{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T06:28:09Z","timestamp":1787120889785,"version":"build-2736575974"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T00:00:00Z","timestamp":1787097600000},"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. Surg."],"abstract":"<jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Osteoporosis substantially influences surgical decision-making, implant fixation, fracture risk, and postoperative outcomes in orthopaedic practice. However, the systemic biological interactions underlying bone loss remain difficult to characterize using conventional statistical models. We aimed to integrate clinically constrained Bayesian network learning with routine biochemical data to identify immune\u2013metabolic factors associated with bone mineral density (BMD) and to provide a clinically interpretable framework for bone health assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      In a retrospective real-world orthopedic cohort of 2,880 individuals with concurrent DXA and biochemical data, we constructed a 20-node network comprising BMD, bone metabolism biomarkers, and immune-inflammatory, metabolic, renal, and endocrine indicators. Gaussian graphical models and prior-constrained Bayesian networks were applied, blacklisting edges that pointed to age or menopause status to enforce physiological plausibility. Bridge mediation analysis systematically scanned source\u2014bone biochemistry\u2014BMD pathways, using bootstrap confidence intervals. Analyses were repeated in the postmenopausal subgroup (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091,546).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Network hubs included parathyroid hormone (PTH), phosphorus, neutrophil-to-lymphocyte ratio (NLR), high-density lipoprotein (HDL), and triglycerides (TG). Prior constraints eliminated all prespecified prohibited edges. NLR\u2013ALP\u2013BMD emerged as the most robust negative bridging pattern (indirect effect for lumbar spine: \u22120.045; mediation proportion: 70.5% in the full cohort), with similar findings in postmenopausal women. NLR\u2013PTH\u2013BMD and NLR\u2013phosphorus\u2013BMD were also significant. The NLR\u2013ALP\u2013BMD indirect associations remained significant after hepatic-marker adjustment and in participants without elevated ALP or ALT, whereas the ALB\u2013ALP\u2013BMD signal was attenuated and inconsistent across subgroups. Positive indirect effects involving glucose, TG, and uric acid may reflect unmeasured adiposity and DXA measurement artifacts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Clinically constrained network analysis identified immune\u2013metabolic factors associated with bone mineral density and provided a biologically plausible framework for interpreting bone\u2013immune\u2013metabolic interrelationships in orthopedic patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fsurg.2026.1934871","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T05:48:49Z","timestamp":1787118529000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinically constrained Bayesian network analysis of immune\u2013metabolic factors associated with bone mineral density in a real-world orthopedic cohort"],"prefix":"10.3389","volume":"13","author":[{"given":"Chong","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Spinal Surgery, Peking University People\u2019s Hospital, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuemei","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Georgia Institute of Technology","place":["Atlanta, GA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenqi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery, Peking University People\u2019s Hospital, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery, Peking University People\u2019s Hospital, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiying","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery, Peking University People\u2019s Hospital, Peking University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,19]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1698647","DOI":"10.3389\/fendo.2025.1698647","article-title":"Macrophages in osteoporotic fractures: from immunometabolic mechanisms to precision therapeutic approaches","volume":"16","author":"Xing","year":"2025","journal-title":"Front Endocrinol (Lausanne)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.3390\/biomedicines13061443","article-title":"Beyond bone loss: a biology perspective on osteoporosis pathogenesis, multi-omics approaches, and interconnected 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