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      Increase in serum albumin concentration is associated with prediabetes development and progression to overt diabetes independently of metabolic syndrome

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          Abstract

          Aim

          Serum albumin concentration is associated with both type 2 diabetes and metabolic syndrome (MetS). We sought to investigate whether baseline serum albumin and change in serum albumin could be independent risk factors for prediabetes in subjects without MetS. We further examined the effect of serum albumin on progression to overt diabetes in subjects who developed prediabetes.

          Methods

          Among 10,792 participants without diabetes and MetS who consecutively underwent yearly health check-ups over six years, 9,807 subjects without incident MetS were enrolled in this longitudinal retrospective study. The risk of developing prediabetes (impared fasting glucose or hemoglobin A1c) was analyzed according to baseline and percent change in serum albumin concentration using Cox regression analysis. Serial changes in serum albumin concentration were measured from baseline to one year before prediabetes diagnosis, and then from the time of prediabetes diagnosis to progression to overt diabetes or final follow-up.

          Results

          A total of 4,398 incident cases of prediabetes developed during 35,807 person-years (median 3.8 years). The hazard ratio for incident prediabetes decreased as percent change in serum albumin concentration (quartiles and per 1%) increased in a crude and fully adjusted model. However, baseline serum albumin concentration itself was not associated with prediabetic risk. Serum albumin levels kept increasing until the end of follow-up in prediabetic subjects who returned to normal glycemic status, whereas these measures did not change in prediabetic subjects who developed type 2 diabetes. Serum albumin concentration measured at the end of follow-up was the highest in the regression group, compared to the stationary (p = 0.014) or progression groups (p = 0.009).

          Conclusions

          Increase in serum albumin concentration might protect against early glycemic deterioration and progression to type 2 diabetes even in subjects without MetS.

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          Most cited references25

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          Metabolic Syndrome and Incident Diabetes

          OBJECTIVE—Our objective was to perform a quantitative review of prospective studies examining the association between the metabolic syndrome and incident diabetes. RESEARCH DESIGN AND METHODS—Using the title terms “diabetes” and “metabolic syndrome” in PubMed, we searched for articles published since 1998. RESULTS—Based on the results from 16 cohorts, we performed a meta-analysis of estimates of relative risk (RR) and incident diabetes. The random-effects summary RRs were 5.17 (95% CI 3.99–6.69) for the 1999 World Health Organization definition (ten cohorts); 4.45 (2.41–8.22) for the 1999 European Group for the Study of Insulin Resistance definition (four cohorts); 3.53 (2.84–4.39) for the 2001 National Cholesterol Education Program definition (thirteen cohorts); 5.12 (3.26–8.05) for the 2005 American Heart Association/National Heart, Lung, and Blood Institute definition (five cohorts); and 4.42 (3.30–5.92) for the 2005 International Diabetes Federation definition (nine cohorts). The fixed-effects summary RR for the 2004 National Heart, Lung, and Blood Institute/American Heart Association definition was 5.16 (4.43–6.00) (six cohorts). Higher number of abnormal components was strongly related to incident diabetes. Compared with participants without an abnormality, estimates of RR for those with four or more abnormal components ranged from 10.88 to 24.4. Limited evidence suggests fasting glucose alone may be as good as metabolic syndrome for diabetes prediction. CONCLUSIONS—The metabolic syndrome, however defined, has a stronger association with incident diabetes than that previously demonstrated for coronary heart disease. Its clinical value for diabetes prediction remains uncertain.
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            Prevalence of insulin resistance in metabolic disorders: the Bruneck Study.

            The prevalence of insulin resistance in the most common metabolic disorders is still an undefined issue. We assessed the prevalence rates of insulin resistance in subjects with impaired glucose tolerance (IGT), NIDDM, dyslipidemia, hyperuricemia, and hypertension as identified within the frame of the Bruneck Study. The study comprised an age- and sex-stratified random sample of the general population (n = 888; aged 40-79 years). Insulin resistance was estimated by homeostasis model assessment (HOMA(IR)), preliminarily validated against a euglycemic-hyperinsulinemic clamp in 85 subjects. The lower limit of the top quintile of HOMA(IR) distribution (i.e., 2.77) in nonobese subjects with no metabolic disorders (n = 225) was chosen as the threshold for insulin resistance. The prevalence of insulin resistance was 65.9% in IGT subjects, 83.9% in NIDDM subjects, 53.5% in hypercholesterolemia subjects, 84.2% in hypertriglyceridemia subjects, 88.1% in subjects with low HDL cholesterol, 62.8% in hyperuricemia subjects, and 58.0% in hypertension subjects. The prevalence of insulin resistance in subjects with the combination of glucose intolerance (IGT or NIDDM), dyslipidemia (hypercholesterolemia and/or hypertriglyceridemia and/or low HDL cholesterol), hyperuricemia, and hypertension (n = 21) was 95.2%. In isolated hypercholesterolemia, hypertension, or hyperuricemia, prevalence rates of insulin resistance were not higher than that in nonobese normal subjects. An appreciable number of subjects (n = 85, 9.6% of the whole population) was insulin resistant but free of IGT, NIDDM, dyslipidemia, hyperuricemia, and hypertension. These results from a population-based study documented that 1) in hypertriglyceridemia and a low HDL cholesterol state, insulin resistance is as common as in NIDDM, whereas it is less frequent in hypercholesterolemia, hyperuricemia, and hypertension; 2) the vast majority of subjects with multiple metabolic disorders are insulin resistant; 3) in isolated hypercholesterolemia, hyperuricemia, or hypertension, insulin resistance is not more frequent than can be expected by chance alone; and 4) in the general population, insulin resistance can be found even in the absence of any major metabolic disorders.
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              Markers of inflammation and prediction of diabetes mellitus in adults (Atherosclerosis Risk in Communities study): a cohort study.

              Type 2 diabetes mellitus and atherosclerotic cardiovascular disease have common antecedents. Since markers of inflammation predict coronary heart disease and are raised in patients with type 2 diabetes, we investigated whether they predict whether people will develop type 2 diabetes. 12,330 men and women, aged 45-64 years, were followed up for a mean of 7 years. We analysed the association between different markers of acute inflammation and subsequent diagnosis of diabetes. In a subgroup of 610 individuals selected originally for an unrelated atherosclerosis case-control study, we also investigated diabetes associations with total sialic acid and orosomucoid, haptoglobin, and alpha1-antitrypsin. 1335 individuals had a new diagnosis of diabetes. Adjusted odds ratios for developing diabetes for quartile extremes were 1.9 (95% CI 1.6-2.3) for raised white-cell count, 1.3 (1.0-1.5) for low serum albumin, and 1.2 (1.0-1.5) for raised fibrinogen. In the subgroup analysis, individuals with concentrations of orosomucoid and sialic acid of more than the median had odds ratios of 7.9 (2.6-23.7) and 3.7 (1.4-9.8), respectively. Adjustment for body-mass index and waist-to-hip ratio lessened the associations; those for white-cell count (1.5 [1.3-1.8]), orosomucoid (7.1 [2.1-23.7]), and sialic acid (2.8 [1.0-8.1]) remained significant. Markers of inflammation are associated with the development of diabetes in middle-aged adults. Although autoimmunity may partly explain these associations, they probably reflect the pathogenesis of type 2 diabetes.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                21 April 2017
                2017
                : 12
                : 4
                : e0176209
                Affiliations
                [1 ]Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Gangnam-gu, Seoul, Republic of Korea
                [2 ]Department of Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
                [3 ]Division of Endocrinology and Metabolism, Department of Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Republic of Korea
                [4 ]Department of Clinical Research Design & Evaluation, SAIHST, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
                Mathematical Institute, HUNGARY
                Author notes

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

                • Conceptualization: JHK.

                • Data curation: JCB SEL YBL.

                • Formal analysis: JEJ.

                • Investigation: SEL YBL.

                • Methodology: JEJ SMJ JHK.

                • Project administration: JHK.

                • Resources: JHK JHJ.

                • Software: JCB JHJ.

                • Supervision: JHK SMJ KYH MKL.

                • Validation: JHK MKL.

                • Visualization: JEJ KYH.

                • Writing – original draft: JEJ.

                • Writing – review & editing: JEJ JHK.

                Author information
                http://orcid.org/0000-0003-3465-8620
                Article
                PONE-D-17-01453
                10.1371/journal.pone.0176209
                5400249
                28430803
                de2d7fb2-5437-4882-995b-61cecb30663c
                © 2017 Jun et al

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

                History
                : 12 January 2017
                : 6 April 2017
                Page count
                Figures: 2, Tables: 5, Pages: 13
                Funding
                This research did not receive any specific funding from funding agencies in the public, commercial, or not-for-profit sectors.
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Proteins
                Albumins
                Serum Albumin
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Medicine and health sciences
                Diagnostic medicine
                Diabetes diagnosis and management
                HbA1c
                Biology and life sciences
                Biochemistry
                Proteins
                Hemoglobin
                HbA1c
                Medicine and Health Sciences
                Diagnostic Medicine
                Diabetes Diagnosis and Management
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Biology and Life Sciences
                Biochemistry
                Proteins
                Albumins
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Carbohydrate Metabolism
                Glucose Metabolism
                Medicine and Health Sciences
                Metabolic Disorders
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

                Uncategorized
                Uncategorized

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