The objective of this study was to determine potential added value of novel risk factors in predicting the development of type 2 diabetes beyond that provided by standard clinical risk factors.RESEARCH DESIGN AND METHODS
The Atherosclerosis Risk in Communities (ARIC) Study is a population-based prospective cohort study in four U.S. communities. Novel risk factors were either measured in the full cohort or in a case-control sample nested within the cohort. We started with a basic prediction model, previously validated in ARIC, and evaluated 35 novel risk factors by adding them independently to the basic model. The area under the curve (AUC), net reclassification index (NRI), and integrated discrimination index (IDI) were calculated to determine if each of the novel risk factors improved risk prediction.RESULTSThere were 1,457 incident cases of diabetes with a mean of >7.6 years of follow-up among 12,277 participants at risk. None of the novel risk factors significantly improved the AUC. Forced expiratory volume in 1 s was the only novel risk factor that resulted in a significant NRI (0.54%; 95% CI: 0.33-0.86%). Adiponectin, leptin, γ-glutamyl transferase, ferritin, intercellular adhesion molecule 1, complement C3, white blood cell count, albumin, activated partial thromboplastin time, factor VIII, magnesium, hip circumference, heart rate, and a genetic risk score each significantly improved the IDI, but net changes were small.CONCLUSIONS
Evaluation of a large panel of novel risk factors for type 2 diabetes indicated only small improvements in risk prediction, which are unlikely to meaningfully alter clinical risk reclassification or discrimination strategies.
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"Aligned with these results related to vascular biomarkers of endothelial dysfunction and T2D risk are a handful of studies that observed a positive association between other measures of endothelial dysfunction and increased risk of T2D  . Lastly, despite the evidence of an etiologic role of these markers of oxidative stress, inflammation and endothelial dysfunction in the development of T2D the addition of them to established clinical risk prediction equations did not improve predic- tion [38, 45, 46]. Highlighting the differences between etiology and prediction. "
[Show abstract][Hide abstract]ABSTRACT: Background
Oxidative stress, inflammation and endothelial dysfunction are interrelated factors in the etiology of cardiovascular disease, but their linkage to type 2 diabetes is less clear. We examined the association of these biomarkers with incident type 2 diabetes (T2D).
Analysis of 2339 participants in the community-based coronary artery risk development in young adults (CARDIA) study. Participants (age 40.1 ± 3.6 years, 44 % Black, 58 % women) were free of diabetes, and were followed 10 years. Cox regression was used to estimate hazard ratios (HRs) for incident T2D adjusting for the other biomarkers under study, demographic and lifestyle measures, dietary biomarkers, BMI (kg/m2) and metabolic syndrome components.
F2-isoprostanes and oxidized LDL (oxidative stress) were positively associated with incident T2D, but the associations were attenuated by adjustment for BMI. C-reactive protein was positively associated with T2D even with full adjustment: HR (95 % CI) = 2.21 (1.26–3.88) for quartile 4 (Q4) v. quartile 1 (Q1). The HR (95 % CI) for T2D for biomarkers of endothelial dysfunction ICAM-1 and E-selectin for Q4 v. Q1 were 1.64 (0.96–2.81) and 1.68 (1.04–2.71) respectively, with full adjustment. Including these two markers in a common risk score incorporating BMI and clinical measures improved the prediction probability of T2D: relative risk for the average person classified up compared to the average person classified down: 1.09, (1.06–1.13), P < 0.0001.
Biomarkers of inflammation and endothelial dysfunction were positively associated with incident T2D. ICAM-1 and E-selectin add to the prediction of T2D beyond a common risk score.
Full-text · Article · Dec 2016 · Cardiovascular Diabetology
"To provide improved predictive power over conventional risk factors, genetic testing must be sensitive and specific in discriminating subjects who will develop the disease on follow up from those who will not73. In this line, it has been recognized that genetic variants so far identified do not substantially improve the discriminative accuracy of disease prediction based on conventional risk factors74. Even genetic models incorporating thousands of additional putative common variants are likely to offer limited improvement73. "
[Show abstract][Hide abstract]ABSTRACT: Type 2 diabetes is one of the most common complex diseases, of which considerable efforts have been made to unravel the pathophysiological mechanisms. Recently, large-scale genome-wide association (GWA) studies have successfully identified genetic loci robustly associated with type 2 diabetes by searching susceptibility variants across the entire genome in an unbiased, hypothesis-free manner. The number of loci has climbed from just three in 2006 to approximately 70 today. For the common type 2 diabetes-associated variants, three features have been noted. First, genetic impacts of individual variants are generally modest; mostly, allelic odds ratios range between 1.06 and 1.20. Second, most of the loci identified to date are not in or near obvious candidate genes, but some are often located in the intergenic regions. Third, although the number of loci is limited, there might be some population specificity in type 2 diabetes association. Although we can estimate a single or a few target genes for individual loci detected in GWA studies by referring to the data for experiments in vitro, biological function remains largely unknown for a substantial part of such target genes. Nevertheless, new biology is arising from GWA study discoveries; for example, genes implicated in β-cell dysfunction are over-represented within type 2 diabetes-associated regions. Toward translational advances, we have just begun to face new challenges - elucidation of multifaceted (i.e., molecular, cellular and physiological) mechanistic insights into disease biology by considering interaction with the environment. The present review summarizes recent advances in the genetics of type 2 diabetes, together with its realistic potential.
[Show abstract][Hide abstract]ABSTRACT: Traditional risk factors, particularly obesity, do not completely explain the excess risk of diabetes among African Americans compared to whites.
We sought to quantify the impact of recently identified, non-traditional risk factors on the racial disparity in diabetes risk.
Prospective cohort study.
We analyzed data from 2,322 African-American and 8,840 white participants without diabetes at baseline from the Atherosclerosis Risk in Communities (ARIC) Study.
We used Cox regression to quantify the association of incident diabetes by race over 9 years of in-person and 17 years of telephone follow-up, adjusting for traditional and non-traditional risk factors based on literature search. We calculated the mediation effect of a covariate as the percent change in the coefficient of race in multivariate models without and with the covariate of interest; 95 % confidence intervals (95 % CI) were calculated using boot-strapping.
African American race was independently associated with incident diabetes. Body mass index (BMI), forced vital capacity (FVC), systolic blood pressure, and serum potassium had the greatest explanatory effects for the difference in diabetes risk between races, with mediation effects (95 % CI) of 22.0 % (11.7 %, 42.2 %), 21.7 %(9.5 %, 43.1 %), 17.9 % (10.2 %, 37.4 %) and 17.7 % (8.2 %, 39.4 %), respectively, during 9 years of in-person follow-up, with continued effect over 17 years of telephone follow-up.
Non-traditional risk factors, particularly FVC and serum potassium, are potential mediators of the association between race and diabetes risk. They should be studied further to verify their importance and to determine if they mark causal relationships that can be addressed to reduce the racial disparity in diabetes risk.
No preview · Article · Aug 2013 · Journal of General Internal Medicine