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Case study: a patient with diabetes and weight-loss surgery.

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Sue Cummings; Case Study: A Patient With Diabetes and Weight-Loss Surgery. Diabetes Spectr 1 July 2007; 20 (3): 173–176. https://doi.org/10.2337/diaspect.20.3.173

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A.W. is a 65-year-old man with type 2 diabetes who was referred by his primary care physician to the weight center for an evaluation of his obesity and recommendations for treatment options, including weight-loss surgery. The weight center has a team of obesity specialists, including an internist, a registered dietitian (RD), and a psychologist, who perform a comprehensive initial evaluation and make recommendations for obesity treatment. A.W. presented to the weight center team reluctant to consider weight-loss surgery;he is a radiologist and has seen patients who have had complications from bariatric surgery.

Pertinent medical history. A.W.'s current medications include 30 and 70 units of NPH insulin before breakfast and before or after dinner, respectively, 850 mg of metformin twice daily, atorvastatin,lisinopril, nifedipine, allopurinol, aspirin, and an over-the-counter vitamin B 12 supplement. He has sleep apnea but is not using his continuous positive airway pressure machine. He reports that his morning blood glucose levels are 100–130 mg/dl, his hemoglobin A 1c (A1C) level is 6.1%, which is within normal limits, his triglyceride level is 201 mg/dl, and serum insulin is 19 ulU/ml. He weighs 343 lb and is 72 inches tall, giving him a BMI of 46.6 kg/m 2 .

Weight history. A.W. developed obesity as a child and reports having gained weight every decade. He is at his highest adult weight with no indication that medications or medical complications contributed to his obesity. His family history is positive for obesity; his father and one sister are also obese.

Dieting history. A.W. has participated in both commercial and medical weight-loss programs but has regained any weight lost within months of discontinuing the programs. He has seen an RD for weight loss in the past and has also participated in a hospital-based, dietitian-led, group weight-loss program in which he lost some weight but regained it all. He has tried many self-directed diets, but has had no significant weight losses with these.

Food intake. A.W. eats three meals a day. Dinner, his largest meal of the day, is at 7:30 p . m . He usually does not plan a mid-afternoon snack but will eat food if it is left over from work meetings. He also eats an evening snack to avoid hypoglycemia. He reports eating in restaurants two or three times a week but says his fast-food consumption is limited to an occasional breakfast sandwich from Dunkin'Donuts. His alcohol intake consists of only an occasional glass of wine. He reports binge eating (described as eating an entire large package of cookies or a large amount of food at work lunches even if he is not hungry) about once a month, and says it is triggered by stress.

Social history. Recently divorced, A.W. is feeling depressed about his life situation and has financial problems and stressful changes occurring at work. He recently started living with his girlfriend, who does all of the cooking and grocery shopping for their household.

Motivation for weight loss. A.W. says he is concerned about his health and wants to get his life back under control. His girlfriend, who is thin and a healthy eater, has also been concerned about his weight. His primary care physician has been encouraging him to explore weight-loss surgery; he is now willing to learn more about surgical options. He says that if the weight center team's primary recommendation is for weight-loss surgery,he will consider it.

Does A.W. have contraindications to weight-loss surgery, and, if not, does he meet the criteria for weight-loss surgery?

What type of weight-loss surgery would be best for A.W.?

Roles of the obesity specialist team members

The role of the physician as an obesity specialist is to identify and evaluate obesity-related comorbidities and to exclude medically treatable causes of obesity. The physician assesses any need to adjust medications and,if possible, determines if the patient is on a weight-promoting medication that may be switched to a less weight-promoting medication.

The psychologist evaluates weight-loss surgery candidates for a multitude of factors, including the impact of weight on functioning, current psychological symptoms and stressors, psychosocial history, eating disorders,patients' treatment preferences and expectations, motivation, interpersonal consequences of weight loss, and issues of adherence to medical therapies.

The RD conducts a nutritional evaluation, which incorporates anthropometric measurements including height (every 5 years), weight (using standardized techniques and involving scales in a private location that can measure patients who weigh > 350 lb), neck circumference (a screening tool for sleep apnea), and waist circumference for patients with a BMI < 35 kg/m 2 . Other assessments include family weight history,environmental influences, eating patterns, and the nutritional quality of the diet. A thorough weight and dieting history is taken, including age of onset of overweight or obesity, highest and lowest adult weight, usual weight, types of diets and/or previous weight-loss medications, and the amount of weight lost and regained with each attempt. 1  

Importance of type of obesity

Childhood- and adolescent-onset obesity lead to hyperplasic obesity (large numbers of fat cells); patients presenting with hyperplasic and hypertrophic obesity (large-sized fat cells), as opposed to patients with hypertrophic obesity alone, are less likely to be able to maintain a BMI < 25 kg/m 2 , because fat cells can only be shrunk and not eliminated. This is true even after weight-loss surgery and may contribute to the variability in weight loss outcomes after weight loss surgery. Less than 5% of patients lose 100% of their excess body weight. 2 , 3  

Criteria and contraindications for weight-loss surgery

In 1998, the “Clinical Guidelines on the Identification, Evaluation,and Treatment of Overweight and Obesity in Adults: The Evidence Report” 4   recommended that weight-loss surgery be considered an option for carefully selected patients:

with clinically severe obesity (BMI ≥ 40 kg/m 2 or ≥ 35 kg/m 2 with comorbid conditions);

when less invasive methods of weight loss have failed; and

the patient is at high risk for obesity-associated morbidity or mortality.

Contraindications for weight-loss surgery include end-stage lung disease,unstable cardiovascular disease, multi-organ failure, gastric verices,uncontrolled psychiatric disorders, ongoing substance abuse, and noncompliance with current regimens.

A.W. had no contraindications for surgery and met the criteria for surgery,with a BMI of 46.6 kg/m 2 . He had made numerous previous attempts at weight loss, and he had obesity-related comorbidities, including diabetes,sleep apnea, hypertension, and hypercholesterolemia.

Types of procedures

The roux-en-Y gastric bypass (RYGB) surgery is the most common weight-loss procedure performed in the United States. However, the laparoscopic adjustable gastric band (LAGB) procedure has been gaining popularity among surgeons. Both procedures are restrictive, with no malabsorption of macronutrients. There is,however, malabsorption of micronutrients with the RYGB resulting from the bypassing of a major portion of the stomach and duodenum. The bypassed portion of the stomach produces the intrinsic factor needed for the absorption of vitamin B 12 . The duodenum is where many of the fat-soluble vitamins, B vitamins, calcium, and iron are absorbed. Patients undergoing RYGB must agree to take daily vitamin and mineral supplementation and to have yearly monitoring of nutritional status for life.

Weight loss after RYGB and LAGB

The goal of weight-loss surgery is to achieve and maintain a healthier body weight. Mean weight loss 2 years after gastric bypass is ∼ 65% of excess weight loss (EWL), which is defined as the number of pounds lost divided by the pounds of overweight before surgery. 5   When reviewing studies of weight-loss procedures, it is important to know whether EWL or total body weight loss is being measured. EWL is about double the percentage of total body weight loss; a 65% EWL represents about 32% loss of total body weight.

Most of the weight loss occurs in the first 6 months after surgery, with a continuation of gradual loss throughout the first 18–24 months. Many patients will regain 10–15% of the lost weight; a small number of patients regain a significant portion of their lost weight. 6   Data on long-term weight maintenance after surgery indicate that if weight loss has been maintained for 5 years, there is a > 95% likelihood that the patient will keep the weight off over the long term.

The mean percentage of EWL for LAGB is 47.5%. 3   Although the LAGB is considered a lower-risk surgery, initial weight loss and health benefits from the procedure are also lower than those of RYGB.

Weight-loss surgery and diabetes

After gastric bypass surgery, there is evidence of resolution of type 2 diabetes in some individuals, which has led some to suggest that surgery is a cure. 7   Two published studies by Schauer et al. 8   and Sugarman et al. 9   reported resolution in 83 and 86% of patients, respectively. Sjoström et al. 10   published 2-and 10-year data from the Swedish Obese Subjects (SOS) study of 4,047 morbidly obese subjects who underwent bariatric surgery and matched control subjects. At the end of 2 years, the incidence of diabetes in subjects who underwent bariatric surgery was 1.0%, compared to 8.0% in the control subjects. At 10 years, the incidence was 7.0 and 24.0%, respectively.

The resolution of diabetes often occurs before marked weight loss is achieved, often days after the surgery. Resolution of diabetes is more prevalent after gastric bypass than after gastric banding (83.7% for gastric bypass and 47.9% for gastric banding). 5   The LAGB requires adjusting (filling the band through a port placed under the skin),usually five to six times per year. Meta-analysis of available data shows slower weight loss and less improvement in comorbidities including diabetes compared to RYGB. 5  

A.W. had diabetes; therefore, the weight center team recommended the RYGB procedure.

Case study follow-up

A.W. had strong medical indications for surgery and met all other criteria outlined in current guidelines. 4   He attended a surgical orientation session that described his surgical options,reviewed the procedures (including their risks and possible complications),and provided him the opportunity to ask questions. This orientation was led by an RD, with surgeons and post–weight-loss surgical patients available to answer questions. After attending the orientation, A.W. felt better informed about the surgery and motivated to pursue this treatment.

The weight center evaluation team referred him to the surgeon for surgical evaluation. The surgeon agreed with the recommendation for RYGB surgery, and presurgical appointments and the surgery date were set. The surgeon encouraged A.W. to try to lose weight before surgery. 11  

Immediately post-surgery. The surgery went well. A.W.'s blood glucose levels on postoperative day 2 were 156 mg/dl at 9:15 a . m . and 147 mg/dl at 11:15 a . m . He was discharged from the hospital on that day on no diabetes medications and encouraged to follow a Stage II clear and full liquid diet( Table 1 ). 12  

Diet Stages After RYBG Surgery

On postoperative day 10, he returned to the weight center. He reported consuming 16 oz of Lactaid milk mixed with sugar-free Carnation Instant Breakfast and 8 oz of light yogurt, spread out over three to six meals per day. In addition, he was consuming 24 oz per day of clear liquids containing no sugar, calories, or carbonation. A.W.'s diet was advanced to Stage III,which included soft foods consisting primarily of protein sources (diced,ground, moist meat, fish, or poultry; beans; and/or dairy) and well-cooked vegetables. He also attended a nutrition group every 3 weeks, at which the RD assisted him in advancing his diet.

Two months post-surgery. A.W. was recovering well; he denied nausea, vomiting, diarrhea, or constipation. He was eating without difficulty and reported feeling no hunger. His fasting and pre-dinner blood glucose levels were consistently < 120 mg/dl, with no diabetes medications. He continued on allopurinol and atorvastatin and was taking a chewable daily multivitamin and chewable calcium citrate (1,000 mg/day in divided doses) with vitamin D (400 units). His weight was 293 lb, down 50 lb since the surgery. A pathology report from a liver biopsy showed mild to moderate steatatosis without hepatitis.

One year post-surgery. A.W.'s weight was 265 lb, down 78 lb since the surgery, and his weight loss had significantly slowed, as expected. He was no longer taking nifedipine or lisinipril but was restarted at 5 mg daily to achieve a systolic blood pressure < 120 mmHg. His atorvastatin was stopped because his blood lipid levels were appropriate (total cholesterol 117 mg/dl, triglycerides 77 mg/dl, HDL cholesterol 55 mg/dl, and LDL cholesterol 47 mg/dl). His gastroesophageal reflux disease has been resolved, and he continued on allopurinol for gout but had had no flare-ups since surgery. Knee pain caused by osteoarthritis was well controlled without anti-inflammatory medications, and he had no evidence of sleep apnea. Annual medical follow-up and nutritional laboratory measurements will include electrolytes, glucose,A1C, albumin, total protein, complete blood count, ferritin, iron, total iron binding capacity, calcium, parathyroid hormone, vitamin D, magnesium, vitamins B 1 and B 12 , and folate, as well as thyroid, liver, and kidney function tests and lipid measurements.

In summary, A.W. significantly benefited from undergoing RYBP surgery. By 1 year post-surgery, his BMI had decreased from 46.6 to 35.8 kg/m 2 ,and he continues to lose weight at a rate of ∼ 2 lb per month. His diabetes, sleep apnea, and hypercholesterolemia were resolved and he was able to control his blood pressure with one medication.

Clinical Pearls

Individuals considering weight loss surgery require rigorous presurgical evaluation, education, and preparation, as well as a comprehensive long-term postoperative program of surgical, medical, nutritional, and psychological follow-up.

Individuals with diabetes should consider the RYBP procedure because the data on resolution or significant improvement of diabetes after this procedure are very strong, and such improvements occur immediately. Resolution in or improvement of diabetes with the LAGB procedure are more likely to occur only after excess weight has been lost.

Individuals with diabetes undergoing weight loss surgery should be closely monitored; an inpatient protocol should be written regarding insulin regimens and sliding-scale use of insulin if needed. Patients should be educated regarding self-monitoring of blood glucose and the signs and symptoms of hypoglycemia. They should be given instructions on stopping or reducing medications as blood glucose levels normalize.

Patient undergoing RYGB must have lifetime multivitamin supplementation,including vitamins B 1 , B 12 , and D, biotin, and iron, as well as a calcium citrate supplement containing vitamin D (1,000–1,500 mg calcium per day). Nutritional laboratory measurements should be conducted yearly and deficiencies repleted as indicated for the duration of the patient's life.

Sue Cummings, MS, RD, LDN, is the clinical programs coordinator at the MGH Weight Center in Boston, Mass.

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CASE REPORT article

Clinical challenge: patient with severe obesity bmi 46 kg/m 2.

• Section of Endocrinology, Diabetes, Nutrition and Weight Management, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, United States

Obesity causes and exacerbates many disease processes and affects every organ system. Thus it is not surprising that clinical providers are often overwhelmed with the multitude of symptomatology upon initial presentation in patients with obesity. However, despite a “complicated medical history,” a systematic, organized approach in obesity medicine utilizes a personalized-tailored treatment strategy coupled with understanding of the disease state, presence of comorbidities, contraindications, side effects, and patient preferences. Here, we present the case of a young patient with Class 3b severe obesity, several obesity-related complications, and extensive psychological history. Through synergistic and additive treatments (behavioral/nutritional therapy combined with anti-obesity pharmacotherapy and concurrent enrollment in our bariatric surgery program), the patient was able to achieve significant −30.5% total body weight loss with improvement of metabolic parameters. Though these results are not typical of all patients, we must emphasize the need to encompass all available anti-obesity therapies (lifestyle, pharmacotherapy, medical devices, bariatric surgery in monotherapy or combination) in cases of refractory or severe obesity, as we do similarly for other disease modalities such as refractory hypertension or poorly controlled Type 2 diabetes that requires robust escalation in therapy.

Clinical Challenge

A 31 year old patient with a past medical history of Class 3 obesity BMI 46 kg/m 2 , Type 2 diabetes mellitus (A1c <5.7%, well controlled on metformin), polycystic ovarian syndrome, non-alcoholic steatosis of the liver, pulmonary and neurosarcoidosis on infliximab and methotrexate, and chronic worsening pain presents for weight management evaluation. She had a history of opioid use disorder due to the chronic pain, though in remission. She had been on several weight-promoting pain medications for symptom control, including gabapentin, duloxetine and nortriptyline. Contributing factors over the years to her weight gain also included her diagnosis of Bipolar Disorder with antipsychotic medication-induced weight gain (previously trialed aripiprazole, responded to lurasidone with decreasing efficiency, and now finally stable on paliperidone though weight gain promoting). Her highest adult weight was her current weight of 295 pounds with a lowest adult weight of 140 lbs. that pre-dated her Bipolar and sarcoidosis diagnoses several years ago. She had stable eating patterns, and often chose healthy meals such as hummus, vegetables, Greek salads, and lean meats, though had a weakness for sweet cravings. She engaged in structured gym exercise for 30 minutes three times per week despite the chronic pain. Recent stressors included her close aunt who had been diagnosed with cancer. She also suffered from insomnia and had been evaluated closely with sleep therapists and sleep hygiene specialists. Her polysomnogram was negative for sleep apnea.

What Would You Do Next?

A. Offer more aggressive intensive lifestyle therapy intervention

B. Trial of anti-obesity medication if option A above becomes ineffective

C. Metabolic and bariatric surgery only as anti-obesity medication would be contraindicated given her history of opioid use

D. Trial of anti-obesity medication for 3 months with concurrent referral to bariatric surgery

The patient depicted in the case has chronic, debilitating severe obesity classification with several inflammatory obesity-related comorbidities and other contributing etiology to her weight gain.

In regards to lifestyle intervention, the patient was started on a healthy low fat high fiber diet with increased consumption of vegetables, while minimizing intake of processed foods, added sugar, trans fats, and refined flours ( 1 ). Nutrient-dense whole foods prepared at home were encouraged. Acceptable macronutrient distribution range is 45–65% carbohydrates, 20–35% total fat of which <10% should be polyunsaturated fats, and 10–35% protein and amino acids 1 . However, obesity-related comorbidities such as type 2 diabetes mellitus, polycystic ovarian syndrome, and non-alcoholic steatosis of the liver suggesting features of insulin resistance need to be taken into consideration when implementing dietary modifications specific to this case. The patient's daily carbohydrate intake should be reduced to 40–50% to combat insulin resistance. Several studies have shown improvement in metabolic parameters and more rapid weight loss when a low carbohydrate diet was implemented initially in the first 3–6 months ( 2 , 3 ). At presentation, the patient's calculated daily protein intake was <20% of total daily intake and increasing her protein intake to 30% reduced her sweet cravings and increased satiety. In addition, she would benefit from at least 150 min per week of structured moderately intensive exercise as tolerated as recommended by The American College of Sports Medicine ( 4 ). Of note, the patient is also under significant stressors. Stress has been very strongly linked to hyperphagia, binging, and obesity ( 5 , 6 ). Stress management would also provide long-term strategies for emotional/stress eating should they arise. Her sleep has been adequately addressed by a specialist multidisciplinary team. Further, the patient was already under intense behavioral therapy given her underlying psychiatric illness. Early behavioral therapy intervention should be strongly considered in patients with adverse psychological factors, eating disorders and underlying psychiatric conditions that would otherwise impede their overall progress toward health goals. However, it may be difficult to promote more aggressive lifestyle intervention alone, especially in a patient with an advanced obesity disease staging who is already making strides to eat healthy and undergoing behavioral therapy.

Furthermore, the patient also meets criteria for initiation of anti-obesity pharmacotherapy (AOM): BMI >27 kg/m 2 plus the presence of one obesity-related comorbidity and/or BMI >30 kg/m 2 in conjunction with lifestyle intervention ( 7 , 8 ). Though the patient has a history of opioid use disorder, it is in remission and there is no active contraindication to AOM. The patient also does not have underlying heart disease, end-stage-renal disease, or acute angle glaucoma that would negate use of several AOM such as phentermine/topiramate, lorcaserin, and naltrexone/bupropion. Liraglutide 3.0 mg would be a first option given its double benefits in patients with severe obesity and diabetes ( 7 ) and other obesity-related comorbidities such as fatty liver ( 9 ) and polycystic ovarian disease ( 10 ). The medication is also generally well-tolerated and safe. Because anti-obesity medications can exert central effects in a patient with Bipolar Disorder, close monitoring and communication with the patient's psychiatrist would be critical. Because her BMI is already very elevated, clinically, both lifestyle changes and pharmacological treatment would be implemented together, rather than separately. Moreover, based on her current body mass index alone of 40 kg/m 2 , the patient meets National Institutes of Health consensus criteria for metabolic and bariatric surgery ( 11 ): BMI 35 kg/m 2 in the presence of at least one obesity-related comorbidity or BMI 40 kg/m 2 . Therefore, it would be prudent to discuss bariatric surgery in this patient given her disease severity.

The correct answer is D. The patient was actually started on AOM with concurrent referral to the institution's bariatric surgery program. Since the patient's insurance did not provide coverage for liraglutide 3.0 mg, she was alternatively prescribed a combination anti-obesity medication therapy (phentermine/topiramate) after discussion with her psychiatrist and other specialists. AOM were instrumental in improving the patient's overall hunger drive, cravings, and satiety. Despite being the best option for her at presentation, the patient was unwilling to undergo the bariatric procedure. Oftentimes, this may be the case in many patients until they consent to surgical intervention or have weight regain on non-surgical therapy. Future guidelines may need to be more definitive about earlier referral to bariatric surgery.

The patient continued AOM long-term, having lost 90 pounds over a 2 year time period ( Figure 1 ). Her BMI now is 28.7 kg/m 2 , weight 205 lbs. (reversed from Class 3 obesity, BMI 46 kg/m 2 , weight 295 lbs.) with improvement in quality of life and obesity-related comorbidities. Liver transaminases that were previously elevated in the context of fatty liver disease normalized along with return of regular menstrual cycles. In the process of losing weight with related attenuation in disease comorbidity and metabolic profile improvement, the patient's neurosarcoidosis continued to show remarkable recovery with stabilization of her mental health conditions and disability. Her specialists reported that this was the best she had been in many years. The patient lost −30.5% of her total body weight, which is typical weight loss achieved by metabolic and bariatric surgery means, through non-surgical intervention.

Figure 1 . Patient's weight graph derived from the electronic health record. The patient lost a total of 90 lbs. over a 2 year time period with adjunctive anti-obesity pharmacotherapy (phentermine/topiramate) in combination with behavioral and lifestyle intervention.

Though these results may not be usual for all patients, it is important to note that all treatment modalities (behavioral, lifestyle, pharmacological, and/or surgical whether as monotherapy or in combination) must be utilized for patients suffering with severe obesity and its devastating consequences on overall health and quality of life. Many of these patients present with complicated disease states and multiple comorbidities. Thus, important health targets include not only weight loss but treatment-enhanced double benefits leading to improvement of comorbidities.

Data Availability Statement

All datasets for this study were directly generated from the patient's electronic health record and are available upon request.

Informed Consent

Written informed consent to publish this case report was obtained from the patient.

Author Contributions

GS and CA contributed and edited the contents of this manuscript.

No external funding was provided for the creation of this manuscript.

Conflict of Interest

GS served as a consultant for Johnson and Johnson and advisor for Rhythm Pharmaceuticals. CA reports grants from Aspire Bariatrics, Myos, the Vela Foundation, the Dr. Robert C. and Veronica Atkins Foundation, Coherence Lab, Energesis, NIH, and PCORI, grants and personal fees from Orexigen, GI Dynamics, Takeda, personal fees from Nutrisystem, Zafgen, Sanofi-Aventis, NovoNordisk, Scientific Intake, Xeno Biosciences, Rhythm Pharmaceuticals, Eisai, EnteroMedics, Bariatrix Nutrition, and other from Science-Smart LLC, outside the submitted work.

Acknowledgments

We would like to thank the patient for permission to publish.

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Keywords: anti-obesity medications, weight loss drugs, combination therapy, bariatric surgery, lifestyle intervention

Citation: Srivastava G and Apovian CM (2019) Clinical Challenge: Patient With Severe Obesity BMI 46 kg/m 2 . Front. Endocrinol. 10:635. doi: 10.3389/fendo.2019.00635

Received: 30 April 2019; Accepted: 03 September 2019; Published: 02 October 2019.

Reviewed by:

Copyright © 2019 Srivastava and Apovian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Gitanjali Srivastava, geet5sri@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Untargeted lipidomics analysis in women with morbid obesity and type 2 diabetes mellitus: A comprehensive study

Affiliations.

• 1 Department of Medicine and Surgery, Study Group on Metabolic Diseases Associated with Insulin-Resistance (GEMMAIR), Rovira i Virgili University, Hospital Universitari de Tarragona Joan XXIII, IISPV, Tarragona, Spain.
• 2 Department of Electronic, Electric and Automatic Engineering, Higher Technical School of Engineering, Rovira i Virgili University, IISPV, Tarragona, Spain.
• 3 Scientific and Technical Service, Rovira i Virgili University, Tarragona, Spain.
• PMID: 38743756
• DOI: 10.1371/journal.pone.0303569

There is a phenotype of obese individuals termed metabolically healthy obese that present a reduced cardiometabolic risk. This phenotype offers a valuable model for investigating the mechanisms connecting obesity and metabolic alterations such as Type 2 Diabetes Mellitus (T2DM). Previously, in an untargeted metabolomics analysis in a cohort of morbidly obese women, we observed a different lipid metabolite pattern between metabolically healthy morbid obese individuals and those with associated T2DM. To validate these findings, we have performed a complementary study of lipidomics. In this study, we assessed a liquid chromatography coupled to a mass spectrometer untargeted lipidomic analysis on serum samples from 209 women, 73 normal-weight women (control group) and 136 morbid obese women. From those, 65 metabolically healthy morbid obese and 71 with associated T2DM. In this work, we find elevated levels of ceramides, sphingomyelins, diacyl and triacylglycerols, fatty acids, and phosphoethanolamines in morbid obese vs normal weight. Conversely, decreased levels of acylcarnitines, bile acids, lyso-phosphatidylcholines, phosphatidylcholines (PC), phosphatidylinositols, and phosphoethanolamine PE (O-38:4) were noted. Furthermore, comparing morbid obese women with T2DM vs metabolically healthy MO, a distinct lipid profile emerged, featuring increased levels of metabolites: deoxycholic acid, diacylglycerol DG (36:2), triacylglycerols, phosphatidylcholines, phosphoethanolamines, phosphatidylinositols, and lyso-phosphatidylinositol LPI (16:0). To conclude, analysing both comparatives, we observed decreased levels of deoxycholic acid, PC (34:3), and PE (O-38:4) in morbid obese women vs normal-weight. Conversely, we found elevated levels of these lipids in morbid obese women with T2DM vs metabolically healthy MO. These profiles of metabolites could be explored for the research as potential markers of metabolic risk of T2DM in morbid obese women.

Copyright: © 2024 Bertran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

• Case-Control Studies
• Ceramides / blood
• Ceramides / metabolism
• Diabetes Mellitus, Type 2* / blood
• Diabetes Mellitus, Type 2* / complications
• Diabetes Mellitus, Type 2* / metabolism
• Lipid Metabolism
• Lipidomics* / methods
• Lipids / blood
• Metabolomics / methods
• Middle Aged
• Obesity, Morbid* / blood
• Obesity, Morbid* / complications
• Obesity, Morbid* / metabolism
• Sphingomyelins / blood
• Sphingomyelins / metabolism
• Triglycerides / blood

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Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: a case-cohort study

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• Volume 63 , pages 1324–1332, ( 2020 )

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• Theresia M. Schnurr 1 ,
• Hermina Jakupović   ORCID: orcid.org/0000-0001-9667-9406 1 ,
• Germán D. Carrasquilla 1 ,
• Lars Ängquist 1 ,
• Niels Grarup 1 ,
• Thorkild I. A. Sørensen 1 , 2 ,
• Anne Tjønneland 3 , 4 ,
• Kim Overvad 5 , 6 ,
• Oluf Pedersen 1 ,
• Torben Hansen 1 &
• Tuomas O. Kilpeläinen 1  

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Aims/hypothesis

We aimed to investigate whether the impact of obesity and unfavourable lifestyle on type 2 diabetes risk is accentuated by genetic predisposition.

We examined the joint association of genetic predisposition, obesity and unfavourable lifestyle with incident type 2 diabetes using a case-cohort study nested within the Diet, Cancer and Health cohort in Denmark. The study sample included 4729 individuals who developed type 2 diabetes during a median 14.7 years of follow-up, and a randomly selected cohort sample of 5402 individuals. Genetic predisposition was quantified using a genetic risk score (GRS) comprising 193 known type 2 diabetes-associated loci (excluding known BMI loci) and stratified into low (quintile 1), intermediate and high (quintile 5) genetic risk groups. Lifestyle was assessed by a lifestyle score composed of smoking, alcohol consumption, physical activity and diet. We used Prentice-weighted Cox proportional-hazards models to test the associations of the GRS, obesity and lifestyle score with incident type 2 diabetes, as well as the interactions of the GRS with obesity and unfavourable lifestyle in relation to incident type 2 diabetes.

Obesity (BMI ≥ 30 kg/m 2 ) and unfavourable lifestyle were associated with higher risk for incident type 2 diabetes regardless of genetic predisposition ( p  > 0.05 for GRS–obesity and GRS–lifestyle interaction). The effect of obesity on type 2 diabetes risk (HR 5.81 [95% CI 5.16, 6.55]) was high, whereas the effects of high genetic risk (HR 2.00 [95% CI 1.76, 2.27]) and unfavourable lifestyle (HR 1.18 [95% CI 1.06, 1.30]) were relatively modest. Even among individuals with low GRS and favourable lifestyle, obesity was associated with a >8-fold risk of type 2 diabetes compared with normal-weight individuals in the same GRS and lifestyle stratum.

Conclusions/interpretation

Having normal body weight is crucial in the prevention of type 2 diabetes, regardless of genetic predisposition.

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Introduction

Type 2 diabetes is a common disease with a rapidly increasing global prevalence that has been largely attributed to the ongoing pandemic of obesity and a sedentary lifestyle [ 1 , 2 , 3 ]. Public health strategies to prevent type 2 diabetes focus on weight management and promotion of healthy lifestyles [ 4 , 5 ]. Lifestyle interventions designed for weight loss through intensive lifestyle counselling have been shown to delay the onset of type 2 diabetes among individuals with impaired glucose tolerance [ 6 , 7 ]. However, the effects of lifestyle behaviours and weight loss on type 2 diabetes risk may vary between individuals depending on genetic variation [ 6 , 7 ]. To understand the role of genetic variation in the prevention of type 2 diabetes, it is important to elucidate the interaction between genetic predisposition, obesity and lifestyle behaviours.

Recently, a case-cohort study that examined the joint association of genetic predisposition and unfavourable lifestyle with incident type 2 diabetes in the UK Biobank found that type 2 diabetes risk is increased by >10-fold in individuals with an unfavourable lifestyle, independent of genetic risk [ 8 ] . However, unfavourable lifestyle was defined using a multifactorial score that considered obesity as a risk factor equal to smoking behaviour, physical activity and diet. Hence, the study did not allow distinguishing between the effects of obesity and other alterable lifestyle factors in the development of type 2 diabetes. Furthermore, lifestyle recommendations designed for the prevention of cardiovascular disease were applied rather than type 2 diabetes-specific recommendations, and genetic risk was estimated using 38 known type 2 diabetes risk variants rather than ~200 risk variants identified in the most recent genome-wide association study (GWAS) [ 9 ].

In the present study, we address these shortcomings by examining the joint association of genetic risk, obesity and unfavourable lifestyle with incident type 2 diabetes within a case-cohort sample of 4729 cases and a randomly selected subcohort of 5402 individuals from Denmark.

Study population

The present case-cohort study is nested within the Danish Diet, Cancer and Health cohort established in 1993–1997 [ 10 ]. Altogether 160,725 native Danish citizens living in the urban areas of Copenhagen and Aarhus were invited to participate, and 57,053 eligible participants without a previous cancer diagnosis were finally recruited. The study complies with the Declaration of Helsinki and has been approved by the Health Research Ethics, Capital Region of Denmark and the Danish Data Protection Agency. All participants gave written informed consent at inclusion.

The participants were followed up from baseline until the date of the diagnosis of diabetes, death, emigration, date of change of personal identification number, or the end of follow-up on 31 December 2011, whichever came first. Incident cases of type 2 diabetes were identified through the National Diabetes Register, where participants were defined as diabetic if they met one of the following criteria [ 11 , 12 , 13 , 14 ]: registration with a diabetes diagnosis in the National Patient Register; registration of chiropody (as a diabetic patient); five blood glucose measurements in a one year period or two blood glucose measurements per year in five consecutive years; purchase of prescribed insulin recorded in the National Health Service Register; or purchase of oral glucose-lowering drugs in the Danish National Prescription Registry (electronic supplementary material [ESM] Table 1 ). Overall, the positive predictive value for the identification of diabetes cases using the criteria was 89%, and the sensitivity was 86% [ 14 ].

We genotyped a total of 4771 individuals with incident diabetes diagnosis by 31 December 2011 and a randomly drawn subcohort of 5655 individuals who also included 689 individuals who developed diabetes (due to the random selection). The genotyping was performed using the Illumina Human Core Exome BeadChip (Illumina, San Diego, CA, USA) and genotypes were called using the Illumina BeadStudio algorithm. The genotype data were imputed to the Haplotype Reference Consortium (HRC) reference panel 1.1 on the Michigan server using Minimac3.

We excluded closely related individuals, samples with extreme inbreeding coefficients, mislabelled sex or call rate <95%, duplicates, and individuals identified as ethnic outliers based on genome-wide principal component analysis. As indicated in ESM Fig. 1 , we also excluded individuals with type 2 diabetes at baseline and participants that had missing information on lifestyle behaviour or covariates. After the exclusions, a total of 4729 incident type 2 diabetes cases and a randomly selected subcohort of 5402 individuals, of whom 575 developed incident type 2 diabetes, remained in the study population. The median follow-up time was 14.7 years (interquartile range 9.5–15.7 years).

Genetic risk score construction

The SNPs included in the genetic risk score (GRS) were selected based on the most recent DIAMANTE consortium meta-analysis of 32 European-descent GWAS including 74,124 individuals with type 2 diabetes and 824,006 control participants [ 9 ]. Of the 231 SNPs that reached the genome-wide significance threshold ( p  < 5× 10 −8 ) in the BMI-unadjusted primary discovery analysis [ 9 ], we included 218 loci with minor allele frequency (MAF) >1% and good imputation quality (INFO > 0.7) in the Diet, Cancer and Health cohort. We excluded all genetic variants ( n  = 25) that had a genome-wide significant association with BMI or were in strong linkage disequilibrium ( r 2  > 0.8 in 1000 Genomes European panel) with a BMI locus in the largest and most recently published GWAS for BMI [ 15 ], except for the strongest known type 2 diabetes risk locus in TCF7L2 [ 16 ]. We constructed a weighted type 2 diabetes risk-increasing GRS consisting of the remaining 193 loci by summing the number of type 2 diabetes-increasing alleles weighted by the OR of the selected SNPs estimated in the discovery GWAS [ 9 ]. Information on the SNPs included 25 in the GRS, the risk alleles, risk-allele frequencies, imputation INFO scores, and respective ORs that were used as weights for the calculation of the GRS are presented in ESM Table 2 . The GRS was stratified into low (lowest 20%), intermediate and high risk (top 20%) groups.

Assessment of body weight

BMI was calculated from weight and height measured at baseline by dividing body weight in kg by height in metres squared. We defined individuals as normal weight, overweight and obese if they had a BMI <25 kg/m 2 , ≥25 – <30 kg/m 2 and ≥ 30 kg/m 2 , respectively.

Assessment of lifestyle

We adapted lifestyle intervention guidelines established by an initiative of the Danish Ministry of Health [ 4 ] and recommended for type 2 diabetes prevention by the Danish Diabetes Association, to create a multifactorial lifestyle score for type 2 diabetes risk (Table 1 ). Lifestyle was assessed by first defining dichotomous variables (0/1) for adherence to regular physical activity, healthy dietary pattern, smoking status and moderate alcohol consumption, as described in detail below, where 1 point was given for adherence to each favourable lifestyle behaviour. The points were subsequently summed to calculate a lifestyle score for the combined adherence to favourable lifestyle behaviours, where unfavourable lifestyle was defined as adherence to none or one favourable lifestyle behaviour, intermediate lifestyle as adherence to two favourable lifestyle behaviours, and favourable lifestyle as adherence to three or four favourable lifestyle behaviours.

Physical activity

Physical activity was assessed by a previously validated questionnaire [ 17 ] that includes questions on walking, cycling, housework, sports, do-it-yourself activities and gardening. As described previously [ 18 ], each type of physical activity was assigned a metabolic equivalent of task (MET) estimate according to the compendium of physical activities [ 19 ], which allows estimation and classification of the energy costs of different activities based on their rate of energy expenditure. MET min/week were calculated from the median MET of physical activity performed in the summer and winter and multiplied by the number of minutes spent in the activity per week. Individuals were defined as having low or high physical activity levels based on the median 55.0 MET min/week in the study sample.

Dietary pattern

The participants completed a 192-item Food Frequency Questionnaire at baseline [ 20 , 21 , 22 ]. The intake of specific foods and nutrients was calculated by the FoodCalc software [ 23 ]. We adapted the Danish official food-based dietary guidelines that are based on the Nordic Nutrition Recommendations [ 24 ] and referred to by the Danish Diabetes Association [ 25 ], to calculate a healthy diet index comprising seven items (ESM Table 3 ). One of the original recommendations, concerning sodium intake, was excluded in the present analyses, due to lack of data on sodium intake in the Diet, Cancer and Health cohort. One point was given for reaching the recommended consumption of fruits and vegetables, whole grains, fish, low-fat (maximum fat content of 1.5%) dairy products, as well as for recommended low consumption of red meat (including unprocessed and processed meat and meat products), saturated fat, and sugar-sweetened beverages or juices (including fruit and vegetable juice). The points were summed to assess adherence to healthy diet (range 0 [no adherence] to 7 [highest adherence]), and a dichotomous healthy diet variable was formed by defining low adherence as 0 to 2 points and middle to high adherence as 3 to 7 points (Table 1 ). The cut-off for the healthy diet index was set at three out of seven items to ensure sufficient statistical power, as this value was closest to the median of the study population and thus balanced the sample sizes of the two strata.

Alcohol consumption, smoking and educational level

Alcohol consumption was calculated from the 192-item Food Frequency Questionnaire. Based on current Nordic Nutrition Recommendations [ 24 ], moderate alcohol consumption was defined as ≤6 units/week for women and ≤12 units/week for men, and high alcohol consumption as >6 units/week for women and >12 units/week for men, where 1 unit is equivalent to 12 g of pure alcohol. Information on smoking habit (current smoker, non-smoker) and educational level defined by its duration (≤7, 8–9, and ≥10 years) [ 26 , 27 ] was obtained from the baseline questionnaire.

Statistical analysis

We used Prentice-weighted Cox proportional-hazards models using the ‘cch’ command integrated into the R package ‘survival’ to test the associations of GRS, obesity and lifestyle score with incident type 2 diabetes, as well as the interactions of the GRS with obesity and unfavourable lifestyle in relation to incident type 2 diabetes. Consistent with previous studies [ 8 , 28 ], we determined whether participants had a low (quintile 1), intermediate (quintiles 2 to 4) or high (quintile 5) genetic risk for type 2 diabetes and a favourable (3 or 4 favourable lifestyle factors), intermediate (2 favourable lifestyle factors) or an unfavourable lifestyle (0 or 1 favourable lifestyle factors). All analyses were adjusted for age at baseline, sex and educational level. The analyses including the GRS were additionally adjusted for the first three genome-wide principal components to correct for population stratification. Analyses including the GRS and lifestyle score were performed with and without adjustment for BMI. All analyses were performed using RStudio software, version 3.3.1 (2016-06-21; Boston, MA, USA).

Power calculations and assessment of predictive utility of the models

We performed power calculations through 2000 simulation analyses using the simplified approximating setting of logistic regression, hence modelling the odds of incident diabetes (being a case vs a non-case) during the follow-up. The GRS*BMI setting was mimicked using estimated variables from a corresponding fitted model while adjusting for sex and age. We derived the least detectable interaction effect based on the current sample size ( n  = 9556), significance level 5% and 80% power. The power calculations were performed in Stata 15.1 (StataCorp, College Station, TX, USA; www.stata.com ). We also assessed the predictive performance of BMI, the lifestyle score and the genetic risk score for incident type 2 diabetes by performing a receiver operating characteristic (ROC) analysis (ESM Table 4 ).

Population characteristics

The baseline characteristics of the 4726 individuals with type 2 diabetes and the randomly selected subcohort of 5402 participants included in the presented analyses are provided in Table 1 . The mean age of all participants was 56.1 years (range 50–65) and 49.6% were women. Overall, 40.0% of all participants had a favourable lifestyle, 34.6% had an intermediate lifestyle and 25.4% had an unfavourable lifestyle, and 21.8% were classified as obese, 43.0% as overweight and 35.2% as having normal weight.

Associations of the GRS, obesity and lifestyle with incident type 2 diabetes

As provided in ESM Table 5 , participants with high or intermediate genetic risk had an HR of 2.00 (95% CI 1.76, 2.27) or an HR of 1.49 (95% CI 1.34, 1.66) for incident type 2 diabetes, respectively, compared with individuals with low genetic risk. Participants with an unfavourable or intermediate lifestyle had an HR of 1.18 (95% CI 1.06, 1.30) or an HR of 1.10 (95% CI 1.00, 1.20) for incident type 2 diabetes, respectively, compared with participants with a favourable lifestyle. Individuals who were overweight or obese had an HR of 2.37 (95% CI 2.15, 2.62) or an HR of 5.81 (95% CI 5.16, 6.55) for higher risk of incident type 2 diabetes, respectively, than individuals with normal body weight. In sensitivity analyses that excluded 62 individuals who were underweight (BMI < 18.5 kg/m 2 ), the associations of being overweight and obese with type 2 diabetes risk remained virtually unchanged (HR 2.38 [95% CI 2.16, 2.62] and HR 5.82 [95% CI 5.17, 6.56], respectively).

The associations of high genetic risk and unfavourable lifestyle with type 2 diabetes risk remained similar after adjustment for BMI (HR 2.15 [95% CI 1.85, 2.50] and HR 1.29 [95% CI 1.15, 1.43], respectively), suggesting that the associations of GRS and unfavourable lifestyle with type 2 diabetes were not mediated by BMI.

Interaction of the GRS with obesity and lifestyle on incident type 2 diabetes

There was no significant interaction between the GRS and continuous BMI ( p  = 0.35) or the lifestyle score ( p  = 0.72) on incident type 2 diabetes (ESM Table 6 ); higher BMI and unfavourable lifestyle were similarly associated with increased risk of type 2 diabetes across all genetic risk groups (Figs 1 and 2 ). We also did not find significant interactions between the GRS and the four individual lifestyle components comprising the lifestyle score on the risk of type 2 diabetes (ESM Table 7 ). In a sensitivity analysis excluding 62 individuals who were underweight (BMI < 18.5 kg/m 2 ), the interaction between the GRS and continuous BMI on incident type 2 diabetes ( p  = 0.49) remained virtually unchanged (ESM Fig. 2 ).

Associations of GRS and lifestyle with incident type 2 diabetes. Analyses were adjusted for age, sex, educational level and the first three genome-wide principal components. n , number of incident type 2 diabetes cases

Associations of GRS and body-weight status with incident type 2 diabetes. Analyses were adjusted for age, sex, educational level and the first three genome-wide principal components. n , number of incident type 2 diabetes cases

Through power calculation, we estimated that the least detectable GRS*BMI interaction effect that could be detected in the present sample with significance level 0.05 and 80% power is OR = 1.0025.

Combined association of the GRS, obesity and lifestyle with incident type 2 diabetes

Individuals who ranked high for all three risk factors, with obesity, high GRS and unfavourable lifestyle, had an HR of 14.54 (95% CI 8.09, 26.13) for incident type 2 diabetes, compared with normal-weight, individuals low GRS and favourable lifestyle. Notably, even among individuals with low GRS and favourable lifestyle, obesity was strongly associated with higher type 2 diabetes risk with an HR of 8.44 (95% CI 5.43, 13.14) compared with normal weight individuals in the same GRS and lifestyle stratum (Table 2 ). ROC analyses suggested that the GRS and the lifestyle score have very little predictive utility (AUC = 72.85 and 72.05, respectively) on top of BMI, age and sex (AUC = 71.81).

In the present study, we found that obesity and unfavourable lifestyle are associated with increased risk of incident type 2 diabetes regardless of genetic risk. We also found that the associations of the GRS and lifestyle score with risk of incident type 2 diabetes are relatively modest compared with the association of obesity with diabetes risk, underscoring the importance of weight management in diabetes prevention.

Our results show that there was no significant interaction between behavioural lifestyle and genetic risk of type 2 diabetes. An unfavourable lifestyle was associated with similar increase in relative type 2 diabetes risk across each stratum of genetic risk. These findings are consistent with previous studies within the InterAct Consortium [ 29 ] and the UK Biobank [ 8 ]. Overall, the results indicate that a favourable lifestyle should be universally recommended in the prevention of type 2 diabetes, regardless of genetic predisposition, thus supporting current public health guidelines. The study within UK Biobank investigated the effect of a lifestyle score on genetically determined type 2 diabetes risk, finding that a poor lifestyle is associated with a >10-fold increased risk of incident type 2 diabetes, even in individuals with a low genetic risk [ 8 ]. However, the study incorporated being overweight/obese as one component of the multifactorial lifestyle score, which did not allow distinguishing between the effects specific to obesity and other lifestyle factors on type 2 diabetes risk. In the present study, we showed that the association with incident type 2 diabetes is dominated by obesity over the impact of unfavourable lifestyle, with a >8-fold risk of type 2 diabetes found for individuals who were obese, despite a favourable lifestyle.

We found no significant interaction between the GRS and BMI in relation to the risk of incident type 2 diabetes. In contrast, a previous analysis within the InterAct Consortium reported that the effect of a GRS for type 2 diabetes (based on 49 established type 2 diabetes loci) on diabetes risk is modified by BMI, such that the effect of the GRS is significantly greater among individuals who are leaner at baseline [ 29 ]. It is possible that the present analyses were underpowered to detect an interaction between the GRS and BMI. Alternatively, the inclusion of >150 additional recently identified type 2 diabetes risk variants in the GRS used in the present analyses may have incorporated a larger number of genetic loci that have an equal effect between lean and obese individuals, whereas the earlier discovered loci may have included a relatively larger proportion of variants with primary effects on insulin secretion and a larger effect in lean individuals, due to the exclusion of obese individuals in early GWAS for type 2 diabetes [ 30 ]. Indeed, a BMI-stratified analysis, published in 2012, showed a larger OR in normal-weight individuals compared with individuals who were obese for 29 of 36 diabetes loci known at the time [ 31 ]. The InterAct Consortium also did not exclude known obesity risk variants when constructing the GRS, which could lead to spurious interactions between the GRS and BMI due to gene–environment dependence [ 32 ].

Strengths of the present study are the large number of incident type 2 diabetes cases, retrieved objectively from the Danish Diabetes Registry, and the long follow-up period. Information on lifestyle was collected before type 2 diabetes diagnosis, which ensures that recall bias did not influence the present results. Furthermore, we comprised a lifestyle score specific for type 2 diabetes and took advantage of ~200 loci recently identified to be associated with type 2 diabetes [ 9 ]. As a limitation, the present analyses were performed in a population of European genetic ancestry and cannot immediately be generalised to other ancestry groups.

To conclude, we found that individuals with obesity and an unfavourable lifestyle are at greater risk of incident type 2 diabetes regardless of their genetic risk. The results suggest that type 2 diabetes prevention by weight management and healthy lifestyle is critical across all genetic risk groups. Furthermore, we found that the effect of obesity on type 2 diabetes risk is dominant over other risk factors, highlighting the importance of weight management in type 2 diabetes prevention.

Data availability

Due to restrictions related to Danish law and protecting patient privacy, the combined set of data as used in this study can only be made available through a trusted third party, Statistics Denmark. This state organisation holds the data used for this study. University-based Danish scientific organisations can be authorised to work with data within Statistics Denmark and such organisations can provide access to individual scientists inside and outside of Denmark. Requests for data may be sent to Statistics Denmark ( www.dst.dk/en/kontakt ) or the Danish Data Protection Agency ( www.datatilsynet.dk/english ).

Abbreviations

• Genetic risk score

Genome-wide association study

Metabolic equivalent of task

Receiver operating characteristic

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Acknowledgements

Parts of this work were presented in abstract form at the European Human Genetics Conference in Gothenburg, Sweden, 15–18 June 2019, and at the European Association for the Study of Diabetes Conference in Barcelona, Spain, 16–20 September 2019.

The Diet, Cancer and Health cohort is supported by the Danish Cancer Society. DNA purification and genotyping were done using a grant to OP from the UNIK – Food, Fitness & Pharma research initiative. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research centre at the University of Copenhagen, partially funded by an unrestricted donation from the Novo Nordisk Foundation ( cbmr.ku.dk ; grant number NNF18CC0034900). TOK was supported by the Danish Council for Independent Research (DFF – 1333-00124 and Sapere Aude programme grant DFF – 1331-00730B) and the Novo Nordisk Foundation (NNF17OC0026848). HJ, TMS and GDC were supported by the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation (grant number NNF17SA0031406).

Author information

Theresia M. Schnurr and Hermina Jakupović contributed equally to this work.

Authors and Affiliations

Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Copenhagen, Denmark

Theresia M. Schnurr, Hermina Jakupović, Germán D. Carrasquilla, Lars Ängquist, Niels Grarup, Thorkild I. A. Sørensen, Oluf Pedersen, Torben Hansen & Tuomas O. Kilpeläinen

Department of Public Health, Section of Epidemiology, Faculty of Health and Social Sciences, University of Copenhagen, Copenhagen, Denmark

Thorkild I. A. Sørensen

Department of Public Health, Section of Environmental Health, Faculty of Health and Social Sciences, University of Copenhagen, Copenhagen, Denmark

Anne Tjønneland

Danish Cancer Society Research Center, Copenhagen, Denmark

Department of Public Health, Aarhus University, Aarhus, Denmark

Kim Overvad

Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark

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The research initiative and study concept were taken and designed by OP and TOK. TMS, HJ and TOK designed the present analysis. TMS, HJ and LÄ performed statistical analysis. TMS, HJ and TOK interpreted the data and drafted the manuscript. AT and KO collected data and contributed to discussions. GDC, NG, TIAS, OP and TH contributed to discussions. All authors interpreted the results, edited and revised the manuscript and read and approved the final version of the manuscript. TMS and HJ are the guarantors of the work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and accuracy of the data analysis.

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Schnurr, T.M., Jakupović, H., Carrasquilla, G.D. et al. Obesity, unfavourable lifestyle and genetic risk of type 2 diabetes: a case-cohort study. Diabetologia 63 , 1324–1332 (2020). https://doi.org/10.1007/s00125-020-05140-5

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Received : 11 December 2019

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Published : 15 April 2020

Issue Date : July 2020

DOI : https://doi.org/10.1007/s00125-020-05140-5

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Effectiveness of weight management interventions for adults delivered in primary care: systematic review and meta-analysis of randomised controlled trials

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• Peer review
• Claire D Madigan , senior research associate 1 ,
• Henrietta E Graham , doctoral candidate 1 ,
• Elizabeth Sturgiss , NHMRC investigator 2 ,
• Victoria E Kettle , research associate 1 ,
• Kajal Gokal , senior research associate 1 ,
• Greg Biddle , research associate 1 ,
• Gemma M J Taylor , reader 3 ,
• Amanda J Daley , professor of behavioural medicine 1
• 1 Centre for Lifestyle Medicine and Behaviour (CLiMB), The School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK
• 2 School of Primary and Allied Health Care, Monash University, Melbourne, Australia
• 3 Department of Psychology, Addiction and Mental Health Group, University of Bath, Bath, UK
• Correspondence to: C D Madigan c.madigan{at}lboro.ac.uk (or @claire_wm and @lboroclimb on Twitter)
• Accepted 26 April 2022

Objective To examine the effectiveness of behavioural weight management interventions for adults with obesity delivered in primary care.

Design Systematic review and meta-analysis of randomised controlled trials.

Eligibility criteria for selection of studies Randomised controlled trials of behavioural weight management interventions for adults with a body mass index ≥25 delivered in primary care compared with no treatment, attention control, or minimal intervention and weight change at ≥12 months follow-up.

Data sources Trials from a previous systematic review were extracted and the search completed using the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021.

Data extraction and synthesis Two reviewers independently identified eligible studies, extracted data, and assessed risk of bias using the Cochrane risk of bias tool. Meta-analyses were conducted with random effects models, and a pooled mean difference for both weight (kg) and waist circumference (cm) were calculated.

Main outcome measures Primary outcome was weight change from baseline to 12 months. Secondary outcome was weight change from baseline to ≥24 months. Change in waist circumference was assessed at 12 months.

Results 34 trials were included: 14 were additional, from a previous review. 27 trials (n=8000) were included in the primary outcome of weight change at 12 month follow-up. The mean difference between the intervention and comparator groups at 12 months was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, P<0.001), favouring the intervention group. At ≥24 months (13 trials, n=5011) the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, P<0.001) favouring the intervention. The mean difference in waist circumference (18 trials, n=5288) was −2.5 cm (−3.2 to −1.8 cm, I 2 =69%, P<0.001) in favour of the intervention at 12 months.

Conclusions Behavioural weight management interventions for adults with obesity delivered in primary care are effective for weight loss and could be offered to members of the public.

Systematic review registration PROSPERO CRD42021275529.

Introduction

Obesity is associated with an increased risk of diseases such as cancer, type 2 diabetes, and heart disease, leading to early mortality. 1 2 3 More recently, obesity is a risk factor for worse outcomes with covid-19. 4 5 Because of this increased risk, health agencies and governments worldwide are focused on finding effective ways to help people lose weight. 6

Primary care is an ideal setting for delivering weight management services, and international guidelines recommend that doctors should opportunistically screen and encourage patients to lose weight. 7 8 On average, most people consult a primary care doctor four times yearly, providing opportunities for weight management interventions. 9 10 A systematic review of randomised controlled trials by LeBlanc et al identified behavioural interventions that could potentially be delivered in primary care, or involved referral of patients by primary care professionals, were effective for weight loss at 12-18 months follow-up (−2.4 kg, 95% confidence interval −2.9 to−1.9 kg). 11 However, this review included trials with interventions that the review authors considered directly transferrable to primary care, but not all interventions involved primary care practitioners. The review included interventions that were entirely delivered by university research employees, meaning implementation of these interventions might differ if offered in primary care, as has been the case in other implementation research of weight management interventions, where effects were smaller. 12 As many similar trials have been published after this review, an updated review would be useful to guide health policy.

We examined the effectiveness of weight loss interventions delivered in primary care on measures of body composition (weight and waist circumference). We also identified characteristics of effective weight management programmes for policy makers to consider.

This systematic review was registered on PROSPERO and is reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. 13 14

Eligibility criteria

We considered studies to be eligible for inclusion if they were randomised controlled trials, comprised adult participants (≥18 years), and evaluated behavioural weight management interventions delivered in primary care that focused on weight loss. A primary care setting was broadly defined as the first point of contact with the healthcare system, providing accessible, continued, comprehensive, and coordinated care, focused on long term health. 15 Delivery in primary care was defined as the majority of the intervention being delivered by medical and non-medical clinicians within the primary care setting. Table 1 lists the inclusion and exclusion criteria.

Study inclusion and exclusion criteria

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We extracted studies from the systematic review by LeBlanc et al that met our inclusion criteria. 11 We also searched the exclusions in this review because the researchers excluded interventions specifically for diabetes management, low quality trials, and only included studies from an Organisation for Economic Co-operation and Development country, limiting the scope of the findings.

We searched for studies in the Cochrane Central Register of Controlled Trials, Medline, PubMed, and PsychINFO from 1 January 2018 to 19 August 2021 (see supplementary file 1). Reference lists of previous reviews 16 17 18 19 20 21 and included trials were hand searched.

Data extraction

Results were uploaded to Covidence, 22 a software platform used for screening, and duplicates removed. Two independent reviewers screened study titles, abstracts, and full texts. Disagreements were discussed and resolved by a third reviewer. All decisions were recorded in Covidence, and reviewers were blinded to each other’s decisions. Covidence calculates proportionate agreement as a measure of inter-rater reliability, and data are reported separately by title or abstract screening and full text screening. One reviewer extracted data on study characteristics (see supplementary table 1) and two authors independently extracted data on weight outcomes. We contacted the authors of four included trials (from the updated search) for further information. 23 24 25 26

Outcomes, summary measures, and synthesis of results

The primary outcome was weight change from baseline to 12 months. Secondary outcomes were weight change from baseline to ≥24 months and from baseline to last follow-up (to include as many trials as possible), and waist circumference from baseline to 12 months. Supplementary file 2 details the prespecified subgroup analysis that we were unable to complete. The prespecified subgroup analyses that could be completed were type of healthcare professional who delivered the intervention, country, intensity of the intervention, and risk of bias rating.

Healthcare professional delivering intervention —From the data we were able to compare subgroups by type of healthcare professional: nurses, 24 26 27 28 general practitioners, 23 29 30 31 and non-medical practitioners (eg, health coaches). 32 33 34 35 36 37 38 39 Some of the interventions delivered by non-medical practitioners were supported, but not predominantly delivered, by GPs. Other interventions were delivered by a combination of several different practitioners—for example, it was not possible to determine whether a nurse or dietitian delivered the intervention. In the subgroup analysis of practitioner delivery, we refer to this group as “other.”

Country —We explored the effectiveness of interventions by country. Only countries with three or more trials were included in subgroup analyses (United Kingdom, United States, and Spain).

Intensity of interventions —As the median number of contacts was 12, we categorised intervention groups according to whether ≤11 or ≥12 contacts were required.

Risk of bias rating —Studies were classified as being at low, unclear, and high risk of bias. Risk of bias was explored as a potential influence on the results.

Meta-analyses

Meta-analyses were conducted using Review Manager 5.4. 40 As we expected the treatment effects to differ because of the diversity of intervention components and comparator conditions, we used random effects models. A pooled mean difference was calculated for each analysis, and variance in heterogeneity between studies was compared using the I 2 and τ 2 statistics. We generated funnel plots to evaluate small study effects. If more than two intervention groups existed, we divided the number of participants in the comparator group by the number of intervention groups and analysed each individually. Nine trials were cluster randomised controlled trials. The trials had adjusted their results for clustering, or adjustment had been made in the previous systematic review by LeBlanc et al. 11 One trial did not report change in weight by group. 26 We calculated the mean weight change and standard deviation using a standard formula, which imputes a correlation for the baseline and follow-up weights. 41 42 In a non-prespecified analysis, we conducted univariate and multivariable metaregression (in Stata) using a random effects model to examine the association between number of sessions and type of interventionalist on study effect estimates.

Risk of bias

Two authors independently assessed the risk of bias using the Cochrane risk of bias tool v2. 43 For incomplete outcome data we defined a high risk of bias as ≥20% attrition. Disagreements were resolved by discussion or consultation with a third author.

Patient and public involvement

The study idea was discussed with patients and members of the public. They were not, however, included in discussions about the design or conduct of the study.

The search identified 11 609 unique study titles or abstracts after duplicates were removed ( fig 1 ). After screening, 97 full text articles were assessed for eligibility. The proportionate agreement ranged from 0.94 to 1.0 for screening of titles or abstracts and was 0.84 for full text screening. Fourteen new trials met the inclusion criteria. Twenty one studies from the review by LeBlanc et al met our eligibility criteria and one study from another systematic review was considered eligible and included. 44 Some studies had follow-up studies (ie, two publications) that were found in both the second and the first search; hence the total number of trials was 34 and not 36. Of the 34 trials, 27 (n=8000 participants) were included in the primary outcome meta-analysis of weight change from baseline to 12 months, 13 (n=5011) in the secondary outcome from baseline to ≥24 months, and 30 (n=8938) in the secondary outcome for weight change from baseline to last follow-up. Baseline weight was accounted for in 18 of these trials, but in 14 24 26 29 30 31 32 44 45 46 47 48 49 50 51 it was unclear or the trials did not consider baseline weight. Eighteen trials (n=5288) were included in the analysis of change in waist circumference at 12 months.

Studies included in systematic review of effectiveness of behavioural weight management interventions in primary care. *Studies were merged in Covidence if they were from same trial

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Study characteristics

Included trials (see supplementary table 1) were individual randomised controlled trials (n=25) 24 25 26 27 28 29 32 33 34 35 38 39 41 44 45 46 47 50 51 52 53 54 55 56 59 or cluster randomised controlled trials (n=9). 23 30 31 36 37 48 49 57 58 Most were conducted in the US (n=14), 29 30 31 32 33 34 35 36 37 45 48 51 54 55 UK (n=7), 27 28 38 41 47 57 58 and Spain (n=4). 25 44 46 49 The median number of participants was 276 (range 50-864).

Four trials included only women (average 65.9% of women). 31 48 51 59 The mean BMI at baseline was 35.2 (SD 4.2) and mean age was 48 (SD 9.7) years. The interventions lasted between one session (with participants subsequently following the programme unassisted for three months) and several sessions over three years (median 12 months). The follow-up period ranged from 12 months to three years (median 12 months). Most trials excluded participants who had lost weight in the past six months and were taking drugs that affected weight.

Meta-analysis

Overall, 27 trials were included in the primary meta-analysis of weight change from baseline to 12 months. Three trials could not be included in the primary analysis as data on weight were only available at two and three years and not 12 months follow-up, but we included these trials in the secondary analyses of last follow-up and ≥24 months follow-up. 26 44 50 Four trials could not be included in the meta-analysis as they did not present data in a way that could be synthesised (ie, measures of dispersion). 25 52 53 58 The mean difference was −2.3 kg (95% confidence interval −3.0 to −1.6 kg, I 2 =88%, τ 2 =3.38; P<0.001) in favour of the intervention group ( fig 2 ). We found no evidence of publication bias (see supplementary fig 1). Absolute weight change was −3.7 (SD 6.1) kg in the intervention group and −1.4 (SD 5.5) kg in the comparator group.

Mean difference in weight at 12 months by weight management programme in primary care (intervention) or no treatment, different content, or minimal intervention (control). SD=standard deviation

Supplementary file 2 provides a summary of the main subgroup analyses.

Weight change

The mean difference in weight change at the last follow-up was −1.9 kg (95% confidence interval −2.5 to −1.3 kg, I 2 =81%, τ 2 =2.15; P<0.001). Absolute weight change was −3.2 (SD 6.4) kg in the intervention group and −1.2 (SD 6.0) kg in the comparator group (see supplementary figs 2 and 3).

At the 24 month follow-up the mean difference in weight change was −1.8 kg (−2.8 to −0.8 kg, I 2 =88%, τ 2 =3.13; P<0.001) (see supplementary fig 4). As the weight change data did not differ between the last follow-up and ≥24 months, we used the weight data from the last follow-up in subgroup analyses.

In subgroup analyses of type of interventionalist, differences were significant (P=0.005) between non-medical practitioners, GPs, nurses, and other people who delivered interventions (see supplementary fig 2).

Participants who had ≥12 contacts during interventions lost significantly more weight than those with fewer contacts (see supplementary fig 6). The association remained after adjustment for type of interventionalist.

Waist circumference

The mean difference in waist circumference was −2.5 cm (95% confidence interval −3.2 to −1.8 cm, I 2 =69%, τ 2 =1.73; P<0.001) in favour of the intervention at 12 months ( fig 3 ). Absolute changes were −3.7 cm (SD 7.8 cm) in the intervention group and −1.3 cm (SD 7.3) in the comparator group.

Mean difference in waist circumference at 12 months. SD=standard deviation

Risk of bias was considered to be low in nine trials, 24 33 34 35 39 41 47 55 56 unclear in 12 trials, 25 27 28 29 32 45 46 50 51 52 54 59 and high in 13 trials 23 26 30 31 36 37 38 44 48 49 53 57 58 ( fig 4 ). No significant (P=0.65) differences were found in subgroup analyses according to level of risk of bias from baseline to 12 months (see supplementary fig 7).

Risk of bias in included studies

Worldwide, governments are trying to find the most effective services to help people lose weight to improve the health of populations. We found weight management interventions delivered by primary care practitioners result in effective weight loss and reduction in waist circumference and these interventions should be considered part of the services offered to help people manage their weight. A greater number of contacts between patients and healthcare professionals led to more weight loss, and interventions should be designed to include at least 12 contacts (face-to-face or by telephone, or both). Evidence suggests that interventions delivered by non-medical practitioners were as effective as those delivered by GPs (both showed statistically significant weight loss). It is also possible that more contacts were made with non-medical interventionalists, which might partially explain this result, although the metaregression analysis suggested the effect remained after adjustment for type of interventionalist. Because most comparator groups had fewer contacts than intervention groups, it is not known whether the effects of the interventions are related to contact with interventionalists or to the content of the intervention itself.

Although we did not determine the costs of the programme, it is likely that interventions delivered by non-medical practitioners would be cheaper than GP and nurse led programmes. 41 Most of the interventions delivered by non-medical practitioners involved endorsement and supervision from GPs (ie, a recommendation or checking in to see how patients were progressing), and these should be considered when implementing these types of weight management interventions in primary care settings. Our findings suggest that a combination of practitioners would be most effective because GPs might not have the time for 12 consultations to support weight management.

Although the 2.3 kg greater weight loss in the intervention group may seem modest, just 2-5% in weight loss is associated with improvements in systolic blood pressure and glucose and triglyceride levels. 60 The confidence intervals suggest a potential range of weight loss and that these interventions might not provide as much benefit to those with a higher BMI. Patients might not find an average weight loss of 3.7 kg attractive, as many would prefer to lose more weight; explaining to patients the benefits of small weight losses to health would be important.

Strengths and limitations of this review

Our conclusions are based on a large sample of about 8000 participants, and 12 of these trials were published since 2018. It was occasionally difficult to distinguish who delivered the interventions and how they were implemented. We therefore made some assumptions at the screening stage about whether the interventionalists were primary care practitioners or if most of the interventions were delivered in primary care. These discussions were resolved by consensus. All included trials measured weight, and we excluded those that used self-reported data. Dropout rates are important in weight management interventions as those who do less well are less likely to be followed-up. We found that participants in trials with an attrition rate of 20% or more lost less weight and we are confident that those with high attrition rates have not inflated the results. Trials were mainly conducted in socially economic developed countries, so our findings might not be applicable to all countries. The meta-analyses showed statistically significant heterogeneity, and our prespecified subgroups analysis explained some, but not all, of the variance.

Comparison with other studies

The mean difference of −2.3 kg in favour of the intervention group at 12 months is similar to the findings in the review by LeBlanc et al, who reported a reduction of −2.4 kg in participants who received a weight management intervention in a range of settings, including primary care, universities, and the community. 11 61 This is important because the review by LeBlanc et al included interventions that were not exclusively conducted in primary care or by primary care practitioners. Trials conducted in university or hospital settings are not typically representative of primary care populations and are often more intensive than trials conducted in primary care as a result of less constraints on time. Thus, our review provides encouraging findings for the implementation of weight management interventions delivered in primary care. The findings are of a similar magnitude to those found in a trial by Ahern et al that tested primary care referral to a commercial programme, with a difference of −2.7 kg (95% confidence interval −3.9 to −1.5 kg) reported at 12 month follow-up. 62 The trial by Ahern et al also found a difference in waist circumference of −4.1 cm (95% confidence interval −5.5 to −2.3 cm) in favour of the intervention group at 12 months. Our finding was smaller at −2.5 cm (95% confidence interval −3.2 to −1.8 cm). Some evidence suggests clinical benefits from a reduction of 3 cm in waist circumference, particularly in decreased glucose levels, and the intervention groups showed a 3.7 cm absolute change in waist circumference. 63

Policy implications and conclusions

Weight management interventions delivered in primary care are effective and should be part of services offered to members of the public to help them manage weight. As about 39% of the world’s population is living with obesity, helping people to manage their weight is an enormous task. 64 Primary care offers good reach into the community as the first point of contact in the healthcare system and the remit to provide whole person care across the life course. 65 When developing weight management interventions, it is important to reflect on resource availability within primary care settings to ensure patients’ needs can be met within existing healthcare systems. 66

We did not examine the equity of interventions, but primary care interventions may offer an additional service and potentially help those who would not attend a programme delivered outside of primary care. Interventions should consist of 12 or more contacts, and these findings are based on a mixture of telephone and face-to-face sessions. Previous evidence suggests that GPs find it difficult to raise the issue of weight with patients and are pessimistic about the success of weight loss interventions. 67 Therefore, interventions should be implemented with appropriate training for primary care practitioners so that they feel confident about helping patients to manage their weight. 68

Unanswered questions and future research

A range of effective interventions are available in primary care settings to help people manage their weight, but we found substantial heterogeneity. It was beyond the scope of this systematic review to examine the specific components of the interventions that may be associated with greater weight loss, but this could be investigated by future research. We do not know whether these interventions are universally suitable and will decrease or increase health inequalities. As the data are most likely collected in trials, an individual patient meta-analysis is now needed to explore characteristics or factors that might explain the variance. Most of the interventions excluded people prescribed drugs that affect weight gain, such as antipsychotics, glucocorticoids, and some antidepressants. This population might benefit from help with managing their weight owing to the side effects of these drug classes on weight gain, although we do not know whether the weight management interventions we investigated would be effective in this population. 69

What is already known on this topic

Referral by primary care to behavioural weight management programmes is effective, but the effectiveness of weight management interventions delivered by primary care is not known

Systematic reviews have provided evidence for weight management interventions, but the latest review of primary care delivered interventions was published in 2014

Factors such as intensity and delivery mechanisms have not been investigated and could influence the effectiveness of weight management interventions delivered by primary care

What this study adds

Weight management interventions delivered by primary care are effective and can help patients to better manage their weight

At least 12 contacts (telephone or face to face) are needed to deliver weight management programmes in primary care

Some evidence suggests that weight loss after weight management interventions delivered by non-medical practitioners in primary care (often endorsed and supervised by doctors) is similar to that delivered by clinician led programmes

Ethics statements

Ethical approval.

Not required.

Data availability statement

Additional data are available in the supplementary files.

Contributors: CDM and AJD conceived the study, with support from ES. CDM conducted the search with support from HEG. CDM, AJD, ES, HEG, KG, GB, and VEK completed the screening and full text identification. CDM and VEK completed the risk of bias assessment. CDM extracted data for the primary outcome and study characteristics. HEJ, GB, and KG extracted primary outcome data. CDM completed the analysis in RevMan, and GMJT completed the metaregression analysis in Stata. CDM drafted the paper with AJD. All authors provided comments on the paper. CDM acts as guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: AJD is supported by a National Institute for Health and Care Research (NIHR) research professorship award. This research was supported by the NIHR Leicester Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. ES’s salary is supported by an investigator grant (National Health and Medical Research Council, Australia). GT is supported by a Cancer Research UK fellowship. The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: This research was supported by the National Institute for Health and Care Research Leicester Biomedical Research Centre; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years, no other relationships or activities that could appear to have influenced the submitted work.

The lead author (CDM) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported, and that no important aspects of the study have been omitted.

Dissemination to participants and related patient and public communities: We plan to disseminate these research findings to a wider community through press releases, featuring on the Centre for Lifestyle Medicine and Behaviour website ( www.lboro.ac.uk/research/climb/ ) via our policy networks, through social media platforms, and presentation at conferences.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ .

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• Published: 13 May 2024

Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial

• Donna H. Ryan 1 ,
• Ildiko Lingvay   ORCID: orcid.org/0000-0001-7006-7401 2 ,
• John Deanfield 3 ,
• Steven E. Kahn 4 ,
• Eric Barros   ORCID: orcid.org/0000-0001-6613-4181 5 ,
• Bartolome Burguera 6 ,
• Helen M. Colhoun   ORCID: orcid.org/0000-0002-8345-3288 7 ,
• Cintia Cercato   ORCID: orcid.org/0000-0002-6181-4951 8 ,
• Dror Dicker 9 ,
• Deborah B. Horn 10 ,
• G. Kees Hovingh 5 ,
• Ole Kleist Jeppesen 5 ,
• Alexander Kokkinos 11 ,
• A. Michael Lincoff   ORCID: orcid.org/0000-0001-8175-2121 12 ,
• Sebastian M. Meyhöfer 13 ,
• Tugce Kalayci Oral 5 ,
• Jorge Plutzky   ORCID: orcid.org/0000-0002-7194-9876 14 ,
• André P. van Beek   ORCID: orcid.org/0000-0002-0335-8177 15 ,
• John P. H. Wilding   ORCID: orcid.org/0000-0003-2839-8404 16 &
• Robert F. Kushner 17  

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In the SELECT cardiovascular outcomes trial, semaglutide showed a 20% reduction in major adverse cardiovascular events in 17,604 adults with preexisting cardiovascular disease, overweight or obesity, without diabetes. Here in this prespecified analysis, we examined effects of semaglutide on weight and anthropometric outcomes, safety and tolerability by baseline body mass index (BMI). In patients treated with semaglutide, weight loss continued over 65 weeks and was sustained for up to 4 years. At 208 weeks, semaglutide was associated with mean reduction in weight (−10.2%), waist circumference (−7.7 cm) and waist-to-height ratio (−6.9%) versus placebo (−1.5%, −1.3 cm and −1.0%, respectively; P  < 0.0001 for all comparisons versus placebo). Clinically meaningful weight loss occurred in both sexes and all races, body sizes and regions. Semaglutide was associated with fewer serious adverse events. For each BMI category (<30, 30 to <35, 35 to <40 and ≥40 kg m − 2 ) there were lower rates (events per 100 years of observation) of serious adverse events with semaglutide (43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo). Semaglutide was associated with increased rates of trial product discontinuation. Discontinuations increased as BMI class decreased. In SELECT, at 208 weeks, semaglutide produced clinically significant weight loss and improvements in anthropometric measurements versus placebo. Weight loss was sustained over 4 years. ClinicalTrials.gov identifier: NCT03574597 .

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The worldwide obesity prevalence, defined by body mass index (BMI) ≥30 kg m − 2 , has nearly tripled since 1975 (ref. 1 ). BMI is a good surveillance measure for population changes over time, given its strong correlation with body fat amount on a population level, but it may not accurately indicate the amount or location of body fat at the individual level 2 . In fact, the World Health Organization defines clinical obesity as ‘abnormal or excessive fat accumulation that may impair health’ 1 . Excess abnormal body fat, especially visceral adiposity and ectopic fat, is a driver of cardiovascular (CV) disease (CVD) 3 , 4 , 5 , and contributes to the global chronic disease burden of diabetes, chronic kidney disease, cancer and other chronic conditions 6 , 7 .

Remediating the adverse health effects of excess abnormal body fat through weight loss is a priority in addressing the global chronic disease burden. Improvements in CV risk factors, glycemia and quality-of-life measures including personal well-being and physical functioning generally begin with modest weight loss of 5%, whereas greater weight loss is associated with more improvement in these measures 8 , 9 , 10 . Producing and sustaining durable and clinically significant weight loss with lifestyle intervention alone has been challenging 11 . However, weight-management medications that modify appetite can make attaining and sustaining clinically meaningful weight loss of ≥10% more likely 12 . Recently, weight-management medications, particularly those comprising glucagon-like peptide-1 receptor agonists, that help people achieve greater and more sustainable weight loss have been developed 13 . Once-weekly subcutaneous semaglutide 2.4 mg, a glucagon-like peptide-1 receptor agonist, is approved for chronic weight management 14 , 15 , 16 and at doses of up to 2.0 mg is approved for type 2 diabetes treatment 17 , 18 , 19 . In patients with type 2 diabetes and high CV risk, semaglutide at doses of 0.5 mg and 1.0 mg has been shown to significantly lower the risk of CV events 20 . The SELECT trial (Semaglutide Effects on Heart Disease and Stroke in Patients with Overweight or Obesity) studied patients with established CVD and overweight or obesity but without diabetes. In SELECT, semaglutide was associated with a 20% reduction in major adverse CV events (hazard ratio 0.80, 95% confidence interval (CI) 0.72 to 0.90; P  < 0.001) 21 . Data derived from the SELECT trial offer the opportunity to evaluate the weight loss efficacy, in a geographically and racially diverse population, of semaglutide compared with placebo over 208 weeks when both are given in addition to standard-of-care recommendations for secondary CVD prevention (but without a focus on targeting weight loss). Furthermore, the data allow examination of changes in anthropometric measures such as BMI, waist circumference (WC) and waist-to-height ratio (WHtR) as surrogates for body fat amount and location 22 , 23 . The diverse population can also be evaluated for changes in sex- and race-specific ‘cutoff points’ for BMI and WC, which have been identified as anthropometric measures that predict cardiometabolic risk 8 , 22 , 23 .

This prespecified analysis of the SELECT trial investigated weight loss and changes in anthropometric indices in patients with established CVD and overweight or obesity without diabetes, who met inclusion and exclusion criteria, within a range of baseline categories for glycemia, renal function and body anthropometric measures.

Study population

The SELECT study enrolled 17,604 patients (72.3% male) from 41 countries between October 2018 and March 2021, with a mean (s.d.) age of 61.6 (8.9) years and BMI of 33.3 (5.0) kg m − 2 (ref. 21 ). The baseline characteristics of the population have been reported 24 . Supplementary Table 1 outlines SELECT patients according to baseline BMI categories. Of note, in the lower BMI categories (<30 kg m − 2 (overweight) and 30 to <35 kg m − 2 (class I obesity)), the proportion of Asian individuals was higher (14.5% and 7.4%, respectively) compared with the proportion of Asian individuals in the higher BMI categories (BMI 35 to <40 kg m − 2 (class II obesity; 3.8%) and ≥40 kg m − 2 (class III obesity; 2.2%), respectively). As the BMI categories increased, the proportion of women was higher: in the class III BMI category, 45.5% were female, compared with 20.8%, 25.7% and 33.0% in the overweight, class I and class II categories, respectively. Lower BMI categories were associated with a higher proportion of patients with normoglycemia and glycated hemoglobin <5.7%. Although the proportions of patients with high cholesterol and history of smoking were similar across BMI categories, the proportion of patients with high-sensitivity C-reactive protein ≥2.0 mg dl −1 increased as the BMI category increased. A high-sensitivity C-reactive protein >2.0 mg dl −1 was present in 36.4% of patients in the overweight BMI category, with a progressive increase to 43.3%, 57.3% and 72.0% for patients in the class I, II and III obesity categories, respectively.

Weight and anthropometric outcomes

Percentage weight loss.

The average percentage weight-loss trajectories with semaglutide and placebo over 4 years of observation are shown in Fig. 1a (ref. 21 ). For those in the semaglutide group, the weight-loss trajectory continued to week 65 and then was sustained for the study period through week 208 (−10.2% for the semaglutide group, −1.5% for the placebo group; treatment difference −8.7%; 95% CI −9.42 to −7.88; P  < 0.0001). To estimate the treatment effect while on medication, we performed a first on-treatment analysis (observation period until the first time being off treatment for >35 days). At week 208, mean weight loss in the semaglutide group analyzed as first on-treatment was −11.7% compared with −1.5% for the placebo group (Fig. 1b ; treatment difference −10.2%; 95% CI −11.0 to −9.42; P  < 0.0001).

a , b , Observed data from the in-trial period ( a ) and first on-treatment ( b ). The symbols are the observed means, and error bars are ±s.e.m. Numbers shown below each panel represent the number of patients contributing to the means. Analysis of covariance with treatment and baseline values was used to estimate the treatment difference. Exact P values are 1.323762 × 10 −94 and 9.80035 × 10 −100 for a and b , respectively. P values are two-sided and are not adjusted for multiplicity. ETD, estimated treatment difference; sema, semaglutide.

Categorical weight loss and individual body weight change

Among in-trial (intention-to-treat principle) patients at week 104, weight loss of ≥5%, ≥10%, ≥15%, ≥20% and ≥25% was achieved by 67.8%, 44.2%, 22.9%, 11.0% and 4.9%, respectively, of those treated with semaglutide compared with 21.3%, 6.9%, 1.7%, 0.6% and 0.1% of those receiving placebo (Fig. 2a ). Individual weight changes at 104 weeks for the in-trial populations for semaglutide and placebo are depicted in Fig. 2b and Fig. 2c , respectively. These waterfall plots show the variation in weight-loss response that occurs with semaglutide and placebo and show that weight loss is more prominent with semaglutide than placebo.

a , Categorical weight loss from baseline at week 104 for semaglutide and placebo. Data from the in-trial period. Bars depict the proportion (%) of patients receiving semaglutide or placebo who achieved ≥5%, ≥10%, ≥15%, ≥20% and ≥25% weight loss. b , c , Percentage change in body weight for individual patients from baseline to week 104 for semaglutide ( b ) and placebo ( c ). Each patient’s percentage change in body weight is plotted as a single bar.

Change in WC

WC change from baseline to 104 weeks has been reported previously in the primary outcome paper 21 . The trajectory of WC change mirrored that of the change in body weight. At week 208, average reduction in WC was −7.7 cm with semaglutide versus −1.3 cm with placebo, with a treatment difference of −6.4 cm (95% CI −7.18 to −5.61; P  < 0.0001) 21 .

WC cutoff points

We analyzed achievement of sex- and race-specific cutoff points for WC by BMI <35 kg m − 2 or ≥35 kg m − 2 , because for BMI >35 kg m − 2 , WC is more difficult technically and, thus, less accurate as a risk predictor 4 , 25 , 26 . Within the SELECT population with BMI <35 kg m − 2 at baseline, 15.0% and 14.3% of the semaglutide and placebo groups, respectively, were below the sex- and race-specific WC cutoff points. At week 104, 41.2% fell below the sex- and race-specific cutoff points for the semaglutide group, compared with only 18.0% for the placebo group (Fig. 3 ).

WC cutoff points; Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm, non-Asian men <102 cm.

Waist-to-height ratio

At baseline, mean WHtR was 0.66 for the study population. The lowest tertile of the SELECT population at baseline had a mean WHtR <0.62, which is higher than the cutoff point of 0.5 used to indicate increased cardiometabolic risk 27 , suggesting that the trial population had high WCs. At week 208, in the group randomized to semaglutide, there was a relative reduction of 6.9% in WHtR compared with 1.0% in placebo (treatment difference −5.87% points; 95% CI −6.56 to −5.17; P  < 0.0001).

BMI category change

At week 104, 52.4% of patients treated with semaglutide achieved improvement in BMI category compared with 15.7% of those receiving placebo. Proportions of patients in the BMI categories at baseline and week 104 are shown in Fig. 4 , which depicts in-trial patients receiving semaglutide and placebo. The BMI category change reflects the superior weight loss with semaglutide, which resulted in fewer patients being in the higher BMI categories after 104 weeks. In the semaglutide group, 12.0% of patients achieved a BMI <25 kg m − 2 , which is considered the healthy BMI category, compared with 1.2% for placebo; per study inclusion criteria, no patients were in this category at baseline. The proportion of patients with obesity (BMI ≥30 kg m − 2 ) fell from 71.0% to 43.3% in the semaglutide group versus 71.9% to 67.9% in the placebo group.

In the semaglutide group, 12.0% of patients achieved normal weight status at week 104 (from 0% at baseline), compared with 1.2% (from 0% at baseline) for placebo. BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (BMI ≥40 kg m − 2 ).

Weight and anthropometric outcomes by subgroups

The forest plot illustrated in Fig. 5 displays mean body weight percentage change from baseline to week 104 for semaglutide relative to placebo in prespecified subgroups. Similar relationships are depicted for WC changes in prespecified subgroups shown in Extended Data Fig. 1 . The effect of semaglutide (versus placebo) on mean percentage body weight loss as well as reduction in WC was found to be heterogeneous across several population subgroups. Women had a greater difference in mean weight loss with semaglutide versus placebo (−11.1% (95% CI −11.56 to −10.66) versus −7.5% in men (95% CI −7.78 to −7.23); P  < 0.0001). There was a linear relationship between age category and degree of mean weight loss, with younger age being associated with progressively greater mean weight loss, but the actual mean difference by age group is small. Similarly, BMI category had small, although statistically significant, associations. Those with WHtR less than the median experienced slightly lower mean body weight change than those above the median, with estimated treatment differences −8.04% (95% CI −8.37 to −7.70) and −8.99% (95% CI −9.33 to −8.65), respectively ( P  < 0.0001). Patients from Asia and of Asian race experienced slightly lower mean weight loss (estimated treatment difference with semaglutide for Asian race −7.27% (95% CI −8.09 to −6.46; P  = 0.0147) and for Asia −7.30 (95% CI −7.97 to −6.62; P  = 0.0016)). There was no difference in weight loss with semaglutide associated with ethnicity (estimated treatment difference for Hispanic −8.53% (95% CI −9.28 to −7.76) or non-Hispanic −8.52% (95% CI −8.77 to 8.26); P  = 0.9769), glycemic status (estimated treatment difference for prediabetes −8.53% (95% CI −8.83 to −8.24) or normoglycemia −8.48% (95% CI −8.88 to −8.07; P  = 0.8188) or renal function (estimated treatment difference for estimated glomerular filtration rate (eGFR) <60 or ≥60 ml min −1  1.73 m − 2 being −8.50% (95% CI −9.23 to −7.76) and −8.52% (95% CI −8.77 to −8.26), respectively ( P  = 0.9519)).

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and are not adjusted for multiplicity. The dots show estimated treatment differences, and the error bars show 95% CIs. Details of the statistical models are available in Methods . ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Safety and tolerability according to baseline BMI category

We reported in the primary outcome of the SELECT trial that adverse events (AEs) leading to permanent discontinuation of the trial product occurred in 1,461 patients (16.6%) in the semaglutide group and 718 patients (8.2%) in the placebo group ( P  < 0.001) 21 . For this analysis, we evaluated the cumulative incidence of AEs leading to trial product discontinuation by treatment assignment and by BMI category (Fig. 6 ). For this analysis, with death modeled as a competing risk, we tracked the proportion of in-trial patients for whom drug was withdrawn or interrupted for the first time (Fig. 6 , left) or cumulative discontinuations (Fig. 6 , right). Both panels of Fig. 6 depict a graded increase in the proportion discontinuing semaglutide, but not placebo. For lower BMI classes, discontinuation rates are higher in the semaglutide group but not the placebo group.

Data are in-trial from the full analysis set. sema, semaglutide.

We reported in the primary SELECT analysis that serious adverse events (SAEs) were reported by 2,941 patients (33.4%) in the semaglutide arm and by 3,204 patients (36.4%) in the placebo arm ( P  < 0.001) 21 . For this study, we analyzed SAE rates by person-years of treatment exposure for BMI classes (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ) and provide these data in Supplementary Table 2 . We also provide an analysis of the most common categories of SAEs. Semaglutide was associated with lower SAEs, primarily driven by CV event and infections. Within each obesity class (<30 kg m − 2 , 30 to <35 kg m − 2 , 35 to <40 kg m − 2 , and ≥40 kg m − 2 ), there were fewer SAEs in the group receiving semaglutide compared with placebo. Rates (events per 100 years of observation) of SAEs were 43.23, 43.54, 51.07 and 47.06 for semaglutide and 50.48, 49.66, 52.73 and 60.85 for placebo, with no evidence of heterogeneity. There was no detectable difference in hepatobiliary or gastrointestinal SAEs comparing semaglutide with placebo in any of the four BMI classes we evaluated.

The analyses of weight effects of the SELECT study presented here reveal that patients assigned to once-weekly subcutaneous semaglutide 2.4 mg lost significantly more weight than those receiving placebo. The weight-loss trajectory with semaglutide occurred over 65 weeks and was sustained up to 4 years. Likewise, there were similar improvements in the semaglutide group for anthropometrics (WC and WHtR). The weight loss was associated with a greater proportion of patients receiving semaglutide achieving improvement in BMI category, healthy BMI (<25 kg m − 2 ) and falling below the WC cutoff point above which increased cardiometabolic risk for the sex and race is greater 22 , 23 . Furthermore, both sexes, all races, all body sizes and those from all geographic regions were able to achieve clinically meaningful weight loss. There was no evidence of increased SAEs based on BMI categories, although lower BMI category was associated with increased rates of trial product discontinuation, probably reflecting exposure to a higher level of drug in lower BMI categories. These data, representing the longest clinical trial of the effects of semaglutide versus placebo on weight, establish the safety and durability of semaglutide effects on weight loss and maintenance in a geographically and racially diverse population of adult men and women with overweight and obesity but not diabetes. The implications of weight loss of this degree in such a diverse population suggests that it may be possible to impact the public health burden of the multiple morbidities associated with obesity. Although our trial focused on CV events, many chronic diseases would benefit from effective weight management 28 .

There were variations in the weight-loss response. Individual changes in body weight with semaglutide and placebo were striking; still, 67.8% achieved 5% or more weight loss and 44.2% achieved 10% weight loss with semaglutide at 2 years, compared with 21.3% and 6.9%, respectively, for those receiving placebo. Our first on-treatment analysis demonstrated that those on-drug lost more weight than those in-trial, confirming the effect of drug exposure. With semaglutide, lower BMI was associated with less percentage weight loss, and women lost more weight on average than men (−11.1% versus −7.5% treatment difference from placebo); however, in all cases, clinically meaningful mean weight loss was achieved. Although Asian patients lost less weight on average than patients of other races (−7.3% more than placebo), Asian patients were more likely to be in the lowest BMI category (<30 kg m − 2 ), which is known to be associated with less weight loss, as discussed below. Clinically meaningful weight loss was evident in the semaglutide group within a broad range of baseline categories for glycemia and body anthropometrics. Interestingly, at 2 years, a significant proportion of the semaglutide-treated group fell below the sex- and race-specific WC cutoff points, especially in those with BMI <35 kg m − 2 , and a notable proportion (12.0%) fell below the BMI cutoff point of 25 kg m − 2 , which is deemed a healthy BMI in those without unintentional weight loss. As more robust weight loss is possible with newer medications, achieving and maintaining these cutoff point targets may become important benchmarks for tracking responses.

The overall safety profile did not reveal any new signals from prior studies, and there were no BMI category-related associations with AE reporting. The analysis did reveal that tolerability may differ among specific BMI classes, since more discontinuations occurred with semaglutide among lower BMI classes. Potential contributors may include a possibility of higher drug exposure in lower BMI classes, although other explanations, including differences in motivation and cultural mores regarding body size, cannot be excluded.

Is the weight loss in SELECT less than expected based on prior studies with the drug? In STEP 1, a large phase 3 study of once-weekly subcutaneous semaglutide 2.4 mg in individuals without diabetes but with BMI >30 kg m − 2 or 27 kg m − 2 with at least one obesity-related comorbidity, the mean weight loss was −14.9% at week 68, compared with −2.4% with placebo 14 . Several reasons may explain the observation that the mean treatment difference was −12.5% in STEP 1 and −8.7% in SELECT. First, SELECT was designed as a CV outcomes trial and not a weight-loss trial, and weight loss was only a supportive secondary endpoint in the trial design. Patients in STEP 1 were desirous of weight loss as a reason for study participation and received structured lifestyle intervention (which included a −500 kcal per day diet with 150 min per week of physical activity). In the SELECT trial, patients did not enroll for the specific purpose of weight loss and received standard of care covering management of CV risk factors, including medical treatment and healthy lifestyle counseling, but without a specific focus on weight loss. Second, the respective study populations were quite different, with STEP 1 including a younger, healthier population with more women (73.1% of the semaglutide arm in STEP 1 versus 27.7% in SELECT) and higher mean BMI (37.8 kg m − 2 versus 33.3 kg m − 2 , respectively) 14 , 21 . Third, major differences existed between the respective trial protocols. Patients in the semaglutide treatment arm of STEP 1 were more likely to be exposed to the medication at the full dose of 2.4 mg than those in SELECT. In SELECT, investigators were allowed to slow, decrease or pause treatment. By 104 weeks, approximately 77% of SELECT patients on dose were receiving the target semaglutide 2.4 mg weekly dose, which is lower than the corresponding proportion of patients in STEP 1 (89.6% were receiving the target dose at week 68) 14 , 21 . Indeed, in our first on-treatment analysis at week 208, weight loss was greater (−11.7% for semaglutide) compared with the in-trial analysis (−10.2% for semaglutide). Taken together, all these issues make less weight loss an expected finding in SELECT, compared with STEP 1.

The SELECT study has some limitations. First, SELECT was not a primary prevention trial, and the data should not be extrapolated to all individuals with overweight and obesity to prevent major adverse CV events. Although the data set is rich in numbers and diversity, it does not have the numbers of individuals in racial subgroups that may have revealed potential differential effects. SELECT also did not include individuals who have excess abnormal body fat but a BMI <27 kg m − 2 . Not all individuals with increased CV risk have BMI ≥27 kg m − 2 . Thus, the study did not include Asian patients who qualify for treatment with obesity medications at lower BMI and WC cutoff points according to guidelines in their countries 29 . We observed that Asian patients were less likely to be in the higher BMI categories of SELECT and that the population of those with BMI <30 kg m − 2 had a higher percentage of Asian race. Asian individuals would probably benefit from weight loss and medication approaches undertaken at lower BMI levels in the secondary prevention of CVD. Future studies should evaluate CV risk reduction in Asian individuals with high CV risk and BMI <27 kg m − 2 . Another limitation is the lack of information on body composition, beyond the anthropometric measures we used. It would be meaningful to have quantitation of fat mass, lean mass and muscle mass, especially given the wide range of body size in the SELECT population.

An interesting observation from this SELECT weight loss data is that when BMI is ≤30 kg m − 2 , weight loss on a percentage basis is less than that observed across higher classes of BMI severity. Furthermore, as BMI exceeds 30 kg m − 2 , weight loss amounts are more similar for class I, II and III obesity. This was also observed in Look AHEAD, a lifestyle intervention study for weight loss 30 . The proportion (percentage) of weight loss seems to be less, on average, in the BMI <30 kg m − 2 category relative to higher BMI categories, despite their receiving of the same treatment and even potentially higher exposure to the drug for weight loss 30 . Weight loss cannot continue indefinitely. There is a plateau of weight that occurs after weight loss with all treatments for weight management. This plateau has been termed the ‘set point’ or ‘settling point’, a body weight that is in harmony with the genetic and environmental determinants of body weight and adiposity 31 . Perhaps persons with BMI <30 kg m − 2 are closer to their settling point and have less weight to lose to reach it. Furthermore, the cardiometabolic benefits of weight loss are driven by reduction in the abnormal ectopic and visceral depots of fat, not by reduction of subcutaneous fat stores in the hips and thighs. The phenotype of cardiometabolic disease but lower BMI (<30 kg m − 2 ) may be one where reduction of excess abnormal and dysfunctional body fat does not require as much body mass reduction to achieve health improvement. We suspect this may be the case and suggest further studies to explore this aspect of weight-loss physiology.

In conclusion, this analysis of the SELECT study supports the broad use of once-weekly subcutaneous semaglutide 2.4 mg as an aid to CV event reduction in individuals with overweight or obesity without diabetes but with preexisting CVD. Semaglutide 2.4 mg safely and effectively produced clinically significant weight loss in all subgroups based on age, sex, race, glycemia, renal function and anthropometric categories. Furthermore, the weight loss was sustained over 4 years during the trial.

Trial design and participants

The current work complies with all relevant ethical regulations and reports a prespecified analysis of the randomized, double-blind, placebo-controlled SELECT trial ( NCT03574597 ), details of which have been reported in papers describing study design and rationale 32 , baseline characteristics 24 and the primary outcome 21 . SELECT evaluated once-weekly subcutaneous semaglutide 2.4 mg versus placebo to reduce the risk of major adverse cardiac events (a composite endpoint comprising CV death, nonfatal myocardial infarction or nonfatal stroke) in individuals with established CVD and overweight or obesity, without diabetes. The protocol for SELECT was approved by national and institutional regulatory and ethical authorities in each participating country. All patients provided written informed consent before beginning any trial-specific activity. Eligible patients were aged ≥45 years, with a BMI of ≥27 kg m − 2 and established CVD defined as at least one of the following: prior myocardial infarction, prior ischemic or hemorrhagic stroke, or symptomatic peripheral artery disease. Additional inclusion and exclusion criteria can be found elsewhere 32 .

Human participants research

The trial protocol was designed by the trial sponsor, Novo Nordisk, and the academic Steering Committee. A global expert panel of physician leaders in participating countries advised on regional operational issues. National and institutional regulatory and ethical authorities approved the protocol, and all patients provided written informed consent.

Study intervention and patient management

Patients were randomly assigned in a double-blind manner and 1:1 ratio to receive once-weekly subcutaneous semaglutide 2.4 mg or placebo. The starting dose was 0.24 mg once weekly, with dose increases every 4 weeks (to doses of 0.5, 1.0, 1.7 and 2.4 mg per week) until the target dose of 2.4 mg was reached after 16 weeks. Patients who were unable to tolerate dose escalation due to AEs could be managed by extension of dose-escalation intervals, treatment pauses or maintenance at doses below the 2.4 mg per week target dose. Investigators were allowed to reduce the dose of study product if tolerability issues arose. Investigators were provided with guidelines for, and encouraged to follow, evidence-based recommendations for medical treatment and lifestyle counseling to optimize management of underlying CVD as part of the standard of care. The lifestyle counseling was not targeted at weight loss. Additional intervention descriptions are available 32 .

Sex, race, body weight, height and WC measurements

Sex and race were self-reported. Body weight was measured without shoes and only wearing light clothing; it was measured on a digital scale and recorded in kilograms or pounds (one decimal with a precision of 0.1 kg or lb), with preference for using the same scale throughout the trial. The scale was calibrated yearly as a minimum unless the manufacturer certified that calibration of the weight scales was valid for the lifetime of the scale. Height was measured without shoes in centimeters or inches (one decimal with a precision of 0.1 cm or inches). At screening, BMI was calculated by the electronic case report form. WC was defined as the abdominal circumference located midway between the lower rib margin and the iliac crest. Measures were obtained in a standing position with a nonstretchable measuring tape and to the nearest centimeter or inch. The patient was asked to breathe normally. The tape touched the skin but did not compress soft tissue, and twists in the tape were avoided.

The following endpoints relevant to this paper were assessed at randomization (week 0) to years 2, 3 and 4: change in body weight (%); proportion achieving weight loss ≥5%, ≥10%, ≥15% and ≥20%; change in WC (cm); and percentage change in WHtR (cm cm −1 ). Improvement in BMI category (defined as being in a lower BMI class) was assessed at week 104 compared with baseline according to BMI classes: healthy (BMI <25 kg m − 2 ), overweight (25 to <30 kg m − 2 ), class I obesity (30 to <35 kg m − 2 ), class II obesity (35 to <40 kg m − 2 ) and class III obesity (≥40 kg m − 2 ). The proportions of individuals with BMI <35 or ≥35 kg m − 2 who achieved sex- and race-specific cutoff points for WC (indicating increased metabolic risk) were evaluated at week 104. The WC cutoff points were as follows: Asian women <80 cm, non-Asian women <88 cm, Asian men <88 cm and non-Asian men <102 cm.

Overall, 97.1% of the semaglutide group and 96.8% of the placebo group completed the trial. During the study, 30.6% of those assigned to semaglutide did not complete drug treatment, compared with 27.0% for placebo.

Statistical analysis

The statistical analyses for the in-trial period were based on the intention-to-treat principle and included all randomized patients irrespective of adherence to semaglutide or placebo or changes to background medications. Continuous endpoints were analyzed using an analysis of covariance model with treatment as a fixed factor and baseline value of the endpoint as a covariate. Missing data at the landmark visit, for example, week 104, were imputed using a multiple imputation model and done separately for each treatment arm and included baseline value as a covariate and fit to patients having an observed data point (irrespective of adherence to randomized treatment) at week 104. The fit model is used to impute values for all patients with missing data at week 104 to create 500 complete data sets. Rubin’s rules were used to combine the results. Estimated means are provided with s.e.m., and estimated treatment differences are provided with 95% CI. Binary endpoints were analyzed using logistic regression with treatment and baseline value as a covariate, where missing data were imputed by first using multiple imputation as described above and then categorizing the imputed data according to the endpoint, for example, body weight percentage change at week 104 of <0%. Subgroup analyses for continuous and binary endpoints also included the subgroup and interaction between treatment and subgroup as fixed factors. Because some patients in both arms continued to be followed but were off treatment, we also analyzed weight loss by first on-treatment group (observation period until first time being off treatment for >35 days) to assess a more realistic picture of weight loss in those adhering to treatment. CIs were not adjusted for multiplicity and should therefore not be used to infer definitive treatment effects. All statistical analyses were performed with SAS software, version 9.4 TS1M5 (SAS Institute).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Data will be shared with bona fide researchers who submit a research proposal approved by the independent review board. Individual patient data will be shared in data sets in a deidentified and anonymized format. Information about data access request proposals can be found at https://www.novonordisk-trials.com/ .

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Acknowledgements

Editorial support was provided by Richard Ogilvy-Stewart of Apollo, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines ( www.ismpp.org/gpp-2022 ).

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Donna H. Ryan

Department of Internal Medicine/Endocrinology and Peter O’ Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA

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Contributions

D.H.R., I.L. and S.E.K. contributed to the study design. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. were study investigators. D.B.H., I.L., D.D., A.K., S.M.M., A.P.v.B., C.C. and J.P.H.W. enrolled patients. D.H.R. was responsible for data analysis and manuscript preparation. All authors contributed to data interpretation, review, revisions and final approval of the manuscript.

Corresponding author

Correspondence to Donna H. Ryan .

Ethics declarations

Competing interests.

D.H.R. declares having received consulting honoraria from Altimmune, Amgen, Biohaven, Boehringer Ingelheim, Calibrate, Carmot Therapeutics, CinRx, Eli Lilly, Epitomee, Gila Therapeutics, IFA Celtics, Novo Nordisk, Pfizer, Rhythm, Scientific Intake, Wondr Health and Zealand Pharma; she declares she received stock options from Calibrate, Epitomee, Scientific Intake and Xeno Bioscience. I.L. declares having received research funding (paid to institution) from Novo Nordisk, Sanofi, Mylan and Boehringer Ingelheim. I.L. received advisory/consulting fees and/or other support from Altimmune, AstraZeneca, Bayer, Biomea, Boehringer Ingelheim, Carmot Therapeutics, Cytoki Pharma, Eli Lilly, Intercept, Janssen/Johnson & Johnson, Mannkind, Mediflix, Merck, Metsera, Novo Nordisk, Pharmaventures, Pfizer, Regeneron, Sanofi, Shionogi, Structure Therapeutics, Target RWE, Terns Pharmaceuticals, The Comm Group, Valeritas, WebMD and Zealand Pharma. J.D. declares having received consulting honoraria from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk and Bayer, and research grants from British Heart Foundation, MRC (UK), NIHR, PHE, MSD, Pfizer, Aegerion, Colgate and Roche. S.E.K. declares having received consulting honoraria from ANI Pharmaceuticals, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk and Oramed, and stock options from AltPep. B.B. declares having received honoraria related to participation on this trial and has no financial conflicts related to this publication. H.M.C. declares being a stockholder and serving on an advisory panel for Bayer; receiving research grants from Chief Scientist Office, Diabetes UK, European Commission, IQVIA, Juvenile Diabetes Research Foundation and Medical Research Council; serving on an advisory board and speaker’s bureau for Novo Nordisk; and holding stock in Roche Pharmaceuticals. C.C. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Merck, Brace Pharma and Eurofarma. D.D. declares having received consulting honoraria from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and AstraZeneca, and received research grants through his affiliation from Novo Nordisk, Eli Lilly, Boehringer Ingelheim and Rhythm. D.B.H. declares having received research grants through her academic affiliation from Novo Nordisk and Eli Lilly, and advisory/consulting honoraria from Novo Nordisk, Eli Lilly and Gelesis. A.K. declares having received research grants through his affiliation from Novo Nordisk and Pharmaserve Lilly, and consulting honoraria from Pharmaserve Lilly, Sanofi-Aventis, Novo Nordisk, MSD, AstraZeneca, ELPEN Pharma, Boehringer Ingelheim, Galenica Pharma, Epsilon Health and WinMedica. A.M.L. declares having received honoraria from Novo Nordisk, Eli Lilly, Akebia Therapeutics, Ardelyx, Becton Dickinson, Endologix, FibroGen, GSK, Medtronic, Neovasc, Provention Bio, ReCor, BrainStorm Cell Therapeutics, Alnylam and Intarcia for consulting activities, and research funding to his institution from AbbVie, Esperion, AstraZeneca, CSL Behring, Novartis and Eli Lilly. S.M.M. declares having received consulting honoraria from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Daichii-Sankyo, esanum, Gilead, Ipsen, Eli Lilly, Novartis, Novo Nordisk, Sandoz and Sanofi; he declares he received research grants from AstraZeneca, Eli Lilly and Novo Nordisk. J.P. declares having received consulting honoraria from Altimmune, Amgen, Esperion, Merck, MJH Life Sciences, Novartis and Novo Nordisk; he has received a grant, paid to his institution, from Boehringer Ingelheim and holds the position of Director, Preventive Cardiology, at Brigham and Women’s Hospital. A.P.v.B. is contracted via the University of Groningen (no personal payment) to undertake consultancy for Novo Nordisk, Eli Lilly and Boehringer Ingelheim. J.P.H.W. is contracted via the University of Liverpool (no personal payment) to undertake consultancy for Altimmune, AstraZeneca, Boehringer Ingelheim, Cytoki, Eli Lilly, Napp, Novo Nordisk, Menarini, Pfizer, Rhythm Pharmaceuticals, Sanofi, Saniona, Tern Pharmaceuticals, Shionogi and Ysopia. J.P.H.W. also declares personal honoraria/lecture fees from AstraZeneca, Boehringer Ingelheim, Medscape, Napp, Menarini, Novo Nordisk and Rhythm. R.F.K. declares having received consulting honoraria from Novo Nordisk, Weight Watchers, Eli Lilly, Boehringer Ingelheim, Pfizer, Structure and Altimmune. E.B., G.K.H., O.K.J. and T.K.O. are employees of Novo Nordisk A/S.

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Extended data

Extended data fig. 1 effect of semaglutide treatment or placebo on waist circumference from baseline to week 104 by subgroups..

Data from the in-trial period. N  = 17,604. P values represent test of no interaction effect. P values are two-sided and not adjusted for multiplicity. The dots show estimated treatment differences and the error bars show 95% confidence intervals. Details of the statistical models are available in Methods . BMI, body mass index; CI, confidence interval; CV, cardiovascular; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; ETD, estimated treatment difference; HbA1c, glycated hemoglobin; MI, myocardial infarction; PAD, peripheral artery disease; sema, semaglutide.

Supplementary information

Reporting summary, supplementary tables 1 and 2.

Supplementary Table 1. Baseline characteristics by BMI class. Data are represented as number and percentage of patients. Renal function categories were based on the eGFR as per Chronic Kidney Disease Epidemiology Collaboration. Albuminuria categories were based on UACR. Smoking was defined as smoking at least one cigarette or equivalent daily. The category ‘Other’ for CV inclusion criteria includes patients where it is unknown if the patient fulfilled only one or several criteria and patients who were randomized in error and did not fulfill any criteria. Supplementary Table 2. SAEs according to baseline BMI category. P value: two-sided P value from Fisher’s exact test for test of no difference.

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Ryan, D.H., Lingvay, I., Deanfield, J. et al. Long-term weight loss effects of semaglutide in obesity without diabetes in the SELECT trial. Nat Med (2024). https://doi.org/10.1038/s41591-024-02996-7

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DOI : https://doi.org/10.1038/s41591-024-02996-7

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The patient has been treated for hypertension for 10 years, currently with amlodipine 10 mg by mouth daily. She was once told that her cholesterol value was "borderline high" but does not know the value.

She denies symptoms of diabetes, chest pain, shortness of breath, heart disease, stroke, or circulatory problems of the lower extremities.

She estimates her current weight at 165 lbs (75 kg). She thinks she weighed 120 lbs (54 kg) at age 21 years but gained weight with each of her three pregnancies and did not return to her nonpregnant weight after each delivery. She weighed 155 lbs one year ago but gained weight following retirement from her job as an elementary school teacher. No family medical history is available because she was adopted. She does not eat breakfast, has a modest lunch, and consumes most of her calories at supper and in the evening.

On examination, blood pressure is 140/85 mmHg supine and 140/90 mmHg upright with a regular heart rate of 76 beats/minute. She weighs 169 lbs, with a body mass index (BMI) of 30.9 kg/m 2 . Fundoscopic examination reveals no evidence of retinopathy. Vibratory sensation is absent at the great toes, reduced at the medial malleoli, and normal at the tibial tubercles. Light touch sensation is reduced in the feet but intact more proximally. Knee jerks are 2+ bilaterally, but the ankle jerks are absent. The examination is otherwise within normal limits.

• News Releases

Semaglutide can produce clinically meaningful weight loss and reduce waist size for at least 4 years in adults with overweight or obesity who don’t have diabetes, and delivers cardiovascular benefits irrespective of weight lost

European Association for the Study of Obesity

Two important studies based on the largest and longest clinical trial of the effects of semaglutide on weight in over 17,000 adults with overweight and obesity but not diabetes find patients lost on average 10% of their body weight and over 7cm from their waistline after 4 years.

Clinically meaningful weight loss was achieved by men and women of all races, ages, and body sizes, across all regions, with a lower rate of serious adverse events compared with placebo.

Over half of adults taking semaglutide moved down at least one BMI category after 2 years compared to 16% receiving placebo; and 12% reached a healthy BMI (25 kg/m² or less) compared with 1% in the placebo group.

Importantly, the findings also indicate that semaglutide delivers cardiovascular benefits irrespective of starting weight and the amount of weight lost—suggesting that even patients with mild obesity or those not losing weight are likely to gain some advantage.

Two important studies are being presented at this year’s European Congress on Obesity (ECO) in Venice, Italy (12-15 May), based on the landmark Semaglutide and Cardiovascular Outcomes (SELECT) trial from the same international author group. The first new study, led by Professor Donna Ryan from Pennington Biomedical Research Centre, New Orleans, USA, and being published simultaneously in Nature Medicine , examines the long-term weight effects of semaglutide. The second study led by led by Professor John Deanfield from University College London, UK, investigates whether the cardiovascular benefits are related to starting weight or the amount of weight lost.

Semaglutide is a GLP-1 medication primarily prescribed for adults with type 2 diabetes but is also approved for weight loss in people with obesity or overweight who have at least one other health issue. This class of medications simulate the functions of the body’s natural incretin hormones, which help to lower blood sugar levels after a meal. Adjusting these hormone levels can also make people feel full, and in doing so, helps lower their daily calorie intake.

In 2023, the SELECT trial reported that adults with overweight or obesity but not diabetes taking semaglutide for more than 3 years had a 20% lower risk of heart attack, stroke, or death due to cardiovascular disease, and lost an average 9.4% of their bodyweight [1]. 

Between October 2018 and June 2023, 17,604 adults (aged 45 or older; 72% male) from 804 sites in 41 countries with overweight or obesity (BMI of 27 kg/m² or higher) were enrolled and treated with Semaglutide (2.4mg) or placebo for an average of 40 months. They had previously experienced a heart attack, stroke and/or had peripheral artery disease, but did not have type 1 or type 2 diabetes when they joined the study.

The researchers examined markers of obesity that include body composition and fat distribution (waist circumference and waist circumference-to-height ratio [WHtR]), rather than just BMI alone, to help clarify the effect of semaglutide on central abdominal fat which has been proven to cause greater cardiovascular risk than general obesity.

Clinically meaningful weight loss in all sexes, races, body sizes, and regions

The first new study shows that once-weekly treatment with semaglutide can produce clinically meaningful and sustained weight loss and decrease waist size for at least 4 years in adults with overweight or obesity who do not have diabetes, with a lower rate of serious adverse events compared with placebo.

Importantly, men and women of all races, ages, and body sizes, across all geographical regions were able to achieve sustained, clinically meaningful weight loss.

“Our long-term analysis of semaglutide establishes that clinically relevant weight loss can be sustained for up to 4 years in a geographically and racially diverse population of adults with overweight and obesity but not diabetes,” says Professor Ryan. “This degree of weight loss in such a large and diverse population suggests that it may be possible to impact the public health burden of multiple obesity-related illnesses. While our trial focused on cardiovascular events, many other chronic diseases including several types of cancer, osteoarthritis, and anxiety and depression would benefit from effective weight management.”

In the semaglutide group, weight loss continued to week 65 and was sustained for 4 years, with participants’ losing on average 10.2% of their body weight and 7.7cm from their waistline, compared with 1.5% and 1.3cm respectively in the placebo group.

Similarly, in the semaglutide group, average WHtR fell by 6.9% compared with 1% in the placebo group.

These improvements were seen across both sexes and all categories of race and age, irrespective of starting blood sugar (glycaemic) status or metabolically unhealthy body fat. However, women taking semaglutide tended to lose more weight on average than men, and Asian patients lost less weight on average than other races.

Interestingly, after 2 years over half (52%) of participants treated with semaglutide had transitioned to a lower BMI category compared with 16% of those given placebo. For example, the proportion of participants with obesity (BMI 30kg/m² or higher) declined from 71% to 43% in the semaglutide group, and from 72% to 68% in the placebo group. Moreover, 12% of adults in the semaglutide group achieved a healthy weight (BMI 25kg/m² or less) compared with 1.2% in the placebo group

For each BMI category (<30, ≤30-<35, ≤35-<40, and ≥40 kg/m2) there were lower rates (events per 100 years of observation) of SAEs with semaglutide (43.23, 43.54, 51.0, 47.06) than with placebo (50.48, 49.66, 52.73, 60.85) respectively.

There were no unexpected safety issues with semaglutide in the SELECT trial. The proportion of participants with serious adverse events (SAEs) was lower in the semaglutide group than the placebo group (33% vs 36%), mainly driven by differences in cardiac disorders (11.5% vs 13.5%).   More patients receiving semaglutide discontinued the trial due to gastrointestinal symptoms, including nausea and diarrhoea, mainly during the 20-week dose escalation phase. Importantly, semaglutide did not lead to an increased rate of pancreatitis, but rates of cholelithiasis (stones in gallbladder) were higher in the semaglutide group.   

Cardiovascular benefits irrespective of weight loss

The second study examined the relationship between weight measures at baseline, and change in weight during the study with cardiovascular outcomes.  These included time to first major adverse cardiovascular event (MACE) and heart failure measures.

The findings showed that treatment with semaglutide delivered cardiovascular benefits, irrespective of the starting weight and the amount of weight lost. This suggests that even patients with relatively mild levels of obesity, or those who only lose modest amount of weight, may have improved cardiovascular outcome.

“These findings have important clinical implications”, says Professor Deanfield. “Around half of the patients that I see in my cardiovascular practice have levels of weight equivalent to those in the SELECT trial and are likely to derive benefit from taking Semaglutide on top of their usual level of guideline directed care.” 

He adds, “Our findings show that the magnitude of this treatment effect with semaglutide is independent of the amount of weight lost, suggesting that the drug has other actions which lower cardiovascular risk beyond reducing unhealthy body fat. These alternative mechanisms may include positive impacts on blood sugar, blood pressure, or inflammation, as well as direct effects on the heart muscle and blood vessels, or a combination of one or more of these.”

Despite these important findings, the authors caution that SELECT is not a primary prevention trial so that the data cannot be extrapolated to all adults with overweight and obesity to prevent MACE; and despite being large and diverse, it does not include enough individuals from different racial groups to understand different potential effects.

Nature Medicine

COI Statement

DR is an Advisor/consultant: Altimmune, Amgen, Biohaven, Calibrate, Carmot, CINRx, Currax, Epitomee, Gila, Ifa Celtic, Lilly, Nestle, Novo Nordisk, Scientific Intake, Structure Therapeutics, Wondr Health, Xeno Bioscience, Zealand. Speaker’s Bureau: Novo Nordisk, Lilly. Stock Options: Epitomee, Calibrate, Roman, Scientific Intake, Xeno. Research: SELECT Steering Committee (Novo Nordisk). DSMB: IQVIA setmelanotide (2); Lilly(1). JD received CME honoraria and/or consulting fees from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis,  Sanofi, Takeda, Novo Nordisk, Bayer. Research grants from British Heart Foundation, MRC(UK), NIHR, PHE, MSD, Pfizer, Aegerion, Colgate, Roche.  Member of Study Steering Committees for Novo Nordisk (SOUL and SELECT) Editorial support was provided by Richard Ogilvy-Stewart of Titan, OPEN Health Communications, and funded by Novo Nordisk A/S, in accordance with Good Publication Practice guidelines (www.ismpp.org/gpp-2022). Funding Research relating to this abstract was funded by Novo Nordisk.

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World Obesity have collated some of the recent data and case studies available looking pertaining to obesity and the current outbreak of COVID-19. 

Researchers at Johns Hopkins University in the US examined 265 patients to determine if younger patients hospitalised with COVID-19 were more likely to be living with overweight and obesity. They found a correlation, which they hypothesise may be due to physiologic changes from obesity. Other comorbidities these patients may have had were not reported. Read the full study here .

Chinese researchers identified 66 patients with COVID-19 and fatty liver disease and compared the outcomes for those with and without obesity. They found obesity was a significant risk factor for severe illness in this population after accounting for other factors (age, gender, smoking, diabetes, high blood pressure, and dyslipidaemia). Read the full study here . 

The global rise in the prevalence of obesity and type 2 diabetes can be partially explained by a rise in diets high in fats, sugars and refined carbohydrates. Diets high in saturated fatty acids cause inflammation and immune disfunction, which may explain why minority groups (who experience disproportionate rates of diseases linked to nutrition, such as obesity and diabetes) are also hospitalised with COVID-19 at higher rates. Read the full study here .

MicroRNAs (abbreviated miRNAs) are produced in human cells to regulate gene expression. Some research has suggested that these may also defend against viruses. These researchers identified 848 miRNAs that are may be effective against SARS and 873 that could target COVID-19 using genome sequences of each of these viruses. Previous studies have suggested that the elderly and those with underlying conditions (including obesity) may produce less of these miRNAs, possibly explaining why these groups are at increased risk of severe illness from COVID-19. However, trials in human and animal subjects are needed to verify these theoretical results. Read the full study here .  

Given the importance of determining the risk factors for morbidity and mortality related to COVID-19, this retrospective study analysed the frequency and outcomes of COVID-19 patients in critical care who are living with overweight or obesity. “Of the 3,615 individuals who tested positive for COVID-19, 775 (21%) had a body mass index (BMI) 30-34, and 595 (16% of the total cohort) had a BMI >35.” While patients were separated into elderly (over 60) and younger (under 60) groups, it was not reported if the study controlled for other variables that may affect the course of COVID-19. Read the full study here .

This piece describes two patients with obesity that experienced damage to their airways while being intubated due to severe illness from COVID-19. The authors recommend videolaryngoscopy for intubation to protect both patients and healthcare workers. Read the full study here .

These researchers chose to specifically examine how many COVID-19 patients living with obesity or overweight were placed on ventilators. Based in Lille, France, the study included 124 patients, 68.8% of whom ultimately required ventilation. They established a dose-response relationship- increasing body max index (BMI) increased the risk of needing ventilation. This study found that BMI seemed to be associated with ventilator treatments independently of age, diabetes or high blood pressure. However, further research must be conducted before this relationship is proven. Read the full study here .

Researchers obtained medical records of 16,749 people hospitalised for COVID-19 to determine what were some of the factors that made patients more likely to experience severe cases of the illness. Slightly over half of patients had at least one underlying condition (including obesity) and these patients were more likely to die from COVID-19. The study found that obesity is linked to mortality, independently of age, gender and other associated conditions. Read the full study here .

Using a very large sample size of 17,425,455, this cohort study aimed to identify risk factors associated with mortality due to COVID-19 across the general population. Among the comorbidities, most of them were associated with increased risk, including obesity. Furthermore, deprivation was also identified as a major risk factor. Specifically, for patients with overweight and obesity, as their body mass index increased, so did their risk of dying from COVID-19. Read the full study here .

This study included 48 critically ill patients with COVID-19 treated with invasive ventilation in Spain. Of this population, 48% were living with obesity, 44% with hypertension, and 38% with chronic lung disease. Symptoms and patient outcomes were also described. Read the full study here .

This study examined the correlation between severe disease and body mass index (BMI) among 357 patients in France. People diagnosed with severe COVID-19 were 1.35 times more likely to also be living with obesity and people in critical care with COVID-19 were 1.89 times more likely to be living with obesity than the general public. This study adjusted for age and gender of patients but no other cofounding factors. Read the full study here .

Previous research has demonstrated that children tend to gain weight during when school is not in session, so experts have been concerned about the impact of lockdowns due to coronavirus on childhood obesity rates. This study observed lifestyle behaviours in 41 children living with obesity at baseline and then three weeks into quarantine. Scientists found that children reported eating more meals, as well as more potato chips, red meat, and sugar-sweetened beverages. They slept more, exercised less and spent much more time looking at screens. As a result, researchers recommend that lifestyle interventions be delivered through telemedicine while the lockdown lasts. Read the full study here .

A recent study from France examined 1317 COVID-19 patients living with diabetes. Of these, more than 10% passed away and almost 33% needed to be placed on a ventilator within a week of admission to the hospital. Obesity was found to be an independent risk factor for poor outcomes when other cofounding factors were accounted for. Read the full study here .

This study found that, of 5700 patients admitted to 12 selected New York hospitals with COVID-19, 56.6% had hypertension (high blood pressure), 41.7% were living with obesity and 33.8% had diabetes. It also reported data on patient outcomes. Read the full study here .  

Wuhan city, the capital of Hubei province in China, was for a long time the epicentre of the COVID-19 outbreak. This study presents information of patients admitted to two Wuhan hospitals with laboratory-confirmed COVID-19. 191 patients were included in order to determine what risk factors lead to fatalities, describe Covid-19 symptoms over time, determine how long patients are infectious after they recover and record what treatments were tried. It should be noted that almost half of patients had underlying health conditions such as hypertension or heart disease, although obesity was not measured. Read the full study here . 

This study examined 24 adults to determine which populations in the Seattle area were hospitalised with severe illness from COVID-19, what underlying conditions they had, the results of medical imaging tests and whether they recovered. Patients had an average body mass index of 33.2 (give or take 7.2 units) and over half (58%) of patients were diagnosed with diabetes. Scientists concluded that “patients with coexisting conditions and older age are at risk for severe disease and poor outcomes after ICU [intensive care unit] admission.” Read the full study here .

Looking at 383 patients in Shenzen, China, this study was the first to directly examine the correlation between obesity and severe illness from coronavirus. For this study, a person with a body mass index (BMI) between 24.0 - 27.9 was considered overweight and a person with a BMI greater than 28 was considered to be living with obesity. While people living with obesity generally experienced the same length of illness, they were significantly more likely to develop severe pneumonia, even when accounting for other risk factors. Read the full study here .

Based on a sample of 4,103 New York City residents, this paper evaluates what characteristics make people more likely to be admitted to the hospital and critical care.  Overall, it was observed that 39.8% of people living with obesity were hospitalised, compared to 14.5% without. Scientists found “particularly strong associations of older age, obesity, heart failure and chronic kidney disease with hospitalization risk, with much less influence of race, smoking status, chronic pulmonary disease and other forms of heart disease.” Read the full study here .

In order to ensure the proper monitoring of COVID-19-related hospitalisations across the United States, the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was developed. This report “presents age-stratified COVID-19-associated hospitalisation rates for patients admitted during March 1-28, 2020, and clinical data on patients admitted during March 1-30, 2020.” Among the 1,482 patients diagnosed and hospitalised with COVID-19, 90% had at least one comorbidity and 42% were living with obesity, with African Americans and the elderly disproportionately affected. Read the full study here .

This report examined demographic information of patients hospitalised with COVID-19 in China. Of these, older patients, diabetics and those living with obesity were significantly more likely to be considered “severely ill.” The study also looked at symptoms during admission at admission and treatment options. Read the full study here .

In this study, researchers used data from 103 consecutive patients hospitalized in the USA. There were two major findings- a correlation between critical care admissions due to COVID-19 and a body mass index greater than 35 in general, and a correlation between needing invasive mechanical ventilation and having both heart disease and obesity. These findings were adjusted for age, sex, and race. Read the full study here .

This article examined how SARS- CoV-2 impacts pregnancy using 46 patients in the USA. Almost all patients who developed severe disease were living with overweight and obesity. After diagnosis, 16% of patients were admitted to the hospital and 2% were placed in intensive care. Researchers believe this, along with the need to induce labour prematurely in some patients to improve breathing, may suggest that pregnant women should be classified as a vulnerable group. Read the full study here .

School and recreational space closures due to COVID-19 have reduced physical activity among children. Researchers used modeling software to simulate the following scenarios: 

• No school closures (control) 
• Schools closed for two months 
• Schools closed for two months and 10% reduction in physical activity over the summer break  
• Schools closed for four months (April through May and September through October) and 10% reduction in physical activity over the summer break 
• Schools closed for six months (April through May and September through December) and 10% reduction in physical activity over the summer break 

Overall, the pandemic is projected to increase mean standardised body mass index (BMI) between 0.056 (two-month closure) and 0.198 (six-month closure) units. It may also increase the percentage of children living with obesity in the USA by up to 2.373 percentage points. Read the full study here .

This study was conducted to examine the characteristics and course of disease in 50 New York children (under 21 years of age) hospitalised with COVID-19. Of the study population, 11 patients had obesity and 8 had overweight.  Obesity was found to be a significant risk factor for both severe disease and mechanical ventilation while immunosuppression was not.  Read the full study here .

Researchers at the University of Chicago Medical Center found that patients hospitalized with COVID-19 were more likely to die if they were also living with obesity, even when accounting for age, sex, and underlying conditions. 238 patients were included within the study. These researchers did not find a significant connection with admission to critical care units or mechanical ventilation in patients with obesity. Limitations included the makeup of the study population, as the sample size was small and the vast majority were African American, so the results may not be representative of all people. Read the full study here.  

This meta-analysis and systematic review found nine separate articles regarding the link between COVID-19, obesity and more severe diseases. Between all studies, 1817 patients were examined. Researchers found an odds ratio of 1.89 for poor outcomes in patients with obesity, especially among younger patients, which indicates that obesity increases the risk of severe diseases. Read the full study here . 

A meta-analysis concluded that people living with obesity were more likely to have worse outcomes if they also contracted COVID-19. Researchers identified nine articles (six of which were retrospective case-control studies, four of which were retrospective cohort studies, and one of which used both methods) and extracted data from each. Limitations included heterogeneity in study design (particularly regarding the definition of obesity), lack of comorbidity reporting, and low quantity of studies used. Read the full study here .

As almost 75% of American adults over the age of 20 are living with overweight or obesity, this disease should be considered a public health priority, especially given the increased likelihood of poor outcomes in COVID-19 patients with obesity. The paper outlines several mechanisms explaining why obesity may lead to more severe disease, including having more of the receptor the virus uses to enter cells, reduced lung function, chronic inflammation, endothelial disfunction, changes in blood clotting, and physiological changes related to common comorbidities of obesity. Finally, several compelling studies linking obesity to increased risk of complications are included. Read the full study here .

Evidence shows that the impact of COVID-19 tends to be more serious in specific vulnerable groups, including people living with obesity. Furthermore, the pandemic also seems to have a number of indirect repercussions including on eating behaviour patterns among people with obesity. The objective of this study was “to examine the impact of the COVID-19 pandemic on patronage to unhealthy eating establishments in populations with obesity.”   

These researchers combined GPS data with known obesity rates to determine how many people with obesity entered unhealthy restaurants during the COVID-19 pandemic (December 2019- April 2020). Prior to lockdowns, more people in areas with high obesity rates entered fast food restaurants; in March, fewer people did across all areas; however, the numbers of patrons steadily increased during April, at a faster rate in areas with higher obesity rates. While informative, a number of limitations were observed, including the fact that not all consumers exactly match the demographics of the area they live in and that more variables may contribute to restaurant traffic than accounted for here. Read the full study here . 

Various studies over the past few months have linked obesity to a more serious course of illness from COVID-19. It is therefore essential that we improve our understanding of the possible reasons for the link and what it means for those living with obesity. This systematic review looks at the influence of obesity on COVID-19 outcomes and proposes biological mechanisms as to why a more severe courseof illness can occur. It also discusses the implications of COVID-19 for those living with obesity. Read the full study here .

Both COVID-19 and childhood obesity are pandemics raging across America today. Obesity is an independent risk factor for the severity of COVID-19, suggesting that children with obesity could see a more severe course of illness due to COVID-19. The stay-at-home mandates and physical distancing preventative measures have resulted in a lack of access to nutritious foods, physical activity, routines and social interactions, all of which could negatively impact children -especially those living with obesity. Read the full study here .

Obesity has been suggested as a risk factor for poor outcome in those with COVID-19. Studies show that patients with obesity are more likely to require mechanical ventilation. In fact, multiorgan failure in patients with COVID-19 and obesity could be dueto the chronic metabolic inflammation and predisposition to the “enhanced release of cytokines-pathophysiology accompanying severe obesity”. However, the association between body mass index (BMI) and COVID-19 outcomes has yet to be fully explored. This study intends to address that gap. Read the full study here .

Emerging evidence suggests that the severity of COVID-19 in a patient is associated with overweight and obesity. Patients with obesity are at risk for a number of other non-communicable diseases, including cardiovascular dysfunction and hypertension and diabetes. In individuals living with overweight and obesity, macronutrient excess in adipose tissue stimulates adipocytes “to release tumour necrosis factor α(TNF-α), interleukin-6 (IL-6) and other pro-inflammatory mediators and to reduce production of the anti-inflammatory adiponectin, thus predisposing to a proinflammatory state and oxidative stress”. Obesity also impairs immune responses; it has a negative impact on pathogen defences within the body. Therefore, the acceleration of viral inflammatory responses in COVID-19 and more unfavourable prognoses are associated with individuals living with obesity. Read the full study here .

Obesity has been identified as a comorbidity for severe outcomes in patients with COVID-19. In this study, comorbidities associated with increased risk of COVID-19 were determined in a population-based analysis of Mexicans with at least one comorbidity. Data was obtained from the COVID-19 database of the publicly available Mexican Ministry of Health “Dirección General de Epidemiología”. Variables of the patients’ heath were all noted, such as age, gender, smoking status, history of COVID-19 contact, comorbidities, etc. Patients with missing information were excluded in the analysis. To determine the independent effect of each comorbidityon COVID-19 and separate the effect of two or more, “analysis was limited to patients reporting only one comorbidity." Read the full study here .

Obesity has arisen as a major complication for the COVID-19 pandemic, which has been caused by the novel SARS-CoV-2 virus. The former is a major health concern due to its side-effects on human health and association with morbidity and mortality. Evidence points out that obesity can worsen patient prognosis due to COVID-19 infection. There may be a “pathophysiological link that could explain the fact that obese patients are prone to present with SARS-CoV-2 complications”. The authors present mechanistic obesity-related issues that aggravate the SARS-CoV-2 infection in patients living with obesity and the possible molecular links between obesity and SARS-CoV-2 infection. Read the full study here .

The highly infectious serious acute respiratory syndrome COVID-19 has caused high morbidity and mortality all over the world. It has been suggested that SARS-CoV-2, the pathogen of COVID-19, uses angiotensin-converting enzyme 2 (ACE2) as a cell receptor. This receptor is found in the lungs but also many other organs, including the adipose tissue, heart, and oral epithelium. Previous studies have identified obesity as a critical factor in the prognoses of COVID-19 patients, and that, in patients with COVID-19, non-survivors had a higher body mass index (BMI) than survivors. This study intended to “investigate the association between obesity and poor outcomes of COVID-19 patients." Read the full study here .

Approximately 45% of individuals worldwide have overweight or obesity. Obesity is characterized by its pro-inflammatory condition. The excess visceral and omental adiposity seen in individuals with obesity are linked with an increase in pro-inflammatory cytokines that affect systemic cellular processes. Importantly, they “change the nature and frequency of immune cells infiltration”. When a high percentage of a population have obesity, more virulent viral strains tend to develop, and the reach of a virus is wider. Furthermore, the state of obesity is correlated to the presence of comorbidities that are dangerous to human health, such as type 2 diabetes and hypertension. This systematic review includes articles from a myriad of databases in order to address how living with obesity impacts one’s reaction to the SARS-CoV-2 virus and course of COVID-19. Read the full study here .

The psychological impact of COVID-19 lockdown and quarantine on children has been documented to cause “anxiety, worrying, irritability, depressive symptoms, and even post-traumatic stress disorder symptoms”. In particular, children living with severe obesity may struggle with anxieties about the possibility of obesogenic issues that can arise during the course of illness due to COVID-19. In this study, 75 families (one child interviewed per family) were interviewed on anxiety that their child with severe obesity may have, and on what specific type anxieties they are. 24 of 75 children reported having COVID-19 related anxieties. Read the full study here . 

In this multi-centre study focused on retrospective observational data from eight hospitals throughout Greece, the data on 90 critically ill patients positive for COVID-19 is analysed. Those hospitalised due to COVID-19 reflect critically ill patients whodeveloped extremely severe acute respiratory syndrome (SARS) in elderly patients with COVID-19-related pneumonia and/or underlying chronic diseases. Many underlying chronic diseases have been identified as risk factors for developing more severe COVID-19. These include type-2 diabetes, cardiovascular diseases, and hypertension. Obesity has also been associated with disease severity. In this study the relation of comorbidities such as obesity and type-2 diabetes and COVID-19 disease severity is explored. Read the full study here .

According to the World Health Organisation, physical inactivity is the fourth leading cause of death, and increases the risk of a person contracting a “metabolic disease, including obesity and type 2 diabetes (T2D).” This article points out that those seeking treatment for obesity or T2D may find difficulty in doing so during the COVID-19 pandemic due to lockdowns. As it has been found that sedentary behaviour increases one's risk for many chronic diseases, the authors wished to explore hypothetical immunopathologyof COVID-19 in patients living with obesity and how the immune defences against COVID-19 may be related to the “immuno-metabolic dysregulations'' characterised by it. Furthermore, they explore the possibility of exercise as a counteractive measure due to its anti-inflammatory properties. Read the full study here .

Obesity has been linked to a less-efficient immune response in the human body as well as poorer outcomes for respiratory diseases. In this article, researchers hypothesised that a higher Body Mass Index is a risk factor for a more severe course of illness for COVID-19. They followed all patients hospitalised from 11 January to 16 February 2020 until March 26 2020 at the Third People’s Hospital of Shenzhen (China), which was dedicated to COVID-19 treatment. Read the full study here .

As reported by the World Health Organization, the global prevalence of obesity is still on the rise both across high-income as well as low-and middle-income countries. Obesity has been associated with an increase in mortality for patients fighting COVID-19. The authors suggest that the inflammatory profile associated with patients with obesity is conducive to a more severe course of illness in patients with COVID-19. Read the full study here .

Researchers studying COVID-19, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), have concluded that obesity, diabetes, hypertension or cardiovascular disease is correlated to an increased severity of illness due to COVID-19. Obesity has been associated with SARS-CoV-2 due to the “cytokine storm” of the latter; a number of the pro-inflammatory cytokines released in the “storm” which are detrimental to organ function are also found contributing to the chronic low-grade inflammation in patients with obesity. The authors wished to study a Middle Eastern population and assess the outcome of COVID-19 in relation to obesity. They observed clinical data from patients in the Al Kuwait Hospital in Dubai, UAE, to study the correlation between obesity and poor clinical outcomes of COVID-19. Read the full study here .

In many previous studies, underlying conditions such as obesity, hypertension and diabetes have been found to be correlated with an increased rate of hospitalisation and death due to SARS-CoV-2. Obesity is a non-communicable disease marked by an imbalanced energy state due to hypertrophy and hyperplasia of adipose tissue. Increased secretion of various cytokines and hormones, such as interleukin-6, tumour necrosis factor alpha and leptin, establishes a low-grade inflammatory state in patients with obesity. These pro-inflammatory cytokines predispose individuals “to increased risk for infection and adverse outcomes”. The metabolic disorders that are associated with obesity are numerous, including diabetes, hypertension and cardiovascular diseases. Most are associated with an increased risk of severe COVID-19. Due to this link, obesity is “an important factor in determining the morbidity and mortality risk in SARS CoV 2 patients” as well as the need for mechanical ventilation. Read the full study here .

Pulmonary consolidation is the most common complication of COVID-19. A high percentageof COVID-19 related pulmonary consolidationis due to extensive pulmonary fibrosis (PF). Viral infections have been shown to be a risk factor for PF, and both viral infections and aging were“strongly associated cofactors” for PF in this study. Infection with SARS-CoV-2, the virus responsible for COVID-19,suppresses the angiotensin-converting enzyme 1 (ACE2), which is a receptor exploited by the virus for cell entry; this receptor is “a negative regulator of” PF, which therefore links the virus to the progression of PF. Read the full study here .

Elevated body mass index has been marked as a risk factor for COVID-19 severity, hospital admissions and mortality. Diabetes and hypertension have also been associated with severe and fatal cases of COVID-19. Mendelian randomisation (MR) analyses the causal effect of an exposure risk factor on an outcome using genetic variants as instruments of estimation. In this study, the causal relationship between obesity traits (such as elevated BMI and metabolic disorders) and quantitative cardiometabolic biomarkers and COVID-19 susceptibility was examined by MR. Data was obtained from the UK Biobank. 1,211 individuals who had tested positive for COVID-19 and 387,079 individuals who were negativeor untestedwere analysed. Read the full study here .

Obesity and diabetes have both been identified in epidemiological reports as comorbidities “frequently associated with severe forms of COVID-19”. Both have also been identified as an independent risk factor for the severity of COVID-19 in a patient. The presence of these diseases is associated with each other; therefore, they could “confer a particularly high risk of severe COVID-19”. In previous analysis of the CORONAvirus-SARS-CoV-2 and Diabetes Outcomes (CORONADO) Study, it was shown “that body mass index (BMI) was positively and independently associated with severe COVID-19-related outcomes ... in patients with diabetes hospitalised for COVID-19”. In this analysis of the CORONADO data, the course of COVID-19 and its relationship to obesity in patients with type 2 diabetes hospitalised for this disease is explored. The influence of age on BMI and COVID-19 prognosis is also addressed due to the heightened impact of COVID-19 on the elderly population. Read the full study here .

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Case 6–2020: A 34-Year-Old Woman with Hyperglycemia

Presentation of case.

Dr. Max C. Petersen (Medicine): A 34-year-old woman was evaluated in the diabetes clinic of this hospital for hyperglycemia.

Eleven years before this presentation, the blood glucose level was 126 mg per deciliter (7.0 mmol per liter) on routine laboratory evaluation, which was performed as part of an annual well visit. The patient could not recall whether she had been fasting at the time the test had been performed. One year later, the fasting blood glucose level was 112 mg per deciliter (6.2 mmol per liter; reference range, <100 mg per deciliter [<5.6 mmol per liter]).

Nine years before this presentation, a randomly obtained blood glucose level was 217 mg per deciliter (12.0 mmol per liter), and the patient reported polyuria. At that time, the glycated hemoglobin level was 5.8% (reference range, 4.3 to 5.6); the hemoglobin level was normal. One year later, the glycated hemoglobin level was 5.9%. The height was 165.1 cm, the weight 72.6 kg, and the body-mass index (BMI; the weight in kilograms divided by the square of the height in meters) 26.6. The patient received a diagnosis of prediabetes and was referred to a nutritionist. She made changes to her diet and lost 4.5 kg of body weight over a 6-month period; the glycated hemoglobin level was 5.5%.

Six years before this presentation, the patient became pregnant with her first child. Her prepregnancy BMI was 24.5. At 26 weeks of gestation, the result of a 1-hour oral glucose challenge test (i.e., the blood glucose level obtained 1 hour after the oral administration of a 50-g glucose load in the nonfasting state) was 186 mg per deciliter (10.3 mmol per liter; reference range, <140 mg per deciliter [<7.8 mmol per liter]). She declined a 3-hour oral glucose tolerance test; a presumptive diagnosis of gestational diabetes was made. She was asked to follow a meal plan for gestational diabetes and was treated with insulin during the pregnancy. Serial ultrasound examinations for fetal growth and monitoring were performed. At 34 weeks of gestation, the fetal abdominal circumference was in the 76th percentile for gestational age. Polyhydramnios developed at 37 weeks of gestation. The child was born at 39 weeks 3 days of gestation, weighed 3.9 kg at birth, and had hypoglycemia after birth, which subsequently resolved. Six weeks post partum, the patient’s fasting blood glucose level was 120 mg per deciliter (6.7 mmol per liter), and the result of a 2-hour oral glucose tolerance test (i.e., the blood glucose level obtained 2 hours after the oral administration of a 75-g glucose load in the fasting state) was 131 mg per deciliter (7.3 mmol per liter; reference range, <140 mg per deciliter). Three months post partum, the glycated hemoglobin level was 6.1%. Lifestyle modification for diabetes prevention was recommended.

Four and a half years before this presentation, the patient became pregnant with her second child. Her prepregnancy BMI was 25.1. At 5 weeks of gestation, she had an elevated blood glucose level. Insulin therapy was started at 6 weeks of gestation, and episodes of hypoglycemia occurred during the pregnancy. Serial ultrasound examinations for fetal growth and monitoring were performed. At 28 weeks of gestation, the fetal abdominal circumference was in the 35th percentile for gestational age, and the amniotic fluid level was normal. Labor was induced at 38 weeks of gestation; the child weighed 2.6 kg at birth. Neonatal blood glucose levels were reported as stable after birth. Six weeks post partum, the patient’s fasting blood glucose level was 133 mg per deciliter (7.4 mmol per liter), and the result of a 2-hour oral glucose tolerance test was 236 mg per deciliter (13.1 mmol per liter). The patient received a diagnosis of type 2 diabetes mellitus; lifestyle modification was recommended. Three months post partum, the glycated hemoglobin level was 5.9% and the BMI was 30.0. Over the next 2 years, she followed a low-carbohydrate diet and regular exercise plan and self-monitored the blood glucose level.

Two years before this presentation, the patient became pregnant with her third child. Blood glucose levels were again elevated, and insulin therapy was started early in gestation. She had episodes of hypoglycemia that led to adjustment of her insulin regimen. The child was born at 38 weeks 5 days of gestation, weighed 3.0 kg at birth, and had hypoglycemia that resolved 48 hours after birth. After the birth of her third child, the patient started to receive metformin, which had no effect on the glycated hemoglobin level, despite adjustment of the therapy to the maximal dose.

One year before this presentation, the patient became pregnant with her fourth child. Insulin therapy was again started early in gestation. The patient reported that episodes of hypoglycemia occurred. Polyhydramnios developed. The child was born at 38 weeks 6 days of gestation and weighed 3.5 kg. The patient sought care at the diabetes clinic of this hospital for clarification of her diagnosis.

The patient reported following a low-carbohydrate diet and exercising 5 days per week. There was no fatigue, change in appetite, change in vision, chest pain, shortness of breath, polydipsia, or polyuria. There was no history of anemia, pancreatitis, hirsutism, proximal muscle weakness, easy bruising, headache, sweating, tachycardia, gallstones, or diarrhea. Her menstrual periods were normal. She had not noticed any changes in her facial features or the size of her hands or feet.

The patient had a history of acne and low-back pain. Her only medication was metformin. She had no known medication allergies. She lived with her husband and four children in a suburban community in New England and worked as an administrator. She did not smoke tobacco or use illicit drugs, and she rarely drank alcohol. She identified as non-Hispanic white. Both of her grandmothers had type 2 diabetes mellitus. Her father had hypertension, was overweight, and had received a diagnosis of type 2 diabetes at 50 years of age. Her mother was not overweight and had received a diagnosis of type 2 diabetes at 48 years of age. The patient had two sisters, neither of whom had a history of diabetes or gestational diabetes. There was no family history of hemochromatosis.

On examination, the patient appeared well. The blood pressure was 126/76 mm Hg, and the heart rate 76 beats per minute. The BMI was 25.4. The physical examination was normal. The glycated hemoglobin level was 6.2%.

A diagnostic test was performed.

DIFFERENTIAL DIAGNOSIS

Dr. Miriam S. Udler: I am aware of the diagnosis in this case and participated in the care of this patient. This healthy 34-year-old woman, who had a BMI just above the upper limit of the normal range, presented with a history of hyperglycemia of varying degrees since 24 years of age. When she was not pregnant, she was treated with lifestyle measures as well as metformin therapy for a short period, and she maintained a well-controlled blood glucose level. In thinking about this case, it is helpful to characterize the extent of the hyperglycemia and then to consider its possible causes.

CHARACTERIZING HYPERGLYCEMIA

This patient’s hyperglycemia reached a threshold that was diagnostic of diabetes 1 on two occasions: when she was 25 years of age, she had a randomly obtained blood glucose level of 217 mg per deciliter with polyuria (with diabetes defined as a level of ≥200 mg per deciliter [≥11.1 mmol per liter] with symptoms), and when she was 30 years of age, she had on the same encounter a fasting blood glucose level of 133 mg per deciliter (with diabetes defined as a level of ≥126 mg per deciliter) and a result on a 2-hour oral glucose tolerance test of 236 mg per deciliter (with diabetes defined as a level of ≥200 mg per deciliter). On both of these occasions, her glycated hemoglobin level was in the prediabetes range (defined as 5.7 to 6.4%). In establishing the diagnosis of diabetes, the various blood glucose studies and glycated hemoglobin testing may provide discordant information because the tests have different sensitivities for this diagnosis, with glycated hemoglobin testing being the least sensitive. 2 Also, there are situations in which the glycated hemoglobin level can be inaccurate; for example, the patient may have recently received a blood transfusion or may have a condition that alters the life span of red cells, such as anemia, hemoglobinopathy, or pregnancy. 3 These conditions were not present in this patient at the time that the glycated hemoglobin measurements were obtained. In addition, since the glycated hemoglobin level reflects the average glucose level typically over a 3-month period, discordance with timed blood glucose measurements can occur if there has been a recent change in glycemic control. This patient had long-standing mild hyperglycemia but met criteria for diabetes on the basis of the blood glucose levels noted.

Type 1 and Type 2 Diabetes

Now that we have characterized the patient’s hyperglycemia as meeting criteria for diabetes, it is important to consider the possible types. More than 90% of adults with diabetes have type 2 diabetes, which is due to progressive loss of insulin secretion by beta cells that frequently occurs in the context of insulin resistance. This patient had received a diagnosis of type 2 diabetes; however, some patients with diabetes may be given a diagnosis of type 2 diabetes on the basis of not having features of type 1 diabetes, which is characterized by autoimmune destruction of the pancreatic beta cells that leads to rapid development of insulin dependence, with ketoacidosis often present at diagnosis.

Type 1 diabetes accounts for approximately 6% of all cases of diabetes in adults (≥18 years of age) in the United States, 4 and 80% of these cases are diagnosed before the patient is 20 years of age. 5 Since this patient’s diabetes was essentially nonprogressive over a period of at least 9 years, she most likely does not have type 1 diabetes. It is therefore not surprising that she had received a diagnosis of type 2 diabetes, but there are several other types of diabetes to consider, particularly since some features of her case do not fit with a typical case of type 2 diabetes, such as her age at diagnosis, the presence of hyperglycemia despite a nearly normal BMI, and the mild and nonprogressive nature of her disease over the course of many years.

Less Common Types of Diabetes

Latent autoimmune diabetes in adults (LADA) is a mild form of autoimmune diabetes that should be considered in this patient. However, there is controversy as to whether LADA truly represents an entity that is distinct from type 1 diabetes. 6 Both patients with type 1 diabetes and patients with LADA commonly have elevated levels of diabetes-associated autoantibodies; however, LADA has been defined by an older age at onset (typically >25 years) and slower progression to insulin dependence (over a period of >6 months). 7 This patient had not been tested for diabetes-associated autoantibodies. I ordered these tests to help evaluate for LADA, but this was not my leading diagnosis because of her young age at diagnosis and nonprogressive clinical course over a period of at least 9 years.

If the patient’s diabetes had been confined to pregnancy, we might consider gestational diabetes, but she had hyperglycemia outside of pregnancy. Several medications can cause hyperglycemia, including glucocorticoids, atypical antipsychotic agents, cancer immunotherapies, and some antiretroviral therapies and immunosuppressive agents used in transplantation. 8 However, this patient was not receiving any of these medications. Another cause of diabetes to consider is destruction of the pancreas due to, for example, cystic fibrosis, a tumor, or pancreatitis, but none of these were present. Secondary endocrine disorders — including excess cortisol production, excess growth hormone production, and pheochromocytoma — were considered to be unlikely in this patient on the basis of the history, review of symptoms, and physical examination.

Monogenic Diabetes

A final category to consider is monogenic diabetes, which is caused by alteration of a single gene. Types of monogenic diabetes include maturity-onset diabetes of the young (MODY), neonatal diabetes, and syndromic forms of diabetes. Monogenic diabetes accounts for 1 to 6% of cases of diabetes in children 9 and approximately 0.4% of cases in adults. 10 Neonatal diabetes is diagnosed typically within the first 6 months of life; syndromic forms of monogenic diabetes have other abnormal features, including particular organ dysfunction. Neither condition is applicable to this patient.

MODY is an autosomal dominant condition characterized by primary pancreatic beta-cell dysfunction that causes mild diabetes that is diagnosed during adolescence or early adulthood. As early as 1964, the nomenclature “maturity-onset diabetes of the young” was used to describe cases that resembled adult-onset type 2 diabetes in terms of the slow progression to insulin use (as compared with the rapid progression in type 1 diabetes) but occurred in relatively young patients. 11 Several genes cause distinct forms of MODY that have specific disease features that inform treatment, and thus MODY is a clinically important diagnosis. Most forms of MODY cause isolated abnormal glucose levels (in contrast to syndromic monogenic diabetes), a manifestation that has contributed to its frequent misdiagnosis as type 1 or type 2 diabetes. 12

Genetic Basis of MODY

Although at least 13 genes have been associated with MODY, 3 genes — GCK , which encodes glucokinase, and HNF1A and HNF4A , which encode hepatocyte nuclear factors 1A and 4A, respectively — account for most cases. MODY associated with GCK (known as GCK-MODY) is characterized by mild, nonprogressive hyperglycemia that is present since birth, whereas the forms of MODY associated with HNF1A and HNF4A (known as HNF1A-MODY and HNF4A-MODY, respectively) are characterized by the development of diabetes, typically in the early teen years or young adulthood, that is initially mild and then progresses such that affected patients may receive insulin before diagnosis.

In patients with GCK-MODY, genetic variants reduce the function of glucokinase, the enzyme in pancreatic beta cells that functions as a glucose sensor and controls the rate of entry of glucose into the glycolytic pathway. As a result, reduced sensitivity to glucose-induced insulin secretion causes asymptomatic mild fasting hyperglycemia, with an upward shift in the normal range of the fasting blood glucose level to 100 to 145 mg per deciliter (5.6 to 8.0 mmol per liter), and also causes an upward shift in postprandial blood glucose levels, but with tight regulation maintained ( Fig. 1 ). 13 This mild hyperglycemia is not thought to confer a predisposition to complications of diabetes, 14 is largely unaltered by treatment, 15 and does not necessitate treatment outside of pregnancy.

Key features suggesting maturity-onset diabetes of the young (MODY) in this patient were an age of less than 35 years at the diagnosis of diabetes, a strong family history of diabetes with an autosomal dominant pattern of inheritance, and hyperglycemia despite a close-to-normal body-mass index. None of these features is an absolute criterion. MODY is caused by single gene–mediated disruption of pancreatic beta-cell function. In MODY associated with the GCK gene (known as GCK-MODY), disrupted glucokinase function causes a mild upward shift in glucose levels through-out the day and does not necessitate treatment. 13 In the pedigree, circles represent female family members, squares male family members, blue family members affected by diabetes, and green unaffected family members. The arrow indicates the patient.

In contrast to GCK-MODY, the disorders HNF1A-MODY and HNF4A-MODY result in progressive hyperglycemia that eventually leads to treatment. 16 Initially, there may be a normal fasting glucose level and large spikes in postprandial glucose levels (to >80 mg per deciliter [>4.4 mmol per liter]). 17 Patients can often be treated with oral agents and discontinue insulin therapy started before the diagnosis of MODY. 18 Of note, patients with HNF1A-MODY or HNF4A-MODY are typically sensitive to treatment with sulfonylureas 19 but may also respond to glucagon-like peptide-1 receptor agonists. 20

This patient had received a diagnosis of diabetes before 35 years of age, had a family history of diabetes involving multiple generations, and was not obese. These features are suggestive of MODY but do not represent absolute criteria for the condition ( Fig. 1 ). 1 Negative testing for diabetes-associated autoantibodies would further increase the likelihood of MODY. There are methods to calculate a patient’s risk of having MODY associated with GCK , HNF1A , or HNF4A . 21 , 22 Using an online calculator ( www.diabetesgenes.org/mody-probability-calculator ), we estimate that the probability of this patient having MODY is at least 75.5%. Genetic testing would be needed to confirm this diagnosis, and in patients at an increased risk for MODY, multigene panel testing has been shown to be cost-effective. 23 , 24

DR. MIRIAM S. UDLER’S DIAGNOSIS

Maturity-onset diabetes of the young, most likely due to a GCK variant.

DIAGNOSTIC TESTING

Dr. Christina A. Austin-Tse: A diagnostic sequencing test of five genes associated with MODY was performed. One clinically significant variant was identified in the GCK gene ( {"type":"entrez-nucleotide","attrs":{"text":"NM_000162.3","term_id":"167621407","term_text":"NM_000162.3"}} NM_000162.3 ): a c.787T→C transition resulting in the p.Ser263Pro missense change. Review of the literature and variant databases revealed that this variant had been previously identified in at least three patients with early-onset diabetes and had segregated with disease in at least three affected members of two families (GeneDx: personal communication). 25 , 26 Furthermore, the variant was rare in large population databases (occurring in 1 out of 128,844 European chromosomes in gnomAD 27 ), a feature consistent with a disease-causing role. Although the serine residue at position 263 was not highly conserved, multiple in vitro functional studies have shown that the p.Ser263Pro variant negatively affects the stability of the glucokinase enzyme. 26 , 28 – 30 As a result, this variant met criteria to be classified as “likely pathogenic.” 31 As mentioned previously, a diagnosis of GCK-MODY is consistent with this patient’s clinical features. On subsequent testing of additional family members, the same “likely pathogenic” variant was identified in the patient’s father and second child, both of whom had documented hyperglycemia.

DISCUSSION OF MANAGEMENT

Dr. Udler: In this patient, the diagnosis of GCK-MODY means that it is normal for her blood glucose level to be mildly elevated. She can stop taking metformin because discontinuation is not expected to substantially alter her glycated hemoglobin level 15 , 32 and because she is not at risk for complications of diabetes. 14 However, she should continue to maintain a healthy lifestyle. Although patients with GCK-MODY are not typically treated for hyperglycemia outside of pregnancy, they may need to be treated during pregnancy.

It is possible for a patient to have type 1 or type 2 diabetes in addition to MODY, so this patient should be screened for diabetes according to recommendations for the general population (e.g., in the event that she has a risk factor for diabetes, such as obesity). 1 Since the mild hyperglycemia associated with GCK-MODY is asymptomatic (and probably unrelated to the polyuria that this patient had described in the past), the development of symptoms of hyperglycemia, such as polyuria, polydipsia, or blurry vision, should prompt additional evaluation. In patients with GCK-MODY, the glycated hemoglobin level is typically below 7.5%, 33 so a value rising above that threshold or a sudden large increase in the glycated hemoglobin level could indicate concomitant diabetes from another cause, which would need to be evaluated and treated.

This patient’s family members are at risk for having the same GCK variant, with a 50% chance of offspring inheriting a variant from an affected parent. Since the hyperglycemia associated with GCK-MODY is present from birth, it is necessary to perform genetic testing only in family members with demonstrated hyperglycemia. I offered site-specific genetic testing to the patient’s parents and second child.

Dr. Meridale V. Baggett (Medicine): Dr. Powe, would you tell us how you would treat this patient during pregnancy?

Dr. Camille E. Powe: During the patient’s first pregnancy, routine screening led to a presumptive diagnosis of gestational diabetes, the most common cause of hyperglycemia in pregnancy. Hyperglycemia in pregnancy is associated with adverse pregnancy outcomes, 34 and treatment lowers the risk of such outcomes. 35 , 36 Two of the most common complications — fetal overgrowth (which can lead to birth injuries, shoulder dystocia, and an increased risk of cesarean delivery) and neonatal hypoglycemia — are thought to be the result of fetal hyperinsulinemia. 37 Maternal glucose is freely transported across the placenta, and excess glucose augments insulin secretion from the fetal pancreas. In fetal life, insulin is a potent growth factor, and neonates who have hyperinsulinemia in utero often continue to secrete excess insulin in the first few days of life. In the treatment of pregnant women with diabetes, we strive for strict blood sugar control (fasting blood glucose level, <95 mg per deciliter [<5.3 mmol per liter]; 2-hour postprandial blood glucose level, <120 mg per deciliter) to decrease the risk of these and other hyperglycemia-associated adverse pregnancy outcomes. 38 – 40

In the third trimester of the patient’s first pregnancy, obstetrical ultrasound examination revealed a fetal abdominal circumference in the 76th percentile for gestational age and polyhydramnios, signs of fetal exposure to maternal hyperglycemia. 40 – 42 Case series involving families with GCK-MODY have shown that the effect of maternal hyperglycemia on the fetus depends on whether the fetus inherits the pathogenic GCK variant. 43 – 48 Fetuses that do not inherit the maternal variant have overgrowth, presumably due to fetal hyperinsulinemia ( Fig. 2A ). In contrast, fetuses that inherit the variant do not have overgrowth and are born at a weight that is near the average for gestational age, despite maternal hyperglycemia, presumably because the variant results in decreased insulin secretion ( Fig. 2B ). Fetuses that inherit GCK-MODY from their fathers and have euglycemic mothers appear to be undergrown, most likely because their insulin secretion is lower than normal when they and their mothers are euglycemic ( Fig. 2D ). Because fetal overgrowth and polyhydramnios occurred during this patient’s first pregnancy and neonatal hypoglycemia developed after the birth, the patient’s first child is probably not affected by GCK-MODY.

Pathogenic variants that lead to GCK-MODY, when carried by a fetus, change the usual relationship of maternal hyperglycemia to fetal hyperinsulinemia and fetal overgrowth. GCK-MODY–affected fetuses have lower insulin secretion than unaffected fetuses in response to the same maternal blood glucose level. In a hyperglycemic mother carrying a fetus who is unaffected by GCK-MODY, excessive fetal growth is usually apparent (Panel A). Studies involving GCK-MODY–affected hyperglycemic mothers have shown that fetal growth is normal despite maternal hyperglycemia when a fetus has the maternal GCK variant (Panel B). The goal of treatment of maternal hyperglycemia when a fetus is unaffected by GCK-MODY is to establish euglycemia to normalize fetal insulin levels and growth (Panel C); whether this can be accomplished in the case of maternal GCK-MODY is controversial, given the genetically determined elevated maternal glycemic set point. In the context of maternal euglycemia, GCK-MODY–affected fetuses may be at risk for fetal growth restriction (Panel D).

In accordance with standard care for pregnant women with diabetes who do not meet glycemic targets after dietary modification, 38 , 39 the patient was treated with insulin during her pregnancies. In her second pregnancy, treatment was begun early, after hyperglycemia was detected in the first trimester. Because she had not yet received the diagnosis of GCK-MODY during any of her pregnancies, no consideration of this condition was given during her obstetrical treatment. Whether treatment affects the risk of hyperglycemia-associated adverse pregnancy outcomes in pregnant women with known GCK-MODY is controversial, with several case series showing that the birth weight percentile in unaffected neonates remains consistent regardless of whether the mother is treated with insulin. 44 , 45 Evidence suggests that it may be difficult to overcome a genetically determined glycemic set point in patients with GCK-MODY with the use of pharmacotherapy, 15 , 32 and affected patients may have symptoms of hypoglycemia when the blood glucose level is normal because of an enhanced counterregulatory response. 49 , 50 Still, to the extent that it is possible, it would be desirable to safely lower the blood glucose level in a woman with GCK-MODY who is pregnant with an unaffected fetus in order to decrease the risk of fetal overgrowth and other consequences of mildly elevated glucose levels ( Fig. 2C ). 46 , 47 , 51 In contrast, there is evidence that lowering the blood glucose level in a pregnant woman with GCK-MODY could lead to fetal growth restriction if the fetus is affected ( Fig. 2D ). 45 , 52 During this patient’s second pregnancy, she was treated with insulin beginning in the first trimester, and her daughter’s birth weight was near the 16th percentile for gestational age; this outcome is consistent with the daughter’s ultimate diagnosis of GCK-MODY.

Expert opinion suggests that, in pregnant women with GCK-MODY, insulin therapy should be deferred until fetal growth is assessed by means of ultrasound examination beginning in the late second trimester. If there is evidence of fetal overgrowth, the fetus is presumed to be unaffected by GCK-MODY and insulin therapy is initiated. 53 After I have counseled women with GCK-MODY on the potential risks and benefits of insulin treatment during pregnancy, I have sometimes used a strategy of treating hyperglycemia from early in pregnancy using modified glycemic targets that are less stringent than the targets typically used during pregnancy. This strategy attempts to balance the risk of growth restriction in an affected fetus (as well as maternal hypoglycemia) with the potential benefit of glucose-lowering therapy for an unaffected fetus.

Dr. Udler: The patient stopped taking metformin, and subsequent glycated hemoglobin levels remained unchanged, at 6.2%. Her father and 5-year-old daughter (second child) both tested positive for the same GCK variant. Her father had a BMI of 36 and a glycated hemoglobin level of 7.8%, so I counseled him that he most likely had type 2 diabetes in addition to GCK-MODY. He is currently being treated with metformin and lifestyle measures. The patient’s daughter now has a clear diagnosis to explain her hyperglycemia, which will help in preventing misdiagnosis of type 1 diabetes, given her young age, and will be important for the management of any future pregnancies. She will not need any medical follow-up for GCK-MODY until she is considering pregnancy.

FINAL DIAGNOSIS

Maturity-onset diabetes of the young due to a GCK variant.

Acknowledgments

We thank Dr. Andrew Hattersley and Dr. Sarah Bernstein for helpful comments on an earlier draft of the manuscript.

This case was presented at the Medical Case Conference.

No potential conflict of interest relevant to this article was reported.

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org .

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Wednesday briefing: The study that says semaglutide can do much more than help you lose weight

In today’s newsletter: A new study suggests semaglutides reduce not just obesity but risks to your heart too. What will that mean for their availability on the NHS?

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Good morning. If you hear brand names like Ozempic and Wegovy and think of suddenly gaunt A-listers posing on the red carpet, it is surely now time to think again. There has already been substantial evidence that as well as in their initial role as a diabetes treatment, semaglutides – the kind of drug in question – can have a real impact on obesity for people for whom nothing else works. Now a new study has found that they don’t just help those people lose weight – they have a major effect on their heart health , regardless of how much weight they lose.

Obesity affects more than a quarter of adults, and nearly as many children – and those numbers are only going up , which brings serious consequences for public health, especially among the most deprived. So this is hugely significant news, which study author Prof John Deanfield says heralds a class of drugs as powerful as statins, “that could equally transform many chronic diseases of ageing”.

Today’s newsletter, with obesity expert and lecturer in physiology at Anglia Ruskin university Dr Simon Cork, is about what we’ve just learned, why it changes our understanding of semaglutides and whether it will mean they become more widely available. Here are the headlines.

Five big stories

Georgia | Tbilisi has been warned by the US not to turn towards Russia as its parliament defied mass street protests to pass a “Kremlin-inspired” law . A US official said that the “foreign agents” bill, which takes aim at civil society groups with funding from abroad, could jeopardise support from Washington for the former Soviet state “if we are now regarded as an adversary”.

France | Elite French police are searching for gunmen who attacked a prison van in Normandy, killing at least two prison officers and freeing the high-security inmate being transported . The fugitive prisoner was named as Mohamed Amra, who was convicted last week of aggravated robbery and charged in a case of abduction leading to death.

Education | There is no evidence of widespread abuse of the UK’s graduate visa route, a major report has concluded , despite claims from Conservatives that it is being exploited to enter the jobs market. The government is expected to decide next week whether to remove the scheme, a move which would mean financial turmoil for the sector.

US | Donald Trump’s ex-lawyer Michael Cohen has testified in Manhattan court that he submitted phoney invoices for legal services to cover up what were reimbursements for hush money paid to Stormy Daniels . In a second day of evidence, Cohen repeatedly identified Trump as the driver of the Daniels payoff scheme.

Manchester | After a series of humiliating setbacks, the £450m Co-op Live music venue finally opened its doors on Tuesday , with a concert by Elbow that had been meant to be the 15th event on its schedule. The venue’s boss, Tim Leiweke, claimed that it would be “the greatest arena ever built”.

In depth: ‘These drugs aren’t going to solve obesity, but they have a huge amount of promise’

Ozempic and Wegovy are brand names for a snappily named class of drugs called glucagon-like peptide receptor agonists, or GLP-1 agonists. They were first authorised as diabetes treatments, and have proven very effective. The specific GLP-1 agonist in them is semaglutide. It works by mimicking a naturally occurring hormone, GLP-1, which is released by the gut after we eat and makes us feel full.

They don’t work in the same way for everyone, as this set of Guardian reader accounts from last year suggests: sustained healthy weight loss and a liberating release from cravings at one end of the spectrum, total failure and horrible side effects at the other. But overall, studies have shown that semaglutide is more effective than earlier generations of GLP-1 drugs, helping overweight and obese people lose about 15% of their body weight when combined with diet and exercise – much more than those who used diet and exercise alone.

“The existing studies around weight loss cover two years,” Simon Cork said. “We know that the vast majority of patients lose a significant amount of weight, and that that weight loss is associated with a drop in blood pressure and blood glucose” – both linked to heart health. “They don’t cause you to lose weight in and of themselves – to get that effect you have to have an overarching change in your lifestyle.”

It’s also worth noting that users will typically need to maintain their dose for the effects to last. When people stop using semaglutides, studies have shown, “almost everyone regains two-thirds to three-quarters of the weight back – and with that the associated health risks also return”.

What the new study tells us

The previous research on semaglutide as a weight loss treatment has largely focused on that primary role. The evidence about heart health has only been of the impact you would expect to be associated with the corresponding weight loss. But the new study , led by researchers at University College London, sets out evidence that could have a significant impact on how we think about the drug’s potential importance.

The study, which is yet to be published but has been presented at a conference, looked at more than 17,000 overweight or obese adults aged over 45 who had previously experienced a cardiovascular event such as a heart attack, and divided them into two groups – one receiving semaglutide, the other getting a placebo.

It found that those receiving semaglutide were 20% less likely to have a heart attack. Crucially, Cork said, “you can separate out the decrease in cardiovascular risk from the weight changes. We had assumed it was a consequence of losing weight – but this seems to suggest there’s a separate pathway.” We don’t know how those effects would play out in patients at a healthy weight, because they weren’t part of the study – so it doesn’t mean that semaglutides should be considered as a treatment for anyone with heart issues regardless of their weight.

At the same time as the UCL study was released, another study of a new drug, retatrutide, found that it could be even more effective – with participants losing 24% of their body weight over 48 weeks, partly because it has an impact on metabolism as well as appetite. “That can be hugely significant for someone with obesity,” Cork said. But he noted that it also demonstrated how important it is to have such drugs available under proper clinical guidance: “Someone of normal weight or who’s underweight with body dysmorphia using that could be very dangerous.”

What it could mean for availability on the NHS

At the moment, guidance published by the National Institute for Care and Excellence (Nice) says that Wegovy should be prescribed for a maximum of two years – even though the evidence suggests that when it stops being used, the benefits stop too. So another important aspect of the study, Cork said, “is that it was carried out over four years – it’s the first beyond a two-year period. It shows that the health impact is prolonged over four years, and also safe over that period. Obesity is a lifelong condition – we wouldn’t end prescriptions for patients with hypertension or asthma. So that has to change.”

Another barrier: the Nice guidelines also say that Wegovy should only be prescribed by specialist weight management services – but those are badly oversubscribed. “There has been some talk that GPs should be able to prescribe, which would reduce the burden,” Cork said.

Wegovy’s use for weight loss has already caused issues with availability for diabetes patients – and the cost of the drug is also potentially prohibitive. But even from a solely economic perspective, Cork pointed out, obesity “ costs the NHS more than £6bn a year because of the associated risks – heart attacks, diabetes, stroke. So anything we can do to mitigate that has to be helpful in the long run.”

There is no instant fix to the supply issues, although these are primarily the result of private prescriptions rather than those on the NHS. “But if you look at the drugs coming down the line that are in late stage trials, I think it’s very likely that we’re going to see increased availability as they hit the market – and that will have a positive effect on the price, as well.”

What the long-term impact might be

The really remarkable thing about the new study is the hard evidence that it provides of benefits for cardiovascular health that go beyond weight loss alone. One possible impact of that is a shift in the way drugs like Ozempic and Wegovy are discussed: because of the way they came to prominence through stories of celebrities using them to squeeze into outfits, and their usage becoming commonplace among wealthy Americans who want to look good on the beach, they are now caught between being treated as miracle cures and as shortcuts to cosmetic benefits for the terminally lazy.

“That tone is very visible – you only have to look in the comments section on articles or social media,” Cork said. “People simply think the answer is eat less and exercise more – but for obesity that’s not really true.

“We can have a separate conversation about policy to prevent people becoming overweight or obese, whether it’s the affordability of healthy foods or access to green spaces – but the bottom line is that there are people who are overweight and obese because of their genetic makeup, and we have to find effective ways to help those people.”

In that context, and given the new evidence about cardiovascular impact, Cork hopes to see much wider use in the years ahead. “We have no other real way of managing obesity other than ineffective instructions about diet and exercise. These drugs aren’t going to solve the problem, but they have a huge amount of promise.”

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What else we’ve been reading

As the debate over assisted dying in the UK shifts towards a majority view that the ban is an immoral anachronism, it’s really worth reading Anna Moore’s interview with the actor Liz Carr , who’s made a BBC documentary that presents an alternative argument through the prism of her own disability. This line will stay with me: “The biggest catastrophe is that we’d choose it ourselves because there was no more choice for us.” Frances Ryan gives the film five stars . Archie

Hannah Grace Deller and Esther Addley beautifully captured the journeys that NHS nurses have taken since quitting their job after the pandemic in this picture essay. Nimo

Suella Braverman has joined the long list of charities and experts who say that the two-child benefits limit should be scrapped – and on this issue, she’s to the left of Labour. By outflanking the Tories on so many subjects, Gaby Hinsliff writes , Keir Starmer has left “his party defending a frankly implausible swathe of political territory, and not just from an attack by the left”. Archie

This week’s TechScape newsletter is about the Online Safety Act , which Alex Hern describes as “quietly one of the most important pieces of legislation to have come out of this government”. Nimo

Over 100 musicians share their stories with Alfie Packham about the punishing financial landscape that has left many out of pocket and unable to pursue their musical ambitions. Nimo

Football | Erling Haaland scored twice to give Manchester City a 2-0 win at Tottenham to reclaim top spot in the Premier League heading into the last day of the season. Because Tottenham fans are unenthusiastic about Arsenal winning the league, “there was a sense around the crowd of some necessary duty being discharged,” Barney Ronay wrote – but the final result was “a serial champion simply stretching away in the straight”.

Formula One | The More than Equal initiative, a global development programme created to assist women’s progress toward Formula One and in motor racing, has announced its first selection of female drivers . The six teenagers selected will now work with a team of experienced driving coaches in an attempt to address the gender imbalance in the sport.

Rugby | Saracens have confirmed that Billy and Mako Vunipola will leave the club at the end of the season while Gloucester have also announced that Jonny May is making his exit. All three have been England regulars over the past decade but are set to join the growing number of recent Test players in moving abroad in the summer.

The front pages

The Guardian print edition leads this morning with “US warns Georgia not to side with Russia against the west”. The i has “New weight loss jab ‘gold rush’ offers obesity hope to millions”. “Anglo reveal break up plan to thwart BHP takeover” is the top story in the Financial Times ; in the Metro it’s “UK’s record 3m food parcels”. The Times reports “Don’t teach pupils about gender ID, schools told” while the Daily Telegraph splashes on “Tories tell police: Bring back stop and search”. “Sex education to be banned for under 9s – and no more gender dogma” says the Daily Mail . “‘Fighting chance’ migration will fall to 150,000 a year” – that’s the Daily Express. “Mummy’s a legend … I’m a mess” – that’s the Daily Mirror , which covers TV host Ant McPartlin having a baby with his wife, Anne-Marie.

Today in Focus

The ‘foreign agents’ law that has set off mass protests in Georgia

The bill requires any civil society organisation that receives more than 20% of its funds from abroad to register as being under foreign influence. Daniel Boffey reports

Cartoon of the day | Martin Rowson

A bit of good news to remind you that the world’s not all bad

Ncazelo Ncube-Mlilo, a Zimbabwean psychologist, has spent much of her professional career developing “culturally sensitive” therapeutic tools – the most famous is COURRAGE. Each letter in the acronym a theme in an eight-week group counselling program that encourages participants to reframe trauma as stories of survival and strength.

The program is spearheaded by her charity, Phola, which reaches more than 10,000 women, men and children in townships around Johannesburg, South Africa every year. Her method has been adopted in 40 countries including the UK, and participants have gone on to create their own support networks. “Now we are there for each other,” one participant says. “If one of us is going through something, we are just a call away.”

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Childhood obesity 'can cut life expectancy in half' and cause diabetes, new study finds

The analysis used data from 50 existing studies on obesity to estimate the impact of childhood obesity on conditions like type 2 diabetes and overall life expectancy

• 14:37, 15 May 2024

Being very obese as a young child could cut life expectancy by about half, according to a study.

Losing weight however, could add years back on, researchers said. The analysis, presented at the European Congress on Obesity in Venice and led by Germany-based life sciences consultancy stradoo, used data from 50 existing studies on obesity to estimate the impact of childhood obesity on conditions like type 2 diabetes and life expectancy.

The pieces of research combined included more than 10 million people from countries around the world , including about 2.7 million people aged between two and 29. Researchers used a body mass index (BMI) z score which measures how much a youngster's weight deviates from the normal range for their age and gender to estimate how obese children were. The higher the BMI z score, the more a child weighed.

The team found that children that were severely obese at age four with a BMI z score of 3.5 had a life expectancy of 39 years if they did not lose weight. Children with BMI z scores of 2.0 had an estimated life expectancy of 65 without weight loss, while children with a score of 2.5 had a life expectancy of 50 years. Figures published by the Office for National Statistics in January revealed life expectancy at birth in the UK in 2020 to 2022 was 78.6 years for males and 82.6 years for females.

Dr Urs Wiedemann, of stradoo, said: "While it's widely accepted that childhood obesity increases the risk of cardiovascular disease and related conditions such as type 2 diabetes, and that it can reduce life expectancy, evidence on the size of the impact is patchy. A better understanding of the precise magnitude of the long-term consequences and the factors that drive them could help inform prevention policies and approaches to treatment, as well as improve health and lengthen life."

The team found severely obese four-year-olds were also 27 per cent more likely to develop type 2 diabetes by the age of 25, and had a 45 per cent chance of developing the condition by age 35. In comparison, children with BMI z scores of two at age four had a 6.5 per cent chance of developing type 2 diabetes by 25 and a 22 per cent change by 35.

Researchers also used their modelling to determine the impact of weight loss. Children with severe early onset obesity or a BMI z score of 4.0 at age four who do not lose weight had a life expectancy of 37 and a 55 per cent risk of developing type 2 diabetes. However, if this was reduced to a z score of 2.0 in the obese range by age six, life expectancy increased to 64 and the risk of type 2 diabetes fell to 29 per cent.

Dr Wiedemann added: "The impact of childhood obesity on life expectancy is profound. It is clear that childhood obesity should be considered a life-threatening disease. It is vital that treatment isn't put off until the development of type 2 diabetes, high blood pressure or other 'warning signs' but starts early. Early diagnosis should and can improve quality and length of life."

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