Single Nucleotide Variants (SNVs) of the Mesocorticolimbic System Associated with Cardiovascular Diseases and Type 2 Diabetes: A Systematic Review

The mesocorticolimbic (MCL) system is crucial in developing risky health behaviors which lead to cardiovascular diseases (CVDs) and type 2 diabetes (T2D). Although there is some knowledge of the MCL system genes linked to CVDs and T2D, a comprehensive list is lacking, underscoring the significance of this review. This systematic review followed PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. The PubMed and Web of Science databases were searched intensively for articles related to the MCL system, single nucleotide variants (SNVs, formerly single nucleotide polymorphisms, SNPs), CVDs, T2D, and associated risk factors. Included studies had to involve a genotype with at least one MCL system gene (with an identified SNV) for all participants and the analysis of its link to CVDs, T2D, or associated risk factors. The quality assessment of the included studies was performed using the Q-Genie tool. The VEP and DAVID tools were used to annotate and interpret genetic variants and identify enriched pathways and gene ontology terms associated with the gene list. The review identified 77 articles that met the inclusion criteria. These articles provided information on 174 SNVs related to the MCL system that were linked to CVDs, T2D, or associated risk factors. The COMT gene was found to be significantly related to hypertension, dyslipidemia, insulin resistance, obesity, and drug abuse, with rs4680 being the most commonly reported variant. This systematic review found a strong association between the MCL system and the risk of developing CVDs and T2D, suggesting that identifying genetic variations related to this system could help with disease prevention and treatment strategies.


Introduction
Non-communicable diseases (NCDs) pose a significant global health challenge and are among the top causes of adult mortality worldwide [1].In 2022, NCDs were estimated to account for 41 million (71%) of the 57 million global deaths, of which cardiovascular diseases (CVDs) caused 17.9 million (31%) of the global deaths and 44% of all deaths as a result of NCDs [1], whereas diabetes mellitus (DM) was attributed to 1.5 million (3%) of all global deaths and 4% of all NCD deaths [1].Most NCDs share common risk factors, which are often categorized as behavioral or biological [2].
The mesocorticolimbic (MCL) system, originating in the ventral tegmental area (VTA) region of the brain [3], might play a crucial role in the development of key risky health behaviors leading to chronic NCDs of major public health importance.Studies have revealed that there is a strong association between the MCL system and the risk of developing CVDs [4,5].A substantial body of research has demonstrated that certain single nucleotide variants (SNVs) of specific MCL genes are significant in the increased risk of CVDs.For instance, rs7396366, rs4680, and rs4714210 were found to be related to coronary artery disease [6]; rs4680 was associated with hypertension; rs4633 and rs4680 were linked to atherosclerosis [7]; and rs2097603, rs4633, rs4680, and rs174699 were associated with venous thrombosis [8].Additionally, rs324420 was found to be related to an increased heart rate [9].The mesolimbic system plays important roles in the regulation of behavior, vulnerability to stress, and drug abuse [10,11].Stress is a potential activator of mesolimbic and mesocortical projections [12,13].It is also associated with noticeable cardiovascular responses, like differential vasoconstrictor response, change in blood pressure, and heart rate [14,15].The MCL system also regulates optimal cardiovascular responses such as the assimilation of sensory and behavioral information with cardiovascular homeostasis [4,14,16].To sum up, it works as a connector between behaviors like locomotory and cognitive, and cardiovascular homeostasis, which result in CVDs [4,14].
Likewise, studies have revealed that the MCL system has some impacts on the etiology and pathogenesis of type 2 diabetes (T2D) and metabolic syndrome (MS) [17,18].An animal experiment showed that increased dopamine tone in mesolimbic brain areas leads to an increased value of various rewarding stimuli, including food intake [19,20].This fact may have determined an increased motivation for food consumption in the test animals, which at later stages, could result in obesity and deficits in glucose control [21].
Furthermore, environmental and genetic risk factors influence the incidence and severity of CVDs and T2D.Other behavioral risk factors that contribute to the development of CVDs and T2D are smoking, excessive alcohol intake, poor diet, drug addiction, and physical inactivity [22,23].These lifestyle factors are closely linked to the MCL system, which involves a complex interplay between genetic and environmental influences.Research indicates that variations in MCL genes can increase susceptibility to CVDs and T2D among individuals with these risk factors [22,23].Genome-wide association studies have revealed that heterogeneity can result in different susceptible genes being associated with CVDs and T2D [24,25].
Identifying genetic variants linked to the development of, or considered risk factors for, CVDs and T2D is critical for disease prevention and therapy.There is no comprehensive information from genetic association research on MCL system genes that have been identified as risk factors for CVDs and T2D.Therefore, this systematic review was undertaken to give a complete list of SNVs of the MCL system that are related to CVDs and T2D, as well as their possible risk factors.

Study Design and Search Strategy
This review was conducted in accordance with PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions [26].Prior to sorting the studies for inclusion, the review protocol was registered in the international prospective register of systematic reviews, PROSPERO (ID: CRD42021273784).Two databases (PubMed and Web of Science) were searched intensively to identify articles that were related to the MCL system, SNPs, gene variants, and CVDs, T2D, or their risk factors.Those databases were used since they are considered the most fundamental sources of medical research.Search terms and keywords were developed based on the concepts that made up the research question by using the National Library of Medicine's vocabulary thesaurus, MeSH, as indicated in Supplementary Tables S1-S3.To maximize our search sensitivity, the bibliographies of first hit articles, similar articles to those in PubMed, and articles in Google Scholar, ProQuest, and some related journals were manually screened to cover all published and unpublished related articles.The process of selecting studies is illustrated in Figure 1.

Inclusion Criteria
Studies published up to 31 May 2023 were included in this review based on the following criteria: (1) at least one gene (with an identified SNV) related to the MCL system was genotyped for all study participants; (2) the genes (with identified SNVs) were associated with CVDs, T2D, or their risk factors; and (3) primary studies were conducted in the English language and on humans only.

Exclusion Criteria
Studies must not have been conducted on psychiatric-related health statuses like schizophrenia or major depressive disorder (MDD).Furthermore, no limitation was created regarding the study type or characteristics of subjects.

Quality Assessment and Data Extraction
Quality assessment for all included studies was conducted using the standard genetic association study quality assessment tool (Q-Genie tool) [27].Each article was evaluated on a scale of 1-77; the average score of all included articles was 71 (ranging from 52 to 77), which indicates good-quality studies (Supplementary Appendix S1).A preliminary synthesis of the extracted data from the included articles is indicated in Table 1.A thematic analysis was used since it is an appropriate method in the context of a systematic review

Inclusion Criteria
Studies published up to 31 May 2023 were included in this review based on the following criteria: (1) at least one gene (with an identified SNV) related to the MCL system was genotyped for all study participants; (2) the genes (with identified SNVs) were associated with CVDs, T2D, or their risk factors; and (3) primary studies were conducted in the English language and on humans only.

Exclusion Criteria
Studies must not have been conducted on psychiatric-related health statuses like schizophrenia or major depressive disorder (MDD).Furthermore, no limitation was created regarding the study type or characteristics of subjects.

Quality Assessment and Data Extraction
Quality assessment for all included studies was conducted using the standard genetic association study quality assessment tool (Q-Genie tool) [27].Each article was evaluated on a scale of 1-77; the average score of all included articles was 71 (ranging from 52 to 77), which indicates good-quality studies (Supplementary Appendix S1).A preliminary synthesis of the extracted data from the included articles is indicated in Table 1.A thematic analysis was used since it is an appropriate method in the context of a systematic review of heterogeneous data [28].Independently, two authors completed all of the above steps.In case of any inconsistency, the opinion and advice from a third reviewer was considered.

Bioinformatics Analysis
We performed a bioinformatics analysis to annotate and interpret genetic variants and to identify overrepresented biological functions and pathways associated with our identified genes and variant lists.The variant effect prediction (VEP) tool was used to annotate the functional effects of genetic variants [29].The VEP tool was run with the human genome assembly GRCh38.p13 and the Ensembl transcript database release 109.For the functional annotation and enrichment analysis, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) tools was used to identify enriched pathways and gene ontology (GO) terms for our gene list [30].We selected the "Homo sapiens" species database and gene symbol as the gene identifier in DAVID and used the KEGG pathway as the background database.We visualized the enriched terms using a bar plot and performed gene set enrichment analysis using Excel 2019.

Results
Of the 3123 articles retrieved, 77 articles that met the inclusion criteria were included in this review.Out of them, seven were related to CVDs; five were related to T2D; six were related to obesity, and one was related to physical activity, as they were considered risk factors for CVDs and T2D; fourteen were associated with smoking and fifteen, with alcohol consumption; and others were related to drug addiction (three on cocaine, ten on heroin, five on opioids, three on amphetamine, and eight on substance abuse), as they can be risk factors for CVDs as well.Regarding the study designs, the majority of the studies were case-control (n = 50), seventeen were cross-sectional, seven were cohort, and three were randomized controlled trials.
Overall, 117,197 participants were included in 77 studies.Out of them, 27,883 were Asian (65.9% were Chinese), 39,727 were European (16% were European Americans), 6248 were African American, and 158 were Hispanic, although ethnicity was either reported as "Other" or not reported for 49,587 participants.A total of 174 SNVs in 69 different genes of the MCL system that were related to CVDs, T2D, and their potential risk factors were identified.Details on the identified genes and SNVs, including their IDs and other genomic features, are provided in Supplementary Appendix S2 and Supplementary Table S4.The findings were analyzed based on their themes (CVDs, T2D, obesity, smoking and nicotine dependence, alcohol dependence, drug addiction, and exercise behavior), which were related to the review question.Significant and non-significant SNVs for each gene are summarized under those thematic headings in Table 2. Notably, the significant SNVs associated with cardiovascular diseases were related to coronary artery disease, hypertension, venous thrombosis, atherosclerosis, and heart rate.
The significant SNVs were analyzed using the VEP tool [29].The predicted effects of the genetic variants on protein function were synonymous (53%) and missense (47%) (Figure 2).Further analysis of the missense variants using VEP revealed that 48.2% were predicted to be benign, 3.38% were predicted to be likely benign, and 18.42% were predicted to initiate a drug response.CVDs, T2D, and/or their risk factors, while "Non-Significant" indicates SNVs without a statistically significant association, * significant with regular sedative users only.
The significant SNVs were analyzed using the VEP tool [29].The predicted effects of the genetic variants on protein function were synonymous (53%) and missense (47%) (Figure 2).Further analysis of the missense variants using VEP revealed that 48.2% were predicted to be benign, 3.38% were predicted to be likely benign, and 18.42% were predicted to initiate a drug response.Moreover, cellular component and functional enrichment analyses of the 69 identified genes were performed using DAVID [30].For the cellular component enrichment analysis, we found that genes were significantly enriched in several cellular components, including serotonergic and dopaminergic synapses.These results suggest that the 69 genes are involved in various cellular processes and may play important roles in CVDs and T2D development.We also performed a functional enrichment analysis.We found that the 69 genes were significantly enriched in several functional pathways, including "dopamine neurotransmitter receptor activity", "dopamine binding", and "serotonin binding".These pathways are known to be involved in various aspects of CVD and T2D development and progression.The top ten terms for the cellular components, functional enrichments, and phenotypic enrichments of the identified genes are provided in Supplementary Figures S1-S3.Moreover, cellular component and functional enrichment analyses of the 69 identified genes were performed using DAVID [30].For the cellular component enrichment analysis, we found that genes were significantly enriched in several cellular components, including serotonergic and dopaminergic synapses.These results suggest that the 69 genes are involved in various cellular processes and may play important roles in CVDs and T2D development.We also performed a functional enrichment analysis.We found that the 69 genes were significantly enriched in several functional pathways, including "dopamine neurotransmitter receptor activity", "dopamine binding", and "serotonin binding".These pathways are known to be involved in various aspects of CVD and T2D development and progression.The top ten terms for the cellular components, functional enrichments, and phenotypic enrichments of the identified genes are provided in Supplementary Figures S1-S3.

Discussion
The MCL system, originating in the VTA region of the brain, is known to affect a person's adverse health behaviors, which increase their risk for CVDs and T2D development [103,104].Overstimulation of dopamine, as the main neurotransmitter of the MCL, will lead to craving for different substances, and thus, might be related to increasing the risk of developing CVDs and T2D [9].Numerous genes in the MCL system have been found to be related to CVDs and T2D, either directly or indirectly, through their involvement in different risky behaviors [8,51,53,54,60,62,73,96].MCL genes that were frequently found to be associated with multiple traits are discussed herein.
The catechol-O-methyltransferase (COMT) gene was found to be significantly related to all themes of this study.The COMT enzyme is encoded by the COMT gene, as it is responsible for the degradation of dopamine-adrenaline and noradrenaline, and catecholamine [73].Studies show that regulating dopamine activities might have an impact on vascular resistance [73] and numerous reward behaviors like obesity [62].The rs4680 (Val158Met) of the COMT gene was the most prevalent SNV that was related not only to CVDs [8,51,53,60,73] but also to T2D [54,62,96] and other risk factors [22,39,62,68,76,105].A case-control study among subjects of European ancestry found no significant association between rs4680 and nicotine dependence when using the Fagerstrom Test for Nicotine Dependence (FTND) [74].However, the same measurement tool revealed a significant association among two ethnic groups (African American and European American) [39].Furthermore, a study showed a positive relationship between rs4680 and smoking initiation among females and with smoking persistence among males, as smoking status was self-reported, but not with other smoking behaviors.This variation might be due to the absence of a standard measurement tool for smoking behaviors [39].
In regards to drug addiction and rs4680, two case-control studies [66,91] have shown contradictory results for heroin addiction, even though the same standard instrument (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) was applied for both.A study revealed that African American descent were genetically susceptible to heroin addiction, as the Val allele of the COMT gene is a risk allele [66]; in contrast, no relationship was found in another study conducted among people of European descent only [91].These reversing findings might be attributed to the diversity in the ethnic groups and sample sizes of the studies.
A release of mesocorticolimbic dopamine is modulated by a CB1 receptor that is inactivated by fatty acid amide hydrolase (FAAH) enzymes, triggering different aspects of addiction [9,50,89].An SNV variant (rs324420/C385A) of the FAAH gene was found to establish important risk factors for alcohol dependence [50] and marijuana use [9].Under the recessive model of C385A, it was found to be related to increased heart rate following cannabis smoking [50].This proved the connection between MCL and drug addiction, which is considered a risk factor for CVDs.However, a study with a larger sample size conducted among adult Caucasians found that a variant of FAAH was not significantly associated with cannabis use [89].Despite using the same diagnostic criteria for substance use disorder (DSM-IV) in the studies by Schacht et al. [9] and Flanagan et al. [50], the heterogeneity of the sample size, ethnicity, and inclusion criteria might have contributed to the variety in the correlation between the FAAH variant and substance use.
The glucagon-like peptide-1 (GLP-1) is a hormone that regulates appetite and food intake [6,87], and its receptor activation might affect the reduction in driven behavior for alcohol use [87,106].GLP-1R in the mesolimbic area is involved in food-related reward processing [6,87].GLP-1R agonists have a consequence on CVDs through their physiological effects like reduction in fatty acid absorption, increased satiety, and reduction in body weight [6,87].The risk of coronary artery diseases (CADs) was found to be lower among individuals who carried the GG genotypes of the rs4714210 variant of the GLP-1R gene than for AA genotype carriers [107]; however, another study that addressed the targeted SNVs of GLP-1R for the treatment of alcohol use disorder (AUD) among Caucasians and African Americans indicated no relationship between rs4714210 and AUD [106].On the other hand, rs7769547 of the GLP-1R gene was significantly associated with AUD [87], but not with that of CADs [6].This might be due to the fact that different phenotypes were considered; as a consequence, one variant might be a risk for a particular phenotype but not for others.
Different substances such as nicotine, cocaine, alcohol, opiates, and food increase brain dopamine levels and activate the MCL dopaminergic reward pathways of the brain, hence resulting in various risky behaviors such as smoking, alcohol dependence, and obesity [42,67,75,77,79,82,94].There are five dopamine receptor genes, DRD1, DRD2, DRD3, DRD4, and DRD5, which are mainly related to different risky behaviors like substance abuse and addiction [32,38,42,55,63,67,75,77,79,90,94,101].They are considered risk factors for CVDs and T2D.DRD2 TaqI A is an SNV with two variants: A1, the less frequent allele, and A2, the most frequent.The A1 allele is related to a reduction in the concentrations of D2 receptors which results in diverse substance use disorders (SUDs).Studies have identified that TaqI A is significantly associated with smoking [77], heroin [67,79], and opium addiction [75].On the other hand, Ragia et al. [81] showed no interaction between the DRD2 TaqI A polymorphism and smoking initiation; however, they indicated that an interaction between DRD2 TaqI A1 and 5-HT2CR -759T alleles resulted in smoking initiation behavior [81].
Though the genetic risk factors for CVDs and T2D are abundant, no fundamental study has yet been conducted to study all MCL genetic variants in a comprehensive manner.Intensively studying the impacts of these SNVs on chronic diseases might pave the way for establishing new preventive and treatment approaches.Therefore, this systematic review was conducted to compile worthwhile SNVs encoding proteins of the MCL system that were associated with CVDs and T2D.Although some published studies did not consider ethnicity and gender as cofounders, the available data from the literature seem to designate that the MCL system has a strong relationship with increasing the risk of developing CVDs and T2D, either directly or indirectly through modifying their risk factors.Dimorphisms in gender and ethnicity among the included studies might have contributed to the heterogeneity of the outcomes of this review.Another limitation would be that relying on aggregated data restricted our ability to analyze individual patient data, curtailing detailed insights into specific subpopulations.While our comprehensive search strategy aimed to minimize bias in study selection, it is imperative to acknowledge the underrepresentation of studies in languages other than English.Moreover, interpreting biological causality remains challenging; although our review identified statistically significant associations, establishing causation necessitates a more nuanced understanding of the underlying biological mechanisms.Future research should rigorously explore molecular pathways to enhance comprehension.The generalizability of our findings is inherently constrained by the variations in the included study populations, methodologies, and geographic locations, thereby limiting the external validity of our results.Altogether, further studies using these SNVs might help in developing a better understanding of how these SNVs alter CVDs and T2D.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/genes15010109/s1, Figure S1: The top ten cellular component enrichment terms of the identified genes; Figure S2: The top ten functional enrichment terms of the identified genes; Figure S3: The top ten phenotypic enrichment terms of the identified genes; Table S1: Keywords used for PubMed search performed on 2023-03-06; Table S2: Search strategy on PubMed; Table S3: Search strategy on Web of Science; Table S4: Gene Catalog: Chromosome Assignment and Functional Roles.
Author Contributions: S.F. was responsible for the conceptualization, supervision, review, and editing of the manuscript.S.N. and M.M. participated equally in the data extraction/curation, analysis, and review.J.S. contributed by reviewing and adding the institutional background information.All authors have read and agreed to the published version of the manuscript.

Funding:
The Tempus Public Foundation, under the Stipendium Hungaricum Scholarship, funded this research.
Institutional Review Board Statement: Not applicable.

Figure 1 .
Figure 1.PRISMA flow chart of the included studies.

Figure 1 .
Figure 1.PRISMA flow chart of the included studies.

Figure 2 .
Figure 2. Predicted effects of genetic variants on protein function.

Figure 2 .
Figure 2. Predicted effects of genetic variants on protein function.

Table 1 .
Cont. = Number of males available for cases only, -= no data available on gender, RCT = randomized controlled trial. *

Table 2 .
Single nucleotide polymorphisms encoding proteins of the MCL system that are related to cardiovascular diseases, type 2 diabetes, and their risk factors.