The Association between Pulmonary Functions and Incident Diabetes: Longitudinal Analysis from the Ansung Cohort in Korea

Forced vital capacity and body mass index of Xinjiang children and adolescents: an analysis based on seven successive national surveys, 1985-2014

BACKGROUND

Pulmonary function is very important for the healthy development of children and adolescents. However, fewer studies have been conducted on pulmonary function trends in children and adolescents in remote areas. The aim of this study was to estimate the forced vital capacity (FVC) trend and its relationship with body mass index (BMI) among young people in Xinjiang during 1985-2014 using data from seven successive national surveys.

METHODS

A total of 19,449 Xinjiang children and adolescents aged 7-18 years were extracted from the Chinese National Survey on Students' Constitution and Health. Height, weight, and FVC were measured repeatedly in each survey. FVC comparisons between adjacent surveys by age and sex were conducted by nonparametric Kruskal-Wallis after Kolmogorov-Smirnov of normality. One-way ANOVA and least significant difference(LSD) method was used to compare differences in FVC levels of Xinjiang children and adolescents with different BMI. The relationship between BMI and FVC was investigated using a nonlinear regression model.

RESULTS

The FVC levels of Xinjiang children and adolescents peaked in 2000, with overall FVC levels being 8.7% higher in 2000 than in 1985. Since then, a substantial decline occurred, contrasting to 2000, with FVC levels decreasing by 27% in 2014, which was still lower than that in 1985 by 20.73%. The proportion of overnutrition boys increased from 0.2% in 1985 to 22.1% in 2014, and girls from 0.5% in 1985 to 14.5% in 2014. An inverted U-shape association between FVC and BMI values was obtained for Xinjiang children and adolescents.

CONCLUSIONS

Targeted measures should be carried out in schools to control BMI levels to ensure good lung function in children and adolescents in Xinjiang. Future studies should pay more attention to other factors affecting FVC, such as dietary behaviour, physical activity, and racial differences among children and adolescents.

Validation of the Framingham Diabetes Risk Model Using Community-Based KoGES Data

BACKGROUND

An 8-year prediction of the Framingham Diabetes Risk Model (FDRM) was proposed, but the predictor has a gap with current clinical standards. Therefore, we evaluated the validity of the original FDRM in Korean population data, developed a modified FDRM by redefining the predictors based on current knowledge, and evaluated the internal and external validity.

METHODS

Using data from a community-based cohort in Korea (n = 5,409), we calculated the probability of diabetes through FDRM, and developed a modified FDRM based on modified definitions of hypertension (HTN) and diabetes. We also added clinical features related to diabetes to the predictive model. Model performance was evaluated and compared by area under the curve (AUC).

RESULTS

During the 8-year follow-up, the cumulative incidence of diabetes was 8.5%. The modified FDRM consisted of age, obesity, HTN, hypo-high-density lipoprotein cholesterol, elevated triglyceride, fasting glucose, and hemoglobin A1c. The expanded clinical model added γ-glutamyl transpeptidase to the modified FDRM. The FDRM showed an estimated AUC of 0.71, and the model's performance improved to an AUC of 0.82 after applying the redefined predictor. Adding clinical features (AUC = 0.83) to the modified FDRM further improved in discrimination, but this was not maintained in the validation data set. External validation was evaluated on population-based cohort data and both modified models performed well, with AUC above 0.82.

CONCLUSION

The performance of FDRM in the Korean population was found to be acceptable for predicting diabetes, but it was improved when corrected with redefined predictors. The validity of the modified model needs to be further evaluated.

Independent and combined associations of multiple-heavy-metal exposure with lung function: a population-based study in US children

Previous research has found relationships between some single metals and lung function parameters. However, the role of simultaneous multi-metal exposure is poorly understood. The crucial period throughout childhood, when people are most susceptible to environmental dangers, has also been largely ignored. The study aimed to evaluate the joint and individual associations of 12 selected urinary metals with pediatric lung function measures using multi-pollutant approaches. A total of 1227 children aged 6-17 years from the National Health and Nutrition Examination Survey database of the 2007-2012 cycles were used. The metal exposure indicators were 12 urine metals adjusted for urine creatinine, including arsenic (As), barium (Ba), cadmium (Cd), cesium (Cs), cobalt (Co), mercury (Hg), molybdenum (Mo), lead (Pb), antimony (Sb), thallium (Tl), tungsten (Tu), and uranium (Ur). The outcomes of interest were lung function indices, including the 1st second of a forceful exhalation (FEV₁), forced vital capacity (FVC), forced expiratory flow between 25 and 7% of vital capacity (FEF_25-75%), and peak expiratory flow (PEF). Multivariate linear regression, quantile g-computation (QG-C), and Bayesian kernel machine regression models (BKMR) were adopted. A significantly negative overall effect of metal mixtures on FEV₁ (β = - 161.70, 95% CI - 218.12, - 105.27; p < 0.001), FVC (β = - 182.69, 95% CI - 246.33, - 119.06; p < 0.001), FEF_25-75% (β = - 178.86 (95% CI - 274.47, - 83.26; p < 0.001), and PEF (β = - 424.17, 95% CI - 556.55, - 291.80; p < 0.001) was observed. Pb had the largest negative contribution to the negative associations, with posterior inclusion probabilities (PIPs) of 1 for FEV₁, FVC, and FEF_25-75%, and 0.9966 for PEF. And Pb's relationship with lung function metrics showed to be nonlinear, with an approximate "L" shape. Potential interactions between Pb and Cd in lung function decline were observed. Ba was positively associated with lung function metrics. Metal mixtures were negatively associated with pediatric lung function. Pb might be a crucial element. Our findings highlight the need for prioritizing children's environmental health to protect them from later respiratory disorders and to guide future research into the toxic mechanisms of metal-mediated lung function injury in the pediatric population.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.

Development of Various Diabetes Prediction Models Using Machine Learning Techniques

BACKGROUND

There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters.

METHODS

Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method.

RESULTS

The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included.

CONCLUSION

We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

Reduced lung function predicts risk of incident type 2 diabetes: insights from a meta-analysis of prospective studies

Epidemiological studies have repeatedly investigated the association between reduced pulmonary function and incident type 2 diabetes mellitus (T2DM). However, the results have been inconsistent. This meta-analysis aimed to clarify this association with prospective cohort studies. We searched PubMed, Web of Science (ISI), and Google Scholar for all studies (in English) reporting reduced lung function with a risk of T2DM. The measures of lung function included percentage of forced vital capacity for predicted values (FVC_%pre), percentage of forced expiratory volume in the first second after expiration for predicted values (FEV_1%pre) and FEV₁-to-FVC ratio%. Summary risk ratios (RRs) and 95% confidence intervals (CIs) were calculated using fixed-effects or random-effects meta-analyses. A total of 5,480 incident T2DM patients among 88,799 individuals were identified from nine prospective cohort studies. Compared to the highest category of FVC_%pre and FEV_1%pre, the lowest category of FVC_%pre and FEV_1%pre were significantly associated with increased incident T2DM risk (FVC_%pre: RR = 1.49, 95% CI: 1.39-1.59; FEV_1%pre: RR = 1.52, 95% CI: 1.42-1.62). However, no significant relationship was found between the FEV₁/FVC ratio and incident T2DM risk (RR = 1.01, 95% CI: 0.91-1.13). Current evidence suggests that restrictive rather than obstructive impairment of lung function is significantly associated with the incidence of T2DM. Further research is warranted to explore potential mediators of this relationship.