Metabolic diseases are the most common and rapidly growing health issues worldwide. The massive population-based human genetics is crucial for the precise prevention and intervention of metabolic disorders. The China Metabolic Analytics Project (ChinaMAP) is based on cohort studies across diverse regions and ethnic groups with metabolic phenotypic data in China. Here, we describe the centralized analysis of the deep whole genome sequencing data and the genetic bases of metabolic traits in 10,588 individuals from the ChinaMAP. The frequency spectrum of variants, population structure, pathogenic variants and novel genomic characteristics were analyzed. The individual genetic evaluations of Mendelian diseases, nutrition and drug metabolism, and traits of blood glucose and BMI were integrated. Our study establishes a large-scale and deep resource for the genetics of East Asians and provides opportunities for novel genetic discoveries of metabolic characteristics and disorders.

Context: In China, data on the prevalence of metabolic syndrome have been rare recently. Objective: The objective of the study was to evaluate the prevalence of metabolic syndrome and its components in 2010. Design, Setting, and Participants: The study covered all 31 provinces of mainland China and consisted of a nationally representative population sample of 98,658 Chinese adults aged ≥18 years. Of these, 97,098 participants were eligible for the data analysis reported here. Main Outcome Measures: Estimates of the prevalence of metabolic syndrome and its components were calculated. To further explore whether metabolic syndrome is associated with the 10-year coronary heart disease risk, sex-stratified logistic regression models were used. Results: The prevalence of the metabolic syndrome was 33.9% (31.0% in men and 36.8% in women), which indicates that metabolic syndrome affects approximately 454 million adults in China. More than half of total adult population was suffering from low high-density lipoprotein cholesterol (HDL-C), and nearly half of participants had high blood pressure. Abdominal obesity and low HDL-C were more prevalent in women than in men, whereas high blood pressure, high blood glucose, and high triglycerides were more common in men. Metabolic syndrome was associated with a higher 10-year coronary heart disease risk after adjustment for potential risk factors and each component of metabolic syndrome as continuous variables. Conclusion: Our results showed a high prevalence of metabolic syndrome and its components in the general adult population in mainland China. Metabolic syndrome was independently associated with a higher 10-year risk of developing coronary heart disease.

The fatty liver index (FLI), which is an algorithm based on waist circumference, body mass index (BMI), triglyceride, and gamma-glutamyl-transferase (GGT), was initially developed to detect fatty liver in Western countries. Our study aimed to evaluate the accuracy and optimal cut-off point of the FLI for predicting nonalcoholic fatty liver disease (NAFLD) in middle-aged and elderly Chinese. This cross-sectional study included 8626 Chinese adults aged 40 years or above recruited from Jiading District, Shanghai, China. Anthropometric and biochemical features were collected by a standard protocol. NAFLD was diagnosed by hepatic ultrasonography. The accuracy and cut-off point of the FLI to detect NAFLD were evaluated by area under the receiver operator characteristic curve (AUROC) and the maximum Youden index analysis, respectively. The AUROC of the FLI for NAFLD was 0.834 (95% confidence interval: 0.825–0.842), and larger than that of its each individual component [0.786 (0.776–0.796), 0.783 (0.773–0.793), 0.727 (0.716–0.739), and 0.707 (0.695–0.719) for waist circumference, BMI, triglyceride, and GGT, respectively] (all P < 0.001). The optimal cut-off point of the FLI for diagnosing NAFLD was 30 with the maximum Youden Index of 0.51, achieving a high sensitivity of 79.89% and a specificity of 71.51%. The FLI-diagnosed NAFLD individuals were in worse metabolic characteristics (waist circumference, BMI, blood pressure, serum lipids, and aminotransferases) than ultrasonography-diagnosed NAFLD patients (all P < 0.05). The FLI could accurately identify NAFLD and the optimal cut-off point was 30 in middle-aged and elderly Chinese. As FLI-diagnosed NAFLD patients were in worse metabolism, much attention should be paid to the metabolic controls and managements of NAFLD.

OBJECTIVE Uncertainty remains regarding the predictive value of various glycemic measures as they relate to the risk of diabetes and its complications. Using the cutoffs recommended by the American Diabetes Association’s 2010 criteria, we determined the associations of fasting plasma glucose (FPG), 2-h postload glucose (2h-PG), and HbA_1c with the outcomes. RESEARCH DESIGN AND METHODS Baseline medical history, FPG, 2h-PG, and HbA_1c were obtained from a population-based cohort of 193,846 adults aged ≥40 years in China during 2011–2012. A follow-up visit was conducted during 2014–2016 in order to assess incident diabetes, cardiovascular disease (CVD), cancer, and mortality. RESULTS We documented 8,063 cases of diabetes, 3,014 CVD-related events, 1,624 cases of cancer, and 2,409 deaths during up to 5 years of follow-up. Multivariable-adjusted risk ratios (95% CIs) of diabetes associated with prediabetes based on FPG of 100–125 mg/dL, 2h-PG of 140–199 mg/dL, or HbA_1c of 5.7–6.4% (39–47 mmol/mol) were 1.60 (1.43–1.79), 2.72 (2.43–3.04), and 1.49 (1.36–1.62), respectively. Restricted cubic spline analyses suggested J-shaped associations of FPG, 2h-PG, and HbA_1c levels with CVD, cancer, and mortality. Multivariable-adjusted hazard ratios (95% CIs) associated with untreated diabetes based on FPG ≥126 mg/dL, 2h-PG ≥200 mg/dL, or HbA_1c ≥6.5% (48 mmol/mol) were 1.18 (1.05–1.33), 1.31 (1.18–1.45), and 1.20 (1.07–1.34) for CVD; 1.10 (0.92–1.32), 1.44 (1.25–1.67), and 1.08 (0.92–1.28) for cancer; and 1.37 (1.20–1.57), 1.57 (1.41–1.76), and 1.33 (1.17–1.52) for mortality, respectively. 2h-PG remained significantly associated with outcomes in models including FPG and HbA_1c as spline terms. Furthermore, 2h-PG significantly improved the ability of the C statistic to predict diabetes, CVD, and mortality. CONCLUSIONS 2h-PG remains independently predictive of outcomes in models including FPG and HbA_1c. Therefore, in addition to FPG and HbA_1c, routine testing of 2h-PG should be considered in order to better assess the risks of outcomes.

OBJECTIVE: To investigate whether the association between insulin resistance and cardiovascular disease (CVD) differs by glucose tolerance status. RESEARCH DESIGN AND METHODS: We analyzed a nationwide sample of 111,576 adults without CVD at baseline, using data from the China Cardiometabolic Disease and Cancer Cohort Study. Insulin resistance was estimated by sex-specific HOMA of insulin resistance (HOMA-IR) quartiles for participants with normal glucose tolerance, prediabetes, or diabetes, separately, and by 1 SD of HOMA-IR for the overall study participants. We used Cox proportional hazards models to examine the association between insulin resistance and incident CVD according to glucose tolerance status and evaluate the CVD risk associated with the combined categories of insulin resistance and obesity in prediabetes and diabetes, as compared with normal glucose tolerance. Models were adjusted for age, sex, education attainment, alcohol drinking, smoking, physical activity, and diet quality. RESULTS: In participants with normal glucose tolerance, prediabetes, and diabetes defined by three glucose parameters, multivariable-adjusted hazard ratios (95% CIs) for incident CVD associated with the highest versus the lowest quartile of HOMA-IR were 1.03 (0.82–1.30), 1.23 (1.07–1.42), and 1.61 (1.30–2.00), respectively; the corresponding values for CVD per 1-SD increase in HOMA-IR were 1.04 (0.92–1.18), 1.12 (1.06–1.18), and 1.15 (1.09–1.21), respectively (P for interaction = 0.011). Compared with participants with normal glucose tolerance, in participants with prediabetes, the combination of the highest HOMA-IR quartile and obesity showed 17% (95% CI 2–34%) higher risk of CVD, while the combination of the lowest two HOMA-IR quartiles and nonobesity showed 15–17% lower risk of CVD. In participants with diabetes, the upper two HOMA-IR quartiles exhibited 44–77% higher risk of CVD, regardless of obesity status. Consistent findings were observed for glucose tolerance status defined by different combinations of glycemic parameters. CONCLUSIONS: Glucose intolerance status exacerbated the association between insulin resistance and CVD risk. Compared with adults with normal glucose tolerance, adults with prediabetes who were both insulin resistant and obese exhibited higher risks of CVD, while in adults with diabetes, the CVD risk related to insulin resistance remained, regardless of obesity.