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      Prevalence and Predictors of Gestational Diabetes Mellitus and Overt Diabetes in Pregnancy: A Secondary Analysis of Nationwide Data from India

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          Abstract

          Background:

          This analysis was conducted to understand prevalence and determinants of gestational diabetes mellitus (GDM) and (overt) diabetes in pregnancy (DIP) in India, and also ascertain their health-seeking behaviour.

          Materials and Methods:

          The authors analysed data from the fifth National Family Health Survey of India. Binary logistic regression was used to assess the factors independently associated with GDM and (overt) DIP.

          Results:

          The weighted prevalence of GDM was 4.2% (95% confidence interval [CI]: 3.9–4.5), and the age-adjusted prevalence was 5.4% (95% CI: 4.5–6.4). The prevalence of GDM increased with age. The weighted prevalence of (overt) DIP was 0.38% (95% CI: 0.30–0.48), and the age-adjusted prevalence was 1.04% (95% CI: 0.64–1.68). On adjusted analysis, it was found that increasing age and obesity had significantly higher odds of having GDM. Higher odds of access to private facilities were found amongst women with higher education and those who were overweight. Significant regional variation in the prevalence of GDM was observed, with a very low burden observed in northeastern states and a comparatively higher burden in Central, Western and Southern Indian states.

          Conclusions:

          There is an increasing prevalence of GDM in India. Strengthening primary health systems to enhance GDM-related service availability, quality and delivery could be logical policy intervention.

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          Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.

          (2004)
          A WHO expert consultation addressed the debate about interpretation of recommended body-mass index (BMI) cut-off points for determining overweight and obesity in Asian populations, and considered whether population-specific cut-off points for BMI are necessary. They reviewed scientific evidence that suggests that Asian populations have different associations between BMI, percentage of body fat, and health risks than do European populations. The consultation concluded that the proportion of Asian people with a high risk of type 2 diabetes and cardiovascular disease is substantial at BMIs lower than the existing WHO cut-off point for overweight (> or =25 kg/m2). However, available data do not necessarily indicate a clear BMI cut-off point for all Asians for overweight or obesity. The cut-off point for observed risk varies from 22 kg/m2 to 25 kg/m2 in different Asian populations; for high risk it varies from 26 kg/m2 to 31 kg/m2. No attempt was made, therefore, to redefine cut-off points for each population separately. The consultation also agreed that the WHO BMI cut-off points should be retained as international classifications. The consultation identified further potential public health action points (23.0, 27.5, 32.5, and 37.5 kg/m2) along the continuum of BMI, and proposed methods by which countries could make decisions about the definitions of increased risk for their population.
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            Prevalence and risk factors of gestational diabetes mellitus in Asia: a systematic review and meta-analysis

            Kai Wei Kelvin Lee, Siew Mooi Ching, Vasudevan Ramachandran … (2018)
            Background Gestational diabetes mellitus (GDM) is a of the major public health issues in Asia. The present study aimed to determine the prevalence of, and risk factors for GDM in Asia via a systematic review and meta-analysis. Methods We systematically searched PubMed, Ovid, Scopus and ScienceDirect for observational studies in Asia from inception to August 2017. We selected cross sectional studies reporting the prevalence and risk factors for GDM. A random effects model was used to estimate the pooled prevalence of GDM and odds ratio (OR) with 95% confidence interval (CI). Results Eighty-four studies with STROBE score ≥ 14 were included in our analysis. The pooled prevalence of GDM in Asia was 11.5% (95% CI 10.9–12.1). There was considerable heterogeneity (I2 > 95%) in the prevalence of GDM in Asia, which is likely due to differences in diagnostic criteria, screening methods and study setting. Meta-analysis demonstrated that the risk factors of GDM include history of previous GDM (OR 8.42, 95% CI 5.35–13.23); macrosomia (OR 4.41, 95% CI 3.09–6.31); and congenital anomalies (OR 4.25, 95% CI 1.52–11.88). Other risk factors include a BMI ≥25 kg/m2 (OR 3.27, 95% CI 2.81–3.80); pregnancy-induced hypertension (OR 3.20, 95% CI 2.19–4.68); family history of diabetes (OR 2.77, 2.22–3.47); history of stillbirth (OR 2.39, 95% CI 1.68–3.40); polycystic ovary syndrome (OR 2.33, 95% CI1.72–3.17); history of abortion (OR 2.25, 95% CI 1.54–3.29); age ≥ 25 (OR 2.17, 95% CI 1.96–2.41); multiparity ≥2 (OR 1.37, 95% CI 1.24–1.52); and history of preterm delivery (OR 1.93, 95% CI 1.21–3.07). Conclusion We found a high prevalence of GDM among the Asian population. Asian women with common risk factors especially among those with history of previous GDM, congenital anomalies or macrosomia should receive additional attention from physician as high-risk cases for GDM in pregnancy. Trial registration PROSPERO (2017: CRD42017070104).
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              Food Groups and Risk of Overweight, Obesity, and Weight Gain: A Systematic Review and Dose-Response Meta-Analysis of Prospective Studies

              Sabrina Schlesinger, Manuela Neuenschwander, Carolina Schwedhelm … (2019)
              ABSTRACT This meta-analysis summarizes the evidence of a prospective association between the intake of foods [whole grains, refined grains, vegetables, fruit, nuts, legumes, eggs, dairy, fish, red meat, processed meat, and sugar-sweetened beverages (SSBs)] and risk of general overweight/obesity, abdominal obesity, and weight gain. PubMed and Web of Science were searched for prospective observational studies until August 2018. Summary RRs and 95% CIs were estimated from 43 reports for the highest compared with the lowest intake categories, as well as for linear and nonlinear relations focusing on each outcome separately: overweight/obesity, abdominal obesity, and weight gain. The quality of evidence was evaluated with use of the NutriGrade tool. In the dose-response meta-analysis, inverse associations were found for whole-grain (RRoverweight/obesity: 0.93; 95% CI: 0.89, 0.96), fruit (RRoverweight/obesity: 0.93; 95% CI: 0.86, 1.00; RRweight gain: 0.91; 95% CI: 0.86, 0.97), nut (RRabdominal obesity: 0.42; 95% CI: 0.31, 0.57), legume (RRoverweight/obesity: 0.88; 95% CI: 0.84, 0.93), and fish (RRabdominal obesity: 0.83; 95% CI: 0.71, 0.97) consumption and positive associations were found for refined grains (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.10), red meat (RRabdominal obesity: 1.10; 95% CI: 1.04, 1.16; RRweight gain: 1.14; 95% CI: 1.03, 1.26), and SSBs (RRoverweight/obesity: 1.05; 95% CI: 1.00, 1.11; RRabdominal obesity: 1.12; 95% CI: 1.04, 1.20). The dose-response meta-analytical findings provided very low to low quality of evidence that certain food groups have an impact on different measurements of adiposity risk. To improve the quality of evidence, better-designed observational studies, inclusion of intervention trials, and use of novel statistical methods (e.g., substitution analyses or network meta-analyses) are needed.
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                Author and article information

                Journal
                Title: Preventive Medicine: Research & Reviews
                Publisher: Medknow
                ISSN (Print): 2950-5836
                ISSN (Electronic): 2950-5828
                Publication date Created: 2024
                Publication date (Print): 2024
                Publication date (Electronic): November 17 2023
                Volume: 1
                Issue: 1
                Pages: 52-58
                Affiliations
                [1 ]Indian Institute of Public Health – Delhi, Public Health Foundation of India, Haryana, India
                [2 ]AHC Diabetes Centre, Ahmedabad, Gujarat, India
                [3 ]Foundation for People-centric Health Systems, New Delhi, India
                Article
                DOI: 10.4103/PMRR.PMRR_11_23
                SO-VID: 1b2e0678-1dc2-42a3-b3f9-23cc4f910f1e
                Copyright © © 2023
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