Relationship Between BMI and Prediabetes in Chinese Adults: A Cross-Sectional Analytical Study

BackgroundThe purpose of the research was to explore the extent of interaction between triglycerides (TG) and serum uric acid (SUA) level with blood pressure (BP) in middle-aged and elderly individuals in China.MethodsData were selected from the China Health and Retirement Longitudinal Study (CHARLS), a cross-sectional study. 3345(46.99%) men with average ages of 60.24 ± 9.24 years and 3774 (53.01%) women with average ages of 59.91 ± 9.95 years were included in the study. Differences between gender, or between categories of blood pressure levels were evaluated by t-test or chi-square test. The adjusted associations between various characteristics and BP status were first compared using linear regression models, as appropriate. Then, A general linear model adjusted for confounding factors (socio-demographic characteristics [age, educational levels, marital status, place of residence], health behaviors [cigarette smoking, alcohol drinking, eating habits, social and leisure activities, accidental injury, physical activities], medical history [history of cardiovascular diseases, hepatitis history, antidiabetic drugs, history of antilipidemic medication, anti-hypertensive therapy], metabolic measures [C-reactive protein (CRP), hemoglobin A1c (HbA1c), fasting plasma glucose (FPG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), estimated glomerular filtration rate (eGFR), body mass index (BMI)]) was used to examine the synergistic effect of SUA and TG level on BP in middle-aged and elderly individuals in China.ResultsAge-adjusted partial Pearson’s correlation coefficient showed that SUA and TG level positively correlated with both systolic blood pressure (SBP) and diastolic blood pressure (DBP) in both men and women. Multiple linear regression analysis showed the TG level was significantly and positively associated with SBP and DBP in both men (SBP: β =0.068, P = 0.001; DBP: β =0.064, P = 0.002) and women (SBP: β =0.061, P = 0.002; DBP: β =0.084, P = 0.000), but SUA were significantly and positively associated with SBP in both men (SBP: β =0.047, P = 0.013) and women (SBP: β =0.040, P = 0.028), regardless of other confounding factors. After adjusting for related potential confounders, evidence of interaction between SUA and TG level on SBP (men: β = − 1.090, P = 0.726; women: β = − 0.692, P = 0.861) and DBP (men: β = − 1.026, P = 0.572; women: β = − 0.794, P = 0.842) was not observed.ConclusionThe interaction effect of SUA and TG level on BP was not observed in our study. Moreover, high SUA level was significantly associated with SBP, while high TG level was strongly related to both DBP and SBP.

Few studies on the individual and combined analysis between serum uric acid (SUA) and body mass index (BMI) and blood pressure (BP) were conducted in individuals aged ≥45 years. We aimed to assess the extent to which BMI and SUA and their interaction affect BP in Chinese middle-aged and older adults.Data were selected from the China Health and Retirement Longitudinal Study (CHARLS). A total of 5888 individuals aged 45 to 96 was included. Differences between BMI, or between categories of blood pressure were evaluated by t test or chi-square test. The trend of related variables according to four BMI categories was also tested using contrast analysis. The adjusted associations between various characteristics and BP status were first compared using linear regression models, as appropriate. Then, general linear models adjusting for related potential confounders were used to examine the synergistic effect of SUA and BMI level on BP for middle-aged and elderly individuals in China.Age-adjusted partial Pearson correlation coefficient showed that BMI was significantly and positively correlated with BP both in male and female, SUA positively correlated with both systolic blood pressure (SBP) and diastolic blood pressure (DBP) in males with BMI <24.0 kg/m2 and females with BMI <24.0 kg/m2. However, SUA level significantly and positively correlated with DBP, but not with SBP, in females with BMI ≥24.0 kg/m2. Multiple linear regression analysis showed that BMI was independently associated with BP both in male and female, SUA significantly and positively associated with SBP in both males and females with BMI <24.0 kg/m2, However, SUA level positively correlated with DBP in females with BMI <24.0 kg/m2, but not with males with BMI <24.0 kg/m2, independent of other confounding factors. A general linear model analysis adjusted for confounding factors did not reveal interaction between BMI, SUA levels and SBP (β=-1.404, P = .686 in males; β=-2.583, P = .575 in females) and DBP (β=-2.544, P = .263 in males; β=-2.619, P = .622 in females).No interaction between BMI, SUA levels, and BP was observed in either males or females; However, BMI was independently associated with BP both in male and female, SUA independently associated with SBP both in males and females with BMI <24.0 kg/m2, and SUA independently associated with DBP in females with BMI ≥24.0 kg/m2.

Canagliflozin reduced kidney disease progression in participants with type 2 diabetes in the CANagliflozin cardioVascular Assessment Study (CANVAS) Program. This analysis explored potential mediators of the effects of canagliflozin on kidney outcomes. The percent mediating effect of 18 biomarkers indicative of disease was determined by comparing the hazard ratios for the effect of randomized treatment from an unadjusted model and from a model adjusting for the average post-randomization level of each biomarker. Multivariable analyses assessed the joint effects of biomarkers that mediated most strongly in univariable analyses. The kidney outcome was defined as a composite of 40% estimated glomerular filtration rate decline, end-stage kidney disease, or death due to kidney disease. Nine biomarkers (systolic blood pressure [8.9% of effect explained], urinary albumin:creatinine ratio [UACR; 23.9%], gamma glutamyltransferase [4.1%], hematocrit [51.1%], hemoglobin [41.3%], serum albumin [19.5%], erythrocytes [56.7%], serum urate [35.4%], and urine pH [7.5%]) individually mediated the effect of canagliflozin on the kidney outcome. In a parsimonious multivariable model, erythrocyte concentration, serum urate, and systolic blood pressure maximized cumulative mediation (115%). Mediating effects of UACR, but not other mediators, were highly dependent upon the baseline level of UACR: UACR mediated 42% and 7% of the effect in those with baseline UACR 30 mg/g or more and under 30 mg/g, respectively. The identified mediators support existing hypothesized mechanisms for the prevention of kidney outcomes with sodium glucose co-transporter 2 inhibitors. Thus, the disparity in mediating effects across baseline UACR subgroups suggests that the mechanism for kidney protection with canagliflozin may vary across patient subgroups.

Objectives To assess the extent of interaction between body mass index (BMI) and triglyceride (TG) level and its effects on blood pressure (BP) in elderly individuals in China. Design Cross-sectional study. Setting Data were taken from a cross-sectional study called the China Health and Retirement Longitudinal Study. Participants The analytic sample included 3629 subjects aged 45 to 96 years. Main Outcome Measurements Data were obtained from the China Health and Retirement Longitudinal Study, which is a cross-sectional study. Age-adjusted partial Pearson's correlation test was used to compare various characteristics and BP. Adjusted associations were first used as linear regression models, as appropriate. Then, general linear models adjusted for related potential confounders were used to examine the synergistic effects of BMI and TG level on BP. Finally, a binary logistic regression model adjusted for confounding factors was used to examine the association between BMI or TG level and hypertension. Results Age-adjusted partial Pearson's correlation coefficient showed that the TG level was positively correlated with both systolic blood pressure (SBP) and diastolic blood pressure (DBP) in both men and women with BMI < 24.0 kg/m2; however, TG level was positively correlated with DBP in women with BMI ≥ 24.0 kg/m2 but not with DBP in men with BMI ≥ 24.0 kg/m2. Multiple linear regression analysis showed that BMI level was significantly and positively associated with both SBP and DBP in men and women with BMI < 24.0 kg/m2, and TG level was significantly and positively associated with SBP in women with BMI < 24.0 kg/m2, independent of other confounding factors. A general linear model analysis with adjustment for confounding factors (age, educational level, marital status, current residence, smoking, eating habits, taking activities, antidiabetic medication, antihypertensive therapy, fasting plasma glucose [FPG], low-density lipoprotein cholesterol [LDL-C], estimated glomerular filtration rate [eGFR], and serum uric acid [SUA]) showed no interaction between BMI and TG level and SBP (men, β = 0.572, P = 0.845; women, β = 0.122, P = 0.923) and DBP (men, β = -0.373, P = 0.810; women, β = 0.272, P = 0.828). A binary logistic regression model analysis with adjustment for confounding factors (age, educational level, marital status, current residence, smoking, drinking, eating habits, taking activities, major accidental injury, physical activity, history of cardiovascular disease, history of liver disease, antilipidemic medication, antidiabetic medication, antihypertensive therapy, FPG, LDL-C, high-density lipoprotein cholesterol [HDL-C], eGFR, and SUA) showed that overweight and obese men and women were more likely to have hypertension (men: odds ratio [OR] = 1.781, 95% confidence interval [CI] = 1.393–2.277; women: OR = 1.653, 95% CI = 1.330–2.055) and women with high TG were more likely to have hypertension (OR = 1.558, 95% CI = 1.219–1.992). Conclusion An interactive effect of BMI and TG level on BP was not observed in either men or women; however, independent effects of BMI on BP were observed in both men and women, and an association between TG level and hypertension was observed in women.

Bai Ku Yao is an isolated subgroup of the Yao minority in China. Little is known about dyslipidemia in this population. The aim of this study was to compare the effects of demography, diet, and lifestyle on serum lipid levels between the Bai Ku Yao and Han populations. A total of 1,170 subjects of Bai Ku Yao and 1,173 subjects of Han Chinese aged 15-89 years were surveyed by a stratified randomized cluster sampling. The levels of total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A-I (apoA-I), and apoB were significantly lower in Bai Ku Yao than in Han. Physical activity level and total dietary fiber intake were higher, whereas body mass index (BMI), waist circumference, total energy intake, and total fat intake were lower in Bai Ku Yao than in Han. Hyperlipidemia was positively correlated with BMI, waist circumference, and total energy and total fat intakes and negatively associated with physical activity level and total dietary fiber intake in both populations, but it was positively associated with age and alcohol consumption only in Han. The differences in the lipid profiles between the two ethnic groups were associated with different dietary habits, lifestyle choices, and levels of physical activities.

BackgroundStudies on the association between estimated cardiorespiratory fitness (CRF) and changes in metabolic risk in the Chinese population are limited. This study aims to examine the associations between CRF and changes in metabolic risk.Subjects and methodsWe included 4,862 and 2,700 participants recruited from 28 provinces in the China Health and Retirement Longitudinal Study (CHARLS) in the baseline (Wave 1) and follow-up (Wave 4) analyses, respectively. CRF was calculated using sex-specific longitudinal non-exercise equations. Metabolic indicators included systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and fasting plasma glucose (FPG) levels. The metabolic score was calculated as the number of changes in the above metabolic indicators above the 75th percentile of the distribution of changes (equal to or below the 25th percentile for HDL-C).ResultsIn the baseline analysis, CRF was negatively associated with SBP, DBP, TG, and FPG, and positively correlated with HDL-C after adjusting for age, smoking status, and drinking status (all P < 0.0001) in both males and females. In the follow-up analysis, higher baseline CRF was significantly related to a decrease in SBP, DBP, TG, FPG, and metabolic score (all P < 0.0005), and increased HDL-C (P < 0.0001) after further adjustment for corresponding baseline metabolic indicators. The associations remained significant after stratification by sex, except for the changes in HDL-C levels in females. Furthermore, improved CRF was associated with favorable changes in DBP, TG, HDL-C, FPG, and metabolic scores in all populations and males. Significant associations between changes in CRF and DBP, TG, and FPG levels were found in females.ConclusionHigher baseline CRF and improved CRF were associated with favorable changes in metabolic indicators.

Objective To investigate the association of lipoprotein lipase gene Hind Ⅲ and S447X polymorphisms with metabolic syndrome. Methods PCR-RFLP was used to detect lipoprotein lipase Hind Ⅲ and S447X genotypes in 401 subjects(including 201 controls, 200 metabolic syndrome patients). Results ( 1 ) The levels of waist circumference ( WC ) , hip circumference ( HC ) , waist-to-hip ratio ( WHR ) , body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure ( DBP) , total cholesterol ( TC) , triglyceride (TG), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), and fasting plasma glucose (FPG) were significantly different between metabolic syndrome group and control group (all P< 0.05). (2)The frequencies of H+H+ genotype,H+allele,SS genotype, and S allele for metabolic syndrome were all significantly higher than those for controls( H+H+ genotype:66. 5% vs 54.2% ,P=0.012; H+ allele:78.0% vs 71.4%, P=0.031;SS genotype:89.5% vs 77. 1% , P = 0.001; Sallel:94.5% vs 87. 56% , P = 0.001). (3) The levels of WC, HC, WHR, BMI, SBP, DBP, TG, LDL-C, and FPG in H + H-/H-H- genotype were significantly lower than those in H+H+ genotype, HDL-C was significantly higher than that in H+H+ genotype ( all P<0. 05). The levels of WC, HC, WHR, BMI, SBP, DBP, TC, TG, and FPG in SX/XX genotype were significantly lower than those in SS genotype, HDL-C was significantly higher than that in SS genotype ( all P< 0.05). (4)Multi-way logistic regression analysis suggested that risk factors for metabolic syndrome were smoking, drinking, and SS genotype (OR value was 4.289,2.268, and 2. 597, respectively ). (5) Result of interaction analysis among different factors indicated that the risk for metabolic syndrome in smoker with SS genotype was 3. 996 times of non-smokers with SX/XX genotype. Conclusions The lipoprotein lipase gene S447X polymorphism is associated with metabolic syndrome risk in Kazakh, and SS genotype and S allele may serve as genetic risk factors of metabolic syndrome, H + H-/H-H- and SX/XX genotypes yield beneficial effect for lipid and blood pressure. SS genotype and smoking may exist additive effect. Key words: Kazakh; Metabolic syndrome; Lipoprotein lipase; Gene polymorphism

ObjectiveWe investigated the moderation/mediation between the age of menarche and obesity parameters in predicting blood pressure (BP) in middle-aged and elderly Chinese.DesignOur study is a population-based cross-sectional study.SettingParticipants in this...

Objective To provide rationales for preventing and treating dyslipidemia by understanding the current status of lipids and related metabolic factors. Methods A total of 2 590 permanent residents aged ≥18 years were selected by random cluster sampling from three urbanized communities of Sijiqing Street. And the rate of abnormal lipid metabolism was calculated for different ages and genders. Spearman's correlation analyses were conducted for the levels of total cholesterol (TC), total triglyceride (TG), low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C), body mass index (BMI), waist circumference(WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting plasma glucose (FPG), glycated hemoglobin (HbA1c) and uric acid (UA) levels. Both χ2 test and logisic regression were employed to examine the correlations between dyslipidemia and overweight/obesity, hypertension, hyperglycemia and hyperuricemia. Results ①The total rate of abnormal lipid metabolism was 60.0%(1 554/2 590) with a standardized rate of 57.2%. High TC rate was 42.9%(1 111/2 590) with a standardized rate of 40.5%. And the edge incremental rate was 31.7%(822/2 590), the standardized rate 30.5%, the incremental rate 11.2%(289/2 590) and the standardized rate 10.0%. High TG rate was 33.0%(855/2 590) with a standardized rate of 30.7%. And the edge incremental rate was 15.3% (397/2 590), the standardized rate 14.3%, the incremental rate 17.7% (458/2 590) and the standardized rate 16.4%. High LDL-C rate was 30.4% (787/2 590) with a standardized rate of 28.4%. And the edge incremental rate was 22.9%(594/2 590), the standardized rate 21.7%, the incremental rate 7.5%(193/2 590) and the standardized rate 6.7%. Low HDL-C rate was 12.6% (327/2 590) with a standardized rate of 12.8%. The rates of high TC, high TG, high LDL-C, low HDL-C and abnormal lipid metabolism among gender showed no statistically significant difference (P>0.05); ② For both males & females, high TC rate, high TG rate, high LDL-C rate and total rate of abnormal lipid metabolism increased with age (P 0.05); ③Spearman's correlation analysis showed that the levels of TC, TG and LDL-C were positively correlated with BMI, WC, SBP, DBP, FBG, HbA1C and UA (all P<0.01) while the level of HDL-C had negative correlations with BMI, WC, SBP, DBP, FBG, HbA1c, and UA (all P<0.05); ④The total rate of abnormal lipid metabolism and various types of abnormal lipid metabolism increased with a rising level of BMI in the upward trend (trend test P<0.01), various types of abnormal lipid metabolism rate between different groups (elevated & non-elevated) of blood pressure, glucose and uric acid also were statistically significant (all P<0.05); ⑤ Non-conditional logistic regression analysis showed that, after adjusting for age and gender, overweight or obesity and hypertension were risk factors of high TC and high LDL-C; overweight or obesity, hyperuricemia was a risk factor for low HDL-C; overweight or obesity, hypertension, hyperglycemia and hyperuricemia were risk factors for high TG and total abnormal blood lipid. Conclusions Urbanized community groups have a high rate of dyslipidemia. And abnormal lipid metabolism is affected by overweight or obesity, hypertension, hyperglycemia and hyperuricemia. The target population should be regularly monitored and comprehensively controlled. Key words: Dyslipidemias; Risk factors; Glucose metabolism disorders

BACKGROUNDSurprisingly, it is estimated that about half of type 2 diabetics remain undetected. The possible causes may be partly attributable to people with normal fasting plasma glucose (FPG) but abnormal postprandial hyperglycemia. We attempted to develop an effective predictive model by using the metabolic syndrome (MeS) components as parameters to identify such persons.SUBJECTS AND METHODSAll participants received a standard 75-g oral glucose tolerance test, which showed that 106 had normal glucose tolerance, 61 had impaired glucose tolerance, and 6 had diabetes-on-isolated postchallenge hyperglycemia. We tested five models, which included various MeS components. Model 0: FPG; Model 1 (clinical history model): family history (FH), FPG, age and sex; Model 2 (MeS model): Model 1 plus triglycerides, high-density lipoprotein cholesterol, body mass index, systolic blood pressure and diastolic blood pressure; Model 3: Model 2 plus fasting plasma insulin (FPI); Model 4: Model 3 plus homeostasis model assessment of insulin resistance. A receiver-operating characteristic (ROC) curve was used to determine the predictive discrimination of these models.RESULTSThe area under the ROC curve of the Model 0 was significantly larger than the area under the diagonal reference line. All the other 4 models had a larger area under the ROC curve than Model 0. Considering the simplicity and lower cost of Model 2, it would be the best model to use. Nevertheless, Model 3 had the largest area under the ROC curve.CONCLUSIONWe demonstrated that Model 2 and 3 have a significantly better predictive discrimination to identify persons with normal FPG at high risk for glucose intolerance.

Relation Between Self-Reported Physical Activity Level, Fitness, and Cardiometabolic Risk

To evaluate the correlation of the serum uric acid and free fatty acid(FFA)levels in Shandong coastal residents. To investigate the correlation between serum uric acid and FFA based on 3 860 individuals who have been long staying in Qingdao, Yantai, Weihai, Rizhao with a randomized, stratified cluster sampling method. According to FFA quartile, subjects were divided into four groups: group Q1 of 908, group Q2 1 016, group Q3 958, and group Q4 978 cases. The prevalence of hyperuricemia and serum uric acid levels increased with the increasing FFA quartile. Compared with Q1, Q2, and Q3 groups, the prevalence of hyperuricemia in Q4 group and the increase of serum uric acid were statistically significant(P<0.05). And in the group Q4, hyperuricemia prevalence is twice as the group A. According to the serum uric acid level, subjects were divided into the normal uric acid group(n=3 331)and the hyperuricemia group(n=529). In the hyperuricemia group, their systolic blood pressure, diastolic blood pressure, waist circumference, hip circumference, triglycerides, total cholesterol, low density lipoprotein-cholesterol(LDL-C), glucose, uric acid, FFA, body mass index etc. were significantly higher than those of the normal uric acid group(all P<0.01), while the high density lipoprotein-cholesterol(HDL-C), cystatin, glomerular filtration rate(eGFR)are significantly lower than those of the normal uric acid group(all P<0.01). Serum uric acid levels are positively correlated with systolic and diastolic blood pressures, waist and hip circumferences, triglycerides, total cholesterol, LDL-C, FFA, blood glucose, body mass index(all P<0.01); and negatively correlated with eGFR(P<0.01). Multiple regression analysis showed that systolic blood pressure, FFA, total cholesterol, triglycerides, LDL-C, blood glucose, body mass index, eGFR were factors influencing serum uric acid independently. Multivariate binary logistic regression analysis showed that systolic blood pressure, waist circumference, total cholesterol, blood glucose, and FFA are independent risk factors to predict hyperuricemia onset while eGFR is a protective factor. Serum uric acid level is closely related to the free fatty acid, and FFA seems to be involved in the development and progression of hyperuricemia. (Chin J Endocrinol Metab, 2017, 33: 765-768) Key words: Uric acid; Free fatty acid; Risk factor

ABSTRACTBackground: The obesity-hypertension pathogenesis is complex. From the phenotype to molecular mechanism, there is a long way to clarify the mechanism. To explore the association between obesity and hypertension, we correlate the phenotypes such as the waist circumference (WC), body mass index (BMI), systolic blood pressure (SB), and diastolic blood pressure (DB) with the clinical laboratory data between four specific Chinese adult physical examination groups (newly diagnosed untreated just-obesity group, newly diagnosed untreated obesity-hypertension group, newly diagnosed untreated just-hypertension group, and normal healthy group), and the results may show something. Objective: To explore the mechanisms from obesity to hypertension by analyzing the correlations and differences between WC, BMI, SB, DB, and other clinical laboratory data indices in four specific Chinese adult physical examination groups. Methods: This cross-sectional study was conducted from September 2012 to July 2014, and 153 adult subjects, 34 women and 119 men, from 21 to 69 years, were taken from four characteristic Chinese adult physical examination groups (newly diagnosed untreated just-obesity group, newly diagnosed untreated obesity-hypertension group, newly diagnosed untreated just-hypertension group, and normal healthy group). The study was approved by the ethics committee of Hangzhou Center for Disease Control and Prevention. WC, BMI, SB, DB, and other clinical laboratory data were collected and analyzed by SPSS. Results: Serum levels of albumin (ALB)，alanine aminotransferase (ALT), low density lipoprotein cholesterol (LDLC), triglyceride (TG), high density lipoprotein cholesterol (HDLC), alkaline phosphatase (ALP), uric acid (Ua), and TC/HDLC (odds ratio) were statistically significantly different between the four groups. WC statistically significantly positively correlated with BMI, ALT, Ua, and serum levels of glucose (GLU), and TC/HDLC, and negatively with ALB, HDLC, and serum levels of conjugated bilirubin (CB). BMI was statistically significantly positively related to ALT, Ua, LDLC, WC, and TC/HDLC, and negatively to ALB, HDLC, and CB. DB statistically significantly positively correlated with ALP, BMI, and WC. SB was statistically significantly positively related to LDLC, GLU, serum levels of fructosamine (FA), serum levels of the total protein (TC), BMI, and WC. Conclusion: The negative body effects of obesity are comprehensive. Obesity may lead to hypertension through multiple ways by different percents.GGT, serum levels of gamma glutamyltransferase; ALB, serum levels of albumin; ALT, serum levels of alanine aminotransferase; LDLC, serum levels of low density lipoprotein cholesterol; TG, serum levels of triglyceride; HDLC, serum levels of high density lipoprotein cholesterol; FA, serum levels of fructosamine; S.C.R, serum levels of creatinine; IB, serum levels of indirect bilirubin; ALP, serum levels of alkaline phosphatase; CB, serum levels of conjugated bilirubin; UREA, Urea; Ua, serum levels of uric acid; GLU, serum levels of glucose; TC, serum levels of the total cholesterol; TB, serum levels of the total bilirubin; TP, serum levels of the total protein; TC/HDLC, TC/HDLC ratio.

Introduction: The triglyceride glucose-body mass index (TyG-BMI) and triglyceride glucose-waist circumference (TyG-WC) are fast and simple clinical indices with proven reliability in predicting heart diseases and stroke. Considering the obesity paradox, where body mass index (BMI) may fail to accurately predict cardiovascular outcomes in obese patients, assessing the effectiveness of TyG-BMI and TyG-WC across the combination categories of BMI and waist circumference (WC) becomes essential. Methods: We included individuals with complete data on TyG, fasting plasma glucose, BMI, and WC at 2011 wave. Incidence of heart disease and stroke was observed during the follow-up visits. All participants were classified into four groups at baseline: both BMI and WC normal, BMI obesity (BMI ≥ 24 kg/m 2 ) but WC normal, BMI normal but WC obesity (WC, male ≥ 90 cm, female ≥ 80 cm) and both BMI and WC obesity. TyG-BMI and TyG-WC were calculated as BMI * TyG index and WC * TyG index, where TyG index = ln[FPG (mg/dL) * TG (mg/dL)/2]. Multivariate Cox proportional hazards models were employed to assess the association between TyG-BMI and TyG-WC with the risk of incident cardiovascular disease within the four groups, adjusted for demographic characteristics, smoking, drinking, blood urea nitrogen, high-density lipoprotein cholesterol, low-density lipoproteins cholesterol, C-Reactive protein, hemoglobin, uric acid, serum creatinine, cystatin C, hypertension, diabetes and kidney disease. Results: Among 7,405 participants (mean age 57.8, 53.9% women), 479 stroke and 1141 heart disease incidents were recorded with average follow-up years of 6.55 and 6.35. The fully adjusted models in Figure 1 revealed that in the BMI and WC obesity group, every 10-unit increase in TyG-BMI and TyG-WC was associated with 4.2% and 1.3% higher risk of heart disease respectively (HR = 1.04, 95% CI = 1.01, 1.07; HR = 1.01, 95% CI = 1.002, 1.03). Additionally, among individuals with normal BMI but obese WC, each 10-unit rise in TyG-WC was associated with a 4.5% increase in stroke risk (HR = 1.05, 95% CI = 1.01, 1.08). Conclusions: The TyG-BMI and TyG-WC indices exhibit differential predictive capabilities for cardiovascular disease risk across diverse obesity phenotypes, where the applicability could not extend to populations characterized by both normal BMI and WC. Our findings stress the importance of applying TyG indices to tailored populations, enhancing the precision of cardiovascular risk assessment.

Objective To investigate the association of carotid intima-media thickness (CIMT) with abnormal glucose and central obesity. Methods A total of 10 207 community residents 40 years of age or older who attended the Chinese patients with Type 2 Diabetes Cancer Risk Epidemic Studies from August 16, 2011 to December 10, 2011. There were 9 556 cases elected according to inclusion and exclusion criteria. Waist, waist-to-hip ratio, body mass index and blood pressure were measured. Fasting plasma glucose, postprandial 2 hours blood glucose, glycosylated hemoglobin, high-density lipoprotein cholesterol, low density lipoprotein cholesterol, total cholesterol, triglycerides and blood uric acid were tested. CIMT was evaluated by neck vascular color Doppler ultrasonography. Respectively compare males and females carotid intima-media thickness in the quartile groups which made by waist circumference. The residents were divided into four groups: normal group (n=955), central obesity group (n=3 919), abnormal glucose group (n=465) and central obesity and abnormal glucose group (n=4 217). Single factor analysis of variance was used to compare the CIMT of each group. Pearson correlation analysis was performed for the correlation between waist circumference, HbA1c and other indicators and CIMT. The optimal cutoff point of carotid endarteral thickening screening was analyzed by using receiver operating characteristic (ROC) curve. Results (1) The CIMT values were (0.95±0.18), (1.00±0.17), (1.02±0.19), and (1.06±0.17) cm for normal group, central obesity group, abnormal glucose group, and central obesity and abnormal glucose group, respectively. CIMT level of central obesity abnormal glucose group was statistically higher than other three groups (F=156.57, P<0.001). (2) CIMT were positively correlated with waist circumference and HbA1c (r=0.209, r=0.186, P<0.001) with Pearson test. Multiple stepwise regression showed that there was a positive correlation between CIMT and waist circumference and HbA1c after adjusted for age, fasting plasma glucose, systolic blood pressure, LDL-C and uric acid (P<0.01). HDL-C and diastolic blood pressure were negatively correlated with CIMT (P<0.001). The areas under the receiver operating characteristic curve for predicting CIMT thickening with waist circumference were 0.615 and 0.604 in male and female, which was statistically significant (P<0.001). When waist circumference was 92.3 cm in male and 89.5 cm in female, the sensitivity was 47.7% and 57.2%, while as specificity was 68.9% and 57.6%, the Youden index was 0.166 and 0.148. The areas under the ROC curve for waist circumference combined HbA1c as a predictor of abnormal CIMT were 0.659 and 0.642 in male and female with sensitivity at 57.3% and 37.7% and specificity 63.3% and 41.0% respectively. Conclusion The CIMT values are significantly increased in the central obesity group and abnormal glucose group. There is significant correlation between waist circumference and CIMT, waist circumference and HbA1c are the independent risk factors for atherosclerosis. Waist circumference can be used to screen high-risk group with CIMT thickening. Key words: Obesity; Atherosclerosis; Impaired glucose; Intima-media thickness