{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T05:38:55Z","timestamp":1772429935269,"version":"3.50.1"},"posted":{"date-parts":[[2026,2,26]]},"group-title":"Public and Global Health","reference-count":63,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2026,2,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Older age is widely considered a risk factor for post-acute sequelae of SARS-CoV-2 infection (PASC), typically attributed to immunosenescence and inflammaging. However, whether this association reflects intrinsic biological ageing or accumulated comorbidity burden remains unclear, with implications for clinical risk stratification.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>We conducted a retrospective cohort study using the Precision PASC Research Cohort (P2RC) from Mass General Brigham, comprising 133,792 COVID-19 patients from 12 hospitals and 20 community health centres in Massachusetts (March 2020-May 2024). PASC was ascertained using a validated computational phenotyping algorithm. We used generalised estimating equations with cluster-robust variance to model PASC risk, causal mediation analysis to decompose age effects through comorbidity burden and acute severity, and specification curve analysis across 768 analytical specifications to assess robustness.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Findings<\/jats:title>\n                  <jats:p>After adjustment for comorbidity burden, each decade of age was associated with 6% lower odds of PASC (OR 0.94; 95% CI 0.93-0.95). Causal mediation analysis revealed that comorbidities accounted for 145% of the total age effect, indicating inconsistent mediation wherein age\u2019s direct protective effect was masked by its indirect harm through chronic disease accumulation. This protection was age-dependent: adults younger than 65 years retained robust resilience independent of comorbidities (ADE:-0.0042, p&lt;0.001), whereas adults 65 years and older showed complete loss of this protection (ADE: +0.0020, p=0.14).<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Interpretation<\/jats:title>\n                  <jats:p>Long COVID susceptibility is driven by physiological reserve rather than chronological age until approximately age 65, beyond which age-related protective mechanisms become exhausted. Risk stratification should prioritise comorbidity burden over birth year in younger adults.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Funding<\/jats:title>\n                  <jats:p>National Institute of Allergy and Infectious Diseases (NIAID).<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.02.24.26346989","type":"posted-content","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T16:35:29Z","timestamp":1772123729000},"source":"Crossref","is-referenced-by-count":0,"title":["The age paradox in post-infectious sequelae: physiological reserve outweighs chronological age in Long COVID susceptibility"],"prefix":"10.64898","author":[{"given":"Alaleh","family":"Azhir","sequence":"first","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Boston, MA, USA"},{"name":"Department of Medicine, Brigham and Women\u2019s Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingya","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0599-3438","authenticated-orcid":false,"given":"Jiazi","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ingrid V.","family":"Bassett","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chirag J.","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey G.","family":"Klann","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shawn N.","family":"Murphy","sequence":"additional","affiliation":[{"name":"Department of Neurology, Massachusetts General Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hossein","family":"Estiri","sequence":"additional","affiliation":[{"name":"Department of Medicine, Massachusetts General Hospital, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026030120500428000_2026.02.24.26346989v1.1","doi-asserted-by":"publisher","DOI":"10.1007\/s11739-023-03397-3"},{"key":"2026030120500428000_2026.02.24.26346989v1.2","doi-asserted-by":"crossref","unstructured":"Wu JW , Yaqub A , Ma Y , Koudstaal W , Hofman A , Ikram MA , et al. 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