{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:16:02Z","timestamp":1786115762759,"version":"build-2736575974"},"reference-count":75,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Epidemiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Globally, Noncommunicable diseases (NCDs) contribute to 75% of all deaths. The precursors to the development of NCDs include the metabolic syndrome (MetS) and key metabolic risk factors outlined by the World Health Organisation, including overweight\/obesity, raised blood pressure, hyperglycemia, and hyperlipidemia. Women in Kirinyaga County in Kenya have the highest prevalence of overweight and obesity at 64.6%, and hypertension at 20%, as per the 2022 Kenyan Demographic Health survey; however, data on MetS, hyperglycemia, and hyperlipidemia in Kirinyaga is scarce. Therefore, this study aimed to determine the relationship between sociodemographic determinants and the prevalence of metabolic risk factors and the metabolic syndrome among women aged 20\u201349 years in Kirinyaga County, Kenya.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A cross-sectional study design was employed, and multi-stage sampling was used to select 425 women aged 20\u201349 years in Kirinyaga County, Kenya. Data were collected on sociodemographic determinants and anthropometric and biochemical measurements. Data analysis was conducted using STATA version 17, frequencies, proportions and binary logistic regression. A total of 425 women participated in the study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The prevalence of MetS was 46.4%; 68.87% were obese or overweight; central obesity, measured by waist circumference, was 79.3%, and by waist-hip ratio, 51.06%. Diabetes was at 8.56%, elevated blood pressure at 22.6%, elevated triglycerides at 17.2%, Low HDL-C at 68.6%, and elevated cholesterol at 7.5%. The primary determinants of metabolic health were wealth and age. Increasing age (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;gt;\u20090.05) elevated the odds of having all risk factors for NCDs and metabolic syndrome, except low HDL cholesterol. Women in the highest wealth quintile had increased odds of obesity (AOR\u2009=\u20091.83,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.036), elevated triglycerides (AOR\u2009=\u20092.22,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.035), and metabolic syndrome (AOR\u2009=\u20091.79,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.040). However, being a student reduced the odds of metabolic syndrome (AOR\u2009=\u20090.24,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.047). Hormonal contraceptive use, on one hand, was associated with reductions in hypertension (AOR\u2009=\u20090.32,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&amp;lt;\u20090.001), but was a significant risk factor for low HDL cholesterol (AOR\u2009=\u20092.08,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.011).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The study reveals a high prevalence of both metabolic syndrome and metabolic risk factors linked to noncommunicable diseases, mainly driven by ageing, multi-parity, and economic affluence. Public health interventions need to focus on wealth-associated lifestyle behaviours.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fepid.2026.1807836","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T05:31:40Z","timestamp":1785994300000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Sociodemographic determinants and prevalence of metabolic risk factors and the metabolic syndrome among women aged 20\u201349 in kirinyaga county, Kenya"],"prefix":"10.3389","volume":"6","author":[{"given":"Umotho Kinya","family":"Mbae","sequence":"first","affiliation":[{"name":"Jomo Kenyatta University of Agriculture and Technology","place":["Nairobi, Kenya"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florence","family":"Kyallo","sequence":"additional","affiliation":[{"name":"Jomo Kenyatta University of Agriculture and Technology","place":["Nairobi, 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