Summary statistics for blood biomarkers.

People living with HIV are at three times greater risk for depressive symptoms. Inflammation is a notable predictor of depression, and people with HIV exhibit chronic inflammation despite antiretroviral therapy. We hypothesised that inflammatory biomarkers may mediate the association between HIV status and depressive symptoms. Participants (N = 60, 53% girls, median [interquartile range (IQR)] age 15.5 [15.0, 16.0] years, 70% living with HIV, of whom 90.5% were virally-suppressed) completed the nine-item Patient Health Questionnaire (PHQ-9). We measured choline and myo-inositol in basal ganglia, midfrontal gray matter, and peritrigonal white matter using magnetic resonance spectroscopy, and 16 inflammatory proteins in blood serum using ELISA and Luminex™ multiplex immunoassays. Using structural equation mediation modelling, we calculated standardised indirect effect estimates with 95% confidence intervals. Median [IQR] total PHQ-9 score was 3 [0, 7]. HIV status was significantly associated with total PHQ-9 score (B = 3.32, p = 0.022). Participants with HIV showed a higher choline-to-creatine ratio in the basal ganglia than those without HIV (β = 0.86, pFDR = 0.035). In blood serum, participants with HIV showed higher monocyte chemoattractant protein-1 (MCP-1, β = 0.59, pFDR = 0.040), higher chitinase-3 like-1 (YKL-40, β = 0.73, pFDR = 0.032), and lower interleukin-1beta (IL-1β, β = -0.67, pFDR = 0.047) than those without HIV. There were no significant associations of any biomarkers with total PHQ-9 score. None of the indirect effects were significant, mediating </div

Unadjusted standardised effect estimates for SEM involving neuroimaging biomarkers.

Unadjusted standardised effect estimates for SEM involving neuroimaging biomarkers.</p

Voxels of interest and representative MRS spectra from one participant.

(A-C) Positions of the voxels of interest (VOI, in yellow) in the (A) basal ganglia (BG), (B) midfrontal gray matter (MFGM), and (C) peritrigonal white matter (PWM), and (D-F) corresponding MRS spectra from the VOIs in (D) BG, (E) MFGM, and (F) PWM. Voxel placement images were produced using Gannet software: https://www.fil.ion.ucl.ac.uk/spm/software/spm12/.</p

Unadjusted standardised effect estimates for SEM involving blood biomarkers.

Unadjusted standardised effect estimates for SEM involving blood biomarkers.</p

Unadjusted standardised effect estimates for associations of all biomarkers with participant age, gender, and HIV status.

Unadjusted standardised effect estimates for associations of all biomarkers with participant age, gender, and HIV status.</p

Fig 4 -

Spearman’s correlations (ρ) between (A) neuroimaging biomarkers and (B) blood biomarkers measured in this study. The strength of correlations is graded across a 3-point scale: −1.0 (in orange), 0.0 (in white), and +1.0 (in purple). For blood biomarkers, the correlation matrix is ordered by hierarchical clustering to optimise visual comparisons.</p

Summary of demographic characteristics self-reported by participants, stratified by HIV status.

Summary of demographic characteristics self-reported by participants, stratified by HIV status.</p

Fig 6 -

Concentration (log2-transformed) of blood biomarkers by (A) HIV status, and (B) total PHQ-9 score. Values shown in (A) and (B) are unadjusted for covariates. (C) Standardised effect estimates with 95% confidence intervals (CI) for structural equation modelling (SEM) involving biomarkers quantified in blood serum, adjusted for age and gender. Path estimates which are significantly different from zero (after correcting p values for multiple comparisons) are shown in blue.</p

Biomarkers assessed in this study and number of participants for whom data was included for each biomarker.

Biomarkers assessed in this study and number of participants for whom data was included for each biomarker.</p

All path estimates for SEM (adjusted) for neuroimaging and blood biomarkers using robust maximum likelihood estimation (MLM).

All path estimates for SEM (adjusted) for neuroimaging and blood biomarkers using robust maximum likelihood estimation (MLM).</p