Cardiovascular-kidney-metabolic (CKM) syndrome has been recently proposed by American Heart Association recently. The triglyceride glucose (TyG) index and TyG-related indices combined with obesity indicators have proven to be associated with the incidence of cardiovascular diseases (CVD). However, there are few studies to explore whether these associations exist among people with CKM syndrome stages 0–3. A total of 7,364 participants from the China Health and Retirement Longitudinal Study were included. Cox hazard regression and restricted cubic spline regression were used to analyze the associations of these indices with CVD incidence. To compare predictive performance, time-dependent Harrell’s C-indices, net reclassification index and integrated discrimination improvement were conducted. The CVD incidence was 20.55% over nine years. The TyG single index and all the modified TyG indices were capable of predicting CVD incidence. RCS regression analyses showed that all indicators had linear relationships with CVD incidence and these linear relationships of TyG combined with waist circumference (TyG-WC) or waist-to-height ratio (TyG-WHtR) still existed in CKM stage 1, stage 2 and stage 3. TyG-WC (C-index: 0.621, p < 0.001) and TyG-WHtR (C-index: 0.621, p < 0.001) almost had the highest C-indices in predicting CVD incidence, compared to single TyG index (C-index: 0.611, p < 0.001) and TyG combined with body mass index (C-index: 0.616, p < 0.001). The TyG index and all the modified TyG indices were independent predictors of CVD incidence among people with CKM syndrome stages 0–3. It was found that modified indices had better predictive performance, especially TyG combined with waist circumference or waist-to-height ratio.

Cervical cancer (CC), primarily caused by human papillomavirus (HPV) infection, remains a significant global health concern. We aimed to comprehensively investigate the epidemiological status of HPV in China. Data from 2,728,321 women undergoing routine cervical examinations at 2127 medical institutions nationwide from January 2017 to June 2023 were analyzed. HPV genotype testing was conducted using HPV DNA typing kits. The overall HPV prevalence was 17.70%, with 13.12% classified as high-risk HPV (HR-HPV) and 4.58% as low-risk HPV (LR-HPV). Notably, HPV52 emerged as the most common carcinogenic type, followed by HPV58 and HPV16. Age-specific prevalence revealed a bimodal distribution, with peaks observed in women under 21 and over 61 years of age. Geographically, the south (19.48%) exhibited the highest infection rate, while the northwest (12.36%) had the lowest. Furthermore, HPV infection rates were higher during winter and spring. Although HPV infection rates have remained stable overall over the past 7 years, the infection rate in 2023 (14.76%) has declined relative to 2017 (16.17%) (P < 0.05). This study provides comprehensive insight into HPV epidemiology in China and guidance for future vaccine development and cervical cancer prevention strategies.

Although some studies have examined the association between the triglyceride glucose-body mass index (TyG-BMI) and cardiovascular outcomes in the cardio-renal-metabolic (CRM) background, none have explored its role in the progression of CRM multimorbidity. In addition, prior research is limited by small sample sizes and a failure to account for the competitive effects of other CRM diseases. In this study, data obtained from the large-scale, prospective UK Biobank cohort were used. CRM multimorbidity was defined as the new-onset of ischemic heart disease, type 2 diabetes mellitus, or chronic kidney disease during follow-up. Multivariable Cox regression was used to analyse the independent association between TyG-BMI and each CRM multimorbidity (first, double, or triple CRM diseases). The C-statistic was calculated for each model, and a restricted cubic spline was applied to assess the dose–response relationship. A multi-state model was used to investigate the association between TyG-BMI and the trajectory of CRM multimorbidity (from baseline [without CRM disease] to the first CRM disease, the first CRM disease to double disease, and double disease to triple disease), with disease-specific analyses. This study included 349,974 participants, with a mean age of 56.05 (standard deviation [SD], 8.08), 55.93% of whom were female. Over a median follow-up of approximately 14 years, 56,659 (16.19%) participants without baseline CRM disease developed at least one CRM disease, including 8451 (14.92%) who progressed to double CRM disease and 789 (9.34%) who further developed triple CRM disease. In the crude model, each SD increase in TyG-BMI was associated with a 47% higher risk of the first CRM disease, a 72% higher risk of double CRM disease, and a 95% higher risk of triple CRM disease, with C-statistics of 0.625, 0.694, and 0.764, respectively. Multi-state model analysis showed a 32% increased risk of new CRM disease, a 24% increased risk of progression to double CRM disease, and a 23% increased risk of further progression for those with double CRM diseases. TyG-BMI was significantly associated with the onset of all individual first CRM diseases (except for stroke) and with the transition to double CRM disease. Significant interactions were also observed, but TyG-BMI remained significantly associated with CRM multimorbidity across subgroups. Sensitivity analyses, including varying time intervals for entering states and an expanded CRM definition (including atrial fibrillation, heart failure, peripheral vascular disease, obesity, and dyslipidaemia), confirmed these findings. TyG-BMI remarkably influences the onset and progression of CRM multimorbidity. Incorporating it into CRM multimorbidity prevention and management could have important public health implications.

Diet plays a critical role in human health and environmental sustainability, particularly in cardiovascular, kidney, and metabolic (CKM) diseases. However, the variations in the Planetary Health Diet Index (PHDI) across populations, regions, and over time, as well as its association with CKM disease burdens, remain insufficiently explored. We assessed PHDI scores using data from 185 countries (1990-2018) from the Global Dietary Database, examining demographic characteristics and temporal trends. The Global Burden of Disease Study was used to evaluate the associations between PHDI and CKM disease burdens, including incidence, prevalence, mortality, and disability-adjusted life years. CKM syndrome was defined by the American Heart Association. Individual-level data from the National Health and Nutrition Examination Survey (NHANES) were also used to assess the impact of PHDI on CKM risks and mortality. From 1990 to 2018, while overall PHDI scores remained relatively stable between sexes, the composition of PHDI scores shifted across different age groups. In 2018, the mean PHDI score was 42.80 (95% uncertainty interval [UI] 42.49-46.50) for males and 44.65 (95% UI 44.53-47.82) for females. Higher PHDI scores were observed among females, older adults, urban residents, individuals with higher education, and those from South Asia. Globally, consumption of red/processed meat, saturated oils/trans fats, and added sugars substantially exceeded the EAT-Lancet Commission's reference values. Higher PHDI scores were generally associated with lower CKM disease burdens, although these associations varied by disease subtype. In individual-level analysis, including 45,460 NHANES participants (weighted mean age: 47.21 years, 51.4% female), each 10-point increase in PHDI was linked to a 13.7% reduction in stage 3/4 CKM syndrome risk, an 11.1% reduction in stage 4 CKM syndrome risk, and lower incidences and mortality rates for cardiovascular diseases, metabolic diseases, and chronic kidney disease. From 1990 to 2018, significant changes occurred in the components of the PHDI, with notable variations by demographics and region. Higher PHDI scores may reduce CKM disease burdens, warranting further investigation into specific disease subtypes.

Background: Although mortality remains high in patients with atrial fibrillation (AF), there have been limited studies exploring machine learning (ML) models on mortality risk prediction in patients with AF.Objectives: This study sought to develop an ML model that captures important variables in order to predict all-cause mortality in AF patients.Methods: In this single center prospective study, an ML-based mortality prediction model was developed and validated using a dataset of 2,012 patients who experienced AF from November 2018 to February 2020 at the First Affiliated Hospital of Shantou University Medical College. The dataset was randomly divided into a training set (70%, n = 1,223) and a validation set (30%, n = 552). A total of 122 features were collected for variable selection. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) algorithms were used for variable selection. Ten ML models were developed using variables selected by LASSO or RF. The best model was selected and compared with conventional risk scores. A nomogram and user-friendly online tool were developed to facilitate the mortality predictions and management recommendations.Results: Thirteen features were selected by the LASSO regression algorithm. The LASSO-Cox model achieved an area under the curve (AUC) of 0.842 in the training dataset, and 0.854 in the validation dataset. A nomogram based on eight independent features was developed for the prediction of survival at 30, 180, and 365 days following discharge. Both the time dependent receiver operating characteristic (ROC) and decision curve analysis (DCA) showed better performances of the nomogram compared to the CHA₂DS₂-VASc and HAS-BLED models.Conclusions: The LASSO-Cox mortality predictive model shows potential benefits in death risk evaluation for AF patients over the 365-day period following discharge. This novel ML approach may also provide physicians with personalized management recommendations.