Prediction model of random forest for the risk of hyperuricemia in a Chinese basic health checkup test

Ensemble machine learning prediction of hyperuricemia based on a prospective health checkup population

Objectives: An accurate prediction model for hyperuricemia (HUA) in adults remain unavailable. This study aimed to develop a stacking ensemble prediction model for HUA to identify high-risk groups and explore risk factors. Methods: A prospective health checkup cohort of 40899 subjects was examined and randomly divided into the training and validation sets with the ratio of 7:3. LASSO regression was employed to screen out important features and then the ROSE sampling was used to handle the imbalanced classes. An ensemble model using stacking strategy was constructed based on three individual models, including support vector machine, decision tree C5.0, and eXtreme gradient boosting. Model validations were conducted using the area under the receiver operating characteristic curve (AUC) and the calibration curve, as well as metrics including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. A model agnostic instance level variable attributions technique (iBreakdown) was used to illustrate the black-box nature of our ensemble model, and to identify contributing risk factors. Results: Fifteen important features were screened out of 23 clinical variables. Our stacking ensemble model with an AUC of 0.854, outperformed the other three models, support vector machine, decision tree C5.0, and eXtreme gradient boosting with AUCs of 0.848, 0.851 and 0.849 respectively. Calibration accuracy as well as other metrics including accuracy, specificity, negative predictive value, and F1 score were also proved our ensemble model's superiority. The contributing risk factors were estimated using six randomly selected subjects, which showed that being female and relatively younger, together with having higher baseline uric acid, body mass index, γ-glutamyl transpeptidase, total protein, triglycerides, creatinine, and fasting blood glucose can increase the risk of HUA. To further validate our model's applicability in the health checkup population, we used another cohort of 8559 subjects that also showed our ensemble prediction model had favorable performances with an AUC of 0.846. Conclusion: In this study, the stacking ensemble prediction model for HUA was developed, and it outperformed three individual models that compose it (support vector machine, decision tree C5.0, and eXtreme gradient boosting). The contributing risk factors were identified with insightful ideas.

An artificial neural network model for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus

Type 2 diabetes with hyperuricaemia may lead to gout, kidney damage, hypertension, coronary heart disease, etc., further aggravating the condition of diabetes as well as adding to the medical and financial burden. To construct a risk model for hyperuricaemia in patients with type 2 diabetes mellitus based on artificial neural network, and to evaluate the effectiveness of the risk model to provide directions for the prevention and control of the disease in this population. From June to December 2022, 8243 patients with type 2 diabetes were recruited from six community service centers for questionnaire and physical examination. Secondly, the collected data were used to select suitable variables and based on the comparison results, logistic regression was used to screen the variable characteristics. Finally, three risk models for evaluating the risk of hyperuricaemia in type 2 diabetes mellitus were developed using an artificial neural network algorithm and evaluated for performance. A total of eleven factors affecting the development of hyperuricaemia in patients with type 2 diabetes mellitus in this study, including gender, waist circumference, diabetes medication use, diastolic blood pressure, γ-glutamyl transferase, blood urea nitrogen, triglycerides, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, fasting glucose and estimated glomerular filtration rate. Among the generated models, baseline & biochemical risk model had the best performance with cutoff, area under the curve, accuracy, recall, specificity, positive likelihood ratio, negative likelihood ratio, precision, negative predictive value, KAPPA and F1-score were 0.488, 0.744, 0.689, 0.625, 0.749, 2.489, 0.501, 0.697, 0.684, 0.375 and 0.659. In addition, its Brier score was 0.169 and the calibration curve also showed good agreement between fitting and observation. The constructed artificial neural network model has better efficacy and facilitates the reduction of the harm caused by type 2 diabetes mellitus combined with hyperuricaemia.

Risk Prediction for the Development of Hyperuricemia: Model Development Using an Occupational Health Examination Dataset

OBJECTIVE

Hyperuricemia has become the second most common metabolic disease in China after diabetes, and the disease burden is not optimistic.

METHODS

We used the method of retrospective cohort studies, a baseline survey completed from January to September 2017, and a follow-up survey completed from March to September 2019. A group of 2992 steelworkers was used as the study population. Three models of Logistic regression, CNN, and XG Boost were established to predict HUA incidence in steelworkers, respectively. The predictive effects of the three models were evaluated in terms of discrimination, calibration, and clinical applicability.

RESULTS

The training set results show that the accuracy of the Logistic regression, CNN, and XG Boost models was 84.4, 86.8, and 86.6, sensitivity was 68.4, 72.3, and 81.5, specificity was 82.0, 85.7, and 86.8, the area under the ROC curve was 0.734, 0.724, and 0.806, and Brier score was 0.121, 0.194, and 0.095, respectively. The XG Boost model effect evaluation index was better than the other two models, and similar results were obtained in the validation set. In terms of clinical applicability, the XG Boost model had higher clinical applicability than the Logistic regression and CNN models.

CONCLUSION

The prediction effect of the XG Boost model was better than the CNN and Logistic regression models and was suitable for the prediction of HUA onset risk in steelworkers.

Development and internal validation of a risk model for hyperuricemia in diabetic kidney disease patients

Purpose

This research aimed to identify independent risk factors for hyperuricemia (HUA) in diabetic kidney disease (DKD) patients and develop an HUA risk model based on a retrospective study in Ningbo, China.

Patients and methods

Six hundred and ten DKD patients attending the two hospitals between January 2019 and December 2020 were enrolled in this research and randomized to the training and validation cohorts based on the corresponding ratio (7:3). Independent risk factors associated with HUA were identified by multivariable logistic regression analysis. The characteristic variables of the HUA risk prediction model were screened out by the least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation, and the model was presented by nomogram. The C-index and receiver operating characteristic (ROC) curve, calibration curve and Hosmer-Lemeshow test, and decision curve analysis (DCA) were performed to evaluate the discriminatory power, degree of fitting, and clinical applicability of the risk model.

Results

Body mass index (BMI), HbA1c, estimated glomerular filtration rate (eGFR), and hyperlipidemia were identified as independent risk factors for HUA in the DKD population. The characteristic variables (gender, family history of T2DM, drinking history, BMI, and hyperlipidemia) were screened out by LASSO combined with 10-fold cross-validation and included as predictors in the HUA risk prediction model. In the training cohort, the HUA risk model showed good discriminatory power with a C-index of 0.761 (95% CI: 0.712-0.810) and excellent degree of fit (Hosmer-Lemeshow test, P > 0.05), and the results of the DCA showed that the prediction model could be beneficial for patients when the threshold probability was 9-79%. Meanwhile, the risk model was also well validated in the validation cohort, where the C-index was 0.843 (95% CI: 0.780-0.906), the degree of fit was good, and the DCA risk threshold probability was 7-100%.

Conclusion

The development of risk models contributes to the early identification and prevention of HUA in the DKD population, which is vital for preventing and reducing adverse prognostic events in DKD.

Prevalence of hyperuricemia and the population attributable fraction of modifiable risk factors: Evidence from a general population cohort in China

Data on updated hyperuricemia prevalence in Beijing-Tianjin-Hebei (BTH) region in China, which is one of the world-class urban agglomerations, is sparse. Overweight/obesity, alcohol consumption, smoking and sedentary behavior are modifiable risk factors (MRFs) for elevated serum uric acid (SUA), but their population attributable fractions (PAFs) for hyperuricemia is still unclear. Using baseline data from the BTH Physical Examination General Population Cohort, we calculated the crude- and adjusted-prevalence of hyperuricemia based on the 30,158 participants aged 18–80 years. Hyperuricemia was defined as SUA >420 μmol/L in men and >360 μmol/L in women, or currently use of uric acid lowering drugs. Overweight/obesity, alcohol consumption, smoking and sedentary behavior were considered as MRFs and their adjusted PAFs were estimated. The prevalence of hyperuricemia was 19.37%, 27.72% in men and 10.69% in women. The PAFs and 95% confidence intervals for overweight, obesity were 16.25% (14.26–18.25%) and 12.08% (11.40–12.77%) in men, 13.95% (12.31–15.59%) and 6.35% (5.97–6.74%) in women, respectively. Alcohol consumption can explain 4.64% (2.72–6.56%) hyperuricemia cases in men, but with no statistical significance in women. Cigarette smoking contributed to 3.15% (1.09–5.21%) cases in men, but a much lower fraction in women (0.85%, 0.49–1.22%). Compared with sedentary time <2 h per day, the PAFs of 2–4 h, 4–6 h, and more than 6 h per day were 3.14% (1.34–4.93%), 6.72% (4.44–8.99%) and 8.04% (4.95–11.13%) in men, respectively. Sedentary time was not found to be associated with hyperuricemia in women. These findings concluded that hyperuricemia is prevalent in this representative Chinese adult general population with substantial sex difference. Four MRFs (overweight/obesity, alcohol consumption, cigarette smoking and sedentary behavior) accounted for a notable proportion of hyperuricemia cases. The PAF estimations enable the exploration of the expected proportion of hyperuricemia cases that could be prevented if the MRFs were removed, which warrants the public health significance of life-style intervention.

Age and Gender Differences Between Carotid Intima-Media Thickness and Serum Uric Acid

Studies have explored the relation between serum uric acid (SUA) and carotid intima-media thickness (CIMT), but the relation remains controversial. The purpose of this study was to examine SUA concentration and its correlation with carotid artery atherosclerosis according to age group and gender. Subjects who underwent physical examinations at the First Affiliated Hospital of Chongqing Medical University from 2016 to 2020 were selected. Using traditional atherosclerosis risk factors as adjustment variables, the association between blood uric acid level and atherosclerosis was assessed by logistic regression analysis. A total of 15,843 subjects (73.90% men) were included, with an average age of 52 ± 12 years. The prevalence of increased CIMT was 9.51%, and the prevalence of plaque was 28.59%. Univariate analysis results showed that there were significant differences in the occurrence of increased CIMT and plaque among different SUA-level groups in both men and women (p <0.0001). After adjustment for conventional cardiovascular risk factors, increased SUA level was significantly associated with an increased risk of carotid intima-media thickening. Correlation analysis in each age subgroup showed that CIMT was significantly associated with SUA in men ≥60 years old and women 45 to 60 years old or ≥60 years old (p <0.0001). In conclusion, in both men and women, increased SUA levels are closely associated with increased CIMT. The age at which this association was observed was lower in women than in men; whether the lower age in women is due to changes in hormone levels between before and after menopause remains to be prospectively studied.