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      Alcohol and the risk for latent autoimmune diabetes in adults: results based on Swedish ESTRID study

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          Abstract

          Objective

          Moderate alcohol consumption is associated with a reduced risk of type 2 diabetes. Our aim was to investigate whether alcohol consumption is associated with the risk of latent autoimmune diabetes in adults (LADA), an autoimmune form of diabetes with features of type 2 diabetes.

          Design

          A population-based case–control study was carried out to investigate the association of alcohol consumption and the risk of LADA.

          Methods

          We used data from the ESTRID case–control study carried out between 2010 and 2013, including 250 incident cases of LADA (glutamic acid decarboxylase antibodies (GADAs) positive) and 764 cases of type 2 diabetes (GADA negative), and 1012 randomly selected controls aged ≥35. Logistic regression was used to estimate the odds ratios (ORs) of diabetes in relation to alcohol intake, adjusted for age, sex, BMI, family history of diabetes, smoking, and education.

          Results

          Alcohol consumption was inversely associated with the risk of type 2 diabetes (OR 0.95, 95% CI 0.92–0.99 for every 5-g increment in daily intake). Similar results were observed for LADA, but stratification by median GADA levels revealed that the results only pertained to LADA with low GADA levels (OR 0.85, 95% CI 0.76–0.94/5 g alcohol per day), whereas no association was observed with LADA having high GADA levels (OR 1.00, 95% CI 0.94–1.06/5 g per day). Every 5-g increment of daily alcohol intake was associated with a 10% increase in GADA levels ( P=0.0312), and a 10% reduction in homeostasis model assessment of insulin resistance ( P=0.0418).

          Conclusions

          Our findings indicate that alcohol intake may reduce the risk of type 2 diabetes and type 2-like LADA, but has no beneficial effects on diabetes-related autoimmunity.

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          Most cited references37

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          Estimation of Relationships for Limited Dependent Variables

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            The many faces of diabetes: a disease with increasing heterogeneity.

            Diabetes is a much more heterogeneous disease than the present subdivision into types 1 and 2 assumes; type 1 and type 2 diabetes probably represent extremes on a range of diabetic disorders. Both type 1 and type 2 diabetes seem to result from a collision between genes and environment. Although genetic predisposition establishes susceptibility, rapid changes in the environment (ie, lifestyle factors) are the most probable explanation for the increase in incidence of both forms of diabetes. Many patients have genetic predispositions to both forms of diabetes, resulting in hybrid forms of diabetes (eg, latent autoimmune diabetes in adults). Obesity is a strong modifier of diabetes risk, and can account for not only a large proportion of the epidemic of type 2 diabetes in Asia but also the ever-increasing number of adolescents with type 2 diabetes. With improved characterisation of patients with diabetes, the range of diabetic subgroups will become even more diverse in the future. Copyright © 2014 Elsevier Ltd. All rights reserved.
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              Estimability and estimation in case-referent studies.

              The concepts that case-referent studies provide for the estimation of "relative risk" only if the illness is "rare", and that the rates and risks themselves are inestimable, are overly superficial and restrictve. The ratio of incidence densities (forces of morbidity)-and thereby the instantaneous risk-ratio-is estimable without any rarity-assumption. Long-term risk-ratio can be computed through the coupling of case-referent data on exposure rates for various age-categories with estimates, possibly from the study itself, or the corresponding age-specific incidence-densities for the exposed and nonexposed combined-but again, no rarity-assumption is involved. Such data also provide for the assessment of exposure-specific absolute incidence-rates and risks. Point estimation of the various parameters can be based on simple relationships among them, and in interval estimation it is sufficient simply to couple the point estimate with the value of the chi square statistic used in significance testing.

                Author and article information

                Journal
                Eur J Endocrinol
                Eur. J. Endocrinol
                EJE
                European Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                0804-4643
                1479-683X
                November 2014
                22 July 2014
                : 171
                : 5
                : 535-543
                Affiliations
                [1 ]Epidemiology Unit, Institute of Environmental Medicine (IMM), Karolinska Institutet , SE 171 77, Stockholm, Sweden
                [2 ]Center for Occupational and Environmental Medicine, Stockholm County Council , Stockholm, Sweden
                [3 ]Department of Medical Sciences, Uppsala University , SE-751 85, Uppsala, Sweden
                [4 ]Department of Clinical Sciences in Malmö, Clinical Research Centre, Lund University , SE-205 02, Malmö, Sweden
                [5 ]NTNU Institute of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim University Hospital , Trondheim, Norway
                [6 ]Department of Public Health and Caring Sciences, Uppsala University , SE-751 22, Uppsala, Sweden
                [7 ]Division of Endocrinology, Department of Medicine, Helsinki University Central Hospital, Research Program for Diabetes and Obesity and Folkhalsan Research Center, University of Helsinki , F-00014, Helsinki, Finland
                Author notes
                Correspondence should be addressed to B Rasouli bahareh.rasouli@ 123456ki.se
                Article
                EJE140403
                10.1530/EJE-14-0403
                4190680
                25117461
                3c721fef-2cda-4c48-8679-6aa2018ce8ff
                © 2014 The authors

                This work is licensed under a Creative Commons Attribution 3.0 Unported License

                History
                : 19 May 2014
                : 6 August 2014
                Categories
                Clinical Study

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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