{"id":54478,"date":"2023-12-31T11:56:25","date_gmt":"2023-12-31T11:56:25","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=54478"},"modified":"2024-01-05T05:50:02","modified_gmt":"2024-01-05T05:50:02","slug":"genetic-polymorphisms-associated-with-metabolic-syndrome-in-north-africa-systematic-review-and-meta-analysis","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol16no4\/genetic-polymorphisms-associated-with-metabolic-syndrome-in-north-africa-systematic-review-and-meta-analysis\/","title":{"rendered":"Genetic Polymorphisms Associated with Metabolic Syndrome in North Africa: Systematic Review and Meta-Analysis"},"content":{"rendered":"\n

Introduction<\/strong><\/p>\n\n\n\n

Metabolic syndrome\n(MetS) is defined by the presence in a same patient of a set of health\ndisorders having a lipid and\/or a carbohydrate leading to a weight excess and\nan increased risk of developing cardiovascular diseases and\/or other disorders 1,2<\/sup>. It is now accepted\nthat the main MetS components are : insulin resistance, abdominal obesity,\ndyslipidemia, and hypertension 3<\/sup> and they are\npopulation-dependent. The MetS constitute nowadays a complex disorder that represents\na global health problem with a prevalence varying between 10 and 40% according\nto population 4<\/sup>, which seems to be\nmainly influenced by obesity and a sedentary lifestyle as behavioral criteria 5<\/sup>. The latter can be considered\nas the inevitable consequences of upheavals in lifestyles (following\ndemographic and dietary transitions) of human populations throughout the world,\nparticularly those in industrialized and developing countries 4<\/sup>.<\/p>\n\n\n\n

In addition to\nconstituting a real socio-economic burden on health systems, the complications\nthat patients affected by MetS can develop have also a very negative impact on\ntheir life quality by increasing the risk of comorbidities and mortality.\nStatistics around the world make it possible to distinguish a very significant\nvariability in the prevalence of MetS, sometimes even between the two sexes. In\nNorth Africa, according to the studies available, the prevalence of MetS is\nalso variable [8.6\u201355%] 5,6,15\u201319,7\u201314<\/sup>, generally more common\nin women than in men, and with a higher incidence among illiterates and married\nsubjects 5,7,15,17\u201319<\/sup>.<\/p>\n\n\n\n

In addition to\ndifferences in lifestyle and habits, the inter-population variability observed\nfor the prevalence of MetS would likely be due to the genetic factor mainly in\nits pathophysiology 20<\/sup>, which could\ninfluence its development in several ways. Indeed, each of the key elements of\nMetS -abdominal obesity, dyslipidemia, hyperglycemia, and arterial hypertension-\nhas its own genetic basis, for which candidate genes have been identified, of\nwhich certain alleles or allelic combinations may lead to a greater\npredisposition to the development of MetS. The analysis of genetic factors should\nmake it possible to better understand the pathophysiology of MetS and to better\nguide the diagnosis towards a specific management adapted to each population.\nIn this context, this systematic review aimed to examine the genetic\ncontribution of candidate gene polymorphisms to MetS susceptibility among populations\nand ethnic groups in the North African region.<\/p>\n\n\n\n

Materials <\/strong>and Methods<\/strong><\/p>\n\n\n\n

This meta-analysis\nwas conducted in accordance with the QUOROM guidelines21<\/sup>. The procedures\nadhered to the Meta-analysis on Genetic Association Studies Checklist and the\nPRISMA 2009 Statement Guidelines 22<\/sup>.<\/p>\n\n\n\n

Research Strategy<\/strong><\/p>\n\n\n\n

Systematic literature\nresearch was performed to identify pertinent articles published on Embase, PubMed,\nand the Web of Science. The keywords used were: (“gene polymorphism”\nor “single nucleotide polymorphism” or “SNP” or “gene\nmutation” or “gene variants”) paired with (“metabolic\nsyndrome” or “syndrome X” or “MetS”) and\n(“Moroccan population” or “Morocco” or “Algerian\npopulation” or “Algeria” or “Tunisian population” or\n“Tunisia” or “Libyan population” or “Libya” or “Egyptian\npopulation” or “Egypt” or “North Africa”). Additionally,\nwe retrieved the references cited in these publications for consultation and\npotential research. The research was restricted to articles published in English\nand we considered publications available until August 2, 2023.<\/p>\n\n\n\n

Study selection\ncriteria and exclusion criteria<\/strong><\/p>\n\n\n\n

We included articles\nthat had MetS as an outcome and met the following criteria :  published in English; constituted original\nresearch involving humans; and tested the primary effects of polymorphisms. All\nexisting MetS definitions were eligible as study outcomes. Included\npolymorphisms were qualified for incorporation in meta-analyses if performed\nfor SNPs with at least two eligible studies available in North African\npopulations.<\/p>\n\n\n\n

For inclusion into our\nmeta-analysis, studies must satisfy the following inclusion criteria : 1) they should\nhave investigated the relationship between the polymorphisms and MetS in North\nAfrican populations; 2) they must be case-control or cohort-designed; 3)\nstudies must provide enough data in control and case groups to calculate an\nodds ratio (OR) and 95% confidence interval (CI); and 4) About duplicate studies,\nwe selected the one with the largest number of samples and\/or the latest publication\ndate.<\/p>\n\n\n\n

The exclusion criteria\nencompassed 1) meta-analyses, reviews article, , letters, case reports, and\nconference abstracts; 2) studies that  lacked complete data necessary to determine\ngenotype numbers in controls and cases for ORs and CI calculations; and 3) studies\nthat not considered humans..<\/p>\n\n\n\n

Data extraction and\nquality assessment<\/strong><\/p>\n\n\n\n

The extraction of\ndata was done by two authors independently and any disagreement was resolved through\ndiscussion and consensus. The informations collected from the publications: the\nfirst author\u2019s name, publication year , country of origin, MetS definition,\nsample, genotype and allele distributions in both case and control groups, and\nthe p-value<\/em> assessing Hardy-Weinberg equilibrium (HWE) in control group.\nFor the quality assessment of every study, we used the Newcastle-Ottawa quality\nassessment scale (NOS). <\/p>\n\n\n\n

Statistical analysis<\/strong><\/p>\n\n\n\n

We computed the\npooled OR and their corresponding 95% CI to evaluate the strength of the\nassociation between the polymorphisms and MetS. Combined ORs and 95% CIs were determined\nunder four genetic models: the allele model (a vs. A), dominant genetic model (aa\n+ Aa vs. AA), recessive genetic model (aa vs. Aa + AA), and additive genetic\nmodel (Aa vs. AA+aa). (Note: a and A, \nrepresent the minor and major alleles of the polymorphism, respectively).\nWe examined the HWE in the control groups using the \u03c72 test, and assessed\nheterogeneity among the studies included in this meta-analysis using \u03c72 based Q-tests and I-square (I2<\/sup>)\ntests. In the absence  heterogeneity (p \u2265\n0.05), we applied a fixed-effect model; otherwise, a random-effect model was used.\nWe conducted a sensitivity analysis to gauge the impact of each study on the combined\nORs and 95% CI. All statistical analyses were carried out using RevMan V.5.1\n(The Cochrane Collaboration).<\/em><\/p>\n\n\n\n

Results<\/strong><\/p>\n\n\n\n

Study characteristics<\/strong><\/p>\n\n\n\n

According to the\nliterature search strategy outlined previously, 785 papers were initially\nobtained in the PubMed, Embase, and Web of Science <\/em>databases, and 15 additional\narticles were obtained using other sources. The sifting process is represented\nin Figure 1. Briefly, 390 records were eliminated due to duplicates; 337 articles\nwere excluded after reviewing their titles and abstracts; and 53 articles with\nfull text were collected for further assessment of eligibility. At last, 29 were\nexcluded for the following reasons: Eleven studies had insufficient data for\nthe calculation of OR and 95% CI; tree studies were meta-analyses; tow study\nhad repeated data; three studies did not include healthy controls; six studies\nhad no full-text articles; and three studies looked at other diseases. Finally,\n 25 studies whose publication dates\nranged from 2011 to 2022, summing 3925 cases and 4431 controls, were retained\nin our study; according to the origin of the samples, the Moroccans were the\nsubject of four studies, the Algerians of a single study, the Tunisians of nine,\nand the Egyptians of eleven studies (we note that no study has yet been carried\nout on the Libyan population) 23,24,33\u201342,25,43,26\u201332<\/sup>. Three  different definitions were applied for the MetS\ndiagnosis : sixteen studies adopted the IDF 2005 definition 3<\/sup>, seven studies\ndiagnosed MetS using the NCEP-ATP III definition 44<\/sup>, and two studies\nrecruited its subjects using the Harmonized definition 1<\/sup>. Two different types\nof genotyping methods were performed: eleven polymorphisms were analyzed using\nrestriction fragment length polymorphism (RFLP), and fourteen polymorphisms\nwere genotyped using real-time PCR.<\/p>\n\n\n\n

Meta-analyses were\nperformed for SNPs with at least two eligible studies available, resulting in\nthe inclusion of 13 studies 24,25,42,43,45,27\u201329,32,36\u201339<\/sup> that considered nine\nSNPs located in six genes (APOA5, APOC3, APOE, FTO, Vaspin, HNF1A).<\/p>\n\n\n\n

Meta-analysis<\/strong><\/p>\n\n\n\n

Firstly, we will elucidate\nthe association between MetS and genes that possess sufficient data for\nmeta-analyses. Secondly, we will present a narrative review of the remaining\nSNP and MetS associations. An overview of all the studied genes and the\npathways in which they are involved are depicted in Table 3. Detailed\ninformation on all studies integrated in the meta-analysis and the genotype\nfrequencies extracted from each are available in Table 1.<\/p>\n\n\n\n

APOA5<\/strong><\/p>\n\n\n\n

The APOA5 gene is\nmapped to chromosome 11q23.3 which forms a cluster with three other\napolipoprotein genes (APOA1\/C3\/A4\/A5). It consists of four exons and encodes a\nprotein composed of 366 amino acids. The APOA5<\/em>\nplays a role in reducing plasma TG levels by stimulating TG hydrolysis through\nthe activation of lipoprotein lipase (LPL) and by inhibiting the production of VLDL\n46<\/sup>. Five polymorphisms\nof the APOA5 gene were studied in North African populations (rs3135506,\nrs662799, rs2075291, rs2266788 and rs651821), but only two of them (rs3135506\nand rs662799) were considered in our meta-analysis. <\/p>\n\n\n\n

The rs3135506 is a\npolymorphic transition also called c.56C>G ; data from two studies39,45<\/sup> totalizing 464 cases\nand 391 controls were included. Heterogeneity was not statistically significant,\n leading to the use of fixed models for\ndata synthesis. This polymorphism showed an association with MetS in two\ngenetic models (allelic and dominant): the G allele of c.56C>G polymorphism\nincreased the MetS risk by 39% (OR = 1.39; 95% CI [1.06-1.84]; Z = 2.35; p =\n0.02), and the dominant model CG+GG vs<\/em> CC (OR = 1.43; 95% CI\n[1.04-1.96]; Z = 2.23; p = 0.03). For rs662799 polymorphism, called also T1131C,\nthe data included in the meta-analysis correspond to three studies 38,39,42<\/sup>. Overall, three genetic\nmodels showed a significant association with an increased risk of developing\nMetS: the allelic model (T vs. C); OR = 2.25; 95% CI [1.42-3.57]; Z = 3.44; p =\n0.0006; the dominant model (TC+CC vs. TT); OR = 3.05; 95% CI [1.57\u20135.90], Z =\n3.31, p = 0.0009; and the additive model (TC vs. TT+CC); OR = 2.87; 95% CI\n[1.61\u20135.14], Z = 3.55, p = 0.0004 (Table 2). In the analysis of these three\ngenetic models, a random-effect model was used due to the presence of statistically\nsignificant heterogeneity among studies.<\/p>\n\n\n\n

However, the other\npolymorphisms of the APOA5 gene does not respect the inclusion criteria, we\nnote that only the rs651821 polymorphism was associated with MetS in the\nTunisian population 45<\/sup> (Table 3).<\/p>\n\n\n\n

APOC3<\/strong><\/p>\n\n\n\n

APOCIII, <\/em>is <\/em>a protein consisting of\n79 amino acids, synthesized by both the liver and intestine, and it is an\nimportant marker of TG-rich lipoprotein levels. the APOC3 gene overexpression\nresults in higher TG levels 47<\/sup>. The APOC3 gene\ncodes for APOCIII<\/em>, and several\npolymorphic sites were detected inside and around the APOC3 gene. The most\nstudied was the rs5128 polymorphism, resulting from the substitution of\ncytosine by guanine at nucleotide 3238C>G (rs5128) at the 3’UTR region of\nexon 4 34<\/sup>. <\/p>\n\n\n\n

Two studies carried\nout in the Moroccan population 34,36<\/sup> were included in our meta-analysis. The\nheterogeneity was not statistically significant, so fixed models were used when\nsynthesizing the results. This polymorphism is associated with the MetS in all\ngenetic models except the recessive one. The G allele of the 3238C>G\nmultiplies the risk of MetS by 2.63 times : OR = 2.63, 95% CI [1.58-4.36], Z =\n3.75, p = 0.0002 for the dominant model (CG+GG vs CC): OR =3.10, 95% CI\n[1.79-5.39], Z =4.02, P <  0.0001, and\nfor the additive model (CG vs. CC+GG): OR = 3.25, 95% CI [1.83-5.79], Z = 4, P\n< 0.0001 (Table 2).<\/p>\n\n\n\n

FTO<\/strong><\/p>\n\n\n\n

The FTO gene located\nin the 16q12.2 chromosomal region is associated with fat mass and obesity, and\nplays an essential role in the energy balance system 48<\/sup>. Three polymorphisms\nhave been studied in North African populations (rs9939609, rs1421085, and\nrs8057044), from which only the rs9939609 polymorphism was included in our\nmeta-analysis. Random-effects models were applied in the recessive genetics\nmodel because the heterogeneity between studies was statistically significant; when\nsynthesizing the results of the other genetic models, the heterogeneity was not\nstatistically significant, and therefore fixed models were used. Thus, this polymorphism\nhas been shown to be associated with MetS in the three genetic models (allelic,\ndominant, and additive). The A allele of the rs9939609 polymorphism increased\nthe risk of MetS, (T vs. A) OR = 1.30 (95%CI= [1.06-1.58]), Z = 2.54, p = 0.01;\nfor the dominant model (TA+AA vs. TT) OR = 1.52 (95% CI= [1.14-2.02]), Z =\n2.87, p = 0.004, finally for the additive model (TA vs. TT+AA) OR = 1.33 (95%\nCI= [1.01-1.76]), Z = 2.05, p = 0.04 (Table 2). The study of Elouej et al.\n(2016) revealed an association of the rs1421085 polymorphism with the MetS,\nwhereas no association was found between the rs8057044 polymorphism and the MetS 49<\/sup> (Table 3).<\/p>\n\n\n\n

Vaspin<\/strong><\/p>\n\n\n\n

Vaspin is an adipocytokine\nderived from visceral adipose tissue, member of serine protease inhibitors family\n50<\/sup>. It is known for its\npotential insulin-sensitizing effects 51<\/sup>. Clinical studies into\nthe regulation of human vaspin have demonstrated a positive association between\nserum levels and insulin resistance. Furthermore, it has been observed that these\nlevels  decrease following a reduction in\nweight and\/or in food intake 52<\/sup>. A single\npolymorphism (rs2236242) has been studied in North African populations. Our\nmeta-analysis included two studies done in the Egyptian population in all every\nthe two samples of 200 cases and 200 controls 27,43<\/sup>. The absence of\nstatistically significant heterogeneity led to utilization of fixed models for data\nsynthesis. This polymorphism was shown to be associated with the MetS and\nappeared to play a protective role in three genetic models (allelic, dominant,\nand recessive) : for allelic model (T vs. A): OR = 0.61 (95% CI= [0.45-0.82]),\nZ = 3.22, p = 0.001; for dominant model (TA+AA vs. TT): OR = 0.48, (95% CI= [0.32-0.73]),\nZ = 3.43, p = 0.0006, and for recessive model (TA vs. TT+AA): OR = 0.60, (95%\nCI= [0.40-0.90]), Z = 2.47, p = 0.01 (Table 2).<\/p>\n\n\n\n

APOE<\/strong><\/p>\n\n\n\n

The APOE gene located\nat locus 19q13.32 codes for a protein that have an important role in clearing\nchylomicron remnants and VLDL from plasma. Only one polymorphism (rs4420638)\nwas included in our meta-analysis 25,35<\/sup>. Random-effects\nmodels were used in all four genetic models because the heterogeneity between\nstudies was statistically significant. In all genetic models rs4420638 was not\nassociated with MetS.: (A vs. G) OR = 0.92, 95% CI [0.68\u20131.26], Z = 0.51, p =\n0.61; (AG+GG vs. AA) OR = 0.90, 95% CI [0.64\u20131.27], Z = 0.59, p = 0.55; (GG vs.\nAA+AG) OR = 1.06, (95% CI [0.36-3.10]), Z = 0.11, p = 0.91; (AG vs. AA+GG) OR =\n0.89, (95% CI [0.63\u20131.27]), Z = 0.65,p = 0.52; (Table 2). In addition, another\nstudy25<\/sup> did not show any\nassociation of the three epsilons polymorphisms and also rs439401 with MetS\n(Table 3).<\/p>\n\n\n\n

HNF1A<\/strong><\/p>\n\n\n\n

HNF1A gene is located on\nchromosome 12q24.2 and codes for hepatocyte nuclear factor 1-alpha 53,54<\/sup>. This protein has a crucial\nrole in regulating gene expression related to the development and function of\nvarious organs, notably the liver 24<\/sup>. HNF1A is mainly\nexpressed in the liver, as well as in the digestive tract, pancreas, and\nkidneys. Its key function extends to the regulation of several genes involved\nin metabolism, glucose and lipid transport , as well as in the blood\ncoagulation process 32<\/sup>.<\/p>\n\n\n\n

In our study, we\nidentified two studies investigating the relationship of three HNF1A\npolymorphisms (rs1169288, rs2464196, and rs735396) with MetS in North African\npopulations 24,32<\/sup>. The results of our\nmeta-analysis indicated that no significant association was observed among the\nthree HNF1A polymorphisms studied and the MetS risk , with the exception of rs1169288\npolymorphism who showed a significant association with the MetS development OR\n= 1.41; 95% IC [1.06-1.86]; Z=2.40; p=0.02, at the additive model (AC vs AA+CC)\nonly (Table2). The p-value did not indicate any significant heterogeneity\nbetween the results of the two studies for this polymorphism, so we have adopted\nthe fixed-effect approach.<\/p>\n\n\n\n

Narrative review:\nPolymorphisms associated with MetS and its components<\/strong><\/p>\n\n\n\n

In this narrative\nreview, we have gathered all the polymorphisms studied in different North\nAfrica populations. In addition to the studies recruited in the meta-analysis,\nwe have included studies that have considered SNPs that were not eligible for\nmeta-analysis (since they were  studied\nin only research project) and others having studied the association between\nSNPs and independent MetS component. <\/p>\n\n\n\n

Polymorphisms associated with obesity<\/strong><\/p>\n\n\n\n

The genetic component\nof obesity is relatively moderate, at 24.56% for obesity and\/or abdominal\nobesity, according to studies (Figure 2). Association studies that have been\nperformed have revealed a multitude of candidate genes to be associated with related\nto obesity traits. In the set of studies included in our review, 11 candidate\ngenes with 14 different polymorphisms were associated with obesity and\/or\nabdominal obesity. Studies considering genetic association with obesity among\nNorth African countries, have not considered the same candidates genes. Indeed,\nin the Moroccan population, two candidate genes, APOA5 (rs662799 and rs2266788)\n39<\/sup> and ADCY5\n(rs11708067) 34<\/sup>, were positively associated.\nIn Tunisians, four others candidate genes, ZNF664 (rs12310367) 35<\/sup>, LEP (rs7799039 and\nrs1137101) 55<\/sup>, and RETN (420C\/G)41<\/sup>, HNF1A (rs2464196\nand rs735396) 31<\/sup>, have shown an\nassociation. In Egyptians, five genes, Vaspin (rs2236242) 27<\/sup>, CD36 (rs1761667) 23<\/sup>, Chemerin\n(rs17173608) 29<\/sup>, ESR1 (rs2234693) 56<\/sup>, and TNF (rs1800629) 26<\/sup>, have been\nassociated (Table 3).<\/p>\n\n\n\n

Polymorphisms associated with hyperglycemia<\/strong><\/p>\n\n\n\n

Hyperglycemia has a\ngenetic component of 22.81%, with 10 candidate genes and 12 polymorphisms. In\nthe present study, the candidate gene approach allowed the unequivocal\nidentification of common genetic variants associated with hyperglycemia (Figure\n2). These polymorphisms are located on the genes : APOA5 (rs3135506, rs662799) 38,45<\/sup>, APOC3 (rs5128) 36<\/sup>, FTO (rs9939609)49<\/sup>, Vaspin (rs2236242) 27<\/sup>, and CD36 (rs1761667) 23<\/sup>, Chemerin\n(rs17173608) 29<\/sup>, ESR1 (rs2234693) 56<\/sup>, HNF1A (rs1169288) 24<\/sup>, PPARy2 (Pro12Ala\nand C161T) 31<\/sup>, and LEP (rs1137101)\n55<\/sup>; it is important to\nemphasize that some polymorphisms are associated with both MetS and\nhyperglycemia and some are not (Table 3).<\/p>\n\n\n\n

Polymorphisms associated with hypertension<\/strong><\/p>\n\n\n\n

Hypertension have\nbetween the other components of MetS the weakest genetic component (13.73%): 7\ngenes with 7 polymorphisms (Figure 2). Two polymorphisms showed an association\nwith hypertension in the Moroccan population: APOA5 (rs662799) 39<\/sup> and HNF1A\n(rs1169288) 24<\/sup>, one in Algerian\nAPOE (rs439401) 25<\/sup>, and three in\nEgyptian Vaspin (rs2236242), Chemerin (rs17173608) 27<\/sup>, CD36 (rs1761667) 23<\/sup>, and ESR1\n(rs2234693) 56<\/sup> (Table 3).<\/p>\n\n\n\n

 Polymorphisms associated with dyslipidemia<\/strong><\/p>\n\n\n\n

 Between all the MetS components, dyslipidemia\nhas the strong genetic component (35.09 %) (Figure 2). Studies carried out in\nNorth Africa with MetS found a high number of candidate genes associated with\ndyslipidemia, around 14 genes with 20 polymorphisms (Table 3).<\/p>\n\n\n\n

Polymorphisms associated with MetS<\/strong><\/p>\n\n\n\n

In this narrative\nreview, we identified 19 candidate genes that have shown a relationship with\nMetS (Table 3). From this list of 19 genes, a total of 23 polymorphisms\n(representing 47.37% of the total polymorphisms analyzed) have presented an\nassociation with MetS in North African populations (Figure 2).<\/p>\n\n\n\n

Indeed, we found some\ndiversity in polymorphisms associated with MetS between North African\npopulations. It is possible for a polymorphism to be associated to MetS in one\npopulation while it show no association in another. Furthermore, although these\ngenes correlate with various MetS components, they do not appear to be linked\nto the MetS development perhaps because of the definition used in the studies (Table\n3).<\/p>\n\n\n\n

Evidence for pleiotropic genetic effects on MetS-related traits<\/strong><\/p>\n\n\n\n

When studying the relationship between of gene polymorphisms with the MetS components , it was possible to  highlight different pleiotropic combinations of traits. Polymorphisms that influence multiple phenotypes can be said to have pleiotropic effects, although the effect may not be directly on the MetS. Genes that impact multiple results can be highly desirable targets for intervention and can help identify connection points between different pathways. Indeed, there are examples of individual polymorphisms that have pleiotropic effects on the suite of MetS traits. According to our review, 21.57% of the polymorphisms studied affect both components (hyperglycemia and dyslipidemia), and 15.69% of the polymorphisms had an association at the same time with obesity and dyslipidemia. The percentage of other combinations is presented in figure 3.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Table 1: Gene polymorphisms characteristics, genotype and allelic distributions in MetS cases and Controls<\/strong><\/p>\n

<\/p>\n

Click here to view Table<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 2: Summary of the combined data used in the meta-analysis<\/strong>.<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Polymorphisme<\/strong><\/p>\n<\/td>\n

\n

Genetic Model<\/strong><\/p>\n<\/td>\n

\n

OR (95 CI)<\/strong><\/p>\n<\/td>\n

\n

Z<\/strong><\/p>\n<\/td>\n

\n

P<\/strong><\/p>\n<\/td>\n

\n

Phet<\/sub><\/strong><\/p>\n<\/td>\n

\n

I2<\/sup> (%)<\/strong><\/p>\n<\/td>\n

\n

Model<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

APOA5 (rs3135506)<\/strong><\/p>\n<\/td>\n

\n

C vs G<\/p>\n<\/td>\n

\n

1.39 [1.06-1.84]\n<\/td>\n

\n

2.35<\/p>\n<\/td>\n

\n

0.02<\/p>\n<\/td>\n

\n

0.13<\/p>\n<\/td>\n

\n

56<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

CG+GG vs CC<\/p>\n<\/td>\n

\n

1.43 [1.04-1.96]\n<\/td>\n

\n

2.23<\/p>\n<\/td>\n

\n

0.03<\/p>\n<\/td>\n

\n

0.19<\/p>\n<\/td>\n

\n

43<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

GG vs CC+CG<\/p>\n<\/td>\n

\n

1.63 [0.72-3.67]\n<\/td>\n

\n

1.17<\/p>\n<\/td>\n

\n

0.24<\/p>\n<\/td>\n

\n

0.23<\/p>\n<\/td>\n

\n

29<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

CG vs CC+GG<\/p>\n<\/td>\n

\n

1.35 [0.97-1.87]\n<\/td>\n

\n

1.81<\/p>\n<\/td>\n

\n

0.07<\/p>\n<\/td>\n

\n

0.53<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

APOA5 (rs662799)<\/strong><\/p>\n<\/td>\n

\n

T vs C<\/p>\n<\/td>\n

\n

2.25 [1.42-3.57]\n<\/td>\n

\n

3.44<\/p>\n<\/td>\n

\n

0.0006<\/p>\n<\/td>\n

\n

0.01<\/p>\n<\/td>\n

\n

77<\/p>\n<\/td>\n

\n

Random<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

TC+CC vs TT<\/p>\n<\/td>\n

\n

3.05 [1.57-5.90]\n<\/td>\n

\n

3.31<\/p>\n<\/td>\n

\n

0.0009<\/p>\n<\/td>\n

\n

0.002<\/p>\n<\/td>\n

\n

84<\/p>\n<\/td>\n

\n

Random<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

CC vs TT+TC<\/p>\n<\/td>\n

\n

1.69 [0.90-3.19]\n<\/td>\n

\n

1.64<\/p>\n<\/td>\n

\n

0.1<\/p>\n<\/td>\n

\n

0.07<\/p>\n<\/td>\n

\n

63<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

TC vs TT+CC<\/p>\n<\/td>\n

\n

2.87 [1.61-5.14]\n<\/td>\n

\n

3.55<\/p>\n<\/td>\n

\n

0.0004<\/p>\n<\/td>\n

\n

0.01<\/p>\n<\/td>\n

\n

78<\/p>\n<\/td>\n

\n

Random<\/p>\n<\/td>\n<\/tr>\n

\n

APOC3 (rs5128)<\/strong><\/p>\n<\/td>\n

\n

C vs G<\/p>\n<\/td>\n

\n

2.63 [1.58-4.36]\n<\/td>\n

\n

3.75<\/p>\n<\/td>\n

\n

0.0002<\/p>\n<\/td>\n

\n

0.6<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

CG+GG vs CC<\/p>\n<\/td>\n

\n

3.10 [1.79-5.39]\n<\/td>\n

\n

4.02<\/p>\n<\/td>\n

\n

P < 0.0001<\/p>\n<\/td>\n

\n

0.54<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

GG vs CC+CG<\/p>\n<\/td>\n

\n

1.33 [0.25-6.98]\n<\/td>\n

\n

0.34<\/p>\n<\/td>\n

\n

0.73<\/p>\n<\/td>\n

\n

0.9<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

CG vs CC+GG<\/p>\n<\/td>\n

\n

3.25 [1.83-5.79]\n<\/td>\n

\n

4<\/p>\n<\/td>\n

\n

P < 0.0001<\/p>\n<\/td>\n

\n

0.49<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

APOE (rs4420638)<\/strong><\/p>\n<\/td>\n

\n

A vs G<\/p>\n<\/td>\n

\n

0.92 [0.68-1.26]\n<\/td>\n

\n

0.51<\/p>\n<\/td>\n

\n

0.61<\/p>\n<\/td>\n

\n

0.46<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

AG+GG vs AA<\/p>\n<\/td>\n

\n

0.90 [0.64-1.27]\n<\/td>\n

\n

0.59<\/p>\n<\/td>\n

\n

0.55<\/p>\n<\/td>\n

\n

0.62<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

GG vs AA+AG<\/p>\n<\/td>\n

\n

1.06 [0.36-3.10]\n<\/td>\n

\n

0.11<\/p>\n<\/td>\n

\n

0.91<\/p>\n<\/td>\n

\n

0.3<\/p>\n<\/td>\n

\n

7<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

AG vs AA+GG<\/p>\n<\/td>\n

\n

0.89 [0.63-1.27]\n<\/td>\n

\n

0.65<\/p>\n<\/td>\n

\n

0.52<\/p>\n<\/td>\n

\n

0.86<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

FTO (rs9939609)<\/strong><\/p>\n<\/td>\n

\n

T vs A<\/p>\n<\/td>\n

\n

1.30 [1.06-1.58]\n<\/td>\n

\n

2.54<\/p>\n<\/td>\n

\n

0.01<\/p>\n<\/td>\n

\n

0.09<\/p>\n<\/td>\n

\n

64<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

TA+AA vs TT<\/p>\n<\/td>\n

\n

1.52 [1.14-2.02]\n<\/td>\n

\n

2.87<\/p>\n<\/td>\n

\n

0.004<\/p>\n<\/td>\n

\n

0.34<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

AA vs TT+TA<\/p>\n<\/td>\n

\n

1.58 [0.53-4.70]\n<\/td>\n

\n

0.82<\/p>\n<\/td>\n

\n

0.41<\/p>\n<\/td>\n

\n

0.03<\/p>\n<\/td>\n

\n

79<\/p>\n<\/td>\n

\n

Random<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

TA vs TT+AA<\/p>\n<\/td>\n

\n

1.33 [1.01-1.76]\n<\/td>\n

\n

2.05<\/p>\n<\/td>\n

\n

0.04<\/p>\n<\/td>\n

\n

0.49<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

Vaspin (rs2236242)<\/strong><\/p>\n<\/td>\n

\n

T vs A<\/p>\n<\/td>\n

\n

0.61 [0.45-0.82]\n<\/td>\n

\n

3.22<\/p>\n<\/td>\n

\n

0.001<\/p>\n<\/td>\n

\n

0.33<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

TA+AA vs TT<\/p>\n<\/td>\n

\n

0.48 [0.32-0.73]\n<\/td>\n

\n

3.43<\/p>\n<\/td>\n

\n

0.0006<\/p>\n<\/td>\n

\n

0.58<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

AA vs TT+TA<\/p>\n<\/td>\n

\n

0.61 [0.31-1.18]\n<\/td>\n

\n

1.48<\/p>\n<\/td>\n

\n

0.14<\/p>\n<\/td>\n

\n

0.78<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/p>\n<\/td>\n

\n

TA vs TT+AA<\/p>\n<\/td>\n

\n

0.60 [0.40-0.90]\n<\/td>\n

\n

2.47<\/p>\n<\/td>\n

\n

0.01<\/p>\n<\/td>\n

\n

0.33<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A (rs1169288)<\/strong><\/p>\n<\/td>\n

\n

A vs C<\/p>\n<\/td>\n

\n

1.42 [0.71-2.82]\n<\/td>\n

\n

0.99<\/p>\n<\/td>\n

\n

0.32<\/p>\n<\/td>\n

\n

0.004<\/p>\n<\/td>\n

\n

88<\/p>\n<\/td>\n

\n

Random<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

AC\u2009+\u2009CC vs AA<\/p>\n<\/td>\n

\n

1.65 [0.76-3.61]\n<\/td>\n

\n

1.26<\/p>\n<\/td>\n

\n

0.21<\/p>\n<\/td>\n

\n

0.01<\/p>\n<\/td>\n

\n

84<\/p>\n<\/td>\n

\n

Random<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

CC vs AC\u2009+\u2009AA<\/p>\n<\/td>\n

\n

1.02 [0.68-1.53]\n<\/td>\n

\n

0.08<\/p>\n<\/td>\n

\n

0.94<\/p>\n<\/td>\n

\n

0.07<\/p>\n<\/td>\n

\n

69<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

AC vs AA+CC<\/p>\n<\/td>\n

\n

1.41 [1.06-1.86]\n<\/td>\n

\n

2.40<\/p>\n<\/td>\n

\n

0.02<\/p>\n<\/td>\n

\n

0.14<\/p>\n<\/td>\n

\n

54<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A (rs2464196)<\/strong><\/p>\n<\/td>\n

\n

G vs A<\/p>\n<\/td>\n

\n

1.14 [0.94-1.39]\n<\/td>\n

\n

1.34<\/p>\n<\/td>\n

\n

0.18<\/p>\n<\/td>\n

\n

0.07<\/p>\n<\/td>\n

\n

69<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

GA\u2009+\u2009AA vs GG<\/p>\n<\/td>\n

\n

1.30 [0.88-1.91]\n<\/td>\n

\n

1.30<\/p>\n<\/td>\n

\n

0.19<\/p>\n<\/td>\n

\n

0.21<\/p>\n<\/td>\n

\n

36<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

AA vs GA\u2009+\u2009GG<\/p>\n<\/td>\n

\n

1.10 [0.77-1.57]\n<\/td>\n

\n

0.54<\/p>\n<\/td>\n

\n

0.59<\/p>\n<\/td>\n

\n

0.11<\/p>\n<\/td>\n

\n

61<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

GA vs GG+AA<\/p>\n<\/td>\n

\n

1.16 [0.88-1.52]\n<\/td>\n

\n

1.05<\/p>\n<\/td>\n

\n

0.29<\/p>\n<\/td>\n

\n

0.87<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A (rs735396)<\/strong><\/p>\n<\/td>\n

\n

T vs C<\/p>\n<\/td>\n

\n

0.92 [0.76-1.12]\n<\/td>\n

\n

0.83<\/p>\n<\/td>\n

\n

0.41<\/p>\n<\/td>\n

\n

0.82<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

TC\u2009+\u2009CC vs TT<\/p>\n<\/td>\n

\n

0.89 [0.66-1.21]\n<\/td>\n

\n

0.73<\/p>\n<\/td>\n

\n

0.47<\/p>\n<\/td>\n

\n

0.23<\/p>\n<\/td>\n

\n

31<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

CC vs TC\u2009+\u2009TT<\/p>\n<\/td>\n

\n

0.91 [0.66-1.25]\n<\/td>\n

\n

0.61<\/p>\n<\/td>\n

\n

0.54<\/p>\n<\/td>\n

\n

0.32<\/p>\n<\/td>\n

\n

0<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n

\n

 <\/strong><\/p>\n<\/td>\n

\n

TC vs TT+CC<\/p>\n<\/td>\n

\n

0.98 [0.75-1.29]\n<\/td>\n

\n

0.14<\/p>\n<\/td>\n

\n

0.89<\/p>\n<\/td>\n

\n

0.05<\/p>\n<\/td>\n

\n

75<\/p>\n<\/td>\n

\n

Fixed<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 3: Summary of the association with the MetS and its components<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

 <\/strong><\/p>\n<\/td>\n

\n

Association with the MetS and its components<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Gene<\/strong><\/p>\n<\/td>\n

\n

Polymorphisms<\/strong><\/p>\n<\/td>\n

\n

Author<\/strong><\/p>\n<\/td>\n

\n

Population<\/strong><\/p>\n<\/td>\n

\n

MetS <\/strong><\/p>\n<\/td>\n

\n

Obesity<\/strong><\/p>\n<\/td>\n

\n

Hypergly
cemia<\/strong><\/p>\n<\/td>\n

\n

Hypert
ension<\/strong><\/p>\n<\/td>\n

\n

Dyslipi
demia<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Meta-analyses<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

APOA5<\/p>\n<\/td>\n

\n

rs3135506<\/p>\n<\/td>\n

\n

Ajjemami  et al 2015<\/p>\n<\/td>\n

\n

Moroccan<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

APOA5<\/p>\n<\/td>\n

\n

rs3135506<\/p>\n<\/td>\n

\n

Kefi et al. 2017<\/p>\n<\/td>\n

\n

Tunisian<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

APOA5<\/p>\n<\/td>\n

\n

rs662799<\/p>\n<\/td>\n

\n

Zaki et al 2014<\/p>\n<\/td>\n

\n

Egyptian<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

APOA5<\/p>\n<\/td>\n

\n

rs662799<\/p>\n<\/td>\n

\n

Ajjemami  et al 2015<\/p>\n<\/td>\n

\n

Moroccan<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

APOA5<\/p>\n<\/td>\n

\n

rs662799<\/p>\n<\/td>\n

\n

Hechmi et al 2020<\/p>\n<\/td>\n

\n

Tunisian<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

APOC3<\/p>\n<\/td>\n

\n

rs5128<\/p>\n<\/td>\n

\n

Ajjemami  et al 2014<\/p>\n<\/td>\n

\n

Moroccan<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

APOC3<\/p>\n<\/td>\n

\n

rs5128<\/p>\n<\/td>\n

\n

Lakbakbi et al 2017<\/p>\n<\/td>\n

\n

Moroccan<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n

\n<\/p><\/td>\n<\/tr>\n

\n

APOE<\/p>\n<\/td>\n

\n

rs4420638<\/p>\n<\/td>\n

\n

Boulenouar  et al 2019<\/p>\n<\/td>\n

\n

Algerian<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n

\n<\/p><\/td>\n<\/tr>\n

\n

APOE<\/p>\n<\/td>\n

\n

rs4420638<\/p>\n<\/td>\n

\n

Elouej  et al 2016<\/p>\n<\/td>\n

\n

Tunisian<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n

\n<\/p><\/td>\n

\n<\/td>\n<\/tr>\n
\n

FTO<\/p>\n<\/td>\n

\n

rs9939609<\/p>\n<\/td>\n

\n

Khell et al 2017<\/p>\n<\/td>\n

\n

Egyptian<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

FTO<\/p>\n<\/td>\n

\n

rs9939609<\/p>\n<\/td>\n

\n

Elouej  et al 2016<\/p>\n<\/td>\n

\n

Tunisian<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

Vaspin<\/p>\n<\/td>\n

\n

rs2236242<\/p>\n<\/td>\n

\n

Mehanna  et al 2015<\/p>\n<\/td>\n

\n

Egyptian<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

Vaspin<\/p>\n<\/td>\n

\n

rs2236242<\/p>\n<\/td>\n

\n

Alnory et al 2016<\/p>\n<\/td>\n

\n

Egyptian<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

HNF1A<\/p>\n<\/td>\n

\n

rs1169288<\/p>\n<\/td>\n

\n

Morjane et al 2017<\/p>\n<\/td>\n

\n

Morocco<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A<\/p>\n<\/td>\n

\n

rs1169288<\/p>\n<\/td>\n

\n

Dalali et al 2022<\/p>\n<\/td>\n

\n

Tunisia<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n
\n

HNF1A<\/p>\n<\/td>\n

\n

rs2464196<\/p>\n<\/td>\n

\n

Morjane et al 2017<\/p>\n<\/td>\n

\n

Morocco<\/p>\n<\/td>\n

\n

(+)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A<\/p>\n<\/td>\n

\n

rs2464196<\/p>\n<\/td>\n

\n

Dalali et al 2022<\/p>\n<\/td>\n

\n

Tunisia<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A<\/p>\n<\/td>\n

\n

rs735396<\/p>\n<\/td>\n

\n

Morjane et al 2017<\/p>\n<\/td>\n

\n

Morocco<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n<\/td>\n\n

x<\/p>\n<\/td>\n<\/tr>\n

\n

HNF1A<\/p>\n<\/td>\n

\n

rs735396<\/p>\n<\/td>\n

\n

Dalali et al 2022<\/p>\n<\/td>\n

\n

Tunisia<\/p>\n<\/td>\n

\n

(-)<\/p>\n<\/td>\n

\n

x<\/p>\n<\/td>\n

\n<\/td>\n\n<\/td>\n\n

 <\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

Narrative review<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

ADCY5<\/p>\n<\/td>\n

\n

rs11708067<\/p>\n<\/td>\n

\n

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\n<\/td>\n\n<\/td>\n\n<\/td>\n\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 1: Flow chart of the studies selected in our meta-analysis.<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 2: Percentage of the polymorphisms associated with the MetS and its components<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n
\"\"<\/td>\n\n

Figure 3: Percentage of the polymorphisms associated with the major combinations of the MetS components<\/strong><\/p>\n

<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Discussion<\/strong><\/p>\n\n\n\n

MetS is a topic of extensive research, particularly due to  its high prevalence. It is identified by the\nassociation of several risk factors or components that can lead to coronary\nartery disease and\/or other pathologies, particularly abdominal obesity,\nhypertension, hyperglycemia, high TG levels and low HDL-c levels 47<\/sup>. The MetS\ndevelopment is believed to be influenced by genetics , making genetic studies\nan active research area 57<\/sup>.<\/p>\n\n\n\n

In this bibliographic review, only polymorphisms that have been the\nsubject of at least two studies, in relation to MetS in North African\npopulations, were included in the meta-analysis. In the same context,\nsignificant SNP-MetS associations that were investigated in only one study were\nincluded in our narrative review. Thus, the overall results of the\nmeta-analyses suggest an association between MetS and the SNPs in the APOA5\n(rs3135506 and rs662799), APOC3 (rs5128), FTO (rs9939609), and vaspin\n(rs2236242) genes (Table 2).<\/p>\n\n\n\n

Our results indicate that the APOA5 gene c.C56G (rs3135506) polymorphism\nsignificantly increases the susceptibility to MetS in North African populations\nby 39% and 43% in the allelic and dominant models, respectively. Furthermore,\nwhen assessing the link between c.C56G and MetS in Asian populations  58<\/sup>, the genotype\ncarrying the G allele significantly increased the MetS risk (OR = 1.32; 95% CI\n[1.15\u20131.50]; p<0.001). On the same gene, the rs662799 showed values of Odd\u2019s\nRatio indicating an increased risk of developing MetS compared to the value of\nthe allelic model of the first polymorphism rs3135506 : (T vs. C), OR =2.25 95%\nCI [1.42-3.57], p = 0.0006; (TC+CC vs. TT), OR =3.05 95% CI [1.57-5.90], p =\n0.0009; (TC vs. TT+CC), OR =2.87 95% CI [1.61-5.14], p = 0.0004. In another\nmeta-analysis, involving a dataset from 12 studies with 5 573 MetS cases and 8\n290 controls subjects in Asians and Europeans, the -1131C allele was associated\nwith an increased risk of MetS with an OR = 1.43, 95% CI [1.29-1.58] in Asian\nand an OR = 1.30, 95% CI [0.94-1.78] in European populations 59<\/sup>. However, among the\nother APOA5 gene polymorphisms that were not included in the meta-analysis,\nonly the rs651821 polymorphism was associated with MetS in the Tunisian\npopulation45<\/sup>.<\/p>\n\n\n\n

In the meta-analysis we investigate the relationship beteween the APOC3\nrs5128 polymorphism and plasma APOC3<\/em>\/lipid\nlevels. The results indicated that carriers of the variant G allele had higher TG\nlevels, total cholesterol, and LDL-c than non-carriers60<\/sup>. The combination of\ntwo studies in our meta-analysis showed increased susceptibility to MetS\ndevelopment in three models (allelic, dominant, and additive). Indeed, the G\nallele of the rs5128 polymorphism multiplies the risk of MetS by 2.63 : (C vs.\nG); OR = 2.63 95% CI [1.58-4.36], p = 0.0002, while with the dominant model\n(CG+GG vs. CC); OR =3.10 95% CI [1.79-5.39], P <  0.0001; and with the additive model (CG vs.\nCC+GG); OR = 3.25 95% CI [1.83-5.79] P < 0.0001.<\/p>\n\n\n\n

Concerning the FTO gene polymorphisms that were shown to be associated\nwith MetS, our meta-analysis indicated that the rs9939609 polymorphism\nincreased the MetS risk in three genetic models: the allelic (T vs. A) OR =\n1.30; 95% IC [1, 06-1.58]; p = 0.01; the dominant model (TA+AA vs. TT) OR =\n1.52; 95% IC [1.14-2.02]; p = 0.004 and the additive model (TA vs. TT+AA) OR =\n1.33; 95% IC [1.01-1.76]; Z=2.05, p=0.04. Similarly, in the combined analysis,\nthe rs9939609 polymorphism significantly increased MetS susceptibility in\ncomparisons by allele (A vs. T) (OR = 1.21; 95% CI [1.10\u20131.35]; p <0.001)\nand in the dominant model (OR = 1.35; 95% CI [1.13\u20131.62]; p < 0.001).\nSubgroup analyses within allele comparisons (A vs. T) indicated that the\nelevated risk was seen in adults (OR = 1.26, 95% CI [1.08-1.47], P = 0.003) but\nnot in children and adolescents (OR = 1.14, 95% CI [0.95-1.36], p = 0.17) [59].\nNevertheless, Elouej et al. found an association between the rs1421085\npolymorphism and the MetS, but no association was found between the rs8057044\npolymorphism and the MetS 49<\/sup>. <\/p>\n\n\n\n

The vaspin gene polymorphism showed a significant association with MetS\nand appeared to play a protective role in three genetic models (allelic,\ndominant, and  additive), respectively:\n(T vs. A) OR = 0.61; 95% CI [0.45-0.82]; p = 0.001; (TA+AA vs. TT) OR = 0.48;\n95% CI [0.32-0.73] Z = 3.43 p = 0.0006; (TA vs. TT+AA) OR = 0.60, 95% CI\n[0.40-0.90] p = 0.01. Similarly, Hashemi et al. (2012) suggested that the\nvaspin polymorphism rs2236242 plays a protective role in susceptibility to MetS\nin Zhedn in the southeastern Iran 61<\/sup>. Furthermore, the\nresults of our meta-analysis revealed the absence of a significant association\nbetween the three HNF1A polymorphisms (rs1169288, rs2464196, rs735396) and MetS\npredisposition. However, it is important to underline that the three HNF1A gene\nSNPs studied have been correlated with the insulin resistance, high LDL-c and\nlow HDL-c levels in some studies 54,62<\/sup>.<\/p>\n\n\n\n

The genetic identification of MetS is made problematic because of its\ncomplexity. This complexity exists at two levels: on the one hand, each of the\nmetabolic processes involved in the development of the syndrome is complex and\ninvolves numerous genes or polymorphisms, on the other hand, the syndrome\netiology remains unknown. The candidate genes choice is then based on two\nhypotheses: either the gene or the polymorphism has a pleiotropic effect and\nplays on different MetS metabolic pathways, or it plays on a specific trait\nthat may be sufficient on its own for the development of MetS. Many studies\nhave investigated whether genes or polymorphisms already associated with one\nMetS component could also be associated with another component or with the risk\nof developing globally the MetS 47,57<\/sup>. In our review, we\nwere able to identify pleiotropic genes: among the genes associated with\nobesity, the APOA5, vaspin, CD 36, chemerin, and ESR1 genes are also associated\nwith hypertension, hyperglycemia, and dyslipidemia 23,27,39,56<\/sup>. In the case-control\nstudies included, 21.57% of the polymorphisms showed an association with\nhyperglycemia and dyslipidemia, and 15.69% of the SNPs showed susceptibility to\nobesity and dyslipidemia (Table 3 and figure 3). However, although these\npolymorphisms are associated with several MetS components, they appear to be\n52.94% associated with the MetS risk 63<\/sup>. For example, the\nAPOA5 (rs3135506) polymorphism is associated with hyperglycemia and\ndyslipidemia in the Tunisian population 45<\/sup>, but its impact on\nthe MetS risk has not yet been shown.<\/p>\n\n\n\n

Association analyses carried out in this narrative review revealed\nnumerous potential candidate genes to MetS and\/or its components. Across all\nthe studies included, we identified 11 candidate genes carrying 14 SNPs\n(representing 24.56% of the total number of SNPs) associated with obesity\nand\/or abdominal obesity. A further 10 candidate genes carrying 13 genetic SNPs\n(22.81%) correspond to common genetic variants linked to hyperglycemia. The\nassociation between hypertension and genetic variations has the lowest prevalence\namong MetS components (12.28%), with the involvement of 7 genes and 7 variants.\nConcerning the genetic association with dyslipidemia, our review revealed the\nhighest prevalence (35.09%) with 14 candidate genes analyzed and 20 SNPs\nassociated. It is important to note that globally the genetic association with\nMetS components presents a great variability between different North African\ncountries (Table 2 and figure 2). Focusing on combination between the principal\nphenotypes (obesity, hyperglycemia, hypertension, and dyslipidemia) could lead\nto the identification of new SNPs that would not have been found associated when\nstudying the phenotypes separately. The present review illustrates that the use\nof this strategy allowed the highlighting of new risk loci shared between MetS\ncomponents (Table 3). Furthermore, we could found that the association of a SNP\nwith the MetS implies its association with at least one of its components;\ntherefore, the association of a SNP with one component does not imply that it\nmust be associated with MetS. For example, the SNPs FTO (rs8057044) 28<\/sup> and HNF1A (rs735396)\n24<\/sup> are linked to\ndyslipidemia but do not appear to be associated with MetS. This distinction\nbetween SNPs associated with a single MetS component and those related to\nseveral components or to MetS as a whole, may simplify the identification of\npathways underlying the clustering observed in the context of MetS 47<\/sup>. Surprisingly, our findings\nrevealed that despite suggestions that disturbances in glucose metabolism,\nweight regulation, and hypertension could trigger MetS 64<\/sup>, most of the SNPs\nassociated with MetS are located in genes involved in lipid metabolism. Then\nassociations of the SNPs on APOA5 (rs3135506 and rs662799), APOC3 (rs5128), and\nFTO (rs9939609) genes with MetS, may be mediated by hypertriglyceridemia and\/or\nhypo-HDL-demia. TG accumulation in the muscles can promote the development of\ninsulin resistance 64<\/sup>. Additionally, dysfunction\nof the APOA5 and APOC3 genes increases free fatty acid levels 65<\/sup>, which may in turn\nstimulate the development of MetS characteristics such as dyslipidemia,\noverweight, insulin resistance, hypertension, or inflammation 64<\/sup>. Alternatively, the\noverrepresentation of SNPs in lipid metabolism may be caused by the emphasis on\nlipid metabolism in the MetS criteria. In the most common MetS definitions, the\nNCEP ATP III definition and the IDF definition, disturbed lipid metabolism is\ncharacterized by two MetS characteristics, namely low HDL-c and increased\ntriglyceride levels, while disturbances in other mechanisms such as weight\nregulation are all characterized by a single MetS feature 47<\/sup>.<\/p>\n\n\n\n

Genetic association studies establish the link between genetic factors\nand a trait using the approach of case-control studies. These studies often\ninvolve multiple SNP markers selected across the genome that are analyzed in\npatient and control samples 57<\/sup>. Other kinds of\nmarkers can also be explored, like insertion\/deletion polymorphisms 66<\/sup>. Variants are chosen\nfrom candidate genes presenting a functional relation with MetS\npathophysiology, like genes of insulin signaling, lipid metabolism,\nhypertension, inflammation, and adipose tissue distribution.<\/p>\n\n\n\n

Several factors contributed to the heterogeneity observed between the studies\nselected, including variations in MetS definitions, populations studied, sample\nsizes, research design, and genetic markers analysed. For instance, while most\nstudies encompassed the two genders in populations, some ones have focused only\nwomen 24,32<\/sup>. Similarly, there\nwas variability in the proportion of men and women as well as the age groups, across\nthe majority of studies; both of which are recognized as well-known sources of\nheterogeneity in meta-analyses 67<\/sup>. It is important to acknowledge\nthe challenges associated with precisely defining North African populations, as\nthey consist of various ethnic subgroups. In addition, we evaluated within-study\nheterogeneity by using statistic I<\/em> 68<\/sup>, which estimates the\nshare of variance within a study attributed to heterogeneity 68<\/sup>. Nevertheless, these\nvalues may lack precison and exhibit substantial bias when the number of\nstudies in a meta-analysis is small, as in our study 69<\/sup>. Due to the small\nnumber of studies available for different genes and genetic variations, we  were unable to ascertain the degree of\npublication bias.<\/p>\n\n\n\n

Limitations of the study<\/strong><\/p>\n\n\n\n

Our study had a number of limitations that should be noted: firstly, we  limited our articles search to English\nlanguage and we have ignored research published in other languages; secondly,\nwe did not considered some additional  risk factors for MetS such as excessive food\nintake and physical inactivity; thirdly, some of the included case-control studies\nhad small sample sizes, which could have resulted in a decreased ability  reducing the power to detect differences in genotype\ndistribution between cases and controls. Furthermore, several studies did not consider\nmatching factors like age, sex, or other variables, which could may have influenced\nthe studies’ findings. The strength of this analysis was the comprehensive\ninclusion of all eligible genetic association studies related to MetS risk,\nleading to a substantially larger sample size, which improved the evaluation of\nthe association effectively.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

This review provides a concise overview of the existing literature concerning the relationship between genetic polymorphisms and MetS susceptibility in North African populations. Our meta-analysis results suggest that four polymorphisms, APOA5 (rs3135506 and rs662799), APOC3 (rs5128) and FTO (rs9939609), contributed significantly to the combined risk of developing MetS. The vaspin polymorphism (rs2236242) appeared to play a protective role against MetS. Each of these SNPs was also linked to some MetS components, but the most of them showed an association with dyslipidemia. The number of genes associated with MetS susceptibility in North African populations is still low, which invite to additional efforts and well-designed epidemiogenetic studies involving large sample sizes of population\u2019s subjects anthropogenetically well defined and considering a large number of polymorphisms implicated in the different metabolic pathways leading to MetS.<\/p>\n\n\n\n

Acknowledgement<\/strong><\/p>\n\n\n\n

None<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

 No conflict of\ninterest to declare.<\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

There are no funding sources.<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

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  69. Von Hippel PT. The heterogeneity statistic I2 can be biased in small meta-analyses. BMC Med Res Methodol<\/em>. 2015;15(1). doi:10.1186\/S12874-015-0024-Z<\/li><\/ol>\n\n\n\n

    Abbreviations<\/strong><\/p>\n\n\n\n

    MetS : Metabolic syndrome; RFLP: Restriction fragment\nlength polymorphism; IDF : International Diabetes Federation; NCEP-ATP III:\nNational Cholesterol Education Program Adult Treatment Panel III; CVDs:\nCardiovascular diseases; LDL-C: Low-density lipoprotein cholesterol; SNPs:\nSingle-nucleotide polymorphisms; VLDL: Very low-density lipoprotein; HDL:\nHigh-density lipoprotein; PRISMA 2009: Preferred Reporting Items for Systematic\nReviews and Meta-Analyses 2009; OR : odds ratio; 95% CI : confidence interval;\nHWE: Hardy-Weinberg equilibrium; NOS: Newcastle-Ottawa scale<\/p>\n","protected":false},"excerpt":{"rendered":"

    Introduction Metabolic syndrome (MetS) is defined by the presence in […]<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[111],"tags":[],"class_list":["post-54478","post","type-post","status-publish","format-standard","hentry","category-vol16no4"],"_links":{"self":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/54478","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/comments?post=54478"}],"version-history":[{"count":5,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/54478\/revisions"}],"predecessor-version":[{"id":55030,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/54478\/revisions\/55030"}],"wp:attachment":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/media?parent=54478"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/categories?post=54478"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/tags?post=54478"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}