Novel risk factors and the prediction of type 2 diabetes in the Atherosclerosis Risk in Communities (ARIC) study

Clonal hematopoiesis as a novel risk factor for type 2 diabetes mellitus in patients with hypercholesterolemia

Introduction

Clonal hematopoiesis of indeterminate potential (CHIP) is associated with atherosclerosis and cardiovascular disease. It has been suggested that CHIP may be related to diabetes, so we investigated the association between CHIP and new-onset type 2 diabetes.

Methods

This study included 4,047 subjects aged >=40 years without diabetes. To detect CHIP, targeted gene sequencing of genomic DNA from peripheral blood cells was performed. The incidence of new-onset type 2 diabetes during the follow-up period was evaluated.

Results

Of the total subjects, 635 (15.7%) had CHIP. During the median follow-up of 5.1 years, the incidence of new-onset diabetes was significantly higher in CHIP carriers than in subjects without CHIP (11.8% vs. 9.1%, p = 0.039). In a univariate analysis, CHIP significantly increased the risk of new-onset diabetes (HR 1.32, 95% CI 1.02-1.70, p = 0.034), but in a multivariate analysis, it was not significant. The CHIP-related risk of new onset diabetes differed according to LDL cholesterol level. In the hyper-LDL cholesterolemia group, CHIP significantly increased the risk of diabetes (HR 1.64, 95% CI 1.09-2.47, p = 0.018), but it did not increase the risk in the non-hyper-LDL cholesterolemia group. The subjects with CHIP and hyper-LDL-cholesterolemia had approximately twice the risk of diabetes than subjects without CHIP and with low LDL cholesterol (HR 2.05, 95% CI 1.40-3.00, p < 0.001).

Conclusion

The presence of CHIP was a significant risk factor for new-onset type 2 diabetes, especially in subjects with high LDL cholesterol. These results show the synergism between CHIP and high LDL cholesterol as a high-risk factor for diabetes.

Association of Complement C3 With Incident Type 2 Diabetes and the Mediating Role of BMI: A 10-Year Follow-Up Study

CONTEXT

Impairment of immune and inflammatory homeostasis is reported to be one of the causal factors of diabetes. However, the association of complement C3 levels with incident diabetes in humans remains unclear.

OBJECTIVE

This study aimed to examine the association between C3 levels and incident type 2 diabetes mellitus (T2DM), and further explore the potential mediating role of body mass index (BMI) in C3-T2DM associations.

METHODS

We determined serum C3 levels of 2662 nondiabetic middle-aged and elderly (64.62 ± 7.25 years) individuals from the Dongfeng-Tongji cohort at baseline. Cox regression was employed to examine the incidence of T2DM in relationship to C3 levels during 10 years of follow-up. Mediation analysis was further applied to assess potential effect of BMI on the C3-T2DM associations.

RESULTS

Overall, 711 (26.7%) participants developed T2DM during 23 067 person-years of follow-up. Higher serum C3 was significantly associated with higher risk of incident T2DM after full adjustment (HR [95% CI] = 1.16 [1.05, 1.27]; per SD higher). Compared with the first quartile of C3 levels, the HR in the fourth quartile was 1.52 (95% CI = [1.14, 2.02]; Ptrend = 0.029). Robust significant linear dose-response relationship was observed between C3 levels and BMI (Poverall < 0.001, Pnonlinear = 0.96). Mediation analyses indicated that BMI might mediate 41.0% of the associations between C3 and T2DM.

CONCLUSION

The present prospective study revealed that C3 could be an early biomarker for incident T2DM, and that BMI might play a potential mediating role in the C3-T2DM associations, which provided clues for the pathogenesis of diabetes.

Predictive model of diabetes mellitus in patients with idiopathic inflammatory myopathies

Objectives

Cardiovascular diseases are the common cause of death in patients with idiopathic inflammatory myopathies (IIMs). Diabetes mellitus was associated with higher cardiovascular mortality, but few studies focused on the risk of diabetes mellitus in IIMs patients. Our study is aimed at developing a predictive model of diabetes mellitus in IIMs patients.

Methods

A total of 354 patients were included in this study, of whom 35 (9.9%) were diagnosed as new-onset diabetes mellitus. The predictive nomogram was drawn based on the features selected by least absolute shrinkage and selection operator (LASSO) regression, univariate logistic regression, multivariable logistic regression, and clinical relationship. The discriminative capacity of the nomogram was assessed by C-index, calibration plot, and clinical usefulness. The predictive model was verified by the bootstrapping validation.

Results

The nomogram mainly included predictors such as age, gender, hypertension, uric acid, and serum creatinine. This predictive model demonstrated good discrimination and calibration in primary cohort (C-index=0.762, 95% CI: 0.677-0.847) and validation cohort (C-index=0.725). Decision curve analysis indicated that this predictive model was clinically useful.

Conclusions

Clinicians can assess the risk of diabetes mellitus in IIMs patients by using this prediction model, and preventive measures should be taken early for high-risk patients, ultimately reducing the adverse cardiovascular prognosis.

Magnesium Supplementation Is Associated with a Lower Cardio-Metabolic Risk in Patients Submitted to Bariatric Surgery

PURPOSE

Literature has demonstrated an inverse relation between magnesium (Mg) consumption and development of type 2 diabetes mellitus (T2DM), hypertension (HT), and dyslipidemia. After bariatric surgery (BS), micronutrients deficiencies are common, it being important to ensure appropriate supplementation. There is no recommendation about Mg supplementation and to our knowledge, its effect has not been studied to date. Our aim was to evaluate the effect of Mg supplementation in cardio-metabolic risk factors on post-bariatric patients.

MATERIALS AND METHODS

A retrospective observational study of patients with obesity who underwent BS was performed. Data was assessed preoperatively and yearly (4-year follow-up).

RESULTS

A total of 3363 patients were included. In the first year of follow-up, 79.8% (n = 2123) of the patients were supplemented with Mg, with evidence of slightly decreased percentages in the following years. Mg deficiency (serum Mg < 1.52 mEq/L) was more common among patients who were not supplemented during each year of follow-up (p < 0.05). Among those who underwent Mg supplementation, the percentage of T2DM, HT, or low-density lipoprotein cholesterol (LDL-C) > 130 mg/dL was significantly lower. In the first year post-surgery, the supplementation group had a lower risk of T2DM (OR = 0.545, p < 0.0001), LDL-C > 130 mg/dL (OR = 0.612, p < 0.0001), and HT (OR = 0.584, p < 0.0001). The OR for having these metabolic comorbidities persisted lower during the 4 years' follow-up. Patients who had Mg deficiency had higher prevalence of T2DM and HT.

CONCLUSION

Mg supplementation seems to have a protective effect on the development of T2DM, HT, and LDL-C > 130 mg/dL in post-bariatric patients.

Circulating Protein Signatures and Causal Candidates for Type 2 Diabetes

The increasing prevalence of type 2 diabetes poses a major challenge to societies worldwide. Blood-based factors like serum proteins are in contact with every organ in the body to mediate global homeostasis and may thus directly regulate complex processes such as aging and the development of common chronic diseases. We applied a data-driven proteomics approach, measuring serum levels of 4,137 proteins in 5,438 elderly Icelanders, and identified 536 proteins associated with prevalent and/or incident type 2 diabetes. We validated a subset of the observed associations in an independent case-control study of type 2 diabetes. These protein associations provide novel biological insights into the molecular mechanisms that are dysregulated prior to and following the onset of type 2 diabetes and can be detected in serum. A bidirectional two-sample Mendelian randomization analysis indicated that serum changes of at least 23 proteins are downstream of the disease or its genetic liability, while 15 proteins were supported as having a causal role in type 2 diabetes.

Agreement between Type 2 Diabetes Risk Scales in a Caucasian Population: A Systematic Review and Report

Early detection of people with undiagnosed type 2 diabetes (T2D) is an important public health concern. Several predictive equations for T2D have been proposed but most of them have not been externally validated and their performance could be compromised when clinical data is used. Clinical practice guidelines increasingly incorporate T2D risk prediction models as they support clinical decision making. The aims of this study were to systematically review prediction scores for T2D and to analyze the agreement between these risk scores in a large cross-sectional study of white western European workers. A systematic review of the PubMed, CINAHL, and EMBASE databases and a cross-sectional study in 59,042 Spanish workers was performed. Agreement between scores classifying participants as high risk was evaluated using the kappa statistic. The systematic review of 26 predictive models highlights a great heterogeneity in the risk predictors; there is a poor level of reporting, and most of them have not been externally validated. Regarding the agreement between risk scores, the DETECT-2 risk score scale classified 14.1% of subjects as high-risk, FINDRISC score 20.8%, Cambridge score 19.8%, the AUSDRISK score 26.4%, the EGAD study 30.3%, the Hisayama study 30.9%, the ARIC score 6.3%, and the ITD score 3.1%. The lowest agreement was observed between the ITD and the NUDS study derived score (κ = 0.067). Differences in diabetes incidence, prevalence, and weight of risk factors seem to account for the agreement differences between scores. A better agreement between the multi-ethnic derivate score (DETECT-2) and European derivate scores was observed. Risk models should be designed using more easily identifiable and reproducible health data in clinical practice.

Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women

BACKGROUND

Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.

METHODS

Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).

RESULTS

The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk (P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P=0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P=0.032).

CONCLUSION

A composite score of blood biomarkers improved T2DM risk prediction among Chinese.

Thyroid diseases increased the risk of type 2 diabetes mellitus: A nation-wide cohort study

Thyroid function may alter carbohydrate metabolism via influence of insulin, which may in terms of derangement of thyroid function and insulin function result in the development of type 2 diabetes mellitus (T2D). We investigated the association of thyroid disorders with T2D by a cohort study of the Taiwan nationwide health insurance database.A sub-dataset of the National Health Insurance Research Database (NHIRD) was used in this study. The thyroid disease (both hyper- and hypo-thyroidism) group was chosen from patients older than 18 years and newly diagnosed between 2000 and 2012. The control group consisted of randomly selected patients who never been diagnosed with thyroid disease and 4-fold size frequency matched with the thyroid disease group. The event of this cohort was T2D (ICD-9-CM 250.x1, 250.x2). Primary analysis was performed by comparing the thyroid disease group to the control group and the second analysis was performed by comparing the hyperthyroidism subgroup, hypothyroidism subgroup, and control group.The occurrence of T2D in the thyroid disease group was higher than the control group with hazard ratio (HR) of 1.23 [95% confidence interval (CI) = 1.16-1.31]. Both hyperthyroidism and hypothyroidism were significantly higher than control. Significantly higher HR was also seen in female patients, age category of 18 to 39-year-old (y/o) and 40 to 64 y/o subgroups. Higher occurrence of T2D was also seen in thyroid disease patients without comorbidity than in the control group with HR of 1.47 (95% CI = 1.34-1.60). The highest HR was found in the half-year follow-up.There was a relatively high risk of T2D development in patients with thyroid dysfunctions, especially in the period of 0.5 to 1 year after presentation of thyroid dysfunctions. The results suggest performing blood sugar tests in patients with thyroid diseases for early detection and treatment of T2D.

A genetic stochastic process model for genome-wide joint analysis of biomarker dynamics and disease susceptibility with longitudinal data

Unraveling the underlying biological mechanisms or pathways behind the effects of genetic variations on complex diseases remains one of the major challenges in the post-GWAS (where GWAS is genome-wide association study) era. To further explore the relationship between genetic variations, biomarkers, and diseases for elucidating underlying pathological mechanism, a huge effort has been placed on examining pleiotropic and gene-environmental interaction effects. We propose a novel genetic stochastic process model (GSPM) that can be applied to GWAS and jointly investigate the genetic effects on longitudinally measured biomarkers and risks of diseases. This model is characterized by more profound biological interpretation and takes into account the dynamics of biomarkers during follow-up when investigating the hazards of a disease. We illustrate the rationale and evaluate the performance of the proposed model through two GWAS. One is to detect single nucleotide polymorphisms (SNPs) having interaction effects on type 2 diabetes (T2D) with body mass index (BMI) and the other is to detect SNPs affecting the optimal BMI level for protecting from T2D. We identified multiple SNPs that showed interaction effects with BMI on T2D, including a novel SNP rs11757677 in the CDKAL1 gene (P = 5.77 × 10-7 ). We also found a SNP rs1551133 located on 2q14.2 that reversed the effect of BMI on T2D (P = 6.70 × 10-7 ). In conclusion, the proposed GSPM provides a promising and useful tool in GWAS of longitudinal data for interrogating pleiotropic and interaction effects to gain more insights into the relationship between genes, quantitative biomarkers, and risks of complex diseases.

Effect of magnesium supplementation on type 2 diabetes associated cardiovascular risk factors: a systematic review and meta-analysis

BACKGROUND

Cardiovascular disorders remain the leading cause of death in type 2 diabetic patients. In the present study, a systematic review and a meta-analysis of randomised controlled trials (RCTs) were conducted aiming to evaluate the effect of magnesium supplementation on type 2 diabetes (T2D) associated cardiovascular risk factors in both diabetic and nondiabetic individuals.

METHODS

PubMed, Scopus, Cochrane, Web of Science and Google Scholar databases were searched from inception to 30 June 2016 aiming to identify RCTs evaluating the effect of magnesium supplementation on T2D associated cardiovascular risk factors. The data were analysed using a random effect model with inverse variance methodology. Sensitivity analysis, risk of bias analysis, subgroup analysis, meta-regression and publication bias analysis were also conducted for the included studies using standard methods.

RESULTS

Following magnesium supplementation, a significant improvement was observed in fasting plasma glucose (FPG) [weighted mean difference (WMD) = -4.641 mg dL-1 , 95% confidence interval (CI) = -7.602, -1.680, P = 0.002], high-density lipoprotein (HDL) (WMD = 3.197 mg dL-1 , 95% CI = 1.455, 4.938, P < 0.001), low-density lipoprotein (LDL) (WMD = -10.668 mg dL-1 , 95% CI = -19.108, -2.228, P = 0.013), plasma triglycerides (TG) (WMD = -15.323 mg dL-1 , 95% CI = -28.821, -1.826, P = 0.026) and systolic blood pressure (SBP) (WMD = -3.056 mmHg, 95% CI = -5.509, -0.603, P = 0.015). During subgroup analysis, a more beneficial effect of magnesium supplementation was observed in diabetic subjects with hypomagnesaemia.

CONCLUSIONS

Magnesium supplementation can produce a favourable effect on FPG, HDL, LDL, TG and SBP. Therefore, magnesium supplementation may decrease the risk T2D associated cardiovascular diseases, although future large RCTs are needed for making robust guidelines for clinical practice.

Individual- and Area-Level SES in Diabetes Risk Prediction: The Multi-Ethnic Study of Atherosclerosis

INTRODUCTION

The purpose of this study was to evaluate if adding SES to risk prediction models based upon traditional risk factors improves the prediction of diabetes.

METHODS

Risk prediction models without and with individual- and area-level SES predictors were compared using the prospective Multi-Ethnic Study of Atherosclerosis. Cox proportional hazards models were utilized to estimate hazard ratios for SES predictors and to generate 10-year predicted risks for 5,021 individuals without diabetes at baseline followed from 2000 to 2012. C-statistics were used to compare model discrimination, and the proportion of individuals reclassified into higher or lower risk categories with the addition of SES predictors was calculated. The accuracy of risk prediction by SES was assessed by comparing observed and predicted risks across tertiles of the SES variables. Statistical analyses were performed in 2015-2016.

RESULTS

Over a median of 9.2 years of follow-up, 615 individuals developed diabetes. Individual- and area-level SES variables did not significantly improve model discrimination or reclassify substantial numbers of individuals across risk categories. Models without SES predictors generally underestimated risk for low-SES individuals or individuals residing in low-SES areas (underestimates ranging from 0.31% to 1.07%) and overestimated risk for high-SES individuals or individuals residing in high-SES areas (overestimates ranging from 0.70% to 1.30%), and the addition of SES variables largely mitigated these differences.

CONCLUSIONS

Standard diabetes risk models may underestimate risk for low-SES individuals and overestimate risk for those of high SES. Adding SES predictors helps correct this systematic misestimation, but may not improve model discrimination.

Lipoprotein insulin resistance score and risk of incident diabetes during extended follow-up of 20 years: The Women's Health Study

BACKGROUND

Type II diabetes (T2D) is preceded by prolonged insulin resistance and relative insulin deficiency incompletely captured by glucose metabolism parameters, high-density lipoprotein (HDL) cholesterol and triglycerides.

OBJECTIVE

Whether lipoprotein insulin resistance (LPIR) score, a metabolomic marker, is associated with incident diabetes and improves risk reclassification over traditional markers on extended follow-up.

METHODS

Among 25,925 nondiabetic women aged 45 years or older, LPIR was measured by nuclear magnetic resonance spectroscopy as a weighted score of very low density lipoprotein, low-density lipoprotein, and HDL particle sizes, and their subsets concentrations. We run adjusted cox regression models for LPIR with incident T2D (20.4 years median follow-up).

RESULTS

Adjusting for demographics, body mass index, life style factors, blood pressure, and T2D family history, the LPIR hazard ratio for T2D (hazard ratio [HR] per standard deviation, 95% confidence interval) was 1.95 (1.85, 2.06). Further adjusting for HbA1c, C-reactive protein, triglycerides, HDL and low-density lipoprotein cholesterol, LPIR HR was attenuated to 1.41 (1.31, 1.53) and had the strongest association with T2D after HbA1C in mutually adjusted models. The association persisted even in those with optimal clinical profiles, adjusted HR per standard deviation 1.91 (1.17, 3.13). In participants deemed at intermediate T2D risk by the Framingham Offspring T2D score, LPIR led to a net reclassification of 0.145 (0.117, 0.175).

CONCLUSION

In middle-aged or older healthy women followed prospectively for over 20 years, LPIR was robustly associated with incident T2D, including among those with an optimal clinical metabolic profile. LPIR improved T2D risk classification and may guide early and targeted prevention strategies.

Utility of existing diabetes risk prediction tools for young black and white adults: Evidence from the Bogalusa Heart Study

AIMS

To evaluate several adult diabetes risk calculation tools for predicting the development of incident diabetes and pre-diabetes in a bi-racial, young adult population.

METHODS

Surveys beginning in young adulthood (baseline age ≥18) and continuing across multiple decades for 2122 participants of the Bogalusa Heart Study were used to test the associations of five well-known adult diabetes risk scores with incident diabetes and pre-diabetes using separate Cox models for each risk score. Racial differences were tested within each model. Predictive utility and discrimination were determined for each risk score using the Net Reclassification Index (NRI) and Harrell's c-statistic.

RESULTS

All risk scores were strongly associated (p<.0001) with incident diabetes and pre-diabetes. The Wilson model indicated greater risk of diabetes for blacks versus whites with equivalent risk scores (HR=1.59; 95% CI 1.11-2.28; p=.01). C-statistics for the diabetes risk models ranged from 0.79 to 0.83. Non-event NRIs indicated high specificity (non-event NRIs: 76%-88%), but poor sensitivity (event NRIs: -23% to -3%).

CONCLUSIONS

Five diabetes risk scores established in middle-aged, racially homogenous adult populations are generally applicable to younger adults with good specificity but poor sensitivity. The addition of race to these models did not result in greater predictive capabilities. A more sensitive risk score to predict diabetes in younger adults is needed.

Serum fibroblast growth factor 21 is a superior biomarker to other adipokines in predicting incident diabetes

OBJECTIVE

Fibroblast growth factor 21 (FGF21) improves glucose and lipid metabolism, but high circulating levels are found in type 2 diabetes, suggesting FGF21 resistance. Serum FGF21 predicts incident diabetes, but its performance compared to established and emerging predictors is not known. We aimed to study the performance of FGF21 in diabetes prediction, relative to other adipokines and established risk factors including 2-h plasma glucose (2hG) during the oral glucose tolerance test (OGTT).

DESIGN/PARTICIPANTS/MEASUREMENTS

We studied 1380 nondiabetic subjects from the Hong Kong Cardiovascular Risk Factor Prevalence Study using the second visit (2000-2004) as baseline when serum levels of FGF21 and other adipokines were measured. Glycaemic status was assessed by OGTT. Incident diabetes was defined as fasting glucose level (FG) ≥ 7 mmol/l or 2hG ≥ 11·1 mmol/l or use of antidiabetic agents, at subsequent visits.

RESULTS

A total of 123 participants developed diabetes over 9·0 years (median). On multivariable logistic regression analysis, FGF21 (P = 0·003), adipocyte fatty acid-binding protein (P = 0·003) and adiponectin (P = 0·035) were independent predictors of incident diabetes. FGF21 had the best change in log likelihood when added to a diabetes prediction model (DP) based on age, family history, smoking, hypertension, BMI, dyslipidaemia and FG. It also improved the area under ROC curve (AUROC) of diabetes prediction (DP) from 0·797 to 0·819 (P = 0·0072), rendering its performance comparable to the 'DP + 2hG' model (AUROC=0·838, P = 0·19).

CONCLUSIONS

As a biomarker for diabetes prediction, serum FGF21 appeared to be superior to other adipokines and, on its own, could be considered as an alternative to the OGTT.

Gamma-glutamyltransferase-friend or foe within?

Gamma-glutamyltransferase (GGT) is a liver enzyme, which is located on the plasma membranes of most cells and organ tissues, but more commonly in hepatocytes, and is routinely used in clinical practice to help indicate liver injury and as a marker of excessive alcohol consumption. Among the liver enzymes, important advances have especially been made in understanding the physiological functions of GGT. The primary role of GGT is the extracellular catabolism of glutathione, the major thiol antioxidant in mammalian cells, which plays a relevant role in protecting cells against oxidants produced during normal metabolism; GGT, therefore, plays an important role in cellular defence. Beyond its physiological functions, circulating serum GGT has been linked to a remarkable array of chronic conditions and diseases, which include nonalcoholic fatty liver disease, vascular and nonvascular diseases and mortality outcomes. This review summarizes the available epidemiological and genetic evidence for the associations between GGT and these adverse outcomes, the postulated biologic mechanisms underlying these associations, outlines areas of outstanding uncertainty and the implications for clinical practice.

Defining a Contemporary Ischemic Heart Disease Genetic Risk Profile Using Historical Data

BACKGROUND

Continued reductions in morbidity and mortality attributable to ischemic heart disease (IHD) require an understanding of the changing epidemiology of this disease. We hypothesized that we could use genetic correlations, which quantify the shared genetic architectures of phenotype pairs and extant risk factors from a historical prospective study to define the risk profile of a contemporary IHD phenotype.

METHODS AND RESULTS

We used 37 phenotypes measured in the ARIC study (Atherosclerosis Risk in Communities; n=7716, European ancestry subjects) and clinical diagnoses from an electronic health record (EHR) data set (n=19 093). All subjects had genome-wide single-nucleotide polymorphism genotyping. We measured pairwise genetic correlations (rG) between the ARIC and EHR phenotypes using linear mixed models. The genetic correlation estimates between the ARIC risk factors and the EHR IHD were modestly linearly correlated with hazards ratio estimates for incident IHD in ARIC (Pearson correlation [r]=0.62), indicating that the 2 IHD phenotypes had differing risk profiles. For comparison, this correlation was 0.80 when comparing EHR and ARIC type 2 diabetes mellitus phenotypes. The EHR IHD phenotype was most strongly correlated with ARIC metabolic phenotypes, including total:high-density lipoprotein cholesterol ratio (rG=-0.44, P=0.005), high-density lipoprotein (rG=-0.48, P=0.005), systolic blood pressure (rG=0.44, P=0.02), and triglycerides (rG=0.38, P=0.02). EHR phenotypes related to type 2 diabetes mellitus, atherosclerotic, and hypertensive diseases were also genetically correlated with these ARIC risk factors.

CONCLUSIONS

The EHR IHD risk profile differed from ARIC and indicates that treatment and prevention efforts in this population should target hypertensive and metabolic disease.

Oxidative stress, inflammation, endothelial dysfunction and incidence of type 2 diabetes

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).

METHODS

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/m(2)) and metabolic syndrome components.

RESULTS

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.

CONCLUSIONS

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.

Discriminatory value of alanine aminotransferase for diabetes prediction: the Insulin Resistance Atherosclerosis Study

AIMS

To examine the incremental usefulness of adding alanine aminotransferase to established risk factors for predicting future diabetes.

METHODS

The study population of the Insulin Resistance Atherosclerosis Study included 724 people aged 40-69 years. We excluded people who had excessive alcohol intake or were treated with lipid-lowering agents. Incident diabetes was assessed after a mean follow-up period of 5.2 years.

RESULTS

Alanine aminotransferase had a non-linear relationship with incident diabetes (Wald chi-squared test, P < 0.001; P for linearity = 0.005) independent of demographic variables, family history of diabetes, BMI and fasting glucose; therefore, we used Youden's J statistic to dichotomize alanine aminotransferase [threshold ≥ 0.43 μkat/L ( ≥ 26 IU/l)]. Dichotomized alanine aminotransferase increased the area under the receiver-operating characteristic curve (0.805 vs. 0.823; P = 0.007) of a model that included demographic variables, family history of diabetes, BMI and fasting glucose as independent variables. The net reclassification improvement was 9.6% (95% CI 1.8-17.4; P = 0.016), and the integrated discrimination improvement was 0.031 (95% CI 0.011-0.050; P = 0.002). Dichotomized alanine aminotransferase reclassified a net of 9.6% of individuals more appropriately.

CONCLUSIONS

Alanine aminotransferase may be useful for classifying individuals who are at risk of future diabetes after accounting for the effect of other risk factors, including family history, adiposity and plasma glucose.

Association between liver enzymes and incident type 2 diabetes in Singapore Chinese men and women

AIMS

To assess the association between liver enzymes and the risk of type 2 diabetes (T2D) in a Chinese population.

METHODS

A nested case-control study comprising 571 T2D cases and 571 matched controls was conducted within the Singapore Chinese Health Study. Alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), and lactate dehydrogenase (LDH) were quantified in baseline plasma collected from them, while γ-glutamyltransferase (GGT) was assayed among 255 T2D cases with baseline hemoglobin A1c <6.5% and 255 matched controls. Participants were free of diagnosed diabetes, cardiovascular disease, and cancer at blood collections (1999-2004). Incident self-reported T2D cases were identified at follow-up II interview (2006-2010). Controls were matched to cases on age, sex, dialect group, and date of blood collection.

RESULTS

Higher levels of ALT and GGT were significantly associated with increased risk of T2D (p for trend <0.001 for ALT, p for trend=0.03 for GGT), and the ORs (95% CIs) comparing highest versus lowest tertiles of ALT and GGT were 2.00 (1.01 to 3.96) and 2.38 (1.21 to 4.66), respectively. A null association was observed for AST, ALP, and LDH with T2D risk. Adding GGT (<23 vs ≥23 IU/L) or ALT (<21 vs ≥21 IU/L) to a prediction model resulted in significant gain in net reclassification improvement and integrated discrimination improvement of T2D prediction (all p<0.001).

CONCLUSIONS

Higher levels of GGT and ALT are associated with increased T2D risk. GGT ≥23 IU/L and ALT ≥21 IU/L may identify people at higher risk of developing T2D in this Chinese population.

Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction

Type 2 diabetes (T2D) has become a leading public health challenge worldwide. To date, a total of 83 susceptibility loci for T2D have been identified by genome-wide association studies (GWAS). Application of meta-analysis and modern genotype imputation approaches to GWAS data from diverse ethnic populations has been key in the effort to discover T2D loci. Genetic information is expected to play a vital role in the prediction of T2D, and many efforts have been made to develop T2D risk models that include both conventional and genetic risk factors. Yet, because most T2D genetic variants identified have small effect size individually (10%-20% increased risk of T2D per risk allele), their clinical utility remains unclear. Most studies report that a genetic risk score combining multiple T2D genetic variants does not substantially improve T2D risk prediction beyond conventional risk factors. In this article, we summarize the recent progress of T2D GWAS and further review the incremental predictive performance of genetic markers for T2D.

The use of genetic information in the prediction of Type 2 diabetes

Type 2 diabetes (T2D) is a metabolic disorder characterized by high blood glucose levels and elevated risk of cardiovascular events. The progression of T2D can be delayed, or prevented, so early prediction is of high importance. More than 70 genetic loci are associated with T2D risk, raising the possibility of early identification of future cases. Results show that the benefits in discrimination by including genes in current risk models are uncertain. Improvements have been shown in reclassification but are too modest for clinical use. Given the current guidelines for T2D risk assessment and the increasing availability of genotyped individuals, we could soon be able to use genetics, not to quantify risk, but to inform clinicians on those requiring earlier observation.

Gamma-glutamyl transferase and risk of type II diabetes: an updated systematic review and dose-response meta-analysis

PURPOSE

We assessed the nature of the dose-response relationship between gamma-glutamyl transferase (GGT) levels and risk of incident type II diabetes mellitus (T2DM) in the general population.

METHODS

Systematic review and dose-response meta-analysis of published prospective studies. Relevant studies were identified in a literature search of MEDLINE, EMBASE, and Web of Science databases up to June 2014. We examined a potential nonlinear relationship using restricted cubic splines.

RESULTS

Of the 300 titles reviewed, we included 24 cohort studies with data on 177,307 participants and 11,155 T2DM cases. In pooled analysis of 16 studies with relevant data, there was evidence of a nonlinear association between GGT and T2DM risk in both males (P for nonlinearity = .02) and females (P for nonlinearity = .0005). In a comparison of extreme thirds of baseline levels of GGT, relative risk for T2DM in pooled analysis of all 24 studies was 1.34 (95% confidence interval, 1.27-1.42). There was heterogeneity among the studies (P < .001), which was to a large part explained by blood sample used, study size, degree of confounder adjustment, and quality of studies.

CONCLUSIONS

Circulating level of GGT contributes to an increased risk of T2DM in the general population in a nonlinear dose-response pattern.

Relationships between adipokines, biomarkers of endothelial function and inflammation and risk of type 2 diabetes

AIMS

Identification of novel biomarkers of diabetes risk help to understand mechanisms of pathogenesis and improve risk prediction. Our objectives were to examine the relationships between adipokines, biomarkers of inflammation and endothelial function and development of type 2 diabetes; and to assess the relevance of including these biomarkers in type 2 diabetes prediction risk models.

METHODS

1345 subjects from the SU.VI.MAX study, who were free of diabetes at baseline and who completed 13 years of follow-up were included in the present analyses. Odds ratios (OR) with 95% confidence intervals (95% CI) of incident type 2 diabetes associated with a 1-SD increase in adiponectin, leptin, C-reactive protein (CRP), soluble intracellular adhesion modecule-1 (sICAM-1), soluble vascular cell adhesion molecule 1 (sVCAM-1), E-selectin and monocyte chemoattractant protein-1 (MCP-1) were estimated. Predicitive performances of models including biomarkers were assessed with area under the receiver operating curves (AUC) and integrated discrimination improvement (IDI) statistics.

RESULTS

82 subjects developed type 2 diabetes during follow-up. The risk of developing type 2 diabetes increased with increasing concentrations of leptin (2.04 (1.28;3.26)), sICAM-1 (1.39 (1.08;1.78)) and sVCAM-1 (1.29 (1.01;1.64)). Type 2 diabetes associations with leptin remained significant after adjusting for a combination of biomarkers. Models adjusted for novel biomarkers had improved performance compared to models adjusted for classical risk factors as assessed by IDI, but not by AUC.

CONCLUSIONS

Adipokines, biomarkers of inflammation and endothelial function were significantly associated to onset of type 2 diabetes. However their inclusion in predictive scores is not supported by the present study.

The complement system in human cardiometabolic disease

The complement system has been implicated in obesity, fatty liver, diabetes and cardiovascular disease (CVD). Complement factors are produced in adipose tissue and appear to be involved in adipose tissue metabolism and local inflammation. Thereby complement links adipose tissue inflammation to systemic metabolic derangements, such as low-grade inflammation, insulin resistance and dyslipidaemia. Furthermore, complement has been implicated in pathophysiological mechanisms of diet- and alcohol induced liver damage, hyperglycaemia, endothelial dysfunction, atherosclerosis and fibrinolysis. In this review, we summarize current evidence on the role of the complement system in several processes of human cardiometabolic disease. C3 is the central component in complement activation, and has most widely been studied in humans. C3 concentrations are associated with insulin resistance, liver dysfunction, risk of the metabolic syndrome, type 2 diabetes and CVD. C3 can be activated by the classical, the lectin and the alternative pathway of complement activation; and downstream activation of C3 activates the terminal pathway. Complement may also be activated via extrinsic proteases of the coagulation, fibrinolysis and the kinin systems. Studies on the different complement activation pathways in human cardiometabolic disease are limited, but available evidence suggests that they may have distinct roles in processes underlying cardiometabolic disease. The lectin pathway appeared beneficial in some studies on type 2 diabetes and CVD, while factors of the classical and the alternative pathway were related to unfavourable cardiometabolic traits. The terminal complement pathway was also implicated in insulin resistance and liver disease, and appears to have a prominent role in acute and advanced CVD. The available human data suggest a complex and potentially causal role for the complement system in human cardiometabolic disease. Further, preferably longitudinal studies are needed to disentangle which aspects of the complement system and complement activation affect the different processes in human cardiometabolic disease.

Insights into the genetic basis of type 2 diabetes

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.

Non-traditional risk factors are important contributors to the racial disparity in diabetes risk: the atherosclerosis risk in communities study

BACKGROUND

Traditional risk factors, particularly obesity, do not completely explain the excess risk of diabetes among African Americans compared to whites.

OBJECTIVE

We sought to quantify the impact of recently identified, non-traditional risk factors on the racial disparity in diabetes risk.

DESIGN

Prospective cohort study.

PARTICIPANTS

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.

MAIN MEASURES

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.

KEY RESULTS

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.

CONCLUSIONS

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.

Introduction to personalized medicine in diabetes mellitus

The world is facing an epidemic rise in diabetes mellitus (DM) incidence, which is challenging health funders, health systems, clinicians, and patients to understand and respond to a flood of research and knowledge. Evidence-based guidelines provide uniform management recommendations for "average" patients that rarely take into account individual variation in susceptibility to DM, to its complications, and responses to pharmacological and lifestyle interventions. Personalized medicine combines bioinformatics with genomic, proteomic, metabolomic, pharmacogenomic ("omics") and other new technologies to explore pathophysiology and to characterize more precisely an individual's risk for disease, as well as response to interventions. In this review we will introduce readers to personalized medicine as applied to DM, in particular the use of clinical, genetic, metabolic, and other markers of risk for DM and its chronic microvascular and macrovascular complications, as well as insights into variations in response to and tolerance of commonly used medications, dietary changes, and exercise. These advances in "omic" information and techniques also provide clues to potential pathophysiological mechanisms underlying DM and its complications.

Differential white cell count and incident type 2 diabetes: the Insulin Resistance Atherosclerosis Study

AIMS/HYPOTHESIS

White cell count has been shown to predict incident type 2 diabetes, but differential white cell count has received scant attention. We examined the risk of developing diabetes associated with differential white cell count and neutrophil:lymphocyte ratio and the effect of insulin sensitivity and subclinical inflammation on white cell associations.

METHODS

Incident diabetes was ascertained in 866 participants aged 40-69 years in the Insulin Resistance Atherosclerosis Study after a 5 year follow-up period. The insulin sensitivity index (SI) was measured by the frequently sampled IVGTT.

RESULTS

C-reactive protein was directly and independently associated with neutrophil (p < 0.001) and monocyte counts (p < 0.01) and neutrophil:lymphocyte ratio (p < 0.001), whereas SI was inversely and independently related to lymphocyte count (p < 0.05). There were 138 (15.9%) incident cases of diabetes. Demographically adjusted ORs for incident diabetes, comparing the top and bottom tertiles of white cell (1.80 [95% CI 1.10, 2.92]), neutrophil (1.67 [1.04, 2.71]) and lymphocyte counts (2.30 [1.41, 3.76]), were statistically significant. No association was demonstrated for monocyte count (1.18 [0.73, 1.90]) or neutrophil:lymphocyte ratio (0.89 [0.55, 1.45]). White cell and neutrophil associations were no longer significant after further adjusting for family history of diabetes, fasting glucose and smoking, but the OR comparing the top and bottom tertiles of lymphocyte count remained significant (1.96 [1.13, 3.37]). This last relationship was better explained by SI rather than C-reactive protein.

CONCLUSIONS/INTERPRETATION

A lymphocyte association with incident diabetes, which was the strongest association among the major white cell types, was partially explained by insulin sensitivity rather than subclinical inflammation.

The association of genetic markers for type 2 diabetes with prediabetic status - cross-sectional data of a diabetes prevention trial

OBJECTIVE

To investigate the association of risk alleles for type 2 diabetes with prediabetes accounting for age, anthropometry, inflammatory markers and lifestyle habits.

DESIGN

Cross-sectional study of 129 men and 157 women of medium-sized companies in northern Germany in the Delay of Impaired Glucose Tolerance by a Healthy Lifestyle Trial (DELIGHT).

METHODS

Besides established risk factors, 41 single nucleotide polymorphisms (SNPs) that have previously been found to be associated with type 2 diabetes were analyzed. As a nonparametric test a random forest approach was used that allows processing of a large number of predictors. Variables with the highest impact were entered into a multivariate logistic regression model to estimate their association with prediabetes.

RESULTS

Individuals with prediabetes were characterized by a slightly, but significantly higher number of type 2 diabetes risk alleles (42.5±4.1 vs. 41.3±4.1, p = 0.013). After adjustment for age and waist circumference 6 SNPs with the highest impact in the random forest analysis were associated with risk for prediabetes in a logistic regression model. At least 5 of these SNPs were positively related to prediabetic status (odds ratio for prediabetes 1.57 per allele (Cl 1.21-2.10, p = 0.001)).

CONCLUSIONS

This explorative analysis of data of DELIGHT demonstrates that at least 6 out of 41 genetic variants characteristic of individuals with type 2 diabetes may also be associated with prediabetes. Accumulation of these risk alleles may markedly increase the risk for prediabetes. However, prospective studies are required to corroborate these findings and to demonstrate the predictive value of these genetic variants for the risk to develop prediabetes.