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      Plasma Branched-Chain Amino Acids and Risk of Incident Type 2 Diabetes: Results from the PREVEND Prospective Cohort Study

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          Abstract

          Plasma branched-chain amino acids (BCAAs) are linked to metabolic disease, but their relevance for prediction of type 2 diabetes development is unclear. We determined the association of plasma BCAAs with type 2 diabetes risk in the prevention of renal and vascular end-stage disease (PREVEND) cohort. The BCAAs were measured by means of nuclear magnetic resonance spectroscopy. We evaluated the prospective associations of BCAAs with type 2 diabetes in 6244 subjects. The BCAAs were positively associated with HOMA-IR after multivariable adjustment ( p < 0.0001). During median follow-up for 7.5 years, 301 cases of type 2 diabetes were ascertained. The Kaplan-Meier plot demonstrated that patients in the highest BCAA quartile presented a higher risk ( p log-rank < 0.001). Cox regression analyses revealed a positive association between BCAA and type 2 diabetes; the hazard ratio (HR) for the highest quartile was 6.15 (95% CI: 4.08, 9.24, p < 0.0001). After adjustment for multiple clinical and laboratory variables, the association remained (HR 2.80 (95% CI: 1.72, 4.53), p < 0.0001). C-statistics, Net reclassification improvement, and −2 log likelihood were better after adding BCAAs to the traditional risk model ( p = 0.01 to <0.001). In conclusions, high concentrations of BCAAs associate with insulin resistance and with increased risk of type 2 diabetes. This association is independent of multiple risk factors, HOMA-IR and β cell function.

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          Use and misuse of the receiver operating characteristic curve in risk prediction.

          The c statistic, or area under the receiver operating characteristic (ROC) curve, achieved popularity in diagnostic testing, in which the test characteristics of sensitivity and specificity are relevant to discriminating diseased versus nondiseased patients. The c statistic, however, may not be optimal in assessing models that predict future risk or stratify individuals into risk categories. In this setting, calibration is as important to the accurate assessment of risk. For example, a biomarker with an odds ratio of 3 may have little effect on the c statistic, yet an increased level could shift estimated 10-year cardiovascular risk for an individual patient from 8% to 24%, which would lead to different treatment recommendations under current Adult Treatment Panel III guidelines. Accepted risk factors such as lipids, hypertension, and smoking have only marginal impact on the c statistic individually yet lead to more accurate reclassification of large proportions of patients into higher-risk or lower-risk categories. Perfectly calibrated models for complex disease can, in fact, only achieve values for the c statistic well below the theoretical maximum of 1. Use of the c statistic for model selection could thus naively eliminate established risk factors from cardiovascular risk prediction scores. As novel risk factors are discovered, sole reliance on the c statistic to evaluate their utility as risk predictors thus seems ill-advised.
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            Lipoprotein particle analysis by nuclear magnetic resonance spectroscopy.

            Laboratory measurements of plasma lipids (principally cholesterol and triglycerides) and lipoprotein lipids (principally low-density lipoprotein [LDL] and low-density lipoprotein [HDL] cholesterol) are the cornerstone of the clinical assessment and management of atherosclerotic cardiovascular disease (CVD) risk. LDL particles, and to a lesser extent very-low-density lipoprotein [VLDL] particles, cause atherosclerosis, whereas HDL particles prevent or reverse this process through reverse cholesterol transport. The overall risk for CVD depends on the balance between the "bad" LDL (and VLDL) and "good" HDL particles. Direct assessment of lipoprotein particle numbers us now possible through nuclear magnetic resonance spectroscopic analysis.
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              Branched-chain amino acid levels are associated with improvement in insulin resistance with weight loss.

              Insulin resistance (IR) improves with weight loss, but this response is heterogeneous. We hypothesised that metabolomic profiling would identify biomarkers predicting changes in IR with weight loss. Targeted mass spectrometry-based profiling of 60 metabolites, plus biochemical assays of NEFA, β-hydroxybutyrate, ketones, insulin and glucose were performed in baseline and 6 month plasma samples from 500 participants who had lost ≥4 kg during Phase I of the Weight Loss Maintenance (WLM) trial. Homeostatic model assessment of insulin resistance (HOMA-IR) and change in HOMA-IR with weight loss (∆HOMA-IR) were calculated. Principal components analysis (PCA) and mixed models adjusted for race, sex, baseline weight, and amount of weight loss were used; findings were validated in an independent cohort of patients (n = 22). Mean weight loss was 8.67 ± 4.28 kg; mean ∆HOMA-IR was -0.80 ± 1.73, range -28.9 to 4.82). Baseline PCA-derived factor 3 (branched chain amino acids [BCAAs] and associated catabolites) correlated with baseline HOMA-IR (r = 0.50, p < 0.0001) and independently associated with ∆HOMA-IR (p < 0.0001). ∆HOMA-IR increased in a linear fashion with increasing baseline factor 3 quartiles. Amount of weight loss was only modestly correlated with ∆HOMA-IR (r = 0.24). These findings were validated in the independent cohort, with a factor composed of BCAAs and related metabolites predicting ∆HOMA-IR (p = 0.007). A cluster of metabolites comprising BCAAs and related analytes predicts improvement in HOMA-IR independent of the amount of weight lost. These results may help identify individuals most likely to benefit from moderate weight loss and elucidate novel mechanisms of IR in obesity.
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                04 December 2018
                December 2018
                : 7
                : 12
                : 513
                Affiliations
                [1 ]Department of Internal Medicine, UMCG, University of Groningen, 9713 GZ Groningen, The Netherlands; m.c.j.oste@ 123456umcg.nl (M.C.J.O.); l.m.kieneker@ 123456umcg.nl (L.M.K.); s.j.l.bakker@ 123456umcg.nl (S.J.L.B.)
                [2 ]Department of Endocrinology, UMCG, University of Groningen, 9713 GZ Groningen, The Netherlands; e.g.gruppen@ 123456umcg.nl (E.G.G.); r.p.f.dullaart@ 123456umcg.nl (R.P.F.D.)
                [3 ]Laboratory Corporation of America Holdings (LabCorp), Morrisville, NC 27560, USA; wolakdj@ 123456labcorp.com (J.W.-D.); otvosj@ 123456labcorp.com (J.D.O.); connem5@ 123456labcorp.com (M.A.C.)
                Author notes
                [* ]Correspondence: j.l.flores.guerrero@ 123456umcg.nl ; Tel.: +31-5036-10137
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-6094-2206
                https://orcid.org/0000-0001-9238-1397
                https://orcid.org/0000-0003-3356-6791
                Article
                jcm-07-00513
                10.3390/jcm7120513
                6306832
                30518023
                da231939-0402-4fb3-8539-1f27b4baed83
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 09 November 2018
                : 27 November 2018
                Categories
                Article

                branched-chain amino acids,risk factor,type 2 diabetes,insulin resistance

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