Estimating the risk of developing type 2 diabetes: a comparison of several risk scores: the Cohorte Lausannoise study

Comparison of five different risk scores to predict incident type 2 diabetes in the Swiss HIV cohort study

OBJECTIVE

People with HIV (PWH) have a higher risk of type 2 diabetes (T2D) than HIV-negative individuals. In the general population, diabetes risk scores are used to identify persons at risk of developing T2D, but little is known regarding their performance in PWH.

DESIGN

Assessment of the capacity of five diabetes risk scores to predict T2D in PWH.

METHODS

A prospective study including all Swiss HIV cohort study (SHCS) participants followed between 2009 and 2019. Five diabetes risk scores were assessed: FINDRISC versions 1 and 2, Balkau, Swiss Diabetes Association (SDA), and Kraege.

RESULTS

Three thousand eight hundred fifty-three T2D-free PWH (78.5% men, 39.9 ± 11.3 years) were included. After a median follow-up of 4.8 years (interquartile range 2.2-7.8), 62 participants (1.6%) developed T2D, corresponding to an incidence rate of 3.18 per 1000 person-years (95% confidence interval = 2.47-4.08). Participants who developed T2D were older (48.7 ± 12.4 vs. 39.8 ± 11.2 years), more likely to be obese (22.6% vs. 7.4%), abdominally obese (9.7% vs. 1.5%), and to have a family history of diabetes (32.3% vs. 19.1%) than those without T2D. The AUC for incident T2D ranged between 0.72 (Kraege 16) and 0.81 (SDA, FINDRISC2 and Balkau). Sensitivity ranged between 3.2% (Balkau) and 67.7% (FINDRISC1) and specificity between 80.9% (FINDRISC1) and 98.3% (Balkau). Positive predictive values of all scores were below 20%, while negative predictive values were above 98%.

CONCLUSION

Our study shows that the performance of conventional diabetes risk scores in PWH is promising, especially for Balkau and FINDRISC2, which showed good discriminatory power. These scores may help identify patients at a low risk of T2D in whom careful assessment of modifiable T2D risk factors can be spared.

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing

BACKGROUND

Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups.

METHODS

Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model.

RESULTS

Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses.

CONCLUSIONS

The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.

Abundance of undiagnosed cardiometabolic risk within the population of a long-stay prison in the UK

BACKGROUND

The health of people in prisons is a public health issue. It is well known that those in prison experience poorer health outcomes than those in the general community. One such example is the burden of non-communicable diseases, more specifically cardiovascular disease (CVD), stroke and type 2 diabetes (T2DM). However, there is limited evidence research on the extent of cardiometabolic risk factors in the prison environment in Wales, the wider UK or globally.

METHODS

Risk assessments were performed on a representative sample of 299 men at HMP Parc, Bridgend. The risk assessments were 30 min in duration and men aged 25-84 years old and free from pre-existing CVD and T2DM were eligible. During the risk assessment, a number of demographic, anthropometric and clinical markers were obtained. The 10-year risk of CVD and T2DM was predicted using the QRISK2 algorithm and Diabetes UK Risk Score, respectively.

RESULTS

The majority of the men was found to be either overweight (43.5%) or obese (37.5%) and/or demonstrated evidence of central obesity (40.1%). Cardiometabolic risk factors including systolic hypertension (25.1%), high cholesterol (29.8%), low HDL cholesterol (56.2%) and elevated total cholesterol: HDL ratios (23.1%) were observed in a considerable number of men. Ultimately, 15.4% were calculated at increased risk of CVD, and 31.8% predicted at moderate or high risk of T2DM.

CONCLUSIONS

Overall, a substantial prevalence of previously undiagnosed cardiometabolic risk factors was observed and men in prison are at elevated risk of cardiometabolic disease at a younger age than current screening guidelines.

Predictive models for diabetes mellitus using machine learning techniques

BACKGROUND

Diabetes Mellitus is an increasingly prevalent chronic disease characterized by the body's inability to metabolize glucose. The objective of this study was to build an effective predictive model with high sensitivity and selectivity to better identify Canadian patients at risk of having Diabetes Mellitus based on patient demographic data and the laboratory results during their visits to medical facilities.

METHODS

Using the most recent records of 13,309 Canadian patients aged between 18 and 90 years, along with their laboratory information (age, sex, fasting blood glucose, body mass index, high-density lipoprotein, triglycerides, blood pressure, and low-density lipoprotein), we built predictive models using Logistic Regression and Gradient Boosting Machine (GBM) techniques. The area under the receiver operating characteristic curve (AROC) was used to evaluate the discriminatory capability of these models. We used the adjusted threshold method and the class weight method to improve sensitivity - the proportion of Diabetes Mellitus patients correctly predicted by the model. We also compared these models to other learning machine techniques such as Decision Tree and Random Forest.

RESULTS

The AROC for the proposed GBM model is 84.7% with a sensitivity of 71.6% and the AROC for the proposed Logistic Regression model is 84.0% with a sensitivity of 73.4%. The GBM and Logistic Regression models perform better than the Random Forest and Decision Tree models.

CONCLUSIONS

The ability of our model to predict patients with Diabetes using some commonly used lab results is high with satisfactory sensitivity. These models can be built into an online computer program to help physicians in predicting patients with future occurrence of diabetes and providing necessary preventive interventions. The model is developed and validated on the Canadian population which is more specific and powerful to apply on Canadian patients than existing models developed from US or other populations. Fasting blood glucose, body mass index, high-density lipoprotein, and triglycerides were the most important predictors in these models.

Importance and Potential of Dentists in Identifying Patients at High Risk of Diabetes

OBJECTIVES

The study was conducted to assess the utilization of medical and dental services by dental patients at two dental school hospitals and to approximate the number of patients having no known previous diagnosis of type 2 diabetes but are at high risk of acquiring it.

METHODS

A cross-sectional study was conducted at two dental school hospitals in India. A 20-item questionnaire was administered as interviews among the dental patients aged 35 to 55 years. Data was collected on past dental and medical visits, medical history, family history relevant to diabetes, cardiovascular health, BMI and waist circumference (measured).

RESULTS

A total of 413 adult patients (males 61.26%, females 38.74%) participated in the surveys. The mean age was 43.06 years. Results revealed that nearly 50% did not have a medical or a dental visit in the last 1 year, 33% had Cardiovascular Diseases (CVD). Among those who did not have medical visit in last one year 45% had BMI >25 kg, 55% had waist circumference above the normal range and 38% were at high risk of diabetes.

CONCLUSION

The high number of patients without a medical visit in the past year or more, as well as the high levels of diabetes risk indicators, affirms the need for dentists to perform chair-side screenings for diabetes. These results suggest the need for additional training among dental students to improve early detection and identification of high-risk patients to minimize potential morbidity due to diabetes.

Metabolomics as a Tool to Understand Pathophysiological Processes

Multiple diseases have a strong metabolic component, and metabolomics as a powerful phenotyping technology, in combination with orthogonal biological and clinical approaches, will undoubtedly play a determinant role in accelerating the understanding of mechanisms that underlie these complex diseases determined by a set of genetic, lifestyle, and environmental exposure factors. Here, we provide several examples of valuable findings from metabolomics-led studies in diabetes and obesity metabolism, neurodegenerative disorders, and cancer metabolism and offer a longer term vision toward personalized approach to medicine, from population-based studies to pharmacometabolomics.

Comparison of traditional diabetes risk scores and HbA1c to predict type 2 diabetes mellitus in a population based cohort study

AIMS

Compare performances of diabetes risk scores and glycated hemoglobin (HbA1c) to estimate the risk of incident type 2 diabetes mellitus (T2DM) in Northeast Germany.

METHODS

We studied 2916 subjects (20 to 81years) from the Study of Health in Pomerania (SHIP) in a 5-year follow-up period. Diabetes risk scores included the Cooperative Health Research in the Region of Augsburg (KORA) base model, the Danish diabetes risk score and the Data from the Epidemiological Study on the Insulin Resistance syndrome (D.E.S.I.R) clinical risk score. We assessed the performance of each of the diabetes risk scores and the HbA1c for 5-year risk of T2DM by the area under the receiver-operating characteristic curve (AUC) and calibration plots.

RESULTS

In SHIP, the incidence of T2DM was 5.4% (n=157) in the 5-year follow-up period. Diabetes risk scores and HbA1c achieved AUCs ranging from 0.76 for the D.E.S.I.R. clinical risk score to 0.82 for the KORA base model. For diabetes risk scores, the discriminative ability was lower for the age group 55 to 74years. For HbA1c, the discriminative ability also decreased for the group 55 to 74years while it was stable in the age group 30 to 64years old.

CONCLUSIONS

All diabetes risk scores and the HbA1c showed a good prediction for the risk of T2DM in SHIP. Which model or biomarker should be used is driven by its context of use, e.g. the practicability, implementation of interventions and availability of measurement.

Association of a Dietary Score with Incident Type 2 Diabetes: The Dietary-Based Diabetes-Risk Score (DDS)

Background

Strong evidence supports that dietary modifications may decrease incident type 2 diabetes mellitus (T2DM). Numerous diabetes risk models/scores have been developed, but most do not rely specifically on dietary variables or do not fully capture the overall dietary pattern. We prospectively assessed the association of a dietary-based diabetes-risk score (DDS), which integrates optimal food patterns, with the risk of developing T2DM in the SUN (“Seguimiento Universidad de Navarra”) longitudinal study.

Methods

We assessed 17,292 participants initially free of diabetes, followed-up for a mean of 9.2 years. A validated 136-item FFQ was administered at baseline. Taking into account previous literature, the DDS positively weighted vegetables, fruit, whole cereals, nuts, coffee, low-fat dairy, fiber, PUFA, and alcohol in moderate amounts; while it negatively weighted red meat, processed meats and sugar-sweetened beverages. Energy-adjusted quintiles of each item (with exception of moderate alcohol consumption that received either 0 or 5 points) were used to build the DDS (maximum: 60 points). Incident T2DM was confirmed through additional detailed questionnaires and review of medical records of participants. We used Cox proportional hazards models adjusted for socio-demographic and anthropometric parameters, health-related habits, and clinical variables to estimate hazard ratios (HR) of T2DM.

Results

We observed 143 T2DM confirmed cases during follow-up. Better baseline conformity with the DDS was associated with lower incidence of T2DM (multivariable-adjusted HR for intermediate (25–39 points) vs. low (11–24) category 0.43 [95% confidence interval (CI) 0.21, 0.89]; and for high (40–60) vs. low category 0.32 [95% CI: 0.14, 0.69]; p for linear trend: 0.019).

Conclusions

The DDS, a simple score exclusively based on dietary components, showed a strong inverse association with incident T2DM. This score may be applicable in clinical practice to improve dietary habits of subjects at high risk of T2DM and also as an educational tool for laypeople to help them in self-assessing their future risk for developing diabetes.

Alcohol consumption and incidence of type 2 diabetes. Results from the CoLaus study

BACKGROUND AND AIMS

Moderate alcohol consumption has been shown to decrease the risk of type 2 diabetes (T2DM), but whether this association is also valid for impaired fasting glucose (IFG) is less well known. We aimed at assessing the impact of alcohol consumption and of type of alcoholic beverage on the incidence of T2DM and T2DM + IFG.

METHODS AND RESULTS

As many as 4765 participants (2613 women, mean age 51.7 ± 10.5 years) without T2DM at baseline and followed for an average of 5.5 years. The association between alcohol consumption, type of alcoholic beverage and outcomes was assessed after adjustment for a validated T2DM risk score. During follow-up 284 participants developed T2DM and 643 developed IFG. On bivariate analysis, alcohol consumption was positively associated with the risk of developing T2DM or T2DM + IFG. Moderate (14-27 units/week) alcohol consumption tended to be associated with a lower risk of T2DM, but no protective effect was found for T2DM + IFG. Multivariable-adjusted odds ratio (OR) and (95% confidence interval) for T2DM: 0.89 (0.65-1.22), 0.66 (0.42-1.03) and 1.63 (0.93-2.84) for 1-13, 14-27 and 28 + units/week, respectively (p for quadratic trend < 0.005). For T2DM + IFG, the corresponding ORs were 1.09 (0.90-1.32), 1.33 (1.02-1.74) and 1.54 (0.99-2.39), respectively, p for trend = 0.03. No specific effect of alcoholic beverage (wine, beer or spirits) was found for T2DM or for T2DM + IFG.

CONCLUSION

Moderate alcohol consumption is associated with a reduced risk of developing T2DM, but not of developing T2DM + IFG. No specific effect of type of alcoholic beverage was found.

Diabetes risk scores and death: predictability and practicability in two different populations

The aim was to examine the capacity of commonly used type 2 diabetes mellitus (T2DM) risk scores to predict overall mortality. The US-based NHANES III (n = 3138; 982 deaths) and the Swiss-based CoLaus study (n = 3946; 191 deaths) were used. The predictive value of eight T2DM risk scores regarding overall mortality was tested. The Griffin score, based on few self-reported parameters, presented the best (NHANES III) and second best (CoLaus) predictive capacity. Generally, the predictive capacity of scores based on clinical (anthropometrics, lifestyle, history) and biological (blood parameters) data was not better than of scores based solely on clinical self-reported data. T2DM scores can be validly used to predict mortality risk in general populations without diabetes. Comparison with other scores could further show whether such scores also suit as a screening tool for quick overall health risk assessment.

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

BACKGROUND

The comparative performance of existing models for prediction of type 2 diabetes across populations has not been investigated. We validated existing non-laboratory-based models and assessed variability in predictive performance in European populations.

METHODS

We selected non-invasive prediction models for incident diabetes developed in populations of European ancestry and validated them using data from the EPIC-InterAct case-cohort sample (27,779 individuals from eight European countries, of whom 12,403 had incident diabetes). We assessed model discrimination and calibration for the first 10 years of follow-up. The models were first adjusted to the country-specific diabetes incidence. We did the main analyses for each country and for subgroups defined by sex, age (<60 years vs ≥60 years), BMI (<25 kg/m(2)vs ≥25 kg/m(2)), and waist circumference (men <102 cm vs ≥102 cm; women <88 cm vs ≥88 cm).

FINDINGS

We validated 12 prediction models. Discrimination was acceptable to good: C statistics ranged from 0·76 (95% CI 0·72-0·80) to 0·81 (0·77-0·84) overall, from 0·73 (0·70-0·76) to 0·79 (0·74-0·83) in men, and from 0·78 (0·74-0·82) to 0·81 (0·80-0·82) in women. We noted significant heterogeneity in discrimination (pheterogeneity<0·0001) in all but one model. Calibration was good for most models, and consistent across countries (pheterogeneity>0·05) except for three models. However, two models overestimated risk, DPoRT by 34% (95% CI 29-39%) and Cambridge by 40% (28-52%). Discrimination was always better in individuals younger than 60 years or with a low waist circumference than in those aged at least 60 years or with a large waist circumference. Patterns were inconsistent for BMI. All models overestimated risks for individuals with a BMI of <25 kg/m(2). Calibration patterns were inconsistent for age and waist-circumference subgroups.

INTERPRETATION

Existing diabetes prediction models can be used to identify individuals at high risk of type 2 diabetes in the general population. However, the performance of each model varies with country, age, sex, and adiposity.

FUNDING

The European Union.

Risco para diabetes mellitus tipo 2 e fatores associados

OBJETIVO: Avaliar o risco para diabetes mellitus tipo 2 e sua associação com variáveis clínicas e sociodemográficas. MÉTODOS: Estudo transversal realizado com 419 usuários da Estratégia Saúde da Família. O instrumento de pesquisa foi um questionário validado. RESULTADOS: Verificou-se que 25,3% dos usuários tinham idades ≥45 anos; 59,7% estavam com excesso de peso e 84,0% com obesidade abdominal; 83,3% eram sedentários; 53,7% não comiam frutas/verduras diariamente; 12,9% tomavam anti-hipertensivos; 5,3% mencionaram história prévia de glicose alta e 47% história familiar de diabetes. Foram classificados como de baixo risco 24,6% dos usuários; 63,5% como de risco moderado e 11,7% de risco alto. CONCLUSÃO: Houve associação significante entre o risco para desenvolver diabetes mellitus tipo 2 e as variáveis clínicas: índice de massa corporal, circunferência abdominal, alimentação, uso de anti-hipertensivos, história de glicose alta e história familiar, e as variáveis sociodemográficas gênero e idade.

Comparison of diabetes risk score estimates and cardiometabolic risk profiles in a middle-aged Irish population

Background

To compare diabetes risk assessment tools in estimating risk of developing type 2 diabetes (T2DM) and to evaluate cardiometabolic risk profiles in a middle-aged Irish population.

Methods

Future risk of developing T2DM was estimated using 7 risk scores, including clinical measures with or without anthropometric, biological and lifestyle data, in the cross-sectional Mitchelstown cohort of 2,047 middle-aged men and women. Cardiometabolic phenotypes including markers of glucose metabolism, inflammatory and lipid profiles were determined.

Results

Estimates of subjects at risk for developing T2DM varied considerably according to the risk assessment tool used (0.3% to 20%), with higher proportions of males at risk (0–29.2% vs. 0.1–13.4%, for men and women, respectively). Extrapolated to the Irish population of similar age, the overall number of adults at high risk of developing T2DM ranges from 3,378 to 236,632. Numbers of non-optimal metabolic features were generally greater among those at high risk of developing T2DM. However, cardiometabolic profile characterisation revealed that only those classified at high risk by the Griffin (UK Cambridge) score displayed a more pro-inflammatory, obese, hypertensive, dysglycaemic and insulin resistant metabolic phenotype.

Conclusions

Most diabetes risk scores examined offer limited ability to identify subjects with metabolic abnormalities and at risk of developing T2DM. Our results highlight the need to validate diabetes risk scoring tools for each population studied and the potential for developing an Irish diabetes risk score, which may help to promote self awareness and identify high risk individuals and diabetes hot spots for targeted public health interventions.

Screening for impaired fasting glucose and diabetes using available health plan data

AIMS

To develop and validate prediction equations to identify individuals at high risk for type 2 diabetes using existing health plan data.

METHODS

Health plan data from 2005 to 2009 from 18,527 members of a Midwestern HMO without diabetes, 6% of whom had fasting plasma glucose (FPG) ≥110mg/dL, and health plan data from 2005 to 2006 from 368,025 members of a West Coast-integrated delivery system without diabetes, 13% of whom had FPG ≥110mg/dL were analyzed. Within each health plan, we used multiple logistic regression to develop equations to predict FPG ≥110mg/dL for half of the population and validated the equations using the other half. We then externally validated the equations in the other health plan.

RESULTS

Areas under the curve for the most parsimonious equations were 0.665 to 0.729 when validated internally. Positive predictive values were 14% to 32% when validated internally and 14% to 29% when validated externally.

CONCLUSION

Multivariate logistic regression equations can be applied to existing health plan data to efficiently identify persons at higher risk for dysglycemia who might benefit from definitive diagnostic testing and interventions to prevent or treat diabetes.

Adipocytokines, hepatic and inflammatory biomarkers and incidence of type 2 diabetes. the CoLaus study

CONTEXT

There is contradictory information regarding the prognostic importance of adipocytokines, hepatic and inflammatory biomarkers on the incidence of type 2 diabetes. The objective was to assess the prognostic relevance of adipocytokine and inflammatory markers (C-reactive protein - CRP; interleukin-1beta - IL-1β; interleukin-6- IL-6; tumour necrosis factor-α - TNF-α; leptin and adiponectin) and gamma-glutamyl transpeptidase (γGT) on the incidence of type 2 diabetes.

METHODS

Prospective, population-based study including 3,842 non-diabetic participants (43.3% men, age range 35 to 75 years), followed for an average of 5.5 years (2003-2008). The endpoint was the occurrence of type 2 diabetes.

RESULTS

208 participants (5.4%, 66 women) developed type 2 diabetes during follow-up. On univariate analysis, participants who developed type 2 diabetes had significantly higher baseline levels of IL-6, CRP, leptin and γGT, and lower levels of adiponectin than participants who remained free of type 2 diabetes. After adjusting for a validated type 2 diabetes risk score, only the associations with adiponectin: Odds Ratio and (95% confidence interval): 0.97 (0.64-1.47), 0.84 (0.55-1.30) and 0.64 (0.40-1.03) for the second, third and forth gender-specific quartiles respectively, remained significant (P-value for trend = 0.05). Adding each marker to a validated type 2 diabetes risk score (including age, family history of type 2 diabetes, height, waist circumference, resting heart rate, presence of hypertension, HDL cholesterol, triglycerides, fasting glucose and serum uric acid) did not improve the area under the ROC or the net reclassification index; similar findings were obtained when the markers were combined, when the markers were used as continuous (log-transformed) variables or when gender-specific quartiles were used.

CONCLUSION

Decreased adiponectin levels are associated with an increased risk for incident type 2 diabetes, but they seem to add little information regarding the risk of developing type 2 diabetes to a validated risk score.