Simple risk score to screen for prediabetes: A cross-sectional study from the Qatar Biobank cohort

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.

Performance of a prediabetes risk prediction model: A systematic review

Backgrounds

The prediabetes population is large and easily overlooked because of the lack of obvious symptoms, which can progress to diabetes. Early screening and targeted interventions can substantially reduce the rate of conversion of prediabetes to diabetes. Therefore, this study systematically reviewed prediabetes risk prediction models, performed a summary and quality evaluation, and aimed to recommend the optimal model.

Methods

We systematically searched five databases (Cochrane, PubMed, Embase, Web Of Science, and CNKI) for published literature related to prediabetes risk prediction models and excluded preprints, duplicate publications, reviews, editorials, and other studies, with a search time frame of March 01, 2023. Data were categorized and summarized using a standardized data extraction form that extracted data including author; publication date; study design; country; demographic characteristics; assessment tool name; sample size; study type; and model-related indicators. The PROBAST tool was used to assess the risk of bias profile of included studies.

Findings

14 studies with a total of 15 models were eventually included in the systematic review. We found that the most common predictors of models were age, family history of diabetes, gender, history of hypertension, and BMI. Most of the studies (83.3%) had a high risk of bias, mainly related to under-reporting of outcome information and poor methodological design during the development and validation of models. Due to the low quality of included studies, the evidence for predictive validity of the available models is unclear.

Interpretation

We should pay attention to the early screening of prediabetes patients and give timely pharmacological and lifestyle interventions. The predictive performance of the existing model is not satisfactory, and the model building process can be standardized and external validation can be added to improve the accuracy of the model in the future.

The triglyceride glucose-waist-to-height ratio outperforms obesity and other triglyceride-related parameters in detecting prediabetes in normal-weight Qatari adults: A cross-sectional study

Introduction

The triglyceride-glucose (TyG)-driven indices, incorporating obesity indices, have been proposed as reliable markers of insulin resistance and related comorbidities such as diabetes. This study evaluated the effectiveness of these indices in detecting prediabetes in normal-weight individuals from a Middle Eastern population.

Methods

Using the data of 5,996 adult Qatari participants from the Qatar Biobank cohort, we employed adjusted logistic regression to assess the ability of various obesity and triglyceride-related indices to detect prediabetes in normal-weight (18.5 ≤ BMI <25 kg/m2) adults (≥18 years).

Results

Of the normal-weight adults, 13.62% had prediabetes. TyG-waist-to-height ratio (TyG-WHTR) was significantly associated with prediabetes among normal-weight men [OR per 1-SD 2.68; 95% CI (1.67–4.32)] and women [OR per 1-SD 2.82; 95% CI (1.61–4.94)]. Compared with other indices, TyG-WHTR had the highest area under the curve (AUC) value for prediabetes in men [AUC: 0.76, 95% CI (0.70–0.81)] and women [AUC: 0.73, 95% CI (0.66–0.80)], and performed significantly higher than other indices (p < 0.05) in detecting prediabetes in men. Tyg-WHTR shared similar diagnostic values as fasting plasma glucose (FPG).

Discussion

Our findings suggest that the TyG-WHTR index could be a better indicator of prediabetes for general clinical usage in normal weight Qatari adult men than other obesity and TyG-related indices. TyG-WHTR can help identify a person’s risk for developing prediabetes in both men and women when combined with FPG results.

Non-Laboratory-Based Risk Prediction Tools for Undiagnosed Pre-Diabetes: A Systematic Review

Early detection of pre-diabetes (pre-DM) can prevent DM and related complications. This review examined studies on non-laboratory-based pre-DM risk prediction tools to identify important predictors and evaluate their performance. PubMed, Embase, MEDLINE, CINAHL were searched in February 2023. Studies that developed tools with: (1) pre-DM as a prediction outcome, (2) fasting/post-prandial blood glucose/HbA1c as outcome measures, and (3) non-laboratory predictors only were included. The studies’ quality was assessed using the CASP Clinical Prediction Rule Checklist. Data on pre-DM definitions, predictors, validation methods, performances of the tools were extracted for narrative synthesis. A total of 6398 titles were identified and screened. Twenty-four studies were included with satisfactory quality. Eight studies (33.3%) developed pre-DM risk tools and sixteen studies (66.7%) focused on pre-DM and DM risks. Age, family history of DM, diagnosed hypertension and obesity measured by BMI and/or WC were the most common non-laboratory predictors. Existing tools showed satisfactory internal discrimination (AUROC: 0.68–0.82), sensitivity (0.60–0.89), and specificity (0.50–0.74). Only twelve studies (50.0%) had validated their tools externally, with a variance in the external discrimination (AUROC: 0.31–0.79) and sensitivity (0.31–0.92). Most non-laboratory-based risk tools for pre-DM detection showed satisfactory performance in their study populations. The generalisability of these tools was unclear since most lacked external validation.

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire

We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20–64 years) from Tokyo and surrounding areas were recruited. Participants underwent an oral glucose tolerance test (OGTT) and completed a questionnaire regarding lifestyle and physical characteristics. They were classified into four glycometabolic categories based on the OGTT results: category 1: best glucose metabolism, category 2: low insulin sensitivity, category 3: low insulin secretion, and category 4: combined characteristics of categories 2 and 3. A total of 977 individuals were included; the ratios of participants in categories 1, 2, 3, and 4 were 46%, 21%, 14%, and 19%, respectively. Machine learning models (decision tree, support vector machine, random forest, and XGBoost) were developed for identifying the glycometabolic category using questionnaire responses. Then, the top 10 most important variables in the random forest model were selected, and another random forest model was developed using these variables. Its areas under the receiver operating characteristic curve (AUCs) to classify category 1 and the others, category 2 and the others, category 3 and the others, and category 4 and the others were 0.68 (95% confidence intervals: 0.62–0.75), 0.66 (0.58–0.73), 0.61 (0.51–0.70), and 0.70 (0.62–0.77). For external validation of the model, the same dataset of 452 Japanese adults in Hokkaido was obtained. The AUCs to classify categories 1, 2, 3, and 4 and the others were 0.66 (0.61–0.71), 0.57 (0.51–0.62), 0.60 (0.50–0.69), and 0.64 (0.57–0.71). In conclusion, our model could identify the glucose metabolism status using only 10 factors of lifestyle and physical characteristics. This model may help the larger general population without diabetes to understand their glucose metabolism status and encourage lifestyle improvement to prevent diabetes.

Prediabetes in Syria and Its Associated Factors: A Single-Center Cross-Sectional Study

INTRODUCTION

Prediabetes is a major risk factor for diabetes and many chronic complications, particularly cardiovascular disease (CVD). Risk factors vary among races and demographics. This is the first study to assess prediabetes in Syria and its relevant risk factors.

METHODS

This cross-sectional study was conducted in a primary health clinic in Al-Mouwasat University Hospital, the major Hospital in Damascus, Syria. Interviews, examinations, and blood investigations were carried out by qualified physicians in the clinic.

RESULTS

This study included 406 participants, of which 363 (89.4%) were females, 43(10.6%) were males, 91 (22.4%) had prediabetes, 108 (26.6%) were overweight, and 231 (56.9%) were obese. Older age, positive family history of diabetes, obesity, abdominal obesity in females, high cholesterol, being married, and CVD were statistically significantly associated with prediabetes (p < 0.05). However, prediabetes was not associated with gender, living in the city or country, cigarette smoking, hypertension, diet, triglycerides, or polycystic ovary syndrome (p > 0.05). However, in the multivariable analysis, only high cholesterol, familial diabetes, and waist diameter had significant association.

CONCLUSIONS

Prevalence of prediabetes in our study in Syria was higher than what was estimated by previous studies. While many risk factors were similar to other countries in the regions, other risk factors differed. These results were highly reflective of high burden of prediabetes and diabetes, mainly in relatively young females. Further studies are required to tackle this rising issue as it imposes major complications in the long term, and the high financial burden on the health care system.