Findings support latent phenotyping as a descriptive strategy for organising long COVID heterogeneity and identify ICAM-1 as the principal hypothesis-generating inflammatory-endothelial correlate of the clinical profiles.
Abstract
Long COVID is clinically heterogeneous, and reproducible biomarker patterns remain limited. This cross-sectional study aimed to identify clinical-cognitive profiles in 304 patients with long COVID and examine their associations with inflammatory-endothelial biomarkers and intermediate vascular measures. Latent profile analysis was performed using cognitive, subjective memory, fatigue, sleep, pain, affective, frailty, dyspnea, functional and health-related quality-of-life indicators. Associations between retained profiles and biomarkers or vascular variables were examined using linear models adjusted for age, sex, educational level and body mass index. A four-profile solution was retained, comprising a low global burden profile (42.1%), an intermediate affective-mental profile (22.1%), a high multisystem burden profile (17.9%) and an intermediate physical-functional profile (17.9%), with mean posterior probabilities from 0.924 to 0.965. The two intermediate profiles showed a cross-over SF-36 pattern, with lower MCS and relatively preserved PCS in the affective-mental profile and the reverse pattern in the physical-functional profile. Profiles were associated with ICAM-1, F(3,267) = 5.84, BH-adjusted
p
= .008, partial
ηp
2
= .065, but not with vascular measures. ICAM-1 remained significant across analyses accounting for classification uncertainty, whereas the endothelin-1 finding was not consistently reproduced. These findings support latent phenotyping as a descriptive strategy for organising long COVID heterogeneity and identify ICAM-1 as the principal hypothesis-generating inflammatory-endothelial correlate of the clinical profiles.
Strong evidence supports the role of low-grade systemic inflammation in neurodegeneration, including cognitive decline. Given the literature documenting chronic persistent inflammation in older Black adults, we investigated the association between circulating inflammatory proteins and cognitive decline in this high-risk population. We used data (n = 642) from the Minority Aging Research Study (MARS) and the Rush Clinical Core, including plasma samples to assess circulating inflammatory proteins (Olink® Target-96 Inflammation) and cognition (global cognition and five cognitive domains) assessed annually following proteomics measurement using previously stored blood samples. Linear mixed-effect and latent class mixed models (age at blood draw for proteomics measurement, sex, and education-adjusted), and elastic-net regression were used. Statistical significance was determined using an FDR threshold of 10%. Participants (62.3 to 99.4 years) were mostly women (80.68%) with 15.1 years of education. In multivariable linear mixed-effect models, we found that higher levels of osteoprotegerin (OPG) and chemokine (C-C motif) ligand 23 (CCL23) were cross-sectionally associated with poorer global cognition (beta=-0.205 and beta=-0.148), and higher CCL23 was associated with poorer semantic memory (beta=-0.206). Furthermore, protein × time interaction analyses indicated that higher OPG, Stem cell factor (SCF), and chemokine (C-X-C motif) ligand 9 (CXCL9) levels were associated with faster decline in global cognition (OPG × time term: beta=-0.042), semantic memory (SCF × time term: beta=-0.059, OPG × time term: beta=-0.043), episodic memory (SCF × time term: beta=-0.064; OPG × time term: beta=-0.044), and visuospatial ability (CXCL9×time term: beta=-0.012). In latent class mixed models, several significant protein × time interactions were observed for episodic memory in the subgroup with initial below-average performance and gradual decline. Using elastic net regression, we identified signatures of global cognitive level (26 proteins), but the prediction of cognitive decline was poor. In older Black adults, circulating inflammatory proteins were linked to cognition, reinforcing the role of systemic inflammation as a potential driver of neurodegeneration in this population.
Anat Yaskolka Meir, H. Adeola, S. Tasaki et al.· Brain : a journal of neurolo...· 0 citations
Post-stroke cognitive impairment (PSCI) is common, but the reproducibility and clinical meaning of allostatic load (AL)-based latent profiles remain uncertain. We examined empirical AL profiles and their associations with PSCI in older stroke survivors. This retrospective cross-sectional study included 356 adults aged 60 years or older with stroke. Latent profile analysis used 13 continuous biomarkers. One- through five-class diagonal Gaussian mixture models were fitted with 100 random starts; Bayesian information criterion guided selection among models meeting prespecified convergence, likelihood-replication, and minimum-class-size criteria. PSCI was defined as Mini-Mental State Examination (MMSE) < 27. Adjusted logistic regression, a continuous AL-score model, and biomarker-transformation and MMSE-cutoff sensitivity analyses were performed. The eligible four-profile solution had the lowest BIC (12,033.77; entropy = .908), with 25 (7.0%), 93 (26.1%), 160 (44.9%), and 78 (21.9%) participants. PSCI prevalence increased across profiles (20.0%, 23.7%, 45.6%, and 73.1%; p < .001). After full adjustment, Profiles 3 (OR = 3.51, 95% CI [1.19, 10.34], p = .023) and 4 (OR = 11.54, 95% CI [3.24, 41.04], p < .001), but not Profile 2, had higher PSCI odds than Profile 1. The continuous AL score was also associated with PSCI (OR = 1.21 per point, 95% CI [1.11, 1.32], p < .001). Log transformation of high-sensitivity C-reactive protein and triglycerides selected three rather than four classes (adjusted Rand index = .788), meeting the prespecified instability criterion. Higher multisystem burden was associated with PSCI, but the number of latent profiles was transformation-sensitive. These profiles are exploratory empirical descriptions rather than validated clinical risk categories and require prospective external validation.
Chunbo Xue, Sisi Lin, Tianxiang Liu· Biological Research for Nurs...· 0 citations
Introduction Allostatic load (AL) reflects the cumulative physiological burden on the body, quantified using biomarkers across multiple systems. The current study identified latent AL profiles in young adults and examined whether resilience moderates associations between AL and depressive symptoms, social functioning, and role functioning. Methods A total of 165 nonclinical young adults (84 women; aged 19–30 years) provided data on 15 biomarkers spanning the hypothalamic-pituitary-adrenal axis, oxidative stress, inflammatory-immune, lipid/glucose-metabolic, and renal systems, which were z-standardized. Latent profile analysis was conducted using the mclust package in R. Moderated regression models examined depressive symptoms, social functioning, and role functioning as outcomes of AL profile, resilience, and their interaction, adjusting for age, sex, and cognitive ability. Percentile bootstrap confidence intervals were estimated (R = 5,000). Results A two-profile solution was retained based on classification quality and interpretability, yielding control and higher-dysregulation profiles (n = 132 and n = 33, respectively; entropy = 0.965; average posterior probability = 0.993). The higher-dysregulation profile showed relatively elevated dysregulation, particularly in inflammatory-immune and lipid/glucose-metabolic markers. Resilience was inversely associated with depressive symptoms [b = −0.138, p < 0.001, 95% CI (−0.199, −0.079)], and a significant AL profile × resilience interaction [b = −0.335, p < 0.001, 95% CI (−0.645, −0.055)] indicated that this association was stronger in the higher-dysregulation group. A similar interaction was observed for social functioning (b = 0.038, p < 0.001, 95% CI [0.003, 0.063]), whereas the interaction for role functioning was not significant. Discussion These findings suggest that person-centered AL profiles capture heterogeneous physiological risk patterns. Resilience showed stronger associations with lower depressive symptoms and better social functioning among individuals with relatively greater physiological dysregulation, highlighting profile-dependent links between psychological resilience and psychosocial adaptation in young adulthood.
S. Koo, Jung Woo Park, Jee Eun Min et al.· Frontiers in Psychology· 0 citations
Background Cognitive aging is heterogeneous, and growing evidence implicates chronic low-grade inflammation and metabolic dysregulation in neuropathology and cognitive decline. This study examined the multivariate relationship between inflammatory-metabolic biomarkers and neuropsychological performance in a large, community-based cohort of adults from rural West Texas enrolled in Project FRONTIER. Methods Participants aged ≥ 40 years who completed a study visit in the Project FRONTIER study were included (n = 1,357). Associations between systemic metabolic health and cognitive performance were examined using a domain-driven metabolic marker set and a cognitive variables set. Canonical correlation analysis (CCA) was used to assess shared variance between biomarker and cognitive domains, followed by structural equation modeling (SEM) using variables with the highest canonical loadings as an exploratory model. Analyses were conducted in R version 4.5.2. Results CCA revealed a significant multivariate association between inflammatory-metabolic biomarkers and cognitive performance. The first canonical correlation was 0.242 (95% CI: 0.171–0.310), accounting for 5.8% of the shared variance between the biological and cognitive domains. This association was statistically significant based on both Wilks’ lambda (p = 1.05 × 10–6) and permutation testing (Fisher combined p < 0.001) and remained stable in bootstrap validation (r = 0.255, 95% CI: 0.219–0.294, 5,000 samples). Among biomarkers, C-reactive protein showed the strongest loading (0.69), followed by gamma-glutamyl transferase (0.28), fasting blood sugar (0.21), abdominal circumference (0.20), and hemoglobin A1c (-0.18). Among cognitive measures, EXIT-25 (loading = −0.59) showed the strongest contribution, followed by Trail Making Test Part B (loading = −0.45), RBANS-visuospatial/constructional (loading = −0.41), clock drawing (loading = −0.31), and RBANS-Language (loading = −0.30). SEM showed acceptable fit and further supported the finding that a higher inflammatory-metabolic burden was associated with worse executive function after adjusting for age, sex, ethnicity, education, and income (β = −0.310, standardized β = −0.202, p < 0.001). Conclusion Systemic inflammation and metabolic dysregulation were associated with poorer cognitive performance, particularly executive dysfunction, in this rural aging cohort. These findings, while modest in effect size, support the importance of cardiometabolic health in cognitive aging, motivating future longitudinal and mechanistic studies.
C. S. Dhanasekara, Chanaka Kahathuduwa, Volker Neugebauer· Frontiers in Aging Neuroscie...· 0 citations
Introduction Anxiety and depression are prevalent, disabling, yet frequently underdiagnosed non-motor symptoms in Parkinson’s disease (PD). This study aimed to develop and validate a non-invasive model predicting these affective disorders by integrating peripheral blood biomarkers with standardized clinical scales to facilitate early screening. Methods We retrospectively analyzed data from 290 patients with PD, who were randomly allocated into a training cohort (n = 203) and a validation cohort (n = 87). Baseline plasma neurofilament light chain (NfL) levels and clinical phenotypes were assessed. Independent risk factors were determined via multivariate logistic regression analysis to construct the clinical prediction nomogram. Model performance was comprehensively evaluated using the area under the receiver operating characteristic curve (AUC) for discrimination, calibration curves for risk consistency, and decision curve analysis (DCA) for clinical utility. Results Multivariate logistic regression identified plasma NfL, Hoehn and Yahr stage, MMSE score, and MDS-UPDRS Part III score as independent predictors for anxiety and depression in PD patients (all p < 0.05). The established model exhibited high discriminative power, achieving an AUC of 0.94 (95% CI: 0.873–0.964) in the training cohort and 0.84 (95% CI: 0.782–0.879) in the validation cohort. Calibration curves demonstrated excellent consistency between predicted and actual probabilities, and DCA confirmed strong clinical net benefits. Discussion In conclusion, combining peripheral plasma NfL levels with standard clinical phenotypes provides an objective, quantifiable, and non-invasive tool for early risk stratification of affective disorders in PD. This multimodal nomogram effectively expands the therapeutic window for timely personalized psychiatric interventions, potentially improving long-term quality of life and clinical outcomes for PD patients.
Guidong Liu, Yan-Qin Geng, Hanwen Zhang et al.· Frontiers in Aging Neuroscie...· 0 citations
CircS is associated with SCD severity, while exploratory analyses suggest possible nonlinear associations between CircS score and selected plasma biomarkers and male participants showed a stronger association with SCD-domain scores.
Dan Liu, C. Cai, Jingjing Zhang et al.· Journal of Affective Disorde...· 0 citations
