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  • Systematic Review
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17 January 2024

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

,
,
and
1
Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
2
Doctoral School of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary
3
ELKH-DE Public Health Research Group, Department of Public Health and Epidemiology, Faculty of Medicine, University of Debrecen, 4032 Debrecen, Hungary
*
Author to whom correspondence should be addressed.
Genes2024, 15(1), 109;https://doi.org/10.3390/genes15010109
This article belongs to the Section Human Genomics and Genetic Diseases
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Abstract

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.

1. 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.

2. Materials and Methods

2.1. 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.
Figure 1. PRISMA flow chart of the included studies.

2.2. 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.

2.3. 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.

2.4. 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.

2.5. 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.

3. 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.
Our systematic review identified a significant association between the COMT gene and various themes related to CVDs, T2D, and their risk factors. The COMT gene was found to be significantly related to hypertension, dyslipidemia, insulin resistance, obesity, and drug abuse. The rs4680 SNP within the COMT gene was the most frequently reported genetic variant associated with these diseases and their risk factors. This SNP has been shown to affect the activity of the COMT enzyme, which may impact various physiological processes related to CVDs and T2D.
Table 1. Characteristics of the included articles (n = 77).
Table 1. Characteristics of the included articles (n = 77).
No.First Author, Year CountryRisk Factor/DiseaseSample Size (Male)Study Design
1Adamska-Patruno et al., 2019 [31]PolandObesity927 (473)Case–control
2Al-Eitan et al., 2012 [32]JordanDrug use460 (220)Case–control
3Aliasghari et al., 2021 [33]IranObesity 531 (0)Case–control
4Anney et al., 2007 [34]AustraliaSubstance dependence815 (–)Cohort study
5Aroche et al., 2020 [35]Brazil Crack cocaine addiction1069 (605)Case–control
6Avsar et al., 2017 [36]TurkeyObesity 448 (142)Case–control
7Bach et al., 2015 [37]GermanyAlcohol dependence81 (43)Cross-sectional
8Batel et al., 2008 [38]FranceAlcohol dependence 230 (138)Case–control
9Beuten et al., 2006 [39]USANicotine dependence 2037 (668)Cross-sectional
10Beuten et al., 2007 [40]USANicotine dependence 2037 (–)Cohort study
11Céspedes et al., 2021 [41]Brazil Alcohol dependence401 (366)Case–control
12Carr et al., 2014 [42]USAObesity245 (119)Cross-sectional
13Clarke et al., 2014 [43]USAOpioid and cocaine addiction 3311 (1554)Case–control
14da Silva Junior et al., 2020 [44]BrazilAlcohol dependence300 (300)Case–control
15Doehring et al., 2009 [45]GermanyOpioid dependence 88 (62)Case–control
16Erlich et al., 2010 [28]USANicotine and opioid dependence 505 (153)Cross-sectional
17Fedorenko et al., 2012 [46]Russia Alcohol dependence501 (501)Case–control
18Fehr et al., 2013 [47]GermanyAlcohol dependence1159 (804)Case–control
19Fernàndez-Castillo et al., 2010 [48]SpainCocaine dependence338 (142)Case–control
20Fernàndez-Castillo et al., 2013 [49]SpainCocaine dependence914 (755)Case–control
21Flanagan et al., 2006 [50]USADrug addiction (cocaine, alcohol, heroin, methadone, and methamphetamine)1024 (–)Case–control
22Ge et al., 2015 [51]ChinaBlood pressure and lipid level 3079 (1864)Cohort study
23Gellekink et al., 2007 [8]NetherlandVenous thrombosis607 (302)Case–control
24Gold et al., 2012 [52]USASmoking cessation1217 (553)RCT
25Hall et al., 2014 [53]USACVD, aspirin and vitamin E23,273 (0)RCT
26Hall et al., 2016 [54]USAT2D909 (0)Cross-sectional
27Harrell et al., 2016 [55]USASmoking96 (71)Cross-sectional
28Huang et al., 2009 [56]USANicotine dependence 2037 (–)Cohort study
29Johnstone et al., 2004 [57]USASmoking behavior975 (399)Cohort study
30Joshua WB, 2013 [58]USAObesity and drug abuse 59 (29)Cross-sectional
31Kaminskaite et al., 2021 [59]LithuaniaAlcohol dependence329 (127)Case–control
32Kishi et al., 2008 [7]JapanMeth use disorder 944 (479)Case–control
33Ko et al., 2012 [60]ChinaAtherosclerosis 1503 (696)Cross-sectional
34Koijam et al., 2021 [61]India Heroin dependence279 (110)Case–control
35Kring et al., 2009 [62]DenmarkT2D and obesity 1557 (1557)Cross-sectional
36Kuo et al., 2018 [63]ChinaAmphetamine dependence1063 (854)Case-control
37Lachowicz et al., 2020 [64]PolandPolysubstance addiction601 (601)Case–control
38Landgren et al., 2011 [33]SwedenAlcohol dependence115 (88)Case–control
39Långberg et al., 2013 [65]SwedenObesity and Type 2 diabetes1177 (827)Case–control
40Levran et al., 2015 [66]USAHeroin (OD) and cocaine (CD) addictions 522 (281)Case–control
41Li et al., 2006 [67]ChinaHeroin dependence 420 (–)Cross-sectional
42Li et al., 2016 [68]ChinaHeroin addiction 1080 (–)Case–control
43Lind et al., 2009 [69]AustraliaAlcohol consumption behavior305 (305)Case–control
44Lohoff et al., 2009 [70]USACocaine dependence 608 (328)Case–control
45Ma et al., 2005 [71]USANicotine dependence 2037 (686)Case–control
46Ma et al., 2018 [6]China Coronary artery disease 611 (471)Case–control
48Mattioni et al., 2022 [72]France Alcohol use, nicotine, and cannabis dependence3056 (1834)Case–control
47Mir et al., 2018 [73]IndiaCardiovascular disease 200 (96)Cohort study
49Mutschler et al., 2013 [74]GermanySmoking behavior551 (–)Case–control
50Najafabadi et al., 2005 [75]IranOpium dependence 230 (230)Case–control
51Nelson et al., 2014 [76]USA and AustraliaHeroin dependence 3485 (2095)Case–control
52Noble et al., 1994 [77]USASmoking354 (190)Case–control
53Peng et al., 2013 [78]ChinaHeroin dependence 844 (436)Case–control
54Perez de los Cobos et al., 2007 [79]SpainHeroin dependence426 (305)Case–control
55Prado-Lima et al., 2004 [80]BrazilSmoking behaviors 625 (266)Cross-sectional
56Ragia et al., 2013 [81]GreekSmoking initiation 410 (215)Case–control
57Ragia et al., 2016 [82]TurkeyAlcohol dependence 146 (111)Case–control
58Schacht et al., 2009 [9]USA Smoking marijuana 40 (30)Cross-sectional
59Schacht et al., 2022 [83]USAAlcohol dependence87 (33)RCT
60Shiels et al., 2009 [84]USASmoking 10,059 (3873)Cross-sectional
61Sipe, et al., 2002 [85]USADrug users (drugs, alcohol, nicotine)2881 (–)Case–control
62Spitta et al., 2022 [86]GermanyAlcohol dependence29 (26)Case–control
63Suchankova et al., 2015 [87]USAAlcohol dependence 2671 (2405)Case–control
64Sun et al., 2021 [88]China Methamphetamine, heroin, and alcohol addiction6146 (4364)Case–control
65Tyndale et al., 2006 [89]CanadaDrug addiction749 (242)Cross-sectional
66Van Der Mee et al., 2018 [90]GreeceExercise behavior12,929 (5144)Cohort study
67Vereczkei et al., 2013 [91]HungaryHeroin dependence 858 (597)Case–control
68Voisey et al., 2011 [92]AustraliaAlcohol, nicotine, and opiate dependence 748 (443)Case–control
69Wang et al., 2018 [93]ChinaCoronary artery disease 707 (311)Case–control
70Wei et al., 2012 [94]ChinaNicotine dependence 480 (480)Cross-sectional
71Xie et al., 2013 [95]ChinaHeroin addiction 533 (533)Case–control
72Xiu et al., 2015 [96]ChinaType 2 diabetes 1320 (758)Case–control
73Xu et al., 2004 [97]Germany and ChinaHeroin dependence 1462 (–)Case–control
74Ying et al., 2009 [98]ChinaObesity 426 (217)Case–control
75Yu et al., 2006 [99]USANicotine dependence 1590 (730)Cross-sectional
76Zain et al., 2015 [100]PakistanType 2 diabetes191 (107)Cross-sectional
77Zhu et al., 2013 [101]ChinaOpioid dependence 939 (343 *)Case–control
Total number of participants (accumulative) 117,197 (43,839)
* = 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.
Table 2. Single nucleotide polymorphisms encoding proteins of the MCL system that are related to cardiovascular diseases, type 2 diabetes, and their risk factors.
No.Risk Factor/Disease Gene Name ‡Significant SNVs †Non-Significant SNVs †
1Cardiovascular diseases (CVDs) AP2A2rs7396366 [6]
BZRAP1 rs2526378 [93]
COMTrs4680 [51,53,60,73]
Haplotype: rs2097603–rs4633–rs4680–rs174699 (G–C–G–T) [8]
rs4633 [60]
rs4818 [53]		(rs2097603
rs4633
rs174699) [8]
Haplotypes: rs2097603–rs4633–rs4680–rs174699 (A–C-G–T, A–T-A–T, A–C–G–C) [8]
FAAHC385A (rs324420) [9]
GLP1Rrs4714210 [6](rs761387
rs2268635
rs7769547
rs910162
rs3765468
rs3765467
rs3765466
rs10305456
rs10305518
rs1820) [6]
2Type 2 diabetes (T2D)5HT2A rs6311 [62]
5HT2Crs3813929 [62]
ADRA2A(rs553668
rs521674) [65]		rs11195419 [65]
COMTrs4646312 [96]
rs4680 [54,62,96]
(900 I/D C) [100]
(rs4633
rs4818) [54]
DRD3 (rs167771
rs324029
rs8076005
rs20667) [96]
SLC6A4Haplotypes: rs4646312, rs4680 (C–G, T–A) [96]
Diplotype: rs4646312–rs4680 (C–G_T–G)
SNP–SNP interactions
Additive × additive (rs4680 × rs2066713)
Dominant × dominant (rs4680 × rs2066713) [11]		Haplotypes: rs8076005, rs2066713 (A–A, A–G, G–G) [96]
3Obesity 5HT2AR–c.1438 A>G [98]
5HT2CCombined genotype with COMT (rs3813929
rs4680) [62]
ANNK1rs1800497 [33]
ADRA2A(rs553668
rs521674) [65]		rs11195419 [65]
COMTrs4680 [62]rs4580 [42]
DAT1 rs28363170 [42]
DBH (rs77905
rs6271
rs1611115
rs1108580) [42]
DDC (rs2060762
rs11575543
rs11575542
rs11575522
rs11238131) [42]
DRD1 rs4532 [42]
DRD2rs1799732 [33]
rs1800497 [42]
(rs1800498
rs6277) [72]
DRD3 rs6280 [42]
DRD4 rs4646984 [42]
HTR1A (rs6295
rs1800044
rs1799920
rs10042486) [42]
HTR1B (rs6296
rs13212041
rs130058) [42]
HTR2Ars6314 [42](rs927544
rs7997012
rs6313
rs6311
rs2770296
rs1923886) [42]
LEPRrs1137100 [58]rs1137101 [58]
MAOAMAOA-LPR (3.5R/4R) [42]
u VNTR [36]
MC4R(rs1350341
rs17782313
rs633265) [31]
OPRD (rs569356
rs2236861
rs204076
rs7773995
rs514980
rs2281617
rs1799971
rs12205732
rs10485057
rs17174801) [42]
SERT (rs2066713
rs2020933
rs16965628
rs1042173) [42]
SPR (rs2421095
rs1876487) [42]
TH rs71029110 [42]
TPH2 (rs7963720
rs7305115
rs4290270
rs17110690
rs1487275
rs17110747) [42]
4Smoking and nicotine dependence 5HT2AT102C [80]
ANKK1(rs11604671
rs2734849) [56]		(rs10891545
rs7945132
rs4938013
rs7118900
rs1800497) [56]
CHRNA3(rs660652
rs1051730) [28]		(rs6495308
rs12443170) [28]
CHRNA4rs2236196 [94]
CHRNA5(DRD2/5-HT2CR –759C>T genotype combinations: A1–/–759T–, A1+/–759T–, A1–/–759T + A1+/–759T+;
DRD2/5-HT2CR –697G>C genotype combinations: A1–/–697C–, A1+/–697C–, A1–/–697C+ A1+/–697C+, 5-HT2CR –759C>T; interaction of 5-HT2CR –759C>T and DRD2 TaqIA; 5-HT2CR –697G>C; interaction of 5-HT2CR –697G>C and DRD2 TaqIA) [28]
(rs936460
rs936461
rs12280580) [55]		rs16969968 [28]
CHRNB3rs4954 [94]
rs660652 [28]
COMTrs4680 [39,84]
(rs740603
rs4680
rs174699
rs933271
rs174699) [39]
Haplotype: rs740603–rs4680–rs174699 (A–G–T)
rs933271–rs4680–rs174699 (T–G–T, C–A–T) [39] 		rs4633 [39]
rs4680 [74]
DBHrs77905 [84]
DDCrs11575461 [94]
(rs12718541
rs1470747
rs11238214
rs2060761) [99]
rs921451 [71,99]
Haplotype: rs921451–rs3735273–rs1451371–rs2060762 (T–G–T–G) rs921451–rs3735273–rs1451371–rs3757472 (T–G–T–G) [71]		(rs11575542
rs732215
rs1451371
rs3823674
rs1470750
rs11575334
rs4947644) [99]
(rs998850
rs3735273
rs1470750
rs1451371
rs732215
rs3757472
rs2060762) [71]
DRD2(rs11214613
rs6589377) [94]
TaqIA1 [77]		(rs6278
rs6279
rs1079594
rs6275
rs2075654
rs2587548
rs2075652
rs1079596
rs4586205
rs7125415
rs4648318
rs4274224
rs7131056
rs4648317
rs4350392
rs6589377) [56]
C32806T [57]
(rs1800498
rs6277) [72]
DRD3rs2630351 [94]
DRD4(rs936460
rs936461
rs12280580) [55]		rs1805186 [55]
DRD5rs1967550 [94]
FIGNL1rs10230343 [99]
GABBR2rs2779562 [40]
GALR1rs2717162 [52]
GRB10 (rs12669770
rs12540874
rs2715129) [99]
MAOArs1801291 [84]
MAP3K4rs2314378 [94]
PPP1R1BHaplotype: rs2271309–rs907094–rs3764352–rs3817160 (–C–T–G–C)
rs879606 [40]		rs1874228 [40]
ZNFN1A1 (rs11980407
rs1110701) [99]
5Alcohol dependence ADH1Brs1229984 [88]
AGBL4 rs147247472 [88]
ANKK1 rs1800497 [59]
(rs4938015
rs1800497) [72,86]
ANKS1B rs2133896 [88]
CHRNA3 (rs6495307
rs1317286
rs12443170
rs8042059) [34]
CHRNA4 (rs1044396
snp12284
rs6011776
rs6010918) [34]
CHRNA6 (rs17621710
rs10087172
rs10109429
rs2196129
rs16891604) [34]
CHRNB2 (rs2072659
rs2072660) [34]
CHRNB3rs13261190 [34](rs62518216
rs62518217
rs62518218
rs16891561) [34]
COMT(rs165774
rs4680) [59,83]
Haplotype: rs4680–rs165774 (–A–A) [92]		(rs4633
rs740602
rs4818
rs4680
rs4646315) [41]
CRHrs6999100 [58]
CSNK1Ers135745 [58]
CTNNA2 rs10196867 [88]
DDCrs11575457 [41](rs5884156
rs4490786
rs11575457
rs58085392
rs2876829
rs11575375
rs3735273
rs6950777
rs6264) [41]
DAT1(rs6350
rs463379) [69]		(rs10064219
rs12516948
rs40184
rs6347
rs464049
rs403636) [69]
DRD1rs686 [38]
(rs2283265
rs1076560
rs2075654
rs1125394
rs2734836
rs1799732) [32]
Haplotype: rs686–rs4532 (–T–G) [38]		(rs686
rs155417
rs4532) [41]
DRD2(rs6277
rs1800498) [72]		A2/A1 [82]
rs1800497 [34]
(rs6277
rs6275
rs1076560
rs35352421
rs11608185
rs12808482) [41]
DRD3 Ser9Gly [82]
(rs149281192
rs2251177
rs3732783
rs6280) [41]
DRD4 rs7124601
DRD5 (rs2076907
rs6283
rs1967551) [41]
DβH 1021 C/T [82]
FAAH385 C/A [85]
GHRL(rs42451
rs35680) [34]		(rs4684677
rs34911341
rs696217
rs26802) [34]
GHSRrs495225 [34](rs2948694
rs572169
rs2232165) [34]
GLP1R(rs7766663
rs2235868
rs7769547
rs10305512
rs2143734
rs2268650
rs874900
rs6923761
rs7341356
rs932443
rs2300613) [87]		(rs7738586
rs9296274
rs2268657
rs3799707
rs3799707
rs910170
rs1042044
rs12204668
rs1076733
rs2268640
rs2206942
rs10305514
rs4714210
rs4254984
rs9968886) [87]
GRIK1 rs2832407 [82]
HTR2A(rs6313
rs6311) [44]
OPRM1rs1799971 [37]A118G [82]
PIP4K2A(rs746203
rs2230469) [46]		(rs8341
rs943190
rs1132816
rs1417374
rs11013052) [46]
SLC6A3 (rs429699
rs8179029
rs6347
rs6348
rs460000
rs465130
rs465989
rs13189021
rs2254408
rs2270914
rs2270913
rs8179023
rs6350) [41]
TH (rs6578990
rs12419447
rs6357
rs7925924
rs4074905
rs6356
rs7925375) [41]
VMAT2rs363387 [47]
Haplotypes: rs363332, rs363387
(–G–T, –G–G)
rs363387–rs363333 (–T–T) rs363333–rs363334 (C–T)
rs363387–rs363333–rs363334 (–T–T–C)
rs363332–rs363387–rs363333–rs363334 (–G–T–T–C) [47]		(rs363371
rs363324
rs11197931) [47]
6Drug addiction ADH1B rs1229984 [88]
AGBL4rs147247472 [88]
ANKK1(rs877137
rs877138
rs12360992
rs4938013
rs2734849
rs2734848) [76]
rs1800497 [45,91]		rs1800497 [76]
rs7118900 [66]
ANKS1Brs2133896 [88]
CDNF (rs11259365
rs7094179
rs7900873
rs2278871) [70]
CHRM5rs7162140 [102](rs661968
257A>T
rs2702309
rs2702304
rs2576302
rs2705353) [102]
CHRNA4 (rs755203
rs2273506
rs2273505
rs3787141
rs3787140
rs2273504
rs2273502
rs2273501
rs1044396
rs1044397
rs3787137
rs2236196
rs4522666) [7]
CHRNA5rs16969968 [35]
Haplotypes: rs16969968–rs660652–rs1051730–rs6495308–rs12443170 (A–G–A–T–G, G–G–G–T–G)) [28]
(rs588765
rs514743) [35]
CHRNB2 (rs4845652
rs2072658
rs2072659
rs2072660
rs3811450) [7]
CNTFRrs7036351 [49]
COMTrs4680 [66]rs4680 [91]
(rs933271
rs2239393
rs4818) [66]
(rs265981
rs1800497
VNTR 130–166 bp
rs2519152
VNTR) [90]
CSNK1E rs5757037 [66]
CTNNA2rs10196867 [88]
DAT1Int8 VNTR [48]
(rs28363170
rs3836790
rs246997) [61]		SLC6A3 VNTR [67]
3′UTR VNTR [48]
(rs40184
rs27048
rs37021
rs250683
rs250682
rs427284)
rs458609) [61]
DBHrs6479643 [49]rs1611115 [95]
rs1108580 [66]
1021C>T [81]
(rs1108580
5UTR ins/del) [48]
rs2519152 [90]
DCC(rs16956878
rs12607853
rs2292043) [68]		(rs2122822
rs2329341) [66]
(rs17753970
rs934345
rs2229080) [68]
DLG2 (rs575050, rs2512676, rs17145219, rs2507850) [68]
DRD1(rs4532
rs686) [101]		(rs4532
rs5326
rs2168631
rs6882300
rs267418) [78]
(rs686
rs5326) [66]
(rs10078866
rs10063995
rs5326
rs1799914
rs4867798) [101]
rs265981 [90]
DRD2TaqI A1 [67,75,79]
(rs2234689
rs1554929
rs2440390
rs1076563) [76]
rs1079597 [91]
rs1076560 [43,45]
(241 A>G; TaqIB A>G; TaqID G>A; and intron 4 T>C) [97]
(759 C>T; 697 G>C) [81]
Haplotypes: rs1076560, rs1800498, rs1079597, rs6276, and rs180049 of the ANKK1
(C–T–G–A–T, C–T–G–A–C) [64]		rs7125415 [76]
(141 ins/del C; intron 6 ins/del G; 311 Ser>Cys; 20236 C>T; exon 822640 C>G; and TaqIA G>A) [97]
rs1800498 [72,91]
(rs1076560
rs2283265
rs2587548
rs1076563
rs1079596
rs1125394
rs2471857
rs4648318
rs4274224
rs1799978) [66]
TaqIA [81]
rs1079597 [48]
rs1800497 [48,90]
(rs12364283
rs1799978
rs1799732
rs4648317
rs1800496
rs1801028
rs6275
rs6277) [45,72]
DRD3Haplotype: rs324029–rs6280–rs9825563 (A–T–A)
rs2134655–rs963468–rs9880168 (A–T–A) [63]		(rs3773678
rs167771) [66]
rs6280 [90]
(rs2046496
rs2630351) [63]
DRD4rs1800955 [91](rs936462
rs747302) [91]
VNTR 48 bp [90]
DRD5 DRP (A9/A9) [67]
rs2867383 [66]
VNTR 130–166 bp [90]
FAAH(rs12075550
rs6658556
796A>G
rs932816
rs4660930) [50]		385 C/A * [50,89]
FAT3 (rs10765565
rs4753069
rs2197678
rs7927604) [68]
HTR1Ers1408449 [49]
HTR2A(rs6561332
rs6561333) [49]
KTN1 (rs10146870
rs1138345
rs10483647
rs1951890
rs17128657
rs945270) [68]
NCAM1(rs4492854
rs587761) [76] 		rs11214546 [76]
NGFRrs534561 [49]
NTF3rs4073543 [49]
NTRK2rs1147193 [49]
NTRK3(rs12595249
rs744994
rs998636) [49]
THrs2070762 [49]
TTC12(rs2303380
rs10891536
rs4938009
rs7130431
rs12804573) [76] 		rs719804 [76]
7Exercise BehaviorCOMTrs4680 [90]
DAT1VNTR 440 bp [90]
DBH rs2519152 [90]
DRD1 rs265981 [90]
DRD2/ANKK1 rs1800497 [90]
DRD3 rs6280 [90]
DRD4VNTR 48 bp (7r) [90]
DRD5 VNTR 130–166 bp [90]
MAOA VNTR 30 bp [90]
‡ A concise summary of the role of each gene and the chromosome where it is located is provided in Supplementary Table S4, † “Significant” denotes SNVs with a statistically significant association with 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.
Figure 2. Predicted effects of genetic variants on protein function.
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.

4. 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.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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