Introduction: South Asians (SA) and Pima Indians have high prevalence of diabetes but differ markedly in body size. We hypothesize that young SA will have higher diabetes incidence than Pima Indians at comparable body mass index (BMI) levels.
Research Design And Methods: We used prospective cohort data to estimate age-specific, sex, and BMI-specific diabetes incidence in SA aged 20-44 years living in India and Pakistan from the Center for Cardiometabolic Risk Reduction in South Asia Study (n=6676), and compared with Pima Indians, from Pima Indian Study (n=1852).
Results: At baseline, SA were considerably less obese than Pima Indians (BMI (kg/m): 24.4 vs 33.8; waist circumference (cm): 82.5 vs 107.0). Age-standardized diabetes incidence (cases/1000 person-years, 95% CI) was lower in SA than in Pima Indians (men: 14.2, 12.2-16.2 vs 37.3, 31.8-42.8; women: 14.8, 13.0-16.5 vs 46.1, 41.2-51.1). Risk of incident diabetes among 20-24-year-old Pima men and women was six times (relative risk (RR), 95% CI: 6.04, 3.30 to 12.0) and seven times (RR, 95% CI: 7.64, 3.73 to 18.2) higher as compared with SA men and women, respectively. In those with BMI <25 kg/m, however, the risk of diabetes was over five times in SA men than in Pima Indian men. Among those with BMI ≥30 kg/m, diabetes incidence in SA men was nearly as high as in Pima men. SA and Pima Indians had similar magnitude of association between age, sex, BMI, and insulin secretion with diabetes. The effect of family history was larger in SA, whereas that of insulin resistance was larger in Pima Indians CONCLUSIONS: In the background of relatively low insulin resistance, higher diabetes incidence in SA is driven by poor insulin secretion in SA men. The findings call for research to improve insulin secretion in early natural history of diabetes.
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http://dx.doi.org/10.1136/bmjdrc-2020-001988 | DOI Listing |
Sci Rep
January 2025
Department of Computer Science, Faculty of Computers and Information, Suez University, P. O. Box 43221, Suez, Egypt.
Diabetes is a long-term condition characterized by elevated blood sugar levels. It can lead to a variety of complex disorders such as stroke, renal failure, and heart attack. Diabetes requires the most machine learning help to diagnose diabetes illness at an early stage, as it cannot be treated and adds significant complications to our health-care system.
View Article and Find Full Text PDFVaccine X
January 2025
Mel & Enid Zuckerman College of Public Health, University of Arizona, Tucson, United States of America.
Introduction: Vaccine hesitancy among marginalized populations particularly in the Hispanic community over the course of the COVID-19 pandemic has presented as a public health issue. This study examined the relationship between political affiliation and vaccination decisions of Hispanic adults in Pima County, Arizona.
Methods: Between January and October 2022, 623 participants completed surveys in English or Spanish after completing informed consent process.
Sleep Med
January 2025
Department of Child Neurology and Department of Sleep Medicine, Geisinger Commonwealth School of Medicine, Geisinger Medical Center, Janet Weis Children's Hospital, Danville, PA, USA.
J Am Med Inform Assoc
November 2024
Pima County Health Department, Tucson, AZ 85714, United States.
Objective: This communication presents the results of defining a tribal health jurisdiction by a combination of tribal affiliation (TA) and case address.
Materials And Methods: Through a county-tribal partnership, Geographic Information System (GIS) software and custom code were used to extract tribal data from county data by identifying reservation addresses in county extracts of COVID-19 case records from December 30, 2019, to December 31, 2022 (n = 374 653) and COVID-19 vaccination records from December 1, 2020, to April 18, 2023 (n = 2 355 058).
Results: The tool identified 1.
Technol Cancer Res Treat
May 2024
Department of Computed Tomography and Magnetic Resonance, Xing Tai People's Hospital, Xing Tai, He Bei, China.
To develop and validate predictive models based on clinical parameters, and radiomic features to distinguish pulmonary pure invasive mucinous adenocarcinoma (pIMA) from mixed mucinous adenocarcinoma (mIMA) before surgery. From January 2017 to December 2022, 193 pIMA and 111 mIMA were retrospectively analyzed at our hospital in this retrospective study. From contrast-enhanced computed tomography, 1037 radiomic features were extracted.
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