Introduction
Exposure to fine particulate matter has been associated with several cardiovascular and cardiometabolic diseases. However, such evidence mostly originates from low-pollution settings or cross-sectional studies, thus necessitating evidence from regions with high air pollution levels, such as India, where the burden of non-communicable diseases is high.
Research design and methods
We studied the associations between ambient PM2.5 levels and fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c) and incident type 2 diabetes mellitus (T2DM) among 12 064 participants in an adult cohort from urban Chennai and Delhi, India. A meta-analytic approach was used to combine estimates, obtained from mixed-effects models and proportional hazards models, from the two cities.
Results
We observed that 10 μg/m3 differences in monthly average exposure to PM2.5 was associated with a 0.40 mg/dL increase in FPG (95% CI 0.22 to 0.58) and 0.021 unit increase in HbA1c (95% CI 0.009 to 0.032). Further, 10 μg/m3 differences in annual average PM2.5 was associated with 1.22 (95% CI 1.09 to 1.36) times increased risk of incident T2DM, with non-linear exposure response.
Conclusions
We observed evidence of temporal association between PM2.5 exposure, and higher FPG and incident T2DM in two urban environments in India, thus highlighting the potential for population-based mitigation policies to reduce the growing burden of diabetes.
Several international studies have stratified people with diabetes into phenotypical clusters. However, there has not been a systematic examination of the variation in these clusters across ethnic groups. For example, some clusters appear more frequent among Asians and may have lower weight, age at diagnosis and poorer beta cell function.
Background
Structured lifestyle change education reduces the burden of cardiometabolic diseases such as diabetes. Delivery of these programs at worksites could overcome barriers to program adoption and improve program sustainability and reach; however, tailoring to the worksite setting is essential.
Methods
The Integrating Diabetes Prevention in Workplaces (INDIA-WORKS) study tested the implementation and effectiveness of a multi-level program for reducing cardiometabolic disease risk factors at 11 large and diverse worksites across India. Herein, we describe and classify program adaptations reported during in-depth interviews and focus group discussions with worksite managers, program staff, and peer educators involved in program delivery, and program participants and drop-outs. We used thematic analysis to identify key themes in the data and classified reported program adaptations using the FRAME classification system.
Results
Adaptations were led by worksite managers, peer educators, and program staff members. They occurred both pre- and during program implementation and were both planned (proactive) and unplanned (proactive and reactive). The most frequently reported adaptations to the individual-level intervention were curriculum changes to tailor lessons to the local context, make the program more appealing to the workers at the site, or add a wider variety of exercise options. Other content adaptations included improvements to the screening protocol, intervention scheduling, and outreach plans to tailor participant recruitment and retention to the sites. Environment-level content adaptations included expanding or leveraging healthy food and exercise options at the worksites. Challenges to adaptation included scheduling and worksite-level challenges. Participants discussed the need to continue adapting the program in the future to continue making it relevant for worksite settings and engaging for employees.
Conclusion
This study describes and classifies site-specific modifications to a structured lifestyle change education program with worksite-wide health improvements in India. This adds to the literature on implementation adaptation in general and worksite wellness in India, a country with a large and growing workforce with, or at risk of, serious cardiometabolic diseases. This information is key for program scale-up, dissemination, and implementation in other settings.
Importance
Hypertension is a major cause of morbidity and mortality worldwide. Previous efforts to characterize gaps in the hypertension care continuum—including diagnosis, treatment, and control—in India did not assess district-level variation. Local data are critical for planning, implementation, and monitoring efforts to curb the burden of hypertension.
Objective
To examine the hypertension care continuum in India among individuals aged 18 to 98 years.
Design, Setting, and Participants
The nationally representative Fifth National Family Health Survey study was conducted in 2 phases from June 17, 2019, to March 21, 2020, and from November 21, 2020, to April 30, 2021, among 1 895 297 individuals in 28 states, 8 union territories, and 707 districts of India.
Exposures
District and state of residence, urban classification, age (18-39, 40-64, and ≥65 years), sex, and household wealth quintile.
Main Outcomes and Measures
Hypertension was defined as a self-reported diagnosis or a newly measured blood pressure of 140/90 mm Hg or more. The proportion of individuals diagnosed (self-reported), the proportion of individuals treated among those diagnosed (self-reported medication use), and the proportion of individuals with blood pressure control among those treated (blood pressure <140/90 mm Hg [aged 18-79 years] or <150/90 mm Hg [aged ≥80 years]) were calculated based on national guidelines. Age-standardized estimates of treatment and control were also provided among the total with hypertension. To assess differences in the care continuum between or within states (ie, between districts), the variance was partitioned using generalized linear mixed models.
Results
Of the 1 691 036 adult respondents (52.6% women; mean [SD] age, 41.6 [16.5] years), 28.1% (95% CI, 27.9%-28.3%) had hypertension, of whom 36.9% (95% CI, 36.4%-37.3%) received a diagnosis. Among those who received a diagnosis, 44.7% (95% CI, 44.1%-45.3%) reported taking medication (corresponding to 17.7% [95% CI, 17.5%-17.9%] of the total with hypertension). Among those treated, 52.5% (95% CI, 51.7%-53.4%) had blood pressure control (corresponding to 8.5% [95% CI, 8.3%-8.6%] of the total with hypertension). There were substantial variations across districts in blood pressure diagnosis (range, 6.3%-77.5%), treatment (range, 8.7%-97.1%), and control (range, 2.7%-76.6%). Large proportions of the variation in hypertension diagnosis (94.7%), treatment (93.6%), and control (97.3%) were within states, not just between states.
Conclusions and Relevance
In this cross-sectional survey study of Indian adults, more than 1 in 4 people had hypertension, and of these, only 1 in 3 received a diagnosis, less than 1 in 5 were treated, and only 1 in 12 had blood pressure control. National mean values hide considerable state-level and district-level variation in the care continuum, suggesting the need for targeted, decentralized solutions to improve the hypertension care continuum in India.
The global diabetes burden continues to rise and disproportionately impacts low- and middle-income countries (LMICs) (Guariguata et al., 2014; International Diabetes Federation, 2020). In South Asia, approximately 90 million adults have diabetes, representing over 16% of total diabetes cases worldwide (International Diabetes Federation, 2020). Patients with diabetes in Asia and the Middle East only achieve recommended diabetes care targets (i.e., glycemia, blood pressure, cholesterol) less than 10% of the time (Anjana et al., 2021; Anjana et al., 2022; Shivashankar et al., 2015). As such, there is a need to address implementation gaps regarding diabetes care practices in these regions of the world.
Efforts to improve diabetes care in South Asia have demonstrated the effectiveness of national training programs (Department of Endocrinology Diabetes and Metabolism, 2011; World Diabetes Foundation, 2009) and multi-component diabetes care models (Ali et al., 2020; Shah et al., 2020; Bhurji et al., 2016; Kowalski et al., 2017; Misra et al., 2018; Ali et al., 2016; Shal et al., 2012). The determinants for sustaining these programs, however, remain unclear. With numerous identified health system-level challenges to developing and managing diabetes care models in LMICs (Karachaliou et al., 2020), further research is needed that examines how organizational characteristics can shape the implementation and sustainment of these care models.
In the originally published version of the article, the name of the 12th author was given as Venkat K. M. Narayan and is now changed to K. M. Venkat Narayan. Furthermore, in the authors’ contributions section, the initials VKMN are now changed to KMVN. The mentioned changes are corrected also online.
Introduction: Several interventions have been found to be effective for reversing prediabetes in adults. This systematic review and meta-analysis aims to compare the effectiveness of such interventions. Methods: MEDLINE, Embase, and Cochrane Library databases were searched for articles published between January 1, 2000 and June 27, 2018. RCTs in adults with prediabetes, testing nonsurgical interventions lasting for ≥3 months, and reporting the number of participants achieving normal glucose levels at intervention end were eligible. The pooled risk difference and number needed to treat for achieving normoglycemia were estimated using a random-effects, arm-based network meta-analysis. The strength of the evidence was assessed using Grading of Recommendations Assessment, Development, and Evaluation. Data were obtained in 2018 and analyzed in 2019 and 2021. Results: Of 54 studies included in the systematic review, 47 were meta-analyzed (n=26,460, mean age=53 years, 46% male, 31% White). Studies included 27 arms testing lifestyle modification interventions, 25 testing medications, 5 testing dietary supplements, and 10 testing Chinese medicine. There were 35 control/placebo arms. At a median follow-up of 1.6 years, more participants in the lifestyle modification groups achieved normoglycemia than those in the control (risk difference=0.18, number needed to treat=6). The strength of the evidence was strong for lifestyle modification. Over a median follow-up of 2.7 years, more participants receiving glucagon-like peptide-1 receptor agonists (risk difference=0.47, number needed to treat=2), α-glucosidase inhibitors (risk difference=0.29, number needed to treat=4), and insulin sensitizers (risk difference=0.23, number needed to treat=4) achieved normoglycemia than control. The strength of evidence was moderate for these medications. Discussion: Although several pharmacological approaches can reverse prediabetes, lifestyle modification provides the strongest evidence of effectiveness and should remain the recommended approach to address this condition.
Individuals with non-communicable diseases (NCDs) such as diabetes are susceptible to communicable diseases (CDs) as the current COVID-19 pandemic illustrates. The co-occurrence of diabetes as well as other co-morbid conditions with COVID-19 augurs greater risk for severe outcomes and mortality. Hence, NCD and CD pandemics are closely linked and require global efforts to thwart and disrupt their nexus before the next viral outbreaks occurs. This will require steadfast dedication and resolve to address NCDs previously committed to by the global community.
OBJECTIVE-To examine β-cell function across a spectrum of glycemia among Asian Indians, a population experiencing type 2 diabetes development at young ages despite low BMI. RESEARCH DESIGN AND METHODS-One-thousand two-hundred sixty-four individuals without known diabetes in the Diabetes Community Lifestyle Improvement Program in Chennai, India, had a 75-g oral glucose tolerance test,with glucose and insulinmeasured at 0, 30, and 120 min. Type 2 diabetes, isolated impaired fasting glucose (iIFG), isolated impaired glucose tolerance (iIGT), combined impaired fasting glucose and impaired glucose tolerance, and normal glucose tolerance (NGT) were defined by American Diabetes Association guidelines. Measures included insulin resistance and sensitivity (homeostasis model assessment of insulin resistance [HOMA-IR], modified Matsuda Index, 1/fasting insulin) and β-cell function (oral disposition index = [Δinsulin0-30/Δglucose0-30] × [1/fasting insulin]). RESULTS-Mean age was 44.2 years (SD, 9.3) and BMI 27.4 kg/m 2 (SD, 3.8); 341 individuals had NGT, 672 had iIFG, IGT, or IFG plus IGT, and 251 had diabetes. Patterns of insulin resistance or sensitivity were similar across glycemic categories. With mild dysglycemia, the absolute differences in age- and sex-adjusted oral disposition index (NGT vs. iIFG, 38%; NGT vs. iIGT, 32%) were greater than the differences in HOMA-IR (NGT vs. iIFG, 25%; NGT vs. iIGT, 23%; each P < 0.0001). Compared with NGT and adjusted for age, sex, BMI, waist circumference, and family history, the odds of mild dysglycemia were more significant per SD of oral disposition index (iIFG: odds ratio [OR], 0.36; 95% CI, 0.23-0.55; iIGT: OR, 0.37; 95% CI, 0.24-0.56) than per SD of HOMA-IR (iIFG: OR, 1.69; 95% CI, 1.23-2.33; iIGT: OR, 1.53; 95% CI, 1.11-2.11). CONCLUSIONS-Asian Indians with mild dysglycemia have reduced β-cell function, regardless of age, adiposity, insulin sensitivity, or family history. Strategies in diabetes prevention should minimize loss of β-cell function.
Background:
Diabetes is an important contributor to global morbidity and mortality. The contributions of population aging and macroeconomic changes to the growth in diabetes prevalence over the past 20 years are unclear.
Methods: We used cross-sectional data on age- and sex-specific counts of people with diabetes by country, national population estimates, and country-specific macroeconomic variables for the years 1990, 2000, and 2008. Decomposition analysis was performed to quantify the contribution of population aging to the change in global diabetes prevalence between 1990 and 2008. Next, age-standardization was used to estimate the contribution of age composition to differences in diabetes prevalence between high-income (HIC) and low-to-middle-income countries (LMICs). Finally, we used non-parametric correlation and multivariate first-difference regression estimates to examine the relationship between macroeconomic changes and the change in diabetes prevalence between 1990 and 2008.
Results: Globally, diabetes prevalence grew by two percentage points between 1990 (7.4 %) and 2008 (9.4 %). Population aging was responsible for 19 % of the growth, with 81 % attributable to increases in the age-specific prevalences. In both LMICs and HICs, about half the growth in age-specific prevalences was from increasing levels of diabetes between ages 45-65 (51 % in HICs and 46 % in LMICs). After age-standardization, the difference in the prevalence of diabetes between LMICs and HICs was larger (1.9 % point difference in 1990; 1.5 % point difference in 2008). We found no evidence that macroeconomic changes were associated with the growth in diabetes prevalence.
Conclusions: Population aging explains a minority of the recent growth in global diabetes prevalence. The increase in global diabetes between 1990 and 2008 was primarily due to an increase in the prevalence of diabetes at ages 45-65. We do not find evidence that basic indicators of economic growth, development, globalization, or urbanization were related to rising levels of diabetes between 1990 and 2008.