Objective: Our study aimed to explore the association between trimethylamine N-oxide and frailty in older adults with cardiovascular disease. Patients and Methods: This cross-sectional study analyzed a total of 451 people aged 65 years or older who underwent comprehensive geriatric assessments. Frailty status was determined using a frailty index constructed with 48 variables according to the cumulative deficits model. Physical frailty and cognitive frailty were also assessed in detail. Fasting plasma TMAO was measured by mass spectrometry. Results: The proportion of frail subjects was 29.9% (135/451). Plasma TMAO levels were significantly higher in frail patients than in nonfrail individuals (4.04 [2.84– 7.01] vs 3.21 [2.13– 5.03] μM; p< 0.001). Elevated plasma TMAO levels were independently associated with the likelihood of frailty (OR 2.12, 95% CI 1.01– 4.38, p=0.046). Dose–response analysis revealed a linear association between the TMAO concentration and the OR for frailty. A 2-unit increase in TMAO was independently correlated with physical frailty (OR 1.23, 95% CI 1.08– 1.41, p for trend 0.002) and cognitive frailty (OR 1.21, 95% CI 1.01– 1.45, p for trend 0.04). Conclusion: Elevated circulating TMAO levels are independently associated with frailty among older adults with cardiovascular disease.

Background: Frailty increases the adverse outcomes of clinical heart failure; however, the relationship between frailty and stage-B heart failure (SBHF) remains unknown. We aimed to explore the epidemiology and predictive value of frailty in older adults with SBHF. Methods: A prospective cohort of SBHF inpatients aged 65 years or older who were hospitalized between September 2018 and February 2019 and were followed up for 6 months were included. SBHF was defined as systolic abnormality, structural abnormality (left ventricular enlargement, left ventricular hypertrophy, wall motion abnormalities, valvular heart disease), or prior myocardial infarction. Frailty was assessed by the Fried frailty phenotype. Multivariable Cox proportional hazards regression was used to explore the independent risk and prognostic factors. Results: Data of 443 participants (age: 76.1 ± 6.79 years, LVEF: 62.8 ± 4.92%, men: 225 [50.8%], frailty: 109 [24.6%]) were analyzed. During the 6-month follow-up, 83 (18.7%) older SBHF inpatients experienced all-cause mortality or readmission, and 29 (6.5%) of them developed clinical HF. Frail individuals had a 1.78–fold (95%CI: 1.02–3.10, P = 0.041) higher risk of 6-month mortality or readmission and a 2.83–fold (95%CI 1.24–6.47, P = 0.014) higher risk of developing clinical HF, independent of age, sex, left ventricular ejection fraction, and N-terminal pro-B-type natriuretic peptide level. Conclusions: Frailty is common in older SBHF inpatients and should be considered to help identify individuals with an increased risk of mortality or readmission, and developing clinical HF. Trial registration: ChiCTR1800017204.

High-mobility group box 1 (HMGB1) is newly discovered protein, which play a crucial role in the pathogenesis of systemic inflammation. Recent studies showed that HMGB1 is one of the important pathophysiological mechanisms in the occurrence and development of atherosclerosis. The purpose of the present study was to investigate the relationship between serum HMGB1 levels and endothelial function in patients with polycystic ovary syndrome (PCOS). Eighty newly diagnosed patients with PCOS and eighty normal women of similar age were selected. Metformin treatment (1,500 mg/day) was initiated in all patients for a period of consecutive 3 months. Serum HMGB1 levels were measured by ELISA. High resolution ultrasound was used to measure the brachial artery diameter at rest, after reactive hyperemia (flow-mediated arterial dilation, FMD) and after sublingual glyceryltrinitrate. Serum HMGB1 levels in PCOS were 24.87±14.93 ng/ml, which were significantly higher than that in controls (8.82±3.55 ng/ml, p<0.01). After 3 months treatment, serum HMGB1 levels decreased significantly (p<0.05). By dividing the distribution of HMGB1 levels into quartiles, serum HMGB1 levels were increased gradually with the increase of testosterone levels (p<0.05), whereas the FMD levels decreased (p<0.05). Multiple stepwise linear regression analysis showed that FMD (estimated coefficient β=-0.69, p=0.005), testosterone (β=0.31, p=0.045), TBARS (β=0.69, p=0.012) and hs-CRP levels (β=0.68, p=0.001) were significantly associated with HMGB1. The absolute changes in HMGB1 showed a positive correlation with the changes in testosterone (p<0.05) and negative correlation with the changes in FMD (p<0.05) in patients with PCOS during the course of metformin therapy. Serum HMGB1 levels are correlated with endothelial dysfunction in patients with PCOS. Our study suggests that HMGB1 may contribute to the early stage of atherosclerosis in patients with PCOS

Chronic inflammation is associated with major adverse cardiovascular events (MACEs), mortality, and frailty. Our aim was to add high-sensitivity C-reactive protein (hsCRP) to the frailty assessment to predict its association with prognosis of older adults with cardiovascular disease (CVD). A comprehensive geriatric assessment was conducted at baseline in 720 in-patients aged ≥65 years with CVD. We divided the population into frailty and non-frailty groups according to the Fried phenotype, and hsCRP was further combined with frailty to stratify all patients into c-frailty and non-c-frailty groups. Predictive validity was tested using Cox proportional hazards regression model analysis and the discriminative ability was evaluated by receiver operating characteristic (ROC) curves. Of all the subjects enrolled, 51.0% were male and the mean age was 75.32 ± 6.52 years. The all-cause death and MACE rate was 6.4% at the 1-year follow-up. Frailty and c-frailty were independent predictors of all-cause death and MACE (hazard ratio [HR]: 2.55, 95% confidence interval [CI]: 1.35-4.83, p = 0.004; HR: 3.67, 95% CI: 1.83-7.39, p < 0.001). Adding hsCRP to the frailty model resulted in a significant increase in the area under the ROC curve from 0.74 (95% CI: 0.70-0.77) to 0.77 (95% CI: 0.71-0.84) (p = 0.0132) and a net reclassification index of 7.9% (95% CI: 1.96%-12.56%, p = 0.012). Adding hsCRP to the frailty assessment is helpful to identify a subgroup of older CVD patients with a higher risk of death and MACE over a period of 1 year. ChiCTR1800017204; date of registration: 07/18/2018. URL: http://www.chictr.org.cn/showproj.aspx?proj=28931.

Objective: To evaluate the prognostic value of frailty in gerontal pre-clinical heart failure (stage B heart failure, SBHF) inpatients.Background: The association between frailty and SBHF remains unknown.Methods: We conducted a subgroup analysis of a prospective observational cohort study on frailty. The previous study recruited 1,000 elderly inpatients who were consecutively admitted to a tertiary referral hospital in Beijing, China, from September 2018 to February 2019. The outcomes were all-cause death or readmission at 1-year follow-up. SBHF was diagnosed for asymptomatic cardiac structural or functional abnormalities. Frailty was assessed using the Comprehensive Geriatric Assessment-Frailty Index (CGA-FI).Results: Overall, 531 inpatients aged ≥65 years were deemed to have SBHF and followed up for 1 year. Of them, 34.5% exhibited frailty. During the follow-up period, all-cause death or readmission occurred in 157 (29.5%) participants. Of these participants, 36.6% (67/183) and 25.9% (90/348) belonged to the frail and non-frail groups, respectively (χ² = 6.655, P = 0.010). Frailty, defined by the CGA-FI, rather than Fried frailty phenotype, could independently predict 1-year all-cause death or readmission (hazard ratio, 1.56; 95% confidence interval, 1.03–2.35; P = 0.034) and was more suitable for predicting all-cause death or readmission than N-terminal pro-B-type natriuretic peptide in female SBHF inpatients aged 80 years or over(AUC_CGA−FI vs. AUC_NT−proBNP 0.654 vs. 0.575, P = 0.017).Conclusions: Frailty is highly prevalent even among SBHF inpatients aged ≥65 years. The CGA-FI can independently predict 1-year all-cause death or readmission, rather than Fried frailty phenotype. Frailty in gerontal SBHF inpatients deserves more attention.Clinical Trial registration: ChiCTR1800017204; date of registration: 07/18/2018.

Frailty is a multifactorial syndrome associated with adverse health outcomes. The metabolic underpinnings of frailty, particularly lipid metabolism, are not fully understood. Unlike isolated lipid fractions or inflammatory markers, atherogenic index of plasma (AIP) integrates atherogenic lipid profiles and systemic inflammation. However, its association with frailty has not been extensively studied. Six thousand four hundred participants from the National Health and Nutrition Examination Survey (NHANES) were enrolled. Frailty was calculated with the frailty index (FI), with scores ≥ 0.21 indicating frailty. Logistic regression adjusted for demographic, socioeconomic, and lifestyle factors evaluated the association between AIP and frailty. Restricted cubic splines (RCS) explored nonlinear associations, and subgroup analyses assessed interactions across age, sex, race, poverty income ratio, smoking status, drinking status, and marital status. This study demonstrated a strong dose–response relationship between AIP and frailty. After full adjustment, Individuals in quartile 3 and 4 showed higher odds of frailty than those in lowest quartile, with ORs (95% CI) of 1.26(1.01,1.57) and 1.73(1.34,2.23), respectively. Continuous AIP measures also exhibited significant associations (OR: 1.82, 95% CI: 1.34–2.47). RCS analysis showed that AIP exhibited a nonlinear association with the risk of frailty. Subgroup analyses showed the associations were more pronounced in the females. The sensitivity analyses substantiated the stability and strength of the results. Our findings suggest that elevated AIP levels are independently associated with frailty risk, particularly in females, highlighting its potential as a cost-effective biomarker for risk stratification. Future longitudinal studies are needed to validate AIP’s predictive utility and uncover the underlying mechanisms.