Sex-Specific causal dynamic between Insulin resistance and MDD, a bidirectional Mendelian randomization study

Sex-Specific causal dynamic between Insulin resistance and MDD, a bidirectional Mendelian randomization study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Sex-Specific causal dynamic between Insulin resistance and MDD, a bidirectional Mendelian randomization study View ORCID Profile Qizhou Xia , Patricia P. Silveira doi: https://doi.org/10.1101/2024.07.16.24310339 Qizhou Xia 1 Integrated Program in Neurosciences, McGill University , Montreal, QC, Canada 2 Douglas Mental Health University Institute, McGill University , Montreal, QC, Canada BSc Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Qizhou Xia Patricia P. Silveira 2 Douglas Mental Health University Institute, McGill University , Montreal, QC, Canada 3 Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Centre, McGill University , Montreal, QC, Canada 4 Department of Psychiatry, Faculty of Medicine and Health Sciences, McGill University , Montreal, QC, Canada MD, PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: patricia.silveira{at}mcgill.ca Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Aims/hypothesis Observational studies have shown a bidirectional association between major depressive disorder (MDD), Insulin resistance (IR), and related diseases, which varies between sexes and ancestries. We conducted a sex-specific two-sample bidirectional Mendelian randomization (MR) study to assess the causal associations of MDD with Insulin resistance measured through the TG: HDL-C ratio and vice versa using Caucasian and East Asian data. Methods We extracted summary-level data for MDD and insulin resistance from corresponding published large genome-wide association studies of individuals of European descent and partially replicated the analyses using available summary data from studies of individuals of East Asian descent. The random-effects inverse-variance weighted method and MRlap method were used for the main analyses for non-overlapping and overlapping samples, respectively. Results Genetic liability to MDD was significantly associated with insulin resistance both generally and sex-specifically, while the causal effect of Insulin resistance on MDD is only consistently significant in females. We did not find any significant causal association between IR and MDD using East Asian data, though the beta estimates suggest potentially ancestry-related differences in the direction of effect. Conclusions/interpretation The present study strengthened the evidence that MDD is a potential risk factor for insulin resistance and that insulin resistance plays a sex—and ancestry-specific role in MDD pathology. Together, these findings could contribute to further our understanding of the comorbidity between MDD and IR-related diseases, allowing for more individualized treatment and diagnosis. Introduction Major Depressive Disorder (MDD) is one of the most prevalent and socially debilitating mental illnesses globally, affecting an estimated 200-300 million individuals 1 . Similarly, cardiometabolic disorders such as Type 2 Diabetes Mellitus (T2DM) impact an equivalent number of people worldwide 2 . Clinical studies have repeatedly demonstrated significant comorbidity between these two groups of disorders, with a substantial percentage of patients with MDD being diagnosed with diabetes and vice versa 3 , 4 . Current literature suggests several shared pathogenic mechanisms between these conditions, notably insulin resistance (IR) 5 , 6 . IR is a pathological, modifiable, metabolic, and inflammatory precursor state to T2DM characterized by decreased peripheral tissue responsiveness to insulin 7 . This condition and related diseases are speculated to have a bidirectional association with depression 8 : IR is associated with an increased likelihood of developing depression and exacerbating its symptoms 9 , while higher depression severity is likewise positively associated with IR levels 10 , thus constituting a worsening cycle between the two disorders. This relationship could be underpinned by an underlying state of inflammation and various shared biological mechanisms, including abnormalities in insulin signal processing and hypothalamic-pituitary-adrenal axis function disruptions 11 . The presence of this bidirectional causal association and its nature as a malleable pathophysiological condition suggests that IR could be a target for intervention and treatment in both MDD and T2DM 5 . However, a notable sex disparity exists in the prevalence of comorbidity between MDD and T2DM; diagnosis of T2DM leads to more than a two-fold increase in the odds of developing MDD in females, whereas this effect is significantly less pronounced in males 3 . The significant sex disparity between the prevalence of comorbidity between T2DM and MDD requires intervention and treatment strategies where biological sex is taken into consideration. Observational studies focusing on the sex-specific patterns in the relationship between IR and MDD yielded mixed results 12 , as positive associations between IR and MDD have been found in both males and females 13 , 14 , 15 . Furthermore, the observational nature of these previous studies led to issues such as residual confounding and potential reverse causality 16 , which limit interpretative power and applicability in clinical and pharmaceutical settings. Within this context, this study will utilize summary-level data from extensive previous genome-wide association studies (GWAS) to address these limitations and provide robust causal evidence through Mendelian randomization 17 . Our objectives are : 1) to substantiate the association between insulin resistance and MDD through Mendelian causal inference, and 2) to investigate the existence of a sex-specific causal dynamic between insulin resistance and major depressive disorders using a series of bidirectional Mendelian randomization analyses. These approaches aim to clarify the mechanisms contributing to the observed sex differences in the comorbidity of these complex diseases by investigating the sex-specificity causal effect of Insulin resistance as a pathological metabolic state associated with MDD in order to guide more targeted therapeutic and intervention strategies. Methods Data sources and instrument selections for two sample bidirectional Mendelian randomization Insulin resistance (IR) The genetic summary statistics for IR were extracted from the most recent comprehensive genome-wide association study on this trait by Oliveri et al. 18 , which includes a total of 402398 European subjects (185749 males and 216649 females) from UKB. Insulin resistance is measured in this study using the ratio of triglycerides to high-density lipoprotein cholesterol (TG: HDL-C) as a surrogate marker. Overall, this study identified 369 independent SNPs reaching genome-wide significance in the combined cohort, of which 318 have not been previously reported for IR, 141 independent SNPs in the male-only cohort, and 199 independent SNPs in females. To impose a more stringent and conservative level of independence (r2<0.01, window size=3000kb), we clumped the variants based on linkage disequilibrium (LD) between variants using the European subset of 1000 Genomes 19 . Finally, we removed pleiotropic SNPs, which are significantly associated with both exposure and outcome traits, resulting in 155 and 103 instrumental variables for females and males, respectively. Major depressive disorder (MDD) general population The general population genetic summary statistic for MDD was derived from a recent genome-wide association study (GWAS) meta-analysis by Howard et al. 20 , which includes 807553 individuals (246363 cases) from three cohorts: the Psychiatric Genomics Consortium (PGC), UK Biobank, and 23andMe307K; as well as a replication 23andMe cohort comprising 1306353 participants with 414574 cases. Depression in this study was defined broadly as either self-reported (UKB and 23andMe) or clinically diagnosed in PGC, with a strong genetic correlation observed between data sets (0.85-0.87) despite the different case definitions 20 . The study identified 102 SNPs independently associated with MDD, reaching genome-wide significance in the test cohort. From these SNPs, we filtered those who did not reach a genome-wide significance level of 5e-8 in the combined meta-analysis+replication cohort. Furthermore, we clumped the SNPs based on linkage disequilibrium (LD) between variants using the European subset of 1000 Genomes 19 , using the same parameters as mentioned before. Finally, we excluded palindromic SNPs with intermediate allele frequency information. The application of these filters leads to 87 independent variants as genetic instrumental variables to act as proxies for the effect of MDD exposure on IR levels. For analyses where major depressive disorder (MDD) in the general population was the outcome, genetic estimates were limited to the Psychiatric Genomics Consortium (PGC) and UK Biobank cohorts due to restricted access to the genome-wide data from 23andMe. Consequently, this part of the study comprised a total of 500,199 participants, including 170,756 individuals with depression and 329,443 controls. Sex-specific MDD The sex-specific genetic summary statistic for MDD was derived from a recent sex-stratified genome-wide association study by Silveira et al. 21 , which included 127867 non-related males (34923 cases) and 146274 non-related females (62066 cases). All subjects self-identified as Caucasian descent. Depression in this study is defined based on self-reported help-seeking behaviour for mental health difficulties from either a general practitioner or psychiatrist, with a strong correlation of a beta coefficient of 0.91 with the Howard broad depression GWAS 21 . This study reported 11 independent loci reaching genome-wide significance for females and 1 for males; therefore, we lowered the threshold for significance to 5e-6 and applied a clumping parameter of 3000KB, r2<0.01, resulting in a total of 45 and 37 instrumental variables (SNPs) for females and males, respectively. Additional East-Asian cohort replication Due to the known ancestry-related differences in the clinical outlook of MDD and its association with insulin resistance and cardiometabolic disorders 22 , 23 , 24 , we seek to examine whether the observed pattern of association between IR and MDD in the Caucasian cohort could also be observed using East Asian data. We extracted genetic summary statistics of both MDD 24 and IR 18 from corresponding GWASes (MDD summary is derived from a study by Giannakopoulo et al., while IR statistics is derived from East Asian-specific cohort data in the study by Oliveri et al.), and applied the same uniform instrument selection pipeline as described before. Additionally, we lowered the significance threshold for instrument selection to 5e-6 due to the small number of significant independent SNPs identified. Overall, 8 instruments were selected for MDD and 4 for IR ( Table 1 ). View this table: View inline View popup Download powerpoint Table 1: Source and characteristics of GWAS summary statistics used in analyses Statistical analysis Two sampled Mendelian randomization We first utilized the multiplicative random-effects inverse-variance weighted (IVW) method to estimate potential causal associations 25 . This approach, employing the IVW method with random effects accounts for potential heterogeneity across the individual SNP Wald ratio estimates 26 . We further conducted several sensitivity analyses using the weighted median 27 , penalized median 27 , MR-Egger regression 28 , and MR-PRESSO methods 29 , to evaluate the consistency of our results and to identify possible violations of the MR assumptions such as directional 28 and horizontal pleiotropy 29 . Due to the sample overlap among some of the datasets analyzed, we additionally calculated causal effects with the inverse variance weighted (IVW) method within the MRlap method 30 , which is designed to address weak instrument bias and winner’s curse while accounting for potential sample overlap among datasets used in two-sample MR analyses. Furthermore, leave-one-out analysis using the default IVW method was performed to further identify outlier SNPs, when a potential outlier is identified, the full range of sensitivity analyses are performed on the remaining SNPs to assess result robustness. Finally, the SNPs that were unavailable in the outcome datasets were replaced by proxies at R2 > 0.90 using LDlink 31 .Unless otherwise specified, we report MRlap outputs as the main results for MDD-IR bidirectional association analyses in Caucasians due to sample overlap and IVW outputs for the analyses in East Asians. To verify that MR estimates are not under the influence of weak instrument bias, we calculated F -statistics for each genetic instrument j using the formula: Fj = γj 2/ σXj 2where γj is the SNP-exposure estimate and σXj is the corresponding standard error 32 . An F-statistic>10 indicates strong instrument strength 33 . All analyses, except MRLap estimation, were conducted using the TwoSampleMR package ( https://mrcieu.github.io/TwoSampleMR/ ) in R software (version 4.3.1; www.r-project.org/ ). Results Main analyses MDD-IR causal association Genetic liability to MDD in general and both female and male-specific cohorts was significantly associated with increased IR levels (Suppl. Table 1, a-c, Figure 1, a-c ). The increase in the rank-based inverse-normalized TG:HDL-C ratio per one-unit increase in log odds of MDD was 0.269 (pval<1.67E-4) in the general population analysis, 0.149 (pval<2.44E-3) in the female-specific and 0.149 (pval <.5.55E-3) in the male-specific analysis The Cochran’s Q test reveals substantial heterogeneity among estimates of individual SNPs in all three analyses, while MR-Egger analysis reveals no evidence for directional pleiotropy (Suppl. Table 1. a-c). The association was consistently significant across sensitivity analysis except in MR-Egger (Suppl. Table 1. a-c). For all three analyses, leave-one-out analysis rejects the possibility that a single SNP is driving the association (Suppl. Figure 1-3). However, for all three analyses, MR-PRESSO reveals a significant global index of horizontal pleiotropy with no evidence of significant distortion (Suppl. Table 1. a-c), suggesting that although there is evidence of horizontal pleiotropy, the outliers detected are not substantially influencing the overall MR estimate and that the results obtained are robust. Download figure Open in new tab Download figure Open in new tab Figure 1: Forest plots showing the MR analysis results between Major depressive disorder (exposure) and Insulin resistance level (outcome) in European (A), European females (B) and European males (C). The 95% CIs and P values are shown on the right. Sensitivity analyses except MR-Egger yielded consistent results, where MDD genetic liability is positively causally associated with IR level. The causal effect of IR on MDD The general population analysis does not support a significant causal association between IR and MDD (b=0.0093,pval<0.103), with significant heterogeneity amongst estimates obtained from individual SNPs (Q=516.1, pval<8.89E-15) and no evidence of directional pleiotropy(bi=-0.00039,pval<0.561) (Suppl. Table 2. a, Figure 2.a ). However, the sex-specific analysis revealed a significant positive association between IR levels and MDD risk in females (b=0.021, pval<0.0232) but not in males(b=0.014,p<0.178) (Suppl. Table 2. b-c, Figure 2. b-c ). This pattern of association is consistent across sensitivity analysis, except the MR-Egger analysis, which yielded non-significant results for all three groups (Suppl. Table 2, Figure 2 ). Non-significant heterogeneity and no evidence for directional and horizontal pleiotropy were detected for both sex-specific analyses, and although there is evidence of horizontal pleiotropy in the general population analysis, we found no evidence for significant distortion (Suppl. Table 2. a). Leave-one-out analysis suggests that no single SNP is driving the observed association in the sex-specific analyses(Suppl. Figure 5-6). However, in the general population analysis, the removal of the outlier rs72786786 results in a weakly significant P-value of 0.047 using MR-IVW (Suppl. Figure 4) and MR-LASSO estimation but not while using any other MR methods (Suppl. Table 12). Download figure Open in new tab Download figure Open in new tab Figure 2: Forest plots showing the MR analysis results between Insulin resistance level (exposure) and Major depressive disorder risk (outcome) in European (A), European females (B) and European males (C). The 95% CIs and P values are shown on the right. Sensitivity analyses except MR-Egger yielded consistent results, where IR level genetic liability is positively causally associated with MDD risk only in females. East Asian cohort analyses No significant effect has been found in either the forward or the reverse direction analysis, though IR has a suggestive positive effect on MDD risk (b=0.071, pval<6.92e-2). Interestingly, we observed a consistent negative effect of MDD genetic liability on the IR level in several sensitivity analyses, in contrast to the findings in the analyses using Caucasian data (Suppl. Table 3), suggesting possible underlying ancestry-related differences in the direction of effect. Discussion This current study consolidates MDD as a risk factor for insulin resistance, with consistent causal effects across sexes. Our findings align with previous research; whereas clinical studies have shown that MDD is associated with increased insulin resistance, possibly mediated by systemic inflammation and abnormalities in the hypothalamic-pituitary-adrenal axis function, some studies leveraging genetic data through the use of mendelian randomization have also supported a causal association between MDD and pathological cardiometabolic traits such as T2DM 34 as well as metabolic syndrome and its component 35 . However, more importantly, to our best knowledge, this current study is the first to provide causal evidence for the sex-specific role of Insulin resistance in MDD pathology, where IR genetic liability significantly predicts MDD risk only in females, but not in males. Whilst sex-specific patterns have been documented in epidemiological studies, with women being more susceptible to comorbidities between MDD and Type 2 Diabetes Mellitus (T2DM), no previous study has attempted to evaluate the sex-specificity of insulin resistance as a proposed common pathogenic mechanism or applied a sex-stratified method in evaluating the effect of cardiometabolic disorders on depression risks. Consequently, previous studies examining the causal effect of cardiometabolic traits on depression risks through Mendelian randomization or other methods have often failed to establish any significant results 34 , 35 , 36 . We propose that the significant causal effect of IR observed uniquely in females but not in males could be partially attributed to the known sex difference in inflammation and immune responses. Women generally have stronger baseline immune responses than men 37 , which can lead to higher levels of systemic inflammation. Inflammation is a prominent link between insulin resistance (IR) and Major Depressive Disorder (MDD), where IR is associated with the production of pro-inflammatory cytokines like TNF-α 5 , which disrupts the blood-brain barrier 38 and causes neuroinflammation, disrupting the neurotransmitter system such as serotonin signalling and thus leading to depressive symptoms 39 , 40 . Therefore, the stronger inflammatory response in women may amplify these pathways, contributing to the observed sex-specific relationship between IR and MDD. Though the analyses using East Asian data yielded non-significant results, we note an interesting pattern of the possible negative effects of MDD on IR level by observing the beta estimates from several MR methods. The ancestry-related difference in the association between cardiometabolic traits and Major depression has been frequently observed in various studies 23 , 24 . Most recently, a study by Meng et al. 22 has shown that while Triglyceride level is positively associated with MDD risk in Caucasians, the reverse is true in East Asians. However, the lack of statistical power due to a smaller sample size impedes us from revealing any significant ancestry-related difference in the causal dynamic between Insulin resistance and major depressive disorder. Similarly, the lack of sex-stratified data also prevents us from exploring the sex-specificity of this association in East Asians. A limitation of this current study is the broad definition of depression. A previous meta-analysis focusing on the association between insulin resistance and MDD has suggested that this association is only present in the atypical metabolic subtype but not typical depression 41 . The use of GWAS for broadly defined depression with no subtype information in this study could limit the clinical applicability of the findings, as it’s unclear whether the associations observed are unique to the atypical metabolic subtype of depression or pertain to general depression as well. However, it is worth noting that no sex-specific GWAS has been performed on the atypical subtype of MDD, yet future research endeavours are needed to address this gap in the understanding of the genetic architecture of MDD as a heterogeneous disorder. Furthermore, whilst the simple and easy-to-gather TG: HDL-C measure of insulin resistance in the Oliveri study 18 , from which our instrumental variables are derived, permits the performance of genome-wide association analyses on a larger sample size, whether its clinical validity and correlation with other common measures of IR, such as HOMA-IR and fasting insulin measure, remains an area in debate 18 . We selected a GWAS with this measure of Insulin resistance due to its larger sample size and higher number of independent SNPs identified, however, future studies need to consider the trade-offs between statistical power and the precision of the insulin resistance measurement. Although the TG: HDL-C ratio provides a convenient proxy, its comparability to more direct measures like HOMA-IR or fasting insulin levels should be critically evaluated in the context of a genetic study. Future studies may benefit from incorporating these alternative metrics to confirm findings and enhance understanding of the biological pathways involved. This dual approach could potentially lead to more robust conclusions about the genetic determinants of insulin resistance and its implications for related metabolic and mental disorders. In conclusion, this current study strengthens the evidence that MDD is a potential risk factor for IR, and it is the first to provide causal evidence of IR’s sex-specific role in MDD pathology, overcoming the shortcomings of traditional observational studies such as reverse causality. However, future GWAS studies with larger sample sizes are necessary to improve statistical power for East Asian cohorts to establish significant ancestry-related differences. We hope this study could further our understanding of the underlying mechanism for the comorbidity between MDD and cardiometabolic diseases, paving the way for more individualized therapeutic and intervention strategies. Data Availability All data produced in the present study are available upon reasonable request to the authors References 1. ↵ U.S. Department of Health and Human Services. (n.d .). Major depression . National Institute of Mental Health . https://www.nimh.nih.gov/health/statistics/major-depression 2. ↵ World Health Organization. (n.d .). Diabetes . World Health Organization . https://www.who.int/health-topics/diabetes 3. ↵ Katon , W. J. ( 2008 ). The comorbidity of diabetes mellitus and depression . The American Journal of Medicine , 121 ( 11 ). doi: 10.1016/j.amjmed.2008.09.008 OpenUrl CrossRef PubMed Web of Science 4. ↵ Anderson , R. J. , Freedland , K. E. , Clouse , R. E. , & Lustman , P. J. ( 2001 ). The prevalence of comorbid depression in adults with diabetes . Diabetes Care , 24 ( 6 ), 1069 – 1078 . doi: 10.2337/diacare.24.6.1069 OpenUrl Abstract / FREE Full Text 5. ↵ Lyra e Silva , N. de , Lam , M. P. , Soares , C. N. , Munoz , D. P. , Milev , R. , & De Felice , F. G. ( 2019 ). Insulin resistance as a shared pathogenic mechanism between depression and type 2 diabetes . Frontiers in Psychiatry , 10 . doi: 10.3389/fpsyt.2019.00057 OpenUrl CrossRef 6. ↵ Watson , K. , Nasca , C. , Aasly , L. , McEwen , B. , & Rasgon , N. ( 2018 ). Insulin resistance, an unmasked culprit in Depressive Disorders: Promises for interventions . Neuropharmacology , 136 , 327 – 334 . doi: 10.1016/j.neuropharm.2017.11.038 OpenUrl CrossRef PubMed 7. ↵ Lebovitz , H. ( 2001 ). Insulin resistance: Definition and consequences . Experimental and Clinical Endocrinology & Diabetes , 109 ( Suppl 2 ). doi: 10.1055/s-2001-18576 OpenUrl CrossRef 8. ↵ Kan , C. , Silva , N. , Golden , S. H. , Rajala , U. , Timonen , M. , Stahl , D. , & Ismail , K. ( 2013 ). A systematic review and meta-analysis of the association between depression and insulin resistance . Diabetes Care , 36 ( 2 ), 480 – 489 . doi: 10.2337/dc12-1442 OpenUrl Abstract / FREE Full Text 9. ↵ Jin , M. , Lv , P. , Liang , H. , Teng , Z. , Gao , C. , Zhang , X. , Ni , A. , Cui , X. , Meng , N. , & Li , L. ( 2023 ). Association of Triglyceride-Glucose index with major depressive disorder: A cross-sectional study . Medicine , 102 ( 24 ). doi: 10.1097/md.0000000000034058 OpenUrl CrossRef 10. ↵ Timonen , M. , Laakso , M. , Jokelainen , J. , Rajala , U. , Meyer-Rochow , V. B. , & Keinänen-Kiukaanniemi , S. ( 2004 ). Insulin resistance and depression: Cross sectional study . BMJ , 330 ( 7481 ), 17 – 18 . doi: 10.1136/bmj.38313.513310.f71 OpenUrl CrossRef 11. ↵ Dantzer , R. , O’Connor , J. C. , Freund , G. G. , Johnson , R. W. , & Kelley , K. W. ( 2008 ). From inflammation to sickness and depression: When the immune system subjugates the brain . Nature Reviews Neuroscience , 9 ( 1 ), 46 – 56 . doi: 10.1038/nrn2297 OpenUrl CrossRef PubMed Web of Science 12. ↵ Krupa , A. J. , Dudek , D. , & Siwek , M. ( 2023 ). Consolidating evidence on the role of insulin resistance in major depressive disorder . Current Opinion in Psychiatry , 37 ( 1 ), 23 – 28 . doi: 10.1097/yco.0000000000000905 OpenUrl CrossRef 13. ↵ He , Y. , Tong , L. , Guo , F. , Zhao , S. , Zhang , J. , Guo , X. , Tao , Y. , Lin , X. , & Jin , L. ( 2022 ). Depression status and insulin resistance in adults with obesity: A cross-sectional study . Journal of Psychosomatic Research , 163 , 111049 . doi: 10.1016/j.jpsychores.2022.111049 OpenUrl CrossRef 14. ↵ Zheng , L. , Cui , C. , Yue , S. , Yan , H. , Zhang , T. , Ding , M. , Sun , Q. , He , C. , & Ren , H. ( 2023 ). Longitudinal association between triglyceride glucose index and depression progression in middle-aged and elder adults: A national retrospective cohort study . Nutrition, Metabolism and Cardiovascular Diseases , 33 ( 3 ), 507 – 515 . doi: 10.1016/j.numecd.2022.11.015 OpenUrl CrossRef 15. ↵ Łapińska , L. , Stachurska , Z. , Raczkowski , A. , Waszkiewicz , N. , Kamiński , K. , & Kowalska , ( 2022 ). The relationship between subclinical depressive symptoms and metabolic parameters in women: A subanalysis of a Bialystok plus study . Polish Archives of Internal Medicine . doi: 10.20452/pamw.16261 OpenUrl CrossRef 16. ↵ Grimes , D. A. , & Schulz , K. F. ( 2002 ). Bias and causal associations in observational research . The Lancet , 359 ( 9302 ), 248 – 252 . doi: 10.1016/s0140-6736(02)07451-2 OpenUrl CrossRef 17. ↵ Lawlor , D. A. , Harbord , R. M. , Sterne , J. A. , Timpson , N. , & Davey Smith , G. ( 2008 ). Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology . Statistics in Medicine , 27 ( 8 ), 1133 – 1163 . doi: 10.1002/sim.3034 OpenUrl CrossRef PubMed 18. ↵ Oliveri , A. , Rebernick , R. J. , Kuppa , A. , Pant , A. , Chen , Y. , Du , X. , Cushing , K. C. , Bell , H. N. , Raut , C. , Prabhu , P. , Chen , V. L. , Halligan , B. D. , & Speliotes , E. K. ( 2024 ). Comprehensive genetic study of the insulin resistance marker TG:HDL-C in the UK Biobank . Nature Genetics , 56 ( 2 ), 212 – 221 . doi: 10.1038/s41588-023-01625-2 OpenUrl CrossRef 19. ↵ The 1000 Genomes Project Consortium . A global reference for human genetic variation . Nature 526 , 68 – 74 ( 2015 ). doi: 10.1038/nature15393 OpenUrl CrossRef PubMed 20. ↵ Howard , D. M. , Adams , M. J. , Clarke , T.-K. , Hafferty , J. D. , Gibson , J. , Shirali , M. , Coleman , J. R. , Hagenaars , S. P. , Ward , J. , Wigmore , E. M. , Alloza , C. , Shen , X. , Barbu , M. C. , Xu , E. Y. , Whalley , H. C. , Marioni , R. E. , Porteous , D. J. , Davies , G. , Deary , I. J. , … McIntosh , A. M. ( 2019 ). Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions . Nature Neuroscience , 22 ( 3 ), 343 – 352 . doi: 10.1038/s41593-018-0326-7 OpenUrl CrossRef PubMed 21. ↵ Silveira , P. P. , Pokhvisneva , I. , Howard , D. M. , & Meaney , M. J. ( 2023 ). A sex-specific genome-wide association study of depression phenotypes in UK Biobank . Molecular Psychiatry , 28 ( 6 ), 2469 – 2479 . doi: 10.1038/s41380-023-01960-0 OpenUrl CrossRef PubMed 22. ↵ Meng , X. , Navoly , G. , Giannakopoulou , O. , Levey , D. F. , Koller , D. , Pathak , G. A. , Koen , N. , Lin , K. , Adams , M. J. , Rentería , M. E. , Feng , Y. , Gaziano , J. M. , Stein , D. J. , Zar , H. J. , Campbell , M. L. , van Heel , D. A. , Trivedi , B. , Finer , S. , McQuillin , A. , … Kuchenbaecker , K. ( 2024 ). Multi-ancestry genome-wide association study of Major Depression AIDS locus discovery, fine mapping, gene prioritization and causal inference . Nature Genetics , 56 ( 2 ), 222 – 233 . doi: 10.1038/s41588-023-01596-4 OpenUrl CrossRef 23. ↵ O’Loughlin , J. , Casanova , F. , Fairhurst-Hunter , Z. , Hughes , A. , Bowden , J. , Watkins , E. R. , Freathy , R. M. , Millwood , I. Y. , Lin , K. , Chen , Z. , Li , L. , Lv , J. , Walters , R. G. , Howe , L. D. , Kuchenbaecker , K. , & Tyrrell , J. ( 2023 ). Mendelian randomisation study of body composition and depression in people of East Asian ancestry highlights potential setting-specific causality . BMC Medicine , 21 ( 1 ). doi: 10.1186/s12916-023-02735-8 OpenUrl CrossRef 24. ↵ Giannakopoulou , O. , Lin , K. , Meng , X. , Su , M.-H. , Kuo , P.-H. , Peterson , R. E. , Awasthi , S. , Moscati , A. , Coleman , J. R. , Bass , N. , Millwood , I. Y. , Chen , Y. , Chen , Z. , Chen , H.-C. , Lu , M.-L. , Huang , M.-C. , Chen , C.-H. , Stahl , E. A. , Loos , R. J. , … Zou , M. ( 2021 ). The genetic architecture of depression in individuals of East Asian ancestry . JAMA Psychiatry , 78 ( 11 ), 1258 . doi: 10.1001/jamapsychiatry.2021.2099 OpenUrl CrossRef 25. ↵ Burgess S , Butterworth A , Thompson SG : Mendelian randomization analysis with multiple genetic variants using summarized data . Genet Epidemiol . 2013 ; 37 ( 7 ): 658 – 665 . doi: 10.1002/gepi.21758 OpenUrl CrossRef PubMed 26. ↵ Bowden , J. , Del Greco M F. , Minelli , C. , Davey Smith , G. , Sheehan , N. , & Thompson , J. ( 2017 ). A framework for the investigation of Pleiotropy in two-sample summary data mendelian randomization . Statistics in Medicine , 36 ( 11 ), 1783 – 1802 . doi: 10.1002/sim.7221 OpenUrl CrossRef PubMed 27. ↵ Bowden , J. , Davey Smith , G. , Haycock , P. C. , & Burgess , S. ( 2016 ). Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator . Genetic Epidemiology , 40 ( 4 ), 304 – 314 . doi: 10.1002/gepi.21965 OpenUrl CrossRef PubMed 28. ↵ Jack Bowden , George Davey Smith , Stephen Burgess , Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression , International Journal of Epidemiology , Volume 44 , Issue 2 , April 2015 , Pages 512 – 525 , doi: 10.1093/ije/dyv080 OpenUrl CrossRef PubMed 29. ↵ Verbanck , M. , Chen , C.-Y. , Neale , B. , & Do , R. ( 2018 ). Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases . Nature Genetics , 50 ( 5 ), 693 – 698 . doi: 10.1038/s41588-018-0099-7 OpenUrl CrossRef PubMed 30. ↵ Mounier , N. , & Kutalik , Z. ( 2023 ). Bias correction for inverse variance weighting Mendelian randomization . Genetic Epidemiology , 47 ( 4 ), 314 – 331 . doi: 10.1002/gepi.22522 OpenUrl CrossRef 31. ↵ Machiela , M. J. , & Chanock , S. J. ( 2015 ). LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants . Bioinformatics , 31 ( 21 ), 3555 – 3557 . doi: 10.1093/bioinformatics/btv402 OpenUrl CrossRef PubMed 32. ↵ Harroud , A. , Marrie , R. A. , Fitzgerald , K. C. , Salter , A. , Lu , Y. , Patel , M. , & Kowalec , K. ( 2021 ). Mendelian randomization provides no evidence for a causal role in the bidirectional relationship between depression and multiple sclerosis . Multiple Sclerosis Journal , 27 ( 13 ), 2077 – 2084 . doi: 10.1177/1352458521993075 OpenUrl CrossRef 33. ↵ Staiger D , Stock JH . Instrumental Variables Regression with Weak Instruments . Econometrica . 1997 ; 65 ( 3 ) : 557 – 86 OpenUrl CrossRef PubMed Web of Science 34. ↵ Tang , B. , Yuan , S. , Xiong , Y. , He , Q. , & Larsson , S. C. ( 2020 ). Major depressive disorder and cardiometabolic diseases: A bidirectional mendelian randomisation study . Diabetologia , 63 ( 7 ), 1305 – 1311 . doi: 10.1007/s00125-020-05131-6 OpenUrl CrossRef PubMed 35. ↵ Zhang , M. , Chen , J. , Yin , Z. , Wang , L. , & Peng , L. ( 2021 ). The association between depression and metabolic syndrome and its components: A bidirectional two-sample mendelian randomization study . Translational Psychiatry , 11 ( 1 ). doi: 10.1038/s41398-021-01759-z OpenUrl CrossRef 36. ↵ Jared G. Maina , Zhanna Balkhiyarova , Arie Nouwen , Igor Pupko , Anna Ulrich , Mathilde Boissel , Amélie Bonnefond , Philippe Froguel , Amna Khamis , Inga Prokopenko , Marika Kaakinen ; Bidirectional Mendelian Randomization and Multiphenotype GWAS Show Causality and Shared Pathophysiology Between Depression and Type 2 Diabetes . Diabetes Care 1 September 2023 ; 46 ( 9 ): 1707 – 1714 . doi: 10.2337/dc22-2373 OpenUrl CrossRef 37. ↵ Klein , S. L. , & Flanagan , K. L. ( 2016 ). Sex differences in immune responses . Nature Reviews Immunology , 16 ( 10 ), 626 – 638 . doi: 10.1038/nri.2016.90 OpenUrl CrossRef PubMed 38. ↵ Cheng Y , Desse S , Martinez A , Worthen RJ , Jope RS , Beurel E. TNFalpha disrupts blood brain barrier integrity to maintain prolonged depressive-like behavior in mice . Brain Behav Immun . ( 2018 ) 69 : 556 – 67 . doi: 10.1016/j.bbi.2018.02.003 OpenUrl CrossRef 39. ↵ Popov A , Abdullah Z , Wickenhauser C , Saric T , Driesen J , Hanisch FG , et al. Indoleamine 2,3-dioxygenase-expressing dendritic cells form suppurative granulomas following Listeria monocytogenes infection . J Clin Invest . ( 2006 ) 116 : 3160 – 70 . doi: 10.1172/JCI28996 OpenUrl CrossRef PubMed Web of Science 40. ↵ Zhu CB , Blakely RD , Hewlett WA . The proinflammatory cytokines interleukin-1beta and tumor necrosis factor-alpha activate serotonin transporters . Neuropsychopharmacology ( 2006 ) 31 : 2121 – 31 . doi: 10.1038/sj.npp.1301029 OpenUrl CrossRef PubMed Web of Science 41. ↵ Fernandes , B. S. , Salagre , E. , Enduru , N. , Grande , I. , Vieta , E. , & Zhao , Z. ( 2022 ). Insulin resistance in depression: A large meta-analysis of metabolic parameters and variation . Neuroscience & Biobehavioral Reviews , 139 , 104758 . doi: 10.1016/j.neubiorev.2022.104758 OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted July 16, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Sex-Specific causal dynamic between Insulin resistance and MDD, a bidirectional Mendelian randomization study Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Sex-Specific causal dynamic between Insulin resistance and MDD, a bidirectional Mendelian randomization study Qizhou Xia , Patricia P. Silveira medRxiv 2024.07.16.24310339; doi: https://doi.org/10.1101/2024.07.16.24310339 Share This Article: Copy Citation Tools Sex-Specific causal dynamic between Insulin resistance and MDD, a bidirectional Mendelian randomization study Qizhou Xia , Patricia P. Silveira medRxiv 2024.07.16.24310339; doi: https://doi.org/10.1101/2024.07.16.24310339 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Psychiatry and Clinical Psychology Subject Areas All Articles Addiction Medicine (574) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4462) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (611) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15251) Forensic Medicine (31) Gastroenterology (1132) Genetic and Genomic Medicine (6621) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4564) Health Policy (1372) Health Systems and Quality Improvement (1617) Hematology (544) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15938) Intensive Care and Critical Care Medicine (1107) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6643) Nursing (346) Nutrition (1001) Obstetrics and Gynecology (1149) Occupational and Environmental Health (957) Oncology (3350) Ophthalmology (981) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1698) Pharmacology and Therapeutics (694) Primary Care Research (714) Psychiatry and Clinical Psychology (5465) Public and Global Health (9259) Radiology and Imaging (2212) Rehabilitation Medicine and Physical Therapy (1372) Respiratory Medicine (1199) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (533) Surgery (715) Toxicology (100) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a03db37e7c404193',t:'MTc4MDE0MzcxMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();