<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>49</volume><submitter>Hallab A</submitter><pubmed_abstract>&lt;h4>Introduction&lt;/h4>Therapy-resistant depression is associated with higher levels of systemic inflammation and increased odds of metabolic disorders. It is, therefore, crucial to identify the biomarkers of high-risk individuals and understand the key features of depression-immunometabolic networks.&lt;h4>Methods&lt;/h4>The multiethnic ≥50-year-old study population is a subset of the Health and Aging Brain Study: Health Disparities (HABS-HD) study. Spearman's rank correlation network analysis was performed between immunological, metabolic, and subscales of the Geriatric Depression Scale (GDS). Significant correlations were then evaluated using a multivariable linear regression analysis, including testing for non-linearity and clinical cutoffs.&lt;h4>Results&lt;/h4>Two clusters were formed: the first i</pubmed_abstract><journal>Brain, behavior, &amp; immunity - health</journal><pagination>101103</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC12523063</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Networks and clusters of immunometabolic biomarkers and depression-associated features in middle-aged and older community-dwelling US adults with and without depression.</pubmed_title><pmcid>PMC12523063</pmcid><pubmed_authors>Petersen M</pubmed_authors><pubmed_authors>Vardarajan B</pubmed_authors><pubmed_authors>Zhang F</pubmed_authors><pubmed_authors>Johnson L</pubmed_authors><pubmed_authors>Hall JR</pubmed_authors><pubmed_authors>Lee J</pubmed_authors><pubmed_authors>Cheema A</pubmed_authors><pubmed_authors>Barber R</pubmed_authors><pubmed_authors>Ances B</pubmed_authors><pubmed_authors>Vig R</pubmed_authors><pubmed_authors>Mason D</pubmed_authors><pubmed_authors>Mielke M</pubmed_authors><pubmed_authors>Shi Y</pubmed_authors><pubmed_authors>McColl R</pubmed_authors><pubmed_authors>Babulal G</pubmed_authors><pubmed_authors>Hill C</pubmed_authors><pubmed_authors>King K</pubmed_authors><pubmed_authors>Borzage M</pubmed_authors><pubmed_authors>Mindt MR</pubmed_authors><pubmed_authors>Okonkwo O</pubmed_authors><pubmed_authors>Large S</pubmed_authors><pubmed_authors>Llibre-Guerra J</pubmed_authors><pubmed_authors>HABS-HD Investigators</pubmed_authors><pubmed_authors>Hallab A</pubmed_authors><pubmed_authors>Mapstone M</pubmed_authors><pubmed_authors>Christian B</pubmed_authors><pubmed_authors>HABS-HD MPIs</pubmed_authors><pubmed_authors>Kind A</pubmed_authors><pubmed_authors>Palmer R</pubmed_authors><pubmed_authors>Barnes L</pubmed_authors><pubmed_authors>Rissman R</pubmed_authors><pubmed_authors>Braskie M</pubmed_authors><pubmed_authors>Nandy R</pubmed_authors><pubmed_authors>Phillips N</pubmed_authors><pubmed_authors>Donohue M</pubmed_authors><pubmed_authors>Zhou Z</pubmed_authors><pubmed_authors>Raman R</pubmed_authors><pubmed_authors>Cohen A</pubmed_authors><pubmed_authors>O'Bryant SE</pubmed_authors><pubmed_authors>Yaffe K</pubmed_authors><pubmed_authors>Toga A</pubmed_authors><pubmed_authors>Vintimilla R</pubmed_authors><pubmed_authors>Health and Aging Brain Study (HABS-HD) Study Team</pubmed_authors></additional><is_claimable>false</is_claimable><name>Networks and clusters of immunometabolic biomarkers and depression-associated features in middle-aged and older community-dwelling US adults with and without depression.</name><description>&lt;h4>Introduction&lt;/h4>Therapy-resistant depression is associated with higher levels of systemic inflammation and increased odds of metabolic disorders. It is, therefore, crucial to identify the biomarkers of high-risk individuals and understand the key features of depression-immunometabolic networks.&lt;h4>Methods&lt;/h4>The multiethnic ≥50-year-old study population is a subset of the Health and Aging Brain Study: Health Disparities (HABS-HD) study. Spearman's rank correlation network analysis was performed between immunological, metabolic, and subscales of the Geriatric Depression Scale (GDS). Significant correlations were then evaluated using a multivariable linear regression analysis, including testing for non-linearity and clinical cutoffs.&lt;h4>Results&lt;/h4>Two clusters were formed: the first i</description><dates><release>2025-01-01T00:00:00Z</release><publication>2025 Nov</publication><modification>2026-06-04T12:20:17.862Z</modification><creation>2026-05-09T03:08:27.248Z</creation></dates><accession>S-EPMC12523063</accession><cross_references><pubmed>41104357</pubmed><doi>10.1016/j.bbih.2025.101103</doi></cross_references></HashMap>