The binary diagnosis of Metabolic Syndrome(MetS) fails to accurately evaluate its severity, and the association between MetS severity and frailty progression remains inadequately elucidated. This study aims to clarify the relationship between the severity of MetS and the progression of frailty among the middle-aged and elderly population in China. Participants from the 2011–2018 China Health and Retirement Longitudinal Study(CHARLS) were included for a longitudinal analysis. The study employs a frailty index(FI) based on 32 health deficits to diagnose frailty and to assess FI trajectories. An age-sex-ethnicity-specific MetS scoring model (MetS score) was used to assess metabolic syndrome severity in Chinese adults. The Cumulative MetS score from 2012 to 2015 was calculated using the formula: (MetS score in wave 1 + MetS score in wave 3) / 2 × time(2015 - 2012). The association between MetS score, Cumulative MetS score, and the risk and trajectory of frailty were evaluated using Cox regression/logistic regression, and linear mixed models. Restricted Cubic Splines(RCS) models were utilized to detect potential non-linear associations. A higher MetS score was significantly associated with an increased risk of frailty(HR per 1 SD increase = 1.205; 95%CI: 1.14 to 1.273) and an accelerated FI trajectory(β per 1 SD increase = 0.113 per year; 95%CI: 0.075 to 0.15 per year). Evaluating changes in MetS score using a Cumulative MetS score indicated that each 1 SD increase in the Cumulative MetS score increased the risk of frailty by 22.2%(OR = 1.222; 95%CI: 1.133 to 1.319) and accelerated the rate of increase in FI(β = 0.098 per year; 95%CI: 0.058 to 0.138 per year). RCS model results demonstrated a dose-response curve relationship between MetS score and Cumulative MetS score with frailty risk. Stratified analysis showed consistency across subgroups. The interaction results indicate that in males and individuals under aged 60, MetS score may accelerate the increase in FI, a finding consistent across both models. Our findings underscore the positive correlation between the severity of MetS and frailty progression in the middle-aged and elderly, highlighting the urgent need for early identification of MetS and targeted interventions to reduce the risk of frailty.

Background: Blood glucose levels significantly affect the clinical prognosis of patients with coronary artery disease (CAD), and systemic immune inflammation is a common risk factor for both CAD and diabetes. However, the relationship between immune inflammation levels and poor prognosis in patients with CAD with different glucose metabolic statuses remains unclear. Methods: Between January 2007 and December 2020, we recruited 84,645 patients with CAD. The systemic immune inflammation index (SII) was used to comprehensively reflect the immune and inflammatory levels of patients and was calculated using the following formula: neutrophils × platelets/lymphocytes. The patients were classified into nine groups according to their glucose metabolism status (diabetes mellitus [DM], pre-diabetes mellitus [pre-DM], and normal glucose regulation [NGR]). Cox regression models and competing risk Fine and Gray models were used to investigate the association between SII and clinical outcomes. Results: During the follow-up period, 12,578 patients died, including 5857 cardiovascular-related and 1251 cancer-related deaths. The risk of all-cause and cause-specific mortality increased with increasing SII tertiles in CAD patients with NGR, pre-DM, and DM. When considering glucose metabolism status, the multivariate cox regression revealed that CAD patients with DM and SII-H levels had the highest risk of all-cause mortality (1.69 [1.56– 1.83]), cardiovascular mortality (2.29 [2.02– 2.59]), and cancer mortality (1.29 [1.01– 1.66]). Moreover, incorporating the SII into traditional risk factor models significantly improved the C-index for predicting all-cause and cardiovascular mortality. Conclusion: Systemic immune inflammation levels on admission were correlated with a higher risk of all-cause and cause-specific mortality in patients with CAD, particularly in those with DM.

Background: Metabolic syndrome (MetS) is a pathological condition characterized by the abnormal clustering of several metabolic components and has become a major public health concern. We aim to investigate the potential link of Systemic immunity-inflammation index (SII) on MetS and its components. Methods and result: Weighted multivariable logistic regression was conducted to assess the relationship between SII and MetS and its components. Restricted cubic spline (RCS) model and threshold effect analysis were also performed. A total of 6,999 U.S. adults were enrolled. Multivariate model found that SII were positively associated with MetS (OR = 1.18;95CI%:1.07–1.30) and hypertension (OR = 1.22; 95CI%:1.12–1.34) in a dose-dependent manner. When SII was converted into a categorical variable, the risk of MetS increased by 36% and the risk of hypertension increased by 53% in the highest quantile of SIIs. The RCS model confirmed linear associations between SII and MetS, as well as a non-linear association between SII and certain components of MetS, including hypertension, hyperglycemia, low HDL, and hyperlipidemia. Meanwhile, the relationship between SII and hypertension presents a J-shaped curve with a threshold of 8.27, above which the risk of hypertension increases. Furthermore, in MetS and hypertension, age, sex, body mass index (BMI), and race were not significantly associated with this positive association based on subgroup analyses and interaction tests(p for interaction > 0.05). Conclusions: The present study indicated that there was a higher SII association with an increased risk of MetS and hypertension in adults. However, further prospective cohort studies are required to establish a causal relationship between SII and MetS, as well as its components.

Previous studies indicated that obesity defined by BMI accelerate frailty, but this effect weakens when stratified by metabolic status. The Weight-adjusted Waist Index (WWI), a better indicator of central obesity, may provide a more accurate measurement. The primary aim of this study was to estimate the impact of WWI on frailty progression and compare its effects across different metabolic statuses. We used data from 10,440 participants aged 45 and older from the China Health and Retirement Longitudinal Study (CHARLS).The Frailty Index (FI) was derived from 32 health deficits. K-means clustering identified three trajectories for WWI or BMI: stable low, stable moderate, and stable high. Longitudinal associations were assessed using Accelerated Failure Time and linear mixed models. Each Standard deviation (SD) increment in WWI was linked to a faster onset of frailty [Time ratio (TR) = 0.899; 95% CI 0.872 to 0.926; p < 0.001] and accelerated FI progression (β = 0.186/year; 95% CI 0.152 to 0.220/year; p < 0.001). For BMI, each SD increment was associated with a shorter time to frailty onset (TR = 0.943; 95% CI 0.917 to 0.970; p < 0.001), which was positively correlated with the accelerated FI, but this estimate is imprecise. Smooth curve fitting revealed a dose–response relationship between WWI and FI and a U-shaped relationship between BMI and FI. In WWI trajectories, stable moderate, and stable high groups presented shorter frailty onset time and accelerated FI progression. For BMI trajectories, only the stable high group was associated with frailty progression in participants without baseline underweight. Stratified analysis showed that the association between WWI and FI progression remained consistent across different metabolic statuses, while the association between BMI and FI progression was weakened in all subgroups. WWI accelerates the progression of frailty,and remains consistent across different metabolic statuses, unlike BMI. This indicates that WWI may better capture obesity-related frailty risk, emphasizing the role of central obesity in frailty assessment.

Acute coronary syndrome (ACS) is a leading global cause of death, and its incidence is increasingly rising in young adults, who exhibit distinct clinical characteristics from elderly patients. However, multi-omics studies focusing specifically on young coronary heart disease (CHD) patients remain scarce, hindering precise diagnosis and mechanism exploration. Here, we enrolled 206 young chest pain patients (18-45 years old), including 122 ACS patients, 38 chronic coronary syndrome (CCS) patients, and 46 individuals with healthy coronary arteries (NC). We performed integrated analyses of peripheral blood mononuclear cell transcriptomics, serum metabolomics, stool metabolomics, and gut microbiome metagenomics to characterize CHD subtypes and develop targeted diagnostic tools. Our results showed that single omics layers had limited ability to distinguish CHD subtypes, while multi-omics integration significantly improved diagnostic efficacy. We identified unique molecular signatures for different subtypes: STEMI was associated with abnormal amino acid and carbohydrate metabolism, CCS was dominated by amino acid metabolism disturbances, and both STEMI and ACS showed enriched inflammation-related pathways. Novel biomarkers including p-chlorobenzene sulfonamide, cotinine, and the gut bacterium Streptococcus parasanguinis were identified, with Streptococcus parasanguinis validated as an atherogenic pathogen in a murine model. We constructed three multi-omics fusion diagnostic models (ACS vs. NACS, CCS vs. NC, STEMI vs. NSTE-ACS) with AUC values of 0.99, 0.95, and 0.96, respectively, and integrated them into a comprehensive diagnostic pipeline. Furthermore, multi-omics functional analysis unraveled a synergistic "microbiota-metabolism-immunity" regulatory network underlying CHD subtypes, linked to disordered amino acid and carbohydrate metabolism and aberrant inflammatory activation. This study provides a systematic molecular landscape of young CHD, a high-precision diagnostic strategy, and novel targets for mechanism research and targeted intervention, addressing the unmet clinical need for precise management of young CHD patients.