A nomogram for predicting 5-year incidence of type 2 diabetes in a Chinese population

[Construction of a Predictive Model for Diabetes Mellitus Type 2 in Middle-Aged and Elderly Populations Based on the Medical Checkup Data of National Basic Public Health Service]

Objective

To establish a universally applicable logistic risk prediction model for diabetes mellitus type 2 (T2DM) in the middle-aged and elderly populations based on the results of a Meta-analysis, and to validate and confirm the efficacy of the model using the follow-up data of medical check-ups of National Basic Public Health Service.

Methods

Cohort studies evaluating T2DM risks were identified in Chinese and English databases. The logistic model utilized Meta-combined effect values such as the odds ratio (OR) to derive β, the partial regression coefficient, of the logistic model. The Meta-combined incidence rate of T2DM was used to obtain the parameter α of the logistic model. Validation of the predictive performance of the model was conducted with the follow-up data of medical checkups of National Basic Public Health Service. The follow-up data came from a community health center in Chengdu and were collected between 2017 and 2022 from 7602 individuals who did not have T2DM at their baseline medical checkups done at the community health center. This community health center was located in an urban-rural fringe area with a large population of middle-aged and elderly people.

Results

A total of 40 cohort studies were included and 10 items covered in the medical checkups of National Basic Public Health Service were identified in the Meta-analysis as statistically significant risk factors for T2DM, including age, central obesity, smoking, physical inactivity, impaired fasting glucose, a reduced level of high-density lipoprotein cholesterol (HDL-C), hypertension, body mass index (BMI), triglyceride glucose (TYG) index, and a family history of diabetes, with the OR values and 95% confidence interval (CI) being 1.04 (1.03, 1.05), 1.55 (1.29, 1.88), 1.36 (1.11, 1.66), 1.26 (1.07, 1.49), 3.93 (2.94, 5.24), 1.14 (1.06, 1.23), 1.47 (1.34, 1.61), 1.11 (1.05, 1.18), 2.15 (1.75, 2.62), and 1.66 (1.55, 1.78), respectively, and the combined β values being 0.039, 0.438, 0.307, 0.231, 1.369, 0.131, 0.385, 0.104, 0.765, and 0.507, respectively. A total of 37 studies reported the incidence rate, with the combined incidence being 0.08 (0.07, 0.09) and the parameter α being -2.442 for the logistic model. The logistic risk prediction model constructed based on Meta-analysis was externally validated with the data of 7602 individuals who had medical checkups and were followed up for at least once. External validation results showed that the predictive model had an area under curve (AUC) of 0.794 (0.771, 0.816), accuracy of 74.5%, sensitivity of 71.0%, and specificity of 74.7% in the 7602 individuals.

Conclusion

The T2DM risk prediction model based on Meta-analysis has good predictive performance and can be used as a practical tool for T2DM risk prediction in middle-aged and elderly populations.

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

BACKGROUND

People with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.

METHODS

A systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test).

RESULTS

Forty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52-0.60, 0.50-0.59, and 0.50-0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54-0.73, 0.52-0.67, and 0.59-0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).

CONCLUSIONS

In Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

Elevated gamma-glutamyl transferase to high-density lipoprotein cholesterol ratio has a non-linear association with incident diabetes mellitus: A second analysis of a cohort study

OBJECTIVE

Evidence regarding the association between the GGT/HDL-c ratio and incident diabetes is still limited. On that account, our research aims to survey the link of the GGT/HDL-c ratio with the risk of diabetes.

METHODS

In this retrospective cohort study, data of 15,171 participants who participated in the medical examination program were collected in Murakami Memorial Hospital in Japan from 2004 to 2015. The independent and dependent variables were the baseline GGT/HDL-c ratio and diabetes during the follow-up, respectively. The Cox proportional-hazards regression model was used to explore the association between the GGT/HDL-c ratio and diabetes risk. A Cox proportional hazards regression with the cubic spline smoothing was used to recognize non-linear relationships between the GGT/HDL-c ratio and incident diabetes.

RESULTS

After adjusting covariates, the results showed that the GGT/HDL-c ratio was positively associated with incident diabetes (HR = 1.013, 95% CI: 1.002, 1.024). There was also a non-linear relationship between the GGT/HDL-c ratio and the risk of diabetes, and the inflection point of the GGT/HDL-c ratio was 6.477. The HR on the left and right sides of the inflection point was 2.568 (1.157, 5.699) and 1.012 (1.001, 1.023), respectively. The sensitivity analysis demonstrated the robustness of the results. Besides, the performance of the FPG + GGT/HDL-c ratio was better than FPG + GGT, FPG + HDL-c, and FPG in predicting diabetes.

CONCLUSION

This study demonstrates a positive and non-linear relationship between the GGT/HDL-c ratio and incident diabetes in the Japanese population. The GGT/HDL-c ratio is strongly related to diabetes risk when it is <6.477.

High incidence of type 2 diabetes in a population with normal range body mass index and individual prediction nomogram in Vietnam

AIMS

The study aimed at determining 5-year incidence and prediction nomogram for new-onset type 2 diabetes (T2D) in a middle-aged population in Vietnam.

METHODS

A population-based prospective study was designed to collect socio-economic, anthropometric, lifestyle and clinical data. Five-year T2D incidence was estimated and adjusted for age and sex. Hazard ratio (HR) for T2D was investigated using discrete-time proportional hazards model. T2D prediction model entering the most significant risk factors was developed using the multivariable logistic-regression algorithm. The corresponding prediction nomogram was constructed and checked for discrimination, calibration and clinical usefulness.

RESULTS

The age- and sex-adjusted incidence was 21.0 cases (95% CI: 12.2-40.0) per 1000 person-years in people with mean BMI of 22.2 (95% CI: 21.9-22.7 kg/m² ). The HRs (95% CI) for T2D were 1.14 (1.05-1.23) per 10 mmHg systolic blood pressure, 1.05 (1.03-1.08) per 1 cm waist circumference, 1.40 (1.13-1.73) per 1 mmol/L fasting blood glucose, 1.77 (1.15-2.71) per sleeping time (<6 h/day vs 6-7 h/day) and 2.12 (1.25-3.61) per residence (urban vs rural). The prediction nomogram for new-onset T2D had a good discrimination (area under curve: 0.711, 95% CI: 0.666-0.755) and fit calibration (mean absolute error: 0.009). For the predicted probability thresholds between 0.03 and 0.36, the nomogram showed a positive net benefit, without increasing the number of false positives.

CONCLUSION

This study highlighted an alarmingly high incidence of T2D in a middle-aged population with a normal range BMI in Vietnam. The individual prediction nomogram with decision curve analysis for new-onset T2D would be valuable for early detection, intervention and treatment of the condition.

A Prediction Model of the Incidence of Type 2 Diabetes in Individuals with Abdominal Obesity: Insights from the General Population

BACKGROUND

This study aimed to distinguish the risk factors for type 2 diabetes mellitus (T2DM) and construct a predictive model of T2DM in Japanese adults with abdominal obesity.

METHODS

This study was a post hoc analysis. A total of 2012 individuals with abdominal obesity were included and randomly divided into training and validation groups at 70% (n = 1518) and 30% (n = 494), respectively. The LASSO method was used to screen for risk variables for T2DM, and to construct a nomogram incorporating the selected risk factors in the training group. We used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration and clinical significance of the nomogram.

RESULTS

In the training cohort, the C-index and receiver operating characteristic were 0.819 and the 95% CI was 0.776-0.858, with a specificity and sensitivity of 77% and 74.68%, respectively. In the validation cohort, the C-index was 0.853; sensitivity and specificity were 77.6% and 88.1%, respectively. The decision curve analysis showed that the model's prediction was effective and cumulative hazard analysis demonstrated that the high-risk score group was more likely to develop T2DM than the low-risk score group.

CONCLUSION

This nomogram may help clinicians screen abdominal obesity at a high risk for T2DM.

New risk score model for identifying individuals at risk for diabetes in southwest China

The prevalence of diabetes is increasing rapidly and becoming a major public health issue worldwide. We aimed to develop a novel nomogram model for long-term diabetic risk prediction in a Chinese population. A prospective cohort study was performed on 687 nondiabetic individuals who underwent routine physical examination in 1992 and 2007. Using the least absolute shrinkage and selection operator model to optimize feature selection. Multiple Cox regression analysis was performed, and a simple nomogram was constructed. The area under receiver operating characteristic curve (AUC) and calibration plot were conducted to assess the predictive accuracy of the model. The model was subjected to bootstrap internal validation. Of the 687 participants without diabetes at baseline, 74 developed diabetes during the follow-up time. This simple nomogram model was constructed by family history of diabetes, height, waist circumference, triglycerides, fasting plasma glucose and white blood cell count. The AUCs were 0.812 (95% CI: 0.729-0.895) and 0.794 (95% CI: 0.734-0.854) for 10-year and 15-year diabetic risk. The bootstrap corrected c-index was 0.771 (95% CI: 0.721-0.821). The calibration plot also achieved good agreement between observational and actual diabetic incidence. The stratification into different risk groups by optimal cut-off value of 12.8 allowed significant distinction between cumulative diabetic incidence curves in the whole cohort and several subgroups. We established and internally validated a novel nomogram which can provide individual diabetic risk prediction for Chinese population and this practical screening model may help clinicians to identify individuals at high risk of diabetes.

Applying latent class analysis to risk stratification of incident diabetes among Chinese adults

OBJECTIVE

To use latent class analysis to identify unobservable subpopulations amongst the heterogeneous population and explore the relationship between subpopulations and incident diabetes among Chinese adults.

METHODS

The retrospective study included 32,312 Chinese adults without diabetes at baseline. Latent class indicators included demographic and clinical variables. The outcome was incident diabetes. The relationship between latent class and outcome was evaluated with Cox proportional hazard regression analysis.

RESULTS

After screening, the two-class latent class model best fits the population. Participants in class 2 are characterized by higher age, body mass index, systolic and diastolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, serum creatinine, serum urea nitrogen, alanine aminotransferase, and a higher proportion of males, ever/current smokers and drinkers, but lower high-density lipoprotein cholesterol and a lower proportion of family history of diabetes. The risk of diabetes in class 2 was 5.451 times (HR: 6.451, 95%CI: 4.179-9.960, P < 0.00001) and 5.264 times (HR: 6.264, 95%CI: 4.680-8.385, P < 0.00001) higher than that in class 1 during 3-year and 5-year follow-up, respectively.

CONCLUSIONS

We used latent class analysis to identify two distinct subpopulations with differential risk of diabetes during 3-year and 5-year follow-up.

Prediction Model for Screening Patients at Risk of Malnutrition After Gastric Cancer Surgery

BACKGROUND

Malnutrition after gastrectomy is associated with a poor prognosis; however, no accurate model for predicting post-gastrectomy malnutrition exists. Hence, we conducted a retrospective study to develop a prediction model identifying gastric cancer patients at high risk of malnutrition after gastrectomy.

METHOD

Gastric cancer patients who underwent curative gastrectomy with more than one weight measurement during a 3-year follow-up period were included. Malnutrition was defined as body mass index (BMI) < 18.5 kg/m² according to the European Society of Clinical Nutrition and Metabolism diagnostic criteria. BMI-loss pattern was analyzed using a group-based trajectory model. A prediction model for malnutrition 6 months after gastrectomy was developed based on significant risk factors, and then validated.

RESULTS

Overall, 1421 patients were examined. The BMI-loss trajectory model showed significant BMI loss at 6 months after gastrectomy. Severe BMI loss (mean 21.5%; n = 109) was significantly associated with the elderly, female sex, higher preoperative BMI, advanced cancer stage, open surgery, total gastrectomy, Roux-en-Y reconstruction, chemotherapy, and postoperative complications (all p < 0.05). Malnutrition 6 months after gastrectomy was observed in 152 (11.9%) of 1281 patients. Preoperative BMI, sex, and type of operation were included in the final prediction model as predictive factors (p < 0.05). The C-index of the developmental set and bootstrap validation of the prediction model was 0.91 (95% confidence interval 0.89-0.94) and 0.91, respectively.

CONCLUSION

The prediction model for the risk of malnutrition 6 months after gastrectomy was accurately developed, with three independent risk factors: low preoperative BMI, female sex, and total or proximal gastrectomy.

Exploration of the risk factors of essential hypertension with hyperhomocysteinemia: A hospital-based study and nomogram analysis

OBJECTIVES

To explore the risk factors of essential hypertension with hyperhomocysteinemia (H-type hypertension) and design a nomogram to predict this risk.

METHODS

A hospital-based study was conducted on 1,712 individuals, including 282 patients with H-type hypertension, 105 patients with simple hypertension, 645 individuals with hyperhomocysteinemia, and 680 healthy controls. Logistic regression and nomogram models were applied to evaluate the risk factors.

RESULTS

Logistic regression showed that advanced age, male sex, high body mass index (BMI), high total cholesterol levels, high glucose levels, and high creatinine levels were risk factors of H-type hypertension in the healthy population and were integrated into the nomogram model. Advanced age, male sex, high BMI, high total cholesterol levels, and high glucose levels were shown to be risk factors of H-type hypertension in the hyperhomocysteinemia population. Male sex and high creatinine levels were shown to be risk factors of H-type hypertension in the hypertension population. Nomogram analysis showed that the total factor score ranged from 106 to 206, and the corresponding risk rate ranged from 0.05 to 0.95.

CONCLUSIONS

Men are more likely to have H-type hypertension, and advanced age, high BMI, high total cholesterol levels, and high glucose levels are risk factors of H-type hypertension in healthy and hyperhomocysteinemia populations. Furthermore, high creatinine level is a risk factor of H-type hypertension in healthy and hypertension populations. Nomogram models may be used to intuitively evaluate H-type hypertension risk and provide a basis for personalized interventions.

Nomogram Predicting the Risk of Progression from Prediabetes to Diabetes After a 3-Year Follow-Up in Chinese Adults

PURPOSE

To develop a nomogram for predicting the risk of progression from prediabetes to diabetes and provide a quantitative predictive tool for early clinical screening of high-risk populations of diabetes.

MATERIALS AND METHODS

This study was a retrospective cohort study and part of the investigation conducted for the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 1857 prediabetic participants at baseline underwent oral glucose tolerance test and hemoglobin A1c (HbA1c) testing after 3 years. The areas under the receiver operating characteristic curves (AUCs) were adopted to measure the predictive value of progression to diabetes, using baseline fasting plasma glucose (FPG), 2-hr postprandial plasma glucose (2hPG), HbA1c or combined models. Decision curve analysis determined the model with the best discriminative ability. A nomogram was formulated and internally validated, providing an individualized predictive tool by calculating total scores.

RESULTS

After 3 years, 145 participants developed diabetes, and the annual incidence was estimated to be 2.60%. Among the three single indicators and four combined models, model 4 combined of FPG, 2hPG, and HbA1c showed the best performance in risk predication, with an AUC of 0.742. The nomogram constructed via model 4 was validated and demonstrated good prediction for the risk of diabetes. The nomogram score/predicted probability was a numeric value representing the prediction model score of individual patients. Notably, all nomogram scores showed relatively high negative predictive values.

CONCLUSION

The nomogram constructed in this study effectively predicts and quantifies the risk of progression from prediabetes to diabetes after a 3-year follow-up and could be adopted to identify Chinese patients at high risk for diabetes in order to provide timely interventions.

Derivation and Validation of a Prediction Model for Predicting the 5-Year Incidence of Type 2 Diabetes in Non-Obese Adults: A Population-Based Cohort Study

PURPOSE

The aim of this study was to derivate and validate a nomogram based on independent predictors to better evaluate the 5-year risk of T2D in non-obese adults.

PATIENTS AND METHODS

This is a historical cohort study from a collection of databases that included 12,940 non-obese participants without diabetes at baseline. All participants were randomised to a derivation cohort (n = 9651) and a validation cohort (n = 3289). In the derivation cohort, the least absolute shrinkage and selection operator (LASSO) regression model was used to determine the optimal risk factors for T2D. Multivariate Cox regression analysis was used to establish the nomogram of T2D prediction. The receiver operating characteristic (ROC) curve, C-index, calibration curve, and decision curve analysis were performed by 1000 bootstrap resamplings to evaluate the discrimination ability, calibration, and clinical practicability of the nomogram.

RESULTS

After LASSO regression analysis of the derivation cohort, it was found that age, fatty liver, γ-glutamyltranspeptidase, triglycerides, glycosylated hemoglobin A1c and fasting plasma glucose were risk predictors, which were integrated into the nomogram. The C-index of derivation cohort and validation cohort were 0.906 [95% confidence interval (CI), 0.878-0.934] and 0.837 (95% CI, 0.760-0.914), respectively. The AUC of 5-year T2D risk in the derivation cohort and validation cohort was 0.916 (95% CI, 0.889-0.943) and 0.829 (95% CI, 0.753-0.905), respectively. The calibration curve indicated that the predicted probability of nomogram is in good agreement with the actual probability. The decision curve analysis demonstrated that the predicted nomogram was clinically useful.

CONCLUSION

Our nomogram can be used as a reasonable, affordable, simple, and widely implemented tool to predict the 5-year risk of T2D in non-obese adults. With this model, early identification of high-risk individuals is helpful to timely intervene and reduce the risk of T2D in non-obese adults.

Nomogram Model for Screening the Risk of Type II Diabetes in Western Xinjiang, China

OBJECTIVE

A simple type 2 diabetes mellitus (T2DM) screening model was established preciously based on easily available variables for identifying high-risk individuals in western Xinjiang, China.

METHODS

A total of 458,153 cases participating in the national health examination were recruited. Logistic regression and the least absolute shrinkage and selection operator (LASSO) models were used for univariate analysis, factors selection, and the establishment of prediction model. Receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test and clinical decision curve (CDA) were applied for evaluating the discrimination, calibration and clinical validity, respectively. The optimal threshold for predicting risk factors for T2DM has been estimated as well.

RESULTS

The nomogram depicted the risk of T2DM based on different genders, the factors mainly consisted of age, family history of T2DM (FHOT), waist circumference (WC), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDLc), body mass index (BMI), high-density lipoprotein cholesterol (HDLc), etc. The area under ROC of men and women was 0.864 and 0.816 in the development group, similarly in the validation group, which was 0.865 and 0.815, respectively. The calibration curve showed that the nomogram was accurate for predicting the risk of T2DM, and the CDA proved great clinical application value of the nomogram. Threshold values of the age, WC, TC, TG, HDLc, BMI in different genders were 52.5 years old (men) and 48.5 years old (women), 85.50 cm (men) and 89.9 cm (women), 4.94 mmol/L (men) and 4.94mmol/L (women), 1.26mmol/L (men) and 1.67mmol/L (women), 1.40mmol/L (men) and 1.40mmol/L (women), 24.70kg/m² (men) and 24.95kg/m² (women), respectively.

CONCLUSION

Our results give a clue that the nomogram may be useful for identifying adults who have high risk for diabetes, which is simple, affordable, with high credibility and can be widely implemented. Further studies are needed to evaluate the utility and feasibility of this model in various settings.

A prediction nomogram for the 3-year risk of incident diabetes among Chinese adults

Identifying individuals at high risk for incident diabetes could help achieve targeted delivery of interventional programs. We aimed to develop a personalized diabetes prediction nomogram for the 3-year risk of diabetes among Chinese adults. This retrospective cohort study was among 32,312 participants without diabetes at baseline. All participants were randomly stratified into training cohort (n = 16,219) and validation cohort (n = 16,093). The least absolute shrinkage and selection operator model was used to construct a nomogram and draw a formula for diabetes probability. 500 bootstraps performed the receiver operating characteristic (ROC) curve and decision curve analysis resamples to assess the nomogram's determination and clinical use, respectively. 155 and 141 participants developed diabetes in the training and validation cohort, respectively. The area under curve (AUC) of the nomogram was 0.9125 (95% CI, 0.8887-0.9364) and 0.9030 (95% CI, 0.8747-0.9313) for the training and validation cohort, respectively. We used 12,545 Japanese participants for external validation, its AUC was 0.8488 (95% CI, 0.8126-0.8850). The internal and external validation showed our nomogram had excellent prediction performance. In conclusion, we developed and validated a personalized prediction nomogram for 3-year risk of incident diabetes among Chinese adults, identifying individuals at high risk of developing diabetes.

Glycosylated Hemoglobin A1c Improves the Performance of the Nomogram for Predicting the 5-Year Incidence of Type 2 Diabetes

AIM

To develop and validate a model, which combines traditional risk factors and glycosylated hemoglobin A1c (HbA1c) for predicting the risk of type 2 diabetes (T2DM).

MATERIALS AND METHODS

This is a historical cohort study from a collected database, which included 8419 males and 7034 females without diabetes at baseline with a median follow-up of 5.8-years and 5.1-years, respectively. Multivariate cox regression analysis was used to select significant prognostic factors of T2DM. Two nomograms were constructed to predict the 5-year incidence of T2DM based on traditional risk factors (Model 1) and traditional risk factors plus HbA1c (Model 2). C-index, calibration curve, and time-dependent receiver-operating characteristic (ROC) curve were conducted in the training sets and validation sets.

RESULTS

In males, the C-index was 0.824 (95% CI: 0.795-0.853) in Model 1 and 0.867 (95% CI: 0.840-0.894) in Model 2; in females, the C-index was 0.830 (95% CI: 0.770-0.890) in Model 1 and 0.856 (95% CI: 0.795-0.917) in Model 2. The areas under curve (AUC) in Model 2 for prediction of T2DM development were higher than in Model 1 at each time point. The calibration curves showed excellent agreement between the predicted possibility and the actual observation in both models. The results of validation sets were similar to the results of training sets.

CONCLUSION

The proposed nomogram can be used to accurately predict the risk of T2DM. Compared with the traditional nomogram, HbA1c can improve the performance of nomograms for predicting the 5-year incidence of T2DM.