Circulating adipokines and major depressive disorder: a two-sample Mendelian randomization study

doi:10.21203/rs.3.rs-5043023/v1

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Circulating adipokines and major depressive disorder: a two-sample Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-5043023/v1

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Background

Observational research has revealed correlations between adipokines and major depressive disorder (MDD). However, the causality of this association remain unknown.

Method

Two-sample Mendelian randomization (TSMR) was performed to assess the causal effect between adipokines and major depression risk. The analyses were conducted using methods such as inverse variance-weighted-fixed effects (IVW-FE), MR‒Egger, weighted median, weighted mode, and simple mode, which were calculated from the summarized results of a comprehensive genome-wide association study (GWAS). Subsequently, sensitivity analyses were performed to evaluate the robustness of the outcomes.

Results

Genetically predicted circulating leptin levels showed a positive relationship with MDD risk (OR_IVW =1.12; 95% CI 1.04–1.22; P = 0.005). No causal effect of PAI-1 or resistin on MDD risk was observed. The robustness of this research was ensured by the results derived from the sensitivity analysis.

Conclusion

These data provide the first evidence of a potential causal relationship between adipokines and MDD. These results indicate that monitoring leptin levels is an effective prevention and control strategy for MDD.

Major depressive disorder

adiponectin

leptin

PAI-1

resistin

causal effect

Mendelian randomization

Major depressive disorder (MDD) is a highly prevalent mental illness that is defined by a persistently depressed mood, decreased interest or pleasure, fatigue, insomnia, etc. Most patients often experience inattention, feelings of worthlessness or guilt, thoughts about death, or suicidal ideation or attempts. It is disabling and lethal^[1]. Depression is a major cause of disability worldwide. In addition, the prevalence of depressive disorders has increased significantly in recent years^{[2, 3]}. The high prevalence and disability of depression indicate an urgent need to identify its etiology and pathogenesis.

Adipokines are bioactive compounds secreted by adipose tissue and play various roles in the body; they can affect factors such as energy balance, inflammation, and insulin sensitivity. According to previous studies, three adipokines including leptin, plasminogen activator inhibitor-1 (PAI-1) and resistin are thought to directly or indirectly affect MDD through proinflammatory or other regulatory effects^[4]. Leptin, a peptide hormone, may be involved in cranial neurogenesis pathways^{[5, 6, 7]}. Based on observational studies, there is a specific association between leptin and depression, but these associations vary widely across studies^[8]. Some studies have shown lower levels of leptin in individuals suffering from depression and have shown antidepressant and anxiolytic properties in animal models ^{[9, 10, 11, 12]}. Nevertheless, individuals with MDD have been found to exhibit elevated levels of leptin in comparison to individuals without the disorder^{[12, 14]}. Therefore, it is crucial to elucidate the impact of leptin on depression. Resistin is a cytokine produced by inflammatory cells such as macrophages. Its expression can inhibit dopamine and norepinephrine synthesis, reduce the level of monoamines in synapses, and participate in the development of depression^{[15, 16, 17, 18]}. In addition, observational studies have shown an association between the PAI-1 score and MDD. The PAI-1 level is elevated in patients suffering from depression, and some scholars have suggested that it affects the metabolism of dopamine and serotonin in depression^[19].

However, there are some limitations in observational studies, and a causal relationship between the above factors and depression cannot be established because there are some unpredictable confounding factors. This limitation makes observational studies unable to clarify the causality between the influencing factors and the disease^[20].

Mendelian randomization (MR) analysis is a novel approach that uses genetic variation as a tool to assess the potential causal relationship between a phenotype and a disease, which can avoid confounders effectively^[21]. In this paper, the potential causal relationship between adipokines and the risk of MDD was assessed by MR analysis, aimed to provide valuable insights for further observational studies on MDD.

Study design

This research employed nonoverlapping large-scale summary level genome-wide association study (GWAS) data within a conventional two-sample Mendelian randomization (MR) framework. Figure 1presents the study design of the research.

Figure 1. Study design

Source of adipokine data

For leptin levels, we utilized the findings from the NHGRI-EBI GWAS meta-analysis, which included data from 4,9909 subjects from 35 cohorts, and identified 6 genome-wide significant leptin-related variants of European ancestry, which we selected as genetic tools for leptin^[22]. For PAI-1, 10 SNPs were identified in a meta-analysis of 23,081 subjects, which were corrected for relevant demographic characteristics for each subject^{[24, 25]}. The GWAS summary dataset for levels of resistin data was derived from a genome-wide meta-analysis of 13 cohorts with a total of 21,758 participants ^[23], and we selected 22 independent SNPs as genetic tools for MR analysis.

Selection of Genetic Instrumental Variables

Instrumental Variables (IVs) were collected from each published GWAS at the significance threshold of P < 5×10^− 6 for each exposure. To ensure the independence of each SNP from one another, we evaluated the linkage disequilibrium (LD) between the SNPs (r² = 0.001 and a window size = 10 Mb)^{[26, 27, 28]}. If a SNP related to exposure was not found in the resulting dataset, a substitute SNP was not utilized. The F statistics (F = beta²/se²) for each SNP were calculated, and an F statistic < 10 indicate the weak IV^{[29, 30, 31]}.

Data sources for the MDD patients

A large-scale GWAS that meta-analyzed 170756 cases and 329443 controls from the Psychiatric Genomics Consortium (PGC) provided summary data for MDD, and the dataset excluded samples from 23andme. MDD patients were identified primarily by self-reports of three depression phenotype ranges, anxiety, tension, and depression, and by admission records^[4]. Overall, in the GWAS analysis of MDD in this case‒control study, genomic data from 246,363 patients and 561,190 controls were used as outcome variables, and 102 independent genetic variants associated with MDD were identified after removing sample overlap. Finally, we performed MR analysis using the results of the meta-analysis of MDD patients.

Data availability

GWAS data for adipokines can be accessed on the OPEN GWAS (https://gwas.mrcieu.ac.uk). In addition, aggregate data for MDD patients are available, and public databases provide all data, which does not require ethical or moral review.

Statistical analysis

Based on the SNPs retained after pleiotropy screening, we tested between-SNP heterogeneity^[32]. To determine whether heterogeneity existed, Cochran’s Q and Rucker’s Q statistics were used. During this stage, if there was a notable heterogeneity (indicated by a p value of less than 0.05 for both Cochran's Q and Rucker's Q statistics) and there was no evidence of horizontal pleiotropy^[33], we proceeded to employ both the radial IVW approach and the Egger approach to detect any outliers with p values below 0.05. Following the removal of outliers, we again implemented the above calculations to determine whether heterogeneity persisted.

Subsequently, we employed the MR‒Egger regression assessment and MR-PRESSO global test to assess pleiotropy. A p value < 0.05 in this study indicated that there was pleiotropy. Following the aforementioned procedures, if pleiotropy were absent, and then we proceeded with primary MR analysis using the inverse variance-weighted-fixed effects (IVW-FE) method. The weighted median^[34], weighted mode^[35], MR‒Egger^[36], and simple mode^[37] were utilized as supplementary procedures. Ultimately, a “leave-one-out” analysis was performed to identify the influential SNPs, if such SNPs were not present, we considered the conclusions to be reliable^{[38, 39, 40, 41]}.

The “TwoSampleMR”, “RadialMR”, and “MRPRESSO” packages in R software were used for all the statistical analyses in this study.

The F-statistic for all IVs exceeded 20.90, indicating no instrument bias. Table 1shows the selected SNPs.

Table 1

the selected SNPs.
exposure	SNP	effect_allele	other_allele	beta	eaf	se	pval	F
Leptin	rs10195252	C	T	0.054764	0.382155	0.007223	3.40E-14	57.49
	rs10938397	G	A	0.031978	0.438977	0.006671	1.64E-06	22.98
	rs1121980	A	G	0.054863	0.4317	0.006582	7.71E-17	69.48
	rs713586	C	T	0.031133	0.466499	0.006606	2.45E-06	22.21
	rs900399	G	A	-0.03305	0.396124	0.00701	2.43E-06	22.22
	rs972283	G	A	-0.04137	0.521301	0.006415	1.12E-10	41.59
PAI-1	rs10430514	T	C	-0.28585	0.124	0.060698	3.41E-06	22.18
	rs111425148	C	T	-1.14955	0.007	0.236947	1.69E-06	23.54
	rs111468958	T	C	-0.81056	0.014	0.167142	1.88E-06	23.52
	rs11594435	C	G	-0.27396	0.14	0.058997	4.74E-06	21.56
	rs140060891	A	G	1.34679	0.005	0.287937	3.87E-06	21.88
	rs148866121	T	A	0.632873	0.031	0.113028	4.12E-08	31.35
	rs3826772	T	C	-0.27176	0.853	0.058548	4.47E-06	21.54
	rs4492605	T	C	-0.23256	0.797	0.049007	3.01E-06	22.52
	rs75567046	A	G	-0.31781	0.104	0.065176	1.48E-06	23.78
	rs80067710	A	G	-0.36317	0.078	0.073131	9.61E-07	24.66
Resistin	rs10103048	C	A	-0.0596	0.5849	0.0096	5.25E-10	38.54
	rs10162788	T	C	0.08	0.0617	0.0175	4.83E-06	20.90
	rs1144700	T	C	-0.0606	0.19	0.012	4.07E-07	25.50
	rs120858	A	G	-0.0482	0.4588	0.0102	2.41E-06	22.33
	rs140711477	G	A	0.1929	0.0222	0.0412	2.84E-06	21.92
	rs16838212	C	G	-0.2403	0.0149	0.0516	3.25E-06	21.69
	rs17405635	A	G	0.0799	0.2625	0.0107	6.61E-14	55.76
	rs2074038	T	G	0.069	0.1	0.0148	3.10E-06	21.74
	rs2239619	A	C	-0.0532	0.6221	0.0097	4.06E-08	30.08
	rs2977789	C	T	-0.048	0.6951	0.0104	3.69E-06	21.30
	rs4000725	C	A	-0.0905	0.8548	0.0165	3.86E-08	30.08
	rs4876578	A	G	0.0959	0.0459	0.0203	2.41E-06	22.32
	rs4981264	T	C	0.0531	0.3872	0.0104	3.80E-07	26.07
	rs56048629	T	C	-0.0458	0.3703	0.0098	2.95E-06	21.84
	rs570113	T	C	-0.0568	0.3249	0.0111	2.72E-07	26.18
	rs597808	G	A	-0.0523	0.525	0.0105	5.83E-07	24.81
	rs61805072	T	C	-0.0932	0.0602	0.0184	4.23E-07	25.66
	rs6775731	C	T	0.0632	0.6978	0.0107	3.17E-09	34.89
	rs73008259	A	G	0.1926	0.0527	0.0204	3.68E-21	89.14
	rs7306680	A	G	-0.0478	0.6077	0.0097	7.86E-07	24.28
	rs7589428	G	A	-0.0626	0.491	0.0095	4.64E-11	43.42
	rs7667235	T	C	-0.0499	0.3904	0.0104	1.77E-06	23.02

The IVW results suggested that elevated circulating leptin levels were associated with a greater risk of MDD (OR = 1.12; 95% CI 1.04–1.22; P = 0.005). The results of other MR complementary analysis methods, including MR‒Egger (OR = 1.10; 95% CI 0.78–1.54; P = 0.691), weighted median (OR = 1.13; 95% CI 1.02–1.24; P = 0.021), weighted mode (OR = 1.13; 95% CI 0.98–1.31; P = 0.142), and simple mode (OR = 1.17; 95% CI 1.01–1.35; P = 0.001), showed a consistent effect. For PAI-1 and resistin, there was no evidence of a causal relationship with MDD risk (PAI-1: OR = 1.01, 95% CI 1.00-1.02, P = 0.23; resistin: OR = 1.00, 95% CI 0.97–1.04, P = 0.798). All the results are presented in Fig. 2. The scatter plots (Fig. 3) of the MR analysis revealed no variation in genetic composition or multiple effects of a single gene, as all p values of the Q test, Egger intercept and MR PRESSO Global test were above 0.05. The leave-one-out results (Fig. 4) indicated that there was no single SNP linked to the relationships between the three adipokines and the likelihood of MDD.

MDD is a mental illness characterized by significant mood disorders. According to existing research, the number of patients with MDD is increasing rapidly^[42], and MDD is the main cause of disability worldwide, but its etiology is not fully understood, which makes the clinical cure of MDD particularly difficult, and more measures can only be taken to relieve the symptoms of patients. Although a complete explanation of the pathogenesis of MDD is beyond our current capabilities, the focus can shift to identifying biomarkers for its pathogenesis^[43]. Several observational studies have demonstrated specific alterations in blood indicators in patients with depression. Clarifying the relevant causal relationships may provide information for disease assessment, prevention strategies, and interventions for MDD.

The results showed that increased leptin levels significantly increased the risk of MDD. Although the results of the IVW analysis failed to demonstrate a cause-and-effect connection between the levels of leptin in circulation and the risk of MDD, significant results were observed in the IVW analysis, and the same trend direction was observed in all the MR methods employed. These results collectively suggest that circulating leptin levels tend to increase the risk of MDD. In line with our findings, an observational study revealed a substantial increase in plasma leptin levels in female patients with MDD ^[13]. In addition, a large cohort epidemiologic study revealed a relationship between leptin and depression, with higher leptin levels in women with depression and elevated leptin levels possibly predicting depression in the overall cohort^[44]. In addition, a positive correlation was observed between leptin mRNA and protein expression and the severity of depression in a clinical study^[45].

Interestingly, in contrast to previous observational studies, antidepressant effects of leptin have been observed in animal studies^[12], and the most likely explanation for these conflicting results is that leptin is confounded by potential confounders such as sex, age, fat content, and treatment^[45], as well as animal and human heterogeneity. The above factors have also been shown to influence leptin concentrations in both men and women^[46]. Compared with clinical research, MR eliminates the influence of interference factors in the process of research, increases the reliability of the results, decreases the time and cost, and can be utilized to assess the correlation between one phenotype and another phenotype, which makes our results more realistic and reliable.

Leptin plays a key role in the regulation of energy homeostasis by acting as a negative feedback signal to suppress appetite while stimulating neurons to produce anorexigenic signals that collectively reduce food intake^[47]. In addition, the central nervous system is significantly impacted by leptin, which affects neurotransmitters such as dopaminergic neurotransmitters and gray matter plasticity^{[48, 49]}. These studies provide potential evidence for a link between leptin and major depressive disorder, and we believe that leptin may play a role in the development of MDD by affecting neurotransmitter transmission and appetite. However, the specific underlying mechanism is unclear, and more experiments are needed to explore and verify this phenomenon.

Within our research, we did not detect an association between the PAI-1 or resistin concentration and the risk of MDD. However, all the results suggested the same trend: the PAI-1 was positively correlated with the risk of MDD, and resistin was more inclined to reduce the risk of MDD, although the results were not statistically significant. This finding contradicts the findings of some scholars, such as clinical studies showing high levels of PAI-1 in the plasma of patients with depression^{[50, 51]}. On the basis of studies in mice, Helene et al. suggested that PAI-1 may serve as a protective factor for MDD, with its absence leading to depression and resistance to subsequent treatment^[52]. Another study showed that increased PAI-1 expression was associated with a depressive phenotype in mice^[18]. Ham-d scores in patients with MDD and serum resistin scores were significantly correlated in a prospective study^[17]. Although the above observational studies described the correlation between the PAI-1 score and MDD incidence, these findings are susceptible to interference from confounding factors such as individual patient heterogeneity and racial differences. Therefore, further studies are needed to determine the causal relationships among these factors.

This study is the first to evaluate whether adipokines are associated with MDD risk. To reduce the impact of biased outcomes, thorough sensitivity analyses were conducted to evaluate the accuracy of the MR hypothesis. The IVW method has a stringent condition in which all SNPs are not horizontally pleiotropic. The use of IVW as the main method increases the statistical power over other methods ^[19]. We used MR‒Egger regression to ensure the absence of potential pleiotropy of SNPs and thus to make the analysis more stable. Our study endeavored to maintain the prerequisite that all MR methods be consistent, enhancing the reliability of the results. However, some limitations need to be considered. First, the dataset was based on individuals of European ancestry and may be heterogeneous compared with other ethnic groups. Second, MDD cases from GWASs were identified from self-reported depression phenotypes as well as hospital admission records, which may affect the diagnostic accuracy. Third, the functional biological role of genetic variation is not yet fully understood, and completely ruling out pleiotropy remains challenging. Finally, our results indicated that leptin and MDD risk are likely linked, and the underlying mechanisms for this association need to be further explored.

Our research provides evidence of a causal relationship between leptin and MDD risk. More studies are needed to explore the underlying mechanisms involved. Considering the prevalence and persistence of MDD, the prevention and management of MDD should be emphasized.

Ethics approval and consent to participate

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Consent for publication

Not applicable

Competing interests

The authors hereby declare that they have no conflicts of interest to disclose

Sources of funding

This study was supported by the Xingdian talent support program of Yunnan Province (Grant No.RLQB20220010 and RLMY20200024)

Availability of data and meterials

All data generated or analysed during this study are included in this published article and its Additional files.

Author contributions

Shuyi Qiu designed the study and drafted the manuscript. Yong Duan and Junxi Pan performed the data collection and analysis. Binjing Dou, Jin Guo, Jiahong Li, Chunmei Li, Ziyu Yuan and Xianglin Hu contributed to methodological guidance and manuscript revision. All authors read and approved the final manuscript.

Liu L, Wang H, Chen X, Zhang Y, Zhang H, Xie P. Gut microbiota and its metabolites in depression: from pathogenesis to treatment. EBioMedicine. 2023;90:104527.
de Oliveira LRS, Machado FSM, Rocha-Dias I, E Magalhães COD, De Sousa RAL, Cassilhas RC. An overview of the molecular and physiological antidepressant mechanisms of physical exercise in animal models of depression. Mol Biol Rep. 2022;49(6):4965-4975.
COVID-19 Mental Disorders Collaborators. Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic. Lancet. 2021;398(10312):1700-1712.
Howard DM, Adams MJ, Clarke TK, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343-352. doi:10.1038/s41593-018-0326-7
Hinze-Selch D, Schuld A, Kraus T, et al. Effects of antidepressants on weight and on the plasma levels of leptin, TNF-alpha and soluble TNF receptors: A longitudinal study in patients treated with amitriptyline or paroxetine. Neuropsychopharmacology. 2000;23(1):13-19.
Duman RS, Li N. A neurotrophic hypothesis of depression: role of synaptogenesis in the actions of NMDA receptor antagonists. Philos Trans R Soc Lond B Biol Sci. 2012;367(1601):2475-2484.
Duman RS, Monteggia LM. A neurotrophic model for stress-related mood disorders. Biol Psychiatry. 2006;59(12):1116-1127.
Chirinos DA, Goldberg R, Gellman M, et al. Leptin and its association with somatic depressive symptoms in patients with the metabolic syndrome. Ann Behav Med. 2013;46(1):31-39.
Eikelis N, Esler M, Barton D, Dawood T, Wiesner G, Lambert G. Reduced brain leptin in patients with major depressive disorder and in suicide victims. Mol Psychiatry. 2006;11(9):800-801.
Jow GM, Yang TT, Chen CL. Leptin and cholesterol levels are low in major depressive disorder, but high in schizophrenia. J Affect Disord. 2006;90(1):21-27.
Yang K, Xie G, Zhang Z, et al. Levels of serum interleukin (IL)-6, IL-1beta, tumoour necrosis factor-alpha and leptin and their correlation in depression. Aust N Z J Psychiatry. 2007;41(3):266-273.
Kraus T, Haack M, Schuld A, Hinze-Selch D, Pollmächer T. Low leptin levels but normal body mass indices in patients with depression or schizophrenia. Neuroendocrinology. 2001;73(4):243-247.
Liu J, Garza JC, Bronner J, Kim CS, Zhang W, Lu XY. Acute administration of leptin produces anxiolytic-like effects: a comparison with fluoxetine. Psychopharmacology (Berl). 2010;207(4):535-545.
Zeman M, Jirak R, Jachymova M, Vecka M, Tvrzicka E, Zak A. Leptin, adiponectin, leptin to adiponectin ratio and insulin resistance in depressive women. Neuro Endocrinol Lett. 2009;30(3):387-395.
Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. J Immunol. 2005;174(9):5789-5795.
Lehto SM, Huotari A, Niskanen L, et al. Serum adiponectin and resistin levels in major depressive disorder. Acta Psychiatr Scand. 2010;121(3):209-215.
Griffith TA, Russell JS, Naghipour S, et al. Behavioural disruption in diabetic mice: Neurobiological correlates and influences of dietary α-linolenic acid. Life Sci. 2022;311(Pt A):121137.
Rahman S, Shanta AA, Daria S, et al. Increased serum resistin but not G-CSF levels are associated in the pathophysiology of major depressive disorder: Findings from a case-control study. PLoS One. 2022;17(2):e0264404. Published 2022 Feb 25.
Girard RA, Chauhan PS, Tucker TA, et al. Increased expression of plasminogen activator inhibitor-1 (PAI-1) is associated with depression and depressive phenotype in C57Bl/6J mice. Exp Brain Res. 2019;237(12):3419-3430.
Lin Z, Deng Y, Pan W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet. 2021;17(11):e1009922. Published 2021 Nov 18.
Nowak C, Ärnlöv J. A Mendelian randomization study of the effects of blood lipids on breast cancer risk. Nat Commun. 2018;9(1):3957. Published 2018 Sep 27.
Maina JG, Balkhiyarova Z, Nouwen A, et al. Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes. Diabetes Care. 2023;46(9):1707-1714. doi:10.2337/dc22-2373
Yaghootkar H, Zhang Y, Spracklen CN, et al. Genetic Studies of Leptin Concentrations Implicate Leptin in the Regulation of Early Adiposity. Diabetes. 2020;69(12):2806-2818.
Folkersen L, Gustafsson S, Wang Q, et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab. 2020;2(10):1135-1148.
Gilly A, Park YC, Png G, et al. Whole-genome sequencing analysis of the cardiometabolic proteome. Nat Commun. 2020;11(1):6336. Published 2020 Dec 10.
Yang Q, Sanderson E, Tilling K, Borges MC, Lawlor DA. Exploring and mitigating potential bias when genetic instrumental variables are associated with multiple non-exposure traits in Mendelian randomization. Eur J Epidemiol. 2022;37(7):683-700.
Bahls M, Leitzmann MF, Karch A, et al. Physical activity, sedentary behavior and risk of coronary artery disease, myocardial infarction and ischemic stroke: a two-sample Mendelian randomization study. Clin Res Cardiol. 2021;110(10):1564-1573.
Chen X, Hong X, Gao W, et al. Causal relationship between physical activity, leisure sedentary behaviors and COVID-19 risk: a Mendelian randomization study. J Transl Med. 2022;20(1):216. Published 2022 May 13.
Lin T, Zhou F, Mao H, Xie Z, Jin Y. Vitamin D and idiopathic pulmonary fibrosis: a two-sample mendelian randomization study. BMC Pulm Med. 2023;23(1):309. Published 2023 Aug 23.
Burgess S, Thompson SG; CRP CHD Genetics Collaboration. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755-764.
Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data [published correction appears in PLoS Genet. 2017 Dec 29;13(12 ):e1007149]. PLoS Genet. 2017;13(11):e1007081. Published 2017 Nov 17.
Milaneschi Y, Peyrot WJ, Nivard MG, Mbarek H, Boomsma DI, W J H Penninx B. A role for vitamin D and omega-3 fatty acids in major depression? An exploration using genomics. Transl Psychiatry. 2019;9(1):219. Published 2019 Sep 5.
Burgess S, Davey Smith G, Davies NM, et al. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res. 2023;4:186. Published 2023 Aug 4.
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304-314.
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985-1998.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512-525.
Zhu G, Zhou S, Xu Y, et al. Mendelian randomization study on the causal effects of omega-3 fatty acids on rheumatoid arthritis. Clin Rheumatol. 2022;41(5):1305-1312.
Hemani G, Zheng J, Elsworth B, et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. Published 2018 May 30.
Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195-R208.
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases [published correction appears in Nat Genet. 2018 Aug;50(8):1196]. Nat Genet. 2018;50(5):693-698.
Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28(1):30-42.
Greenberg PE, Fournier AA, Sisitsky T, et al. The Economic Burden of Adults with Major Depressive Disorder in the United States (2010 and 2018). Pharmacoeconomics. 2021;39(6):653-665.
Nobis A, Zalewski D, Waszkiewicz N. Peripheral Markers of Depression. J Clin Med. 2020;9(12):3793. Published 2020 Nov 24.
Pasco JA, Jacka FN, Williams LJ, et al. Leptin in depressed women: cross-sectional and longitudinal data from an epidemiologic study. J Affect Disord. 2008;107(1-3):221-225.
Ge T, Fan J, Yang W, Cui R, Li B. Leptin in depression: a potential therapeutic target. Cell Death Dis. 2018;9(11):1096. Published 2018 Oct 26.
Mantzoros CS, Magkos F, Brinkoetter M, et al. Leptin in human physiology and pathophysiology. Am J Physiol Endocrinol Metab. 2011;301(4):E567-E584.
Cowley MA, Smart JL, Rubinstein M, et al. Leptin activates anorexigenic POMC neurons through a neural network in the arcuate nucleus. Nature. 2001;411(6836):480-484.
Ishibashi K, Berman SM, Paz-Filho G, et al. Dopamine D2/D3 receptor availability in genetically leptin-deficient patients after long-term leptin replacement. Mol Psychiatry. 2012;17(4):352-353.
London ED, Berman SM, Chakrapani S, et al. Short-term plasticity of gray matter associated with leptin deficiency and replacement [published correction appears in J Clin Endocrinol Metab. 2011 Nov;96(11):3576. Dosage error in article text]. J Clin Endocrinol Metab. 2011;96(8):E1212-E1220.
Eskandari F, Mistry S, Martinez PE, et al. Younger, premenopausal women with major depressive disorder have more abdominal fat and increased serum levels of prothrombotic factors: implications for greater cardiovascular risk. Metabolism. 2005;54(7):918-924.
Lahlou-Laforet K, Alhenc-Gelas M, Pornin M, et al. Relation of depressive mood to plasminogen activator inhibitor, tissue plasminogen activator, and fibrinogen levels in patients with versus without coronary heart disease. Am J Cardiol. 2006;97(9):1287-1291.
Party H, Dujarrier C, Hébert M, et al. Plasminogen Activator Inhibitor-1 (PAI-1) deficiency predisposes to depression and resistance to treatments. Acta Neuropathol Commun. 2019;7(1):153. Published 2019 Oct 14.

No competing interests reported.

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Circulating adipokines and major depressive disorder: a two-sample Mendelian randomization study

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Conclusion

Figures

Introduction

Method

Study design

Figure 1. Study design

Source of adipokine data

Data sources for the MDD patients

Data availability

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1