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      Risk factors for type 2 diabetes mellitus: An exposure-wide umbrella review of meta-analyses

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          Abstract

          Background

          Type 2 diabetes mellitus (T2DM) is a global epidemic associated with increased health expenditure, and low quality of life. Many non-genetic risk factors have been suggested, but their overall epidemiological credibility has not been assessed.

          Methods

          We searched PubMed to capture all meta-analyses and Mendelian randomization studies for risk factors of T2DM. For each association, we estimated the summary effect size, its 95% confidence and prediction interval, and the I 2 metric. We examined the presence of small-study effects and excess significance bias. We assessed the epidemiological credibility through a set of predefined criteria.

          Results

          We captured 86 eligible papers (142 associations) covering a wide range of biomarkers, medical conditions, and dietary, lifestyle, environmental and psychosocial factors. Adiposity, low hip circumference, serum biomarkers (increased level of alanine aminotransferase, gamma-glutamyl transferase, uric acid and C-reactive protein, and decreased level of adiponectin and vitamin D), an unhealthy dietary pattern (increased consumption of processed meat and sugar-sweetened beverages, decreased intake of whole grains, coffee and heme iron, and low adherence to a healthy dietary pattern), low level of education and conscientiousness, decreased physical activity, high sedentary time and duration of television watching, low alcohol drinking, smoking, air pollution, and some medical conditions (high systolic blood pressure, late menarche age, gestational diabetes, metabolic syndrome, preterm birth) presented robust evidence for increased risk of T2DM.

          Conclusions

          A healthy lifestyle pattern could lead to decreased risk for T2DM. Future randomized clinical trials should focus on identifying efficient strategies to modify harmful daily habits and predisposing dietary patterns.

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          Most cited references68

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          Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future.

          Steven E. Kahn, Mark E. Cooper, Stefano Del Prato (2014)
          Glucose metabolism is normally regulated by a feedback loop including islet β cells and insulin-sensitive tissues, in which tissue sensitivity to insulin affects magnitude of β-cell response. If insulin resistance is present, β cells maintain normal glucose tolerance by increasing insulin output. Only when β cells cannot release sufficient insulin in the presence of insulin resistance do glucose concentrations rise. Although β-cell dysfunction has a clear genetic component, environmental changes play an essential part. Modern research approaches have helped to establish the important role that hexoses, aminoacids, and fatty acids have in insulin resistance and β-cell dysfunction, and the potential role of changes in the microbiome. Several new approaches for treatment have been developed, but more effective therapies to slow progressive loss of β-cell function are needed. Recent findings from clinical trials provide important information about methods to prevent and treat type 2 diabetes and some of the adverse effects of these interventions. However, additional long-term studies of drugs and bariatric surgery are needed to identify new ways to prevent and treat type 2 diabetes and thereby reduce the harmful effects of this disease. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            Quantity and Quality of Sleep and Incidence of Type 2 Diabetes

            Francesco Cappuccio, Lanfranco D'Elia, Pasquale Strazzullo … (2009)
            OBJECTIVE To assess the relationship between habitual sleep disturbances and the incidence of type 2 diabetes and to obtain an estimate of the risk. RESEARCH DESIGN AND METHODS We conducted a systematic search of publications using MEDLINE (1955–April 2009), EMBASE, and the Cochrane Library and manual searches without language restrictions. We included studies if they were prospective with follow-up >3 years and had an assessment of sleep disturbances at baseline and incidence of type 2 diabetes. We recorded several characteristics for each study. We extracted quantity and quality of sleep, how they were assessed, and incident cases defined with different validated methods. We extracted relative risks (RRs) and 95% CI and pooled them using random-effects models. We performed sensitivity analysis and assessed heterogeneity and publication bias. RESULTS We included 10 studies (13 independent cohort samples; 107,756 male and female participants, follow-up range 4.2–32 years, and 3,586 incident cases of type 2 diabetes). In pooled analyses, quantity and quality of sleep predicted the risk of development of type 2 diabetes. For short duration of sleep (≤5–6 h/night), the RR was 1.28 (95% CI 1.03–1.60, P = 0.024, heterogeneity P = 0.015); for long duration of sleep (>8–9 h/night), the RR was 1.48 (1.13–1.96, P = 0.005); for difficulty in initiating sleep, the RR was 1.57 (1.25–1.97, P < 0.0001); and for difficulty in maintaining sleep, the RR was 1.84 (1.39–2.43, P < 0.0001). CONCLUSIONS Quantity and quality of sleep consistently and significantly predict the risk of the development of type 2 diabetes. The mechanisms underlying this relation may differ between short and long sleepers.
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              Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction

              Fumiaki Imamura, Laura O’Connor, Zheng Ye … (2015)
              Objectives To examine the prospective associations between consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice with type 2 diabetes before and after adjustment for adiposity, and to estimate the population attributable fraction for type 2 diabetes from consumption of sugar sweetened beverages in the United States and United Kingdom. Design Systematic review and meta-analysis. Data sources and eligibility PubMed, Embase, Ovid, and Web of Knowledge for prospective studies of adults without diabetes, published until February 2014. The population attributable fraction was estimated in national surveys in the USA, 2009-10 (n=4729 representing 189.1 million adults without diabetes) and the UK, 2008-12 (n=1932 representing 44.7 million). Synthesis methods Random effects meta-analysis and survey analysis for population attributable fraction associated with consumption of sugar sweetened beverages. Results Prespecified information was extracted from 17 cohorts (38 253 cases/10 126 754 person years). Higher consumption of sugar sweetened beverages was associated with a greater incidence of type 2 diabetes, by 18% per one serving/day (95% confidence interval 9% to 28%, I2 for heterogeneity=89%) and 13% (6% to 21%, I2=79%) before and after adjustment for adiposity; for artificially sweetened beverages, 25% (18% to 33%, I2=70%) and 8% (2% to 15%, I2=64%); and for fruit juice, 5% (−1% to 11%, I2=58%) and 7% (1% to 14%, I2=51%). Potential sources of heterogeneity or bias were not evident for sugar sweetened beverages. For artificially sweetened beverages, publication bias and residual confounding were indicated. For fruit juice the finding was non-significant in studies ascertaining type 2 diabetes objectively (P for heterogeneity=0.008). Under specified assumptions for population attributable fraction, of 20.9 million events of type 2 diabetes predicted to occur over 10 years in the USA (absolute event rate 11.0%), 1.8 million would be attributable to consumption of sugar sweetened beverages (population attributable fraction 8.7%, 95% confidence interval 3.9% to 12.9%); and of 2.6 million events in the UK (absolute event rate 5.8%), 79 000 would be attributable to consumption of sugar sweetened beverages (population attributable fraction 3.6%, 1.7% to 5.6%). Conclusions Habitual consumption of sugar sweetened beverages was associated with a greater incidence of type 2 diabetes, independently of adiposity. Although artificially sweetened beverages and fruit juice also showd positive associations with incidence of type 2 diabetes, the findings were likely to involve bias. None the less, both artificially sweetened beverages and fruit juice were unlikely to be healthy alternatives to sugar sweetened beverages for the prevention of type 2 diabetes. Under assumption of causality, consumption of sugar sweetened beverages over years may be related to a substantial number of cases of new onset diabetes.
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                Author and article information

                Contributors
                Vanesa Bellou: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Lazaros Belbasis: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Ioanna Tzoulaki: Role: MethodologyRole: SupervisionRole: Writing – review & editing
                Evangelos Evangelou:
                ORCID: http://orcid.org/0000-0002-5488-2999
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Pratibha V. Nerurkar: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 20 March 2018
                Publication date Collection: 2018
                Volume: 13
                Issue: 3
                Electronic Location Identifier: e0194127
                Affiliations
                [1 ] Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
                [2 ] Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
                [3 ] MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
                University of Hawai'i at Manoa College of Tropical Agriculture and Human Resources, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-5488-2999
                Article
                Publisher ID: PONE-D-17-16562
                DOI: 10.1371/journal.pone.0194127
                PMC ID: 5860745
                PubMed ID: 29558518
                SO-VID: da175fbe-37e2-4dd6-ae7e-2d0f3d0de482
                Copyright © © 2018 Bellou et al
                License:

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

                History
                Date received : 30 April 2017
                Date accepted : 26 February 2018
                Page count
                Figures: 5, Tables: 3, Pages: 27
                Funding
                The authors received no specific funding for this work.
                Categories
                Subject: Research Article
                Subject: Research and Analysis Methods
                Subject: Mathematical and Statistical Techniques
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Physical Sciences
                Subject: Mathematics
                Subject: Statistics (Mathematics)
                Subject: Statistical Methods
                Subject: Meta-Analysis
                Subject: Medicine and Health Sciences
                Subject: Endocrinology
                Subject: Endocrine Disorders
                Subject: Diabetes Mellitus
                Subject: Type 2 Diabetes
                Subject: Medicine and Health Sciences
                Subject: Metabolic Disorders
                Subject: Diabetes Mellitus
                Subject: Type 2 Diabetes
                Subject: Physical sciences
                Subject: Chemistry
                Subject: Chemical compounds
                Subject: Organic compounds
                Subject: Vitamins
                Subject: Vitamin D
                Subject: Physical sciences
                Subject: Chemistry
                Subject: Organic chemistry
                Subject: Organic compounds
                Subject: Vitamins
                Subject: Vitamin D
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Physiological Parameters
                Subject: Body Weight
                Subject: Obesity
                Subject: Medicine and Health Sciences
                Subject: Physiology
                Subject: Physiological Parameters
                Subject: Body Weight
                Subject: Obesity
                Subject: Biology and Life Sciences
                Subject: Agriculture
                Subject: Animal Products
                Subject: Meat
                Subject: Biology and Life Sciences
                Subject: Nutrition
                Subject: Diet
                Subject: Food
                Subject: Meat
                Subject: Medicine and Health Sciences
                Subject: Nutrition
                Subject: Diet
                Subject: Food
                Subject: Meat
                Subject: Physical Sciences
                Subject: Chemistry
                Subject: Chemical Compounds
                Subject: Acids
                Subject: Uric Acid
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Immune Physiology
                Subject: Cytokines
                Subject: Adipokines
                Subject: Adiponectin
                Subject: Medicine and Health Sciences
                Subject: Physiology
                Subject: Immune Physiology
                Subject: Cytokines
                Subject: Adipokines
                Subject: Adiponectin
                Subject: Biology and Life Sciences
                Subject: Immunology
                Subject: Immune System
                Subject: Innate Immune System
                Subject: Cytokines
                Subject: Adipokines
                Subject: Adiponectin
                Subject: Medicine and Health Sciences
                Subject: Immunology
                Subject: Immune System
                Subject: Innate Immune System
                Subject: Cytokines
                Subject: Adipokines
                Subject: Adiponectin
                Subject: Biology and Life Sciences
                Subject: Developmental Biology
                Subject: Molecular Development
                Subject: Cytokines
                Subject: Adipokines
                Subject: Adiponectin
                Subject: Biology and Life Sciences
                Subject: Biochemistry
                Subject: Hormones
                Subject: Peptide Hormones
                Subject: Adiponectin
                Subject: Biology and Life Sciences
                Subject: Nutrition
                Subject: Diet
                Subject: Alcohol Consumption
                Subject: Medicine and Health Sciences
                Subject: Nutrition
                Subject: Diet
                Subject: Alcohol Consumption
                Custom metadata
                Data Availability All relevant data are within the paper and its Supporting Information files.

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