{"doi":"10.1016/j.bbih.2025.101103","title":"Networks and clusters of immunometabolic biomarkers and depression-associated features in middle-aged and older community-dwelling US adults with and without depression","abstract":"Introduction: 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. Methods: 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. Results: Two clusters were formed: the first included the immunometabolic biomarkers, and the second included the different subscales of GDS. The two clusters were significantly correlated at six edges. IL-6 and HbA1c were significantly correlated with anhedonic and melancholic features. Abdominal circumference and BMI were significantly correlated with anhedonic features. In the subgroup without current depression, IL-6 and Abdominal circumference maintained a significant edge with anhedonic features. TNF-alpha/melancholia and IL-6/cognitive concerns were additional relevant edges in older adults. The observed correlations remained statistically significant in the confounder-adjusted regression analysis and followed specific patterns. Conclusions: Symptom clustering showed its superiority over relying on dichotomized depression diagnoses for identifying relevant immunometabolic biomarkers. This study is a first step toward understanding the particularities of immunometabolic depression for better risk stratification and to direct personalized preventive and therapeutic strategies in multiethnic aging populations.","journal":"Brain Behavior & Immunity - Health","year":2025,"id":575091,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5476,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":270462,"name":"Sid E. 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