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We recently reported a data-mining procedure for operationalizing empirically-derived composite gender variables in archival databases. The present study extends the prior cross-sectional approach by examining sex and gender as separate and interactive predictors of longitudinal data-driven memory trajectory classes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Participants (\n                      <jats:italic>N\u2009=<\/jats:italic>\n                      \u2009746) contributed baseline data for binary sex (female\/male), education (years), and empirically-derived composite variables representing complementary gender facets. These facets included\n                      <jats:italic>Manual Tasks and Physical Activities; Social and Household Management<\/jats:italic>\n                      ;\n                      <jats:italic>Leisure<\/jats:italic>\n                      ,\n                      <jats:italic>Socializing<\/jats:italic>\n                      ,\n                      <jats:italic>and Travel; Cognitive Activity and Brain Games<\/jats:italic>\n                      ;\n                      <jats:italic>Health Perceptions and Practices<\/jats:italic>\n                      ; and\n                      <jats:italic>Subjective Memory Beliefs<\/jats:italic>\n                      . We integrated these into a longitudinal episodic memory trajectory distribution spanning 42 years (53\u201395) of aging. Data-driven latent class growth analysis (LCGA) on the trajectory distribution identified discriminable classes. Using the R3STEP approach, we separately tested sex, gender facets, and education as predictors of membership in the higher (healthier) trajectory classes relative to the lowest (benchmark) class. We then included interaction terms to test for sex moderation of gender effects. Finally, we identified all genotyped participants and tested whether sex and gender effects were moderated by\n                      <jats:italic>Apolipoprotein E<\/jats:italic>\n                      (\n                      <jats:italic>APOE<\/jats:italic>\n                      ).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      LCGA revealed three memory classes:\n                      <jats:italic>High-Stable<\/jats:italic>\n                      (highest level\/relatively stable),\n                      <jats:italic>Moderate\/Normal-Declining<\/jats:italic>\n                      (average level\/moderate decline), and\n                      <jats:italic>Low-Declining<\/jats:italic>\n                      (lowest level\/steepest decline). Several variables separately predicted High-Stable membership. For sex, females were more likely than males to belong to this class. For gender, (a) higher scores for\n                      <jats:italic>Social and Household Management<\/jats:italic>\n                      ,\n                      <jats:italic>Cognitive Activity and Brain Games<\/jats:italic>\n                      , and\n                      <jats:italic>Subjective Memory Beliefs<\/jats:italic>\n                      predicted High-Stable membership; and (b) higher scores for\n                      <jats:italic>Manual Tasks and Physical Activities<\/jats:italic>\n                      and\n                      <jats:italic>Health Perceptions and Practices<\/jats:italic>\n                      decreased the likelihood of High-Stable membership (relative to Low-Declining). Moderate\/Normal-Declining membership was predicted by\n                      <jats:italic>Social and Household Management<\/jats:italic>\n                      (higher). For education, more years predicted High-Stable membership. Moderation analyses indicated that gender effects were consistent across both sexes and\n                      <jats:italic>APOE<\/jats:italic>\n                      carrier status.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Data-driven analyses show that biological sex and measurable facets of gender differentially contribute to memory trajectory patterns over a 42-year span of cognitively unimpaired aging.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13293-026-00864-2","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T16:03:33Z","timestamp":1773072213000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Evaluating sex and gender as separate and interactive predictors of memory aging trajectory classes: an integrative data-driven approach"],"prefix":"10.1186","volume":"17","author":[{"given":"Linzy","family":"Bohn","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paige","family":"Vandenberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harrison","family":"Fah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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