Background and Aims: Previous studies found that frailty was an important risk factor for cardiovascular disease (CVD). However, previous studies only focused on baseline frailty status, not taking into consideration the changes in frailty status during follow-up. The aim of this study was to investigate the associations of changes in frailty status with incident CVD. Methods: This study used data of three prospective cohorts: China Health and Retirement Longitudinal Study (CHARLS), English Longitudinal Study of Ageing (ELSA), and Health and Retirement Study (HRS). Frailty status was evaluated by the Rockwood frailty index and classified as robust, pre-frail, or frail. Changes in frailty status were assessed by frailty status at baseline and the second survey which was two years after the baseline. Cardiovascular disease was ascertained by self-reported physician-diagnosed heart disease (including angina, heart attack, congestive heart failure, and other heart problems) or stroke. Cox proportional hazard models were used to calculate the hazard ratio (HR) and 95% confidence interval (95% CI) after adjusting for potential confounders. Results: A total of 7116 participants from CHARLS (female: 48.6%, mean age: 57.4 years), 5303 from ELSA (female: 57.7%, mean age: 63.7 years), and 7266 from HRS (female: 64.9%, mean age: 65.1 years) were included according to inclusion and exclusion criteria. The median follow-up periods were 5.0 years in the CHARLS, 10.7 years in the ELSA, and 9.5 years in the HRS. Compared with stable robust participants, robust participants who progressed to pre-frail or frail status had increased risks of incident CVD (CHARLS, HR = 1.84, 95% CI: 1.54–2.21; ELSA, HR = 1.53, 95% CI: 1.25–1.86; HRS, HR = 1.59, 95% CI: 1.31–1.92). In contrast, frail participants who recovered to robust or pre-frail status presented decreased risks of incident CVD (CHARLS, HR = 0.62, 95% CI: 0.47–0.81; ELSA, HR = 0.49, 95% CI: 0.34–0.69; HRS, HR = 0.70, 95% CI: 0.55–0.89) when compared with stable frail participants. These decreased risks of incident CVD were also observed in pre-frail participants who recovered to robust status (CHARLS, HR = 0.66, 95% CI: 0.52–0.83; ELSA, HR = 0.65, 95% CI: 0.49–0.85; HRS, HR = 0.71, 95% CI: 0.56–0.91) when compared with stable pre-frail participants. Conclusions: Different changes in frailty status are associated with different risks of incident CVD. Progression of frailty status increases incident CVD risks, while recovery of frailty status decreases incident CVD risks.

Background: SII and SIRI are two novel systemic inflammation indexes that were suggested in predicting poor outcomes in cancers. However, no studies have examined their effect on cardiovascular diseases (CVDs) and all-cause mortality. Thus, this study aims to investigate associations between SII, SIRI, and the risks for CVDs and all-cause mortality. Methods: A total of 85,154 participants from the Kailuan cohort were included and followed up for incidents of CVDs (including MI, stroke) and all-cause death for 10 years. Multiple Cox regression was used to calculate the adjusted hazard ratios (HRs). Results: During the follow-up period, 4262 stroke events, 1233 MI events, and 7225 all-cause deaths were identified, respectively. Compared with the lowest quantile (Q1) of SII or SIRI, after adjusted for most cardiovascular risk factors, both indexes showed positive associations with the risk for stroke (adjusted HRs in Q4 were 1.264 (95% CI: 1.157,1.382) for SII, 1.194 (95% CI: 1.087,1.313) for SIRI), and all-cause death (adjusted HRs in Q4 were 1.246 (95% CI: 1.165,1.331) for SII, 1.393 (95% CI: 1.296,1.498) for SIRI). Additionally, higher SII and SIRI are also associated with increased risk of hemorrhagic stroke and ischemic stroke. Higher SIRI but not SII exhibited a higher MI risk, the adjusted HR in Q4 was 1.204 (1.013,1.431). The significant association remained after additional adjustment for CRP. Subgroup analysis and sensitivity analysis displayed consistent results except for SIRI with MI, where the association did not arrive at significance in subjects aged ≥ 60. Conclusion: Elevated SII and SIRI increased the risk of stroke, two stroke subtypes, and all-cause death. Higher SIRI, but not SII associated with increased MI incidence, and the association of SIRI was only significant in subjects aged < 60.

Background: Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear.Methods: A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed‐effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression.Results: In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (β: ‐0.011, 95% CI: −0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (β: 0.103, 95% CI: −0.005 to 0.210, P = 0.061) and MUNW (β: 0.157, 95% CI: −0.011 to 0.324, P = 0.066), but not for MHOO (β: ‐0.047, 95% CI: −0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, β: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, β: 0.210, 95% CI: 0.049 to 0.370, P = 0.011).Conclusions: Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on metabolic status.

Background: The pathogenesis of obesity is reportedly related to variations in the fat mass and an obesity-associated gene (FTO); however, as the number of reports increases, particularly with respect to varying ethnicities, there is a need to determine more precisely the effect sizes in each ethnic group. In addition, some reports have claimed ethnic-specific associations with alternative SNPs, and to that end there has been a degree of confusion. Methods: We searched PubMed, MEDLINE, Web of Science, EMBASE, and BIOSIS Preview to identify studies investigating the associations between the five polymorphisms and obesity risk. Individual study odds ratios (OR) and their 95% confidence intervals (CI) were estimated using per-allele comparison. Summary ORs were estimated using a random effects model. Results: We identified 59 eligible case-control studies in 27 articles, investigating 41,734 obesity cases and 69,837 healthy controls. Significant associations were detected between obesity risk and the five polymorphisms: rs9939609 (OR: 1.31, 95% CI: 1.26 to 1.36), rs1421085 (OR: 1.43, 95% CI: 1.33 to 1.53), rs8050136 (OR: 1.25, 95% CI: 1.13 to 1.38), rs17817449 (OR: 1.54, 95% CI: 1.41 to 1.68), and rs1121980 (OR: 1.34, 95% CI: 1.10 to 1.62). Begg's and Egger's tests provided no evidence of publication bias for the polymorphisms except rs1121980. There is evidence of higher heterogeneity, with I ² test values ranging from 38.1% to 84.5%. Conclusions: This meta-analysis suggests that FTO may represent a low-penetrance susceptible gene for obesity risk. Individual studies with large sample size are needed to further evaluate the associations between the polymorphisms and obesity risk in various ethnic populations.

Thyroid cancer is the most common malignant disease of the endocrine system. Previous studies indicate a rapid increase in the incidence of thyroid cancer in recent decades, and this increase has aroused the great public concern. The aim of this study was to analyze the trends in incidence, mortality and clinical-pathological patterns of thyroid cancer in Zhejiang province. Population-based incidence and mortality rates of thyroid cancer were collected from eight cancer registries in Zhejiang from 2000 to 2012. The incidence and mortality rates were age-standardized to Segi’s world population. A Joinpoint model was used to examine secular trends in age-adjusted thyroid cancer rates with the Joinpoint Regression Program Version 4.0.0. Thyroid cancer patients were recruited from Zhejiang Cancer Hospital from 1972 to 2014. Patient demographics, tumor histology and tumor size were compared among the different periods of 1972–1985, 1986–1999 and 2000–2014. The age-standardized incidence rate of thyroid cancer in Zhejiang cancer registries was 2.75/105 in 2000, and increased to 19.42/105 in 2012. Additionally, we observed significantly increasing incidence rates with the Annual Percent Change (APC) of 22.86% (95%CI, 19.2%–26.7%). The age-standardized mortality of thyroid cancer in Zhejiang cancer registries was 0.23/105 in 2000 and 0.25/105 in 2012. No significant change in mortality rate was found. We observed a rapid increase in the proportions of papillary thyroid carcinoma (PTC) in 12,508 patients with thyroid carcinoma identified in the Zhejiang Cancer Hospital from 1972 to 2014 while the proportions of poorly differentiated thyroid cancer (PDTC), medullary thyroid carcinoma (MTC) and follicular thyroid carcinoma (FTC) decreased over the decades. In the PTC cases, the proportion of patients with maximum tumor diameter (MTD) < 1 cm dramatically and significantly increased from 0 in 1972–1985 to 32.1% in 2000–2014. A rapid increase in incidence and a stable trend in mortality of thyroid cancer were found in the distribution of thyroid cancer. Most of the increased incidence was PTC, especially the papillary thyroid microcarcinoma (PTMC) with MTD < 1 cm. This increase in incidence might be due to increased diagnosis with advanced technology.

Background: Considerable evidence has been reported that tobacco use could cause alterations in gut microbiota composition. The microbiota-gut–brain axis also in turn hinted at a possible contribution of the gut microbiota to smoking. However, population-level studies with a higher evidence level for causality are lacking. Methods: This study utilized the summary-level data of respective genome-wide association study (GWAS) for 211 gut microbial taxa and five smoking phenotypes to reveal the causal association between the gut microbiota and tobacco smoking. Two-sample bidirectional Mendelian randomization (MR) design was deployed and comprehensively sensitive analyses were followed to validate the robustness of results. We further performed multivariable MR to evaluate the effect of neurotransmitter-associated metabolites on observed associations. Results: Our univariable MR results confirmed the effects of smoking on three taxa (Intestinimonas, Catenibacterium, and Ruminococcaceae, observed from previous studies) with boosted evidence level and identified another 13 taxa which may be causally affected by tobacco smoking. As for the other direction, we revealed that smoking behaviors could be potential consequence of specific taxa abundance. Combining with existing observational evidence, we provided novel insights regarding a positive feedback loop of smoking through Actinobacteria and indicated a potential mechanism for the link between parental smoking and early smoking initiation of their children driven by Bifidobacterium. The multivariable MR results suggested that neurotransmitter-associated metabolites (tryptophan and tyrosine, also supported by previous studies) probably played a role in the action pathway from the gut microbiota to smoking, especially for Actinobacteria and Peptococcus. Conclusions: In summary, the current study suggested the role of the specific gut microbes on the risk for cigarette smoking (likely involving alterations in metabolites) and in turn smoking on specific gut microbes. Our findings highlighted the hazards of tobacco use for gut flora dysbiosis and shed light on the potential role of specific gut microbiota for smoking behaviors.