Association of 24 Conventional and Unconventional Anthropometric Indicators with Pre-Hypertension or Hypertension: A Population-Based Cross-Sectional Study in Chinese Population

Background: Anthropometric indicators are known to be closely related to pre-hypertension and hypertension. This study aimed to explore the association of conventional and unconventional anthropometric indicators with pre-hypertension and hypertension. Methods: About 8787 adults (aged ≥ 18 years) who joined in the Chinese Residents Cardiovascular Disease and Risk Factors Surveillance Project (2020) were included. Twenty-four anthropometric indicators were measured and calculated, including conventional anthropometric indicators [eg, weight, fat mass (FM), body mass index (BMI), height-adjusted weight (HtaW), fat mass index (FMI), fat-free mass index (FFMI)] and unconventional anthropometric indicators [eg, atherogenic index of plasma (AIP), triglyceride-glucose index (TyG)]. LASSO regressions were used to identify key anthropometric indicators associated with pre-hypertension/hypertension. Multivariate logistic regression model, RCS regression analysis, subgroup analyses and sensitivity analyses were conducted to explore the association between anthropometric indicators and pre-hypertension/hypertension. ROC and AUC were also utilized to evaluate the performance of anthropometric indicators in identifying pre-hypertension/ hypertension. Results: After adjustment for potential confounders, multivariate logistic regression showed that weight, BMI, HtaW, FMI, FFMI, AIP, and TyG were significantly associated with pre-hypertension, while FM, AIP, and TyG were significantly associated with hypertension. ROC analysis showed that conventional anthropometric indicators were slightly superior to unconventional anthropometric indicators in identifying pre-hypertension and hypertension. RCS models suggested that weight, FMI, FFMI, AIP, and TyG had linear dose-response relationships with pre-hypertension risk, while BMI and HtaW were nonlinearly associated with pre-hypertension risk; FM, AIP and TyG had nonlinear dose-response relationships with hypertension risk ( P nonlinear < 0.05). The results from the subgroup analysis and sensitivity analysis basically supported the primary findings. Conclusion: As one of the first comprehensive comparisons of 24 anthropometric indicators in a large Chinese population, this study found that BMI and TyG were the best anthropometric indicators for identifying pre-hypertension, while TyG showed a significantly stronger association with hypertension. Plain Language Summary: Many studies have shown that common body measures such as body mass index (BMI) and waist circumference can predict whether someone is likely to develop high blood pressure. Recently, researchers have started looking at new body measures like the atherogenic index of plasma (AIP) and triglyceride-glucose index (TyG). However, not many studies have compared how well these new body measures predict high blood pressure compared to the body traditional ones. In our research, we used data from the Fujian Province part of the Chinese Residents Cardiovascular Disease and Risk Factors Surveillance Project, 2020 (CRCDRFSP-2020), to study how 24 different body measures relate to high blood pressure and its early stage, called pre-hypertension. We used detailed statistical methods to find out which measures are the best predictors of high blood pressure and its early stage. We discovered that weight, BMI, and a few other measures are closely linked to pre-hypertension. Specifically, BMI and TyG were the most strongly connected. For high blood pressure, fat mass, AIP, and TyG were important, with TyG being particularly strong in predicting high blood pressure. From these results, we have suggested a practical method for doctors to pick the best body measures to predict high blood pressure early, based on a person’s specific situation. We also created a decision diagram to help with this. We believe this approach will help in early recognition of high blood pressure, allowing for better prediction and helping to reduce its occurrence.