Nomogram model and risk score to predict 5-year risk of progression from prediabetes to diabetes in Chinese adults: Development and validation of a novel model

Building a predictive model for depression risk in fracture patients: insights from cross-sectional NHANES 2005-2020 data and an external hospital-based dataset

BACKGROUND

Depression represents a frequent mental health challenge in individuals with fractures, negatively impacting their recuperation and overall well-being. The purpose of this research was to formulate and corroborate a prognostic framework for pinpointing depression risk among fracture sufferers by utilizing data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2020 and a separate hospital-based group.

METHODS

We analyzed records from 1,748 individuals with fractures documented in the NHANES database spanning 2005 to 2020, of which 362 were diagnosed with depression, as indicated by a Patient Health Questionnaire-9 (PHQ-9) score of 10 or higher. An additional validation group comprised 360 fracture patients sourced from a medical center. Considered variables for prediction encompassed demographic details, lifestyle habits, past medical conditions, and laboratory results. The method of least absolute shrinkage and selection operator (LASSO) regression facilitated the narrowing down of variables, while multivariate logistic regression was employed to pinpoint significant predictors. To assist in prediction, a nomogram was designed and subsequently validated.

RESULTS

Five independent predictors were identified: drinking, insomnia, poverty-to-income ratio, education level, and white blood cell count. The nomogram showed good discrimination in the NHANES cohorts (training area under the curve (AUC) 0.734, validation AUC 0.740) and hospital-based external validation (AUC 0.711). Calibration curves and decision analysis supported its predictive accuracy and clinical value.

CONCLUSION

The constructed nomogram offers a precise and clinically relevant instrument for forecasting depression risk in patients with fractures, facilitating the early detection of individuals at high risk and enabling prompt intervention.

Development of an immunoinflammatory indicator-related dynamic nomogram based on machine learning for the prediction of intravenous immunoglobulin-resistant Kawasaki disease patients

BACKGROUND

Approximately 10-20% of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance, placing them at higher risk of developing coronary artery aneurysms. Therefore, we aimed to construct an IVIG resistance prediction tool for children with KD in Shanghai, China.

METHODS

Retrospective analysis was conducted on data from 1271 patients diagnosed with KD and the patients were randomly divided into a training set and a validation set in a 2:1 ratio. Machine learning algorithms were employed to identify important predictors associated with IVIG resistance and to build a predictive model. The best-performing model was used to construct a dynamic nomogram. Moreover, receiver operating characteristic curves, calibration plots, and decision-curve analysis were utilized to measure the discriminatory power, accuracy, and clinical utility of the nomogram.

RESULTS

Six variables were identified as important predictors, including C-reactive protein, neutrophil ratio, procalcitonin, CD3 ratio, CD19 count, and IgM level. A dynamic nomogram constructed with these factors was available at https://hktk.shinyapps.io/dynnomapp/. The nomogram demonstrated good diagnostic performance in the training and validation sets (area under the receiver operating characteristic curve = 0.816 and 0.800, respectively). Moreover, the calibration curves and decision curves analysis indicated that the nomogram showed good consistency between predicted and actual outcomes and had good clinical benefits.

CONCLUSION

A web-based dynamic nomogram for IVIG resistance was constructed with good predictive performance, which can be used as a practical approach for early screening to assist physicians in personalizing the treatment of KD patients in Shanghai.

Elevated triglyceride-glucose-body mass index associated with lower probability of future regression to normoglycemia in Chinese adults with prediabetes: a 5-year cohort study

Objective

Despite the clear association of TyG-BMI with prediabetes and the progression of diabetes, no study to date has examined the relationship between TyG-BMI and the reversal of prediabetes to normoglycemia.

Methods

25,279 participants with prediabetes who had physical examinations between 2010 and 2016 were enrolled in this retrospective cohort study. The relationship between baseline TyG-BMI and regression to normoglycemia from prediabetes was examined using the Cox proportional hazards regression model in this study. Additionally, the nonlinear association between TyG-BMI and the likelihood of regression to normoglycemia was investigated using the Cox proportional hazards regression with cubic spline function. Competing risk multivariate Cox regression analysis was conducted, with progression to diabetes as a competing risk for prediabetes reversal to normoglycemia. Furthermore, subgroup analyses and a series of sensitivity analyses were performed.

Results

After adjusting for covariates, the results showed that TyG-BMI was negatively associated with the probability of returning to normoglycemia (per 10 units, HR=0.970, 95% CI: 0.965, 0.976). They were also nonlinearly related, with an inflection point for TyG-BMI of 196.46. The effect size (HR) for TyG-BMI to the right of the inflection point (TyG-BMI ≥ 196.46) and the probability of return of normoglycemia was 0.962 (95% CI: 0.954, 0.970, per 10 units). In addition, the competing risks model found a negative correlation between TyG-BMI and return to normoglycemia (SHR=0.97, 95% CI: 0.96-0.98). Sensitivity analyses demonstrated the robustness of our results.

Conclusion

This study demonstrated a negative and nonlinear relationship between TyG-BMI and return to normoglycemia in Chinese adults with prediabetes. Through active intervention, the combined reduction of BMI and TG levels to bring TyG-BMI down to 196.46 could significantly increase the probability of returning to normoglycemia.

Association between high-density lipoprotein cholesterol and reversion to normoglycemia from prediabetes: an analysis based on data from a retrospective cohort study

The available evidence on the connection between high-density lipoprotein cholesterol (HDL-C) levels and the reversion from prediabetes (Pre-DM) to normoglycemia is currently limited. The present research sought to examine the connection between HDL-C levels and the regression from Pre-DM to normoglycemia in a population of Chinese adults. This historical cohort study collected 15,420 Pre-DM patients in China who underwent health screening between 2010 and 2016. The present research used the Cox proportional hazards regression model to investigate the connection between HDL-C levels and reversion from Pre-DM to normoglycemia. The Cox proportional hazards regression model with cubic spline functions and smooth curve fitting was employed to ascertain the nonlinear association between HDL-C and reversion from Pre-DM to normoglycemia. Furthermore, a set of sensitivity analyses and subgroup analyses were employed. Following the adjustment of covariates, the findings revealed a positive connection between HDL-C levels and the likelihood of reversion from Pre-DM to normoglycemia (HR 1.898, 95% CI 1.758-2.048, P < 0.001). Furthermore, there was a non-linear relationship between HDL-C and the reversion from Pre-DM to normoglycemia in both genders, and the inflection point of HDL-C was 1.540 mmol/L in males and 1.620 mmol/L in females. We found a strong positive correlation between HDL-C and the reversion from Pre-DM to normoglycemia on the left of the inflection point (Male: HR 2.783, 95% CI 2.373-3.263; Female: HR 2.217, 95% CI 1.802-2.727). Our sensitivity analysis confirmed the robustness of these findings. Subgroup analyses indicated that patients with SBP < 140 mmHg and ever smoker exhibited a more pronounced correlation between HDL-C levels and the reversion from Pre-DM to normoglycemia. In contrast, a less robust correlation was observed among patients with SBP ≥ 140 mmHg, current and never smokers. This study provides evidence of a positive and nonlinear association between HDL-C levels and the reversion from Pre-DM to normoglycemia in Chinese patients. Implementing intensified intervention measures to control the HDL-C levels of patients with Pre-DM around the inflection point may substantially enhance the likelihood of regression to normoglycemia.

Differential effect of interventions in patients with prediabetes stratified by a machine learning-based diabetes progression prediction model

AIM

To investigate whether stratifying participants with prediabetes according to their diabetes progression risks (PR) could affect their responses to interventions.

METHODS

We developed a machine learning-based model to predict the 1-year diabetes PR (ML-PR) with the least predictors. The model was developed and internally validated in participants with prediabetes in the Pinggu Study (a prospective population-based survey in suburban Beijing; n = 622). Patients from the Beijing Prediabetes Reversion Program cohort (a multicentre randomized control trial to evaluate the efficacy of lifestyle and/or pioglitazone on prediabetes reversion; n = 1936) were stratified to low-, medium- and high-risk groups using ML-PR. Different effect of four interventions within subgroups on prediabetes reversal and diabetes progression was assessed.

RESULTS

Using least predictors including fasting plasma glucose, 2-h postprandial glucose after 75 g glucose administration, glycated haemoglobin, high-density lipoprotein cholesterol and triglycerides, and the ML algorithm XGBoost, ML-PR successfully predicted the 1-year progression of participants with prediabetes in the Pinggu study [internal area under the curve of the receiver operating characteristic curve 0.80 (0.72-0.89)] and Beijing Prediabetes Reversion Program [external area under the curve of the receiver operating characteristic curve 0.80 (0.74-0.86)]. In the high-risk group pioglitazone plus intensive lifestyle therapy significantly reduced diabetes progression by about 50% at year l and the end of the trial in the high-risk group compared with conventional lifestyle therapy with placebo. In the medium- or low-risk group, intensified lifestyle therapy, pioglitazone or their combination did not show any benefit on diabetes progression and prediabetes reversion.

CONCLUSIONS

This study suggests personalized treatment for prediabetes according to their PR is necessary. ML-PR model with simple clinical variables may facilitate personal treatment strategies in participants with prediabetes.

A nomogram model for predicting 5-year risk of prediabetes in Chinese adults

Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290-0.7392) for the training cohort and 0.7336 (95% CI 0.7285-0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.

Development and validation of a nomogram to estimate future risk of type 2 diabetes mellitus in adults with metabolic syndrome: prospective cohort study

OBJECTIVES

To develop and validate the 4-year risk of type 2 diabetes mellitus among adults with metabolic syndrome.

DESIGN

Retrospective cohort study of a large multicenter cohort with broad validation.

SETTINGS

The derivation cohort was from 32 sites in China and the geographic validation cohort was from Henan population-based cohort study.

RESULTS

568 (17.63) and 53 (18.67%) participants diagnosed diabetes during 4-year follow-up in the developing and validation cohort, separately. Age, gender, body mass index, diastolic blood pressure, fasting plasma glucose and alanine aminotransferase were included in the final model. The area under curve for the training and external validation cohort was 0.824 (95% CI, 0.759-0.889) and 0.732 (95% CI, 0.594-0.871), respectively. Both the internal and external validation have good calibration plot. A nomogram was constructed to predict the probability of diabetes during 4-year follow-up, and on online calculator is also available for a more convenient usage ( https://lucky0708.shinyapps.io/dynnomapp/ ).

CONCLUSION

We developed a simple diagnostic model to predict 4-year risk of type 2 diabetes mellitus among adults with metabolic syndrome, which is also available as web-based tools ( https://lucky0708.shinyapps.io/dynnomapp/ ).