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Data from 1,001 factors retrieved from the MR-base database were analyzed. The metabolites, disease, and risk factors were selected and collated to form 38 subcategories. The results contained 459 factors, 17,826 SNPs, and 15 subcategories of depression. The subcategories that were significantly (P-value < 0.05) associated with depression included amino acid, anthropometric, autoimmune/inflammatory, behavioral, diabetes, education, fatty acid, glycemic, lipid, nucleotide, peptide, personality, protein, psychiatric/neurological, and sleeping (Supplementary Table S1, available in www.besjournal.com). Additionally, the results contained 424 factors, 18,211 SNPs, and 7 MDD subcategories. The subcategories that were significantly (P-value < 0.05) associated with MDD included anthropometric, behavioral, education, glycemic, personality, psychiatric/neurological, and sleeping (Supplementary Table S2, available in www.besjournal.com).
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The IVW calculations for exposure factors, depression, and MDD were visualized in the MR-Base database. The x-axis represented the log change or the change caused by a decrease in each SD for the 459 and 424 selected traits, respectively. In contrast, the y-axis represented the reference evidence indicating a causal relationship between a single environmental factor and depression with -log10 (Pval) > 1.3 (P-value < 0.05). The three colors in the figure represent disease, metabolites, and risk factors, respectively. The overall distribution was symmetrical, but the levels of the three environmental factors were significantly different. Metabolic factors were predominant, followed by risk factors and diseases (Figure 2A). Psychiatric/neurological and autoimmune/inflammatory diseases were significantly associated with depression (Figure 2B). Moreover, amino acids, fatty acids, and peptides were the representative metabolites associated with the risk of depression (Figure 2C). Among the risk factors, education, behaviors, and anthropometrics were significantly associated with depression (Figure 2D). The overall distribution was symmetrical, showing that the risk factors were positively distributed (Figure 3A). Psychiatric/neurological and autoimmune/inflammatory diseases were significantly associated with the subcategories of depression (Figure 3B). Fatty acid, metabolite ratio, and lipid are significantly correlated with the risk of depression (Figure 3C). The risk factors significantly correlated with depression were education, anthropometrics, and lipid (Figure 3D).
Figure 2. The x-axis shows the change of log OR caused by the decrease of each SD in 459 traits, and the y-axis displays the relevant P-value. Setting -log10 (Pval) > 1.3 (i.e. P-value < 0.05). (A) Effect of 459 traits on depression; (B) Effect of disease on depression; (C) Effect of metabolites on depression; (D) Effect of risk factor traits on depression. OR, odds ratio; SD, standard deviation
Figure 3. The x-axis shows the change of log OR caused by the decrease of each SD in 424 traits, and the y-axis displays the relevant P-value. Setting -log10 (Pval) > 1.3 (i.e. P-value < 0.05). (A) Effect of 459 traits on MDD; (B) Effect of disease on MDD; (C) Effect of metabolites on MDD; (D) Effect of risk factor traits on MDD. MDD, major depressive disorder; OR, odds ratio; SD, standard deviation.
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The 1,001 environmental risk factors collected from the MR-Base database included 459 for depression and 43 factors with P < 0.05 (Supplementary Table S3, available in www.besjournal.com) (Figure 4). Laurate (IVW, OR = 0.78, 95% CI: 0.66–0.93, P = 0.01), alcohol consumption (IVW, OR = 0.91, 95% CI: 0.84–0.99, P = 0.02), 3-dehydrocarnitine (IVW, OR = 0.91, 95% CI: 0.84–0.99, P = 0.02), and chronotype (IVW, OR = 0.94, 95% CI: 0.98–1.00, P = 0.05) were significantly associated with decreased risk of depression. However, uridine (IVW, OR = 1.17, 95% CI: 1.02–1.34, P = 0.02) and neuroticism (IVW, OR = 1.15, 95% CI: 1.07–1.23, P < 0.01) (Table 1) were significantly associated with increased risk depression.
Figure 4. MR analysis of factors associated significantly with risk of depression. There are 43 different types of risk factors exposures on depression per unit of exposure. IVW: Inverse variance weighted; SNP: single nucleotide polymorphisms; MR, Mendelian randomization; OR: odds ratio; SD, standard deviation.
Study Method SNP (n) OR 95% CI P-value Years of schooling || id: ieu-a-1239 Inverse variance weighted 299 0.95 0.94−0.97 9.62 × 10−9 Neuroticism || id: ieu-a-1007 Inverse variance weighted 10 1.15 1.07−1.23 7.39 × 10−5 Primary sclerosing cholangitis || id: ieu-a-1112 Inverse variance weighted 17 1.00 0.99−1.00 8.47 × 10−4 Schizophrenia || id: ieu-a-22 Inverse variance weighted 71 1.01 1.00−1.02 1.21 × 10−3 Mean diameter for VLDL particles || id: met-c-941 Inverse variance weighted 13 1.01 1.00−1.02 1.36 × 10−3 Inflammatory bowel disease || id: ieu-a-295 Inverse variance weighted 2 1.01 1.00−1.02 1.41 × 10−3 Systemic lupus erythematosus || id: ieu-a-815 Inverse variance weighted 2 1.01 1.00−1.01 2.37 × 10−3 Ulcerative colitis || id: ieu-a-971 Inverse variance weighted 3 1.01 1.00−1.01 3.13 × 10−3 Glutaroyl carnitine || id: met-a-699 Inverse variance weighted 8 0.95 0.91−0.98 3.66 × 10−3 Laurate (12:0) || id: met-a-350 Inverse variance weighted 2 0.78 0.66−0.93 0.01 X-12040 || id: met-a-568 Inverse variance weighted 2 1.01 1.00−1.02 0.01 Average number of methylene groups in a fatty acid chain || id: met-c-848 Inverse variance weighted 3 1.01 1.00−1.02 0.02 Glutamine || id: met-c-860 Inverse variance weighted 5 0.98 0.97−1.00 0.02 Hippocampus volume || id: ieu-a-1045 Inverse variance weighted 2 1.00 1.00 0.02 Alcohol consumption || id: ieu-a-1283 Inverse variance weighted 4 0.91 0.84−0.99 0.02 X-11327 || id: met-a-498 Inverse variance weighted 2 1.28 1.03−1.58 0.02 Uridine || id: met-a-316 Inverse variance weighted 3 1.17 1.02−1.34 0.02 Average number of double bonds in a fatty acid chain || id: met-c-851 Inverse variance weighted 5 0.99 0.98−1.00 0.02 Type 2 diabetes || id: Ieu-a-24 Inverse variance weighted 35 0.99 0.99−1.00 0.03 3-dehydrocarnitine* || id: met-a-500 Inverse variance weighted 2 0.91 0.84−0.99 0.03 Concentration of very large VLDL particles || id: met-c-950 Inverse variance weighted 8 1.01 1.00−1.02 0.03 Total lipids in very large VLDL || id: met-c-949 Inverse variance weighted 8 1.01 1.00−1.02 0.03 Triglycerides in chylomicrons and largest VLDL particles || id: met-c-960 Inverse variance weighted 9 1.01 1.00−1.02 0.03 Cholesterol esters in very large HDL || id: met-c-943 Inverse variance weighted 11 0.99 0.99−1.00 0.03 Fasting insulin || id: ieu-b-116 Inverse variance weighted 13 0.95 0.91−1.00 0.03 Bradykinin, des-arg (9) || id: met-a-656 Inverse variance weighted 3 0.99 0.98−1.00 0.03 Concentration of small HDL particles || id: met-c-922 Inverse variance weighted 5 1.01 1.00−1.02 0.03 Apolipoprotein A-I || id: met-c-842 Inverse variance weighted 10 0.99 0.98−1.00 0.04 X-11792 || id: met-a-542 Inverse variance weighted 3 0.98 0.97−1.00 0.04 Total cholesterol in large HDL || id: met-c-874 Inverse variance weighted 14 0.99 0.99−1.00 0.04 Mean diameter for LDL particles || id: met-c-896 Inverse variance weighted 8 0.99 0.98−1.00 0.04 Cholesterol esters in large VLDL || id: met-c-887 Inverse variance weighted 12 1.01 1.00−1.02 0.04 Ratio of bisallylic groups to total fatty acids || id: met-c-845 Inverse variance weighted 4 0.99 0.99−1.00 0.04 X-13215 || id: met-a-675 Inverse variance weighted 2 0.87 0.76−0.99 0.04 PGC cross-disorder traits || id: ieu-a-803 Inverse variance weighted 4 1.03 1.00−1.06 0.04 Free cholesterol in large VLDL || id: met-c-888 Inverse variance weighted 11 1.01 1.00−1.02 0.05 X-12244--N-acetylcarnosine || id: met-a-596 Inverse variance weighted 3 0.92 0.86−1.00 0.05 Concentration of large VLDL particles || id: met-c-890 Inverse variance weighted 10 1.01 1.00−1.02 0.05 Phospholipids in medium VLDL || id: met-c-914 Inverse variance weighted 15 1.01 1.00−1.01 0.05 Waist circumference || id: ieu-a-68 Inverse variance weighted 25 1.02 1.00−1.04 0.05 Chronotype || id: ieu-a-1087 Inverse variance weighted 9 0.94 0.89−1.00 0.05 Cholesterol esters in large HDL || id: met-c-875 Inverse variance weighted 13 0.99 0.99−1.00 0.05 Note. 2SMR, two-sample mendelian randomization; SNP, single nucleotide polymorphism. Table 1. 2SMR estimates of the significant results in depression
Furthermore, the 1,001 environmental risk factors collected from the MR-Base database included 424 results for MDD, and MDD was considered an outcome factor (Supplementary Table S4, available in www.besjournal.com). Thirty environmental risk factors were associated with MDD (Figure 5). Laurate (IVW, OR = 0.21, 95% CI: 0.07−0.66, P < 0.01), years of education (IVW, OR = 0.72, 95% CI: 0.64−0.81, P < 0.01), and the ratio of bisallylic groups to double bonds (IVW, OR = 0.94, 95% CI: 0.90−0.98, P < 0.01) were significantly associated with the decreased risk of MDD. The risk factors significantly correlated with increased risk factors included PGC cross-disorder traits (IVW, OR = 1.32, 95% CI: 1.15−1.51, P < 0.01), attention deficit hyperactivity disorder (ADHD) (IVW, OR = 1.20, 95% CI: 1.12−1.29, P < 0.01), and obesity class 1 (IVW, OR = 1.13, 95% CI: 1.05−1.21, P < 0.01) (Table 2).
Figure 5. MR analysis of factors associated significantly with risk of MDD. There are 30 different types of risk factors exposures on MDD per unit of exposure. IVW: Inverse variance weighted; SNP: single nucleotide polymorphisms; MDD, major depressive disorder; MR, Mendelian randomization; OR: odds ratio.
Study Method SNP (n) OR 95% CI P-value Years of schooling || id: ieu-a-1239 Inverse variance weighted 257 0.72 0.64−0.81 2.31 × 10−8 ADHD || id: ieu-a-1183 Inverse variance weighted 10 1.2 1.12−1.29 1.20 × 10−7 Schizophrenia || id: ieu-a-22 Inverse variance weighted 72 1.09 1.05-1.14 5.68 × 10−5 PGC cross-disorder traits || id: ieu-a-803 Inverse variance weighted 4 1.32 1.15−1.51 8.52 × 10−5 Obesity class 1 || id: ieu-a-90 Inverse variance weighted 14 1.13 1.05−1.21 4.90 × 10−4 Average number of double bonds in a fatty acid chain || id: met-c-851 Inverse variance weighted 5 0.92 0.88-0.97 2.01 × 10−3 Ratio of bisallylic groups to double bonds || id: met-c-844 Inverse variance weighted 4 0.94 0.90−0.98 2.80 × 10−3 Ratio of bisallylic groups to total fatty acids || id: met-c-845 Inverse variance weighted 6 0.94 0.9-0.98 2.88 × 10−3 Waist circumference || id: ieu-a-102 Inverse variance weighted 2 1.27 1.08−1.49 4.10 × 10−3 Obesity class 3 || id: ieu-a-92 Inverse variance weighted 2 1.06 1.02−1.1 4.96 × 10−3 Urate || id: ieu-a-789 Inverse variance weighted 4 0.96 0.94−0.99 0.01 Waist circumference || id: ieu-a-69 Inverse variance weighted 21 1.17 1.05−1.31 0.01 Laurate (12:0) || id: met-a-350 Inverse variance weighted 2 0.21 0.07−0.66 0.01 Average number of methylene groups per double bond || id: met-c-847 Inverse variance weighted 5 1.06 1.02−1.1 0.01 Obesity class 2 || id: ieu-a-91 Inverse variance weighted 11 1.08 1.02−1.15 0.01 Body mass index || id: ieu-a-974 Inverse variance weighted 35 1.17 1.04−1.33 0.01 Body mass index || id: ieu-a-785 Inverse variance weighted 28 1.21 1.05−1.4 0.01 Body mass index || id: ieu-a-95 Inverse variance weighted 9 1.32 1.06−1.63 0.01 Crohn's disease || id: ieu-a-10 Inverse variance weighted 97 0.98 0.97−1 0.02 1-linoleoylglycerophosphoethanolamine* || id: met-a-497 Inverse variance weighted 2 1.45 1.04−2.02 0.03 Overweight || id: ieu-a-93 Inverse variance weighted 10 1.16 1.02−1.33 0.03 Alzheimer's disease || id: ieu-a-298 Inverse variance weighted 20 0.96 0.92−1 0.03 Waist circumference || id: ieu-a-65 Inverse variance weighted 13 1.23 1.02−1.48 0.03 HDL cholesterol || id: ieu-a-299 Inverse variance weighted 83 1.06 1−1.12 0.04 X-08402 || id: met-a-426 Inverse variance weighted 2 0.74 0.56−0.99 0.04 1-arachidonoylglycerophosphocholine* || id: met-a-558 Inverse variance weighted 3 0.76 0.58−0.99 0.04 Inflammatory bowel disease || id: ieu-a-31 Inverse variance weighted 54 0.98 0.97−1 0.04 Body mass index || id: ieu-a-2 Inverse variance weighted 74 1.13 1−1.26 0.04 Urate || id: ieu-a-797 Inverse variance weighted 3 0.96 0.92−1 0.04 LDL cholesterol || id: ieu-a-300 Inverse variance weighted 75 0.96 0.92−1 0.05 Note. MDD, major depressive disorder; MR, Mendelian randomization; SNP, single nucleotide polymorphism. Table 2. 2SMR estimates of the significant results in MDD
We performed heterogeneity tests, multiple validity tests, and leave-one-out sensitivity analyses for each significant outcome. The heterogeneity analysis generated 16 results with < 0.05 Q_pval. Thus, we used a random effects model to estimate the MR effect size, which confirmed that the outcomes were causally related to depression (Pval < 0.05). Besides, the multiplicity test showed that each significant outcome (Pval > 0.05) lacked horizontal multiplicity. The leave-one-out sensitivity analyses of each significant outcome showed that no specific SNPs influenced the results (Supplementary Table S5 and Supplementary Figures, available in www.besjournal.com).
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The conditions with significant risk factors, including primary sclerosing cholangitis (PSC), were confirmed through tests. The purpose was to prove the reliability of the results. Patients with chronic diseases have a higher rate of depression than the normal population[18], but PSC (IVW, OR = 1.00, 95% CI: 0.99−1.00, P < 0.01) does not increase the risk of depression. Research trials showed that depression is less prevalent in PSC than in the general population. Schizophrenia (SCZ) (IVW, OR = 1.01, 95% CI: 1.00−1.02, P < 0.01) and MDD are two psychiatric disorders with overlapping symptoms and risk factors[19]. However, patients with SCZ have an increased risk of MDD. Approximately 50% of SCZ patients also experience a major depressive episode at some point, with higher risks of hospitalization, suicide attempts, and poorer treatment outcomes than those who never suffered depression[20,21]. Thus, this evidence suggests an association between the two diseases.
Systemic lupus erythematosus (SLE) was another significant (IVW, OR = 1.01, 95% CI: 1.00−1.01, P < 0.01) risk factor for depression[22]. This autoimmune disease causes inflammation and damages various organs and tissues in the body. Although SLE primarily affects the physical health of an individual, there is growing evidence that it can also affect mental health, including an increased risk of developing MDD[23]. The prevalence of depression in SLE patients is approximately three times higher than in the general population[24]. Moreover, SLE patients with a history of depression had worse disease outcomes and quality of life than patients without depression[25]. Therefore, the MR results are consistent with the trial findings, confirming the reliability of the MR calculations.
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Genetically variant SNPs of the therapeutic target genes were selected to identify potential therapeutic drugs for depression using ADHD, SCZ, Inflammatory bowel disease (IBD) (IVW, OR = 0.98, 95% CI: 0.97−1.00, P = 0.04), Alzheimer’s disease (AD) (IVW, OR = 0.96, 95% CI: 0.92−1.00, P = 0.03) as instrumental exposure variables. Major depressive disorder was used as the outcome variable for 2SMR analysis. The DrugBank database search retrieved 16 drugs for ADHD, corresponding to 135 drug targets; 35 drugs for SCZ, corresponding to 452 drug targets; 12 drugs for IBD, corresponding to 24 drug targets; and 8 drugs for AD, corresponding to 95 drug targets (Supplementary Table S5). Crohn’s disease was among IBD diseases, but PGC cross-disorder traits could not be calculated as independent diseases; thus, only ADHD, SCZ, IBD, and AD were evaluated.
The top nine targets selected through MR analysis included sodium-dependent noradrenaline transporter (SLC6A4), glutathione S-transferase P (GSTP1), glutamate receptor ionotropic NMDA 2C (GRIN2C), neuronal acetylcholine receptor subunit alpha-3 (CHRNA3), glutamate receptor ionotropic NMDA 2A (GRIN2A), gamma-aminobutyric acid receptor subunit gamma-2 (GABRG2), sodium channel protein type 10 subunit alpha (SCN10A), 5-hydroxytryptamine receptor 3A (HTR3A), and interleukin-1 beta (IL1B). The findings showed that SLC6A4 (IVW, OR = 1.03, 95% CI: 1.00−1.06, P = 0.05), GRIN2A (IVW, OR = 1.04, 95% CI: 1.01−1.06, P < 0.01), GRIN2C (IVW, OR = 1.02, 95% CI: 1.01−1.04, P < 0.01), SCN10A (IVW, OR = 1.08, 95% CI: 1.01–1.16, P = 0.03), and IL1B (IVW, OR = 1.03, 95% CI: 1.01−1.06, P = 0.01) are positively correlated with the risk of MDD. Thus, high expression of SLC6A4, GRIN2A, GRIN2C, CHRNA3, SCN10A, and IL1B may increase the risk of MDD. Additionally, CHRNA3 (IVW, OR = 0.95, 95% CI: 0.92−0.98, P < 0.01), GSTP1 (IVW, OR = 0.84, 95% CI: 0.73−0.98, P = 0.02), HTR3A (IVW, OR = 0.96, 95% CI: 0.92−1.00, P = 0.04), GRIN2A (IVW, OR = 0.84, 95% CI: 0.73−0.92, P = 0.02) with high expression may reduce the risk of MDD (Supplementary Table S6, available in www.besjournal.com).
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Associations between Risk Factors, Depression, and MDD
Visualization of the Results with Depression and MDD
Data Statistics
In-silico Results Correspond with Disease Tests
MDD Drug Targets Searching
23037+Supplementary Materials.zip |