Non-invasive risk scores for prediction of type 2 diabetes (EPIC-InterAct): a validation of existing models

Deep learning to estimate impaired glucose metabolism from Magnetic Resonance Imaging of the liver: An opportunistic population screening approach

AIM

Diabetes is a global health challenge, and many individuals are undiagnosed and not aware of their increased risk of morbidity/mortality although dedicated tests are available, which indicates the need for novel population-wide screening approaches. Here, we developed a deep learning pipeline for opportunistic screening of impaired glucose metabolism using routine magnetic resonance imaging (MRI) of the liver and tested its prognostic value in a general population setting.

METHODS

In this retrospective study a fully automatic deep learning pipeline was developed to quantify liver shape features on routine MR imaging using data from a prospective population study. Subsequently, the association between liver shape features and impaired glucose metabolism was investigated in individuals with prediabetes, type 2 diabetes and healthy controls without prior cardiovascular diseases. K-medoids clustering (3 clusters) with a dissimilarity matrix based on Euclidean distance and ordinal regression was used to assess the association between liver shape features and glycaemic status.

RESULTS

The deep learning pipeline showed a high performance for liver shape analysis with a mean Dice score of 97.0±0.01. Out of 339 included individuals (mean age 56.3±9.1 years; males 58.1%), 79 (23.3%) and 46 (13.6%) were classified as having prediabetes and type 2 diabetes, respectively. Individuals in the high risk cluster using all liver shape features (n = 14) had a 2.4 fold increased risk of impaired glucose metabolism after adjustment for cardiometabolic risk factors (age, sex, BMI, total cholesterol, alcohol consumption, hypertension, smoking and hepatic steatosis; OR 2.44 [95% CI 1.12-5.38]; p = 0.03). Based on individual shape features, the strongest association was found between liver volume and impaired glucose metabolism after adjustment for the same risk factors (OR 1.97 [1.38-2.85]; p<0.001).

CONCLUSIONS

Deep learning can estimate impaired glucose metabolism on routine liver MRI independent of cardiometabolic risk factors and hepatic steatosis.

Multi-omic prediction of incident type 2 diabetes

AIMS/HYPOTHESIS

The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes.

METHODS

We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c.

RESULTS

Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period.

CONCLUSIONS/INTERPRETATION

Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions.

Validation of prevalent diabetes risk scores based on non-invasively measured predictors in Ghanaian migrant and non-migrant populations - The RODAM study

Background

Non-invasive diabetes risk models are a cost-effective tool in large-scale population screening to identify those who need confirmation tests, especially in resource-limited settings.

Aims

This study aimed to evaluate the ability of six non-invasive risk models (Cambridge, FINDRISC, Kuwaiti, Omani, Rotterdam, and SUNSET model) to identify screen-detected diabetes (defined by HbA1c) among Ghanaian migrants and non-migrants.

Study design

A multicentered cross-sectional study.

Methods

This analysis included 4843 Ghanaian migrants and non-migrants from the Research on Obesity and Diabetes among African Migrants (RODAM) Study. Model performance was assessed using the area under the receiver operating characteristic curves (AUC), Hosmer-Lemeshow statistics, and calibration plots.

Results

All six models had acceptable discrimination (0.70 ≤ AUC <0.80) for screen-detected diabetes in the overall/combined population. Model performance did not significantly differ except for the Cambridge model, which outperformed Rotterdam and Omani models. Calibration was poor, with a consistent trend toward risk overestimation for screen-detected diabetes, but this was substantially attenuated by recalibration through adjustment of the original model intercept.

Conclusion

Though acceptable discrimination was observed, the original models were poorly calibrated among populations of African ancestry. Recalibration of these models among populations of African ancestry is needed before use.

Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST

BACKGROUND

The prediction model of intravenous immunoglobulin (IVIG) resistance in Kawasaki disease can calculate the probability of IVIG resistance and provide a basis for clinical decision-making. We aim to assess the quality of these models developed in the children with Kawasaki disease.

METHODS

Studies of prediction models for IVIG-resistant Kawasaki disease were identified through searches in the PubMed, Web of Science, and Embase databases. Two investigators independently performed literature screening, data extraction, quality evaluation, and discrepancies were settled by a statistician. The checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) was used for data extraction, and the prediction models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS

Seventeen studies meeting the selection criteria were included in the qualitative analysis. The top three predictors were neutrophil measurements (peripheral neutrophil count and neutrophil %), serum albumin level, and C-reactive protein (CRP) level. The reported area under the curve (AUC) values for the developed models ranged from 0.672 (95% confidence interval [CI]: 0.631-0.712) to 0.891 (95% CI: 0.837-0.945); The studies showed a high risk of bias (ROB) for modeling techniques, yielding a high overall ROB.

CONCLUSION

IVIG resistance models for Kawasaki disease showed high ROB. An emphasis on improving their quality can provide high-quality evidence for clinical practice.

IMPACT STATEMENT

This study systematically evaluated the risk of bias (ROB) of existing prediction models for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease to provide guidance for future model development meeting clinical expectations. This is the first study to systematically evaluate the ROB of IVIG resistance in Kawasaki disease by using PROBAST. ROB may reduce model performance in different populations. Future prediction models should account for this problem, and PROBAST can help improve the methodological quality and applicability of prediction model development.

Risk prediction models for type 2 diabetes using either fasting plasma glucose or HbA1c in Chinese, Malay, and Indians: Results from three multi-ethnic Singapore cohorts

AIMS

To assess three well-established type 2 diabetes (T2D) risk prediction models based on fasting plasma glucose (FPG) in Chinese, Malays, and Indians, and to develop simplified risk models based on either FPG or HbA1c.

METHODS

We used a prospective multiethnic Singapore cohort to evaluate the established models and develop simplified models. 6,217 participants without T2D at baseline were included, with an average follow-up duration of 8.3 years. The simplified risk models were validated in two independent multiethnic Singapore cohorts (N = 12,720).

RESULTS

The established risk models had moderate-to-good discrimination (area under the receiver operating characteristic curves, AUCs 0.762 - 0.828) but a lack of fit (P-values < 0.05). Simplified risk models that included fewer predictors (age, BMI, systolic blood pressure, triglycerides, and HbA1c or FPG) showed good discrimination in all cohorts (AUCs ≥ 0.810), and sufficiently captured differences between the ethnic groups. While recalibration improved fit the simplified models in validation cohorts, there remained evidence of miscalibration in Chinese (p ≤ 0.012).

CONCLUSIONS

Simplified risk models including HbA1c or FPG had good discrimination in predicting incidence of T2D in three major Asian ethnic groups. Risk functions with HbA1c performed as well as those with FPG.

Synergistic effect of non-alcoholic fatty liver disease and history of gestational diabetes to increase risk of type 2 diabetes

Whether non-alcoholic fatty liver disease (NAFLD) improves risk prediction for subsequent type 2 diabetes mellitus (T2DM) in women with prior gestational diabetes mellitus (pGDM) is uncertain. We examined the combined effects of NAFLD and pGDM on risk prediction for incident T2DM. This retrospective cohort study included 97,347 Korean parous women without diabetes mellitus at baseline whose mean (SD) age was 39.0 (7.8) years. Cox proportional hazards models were used to estimate hazard ratios (HRs) for incident T2DM according to self-reported pGDM and ultrasound-diagnosed NAFLD at baseline. When combined with conventional diabetes risk factors, the incremental predictive ability of NAFLD and pGDM to identify incident T2DM was assessed. During a median follow-up of 3.9 years, 1,515 cases of incident T2DM occurred. Multivariable-adjusted HRs (95% confidence intervals [CIs]) for incident T2DM comparing pGDM alone, NAFLD alone, and both NAFLD and pGDM to the reference (neither NAFLD nor pGDM) were 2.61 (2.06-3.31), 2.26 (1.96-2.59), and 6.45 (5.19-8.00), respectively (relative excess risk of interaction = 2.58 [95% CI, 1.21-3.95]). These associations were maintained after adjusting for insulin resistance, body mass index, and other potential confounders as time-dependent covariates. The combination of NAFLD and pGDM improved risk prediction for incident T2DM (based on Harrell's C-index, also known as the concordance index, and net reclassification improvement) compared to conventional diabetes risk factors. In conclusion, NAFLD synergistically increases the risk of subsequent T2DM in women with pGDM. The combination of NAFLD and pGDM improves risk prediction for T2DM.

Metabolic health and cardiometabolic risk clusters: implications for prediction, prevention, and treatment

Among 20 leading global risk factors for years of life lost in 2040, reference forecasts point to three metabolic risks-high blood pressure, high BMI, and high fasting plasma glucose-as being the top risk variables. Building upon these and other risk factors, the concept of metabolic health is attracting much attention in the scientific community. It focuses on the aggregation of important risk factors, which allows the identification of subphenotypes, such as people with metabolically unhealthy normal weight or metabolically healthy obesity, who strongly differ in their risk of cardiometabolic diseases. Since 2018, studies that used anthropometrics, metabolic characteristics, and genetics in the setting of cluster analyses proposed novel metabolic subphenotypes among patients at high risk (eg, those with diabetes). The crucial point now is whether these subphenotyping strategies are superior to established cardiometabolic risk stratification methods regarding the prediction, prevention, and treatment of cardiometabolic diseases. In this Review, we carefully address this point and conclude, firstly, regarding cardiometabolic risk stratification, in the general population both the concept of metabolic health and the cluster approaches are not superior to established risk prediction models. However, both subphenotyping approaches might be informative to improve the prediction of cardiometabolic risk in subgroups of individuals, such as those in different BMI categories or people with diabetes. Secondly, the applicability of the concepts by treating physicians and communication of the cardiometabolic risk with patients is easiest using the concept of metabolic health. Finally, the approaches to identify cardiometabolic risk clusters in particular have provided some evidence that they could be used to allocate individuals to specific pathophysiological risk groups, but whether this allocation is helpful for prevention and treatment still needs to be determined.

Predicting type 2 diabetes prevalence for people with severe mental illness in a multi-ethnic East London population

BACKGROUND AND AIMS

Prevalence of type two diabetes mellitus (T2DM) in people with severe mental illness (SMI) is 2-3 times higher than in general population. Predictive modelling has advanced greatly in the past decade, and it is important to apply cutting-edge methods to vulnerable groups. However, few T2DM prediction models account for the presence of mental illness, and none seemed to have been developed specifically for people with SMI. Therefore, we aimed to develop and internally validate a T2DM prevalence model for people with SMI.

METHODS

We utilised a large cross-sectional sample representative of a multi-ethnic population from London (674,000 adults); 10,159 people with SMI formed our analytical sample (1,513 T2DM cases). We fitted a linear logistic regression and XGBoost as stand-alone models and as a stacked ensemble. Age, sex, body mass index, ethnicity, area-based deprivation, past hypertension, cardiovascular diseases, prescribed antipsychotics, and SMI illness were the predictors.

RESULTS

Logistic regression performed well while detecting T2DM presence for people with SMI: area under the receiver operator curve (ROC-AUC) was 0.83 (95 % CI 0.79-0.87). XGBoost and LR-XGBoost ensemble performed equally well, ROC-AUC 0.83 (95 % CI 0.79-0.87), indicating a negligible contribution of non-linear terms to predictive power. Ethnicity was the most important predictor after age. We demonstrated how the derived models can be utilised and estimated a 2.14 % (95 %CI 2.03 %-2.24 %) increase in T2DM prevalence in East London SMI population in 20 years' time, driven by the projected demographic changes.

CONCLUSIONS

Primary care data, the setting where prediction models could be most fruitfully used, provide enough information for well-performing T2DM prevalence models for people with SMI. We demonstrated how thorough internal cross-validation of an ensemble of a linear and machine-learning model can quantify the predictive value of non-linearity in the data.

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.

Development and validation of a risk score for diabetic kidney disease prediction in type 2 diabetes patients: a machine learning approach

PURPOSE

This study aims to develop and validate a risk score to predict the occurrence of DKD in individuals with type 2 diabetes using a machine learning (ML) approach.

METHODS

By implementing Recursive Feature Elimination with Cross-Validation (RFECV) and RFE on the Diabetes Clinic of Imam Khomeini Hospital Complex (IKHC) dataset, the most critical features were identified. These features were used in the multivariate logistic regression (LR) analysis, and the discrimination and calibration of the model were evaluated. Finally, external validation of the model was assessed.

RESULTS

The development dataset included 1907 type 2 diabetic patients, 763 of whom developed DKD over 5 years. The predictive model performed well in the development dataset by implementing RFECV with the RF algorithm and considering six features (AUC: 79%). Using these features, the LR-based risk score indicated appropriate discrimination (AUC: 75.5%, 95% CI 73-78%) and acceptable calibration ([Formula: see text]= 7.44; p value = 0.49). This risk score was then used for 1543 diabetic patients in the validation dataset, including 633 patients with DKD over 5 years. The results showed sufficient discrimination (AUC: 75.8%, 95% CI 73-78%) of the risk score in the validation dataset.

CONCLUSIONS

We developed and validated a new risk score for predicting DKD via ML approach, which used common features in the periodic screening of type 2 diabetic patients that are readily available. In addition, a web-based online tool that is readily available to the public was developed to calculate the DKD risk score.

NAFLD improves risk prediction of type 2 diabetes: with effect modification by sex and menopausal status

BACKGROUND AND AIMS

The effects of sex and menopausal status on the association between NAFLD and incident type 2 diabetes (T2D) remain unclear. We investigated the effect modification by sex and menopause in the association between NAFLD and T2D; also, the added predictive ability of NAFLD for the risk of T2D was assessed.

APPROACH AND RESULTS

This cohort study comprised 245,054 adults without diabetes (109,810 premenopausal women; 4958 postmenopausal women; 130,286 men). Cox proportional hazard models were used to estimate hazard ratios (HRs; 95% confidence intervals [CIs]) for incident T2D according to NAFLD status. The incremental predictive role of NAFLD for incident T2D was assessed using the area under the receiver operating characteristic curve, net reclassification improvement, and integrated discrimination improvement. A total of 8381 participants developed T2D (crude incidence rate/10³ person-years: 2.9 premenopausal women; 12.2 postmenopausal women; 9.3 men) during median follow-up of 5.3 years. NAFLD was positively associated with incident T2D in all groups. After adjustment for potential confounders, the multivariable-adjusted HRs (95% CIs) for incident T2D comparing NAFLD to no NAFLD were 4.63 (4.17-5.14), 2.65 (2.02-3.48), and 2.16 (2.04-2.29) in premenopausal women, postmenopausal women, and men, respectively. The risks of T2D increased with NAFLD severity as assessed by serum fibrosis markers, and the highest relative excess risks were observed in premenopausal women. The addition of NAFLD to conventional risk factors improved risk prediction for incident T2D in both sexes, with a greater improvement in women than men.

CONCLUSIONS

NAFLD, including more severe NAFLD, is a stronger risk factor for incident T2D in premenopausal women than in postmenopausal women or men; protection against T2D is lost in premenopausal women with NAFLD.

Personalised prevention of type 2 diabetes

It is well established from clinical trials that behavioural interventions can halve the risk of progression from prediabetes to type 2 diabetes but translating this evidence of efficacy into effective real-world interventions at scale is an ongoing challenge. A common suggestion is that future preventive interventions need to be more personalised in order to enhance effectiveness. This review evaluates the degree to which existing interventions are already personalised and outlines how greater personalisation could be achieved through better identification of those at high risk, division of type 2 diabetes into specific subgroups and, above all, more individualisation of the behavioural targets for preventive action. Approaches using more dynamic real-time data are in their scientific infancy. Although these approaches are promising they need longer-term evaluation against clinical outcomes. Whatever personalised preventive approaches for type 2 diabetes are developed in the future, they will need to be complementary to existing individual-level interventions that are being rolled out and that are demonstrably effective. They will also need to ideally synergise with, and at the very least not detract attention from, efforts to develop and implement strategies that impact on type 2 diabetes risk at the societal level.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01622-5.

Non-laboratory-based risk assessment model for case detection of diabetes mellitus and pre-diabetes in primary care

INTRODUCTION

More than half of diabetes mellitus (DM) and pre-diabetes (pre-DM) cases remain undiagnosed, while existing risk assessment models are limited by focusing on diabetes mellitus only (omitting pre-DM) and often lack lifestyle factors such as sleep. This study aimed to develop a non-laboratory risk assessment model to detect undiagnosed diabetes mellitus and pre-diabetes mellitus in Chinese adults.

METHODS

Based on a population-representative dataset, 1,857 participants aged 18-84 years without self-reported diabetes mellitus, pre-diabetes mellitus, and other major chronic diseases were included. The outcome was defined as a newly detected diabetes mellitus or pre-diabetes by a blood test. The risk models were developed using logistic regression (LR) and interpretable machine learning (ML) methods. Models were validated using area under the receiver-operating characteristic curve (AUC-ROC), precision-recall curve (AUC-PR), and calibration plots. Two existing diabetes mellitus risk models were included for comparison.

RESULTS

The prevalence of newly diagnosed diabetes mellitus and pre-diabetes mellitus was 15.08%. In addition to known risk factors (age, BMI, WHR, SBP, waist circumference, and smoking status), we found that sleep duration, and vigorous recreational activity time were also significant risk factors of diabetes mellitus and pre-diabetes mellitus. Both LR (AUC-ROC = 0.812, AUC-PR = 0.448) and ML models (AUC-ROC = 0.822, AUC-PR = 0.496) performed well in the validation sample with the ML model showing better discrimination and calibration. The performance of the models was better than the two existing models.

CONCLUSIONS

Sleep duration and vigorous recreational activity time are modifiable risk factors of diabetes mellitus and pre-diabetes in Chinese adults. Non-laboratory-based risk assessment models that incorporate these lifestyle factors can enhance case detection of diabetes mellitus and pre-diabetes.

Early prediction of diabetes by applying data mining techniques: A retrospective cohort study

BACKGROUND

Saudi Arabia ranks 7th globally in terms of diabetes prevalence, and its prevalence is expected to reach 45.36% by 2030. The cost of diabetes is expected to increase to 27 billion Saudi riyals in cases where undiagnosed individuals are also documented. Prevention and early detection can effectively address these challenges.

OBJECTIVE

To improve healthcare services and assist in building predictive models to estimate the probability of diabetes in patients.

METHODS

A chart review, which was a retrospective cohort study, was conducted at the National Guard Health Affairs in Riyadh, Saudi Arabia. Data were collected from 5 hospitals using National Guard Health Affairs databases. We used 38 attributes of 21431 patients between 2015 and 2019. The following phases were performed: (1) data collection, (2) data preparation, (3) data mining and model building, and (4) model evaluation and validation. Subsequently, 6 algorithms were compared with and without the synthetic minority oversampling technique.

RESULTS

The highest performance was found in the Bayesian network, which had an area under the curve of 0.75 and 0.71.

CONCLUSION

Although the results were acceptable, they could be improved. In this context, missing data owing to technical issues played a major role in affecting the performance of our model. Nevertheless, the model could be used in prevention, health monitoring programs, and as an automated mass population screening tool without the need for extra costs compared to traditional methods.

Prediction of type 2 diabetes mellitus onset using logistic regression-based scorecards

Background

Type 2 diabetes (T2D) accounts for ~90% of all cases of diabetes, resulting in an estimated 6.7 million deaths in 2021, according to the International Diabetes Federation. Early detection of patients with high risk of developing T2D can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since populations of lower socio-demographic status are more susceptible to T2D and might have limited resources or access to sophisticated computational resources, there is a need for accurate yet accessible prediction models.

Methods

In this study, we analyzed data from 44,709 nondiabetic UK Biobank participants aged 40-69, predicting the risk of T2D onset within a selected time frame (mean of 7.3 years with an SD of 2.3 years). We started with 798 features that we identified as potential predictors for T2D onset. We first analyzed the data using gradient boosting decision trees, survival analysis, and logistic regression methods. We devised one nonlaboratory model accessible to the general population and one more precise yet simple model that utilizes laboratory tests. We simplified both models to an accessible scorecard form, tested the models on normoglycemic and prediabetes subcohorts, and compared the results to the results of the general cohort. We established the nonlaboratory model using the following covariates: sex, age, weight, height, waist size, hip circumference, waist-to-hip ratio, and body mass index. For the laboratory model, we used age and sex together with four common blood tests: high-density lipoprotein (HDL), gamma-glutamyl transferase, glycated hemoglobin, and triglycerides. As an external validation dataset, we used the electronic medical record database of Clalit Health Services.

Results

The nonlaboratory scorecard model achieved an area under the receiver operating curve (auROC) of 0.81 (95% confidence interval [CI] 0.77-0.84) and an odds ratio (OR) between the upper and fifth prevalence deciles of 17.2 (95% CI 5-66). Using this model, we classified three risk groups, a group with 1% (0.8-1%), 5% (3-6%), and the third group with a 9% (7-12%) risk of developing T2D. We further analyzed the contribution of the laboratory-based model and devised a blood test model based on age, sex, and the four common blood tests noted above. In this scorecard model, we included age, sex, glycated hemoglobin (HbA1c%), gamma glutamyl-transferase, triglycerides, and HDL cholesterol. Using this model, we achieved an auROC of 0.87 (95% CI 0.85-0.90) and a deciles' OR of ×48 (95% CI 12-109). Using this model, we classified the cohort into four risk groups with the following risks: 0.5% (0.4-7%); 3% (2-4%); 10% (8-12%); and a high-risk group of 23% (10-37%) of developing T2D. When applying the blood tests model using the external validation cohort (Clalit), we achieved an auROC of 0.75 (95% CI 0.74-0.75). We analyzed several additional comprehensive models, which included genotyping data and other environmental factors. We found that these models did not provide cost-efficient benefits over the four blood test model. The commonly used German Diabetes Risk Score (GDRS) and Finnish Diabetes Risk Score (FINDRISC) models, trained using our data, achieved an auROC of 0.73 (0.69-0.76) and 0.66 (0.62-0.70), respectively, inferior to the results achieved by the four blood test model and by the anthropometry models.

Conclusions

The four blood test and anthropometric models outperformed the commonly used nonlaboratory models, the FINDRISC and the GDRS. We suggest that our models be used as tools for decision-makers to assess populations at elevated T2D risk and thus improve medical strategies. These models might also provide a personal catalyst for changing lifestyle, diet, or medication modifications to lower the risk of T2D onset.

Funding

The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.

Performance assessment across different care settings of a heart failure hospitalisation risk-score for type 2 diabetes using administrative claims

Predicting the risk of cardiovascular complications, in particular heart failure hospitalisation (HHF), can improve the management of type 2 diabetes (T2D). Most predictive models proposed so far rely on clinical data not available at the higher Institutional level. Therefore, it is of interest to assess the risk of HHF in people with T2D using administrative claims data only, which are more easily obtainable and could allow public health systems to identify high-risk individuals. In this paper, the administrative claims of > 175,000 patients with T2D were used to develop a new risk score for HHF based on Cox regression. Internal validation on the administrative data cohort yielded satisfactory results in terms of discrimination (max AUROC = 0.792, C-index = 0.786) and calibration (Hosmer-Lemeshow test p value < 0.05). The risk score was then tested on data gathered from two independent centers (one diabetes outpatient clinic and one primary care network) to demonstrate its applicability to different care settings in the medium-long term. Thanks to the large size and broad demographics of the administrative dataset used for training, the proposed model was able to predict HHF without significant performance loss concerning bespoke models developed within each setting using more informative, but harder-to-acquire clinical variables.

MRI-Derived Radiomics Features of Hepatic Fat Predict Metabolic States in Individuals without Cardiovascular Disease

RATIONALE AND OBJECTIVES

To investigate radiomics features of hepatic fat as potential biomarkers of type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) in individuals without overt cardiovascular disease, and benchmarking against hepatic proton density fat fraction (PDFF) and the body mass index (BMI).

MATERIALS AND METHODS

This study collected liver radiomics features of 310 individuals that were part of a case-controlled imaging substudy embedded in a prospective cohort. Individuals had known T2DM (n = 39; 12.6 %) and MetS (n = 107; 34.5 %) status, and were divided into stratified training (n = 232; 75 %) and validation (n = 78; 25 %) sets. Six hundred eighty-four MRI radiomics features were extracted for each liver volume of interest (VOI) on T₁-weighted dual-echo Dixon relative fat water content (rfwc) maps. Test-retest and inter-rater variance was simulated by additionally extracting radiomics features using noise augmented rfwc maps and deformed volume of interests. One hundred and seventy-one features with test-retest reliability (ICC(1,1)) and inter-rater agreement (ICC(3,k)) of ≥0.85 on the training set were considered stable. To construct predictive random forest (RF) models, stable features were filtered using univariate RF analysis followed by sequential forward aggregation. The predictive performance was evaluated on the independent validation set with area under the curve of the receiver operating characteristic (AUROC) and balanced accuracy (Accuracy_B).

RESULTS

On the validation set, the radiomics RF models predicted T2DM with AUROC of 0.835 and Accuracy_B of 0.822 and MetS with AUROC of 0.838 and Accuracy_B of 0.787, outperforming the RF models trained on the benchmark parameters PDFF and BMI.

CONCLUSION

Hepatic radiomics features may serve as potential imaging biomarkers for T2DM and MetS.

Non-Invasive radial pressure wave analysis may digitally predict women's risks of type 2 diabetes (T2DM): A case and control group study

Background

Women not only have worse diabetes complications, but also have menstrual cycle, pregnancy, and menopause which can make managing diabetes more difficult. The aim of this study was to investigate if radial pressure wave analysis may non-invasively screen for women’s risk of type 2 diabetes.

Methods

Spectrum analysis of the radial pressure wave was performed to evaluate the first five harmonic components, C1 to C5. The study consisted of a total of 808 non-pregnant female subjects aged 20–95 over the period of 4 years, and 404 of them were diagnosed with Type 2 diabetes as the case group.

Result

The first five harmonic components are significantly different in a comparison of the case group and the control group. In the logistic regression analysis, T2DM was found to be associated with C1 (OR = 1.055, CI = 1.037–1.074, p < 0.001), C2 (OR = 1.051, CI = 1.019–1.085, p = 0.002), and C3 (OR = 0.972, CI = 0.950–0.994, p = 0.013). In the Receiver Operating Characteristic curve analysis, the Area Under Curve of using C3 only (70%, p <0.05), weighted C1, C2 and C3, (75%, p < 0.05), and weighted C1, C2 and C3 and Body mass Index (84%, p <0.05) were tested for the accuracy on how well these tests separate the women into the groups with and without the T2DM.

Conclusion

We thus concluded that pulse spectrum was a non-invasive predictor for women’s risk of T2DM.

Characteristic of FINDRISC Score and Association with Diabetes Development in 6-Year Follow-Up in PURE Poland Cohort Study

Purpose

The aim of the study was to assess if FINDRISC score was associated with diabetes development after 6 years of observation.

Methods

Polish cohort is a part of global PURE study. Hereby analysis presents data from baseline (2007–2010) and 6-year follow-up (2013–2016) and was conducted on 1090 participants (702 women) from urban and rural areas in Lower Silesia region (Poland) without diabetes at the baseline and with complete data throughout course of the study.

Results

At the baseline, women had significantly higher FINDRISC score than men (10.43 vs 8.91; p=0.000) and participants from rural areas had higher score than from urban areas (10.97 vs 9.33; p=0.000). At the baseline, 25.87% of the participants had low risk of diabetes according to FINDRISC score, 38.90% had slightly elevated risk, 16.79% moderate risk, 16.42% high risk and 2.02% very high risk. Participants, who were healthy at baseline, but developed diabetes after 6 years of observation had significantly higher FINDRISC, than those who did not (13.39 vs 9.36; p=0.000). In 6-year follow-up, diabetes was diagnosed in 2.8% of participants, who were ascertained to “low risk” according to FINDRISC score in baseline; in 9.9% of participants of “slightly elevated risk”, 17.5% of participants of “moderate risk”, 26.8% in participants of “high risk” and 50.0% of participants of “very high risk”.

Conclusions

Results of PURE Poland cohort study indicates that higher FINDRISC score at the baseline was associated with higher risk of diabetes development during 6 years of observation.

Risk prediction of covid-19 related death and hospital admission in adults after covid-19 vaccination: national prospective cohort study

OBJECTIVES

To derive and validate risk prediction algorithms to estimate the risk of covid-19 related mortality and hospital admission in UK adults after one or two doses of covid-19 vaccination.

DESIGN

Prospective, population based cohort study using the QResearch database linked to data on covid-19 vaccination, SARS-CoV-2 results, hospital admissions, systemic anticancer treatment, radiotherapy, and the national death and cancer registries.

SETTINGS

Adults aged 19-100 years with one or two doses of covid-19 vaccination between 8 December 2020 and 15 June 2021.

MAIN OUTCOME MEASURES

Primary outcome was covid-19 related death. Secondary outcome was covid-19 related hospital admission. Outcomes were assessed from 14 days after each vaccination dose. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance was evaluated in a separate validation cohort of general practices.

RESULTS

Of 6 952 440 vaccinated patients in the derivation cohort, 5 150 310 (74.1%) had two vaccine doses. Of 2031 covid-19 deaths and 1929 covid-19 hospital admissions, 81 deaths (4.0%) and 71 admissions (3.7%) occurred 14 days or more after the second vaccine dose. The risk algorithms included age, sex, ethnic origin, deprivation, body mass index, a range of comorbidities, and SARS-CoV-2 infection rate. Incidence of covid-19 mortality increased with age and deprivation, male sex, and Indian and Pakistani ethnic origin. Cause specific hazard ratios were highest for patients with Down's syndrome (12.7-fold increase), kidney transplantation (8.1-fold), sickle cell disease (7.7-fold), care home residency (4.1-fold), chemotherapy (4.3-fold), HIV/AIDS (3.3-fold), liver cirrhosis (3.0-fold), neurological conditions (2.6-fold), recent bone marrow transplantation or a solid organ transplantation ever (2.5-fold), dementia (2.2-fold), and Parkinson's disease (2.2-fold). Other conditions with increased risk (ranging from 1.2-fold to 2.0-fold increases) included chronic kidney disease, blood cancer, epilepsy, chronic obstructive pulmonary disease, coronary heart disease, stroke, atrial fibrillation, heart failure, thromboembolism, peripheral vascular disease, and type 2 diabetes. A similar pattern of associations was seen for covid-19 related hospital admissions. No evidence indicated that associations differed after the second dose, although absolute risks were reduced. The risk algorithm explained 74.1% (95% confidence interval 71.1% to 77.0%) of the variation in time to covid-19 death in the validation cohort. Discrimination was high, with a D statistic of 3.46 (95% confidence interval 3.19 to 3.73) and C statistic of 92.5. Performance was similar after each vaccine dose. In the top 5% of patients with the highest predicted covid-19 mortality risk, sensitivity for identifying covid-19 deaths within 70 days was 78.7%.

CONCLUSION

This population based risk algorithm performed well showing high levels of discrimination for identifying those patients at highest risk of covid-19 related death and hospital admission after vaccination.

Development and validation of the type 2 diabetes mellitus 10-year risk score prediction models from survey data

AIMS

In this paper, we demonstrate the development and validation of the 10-years type 2 diabetes mellitus (T2DM) risk prediction models based on large survey data.

METHODS

The Survey of Health, Ageing and Retirement in Europe (SHARE) data collected in 12 European countries using 53 variables representing behavioural as well as physical and mental health characteristics of the participants aged 50 or older was used to build and validate prediction models. To account for strongly unbalanced outcome variables, each instance was assigned a weight according to the inverse proportion of the outcome label when the regularized logistic regression model was built.

RESULTS

A pooled sample of 16,363 individuals was used to build and validate a global regularized logistic regression model that achieved an area under the receiver operating characteristic curve of 0.702 (95% CI: 0.698-0.706). Additionally, we measured performance of local country-specific models where AUROC ranged from 0.578 (0.565-0.592) to 0.768 (0.749-0.787).

CONCLUSIONS

We have developed and validated a survey-based 10-year T2DM risk prediction model for use across 12 European countries. Our results demonstrate the importance of re-calibration of the models as well as strengths of pooling the data from multiple countries to reduce the variance and consequently increase the precision of the results.

Comparing non-invasive diabetes risk scores for detecting patients in clinical practice: a cross-sectional validation study

Background: Type 2 diabetes (T2DM) is a significant cause of morbidity and mortality, thus early identification is of paramount importance. A high proportion of T2DM cases are undiagnosed highlighting the importance of effective detection methods such as non-invasive diabetes risk scores (DRSs). Thus far, no DRS has been validated in an Irish population. Therefore, the aim of this study was to compare the ability of nine DRSs to detect T2DM cases in an Irish population. Methods: This was a cross-sectional study of 1,990 men and women aged 46–73 years. Data on DRS components were collected from questionnaires and clinical examinations. T2DM was determined according to a fasting plasma glucose level ≥7.0 mmol/l or a glycated haemoglobin A1c level ≥6.5% (≥48 mmol/mol). Receiver operating characteristic curve analysis assessed the ability of DRSs and their components to discriminate T2DM cases. Results: Among the examined scores, area under the curve (AUC) values ranged from 0.71–0.78, with the Cambridge Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Leicester Diabetes Risk Score (AUC=0.78, 95% CI: 0.75–0.82), Rotterdam Predictive Model 2 (AUC=0.78, 95% CI: 0.74–0.82) and the U.S. Diabetes Risk Score (AUC=0.78, 95% CI: 0.74–0.81) demonstrating the largest AUC values as continuous variables and at optimal cut-offs. Regarding individual DRS components, anthropometric measures displayed the largest AUC values. Conclusions: The best performing DRSs were broadly similar in terms of their components; all incorporated variables for age, sex, BMI, hypertension and family diabetes history. The Cambridge Diabetes Risk Score, had the largest AUC value at an optimal cut-off, can be easily accessed online for use in a clinical setting and may be the most appropriate and cost-effective method for case-finding in an Irish population.

Burden and management of type 2 diabetes in rural United States

In the United States, rural areas have a higher burden of type 2 diabetes (T2DM) compared to urban areas. However, there is limited information on risk factors and interventions that improve the primary prevention and management of T2DM in rural areas. To synthesize current knowledge on T2DM in rural areas and to guide healthcare providers and policy makers, we reviewed five scientific databases and the grey literature over the last decade (2010-2020). We described classification systems for rurality and the T2DM burden based on rurality and region (West, South, Midwest, and Northeast). We highlighted risk factors for T2DM in rural compared to urban areas, and summarized interventions to screen and manage T2DM based on opportunistic screening, T2DM self-management, community-based initiatives, as well as interventions targeting comorbidities and T2DM. Several studies identified the co-existence of T2DM and depression/psychological symptoms, which could reduce adherence to non-pharmacologic and pharmacologic management of T2DM. We highlighted the role of technology in education and counselling of patients with geographic and financial barriers to accessing care, which is exacerbated by the SARS-CoV-2 coronavirus disease-19 pandemic. We identified knowledge gaps and next steps in improving T2DM care in rural areas. There is an urgent need for interventions tailored to rural areas given that rural Americans currently experience a disproportionate burden of T2DM and are encumbered by its associated morbidity, mortality, and loss in economic productivity.

Association of a body shape index and hip index with cardiometabolic risk factors in children and adolescents: the CASPIAN-V study

OBJECTIVE

This study designed to discover the link between a body shape index (ABSI) and hip index (HI) with cardiometabolic risk factors (CMRFs) in Iranian children and adolescents.

SUBJECTS AND METHODS

In a nationwide cross-sectional survey, 4200 students who were 7-18 years old were chosen via a multistage cluster sampling method in 30 provinces of Iran in 2015. Metabolic syndrome (MetS) was defined in line with the Adult Treatment Panel III criteria. ABSI and HI were defined as waist circumference (m)/ [body mass index 2/3 * height (m)1/2] and hip circumference (cm) *(height/ 166 cm)0.310 *(weight / 73 kg)-0.482 respectively. Association between ABSI and HI with CMRFs as categorical and continuous variables were evaluated using multivariable logistic and linear regression analysis respectively.

RESULTS

Totally, information of 14,002 students and findings of blood samples of 3483 of them were involved in the current study. In the multivariable logistic regression, an association of HI with high triglyceride (TG) (OR: 0.99, 95 % CI: 0.98-0.99) and ABSI with MetS (OR: 11.41, 2.61-49.88) was statistically significant (P < 0.05). Also, both indices were significantly associated with overweight, generalized, and abdominal obesity. In the multivariable linear regression analysis, increasing HI (per one unit) was associated with body mass index z-score (z-BMI) (β: -0.01), waist circumference (WC) (β: 0.15), TG (β: -0.16), and systolic blood pressure (SBP) (β: -0.02). Moreover, in the multivariable linear models, ABSI was significantly associated with z-BMI, WC, SBP, and diastolic blood pressure (P < 0.001).

CONCLUSIONS

ABSI and HI as novel body shape indices were significantly associated with some CMRFs. Therefore, these indices can be used as some useful anthropometric risk indices for predicting MetS.

Validation of Indian Diabetes Risk Score for Screening Prediabetes in West Tripura District of India

Background:

Viswanathan Mohan and his team have developed “Indian Diabetes Risk Score” (IDRS) for identifying the Indians at risk for developing diabetes and prediabetes. Due to heterogeneity of Indian population, this risk score needs further validation in different parts across the country.

Objectives:

The objective is to estimate the sensitivity, specificity, positive, and negative predictive values of IDRS for screening prediabetes in West Tripura District.

Methodology:

It was a community-based cross-sectional study conducted in West Tripura district during January 1, 2018–December 31, 2019 among 325 self-declared nondiabetic individuals, selected by multistage sampling. Fasting blood sugar value was used as the gold standard to validate IDRS. Data were collected using a validated and pretested interview schedule. Data entry and analysis were performed in computer using SPSS-24. Receiver operating characteristic (ROC) curve was constructed to validate IDRS.

Results:

Among the study individuals, 19% and 6.5% were identified as prediabetic and diabetics, respectively. Optimum sensitivity of 83.13% and specificity of 82.64%, with positive and negative predictive values 62.16% and 93.45%, respectively, were observed at an IDRS score of ≥60 for identifying prediabetes and diabetes in this study population. IDRS showed good accuracy with an area under ROC curve of 0.832 (95% confidence interval: 0.77–0.88).

Conclusion:

IDRS is found to be a valid tool for screening prediabetes at community level in West Tripura district of India.

Artificial intelligence in health data analysis: The Darwinian evolution theory suggests an extremely simple and zero-cost large-scale screening tool for prediabetes and type 2 diabetes

AIMS

The effective identification of individuals with early dysglycemia status is key to reduce the incidence of type 2 diabetes. We develop and validate a novel zero-cost tool that significantly simplifies the screening of undiagnosed dysglycemia.

METHODS

We use NHANES cross-sectional data over 10 years (2007-2016) to derive an equation that links non-laboratory exposure variables to the possible presence of undetected dysglycemia. For the first time, we adopt a novel artificial intelligence approach based on the Darwinian evolutionary theory to analyze health data. We collected data for 47 variables.

RESULTS

Age and waist circumference are the only variables required to use the model. To identify undetected dysglycemia, we obtain an area under the curve (AUC) of 75.3%. Sensitivity and specificity are 0.65 and 0.73 by using the optimal threshold value determined from external validation data.

CONCLUSIONS

The use of uniquely two variables allows to obtain a zero-cost screening tool of analogous precision than that of more complex tools widely adopted in the literature. The newly developed tool has clinical use as it significantly simplifies the screening of dysglycemia. Furthermore, we suggest that the definition of an age-related waist circumference cut-off might help to improve existing diabetes risk factors.

Performance of Risk Assessment Models for Prevalent or Undiagnosed Type 2 Diabetes Mellitus in a Multi-Ethnic Population-The Helius Study

Background:

Most risk assessment models for type 2 diabetes (T2DM) have been developed in Caucasians and Asians; little is known about their performance in other ethnic groups.

Objective(s):

We aimed to identify existing models for the risk of prevalent or undiagnosed T2DM and externally validate them in a multi-ethnic population currently living in the Netherlands.

Methods:

A literature search to identify risk assessment models for prevalent or undiagnosed T2DM was performed in PubMed until December 2017. We validated these models in 4,547 Dutch, 3,035 South Asian Surinamese, 4,119 African Surinamese, 2,326 Ghanaian, 3,598 Turkish, and 3,894 Moroccan origin participants from the HELIUS (Healthy LIfe in an Urban Setting) cohort study performed in Amsterdam. Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer-Lemeshow test). We identified 25 studies containing 29 models for prevalent or undiagnosed T2DM. C-statistics varied between 0.77–0.92 in Dutch, 0.66–0.83 in South Asian Surinamese, 0.70–0.82 in African Surinamese, 0.61–0.81 in Ghanaian, 0.69–0.86 in Turkish, and 0.69–0.87 in the Moroccan populations. The C-statistics were generally lower among the South Asian Surinamese, African Surinamese, and Ghanaian populations and highest among the Dutch. Calibration was poor (Hosmer-Lemeshow p < 0.05) for all models except one.

Conclusions:

Generally, risk models for prevalent or undiagnosed T2DM show moderate to good discriminatory ability in different ethnic populations living in the Netherlands, but poor calibration. Therefore, these models should be recalibrated before use in clinical practice and should be adapted to the situation of the population they are intended to be used in.

Development and validation of a diabetes risk score among two populations

The purpose of this study was to assess the validity of a practical diabetes risk score amongst two heterogenous populations, a working population and a non-working population. Study population 1 (n = 2,089) participated in a large-scale screening program offered to retired workers to discover previously undetected/incipient chronic illness. Study population 2 (n = 3,293) was part of a Colorado worksite wellness program health risk assessment. We assessed the relationship between a continuous diabetes risk score at baseline and development of diabetes in the future using logistic regression. Receiver operating curves and sensitivity/specificity of the models were calculated. Across both study populations, we observed that participants with diabetes at follow-up had higher diabetes risk scores at baseline than participants who did not have diabetes at follow-up. On average, the odds ratio of developing diabetes in the future was 1.38 (95% CI: 1.26-1.50, p < 0.0001) for study population 1 and 1.68 (95% CI: 1.45-1.95, p-value < 0.0001) for study population 2. These findings indicate that the diabetes risk score may be generalizable to diverse individuals, and thus potentially a population level diabetes screening tool. Minimally-invasive diabetes risk scores can aid in the identification of sub-populations of individuals at risk for diabetes.

Risk Factors and Wellness Measures Associated with Prediabetes and Newly Diagnosed Type 2 Diabetes Mellitus in Hispanic Adults

Background: To characterize the associations of clinical risk factors, lifestyle factors, and wellness measures with prediabetes and new type 2 diabetes mellitus (T2DM) diagnosis in Hispanic adults and guide primary prevention. Methods: Sangre Por Salud Biobank enrolled 3733 Hispanic adults from Phoenix, AZ, United States, from 2013 to 2018. This analysis included participants with euglycemia, prediabetes, or new T2DM diagnosis (i.e., no prior T2DM diagnosis) at enrollment. Participants completed a baseline questionnaire on cardiometabolic risk factors and wellness measures and provided biometric measurements. The associations of factors and measures with odds (95% confidence interval) of prediabetes and new T2DM diagnosis were analyzed in logistic regression models. Results: Among 3299 participants with euglycemia (n = 1301), prediabetes (n = 1718), and new T2DM diagnosis (n = 280) at enrollment, 72% were women (n = 2376/3299). In adjusted models, most cardiometabolic risk factors were positively associated with prediabetes and new T2DM diagnosis, with stronger associations for new T2DM diagnosis. Obesity (body mass index ≥30 kg/m² vs. lower) was associated with higher odds of new T2DM diagnosis (3.14 [2.30-4.28]; P < 0.01) than prediabetes versus euglycemia (1.96 [1.66-2.32]; P < 0.01) and Interaction (P = 0.01). Similarly, waist circumference, family history of diabetes, and average systolic and diastolic blood pressure were associated with higher odds of new T2DM diagnosis versus euglycemia than prediabetes versus euglycemia. Using stepwise logistic regression modeling, a parsimonious model of age, family history of diabetes, waist circumference, diastolic blood pressure, passive tobacco exposure, and self-rated general health were associated with new T2DM diagnosis versus euglycemia. Conclusions: In Hispanic adults, modifiable cardiometabolic and lifestyle factors were associated with prediabetes and new T2DM diagnosis. Personalized interventions targeting these factors and measures could guide T2DM primary prevention efforts among Hispanic adults.

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults

Purpose: We aimed to establish and validate a risk assessment system that combines demographic and clinical variables to predict the 3-year risk of incident diabetes in Chinese adults. Methods: A 3-year cohort study was performed on 15,928 Chinese adults without diabetes at baseline. All participants were randomly divided into a training set (n = 7,940) and a validation set (n = 7,988). XGBoost method is an effective machine learning technique used to select the most important variables from candidate variables. And we further established a stepwise model based on the predictors chosen by the XGBoost model. The area under the receiver operating characteristic curve (AUC), decision curve and calibration analysis were used to assess discrimination, clinical use and calibration of the model, respectively. The external validation was performed on a cohort of 11,113 Japanese participants. Result: In the training and validation sets, 148 and 145 incident diabetes cases occurred. XGBoost methods selected the 10 most important variables from 15 candidate variables. Fasting plasma glucose (FPG), body mass index (BMI) and age were the top 3 important variables. And we further established a stepwise model and a prediction nomogram. The AUCs of the stepwise model were 0.933 and 0.910 in the training and validation sets, respectively. The Hosmer-Lemeshow test showed a perfect fit between the predicted diabetes risk and the observed diabetes risk (p = 0.068 for the training set, p = 0.165 for the validation set). Decision curve analysis presented the clinical use of the stepwise model and there was a wide range of alternative threshold probability spectrum. And there were almost no the interactions between these predictors (most P-values for interaction >0.05). Furthermore, the AUC for the external validation set was 0.830, and the Hosmer-Lemeshow test for the external validation set showed no statistically significant difference between the predicted diabetes risk and observed diabetes risk (P = 0.824). Conclusion: We established and validated a risk assessment system for characterizing the 3-year risk of incident diabetes.

Risk prediction model of gestational diabetes mellitus based on nomogram in a Chinese population cohort study

To build a risk prediction model of gestational diabetes mellitus using nomogram to provide a simple-to-use clinical basis for the early prediction of gestational diabetes mellitus (GDM). This study is a prospective cohort study including 1385 pregnant women. (1) It is showed that the risk of GDM in women aged ≥ 35 years was 5.5 times higher than that in women aged < 25 years (95% CI: 1.27–23.73, p < 0.05). In the first trimester, the risk of GDM in women with abnormal triglyceride who were in their first trimester was 2.1 times higher than that of lipid normal women (95% CI: 1.12–3.91, p < 0.05). The area under the ROC curve of the nomogram of was 0.728 (95% CI: 0.683–0.772), the sensitivity and specificity of the model were 0.716 and 0.652, respectively. This study provides a simple and economic nomogram for the early prediction of GDM risk in the first trimester, and it has certain accuracy.

Increased risk of type 2 diabetes in antidepressant users: evidence from a 6-year longitudinal study in the E3N cohort

AIM

To examine the association between antidepressant medication use and the risk of type 2 diabetes.

METHODS

Data were obtained from the E3N study (Étude Épidémiologique de Femmes de la Mutuelle Générale de l'Éducation Nationale), a French cohort study initiated in 1990, with questionnaire-based follow-up every 2 or 3 years. Exposure to antidepressants was obtained from drug reimbursement files available from 2004 onwards, and individually matched with questionnaire data. Cases of type 2 diabetes were identified from drug reimbursements. Cox proportional-hazard regression models were used, with drug exposure considered as a time-varying parameter.

RESULTS

Of the 63 999 women who were free of drug-treated type 2 diabetes at baseline in 2005, 1124 developed type 2 diabetes over the 6-year follow-up. Current use of antidepressants was associated with an increased risk of type 2 diabetes [hazard ratio 1.34 (95% CI 1.12, 1.61)] compared to non-users. When the different types of antidepressants were considered, women who currently used selective serotonin reuptake inhibitors, imipramine-type, 'other' or 'mixed' antidepressants had a 1.25-fold (95% CI 0.99, 1.57), 1.66-fold (95% CI 1.12, 2.46), 1.35-fold (95% CI 1.00, 1.84) and 1.82-fold (95% CI 0.85, 3.86) increase in risk of type 2 diabetes compared to non-users, respectively.

CONCLUSION

Our study suggests a positive association between antidepressant use and the risk of type 2 diabetes among women. If this association is confirmed, screening and surveillance of glucose levels should be considered in the context of antidepressant therapy. Further studies assessing the underlying mechanisms of this association are needed. (ClinicalTrials.gov identifier: NCT03285230).

Development and validation of a tool for predicting type 2 diabetes in Mexican women of reproductive age

INTRODUCTION

Diabetes is a worldwide problem with a greater impact in developing countries, where many people are unaware of their risk. In Mexico, women show the greatest risk for T2D. Current risk scores have been developed and validated in predominantly older European cohorts. They are not the best option in Mexican women. The development of a risk model/score in this population would be useful.

OBJECTIVE

To develop and validate a risk model and score that incorporates the most relevant risk factors for T2D in Mexican women of reproductive age.

METHODS

The study was carried out in two phases, with the first phase being the development of the predictive model and the second phase the validation of the model in a separate independent population. A cohort of Mexican patients of reproductive age ("Derivation Cohort") was used to create the predictive model. It included data on 3161 women. Risk factors for identification were assessed using Cox proportional hazards regression. Finally a score with a range of 0 to 19 points was developed to identify the 2.4 year probability of developing DM2 in Mexican women of reproductive age.

RESULTS

147 new cases of T2D (4.6%) were identified in the Derivation Cohort model, 97 of 925 participants (10.48%) in the validation cohort. The risk factor predictors of T2D were: history of gestational diabetes (HR 2.69, 95% CI 1.10-6.58), BMI (HR 1.03, 95% CI 1.01-1.06), hypertriglyceridemia (HR 1.54, 95% CI 1.11-2.14) and fasting blood glucose (HR 1.06, 95% CI 1.05-1.08), with an AUC of 0.75. The AUC in the validation cohort was 0.91 (95% CI 0.87-0.94). The score had a sensitivity of 73% and specificity of 67% at a cutoff of ≥15.

CONCLUSIONS

A predictive model and risk score was developed to detect cases at risk for incident T2D. It was generated using the characteristics of Mexican women of reproductive age. This risk score is a step forward in attempting to address the generational legacy that diabetes in pregnancy could have on women and their children.

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

OBJECTIVE

To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults.

DESIGN

Population based cohort study.

SETTING AND PARTICIPANTS

QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020.

MAIN OUTCOME MEASURES

The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period.

RESULTS

4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R²); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19.

CONCLUSION

The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.

Integrating Genetics and the Plasma Proteome to Predict the Risk of Type 2 Diabetes

PURPOSE OF THE REVIEW

Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D).

RECENT FINDINGS

Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.

The Sickle Cell Disease Ontology: Enabling Collaborative Research and Co-Designing of New Planetary Health Applications

Sickle cell disease (SCD) is one of the most common blood disorders impacting planetary health. Over 300,000 newborns are diagnosed with SCD each year globally, with an increasing trend. The sickle cell disease ontology (SCDO) is the most comprehensive multidisciplinary SCD knowledge portal. The SCDO was collaboratively developed by the SCDO working group, which includes experts in SCD and data standards from across the globe. This expert review presents highlights and lessons learned from the fourth SCDO workshop that marked the beginning of applications toward planetary health impact, and with an eye to empower and cultivate multisite SCD collaborative research. The workshop was organized by the Sickle Africa Data Coordinating Center (SADaCC) and attended by 44 participants from 14 countries, with 2 participants connecting remotely. Notably, from the standpoint of democratizing and innovating scientific meeting design, an SCD patient advocate also presented at the workshop, giving a broader real-life perspective on patients' aspirations, needs, and challenges. A major component of the workshop was new approaches to harness SCDO to harmonize data elements used by different studies. This was facilitated by a web-based platform onto which participants uploaded data elements from previous or ongoing SCD-relevant research studies before the workshop, making multisite collaborative research studies based on existing SCD data possible, including multisite cohort, SCD global clinical trials, and SCD community engagement approaches. Trainees presented proposals for systematic literature reviews in key SCD research areas. This expert review emphasizes potential and prospects of SCDO-enabled data standards and harmonization to facilitate large-scale global SCD collaborative initiatives. As the fields of public and global health continue to broaden toward planetary health, the SCDO is well poised to play a prominent role to decipher SCD pathophysiology further, and co-design diagnostics and therapeutics innovation in the field.

A Newly Developed Diabetes Risk Index, Based on Lipoprotein Subfractions and Branched Chain Amino Acids, is Associated with Incident Type 2 Diabetes Mellitus in the PREVEND Cohort

Objective: Evaluate the ability of a newly developed diabetes risk score, the Diabetes Risk Index (DRI), to predict incident type 2 diabetes mellitus (T2D) in a large adult population. Methods: The DRI was developed by combining the Lipoprotein Insulin Resistance Index (LP-IR), calculated from 6 lipoprotein subspecies and size parameters, and the branched chain amino acids, valine and leucine, all of which have been shown previously to be associated with future T2D. DRI scores were calculated in a total of 6134 nondiabetic men and women in the Prevention of Renal and Vascular End-Stage Disease (PREVEND) Study. Cox proportional hazards regression was used to evaluate the association of DRI scores with incident T2D. Results: During a median follow-up of 8.5 years, 306 new T2D cases were ascertained. In analyses adjusted for age and sex, there was a significant association between DRI scores and incident T2D with the hazard ratio (HR) for the highest versus lowest quartile being 12.07 (95% confidence interval: 6.97–20.89, p < 0.001). After additional adjustment for body mass index (BMI), family history of T2D, alcohol consumption, diastolic blood pressure, total cholesterol, triglycerides, HDL cholesterol and HOMA-IR, the HR was attenuated but remained significant (HR 3.20 (1.73–5.95), p = 0.001). Similar results were obtained when DRI was analyzed as HR per 1 SD increase (HR 1.37 (1.14–1.65), p < 0.001). The Kaplan–Meier plot demonstrated that patients in the highest quartile of DRI scores presented at higher risk (p-value for log-rank test <0.001). Conclusions: Higher DRI scores are associated with an increased risk of T2D. The association is independent of clinical risk factors for T2D including HOMA-IR, BMI and conventional lipids.

Predicting the Development of Type 2 Diabetes in a Large Australian Cohort Using Machine-Learning Techniques: Longitudinal Survey Study

BACKGROUND

Previous conventional models for the prediction of diabetes could be updated by incorporating the increasing amount of health data available and new risk prediction methodology.

OBJECTIVE

We aimed to develop a substantially improved diabetes risk prediction model using sophisticated machine-learning algorithms based on a large retrospective population cohort of over 230,000 people who were enrolled in the study during 2006-2017.

METHODS

We collected demographic, medical, behavioral, and incidence data for type 2 diabetes mellitus (T2DM) in over 236,684 diabetes-free participants recruited from the 45 and Up Study. We predicted and compared the risk of diabetes onset in these participants at 3, 5, 7, and 10 years based on three machine-learning approaches and the conventional regression model.

RESULTS

Overall, 6.05% (14,313/236,684) of the participants developed T2DM during an average 8.8-year follow-up period. The 10-year diabetes incidence in men was 8.30% (8.08%-8.49%), which was significantly higher (odds ratio 1.37, 95% CI 1.32-1.41) than that in women at 6.20% (6.00%-6.40%). The incidence of T2DM was doubled in individuals with obesity (men: 17.78% [17.05%-18.43%]; women: 14.59% [13.99%-15.17%]) compared with that of nonobese individuals. The gradient boosting machine model showed the best performance among the four models (area under the curve of 79% in 3-year prediction and 75% in 10-year prediction). All machine-learning models predicted BMI as the most significant factor contributing to diabetes onset, which explained 12%-50% of the variance in the prediction of diabetes. The model predicted that if BMI in obese and overweight participants could be hypothetically reduced to a healthy range, the 10-year probability of diabetes onset would be significantly reduced from 8.3% to 2.8% (P<.001).

CONCLUSIONS

A one-time self-reported survey can accurately predict the risk of diabetes using a machine-learning approach. Achieving a healthy BMI can significantly reduce the risk of developing T2DM.

Selecting the optimal risk threshold of diabetes risk scores to identify high-risk individuals for diabetes prevention: a cost-effectiveness analysis

AIMS

Although risk scores to predict type 2 diabetes exist, cost-effectiveness of risk thresholds to target prevention interventions are unknown. We applied cost-effectiveness analysis to identify optimal thresholds of predicted risk to target a low-cost community-based intervention in the USA.

METHODS

We used a validated Markov-based type 2 diabetes simulation model to evaluate the lifetime cost-effectiveness of alternative thresholds of diabetes risk. Population characteristics for the model were obtained from NHANES 2001-2004 and incidence rates and performance of two noninvasive diabetes risk scores (German diabetes risk score, GDRS, and ARIC 2009 score) were determined in the ARIC and Cardiovascular Health Study (CHS). Incremental cost-effectiveness ratios (ICERs) were calculated for increasing risk score thresholds. Two scenarios were assumed: 1-stage (risk score only) and 2-stage (risk score plus fasting plasma glucose (FPG) test (threshold 100 mg/dl) in the high-risk group).

RESULTS

In ARIC and CHS combined, the area under the receiver operating characteristic curve for the GDRS and the ARIC 2009 score were 0.691 (0.677-0.704) and 0.720 (0.707-0.732), respectively. The optimal threshold of predicted diabetes risk (ICER < $50,000/QALY gained in case of intervention in those above the threshold) was 7% for the GDRS and 9% for the ARIC 2009 score. In the 2-stage scenario, ICERs for all cutoffs ≥ 5% were below $50,000/QALY gained.

CONCLUSIONS

Intervening in those with ≥ 7% diabetes risk based on the GDRS or ≥ 9% on the ARIC 2009 score would be cost-effective. A risk score threshold ≥ 5% together with elevated FPG would also allow targeting interventions cost-effectively.

Sociodemographic and lifestyle-related risk factors for identifying vulnerable groups for type 2 diabetes: a narrative review with emphasis on data from Europe

BACKGROUND

Type 2 diabetes mellitus (T2DM) comprises the vast majority of all diabetes cases in adults, with alarmingly increasing prevalence over the past few decades worldwide. A particularly heavy healthcare burden of diabetes is noted in Europe, where 8.8% of the population aged 20-79 years is estimated to have diabetes according to the International Diabetes Federation. Multiple risk factors are implicated in the pathogenesis of T2DM with complex underlying interplay and intricate gene-environment interactions. Thus, intense research has been focused on studying the role of T2DM risk factors and on identifying vulnerable groups for T2DM in the general population which can then be targeted for prevention interventions.

METHODS

For this narrative review, we conducted a comprehensive search of the existing literature on T2DM risk factors, focusing on studies in adult cohorts from European countries which were published in English after January 2000.

RESULTS

Multiple lifestyle-related and sociodemographic factors were identified as related to high T2DM risk, including age, ethnicity, family history, low socioeconomic status, obesity, metabolic syndrome and each of its components, as well as certain unhealthy lifestyle behaviors. As Europe has an increasingly aging population, multiple migrant and ethnic minority groups and significant socioeconomic diversity both within and across different countries, this review focuses not only on modifiable T2DM risk factors, but also on the impact of pertinent demographic and socioeconomic factors.

CONCLUSION

In addition to other T2DM risk factors, low socioeconomic status can significantly increase the risk for prediabetes and T2DM, but is often overlooked. In multinational and multicultural regions such as Europe, a holistic approach, which will take into account both traditional and socioeconomic/socioecological factors, is becoming increasingly crucial in order to implement multidimensional public health programs and integrated community-based interventions for effective T2DM prevention.

Machine learning for characterizing risk of type 2 diabetes mellitus in a rural Chinese population: the Henan Rural Cohort Study

With the development of data mining, machine learning offers opportunities to improve discrimination by analyzing complex interactions among massive variables. To test the ability of machine learning algorithms for predicting risk of type 2 diabetes mellitus (T2DM) in a rural Chinese population, we focus on a total of 36,652 eligible participants from the Henan Rural Cohort Study. Risk assessment models for T2DM were developed using six machine learning algorithms, including logistic regression (LR), classification and regression tree (CART), artificial neural networks (ANN), support vector machine (SVM), random forest (RF) and gradient boosting machine (GBM). The model performance was measured in an area under the receiver operating characteristic curve, sensitivity, specificity, positive predictive value, negative predictive value and area under precision recall curve. The importance of variables was identified based on each classifier and the shapley additive explanations approach. Using all available variables, all models for predicting risk of T2DM demonstrated strong predictive performance, with AUCs ranging between 0.811 and 0.872 using laboratory data and from 0.767 to 0.817 without laboratory data. Among them, the GBM model performed best (AUC: 0.872 with laboratory data and 0.817 without laboratory data). Performance of models plateaued when introduced 30 variables to each model except CART model. Among the top-10 variables across all methods were sweet flavor, urine glucose, age, heart rate, creatinine, waist circumference, uric acid, pulse pressure, insulin, and hypertension. New important risk factors (urinary indicators, sweet flavor) were not found in previous risk prediction methods, but determined by machine learning in our study. Through the results, machine learning methods showed competence in predicting risk of T2DM, leading to greater insights on disease risk factors with no priori assumption of causality.

Associations Between Migraine and Type 2 Diabetes in Women: Findings From the E3N Cohort Study

Importance

Little is known about the associations between migraine and type 2 diabetes and the temporality of the association between these 2 diseases.

Objective

To evaluate the association between migraine and type 2 diabetes incidence as well as the evolution of the prevalence of active migraine before and after type 2 diabetes diagnosis.

Design, Setting, and Participants

We used data from the E3N cohort study, a French prospective population-based study initiated in 1990 on a cohort of women born between 1925 and 1950. The E3N study participants are insured by a health insurance plan that mostly covers teachers. From the eligible women in the E3N study, we included those who completed the 2002 follow-up questionnaire with information available on migraine. We then excluded prevalent cases of type 2 diabetes, leaving a final sample of women who were followed up between 2004 and 2014. All potential occurrences of type 2 diabetes were identified through a drug reimbursement database. Statistical analyses were performed in March 2018.

Exposures

Self-reported migraine occurrence.

Main Outcomes and Measures

Pharmacologically treated type 2 diabetes.

Results

From the 98 995 women in the study, 76 403 women completed the 2002 follow-up survey. Of these, 2156 were excluded because they had type 2 diabetes, leaving 74 247 women. Participants had a mean (SD) age of 61 (6) years at baseline, and all were free of type 2 diabetes. During 10 years of follow-up, 2372 incident type 2 diabetes cases occurred. A lower risk of type 2 diabetes was observed for women with active migraine compared with women with no migraine history (univariate hazard ratio, 0.80 [95% CI, 0.67-0.96], multivariable-adjusted hazard ratio, 0.70 [95% CI, 0.58-0.85]). We also observed a linear decrease in active migraine prevalence from 22% (95% CI, 16%-27%) to 11% (95% CI, 10%-12%) during the 24 years prior to diabetes diagnosis, after adjustment for potential type 2 diabetes risk factors. A plateau of migraine prevalence around 11% was then observed for 22 years after diagnosis.

Conclusions and Relevance

We observed a lower risk of developing type 2 diabetes for women with active migraine and a decrease in active migraine prevalence prior to diabetes diagnosis. Further targeted research should focus on understanding the mechanisms involved in explaining these findings.

Predicting diabetes clinical outcomes using longitudinal risk factor trajectories

BACKGROUND

The ubiquity of electronic health records (EHR) offers an opportunity to observe trajectories of laboratory results and vital signs over long periods of time. This study assessed the value of risk factor trajectories available in the electronic health record to predict incident type 2 diabetes.

STUDY DESIGN AND METHODS

Analysis was based on a large 13-year retrospective cohort of 71,545 adult, non-diabetic patients with baseline in 2005 and median follow-up time of 8 years. The trajectories of fasting plasma glucose, lipids, BMI and blood pressure were computed over three time frames (2000-2001, 2002-2003, 2004) before baseline. A novel method, Cumulative Exposure (CE), was developed and evaluated using Cox proportional hazards regression to assess risk of incident type 2 diabetes. We used the Framingham Diabetes Risk Scoring (FDRS) Model as control.

RESULTS

The new model outperformed the FDRS Model (.802 vs .660; p-values <2e-16). Cumulative exposure measured over different periods showed that even short episodes of hyperglycemia increase the risk of developing diabetes. Returning to normoglycemia moderates the risk, but does not fully eliminate it. The longer an individual maintains glycemic control after a hyperglycemic episode, the lower the subsequent risk of diabetes.

CONCLUSION

Incorporating risk factor trajectories substantially increases the ability of clinical decision support risk models to predict onset of type 2 diabetes and provides information about how risk changes over time.

Cardiometabolic risk factors correlated with the incidence of dysglycaemia in a Brazilian normoglycaemic sample: the Baependi Heart Study cohort

BACKGROUND

Dysglycaemia is defined by elevated glucose levels in the blood, commonly characterized by impaired fasting glucose, impaired glucose tolerance, elevated glycated haemoglobin, or diabetes mellitus (DM) diagnosis. The abnormal levels of glucose may occur many years before DM, a condition known as prediabetes, which is correlated with comorbidities such as cardiovascular diseases. Therefore, the aim of this study was to investigate the incidence of prediabetic dysglycaemia and its relationship with cardiometabolic risk factors at a 5-year follow-up, based on an initially normoglycaemic sample in the Baependi Heart Study cohort.

METHODS

The data used comes from the Baependi Heart Study cohort, which consists of two periods: cycle 1 (2005-2006) and cycle 2 (2010-2013). For this study, we excluded those who had fasting blood glucose ≥ 100 mg/dL or were taking anti-diabetic medications at baseline, and those that had diabetes diagnosed in cycle 2. Mixed-effects logistic regression models were used to assess the association between cardiometabolic risk factors and the incidence of dysglycaemia, including a familiar random effect such as a cluster.

RESULTS

The incidence of prediabetic dysglycaemia was 12.8%, and it did not differ between men and women (14.4% and 11.6%, respectively). Two models were analysed to investigate the relationship between cardiometabolic risk factors and the occurrence of prediabetic dysglycaemia. The model that better explained the occurrence of dysglycaemia over the 5 years, after correction, included the waist circumference (WC) (measures and Δ), systolic blood pressure (SBP), HDL-c levels, and age. Although sex was not associated with the incidence of dysglycaemia, women and men showed differences in cardiometabolic risk factors related to glucose impairment: men who developed dysglycaemia showed, in parallel, higher LDL-c levels, TC/HDL-c ratio and DBP measurements; while these parameters remained similar between women who developed dysglycaemia and dysglycaemia-free women, after 5 years.

CONCLUSIONS

In an initially normoglycaemic sample of a highly mixed population living in a traditional Brazilian lifestyle, important cardiometabolic risk factors were associated with the occurrence of prediabetic dysglycaemia, and this relationship appeared to be more important in men. These results provide important insights about cardiovascular risk in prediabetic individuals.

Evidence-Based Utilization of Prognostic Prediction Models in Cardiovascular Medicine

Prediction models are combinations of predictors to assess the risks of specific endpoints such as the presence or prognosis of a disease. Many novel predictors have been developed, modelling techniques have been evolving, and prediction models are currently abundant in the medical literature, especially in cardiovascular medicine, but evidence is still lacking regarding how to use them. Recent methodological advances in systematic reviews and meta-analysis have enabled systematic evaluation of prediction model studies and quantitative analysis to identify determinants of model performance. Knowing what is critical to model performance, under what circumstances model performance remains adequate, and when a model might require further adjustment and improvement will facilitate effective utilization of prediction models and will enhance diagnostic and prognostic accuracy in clinical practice. In this review article, we provide a current methodological overview of the attempts to implement evidence-based utilization of prognostic prediction models for all potential model users, including patients and their families, health-care providers, administrators, researchers, guideline developers and policy makers.

Risk scores for type 2 diabetes mellitus in Latin America: a systematic review of population-based studies

AIM

To summarize the evidence on diabetes risk scores for Latin American populations.

METHODS

A systematic review was conducted (CRD42019122306) looking for diagnostic and prognostic models for type 2 diabetes mellitus among randomly selected adults in Latin America. Five databases (LILACS, Scopus, MEDLINE, Embase and Global Health) were searched. type 2 diabetes mellitus was defined using at least one blood biomarker and the reports needed to include information on the development and/or validation of a multivariable regression model. Risk of bias was assessed using the PROBAST guidelines.

RESULTS

Of the 1500 reports identified, 11 were studied in detail and five were included in the qualitative analysis. Two reports were from Mexico, two from Peru and one from Brazil. The number of diabetes cases varied from 48 to 207 in the derivations models, and between 29 and 582 in the validation models. The most common predictors were age, waist circumference and family history of diabetes, and only one study used oral glucose tolerance test as the outcome. The discrimination performance across studies was ~ 70% (range: 66-72%) as per the area under the receiving-operator curve, the highest metric was always the negative predictive value. Sensitivity was always higher than specificity.

CONCLUSION

There is no evidence to support the use of one risk score throughout Latin America. The development, validation and implementation of risk scores should be a research and public health priority in Latin America to improve type 2 diabetes mellitus screening and prevention.

Use of capillary glucose combined with other non-laboratory examinations to screen for diabetes and prediabetes

AIM

To evaluate the value and feasibility of capillary glucose assessment, combined with other non-laboratory measures, in screening for diabetes and prediabetes in the community.

METHODS

In this cross-sectional study, we assessed fasting capillary glucose, fasting plasma glucose, and both capillary glucose and plasma glucose values after 2-h oral glucose tolerance tests in a total of 3736 samples. We determined the optimal threshold of capillary glucose using receiver-operating characteristic curve analysis. The effect of screening methods using capillary glucose combined with other variables, such as age, BMI and waist circumference, was assessed according to area under the receiver-operating characteristic curve.

RESULTS

There was a strong positive correlation between capillary glucose and venous plasma glucose. The area under the curve for the model using fasting capillary glucose to screen for impaired fasting glucose was 0.722, while that for the model using capillary glucose after a 2-h oral glucose tolerance test to screen for impaired glucose tolerance was 0.916. The area under the curve for the model using fasting capillary glucose to screen for diabetes was 0.835, while that for the model using 2-h oral glucose tolerance test capillary glucose was 0.912. The area under the curve for the model using fasting capillary glucose + 2-h oral glucose tolerance test capillary glucose to screen for diabetes was 0.945. The discriminatory capability of models using capillary glucose was somewhat improved by adding non-laboratory variables.

CONCLUSIONS

Capillary glucose could be an alternative for screening for diabetes and prediabetes, especially in low-resource areas.

Comparative Performance of Prediction Models for Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention

BACKGROUND

Identifying patients at increased risk of contrast-associated acute kidney injury (CA-AKI) can help target risk mitigation strategies toward these individuals during percutaneous coronary intervention. Illuminating which risk models best stratify risk is an important foundation for such quality improvement efforts.

METHODS AND RESULTS

Seven previously published risk prediction models for CA-AKI and 3 models for kidney injury requiring dialysis were validated using 2 definitions for CA-AKI (the Kidney Disease: Improving Global Outcomes definition of ≥0.3 mg/dL within 48 hours or ≥50% increase in serum creatinine from baseline within 7 days and the historical definition of ≥0.5 mg/dL or ≥25% increase in serum creatinine from baseline within 48 hours), and AKI requiring dialysis within 30 days of percutaneous coronary intervention. Model performance was compared based on discrimination, calibration, and categorical net reclassification index before and after model recalibration. Among 7888 patients who underwent percutaneous coronary intervention in Alberta Canada, CA-AKI occurred in 330 patients (4.2%) when CA-AKI was defined using the Kidney Disease: Improving Global Outcomes definition and 571 (7.3%) when using the historical definition. CA-AKI requiring dialysis occurred in 42 (0.6%) patients. When validated using the Kidney Disease: Improving Global Outcomes definition for CA-AKI, the 2 most recently published models for CA-AKI showed better discrimination (C statistics, 0.75-0.76) than older models (C statistics, 0.61-0.68). C statistics of models for kidney injury requiring dialysis ranged from 0.70 to 0.86. The calibration of all models for CA-AKI deviated from ideal, and the proportion of patients classified into different risk categories for CA-AKI differed substantially for the 2 most recent models. Recalibration significantly improved risk stratification of patients into clinical risk categories for some models.

CONCLUSIONS

Recent prediction models for CA-AKI show better discrimination compared with older models; however, model recalibration should be examined in external cohorts to improve the accuracy of predictions, particularly if predicted risk strata are used to guide management approaches.