Differential associations of depression-related phenotypes with cardiometabolic risks: Polygenic analyses and exploring shared genetic variants and pathways

Unraveling the heart-brain axis: shared genetic mechanisms in cardiovascular diseases and Schizophrenia

The comorbidity between cardiovascular diseases (CVD) and schizophrenia (SCZ) has attracted widespread attention from researchers, with shared genetic causes potentially providing important insights into their association. This study conducted a comprehensive analysis of genetic data from 17 types of CVD and SCZ using genome-wide multi-trait association studies (GWAS), employing statistical methods such as LDSC, MTAG, LAVA, and bidirectional Mendelian randomization to explore global and local genetic correlations and identify pleiotropic single nucleotide variants (SNVs). The analysis revealed a significant genetic correlation between CVD and SCZ, identifying 842 potential pleiotropic single nucleotide variants (SNVs) and multiple associated biological pathways. Notably, genes such as TRIM27, CENPM, and MYH7B played critical roles in the shared genetic variations of both types of diseases. This study reveals the complex genetic relationship between CVD and SCZ, highlighting potential shared biological mechanisms involving immune responses, metabolic factors, and neurodevelopmental processes, thereby providing new directions for future interventions and treatments.

Genetic architecture of DCC and influence on psychological, psychiatric and cardiometabolic traits in multiple ancestry groups in UK Biobank

BACKGROUND

People with severe mental illness have a higher risk of cardiometabolic disease than the general population. Traditionally attributed to sociodemographic, behavioural factors and medication effects, recent genetic studies have provided evidence of shared biological mechanisms underlying mental illness and cardiometabolic disease. We aimed to determine whether signals in the DCC locus, implicated in psychiatric and cardiometabolic traits, were shared or distinct.

METHODS

In UK Biobank, we systematically assessed genetic variation in the DCC locus for association with metabolic, cardiovascular and psychiatric-related traits in unrelated "white British" participants (N = 402,837). Logistic or linear regression were applied assuming an additive genetic model and adjusting for age, sex, genotyping chip and population structure. Bonferroni correction for the number of independent variants was applied. Conditional analyses (including lead variants as covariates) and trans-ancestry analyses were used to investigate linkage disequilibrium between signals.

RESULTS

Significant associations were observed between DCC variants and smoking, anhedonia, body mass index (BMI), neuroticism and mood instability. Conditional analyses and linkage disequilibrium structure suggested signals for smoking and BMI were distinct from each other and the mood traits, whilst individual mood traits were inter-related in a complex manner.

LIMITATIONS

Restricting analyses in non-"white British" individuals to the phenotypes significant in the "white British" sample is not ideal, but the smaller samples sizes restricted the phenotypes possible to analyse.

CONCLUSIONS

Genetic variation in the DCC locus had distinct effects on BMI, smoking and mood traits, and therefore is unlikely to contribute to shared mechanisms underpinning mental and cardiometabolic traits.

The role of daily adjustment disorder, depression and anxiety symptoms for the physical activity of cardiac patients

BACKGROUND

Physical activity (PA) is crucial in the treatment of cardiac disease. There is a high prevalence of stress-response and affective disorders among cardiac patients, which might be negatively associated with their PA. This study aimed at investigating daily differential associations of International Classification of Diseases (ICD)-11 adjustment disorder, depression and anxiety symptoms with PA and sedentary behaviour (SB) during and right after inpatient cardiac rehabilitation.

METHODS

The sample included N = 129 inpatients in cardiac rehabilitation, Mage = 62.2, s.d.age = 11.3, 84.5% male, n = 2845 days. Adjustment disorder, depression and anxiety symptoms were measured daily during the last 7 days of rehabilitation and for 3 weeks after discharge. Moderate-to-vigorous PA (MVPA), light PA (LPA) and SB were measured with an accelerometer. Bayesian lagged multilevel regressions including all three symptoms to obtain their unique effects were conducted.

RESULTS

On days with higher adjustment disorder symptoms than usual, patients engaged in less MVPA, and more SB. Patients with overall higher depression symptoms engaged in less MVPA, less LPA and more SB. On days with higher depression symptoms than usual, there was less MVPA and LPA, and more SB. Patients with higher anxiety symptoms engaged in more LPA and less SB.

CONCLUSIONS

Results highlight the necessity to screen for and treat adjustment disorder and depression symptoms during cardiac rehabilitation.

Association Between Psychiatric Polygenic Scores, Healthcare Utilization and Comorbidity Burden

Purpose

To estimate the association of psychiatric polygenic scores with healthcare utilization and comorbidity burden.

Methods

Observational cohort study (N = 118,882) of adolescent and adult biobank participants with linked electronic health records (EHRs) from three diverse study sites; (Massachusetts General Brigham, Vanderbilt University Medical Center, Geisinger). Polygenic scores (PGS) were derived from the largest available GWAS of major depressive depression, bipolar disorder, and schizophrenia at the time of analysis. Negative binomial regression models were used to estimate the association between each psychiatric PGS and healthcare utilization and comorbidity burden. Healthcare utilization was measured as frequency of emergency department (ED), inpatient (IP), and outpatient (OP) visits. Comorbidity burden was defined by the Elixhauser Comorbidity Index and the Charlson Comorbidity Index.

Results

Participants had a median follow-up duration of 12 years in the EHR. Individuals in the top decile of polygenic score for major depressive disorder had significantly more ED visits (RR=1.22, 95% CI; 1.17, 1.29) compared to those the lowest decile. Increases were also observed with IP and comorbidity burden. Among those diagnosed with depression and in the highest decile of the PGS, there was an increase in all utilization types (ED: RR=1.56, 95% CI 1.41, 1.72; OP: RR=1.16, 95% CI 1.08, 1.24; IP: RR=1.23, 95% CI 1.12, 1.36) post-diagnosis. No clinically significant results were observed with bipolar and schizophrenia polygenic scores.

Conclusions

Polygenic score for depression is modestly associated with increased healthcare resource utilization and comorbidity burden, in the absence of diagnosis. Following a diagnosis of depression, the PGS was associated with further increases in healthcare utilization. These findings suggest that depression genetic risk is associated with utilization and burden of chronic disease in real-world settings.

Exploring the genetic correlation of cardiovascular diseases and mood disorders in the UK Biobank

AIMS

Cardiovascular diseases (CVDs) are the leading cause of deaths globally. Mortality and incidence of CVDs are significantly higher in people with mood disorders. About 81.1% of CVD patients were reported with comorbidities in 2019, where the second most common comorbidity was due to major depressive disorder (MDD). This study, therefore, aimed to evaluate the genetic correlation between CVDs and mood disorders by using data from the UK Biobank towards understanding the influence of genetic factors on the comorbidity due to CVDs and mood disorders.

METHODS

The UK Biobank database provides genetic and health information from half a million adults, aged 40-69 years, recruited between 2006 and 2010. A total of 117,925 participants and 6,128,294 variants were included for analysis after applying exclusion criteria and quality control steps. This study focused on two CVD phenotypes, two mood disorders and 12 cardiometabolic-related traits to conduct association studies.

RESULTS

The results indicated a significant positive genetic correlation between CVDs and overall mood disorders and MDD specifically, showing substantial genetic overlap. Genetic correlation between CVDs and bipolar disorder was not significant. Furthermore, significant genetic correlation between mood disorders and cardiometabolic traits was also reported.

CONCLUSIONS

The results of this study can be used to understand that CVDs and mood disorders share a great deal of genetic liability in individuals of European ancestry.

Determining the stability of genome-wide factors in BMI between ages 40 to 69 years

Genome-wide association studies (GWAS) have successfully identified common variants associated with BMI. However, the stability of aggregate genetic variation influencing BMI from midlife and beyond is unknown. By analysing 165,717 men and 193,073 women from the UKBiobank, we performed BMI GWAS on six independent five-year age intervals between 40 and 72 years. We then applied genomic structural equation modeling to test competing hypotheses regarding the stability of genetic effects for BMI. LDSR genetic correlations between BMI assessed between ages 40 to 73 were all very high and ranged 0.89 to 1.00. Genomic structural equation modeling revealed that molecular genetic variance in BMI at each age interval could not be explained by the accumulation of any age-specific genetic influences or autoregressive processes. Instead, a common set of stable genetic influences appears to underpin genome-wide variation in BMI from middle to early old age in men and women alike.

Shared genetic loci between depression and cardiometabolic traits

Epidemiological and clinical studies have found associations between depression and cardiovascular disease risk factors, and coronary artery disease patients with depression have worse prognosis. The genetic relationship between depression and these cardiovascular phenotypes is not known. We here investigated overlap at the genome-wide level and in individual loci between depression, coronary artery disease and cardiovascular risk factors. We used the bivariate causal mixture model (MiXeR) to quantify genome-wide polygenic overlap and the conditional/conjunctional false discovery rate (pleioFDR) method to identify shared loci, based on genome-wide association study summary statistics on depression (n = 450,619), coronary artery disease (n = 502,713) and nine cardiovascular risk factors (n = 204,402–776,078). Genetic loci were functionally annotated using FUnctional Mapping and Annotation (FUMA). Of 13.9K variants influencing depression, 9.5K (SD 1.0K) were shared with body-mass index. Of 4.4K variants influencing systolic blood pressure, 2K were shared with depression. ConjFDR identified 79 unique loci associated with depression and coronary artery disease or cardiovascular risk factors. Six genomic loci were associated jointly with depression and coronary artery disease, 69 with blood pressure, 49 with lipids, 9 with type 2 diabetes and 8 with c-reactive protein at conjFDR < 0.05. Loci associated with increased risk for depression were also associated with increased risk of coronary artery disease and higher total cholesterol, low-density lipoprotein and c-reactive protein levels, while there was a mixed pattern of effect direction for the other risk factors. Functional analyses of the shared loci implicated metabolism of alpha-linolenic acid pathway for type 2 diabetes. Our results showed polygenic overlap between depression, coronary artery disease and several cardiovascular risk factors and suggest molecular mechanisms underlying the association between depression and increased cardiovascular disease risk.

Association between single and multiple cardiometabolic diseases and depression: A cross-sectional study of 391,083 participants from the UK biobank

Background

Individual cardiometabolic diseases (CMDs) are associated with an increased risk of depression, but it's unclear whether having more than one CMD is associated with accumulative effects on depression. We aimed to assess the associations between CMDs and depression and determine the accumulative extent.

Methods

In this cross-sectional study based on UK Biobank, participants with available information on CMDs and depression were enrolled. The history of CMDs was derived from self-reported medical history and electrical health-related records. Depression status was assessed by the aggregation of self-reported history and antidepressant use, depression (Smith), and hospital inpatient diagnoses. Logistic regression models were fitted to assess the association between the number or specific patterns of CMDs and depression and to test the accumulative effect of CMD number, adjusting for confounding factors.

Results

391,083 participants were enrolled in our analyses. After multivariable adjustments, CMDs of different number or patterns were associated with a higher risk of depression compared with the reference group (all P < 0.001). In the full-adjusted model, participants with one [odds ratio (OR) 1.26, 95% confidence interval (CI) 1.23-1.29], two (OR 1.50, 95% CI 1.44-1.56), and three or more (OR 2.13, 95% CI 1.97-2.30) CMD(s) had an increased risk of depression. A significant, accumulative dose-related relationship between the number of CMDs and depression was observed (OR 1.25, 95% CI 1.24-1.27). The dose-dependent accumulative relationship was consistent in stratified analyses and sensitivity analyses.

Conclusions

CMDs were associated with a higher risk of depression, and there was an accumulative relationship between CMD number and depression.

Causal relationships between blood lipids and depression phenotypes: a Mendelian randomisation analysis

BACKGROUND

The etiology of depression remains poorly understood. Changes in blood lipid levels were reported to be associated with depression and suicide, however study findings were mixed.

METHODS

We performed a two-sample Mendelian randomisation (MR) analysis to investigate the causal relationship between blood lipids and depression phenotypes, based on large-scale GWAS summary statistics (N = 188 577/480 359 for lipid/depression traits respectively). Five depression-related phenotypes were included, namely major depression (MD; from PGC), depressive symptoms (DS; from SSGAC), longest duration and number of episodes of low mood, and history of deliberate self-harm (DSH)/suicide (from UK Biobank). MR was conducted with inverse-variance weighted (MR-IVW), Egger and Generalised Summary-data-based MR (GSMR) methods.

RESULTS

There was consistent evidence that triglyceride (TG) is causally associated with DS (MR-IVW β for one-s.d. increase in TG = 0.0346, 95% CI 0.0114-0.0578), supported by MR-IVW and GSMR and multiple r2 clumping thresholds. We also observed relatively consistent associations of TG with DSH/suicide (MR-Egger OR = 2.514, CI 1.579-4.003). There was moderate evidence for positive associations of TG with MD and the number of episodes of low mood. For HDL-c, we observed moderate evidence for causal associations with DS and MD. LDL-c and TC did not show robust causal relationships with depression phenotypes, except for weak evidence that LDL-c is inversely related to DSH/suicide. We did not detect significant associations when depression phenotypes were treated as exposures.

CONCLUSIONS

This study provides evidence to a causal relationship between TG, and to a lesser extent, altered cholesterol levels with depression phenotypes. Further studies on its mechanistic basis and the effects of lipid-lowering therapies are warranted.

Depression and anxiety in cardiac rehabilitation: differential associations with changes in exercise capacity and quality of life

BACKGROUND

Cardiac rehabilitation (CR) has been successful in improving exercise capacity (EC) and quality of life (QoL). However, depression and anxiety are highly prevalent among cardiac patients and might represent risk factors for rehabilitation outcomes. The aim of this study was to investigate the role of depression and anxiety as possible independent risk factors for CR outcomes.

METHODS

The study applied a pre-post-design. The sample comprised N = 3'434 cardiac disease patients taking part in a Swiss inpatient CR center. Variables measured at the beginning (T1) and end of rehabilitation (T2) included depression and anxiety (HADS), EC, and QoL (MacNew). A path analysis was conducted.

RESULTS

Depression at T1 had a significant negative relationship with improvements in EC and in all aspects of QoL during rehabilitation. Anxiety at T1 was positively related to improvements in EC and in emotional and physical QoL. Improvements in depression during CR were positively related with improvements in all outcomes. Improvements in anxiety showed no significant association with the outcomes.

CONCLUSION

Depression and anxiety should be screened for during CR. Depression should be treated due to the negative association found with rehabilitation outcomes. Underlying mechanisms of the positive association of anxiety with rehabilitation outcomes need further investigation.

Shared genetic architecture between neuroticism, coronary artery disease and cardiovascular risk factors

Neuroticism is associated with poor health, cardiovascular disease (CVD) risk factors and coronary artery disease (CAD). The conditional/conjunctional false discovery rate method (cond/conjFDR) was applied to genome wide association study (GWAS) summary statistics on neuroticism (n = 432,109), CAD (n = 184,305) and 12 CVD risk factors (n = 188,577-339,224) to investigate genetic overlap between neuroticism and CAD and CVD risk factors. CondFDR analyses identified 729 genomic loci associated with neuroticism after conditioning on CAD and CVD risk factors. The conjFDR analyses revealed 345 loci jointly associated with neuroticism and CAD (n = 30), body mass index (BMI) (n = 96) or another CVD risk factor (n = 1-60). Several loci were jointly associated with neuroticism and multiple CVD risk factors. Seventeen of the shared loci with CAD and 61 of the shared loci with BMI are novel for neuroticism. 21 of 30 (70%) neuroticism risk alleles were associated with higher CAD risk. Functional analyses of the genes mapped to the shared loci implicated cell division, nuclear receptor, elastic fiber formation as well as starch and sucrose metabolism pathways. Our results indicate polygenic overlap between neuroticism and CAD and CVD risk factors, suggesting that genetic factors may partly cause the comorbidity. This gives new insight into the shared molecular genetic basis of these conditions.

M1/M2 polarization in major depressive disorder: Disentangling state from trait effects in an individualized cell-culture-based approach

Accumulating evidence indicates the specific involvement of inflammatory processes in major depressive disorder (MDD), particularly affecting innate immunity. Most immune alterations have so far been determined based on plasma or cerebrospinal fluid cytokine levels. To precisely characterize putative innate immune-mediated mechanisms in MDD pathogenesis, we sought to disentangle "state" from "trait" effects in a patient-specific cell model by quantifying the impact of patient-derived autologous sera (AS) on patient-specific monocyte-derived macrophages (Mo-MФs) polarization in vitro. Mo-MФs were generated from 28 patients with moderate to severe MDD and 28 age-, sex-, smoking status- and BMI-matched healthy controls (HC). Cells were treated either with AS or fetal calf serum (FCS) and polarized into M1 (LPS), M2 (IL-10, IL-4, TGF-β) or M0 (unstimulated) macrophages. Polarization capacity was quantified by means of specific M1 (CCR7, CD86, CXCL10, IL-12p70, TNF-α, IL-6, IL-1β, IL-12p40, IL-23, IP-10) and M2 (CD206, IL-10, TARC, IL-1RA) markers. Compared to HC, significantly increased M1-polarization was observed for MDD patients in the presence of FCS, however, polarization in AS enriched media determined an increased M2-polarization in patients. Moreover, female MDD patients exhibited increased M1- and decreased M2-polarization in both conditions compared to male MDD patients. Our data suggests that Mo-MФs derived from patients with MDD exhibit facilitated M1-polarization under traditional cell culture conditions and an increased potential for M2-polarization when cultured in AS. Striking inter-individual variation and pronounced gender effects highlight the potential utility of our personalized cell model-based approach to aid diagnostic and therapeutic decisions.

Depression and metabolic syndrome in participants of the "Seguimiento Universidad de Navarra" (SUN) cohort study

BACKGROUND

Depression is a major public health concern worldwide and its association with metabolic syndrome (MetS) remains unclear. Thus, we prospectively examined the association between depression and the risk of MetS, according to different diagnosis criteria.

METHODS

This study included 9,237 participants of a Spanish dynamic prospective cohort of adult university graduates [mean (SD) age: 36.7 year (10.7)], initially free of any specific criterion of MetS, who were followed-up for a median of 8.3 years. The exposure variables were medical diagnosis of depression at baseline or in the first 2-year follow-up questionnaire. The outcome variable was the incidence of MetS, assessed according to each of three different criteria proposed by: International Diabetes Federation (IDF); National Cholesterol Education Program's Adult Treatment Panel III (NCEP-ATP III); IDF/NCEP-ATP III (updated harmonizing definition). Multivariable-adjusted Relative Risks (RR) of new-onset MetS and their 95% Confidence Intervals (95% CI) were estimated, using Poisson regression models.

RESULTS

The cumulative incidences of MetS were 475 cases (IDF definition), 288 cases (NCEP-ATP III definition) and 492 cases (update harmonized definition). No association was observed between baseline depression and incidence of MetS, but the presence of depression after 2-years of follow-up was significantly associated with a higher risk of new-onset MetS, according to NCEP-ATP III definition (multivariable-adjusted RR, 2.46; 95% CI, 1.06-5.67).

LIMITATIONS

Diagnosis of depression and MetS were self-reported.

CONCLUSIONS

In this large prospective cohort of Spanish middle-aged adult university graduates, a direct association between depression and the risk of MetS according to NCEP-ATP III definition was found.

Understanding the relationship between type 2 diabetes and depression: lessons from genetically informative study designs

AIMS

To conduct a systematic review in order to comprehensively synthesize the findings from a diverse range of genetically informative studies on comorbid depression and type 2 diabetes.

METHODS

Database searches (1 January 2008 to 1 June 2020) in PubMed and EMBASE were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Eligible reports employed any type of genetically informed design, including twin modelling, Mendelian randomization, genome-wide association studies, polygenetic risk scores, or linkage disequilibrium score regression. Searches generated 451 unique citations, and 16 manuscripts met the inclusion criteria.

RESULTS

The included studies addressed three aetiological models of the depression-diabetes relationship: uni- or bi-directional phenotypic causation; shared genetic liability; or gene-environment interaction. From these studies, there is modest evidence that type 2 diabetes is causally related to risk of developing depression, but much more limited evidence that depression is causally related to risk of diabetes. There is little evidence of shared genetic liability between depression and diabetes or of gene-environment interaction.

CONCLUSIONS

Findings from genetically informed studies are mixed but provide some support for the uni- or bi-directional phenotypic model of depression and type 2 diabetes. Future studies should also explore the hypothesis that this relationship may be influenced by shared environmental risk factors. Findings can inform multifaceted approaches to diabetes prevention and care that reflect how psychosocial factors contribute to type 2 diabetes risk and outcomes.

Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression

It is imperative to understand the specific and shared etiologies of major depression and cardio-metabolic disease, as both traits are frequently comorbid and each represents a major burden to society. This study examined whether there is a genetic association between major depression and cardio-metabolic traits and if this association is stratified by age at onset for major depression. Polygenic risk scores analysis and linkage disequilibrium score regression was performed to examine whether differences in shared genetic etiology exist between depression case control status (N cases = 40,940, N controls = 67,532), earlier (N = 15,844), and later onset depression (N = 15,800) with body mass index, coronary artery disease, stroke, and type 2 diabetes in 11 data sets from the Psychiatric Genomics Consortium, Generation Scotland, and UK Biobank. All cardio-metabolic polygenic risk scores were associated with depression status. Significant genetic correlations were found between depression and body mass index, coronary artery disease, and type 2 diabetes. Higher polygenic risk for body mass index, coronary artery disease, and type 2 diabetes was associated with both early and later onset depression, while higher polygenic risk for stroke was associated with later onset depression only. Significant genetic correlations were found between body mass index and later onset depression, and between coronary artery disease and both early and late onset depression. The phenotypic associations between major depression and cardio-metabolic traits may partly reflect their overlapping genetic etiology irrespective of the age depression first presents.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Genetic Advance in Depressive Disorder

Major depressive disorder (MDD) and bipolar disorder (BPD) are both chronic, severe mood disorder with high misdiagnosis rate, leading to substantial health and economic burdens to patients around the world. There is a high misdiagnosis rate of bipolar depression (BD) just based on symptomology in depressed patients whose previous manic or mixed episodes have not been well recognized. Therefore, it is important for psychiatrists to identify these two major psychiatric disorders. Recently, with the accumulation of clinical sample sizes and the advances of methodology and technology, certain progress in the genetics of major depression and bipolar disorder has been made. This article reviews the candidate genes for MDD and BD, genetic variation loci, chromosome structural variation, new technologies, and new methods.

Association of SGK1 Polymorphisms With Susceptibility to Coronary Heart Disease in Chinese Han Patients With Comorbid Depression

There is a strong link between heart disease and depression, both of which are closely related to lifetime stress exposure. Serum/glucocorticoid-regulated kinase 1 (SGK1) is a stress-responsive gene with a pivotal role in both the heart and brain. To determine the role of SGK1 polymorphisms (rs2758151, rs1743963, rs9493857, rs1763509, rs9376026, and rs9389154) in susceptibility to comorbid coronary heart disease (CHD) and depression, we conducted a hospital-based case–control study involving 257 CHD cases (including 69 cases with depression and 188 cases without depression) and 107 controls in a Chinese Han population. Six single-nucleotide polymorphisms (SNPs) in the SGK1 gene were successfully genotyped by polymerase chain reaction–ligase detection reaction (PCR-LDR) assay. Our results showed no significant differences in SGK1 genetic polymorphisms between CHD patients and controls, whereas significant associations were observed between SGK1 SNPs (rs1743963 and rs1763509) and the development of depression in CHD patients (P = 0.018 by genotype, P = 0.032 by allele; P = 0.017 by genotype, P = 0.003 by allele, respectively). However, none of these associations remained significant after Bonferroni correction (P = 0.054 for rs1743963; P = 0.051 for rs1763509). Interestingly, both the GG genotype of SGK1 rs1743963 and AA genotype of SGK1 rs1763509 were associated with a higher risk of depression in CHD patients; for rs1763509, the Patient Health Questionnaire-9 (PHQ-9) scores in the carriers of the risk genotype for comorbid depression, AA, were significantly higher than in GG and AG carriers (P = 0.008). Notably, haplotype analysis indicated that haplotype GGA significantly increased the risk of depression in CHD patients (P = 0.011, odds ratio (OR) = 1.717, 95% confidence interval (CI) = 1.132–2.605), whereas haplotype AAG may be a protective factor for CHD patients with comorbid depression (P = 0.038, OR = 0.546, 95% CI = 0.307–0.972). It should be noted that only the significance of haplotype GGA survived after Bonferroni adjustment (P = 0.044) and that no significant differences were found for other SGK1 SNPs (rs2758151, rs9493857, rs9376026, and rs9389154) between CHD patients with and without depression. These findings, for the first time, elucidate the important role of SGK1 variants in the comorbidity of CHD and depression.

Pharmacogenetics and Depression: A Critical Perspective

Depression leads the higher personal and socio-economical burden within psychiatric disorders. Despite the fact that over 40 antidepressants (ADs) are available, suboptimal response still poses a major challenge and is thought to be partially a result of genetic variation. Pharmacogenetics studies the effects of genetic variants on treatment outcomes with the aim of providing tailored treatments, thereby maximizing efficacy and tolerability. After two decades of pharmacogenetic research, variants in genes coding for the cytochromes involved in ADs metabolism (CYP2D6 and CYP2C19) are now considered biomarkers with sufficient scientific support for clinical application, despite the lack of conclusive cost/effectiveness evidence. The effect of variants in genes modulating ADs mechanisms of action (pharmacodynamics) is still controversial, because of the much higher complexity of ADs pharmacodynamics compared to ADs metabolism. Considerable progress has been made since the era of candidate gene studies: the genomic revolution has made possible to assess genetic variance on an unprecedented scale, throughout the whole genome, and to analyze the cumulative effect of different variants. The results have revealed key information on the biological mechanisms mediating ADs effect and identified hypothetical new pharmacological targets. They also paved the way for future availability of polygenic pharmacogenetic panels to predict treatment outcome, which are expected to explain much higher variance in ADs response compared to CYP2D6 and CYP2C19 only. As the demand and availability of AD pharmacogenetic testing is projected to increase, it is important for clinicians to keep abreast of this evolving area to facilitate informed discussions with their patients.