A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

Construction and evaluation of a diagnostic model for metabolic dysfunction-associated steatotic liver disease based on advanced glycation end products and their receptors

Background

Effective biomarkers for the diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) remain limited. This study aims to evaluate the potential of advanced glycation end products (AGEs) and their endogenous secretory receptor (esRAGE) as non-invasive biomarkers for diagnosing MASLD, to explore differences between obese and non-obese MASLD patients, and to develop a novel diagnostic model based on these biomarkers.

Methods

This study enrolled 341 participants, including 246 MASLD patients (118 non-obese, 128 obese) and 95 healthy controls. Serum AGEs and esRAGE levels were measured by ELISA. Key predictors were identified using the Lasso algorithm, and a diagnostic model was developed with logistic regression and visualized as nomograms. Diagnostic accuracy and utility were evaluated through the area under the curve (AUC), bootstrap validation, calibration curves, and decision curve analysis (DCA).

Results

Serum AGEs and esRAGE levels were significantly higher in MASLD patients compared to controls. Moreover, obese MASLD patients had higher esRAGE levels than non-obese ones, but no significant difference in AGEs levels was found. A diagnostic model incorporating age, WC, BMI, ALT, TG, HDL, AGEs, and esRAGE achieved an AUC of 0.963, with 94.3% sensitivity and 85.3% specificity. The AUC for bootstrap internal validation was 0.963 (95% CI: 0.944-0.982). Calibration curves showed strong predictive accuracy, and DCA demonstrated high net clinical benefit.

Conclusion

Serum AGEs and esRAGE serve as non-invasive biomarkers for distinguishing MASLD patients. We developed and validated diagnostic models for MASLD, offering valuable tools to identify at-risk populations and improve prevention and treatment strategies.

A nomograph model for predicting the risk of diabetes nephropathy

OBJECTIVE

Using machine learning to construct a prediction model for the risk of diabetes kidney disease (DKD) in the American diabetes population and evaluate its effect.

METHODS

First, a dataset of five cycles from 2009 to 2018 was obtained from the National Health and Nutrition Examination Survey (NHANES) database, weighted and then standardized (with the study population in the United States), and the data were processed and randomly grouped using R software. Next, variable selection for DKD patients was conducted using Lasso regression, two-way stepwise iterative regression, and random forest methods. A nomogram model was constructed for the risk prediction of DKD. Finally, the predictive performance, predictive value, calibration, and clinical effectiveness of the model were evaluated through the receipt of ROC curves, Brier score values, calibration curves (CC), and decision curves (DCA). In addition, we will visualize it.

RESULTS

A total of 4371 participants were selected and included in this study. Patients were randomly divided into a training set (n = 3066 people) and a validation set (n = 1305 people) in a 7:3 ratio. Using machine learning algorithms and drawing Venn diagrams, five variables significantly correlated with DKD risk were identified, namely Age, Hba1c, ALB, Scr, and TP. The area under the ROC curve (AUC) of the training set evaluation index for this model is 0.735, the net benefit rate of DCA is 2%-90%, and the Brier score is 0.172. The area under the ROC curve of the validation set (AUC) is 0.717, and the DCA curve shows a good net benefit rate. The Brier score is 0.177, and the calibration curve results of the validation set and training set are almost consistent.

CONCLUSION

The DKD risk nomogram model constructed in this study has good predictive performance, which helps to evaluate the risk of DKD as early as possible in clinical practice and formulate relevant intervention and treatment measures. The visual result can be used by doctors or individuals to estimate the probability of DKD risk, as a reference to help make better treatment decisions.

Insulin resistance, combined with health-related lifestyles, psychological traits and adverse cardiometabolic profiles, is associated with cardiovascular diseases: findings from the BHMC study

The triglyceride glucose (TyG) index is a reliable marker of insulin resistance; however, its combined impact with modifiable lifestyle risk factors and psychological traits on cardiovascular diseases (CVDs) remains unclear. The aim of this study was to explore the relationship between the TyG index, various behavioral factors, psychological traits, and CVDs. A total of 77 752 adults aged 18 and over from the baseline survey of the Beijing Health Management Cohort study were investigated. Associations of the TyG index, body roundness index (BRI), dietary habits, psychological traits, and sleep habits with CVDs were estimated using multivariable logistic regression models. Compared to the Q1 level, the Q4 level of the TyG index had an odds ratio (OR) and 95% confidence interval (CI) of 2.30 (1.98-2.68) for CVD risk in men and 2.12 (1.81-2.48) in women. Compared to a sleep duration of more than 7 hours, a sleep duration less than 5 hours had a 32% (8%-61%) higher risk in men and 22% (1%-48%) in women. The ORs (95% CIs) for fast eating compared to normal speed were 1.47 (1.23-1.76) in men and 1.17 (1.05-1.29) in women. Compared to individuals with a passive and depressed psychological trait, those who were positive and optimistic had a 47% (36%-56%) decreased risk in men and 43% (31%-53%) in women. In the age-stratified analysis, a higher BRI level showed a sex-differential effect on CVDs, which is potentially related to a lower risk of CVDs in elderly men. A high level of the TyG index combined with unhealthy lifestyle factors indicates a higher risk of CVDs, while maintaining a positive and optimistic psychological trait acts as a protective factor. These findings may be valuable for identifying high-risk populations for CVDs in community settings.

Development of hypertension models for lung cancer screening cohorts using clinical and thoracic aorta imaging factors

This study aims to develop and validate nomogram models utilizing clinical and thoracic aorta imaging factors to assess the risk of hypertension for lung cancer screening cohorts. We included 804 patients and collected baseline clinical data, biochemical indicators, coexisting conditions, and thoracic aorta factors. Patients were randomly divided into a training set (70%) and a validation set (30%). In the training set, variance, t-test/Mann-Whitney U-test and standard least absolute shrinkage and selection operator were used to select thoracic aorta imaging features for constructing the AIScore. Multivariate logistic backward stepwise regression was utilized to analyze the influencing factors of hypertension. Five prediction models (named AIMeasure model, BasicClinical model, TotalClinical model, AIBasicClinical model, AITotalClinical model) were constructed for practical clinical use, tailored to different data scenarios. Additionally, the performance of the models was evaluated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analyses (DCA). The areas under the ROC curve for the five models were 0.73, 0.77, 0.83, 0.78, 0.84 in the training set, and 0.77, 0.78, 0.81, 0.78, 0.82 in the validation set, respectively. Furthermore, the calibration curves and DCAs of both sets performed well on accuracy and clinical practicality. The nomogram models for hypertension risk prediction demonstrate good predictive capability and clinical utility. These models can serve as effective tools for assessing hypertension risk, enabling timely non-pharmacological interventions to preempt or delay the future onset of hypertension.

Lifestyle and clinical factors as predictive indicators of cardiometabolic multimorbidity in Chinese adults: Baseline findings of the Beijing Health Management Cohort (BHMC) study

BACKGROUND

Cardiometabolic multimorbidity (CMM) is increasing globally as a result of lifestyle changes and the aging population. Even though previous studies have examined risk factors associated with CMM, there is a shortage of prediction models that can accurately identify high-risk individuals for early prevention.

METHODS

In the baseline survey of the Beijing Health Management Cohort, a total of 77,752 adults aged 18 years or older were recruited from 2020 to 2021. Data on lifestyle factors, clinical profiles, and diagnoses of diabetes, coronary heart disease, and stroke were collected. Logistic regression models were used to identify risk factors for CMM. Nomograms were developed to estimate an individual's probability of CMM based on the identified risk factors. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

In men, the top three risk factors for CMM were hypertension (OR: 3.52, 95 % CI: 2.97-4.18), eating very fast (3.43, 2.27-5.16), and dyslipidemia (2.59, 2.20-3.06). In women, hypertension showed the strongest association with CMM (3.62, 2.90-4.52), followed by night sleep duration less than 5 h per day (2.41, 1.67-3.50) and dyslipidemia (1.91, 1.58-2.32). The ORs for holding passive and depressed psychological traits were 1.49 (95%CI: 1.08-2.06) in men and 1.58 (1.03-2.43) in women. Prediction models incorporating these factors demonstrated good discrimination in the test set, with AUC 0.84 (0.83-0.86) for men and 0.90 (0.89-0.91) for women. The sex-specific nomograms were established based on selected predictors.

CONCLUSIONS

Modifiable lifestyle factors, metabolic health and psychological trait are associated with the risk of CMM. The developed prediction models and nomograms could facilitate early identification of individuals at high-risk of CMM.

Body mass index, neck circumference, and hypertension: a prospective cohort study

Objective

This study aimed to investigate the association between BMI combined with neck circumference and the risk of hypertension.

Methods

We selected participants from the Kailuan study in 2014 who were normotensive as our research subjects. We compared the risk of hypertension among individuals in group 1 (non-obese with low neck circumference), group 2 (non-obese with high neck circumference), group 3 (obese with low neck circumference), and group 4 (obese with high neck circumference).

Results

After a median observation period of 3.86 years, hypertension occurred in 13,383 participants. Subjects in Group 2, 3, and 4 had significantly higher risks of hypertension compared to Group 1, with hazard ratios (HRs) of 1.066 (95% CI: 1.025, 1.110), 1.322 (95% CI: 1.235, 1.415), and 1.422 (95% CI: 1.337, 1.512), respectively. Additionally, adding BMI to a conventional model had a greater incremental effect on predicting hypertension compared to adding neck circumference alone. However, considering both BMI and neck circumference together further improved the prediction of hypertension.

Conclusion

Individuals with both high BMI and high neck circumference face a higher risk of hypertension. Moreover, BMI is a superior predictor of hypertension risk compared to neck circumference, but using both of these measures can further enhance the accuracy of hypertension risk prediction.

Fat-to-Muscle Ratio Is Independently Associated with Hyperuricemia and a Reduced Estimated Glomerular Filtration Rate in Chinese Adults: The China National Health Survey

BACKGROUND

The effects of the fat-to-muscle ratio (FMR) on hyperuricemia and a reduction in the estimated glomerular filtration rate (eGFR) are still unclear.

METHODS

Data from the China National Health Survey were used to explore the associations of the FMR with hyperuricemia and reduced eGFR. The fat mass and muscle mass were measured through bioelectrical impedance analysis. Mediation analysis was used to estimate the mediated effect of hyperuricemia on the association between the FMR and reduced eGFR.

RESULTS

A total of 31171 participants were included. For hyperuricemia, compared with the Q1 of the FMR, the ORs (95% CI) of Q2, Q3 and Q4 were 1.60 (1.32-1.95), 2.31 (1.91-2.80) and 2.71 (2.15-3.43) in men and 1.91 (1.56-2.34), 2.67 (2.12-3.36) and 4.47 (3.40-5.89) in women. For the reduced eGFR, the ORs (95% CI) of Q2, Q3 and Q4 of the FMR were 1.48 (1.18-1.87), 1.38 (1.05-1.82) and 1.45 (1.04-2.04) in men aged 40-59, but no positive association was found in younger men or in women. Hyperuricemia mediated the association between the FMR and reduced eGFR in men. The OR (95% CI) of the indirect effect was 1.08 (1.05-1.10), accounting for 35.11% of the total effect.

CONCLUSIONS

The FMR was associated with hyperuricemia and reduced eGFR, and the associations varied based on sex and age. The effect of the FMR on the reduced eGFR was significantly mediated by hyperuricemia in men.