Postpartum glycemic and cardiometabolic profile of women testing positive for gestational diabetes mellitus by International Association of Diabetes and Pregnancy Study Groups (IADPSG) but negative by alternate criteria: Insights from CHIP-F study.

OBJECTIVE: To evaluate burden of postpartum diabetes and other cardiometabolic risk factors among women who test positive for gestational diabetes mellitus (GDM) by International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria, but negative by alternate criteria. METHODS: This prospective cross-sectional study was conducted from 2019 to 2022 and is a sub-study of the CHIP-F cohort (Cohort Study of Indian Women with Hyperglycemia in Pregnancy and their Families). RESULTS: Study participants (n = 826; 183 with normoglycemia and 643 with GDM using IADPSG criteria) were evaluated at a median (IQR) postpartum interval of 31 (21-45) months. Using the United Kingdom National Institute of Health and Care Excellence (UK NICE), Canadian Diabetes Association (CDA), and Diabetes in Pregnancy Study Group India (DIPSI) criteria, 251 (39.0 %), 148 (23.0 %) and 384 (59.7 %) women who tested positive for GDM by IADPSG criteria, would have tested negative. The incidence of postpartum diabetes among such women was 30.4, 34.3, and 48.2 per 1000 women-years, respectively, which was significantly higher than those testing negative by both IADPSG and UK NICE (5.0 per 1000 women-years), IADPSG and CDA (9.2/1000 women-years) and IADPSG and DIPSI criteria (5.0/1000 women-years). The burden of obesity and metabolic syndrome was also significantly higher in such women. CONCLUSIONS: We found a significant burden of postpartum diabetes and cardiometabolic risk factors among women who tested positive for GDM by IADPSG, but negative by alternate criteria. There are potential clinical implications of a "failed" diagnosis for future cardiometabolic diseases that need to be carefully examined.

Cardiometabolic profile of women with a history of overt diabetes compared to gestational diabetes and normoglycemia in index pregnancy: Results from CHIP-F study.

PURPOSE: We aimed to evaluate the prevalence of postpartum diabetes among women with a history of overt diabetes in pregnancy (ODiP) and compare with women having a history of gestational diabetes mellitus (GDM) and normoglycemia in pregnancy. METHODS: We have an established longitudinal cohort of postpartum women with a history of hyperglycemia (preexisting diabetes [PED] [n = 101], ODiP [n = 92], GDM [n = 643]), and normoglycemia (n = 183) in pregnancy. For this study, we excluded women with PED and invited other eligible women in a fasting state for clinical and biochemical evaluation. RESULTS: We evaluated 918 women with a mean (SD) age of 33.6 (5.0) years and at a median (interquartile range) postpartum interval of 31 (20-45) months. Diabetes was diagnosed in 65 (70.7%) women in ODiP compared to 99 (15.4%) in GDM (p < .001) and 4 (2.2%) in normoglycemia group (p < .001). In the ODiP group, the prevalence of diabetes was 47.4% among women tested in the first year postpartum, increasing to 86.8% among women tested at >3 years postpartum. Diabetes was more common when ODiP was diagnosed in the first (27/29, 93.1%) compared to the second trimester of pregnancy (35/57, 61.4%). The adjusted odds ratio for diabetes in ODiP was 14.82 (95% confidence interval, 8.49-25.87; p < .001; reference category: GDM). CONCLUSIONS: The prevalence of postpartum diabetes was significantly higher in women with ODiP compared to GDM. Nearly 50% of women with ODiP did not develop diabetes in the first year of follow-up, especially when ODiP was diagnosed after the first trimester of pregnancy and on the basis of a 2-h oral glucose tolerance test value. Such women are amenable to prevention strategies.

SARS-CoV-2 Seroprevalence in Individuals With Type 1 and Type 2 Diabetes Compared With Controls.

OBJECTIVE: Data for the association between diabetes and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) susceptibility are conflicting. We aimed to evaluate this association using an analytical cross-sectional study design. METHODS: Study participants were recruited from endocrine clinics of our hospital and belonged to 3 groups: group 1 (type 1 diabetes mellitus [T1DM]), group 2 (type 2 diabetes mellitus [T2DM]), and group 3 (controls). All participants submitted blood samples for SARS-CoV-2 S1/S2 immunoglobulin G antibody test (LIAISON; DiaSorin) and were interviewed for a history of documented infection. RESULTS: We evaluated a total of 643 participants (T1DM, 149; T2DM, 160; control, 334; mean age, 37.9 ± 11.5 years). A total of 324 (50.4%) participants were seropositive for SARS-CoV-2. The seropositivity rate was significantly higher in the T1DM (55.7% vs 44.9%, P = .028) and T2DM (56.9% vs 44.9%, P = .013) groups than in the control group. The antibody levels in seropositive participants with T1DM and T2DM were not significantly different from those in seropositive controls. On multivariable analysis, low education status (odds ratio [OR], 1.41 [95% CI, 1.03-1.94]; P = .035), diabetes (OR, 1.68 [95% CI, 1.20-2.34]; P = .002), and overweight/obesity (OR, 1.52 [95% CI, 1.10-2.10]; P = .012) showed a significant association with SARS-CoV-2 seropositivity. The association between diabetes and SARS-CoV-2 seropositivity was found to further increase in participants with coexisting overweight/obesity (adjusted OR, 2.63 [95% CI, 1.54-4.47]; P < .001). CONCLUSION: SARS-CoV-2 seropositivity, assessed before the onset of the national vaccination program, was significantly higher in participants with T1DM and T2DM than in controls. The antibody response did not differ between seropositive participants with and without diabetes. These findings point toward an increased SARS-CoV-2 susceptibility for patients with diabetes, in general, without any differential effect of the diabetes type.

Change in cardiometabolic risk factors among Asian Indian adults recruited in a mHealth-based diabetes prevention trial.

OBJECTIVE: India is experiencing an increasing prevalence of type 2 diabetes and cardiovascular diseases. Mobile health technology may be a strategy to reduce the risk of cardiometabolic disorders. This paper reports on the effect of a mobile health intervention on cardiometabolic risk factors. METHODS: The mobile health and diabetes intervention was a 12-week reality television-based mobile health program application delivered via videos, short message service and infographics through a smartphone application followed-up weekly by health coach calls. mobile health and diabetes was conducted in a randomized control trial mode randomized controlled trial methodology in three Indian cities (Chennai, Bengaluru and New Delhi) with participants recruited via community screening events. This paper looks at the pre-post changes in cardiometabolic risks among the participants and the place of demography in influencing these. RESULTS: The mobile health and diabetes intervention group experienced a small reduction in waist circumference (1.8 cm) compared to the control group (0.5 cm, p < 0.05) and a greater decrease in systolic blood pressure (2.7 mmHg) compared to the control group (p < 0.05). The improvements in cardiometabolic risk factors were more pronounced in individuals with obesity, although overall effects were very modest. CONCLUSIONS: Cardiometabolic risk factors can be reduced with a mobile health application using human coaching, especially in obese individuals, but the improvements are small. To be more effective and clinically meaningful, intensive engagement with the participants is probably required.

Prevalence of non-alcoholic fatty liver disease and factors associated with it in Indian women with a history of gestational diabetes mellitus.

AIMS/INTRODUCTION: This study aims to evaluate the prevalence of and factors associated with non-alcoholic fatty liver disease (NAFLD) in Indian women with prior gestational diabetes mellitus (GDM) diagnosed using International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria. MATERIALS AND METHODS: This cross-sectional study (2018-2019) enrolled women with and without prior GDM. Study participants underwent detailed assessments, including relevant medical, obstetric and demographic details; 75-g oral glucose tolerance test with glucose and insulin estimation at 0, 30 and 120 min; and other relevant biochemical and anthropometric measurements. NAFLD status was defined by ultrasonography. RESULTS: We evaluated a total of 309 women (201 and 108 with and without prior GDM, respectively) at a mean age of 31.9 ± 5.0 years and median of 16 months (interquartile range 9-38 months) following the index delivery. The prevalence of NAFLD was significantly higher in women with prior GDM (62.7% vs 50.0%, P = 0.038; grade 2 and 3 disease, 13.9% vs 6.5%). On logistic regression analysis (fully adjusted model), the odds of NAFLD were 2.11-fold higher in women with prior GDM (95% confidence interval 1.16-3.85, P = 0.014). Overweight/obesity, metabolic syndrome, prediabetes and homeostasis model of assessment of insulin resistance (a measure of insulin resistance) were positively associated with NAFLD, whereas the Matsuda index (a measure of insulin sensitivity) showed a negative association with NAFLD. CONCLUSIONS: The prevalence of NAFLD is high in women with prior GDM. Such women also have a high burden of cardiometabolic risk factors. Future studies should evaluate the intermediate and long-term hepatic and cardiovascular risk, and the impact of lifestyle interventions in reducing morbidity in such women.

Engagement and Weight Loss: Results from the Mobile Health and Diabetes Trial.

Background: Prevalence of type 2 diabetes (T2D) is increasing worldwide. Identifying and targeting individuals at high risk, is essential for preventing T2D. Several studies point to mobile health initiatives delivered through personal smart devices being a promising approach to diabetes prevention, through weight loss. The aim of the mobile health and diabetes (mDiab) trial was twofold: to achieve 5% weight loss and to look at the association of weight loss with degree of engagement with the mDiab app. Methods: The mDiab randomized control trial was carried out among smartphone users who are at high risk for T2D mellitus in three cities-Chennai, Bengaluru, and New Delhi in India. The intervention was delivered through a mobile phone application along with weekly coach calls for 12 weeks. While individuals in the intervention group individuals received the app, which enabled tracking their weight, physical activity, and diet along with 12 weekly video lessons on T2D prevention and coach calls, the control group received usual care. Results: The intervention group experienced a significant 1 kg weight loss while the control group lost 0.3 kg (P < 0.05). More individuals in the intervention group (n = 139, 15%) met the 5% weight loss target than in the control group (n = 131, 9%). In the intervention group those who viewed the videos experienced greater weight loss (2.4 kg) than those who only attended coach calls (0.9 kg) (P < 0.01). Conclusions: An mHealth intervention helped to achieve moderate weight loss. Future studies should explore the sustainability of this weight loss.