{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:52:00Z","timestamp":1791525120258,"version":"4.3.4"},"reference-count":56,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004336","name":"Novartis","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004336","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>In low- and middle-income countries, adverse body composition risk is increasingly driven by structural determinants, including rapid urbanisation, nutrition transitions, and reduced opportunities for physical activity. Quantifying and interpreting bone, fat and lean mass risks require appropriate measurement and context-sensitive thresholds to detect subclinical vulnerability in under-resourced settings. Few studies, particularly from Africa, have quantified how these risks intersect and cluster with cardiometabolic and socio-demographic determinants. The purpose of this study was to characterise body composition profiles, estimate the prevalence and co-occurrence of risk phenotypes and examine their distribution across health determinants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      The study was carried out in under-resourced public schools in Gqeberha, Eastern Cape, South Africa, as part of the\n                      <jats:italic>KaziBantu<\/jats:italic>\n                      project (ISRCTN18485542). A school-based workplace health intervention was conducted, using cross-sectional assessments. We employed dual-energy X-ray absorptiometry (DXA), assessed metabolic indicators, behavioural indicators (alcohol and tobacco use, device-measured physical activity, handgrip strength), and obtained demographic and medical data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 136 apparently healthy teachers (85% women; age 21\u201372\u202fyears; mean\u202f=\u202f47\u202fyears; standard deviation\u202f=\u202f11\u202fyears), body composition risk was prevalent: fat mass risk 91.8% (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f123\/134; 95%CI 85.8\u201395.8%), lean mass risk 15.7% (\n                      <jats:italic>n<\/jats:italic>\n                      =\u202f21\/134; 95%CI 10\u201323%) and bone mass risk 29.9% (\n                      <jats:italic>n<\/jats:italic>\n                      =\u202f40\/134; 95%CI 22.3\u201338.4%). The most frequent combined phenotype was bone-fat risk (27.6%;\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f37; 95%CI 20.2\u201336%), while 6% (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f8; 95%CI 2.6\u201311.4%) exhibited all three risks concurrently. Metabolic syndrome criteria were met in 53% (\n                      <jats:italic>n<\/jats:italic>\n                      \u202f=\u202f70; 95%CI 44.2\u201361.8%) of teachers, and adverse risk phenotypes were associated with elevated multimorbidity and poorer metabolic health.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This is the first study from South Africa\u2019s Eastern Cape to report DXA-derived body composition profiles. Body composition risks and multimorbidity were prevalent, with likely substantial impacts on quality of life. Protecting teachers\u2019 health will require multi-level strategies, including workplace-based screening with low-cost tools (e.g., bioelectrical impedance), culturally responsive prevention such as targeted weight-bearing and resistance activity, and action on structural inequities, such as more equitable resourcing across schools to reduce occupational strain and stronger pathways that turn teachers\u2019 existing health coverage into timely screening and treatment. These strategies aim to safeguard well-being and sustain teachers\u2019 pivotal role in educating future generations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1794604","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:33:19Z","timestamp":1791523999000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Bone health, obesity, and multimorbidity risk among teachers in under-resourced South African public primary schools: results from a cross-sectional DXA study"],"prefix":"10.3389","volume":"14","author":[{"given":"Nandi","family":"Joubert","sequence":"first","affiliation":[{"name":"Department of Human Movement Science, Nelson Mandela University","place":["Gqeberha, South Africa"]},{"name":"Swiss Tropical and Public Health Institute","place":["Allschwil, Switzerland"]},{"name":"University of Basel","place":["Basel, Switzerland"]},{"name":"Department of 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