Cardiovascular–kidney–metabolic (CKM) syndrome, proposed by the American Heart Association, is strongly linked to cardiovascular disease (CVD) incidence and mortality. Triglyceride-glucose (TyG)-related indices are established predictors of CVD risk, but their associations with CVD and mortality in individuals with CKM syndrome remain understudied. This prospective study analyzed 282,920 UK Biobank participants with CKM syndrome stages 0–3, free of CVD at baseline. Four TyG-related indices were evaluated: TyG index, TyG-body mass index (BMI), TyG-waist circumference (TyG-WC), and TyG-waist-to-height ratio (TyG-WHtR). Outcomes, including overall CVD, coronary heart disease (CHD), stroke, all-cause mortality, and cardiovascular mortality, were identified via electronic medical records and death registries. Associations were analyzed via Cox proportional hazards and restricted cubic spline (RCS) models, with incremental predictive performance evaluated by the net reclassification index (NRI), integrated discrimination improvement (IDI), and area under the curve (AUC). During a median follow-up of 13.6 years, 27,134 overall CVD cases, 21,658 CHD, 6,717 strokes, 19,381 all-cause deaths, and 3,466 cardiovascular deaths occurred. Compared with the lowest quartile, the fully adjusted hazard ratios (HRs) for participants in the highest TyG quartile were 1.30 (95% CI 1.25–1.35) for overall CVD. Consistent positive associations were observed for TyG-BMI (HR: 1.49, 95% CI 1.43–1.55), TyG-WC (HR: 1.58, 95% CI 1.51–1.65), and TyG-WHtR (HR: 1.58, 95% CI 1.51–1.65). For all-cause mortality, HRs (95% CIs) in the highest versus the lowest quartile were 1.11 (1.06–1.16) for TyG-BMI, 1.24 (1.18–1.31) for TyG-WC, and 1.18 (1.13–1.24) for TyG-WHtR. Similar patterns were seen for cardiovascular mortality: TyG-BMI (HR: 1.42, 95% CI 1.27–1.59); TyG-WC(HR: 1.51, 95% CI 1.33–1.72); TyG-WHtR (HR: 1.48, 95% CI 1.31–1.67). RCS analyses revealed nonlinear associations between TyG-related indices and overall CVD (all P values for nonlinearity < 0.05), except for the TyG index, which showed a linear trend. Associations with cardiovascular mortality were predominantly linear. Furthermore, TyG-WHtR, TyG-WC, and TyG-BMI exhibited significantly higher NRI, IDI, and AUC values. Higher TyG-related indices, especially TyG-BMI, TyG-WC, and TyG-WHtR, were significantly associated with a higher risk of incident CVD and mortality in individuals with CKM syndrome stage 0 to 3. Integrating TyG indices with obesity measures could enhance the incremental predictive performance for prognostic outcomes in CKM syndrome patients.

Our study aims to investigate the association between SRH and all-cause mortality, and to investigate whether the SRH-mortality association varies across different socioeconomic status (SES) groups among middle-aged and older Chinese adults. We used data from China Health and Retirement Longitudinal Study (CHARLS), including 11,762 participants for the final analysis. Cox proportional hazards regression was conducted to investigate the association between SRH status and subsequent mortality. There were 724 death events occurred. The results were shown that fair/poor SRH participants tend to die than better SRH peers (HR 1.46, 95% CI 1.12–1.91). The association only occurred in those with rural residency (HR 1.46, 95% CI 1.05–2.04), those who were literate (HR 1.65, 95% CI 1.17–2.33), those with above-average household income (HR 1.95, 95% CI 1.15–3.29) and those working in agriculture and below (HR 1.38, 95% CI 1.02–1.88). In conclusion, worse SRH may be a predictor of all-cause mortality among middle-aged and elderly Chinese, especially in people with rural residency, literacy, above-average household income and working in agriculture and below.

Previous studies reported that depressive symptoms were associated with a high risk of all-cause mortality. However, the effect of different long-term depressive symptom trajectory patterns on the risk of all-cause mortality has not been evaluated. Our research aimed to explore the association between different depressive symptom trajectories and the risk of all-cause mortality in Chinese adults. The data we used were from the China Health and Retirement Longitudinal Study. In total, 13,624 subjects aged over 45 years were ultimately included in the analysis. Group-based trajectory modeling was used to identify the different trajectories of depressive symptoms. The multivariable Cox regression model was used to examine the association between long-term depressive symptom trajectories and all-cause mortality. The results show that a total of five depressive symptom trajectories were identified in our study, including stable-low, stable-moderate, increasing, decreasing and stable-high. Compared with individuals in the stable-low depressive symptom trajectories group, those in the increasing and stable-high trajectory groups possessed a greater mortality rate, with a multivariable-adjusted hazard ratio (95% CIs) for mortality were 1.30 (1.06, 1.60) and 1.59 (1.26, 2.02), respectively. In addition, we have not identified the significant risk of all-cause mortality in people with decreased and stable-moderate symptom trajectories. Moreover, the risk of all-cause mortality had an increasing trend among the different trajectory groups. In the sensitivity analysis, the association was robust in most of the subgroups. In conclusion, people with increasing and persistent higher depressive symptom trajectories were associated with an increased risk of all-cause mortality.

Aims: Frailty and poor sleep are both risk factors for delirium, but their joint and interactive associations with incident delirium are unknown. The study aimed to examine the associations of frailty status and sleep quality with incident delirium. Methods: A total of 346,846 participants from the UK Biobank without delirium, dementia, or cognitive impairment at baseline were included. Incident delirium cases were identified through the International Classification of Diseases, Tenth Revision codes. A modified‐version Fried phenotype was utilized to evaluate frailty status. A sleep score comprising five sleep characteristics was used to quantify sleep quality. Cox proportional hazard models were applied to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations. Also, their interactions on delirium incidence on the multiplicative and additive scales were quantified. Results: During a median follow‐up of 13.8 years, 6554 (1.9%) cases of incident delirium were documented. Prefrail and frail individuals had a 34% (HR = 1.34; 95% CI: 1.27–1.41) and 98% (HR = 1.98; 95% CI: 1.78–2.19) increased risk of delirium, respectively, compared with nonfrail controls. Each 1‐point increment in frailty score was associated with a 25% (HR = 1.25; 95% CI: 1.21–1.28) increased risk of delirium. Healthy sleep significantly counteracted the increased risk of delirium due to frailty ( P for multiplicative interaction = 0.026). In the poor and healthy sleep stratification, the corresponding HRs (95% CIs) were 2.07 (1.86–2.31) and 1.28 (0.83–1.97), respectively. Moreover, we observed the highest delirium risk in frail individuals with poor sleep quality (HR = 2.08; 95% CI: 1.83–2.35), with evidence of an additive interaction. Conclusions: Both physical frailty and poor sleep are significantly associated with higher delirium risk. Achieving healthy sleep could not only lower the risk of delirium but also greatly offset the adverse impact of frailty on delirium.

Objective: While metabolic-associated fatty liver disease (MAFLD) has been associated with individual cardiometabolic diseases (CMDs), its role in the dynamic progression to cardiometabolic multimorbidity (CMM) remains unclear. We investigated the association of MAFLD, its severity and subtypes with CMM in individuals with no or one CMD at baseline. Methods: This prospective cohort study involved 386,651 individuals (344,415 without and 42,236 with a single CMD at baseline) from the UK Biobank. MAFLD was defined as the presence of hepatic steatosis plus overweight/obesity, type 2 diabetes (T2D), or metabolic abnormalities. CMM was defined as the coexistence of two or more CMDs in the same person, including T2D, coronary heart disease (CHD) and stroke. Cox proportional hazard models and multistate models were performed to estimate the hazard ratios (HRs) and 95% confidence intervals (95% CIs). Results: During a median follow-up of 13.85 years, 4,622 new-onset CMM cases emerged among participants free of CMD at baseline. MAFLD was significantly associated with an increased risk of incident CMM (adjusted HR: 2.78, 95% CI: 2.60-2.96). Multistate models showed that MAFLD adversely affected most transitions from baseline to single CMDs and then to CMM. Among the single-CMD participants, the adjusted HRs of incident CMM in the MAFLD group were 1.21 (95% CI: 1.13-1.31) for T2D patients, 1.90 (1.75-2.05) for CHD patients, and 1.65 (1.45-1.87) for stroke patients, respectively. Conclusion: MAFLD independently elevated the risk of incident CMM, regardless of the baseline CMD status. These findings emphasize the necessity of targeted MAFLD interventions for CMM prevention.

Objective: To address the relationship between the proportions of carbohydrates and fat and hyperglycaemia in the Chinese population. Design: A cross-section research involving data from the China Health and Nutrition Survey in 2009, and nutritional status and health indicators were mainly focused. Setting: China. Participants: 8197 Chinese individuals aged over 16 years, including 1345 subjects who had a low-carbohydrate and high-fat diet, 3951 individuals who had a medium proportion of carbohydrate and fat diet, 2660 participants who had a high-carbohydrate and low-fat diet and 241 people who had a very-high-carbohydrate and low-fat diet. Results: Subjects with the high-carbohydrate and low-fat diet were significantly associated with an increased risk of hyperglycaemia (OR: 1·142; 95 % CI: 1·022, 1·276) when compared with the individuals with the medium proportion of carbohydrate and fat diet. Meanwhile, people with a very-high-carbohydrate and low-fat diet had a higher risk of hyperglycaemia (OR: 1·829; 95 % CI: 1·377, 2·429). In contrast, the association between participants with a low-carbohydrate and high-fat diet and hyperglycaemia was NS (OR: 1·082; 95 % CI: 0·942, 1·243) with adjusting a series of confounding factors. Furthermore, people with a very-high-carbohydrate and low-fat diet were significantly associated with a higher risk of hyperglycaemia in the major energy levels and social characteristics subgroup. Conclusions: We found the high-carbohydrate and low-fat and very-high-carbohydrate and low-fat diets were significantly associated with a high risk of hyperglycaemia. And, the association between low-carbohydrate and high-fat diets and the risk of hyperglycaemia was NS.