Abstract
BACKGROUND: About every fourth patient with major depressive disorder (MDD) shows evidence of systemic inflammation. Previous studies have shown inflammation-depression associations of multiple serum inflammatory markers and multiple specific depressive symptoms. It remains unclear, however, if these associations extend to genetic/lifetime predisposition to higher inflammatory marker levels and what role metabolic factors such as Body Mass Index (BMI) play. It is also unclear whether inflammation-symptom associations reflect direct or indirect associations, which can be disentangled using network analysis.
METHODS: This study examined associations of polygenic risk scores (PRSs) for immuno-metabolic markers (C-reactive protein [CRP], interleukin [IL]-6, IL-10, tumour necrosis factor [TNF]-α, BMI) with seven depressive symptoms in one general population sample, the UK Biobank study (n=110,010), and two patient samples, the Munich Antidepressant Response Signature (MARS, n=1,058) and Sequenced Treatment Alternatives to Relieve Depression (STAR*D, n=1,143) studies. Network analysis was applied jointly for these samples using fused graphical least absolute shrinkage and selection operator (FGL) estimation as primary analysis and, individually, using unregularized model search estimation. Stability of results was assessed using bootstrapping and three consistency criteria were defined to appraise robustness and replicability of results across estimation methods, network bootstrapping, and samples.
RESULTS: Network analysis results displayed to-be-expected PRS-PRS and symptom-symptom associations (termed edges), respectively, that were mostly positive. Using FGL estimation, results further suggested 28, 29, and six PRS-symptom edges in MARS, STAR*D, and UK Biobank samples, respectively. Unregularized model search estimation suggested three PRS-symptom edges in the UK Biobank sample. Applying our consistency criteria to these associations indicated that only the association of higher CRP PRS with greater changes in appetite fulfilled all three criteria. Four additional associations fulfilled at least two consistency criteria; specifically, higher CRP PRS was associated with greater fatigue and reduced anhedonia, higher TNF-α PRS was associated with greater fatigue, and higher BMI PRS with greater changes in appetite and anhedonia. Associations of the BMI PRS with anhedonia, however, showed an inconsistent valence across estimation methods.
CONCLUSIONS: Genetic predisposition to higher systemic inflammatory markers are primarily associated with somatic/neurovegetative symptoms of depression such as changes in appetite and fatigue, consistent with previous studies based on circulating levels of inflammatory markers. We extend these findings by providing evidence that associations are direct (using network analysis) and extend to genetic predisposition to immuno-metabolic markers (using PRSs). Our findings can inform selection of patients with inflammation-related symptoms into clinical trials of immune-modulating drugs for MDD.
| Original language | English |
|---|---|
| Pages (from-to) | 256-268 |
| Number of pages | 13 |
| Journal | Brain, Behavior, and Immunity |
| Volume | 95 |
| Early online date | 29 Mar 2021 |
| DOIs | |
| Publication status | Published - Jul 2021 |
Bibliographical note
Funding Information:We are grateful to all original authors, technical assistants and patients who contributed to the MARS study. We are grateful for the National Institute of Mental Health (NIMH) and the NIMH Repository and Genomics Resource (NRGR) for the possibility of analysing the STAR*D data. We are also grateful to the original STAR*D authors, and particularly for the contributions of all patients and families who participated in the study. Data were obtained from the limited access datasets distributed from the NIH-supported ?Sequenced Treatment Alternatives to Relieve Depression? (STAR*D). The study was supported by NIMH Contract No. N01MH90003 to the University of Texas Southwestern Medical Center. The ClinicalTrials.gov identifier is NCT00021528. This research has been conducted using the UK Biobank Resource. We are grateful for all scientists and participants who made this large-scale effort and resource possible.
Funding Information:
This study is funded by the Max Planck Institute of Psychiatry. This research was funded in whole, or in part, by the Wellcome Trust (grant code: 201486/Z/16/Z). NK, NR, and SM are supported by the International Max Planck Research School of Translational Psychiatry (IMPRS-TP). NR received funding from the Bavarian Ministry of Economic Affairs, Regional Development and Energy (BayMED, PBN_MED-1711–0003). GMK acknowledges funding support from the Wellcome Trust (grant code: 201486/Z/16/Z), the MQ: Transforming Mental Health (grant code: MQDS17/40), the Medical Research Council, UK (grant code: MC_PC_17213 and grant code: MR/S037675/1), and the BMA Foundation (J Moulton grant 2019). JA received support by a NARSAD Young Investigator Grant from Brain and Behavior Research Foundation.
Funding Information:
We are grateful to all original authors, technical assistants and patients who contributed to the MARS study. We are grateful for the National Institute of Mental Health (NIMH) and the NIMH Repository and Genomics Resource (NRGR) for the possibility of analysing the STAR*D data. We are also grateful to the original STAR*D authors, and particularly for the contributions of all patients and families who participated in the study. Data were obtained from the limited access datasets distributed from the NIH-supported ‘Sequenced Treatment Alternatives to Relieve Depression’ (STAR*D). The study was supported by NIMH Contract No. N01MH90003 to the University of Texas Southwestern Medical Center. The ClinicalTrials.gov identifier is NCT00021528. This research has been conducted using the UK Biobank Resource. We are grateful for all scientists and participants who made this large-scale effort and resource possible.
Publisher Copyright:
© 2021 Elsevier Inc.