Triglyceride-glucose index and risk of all-cause and cardiovascular mortality in patients with cardiovascular disease: Analysis from 1999 to 2018 NHANES data

This research seeks to examine the correlation between the triglyceride-glucose index and the hazard of all-cause and cardiovascular death in individuals with cardiovascular disease (CVD). By evaluating the index, we can better anticipate and assess the risk and prognosis of CVD patients, and provide precise and individualized guidance for clinical treatment and management. Demographic and clinical data of 2185 CVD patients from 10 cycles of the National Health and Nutrition Examination Survey database from 1999 to 2018 were extracted for analysis. Employed the 3-level quantile method to group data, and a multivariate Cox proportional hazard model along with subgroup analysis to study the correlation between index and both mortalities. Additionally, restricted cubic spline examination was employed to assess the correlation, aiming to enhance the comprehension of their interrelation. Over the course of an average post-observation duration of 89.5 months involving 2185 CVD individuals, 607 patients suffered from all-cause mortality and 313 patients suffered from CVD-related mortality. An inverted U-shaped correlation was identified through restricted cubic spline analysis. During the multivariate COX regression analysis, it was found that individuals in the T2 and T3 had a dramatically lower hazard of both mortalities as opposed to those in the T1. The results were overall consistent across subgroup analyses according to age, gender, race, body mass index, diabetes, and hypertension, the interaction between these characteristics and the index was not remarkable ( P > .05). Studies conducted on CVD individuals in the US have revealed a U-shaped correlation between triglyceride-glucose index and hazard of both all-cause and CVD-related death. However, further investigations are required to examine the particular function of index in forecasting the prognosis of CVD individuals. This will be helpful in accurately evaluating the risk and prognosis of CVD patients, and ultimately in developing more precise and personalized treatment and management strategies.