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Objective The triglyceride–glucose (TyG) index has been shown to be a new alternative measure for insulin resistance. However, no study has attempted to investigate the association of the TyG index with incident atrial fibrillation (AF) in the general population without known cardiovascular diseases. Methods Individuals without known cardiovascular diseases (heart failure, coronary heart disease, or stroke) from the Atherosclerosis Risk in Communities (ARIC) cohort were recruited. The baseline TyG index was calculated as the Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The association between the baseline TyG index and incident AF was examined using Cox regression. Results Of 11,851 participants, the mean age was 54.0 years; 6586 (55.6%) were female. During a median follow-up of 24.26 years, 1925 incidents of AF cases (0.78/per 100 person-years) occurred. An increased AF incidence with a graded TyG index was found by Kaplan‒Meier curves ( P  < 0.001). In multivariable-adjusted analysis, both < 8.80 (adjusted hazard ratio [aHR] = 1.15, 95% confidence interval [CI] 1.02, 1.29) and > 9.20 levels (aHR 1.18, 95% CI 1.03, 1.37) of the TyG index were associated with an increased risk of AF compared with the middle TyG index category (8.80–9.20). The exposure-effect analysis confirmed the U-shaped association between the TyG index and AF incidence ( P  = 0.041). Further sex-specific analysis showed that a U-shaped association between the TyG index and incident AF still existed in females but not in males. Conclusions A U-shaped association between the TyG index and AF incidence is observed in Americans without known cardiovascular diseases. Female sex may be a modifier in the association between the TyG index and AF incidence. Graphical Abstract

Background Triglyceride-glucose (TyG) index is considered an alternative indicator of insulin resistance and is associated with cardiovascular diseases. However, its association with subclinical myocardial injury in a general population without known cardiovascular disease has not been investigated, nor has the effect of diabetes. Methods Individuals without known cardiovascular disease were included from the Atherosclerosis Risk in Communities (ARIC) cohort. The baseline TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The association between the baseline TyG index and subclinical myocardial injury [high-sensitivity cardiac troponin T (hs-cTnT≥ 14 ng/L)] was assessed using both cross-sectional and prospective cohort designs. Results A total of 11,478 participants were involved in the cross-sectional study (mean age, 56.78 years; 42.44% male) and 8801 participants were involved in the prospective cohort study (mean age, 56.57 years; 41.36% male). Both linear (r = 0.13, p  < 0.001) and logistic regression analyses (adjusted odds ratio [aOR] = 1.33, p  < 0.001) showed a positive association between the TyG index and baseline hs-cTnT level, which was consistent in diabetic participants (aOR = 1.64, p  = 0.020) but not significant in non-diabetic participants (aOR = 0.89, p  = 0.374). After a six-year follow-up, a U-shaped association between the TyG index and incidence of hs-cTnT elevation was observed among the overall participants. Further subgroup analyses showed an L-shaped (aOR = 0.72, p  = 0.006) and a J-shaped (aOR = 2.09, p  < 0.001) association between the TyG index and incidence of hs-cTnT elevation in participants without and with diabetes, respectively. Conclusion A U-shaped association between the TyG index and the incidence of subclinical myocardial injury was observed for the first time. Diabetes may be a critical modifier of the association between the TyG index and subclinical myocardial injury. Considering the risk stratification value of the TyG index based on diabetes status may hold significant clinical value. Graphical abstract

Background The previous study investigated whether the functions of small, medium, and large high density lipoprotein (S/M/L-HDL) are correlated with protein changes in mice. Herein, the proteomic and functional analyses of high density lipoprotein (HDL) subclasses were performed in humans and rats. Methods After purifying S/M/L-HDL subclasses from healthy humans ( n  = 6) and rats ( n  = 3) using fast protein liquid chromatography (FPLC) with calcium silica hydrate (CSH) resin, the proteomic analysis by mass spectrometry was conducted, as well as the capacities of cholesterol efflux and antioxidation was measured. Results Of the 120 and 106 HDL proteins identified, 85 and 68 proteins were significantly changed in concentration among the S/M/L-HDL subclasses in humans and rats, respectively. Interestingly, it was found that the relatively abundant proteins in the small HDL (S-HDL) and large HDL (L-HDL) subclasses did not overlap, both in humans and in rats. Next, by searching for the biological functions of the relatively abundant proteins in the HDL subclasses via Gene Ontology, it was displayed that the relatively abundant proteins involved in lipid metabolism and antioxidation were enriched more in the medium HDL (M-HDL) subclass than in the S/L-HDL subclasses in humans, whereas in rats, the relatively abundant proteins associated with lipid metabolism and anti-oxidation were enriched in M/L-HDL and S/M-HDL, respectively. Finally, it was confirmed that M-HDL and L-HDL had the highest cholesterol efflux capacity among the three HDL subclasses in humans and rats, respectively; moreover, M-HDL exhibited higher antioxidative capacity than S-HDL in both humans and rats. Conclusions The S-HDL and L-HDL subclasses are likely to have different proteomic components during HDL maturation, and results from the proteomics-based comparison of the HDL subclasses may explain the associated differences in function.

ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. The study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42%, 37%, and 17%, respectively. ChatGPT achieved a 90% concordance across all questions. Among correct responses, the concordance was 100%, significantly exceeding that of incorrect responses (n=57, 50%; P<.001). ChatGPT provided innovative insights for 80% (n=132) of all questions, with an average of 2.95 insights per accurate response. Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context.

Background Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have shown benefits for cardiorenal outcomes in patients with type 2 diabetes mellitus. Although some observational studies suggest that GLP-1RAs protect against arrhythmias, the evidence remains inconclusive. Methods This study aimed to assess the causal relationship between GLP-1RAs and arrhythmias, including atrial fibrillation (AF), cardiac arrest, and ventricular fibrillation. We performed a two-sample Mendelian randomization (MR) analysis to examine the associations between genetically proxied GLP-1RAs and the risk of arrhythmias. Genetic instruments for GLP-1RAs were obtained from the cis-expression quantitative trait loci of the GLP1R gene, on the basis of data from the eQTLGen Consortium. Genome-wide association study (GWAS) data for AF were sourced from FinnGen10, whereas data for cardiac arrest and ventricular fibrillation came from the GWAS Catalog. Bayesian colocalization and multivariable Mendelian randomization (MVMR) analyses were conducted as supplementary analyses. Results Twelve independent single nucleotide polymorphisms were identified as genetic instruments for GLP-1RAs. MR analysis indicated that genetically proxied GLP-1RAs were associated with a reduced risk of AF (odds ratio [OR] = 0.78, 95% confidence interval [CI] = 0.71–0.85, p  = 4.45E-08, posterior probability of hypothesis 4 [PP.H4] = 0.007) and a lower risk of cardiac arrest and ventricular fibrillation (OR = 0.60, 95% CI = 0.42–0.85, p  = 0.0039, PP.H4 = 0.018). Bayesian colocalization analysis revealed that genetically proxied GLP-1RAs did not share genetic variation with arrhythmias. MVMR analysis revealed that, after adjusting for body mass index and type 2 diabetes mellitus, genetically proxied GLP-1RAs did not have a significant effect on the risk of arrhythmias. Conclusions Our findings suggest that genetically proxied GLP-1RAs are causally associated with a reduced risk of AF, cardiac arrest, and ventricular fibrillation. Further randomized controlled trials are needed to confirm these results. Graphical abstract

Background Several kinds of motor dysfunction have been studied for predicting future fall risk in community-dwelling older individuals. However, no study has tested the ability of the fine motor index (FINEA) and gross motor index (GROSSA) to predict the risk of falling, as well as the specific fall type. Objective We investigated the associations of FINEA/GROSSA scores with fall risk, explained falls, and unexplained falls. Methods A total of 6267 community-dwelling adults aged ≥ 50 years from the Irish Longitudinal Study on Aging (TILDA) cohort were included. First, the associations of FINEA and GROSSA scores with the history of total falls, explained falls and unexplained falls were assessed in a cross-sectional study and further verified in a prospective cohort after 2 years of follow-up by Poisson regression analysis. Results We found that high FINEA and GROSSA scores were positively associated with almost all fall histories (FINEA scores: total falls: adjusted prevalence ratio [aPR] = 1.28, P  = 0.009; explained falls: aPR = 1.15, P  = 0.231; unexplained falls: aPR = 1.88, P  < 0.001; GROSSA scores: total falls: aPR = 1.39, P  < 0.001; explained falls: aPR = 1.28, P  = 0.012; unexplained falls: aPR = 2.18, P  < 0.001) in a cross-sectional study. After 2 years of follow-up, high FINEA scores were associated with an increased incidence of total falls (adjusted rate ratio [aRR] = 1.42, P  = 0.016) and explained falls (aRR = 1.51, P  = 0.020) but not with unexplained falls (aRR = 1.41, P  = 0.209). High GROSSA scores were associated with an increased incidence of unexplained falls (aRR = 1.57, P  = 0.041) and were not associated with either total falls (aRR = 1.21, P  = 0.129) or explained falls (aRR = 1.07, P  = 0.656). Compared with individuals without limitations in either the FINEA or GROSSA, individuals with limitations in both indices had a higher risk of falls, including total falls (aRR = 1.35, P  = 0.002), explained falls (aRR = 1.31, P  = 0.033) and unexplained falls (aRR = 1.62, P  = 0.004). Conclusion FINEA scores were positively associated with accidental falls, while GROSSA scores were positively associated with unexplained falls. The group for whom both measures were impaired showed a significantly higher risk of both explained and unexplained falls. FINEA or GROSSA scores should be investigated further as possible tools to screen for and identify community-dwelling adults at high risk of falling.

Aims The prognostic significance of N‐terminal pro B‐type natriuretic peptide (NT‐proBNP) in heart failure with preserved ejection fraction (HFpEF) has been well established. HFpEF and atrial fibrillation (AF) commonly coexist, and each contributes to poor outcomes independently. Nevertheless, the ability of NT‐proBNP to predict AF in HFpEF patients remains uncertain. Methods and results A total of 367 HFpEF patients without baseline AF from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial were included. The Cox proportional hazard model was used to assess the association of NT‐proBNP with the risk of AF. The C‐statistic, categorical net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to evaluate the ability of NT‐proBNP in new‐onset AF prediction. During a median follow‐up of 2.91 years, 17 (4.63%) new‐onset AF cases occurred. Every 1000 pg/mL increase in NT‐proBNP was associated with a 16% increase in the risk of AF occurrence after adjustments (hazard ratio, 1.16 [95% CI, 1.02–1.32]). NT‐proBNP showed a moderate performance for new‐onset AF at 3 years (C‐statistic, 0.67). Adding NT‐proBNP to CHADS2/R2CHADS2/CHA2DS2‐VASc/C2HSET scores improved their predictive performance for AF risk (CHADS2: C‐statistic, 0.63, CHADS2+NT: C‐statistic, 0.69, NRI, 47.46%, IDI, 1.18%; R2CHADS2: C‐statistic, 0.65, R2CHADS2+NT: C‐statistic, 0.70, NRI, 48.03%, IDI, 0.51%; CHA2DS2‐VASc: C‐statistic, 0.67, CHA2DS2‐VASc+NT: C‐statistic, 0.72, NRI, 49.41%, IDI, 0.86%; C2HSET: C‐statistic, 0.77, C2HSET+NT: C‐statistic, 0.80, NRI, 50.32%, IDI, 1.58%). Conclusions Among patients with HFpEF, the NT‐proBNP level was positively associated with the incidence of new‐onset AF and may be a promising predictor.

Background: Although elevation of HDL-C levels by pharmaceutical drugs have no benefit of cardiovascular endpoint, the effect of high-density lipoprotein/apolipoprotein A1 (HDL/apoA-1) replacement therapy on atherosclerosis is controversial. The current meta-analysis analyzed the effects of HDL/apoA-1 replacement therapies on atherosclerotic lesions both in humans and mice. Methods: The PubMed, Cochrane Library, Web of Science, and EMBASE databases were searched through June 6, 2020. The methodological quality of the human studies was assessed using Review Manager (RevMan, version 5.3.). The methodological quality of the mouse studies was assessed using a stair list. STATA (version 14.0) was used to perform all statistical analyses. Results: Fifteen randomized controlled human trials and 17 animal studies were included. The pooled results showed that HDL/apoA-1 replacement therapy use did not significantly decrease the percent atheroma volume ( p = 0.766) or total atheroma volume ( p = 0.510) in acute coronary syndrome (ACS) patients ( N = 754). However, HDL/apoA-1 replacement therapies were significantly associated with the final percent lesion area, final lesion area, and changes in lesion area (SMD, −1.75; 95% CI: −2.21~-1.29, p = 0.000; SMD, −0.78; 95% CI: −1.18~-0.38, p = 0.000; SMD: −2.06; 95% CI, −3.92~-0.2, p = 0.03, respectively) in mice. Conclusions: HDL/apoA-1 replacement therapies are safe but do not significantly improve arterial atheroma volume in humans. The results in animals suggest that HDL/apoA-1 replacement therapies decrease the lesion area. Additional studies are needed to investigate and explain the differences in HDL/apoA-1 replacement therapy efficacies between humans and animals. Trial registration number: Human pooled analysis: PROSPERO, CRD42020210772. prospectively registered.

The association between high-density lipoprotein cholesterol (HDL-C) and cardiovascular disease (CVD) is controversial. HDL-C is one content type of high-density lipoprotein (HDL). HDL consists of diverse proteins and lipids and can be classified into different subclasses based on size, shape, charge, and density, and can change dynamically in disease states. Therefore, HDL-C levels alone cannot represent HDLs’ cardioprotective role. In this review, we summarized the methods for separating HDL subclasses, the studies on the association between HDL subclasses and cardiovascular risk (CVR), and the impact of lipid-modifying medications and nonpharmacological approaches (exercise training, dietary omega fatty acids, and low-density lipoprotein apheresis) on HDL subclasses. As HDL is a natural nanoplatform, recombinant HDLs (rHDLs) have been used as a delivery system in vivo by loading small interfering RNA, drugs, contrast agents, etc. Therefore, we further reviewed the HDL subclasses used in rHDLs and their advantages and disadvantages. This review would provide recommendations and guidance for future studies on HDL subclasses’ cardioprotective roles.

Carotid plaque radiomics-included models have increased the predictive capacity of cardiovascular risk, but the radiomic features of these models were inconsistent in previous studies. Lipids could be used to select the most important radiomic feature. A retrospective case‒control study was performed in 153 diabetic and 76 non-diabetic patients with carotid plaque detected by ultrasound. Cerebro-cardiovascular disease (CCD), comprising coronary heart disease (CHD) and stroke, was the primary outcome. Clinical variables and radiomic features of longitudinal carotid plaque images were collected. Principal component analyses were used to compare the power of radiomic and lipid features in discrimination between diabetes, CCD patients, and their opposites. Partial least square regression, logistic regression analyses, and least absolute shrinkage and selection operator (LASSO) regression were performed for high-risk radiomic features. The diagnostic capacity of the models was evaluated. PCA based on radiomics or lipids did not show good discrimination of diabetes, CCD, and their opposites. There were 6 overlapping radiomic features associated with lipid profiles, but only original_firstorder_Mean was negatively associated with diabetic stroke [adjusted OR  = 0.468 (0.243–0.902), P  = 0.023] and nondiabetic CHD [adjusted OR = 0.311 (0.123–0.783), P  = 0.013]. The associations remained independent in the LASSO regression models ( β =-0.032 for diabetic stroke, and − 0.026 for non-diabetic CHD). The diagnostic capacity of lipid-related radiomics for diabetic stroke (0.556 to 0.688) and non-diabetic CHD (0.690 to 0.783) was increased by the combination of these clinical variables. Carotid plaque radiomics is associated with lipids and stroke in diabetes, and quantitative features are useful for therapeutic guidance and cardiovascular risk evaluation in clinical use.