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The Mariani Foundation Centre for Hereditary Metabolic Diseases of Childhood in Monza (Italy) is organizing the workshop “The Extended Neonatal Screening (ENS): Multidisciplinary Approach and Psychological Support to the Family” which will be held online on 8 April and on 15 April 2024. The Workshop aims: The Workshop will be conducted in Italian and receive accreditation […]

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Prevention of metabolic diseases through measures at the workplace

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J Schätzer, M Schätzer, C Putzhammer, N Moser, J Bhardwaj, F Hoppichler, Prevention of metabolic diseases through measures at the workplace, European Journal of Public Health , Volume 31, Issue Supplement_3, October 2021, ckab165.393, https://doi.org/10.1093/eurpub/ckab165.393

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Metabolic diseases such as type 2 diabetes pose a risk for both individuals and companies. According to the current Austrian health report, only 13.5% of adults participate in preventive medical examinations. As a result, there is a lack of information on important health parameters. To counteract this, a modular system with health checks that take place directly at the workplace was developed.

In 2018 and 2019, a health check was carried out on 877 people (male: 70.7%; female: 29.3%) at their workplace. In addition to checking the abdominal girth and calculating the BMI, parameters such as blood pressure, blood sugar, LDL, and total cholesterol levels were measured.

62.5% of all participants were overweight or obese (male: 66.3%; female: 53.5%). The proportion of people with obesity was 11.5% (male: 11.4%; female: 11.7%). The abdominal girth was too high in 50.2% of the participants (male: 47.6%; female: 56.5%). 36.5% of the examined persons had elevated blood pressure readings (male: 42.3%; female: 22.5%). Total cholesterol was too high in 37.6% of the persons (m: 38.8%; w: 34.9%), LDL cholesterol was too high in 22.5% (m: 24.2%; 20.4%). The blood sugar levels were too high in 9.9% of the participants (male: 9.4%; female: 12.6%), regardless of whether they fasted or not. 81% of the participants stated that they had received new information about their state of health in the course of the examinations.

Measures to monitor the central metabolic parameters directly at the workplace represent an important component in prevention, which is essential for both the individual and the company.

Metabolic diseases and obesity, as well as their consequences, pose a risk for both individuals and the companies they work for.

Health checks directly at the workplace represent an important component in prevention and support companies in keeping their employees healthy.

• hypertension
• ldl cholesterol lipoproteins
• body mass index procedure
• diabetes mellitus, type 2
• blood pressure
• metabolic diseases
• blood glucose
• arterial pressure, increased
• total cholesterol

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Metabolomics in epidemiologic research: challenges and opportunities for early-career epidemiologists

• Review Article
• Published: 07 January 2019
• Volume 15 , article number  9 , ( 2019 )

Cite this article

• Eline H. van Roekel   ORCID: orcid.org/0000-0002-9402-2029 1 ,
• Erikka Loftfield 2 ,
• Rachel S. Kelly 3 ,
• Oana A. Zeleznik 3 &
• Krista A. Zanetti 4  

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The application of metabolomics to epidemiologic studies is increasing.

Aim of Review

Here, we describe the challenges and opportunities facing early-career epidemiologists aiming to apply metabolomics to their research.

Key Scientific Concepts of Review

Many challenges inherent to metabolomics may provide early-career epidemiologists with the opportunity to play a pivotal role in answering critical methodological questions and moving the field forward. Although generating large-scale high-quality metabolomics data can be challenging, data can be accessed through public databases, collaboration with senior researchers or participation within interest groups. Such efforts may also assist with obtaining funding, provide knowledge on training resources, and help early-career epidemiologists to publish in the field of metabolomics.

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Alkawaa, F. M., et al. (2017). Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. Journal of Proteome Research, 17 (1), 337–347.

Google Scholar  

Assi, N., et al. (2015). A statistical framework to model the meeting-in-the-middle principle using metabolomic data: Application to hepatocellular carcinoma in the EPIC study. Mutagenesis, 30 (6), 743–753.

CAS   PubMed   PubMed Central   Google Scholar  

Baldassarre, M. E., et al. (2018). Effectiveness and safety of a probiotic-mixture for the treatment of infantile colic: A double-blind, randomized, placebo-controlled clinical trial with fecal real-time PCR and nmr-based metabolomics analysis. Nutrients, 10 (2), 195.

Article   PubMed Central   Google Scholar  

Bictash, M., et al. (2010). Opening up the “Black Box”: Metabolic phenotyping and metabolome-wide association studies in epidemiology. Journal of Clinical Epidemiology, 63 (9), 970–979.

Article   PubMed   PubMed Central   Google Scholar  

Broadhurst, D., et al. (2018). Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics, 14 (6), 72.

Carayol, M., et al. (2017). Blood metabolic signatures of body mass index: A targeted metabolomics study in the EPIC cohort. Journal of Proteome Research, 16 (9), 3137–3146.

Article   Google Scholar  

Cheng, S., et al. (2017). Potential impact and study considerations of metabolomics in cardiovascular health and disease: A scientific statement from the American Heart Association. Circulation Cardiovascular Genetics, 10 (2), e000032.

Cross, A. J., et al. (2014). A prospective study of serum metabolites and colorectal cancer risk. Cancer, 120 (19), 3049–3057.

Article   CAS   PubMed   Google Scholar  

Cui, L., et al. (2016). Serum metabolomics reveals serotonin as a predictor of severe dengue in the early phase of dengue fever. PLoS Neglected Tropical Diseases, 10 (4), e0004607.

Derkach, A., et al. (2017). Effects of dietary sodium on metabolites: The dietary approaches to stop hypertension (DASH)-sodium feeding study. The American Journal of Clinical Nutrition, 106 (4), 1131–1141.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Dudzik, D., et al. (2018). Quality assurance procedures for mass spectrometry untargeted metabolomics. A review. Journal of Pharmaceutical and Biomedical Analysis, 147 , 149–173.

Fages, A., et al. (2014). Investigating sources of variability in metabolomic data in the EPIC study: The principal component partial R-square (PC-PR2) method. Metabolomics, 10 (6), 1074–1083.

Article   CAS   Google Scholar  

Fages, A., et al. (2015). Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort. BMC Medicine, 13 , 242.

Floegel, A., et al. (2013). Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes, 62 (2), 639–648.

Floegel, A., et al. (2018). Serum metabolites and risk of myocardial infarction and ischemic stroke: A targeted metabolomic approach in two German prospective cohorts. European Journal of Epidemiology, 33 (1), 55–66.

Guertin, K. A., et al. (2015). Serum biomarkers of habitual coffee consumption may provide insight into the mechanism underlying the association between coffee consumption and colorectal cancer. American Journal of Clinical Nutrition, 101 (5), 1000–1011.

Hernandes, V. V., et al. (2017). A review of blood sample handling and pre-processing for metabolomics studies. Electrophoresis, 38 (18), 2232–2241.

Ho, J. E., et al. (2016). Metabolomic profiles of body mass index in the framingham heart study reveal distinct cardiometabolic phenotypes. PLoS ONE, 11 (2), e0148361.

Holmes, E., et al. (2008). Human metabolic phenotype diversity and its association with diet and blood pressure. Nature, 453 (7193), 396–400.

Hrydziuszko, O., & Viant, M. R. (2012). Missing values in mass spectrometry based metabolomics: An undervalued step in the data processing pipeline. Metabolomics, 8 (1), 161–174.

Huang, J., et al. (2016). Serum metabolomic profiling of prostate cancer risk in the prostate, lung, colorectal, and ovarian cancer screening trial. British Journal of Cancer, 115 (9), 1087–1095.

Huang, S., et al. (2016). Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis. Genome Medicine, 8 (1), 34.

Ke, C., et al. (2015). Large-scale profiling of metabolic dysregulation in ovarian cancer. International Journal of Cancer, 136 (3), 516–526.

CAS   PubMed   Google Scholar  

Kirwan, J. A., et al. (2018). Preanalytical processing and biobanking procedures of biological samples for metabolomics research: A white paper, community perspective (for “precision Medicine and pharmacometabolomics task group"-the metabolomics society initiative). Clinical Chemistry, 64 (8), 1158–1182.

Liesenfeld, D. B., et al. (2013). Review of mass spectrometry-based metabolomics in cancer research. Cancer Epidemiology Prevention Biomarkers, 22 (12), 2182–2201.

Loftfield, E., et al. (2016). Comparison of collection methods for fecal samples for discovery metabolomics in epidemiologic studies. Cancer Epidemiology Prevention Biomarkers, 25 (11), 1483–1490.

Mathe, E. A., et al. (2014). Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Research, 74 (12), 3259–3270.

Menni, C., et al. (2013). Biomarkers for type 2 diabetes and impaired fasting glucose using a nontargeted metabolomics approach. Diabetes, 62 (12), 4270–4276.

Molins, C. R., et al. (2015). Development of a metabolic biosignature for detection of early Lyme disease. Clinical Infectious Diseases, 60 (12), 1767–1775.

Mondul, A. M., et al. (2015). Metabolomic analysis of prostate cancer risk in a prospective cohort: The alpha-tocolpherol, beta-carotene cancer prevention (ATBC) study. International Journal of Cancer, 137 (9), 2124–2132.

Moore, S. C., et al. (2014). Human metabolic correlates of body mass index. Metabolomics, 10 (2), 259–269.

Moore, S. C., et al. (2018). A metabolomics analysis of body mass index and postmenopausal breast cancer risk. Journal of National Cancer Institute, 110 (6), 588–597.

Nishiumi, S., et al. (2012). A novel serum metabolomics-based diagnostic approach for colorectal cancer. PLoS ONE, 7 (7), e40459.

Playdon, M. C., et al. (2017). Nutritional metabolomics and breast cancer risk in a prospective study. The American Journal of Clinical Nutrition, 106 (2), 637–649.

Ren, S., et al. (2015). Computational and statistical analysis of metabolomics data. Metabolomics, 11 (6), 1492–1513.

Saccenti, E., et al. (2014). Reflections on univariate and multivariate analysis of metabolomics data. Metabolomics, 10 (3), 361–374.

Scalbert, A., et al. (2009). Mass-spectrometry-based metabolomics: Limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics, 5 (4), 435–458.

Stepien, M., et al. (2016). Alteration of amino acid and biogenic amine metabolism in hepatobiliary cancers: Findings from a prospective cohort study. International Journal of Cancer, 138 (2), 348–360.

Su, L. J., et al. (2014). The use of metabolomics in population-based research. Advances in Nutrition, 5 (6), 785–788.

Sumner, L. W., et al. (2007). Proposed minimum reporting standards for chemical analysis. Metabolomics, 3 (3), 211–221.

Townsend, M. K., et al. (2013). Reproducibility of metabolomic profiles among men and women in 2 large cohort studies. Clinical Chemistry, 59 (11), 1657–1667.

Tzoulaki, I., et al. (2014). Design and analysis of metabolomics studies in epidemiologic research: A primer on -omic technologies. American Journal of Epidemiology, 180 (2), 129–139.

Article   PubMed   Google Scholar  

Wang, T. J., et al. (2011). Metabolite profiles and the risk of developing diabetes. Nature Medicine, 17 (4), 448–453.

Wang-Sattler, R., et al. (2012). Novel biomarkers for pre-diabetes identified by metabolomics. Molecular Systems Biology, 8 , 615.

Welsh, P., et al. (2018). Circulating amino acids and the risk of macrovascular, microvascular and mortality outcomes in individuals with type 2 diabetes: Results from the advance trial. Diabetologia, 61 (7), 1581–1591.

Wurtz, P., et al. (2015). Metabolite profiling and cardiovascular event risk: A prospective study of 3 population-based cohorts. Circulation, 131 (9), 774–785.

Xiao, Q., et al. (2014). Sources of variability in metabolite measurements from urinary samples. PLoS ONE, 9 (5), e95749.

Zanetti, K., et al. (2014). The future of metabolomic profiling in population-based research: Opportunities and challenges. Journal of Analytical and Bioanalytical Techniques, 5 , 203.

Zheng, Y., et al. (2013a). Associations between metabolomic compounds and incident heart failure among African Americans: The ARIC Study. American Journal of Epidemiology, 178 (4), 534–542.

Zheng, Y., et al. (2013b). Metabolomics and incident hypertension among blacks: The atherosclerosis risk in communities study. Hypertension, 62 (2), 398–403.

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E.H. van Roekel was financially supported by Wereld Kanker Onderzoek Fonds (WKOF), as part of the World Cancer Research Fund International grant programme (Grant No. 2016/1620) and the GROW School for Oncology and Developmental Biology. E. Loftfield was supported by the Intramural Research Program of the National Institutes of Health, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Department of Health and Human Services. R.S. Kelly was supported by a Discovery Award from The US Department of Defense (Grant No. W81XWH-17-1-0533), and a grant from the US NIH (Grant No. 1R01HL123915-01). O.A. Zeleznik was supported by grants from the NIH (Grant Nos. CA087969, CA050385). K.A. Zanetti was supported by the Extramural Research Program of the National Institutes of Health, Division of Cancer Control and Populations Sciences, National Cancer Institute, Department of Health and Human Services.

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Department of Epidemiology, GROW School for Oncology and Developmental Biology, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, Netherlands

Eline H. van Roekel

Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, USA

Erikka Loftfield

Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA

Rachel S. Kelly & Oana A. Zeleznik

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, USA

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van Roekel, E.H., Loftfield, E., Kelly, R.S. et al. Metabolomics in epidemiologic research: challenges and opportunities for early-career epidemiologists. Metabolomics 15 , 9 (2019). https://doi.org/10.1007/s11306-018-1468-z

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Received : 30 July 2018

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Published : 07 January 2019

DOI : https://doi.org/10.1007/s11306-018-1468-z

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Metabolic Disorders

Given the increasing prevalence of metabolic diseases, obesity and diabetes worldwide, there is an urgent need to fight against this major societal burden.

The Graduate School of Metabolic Disorders is an innovative teaching program providing a broad view of these metabolism-related pathologies.

It combines teaching on: biological aspects of metabolism from the molecular mechanism to integrated physiopathology as well as stimulating reflection on the recent societal changes largely contributing to this new metabolic pandemic.

Presentation

Metabolic dysfunctions and obesity disrupt homeostasis leading to chronic inflammatory responses. Amongst the obesity devastating complications, there is the increased incidence of type-2 diabetes, which accounts for over 90 % of the diagnosed cases of diabetes and affects some 200 million people around the world. The incidence of autoimmune type 1 diabetes is also increasing worldwide and the role of the environment altering the microbiota as well as pollutants have been involved.

Diabetes is a chronic disease requiring long-term therapy to avoid complications leading to cardiovascular and renal co-morbidities. A better understanding of these pathologies is a prerequisite for the prevention as well as the development of new effective therapies to acting at different stages of metabolic diseases and their complications.

The Graduate School of Metabolic Disorders is based on: the existence of a centre of excellence in metabolism already recognised at the national and international level and develops teaching resources in symbiosis with the Laboratories of Excellence (Labex) and the University Hospital Research (RHU).

The Graduate School of Metabolic Disorders  develops an ambitious and exceptional teaching program that combines:

• Basic science on metabolism (cellular, tissue and systemic energy control) in different organisms ranging from bacteria to humans.
• Pathophysiology of metabolic diseases in animal models and patients with components on: metabolic, endocrinological, immunological, neurological, genetic and epigenetic aspects, alteration of microbiota and impact of environmental factors (pollutants, stress,etc.).
• Big data analysis and artificial intelligence, for example to analyse data from the Nutrinet cohort, the Inserm RHU QUID NASH program and Microbiota in interaction with the University Chair in AI and companies such as: Dassault system, Mydiabby health care and Swiss Institute of Bioinformatics with which researchers from Université Paris Cité interact. All of these programs help generate new algorithms and promote training in telemedicine and telehealth services.
• Innovative therapies (e.g. CAR Treg cells, antibodies, modified peptides, probiotics, small molecules, etc.) including interactions with biotech and bid pharma companies such as: Diabeloop, Diacurrate, Imcyse, Astrazeneca, Novo Nordisk, etc.
• Societal aspects with the impact of lifestyle changes (nutrition, physical activity, education, etc.) including meetings with patient associations such as the Juvenile Diabetes Association (AJD) and the National Collective of Obese Associations (CNAO).
• International courses (linked to the Franco-German School of Diabetology, the European Magisterium of Genetics (MED) and the Master Inflammation and Inflammatory Diseases (IMI) which recruits foreign students in Master and PhD programs all partly paid by Labex Inflamex.

Faculty of Health Agnès LEHUEN [email protected]

Faculty of sciences christophe magnan [email protected].

Université Paris Cité offers many courses that are relevant to the students of the Graduate School of Metabolic Disorders. Many professors and researchers from Université Paris Cité teach skills and experiences as they are already involved in several master programmes. Courses are taught in French and English.

• Physiology of adapted physical activity, health and nutrition
• Biology, pathophysiology, pharmacology of the heart and circulation
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The Sorbonne University and INRAE also offer courses and/or resources that may be available to Graduate School students:

• Nutrition, quality and health
• “Endocrinology” in Master physiology, metabolism and human physiopathologies

The laboratories associated with the Graduate School of Metabolic Disorders include teams working on molecular and cellular aspects as well as integrative biology. Thus, the models studied range from cells to preclinical models (rats, rodents) through modelling and in silico approaches.

The bioinformatics resources of Université Paris Cité also allow access to numerous databases. Finally, clinical teams are also associated with the Graduate School, thus favouring cross-sectional studies from bench to bed.

Up to now, the majority of the research teams of the Graduate School of Metabolic Disorders are attached to the Institut du Diabète created by the Faculty of Health of Université Paris Cité , and the LabEx Inflamex .

Laboratories

• BFA – Unit of Functional and Adaptive Biology (UMR CNRS 8251)
• Comete – Chemistry, Analytical and Cellular Toxicology (UMR 8015)
• CRC – Cordeliers Research Centre (UMRS 1138)
• CRESS – Centre of Research in Epidemiology and StatisticS (UMRS 1153)
• CRI – Centre for Research on Inflammation (UMRS 1149)
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• Institut Cochin (UMR 8104/1016)
• LVTS – Laboratory for Vascular Translational Science (UMRS 1148)
• PARCC – Paris Cardiovascular Research Centre (UMRS 970)

Doctoral schools

• BioSPC – Bio Sorbonne Paris Cité (DS 562)
• HOB – Hemaytology, oncogenesis, biotherapies (DS 561)
• MTCI – Medication, toxicology, chemistry, imaging (DS 563)
• Pierre Louis of Public Health in Paris: Epidemiology and Biomedical Information Sciences (DS 393)
• Agnès LEHUEN Director of Research at the  French National Centre for Scientific Research  (CNRS)
• Christophe MAGNAN Professor at Université Paris Cité

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• Open access
• Published: 19 December 2019

Effect of workplace physical activity interventions on the cardio-metabolic health of working adults: systematic review and meta-analysis

• Rubina Mulchandani 1 ,
• Ambalam M. Chandrasekaran 2 ,
• Roopa Shivashankar 2 ,
• Dimple Kondal 2 ,
• Anurag Agrawal 3 ,
• Jeemon Panniyammakal 4 , 5 ,
• Nikhil Tandon 6 ,
• Dorairaj Prabhakaran 2 , 5 ,
• Meenakshi Sharma 7 &
• Shifalika Goenka   ORCID: orcid.org/0000-0001-6993-2883 1 , 2  

International Journal of Behavioral Nutrition and Physical Activity volume  16 , Article number:  134 ( 2019 ) Cite this article

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Adults in urban areas spend almost 77% of their waking time being inactive at workplaces, which leaves little time for physical activity. The aim of this systematic review and meta-analysis was to synthesize evidence for the effect of workplace physical activity interventions on the cardio-metabolic health markers (body weight, waist circumference, body mass index (BMI), blood pressure, lipids and blood glucose) among working adults.

All experimental studies up to March 2018, reporting cardio-metabolic worksite intervention outcomes among adult employees were identified from PUBMED, EMBASE, COCHRANE CENTRAL, CINAHL and PsycINFO. The Cochrane Risk of Bias tool was used to assess bias in studies. All studies were assessed qualitatively and meta-analysis was done where possible. Forest plots were generated for pooled estimates of each study outcome.

A total of 33 studies met the eligibility criteria and 24 were included in the meta-analysis. Multi-component workplace interventions significantly reduced body weight (16 studies; mean diff: − 2.61 kg, 95% CI: − 3.89 to − 1.33) BMI (19 studies, mean diff: − 0.42 kg/m 2 , 95% CI: − 0.69 to − 0.15) and waist circumference (13 studies; mean diff: − 1.92 cm, 95% CI: − 3.25 to − 0.60). Reduction in blood pressure, lipids and blood glucose was not statistically significant.

Conclusions

Workplace interventions significantly reduced body weight, BMI and waist circumference. Non-significant results for biochemical markers could be due to them being secondary outcomes in most studies. Intervention acceptability and adherence, follow-up duration and exploring non-RCT designs are factors that need attention in future research.

Prospero registration number: CRD42018094436.

Physical activity as a modifiable health behavior for cardiovascular disease (CVD) prevention

According to the INTERHEART study, physical inactivity is one of the 9 major modifiable risk factors responsible for CVDs in both sexes worldwide [ 1 ]. It is responsible for 10% of the premature mortality, 6% of coronary heart disease burden and 7% of the diabetes burden worldwide [ 2 ]. Approximately 3.2 million annual deaths are attributable to insufficient activity [ 3 ] and 25% reduction in inactivity can avert 1.3 million deaths annually [ 2 ]. Physical activity (PA) aids in better glycemic control and it is a vital component of diabetes prevention and management [ 4 ]. The World Health Organization (WHO) now recommends 150–300 min of moderate to vigorous aerobic physical activity (MVPA) for adults aged 18–64 [ 5 ]. Some of the most common reasons for inactivity among adults are an unsupportive social and physical environment [ 6 , 7 ] and lack of time [ 8 ]. Adults in urban areas spend almost 77% of their waking time being inactive at work or otherwise, leaving little time for exercise [ 9 , 10 ].

Worksite physical activity programs are specifically designed with the aim of enhancing employee physical activity levels and improving their dietary behavior at the workplace [ 11 ]. Worksite settings provide effective channels to reach defined populations, disseminate information, create an effective medium for program delivery and study the impact to maximize benefits [ 12 , 13 ]. These can be suitable settings for advocating an active lifestyle, improving employee productivity and reducing healthcare costs [ 14 , 15 ]. Contemporary workplaces are thus ideal for interventions that promote higher levels of physical activity amongst employees, to improve health and optimize performance [ 16 ].

Rationale for the current systematic review and meta-analysis

A number of narrative and systematic reviews have demonstrated the positive effect of various worksite physical activity interventions on physical activity, productivity and cost outcomes [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 ]. However, only a handful of them have comprehensively evaluated the effects of these interventions on the major measurable cardiovascular disease markers. The last comprehensive review on the topic was done in 2010, included only randomized controlled trials (RCTs) and did not meta-analyze the effects [ 26 ]. Worksite PA interventions can provide an effective lever to address the CVD burden. However, the effectiveness of these interventions needs to be quantified. Given the availability of numerous primary studies in the area, it becomes imperative to present not only an overview but also obtain an overall quantitative estimate of intervention effects from different studies, both randomized and non-randomized.

Therefore, we aimed to undertake a comprehensive and systematic synthesis of literature and meta-analysis of available evidence, to obtain a holistic view, of the potential of worksite PA interventions in improving the cardio-metabolic health of working adults.

To summarize evidence for the effect of worksite physical activity interventions on CVD risk markers (body weight, waist circumference, body mass index, blood pressure, lipids and blood glucose) among working adults and describe the intervention approaches used in the different studies.

Research question

Do worksite physical activity interventions lower the cardio-metabolic disease risk of adults?

The review methodology was registered with PROSPERO (registration ID: CRD42018094436) and has been described in detail in the protocol [ 27 ].

Search strategy and inclusion of studies

We searched Cochrane Central, PUBMED, CINAHL, PSYCINFO and EMBASE to identify relevant studies on workplace physical activity interventions published till March 2018 using keywords like “workplace”, “workers”, “physical activity”, “exercise”, “wellness”, “counseling”, “RCTs”, “trials” etc. A comprehensive strategy was prepared by one researcher (RM) and reviewed by the second (CS) researcher. The PUBMED search strategy is illustrated in the Additional File 1 . It was then modified as per the indexing system of other databases.

Eligibility criteria for inclusion of studies

Study designs- Experimental study designs with a comparator group including randomized controlled trials, controlled trials, cluster RCTs, quasi-experimental studies; a comparator could be no intervention, minimal intervention, usual care, waitlisted control.

Study populations- Studies involving individuals aged 18 and above; healthy populations as well as populations at risk of CVD were included

Study outcomes- Studies reporting any of the CVD outcomes (body weight, body fat, waist circumference, BMI, blood pressure, plasma glucose, lipids and triglycerides)

Study interventions- Workplace studies implementing physical activity based interventions targeting inactivity to improve the cardio-metabolic disease markers (anthropometric and biochemical) in adult employees

Exclusion criteria: Studies not published in the English language, those with a follow-up period of less than 6 months, observational studies and experimental studies without a comparator.

Referencing software Zotero was used to import the search results and remove the duplicates. Titles and abstracts of all the retrieved articles were screened independently by RM; CS independently screened 10% of the citations. The reference list of relevant studies obtained was further hand searched. Full texts of eligible studies were screened by RM and reviewed by CS. Wherever data for meta-analysis was unavailable in the public domain, the study authors were electronically contacted.

Data extraction, quality assessment and analysis

Data extraction was performed independently by the two researchers. Disagreements were resolved within the team. Items in the data extraction form were prepared by RM using the Cochrane Handbook recommendations and were verified by CS. Outcomes were appropriately converted to the International System of Units for studies that reported them in other units. Findings from all the studies were included in the narrative synthesis. Review Manager (RevMan version 5.3) was used for the meta-analysis. The inverse-variance method was used to combine effect sizes using the random effects models (REMs) [ 28 ]. The treatment effect was reported as mean difference (MD) with 95% confidence intervals (CIs) wherein CIs excluding 0 were considered to be statistically significant. Forest plots were generated using RevMan to compare each of the proposed outcome measures in the intervention vs the control groups in the included studies. Studies that did not provide this data were excluded from the meta-analysis. REMs were used to report the overall mean difference with 95% CIs. The confidence intervals for each study in the meta-analysis were observed for their level of overlap, for a visual assessment of heterogeneity. I 2 values, defined as ‘the percentage of variability in effect estimates that is due to heterogeneity rather than sampling error’, were used to determine the magnitude of variation beyond chance. It is calculated as [(Q-df)/Q]*100 where Q is the chi-square statistic and df is its degrees of freedom. A chi-square p -value of less than 0.05 was considered statistically significant for the presence of heterogeneity. Degree of heterogeneity was ascertained based on the cut-offs mentioned in the Cochrane handbook (0–40%: not important, 30–60%: moderate, 50–90%: substantial, 75–100%:considerable heterogeneity) [ 29 ].

The intervention effects on various CVD markers were also assessed under the sub-groups of study design (RCTs vs cRCTs), duration (6–12 months vs > 12 months), intervention type (predominantly educational vs predominantly behavioral change vs predominantly environmental changed based) and employee health status (all employees vs those at risk of CVD). The chi-square test p-value for sub-group differences was assessed for significant sub-group effects (a p  < 0.05 indicates significant sub-group effect).

We classified the various intervention approaches used in the included studies based on a 2012 review by Heath et al. [ 30 ]. The interventions were broadly categorized as follows:

Campaigns and informational approaches: This involves information dissemination through different mediums like text messages, emails, newspapers, television, radio, to raise awareness and encourage a change in health behaviors mainly increasing activity and improving diet.

Behavioral and social approaches: This involves a change in individual behavior to incorporate more physical activity in their regular routine through goal setting, peer support and self-rewards. It can be implemented in groups (through technological means) as well as on an individual level with the help of a health provider/trainer and personalized activity plans.

Environmental and policy approaches: This involves making the office infrastructure and physical environment more activity friendly through construction of walking paths, changes to the vending machines, introduction of ergonomic workstations, break rooms, fitness facilities etc.

The Cochrane risk of bias tool [ 31 ] was used to assess the bias in included studies. The assessment was independently performed by RM and CS and disagreements were resolved by consensus. Possible publication bias among the studies was visually assessed using funnel plots.

Literature search and characteristics of included studies

Our search identified a total of 3774 records (Fig. 1 ). Out of these, 1873 were retrieved through Pubmed via MEDLINE, 696 through EMBASE, 922 through CENTRAL and 283 through CINAHL and PsychInfo. An additional 10 records were identified through other sources (identified by manually searching the reference list of included studies). After removal of duplicates, we screened 2517 records and identified 101 full text articles for eligibility assessment. Of these, 33 studies were included in the narrative synthesis. Studies reported various outcomes: weight ( n  = 16) [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], BMI ( n  = 19) [ 32 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 48 , 49 , 50 , 51 , 52 , 53 ], waist circumference ( n  = 13) [ 32 , 33 , 34 , 35 , 36 , 39 , 42 , 43 , 45 , 46 , 47 , 51 , 54 ], lipids ( n  = 15) [ 32 , 34 , 35 , 36 , 37 , 39 , 42 , 44 , 45 , 46 , 47 , 49 , 51 , 52 , 55 ], triglycerides ( n  = 8) [ 37 , 39 , 44 , 45 , 46 , 47 , 49 , 52 ], blood pressure (n = 16) [ 32 , 33 , 34 , 35 , 36 , 37 , 39 , 42 , 43 , 44 , 45 , 46 , 47 , 49 , 51 , 52 ] and glucose ( n  = 10) [ 32 , 34 , 37 , 39 , 44 , 45 , 46 , 47 , 49 , 52 ]. A total of 24 studies were included in the meta-analysis. Data from other studies was not available.

PRISMA FLOW diagram for study selection

Common reasons for excluding studies from the review are reported in the PRISMA diagram. Twelve RCTs [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 49 , 54 , 56 , 57 ], 15 cluster RCTs [ 40 , 41 , 43 , 44 , 46 , 47 , 48 , 50 , 53 , 58 , 59 , 60 , 61 , 62 , 63 ], 3 quasi experimental trials [ 42 , 52 , 64 ], and 3 controlled trials [ 45 , 51 , 55 ] were included in the review. A total of 36,188 men and women aged 32 to 55 years participated in these studies with the study sample sizes ranging from 45 to 10,281.

The descriptive characteristics of the included studies are presented in Table  1 . The studies had a varied population which included school and university personnel, employees of public and private sectors, blue collar workers (carpenters, bricklayers, road workers, crane operators, locomotive maintenance workers, gardeners, drivers, transportation workers, garage staff and factory workers), professional and technical, salaried and hourly workers, hospital staff, security guards, healthcare workers, casino employees and industry workers.

Out of the 33 studies reviewed, 13 studies [ 32 , 33 , 34 , 36 , 38 , 39 , 40 , 43 , 46 , 49 , 51 , 56 , 60 ] (8 RCTs, 4 cluster RCTs and 1 controlled trial) included only employees who had at least one raised CVD risk factor while the other 20 studies [ 35 , 37 , 41 , 42 , 44 , 45 , 47 , 48 , 50 , 52 , 53 , 54 , 55 , 57 , 58 , 59 , 61 , 62 , 63 , 64 ] included all employees irrespective of their health status.

Narrative analysis

Study interventions.

The studies used different types of interventions like campaigns, workshops and education; individual level behavioral change; and changes to the office environment and policies. Out of the 33 studies reviewed, 28 studies [ 32 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ] used a mix of the three approaches whereas the other 5 studies [ 33 , 34 , 35 , 38 , 48 ] implemented any one of these three approaches. The intervention duration in all the studies ranged from 6 months to 5 years. Campaign approach included lifestyle coaches to educate on physical activity, workshops on cardiac risk factors, wellness fairs, point of choice prompts and information dissemination through newsletters, brochures, internet etc. Behavioral change included incentivized group activities or tailored-for-individual weight loss regimes through physical activity, goal setting and rewards. Organizational changes included making stairs and walls more aesthetic, mapping of walking routes and more. Detailed description of the intervention and control groups is presented in Additional File 2 .

Risk of bias in included studies

Risk of bias among the included studies was assessed using the Cochrane risk of bias assessment tool as shown in Fig. 2 . The risk of bias summary for individual studies has been presented in Additional File 4 .

Risk of bias graph- review authors’ judgments about each risk of bias item presented as percentages across all included studies

The highest risk of bias emanated from performance bias due to unblinded participants and study personnel. There was also a high unclear risk of selection bias and detection bias due to lack of adequate data reported on randomization, allocation concealment and blinding of study outcome assessors.

Meta-analysis

Intervention effects on cardio-metabolic risk markers.

We undertook exploratory meta-analyses to pool the effect estimates for body weight, body mass index, waist circumference, systolic and diastolic blood pressure, total cholesterol, low density lipoprotein (LDL-C) and high density lipoprotein (HDL-C) cholesterol, triglycerides and blood glucose. Review Manager Software (RevMan version 5.3) was used to generate forest plots. The random effects model was used to generate intervention effects.

Results from the meta-analyses showed an overall significant intervention effect for body weight (16 studies, Mean difference: -2.61, 95% CI- -3.89, − 1.33), body mass index (19 studies, Mean difference: -0.42, 95% CI- -0.69, − 0.15) and waist circumference (13 studies, Mean difference: -1.92, 95% CI- -3.25, − 0.60) but there was considerable heterogeneity among estimates (I 2  = 94, 89 and 92% respectively; p -value < 0.0001). The pooled estimates for lipids, blood pressure and blood glucose were not statistically significant.

The overall mean difference and 95% CIs for each outcome, along with the heterogeneity in individual studies have been presented in Table 2 . Exploratory sub-group analysis showed a significant sub-group effect by study design for body weight ( p  = 0.0008) and BMI ( p  < 0.00001) and by intervention type for BMI ( p  = 0.008) and TC ( p  = 0.0007). However, there was no sub-group effect for the other outcomes (waist circumference and biochemical markers). (Additional File 3 ) In conclusion, sub-groups could not explain the high levels of heterogeneity responsible for the variability in study effect size estimates because the I-squared values were not reduced substantially.

Also, since these analyses usually involve multiple testing in case of many outcomes and would ideally require a much smaller p -value cut-off for significance, sub-group analysis estimates are observational and should be interpreted with caution.

The forest plots for all the individual outcomes have been shown in the figures below. Each forest plot shows the individual effect estimates for the intervention and control groups and the mean difference in each study, along with the overall pooled mean difference and corresponding CIs. (Figs. 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ).

Forest plot for change in body weight

Forest plot for change in body mass index

Forest plot for change in waist circumference

Forest plot for change in systolic blood pressure

Forest plot for change in diastolic blood pressure

Forest plot for change in total cholesterol

Forest plot for change in HDL-cholesterol

Forest plot for change in LDL-cholesterol

Forest plot for change in triglycerides

Forest plot for change in blood glucose

(A visual assessment of the funnel plots for each outcome showed the presence of some asymmetry for a few biochemical outcomes but we did not conduct any formal statistical tests to assess the same.)

Based on the 33 studies reviewed, we found that changes in diet and physical activity at worksites had a significant and positive effect on body weight, body mass index and waist circumference of working adults. It can be concluded that workplace based physical activity interventions can positively affect anthropometric outcomes and thus have the potential to alter the biochemical risk markers too. The results need to be interpreted with caution though, due to high heterogeneity among studies. The p -values for the chi-square test for heterogeneity were quite significant, suggesting a high degree of variability in effect estimates due to actual differences in studies and not due to sampling error (chance). This may be due to variability in sample sizes (they ranged from 45 to 10,281 participants) as well as the different study designs used in different studies. The various intervention approaches used across studies might also have contributed to the heterogeneity, as indicated by the exploratory sub-group analyses.

There could be a few reasons for the lack of a stronger evidence for the effect on biochemical variables. Anthropometric outcomes were the primary outcomes in almost all the studies whereas biochemical outcomes in a third, and only as secondary outcomes in half of the studies included in the meta-analyses. Those studies were therefore not adequately powered to detect significant changes in blood pressure, lipids and glucose levels.

Some reviews done in the past have shown a similar pattern with most included studies focusing only on anthropometric outcomes, which underscores the need for more high quality trials studying the effect of physical activity interventions on blood pressure and biochemical measures as well [ 65 ] [ 66 ] .

A few previously done reviews such as the one by Fleming et al. [ 67 ] , a 2010 review by Groeneveld et al. [ 26 ] and a brief overview of worksite health promotion programs and non-communicable disease prevention [ 68 ] have all suggested the possibility of greater intervention effectiveness among populations already at risk of CVDs compared to mixed populations. Hence, there is need for better quality studies to ascertain the role of employee health status in intervention effectiveness. A comparison of the effects of individual level behavior change on CVD risk reduction, compared to educational approaches and changes to the office environment is also an interesting facet that can be further explored.

Another aspect that needs consideration is participant compliance and barriers to intervention adherence. Unlike clinical or medical interventions which can be constantly monitored for acceptability, the effectiveness of lifestyle based interventions is difficult to evaluate since intervention uptake is a complex measure [ 69 ]. Some studies concluded lack of compliance, issues with intervention adherence, low participation and retention rates and inadequately motivated employees as some of the reasons which could have affected the study results. Non-adherence could also be one reason for very small effect sizes in studies with larger sample sizes [ 70 ].

Long-term participation and employee adherence thus seem to be major challenges in implementation of worksite physical activity interventions [ 70 ] [ 71 ]. It becomes paramount to devise innovative and practical ways to motivate the workforce and ensure sustained interest of the participants throughout the study [ 72 ]. Greater adherence and acceptability would ensure greater uptake that would in-turn result in more tangible health benefits to the employees.

Limitations and strengths

Our review has a few strengths. To the best of our knowledge, this is the first meta-analysis focused solely on the anthropometric and biochemical outcomes related to physical activity interventions at worksite. Secondly, the last review reporting the effects of worksite interventions on anthropometric and biochemical CVD risk markers was done in 2010 and our work provides updated literature on the topic. Thirdly, considering that we were dealing with multi-component PA interventions with multiple outcomes (and not a drug trial) we used a broad search strategy and covered 5 different databases to obtain a synthesis of all the relevant literature for practical understanding and future research. Fourthly, unlike a majority of previous reviews assessing the effect of worksite PA interventions primarily on physical activity, the proximal outcome, our review goes to the next level and summarizes the effects on the more distant anthropometric and biochemical outcomes.

A limitation of our study was that assessment of bias in individual studies was based on the data as reported in them. In some studies, relevant information on aspects of randomization and reporting of data was not presented which may have led to an underestimation of their quality. Another limitation was that we could not include data from nine studies in our meta-analyses since the estimates required for the analysis were not available. We wrote to the study authors but unfortunately only one of them provided data for our analyses. Additionally, it is possible that the interventions caused a change in other health behaviors like diet too, apart from physical activity, which in-turn could have led to an improvement in CVD outcomes.

Worksite physical activity interventions were effective in improving anthropometric measures, namely body weight, BMI and waist circumference. We were however unable to demonstrate a significant effect on biochemical variables. A possible reason could be that almost two-third of the studies were either not reporting the biochemical outcomes or not adequately powered to assess intervention effects on these variables. The potential of such interventions to prevent CVD and overall non-communicable diseases (NCDs) needs attention by employers and policy makers for improving the health status of the population. This can significantly contribute to achieving the UN targets of a 25% relative reduction in premature deaths from NCDs by 2025 [ 73 ].

Implications for future research

Overall, the evidence on the wide-ranging benefits of physical activity interventions is robust for action, and the absence of statistically significant biochemical improvements should not act as a deterrent to adoption by worksites. Ways to enhance uptake of worksite physical activity interventions by employers, employees and the environment need to be studied. A robust process evaluation framework along with assessment of factors like dietary changes, frequency of sickness, back pain, absenteeism etc., would provide greater insights into the relative effectiveness and complementarity of the different types of interventions. A design based on a theoretical framework like the Medical Research Council framework [ 74 ] for designing and evaluating complex intervention studies is an option. Also, future worksite PA intervention studies should adequately power for the biochemical outcomes and have longer follow-up durations. Hard-endpoints should be strived for wherever possible.

Availability of data and materials

Not Applicable

Abbreviations

Body Mass Index

Confidence Interval

Cardiovascular Disease

High Density Lipoprotein Cholesterol

Low Density Lipoprotein Cholesterol

Moderate to Vigorous Physical Activity

Physical Activity

Randomized Controlled Trial

World Health Organization

Yusuf S, Hawken S, Ôunpuu S, Dans T, Avezum A, Lanas F, et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case-control study. Lancet. 2004;364(9438):937–52.

Article   PubMed   Google Scholar  

Lee I-M, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmarzyk PT. Impact of physical inactivity on the World’s major non-communicable diseases. Lancet. 2012;380(9838):219–29.

Article   PubMed   PubMed Central   Google Scholar  

WHO | Physical Inactivity: A Global Public Health Problem [Internet]. WHO. 2017 [cited 2017 Dec 27]. Available from: http://www.who.int/dietphysicalactivity/factsheet_inactivity/en/

Diabetes mellitus and exercise [Internet]. [cited 2018 Jul 9]. Available from: http://care.diabetesjournals.org/content/diacare/25/suppl_1/s64.full.pdf

2018 Physical Activity Guidelines Advisory Committee Scientific Report. 2018;779.

Andajani-Sutjahjo S, Ball K, Warren N, Inglis V, Crawford D. Perceived personal, social and environmental barriers to weight maintenance among young women: a community survey. Int J Behav Nutr Phys Act. 2004;1(1):15.

Anjali SM. Perceived barriers of young adults for participation in physical activity. Curr Res Nutr Food Sci J. 2018;6(2):437–49.

Article   Google Scholar  

Trost SG, Owen N, Bauman AE, Sallis JF, Brown W. Correlates of adults’ participation in physical activity: review and update. Med Sci Sports Exerc. 2002;34(12):1996–2001.

van Uffelen JGZ, Wong J, Chau JY, van der Ploeg HP, Riphagen I, Gilson ND, et al. Occupational sitting and health risks: a systematic review. Am J Prev Med. 2010;39(4):379–88.

Tudor-Locke C, Leonardi C, Johnson WD, Katzmarzyk PT. Time spent in physical activity and sedentary behaviors on the working day: the American time use survey. J Occup Environ Med. 2011;53(12):1382–7.

Jirathananuwat A, Pongpirul K. Promoting physical activity in the workplace: a systematic meta-review. J Occup Health. 2017;59(5):385–93.

Phillips D. A settings approach: Healthy@Work—a model of a health promoting workplace. :7.

The Settings Approach: A Guiding Framework for HECA [Internet]. [cited 2018 Jul 9]. Available from: http://www.who.int/heca/alliancebuilding/heca2wg_draftsettings_180303.PDF

Proper K, van Mechelen W. Effectiveness and economic impact of worksite interventions to promote physical activity and healthy diet. :63.

Worksite Physical Activity | Physical Activity | CDC [Internet]. 2019 [cited 2019 Sep 15]. Available from: https://www.cdc.gov/physicalactivity/worksite-pa/index.htm

Pronk NP. Physical activity promotion in business and industry: evidence, context, and recommendations for a national plan. J Phys Act Health. 2009;6(Suppl 2):S220–35.

Chu AHY, Ng SHX, Tan CS, Win AM, Koh D, Müller-Riemenschneider F. A systematic review and meta-analysis of workplace intervention strategies to reduce sedentary time in white-collar workers. Obes Rev Off J Int Assoc Study Obes. 2016;17(5):467–81.

Article   CAS   Google Scholar  

Shrestha N, Harjula K, Verbeek J, Ijaz S. Workplace interventions for reducing sitting at work - Shrestha - 2016 - The Cochrane Library - Wiley Online Library [Internet]. 2017 [cited 2017 Aug 17]. Available from: http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD010912.pub3/full

Hutcheson AK, Piazza AJ, Knowlden AP. Work site–based environmental interventions to reduce sedentary behavior: a systematic review. Am J Health Promot. 2016:0890117116674681.

Reed JL, Prince SA, Elliott CG, Mullen K-A, Tulloch HE, Hiremath S, et al. Impact of Workplace Physical Activity Interventions on Physical Activity and Cardiometabolic Health Among Working-Age Women: A Systematic Review and Meta-Analysis. Circ Cardiovasc Qual Outcomes. 2017;10(2).

Commissaris DA, Huysmans MA, Mathiassen SE, Srinivasan D, Koppes LL, Hendriksen IJ. Interventions to reduce sedentary behavior and increase physical activity during productive work: a systematic review. Scand J Work Environ Health. 2016;42(3):181–91.

PubMed   Google Scholar  

Plotnikoff R, Collins CE, Williams R, Germov J, Callister R. Effectiveness of interventions targeting health behaviors in university and college staff: a systematic review. Am J Health Promot AJHP. 2015;29(5):e169–87.

To QG, Chen TTL, Magnussen CG, To KG. Workplace physical activity interventions: a systematic review. Am J Health Promot AJHP. 2013;27(6):e113–23.

Barr-Anderson DJ, AuYoung M, Whitt-Glover MC, Glenn BA, Yancey AK. Integration of short bouts of physical activity into organizational routine a systematic review of the literature. Am J Prev Med. 2011;40(1):76–93.

Chau JY, der Ploeg HP van, van Uffelen JGZ, Wong J, Riphagen I, Healy GN, et al. Are workplace interventions to reduce sitting effective? A systematic review. Prev Med 2010;51(5):352–356.

Groeneveld IF, Proper KI, van der Beek AJ, Hildebrandt VH, van Mechelen W. Lifestyle-focused interventions at the workplace to reduce the risk of cardiovascular disease--a systematic review. Scand J Work Environ Health. 2010;36(3):202–15.

Prospero Review Protocol [Internet]. [cited 2018 Jul 9]. Available from: https://www.crd.york.ac.uk/prospero/display_record.php? RecordID=94436.

DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.

Article   CAS   PubMed   Google Scholar  

9.5.2 Identifying and measuring heterogeneity [Internet]. [cited 2019 Sep 16]. Available from: https://handbook-5-1.cochrane.org/chapter_9/9_5_2_identifying_and_measuring_heterogeneity.htm

Heath GW, Parra DC, Sarmiento OL, Andersen LB, Owen N, Goenka S, et al. Evidence-based intervention in physical activity: lessons from around the world. Lancet Lond Engl. 2012;380(9838):272–81.

Assessing Risk of Bias in Included Studies [Internet]. [cited 2018 Aug 6]. Available from:  https://handbook-5-1.cochrane.org/chapter_8/8_assessing_risk_of_bias_in_included_studies.htm .

Kramer M, Molenaar D, Arena V, Venditti E, Meehan R, Miller R, et al. Improving employee health: evaluation of a worksite lifestyle change program to decrease risk factors for diabetes and cardiovascular disease. J Occup Environ Med Am Coll Occup Environ Med. 2015;57(3):284–91.

Morgan PJ, Collins CE, Plotnikoff RC, Cook AT, Berthon B, Mitchell S, et al. Efficacy of a workplace-based weight loss program for overweight male shift workers: the workplace POWER (preventing obesity without eating like a rabbit) randomized controlled trial. Prev Med. 2011;52(5):317–25.

Limaye T, Kumaran K, Joglekar C, Bhat D, Kulkarni R, Nanivadekar A, et al. Efficacy of a virtual assistance-based lifestyle intervention in reducing risk factors for type 2 diabetes in young employees in the information technology industry in India: LIMIT, a randomized controlled trial. Diabet Med J Br Diabet Assoc. 2017;34(4):563–8.

Viester L, Verhagen EALM, Bongers PM, van der Beek AJ. Effectiveness of a worksite intervention for male construction workers on dietary and physical activity behaviors, body mass index, and health outcomes: results of a randomized controlled trial. Am J Health Promot AJHP. 2017;1:890117117694450.

Google Scholar  

Weinhold KR, Miller CK, Marrero DG, Nagaraja HN, Focht BC, Gascon GM. A Randomized Controlled Trial Translating the Diabetes Prevention Program to a University Worksite, Ohio, 2012–2014. Prev Chronic Dis [Internet]. 2015 Nov 25 [cited 2017 Dec 12];12. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4674443/

Muto T, Yamauchi K. Evaluation of a multicomponent workplace health promotion program conducted in Japan for improving employees’ cardiovascular disease risk factors. Prev Med. 2001;33(6):571–7.

Kim J-Y, Oh S, Steinhubl S, Kim S, Bae WK, Han JS, et al. Effectiveness of 6 Months of Tailored Text Message Reminders for Obese Male Participants in a Worksite Weight Loss Program: Randomized Controlled Trial. JMIR MHealth UHealth [Internet]. 2015 Feb 3 [cited 2017 Dec 12];3(1). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4342743/

Jamal SN, Moy FM, Mohamed MNA, Mukhtar F. Effectiveness of a group support lifestyle modification (GSLiM) Programme among obese adults in workplace: a randomised controlled trial. PLoS One. 2016;11(8):e0160343.

Article   PubMed   PubMed Central   CAS   Google Scholar  

Almeida FA, You W, Harden SM, Blackman KCA, Davy BM, Glasgow RE, et al. Effectiveness of a worksite-based weight loss randomized controlled trial: the WORKSITE study. Obes Silver Spring Md. 2015;23(4):737–45.

Wilson MG, DeJoy DM, Vandenberg R, Padilla H, Davis M. FUEL your life: a translation of the diabetes prevention program to worksites. Am J Health Promot AJHP. 2016;30(3):188–97.

Chen M-M, Tsai AC, Wang J-Y. The effectiveness and barriers of implementing a workplace health promotion program to improve metabolic disorders in older workers in Taiwan. Glob Health Promot. 2016;23(2):6–14.

Christensen J, Overgaard K, Carneiro I, Holtermann A, Søgaard K. Weight loss among female health care workers- a 1-year workplace based randomized controlled trial in the FINALE-health study. BMC Public Health. 2012;12:625.

Racette SB, Deusinger SS, Inman CL, Burlis TL, Highstein GR, Buskirk TD, et al. Worksite opportunities for wellness (WOW): effects on cardiovascular disease risk factors after 1 year. Prev Med. 2009;49(0):108–14.

Prabhakaran D, Jeemon P, Goenka S, Lakshmy R, Thankappan KR, Ahmed F, et al. Impact of a worksite intervention program on cardiovascular risk factors: a demonstration project in an Indian industrial population. J Am Coll Cardiol. 2009;53(18):1718–28.

Shrivastava U, Fatma M, Mohan S, Singh P, Misra A. Randomized Control Trial for Reduction of Body Weight, Body Fat Patterning, and Cardiometabolic Risk Factors in Overweight Worksite Employees in Delhi, India. J Diabetes Res [Internet]. 2017 [cited 2018 9];2017. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5727835/

Healy GN, Winkler EAH, Eakin EG, Owen N, Lamontagne AD, Moodie M, et al. A cluster RCT to reduce workers’ sitting time: impact on Cardiometabolic biomarkers. Med Sci Sports Exerc. 2017;49(10):2032–9.

Siegel JM, Prelip ML, Erausquin JT, Kim SA. A worksite obesity intervention: results from a group-randomized trial. Am J Public Health. 2010;100(2):327–33.

Nilsson PM, Klasson EB, Nyberg P. Life-style intervention at the worksite--reduction of cardiovascular risk factors in a randomized study. Scand J Work Environ Health. 2001;27(1):57–62.

Fernandez ID, Chin NP, Devine CM, Dozier AM, Martina CA, McIntosh S, et al. Images of a healthy worksite: a group-randomized trial for worksite weight gain prevention with employee participation in intervention design. Am J Public Health. 2015;105(10):2167–74.

Engbers LH, van Poppel MNM, van Mechelen W. Modest effects of a controlled worksite environmental intervention on cardiovascular risk in office workers. Prev Med. 2007;44(4):356–62.

Moy F, Sallam AAB, Wong M. The results of a worksite health promotion programme in Kuala Lumpur. Malaysia Health Promot Int. 2006;21(4):301–10.

Linde JA, Nygaard KE, MacLehose RF, Mitchell NR, Harnack LJ, Cousins JM, et al. HealthWorks: results of a multi-component group-randomized worksite environmental intervention trial for weight gain prevention. Int J Behav Nutr Phys Act. 2012;9:14.

Atlantis E, Chow C-M, Kirby A, Fiatarone Singh MA. Worksite intervention effects on physical health: a randomized controlled trial. Health Promot Int. 2006;21(3):191–200.

Naito M, Nakayama T, Okamura T, Miura K, Yanagita M, Fujieda Y, et al. Effect of a 4-year workplace-based physical activity intervention program on the blood lipid profiles of participating employees: the high-risk and population strategy for occupational health promotion (HIPOP-OHP) study. Atherosclerosis. 2008;197(2):784–90.

Barham K, West S, Trief P, Morrow C, Wade M, Weinstock RS. Diabetes prevention and control in the workplace: a pilot project for county employees. J Public Health Manag Pract JPHMP. 2011;17(3):233–41.

Chockalingam A, Kishchuk N, LeLorier J, Alloul K, Makrides L, Richard J, et al. Evaluation of a workplace health program to reduce coronary risk factors. Clin Gov Int J. 2008;13(2):95–105.

Milani RV, Lavie CJ. Impact of worksite wellness intervention on cardiac risk factors and one-year health care costs. Am J Cardiol. 2009;104(10):1389–92.

Lemon SC, Zapka J, Li W, Estabrook B, Rosal M, Magner R, et al. Step ahead. Am J Prev Med. 2010;38(1):27.

Williams AE, Stevens VJ, Albright CL, Nigg CR, Meenan RT, Vogt TM. The results of a 2-year randomized trial of a worksite weight management intervention. Am J Health Promot AJHP. 2014;28(5):336–9.

Lemon SC, Wang ML, Wedick NM, Estabrook B, Druker S, Schneider KL, et al. Weight gain prevention in the school worksite setting: results of a multi-level cluster randomized trial. Prev Med. 2014;60:41–7.

Brehm BJ, Gates DM, Singler M, Succop PA, D’Alessio DA. Environmental changes to control obesity: a randomized controlled trial in manufacturing companies. Am J Health Promot AJHP. 2011;25(5):334–40.

French SA, Harnack LJ, Hannan PJ, Mitchell NR, Gerlach AF, Toomey TL. Worksite environment intervention to prevent obesity among metropolitan transit workers. Prev Med. 2010;50(4):180–5.

Goetzel RZ, Baker KM, Short ME, Pei X, Ozminkowski RJ, Wang S, et al. First-year results of an obesity prevention program at the Dow Chemical Company. J Occup Environ Med Am Coll Occup Environ Med. 2009;51(2):125–38.

A Systematic Review of Internet-Based Worksite Wellness Approaches for Cardiovascular Disease Risk Management: Outcomes, Challenges & Opportunities [Internet]. [cited 2018 Sep 26]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0083594

Proper KI, Koning M, van der Beek AJ, Hildebrandt VH, Bosscher RJ, van Mechelen W. The effectiveness of worksite physical activity programs on physical activity, physical fitness, and health. Clin J Sport Med Off J Can Acad Sport Med. 2003;13(2):106–17.

Fleming P, Godwin M. Lifestyle interventions in primary care. Can Fam Physician. 2008;54(12):1706–13.

PubMed   PubMed Central   Google Scholar  

Kolbe-Alex T. E, Ev L. non-communicable disease prevention and worksite health promotion programs: a brief review. Occup Med Health Aff. 2013;1(7):1–8.

Visscher TL, Bell C, Gubbels JS, Huang TT, Bryant MJ, Peeters A, et al. Challenges in lifestyle and community interventions research; a call for innovation. BMC Obes [Internet]. 2014 21 [cited 2019 Sep 15];1. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4511431/

Robroek SJ, van Lenthe FJ, van Empelen P, Burdorf A. Determinants of participation in worksite health promotion programmes: a systematic review. Int J Behav Nutr Phys Act. 2009;6:26.

Linnan LA, Sorensen G, Colditz G, Klar DN, Emmons KM. Using theory to understand the multiple determinants of low participation in worksite health promotion programs. Health Educ Behav Off Publ Soc Public Health Educ. 2001;28(5):591–607.

CAS   Google Scholar  

Seifert CM, Chapman LS, Hart JK, Perez P. Enhancing intrinsic motivation in health promotion and wellness. Am J Health Promot. 2012;26(3):1–12.

WHO | About 9 voluntary global targets [Internet]. WHO. [cited 2019 Sep 15]. Available from: http://www.who.int/nmh/ncd-tools/definition-targets/en/

MRC-Developing and evaluating complex interventions [Internet]. [cited 2019 Nov 4]. Available from: https://mrc.ukri.org/documents/pdf/complex-interventions-guidance/

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Acknowledgements

SG was supported by Wellcome Trust (M-Wellcare Grant No: 096735/B/11/Z), (2015-17); Bernard Lown Scholars in Cardiovascular Health Program, Harvard School of Public Health (2015-17). RM was supported by SG’s fellowship ‘Bernard Lown Scholars in Cardiovascular Health Program’ (2015-17) and partially from Wellcome Trust Grant No: 203124/Z/16/Z 2018.

Publication cost of the manuscript is supported by the Fogarty International Center of the National Institutes of Health Award Number D43TW009135. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Public Health Foundation of India.

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Additional file 1..

Search terms. This file provides the search strategy used to obtain relevant articles from PUBMED.

Additional file 2.

Study interventions. This file includes a table that describes the purpose, characteristics, interventions and results of each study included in the review.

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Exploratory Sub-group analyses. This file includes the tables describing the effects of workplace interventions on outcomes, analyzed under sub-groups.

Additional file 4.

Risk of bias summary for individual studies in the review.

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Mulchandani, R., Chandrasekaran, A.M., Shivashankar, R. et al. Effect of workplace physical activity interventions on the cardio-metabolic health of working adults: systematic review and meta-analysis. Int J Behav Nutr Phys Act 16 , 134 (2019). https://doi.org/10.1186/s12966-019-0896-0

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• Published: 14 April 2021

Prevalence of metabolic syndrome and its associated risk factors among staffs in a Malaysian public university

• Mohd Rizal Abdul Manaf 1 ,
• Azmawati Mohammed Nawi 1 ,
• Noorlaili Mohd Tauhid 2 ,
• Hanita Othman 3 ,
• Mohd Rizam Abdul Rahman 1 ,
• Hanizah Mohd Yusoff 1 ,
• Nazaruddin Safian 1 ,
• Pei Yuen Ng 4 ,
• Zahara Abdul Manaf 5 ,
• Nor Ba’yah Abdul Kadir 6 ,
• Kevina Yanasegaran 4 ,
• Siti Munirah Abdul Basir 5 ,
• Sowmya Ramakrishnappa 1 &
• Kurubaran Ganasegeran 7  

Scientific Reports volume  11 , Article number:  8132 ( 2021 ) Cite this article

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• Endocrinology
• Health occupations
• Risk factors

Public health systems are concerned with the commensurate rise of metabolic syndrome (MetS) incidence across populations worldwide, due to its tendency to amplify greater risk of diabetes and cardiovascular diseases within communities. This study aimed to determine the prevalence of MetS and its associated risk factors among staffs in a Malaysian public university. A cross-sectional study was conducted among 538 staffs from the Universiti Kebangsaan Malaysia (UKM) between April and June 2019. MetS was defined according to JIS “Harmonized” criteria. A questionnaire that consisted of items on socio-demographics, lifestyle risk behaviors and personal medical history information was administered to participants. Subsequently, a series of physical examination and biochemical assessment was conducted at the hall or foyer of selected faculties in the university. Descriptive and inferential statistics were conducted using SPSS version 22.0. Multivariate models were yielded to determine the risk factors associated with MetS. Statistical significance was set at P  < 0.05. The overall prevalence of MetS was 20.6%, with men having greater prevalence than women (24.9% vs. 18.3%). Prevalence of MetS increased with age. Factors contributed to MetS in the overall sample were BMI, hypertension, diabetes and physical activity of moderate intensity. Diabetes and hypertension were significantly associated with MetS in men, whereas BMI, diabetes and hyperlipidemia were significantly associated with MetS in women. Lifestyle behaviors and cardio-metabolic risk factors were associated with MetS for the overall sample, and across genders.

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Introduction

The burden of non-communicable diseases (NCDs) has caused substantial impact to health systems and economies worldwide. NCDs cause greater increase to morbidities and mortalities, reduced quality of life and escalated healthcare expenditures to governments, particularly in low- and middle-income countries (LMICs) 1 , 2 . Coupled with these unprecedented consequences of NCDs, global public health systems are being challenged with the rise of metabolic syndrome (MetS) incidence 3 . MetS constitutes a cluster of at least three out of five interrelated cardio-metabolic dysfunctions that occur concurrently 4 , 5 , 6 . The abnormalities include abdominal obesity, raised blood pressure, hyperglycemia, hypertriglyceridemia and low high-density lipoprotein (HDL) cholesterol levels 4 , 5 , 6 . These features predispose individuals to the development of diabetes and cardiovascular diseases 7 .

The global epidemic proportions of MetS was estimated to be around 20–25% 6 . When compared across regions, it was estimated that 12–37% of the Asian population were afflicted with MetS, while around 12–26% of the European population suffered the condition 8 . MetS affects approximately 25–40% of Malaysian adults, with its risks being elevated with advancing age 9 . The magnitude of MetS prevalence varies globally, especially in Asian countries as a result of differences in lifestyle behaviors and ethnicities 10 . As the proportion and distribution of body fat in Asians differed across populations in Europe or North American regions, it became fundamental to consider that the definition of obesity applied to Western populations cannot be adopted for Asian populations 11 , 12 . This could be observed with the rising trend of MetS prevalence reported in Singapore 13 , China 14 and Malaysia 15 when used the Asian adapted definitions on the National Cholesterol Education Program (NCEP)–Adult Treatment Panel III (ATP III) criteria. The Joint Interim Statement (JIS) “Harmonized” criteria definition that was later adopted was found to be more suitable to determine the proportions of MetS in Asian populations 16 .

While literature to determine MetS in populations was burgeoning rapidly, the exploration of such investigations to occupational groups were limited. Previous studies have identified that the prevalence of MetS among employees in a Taiwanese hospital was 12% 17 , among Chinese police officers was 23.2% 18 , among academic staffs in a Malaysian public university was 38.3% 19 and among Malaysian government employees was 57.1% 20 . Literature has identified a multitude of factors to be associated with MetS. Demographic characteristics such as being a woman or older age was shown to escalate the risk of having MetS 21 , 22 , whereas lifestyle behaviors like physical activity, alcohol consumption, smoking, overweight or obesity 23 , 24 , 25 , 26 were commonly linked to MetS across different geographies and populations. The current study was aimed to determine the prevalence of MetS and its associated risk factors using the JIS “Harmonized” criteria among staffs in a Malaysian public university.

Study design, setting and participants

This descriptive-analytical cross-sectional single-center study was conducted from April to June 2019 among staffs at the Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia. Based on a sample-size calculation for a study of finite population 27 , with approximately 4000 employees at the Universiti Kebangsaan Malaysia (Bangi Campus), a minimum sample size of 522 staffs was calculated to represent a cross-section of the population and to allow the study to determine the prevalence of metabolic syndrome with a margin of error of ± 4%, as recommended by previous literature with a similar study population 28 . An additional 15% was included in the calculated sample to compensate for missing data and non-response 29 , for a final sample size of 600 staffs. A total of six hundred staffs from both academics and non-academics were randomly invited to participate in the study. Random selection was conducted using a free computer aided software (Research Randomizer) 30 . The sampling frame included the entire university’s staffs population, with their employment identity number provided by the Department of Registrar, Universiti Kebangsaan Malaysia. The single generated set of 600 random samples were identified, and subsequently, an invitation was sent out to the employees through their official email registered in the personal profile. The study was conducted at the hall or foyer of selected faculties in the university.

Study inclusion and exclusion criteria

Permanent and contract staffs aged between 18 and 60 years old were included in the study. Staffs who were pregnant, breast-feeding and those who were on maternity leaves or sabbaticals were excluded.

Ethics statement

This study complied with the guidelines convened in the Declaration of Helsinki. Ethical approval was obtained from the Universiti Kebangsaan Malaysia Research and Ethics Committee (approval number: UKM PPI.800-1/1/5/JEP-2019-391). Study objectives and benefits were explained verbally and in written form. Respondent’s confidentiality and anonymity were assured. Written consent was obtained from those who agreed to participate.

Data collection and procedure

The study involved two stages. In stage one, respondents were required to complete a self-administered questionnaire that consisted of items on socio-demographic characteristics (gender and age), lifestyle risk behaviors (smoking status, alcohol consumption and physical activity level) and personal medical history (hypertension, diabetes and hyperlipidemia).

Smokers were defined as those who have smoked at least 100 cigarettes during their lifetime 31 . The item was assessed using a dichotomized response (yes/no). Alcohol consumption was assessed using a dichotomized response (yes/no), defined as monthly user consumption of alcoholic beverages, either wine, liquor or beer within the recent year 31 . Physical activity (PA) was assessed using the validated Malay version of the Global Physical Activity Questionnaire (GPAQ-M) 32 . The GPAQ-M comprises of 16 questions that asked participants about the intensity, frequency and duration of PA across 3 major domains, namely PA at work, PA during travel or transport and PA during recreation or leisure time, in addition, to an extra question that collected data on sedentary behavior and time, in minutes/day. A metabolic equivalent task (MET) value of 4 was designated as moderate intensity PA, while a value of 8 was assigned as vigorous intensity PA. These values of MET were subsequently multiplied by the number of days per week of PA and the duration on a typical day for each PA domain to tabulate the total PA (MET-minutes/week). The MET-minutes/week spent on each domain was subsequently computed to yield the overall PA level. High PA level was defined as vigorous-intensity activity on at least 3 days with at least 1500 MET-minutes/week or 7 days or more on any combinations of walking, moderate or vigorous intensity activities of at least 3000 MET-minutes/week. Moderate PA level was defined as 3 or more days of vigorous-intensity activity of at least 20 min/day or 5 or more days of moderate-intensity activity or walking of at least 30 min/day or 5 or more days of any combination of walking, moderate- or vigorous-intensity activities, that achieved a minimum of at least 600 MET-minutes/week. Participants who neither meet any of the previous two criteria were classified as having low PA level 33 , 34 , 35 . Personal medical history was based on respondents self-reported hypertension, diabetes or hyperlipidemia as diagnosed by a doctor or under current use of anti-hypertensives, anti-diabetics or lipid-lowering drugs.

In stage two, respondents were required to undergo a health screening session that constituted of general physical examination such as participant’s height, weight, body mass index (BMI), waist circumference (WC) and blood pressure (BP) measurements. Subsequently, medical laboratory tests for fasting blood glucose and lipid profiles were undertaken. Fasting blood (5 ml) was collected from each subject, separated using EDTA and serum separator tubes for biochemical investigations. Fasting plasma glucose (in mmol/L) was determined using the glucose oxidase method. Serum triglyceride (TG) (in mmol/L) was measured enzymatically after hydrolyzation of glycerol. High density lipoprotein cholesterol (HDL-c) (in mmol/L) was measured after precipitation of other lipoproteins with heparin manganese chloride mixture. Colorimetry/spectrophotometry for biochemistry assay and hexokinase for glucose were performed using Architect c16000 and c8000 (Abbott, Illinois, USA).

Height of the participant was measured barefooted by using a portable stadiometer (SECA, Germany) to the nearest 0.1 cm. Weight of the participant was measured using a digital lithium weighing scale (Tanita, Japan) calibrated to the nearest 0.1 kg, with the individual being dressed in light clothing and barefooted. The BMI was calculated by dividing body weight by the squared of height (kg/m 2 ). BMI was later categorized based on the WHO BMI guideline (1998), which was also adopted in the National Health and Morbidity Survey (NHMS) 2015 Malaysia (< 25 kg/m 2 as underweight to normal weight, 25.0 to 29.9 kg/m 2 as overweight and ≥ 30 kg/m 2 as obesity) 36 . To ease interpretations, the current study dichotomized the BMI category as < 30 kg/m 2 (non-obese) and ≥ 30 kg/m 2 (obese) 36 . WC was measured with participants wearing light clothing at mid-point between the lower rib margin and iliac crest using a flexible measuring tape to the nearest 0.1 cm. All anthropometric indices were measured twice and averaged to reduce measurement error as recommended by the International Standards for Anthropometric Assessment 37 . BP was measured twice on the same arm with a digital BP monitor (OMRON) after the individual had been seated at rest for at least 10 min. The systolic and diastolic BP measurements (in mmHg) were the mean of two readings.

Criteria for MetS

This study adopted the Joint Interim Statement (JIS) “Harmonized” criteria for MetS as advocated by the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity 6 . MetS was defined as having at least three of the following five risk factors: (1) raised waist circumference (WC) of ≥ 90 cm for men and ≥ 80 cm for women; (2) Raised serum triglycerides (TG) of ≥ 1.7 mmol/L; (3) Low high density lipoprotein cholesterol (HDL-c), defined as < 1.0 mmol/L for men and < 1.3 mmol/L for women; (4) Raised BP, defined as a systolic blood pressure (SBP) ≥ 130 mmHg or a diastolic blood pressure (DBP) ≥ 85 mmHg, or under current use of anti-hypertensive medications; and (5) Raised fasting blood sugar (hyperglycemia), defined as ≥ 5.6 mmol/L, or under current use of anti-diabetic medications.

Statistical analysis

Analysis was conducted using IBM SPSS Statistics version 22.0 38 . Descriptive statistics were conducted for all variables in the study. Pearson chi-square test and binary logistic regressions were used to assess the associations between MetS and categorical independent variables such as demographics, lifestyle risk behaviors and personal medical history in this study. Crude odds ratios (cOR) were reported. Multiple logistic regression analysis using “Backward,” “Forward,” and “Enter” regression techniques were employed to determine the predictors of MetS in this sample. Adjusted odds ratios (aOR) were reported. Only the most parsimonious model that determined the factors associated with MetS for the overall sample and across both genders was selected. Multi-collinearity between independent variables was checked for the values of variance inflation factor (VIF) values not exceeding 10 39 . A relatively low VIF value (less than 5) confirms no interaction testing is warranted 40 . VIF values were yielded using the procedures recommended by IBM SPSS Statistics software guide 41 . Statistical significance was set at P  < 0.05.

Sample characteristics

Six hundred staffs were invited to participate and 538 consented to participate (participation rate: 89.7%). Of the total, 349 (64.9%) were women and 189 (35.1%) were men. The mean (SD) age of the participants was 43.4 (7.7) years and the age ranged between 27 and 60 years old. Most participants were aged between 35 and 44 years old, 309 (57.4%). Only thirty participants (7.4%) were smokers and six (1.5%) were alcohol drinkers, but the majority had a BMI < 30 kg/m 2 , 140 (74.1%). Most respondents reported to undertake high intensity PA, 226 (43.1%). Eighty-three (15.7%) of the participants had hypertension, 26 (4.9%) had diabetes and 74 (14%) had hyperlipidemia. The prevalence of MetS in this sample was 20.6% (Table 1 ).

Prevalence of MetS and its components by age groups

The overall prevalence of MetS was 20.6% (24.9% in men and 18.3% in women). As exhibited in Table (2), this study showed a significantly higher prevalence of MetS in the older aged group for the overall sample and in women. The prevalence of MetS increased from 10.7 and 9.5% for those aged less than 35 years to 31.5% and 36.1% for those aged 55 years or more in the overall sample and in women respectively. Two MetS components (glucose and SBP) showed similar trends in both men and women. Both genders had the highest glucose and SBP levels at the age of 55 years or older. However, other MetS components (WC, TG and DBP) were significantly prevalent in women. The highest WC and TG levels were found among women aged 55 years or older, whereas raised DBP levels were mostly found in women between the ages 45–54 years (Table 2 ).

Risk factors associated with MetS by binary logistic regression

MetS for the overall sample was significantly higher among alcohol drinkers (cOR 8.2, 95% CI 1.4–45), those with BMI ≥ 30 kg/m 2 (cOR 3.5, 95% CI 2.2–5.4), those who practice moderate intensity PA (cOR 1.8, 95% CI 1.1–3.3), and among those with hypertension (cOR 3.9, 95% CI 2.3–6.4), diabetes (cOR 5.1, 95% CI 2.3–11.3) or hyperlipidemia (cOR 1.8, 95% CI 1.1–3.1). In men, MetS was significantly higher among those with BMI ≥ 30 kg/m 2 (cOR 2.2, 95% CI 1.1–4.5), and among those having hypertension (cOR 3.4, 95% CI 1.6–7.1) or diabetes (cOR 4.6, 95% CI 1.6–13.1). In women, MetS was significantly higher among alcohol drinkers (cOR 9.4, 95% CI 1.2–100.0), those with BMI ≥ 30 kg/m 2 (cOR 4.7, 95% CI 2.7–8.3), and those having hypertension (cOR 4.1, 95% CI 2.1–8.1), diabetes (cOR 4.8, 95% CI 1.3–16.7) or hyperlipidemia (cOR 3.3, 95% CI 1.5–7.4) respectively. These associations were statistically significant (Table 3 ).

Risk factors associated with MetS by multiple logistic regression analyses

All statistically significant risk factors associated with MetS in the univariate analyses were included in the multivariate analyses. For the overall sample, the multivariable model had four statistically significant risk factors associated with MetS: BMI ≥ 30 kg/m 2 (aOR 3.1, 95% CI 1.8–5.5; P  < 0.001), moderate intensity PA (aOR 2.5, 95% CI 1.2–5.0; P  = 0.015), having hypertension (aOR 2.0, 95% CI 1.1–4.3; P  = 0.023) and diabetes (aOR 3.9, 95% CI 1.3–11.4; P  = 0.011). The total model for the overall sample was significant and accounted for 19% of the variance (Table 4 ). In men, the multivariable model retained two statistically significant risk factors associated with MetS: having hypertension (aOR 2.4, 95% CI 1.1–5.4; P  = 0.029) and diabetes (aOR 3.8, 95% CI 1.3–11.1; P  = 0.031). The total model was significant and accounted for 15% of the variance (Table 5 ). For women, three risk factors associated with MetS were retained in the multivariable model: BMI ≥ 30 kg/m 2 (aOR 4.6, 95% CI 2.3–8.2; P  < 0.001), having diabetes (aOR 4.2, 95% CI 1.4–9.2; P  = 0.023) and hyperlipidemia (aOR 3.2, 95% CI 1.1–6.0; P  = 0.030). The total model was significant and accounted for 18% of the variance (Table 6 ). There was no multi-collinearity between independent variables in all three models, hence interaction analysis was not warranted. In all three multivariable analyses, the “Backward Wald” technique yielded the most parsimonious models.

Core summary findings

This study aimed to determine the prevalence of MetS and its associated risk factors among staffs in a Malaysian public university. The overall prevalence rate of MetS in this sample was 20.6%. Prevalence rate exhibited a commensurate rise with age, and was significantly higher in older aged people for the overall sample and in women, however, for men, the prevalence rate trended a non-significant S-shaped pattern with increasing age. Factors significantly associated with MetS in the overall sample were BMI ≥ 30 kg/m 2 , hypertension, diabetes and physical activity of moderate intensity. Gender-specific-risks regression models found that diabetes was the most important factor to be significantly associated with MetS in men (Wald value = 5.6), while a BMI ≥ 30 kg/m 2 was the most substantial factor to be significantly associated with MetS in women (Wald value = 18.5).

Comparison with existing literature

Prevalence of mets and its components.

This study found that the overall prevalence of MetS was 20.6%. The prevalence rate reported in this study was higher than that found in Taiwanese high-tech industry workers (8.2%) 42 , the Philippine general population (18.6%) 43 and a rural Ugandan adult cohort (19.1%) 44 , but lower than that found in populations across Iran (ranged between 33.1% and 37.1%) 26 , 45 , Brazil (34.1%) 24 , Indonesia and the Netherlands (39% vs. 29.2%) 46 , China (ranged between 24.2 and 42.6%) 23 , 25 , 31 , Canada (25%) 47 and Australia (ranged between 21.1 and 30.7%) 48 . From the Malaysian context, population-based prevalence estimates for MetS ranged between 25 and 40% 9 , 16 , 49 , while for specific sub-groups, the prevalence rates of MetS for patients with type 2 diabetes mellitus ranged between 73 and 85% 50 , among elderly people was 43.4% 51 , non-diabetic women post gestational diabetes mellitus was 22% 52 , and for vegetarians accounted for approximately 24.2% 53 . The bulk of existing literature reported that the prevalence of MetS was significantly higher in women than men 26 , 43 , 54 , 55 , 56 , 57 . In contrary to those findings, the current study showed that the prevalence of MetS in men was higher than in women (24.9% vs. 18.3%) and that no statistically significant difference was observed. This result was consistent with emerging works from the Indian 58 and Chinese 25 , 59 cohorts that evaluated prevalence rates across genders. The stratified analysis by age showed some interesting epidemiological observations in this study. Men aged < 55 years of age had higher prevalence of MetS than in women within the same aged group, but this observation was not statistically significant. However, a reversed phenomenon occurred for those aged ≥ 55 years of age, with women exhibiting greater prevalence rate of MetS than men, and this association was statistically significant. Similar observation was found in a previous study 59 . The current study found higher prevalence of MetS components in women than men. Two components (raised glucose and SBP) showed a significant upward linear trend with increasing age in both genders. However, other statistically significant MetS components (raised WC, TG and DBP) were only prevalent in women. These findings were inconsistent with previous studies 26 , 31 .

Investigators to-date often struggled to explain the controversial linkages between MetS with age- and gender-specific associations. The complexities surrounding the variation of prevalence rates across different study populations, regions, countries and settings were difficult to decipher and should be interpreted with caution. Four plausible attributes could be advocated to explain probable associations. These include methodological, environmental, hormonal, and lifestyle influences on the burden of MetS across populations. From the methodological domain, a prominent factor leading to variation of prevalence rates across studies could be attributed to different criteria and operational definitions applied to diagnose MetS. Four distinctive criteria were used across the scientific literature to determine the diagnosis and epidemiology of MetS till date, namely the World Health Organization (WHO) 60 , the National Cholesterol Education Program (NCEP)—Adult Treatment Panel III (ATP III) 4 , 61 , the International Diabetes Federation (IDF) 62 and the Joint Interim Statement (JIS) “Harmonized” 6 definitions. As the proportion and composition of body fat in Asians differed to European populations, it became apparent that the JIS Harmonized criteria was more suitable to determine the risk of MetS for populations in Asia, given its pre-defined cut-offs for central obesity (waist circumference for men ≥ 90 cm and women ≥ 80 cm) and reduced cut-off for hyperglycemia (≥ 5.6 mmol/L instead of 6.1 mmol in the NCEP-ATP III criteria). This was evident as Asian studies that used JIS Harmonized criteria to diagnose MetS showed distinctive variations when compared to other definitions 13 , 14 , 15 , 63 . From the environmental perspective, it was hypothesized that post-exposure serum perfluoroalkyl chemicals (PFCs) that are widely used in consumer products manufacture distorts glucose homeostasis and influence gender-specific MetS indicators 64 , 65 . Hormonal factors such as postmenopausal weight gain and confounded risk profiles might have accounted higher prevalence rate of MetS in women than men 51 . This postulation may be somewhat true for the current study, as older aged women were observed to be more susceptible to MetS in comparison to men. Although gender and age are non-modifiable risk factors for MetS with extensive controversies being postulated, modifiable risk factors such as lifestyle behaviors could provide more meaningful real-life interpretations. One such lifestyle behavior that could post substantial risk for people to be afflicted with MetS is sedentary work nature, which could be highly prevalent in the current study, as the study sample involved white-color workers in an academic institution whose occupational nature were mostly related to desk-jobs, thus predisposing to obesogenic effects.

Risk factors associated with MetS

The final regression model for the overall sample retained five risk factors to be associated with MetS, in which four variables (BMI, PA, hypertension and diabetes) showed statistical significance. When compared across gender, it was found that greater BMI, diabetes and hypertension increased the risk of MetS in women, while for men, although three factors were likely to increase the risk of MetS, only two (hypertension and diabetes) showed statistical significance. The regression model concluded that greater BMI in the overall sample (Wald value = 14.5) and in women (Wald value = 18.5) significantly predicted MetS. This finding was consistent with previous studies 26 , 42 , 66 , 67 . In obese individuals, free fatty acids and cytokines like tumor necrosis factor-alpha (TNF-α) are released by adipose cells. These substances block phosphatidylinositide-3-kinase-dependent signal transduction pathways, thus reducing glucose uptake in the liver and skeletal muscles 68 , 69 . As a consequence, pancreatic β-cells are forced to secrete excessive insulin. These conditions lead to hyperglycemia or diabetes 42 . As age advances, blood vessels tend to undergo gradual loosening of their elasticity, gaining increased resistance, and slowing of blood flow. With poor circulation, lipid is prone to pile up in the abdomen and release free fatty acids into the serum, causing greater insulin resistance and elevated serum triglycerides 70 . These physiological and biological processes, coupled with greater adiposity, predispose greater risk of MetS as observed in the current study, with the prevalence of MetS and its components being mostly increased with advancing age, and its associated factors, particularly diabetes, hypertension and hyperlipidemia being statistically significant in the final regression models.

The bulk of the literature had found that lifestyle behaviors such as PA, smoking and alcohol consumption escalates the risk of MetS 23 , 25 , 31 , 44 , 59 . In contrast to those investigations, the current study found a somewhat debatable finding on the associations between lifestyle behaviors and MetS. PA was significantly associated with MetS in the current study, consistent with previous reports 23 , 25 , 31 . However, moderate intensity PA was not a protective effect to MetS in the current study, similar to previous reports 71 , 72 . This could be attributed to the fact that moderate intensity PA may not suffice to accelerate metabolism and burn calories in a sample of individuals whose work is highly sedentary in nature. The other lifestyle behaviors, particularly smoking and alcohol consumption showed a peculiar pattern of association in the current study. Alcohol consumption which was significantly associated with MetS in the overall sample and in women at the univariate level showed a reversed observation across final regression models (not statistically significant for the overall sample and eliminated as a predictor for MetS in women). These findings contradict available literature 23 , 25 , 31 .

Limitations

The cross-sectional nature of this study could not establish causal relationships. The relatively small sample size from a single center, coupled with non-representative demographics of the study population (such as majority being women) may limit the generalizability of the study findings, thus extrapolation of the study findings to a nationally representative estimate could not be established. Concurrently, the size of the sample in the current study may have increased the possibility of type II error in the current analysis. For example, alcohol drinking in the overall sample and greater BMI in men may have achieved statistical significance in association with MetS with a larger sample size (p = 0.079 and p = 0.058 respectively). The relatively wide confidence intervals (CIs) gap observed in certain variables, such as for the association between alcohol drinking and MetS suggests weak relationships, hence was not sufficiently powered enough to actually predict MetS in the current sample. As smoking and alcohol consumption were self-reported measures in the current study, the findings could be attributed to social-desirability bias. Such circumstances could be highly possible, as Malaysia, a country being shaped with cultural and religious norms, roles and behaviors may have predisposed respondents in the current sample to under-report alcohol consumption behaviors due to the apprehended harsh social etiquette issues or stigmatization within communities. This could be the reason on the relatively weak association of alcohol consumption and MetS in women at the univariate level, and its elimination or non-significance at the multivariate model. Similar under-reporting may have been attributed to smoking behaviors.

It should be noted that biochemical or physical examination data obtained during health screening sessions may be inconsistent with self-reported medical history by the respondents. For example, during examination, newly diagnosed hypertension or diabetes could have been reported, causing raised prevalence rates based on onsite blood pressure or blood glucose measurement as compared to self-reported medical history. In contrast, respondents who had their medical conditions controlled via compliance to anti-hypertensives, oral hypoglycemic agents or behavioral interventions would have observed reduced prevalence rates of hypertension, diabetes or hyperlipidemia at the time of study as compared to self-reported medical history. Succinctly, differences between baseline clinical parameters and MetS definition may pose inconsistencies as well. One of the definitions of MetS using the JIS criteria as adopted in this study was systolic BP ≥ 130 mmHg, and diastolic BP ≥ 85 mmHg. However, based on Malaysian Clinical Practice Guidelines, hypertension is defined as systolic BP ≥ 140 mmHg and diastolic BP ≥ 90 mmHg 73 . Despite these inconsistencies, the current study maintained self-reported medical history as independent variables, and not variables that defined the dependent variable (MetS) based on biochemical or physical examination data. This offsets redundancy of variable analytical procedures, which may escalate effect sizes with wide confidence interval gaps, resulting in risk for error to the study results.

The overall prevalence of MetS in this sample was 20.6%. Older aged people were more likely to have MetS. Factors significantly associated with MetS in the overall sample were greater BMI, hypertension, diabetes and physical activity of moderate intensity. Gender-specific-risks regression models found that diabetes and hypertension were significantly associated with MetS in men, while greater BMI, diabetes and hyperlipidemia were significantly associated with MetS in women.

The results of the current study may have significant theoretical and practical implications within the academic settings. Operational definitions for components of MetS should be normalized with the national clinical practice guidelines after an expert panel review for the Malaysian population to halt inconsistencies of reported prevalence rates.

Based on gender specific prevalence trends of MetS, the current study may suggest post-menopausal age as a risk factor for MetS. Previous study from India have confirmed such associations 74 . However, as menopausal age of women (ranges between 40 and 55 years) was not confirmed, and given the cross-sectional nature of this investigation, the current study was not powered to establish its causality with MetS. This postulation could direct future studies for further exploration. The distinct gender-specific patterns of MetS risk factors are likely due to complex interactions of lifestyle, physiological, psychological and cultural influences. Such attributes are closely related to the nature of academics’ work environment which are mostly sedentary, multi-tasking and stressful. It would be worthwhile to note that treating individual risk components may be less useful to control MetS, but improvement of lifestyle behaviors and health interventions to identify high-risk employees would reduce the prevalence of MetS.

The findings of the current study catalyze the need to initiate employer-based interventions across the Malaysian academic settings. Workplace health promotion activities such as on-site diet and exercise programs should be executed more rigorously. Office of deaneries and departmental managers should actively plan and execute in-service health screening concerning MetS amongst their employees for physical and mental health monitoring.

Bloom, D.E. et al. The global economic burden of noncommunicable diseases. Geneva: World Economic Forum (2011).

WHO. Non-communicable diseases country profiles 2018 (2018).

Solomon, S. & Mulugeta, W. Disease burden and associated risk factors for metabolic syndrome among adults in Ethiopia. BMC Cardiovasc. Disord. 19 , 236 (2019).

Article   PubMed   PubMed Central   CAS   Google Scholar  

Grundy, S. M. et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 112 , 2735–2752 (2005).

Article   PubMed   Google Scholar  

Federation, I.D. The IDF consensus worldwide definition of the metabolic syndrome http://www.idf.org/webdata/docs/MetS_def_update (2006).

Alberti, K. G. et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120 , 1640–1645 (2009).

Article   CAS   PubMed   Google Scholar  

Mottillo, S. et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J. Am. Coll. Cardiol. 56 , 1113–1132 (2010).

Ranasinghe, P. et al. Prevalence and trends of metabolic syndrome among adults in the Asia-Pacific region: a systematic review. BMC Public Health 17 , 101 (2017).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Ghee, L. K. & Kooi, C. W. A review of metabolic syndrome research in Malaysia. Med. J. Malay. 71 , 20–28 (2016).

Google Scholar  

Pan, W. H., Yeh, W. T. & Weng, L. C. Epidemiology of metabolic syndrome in Asia. Asia Pac. J. Clin. Nutr. 17 , 37–42 (2008).

CAS   PubMed   Google Scholar  

Deurenberg, P., Deurenberg-Yap, M. & Guricci, S. Asians are different from Caucasians and from each other in their body mass index/body fat percent relationship. Obes. Rev. 3 , 141–146 (2002).

WHO expert consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363 , 157–163 (2004).

Article   Google Scholar  

Tan, C. E., Ma, S., Wai, D., Chew, S. K. & Tai, E. S. Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians?. Diabetes Care 27 , 1182–1186 (2004).

Dou, X. F. et al. Metabolic syndrome strongly linked to stroke in Chinese. Zhonghua Yi Xue Za Zhi 84 , 539–542 (2004).

PubMed   Google Scholar  

Mohamud, W. N. et al. Prevalence of metabolic syndrome and its risk factors in adult Malaysians: results of a nationwide survey. Diabetes Res. Clin. Pract. 91 , 239–245 (2011).

Iqbal, S. P. et al. Relationship of socio-demographic and lifestyle factors and diet habits with metabolic syndrome (MetS) among three ethnic groups of the Malaysian population. PLoS ONE 15 , e0224054 (2020).

Yeh, W. C., Chuang, H. H., Lu, M. C., Tzeng, I. S. & Chen, J. Y. Prevalence of metabolic syndrome among employees of a Taiwanese hospital varies according to profession. Medicine 97 , e11664 (2018).

Zhang, J., Liu, Q., Long, S., Guo, C. & Tan, H. Prevalence of metabolic syndrome and its risk factors among 10,348 police officers in a large city of China: a cross-sectional study. Medicine 98 , 40 (2019).

Heng, K. S., Hejar, A. R., Rushdan, A. Z. & Loh, S. P. Prevalence of metabolic syndrome among staff in a Malaysian public university based on Harmonized, International Diabetes Federation and National Cholesterol Education Program Definitions. Malays. J. Nutr. 19 , 77–86 (2013).

Chee, H. P., Hazizi, A. S., BarakatunNisak, M. Y. & MohdNasir, M. T. Metabolic risk factors among government employees in Putrajaya, Malaysia. Sains Malaysiana 43 , 1165–1174 (2014).

Xi, B., He, D., Hu, Y. & Zhou, D. Prevalence of metabolic syndrome and its influencing factors among the Chinese adults: the China Health and Nutrition Survey in 2009. Prev. Med. 57 , 867–871 (2013).

Article   PubMed   PubMed Central   Google Scholar  

Hajian-Tilaki, K. et al. Prevalence of metabolic syndrome and the association with socio-demographic characteristics and physical activity in urban population of Iranian adults: a population-based study. Diabetes Metab. Syndr. 8 , 170–176 (2014).

Yu, S., Guo, X., Yang, H., Zheng, L. & Sun, Y. An update on the prevalence of metabolic syndrome and its associated factors in rural northeast China. BMC Public Health 14 , 877 (2014).

Franca, S. L., Lima, S. S. & Vieira, J. R. D. S. Metabolic syndrome and associated factors in adults of the Amazon Region. PLoS ONE 11 , e0167320 (2016).

Li, Y., Zhao, L., Yu, D., Wang, Z. & Ding, G. Metabolic syndrome prevalence and its risk factors among adults in China: a nationally representative cross-sectional study. PLoS ONE 13 , e0199293 (2018).

Jahangiry, L. et al. Prevalence of metabolic syndrome and its determinants among Iranian adults: evidence of IraPEN survey on a bi-ethnic population. Sci. Rep. 9 , 7937 (2019).

Article   ADS   PubMed   PubMed Central   CAS   Google Scholar  

Creative Research Systems. Sample size calculator http://www.surveysystem.com/sscalc.htm (2012).

Rossa, C. E. B., Caramori, P. R. A. & Manfroi, W. C. Metabolic syndrome in workers in a university hospital. Rev. Port. Cardiol. 31 , 629–636 (2012).

Naing, L., Winn, T. & Rusli, B. N. Practical issues in calculating the sample size for prevalence studies. Arch. Orofac. Sci. 1 , 9–14 (2006).

Urbaniak, G.C. & Plous, S. Research randomizer—random sampling and random assignment made easy https://www.randomizer.org/ (2021)

Xiao, J. et al. Prevalence of metabolic syndrome and its risk factors among rural adults in Nantong, China. Sci. Rep. 6 , 38089 (2016).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Soo, K. L., Wan Abdul Manan, W. M. & Wan Suriati, W. N. The Bahasa Melayu version of the Global Physial Activity Questionnaire: reliability and validity study in Malaysia. Asia Pac. J. Public Health 27 , 184–193 (2015).

World Health Organization. Global Physical Activity Questionnaire (GPAQ) Analysis Guide. World Health Organization http://www.who.int/chp/steps/resources/GPAQ_Analysis_Guide.pdf (2020).

Lingesh, G. et al. Comparing physical activity levels or Malay version of the IPAQ and GPAQ with accelerometer in nurses. Int. J. Appl. Exerc. Physiol. 5 , 8–17 (2016).

Thanamee, S. et al. A population-based survey on physical inactivity and leisure time physical activity among adults in Chiang Mai, Thailand, 2014. Arch. Public Health 75 , 41 (2017).

Ariaratnam, S. et al. Prevalence of obesity and its associated risk factors among the elderly in Malaysia: findings from The National Health and Morbidity Survey (NHMS) 2015. PLoS ONE 15 , e0238566 (2020).

Marfell-Jones, M.J., Olds, T., Stewart, A.D. & Carter, L. International standards for anthropometric assessment. Potchefstroom, South Africa: International Society for the Advancement of Kinanthropometry (ISAK) (2006).

IBM Corp. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp, (2013).

Hair, J. F. Jr., Anderson, R. E., Tatham, R. L. & Black, W. C. Multivariate data analysis 3rd edn. (Macmillan, New York, 1995).

Ringle, C.M., Wende, S. & Becker, J.M. SmartPLS 3. Bonningstedt: SmartPLS http://www.smartpls.com (2015).

IBM SPSS Statistics. Troubleshooting guide—multicollinearity diagnostics for logistic regression, NOMREG or PLUM https://www.ibm.com/support/pages/multicollinearity-diagnostics-logistic-regression-nomreg-or-plum (2018).

Tsai, T. Y., Cheng, J. F. & Lai, Y. M. Prevalence of metabolic syndrome and related factors in Taiwanese high-tech industry workers. Clinics 66 , 1531–1535 (2011).

Morales, D. D., Punzalan, F. E. & Paz-Pacheco, E. Metabolic syndrome in the Philippine general population: prevalence and risk for atherosclerotic cardiovascular disease and diabetes mellitus. Diab. Vasc. Dis. Res. 5 , 36–43 (2008).

Ben-Yacov, L., Ainembabazi, P., Stark, A. H., Kizito, S. & Bahendeka, S. Prevalence and sex-specific patterns of metabolic syndrome in rural Uganda. BMJ Nutr. Prev. Health 3 , 1–7 (2020).

Nikbakhta, H. A., Rezaianzadeh, A., Seif, M. & Ghaem, H. Prevalence of metabolic syndrome and its components among a population-based study in south of Iran, PERSIAN Kharameh cohort study. Clin. Epidemiol. Glob. Health 8 , 678–683 (2020).

Sigit, F. S. et al. The prevalence of metabolic syndrome and its association with body fat distribution in middle-aged individuals from Indonesia and the Netherlands: a cross-sectional analysis of two population-based studies. Diabetol. Metab. Syndr. 12 , 2 (2020).

Anand, S. S. et al. Study of health assessment and risk in ethnic groups, study of health assessment and risk evaluation in aboriginal peoples investigators. Relationship of metabolic syndrome and fibrinolytic dysfunction to cardiovascular disease. Circulation 108 , 420–425 (2003).

Cameron, A. J., Magliano, D. J., Zimmet, P. Z., Welborn, T. & Shaw, J. E. The metabolic syndrome in Australia: prevalence using four definitions. Diabetes Res. Clin. Pract 77 , 471–478 (2007).

Yeow, T. P. et al. Predictors of ischaemic heart disease in a Malaysian population with the metabolic syndrome. Diabet. Med. 29 , 1378–1384 (2012).

Saif-Ali, R., Kamaruddin, N. A., Al-Habori, M., Al-Dubai, S. A. & Ngah, W. Z. W. Relationship of metabolic syndrome defined by IDF or revised NCEP ATP III with glycemic control among Malaysians with Type 2 Diabetes. Diabetol. Metab. Syndr. 12 , 67 (2020).

Johari, S. M. & Shahar, S. Metabolic syndrome: the association of obesity and unhealthy lifestyle among Malaysian elderly people. Arch. Gerontol. Geriatr. 59 , 360–366 (2014).

Shyam, S. et al. Metabolic syndrome, abnormal glucose tolerance and high sensitivity-C-reactive protein among women with a history of gestational diabetes mellitus. J. Diabetes Metab. 5 , 424 (2014).

Ching, Y. K. et al. Prevalence of metabolic syndrome and its associated factors among vegetarians in Malaysia. Int. J. Environ. Res. Public Health. 15 , piiE2031 (2018).

Article   CAS   Google Scholar  

Ford, E. S., Giles, W. H. & Mokdad, A. H. Increasing prevalence of the metabolic syndrome among U.S. Adults. Diabetes Care 27 , 2444–2449 (2004).

Chen, M. et al. Different physical activity subtypes and risk of metabolic syndrome in middle-aged and older Chinese people. PLoS ONE 8 , e53258 (2013).

Bhanushali, C.J. et al. Association between lifestyle factors and metabolic syndrome among African Americans in the United States. J. Nutr. Metab. Article ID 516475 (2013).

Gebreegziabiher, G., Belachew, T., Mehari, K. & Tamiru, D. Magnitude and associated factors of metabolic syndrome among adult urban dwellers of Northern Ethiopia. Diabetes Metab. Syndr. Obes. 14 , 589–600 (2021).

Deshmukh, P. R., Kamble, P., Goswami, K. & Garg, N. Metabolic syndrome in the rural population of Wardha, central India: an exploratory factor analysis. Indian J. Community Med. 38 , 33–38 (2013).

Song, Q. B. et al. Sex difference in the prevalence of metabolic syndrome and cardiovascular-related risk factors in urban adults from 33 communities of China: the CHPSNE study. Diab. Vasc. Dis. Res. 12 , 189–198 (2015).

Balkau, B. et al. Frequency of the WHO metabolic syndrome in European cohorts, and an alternative definition of an insulin resistance syndrome. Diabetes Metab. 28 , 364–376 (2002).

Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 285 , 2486–2497 (2001).

Alberti, K. G., Zimmet, P. & Shaw, J. Metabolic syndrome—a new world-wide definition. A consensus statement from the international diabetes federation. Diabet. Med. 23 , 469–480 (2006).

Ramli, A.S. et al. JIS definition identifies more Malaysian adults with metabolic syndrome compared to NCEP-ATP III and IDF criteria. BioMed Res. Int. 2013, ID 760963 (2013).

Lin, C. Y., Chen, P. C. & Lin, Y. C. Association among serum perfluoroalkyl chemicals, glucose homeostasis, and metabolic syndrome in adolescents and adults. Diabetes Care 32 , 702–707 (2009).

Lin, H. S., Wen, L. L., Chu, P. L. & Lin, C. Y. Association among total serum isomers of perfluorinated chemicals, glucose homeostasis, lipid profiles, serum protein and metabolic syndrome in adults: NHANES, 2013–2014. Environ. Pollut. 232 , 73–79 (2018).

Article   PubMed   CAS   Google Scholar  

Hwang, L. C., Bai, C. H. & Chen, C. J. Prevalence of obesity and metabolic syndrome in Taiwan. J. Formos. Med. Assoc. 105 , 626–635 (2006).

Aminuddin, A. et al. High C reactive protein associated with increased pulse wave velocity among urban men with metabolic syndrome in Malaysia. Saudi Med. J. 34 , 266–275 (2013).

Arner, P. Insulin resistance in type 2 diabetes: role of fatty acids. Diabetes Metab. Res. Rev. 18 , S5-9 (2002).

Article   ADS   CAS   PubMed   Google Scholar  

Cornier, M. A. et al. The metabolic syndrome. Endocr. Rev. 29 , 777–822 (2008).

Ai, M. et al. Small dense LDL cholesterol and coronary heart disease: results from the Framingham Offspring study. Clin. Chem. 56 , 967–976 (2010).

Faam, B. et al. Leisure-time physical activity and its association with metabolic risk factors in Iranian adults: Tehran Lipid and Glucose Study, 2005–2008. Prev. Chronic Dis. 10 , E36 (2013).

Walle, B. et al. Prevalence of metabolic syndrome and factors associated with it among adults of West Gojjam: a community-based cross-sectional study. Diabetes Metab. Syndr. Obes. 14 , 875–883 (2021).

Malaysia Hypertension Guideline Working Group. Management of hypertension. Clinical Practice Guidelines. 4, (2013).

Meher, T. & Sahoo, H. The epidemiological profile of metabolic syndrome in Indian population: a comparative study between men and women. Clin. Epidemiol. Glob. Health 8 , 1047–1052 (2020).

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Acknowledgments

We would like to extend our gratitude to Sazman Abdul Wahab, Mohd Izhar Ariff and Siti Zaleha Sahibulddin from Faculty of Medicine, Universiti Kebangsaan Malaysia for their kind assistance.

This study was funded by the Cabaran Perdana Grant (DCP-2018-005/1) from Universiti Kebangsaan Malaysia.

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M.R.A.M. had the original idea, designed and coordinated the study, and revised the final draft critically for important intellectual content. A.M.N., N.M.T., H.O., M.R.A.R., H.M.Y., N.S., P.Y.N., Z.A.M., N.B.A.K., K.Y., S.M.A.B. and S.R. conceptualized the study and involved in data collection. K.G. assisted in statistical analysis, interpreted the results and drafted the manuscript.

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Manaf, M.R.A., Nawi, A.M., Tauhid, N.M. et al. Prevalence of metabolic syndrome and its associated risk factors among staffs in a Malaysian public university. Sci Rep 11 , 8132 (2021). https://doi.org/10.1038/s41598-021-87248-1

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Metabolic Disease Epidemics: Emerging Challenges in Regenerative Medicine

Affiliations.

• 1 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Parque de Ciência e Tecnologia, Zona Industrial da Gandra, 4805-017 Barco, Guimarães, Portugal; ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.
• 2 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Parque de Ciência e Tecnologia, Zona Industrial da Gandra, 4805-017 Barco, Guimarães, Portugal; ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal; The Discoveries Centre for Regenerative and Precision Medicine, Headquarters at University of Minho, Avepark, 4805-017 Barco, Guimarães, Portugal.
• 3 3B's Research Group, I3Bs - Research Institute on Biomaterials, Biodegradables and Biomimetics, University of Minho, Headquarters of the European Institute of Excellence on Tissue Engineering and Regenerative Medicine, AvePark, Parque de Ciência e Tecnologia, Zona Industrial da Gandra, 4805-017 Barco, Guimarães, Portugal; ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal; The Discoveries Centre for Regenerative and Precision Medicine, Headquarters at University of Minho, Avepark, 4805-017 Barco, Guimarães, Portugal. Electronic address: [email protected].
• PMID: 30704823
• DOI: 10.1016/j.tem.2019.01.001

The interplay between cell/tissue damage caused by metabolic dysfunction and regenerative potential remains elusive. The tissue engineering and regenerative medicine (TERM) field is now facing a worldwide epidemic of obesity. This Forum article uncovers prospective questions to be addressed in TERM toward the development of effective regenerative therapies adjusted to these new demands.

Keywords: adipose stem cells; diabetes; metabolic syndrome; obesity; tissue engineering.

Copyright © 2019 Elsevier Ltd. All rights reserved.

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• Research Support, Non-U.S. Gov't
• Metabolic Diseases / therapy*
• Regenerative Medicine / methods*
• Tissue Engineering / methods

The six drivers of employee health every employer should know

Investing in employee health can lead to increased productivity, reduced absenteeism, and better talent attraction and retention. Image:  Pexels/fauxels

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Stay up to date:, future of work.

• Investing in employee health and well-being can lead to increased productivity, reduced absenteeism and better talent attraction and retention.
• This translates to billions of dollars in potential economic gains globally.
• While employers play a crucial role, a multi-pronged approach is needed to move beyond basic healthcare to create optimal health conditions.

Imagine a world in which employers make evidence-based investments in the health of their employees. In return, they reap a manifold benefit to those investments: their employees thrive, their business thrives, and the societies in which they operate thrive. There's a positive opportunity that arises when employers address the inherent interconnectedness between work and health.

The McKinsey Health Institute (MHI) has previously identified 23 drivers of health . Employment can greatly influence some of these drivers, such as social interaction and sleep. In this article, we zoom in on six drivers of health that employers can influence and could be wise to support. By improving employees’ health, employers could add trillions of dollars to the global economy and have a positive impact on society. When employers and employees work together to improve modifiable drivers of health, everyone benefits.

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Modifiable drivers of health in the workplace: What does the research say?

Six modifiable drivers of health in the workplace—social interaction, mindsets and beliefs, productive activity, stress, economic security, and sleep—were identified from the growing body of research that connects the dots among drivers of health and the workplace. Researchers are building a greater understanding of how employers can address modifiable drivers to create change in favor of optimal employee health.

Considering that the average person spends a third of their life at work (more than 90,000 hours in a lifetime), employment can be a critical piece of the puzzle when working toward the goal of improving global health. MHI analyzed 26 workplace factors to understand how they influence a range of health- and work-related outcomes across 30 countries. That research showed there are important differences between the workplace factors that lead to poor health and those that lead to good health. Our analysis found that employee self-efficacy, adaptability, and feelings of belonging at work were top predictors of good health, whereas toxic workplace behaviors, role ambiguity, and role conflict at work were top predictors of poor health.

Previously, researchers at the University of Oxford’s Wellbeing Research Centre analyzed data from more than 15 million employees on their well-being and the underlying workplace factors driving it. The researchers identified and tested 11 factors, including compensation, flexibility, purpose, inclusion, achievement, support, trust, belonging, management, and learning. The three top factors for the companies that scored best on well-being were feeling energized, belonging, and trust. Interestingly, they are different from the top drivers that employees think will make them happy and drive well-being at work: pay and flexibility.

Together, all the research led us to identify six drivers of health that employers can most easily influence.

Employers can improve employee health through six modifiable drivers

Our analysis shows that employers can effect significant change through six modifiable drivers of health: social interaction, mindsets and beliefs, productive activity, stress, economic security, and sleep.

Social interaction

The positive effects of regular social interactions on health have been widely reported. For instance, a study reviewing mortality rates has documented an average 50 percent increase in likelihood for survival if participants have strong social relationships. Furthermore, social integration during childhood is related to lower blood pressure and body mass index in adulthood.

Social interactions at work experienced by employees strongly influence health and workplace outcomes. Feeling connected at work is associated with greater innovation, engagement, and quality of work—and may be especially impactful for those with smaller social networks outside of their jobs. MHI’s 2023 research shows experiencing toxic workplace behavior is a strong predictor of negative health outcomes at work, including loneliness at work, the intention to leave an organization, and burnout symptoms.

Toxic workplace behavior is a critical workplace driver to combat. If left unaddressed, it can mitigate the benefits of any health and well-being initiatives pursued. Examples of interventions to counter toxic workplace behavior include establishing a zero-tolerance policy for it and creating anonymous feedback processes through which employees can report it—which also normalizes a culture of providing concrete, specific feedback to colleagues.

Meanwhile, experiencing psychological safety on a team and support from coworkers and managers predicts positive health outcomes, including better holistic health . In 2023, MIT Sloan School of Management researchers outlined proven social-health initiatives that helped managers build psychological safety on their teams. They included training managers to use one-on-one meetings to increase employee individuation by asking employees what was important to them and where they needed support. Another use of the meetings was to remove blockers for employees by helping them prioritize among tasks. Interestingly, individuation has been shown to increase psychological safety the most when psychological safety is relatively low, while removing blockers is more effective when psychological safety is relatively high.

Mindsets and beliefs

Research, including MHI analysis, has demonstrated a connection between positive mindsets and beliefs and better health experience. This includes the positive effects of a growth mindset on mental health and the benefits of gratitude on physical health. Positive mindsets and beliefs in the workplace are also greatly influential in good holistic health.

In fact, good holistic health isn’t achieved by completely avoiding workplace stressors. Instead, it can be maintained through creating positive experiences at work, such as experiencing high self-efficacy, high adaptability, a feeling of meaning, and a feeling of belonging at work. For example, an individual may be able to tolerate the stress of a looming deadline on a big project if they believe that they have the support of their team.

Employers can foster meaning and belonging by engaging employees through compelling storytelling and fostering a connection to an organization’s mission. Purpose-driven companies that excel at this grow two times faster than their competitors do and achieve gains in employee satisfaction, employee retention, and consumer trust. Some of these outcomes may be attributed to employees who are intrinsically motivated and able to maintain better well-being over time, creating a positive performance loop. Additionally, employee self-efficacy and adaptability are capabilities that can be cultivated among employees to make a more resilient and healthy workforce.

Productive activity

Productive activity includes employment- and nonemployment-related activities. Examples include volunteering, caregiving, spending time on hobbies, worshiping, spending time on activism, playing music, and traveling.

Employment has been linked to improved life expectancy. According to MHI research, one of the top contributors to productivity at work is an individual’s sense of self-efficacy—an employee’s belief that they can cope with difficult or changing situations. Self-efficacy can be improved through interventions, suggesting that employers can target self-efficacy to improve employee productivity.

Furthermore, employers have the opportunity to help the people in their communities connect to meaningful and productive activities that support their long-term health and well-being. Enjoyable leisure activities are also associated with improved psychosocial and physical measures that support good health and well-being, including greater life satisfaction and engagement and lower rates of depression, blood pressure, cortisol, and physical function.

In discussing workplace stressors, it’s important to acknowledge that stress itself isn’t necessarily a bad thing, as it’s actually needed to learn, grow, and develop. Optimal levels of stress can contribute to better performance. After that point, the benefits diminish into worse well-being because of the excessive demands of high stress and lack of replenishment of energy resources. The employer’s role is to ensure that employees are stimulated, challenged, and motivated—but not overwhelmed—by the demands they experience in the workplace.

Chronically elevated levels of stress can increase the risk of cardiovascular disease, neurodegenerative disease, and metabolic disease. Job strain and effort–reward imbalance can predict several common mental disorders. Additionally, MHI research shows that an increase in workplace demands is the driver most predictive of burnout and distress symptoms at work.

Some jobs are high in demand by structure. For example, some organizations have seasonal or other cyclical patterns in work demand. In these situations, interventions should focus on building in recovery time so that employees can regain their energy after high-demand periods.

Economic security

Economic opportunity and economic security can influence many facets of health and productivity. For example, high-income individuals are five times more likely than low-income individuals to report strong health. Employees who are struggling financially are more likely than others to experience signs of poor mental health that might affect their ability to function at work. A lack of job stability links with poor mental health, as well as poor physical well-being (for example, cardiovascular disease). Any short-term rise in employee performance fueled by job insecurity is often negated by the additional burden on employee physical and mental health.

MHI research shows that the greatest contributor to employees’ feelings of financial insecurity is whether they are paid sufficiently to cover their basic needs. While what it takes to feel economically secure is unique to each person, employers can reduce feelings of financial insecurity by ensuring that compensation covers basic needs.

There’s a strong association between sleep hours and both employee health and workplace outcomes. The cost to employers when employees have insufficient or poor-quality sleep can be substantial.

Employees with untreated insomnia cost employers an average of $2,280 more annually than employees without untreated insomnia because of absenteeism, “presenteeism,” poor performance, and increased incidents of accident and injury. According to the MHI 2023 survey, 31 percent of employees across the world average fewer than seven hours of sleep per night. Although everyone has unique needs, this falls below the ballpark number of hours recommended to maintain good health. Researchers have shown severe sleep loss can even lead to death, as our bodies conduct necessary reparative processes when we sleep.

The MHI survey found that one of the main contributors to an employee’s average number of sleep hours is the experienced volume of work required of them. Furthermore, one of the top contributors to an employee’s satisfaction with their sleep is their ability to adjust to unexpected changes. This may suggest that employee programs that look to improve adaptability may in turn improve employees’ satisfaction with their sleep.

Employers have additional interventions they can consider if their employees are struggling with getting consistent, high-quality sleep. They include creating work environments with ample natural light and access to healthy foods, limiting or disabling employees from being online after hours, creating incentives for employees who prioritize sleep, and encouraging and rewarding leaders who model the prioritization of sleep over work.

Many employers are already investing in employee health and well-being, but we would encourage them to reflect on where they currently provide support and if they might want to change resources or add more interventions. For example, many employee assistance programs (EAPs) provide coverage of interventions for factors such as stress and economic security but less coverage of those for factors such as social interactions at work. Additionally, while EAPs are widely available, they tend to be underused by employees and focus on a reactive instead of a proactive approach to health.

In rethinking a workplace strategy on employee health and well-being, current EAP offerings can be useful starting points for action but are unlikely to be the full solution. They are also unlikely, by themselves, to yield the ROI that employers increasingly expect. Strengthening the measurement of intervention outcomes may also help guide an organization’s overall investment strategy.

In rethinking a workplace strategy on employee health and well-being, current EAP offerings can be useful starting points for action but are unlikely to be the full solution.

Improving global employee health can create trillions of dollars of economic value

It makes good business sense to invest in employee health and well-being. We estimate that the total global opportunity for optimizing employee health and well-being is $3.7 trillion to $11.7 trillion, which is equivalent to raising global GDP by 4 to 12 percent. Together, high- and middle-income economies represent 95 percent of this total opportunity (exhibit).

While it may not be feasible in the near term to bring all employees everywhere to optimal well-being, capturing just 10 percent of the total opportunity could yield up to $1.17 trillion of annual value and raise the global GDP by more than 1 percent.

In addition to contributing to increased productivity at work, our calculations indicate that investing in employee health and well-being provides a positive opportunity for attracting and retaining talent. As noted in McKinsey research, employees facing mental-health and well-being challenges are four times more likely than others to want to leave their organizations .

Better health correlates with higher productivity across countries and workplace settings and is also strongly correlated with workforce participation at all ages. Every 1 to 3 percent increase in global workforce participation is worth a further $1.4 billion to $4.2 billion, benefiting employees, their health, the societies in which they live, and government finances.

To capture these economic benefits fully, employers need to move from a sole focus of protecting against incidental risk and illness to helping employees achieve more optimal health. This is particularly important when considering that employees move along a continuum of health over time and may draw upon different workplace resources throughout their employment with a company. Ultimately, a focus on improving health could lead to a virtuous circle of positive change, as employees gain health literacy, and employers in turn respond to employee health concerns.

To capture the economic benefits of good health fully, employers need to move from a sole focus of protecting against incidental risk and illness to helping employees achieve more optimal health.

Acting now also reduces future brand and business risk. In Australia, a lawsuit resulted in a fine for an organization that tolerated a toxic workplace culture. Recently, the European Union adopted the European Sustainability Reporting Standards, requiring organizations by law to report on working conditions such as working time, social dialogue, and work–life balance. As employees develop higher standards for what is tolerable in the workplace, more pushback and litigation may be possible.

Furthermore, investors such as asset managers, private equity companies, and venture capitalists are increasingly weighing environmental, social, and governance (ESG) considerations in their investment decisions. They are guided by ESG ratings released by various agencies and standards issued by the International Sustainability Standards Board.

Have you read?

The future of work: what does it mean for employees, how employers can make emotional well-being a top priority, from hierarchy to partnership: rethinking the employee/employer relationship in 2024, how companies can support employees working with cancer to drive better business and health outcomes, improving employee health and well-being involves more than just employers.

We have highlighted practical examples of how employers can play a role in changing norms and catalyzing innovation around employee health and well-being. However, employers alone can’t complete this task. Employees, policy makers, and local governments will need to help.

Employees can play a role in their own health by taking advantage of the workplace resources that do exist and helping cultivate a community and culture of healthy practices among colleagues. They can make their desires known to employers as a means of holding leaders accountable for responding to the health needs and aspirations of their workforces. These might include benefits such as paid parental leave and caregiving support, which aim to help employees balance work and family responsibilities while tending to their own overall health and well-being.

Policy and decision makers may want to consider a variety of ways to protect and promote employee health. Possibilities include mandating upper limits on total working hours, health coverage paid by employers, and employee access to therapy and other psychological resources. Enhancing standards and transparency could enable employees to make informed choices about their employment while also allowing policy makers to audit progress on a wider scale.

Through investment in public health (such as funding and grants), policy makers can encourage and enable employers to take employee health seriously and professionalize how they track the impact of their initiatives on employee health and well-being. Finally, policy and decision makers can lead by example in acting to promote their own employees’ health. This may be done in partnership with both private and other public sector employers, such as those that play a critical role in educating individuals about health—school systems, healthcare systems, and community programs—down to the city level.

City governments can play an important role in unlocking positive health outcomes. Given that most large employers are concentrated in cities, there’s a unique opportunity for companies and employees to come together to set broader aspirations on health and identify targeted interventions to pursue jointly.

Employment can and does have a profound impact on health, both positive and negative. Adapting how and where people work to support optimal employee health could result in billions of employees and their families around the world living longer, higher-quality lives—and simultaneously benefiting their employers and the societies in which they live.

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Networking Across Borders for Individuals with Organic Acidurias and Urea Cycle Disorders: The E-IMD Consortium

Stefan kölker.

Division of Inherited Metabolic Diseases, Department of General Pediatrics, University Children’s Hospital Heidelberg, Im Neuenheimer Feld 430, 69120 Heidelberg, Germany

Dries Dobbelaere

Centre de Référence des Maladies Héréditaires du Métabolisme de l’Enfant et de l’Adulte, Hôpital Jeanne de Flandre, Lille, France

Johannes Häberle

Division of Metabolism and Children’s Research Centre, University Children’s Hospital Zurich, Steinwiesstraße 75, 8032 Zurich, Switzerland

Peter Burgard

Florian gleich, marshall l. summar.

Children’s National Medical Center, 111 Michigan Avenue, N.W., Washington, DC, 20010 USA

Steven Hannigan

CLIMB, National Information Centre for Metabolic Diseases, 176 Nantwich Road, Crewe, CW2 6BG UK

Samantha Parker

Orphan Europe Sarl, Immeuble “Le Wilson”70 Avenue du Général de Gaulle, 92058 Paris La Défense, France

Anupam Chakrapani

Birmingham Children’s Hospital NHS Foundation Trust, Steelhouse Lane, Birmingham, B4 6NH UK

Matthias R. Baumgartner

Johannes zschocke.

Innsbruck, Austria

Background : Patients with organic acidurias (OAD) and urea cycle disorders (UCD) are at increased risk of disability, impaired quality of life and reduced life expectancy. Clinical care in any one centre is constrained by small patient numbers; and furthermore diagnostic and treatment strategies vary between metabolic centres and countries, resulting in significant inequalities and disparity in patient outcome.

Aims / methods : The overall objective of the EU-funded activity ‘European registry and network for intoxication type metabolic diseases’ (E-IMD) is to collect systematic data to improve the knowledge of these diseases, to develop consensus care guidelines and to provide detailed information materials for families and professionals.

Results : Within three years E-IMD has (1) established a network of 87 partners in 25 countries (2) set up a patient registry of more than 1,000 individuals with OAD and UCD, (3) launched a website ( www.e-imd.org ) including detailed information materials in 11 languages, (4) developed guidelines for OAD and UCD, (5) organised two teaching courses and various scientific meetings, (6) extended the IT platform clustering with other inherited metabolic diseases (IMD) and (7) strengthened the collaboration with other international scientific consortia.

Conclusions : E-IMD has made important steps towards improving and sharing knowledge on OAD and UCD and harmonisation of diagnostic and therapeutic strategies. Through the establishment of a modular patient registry, clustering with other IMD and stepwise extension of the network, E-IMD has implemented the core components of a European Reference Network for rare diseases.

Introduction

Organic acidurias (OAD) and urea cycle disorders (UCD) are rarely inherited metabolic diseases (IMD) with an estimated cumulative incidence of 1 in 35,000 newborns (UCD) or 1 in 14,000–30,000 newborns (OAD), respectively (Kasper et al. 2010 ; Schulze et al. 2003 ; Summar et al. 2013 ; Wilcken et al. 2003 ). Affected individuals often present with first symptoms in the newborn period or infancy and are at an increased risk of severe disability, impaired quality of life, and reduced life expectancy (Bachmann 2003 ; Enns et al. 2007 ; Grünert et al. 2013 ; Hörster et al. 2007 ; Kido et al. 2012 ; Kölker et al. 2006 ; Nassogne et al. 2005 ; Pena et al. 2012 ; Strauss et al. 2003 ; Summar et al. 2008 ). Because of their life-threatening character and the permanent risk of metabolic crisis OAD and UCD are also called intoxication type IMD. In some countries these diseases are included in newborn screening programmes, hereby allowing early detection and start of treatment in asymptomatic individuals. This early intervention hopefully leads to improved health outcome, as it has been shown for glutaric aciduria type 1 (GA-1) and isovaleric aciduria (IVA) (Grünert et al. 2012 ; Heringer et al. 2010 ; Kölker et al. 2007a ).

Patients with rare diseases like intoxication type IMD have become a healthcare priority in developed countries where other causes of infant mortality such as infectious diseases are now treatable (Commission of the European Communities 2008 ). OAD and UCD patients are scattered across countries and as a result medical expertise for each of these diseases is a scarce resource. Fragmented disease knowledge means that care is not optimal, and there are significant differences in the infrastructure, expertise, diagnostic procedures, time to diagnosis, strategies and outcome. In analogy to other rare diseases it can be expected that this diversity has a negative impact on health outcome and on socio-economics (Brimley et al. 2013 ; Linertová et al. 2012 ; López-Bastida et al. 2008 ; López-Bastida et al. 2009 ).

The overall aim of the European registry and network for intoxication type metabolic diseases (E-IMD) is to promote health for individuals affected with OAD or UCD by pooling of expertise and networking and by reducing avoidable inequity. E-IMD has two specific objectives: (1) to establish a European patient registry describing the disease course, epidemiology, diagnostic and therapeutic strategies for OAD and UCD and (2) to provide European evidence-based consensus care protocols for patients with OAD and UCD serving as a template for the development of guidelines and patient brochures. This paper describes the establishment of E-IMD, major achievements within the first three years of the project and important obstacles that the consortium learnt to deal with.

Methods and Results

The network.

E-IMD partners have already successfully collaborated for several years in various projects on OAD and UCD. In 2010, a strategic decision was made to formalise cooperation by establishing E-IMD. E-IMD has been partly funded from 1 January 2011 to 30 April 2014 by the European Union [via the European Agency for Health and Consumers (EAHC); agreement no. 2010 12 01], in the framework of the Health Programme 2008-2013. It is coordinated by the University Hospital Heidelberg and started with 28 project partners (coordinator, 12 associated and 15 collaborating partners) from 15 European countries. Associated partners received on average 60% EU co-funding from the grant whilst collaborating partners participated on a voluntary basis. The network has developed beyond expectations and now includes 87 partners from 25 countries on four continents (Fig.  1 ). Sixteen patient organisations (PO), four industrial partners and 67 clinical partners form the network. Representatives of the adult metabolic and dieticians’ groups of the Society for the Study of Inborn Errors of Metabolism, the Urea Cycle Disorders Consortium (UCDC) and the Japanese Consortium for Urea Cycle Disorders are E-IMD partners. Applications for memberships of clinical partners have been evaluated by the steering committee based on the following criteria: (1) clinical and scientific expertise in intoxication type metabolic diseases, (2) metabolic service provided by an interdisciplinary team of experts and (3) capacity and relevant infrastructure to contribute to E-IMD.

Crossing borders for patients with rare OAD and UCD. The E-IMD network so far includes 87 partners from 25 countries on four continents. Note that the coloured areas do not reflect the exact geographical coverage of E-IMD

The E-IMD advisory board including all network partners is the principal decision-making and arbitration body for the network and registry. The steering group comprising the lead of each work package and a representative of collaborating partners has the overall responsibility to ensure satisfactory progress of the work and promptly deal with problems. It assists the network director in the implementation of the integrative management of the network. All E-IMD partners have signed a consortium agreement which specifies and defines the organisation, management, responsibilities and tasks of the network.

The Patient Registry

The E-IMD patient registry is a web-based, password-protected registry ( https://www.eimd-registry.org ) launched on the web in August 2011. It contains comprehensive information on patients with confirmed diagnosis of OAD, i.e. glutaric aciduria type 1 (GA1; OMIM #231670), methylmalonic aciduria (MMA; OMIM #251000, #251100, #251110, #277400, #277410), propionic aciduria (PA; OMIM #606054), IVA (OMIM #243500) and UCD, i.e. inherited deficiency of N -acetylglutamate synthase (OMIM #237310), carbamylphosphate synthetase 1 (OMIM #237300), ornithine transcarbamylase (also including heterozygous female carriers; OMIM #311250), argininosuccinate synthetase (OMIM #215700), argininosuccinate lyase (OMIM #207900) and arginase 1 (OMIM #207800), as well as hyperornithinemia-hyperammonemia-homocitrullinuria syndrome (OMIM #238970). Written informed consent is obtained from all study patients before enrolment and baseline visit. The study was approved by the local ethics committee of the coordinating centre (i.e. University Hospital Heidelberg, application no. S-525/2010) on 31 January 2011 and then approved by the ethics committees of clinical partners contributing to the registry ( n  = 44). As of 30 April 2014, 1,009 patients with a confirmed diagnosis of OAD and UCD have been registered. The registry collects prospective data and contains detailed information on 949 baseline, 1,076 regular (annual), 437 emergency and 17 fatal disease course visits, averaging 2.5 visits per patient. A detailed description of the clinical phenotype will be published separately (Kölker et al. 2015a , b ).

Forty clinical partners (of the 44 partners with ethical approval) from 20 countries – 17 European countries [15 EU member states (MS), Switzerland and Republic of Serbia], India, Japan and the USA – have so far contributed to the registry. The maximal population size of EU MS covered by clinical E-IMD partners was 453,288,893 citizens, i.e. 89.6% of the population in 28 EU MS according to Eurostat 2013 ( http://epp.eurostat.ec.europa.eu ). However, since recruitment to E-IMD varies between countries, estimated prevalences of OAD and UCD are likely to be underestimated. For instance, the minimum prevalence of patients with OAD and UCD was 2.10 per million citizens, with a range of 0.15 (Romania) to 8.39 (Denmark) (Table  1 ). This may skew the results, but with continued registration of patients, this current drawback will diminish.

Minimum cumulative prevalence of patients with OAD and UCD in Europe

EU MS member states of the European Union, pat patients, pop population

a Population size in 2013 according to EUROSTAT. Patients from non-European countries ( n  = 24) are not listed

This variable frequency of UCD and OAD patients is unlikely to reflect true epidemiological differences, but merely shows the effect of a number of modulatory factors including the number and capacity of E-IMD partners in different countries as well as the rapidity and success of the activation process in individual centres. The activation process is the time interval from becoming a member of the E-IMD consortium and registering the first patient. The process time has been highly variable reflecting national and local differences and ranged from 2 to 20 months.

An important source of variability has been the ethical review process which includes translation into national languages, adaptation of the application to national and local requirements and submission to the board. The regulations governing this process are highly variable in different countries and local centres. Noteworthily, associated partners who have received EU co-funding have achieved ethical approval and started registering patients earlier than collaborating partners who contributed to the project on a voluntary basis (Fig.  2 ). Furthermore, the 12 associated clinical partners have registered more patients (74% of total) than 28 collaborating partners (26%). Despite the shortcomings arising from the project funding mechanism, the process of recruiting and registering patients was almost linear with approximately 26 patients with OAD and UCD being newly registered during each month between February 2011 (first patient recruited) and April 2014 (end of the EU funding period). Meanwhile, the follow-up visits outnumber the baseline visits showing that the registry is now increasingly used for systematic follow-up of registered patients (Fig.  3 ).

Milestones of the activation process in associated and collaborating partners. The time required for receiving ethics approval was adjusted to the date when the coordinating partner had received ethics approval and to the individual dates of signing the consortium agreement (for collaborating partners who have joined the consortium after the kick-off meeting)

Timeline of recruiting and registering patients. Lines indicate cumulative frequencies of registered patients, baseline visits and regular follow-up visits during the funding period. Dotted lines indicate the cut-off date for the interim analysis (22 October 2013) and the end of the EU funding period (30 April 2014)

Dissemination

Dissemination is an integral part of the E-IMD strategy with targeted distribution of disease information and intervention materials, such as protocols and guidelines. E-IMD developed a website ( www.e-imd.org ) as the main dissemination vehicle for the network. At present, the website provides information for patients and their family (translated into 11 languages) and for healthcare professionals, newsletters and contact addresses. The number of visitors has been increased to approximately 250 visitors per day and is still increasing. The patient information is the most frequently visited part of the website. In addition, the number of cases can be viewed in real-time mode in the public domain of the patient registry ( https://www.eimd-registry.org ).

E-IMD has actively sought to publish its achievements in peer-reviewed journals focusing on epidemiology, clinical phenotyping, therapy and guideline development Baumgartner et al. 2014 ; Boy et al. 2013 ; Chapman et al. 2012 ; Häberle et al. 2012 ; Kölker et al. 2011 ; Pena et al. 2012 ; Rüegger et al. 2014 ; Summar et al. 2013 ). Four E-IMD advisory board meetings have been held during annual SSIEM and ICIEM symposia between 2011 and 2014. Since 2013, joint meetings with the E-HOD ( www.e-hod.org ) and UCDC consortia have been organised.

Finally, E-IMD has partnered with the Recordati Rare Diseases Fondation d’entreprise ( http://www.rrdacademy.org ) to organise three training courses for young doctors in the field of IMD.

Guideline Development

Guidelines are systematically developed statements assisting practitioner and patient decisions in appropriate healthcare for specific clinical circumstances. The Scottish Intercollegiate Guidelines Network ( http://www.sign.ac.uk ) methodology was employed to develop these guidelines.

European consensus guidelines for UCD (Häberle et al. 2012 ), GA1 (Kölker et al. 2011 ), and MMA/PA (Baumgartner et al. 2014 ) have been published under the umbrella of E-IMD. Those for IVA are still under development. Short guideline versions are available online via the E-IMD website. Based on the available evidence from literature, the statements for OAD and UCD are mostly graded level C or D. The process of guideline development lasted over several years and can be considered as outstanding in the field of metabolic medicine. Importantly, the use of GA1 guideline recommendations, when evaluated after some years, was shown to improve the neurological outcome and to support normal growth (Boy et al. 2013 ; Heringer et al. 2010 ).

Along this, it is anticipated that the achieved publication and dissemination of consensus evidence-based guidelines will further improve care of patients with OAD and UCD.

The evaluation of the project is led by the steering group with strong patient representation. Its overall aim is to appreciate how E-IMD achieved its main goal of building knowledge and whether diagnosis and care improved in the different European countries. The following sources of information and indicators have been used: (1) a survey sent out via national PO, (2) analysis of the use of the E-IMD website and (3) analysis of the patient registry for completeness of geographical coverage, quality and completeness of records and time to diagnosis.

The evaluation highlighted that there is a need to develop care pathways for OAD and UCD patients and to resolve the difficult issue of transition of adolescents into adult care. From the patient perspective there is also a need to develop the PO community for IMD through the establishment of a helpline and online community. E-IMD has been the catalyser in the funding and launching of the European Metabolic Disease Alliance ( www.eumda.org ).

The website is the principal means of public communication. E-IMD comes up on the first page of Google search engine using the key words ‘organic aciduria’ or ‘urea cycle disorder’; however, it was still difficult to get traffic to the website.

The registry contains data on 1,009 individuals with an OAD or UCD. There must be a concerted effort amongst partners to complete patient records and follow up this unique cohort. Further data is needed in the registry to better understand the outcome of patients diagnosed through newborn screening compared to those diagnosed after the onset of symptoms.

In the last three years, E-IMD has started the first collaborative initiative on UCD patients in Europe and the largest initiative for OAD patients worldwide. E-IMD has fostered international collaboration with the American UCDC consortium (Seminara et al. 2010 ) and the newly established Japanese UCDC. The E-IMD network has developed beyond expectations and now includes 87 partners in 25 countries. The specific objectives of this EU-funded activity are to improve the knowledge base, to develop European consensus guidelines and to foster networking for patients with OAD and UCD in Europe. These goals have all been achieved despite important obstacles and hurdles.

Project Funding Mechanism

A major drawback was the funding mechanism of this project. The activation process time and the number of patients registered differed between associated and collaborating partners. Whereas associated partners received partial EU funding, collaborating partners contributed on a voluntary basis without financial compensation for their working time. Therefore, it can be assumed that the activation process would have been accelerated and the total number of patients would have been significantly increased, if a larger proportion of clinical partners had received project funding. In addition, the shared cost principle and financial mechanism have been a challenge as hospital administrators are often unable to prefinance. In conclusion, the management of this project could have been improved if European regulations had been harmonious on a national level and the financial plan for such projects had been more flexible.

Ethical Review Process

Another source of significant variability in the activation process has been the ethical review. In some countries this is painstakingly long and administrative, whilst in others formal ethical review is not required as the study is regarded as noninterventional audit. It would be of great benefit for rare disease registries to harmonise regulations and to distinguish between noninterventional studies with low or even no risk of potential harm for participating individuals and other types of research, at a European level.

Guidelines for Rare Diseases

Guidelines for OAD and UCD have been developed and published under the umbrella of E-IMD (Baumgartner et al. 2014 ; Häberle et al. 2012 ; Kölker et al. 2011 ). The formal process of guideline development, which was in line with the SIGN methodology, was considered unhelpful by some authors since guidelines for rare diseases often result in low grades of recommendations. It was assumed that such guidelines might be liable to misinterpretation and misuse, not be prescriptive enough and prove to be a hindrance in obtaining funding for treatment (Vockley et al. 2013 ). However, there are strong refutations to this position:

• The level of published evidence and grading of a recommendation do not necessarily correlate with its clinical relevance. For instance, although low phenylalanine diet for phenylketonuria has never been tested in a randomised controlled trial, and, therefore, the level of evidence for this intervention has to be formally evaluated as relatively low (Yi and Singh 2008 ), no metabolic specialist would doubt that this therapeutic intervention in general is extremely relevant for affected individuals (Blau et al. 2010 ; Burgard 2000 ; Camp et al. 2014 ).
• The effect size of therapeutic interventions in patients with an IMD is often huge. Therefore, it can be reliably identified by a cohort study with low risk of confounding bias (Kölker et al. 2007b ; Heringer et al. 2010 ). There is no doubt that, if affordable and achievable, randomised controlled trials for rare IMDs should be performed, but this is often not feasible due to low number of patients and would require the interest of the pharmaceutical industry (Enns et al. 2007 ; Levy et al. 2007 ; Wraith et al. 2004 ). However, there are also examples of carefully designed n of one trials (Bickel et al. 1953 ).
• Low grading helps to identify the gaps in current knowledge thereby setting the scene for further research.
• Setting standards of practice is important to minimise unnecessary variance or – even worse – trial and error.
• Identification of alternative approaches is important since these are often required when adverse events occur or a drug is not available in a national health system.
• Practices based upon expert opinion of single physicians or centres with a long-standing experience do not gain wide appraisal and approval without independent and critical evaluation.

The E-IMD consortium will foster the implementation of the guidelines for OAD and UCD in daily practice and will investigate whether the use of guideline recommendations improves the health outcomes and quality of life of affected individuals. First promising results have already been published for the use of the GA1 guideline on a national level (Heringer et al. 2010 ) and for the UCD guideline (Häberle and Huemer 2015 ). To evaluate the effect of newborn screening and adherence to guideline recommendations in rare diseases with a broad clinical spectrum will become challenging, since reliable clinical endpoints and a large number of patients are required for the analysis.

In 2013, after the development of the E-IMD guidelines, the Scottish Intercollegiate Guidelines Network implemented the principles of the GRADE methodology. This change will facilitate the process of recommendation development for rare diseases.

Looking Forward: Towards a European Reference Network for Inherited Metabolic Diseases

Despite some significant challenges, E-IMD has succeeded beyond expectations which will ultimately promote health for individuals with OAD and UCD. However this goal can only be achieved through long-term follow-up, which requires a sustainable funding mechanism. An opportunity for E-IMD could be its establishment as a European Reference Network (ERN) for inherited metabolic diseases. The general concept and implementation of ERNs are defined in Article 12 of the Cross-Border Healthcare Directive (The European Parliament and the Council of the European Union 2011 ). According to the recommendations of the European Union Committee of Experts on Rare Diseases (EUCERD) on rare disease European Union Committee of Experts on Rare Diseases ( 2013 ), which are designed to be complementary to the Cross-Border Healthcare Expert Group on ERNs, a rare disease ERN should cover various core tools and activities, amongst the disease registries, mechanisms for information flow for good practice guidelines, training and education tools and communications infrastructure to ensure visibility. E-IMD has developed these tools and activities.

In addition, E-IMD has proceeded with the concept of disease clustering. Starting with 11 IMD, the modular IT platform has been extended to 26 IMD by inclusion of homocystinurias and methylation defects within the EU-funded project E-HOD (project lead: Prof. Henk Blom, Freiburg, Germany). Since 2013, the IT platform has also been used to gather post-marketing surveillance data for the orphan drug Cystadane™ (betaine anhydrous) which is licensed for adjunctive treatment of homocystinuria caused by deficiencies or defects in cystathionine beta-synthase, 5,10-methylene tetrahydrofolate reductase, and cobalamin cofactor metabolism. The ‘Cystadane Surveillance Protocol’ project has been realised within a public private partnership between the E-HOD consortium and the drug licence holder, Orphan Europe Sarl. This collaboration maps onto the 2011 recommendations of the European Medicines Agency and EUCERD stressing the need to support public private partnerships in the development of registries and collaboration for post-marketing surveillance (European Union Committee of Experts on Rare Diseases 2011 ). In 2014, the same IT solution will be used for implementing the IMD group of biogenic amine and pterin biosynthesis and recycling disorders (iNTD, project lead: Dr Thomas Opladen, Heidelberg, Germany; funded by Dietmar Hopp Foundation, Germany).

This clearly shows that disease clustering and the development of new applications using the same IT platform and network have many advantages and are a favourable strategy for both sustainment and extension. The strategy for extending the IT platform and the network towards an ERN for inherited metabolic diseases has been elaborated and shall be realised step by step.

Acknowledgements

This publication arises from the project ‘European registry and network for intoxication type metabolic diseases (E-IMD)’ (EAHC no 2010 12 01) which has received funding from the European Union, in the framework of the Health Programme. After the end of the EU funding period the E-IMD patient registry will be sustained by funding from the Kindness for Kids Foundation (Munich, Germany). MRB and JH are supported by radiz – Rare Disease Initiative Zurich – a clinical research priority programme of the University of Zurich, Switzerland.

We are grateful to the following partners for their valuable contribution to establish the E-IMD consortium and for their fruitful ongoing collaboration (listed in alphabetical order of countries):

Austria : Daniela Karall, Johannes Zschocke and Sabine Scholl-Bürgi (Medizinische Universität Innsbruck, Universitätskinderklinik und Sektionen für Humangenetik und Klinische Genetik, Innsbruck).

Australia : Avihu Boneh (the Murdoch Childrens Research Institute, Royal Children’s Hospital, Melbourne)

Belgium : Francois Eyskens and Tine Maes (Universitair Ziekenhuis Antwerpen, Antwerpen), Linda de Meirleir (University Hospital Vrije Universiteit Brussels, Department of Pediatric Neurology, Brussels), Corinne de Laet (Hôpital Universitaire des Enfants Reine Fabiola, Nutrition and Metabolism Unit, Brussels), Etienne Sokal and Florence Defresne (Université Catholique de Louvain, Clinique Universitaires St. Luc, Brussels), Dominique Roland (Institute of Pathology and Genetics, Center for Inherited Metabolic Diseases, Gosselies), Guillaume Debray (Centre Hospitalier Universitaire, Department of Genetics, Liège) and Lut de Baere and Nathalie Stroobant [Belgische Organisatie voor Kinderen en Volwassenen met een Stofwisselingsziekte VZW (BOKS), Melsele, PO group]

Canada : Cheryl R. Greenberg (University of Manitoba, Department of Pediatrics and Child Health, Department of Biochemistry and Medical Genetics, Winnipeg)

Croatia : Ivo Baric, Mario Cuk and Slobodan Galic (Sveuciliste u Zagrebu, Medicinski fakultet, University Hospital Centre, Department of Pediatrics, Zagreb) and Nelia Caric (Hrvatska udruga za rijetke bolesti, PO group)

Czech Republic : Jiri Zeman and Tomas Honzik (Charles University of Prague, First Faculty of Medicine, Department of Pediatrics, Prague)

Denmark : Allan M. Lund, Ernst Christensen, Lise Aksglaede and Malene Bøgehus Rasmussen (Rigshospitalet, Centre for Inherited Metabolic Diseases, Department of Clinical Genetics, Copenhagen) and Annika and Kennet Rovsing (PND – Protein Nedbrydnings Defekt foreningen, PO group)

France : Vassili Valayannopoulos, Jean-Baptiste Arnoux, Pascale de Lonlay, Ulf Aringer, Kim-Hanh Le Quan Sang and Eric Bauchart (Hôpital Necker-Enfants Malades, Assistance Publique-Hôpitaux de Paris, Reference Center for Inherited Metabolic Disease, Necker-Enfants Malades University Hospital and Imagine Institute, Paris); Hélène Ogier de Baulny (Centre de Référence Maladies Héréditaires du Metabolisme, Hôpital Robert Debré, Université Paris VII, Paris); Brigitte Chabrol (Centre de Référence Maladies Héréditaires du Metabolisme, Hôpital d’Enfants Service de Neurologie, Marseille); Pierre Broue (Centre de Référence Maladies Héréditaires du Metabolisme, Hôpital des Enfants – CHU Toulouse, Toulouse); EURORDIS, European Organisation for Rare Disease (Paris); and Orphan Europe SARL (Paris)

Germany : S. P. Nikolas Boy, Corinna Bürger, Esther M. Glahn, Friederike Hörster, Gisela Haege, Jana Heringer, Marina A. Morath, Roland Posset, Christian Staufner, Kathrin Zangerl and Matthias Zielonka (Universitätsklinikum Heidelberg, Zentrum für Kinder- und Jugendmedizin, Kinderklinik I); Chris Mühlhausen and René Santer (Universitätsklinikum Hamburg-Eppendorf, Klinik für Kinder- und Jugendmedizin, Hamburg); Regina Ensenauer (Ludwig-Maximilian-Universität München, Dr. von Haunersches Kinderspital, München); Thomas Meissner (Universitätsklinikum Düsseldorf, Zentrum für Kinder- und Jugendmedizin, Düsseldorf); Peter Freisinger (Klinik für Kinder- und Jugendmedizin, Klinikum am Steinenberg, Reutlingen); Sarah Grünert and Ute Spiekerkötter (Universitätsklinikum Freiburg, Zentrum für Kinder- und Jugendmedizin, Freiburg); Martin Lindner (Universitätsklinikum Frankfurt, Klinik für Kinder- und Jugendmedizin, Frankfurt); Markus Ott and Beate Szczerbak (Nutricia Metabolics GmbH, Friedrichsdorf); Hubertus von Voss and Raimund Schmid (Kindernetzwerk e.V., Aschaffenburg); Mandy Kretschmer (Glutarazidurie e.V.); and Reinhild Link (Wiesbaden, representing the SSIEM Dieticians Group)

Greece : Persephone Augoustides-Savvopoulou and Harikleia Ioannou (University A’Pediatric Department, Metabolic Laboratory, ‘Hippokration’ General Hospital of Thessaloniki, Thessaloniki, and KRIKOS ZOIS, PO group), Evriki Drogari (University of Athens, Aghia Sophia Children’s Hospital, Unit of Metabolic Diseases, Athens) and Zarifis Dimitroulis (KRIKOS ZOIS – Society for Patients and Friends of Patients with Inherited Metabolic diseases, PO group)

India : Anil Jalan (N.I.R.M.A.N., Om Rachna Society, Mumbai)

Italy : Alberto B. Burlina, Andrea Bordugo and Francesca Furlan (Azienda Ospedaliera di Padova, U.O.C. Malattie Metaboliche Ereditarie, Dipartimento di Pediatria, Padova); Carlo Dionisi-Vici and Diego Martinelli (Ospedale Pediatrico Bambino Gesù, U.O.C. Patologia Metabolica, Rome); Renza Barbon Galluppi (UNIAMO FIMR, PO group); and Susan Udina (COMETA ASMME – Associazione Studio Malattie Metaboliche Ereditarie – ONLUS, PO group)

Japan : Fumio Endo and Shirou Matsumoto (Kumamoto University Hospital, Department of Pedatrics, Kumamoto, and representing the Japanese Urea Cycle Disorders Consortium)

Netherlands : Frits A. Wijburg and Eveline Langereis (Academisch Medisch Centrum, Department of Pediatrics, Amsterdam), Monique Williams (Erasmus Universiteit Rotterdam, Erasmus MC-Sophia Kinderziekenhuis, Rotterdam) and Hanka Meutgeert (Volwassenen en Kinderen met Stofwisselingsziekten [VKS], Zwolle, PO group)

Poland : Jolanta Sykut-Cegielska (Institute of Mother and Child, Screening Department, Warsaw) and Wanda Gradowska (Instytut ‘Pomnik-Centrum Zdrowia Dziecka’, The Children’s Memorial Health Institute, Department of Metabolic Diseases, Endocrinology and Diabetology, Warsaw)

Portugal : Elisa Teles Leao, Susana Soares and Esmeralda Rodrigues (Unidade de Doenças Metabólicas, Serviço de Pediatria, Hospital de S. João, EPE, Porto); Laura Vilarinho (Newborn Screening Unit, Metabolic Genetics Center, National Institute of Health [INSA], Porto); Ana Gaspar (Unidade de Doenças Metabólicas, Serviço de Pediatria Hospital Santa Maria Lisboa, Lisbon); Isabel Tavares de Almeida (Faculdade de Farmácia da Universidade de Lisboa, Lisbon); Vanessa Ferreira (Associação Portuguesa CDG, PO group); Miguel Macedo (Apofen, PO group); and Sérgio Braz Antão (Rarrisimas, PO group)

Republic of Serbia : Adrijan Sarajlija and Maja Djordjevic (Institut za zdravstvenu zaštitu majke i deteta Srbije, Belgrade)

Romania : Paula Avram (Institute for Mother and Child Care ‘Alfred Rusescu’, Bucharest)

Spain : Juan-Luque Moreno (CIBERER, Centro de Investigacíon Biomédica en Red de Enfermedades Raras); Angeles Garcia-Cazorla, Jaume Campistol, Carlos Ortez and Elisenda Cortès i Saladelafont (Hospital Sant Joan de Deu, Servicio de Neurologica, Barcelona); Elena Balmaseda and M. Carmen Carrascosa (Complejo Hospitalario Universitario de Albacete, Albacete); Guillem Pintos-Morell (University hospital ‘German Trias i Pujol’, Badalona); Antonia Ribes (Institut Bioquimica Clinica, Corporacio Sanitaria Clinic, Barcelona); Eduardo Lopéz (Pediatric Neurology Unit Department of Pediatrics, University Hospital Reina Sofia, Cordoba); Immaculada Vives and David Gil Ortega (Hospital Virgen de la Arrixaca de Murcia, El Palmar); Juana Maria de Haro Catellano (Secretaria Tecnica del CEI-Granada (HUVN), Edificio Licinio de la Fuente, Granada); Luis Pena-Quintana (Hospital Universitario Materno-Infantil de Canarias, Universidad de Las Palmas de Gran Canaria, Las Palmas de Gran Canaria); Magdalena Ugarte and Begona Merinero (Universidad Autonoma de Madrid, Madrid); Consuelo Pedròn-Giner (Hospital Infantil Universitario Niño Jesús, Sección de Gastroenterología y Nutrición, Madrid); Javier Blasco-Alonso (Hospital Materno-Infantil de Malaga, Unidad de Gastroenterologia, Hepatologia, Nutricion y Metabolopatias, Malaga); Angeles Ruiz Gómez (Hospital Universitario Son Espases, Palma de Mallorca); Maria L. Couce (Hospital Clinico Universitario de Santiago de Compostela, Santiago de Compostela); Vicente Rubio (Instituto de Biomedicina de Valencia, Valencia); and Sergi Faber (Catalana de Trastorns Metabòlics Hereditaris, PO group)

Sweden : Sofia Nordin (Swedish Orphan Biovitrum AB [SOBI], Stockholm)

Switzerland : Jörn-Oliver Sass (Kinderspital Zürich, Universitäts-Kinderkliniken, Eleonoren-Stiftung, Department of Clinical Chemistry and Biochemistry, Zürich) and Jean-Marc Nuoffer (Universitätsspital Bern, Universitätsklinik für Kinderheilkunde, Bern)

Taiwan : Wu-Liang Hwu, Yin-Hsiu Chien and Ni-Chung Lee (National Taiwan University Hospital, Department of Medical Genetics, Taipei)

Turkey : Mübeccel Demirkol and Gülden Gökcay (Istanbul University, Children’s Hospital, Department of Nutrition and Metabolism, Istanbul)

UK : Victoria Riches (Birmingham Children’s Hospital NHS Foundation Trust, Birmingham); CLIMB, Children Living with Inherited Metabolic Diseases, National Information Centre for Metabolic Diseases (Crewe); Stephanie Grünewald and Nick Thompson (Great Ormond Street Hospital for Children NHS Trust, London); Robin Lachmann and Elaine Murphy (National Hospital for Neurology and Neurosurgery, Charles Dent Metabolic Unit, London, and representing the SSIEM Adult Metabolic Group); Roshni Vara (Evelina Children’s Hospital, St Thomas’ Hospital, Department of Inherited Metabolic Disease, London); John Walter and Andrew Morris (Central Manchester and Manchester Children’s University Hospital, Willink Biochemical Genetics Unit, Manchester); Bradford Teaching Hospitals NHS Trust, St Luke’s Hospital (Bradford); and EMDA, the European Metabolic Disorders Alliance (PO group)

USA : Kimberly Chapman (Children’s National Medical Center, Center for Genetic Medicine Research, Washington DC, and representing the Urea Cycle Disorders Consortium) and Jerry Vockley (Children’s Hospital of Pittsburgh of UPMC, Pittsburgh)

Abbreviations

E-IMD has established an international network, has improved knowledge about organic acidurias and urea cycle disorders and has started harmonising diagnostic and therapeutic strategies. This is the prerequisite for establishing a European Reference Network for inherited metabolic diseases.

Compliance with Ethics Guidelines

Conflict of interest.

Stefan Kölker, Matthias R. Baumgartner, Peter Burgard, Anupam Chakrapani, Dries Dobbelaere, Florian Gleich, Johannes Häberle, Marshall L. Summar, and Steven Hannigan declare that they have no conflict of interest. Samantha Parker is employed by Orphan Europe Sarl being part of the Recordati Group.

Informed Consent

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all patients or their legal guardians for being included in the study.

Details of the Contributions of Individual Authors

Designing, planning and conducting the study: all authors

Statistical analysis: Peter Burgard, Florian Gleich and Stefan Kölker

Manuscript writing: All authors

Animal Rights

This article does not contain any studies with animal subjects performed by any of the authors.

Competing interests: None declared

• Bachmann C. Outcome and survival of 88 patients with urea cycle disorders: a retrospective evaluation. Eur J Pediatr. 2003; 162 :410–416. doi: 10.1007/s00431-003-1347-z. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Baumgartner MR, Hörster F, Assoun M, et al. Suggested guidelines for the diagnosis and management of methylmalonic and propionic acidemias. Orphanet J Rare Dis. 2014; 9 :130. doi: 10.1186/s13023-014-0130-8. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Bickel H, Gerrard J, Hickmans EM (1953) Influence of phenylalanine intake on phenylketonuria. Lancet 2:812–813 [ PubMed ]
• Blau N, Bélanger-Quintana A, Demirkol M, et al. Management of phenylketonuria in Europe: survey results from 19 countries. Mol Genet Metab. 2010; 99 :109–115. doi: 10.1016/j.ymgme.2009.09.005. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boy N, Haege G, Heringer J, et al. Low lysine diet in glutaric aciduria type I – effect on anthropometric and biochemical follow-up parameters. J Inherit Metab Dis. 2013; 36 :525–533. doi: 10.1007/s10545-012-9517-7. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brimley CJ, Lopez J, van Haren K, et al. National variation in costs and mortality for leukodystrophy patients in US children’s hospitals. Pediatr Neurol. 2013; 49 :156–162. doi: 10.1016/j.pediatrneurol.2013.06.006. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burgard P. Development of intelligence in early treated phenylketonuria. Eur J Pediatr. 2000; 159 (Suppl 2):S74–S79. doi: 10.1007/PL00014388. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Camp KM, Parisi MA, Acosta PB et al (2014) Phenylketonuria scientific review conference: state of the science and future research needs. Mol Genet Metab pii:S1096–7192(14)00085-7. doi:10.1016/j.ymgme.2014.02.013 [ PubMed ]
• Chapman KA, Gropman A, MacLeod E, et al. Acute management of propionic acidemia. Mol Genet Metab. 2012; 105 :16–25. doi: 10.1016/j.ymgme.2011.09.026. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Commission of the European Communities (2008) Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the regions. http://ec.europa.eu/health/rare_diseases/policy/index_en.htm
• Enns GM, Berry SA, Berry GT, Rhead WJ, Brusilow SW, Hamosh A. Survival after treatment with phenylacetate and benzoate for urea-cycle disorders. N Engl J Med. 2007; 356 :2282–2292. doi: 10.1056/NEJMoa066596. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• European Union Committee of Experts on Rare Diseases (2011) EUCERD/EMA workshop report: Towards a public-private partnership in the field of registries for rare diseases. http://www.eucerd.eu/?post_type=document&p=1234
• European Union Committee of Experts on Rare Diseases (2013) EUCERD recommendations on rare disease European Reference Networks (RD ERNS). http://www.eucerd.eu/?post_type=document&p=2207
• Grünert SC, Wendel U, Lindner M, et al. Clinical and neurocognitive outcome in symptomatic isovaleric acidemia. Orphanet J Rare Dis. 2012; 7 :9. doi: 10.1186/1750-1172-7-9. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grünert SC, Müllerleile S, De Silva L, et al. Propionic acidemia: clinical course and outcome in 55 pediatric and adolescent patients. Orphanet J Rare Dis. 2013; 8 :6. doi: 10.1186/1750-1172-8-6. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Häberle J, Huemer M (2015) Evaluation of implementation, adaptation and use of the recently proposed urea cycle disorders guidelines. J Inherit Metab Dis (Reports), doi:10.1007/8904_2014_387 [ PMC free article ] [ PubMed ]
• Häberle J, Boddaert N, Burlina A, et al. Suggested guidelines for the diagnosis and management of urea cycle disorders. Orphanet J Rare Dis. 2012; 7 :32. doi: 10.1186/1750-1172-7-32. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Heringer J, Boy SP, Ensenauer R, et al. Use of guidelines improves the neurological outcome in glutaric aciduria type I. Ann Neurol. 2010; 68 :743–752. doi: 10.1002/ana.22095. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hörster F, Baumgartner MR, Viardot C, et al. Long-term outcome in methylmalonic acidurias is influenced by the underlying defect (mut0, mut-, cblA, cblB) Pediatr Res. 2007; 62 :225–230. doi: 10.1203/PDR.0b013e3180a0325f. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kasper DC, Ratschmann R, Metz TF, et al. The national newborn screening program – eight years experience with mass spectrometry. Past, present and future goals. Wien Klin Wochenschr. 2010; 122 :607–613. doi: 10.1007/s00508-010-1457-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kido J, Nakamura K, Mitsubuchi H, et al. Long-term outcome and intervention of urea cycle disorders in Japan. J Inherit Metab Dis. 2012; 35 :777–785. doi: 10.1007/s10545-011-9427-0. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kölker S, Garcia Cazorla A, Valayannopoulos V et al (2015a) The phenotypic spectrum of organic acidurias and urea cycle disorders. Part 1: The initial presentation. J Inherit Metab Dis. doi:10.1007/s10545-015-9817-9 [ PubMed ]
• Kölker S, Valayannopoulos V, Garcia Cazorla A et al (2015b) The phenotypic spectrum of organic acidurias and urea cycle disorders. Part 2: The initial presentation. J Inherit Metab Dis. doi:10.1007/s10545-015-9818-8 [ PubMed ]
• Kölker S, Garbade SF, Greenberg CR, et al. Natural history, outcome, and treatment efficacy in children and adults with glutaryl-CoA dehydrogenase deficiency. Pediatr Res. 2006; 59 :840–847. doi: 10.1203/01.pdr.0000219387.79887.86. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kölker S, Christensen E, Leonard JV, et al. Guideline for the diagnosis and management of glutaryl-CoA dehydrogenase deficiency (glutaric aciduria type I) J Inherit Metab Dis. 2007; 30 :5–22. doi: 10.1007/s10545-006-0451-4. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kölker S, Garbade SF, Boy N, et al. Decline of acute encephalopathic crises in children with glutaryl-CoA dehydrogenase deficiency identified by newborn screening in Germany. Pediatr Res. 2007; 62 :225–260. doi: 10.1203/PDR.0b013e318137a124. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kölker S, Christensen E, Leonard JV, et al. Diagnosis and management of glutaric aciduria type I – revised recommendations. J Inherit Metab Dis. 2011; 34 :677–694. doi: 10.1007/s10545-011-9289-5. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levy HL, Milanowski A, Chakrapani A, et al. Efficacy of sapropterin dihydrochloride (tetrahydrobiopterin, 6R-BH4) for reduction of phenylalanine concentration in patients with phenylketonuria: a phase III randomised placebo-controlled study. Lancet. 2007; 370 :504–510. doi: 10.1016/S0140-6736(07)61234-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Linertová R, Serrano-Aguilar P, Posada-de-la-Paz M, et al. Delphi approach to select rare diseases for a European representative survey. The BURQOL-RD study. Health Policy. 2012; 108 :19–26. doi: 10.1016/j.healthpol.2012.08.001. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• López-Bastida J, Perestelo-Pérez L, Montón-Alvarez F, Serrano-Aguilar P. Social economic costs and health-related quality of life in patients with degenerative cerebellar ataxia in Spain. Mov Disord. 2008; 23 :212–217. doi: 10.1002/mds.21798. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• López-Bastida J, Perestelo-Pérez L, Montón-Alvarez F, Serrano-Aguilar P, Alfonso-Sanchez JL. Social economic costs and health-related quality of life in patients with amyotrophic lateral sclerosis in Spain. Amyotroph Lateral Scler. 2009; 10 :237–243. doi: 10.1080/17482960802430781. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nassogne MC, Héron B, Touati G, Rabier D, Saudubray JM. Urea cycle defects: management and outcome. J Inherit Metab Dis. 2005; 28 :407–414. doi: 10.1007/s10545-005-0303-7. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pena L, Franks J, Chapman KA, et al. Natural history of propionic acidemia. Mol Genet Metab. 2012; 105 :5–9. doi: 10.1016/j.ymgme.2011.09.022. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rüegger CM, Lindner M, Ballhausen D, et al. Cross-sectional observational study of 208 patients with non-classical urea cycle disorders. J Inherit Metab Dis. 2014; 37 :21–30. doi: 10.1007/s10545-013-9624-0. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Schulze A, Lindner M, Kohlmüller D, Olgemöller K, Mayatepek E, Hoffmann GF. Expanded newborn screening for inborn errors of metabolism by electrospray ionization-tandem mass spectrometry: results, outcome, and implications. Pediatrics. 2003; 111 :1399–1406. doi: 10.1542/peds.111.6.1399. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Seminara J, Tuchman M, Krivitzky L, et al. Establishing a consortium for the study of rare diseases: The Urea Cycle Disorders Consortium. Mol Genet Metab. 2010; 100 :S97–S105. doi: 10.1016/j.ymgme.2010.01.014. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Strauss KA, Puffenberger EG, Robinson DL, Morton DH (2003) Type I glutaric aciduria, part 1: natural history of 77 patients. Am J Med Genet C Semin Med Genet 121C:38–52 [ PubMed ]
• Summar ML, Dobbelaere D, Brusilow S, Lee B. Diagnosis, symptoms, frequency and mortality of 260 patients with urea cycle disorders from a 21-year, multi-centre study of acute hyperammonaemic episodes. Acta Paediatr. 2008; 97 :1420–1425. doi: 10.1111/j.1651-2227.2008.00952.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Summar ML, Kölker S, Freedenberg D, et al. The incidence of urea cycle disorders. Mol Genet Metab. 2013; 110 :179–180. doi: 10.1016/j.ymgme.2013.07.008. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• The European Parliament and the Council of the European Union (2011) Directive 2011/24/EU of the European Parliament and of the Council of 9 March 2011 on the application of patients’ rights in cross-border healthcare. Official J Eur Union L 88/45:1–21. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2011:088:0045:0065:EN:PDF
• Vockley J, Chapman KA, Arnold GL. Development of clinical guidelines for inbron errors of metabolism: commentary. Mol Genet Metab. 2013; 108 :203–205. doi: 10.1016/j.ymgme.2013.01.013. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wilcken B, Wiley V, Hammond J, Carpenter K. Screening methods for inborn errors of metabolism by tandem mass spectrometry. N Engl J Med. 2003; 348 :2304–2312. doi: 10.1056/NEJMoa025225. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wraith JE, Clarke LA, Beck M, et al. Enzyme replacement therapy for mucopolysaccharidosis I: a randomized, double-blinded, placebo-controlled, multinational study of recombinant human alpha-L-iduronidase (laronidase) J Pediatr. 2004; 144 :581–588. doi: 10.1016/j.jpeds.2004.01.046. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yi S, Singh RH. Protein substitute for children and adults with phenylketonuria. Cochrane Database Syst Rev. 2008; 4 [ PubMed ] [ Google Scholar ]