Performance of European prediction models for classification of type 1 and type 2 diabetes in Indians

AIM

We aimed to determine the performance of European prediction models in an Indian population to classify type 1 diabetes(T1D) and type 2 diabetes(T2D).

METHODS

We assessed discrimination and calibration of published models of diabetes classification, using retrospective data from electronic medical records of 83309 participants aged 18-50 years living in India. Diabetes type was defined based on C-peptide measurement and early insulin requirement. Models assessed combinations of clinical measurements: age at diagnosis, body mass index(mean = 26.6 kg/m2), sex(male = 64.9 %), Glutamic acid decarboxylase(GAD) antibody, serum cholesterol, serum triglycerides, and high-density lipoprotein(HDL) cholesterol.

RESULTS

67955 participants met inclusion criteria, of whom 0.8 % had T1D, which was markedly lower than model development cohorts. Model discrimination for clinical features was broadly similar in our Indian cohort compared to the European cohort: area under the receiver operating characteristic curve(AUC ROC) was 0.90 vs. 0.90 respectively, but was lower in the subset of young participants with measured GAD antibodies(n = 2404): and an AUC ROC of 0.87 when clinical features, sex, lipids and GAD antibodies were combined. All models substantially overestimated the likelihood of T1D, reflecting the lower prevalence of T1D in the Indian population. However, good model performance was achieved after recalibration by updating the model intercept and slope.

CONCLUSION

Models for diabetes classification maintain the discrimination of T1D and T2D in this Indian population, where T2D is far more common, but require recalibration to obtain appropriate model probabilities. External validation and recalibration are needed before these tools can be used in non-European populations.

Altered levels of neurobiological biomarkers at the interface of depression and gestational diabetes mellitus in Asian Indian women

AIM

Gestational diabetes mellitus (GDM) might predispose the mothers to depression. Studies have reported the role of biomarkers either in GDM or depression, but very few have examined them in GDM with depression. The present study profiled the circulating levels of brain-derived neurotrophic factor (BDNF), Beta Endorphin (BE) and nesfatin-1 in women with GDM (with and without depression).

METHODS

160 pregnant women at 24-28 weeks of pregnancy (NGT/GDM with & without depression, n = 40 each) were randomly selected from the ongoing STRiDE (STratification of Risk of Diabetes in Early pregnancy) study. Depression score was derived using PHQ-9 questionnaire and ELISA was used to quantify the biomarkers.

RESULTS

Circulatory levels of BDNF, BE and nesfatin-1 were lower in GDM women with or without depression compared to NGT without depression, however, nesfatin-1 levels were higher in NGT with depression. Notably, GDM with depression had the lowest levels of BDNF and BE. Both BDNF and BE levels were negatively correlated with depression, 1 h and 2 h plasma glucose. Regression analysis confirmed that each standard deviation decreases in BDNF and BE were independently associated with higher odds of GDM with or without depression even after adjusting for potential confounders.

CONCLUSION

Our study has identified altered levels of a panel of neurobiological biomarkers (BDNF/BE/nesfatin-1) in those with combined GDM and depression. BDNF/BE could be potential biomarkers to assess the higher risk of coexisting depression and GDM.

Accuracy of 1-Hour Plasma Glucose During the Oral Glucose Tolerance Test in Diagnosis of Type 2 Diabetes in Adults: A Meta-analysis

OBJECTIVE

One-hour plasma glucose (1-h PG) during the oral glucose tolerance test (OGTT) is an accurate predictor of type 2 diabetes. We performed a meta-analysis to determine the optimum cutoff of 1-h PG for detection of type 2 diabetes using 2-h PG as the gold standard.

RESEARCH DESIGN AND METHODS

We included 15 studies with 35,551 participants from multiple ethnic groups (53.8% Caucasian) and 2,705 newly detected cases of diabetes based on 2-h PG during OGTT. We excluded cases identified only by elevated fasting plasma glucose and/or HbA_1c. We determined the optimal 1-h PG threshold and its accuracy at this cutoff for detection of diabetes (2-h PG ≥11.1 mmol/L) using a mixed linear effects regression model with different weights to sensitivity/specificity (2/3, 1/2, and 1/3).

RESULTS

Three cutoffs of 1-h PG, at 10.6 mmol/L, 11.6 mmol/L, and 12.5 mmol/L, had sensitivities of 0.95, 0.92, and 0.87 and specificities of 0.86, 0.91, and 0.94 at weights 2/3, 1/2, and 1/3, respectively. The cutoff of 11.6 mmol/L (95% CI 10.6, 12.6) had a sensitivity of 0.92 (0.87, 0.95), specificity of 0.91 (0.88, 0.93), area under the curve 0.939 (95% confidence region for sensitivity at a given specificity: 0.904, 0.946), and a positive predictive value of 45%.

CONCLUSIONS

The 1-h PG of ≥11.6 mmol/L during OGTT has a good sensitivity and specificity for detecting type 2 diabetes. Prescreening with a diabetes-specific risk calculator to identify high-risk individuals is suggested to decrease the proportion of false-positive cases. Studies including other ethnic groups and assessing complication risk are warranted.

Contrasting Associations Between Diabetes and Cardiovascular Mortality Rates in Low-, Middle-, and High-Income Countries: Cohort Study Data From 143,567 Individuals in 21 Countries in the PURE Study

OBJECTIVE

We aimed to compare cardiovascular (CV) events, all-cause mortality, and CV mortality rates among adults with and without diabetes in countries with differing levels of income.

RESEARCH DESIGN AND METHODS

The Prospective Urban Rural Epidemiology (PURE) study enrolled 143,567 adults aged 35-70 years from 4 high-income countries (HIC), 12 middle-income countries (MIC), and 5 low-income countries (LIC). The mean follow-up was 9.0 ± 3.0 years.

RESULTS

Among those with diabetes, CVD rates (LIC 10.3, MIC 9.2, HIC 8.3 per 1,000 person-years, P < 0.001), all-cause mortality (LIC 13.8, MIC 7.2, HIC 4.2 per 1,000 person-years, P < 0.001), and CV mortality (LIC 5.7, MIC 2.2, HIC 1.0 per 1,000 person-years, P < 0.001) were considerably higher in LIC compared with MIC and HIC. Within LIC, mortality was higher in those in the lowest tertile of wealth index (low 14.7%, middle 10.8%, and high 6.5%). In contrast to HIC and MIC, the increased CV mortality in those with diabetes in LIC remained unchanged even after adjustment for behavioral risk factors and treatments (hazard ratio [95% CI] 1.89 [1.58-2.27] to 1.78 [1.36-2.34]).

CONCLUSIONS

CVD rates, all-cause mortality, and CV mortality were markedly higher among those with diabetes in LIC compared with MIC and HIC with mortality risk remaining unchanged even after adjustment for risk factors and treatments. There is an urgent need to improve access to care to those with diabetes in LIC to reduce the excess mortality rates, particularly among those in the poorer strata of society.