Conicity-index predicts all-cause mortality in Chinese older people: a 10-year community follow-up

Comparison of obesity indicators for predicting cardiovascular risk factors and multimorbidity among the Chinese population based on ROC analysis

To date, the best obesity-related indicators (ORIs) for predicting hypertension, dyslipidaemia, Type 2 diabetes mellitus (T2DM) and multimorbidity are still controversial. This study assessed the ability of 17 ORIs [body mass index (BMI), body fat percentage (BF%), c-index, Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE), a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), waist-hip ratio (WHR), waist-to-height ratio (WHtR), body roundness index (BRI), abdominal volume index (AVI), triglyceride glucose index (TYG), lipid accumulation product (LAP), visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), waist triglyceride index (WTI) and cardiometabolic index (CMI)] to predict hypertension, dyslipidemia, T2DM, and multimorbidity in populations aged 40-69 years. From November 2017 to December 2022, 10,432 compliant residents participated in this study. Receiver operating characteristic curves were used to assess the ability of ORIs to predict target diseases across the whole population and genders. The DeLong test was used to analyse the heterogeneity of area under curves (AUCs). Multivariable logistic regression was used to analyse the association of ORIs with hypertension, dyslipidaemia, T2DM, and multimorbidity. The prevalence of hypertension, dyslipidaemia, T2DM, and multimorbidity was 67.46%, 39.36%, 12.54% and 63.58%, respectively. After excluding ORIs associated with the target disease components, in the whole population, CVAI (AUC = 0.656), BMI (AUC = 0.655, not significantly different from WC and AVI), CVAI (AUC = 0.645, not significantly different from LAP, CMI, WHR, and WTI), and TYG (AUC = 0.740) were the best predictor of hypertension, dyslipidemia, T2DM, and multimorbidity, respectively (all P < 0.05). In the male population, BF% (AUC = 0.677), BMI (AUC = 0.698), CMI (AUC = 0.648, not significantly different from LAP and CVAI), and TYG (AUC = 0.741) were the best predictors (all P < 0.05). In the female population, CVAI (AUC = 0.677), CUN-BAE (AUC = 0.623, not significantly different from BF%, WC, WHR, WHtR, BRI and BMI), CVAI (AUC = 0.657, not significantly different from WHR), TYG (AUC = 0.740) were the best predictors (all P < 0.05). After adjusting for all covariates, all ORIs were significantly associated with hypertension, dyslipidaemia, T2DM, and multimorbidity (all P < 0.05), except for ABSI and hypertension and BAI and T2DM, which were insignificant. Ultimately, after considering the heterogeneity of prediction of ORIs among different populations, for hypertension, BF% was the best indicator for men and CVAI for the rest of the population. The best predictors of dyslipidaemia, T2DM, and multimorbidity were BMI, CVAI and TYG, respectively. Screening for common chronic diseases in combination with these factors may help to improve the effectiveness.

Optimal cut-off points of anthropometric and body roundness indices associated with diabetes: Persian (Shahedieh) cohort study

Introduction

Diabetes is a chronic and concerning health condition that poses a significant public health challenge. Given that preventing, detecting early, and treating T2DM can enhance public health outcomes, the objective of this study was to identify the most effective obesity indices and determine their optimal cut-off points for predicting the risk of T2DM in an Iranian population.

Methods

This study was conducted on 8,019 male and female participants aged between 35 and 70 years in the context of Shahedieh cohort study. The ROC curve analysis was utilized to determine the optimal cut-off point of each anthropometric index to predict diabetes in age-sex categories.

Results

The overall diabetes incidence in the study population was 2.5%, with 2.5% in men and 2.4% in women. In men, significant differences in most of the anthropometric indices were observed between diabetic individuals and healthy counterparts. This study found that for women 45-65, BMI and weight, and for men under 65 years, weight, WHR, BMI, WC, WHTR, AVI, and BRI are efficient T2DM predictors. The AUC of these indices varied from 0.593 (95% CI: 0.510-0.676) to 0.668 (95% CI: 0.586-0.750) in men, and from 0.587 (95% CI: 0.510-0.664) to 0.644 (95% CI: 0.535-0.754) in women.

Conclusion

Anthropometric indices and body roundness are simple, inexpensive, and noninvasive means markers to predict the risk of diabetes. Our findings show that most of the studied indices had acceptable prediction power for men except for elderly. For women over 45 years old, weight and BMI are appropriate predictors. It seems that the approach of reducing diabetes incidence through early detection and primary prevention is achievable.

Association between two novel anthropometric measures and type 2 diabetes in a Chinese population

AIMS

To investigate the associations of conicity index (C-index) and relative fat mass (RFM) with incident type 2 diabetes mellitus (T2DM) among adults in China.

MATERIALS AND METHODS

A total of 10 813 participants aged over 18 years in Shenzhen Longhua district were enrolled in a follow-up study conducted from 2018 to 2022. The participants were categorized based on quartiles (Q) of C-index and RFM. The Cox proportional hazards model was performed to examine the relationships between C-index, RFM and the risk of T2DM.

RESULTS

After adjusting for potential confounding factors, including age, sex, occupation, marital status, education level, smoking status, alcohol consumption, physical exercise, hypertension status, fasting blood glucose (FBG) and total cholesterol (TC), both C-index and RFM showed positive and independent associations with risk of T2DM. The multivariable-adjusted hazard ratios (95% confidence intervals) for T2DM risk in participants in C-index Q3 and Q4 compared with those in C-index Q1 were 1.50 (1.12, 2.02) and 1.73 (1.29, 2.30), and 1.94 (1.44, 2.63), 3.18 (1.79, 5.64), 4.91 (2.68, 9.00) for participants in RFM Q2, Q3 and Q4 compared with RFM Q1. These differences were statistically significant (all p < 0.05).

CONCLUSION

C-index and RFM are strongly associated with new-onset T2DM and could be used to identify the risk of diabetes in large-scale epidemiological studies.

Obesity-and lipid-related indices as a risk factor of hypertension in mid-aged and elderly Chinese: a cross-sectional study

OBJECTIVE

Hypertension refers to the persistent elevation of blood pressure above the established normal range, resulting in increased pressure exerted by blood on the walls of blood vessels during its circulation. Recent studies have identified significant associations between obesity and lipid-related indices, as well as hypertension. Nevertheless, these studies have yet to comprehensively examine the correlation between the two variables. Our objective is to identify the fat and lipid-related indices that have the strongest correlation with hypertension.

METHOD

There was a total of 9488 elderly and middle-aged Chinese citizens who participated in this investigation. The participants in this research were separated into distinct gender cohorts. The participants were classified into normal and hypertensive categories according to their gender, with hypertension defined as a blood pressure level of 140/90 mmHg or higher, or a history of hypertension. Through the utilization of binary logistic regression analyses and the receiver operator curve (ROC), the optimal among fourteen indicators associated with obesity and lipids were identified.

RESULTS

After adjusting for variables, statistical analysis showed that all 14 measures of obesity and lipid were risk factors for hypertension. The receiver operating characteristic (ROC) curve analysis reveals that the Chinese visceral adiposity index (CVAI) has the highest degree of relationship to hypertension. Simultaneously, a statistically significant association between hypertension and these 14 variables was observed in both males and females.

CONCLUSION

There was a significant independent association between various parameters related to obesity and lipid-related index and the presence of hypertension, indicating that these factors can be considered risk factors for hypertension. CVAI and WHtR (waist height ratio) can be used to screen the high-risk groups of hypertensions in middle-aged and elderly people in China, and then take individualized health care measures to reduce the harm of hypertension.

The Efficacy of Anthropometric Indicators in Predicting Non-Alcoholic Fatty Liver Disease Using FibroScan® CAP Values among the Taiwanese Population

The controlled attenuation parameter (CAP) measurement obtained from FibroScan® is a low-risk method of assessing fatty liver. This study investigated the association between the FibroScan® CAP values and nine anthropometric indicators, including the abdominal volume index (AVI), body fat percentage (BFP), body mass index (BMI), conicity index (CI), ponderal index (PI), relative fat mass (RFM), waist circumference (WC), waist-hip ratio (WHR), and waist-to-height ratio (WHtR), and risk of non-alcoholic fatty liver disease (fatty liver). We analyzed the medical records of adult patients who had FibroScan® CAP results. CAP values <238 dB/m were coded as 0 (non- fatty liver) and ≥238 dB/m as 1 (fatty liver). An individual is considered to have class 1 obesity when their body mass index (BMI) ranges from 30 kg/m2 to 34.9 kg/m2. Class 2 obesity is defined by a BMI ranging from 35 kg/m2 to 39.9 kg/m2, while class 3 obesity is designated by a BMI of 40 kg/m2 or higher. Out of 1763 subjects, 908 (51.5%) had fatty liver. The BMI, WHtR, and PI were found to be more strongly correlated with the CAP by the cluster dendrogram with correlation coefficients of 0.58, 0.54, and 0.54, respectively (all p < 0.0001). We found that 28.3% of the individuals without obesity had fatty liver, and 28.2% of the individuals with obesity did not have fatty liver. The BMI, CI, and PI were significant predictors of fatty liver. The BMI, PI, and WHtR demonstrated better predictive ability, indicated by AUC values of 0.72, 0.68, and 0.68, respectively, a finding that was echoed in our cluster group analysis that showed interconnected clustering with the CAP. Therefore, of the nine anthropometric indicators we studied, the BMI, CI, PI, and WHtR were found to be more effective in predicting the CAP score, i.e., fatty liver.