{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T19:39:50Z","timestamp":1786649990435,"version":"build-2736575974"},"reference-count":31,"publisher":"Ovid Technologies (Wolters Kluwer Health)","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>The population at risk of developing diabetes in future is large and the number of people with diabetes is expected to rise. With sensitivity of 72.5% and a specificity of 60.1%, Indian Diabetes Risk Score (IDRS) can play an important role to screen the risk of diabetes. The objective was to determine the diabetes risk among Indian population aged 45 years and above using the Indian Diabetes Risk Score (IDRS). Additionally, the state-wise risk of diabetes was estimated and its association with various risk factors in this population was determined.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>\n                      This study used the baseline LASI (Longitudinal Ageing Study in India) wave 1 (2017-18) data. LASI was a household survey of adults aged \u2265 45 years and their spouses. It followed a multistage stratified area probability cluster sampling design. This age group was selected to determine the early onset of chronic diseases in this group. The outcome variable was the IDRS score calculated using age category, abdominal obesity-waist circumference (cm), physical activity and family history of diabetes. The data extracted from the LASI dataset included socio-demographic characteristics including sex, education, residence and the monthly per capita expenditure (MPCE) quintile. Metabolic and behavioural factors, such as BMI, self-reported hypertension status, consumption of tobacco and hyperlipidemia were also included along with development of diseases like stroke, chronic heart disease, chronic bone\/joint diseases, cataract and neurological\/psychiatric disorders. Statistical analysis used: Univariate and multivariate analysis using ordinal regression was done on the extracted data, with the calculated IDRS as the dependent variable.\n                      <jats:italic toggle=\"yes\">P<\/jats:italic>\n                      value &lt; 0.05 was considered as statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>This analysis included 52,063 non-diabetic individuals \u226545 years. Half (46.3%) (95% CI: 45.8-46.7) of the LASI population had a high risk of diabetes. Only a small proportion (4.5%) (95% CI: 4.3-4.6) had a low risk of diabetes. The proportion of population with high IDRS varied from 24% to 75%. The highest proportion of individuals with high IDRS were seen in the states\/UTs of Lakshadweep, Kerala, Delhi, Jammu and Kashmir and Goa. In contrast, Nagaland, Assam, Jharkhand, Odisha and Chhattisgarh showed the lowest proportion of individuals in high IDRS category. Females and urban population had 2.26- and 1.62-times the odds of being in the high-risk category of IDRS, respectively. The underweight population had 78% odds of being at high risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>In this population, half were at high risk for diabetes, with a wide variation in the risk among the states. A significant proportion of individuals may be unaware of their diabetes status. Targeted interventions can be applied on this high-risk population. This may include early diagnosis and promotion of a healthy lifestyle.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/ijpvm.ijpvm_178_24","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T12:00:10Z","timestamp":1767182410000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Assessment of Risk of Diabetes Among Indian Population Aged \u226545 Years: Evidence from Longitudinal Aging Study in India"],"prefix":"10.4103","volume":"16","author":[{"given":"Anshul","family":"Mamgai","sequence":"first","affiliation":[{"name":"Department of Community Medicine and School of Public Health, PGIMER, Chandigarh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pritam","family":"Halder","sequence":"additional","affiliation":[{"name":"Department of Community Medicine and School of Public Health, PGIMER, Chandigarh, 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