{"doi":"10.1093/abm/kaab025","title":"Subjective Social Status and Cardiometabolic Risk Markers by Intersectionality of Race/Ethnicity and Sex Among U.S. Young Adults","abstract":"BACKGROUND: Subjective social status (SSS) has shown inverse relationships with cardiometabolic risk, but intersectionalities of race/ethnicity and sex may indicate more nuanced relationships. PURPOSE: To investigate associations of SSS with cardiometabolic risk markers by race/ethnicity and sex. METHODS: Data were from Wave IV (2008) of the National Longitudinal Study of Adolescent to Adult Health (n = 4,847; 24-32 years), which collected biological cardiometabolic risk markers. A 10-step ladder captured SSS; respondents indicated on which step they perceived they stood in relation to other people in the U.S. higher values indicated higher SSS (range: 1-10). We tested the relationship between SSS and individual markers using generalized least square means linear regression models, testing three-way interactions between SSS, race/ethnicity, and sex (p < .10) before stratification. RESULTS: SSS-race/ethnicity-sex interactions were significantly associated with waist circumference (p ≤ .0001), body mass index (BMI; p ≤ .0001), systolic blood pressure (SBP; p ≤ .0001), diastolic blood pressure (DBP; p = .0004), and high-density lipoprotein cholesterol (HDL-C; p = .07). SSS was associated with waist circumference (β [SE]: -1.2 (0.4), p < .05) and BMI (-0.6 [0.2], p < .01) for non-Hispanic White females, compared with males; with HDL-C among non-Hispanic White (0.2 [0.1]; p < .05) and Hispanic (0.3 (0.1); p < .05) females, compared with males; with SBP for non-Hispanic Asian (1.7 [0.8]; p < .05) and Multiracial (1.8 [0.8]; p < .05), versus White, females; and with DBP for non-Hispanic Black (0.8 [0.3]; p < .01), versus White, males. CONCLUSIONS: SSS was differentially related to cardiometabolic risk markers by race/ethnicity and sex, suggesting intersectional aspects. Clinical and research applications of SSS should consider race/ethnicity- and sex-specific pathways influencing cardiometabolic risk.","journal":"Annals of Behavioral Medicine","year":2021,"id":183350,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8009,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":421766,"name":"Linda C. Gallo","orcid":"0000-0002-3678-5888","position":1,"is_corresponding":false},{"id":341235,"name":"Josiemer Mattei","orcid":"0000-0001-5424-8245","position":2,"is_corresponding":false},{"id":448918,"name":"Amanda C. McClain","orcid":"0000-0002-4222-7460","position":0,"is_corresponding":true}],"reference_count":82,"raw_metadata":null,"created_at":"2026-07-18T23:48:22.008011Z","pmid":"33942845","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}