Development and validation of a nomogram model for predicting the risk of MAFLD in the young population

Correlation analysis of the triglyceride-glucose index and related parameters in metabolic dysfunction-associated fatty liver disease

This study aimed to investigate the correlation and predictive value of TyG and related parameters with metabolic dysfunction-associated fatty liver disease (MAFLD) MAFLD. This study retrospectively included individuals who underwent health examinations and abdominal ultrasound from July 2021 to June 2024 at the Affiliated Hospital of Southwest Medical University, Sichuan Province, China. A total of 71,299 subjects' clinical and laboratory data were extracted, the correlation between TyG and related parameters and MAFLD was analyzed via univariate and multivariate logistic regression methods, and the nonlinear relationship between the TyG index and the risk of MAFLD was explored via restricted cubic spline (RCS) analysis. The predictive value of TyG and related parameters for MAFLD was assessed using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). TyG and related parameters were positively correlated with MAFLD, and the results remained unchanged after adjustment for the corresponding parameters. RCS analysis revealed a significant dose‒response relationship between TyG and related parameters and MAFLD. ROC curve analysis revealed AUC values of 0.83 (0.82-0.83), 0.92 (0.91-0.92), 0.90 (0.90-0.91), and 0.87 (0.87-0.88) for TyG, TyG-BMI, TyG-WC, and TyG-WHR, respectively. Subgroup analyses revealed that the TyG index and related parameters had greater predictive value in the female, younger, and BMI < 23.7 populations.

Construction and Validation of a Major Depression Risk Predictive Model for Patients with Coronary Heart Disease: Insights from NHANES 2005-2018

Background

This study aimed to develop and validate a predictive model for major depression risk in adult patients with coronary heart disease (CHD), offering evidence for targeted prevention and intervention.

Methods

Using data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, 1098 adults with CHD were included. A weighted logistic regression model was applied to construct and validate a nomogram-based prediction tool for major depression in this population.

Results

The weighted prevalence of major depression among these patients was 13.95%. Multivariate weighted logistic regression revealed that waist circumference, smoking status, arthritis, sleep disorders, and restricted work capacity were independent risk factors for major depression (odds ratio (OR) >1, p < 0.05). The areas under the receiver operating characteristic (ROC) curve in the nomogram model for both the development and validation cohorts were 0.816 (95% confidence interval (CI): 0.776-0.857) and 0.765 (95% CI: 0.699-0.832), respectively, indicating the model possessed strong discriminative ability. Brier scores in the development and validation cohorts were 0.107 and 0.127, respectively, both well below the 0.25 threshold, demonstrating good calibration. Decision curve analysis (DCA) showed that when the threshold probability for major depression ranged from 0.04 to 0.54 in the development group and from 0.08 to 0.52 in the validation group, the nomogram provided the highest clinical net benefit compared to "Treat All" and "Treat None" strategies, confirming its strong clinical utility.

Conclusions

With a weighted prevalence of 13.95%, this nomogram model shows excellent predictive performance and clinical relevance for predicting major depression risk in patients with CHD. Thus, the model can be applied to aid healthcare professionals in identifying high-risk individuals and implementing targeted preventive strategies, potentially lowering the incidence of major depression in this patient population.

Metabolic dysfunction-associated steatotic liver disease: The question of long-term high-normal alanine aminotransferase as a screening test

The growing prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) is being driven by the obesity epidemic. The quest for solutions continues particularly with regard to early detection. This editorial comments on the utility of long-term high-normal alanine aminotransferase (ALT) in screening for MASLD. Chen et al found that new onset MASLD can be detected by repetitively high normal ALT. Implicit in this concept is the question of what should be the accepted upper limit of normal (ULN) for ALT. It was previously set at 40 IU/L based on studies that included people with subclinical liver disease but the new consensus is 30/19 U/L in healthy males/females. Thus, when Chen et al defines the ULN as 40 U/L, others may view it as excessively high. It is important to recognize the variables affecting ULN e.g. instrumentation, diurnal variations, exercise and ageing. These variables matter when the distinctions are subtle e.g. normal vs high-normal. In this regard, the utility of long-term high normal ALT as a disease marker could be enhanced by combining it with other biomarkers, imaging and MASLD genetics to create machine learning classifiers. All in all, Chen et al's work on long-term high normal ALT as a marker of new-onset MASLD deserves merit.