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Nichols E, Gross AL, Hu P, Sekher TV, Dey AB, Lee J. The association between BMI and cognition in India: data from the Longitudinal Aging Study in India (LASI). BMC Public Health 2024; 24:2720. [PMID: 39369237 PMCID: PMC11456231 DOI: 10.1186/s12889-024-20101-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 09/16/2024] [Indexed: 10/07/2024] Open
Abstract
BACKGROUND High body-mass index (BMI) is an established risk factor for late-life cognitive impairment and dementia, but most evidence comes from high-income contexts. Existing evidence from cross-sectional data in low- and middle-income settings is inconsistent, and many studies do not adequately address potential sources of bias. METHODS We used data from Wave 1 of the Longitudinal Aging Study in India (LASI) (analytic N = 56,753) to estimate the association between BMI categories and cognitive functioning among older adults aged 45 + years using survey-weighted linear regression models stratified by gender and controlling for potential confounders including demographic factors, socio-economic status (SES) characteristics, and health-related behaviors. To probe potential sources of bias, including residual confounding and reverse causation, we used weighting and trimming methods, sample restriction, and explored effect modification. RESULTS In fully adjusted models, relative to normal BMI underweight BMI was associated with lower cognitive scores (Men: -0.16 SD difference, 95% CI -0.18, -0.13; Women: -0.12 SD, -0.15, -0.10). Overweight and obesity were associated with higher cognitive scores in both men (overweight: 0.09; 0.07, 0.12, obese: 0.10; 0.05, 0.15) and women (overweight: 0.09; 0.07-0.12, obese: 0.12; 0.08-0.15). Estimates were similar after weighting and trimming but were attenuated after excluding those with low cognition (≥1 SD below the mean relative to those with similar demographic characteristics). Positive associations between overweight and obese BMI and cognition were attenuated or null in those living in urban settings and those with higher levels of educational attainment. CONCLUSIONS Underweight BMI is a risk factor for poor cognitive outcomes in adults 45 years and older and may be indicative of poor nutritional status and life-course disadvantage in India. In tandem with existing literature, supplemental analyses and effect modification results indicate that unmeasured confounding and reverse causation may explain the observed positive associations between overweight and obese BMI and cognitive functioning from cross-sectional studies in low- and middle-income settings. Future data with longitudinal follow-up will be helpful to further disentangle biases.
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Affiliation(s)
- Emma Nichols
- Center for Economic and Social Research, University of Southern California, 635 Downey Way, VPD, Los Angeles, CA, 90089, USA.
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, United States.
| | - Alden L Gross
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Peifeng Hu
- Division of Geriatrics, UCLA School of Medicine, Los Angeles, CA, USA
| | - T V Sekher
- International Institute for Population Sciences, Mumbai, India
| | - Aparajit B Dey
- Department of Geriatric Medicine, All India Institute of Medical Sciences, New Delhi, India
| | - Jinkook Lee
- Center for Economic and Social Research, University of Southern California, 635 Downey Way, VPD, Los Angeles, CA, 90089, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
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Dlima SD, Hall A, Aminu AQ, Akpan A, Todd C, Vardy ERLC. Frailty: a global health challenge in need of local action. BMJ Glob Health 2024; 9:e015173. [PMID: 39122463 PMCID: PMC11331888 DOI: 10.1136/bmjgh-2024-015173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/24/2024] [Indexed: 08/12/2024] Open
Abstract
Frailty is a complex, age-related clinical condition that involves multiple contributing factors and raises the risk of adverse outcomes in older people. Given global population ageing trends, the growing prevalence and incidence of frailty pose significant challenges to health and social care systems in both high-income and lower-income countries. In this review, we highlight the disproportionate representation of research on frailty screening and management from high-income countries, despite how lower-income countries are projected to have a larger share of older people aged ≥60. However, more frailty research has been emerging from lower-income countries in recent years, paving the way for more context-specific guidelines and studies that validate frailty assessment tools and evaluate frailty interventions in the population. We then present further considerations for contextualising frailty in research and practice in lower-income countries. First, the heterogeneous manifestations of frailty call for research that reflects different geographies, populations, health systems, community settings and policy priorities; this can be driven by supportive collaborative systems between high-income and lower-income countries. Second, the global narrative around frailty and ageing needs re-evaluation, given the negative connotations linked with frailty and the introduction of intrinsic capacity by the World Health Organization as a measure of functional reserves throughout the life course. Finally, the social determinants of health as possible risk factors for frailty in lower-income countries and global majority populations, and potential socioeconomic threats of frailty to national economies warrant proactive frailty screening in these populations.
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Affiliation(s)
- Schenelle Dayna Dlima
- School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research, Applied Research Collaboration - Greater Manchester, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research Policy Research Unit in Older People and Frailty / Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Alex Hall
- School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research Policy Research Unit in Older People and Frailty / Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Abodunrin Quadri Aminu
- School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research Policy Research Unit in Older People and Frailty / Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
| | - Asangaedem Akpan
- Bunbury Regional Hospital, Bunbury, Western Australia, Australia
- University of Western Australia, Perth, Western Australia, Australia
| | - Chris Todd
- School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research, Applied Research Collaboration - Greater Manchester, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research Policy Research Unit in Older People and Frailty / Healthy Ageing, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Manchester, UK
- Manchester University NHS Foundation Trust, Manchester, UK
| | - Emma R L C Vardy
- School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- National Institute for Health and Care Research, Applied Research Collaboration - Greater Manchester, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK
- Manchester Academic Health Science Centre, Manchester, UK
- Oldham Care Organisation, Northern Care Alliance NHS Foundation Trust, Rochdale Road, Oldham, UK
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Kalra S, Anjana RM, Verma M, Pradeepa R, Sharma N, Deepa M, Singh O, Venkatesan U, Elangovan N, Aggarwal S, Kakkar R, Mohan V. Urban-Rural Differences in the Prevalence of Diabetes Among Adults in Haryana, India: The ICMR-INDIAB Study (ICMR-INDIAB-18). Diabetes Ther 2024; 15:1597-1613. [PMID: 38771471 PMCID: PMC11211308 DOI: 10.1007/s13300-024-01602-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
INTRODUCTION Diabetes is a multifactorial disease with far-reaching consequences. Environmental factors, such as urban or rural residence, influence its prevalence and associated comorbidities. Haryana-a north Indian state-has undergone rapid urbanisation, and part of it is included in the National Capital Region (NCR). The primary aim of the study is to estimate the prevalence of diabetes in Haryana with urban-rural, NCR and non-NCR regional stratification and assess the factors affecting the likelihood of having diabetes among adults. METHODS This sub-group analysis of the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study (a nationally representative cross-sectional population-based survey) was done for Haryana using data from 3722 participants. The dependent variable was diabetes, while residence in NCR/non-NCR and urban-rural areas were prime independent variables. Weighted prevalence was estimated using state-specific sampling weights and standardized using National Family Health Survey-5 (NFHS-5) study weights. Associations were depicted using bivariate analysis, and factors describing the likelihood of living with diabetes were explored using a multivariable binary logistic regression analysis approach. RESULTS Overall, the weighted prevalence of diabetes in Haryana was higher than the national average (12.4% vs. 11.4%). The prevalence was higher in urban (17.9%) than in rural areas (9.5%). The prevalence of diabetes in rural areas was higher in the NCR region, while that of prediabetes was higher in rural non-NCR region. Urban-rural participants' anthropometric measurements and biochemical profiles depicted non-significant differences. Urban-rural status, age and physical activity levels were the most significant factors that affected the likelihood of living with diabetes. CONCLUSIONS The current analysis provides robust prevalence estimates highlighting the urban-rural disparities. Urban areas continue to have a high prevalence of diabetes and prediabetes; rural areas depict a much higher prevalence of prediabetes than diabetes. With the economic transition rapidly bridging the gap between urban and rural populations, health policymakers should plan efficient strategies to tackle the diabetes epidemic.
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Affiliation(s)
- Sanjay Kalra
- Department of Endocrinology, Bharti Hospital, Karnal, India.
- University Centre for Research and Development, Chandigarh University, Mohali, India.
| | - Ranjit Mohan Anjana
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Madhur Verma
- Department of Community and Family Medicine, All India Institute of Medical Sciences Bathinda, Bathinda, 151001, India
| | - Rajendra Pradeepa
- Department of Research Operations and Diabetes Complications, Madras Diabetes Research Foundation, Chennai, India
| | - Nikita Sharma
- Department of Community and Family Medicine, All India Institute of Medical Sciences Bilaspur, Bilaspur, India
| | - Mohan Deepa
- Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India
| | - Omna Singh
- Department of Community and Family Medicine, All India Institute of Medical Sciences Bathinda, Bathinda, 151001, India
| | | | - Nirmal Elangovan
- Department of Research Operations and Diabetes Complications, Madras Diabetes Research Foundation, Chennai, India
| | | | - Rakesh Kakkar
- Department of Community and Family Medicine, All India Institute of Medical Sciences Bathinda, Bathinda, 151001, India
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation and Dr. Mohan's Diabetes Specialities Centre, Chennai, India
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Joseph ME, D'Alonzo KT, Btoush R, Fitzgerald N. The Impact of Social Determinants of Health on the Diagnosis of Type 2 Diabetes Mellitus (T2DM) Among Asian Indians (AIs) in New Jersey: A Secondary Analysis of the BRFSS Survey From 2013 to 2017. J Transcult Nurs 2024; 35:125-133. [PMID: 38111158 DOI: 10.1177/10436596231217662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023] Open
Abstract
INTRODUCTION Asian Indians (AIs), the second largest immigrant population in the United States, are disproportionately affected by type 2 diabetes mellitus (T2DM) at a lower age and body mass index (BMI). The purpose of this study was to examine the relationship between social determinants of health (SDOH) and the diagnosis of T2DM among AIs in New Jersey (NJ). METHODOLOGY This was a secondary data analysis of the Behavioral Risk Factor Surveillance System (BRFSS) in NJ from 2013 to 2017. Statistical analyses included descriptive and inferential statistics. RESULTS Among 1,132 AIs, 16% had T2DM or prediabetes (PDM) and 69.2% were overweight or obese. The risk for T2DM was significantly associated with internet use, older age, having medical check-ups, and having a personal doctor (p ≤ .05). DISCUSSION These findings inform culturally congruent care by underscoring the importance of weight management, earlier screening, and provider involvement in diabetes prevention strategies for AIs.
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