Existing studies have extensively explored the association between the triglyceride-glucose index (TyG) and cardiometabolic diseases (CMD), while the relationship between TyG and the occurrence of cardiometabolic multimorbidity (CMM) in the elderly population has been overlooked.
This study aims to investigate the association between TyG and the incidence of CMM in the elderly population.
A prospective cohort study was conducted using the Cheeloo Lifetime Electronic Health Database (Cheeloo LEAD), selecting elderly individuals aged ≥60 years in 2016. Using 2016 as the baseline, the study endpoints were defined as the occurrence of CMM or death, with the follow-up period lasting until December 31, 2022. Participants were divided into four groups based on the quartiles of the baseline TyG: Q1 (5.88≤TyG<8.22), Q2 (8.22≤TyG<8.53), Q3 (8.53≤TyG<8.90), and Q4 (8.90≤TyG<11.33). Kaplan-Meier survival curves were plotted, and Cox proportional hazards models were used to assess the impact of TyG on the risk of incident CMM. Subgroup and sensitivity analyses were also conducted. Restrictive cubic splines (RCS) were applied to explore the relationship between TyG and CMM.
A total of 15 258 participants were included in the analysis, with 3 875 in the Q1 group, 3 776 in the Q2 group, 3 840 in the Q3 group, and 3 767 in the Q4 group. The average follow-up time was 5.63 years, totaling 85 862.48 person-years of follow-up. There were 1 328 new cases of CMM (8.70%). The cumulative incidence rates of new CMM in the Q1-Q4 groups were 5.81%, 7.65%, 9.27%, and 12.16%, respectively. The comparison of CMM incidence rates among the four groups showed statistically significant differences (χ2=104.300, P<0.001). The results of the fully adjusted Cox proportional hazards model showed that, compared to the Q1 group, the risk of incident CMM in the Q2, Q3, and Q4 groups increased by 25.4% (HR=1.254, 95%CI=1.052-1.494, P<0.05), 42.0% (HR=1.420, 95%CI=1.196-1.686, P<0.001), and 83.6% (HR=1.836, 95%CI=1.535-2.195, P<0.001), respectively. The trend test in the Cox model indicated a dose-response relationship between TyG and the risk of incident CMM. This association was consistent in subgroup analyses based on sex and BMI, as well as in sensitivity analyses (P<0.05). RCS analysis showed a dose-response relationship between TyG and the risk of new CMM (P<0.001, Pnon-linearity=0.175) .
TyG is an independent risk factor for incident CMM in the elderly population, with a dose-response relationship between the two. As TyG levels increase, the risk of incident CMM rises, and high TyG levels significantly elevate the risk of CMM, particularly in males and individuals with higher BMI. Controlling TyG levels plays an important role in disease prevention among the elderly population.
With the progression of aging in China and the increase in the population with multiple coexisting diseases, the high risk associated with cardiovascular-metabolic multimorbidity (CMM) has made it a focal point for research. However, most studies have concentrated on individual cardiovascular metabolic diseases rather than exploring the comprehensive correlations within CMM.
To investigate the relationship between the triglyceride-glucose (TyG) index and the risk of CMM in middle-aged and elderly Chinese populations, and to evaluate the role of TyG in the prevention and control of CMM.
Participants were derived from the Anhui Province High-Risk Population Early Screening and Comprehensive Intervention Project for Cardiovascular Diseases conducted between 2017 and 2021. A total of 94 455 subjects were included based on inclusion and exclusion criteria. Baseline characteristics and laboratory examination indices were collected, and the TyG index was calculated. Multivariate logistic stepwise regression analysis was used to explore the impact factors of CMM by TyG as both a continuous variable and different quartiles. Z-tests were applied to compare odds ratio (OR) between groups. Restricted cubic spline (RCS) analysis was employed to assess potential non-linear associations, RCS curves were plotted, and the cutoff point where OR=1 was calculated.
Among the participants, 1 456 cases (664 males, 792 females) were identified with CMM, while 92 999 cases (38 313 males, 54 686 females) did not have CMM. In males, patients with CMM had higher age, BMI, mean arterial pressure (MAP), fasting plasma glucose (FPG), triglycerides (TG), proportion of individuals with high school education or above, diabetes, ischemic heart disease, stroke, hypertension, and TyG index compared to those without CMM (P<0.05) ; they also had lower rates of smoking, drinking, farmer occupation, total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) (P<0.05). Men and women were stratified into quartiles based on their TyG index values. For men, the groups were T1 (TyG 6.90-8.33, n=9 745), T2 (TyG 8.34-8.67, n=9 744), T3 (TyG 8.68-9.08, n=9 744), and T4 (TyG 9.09-11.60, n=9 744). For women, the groups were F1 (TyG 7.07-8.49, n=13 870), F2 (TyG 8.50-8.82, n=13 870), F3 (TyG 8.83-9.21, n=13 869), and F4 (TyG 9.22-11.60, n=13 869). Multivariable Logistic stepwise regression analysis demonstrated that after adjusting for age, marital status, smoking, alcohol consumption, educational level (high school or above), occupation (farmer), TC, HDL-C, LDL-C, BMI, hypertension, and MAP, he odds of CMM were 9.045 times higher in T4 than in T1 (OR=9.045, 95%CI=6.372-13.169, P<0.001) and 7.442 times higher in F4 than in F1 (OR=7.442, 95%CI=5.576-10.080, P<0.001). The Z-test indicated no statistically significant difference in the extent of OR elevation between genders (Z=0.824, P=0.410). For each unit increase in TyG, the risk of CMM increased by 3.960 times in males (OR=3.960, 95%CI=3.388-4.620, P<0.001) and by 4.447 times in females (OR=4.447, 95%CI=3.845-5.137, P<0.001), with no statistically significant difference in OR elevation between genders (Z=-1.216, P=0.224). RCS analysis revealed a significant non-linear relationship between TyG index and CMM risk after adjusting for confounders (Pnonlinear<0.05), indicating an increased risk when TyG>8.82 with a notably steeper slope.
The TyG index is closely related to the risk of CMM occurrence in both genders and exhibits similar predictive power. Individuals with a TyG>8.82 should be particularly monitored, and proactive preventive and intervention measures should be implemented to reduce the risk of CMM.
Cardiometabolic multimorbidity (CMM) is one of the most common patterns of co-morbidity aggregated in middle-aged and older adults. It greatly increases the risk of disability and death in our country. Insulin resistance and obesity are closely related to the occurrence and development of cardiometabolic diseases (CMD). The correlation between obesity and various types of CMD has been previously confirmed. The risk of CMM in residents with varying types of obesity and gender may be different and still unclear.
To identify the correlation of the type of obesity and CMM in male and female middle-aged residents in Anhui Province.
The subjects of this study were derived from the Early Screening and Comprehensive Intervention Program for People at High Risk of Cardiovascular Disease carried out in Anhui Province from 2017 to 2021, with a total of 10 project sites involving community residents in 12 counties and cities. Finally, 70 812 permanent middle-aged residents (45-<65 years) were included. Surveying of the subjects was performed by pre-trained investigators, including the general information (age, gender, smoking, alcohol consumption, remarriage education level of high school and above, farmers), disease history (hypertension, heart disease, diabetes, stroke, dyslipidemia), physical examination (height, body mass index, waist circumference), and laboratory tests (blood glucose, blood lipids). Subjects were divided into male and female, and sub-divided into non-obese, peripheral obesity, central obesity, and compound obesity. Logistic regression analysis was conducted to explore the correlation of CMM with gender and type of obesity. The effect value in male and female groups with different types of obesity was compared by the Z-test in R package.
A total of 26 726 male and 44 086 female residents were included. The prevalence of CMM in the total population, male and female groups was 14.6% (10 361/70 812), 16.6% (4 445/26 726), and 13.4% (5 916/44 086), respectively. The prevalence of hypertension, diabetes mellitus, heart disease, stroke, and dyslipidemia among middle-aged residents of Anhui Province was 27.0%, 7.8%, 0.5%, 2.5%, and 29.5%, respectively. Logistic regression analysis showed that, after adjusting for age, smoking, alcohol consumption, remarriage, education level of high school and above, and occupation of farmers, the risk of CMD significantly increased in the peripheral obesity (OR=1.665, 95% CI=1.599-1.734), central obesity (OR=1.788, 95% CI=1.656-1.930), and compound obesity subgroups (OR=3.020, 95% CI=2.913-3.131) than the non-obesity subgroup (P<0.05). In either the male or female group, the risk of CMM increased sequentially in the peripheral obesity, central obesity, and compound obesity subgroups. In the male group, the OR (95%CI) of an increased risk of CMM in peripheral obesity, central obesity, and compound obesity subgroups compared to the non-obese subgroups was 2.008 (1.822-2.213), 2.281 (1.875-2.774), and 4.137 (3.799-4.504), respectively; while in the female group, it was 1.574 (1.443-1.717), 1.727 (1.509-1.976), and 2.916 (2.721-3.126), respectively (P<0.05). The Z-test results showed a significant difference among the peripheral obesity, central obesity, and compound obesity subgroups in male and female residents (P<0.05). After adjusting for the blood lipids and other related indexes, the risk of CMM in the peripheral obesity, central obesity, and compound obesity subgroups in male and female residents increased sequentially with a significant difference (P<0.05). Z-test showed a significant difference in the risk of CMM in the compound obesity subgroup of male and female residents (Z=2.258, P<0.05) .
The risk of CMM varies in middle-aged residents of Anhui Province with different types of obesity, showing the highest risk in those with compound obesity, followed by central obesity. Male middle-aged residents with compound obesity have a higher risk of CMM than females, serving as a highly concerned population.