The Association between Circadian Clock Gene Polymorphisms and Metabolic Syndrome: A Systematic Review and Meta-Analysis

Simple Summary Metabolic syndrome is a cluster of cardio-metabolic risk factors and comorbidities, including central obesity, hypertension, hyperglycemia, and dyslipidemia. In addition, different studies have shown that the disturbances in circadian rhythm are connected with components of metabolic syndrome. Circadian rhythm is the central regulator of every aspect of human health and metabolism, and metabolic homeostasis is essential in regulating energy metabolism, especially in adipose tissue. Therefore, we aimed to evaluate the association of genetic variations of circadian rhythm genes with metabolic syndrome and its components by a systematic review and meta-analysis. Our findings suggest that some variants of the circadian rhythm gene might be genetic biomarkers applied to predict metabolic syndrome susceptibility. Abstract Metabolic syndrome (MetS) is a combination of cardiovascular risk factors associated with type 2 diabetes, obesity, and cardiovascular diseases. The circadian clock gene polymorphisms are very likely to participate in metabolic syndrome genesis and development. However, research findings of the association between circadian rhythm gene polymorphisms and MetS and its comorbidities are not consistent. In this study, a review of the association of circadian clock gene polymorphisms with overall MetS risk was performed. In addition, a meta-analysis was performed to clarify the association between circadian clock gene polymorphisms and MetS susceptibility based on available data. The PubMed and Scopus databases were searched for studies reporting the association between circadian rhythm gene polymorphisms (ARNTL, BMAL1, CLOCK, CRY, PER, NPAS2, REV-ERBα, REV-ERBβ, and RORα) and MetS, and its comorbidities diabetes, obesity, and hypertension. Thirteen independent studies were analyzed with 17,381 subjects in total. The results revealed that the BMAL1 rs7950226 polymorphism was associated with an increased risk of MetS in the overall population. In contrast, the CLOCK rs1801260 and rs6850524 polymorphisms were not associated with MetS. This study suggests that some circadian rhythm gene polymorphisms might be associated with MetS in different populations and potentially used as predictive biomarkers for MetS.


Introduction
Metabolic syndrome (MetS) is a set of cardiometabolic disorders associated with cardiovascular risk factors [1,2]. Metabolic syndrome is a combination of at least three

Literature Search
The databases (Scopus and PubMed) were searched for studies investigating the association between circadian clock gene polymorphisms and metabolic syndrome and its risk factors, such as diabetes, hypertension, and obesity until 6 July 2021. The searching strategy is presented in Supplementary Table S1. The databases search was conducted following PRISMA guidelines (Supplementary File S1), and the study protocol is registered in PROSPERO (CRD42021291934).

Eligibility Criteria
All retrieved articles were primarily selected for acceptable studies regarding specific criteria: cross-sectional or case-control studies and focused on the association of circadian clock gene polymorphisms with MetS risk and its comorbidities. The exclusion criteria included duplicate records, no control group, and departure from Hardy-Weinberg equilibrium in the control group. In addition, only articles written in English were considered.

Data Extraction
Two investigators (I.Š., N.L.) independently extracted data. Data on the author, publication year, study design, demographic characteristics, risk factors related to MetS, genotyping method, allele frequencies, distribution of genotypes, diagnostic criteria, and clinical characteristics were collected. Discrepancies were resolved by discussion. The quality of the studies evaluated by the Newcastle-Ottawa Scale (NOS) is based on the range from one to nine stars and three components: selection, comparability, and outcome ascertainment [20]. Studies with a higher number of stars were supposed to be of higher quality.

Statistical Analyses
The principal outcome was the odds ratio (OR) and 95% confidence interval (95% CI) of specific polymorphism in patients with risk factors related to MetS compared to the controls under allelic, dominant, and recessive genetic models. The selection bias was estimated by computing the Hardy-Weinberg equilibrium (HWE) and frequencies of the genotypes. A random-effect model was used because the studies were performed in a wide range of settings in different populations. The considerable between-study heterogeneity is anticipated in genetic association studies. The heterogeneity was estimated using the Cochran Q-test and the I-squared statistic, and I 2 higher than 50% was considered significant heterogeneity. The sensitivity analysis was conducted to evaluate studies with high statistical heterogeneity by sequentially removing each study to detect pooled results' stability and heterogeneity source. A meta-analysis was conducted if at least two independent studies for the same polymorphism were available. In addition, publication bias was assessed by Begg's and Egger's tests and visual assessment of the funnel plot. A two-sided p-value < 0.05 was deemed significant. The Comprehensive Meta-analysis software version 3.3.070 (Biostat Inc., Englewood, NJ, USA) was used for statistical data processing.

Characteristics of Eligible Studies
A sum of 1828 articles was recovered through databases search after removing duplicate articles. Screening titles and abstracts excluded the 1791 articles. Furthermore, 24 articles were excluded based on the inclusion/exclusion criteria. Finally, 13 studies [21][22][23][24][25][26][27][28][29][30][31][32][33] were included in the meta-analysis, analyzing 17,381 subjects in total, including 8726 cases and 8655 controls. The study selection process is presented in Figure 1. Quality scores of the chosen studies ranged from six to eight (Supplementary Table S2). In addition, the characteristics of suitable studies are shown Table 1, while clinical characteristics are in  Supplementary Table S3. Among the eligible studies, seven were case-control studies, and six were cross-sectional. are in Supplementary Table S3. Among the eligible studies, seven were case-control stud ies, and six were cross-sectional.

Characteristics of the Circadian Rhythm Gene Polymorphism
In 13 studies included, 11 circadian rhythm genes were analyzed with 46 differen polymorphisms. Six studies evaluated the CLOCK gene polymorphisms in 3644 partici pants with MetS risk factors and 3884 control participants [21,22,25,27,29]. Three studies evaluated the BMAL1 polymorphisms in 3644 participants with MetS risk factors and 3884 control participants [25,27,31]. Three studies evaluated the PER3 gene polymorphisms in 2156 participants with MetS risk factors and 2247 control participants [25,26,33]. Two stud ies evaluated the CRY1 [25,29], CRY2 [23,25], and REV-ERBα [28,32] gene polymorphisms The genotype and allele frequencies of all polymorphisms are presented in Table 2. The majority of the articles and polymorphisms were excluded from the quantitative synthesis for several reasons. Nine polymorphisms in three studies showed selection bias in the control group when assuming Hardy-Weinberg equilibrium [27,28,32]. Many studied polymorphisms were analyzed only in one study [23,26,28,30,32,33]. Finally, only three polymorphisms, BMAL1 rs7950226, CLOCK rs1801260, and rs6850524, were incorporated in the final meta-analysis.

Characteristics of the Circadian Rhythm Gene Polymorphism
In 13 studies included, 11 circadian rhythm genes were analyzed with 46 different polymorphisms. Six studies evaluated the CLOCK gene polymorphisms in 3644 participants with MetS risk factors and 3884 control participants [21,22,25,27,29]. Three studies evaluated the BMAL1 polymorphisms in 3644 participants with MetS risk factors and 3884 control participants [25,27,31]. Three studies evaluated the PER3 gene polymorphisms in 2156 participants with MetS risk factors and 2247 control participants [25,26,33]. Two studies evaluated the CRY1 [25,29], CRY2 [23,25], and REV-ERBα [28,32] gene polymorphisms. The genotype and allele frequencies of all polymorphisms are presented in Table 2. The majority of the articles and polymorphisms were excluded from the quantitative synthesis for several reasons. Nine polymorphisms in three studies showed selection bias in the control group when assuming Hardy-Weinberg equilibrium [27,28,32]. Many studied polymorphisms were analyzed only in one study [23,26,28,30,32,33]. Finally, only three polymorphisms, BMAL1 rs7950226, CLOCK rs1801260, and rs6850524, were incorporated in the final meta-analysis.
Further genetics model analyses were conducted. The BMAL1 rs7950226 polymorphism could lower the risk for MetS comorbidities (G vs. A p = 0.047, OR 0.79, 95% CI 0.62-1.00; GG vs. GA + AA p = 0.037, OR 0.75, 95% CI 0.58-0.98). In contrast, CLOCK rs6850524 polymorphism did not influence the risk for MetS (Table 3). Four studies evaluated the rs180126 CLOCK gene polymorphisms in 1261 participants with MetS risk factors and 1129 control participants [21,24,27,31], while only two studies evaluated rs6850524 in CLOCK [22,29] and rs7950226 in BMAL1 gene [25,31] with 651 and 1835 participants with MetS risk factors and 975 and 2011 control participants, respectively. The stratified analysis was conducted based on ethnicity and MetS risk factors only for CLOCK rs1801260 polymorphism due to the small number of articles for BMAL1 rs7950226 and CLOCK rs6850524 polymorphisms. Genetics model analyses of the CLOCK rs1801260 polymorphism are presented in Figure S2. Polymorphism rs1801260 in the CLOCK gene showed significant association in some particular subgroups (Supplementary Table S4). Although only one study was done in the Asian population, the rs1801260 variant could increase metabolic syndrome risk in the mentioned population (Supplementary Table S4). Likewise, as a component of MetS, hypertension is less likely to lead to MetS. In contrast, insulin resistance is strongly associated with MetS (Supplementary Table S4).

Sensitivity Analysis
Sensitivity analysis was performed to evaluate the weight of some single study on pooled effects by determining the ORs before and after removing individual research from a meta-analysis. After being excluded, no outlying article was recognized to modify the pooled ORs substantially (Supplementary Table S5).

Publication Bias
Moreover, the possible publication bias was estimated for all included studies utilizing Begg's and Egger's tests. No significant publication bias was confirmed in any genetics model of CLOCK rs1801260 polymorphism (Table 4). In addition, the funnel plots of log odds ratio versus standard error were proportional (Supplementary Figure S3).

Discussion
In the present study, a systematic review was conducted to associate circadian clock gene polymorphisms with the overall risk of metabolic syndrome. In addition, a metaanalysis was conducted for three frequently investigated polymorphisms in CLOCK (rs1801260 T > C and rs6850524 G > C) and BMAL1 gene (rs7950226 G > A). The results revealed that BMAL1 rs7950226 polymorphism was linked with MetS risk in the overall population, while CLOCK rs1801260 polymorphism was associated with some specific subgroups. Thus, it is the first time comprehensively assessing the study progress in this domain and the first meta-analysis of MetS-related circadian clock polymorphisms.
CLOCK protein is included in the transcriptional control of circadian output genes and the core circadian clock. Hence, up to 10% of the human transcriptome may be under circadian regulation, and disorder in the CLOCK gene substantially influences transcription control.
The CLOCK rs1801260 (T3111C) polymorphism, placed in the 3 -UTR of the gene, has been extensively studied for its function in different MetS risk factors and in MetS patients on various diets. It is the first polymorphism recognized in the CLOCK gene to be linked with human MetS phenotypes [34]. However, although some studies observed a notable association between rs1801260 polymorphism and MetS susceptibility, the present meta-analysis did not find an association to overall MetS risk. That might happen due to the insufficient number of participants, the ethnic diversity of the studied population, and complex environmental circumstances that vary depending on the study [35]. However, insulin resistance is strongly associated with most risk factors related to MetS in all tested genetic models (allelic, dominant, and recessive). In contrast, hypertension as a sole risk factor is less likely to lead to MetS (Table S4). Moreover, the importance of the rs1801260 polymorphism with MetS risk was observed in the Asian subgroup rather than in the Caucasian and Hispanic subgroups in stratified analysis. The C allele was an independent risk factor for potential insulin resistance in Asian patients with essential hypertension [31] and diabetes [36], while in Caucasian patients was positively associated with hypertension and coronary artery disease and negatively with obesity [27,37,38]. Monteleone et al. [12] noted that the rs1801260 genotypes were not linked with obesity. However, they observed a significant connection of the rs1801260 genotypes among overweight participants. Li et al. [15] observed that patients with the C allele incline towards insulin resistance and MetS, which could lead to the discrepancy. The rs1801260 polymorphism within 3 -UTR could be accountable for changes in the secondary structure of mRNA. Thus, polymorphism within 3 -UTR polymorphism results in various mRNA's secondary structures that interfere with RNA-binding proteins and miRNA-182 binding sites in the 3 -UTR [39]. The CLOCK rs1801260 polymorphism might influence its transcription by modifying mRNA durability and then participating in MetS development. Ozburn et al. showed that rs1801260 polymorphism alters the expression, function, and stability of CLOCK mRNA and consequently affects the PER2 expression [40]. In conclusion, the abovementioned findings showed that BMAL1 rs7950226 polymorphism might be a predictive biomarker for MetS risk in the overall population. Nevertheless, additional molecular analyses require elucidation of all the assumptions regarding relevant mechanisms.
Unlike CLOCK rs1801260, rs6850524 polymorphism is an intron variant with three possible variations: C > G, C > A, and C > T, with C > G being the most common variant. Although studies revealed a discrepancy in the association of CLOCK rs6850524 polymorphism and MetS risk factors, this meta-analysis observed no association. However, some studies found an increased risk for hypertension [37] and obesity [22,29] associated with rs6850524. Still, additional studies are needed to investigate the molecular mechanism involved. Similar to CLOCK rs6850524 polymorphism, BMAL1 rs7950226 polymorphism is an intron variant G > A and has been extensively studied for its function in different MetS risk factors. In the present study, a statistically significant association of BMAL1 rs7950226 with MetS risk was observed in the overall population (Table 3). Based on the results of this meta-analysis, it has been revealed that carriers of the A allele or AA genotype have an elevated risk of developing MetS. Furthermore, Pappa et al. found that the A allele is significantly linked with a higher risk of gestational diabetes mellitus [41], while other studies showed an association with type 2 diabetes mellitus [25,42]. Moreover, rs7950226 GG genotype was associated with insulin resistance in patients with hypertension [31]. First, though, the molecular mechanisms underlying MetS need to be further clarified.
As observed in this study, CLOCK and BMAL1 gene polymorphisms are associated with some components of MetS. Polymorphisms within other circadian rhythm genes are also linked with elements of MetS. CRY1 and CRY2 polymorphisms showed a significant link between diabetes and obesity [23,25,29,43]. CRY1 polymorphisms are negatively associated with obesity [29]. Recent studies reported that PER2 and PER3 polymorphisms are associated with diabetes and obesity, metabolic syndrome components [25,26,33]. PER2 polymorphisms are negatively associated with diabetes, while PER3 polymorphisms are positively associated with obesity. Moreover, some studies observed a link between circadian gene REV-ERBα, REV-ERBβ, and RORα polymorphisms and obesity and diabetes [28,30,32]. RORα variants are linked with an increased possibility of developing diabetes [30], while PER3 and RORα polymorphisms increase the risk of MetS in the Taiwanese population [16]. However, those articles were not incorporated in the quantitative investigation due to a limited number of the same polymorphisms investigated. In addition, circadian rhythm genes might contribute to the risk of MetS independently and via gene-gene and gene-environment interplays. Additionally, to core clock genes, other genes expressed in a circadian manner and lifestyle could affect susceptibility to metabolic syndrome. For example, melatonin is a hormone responsible for regulating circadian rhythm and might affect glucose metabolism, associated with insulin resistance and T2DM and, therefore, MetS [44,45]. Moreover, genetic variants of melatonin receptor 1B (MTNR1B) might affect melatonin function and influence susceptibility to insulin resistance and diet-dependent weight loss [46,47]. Some studies suggest that genetic variants of core clock genes under a specific diet could influence risk factors for MetS, such as glucose levels, dyslipidemia, T2DM [48,49]. However, further similar research is required to be included in the meta-analysis to elucidate the precise link between the different circadian clock gene polymorphisms and the overall MetS risk.
The present study has some shortcomings. First, the database search was limited to English articles, so that some studies could have been overlooked. In addition, although the meta-analysis included a moderate number of participants, the critical studies are somewhat limited. Thus, this area requires further research at the molecular level and updated meta-analysis.

Conclusions
In conclusion, the association of circadian clock gene polymorphisms with the overall risk of MetS was reviewed. Moreover, a meta-analysis was conducted employing all possible data for three often studied polymorphisms among them (CLOCK rs1801260 and rs6850524, and BMAL1 rs7950226). The results revealed that the BMAL1 rs7950226 polymorphism was linked with the risk of MetS in the overall population. In contrast, the CLOCK rs1801260 polymorphism was associated with particular subgroups, implying it might be a possible prognostic biomarker for MetS risk. Thus, the study might present new hints to identify genetic biomarkers related to miRNAs that predict MetS susceptibility.
Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/biology11010020/s1, Supplementary File S1: PRISMA 2020 checklist. Table S1: Search strategy: PubMed and Scopus through 6 July 2021. Table S2: Quality assessment of studies included in the meta-analysis by Newcastle-Ottawa scale, Table S3: Clinical characteristics of participants included in the meta-analysis. Table S4: The meta-analysis of the association between CLOCK rs1801260 polymorphism and metabolic syndrome risk. Table S5: Sensitivity analysis of the rs1801260 polymorphism in the CLOCK gene. Figure S1: Forest plot for the association between metabolic syndrome and BMAL1 and CLOCK gene polymorphisms. (A) Forest plot for the association between metabolic syndrome and rs7950226 in BMAL1 gene with the heterogeneity of 28% (Cochran's Q = 1.39, p = 0.24). (B) Forest plot for the association between metabolic syndrome and rs1801260 in CLOCK gene with the heterogeneity of 93% (Cochran's Q = 42.71, p < 0.001). (C) Forest plot for the association between metabolic syndrome and rs6850524 in CLOCK gene with the heterogeneity of 90% (Cochran's Q = 9.89, p = 0.002). The area of each square is proportional to the weight that the individual study contributed to the meta-analysis. Weights are from the random-effects analysis. Figure S2: Forest plot for the association between metabolic syndrome and CLOCK gene rs1801260 polymorphism. Panel (A) Forest plot for the association between metabolic syndrome and allelic model of rs1801260 polymorphisms with the heterogeneity of 85% (Cochran's Q = 48.18, I 2 = 93.78%, p < 0.001). (B) Forest plot for the association between metabolic syndrome and dominant model of rs1801260 polymorphisms with the heterogeneity of 81% (Cochran's Q = 15.03, I 2 = 80.05%, p = 0.001). Panel (C) Forest plot for the association between metabolic syndrome and recessive model of rs1801260 polymorphisms with the heterogeneity of 92% (Cochran's Q = 42.35, I 2 = 92.92%, p < 0.001). The area of each square is proportional to the weight that the individual study contributed to the meta-analysis. Weights are from the random-effects analysis. Figure

Data Availability Statement:
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.