biostudies-literatureUnknownGholi Zadeh Kharrat FNIAMS NIH HHS<h4>Objective</h4>This study identifies distinct biobehavioral phenotypes among patients with chronic low back pain (cLBP) using Latent Profile Analysis (LPA).<h4>Methods</h4>These phenotypes were derived from baseline data from two cohorts within the NIH HEAL BACPAC consortium: BACKHOME, a large nationwide e-cohort (N = 3,025) utilized for model training, and COMEBACK as external test set, a deep phenotyping cohort (N = 450) utilized for generalization. The analysis incorporated variables including pain characteristics, psychosocial factors, lifestyle habits, and social determinants of health. Model fit was optimized via 10-fold cross-validation with 100 bootstraps and evaluated using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Entropy(uncertainty).<h4>RePain medicine (Malden, Mass.)pnaf095https://www.ebi.ac.uk/biostudies/studies/S-EPMC12419233biostudies-literatureBiobehavioral Phenotypes of Chronic Low Back Pain: Psychosocial Subgroup Identification Using Latent Profile Analysis.PMC12419233U19 AR076737REACH InvestigatorsBishara AShah SFishman SMEwing SKMummaneni PBarylak JMatthew RGuo XSTorrisi SAmar Kumar PBerven SDemarchis EMendoza ELynch JHue TCastellanos JWu PHOrnowski JButte AWu LAZheng PBlack DMKeller AKrug RVashisht RDemir-Deviren SRupanagunta AScheffler ALin FBailey JO'Neill CFerguson ARPeterson TCummings JGuerra SGFields AHunt CADel Rosario KHan MTakegami NGholi Zadeh Kharrat FLotz JNavy CKhattab KTorres-Espin AStrigo IZhou JHuie JRZeidan FLink TWallace MSMehling WAkkaya ZKurillo GPeterson TALyu TVu AJVeres JUmrao SBonnheim NfalseBiobehavioral Phenotypes of Chronic Low Back Pain: Psychosocial Subgroup Identification Using Latent Profile Analysis.<h4>Objective</h4>This study identifies distinct biobehavioral phenotypes among patients with chronic low back pain (cLBP) using Latent Profile Analysis (LPA).<h4>Methods</h4>These phenotypes were derived from baseline data from two cohorts within the NIH HEAL BACPAC consortium: BACKHOME, a large nationwide e-cohort (N = 3,025) utilized for model training, and COMEBACK as external test set, a deep phenotyping cohort (N = 450) utilized for generalization. The analysis incorporated variables including pain characteristics, psychosocial factors, lifestyle habits, and social determinants of health. Model fit was optimized via 10-fold cross-validation with 100 bootstraps and evaluated using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Entropy(uncertainty).<h4>Re2025-01-01T00:00:00Z2025 Jul2026-06-02T17:40:59.439Z2026-04-18T03:11:29.998ZS-EPMC124192334071185410.1093/pm/pnaf095