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Background Triglyceride glucose-body mass index (TyG-BMI) is a recently developed alternative indicator to identify insulin resistance. However, few studies have investigated the association between the TyG-BMI and nonalcoholic fatty liver disease (NAFLD). Therefore, this study aimed to study the relationship between NAFLD and the TyG-BMI in the general population and its predictive value. Methods A cross-sectional study was conducted on 14,251 general subjects who took part in a comprehensive health examination. The anthropological characteristics and many risk factors for NAFLD were measured. Results After fully adjusting for confounding variables, a stable positive correlation was found between NAFLD and the TyG-BMI (OR: 3.90 per SD increase; 95% CI: 3.54 to 4.29; P -trend< 0.00001). This positive correlation was not simply linear but a stable non-linear correlation. Additionally, obvious threshold effects and saturation effects were found, in which a threshold effect occurred when the TyG-BMI was between 100 and 150; when the TyG-BMI was between 300 and 400, the corresponding NAFLD risk appeared saturated. Furthermore, receiver operating characteristic analysis showed that the TyG-BMI could better predict the risk of NAFLD than other traditional indicators [TyG-BMI (AUC): 0.886; 95% CI: 0.8797–0.8927; P  < 0.0001], particularly among young and middle-aged and non-obese people. Conclusions This epidemiological study is the first on the association between the TyG-BMI and NAFLD risk in the general population. In this large data set from the general population, the TyG-BMI showed an independent positive correlation with NAFLD. The discovery of the threshold effect and saturation effect between them provides a new idea to prevent and treat NAFLD.

Ectopic fat obesity and triglycerides are risk factors for diabetes and multiple cardiovascular diseases. However, there have been limited studies on the association between triglycerides and ectopic fat obesity. The purpose of this study was to explore the association between triglycerides and ectopic fat obesity. In this cross-sectional study, we retrospectively analyzed 15464 adult participants recruited by Murakami Memorial Hospital (8430 men and 7034 women, average age of 43.71 ± 8.90). All patients were divided into two groups according to the threshold used to diagnose hypertriglyceridemia. The logistic regression model was used to analyze the association between triglycerides and the risk of ectopic fat obesity, and the generalized additive model was used to identify the nonlinear association. In this study population, the prevalence of ectopic fat obesity was 17.73%. After adjusting other covariables, triglycerides were positively correlated with the risk of ectopic fat obesity (OR: 1.54, 95% CI:1.41-1.69, P<0.0001). Through smooth curve fitting, we found that there was an inverted U-shaped curve association between triglycerides and ectopic fat obesity. This association remained unchanged even if the adjusted covariables were removed from the model, and the inflection point of the curve was 3.98. When triglyceride levels were ≤3.98, triglycerides were positively correlated with the risk of ectopic fat obesity (OR:1.784, 95% CI:1.611-1.975, P<0.0001). When triglyceride levels were >3.98 (right side of the inflection point), there was a negative correlation (OR:0.519, 95% CI:0.333-0.810, P = 0.0039). Our research showed that there is a significant association between triglycerides and ectopic fat obesity. This relation is not a simple linear relationship but instead an inverted U-shaped curve association.

This study examines how weight-adjusted waist index (WWI) correlates with the occurrence of migraine in U.S. adults. Being overweight significantly increases the likelihood of experiencing migraines; nonetheless, conventional metrics like waist circumference (WC) and body mass index (BMI) might not completely capture the level of migraine risk tied to obesity. WWI integrates the strengths of WC while minimizing its correlation with BMI, which might make it a more accurate indicator of central obesity-related migraine susceptibility. This study performed a cross-sectional analysis using data from 9,688 participants obtained from the National Health and Nutrition Examination Survey (NHANES), covering the years 1999-2004. Migraine occurrence was evaluated through questionnaires, and participants' WWI was computed. Weighted multivariable logistic regression models were used to examine the association between WWI and migraines. Restricted cubic splines (RCS) were applied to evaluate the dose-response relationship between WWI and migraines. Furthermore, interaction tests and subgroup analyses were executed. The receiver operating characteristic (ROC) curve, paired with DeLong et al.'s test, was employed to compare the predictive power of WWI, BMI, and WC for migraines. The overall prevalence of migraines was found to be 21.50% (weighted population: 31,888,075 out of 148,278,824). In Model 3, the link between WWI and migraines in women showed no statistical significance (OR = 0.94, 95% CI: 0.82-1.07). In this model, each unit increase in WWI among men was linked to a 22% higher risk of migraines (OR = 1.22, 95% CI: 1.05-1.42). When stratified by quintiles, individuals in the third quintile (Q3) displayed a 69% higher likelihood of experiencing migraines compared to those in the first quintile (Q1) (OR = 1.69, 95% CI: 1.19-2.40), with a significant inflection point observed at 10.95 cm/√kg. Significant interactions were noted among various age groups (p for interaction = 0.018). WWI demonstrated a stronger predictive capability for migraine compared to BMI and WC. A U-shaped positive correlation of WWI with migraines was observerd among adult males in the U.S., while no significant correlation was found in females. Within the context of BMI and WC, WWI exhibited a superior predictive capacity for migraines.

Background It is known that measuring the triglyceride glucose (TyG) index and TyG-related parameters [triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist circumference (TyG-WC), and triglyceride glucose-waist to height ratio (TyG-WHtR)] can predict diabetes; this study aimed to compare the predictive value of the baseline TyG index and TyG-related parameters for the onset of diabetes at different future periods. Methods We conducted a longitudinal cohort study involving 15,464 Japanese people who had undergone health physical examinations. The subject’s TyG index and TyG-related parameters were measured at the first physical examination, and diabetes was defined according to the American Diabetes Association criteria. Multivariate Cox regression models and time-dependent receiver operating characteristic (ROC) curves were constructed to examine and compare the risk assessment/predictive value of the TyG index and TyG-related parameters for the onset of diabetes in different future periods. Results The mean follow-up period of the current study cohort was 6.13 years, with a maximum of 13 years, and the incidence density of diabetes was 39.88/10,000 person-years. In multivariate Cox regression models with standardized hazard ratios (HRs), we found that both the TyG index and TyG-related parameters were significantly and positively associated with diabetes risk and that the TyG-related parameters were stronger in assessing diabetes risk than the TyG index, with TyG-WC being the best parameter (HR per SD increase: 1.70, 95% CI 1.46, 1.97). In addition, TyG-WC also showed the highest predictive accuracy in time-dependent ROC analysis for diabetes occurring in the short-term (2–6 years), while TyG-WHtR had the highest predictive accuracy and the most stable predictive threshold for predicting the onset of diabetes in the medium- to long-term (6–12 years). Conclusions These results suggest that the TyG index combined with BMI, WC, and WHtR can further improve its ability to assess/predict the risk of diabetes in different future periods, where TyG-WC was not only the best parameter for assessing diabetes risk but also the best risk marker for predicting future diabetes in the short-term, while TyG-WHtR may be more suitable for predicting future diabetes in the medium- to long-term.

Background Body mass index (BMI) and lipid parameters are the most commonly used anthropometric parameters and biomarkers for assessing nonalcoholic fatty liver disease (NAFLD) risk. This study aimed to assess and quantify the mediating role of traditional and non-traditional lipid parameters on the association between BMI and NAFLD. Method Using data from 14,251 subjects from the NAGALA (NAfld in the Gifu Area, Longitudinal Analysis) study, mediation analyses were performed to explore the roles of traditional [total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C)] and non-traditional [non-HDL-C, remnant cholesterol (RC), TC/HDL-C ratio, LDL-C/HDL-C ratio, TG/HDL-C ratio, non-HDL-C/HDL-C ratio, and RC/HDL-C ratio] lipid parameters in the association of BMI with NAFLD and quantify the mediation effect of these lipid parameters on the association of BMI with NAFLD using the percentage of mediation. Result After fully adjusting for confounders, multivariate regression analysis showed that both BMI and lipid parameters were associated with NAFLD (All P- value < 0.001). Mediation analysis showed that both traditional and non-traditional lipid parameters mediated the association between BMI and NAFLD (All P -value of proportion mediate < 0.001), among which non-traditional lipid parameters such as RC, RC/HDL-C ratio, non-HDL-C/HDL-C ratio, and TC/HDL-C ratio accounted for a relatively large proportion, 11.4%, 10.8%, 10.2%, and 10.2%, respectively. Further stratified analysis according to sex, age, and BMI showed that this mediation effect only existed in normal-weight (18.5 kg/m 2  ≤ BMI < 25 kg/m 2 ) people and young and middle-aged (30–59 years old) people; moreover, the mediation effects of all lipid parameters except TC accounted for a higher proportion in women than in men. Conclusion The new findings of this study showed that all lipid parameters were involved in and mediated the risk of BMI-related NAFLD, and the contribution of non-traditional lipid parameters to the mediation effect of this association was higher than that of traditional lipid parameters, especially RC, RC/HDL-C ratio, non-HDL-C/HDL-C ratio, and TC/HDL-C ratio. Based on these results, we suggest that we should focus on monitoring non-traditional lipid parameters, especially RC and RC/HDL-C ratio, when BMI intervention is needed in the process of preventing or treating NAFLD.

Remnant cholesterol (RC) is the cholesterol of triglyceride-rich lipoproteins, which has a high degree of atherogenic effect. To date, epidemiological evidence supports that higher RC levels lead to a greater risk of adverse cardiovascular events in patients with diabetes, but the nature of the association between RC levels and diabetes risk remains unclear. This study was designed to assess the association of RC with the risk of new-onset diabetes and to investigate whether there is a causal relationship between the two. The subjects included 15,464 individuals of the general population who participated in a health examination. Subjects were quartered according to the RC quartile, and the Cox proportional hazard model was used to assess the independent association between RC and new-onset diabetes. During an average observation period of 6.13 years, 2.41% of the subjects were diagnosed with new-onset diabetes. Kaplan-Meier analysis showed that the 13-year cumulative diabetes rates corresponding to the RC quartile were 8.62%, 2.49%, 12.78%, and 17.91%. Multivariate Cox regression analysis indicated that higher RC levels were independently associated with an increased risk of new-onset diabetes (HR: 2.44, 95% CI: 1.50-3.89). Additionally, according to the results of receiver operating characteristic curve analysis, RC had the largest area under the curve (0.7314) compared to traditional lipid parameters in predicting new-onset diabetes. These results indicated that RC is an important independent predictor of new-onset diabetes in the general population.

Background Remnant cholesterol (RC) mediates the progression of coronary artery disease, diabetic complications, hypertension, and chronic kidney disease. Limited information is available on the association of RC with nonalcoholic fatty liver disease (NAFLD). This study aimed to explore whether RC can be used to independently evaluate the risk of NAFLD in the general population and to analyze the predictive value of RC for NAFLD. Methods The study included 14,251 subjects enrolled in a health screening program. NAFLD was diagnosed by ultrasound, and the association of RC with NAFLD was assessed using the receiver operating characteristic (ROC) curve and logistic regression equation. Results Subjects with elevated RC had a significantly higher risk of developing NAFLD after fully adjusting for potential confounding factors (OR 1.77 per SD increase, 95% CI 1.64–1.91, P trend< 0.001). There were significant differences in this association among sex, BMI and age stratification. Compared with men, women were facing a higher risk of RC-related NAFLD. Compared with people with normal BMI, overweight and obesity, the risk of RC-related NAFLD was higher in thin people. In different age stratifications, when RC increased, young people had a higher risk of developing NAFLD than other age groups. Additionally, ROC analysis results showed that among all lipid parameters, the AUC of RC was the largest (women: 0.81; men: 0.74), and the best threshold for predicting NAFLD was 0.54 in women and 0.63 in men. Conclusions The results obtained from this study indicate that (1) in the general population, RC is independently associated with NAFLD but not with other risk factors. (2) Compared with traditional lipid parameters, RC has a better predictive ability for NAFLD in men.

M6A methylation is the most prevalent and abundant RNA modification in mammals. Although there are many studies on the regulatory role of m6A methylation in the immune response, the m6A regulators in the pathogenesis of acute ST-segment elevation myocardial infarction (STEMI) remain unclear. We comprehensively analysed the role of m6A regulators in STEMI and built a predictive model, revealing the relationship between m6A methylations and the immune microenvironment. Differential analysis revealed that 18 of 24 m6A regulators were significantly differentially expressed, and there were substantial interactions between the m6A regulator. Then, we established a classifier and nomogram model based on 6 m6A regulators, which can easily distinguish the STEMI and control samples. Finally, two distinct m6A subtypes were obtained and significantly differentially expressed in terms of infiltrating immunocyte abundance, immune reaction activity and human leukocyte antigen genes. Three hub m6A phenotype related genes (RAC2, RELA, and WAS) in the midnightblue module were identified by weighted gene coexpression network analysis, and were associated with immunity. These findings suggest that m6A modification and the immune microenvironment play a key role in the pathogenesis of STEMI.

An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.

Background Atherosclerosis is closely linked with glucose metabolism. We aimed to investigate the role of the atherogenic index of plasma (AIP) in the reversal of prediabetes to normal blood glucose levels or its progression to diabetes. Methods This multi-center retrospective cohort study included 15,421 prediabetic participants from 32 regions across 11 cities in China, under the aegis of the Rich Healthcare Group’s affiliated medical examination institutions. Throughout the follow-up period, we monitored changes in the glycemic status of these participants, including reversal to normal fasting glucose (NFG), persistence in the prediabetic state, or progression to diabetes. Segmented regression, stratified analysis, and restricted cubic spline (RCS) were performed based on the multivariable Cox regression model to evaluate the association between AIP and the reversal of prediabetes to NFG or progression to diabetes. Results During a median follow-up period of 2.9 years, we recorded 6,481 individuals (42.03%) reverting from prediabetes to NFG, and 2,424 individuals (15.72%) progressing to diabetes. After adjusting for confounders, AIP showed a positive correlation with the progression from prediabetes to diabetes [(Hazard ratio (HR) 1.42, 95% confidence interval (CI):1.24–1.64)] and a negative correlation with the reversion from prediabetes to NFG (HR 0.89, 95%CI:0.81–0.98); further RCS demonstrated a nonlinear relationship between AIP and the reversion from prediabetes to NFG/progression to diabetes, identifying a turning point of 0.04 for reversion to NFG and 0.17 for progression to diabetes. In addition, we observed significant differences in the association between AIP and reversion from prediabetes to NFG/progression to diabetes across age subgroups, specifically indicating that the risk associated with AIP for progression from prediabetes to diabetes was relatively higher in younger populations; likewise, a younger age within the adult group favored the reversion from prediabetes to NFG in relation to AIP. Conclusion Our study, for the first time, reveals a negative correlation between AIP and the reversion from prediabetes to normoglycemia and validates the crucial role of AIP in the risk assessment of prediabetes progression. Based on threshold analysis, therapeutically, keeping the AIP below 0.04 was of paramount importance for individuals with prediabetes aiming for reversion to NFG; preventatively, maintaining AIP below 0.17 was vital to reduce the risk of diabetes onset for those with prediabetes.