{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:22:11Z","timestamp":1777396931031,"version":"3.51.4"},"reference-count":59,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["R01AG088637"],"award-info":[{"award-number":["R01AG088637"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000009","name":"Foundation for the National Institutes of Health","doi-asserted-by":"publisher","award":["U19 AG033655"],"award-info":[{"award-number":["U19 AG033655"]}],"id":[{"id":"10.13039\/100000009","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>As life expectancy increases, the prevalence of mild cognitive impairment (MCI) due to Alzheimer's disease is expected to rise. Biomarkers from cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI), combined with age-specific modeling, may enhance understanding of the timing and risk factors of MCI onset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To estimate age-specific incidence rates of MCI onset and examine the impact of biomarkers and demographic factors on risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed data from 307 cognitively unimpaired BIOCARD participants with one or more CSF and MRI measurements. To account for competing risk of death, we applied age-specific cumulative incidence functions and cause-specific Cox proportional hazard models. Time-varying biomarker values were incorporated to assess their influence on MCI risk.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The cumulative incidence rate of MCI onset estimated in age brackets from 50 to 80 was 2.33% at age 55, 5.03% at age 60, 12.38% at age 65, 17.91% at age 70, 28.84% at age 75, and 46.91% at age 80.\n                      <jats:italic toggle=\"yes\">APOE4<\/jats:italic>\n                      carriers had a significantly increased risk before age 70 (HR\u2009=\u20099.55, p\u2009=\u20090.005). Female\n                      <jats:italic toggle=\"yes\">APOE4<\/jats:italic>\n                      carriers showed a non-significant trend toward reduced risk compared to males (HR\u2009=\u20090.35, p\u2009=\u20090.127). Faster decline in entorhinal cortex volume (HR\u2009=\u20091.24, p\u2009=\u20090.028) and elevated p-tau\n                      <jats:sub>181<\/jats:sub>\n                      levels (HR\u2009=\u20091.51, p\u2009=\u20090.012) were both associated with increased MCI risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study presents age-specific incidence rates of MCI and highlights the age-dependent influence of genetic and biomarker risk factors. Incorporating time-varying biomarkers provides valuable insights into dynamic risk prediction for MCI onset.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877251393729","type":"journal-article","created":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T18:07:43Z","timestamp":1763575663000},"page":"483-493","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling the age-specific incidence of mild cognitive impairment incorporating the time-varying relationship of Alzheimer's disease biomarkers over 28 years"],"prefix":"10.1177","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0124-0631","authenticated-orcid":false,"given":"Kexin","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"name":"F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA"},{"name":"Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8441-0290","authenticated-orcid":false,"given":"Yuxin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"},{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"},{"name":"Johns Hopkins Armstrong Institute for Patient Safety and Quality, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marilyn","family":"Albert","sequence":"additional","affiliation":[{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Miller","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christos","family":"Davatzikos","sequence":"additional","affiliation":[{"name":"Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhay","family":"Moghekar","sequence":"additional","affiliation":[{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anja","family":"Soldan","sequence":"additional","affiliation":[{"name":"Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0460-7956","authenticated-orcid":false,"given":"Mei-Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"the BIOCARD Research Team","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,11,19]]},"reference":[{"key":"e_1_3_3_2_2","first-page":"1598","article-title":"Alzheimer\u2019s disease facts and figures","volume":"19","author":"Better MA","year":"2023","unstructured":"Better MA. 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