Aim Metabolic syndrome (MetS) coexists with the occurrence and even death of cardiovascular disease and diabetes mellitus. It is essential to study the factors in the dynamic progression of MetS in the interest of prevention and control. Purpose The aim of this study was to analyze the dynamic progression of Mets and explore the potential factors influencing the progression or reversal of MetS. Patients and Methods This study involved 5581 individuals from two waves of the China Health and Retirement Longitudinal Study: 2011 and 2015. A multistate Markov model containing 4 states (free of metabolic disorder (FMD), mild metabolic disorder (MMD), severe metabolic disorder (SMD) and MetS) was adopted to study the dynamic progression of MetS and its influencing factors. Results After follow-up, a total of 2862 cases (50.28% of the total number) had disease state transition. The intensity of transition from MetS to SMD is the same as that from SMD to MMD, and is greater than that from MMD to Mets (0.06 vs 0.05). For the MetS state, a mean of 1/0.08=12.5 years was spent in the MetS state before recovery. The exercise, smoke, drink, BMI level, hyperuricemia had statistically significant effects on progression of MetS status ( P <0.05). The obesity or overweight, little exercise, smoke, drink and hyperuricemia increased the risk of forward progression of MetS disease status. There were significant nonmodifiable (age, gender) and modifiable factors (exercise, drink, BMI level, or high HbA1c) associated with reversion of MetS state. Conclusion The likelihood of progression from MMD to MetS is less likely than that of reversion from MetS to SMD and SMD to MMD. Old females were more resistant to recover from worse states than males. Prevention and intervention measures should be adopted early when MMD or SMD onset occurs.
Background We aimed to construct simple and practical metabolic syndrome (MetS) risk prediction models based on the data of inhabitants of Urumqi and to provide a methodological reference for the prevention and control of MetS. Methods This is a cross-sectional study conducted in the Xinjiang Uygur Autonomous Region of China. We collected data from inhabitants of Urumqi from 2018 to 2019, including demographic characteristics, anthropometric indicators, living habits and family history. Resampling technology was used to preprocess the data imbalance problems, and then MetS risk prediction models were constructed based on logistic regression (LR) and decision tree (DT). In addition, nomograms and tree diagrams of DT were used to explain and visualize the model. Results Of the 25,542 participants included in the study, 3,267 (12.8%) were diagnosed with MetS, and 22,275 (87.2%) were diagnosed with non-MetS. Both the LR and DT models based on the random undersampling dataset had good AUROC values (0.846 and 0.913, respectively). The accuracy, sensitivity, specificity, and AUROC values of the DT model were higher than those of the LR model. Based on a random undersampling dataset, the LR model showed that exercises such as walking (OR=0.769) and running (OR= 0.736) were protective factors against MetS. Age 60 ~ 74 years (OR=1.388), previous diabetes (OR=8.902), previous hypertension (OR=2.830), fatty liver (OR=3.306), smoking (OR=1.541), high systolic blood pressure (OR=1.044), and high diastolic blood pressure (OR=1.072) were risk factors for MetS; the DT model had 7 depth layers and 18 leaves, with BMI as the root node of the DT being the most important factor affecting MetS, and the other variables in descending order of importance: SBP, previous diabetes, previous hypertension, DBP, fatty liver, smoking, and exercise. Conclusions Both DT and LR MetS risk prediction models have good prediction performance and their respective characteristics. Combining these two methods to construct an interpretable risk prediction model of MetS can provide methodological references for the prevention and control of MetS.
Objective The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze and evaluate the application value of model interpretability methods in dealing with difficult interpretation of predictive models. Methods The study collects data from a chain of health examination institution in Urumqi from 2017 ~ 2019, and performs 39,134 remaining data after preprocessing such as deletion and filling. RFE is used for feature selection to reduce redundancy; MetS risk prediction models (logistic, random forest, XGBoost) are built based on a feature subset, and accuracy, sensitivity, specificity, Youden index, and AUROC value are used to evaluate the model classification performance; post-hoc model-agnostic interpretation methods (variable importance, LIME) are used to interpret the results of the predictive model. Results Eighteen physical examination indicators are screened out by RFE, which can effectively solve the problem of physical examination data redundancy. Random forest and XGBoost models have higher accuracy, sensitivity, specificity, Youden index, and AUROC values compared with logistic regression. XGBoost models have higher sensitivity, Youden index, and AUROC values compared with random forest. The study uses variable importance, LIME and PDP for global and local interpretation of the optimal MetS risk prediction model (XGBoost), and different interpretation methods have different insights into the interpretation of model results, which are more flexible in model selection and can visualize the process and reasons for the model to make decisions. The interpretable risk prediction model in this study can help to identify risk factors associated with MetS, and the results showed that in addition to the traditional risk factors such as overweight and obesity, hyperglycemia, hypertension, and dyslipidemia, MetS was also associated with other factors, including age, creatinine, uric acid, and alkaline phosphatase. Conclusion The model interpretability methods are applied to the black box model, which can not only realize the flexibility of model application, but also make up for the uninterpretable defects of the model. Model interpretability methods can be used as a novel means of identifying variables that are more likely to be good predictors.
Background Close contacts of active pulmonary tuberculosis patients are high-risk groups for tuberculosis. Through active screening of contacts, more contacts who have recently been exposed to pulmonary tuberculosis patients, early existing pulmonary tuberculosis patients, and latent infections can be found. Methods The decision tree Markov model of close contacts screening strategy was established to simulate the cohort of close contacts of tuberculosis. The cost effects of passive screening strategy and active screening once, twice, and once a year were compared. Single-factor and probability sensitivity analyses were conducted to test the impact of assumptions and parameter estimates in the model on cost-effectiveness analysis. Results Compared with passive screening, active screening once, twice, and once a year could reduce the incidence of latent infection by 0.21%, 14.286%, and 63.48%, respectively; Compared with the passive screening strategy, active screening once, twice, and once a year can reduce the incidence of active tuberculosis by 4.13%, 11.22%, and 50.04% respectively; Compared with the passive screening strategy, the active screening once, twice and once a year strategy reduced the incidence of TB deaths by 2.86%, 5.71%, and 11.43% respectively. Compared with the passive screening strategy, active screening once, active screening twice, and active screening once a year will cost 8800.43 RMB, 5781.70 RMB, and 13825.04 RMB more for each additional QALY, which is lower than the willingness of Chinese people to pay and is less than twice the GDP of Xinjiang. The increased cost of obtaining an additional QALY is entirely worth it. All these are advantageous strategies. Conclusion Compared with the passive screening strategy, the cost and effect of the active screening strategy of once, twice, and once a year increase in turn, which can reduce the incidence of LTBI, tuberculosis, and tuberculosis death, and are all advantageous strategies. Continuous active screening of tuberculosis in critical populations is one of the key measures to quickly reduce the epidemic situation of tuberculosis. It is recommended to carry out regular screening for all close contacts of active tuberculosis patients. Reasonably allocate health resources, and propose reasonable screening methods for different age groups and critical groups.
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