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. 2024 Apr 25:15:1345159.
doi: 10.3389/fpsyt.2024.1345159. eCollection 2024.

Identification of gene networks jointly associated with depressive symptoms and cardiovascular health metrics using whole blood transcriptome in the Young Finns Study

Binisha H Mishra  1   2   3 , Emma Raitoharju  4   5 , Nina Mononen  1   2   3 , Aino Saarinen  6   7 , Jorma Viikari  8   9 , Markus Juonala  8   9 , Nina Hutri-Kähönen  10 , Mika Kähönen  2   11 , Olli T Raitakari  12   13   14 , Terho Lehtimäki  1   2   3 , Pashupati P Mishra  1   2   3
Affiliations

Identification of gene networks jointly associated with depressive symptoms and cardiovascular health metrics using whole blood transcriptome in the Young Finns Study

Binisha H Mishra et al. Front Psychiatry. .

Abstract

Background: Studies have shown that cardiovascular health (CVH) is related to depression. We aimed to identify gene networks jointly associated with depressive symptoms and cardiovascular health metrics using the whole blood transcriptome.

Materials and methods: We analyzed human blood transcriptomic data to identify gene co-expression networks, termed gene modules, shared by Beck's depression inventory (BDI-II) scores and cardiovascular health (CVH) metrics as markers of depression and cardiovascular health, respectively. The BDI-II scores were derived from Beck's Depression Inventory, a 21-item self-report inventory that measures the characteristics and symptoms of depression. CVH metrics were defined according to the American Heart Association criteria using seven indices: smoking, diet, physical activity, body mass index (BMI), blood pressure, total cholesterol, and fasting glucose. Joint association of the modules, identified with weighted co-expression analysis, as well as the member genes of the modules with the markers of depression and CVH were tested with multivariate analysis of variance (MANOVA).

Results: We identified a gene module with 256 genes that were significantly correlated with both the BDI-II score and CVH metrics. Based on the MANOVA test results adjusted for age and sex, the module was associated with both depression and CVH markers. The three most significant member genes in the module were YOD1, RBX1, and LEPR. Genes in the module were enriched with biological pathways involved in brain diseases such as Alzheimer's, Parkinson's, and Huntington's.

Conclusions: The identified gene module and its members can provide new joint biomarkers for depression and CVH.

Keywords: cardiovascular health; comorbidity; depressive symptoms; gene networks; multimorbidity; transcriptome.

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Conflict of interest statement

BHM, NM, TL and PPM were affiliated with Fimlab Laboratories. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overall concept of the co/multimorbidity analysis pipeline. (A) Pre-processed genome-wide expression data. (B) Weighted gene co-expression gene network analysis to identify gene co-expression networks or modules. (C) Gene modules identified from step (B). (D) Association analysis of the eigengenes of the gene modules with markers of both depression and cardiovascular health (CVH). The gene modules that are significantly associated with markers of both depression and CVH are considered as candidate gene modules for co/multimorbidity study. (E) Multivariate analysis of variance (MANOVA) test to access joint association of the candidate gene modules as well as its member genes with the markers of both depression and CVH. Biological interpretation of the jointly associated gene modules is done by pathway enrichment analysis of the genes in the modules.
Figure 2
Figure 2
Relationships between gene co-expression modules (x-axis) and markers of cardiovascular health and depression (y-axis). The rows correspond to the different gene modules (named by color) and their summary expression profile; for example, MEdarkred represents the summary expression profile for the module darkred. The columns correspond to the markers of CVH (CVHmetrics) and depression (BDIscore) used in the biostatistical analyses. The values in the cells represent Pearson’s correlation coefficients (r), with the associated p-values in parentheses below the coefficient. The correlation coefficients have a color-coding shown in the color scale (between  −1 and + 1) on the right side of the figure. The highlighted darkred module is significantly associated with the markers of both CVH and depression. CVHmetrics, cardiovascular health metrics; BDIscore, Beck’s depression inventory score.
Figure 3
Figure 3
Scatter plots of gene significance (GS) vs module membership (MM) in the darkred gene module. The left panel corresponds to cardiovascular health (CVH) metrics and the right panel to Beck’s depression inventory score, markers of CVH and depression, respectively. CHV metrics, cardiovascular health metrics; BDI-II score, Beck’s depression inventory score.
Figure 4
Figure 4
Gene Ontology biological processes (GO BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases that were significantly enriched in the genes of the darkred gene module with adjusted p-value <0.05.
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The Young Finns Study has been financially supported by the Academy of Finland: grants 356405, 322098, 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117797 (Gendi), and 141071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; The Sigrid Juselius Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association; EU Horizon 2020 (grant 755320 for TAXINOMISIS and grant 848146 for To Aition); European Research Council (grant 742927 for MULTIEPIGEN project); Tampere University Hospital Supporting Foundation; Finnish Society of Clinical Chemistry; the Cancer Foundation Finland; pBETTER4U_EU (Preventing obesity through Biologically and bEhaviorally Tailored inTERventions for you; project number: 101080117); and the Jane and Aatos Erkko Foundation. PPM was supported by the Academy of Finland (Grant number: 349708) and ER (grants: 330809, 338395).