Genetic overlap between type 2 diabetes and major depressive disorder identified by bioinformatics analysis. Oncotarget. 2016;7:17410-17414. [PMID: 27007159 PMCID: PMC4951221 DOI: 10.18632/oncotarget.8202]

[Genome-wide studies of comorbidity of somatic and mental diseases]

Studies of the genomic architecture of complex phenotypes, which include common somatic and mental diseases, have shown that they are characterized by a high degree of polygenicity, i.e. participation of a large number of genes associated with the risk of developing these diseases. In this regard, it is of interest to establish the genetic overlapping between these two groups of diseases. The aim of the review is to analyze genetic studies of the comorbidity of somatic and mental diseases in terms of the universality and specificity of mental disorders in somatic diseases, the reciprocal relationships of these types of pathologies, and the modulating influence of environmental factors on comorbidity. The results of the analysis indicate the existence of a common genetic predisposition to mental and somatic diseases. At the same time, the presence of common genes does not exclude the specificity of the development of mental disorders depending on a specific somatic pathology. It can be assumed that there are genes that are both unique to a particular somatic and comorbid mental illness, and genes that are common to these diseases. Common genes may have varying degrees of specificity, that is, they may be of a universal nature, which, for example, manifests itself in the development of MDD in various somatic diseases, or be specific only for a couple of individual diseases (schizophrenia - breast cancer). At the same time, common genes can have a multidirectional effect, which also contributes to the specificity of comorbidity. In addition, when searching for common genes for somatic and mental diseases, it is necessary to take into account the modulating influence of such confounders as treatment, unhealthy life style, behavioral characteristics, which can also differ in specificity depending on the diseases under consideration.

Comorbidity of Novel CRHR2 Gene Variants in Type 2 Diabetes and Depression

The corticotropin-releasing hormone receptor 2 (CRHR2) gene encodes CRHR2, contributing to the hypothalamic–pituitary–adrenal stress response and to hyperglycemia and insulin resistance. CRHR2−/− mice are hypersensitive to stress, and the CRHR2 locus has been linked to type 2 diabetes and depression. While CRHR2 variants confer risk for mood disorders, MDD, and type 2 diabetes, they have not been investigated in familial T2D and MDD. In 212 Italian families with type 2 diabetes and depression, we tested 17 CRHR2 single nucleotide polymorphisms (SNPs), using two-point parametric-linkage and linkage-disequilibrium (i.e., association) analysis (models: dominant-complete-penetrance-D1, dominant-incomplete-penetrance-D2, recessive-complete-penetrance-R1, recessive-incomplete-penetrance-R2). We detected novel linkage/linkage-disequilibrium/association to/with depression (3 SNPs/D1, 2 SNPs/D2, 3 SNPs/R1, 3 SNPs/R2) and type 2 diabetes (3 SNPs/D1, 2 SNPs/D2, 2 SNPs/R1, 1 SNP/R2). All detected risk variants are novel. Two depression-risk variants within one linkage-disequilibrium block replicate each other. Two independent novel SNPs were comorbid while the most significant conferred either depression- or type 2 diabetes-risk. Although the families were primarily ascertained for type 2 diabetes, depression-risk variants showed higher significance than type 2 diabetes-risk variants, implying CRHR2 has a stronger role in depression-risk than type 2 diabetes-risk. In silico analysis predicted variants’ dysfunction. CRHR2 is for the first time linked to/in linkage-disequilibrium/association with depression-type 2 diabetes comorbidity and may underlie the shared genetic pathogenesis via pleiotropy.

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.

Genetic association between major depressive disorder and type 2 diabetes mellitus: Shared pathways and protein networks

BACKGROUND

Major depressive disorder (MDD) and type 2 diabetes mellitus (T2DM) are common public health disorders that often co-occur. This study aims to determine whether gene expression profiles from individuals with MDD or T2DM overlap and if there are any functional interconnectivity between identified genes using protein-protein interaction (PPI).

METHODS

The DNA microarray datasets were extracted from the Gene Expression Omnibus. Gene expression dataset GSE98793 from a case-control study of MDD (64 healthy control subjects, 128 patients) and dataset GSE15653 from a case-control study of T2DM (nine controls, nine individuals with T2DM) were used for this secondary and post-hoc analysis. GO enrichment analyses and Reactome pathway enrichment analysis were performed for functional enrichment analyses with the shared genes. PPI networks, PPI clusters and hub genes were performed to detect the potential relationships among differentially expressed genes (DEG) -encoding proteins in both MDD and T2DM.

RESULTS

A total of 3640 DEGs were identified in the MDD group when compared to the control group, whereas 3700 DEGs were identified in the T2DM group when compared to the control groups, among which 244 DEGs were overlap genes. The identified DEGs were enriched for Interleukin-4 and Interleukin-13 signaling, neutrophil degranulation, as well as other select species of the innate immune system. The biological processes of neurofibrillary tangle assembly regulation, tau-protein kinase activity regulation, amyloid-beta clearance regulation, amyloid-beta formation regulation and neuron apoptotic processes were also identified. Molecular function analysis indicated that identified genes were mainly enriched for amyloid-beta binding. 925 out of 1006 protein-protein interactions and six sub-networks were identified reflecting the disparate biological domains of overlapping genes. Ten hub genes further highlight the putative importance of tau-protein kinase activity, inflammatory response and neuron apoptotic regulatory processes across MDD and T2DM.

CONCLUSIONS

Our results indicate that an overlapping genetic architecture subserves MDD and T2DM. Genes relevant to the innate immune system, tau protein formation, and cellular aging were identified. Results indicate that the common, often comorbid, conditions of MDD and T2DM have a pathoetiologic nexus.

Association Between Genetic Risk for Type 2 Diabetes and Structural Brain Connectivity in Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) and type 2 diabetes mellitus (T2D) are known to share clinical comorbidity and to have genetic overlap. Besides their shared genetics, both diseases seem to be associated with alterations in brain structural connectivity and impaired cognitive performance, but little is known about the mechanisms by which genetic risk of T2D might affect brain structure and function and if they do, how these effects could contribute to the disease course of MDD.

METHODS

This study explores the association of polygenic risk for T2D with structural brain connectome topology and cognitive performance in 434 nondiabetic patients with MDD and 539 healthy control subjects.

RESULTS

Polygenic risk score for T2D across MDD patients and healthy control subjects was found to be associated with reduced global fractional anisotropy, a marker of white matter microstructure, an effect found to be predominantly present in MDD-related fronto-temporo-parietal connections. A mediation analysis further suggests that this fractional anisotropy variation may mediate the association between polygenic risk score and cognitive performance.

CONCLUSIONS

Our findings provide preliminary evidence of a polygenic risk for T2D to be linked to brain structural connectivity and cognition in patients with MDD and healthy control subjects, even in the absence of a direct T2D diagnosis. This suggests an effect of T2D genetic risk on white matter integrity, which may mediate an association of genetic risk for diabetes and cognitive impairments.

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.

Chinese Herbal Medicine for the Treatment of Depression: Effects on the Neuroendocrine-Immune Network

The neuroimmune and neuroendocrine systems are two critical biological systems in the pathogenesis of depression. Clinical and preclinical studies have demonstrated that the activation of the neuroinflammatory response of the immune system and hyperactivity of the hypothalamus–pituitary–adrenal (HPA) axis of the neuroendocrine system commonly coexist in patients with depression and that these two systems bidirectionally regulate one another through neural, immunological, and humoral intersystem interactions. The neuroendocrine-immune network poses difficulties associated with the development of antidepressant agents directed toward these biological systems for the effective treatment of depression. On the other hand, multidrug and multitarget Chinese Herbal Medicine (CHM) has great potential to assist in the development of novel medications for the systematic pharmacotherapy of depression. In this narrative essay, we conclusively analyze the mechanisms of action of CHM antidepressant constituents and formulas, specifically through the modulation of the neuroendocrine-immune network, by reviewing recent preclinical studies conducted using depressive animal models. Some CHM herbal constituents and formulas are highlighted as examples, and their mechanisms of action at both the molecular and systems levels are discussed. Furthermore, we discuss the crosstalk of these two biological systems and the systems pharmacology approach for understanding the system-wide mechanism of action of CHM on the neuroendocrine-immune network in depression treatment. The holistic, multidrug, and multitarget nature of CHM represents an excellent example of systems medicine in the effective treatment of depression.

Identification of key genes involved in post-traumatic stress disorder: Evidence from bioinformatics analysis

BACKGROUND

Post-traumatic stress disorder (PTSD) is a serious stress-related disorder.

AIM

To identify the key genes and pathways to uncover the potential mechanisms of PTSD using bioinformatics methods.

METHODS

Gene expression profiles were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified by using GEO2R. Gene functional annotation and pathway enrichment were then conducted. The gene-pathway network was constructed with Cytoscape software. Quantitative real-time polymerase chain reaction (qRT-PCR) analysis was applied for validation, and text mining by Coremine Medical was used to confirm the connections among genes and pathways.

RESULTS

We identified 973 DEGs including 358 upregulated genes and 615 downregulated genes in PTSD. A group of centrality hub genes and significantly enriched pathways (MAPK, Ras, and ErbB signaling pathways) were identified by using gene functional assignment and enrichment analyses. Six genes (KRAS, EGFR, NFKB1, FGF12, PRKCA, and RAF1) were selected to validate using qRT-PCR. The results of text mining further confirmed the correlation among hub genes and the enriched pathways. It indicated that these altered genes displayed functional roles in PTSD via these pathways, which might serve as key signatures in the pathogenesis of PTSD.

CONCLUSION

The current study identified a panel of candidate genes and important pathways, which might help us deepen our understanding of the underlying mechanism of PTSD at the molecular level. However, further studies are warranted to discover the critical regulatory mechanism of these genes via relevant pathways in PTSD.

A cross-sectional study on metabolic similarities and differences between inpatients with schizophrenia and those with mood disorders

BACKGROUND

One of the main causes of death in psychiatric patients is cardiovascular diseases which are closely related with lifestyle-related diseases. Psychiatric disorders include schizophrenia and mood disorders, whose symptoms and treatment medicines are different, suggesting that they might have different metabolic disorders. Thus, we studied the differences of lifestyle-related diseases between schizophrenia and mood disorders in Japan.

METHODS

This cross-sectional study was performed from 2015 to 2017. Study participants were 189 Japanese hospitalized patients (144 schizophrenia group, 45 mood disorders group) in the department of psychiatry at Kohnodai hospital. We examined physical disorders, metabolic status of glucose and lipid, estimated glomerular filtration rate (eGFR) and brain magnetic resonance imaging. We compared these data between schizophrenia and mood disorders groups using analysis of covariance or logistic regression analysis. In comparisons between inpatients with schizophrenia or mood disorders group and the standard, we quoted 'The National Health and Nutrition Survey in Japan 2015' by Ministry of Health, Labor and Welfare as the standard.

RESULTS

eGFR and prevalence of smoking were significantly lower in patients with mood disorder group than those with schizophrenia group by adjustment for age. In comparisons between patients with schizophrenia group or mood disorders group and each standard, the ratio of silent brain infarction (SBI) and cerebral infarction were significantly high in both groups. Schizophrenia group showed significantly higher prevalence of diabetes, low high-density lipoprotein (HDL) cholesterolemia, metabolic syndrome and smoking than the standard. Mood disorders group had significantly high prevalence of low HDL-cholesterolemia compared with the standard. Fasting blood glucose and HbA1c were significantly higher in schizophrenia group and female mood disorders group than the standard. Female mood disorders group had significantly decreased eGFR with increased ratio of eGFR < 60 ml/min than the standard.

CONCLUSIONS

Participants of both groups had increased ratio of SBI and cerebral infarction, accompanied with glucose and lipid disorders. Compared with schizophrenia group, mood disorders group showed significantly low eGFR and prevalence of smoking.

Eveningness Is Associated With Greater Depressive Symptoms in Type 2 Diabetes Patients: A Study in Two Different Ethnic Cohorts

BACKGROUND

Eveningness is associated with greater depressive symptoms in the general population. Depression and type 2 diabetes (T2D) commonly coexist. We aimed to explore the association between morningness-eveningness and depressive symptoms in T2D patients in the United States and in Thailand.

PARTICIPANTS

T2D patients (n = 182) from an endocrinology clinic in Chicago, Illinois, and six hospitals in Thailand (n = 251) were enrolled.

METHODS

Diabetes history was collected. Depressive symptoms were assessed by the Center for Epidemiologic Studies Depression scale (CES-D). The Chicago cohort completed the Morningness-Eveningness Questionnaire (MEQ) and the Thai cohort completed the Composite Scale of Morningness (CSM). Sleep quality was assessed using the Pittsburg Sleep Quality Index (PSQI).

RESULTS

The mean (SD) CES-D score was 13.7 (9.1) in Chicago and 11.9 (6.4) in Thailand. In Chicago participants, after adjusting for age, sex, ethnicity, hemoglobin A1c, insulin use, and PSQI score, greater eveningness (lower MEQ scores) was associated with higher CESD scores (B = -0.117, p = 0.048). In Thai participants, after adjusting for age, sex, and PSQI score, eveningness (lower CSM score) was associated with higher CES-D score (B = -0.147, p = 0.016). In both cohorts, however, eveningness was not independently associated with the likelihood of being in the at-risk range for clinical depression (CES-D ≥ 16).

CONCLUSIONS

Eveningness is independently associated with greater depressive symptoms in T2D in two different ethnic cohorts. The results support the association between individual differences in circadian rhythms and psychological functioning in T2D.

Co-shared genetics and possible risk gene pathway partially explain the comorbidity of schizophrenia, major depressive disorder, type 2 diabetes, and metabolic syndrome

Schizophrenia (SCZ) and major depressive disorder (MDD) in treatment-naive patients are associated with increased risk for type 2 diabetes (T2D) and metabolic syndrome (MetS). SCZ, MDD, T2D, and MetS are often comorbid and their comorbidity increases cardiovascular risk: Some risk genes are likely co-shared by them. For instance, transcription factor 7-like 2 (TCF7L2) and proteasome 26S subunit, non-ATPase 9 (PSMD9) are two genes independently reported as contributing to T2D and SCZ, and PSMD9 to MDD as well. However, there are scarce data on the shared genetic risk among SCZ, MDD, T2D, and/or MetS. Here, we briefly describe T2D, MetS, SCZ, and MDD and their genetic architecture. Next, we report separately about the comorbidity of SCZ and MDD with T2D and MetS, and their respective genetic overlap. We propose a novel hypothesis that genes of the prolactin (PRL)-pathway may be implicated in the comorbidity of these disorders. The inherited predisposition of patients with SCZ and MDD to psychoneuroendocrine dysfunction may confer increased risk of T2D and MetS. We illustrate a strategy to identify risk variants in each disorder and in their comorbid psychoneuroendocrine and mental-metabolic dysfunctions, advocating for studies of genetically homogeneous and phenotype-rich families. The results will guide future studies of the shared predisposition and molecular genetics of new homogeneous endophenotypes of SCZ, MDD, and metabolic impairment.

Quantitative assessment of the bidirectional relationships between diabetes and depression

Diabetes and depression impose an enormous public health burden and the present study aimed to assess quantitatively the bidirectional relationships between the two disorders. We searched databases for eligible articles published until October 2016. A total of 51 studies were finally included in the present bidirectional meta-analysis, among which, 32 studies were about the direction of depression leading to diabetes, and 24 studies about the direction of diabetes leading to depression. Pooled results of the 32 eligible studies covering 1274337 subjects showed that depression patients were at higher risk for diabetes (odds ratio (OR) = 1.34, 95% confidence intervals (CI) = [1.23, 1.46]) than non-depressive subjects. Further gender-subgroup analysis found that the strength of this relationship was stronger in men (OR = 1.63, 95%CI = [1.48, 1.78]) than in women (OR = 1.29, 95%CI = [1.07, 1.51]). For the direction of diabetes leading to depression, pooled data of 24 articles containing 329658 subjects showed that patients with diabetes were at higher risk for diabetes (OR = 1.28, 95%CI = [1.15, 1.42]) than non-diabetic subjects. The available data supports that the relationships between diabetes and depression are bidirectional and the overall strengths are similar in both directions. More mechanistic studies are encouraged to explore the molecular mechanisms underlying the relationships between the two diseases.

Relationships of circular RNA with diabetes and depression

Type 2 diabetes mellitus (T2DM) is closely related to depression; however, the exact molecular mechnisms of this association are unknown. Here, we investigated whether circular RNAs (circRNAs) in the blood are related to the occurrence of depression in patients with T2DM. Fourteen patients with T2DM and depressive symptoms, as assessed by the Self-Rating Depression Scale, were included in this study. Cutoff points of 44 (total coarse points) and 55 (standard score) were used to define depression. The Patient Health Questionnaire 9 was used for common mental disorders, and a score of 5 or more the cutoff for depression. Microarray assays and quantitative real-time reverse transcription polymerase chain reaction showed that 183 hsa-circRNAs were significantly upregulated, whereas 64 were downregulated in the T2DM with depression group (p < 0.05) compared with that in the T2DM group. Differentially expressed hsa-circRNAs could interact with microRNAs to target mRNA expression. KEGG pathway analysis predicted that upregulation of hsa-circRNA_003251, hsa-circRNA_015115, hsa-circRNA_100918, and hsa_circRNA_001520 may participate in the thyroid hormone, Wnt, ErbB, and mitogen-activated protein kinase signalling pathways. We speculate that differentially expressed hsa-circRNAs could help us to clarify the pathogenesis of depression in patients with T2DM and could represent novel molecular targets for clinical diagnosis and therapy.

Common shared genetic variation behind decreased risk of breast cancer in celiac disease

There is epidemiologic evidence showing that women with celiac disease have reduced risk of later developing breast cancer, however, the etiology of this association is unclear. Here, we assess the extent of genetic overlap between the two diseases. Through analyses of summary statistics on densely genotyped immunogenic regions, we show a significant genetic correlation (r = −0.17, s.e. 0.05, P < 0.001) and overlap (P_permuted < 0.001) between celiac disease and breast cancer. Using individual-level genotype data from a Swedish cohort, we find higher genetic susceptibility to celiac disease summarized by polygenic risk scores to be associated with lower breast cancer risk (OR_per-SD, 0.94, 95% CI 0.91 to 0.98). Common single nucleotide polymorphisms between the two diseases, with low P-values (P_CD < 1.00E-05, P_BC ≤ 0.05), mapped onto genes enriched for immunoregulatory and apoptotic processes. Our results suggest that the link between breast cancer and celiac disease is due to a shared polygenic variation of immune related regions, uncovering pathways which might be important for their development.

NLRP3 gene knockout blocks NF-κB and MAPK signaling pathway in CUMS-induced depression mouse model

BACKGROUND

Abundant researches indicate that neuroinflammation has important roles in the pathophysiology of depression. Our previous study found that the NLRP3 inflammasome mediated stress-induced depression-like behavior in mice via regulating neuroinflammation. However, it still remains unclear that how the NLRP3 inflammasome influences related inflammatory signaling pathway to contribute to neuroinflammation in depression.

METHODS

We used wild-type mice and NLRP3 gene knockout mice to explore the role of NLRP3 inflammasome and related inflammatory signaling pathways in chronic unpredictable mild stress (CUMS) induced depression mouse model.

RESULTS

Both wild-type and NLRP3 knockout stress group mice gained less weight than control group mice after 4 weeks CUMS exposure. The wild-type mice subjected to 4 weeks CUMS displayed depression-like behaviors, including decreased sucrose preference and increased immobility time in the tail suspension test. The NLRP3 knockout stress group mice didn't demonstrate depression-like behaviors. The levels of interleukin-1β protein in serum and hippocampi of CUMS exposed wild-type mice were significantly higher, while the NLRP3 knockout stress group mice didn't show an elevation of interleukin-1β levels. Similarly to early researches, we found that CUMS led to promoted NLRP3 expression in hippocampi. In addition, the hippocampi in CUMS exposed wild-type mice had higher p-JNK and p-p38 protein expression, which indicated activation of the mitogen-activated protein kinases (MAPK) pathway. The knockout of NLRP3 gene inhibited CUMS-induced activation of the MAPK pathway. The nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) protein complex was activated in the hippocampi of wild-type mice after CUMS exposure, while knockout of NLRP3 gene hindered its activation.

CONCLUSIONS

These data further proved that the NLRP3 inflammasome mediated CUMS-induced depression-like behavior. The NLRP3 inflammasome regulated CUMS-induced MAPK pathway and NF-κB protein complex activation in depression mouse model. Further researches targeting the NLRP3 inflammasome-signaling pathway might be under a promising future in the prevention and treatment of depression.

Integrative transcriptomic meta-analysis of Parkinson's disease and depression identifies NAMPT as a potential blood biomarker for de novo Parkinson's disease

Emerging research indicates that depression could be one of the earliest prodromal symptoms or risk factors associated with the pathogenesis of Parkinson’s disease (PD), the second most common neurodegenerative disorder worldwide, but the mechanisms underlying the association between both diseases remains unknown. Understanding the molecular networks linking these diseases could facilitate the discovery of novel diagnostic and therapeutics. Transcriptomic meta-analysis and network analysis of blood microarrays from untreated patients with PD and depression identified genes enriched in pathways related to the immune system, metabolism of lipids, glucose, fatty acids, nicotinamide, lysosome, insulin signaling and type 1 diabetes. Nicotinamide phosphoribosyltransferase (NAMPT), an adipokine that plays a role in lipid and glucose metabolism, was identified as the most significant dysregulated gene. Relative abundance of NAMPT was upregulated in blood of 99 early stage and drug-naïve PD patients compared to 101 healthy controls (HC) nested in the cross-sectional Parkinson’s Progression Markers Initiative (PPMI). Thus, here we demonstrate that shared molecular networks between PD and depression provide an additional source of biologically relevant biomarkers. Evaluation of NAMPT in a larger prospective longitudinal study including samples from other neurodegenerative diseases, and patients at risk of PD is warranted.

Metabolic pathways in the periphery and brain: Contribution to mental disorders?

The association between mental disorders and diabetes has a long history. Recent large-scale, well-controlled epidemiological studies confirmed a link between diabetes and psychiatric illnesses. The scope of this review is to summarize our current understanding of this relationship from a molecular perspective. We first discuss the potential contribution of diabetes-associated metabolic impairments to the etiology of mental conditions. Then, we focus on possible shared molecular risk factors and mechanisms. Simple comorbidity, shared susceptibility loci, and common pathophysiological processes in diabetes and mental illnesses have changed our traditional way of thinking about mental illness. We conclude that schizophrenia and affective disorders are not limited to an imbalance in dopaminergic and serotoninergic neurotransmission in the brain. They are also systemic disorders that can be considered, to some extent, as metabolic disorders.