The aim of this study was to investigate the association between serum albumin to serum creatinine ratio (sACR) and the prognosis of heart failure (HF). In this single‐center prospective cohort study, a total of 2625 patients with HF were enrolled between March 2012 and June 2017. All patients were divided into three groups according to the tertiles of sACR. Of 2625 patients, the mean age was 57.0 ± 14.3 years. During a median follow‐up time of 23 months, 666 end point events occurred. Prognosis analysis indicated that the lowest sACR was significantly associated with higher mortality risk of HF (hazard ratio [HR] = 1.920, 95% confidence interval [CI] = 1.585–2.326, p < 0.001) when compared with the highest tertile. After adjusting for covariates including age, gender, diabetes, systolic blood pressure (SBP), diastolic blood pressure, heart rate, total cholesterol, triglycerides, HDL‐C, LDL‐C, white blood cell count, hemoglobin, glycosylated hemoglobin, and β‐blocker use, the HRs for mortality risk of HF was 1.513 (95% CI = 1.070–2.139, p = 0.019). Subgroup analysis indicated that the mortality risk of HF statistically significantly reduced with the rise in sACR in patients with no β‐blocker use, patients with serum creatine less than 97 μmol/L. However, stratification by age, sex, history of hypertension, diabetes, and smoking, level of glycosylated hemoglobin, and albumin have no obvious effect on the association between sACR and the prognosis of HF. Additionally, patients with lower sACR displayed reduced left ventricular ejection fraction and increased left ventricular end‐diastolic diameter. The discriminant power of sACR alone and in combination with age, gender, SBP, heart rate, and glycosylated hemoglobin were excellent with C statistic of 0.655 and 0.889, respectively. Lower sACR was an independent risk factor for mortality risk of HF.

Objectives: Evidence of the relationshipbetween android fat mass and gynoid fat mass with the mortality prediction is still limited. Current study analyzed the NHANES database to investigate the relationship between android fat mass, gynoid fat mass and CVD, with all-cause mortality.Method: The study subjects were NHANES participants over 20 years old, two indicators of regional body composition, android fat and gynoid fat were measured by Dual Energy x-ray Absorptiometry (DEXA). The other various covariates data obtained from the NHANES questionnaire and laboratory measurements, including age, gender, education, race/ethnicity, uric acid, total serum cholesterol, albumin, Vitamin C, folate, alcohol drinking, smoking status, history of diabetes, and hypertension. Mortality status was ascertained from a linked mortality file prepared by the National Center for Health Statistics. The study population was divided quartiles based on the distribution of android fat mass and gynoid fat mass. The relationship between these two indicators with cardiovascular and all-cause mortality was investigated by using Cox regression. The covariates age, gender, smoking status, drinking status, history of diabetes, and history of hypertension were stratified.Results: In the fully adjusted model, Q3 had the lowest HR in android fat mass and gynoid fat mass. When examining the relationship between android fat mass and CVD mortality, current smokers and drinkers had the lowest CVD risk in Q2 [smoking: 0.21 (0.08, 0.52), drinking: 0.14 (0.04, 0.50)]. In diabetic patients, compared with Q1, other groups with increased android fat mass can significantly reduce the risk of CVD [Q4: 0.17 (0.04, 0.75), Q3: 0.18 (0.03, 1.09), Q2: 0.27 (0.09, 0.83)]. In ≥60 years old and female, the greater the gynoid fat mass, the smaller the HR of all-cause mortality [Q4 for ≥60 years old: 0.57 (0.33, 0.96), Q4 for female: 0.37 (0.23, 0.58)]. People <60 years old had a lower risk of all-cause mortality with gynoid fat mass in Q3 than those ≥60 years old [<60 years: 0.50 (0.27, 0.91), ≥60 years: 0.65 (0.45, 0.95)]. Among subjects without hypertension, the group with the largest android fat mass had the lowest risk of CVD mortality, and the group with the largest gynoid fat mass had the lowest risk of all-cause mortality [Android fat mass: 0.36 (0.16, 0.81), gynoid fat mass: 0.57 (0.39, 0.85)].Conclusion: Moderate android fat mass and gynoid fat mass (Q3) had the most protective effect. Smokers and drinkers need to control their body fat. Being too thin is harmful to people with diabetes. Increased gynoid fat mass is a protective factor for all-cause mortality in older adults and females. Young people's gynoid fat mass is more protective in the moderate range than older people's. If no high blood pressure exists, people with more android and gynoid fat mass have a lower risk of CVD or all-cause mortality.

The ratio of non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C) (NHHR) served as a novel comprehensive lipid indicator. This study aimed to explore the association between NHHR and the incidence of cardiometabolic multimorbidity (CMM). This study included 8191 individuals from the China Health and Retirement Longitudinal Study (CHARLS) database. We used multivariable cox proportional hazards regression, logistic regression, and restricted cubic splines (RCS) analysis to evaluate the association between NHHR and CMM. Subgroup analyses and interaction tests were also performed. The incidences of CMM among participants in quartiles (Q) 1–4 of NHHR were 7.03%, 8.3%, 10.06%, and 16.55%, respectively. The NHHR was significantly higher in individuals with CMM compared to those without CMM (P < 0.001). When assessed as a continuous variable, NHHR was independently associated with the risk of CMM, as demonstrated by both multivariable cox proportional hazards regression analysis (HR = 1.05, 95% CI = 1.02–1.07, P < 0.001) and logistic regression analysis (OR = 1.09, 95% CI = 1.04–1.15, P < 0.001). Compared to individuals in the lowest quartiles of the NHHR (Q1), the risk of CMM in the highest quartiles (Q4) was increased by 1.25-fold according to multivariable cox proportional hazards regression analysis (HR = 2.25, 95% CI = 1.73–2.93, P < 0.001) and by 1.48-fold according to logistic regression analysis (OR = 2.48, 95% CI = 1.86–3.31, P < 0.001). This association was consistent across nearly all subgroups. RCS analysis revealed a significant nonlinear association between NHHR and CMM. Additionally, the predictive ability of NHHR for CMM was 0.613, which was superior to that of both HDL-C and non-HDL-C (P < 0.05). Furthermore, the composite variable comprising NHHR and other traditional risk factors exhibited the highest predictive value (C statistic = 0.679). A higher NHHR was closely associated with an increased risk of CMM. Further studies on NHHR could be beneficial for preventing and treating CMM.

Background: Cardiometabolic multimorbidity (CMM) has emerged as a global health challenge with a high mortality risk. This study aimed to explore the association between the metabolic score for insulin resistance (METS-IR) and the incidence of CMM.Methods: This study included 6,977 individuals in the CHARLS database. We used multiple cox proportional hazards regression and restricted cubic splines (RCS) analysis to evaluate the association between METS-IR and CMM. Subgroup analyses and interaction tests were also performed.Results: During a median 109 (108–109) months of follow-up, 745 (10.7%) participants were diagnosed with new-onset CMM. The incidences of CMM among participants in quartiles (Q) 1–4 of METS-IR were 4.99, 7.51, 10.67, and 19.54%, respectively. METS-IR was significantly higher in individuals with CMM compared to those without CMM (p < 0.001). After multivariate adjustment, a higher METS-IR was significantly associated with an increased risk of CMM. Compared to participants in Q1 of METS-IR, the hazard ratios (HRs) (95% confidence intervals [CIs]) using cox proportional hazards regression analysis for those in Q2–4 were 1.52 (1.15–2.00), 2.02 (1.56–2.63), and 3.61 (2.80–4.64), respectively. RCS analysis revealed a significant nonlinear association between METS-IR and CMM (nonlinear p < 0.05). The association between METS-IR and the incidence of CMM was present in almost all the subgroups. Furthermore, the predictive ability of METS-IR for CMM was 0.669, which surpassed that of both the triglyceride to high-density lipoprotein cholesterol ratio and the triglyceride glucose index.Conclusion: A higher METS-IR was closely associated with an increased risk of CMM. Further studies on METS-IR could be beneficial for preventing and treating CMM.

Aims: Apolipoproteins have been reported to be involved in many cardiovascular diseases. The aim of our study was to investigate the prognostic value of apolipoprotein B (ApoB) to apolipoprotein A-I (ApoA-I) ratio (ApoB/ApoA-I) in patients with heart failure (HF). Methods and results: We randomly assigned 2400 HF patients into the training cohort (n = 1400) and the validation cohort (n = 1000). Using a receiver operating characteristic curve, we identified the optimal cut-off value of the ApoB/ApoA-I in the training cohort as 0.69, which was further validated in the validation cohort. A propensity score matching (PSM) analysis was conducted to eliminate the imbalance in the baseline characteristics of the high and low ApoB/ApoA-I group. A total of 2242 HF patients were generated in the PSM cohort. We also validated our results with an independent cohort (n = 838). Univariate and multivariate analyses were conducted to explore the independent prognostic value of ApoB/ApoA-I in the training cohort (n = 1400), the validation cohort (n = 1000), the PSM cohort (n = 2242), and the independent cohort (n = 838). Patients with high ApoB/ApoA-I ratio had significantly poorer prognosis compared with those with low ApoB/ApoA-I ratio in the training cohort, the validation cohort, the PSM cohort, and the independent cohort (P < 0.05). Multivariate analysis indicated that the ApoB/ApoA-I was an independent prognostic factor for HF in the training cohort [hazard ratio (HR) = 1.637, 95% confidence interval (CI) = 1.201–2.231, P = 0.002], the validation cohort (HR = 1.54, 95% CI = 1.051–2.257, P = 0.027), the PSM cohort (HR = 1.645, 95% CI = 1.273–2.125, P < 0.001), and the independent cohort (HR = 1.987, 95% CI = 1.251–3.155, P = 0.004). Conclusions: Serum ApoB/ApoA-I ratio is an independent predictor for the prognosis of HF patients.

Chronic heart failure (CHF) has poor prognosis and polygenic heritability, and the genetic risk score (GRS) to predict CHF outcome has not yet been researched comprehensively. In this study, we sought to establish GRS to predict the outcomes of CHF. We re‐analysed the proteomics data of failing human heart and combined them to filter the data of high‐throughput sequencing in 1000 Chinese CHF cohort. Cox hazards models were used based on single nucleotide polymorphisms (SNPs) to estimate the association of GRS with the prognosis of CHF, and to analyse the difference between individual SNPs and tertiles of genetic risk. In the cohort study, GRS encompassing eight SNPs harboured in seven genes were significantly associated with the prognosis of CHF (P = 2.19 × 10⁻¹⁰ after adjustment). GRS was used in stratifying individuals into significantly different CHF risk, with those in the top tertiles of GRS distribution having HR of 3.68 (95% CI: 2.40‐5.65 P = 2.47 × 10⁻¹⁰) compared with those in the bottom. We developed GRS and demonstrated its association with first event of heart failure endpoint. GRS might be used to stratify individuals for CHF prognostic risk and to predict the outcomes of genomic screening as a complement to conventional risk and NT‐proBNP.