Development and validation of a Diabetes Risk Score for screening undiagnosed diabetes in Sri Lanka (SLDRISK)

Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies

OBJECTIVES

This study aimed to provide an overview of prediction models of undiagnosed type 2 diabetes mellitus (U-T2DM) or the incident T2DM (I-T2DM) using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) checklist and the prediction model risk of the bias assessment tool (PROBAST).

DATA SOURCES

Both PUBMED and EMBASE databases were searched to guarantee adequate and efficient coverage.

STUDY SELECTION

Articles published between December 2011 and October 2019 were considered.

DATA EXTRACTION

For each article, information on model development requirements, discrimination measures, calibration, overall performance, clinical usefulness, overfitting, and risk of bias (ROB) was reported.

RESULTS

The median (interquartile range; IQR) number of the 46 study populations for model development was 5711 (1971 - 27426) and 2457 (2060 - 6995) individuals for I-T2DM and U-T2DM, respectively. The most common reported predictors were age and body mass index, and only the Qrisk-2017 study included social factors (e.g., Townsend score). Univariable analysis was reported in 46% of the studies, and the variable selection procedure was not clear in 17.4% of them. Moreover, internal and external validation was reported in 43% the studies, while over 63% of them reported calibration. The median (IQR) of AUC for I-T2DM models was 0.78 (0.74 - 0.82); the corresponding value for studies derived before October 2011 was 0.80 (0.77 - 0.83). The highest discrimination index was reported for Qrisk-2017 with C-statistics of 0.89 for women and 0.87 for men. Low ROB for I-T2DM and U-T2DM was assessed at 18% and 41%, respectively.

CONCLUSIONS

Among prediction models, an intermediate to poor quality was reassessed in several aspects of model development and validation. Generally, despite its new risk factors or new methodological aspects, the newly developed model did not increase our capability in screening/predicting T2DM, mainly in the analysis part. It was due to the lack of external validation of the prediction models.

Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study

A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the primary objective of our study was to develop a screening model for estimating UDM among people with FHD and enable its validation. We used data from the Korean National Health and Nutrition Examination Survey (KNHANES). KNAHNES (2010-2016) was used as a developmental cohort (n = 5939) and was then evaluated in a validation cohort (n = 1047) KNHANES (2017). We developed the screening model for UDM in male (SMM), female (SMF), and male and female combined (SMP) with FHD using backward stepwise logistic regression analysis. The SMM and SMF showed an appropriate performance (area under curve (AUC) = 76.2% and 77.9%) compared with SMP (AUC = 72.9%) in the validation cohort. Consequently, simple screening models were developed and validated, for the estimation of UDM among patients in the FHD group, which is expected to reduce the burden on the national health care system.

Cardiometabolic risk prediction algorithms for young people with psychosis: a systematic review and exploratory analysis

OBJECTIVE

Cardiometabolic risk prediction algorithms are common in clinical practice. Young people with psychosis are at high risk for developing cardiometabolic disorders. We aimed to examine whether existing cardiometabolic risk prediction algorithms are suitable for young people with psychosis.

METHODS

We conducted a systematic review and narrative synthesis of studies reporting the development and validation of cardiometabolic risk prediction algorithms for general or psychiatric populations. Furthermore, we used data from 505 participants with or at risk of psychosis at age 18 years in the ALSPAC birth cohort, to explore the performance of three algorithms (QDiabetes, QRISK3 and PRIMROSE) highlighted as potentially suitable. We repeated analyses after artificially increasing participant age to the mean age of the original algorithm studies to examine the impact of age on predictive performance.

RESULTS

We screened 7820 results, including 110 studies. All algorithms were developed in relatively older participants, and most were at high risk of bias. Three studies (QDiabetes, QRISK3 and PRIMROSE) featured psychiatric predictors. Age was more strongly weighted than other risk factors in each algorithm. In our exploratory analysis, calibration plots for all three algorithms implied a consistent systematic underprediction of cardiometabolic risk in the younger sample. After increasing participant age, calibration plots were markedly improved.

CONCLUSION

Existing cardiometabolic risk prediction algorithms cannot be recommended for young people with or at risk of psychosis. Existing algorithms may underpredict risk in young people, even in the face of other high-risk features. Recalibration of existing algorithms or a new tailored algorithm for the population is required.

Comparative analysis of screening models for undiagnosed diabetes in Mexico

BACKGROUND

It is estimated that 37% of Mexican adults have undiagnosed diabetes, and are therefore at high risk of developing the severe and devastating complications associated to it. In recent years, a variety of screening tools based on the characteristics of the adult Mexican population have been proposed in order to reduce the negative effects of the disease.

OBJECTIVES

To assess the performance of screening models to diagnose diabetes in the Mexican adult population and to propose a screening model based on HbA1c measurements.

MATERIALS AND METHODS

Data from the 2016 Halfway National Health and Nutrition Survey (NHNS) were used to assess the screening models and to develop and validate the proposed 2016 NHNS model, built using a multivariate logistic regression model. Explanatory variables included in the 2016 NHNS 2016 model were selected through a stepwise backward procedure, using sensitivity and specificity as performance indicators.

RESULTS

Of the screening models assessed, only the model based on the 2006 NHNS survey showed a performance consistent with previous reports. The proposed 2016 NHNS model included age, waist circumference, and systolic blood pressure as explanatory variables and showed a sensitivity of 0.72 and a specificity of 0.80 in the validation data set.

CONCLUSIONS

Age, waist circumference, and systolic blood pressure are variables of special importance for early detection of undiagnosed diabetes in Mexican adults. Based on the consistent performance of the 2006 NHNS model in different data sets, its use as a screening tool for adults with undiagnosed diabetes in Mexico is recommended.

Efficacy of urinary glucose for diabetes screening: a reconsideration

AIMS

Previous studies indicated that urinary glucose (UG) had a limited efficacy in diabetes screening. This study was designed to have a re-evaluation of its efficacy, taking into consideration the collection method of urine and the measurement approach for UG among Chinese adults.

METHODS

This cross-sectional study enrolled a total of 7689 participants without known diabetes, who were fasted and asked to empty bladders before a 75 g glucose loading. Urine was collected 2 h post glucose loading, and UG was measured using quantitative and qualitative approaches. The efficacy of UG in detecting diabetes was assessed by the receiver operating characteristic (ROC) curve.

RESULTS

The area under the ROC curve was 0.89 for quantitative UG and 0.87 for qualitative UG. Quantitative UG was positively correlated with fasting plasma glucose (FPG) and 2 h plasma glucose (2 h PG) (r = 0.55 and 0.56, respectively, both P < 0.001). Quantitative UG displayed a sensitivity of 82.9% and a specificity of 84.7% in detecting diabetes at the corresponding optimal cutoff of 130 mg. Qualitative UG exhibited a sensitivity of 80.2% and a specificity of 85.6% at the optimal cutoff of glycosuria + 1. In addition, the sensitivity of both quantitative and qualitative UG was significantly higher than that of HbA1c (≥ 6.5%) (P < 0.001) and had a comparable sensitivity to 2 h PG (≥ 11.1 mmol/L) (P = 0.493).

CONCLUSIONS

UG, either quantitatively or qualitatively measured at 2 h post glucose loading, was effective in diabetes screening. This indicates that UG is a feasible approach for diabetes screening.

Diabetes Among Non-Overweight Individuals: an Emerging Public Health Challenge

PURPOSE OF REVIEW

Overweight and obesity are well-established risk factors for type 2 diabetes. However, a substantial number of individuals develop the disease at underweight or normal weight. In this review, we discuss the epidemiology of type 2 diabetes in non-overweight adults; pose questions about etiology, pathophysiology, diagnosis, and prognosis; and examine implications for prevention and treatment.

RECENT FINDINGS

In population-based studies, the prevalence of type 2 diabetes ranged from 1.4-10.9%. However, the prevalence of type 2 diabetes in individuals with BMI < 25 kg/m² ranged from 1.4-8.8%. In countries from Asia and Africa, the proportion of individuals with diabetes who were underweight or normal weight ranged from 24 to 66%, which is considerably higher than the US proportion of 10%. Impairments in insulin secretion, in utero undernutrition, and epigenetic alterations to the genome may play a role in diabetes development in this subgroup. A substantial number of individuals with type 2 diabetes, particularly those with recent ancestry from Asia or Africa, are underweight or normal weight. Future research should consist of comprehensive studies of the prevalence of type 2 diabetes in non-overweight individuals; studies aimed at understanding gaps in the mechanisms, etiology, and pathophysiology of diabetes development in underweight or normal weight individuals; and trials assessing the effectiveness of interventions in this population.