Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Mediation role of body fat distribution (FD) on the relationship between CAV1 rs3807992 polymorphism and metabolic syndrome in overweight and obese women

BMC Medical Genomics volume 14, Article number: 202 (2021) Cite this article

Abstract

Background

Metabolic syndrome (MetS) is associated with an increased risk of morbidity and mortality in almost all chronic diseases. The most frequent methods for the calculation of a continuous MetS (cMetS) score have used the standardized residuals in linear regression (z-score). Recently, emerging data suggest that one of the main genetic targets is the CAV1, which plays a crucial role in regulating body fat distribution. This study is designed to investigate the relationship between CAV1 rs3807992 genotypes and cMetS, and to determine whether body fat distribution plays a mediating role in this regard.

Methods

The current cross-sectional study was conducted on 386 overweight and obese females. The CAV1 rs3807992 and body composition were measured by the PCR–RFLP method and bioelectrical impedance analysis, respectively. Serum profile of HDL-C, TGs, FPG, and Insulin were measured by standard protocols.

Results

GG allele carriers had significantly lowered Z-MAP (p = 0.02), total cMetS (p = 0.03) and higher Z-HDL (p = 0.001) compared with (A) allele carriers. There was a significant specific indirect effect (standardized coefficient = 0.19; 95% CI 0.01–0.4) of Visceral fat level (VFL). Although, total body fat was significantly associated with CAV1 rs3807992 and cMetS, the specific indirect effect was not significant (standardized coefficient = 0.21; 95% CI − 0.006, 0.44). VFL contributed to significant indirect effects of 35% on the relationship between CAV1 and cMetS.

Conclusion

Higher visceral adipose tissue may affect the relationship between CAV1 and cMetS. Although CAV1 rs3807992 is linked to VFL in our study, the influence of this polymorphism on MetS is not via total fat.

Peer Review reports

Background

MetS refers to a set of metabolic disorders that are associated with a high risk of cardiovascular disease. Its components include abdominal obesity, high blood pressure (BP), hypertriglyceridemia, hyperglycemia, and low level of high-density lipoprotein-cholesterol (HDL-C) [1, 2]. Based on large investigations, MetS is associated with an increased risk of morbidity and mortality in almost all chronic diseases [1, 3]. There is lack of consensus on a precise definition for MetS. This has brought about several definitions and strategies for this condition [4]. During the last decade, symmetrical to the changes in definitions of the MetS, an alternative approach for the monitoring of MetS was also improved. Given the lack of considerable evidence for using a dichotomous MetS definition because of information loss and the absence of a universal definition for MetS, a potential way for using a continuous score to quantify the MetS was developed [5].

So far, the most frequent methods for the calculation of a cMetS score have used the standardized residuals in linear regression (z-score) [6]. The MetS severity z-score, an inexpensive and clinically-available continuous measure of MetS, was derived from standardized loading coefficients for fasting blood sugar (FBS), triglycerides (TGs), waist circumference (WC), BP, and HDL-cholesterol [7].

Amazingly, evidence suggests that genetic variations, in addition to obesity, play a major role in regulating body fat composition. [8]. It appears that genetic factors have a more prominent impact on visceral fat than on other adipose tissues. It is suggested by some authors that apart from TBF, genetic factors have a great effect on visceral fat [9,10,11]. The availability of enormous genomic information, such as Genome-Wide Association Studies (GWAS), has facilitated the field for fine-mapping and translation of genetic information into clinical practice [12]. Recently, emerging data suggest that one of the main genetic targets is the CAV1, which plays a crucial role in regulating FD [13, 14]. CAV1 is the main part of caveolae and has been extensively studied in dyslipidemia and cardiovascular diseases for its important role in the signal transduction, interaction with steroid receptors, involvement in the activation of ion channels, and cholesterol hemostasis [15, 16].

In this research, we focused on the CAV1 gene which is located on chromosome 7q31.2. CAV1 [15]. CAV1 was found to be expressed at a high level in adipocytes. Moreover, CAV1 is involved in adipogenesis pathway as well as lipogenesis [17]. It is shown by a previous study that a significant association exists between higher CAV1 expression in visceral adipose tissue (VAT) and in subcutaneous adipose tissue (SAT) of obese patients [18].

Numerous gene loci that have regulator action on the body FD are revealed by recent GWAS for a measure of FD [10]. The sexual dimorphic effect has been revealed by sex-stratified studies at twenty Waist-Hip Ratio (WHR)-associated loci. Nineteen out of these twenty loci exhibited more powerful effects in women [19, 20]. Lack of caveolae, as seen in lipoatrophic caveolin-deficient patients, severely compromises fat cell expansion [21]. The risk of developing obesity-related metabolic and cardiovascular complications such as MetS is affected in large quantities by adipose tissue dysfunction and ectopic fat [22].

CAV1 is now specified as a gene involved in FD and potential mechanisms contribute to its variability between VFL and TBF. Regarding this, we have investigated the effect of these pathways and their association with CAV1 and MetS.

The main purpose of this study is to assess the relationship between CAV1 rs3807992 genotypes and the risk of MetS and to determine whether body FD plays a mediating role in this regard.

Methods

Samples

A total of 386 overweight and obese women within the range of 18–55 years old were included in the study [23]. The exclusion criteria were cardiovascular diseases (CVDs), kidney failure, stroke, thyroid disease, liver disease, cancer, inflammatory illnesses, pregnancy, and any therapeutic medications. Each participant provided written informed consent. The study was approved by the Ethics Community of Tehran University of Medical Sciences (TUMS) (97-03-161-41017).

Anthropometric assessments and body composition

Bodyweight and height were measured when the subjects were minimally clothed and not wearing shoes in a standing position using a digital scale (Seca, Germany) and tape measure with a precision of 100 g and 0.1 cm, respectively. WC was measured at the narrowest area below the rib cage and above the umbilicus using a non-elastic tape to the nearest 0.1 cm. Body Mass Index (BMI) was calculated as weight (kg) divided by square of height (m2). A validated International Physical Activity Questionnaire was used for the assessment of the physical activity [24]. After that, for the measurement of BP, participants sat in a rest position for 10 min. They were in suitable positioning including back support. Using a properly sized cuff, their BP was measured from the left bare arm which was supported at heart level [25].

BMI values between 25 and 29.9 kg/m2 and above 30 kg/m2, were defined as overweight and obese, respectively. VFL, TBF, percent body fat (%BF), skeletal muscle mass (SMM), soft lean mass (SLM), fat mass index (FMI), and obesity degree were measured by tetrapolar bioelectrical impedance analysis (InBody 770 scanner, Seoul, Korea), according to the manufacturer's guidelines [26]. First, participants removed their shoes, coats, and sweaters; then they stood barefoot on the balance scale and held the handles of the machine.

Clinical analysis and DNA genotyping

All serum samples were collected after overnight fasting (12-14 h) at the Nutrition and Genomics Laboratory of TUMS [27]. We stored plasma samples at − 80 °C. Baseline circulating fasting blood sugar (FBS), plasma lipid (TGs), and lipoprotein (HDL) were profiled by a standard enzymatic technique with a commercial kit (Pars Azmoon Co., Tehran, Iran). DNA extraction was accomplished from the whole blood sample by a Mini Columns kit (Type G; Genall; Exgene). PCR–RFLP method was used to determine CAV1 rs3807992 polymorphism. For amplification, we utilized the following primers: F:5′AGTATTGACCTGATTTGCCATG-3′, R:3′-GTCTTCTGGAAAAAGCACATGA-5′. Finally, the PCR was conducted in a final volume of.

20 µl, containing 1 µl extracted DNA, 1 µl Forward primers, 1 µl Reverse primers, 7 µl distilled water, and 10 µl Taq DNA Polymerase Master Mix (Ampliqon; Germany) under the following conditions: each reaction started with a cycle of DNA templates denatured at 94 °C for three minutes. Amplification consisted of forty cycles (each cycle includes 94 °C for fifteen seconds, 53 °C for thirty seconds, 72 °C for thirty seconds), with a final extension at 72 °C for three minutes. 10 µl of DNA was digested with 0.5 µl of the Hin1II(NlaIII) enzyme (Fermentase, Germany) at 37 °C overnight. Electrophoresis was conducted to visualize all PCR products. We employed TRIS–Borate-EDTA buffer (TBE) which was prepared as a 10X concentrated stock. PCR–RFLP products were detected by 3.5% gel electrophoresis. The gels were visualized by UV light by two independent individuals.

Calculation of a cMetS score

To derive cMetS, regression analysis of standardized residuals of the WC, FBS, HDL-C, TGs, and mean arterial pressure (MAP) was done on age to adjust them for age-related differences. Since the standardized HDL-C is conversely associated with the MetS’s risk, it was multiplied by (− 1). MAP calculated from measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) variables. MAP was calculated by this formula: [(SBP − DBP)/3] + DBP. The sum of the standardized residuals (z-score) for the subjects’ variables was used to compute a cMetS score.

Statistical methods

The Kolmogorov–Smirnov was used to test normality before computing the z-score. Eighteen of the original 404 participants were identified as outliers and were removed, so that the final analysis was done on 386 participants.

The Exact test was used for the Hardy–Weinberg Equilibrium (HWE). One-Way ANOVA and ANCOVA tests were used for determining the association between quantitative variables. These variables include Age (year), BMI (kg/m2), WC (Cm), WHR, BFM (kg), FFM (kg), SMM (kg), SLM (kg), BF%, VFL, VFA (cm2), Obesity degree %, FFMI (kg), FMI (kg), Total body mineral content (kg). These variables were organized in three groups of genotypes in the crude and adjusted model, respectively. Moreover, the independent-sample t-test was used to compare the above continuous variables according to two groups of an allele in the dominant model. Findings on continuous variables were expressed as means ± standard deviation (SD). p < 0.05 was considered statistically significant.

To investigate the mediatory role of body FD in the relationships between CAV1 polymorphism and cMetS, we used tests of mediation which is an important, new statistical technique in nutrigenetic science to test hypothesized processes (such as body distribution), predictor variable (such as genetic susceptibility) and dependent outcome (such as MetS).

Mediation analysis assesses whether supposed mediating variables explain the relationship between a predictor variable and the outcome variable. For the first time, Baron and Kenny developed this analysis [20]. This method uses bootstrapping to make 95% confidence intervals to detect the indirect effect. If zero is not within this confidence interval, the indirect effect is significant.

As shown in Fig. 1, path c refers to the total effect of CAV1 on cMetS in the absence of the mediators. Specific indirect effects refer to the influence of (path a × path b) via each particular mediator, (a1 × b1) is the indirect effect of CAV1 on cMetS by VFL; (a2 × b2) is the indirect effect of CAV1 on cMetS by TBF. Path c’ refers to the direct effect of CAV1 on cMetS when also controlling for the mediators.

Fig. 1
figure 1

Mediation effects of VFL and TBF on the association between Caveolin-1 rs807992 polymorphism and cMetS (VFL; A), (TBF; B). Confounding factors were age, energy intake. Standardized coefficients were shown along with their estimated p values: “a” is the linear regression coefficient of the CAV-1rs3807992- cMetS association; “b” is the linear regression coefficient of the VFL and TBF of cMetS. *p < 0.05; ** p < 0.001. CAV-1 coded for the number of risk alleles present carried by an individual (GG = 0 AG + AA = 1). The analysis was done using SPSS Process Andrew 3.3 with 5000 bootstrap samples. Statistically significant paths do NOT contain zero between lower and upper level confidence intervals

Full size image

A test of significance of indirect effects was performed by Bias-corrected bootstrap confidence intervals (n = 5000, confidence intervals set at 95%), which were established by Hayes [28].We used SPSS 25 for all statistical analyses.

Results

386 overweight and obese women were investigated in this cross-sectional study. The means and standard deviation (SD) of age, BMI, and WC of individuals were 36.67 ± 9.1 years, 31.26 ± 4.29 kg/m2, and 99.61 ± 10.07 cm, respectively. The overall prevalence of rs3807992 genotypes was 38.34% and 61.66% for the A, and G alleles. The genotype distributions were within HWE (p value > 0.01). Based on CAV1 rs3807992, we had three groups of participants (AA, AG, and GG). The information of their characteristics is shown in Table 1. The mean FMI, VFL, and obesity degree (%) were significantly higher in AA compared to AG and GG (p < 0.05). Also, we couldn’t find any other significant difference among CAV1 rs3807992 groups. Besides, in dominant comparison, the results of the study revealed that mean BFM, VFL, and FMI were significantly lower in GG homozygotes genotypes, compared with A-allele carriers (p < 0.05).

Table 1 Baseline characteristics of all participants according to genotype CAV-1 rs3807992
Full size table

The mean values for each part of the cMetS score which according to different CAV1 genotypes is divided into different groups are presented in Table 2. There was a significant correlation between z-score (MAP, HDL-C) and cMetS with CAV1 polymorphism in this study. GG group had significantly lowered (Z-MAP (p = 0.02), total cMetS (p = 0.03)) and higher Z-HDL-C (p = 0.001) compared with the AA and AG groups. Moreover, in the dominant model, risk allele carriers had significantly higher Z-MAP (p = 0.006), total cMetS (p = 0.01)), and lower Z-HDL (p < 0.001).

Table 2 Mean values of components of continuous metabolic risk score by CAV-1 rs3807992
Full size table

Based on the recessive model, also minor allele homozygotes (AA) have a higher risk of metabolic syndrome, but this association was not significant (p = 0.05). Although we did not observe a significant result in the recessive model, we detected the same direct association as the dominant models. It means that we observed that AA homozygotes have a higher risk of metabolic syndrome compared to major allele carriers (AG and GG) in this model.

Mediation analysis for VFL and TBF is presented in Fig. 1. The analysis showed that the direct effect of CAV1 polymorphism on cMetS was not significant, but only the VFL created a significant specific indirect effect (standardized coefficient = 0.19; 95% CI 0.01–0.4) after controlling for age and energy intake. Although, TBF was significantly associated with CAV1 rs3807992 and cMetS, the specific indirect effect was not significant (standardized coefficient = 0.21; 95% CI − 0.006, 0.44). Therefore, VFL contributed to significant indirect effects of 35% on the relationship between CAV1 rs3807992 and cMetS.

Discussion

The current study aimed to investigate the mediation role of body FD between CAV1 polymorphism and MetS. In the present study, we observed that risk allele carriers (AA, AG) have higher BFM, VFL, and FMI. Therefore, carriers of A allele might be at more risk of developing MetS compared to the individuals with GG genotype. Since the attribution mechanism of CAV1 variants to MetS is unclear at the present, this association has been studied by a small number of existing researchers [29, 30] and the present study is the first human study for showing the association between CAV1 rs3807992 polymorphism and MetS. Similar to our findings, Baudrand et al. revealed that CAV1 rs926198 variant is associated with MetS [29]. Moreover, Mora-Garcia et al. suggest that minor alleles for SNPs rs3779512, rs7804372, and rs1049337 might be associated with a higher risk of hypertriglyceridemia [30].

Furthermore, regardless of the relationships among the caveolae function, adipose tissue, and lipid profile [29, 31, 24], MetS which is linked to CAV1 has not been studied regarding body FD. To a notable extent, we offered a hypothetical mechanism whereby CAV1 can increase MetS risk via FD. As mentioned in the results section, although the rs3807992 polymorphism was statically related with visceral and total body fat, we found evidence in mediation analysis, that only VFL explains the association between rs3807992 and MetS. To the best of our knowledge, the indirect effect of CAV1 rs3807992 on MetS via VFL is a new approach that has not been shown or even suggested before.

A close relationship is present between metabolic disorders and FD; the risk of developing MetS increases in people who have fat deposition in visceral adipose than TBF deposition [32]. Of particular note, the present mediation analysis is especially interesting in that the metabolic difference which is present between visceral and TBF is known [33]. The importance of visceral adipose tissue in the pathophysiology of metabolic disorders can be highlighted by comparing the specifications of VAT with SAT. VAT is linked with the development of insulin resistance, is lipolitically more active, and carries more risk factors for the development of obesity, dyslipidemia, cardiovascular and metabolic disease than subcutaneous fat [34–36]. Regarding these special characteristics, visceral fat led to many metabolic disorders such as alteration in lipid profile (low HDL-C and high TGs levels) and disturbance in glucose homeostasis [37–39]. However, several studies have suggested that these differences in FD in tissues could be genotype-related [19, 40].

According to the mentioned evidence and our pervious study [41], our results cast a new light and mechanism on the mediation role of VAT rather than TBF with consideration to CAV1-linked MetS. Specifically, our primary results suggest a possible pathway to MetS’s risk by VAT and the presence of the CAV1 rs3807992 risk allele.

It is remarkable that lipodystrophies with a defective local fat deposition that are seen in mutations of CAV1 gene indicate a new locus in the metabolic diseases [42]. It has been established by a previous study that CAV1 has a significant regulator role at FD and genetic lipodystrophies in humans [43]. Previous findings had not indicated a special direct or indirect role for VFL in the increased development of MetS in individuals carrying the risk allele. However, studies show the association of CAV1 mRNA expression in VAT in obese women compared with lean subjects [17, 18]. Basic studies have shown that the lean body phenotype of CAV1- null mice are smaller than their wild-type counterparts [36, 44]. Lipodystrophy has been caused in CAV1 null mice due to different functions that are attributed to caveolae in adipocytes; dysfunction of the lipid droplet, disturbance in adipocyte differentiation pathway, abnormality in binding, transport, and storage of cholesterol and fatty acids, and increase in insulin signaling. These possible functional roles for CAV1 may be inherited [43, 45].

The association of CAV1 with body composition in our study could suggest a CAV1–lipogenic pathway interaction. Especially considering that CAV1 is highly expressed in adipose tissue, and the interplay between CAV1 gene and lipogenic genes has been reported [17]. This finding is consistent with what has been found in former studies. Excess visceral fat could be related to changes in circulating fatty acid composition [46]. Altered activities of fatty acid desaturases disturb plasma fatty acid metabolism and cause adipose tissue dysfunction. However, it seems that a possible genetic interaction between CAV1 and fatty acid composition with the lipogenic system genes may also be accountable for our result in this study.

The present study also might have a clinical implication, highlighting the potential of applying this finding in the prevention and treatment of CAV1-linked obesity and cardiovascular disease that is the result of high VFL.

Since the beginning of human studies on the caveolin gene, limitations were present in those studies due to lack of information on body composition [29, 31, 47–51]. So, researchers have not been able to investigate the visceral fat factor which may have been the cause of some observed results. Therefore, with this groundbreaking work, we have researched this gap.

Limitations

Despite our novel findings, it suffers from some limitations. All participants in our study were women, and there have been studies reporting sex differences for the effect of CAV1 on various body compositions [17]. Other limitations of the present study include the cross-sectional design (so that any causality cannot be argued) and small sample size (which may have led to weak statistic determine significant results). Furthermore, our participants were from the country of Iran, so that the results may not be generalized due to racial and regional differences. Finally, many predisposing factors that increase with age, such as insulin resistance, inflammation and hypertension also may contribute to the increase in the prevalence of MetS (4, 5). Although in our study, the participants were within the range of 18–55 years old, we suggest that future studies be done on a wider age range.

Conclusions

It appears that increased VAT fat accounts for the association between CAV1 rs3807992 and MetS. Although rs3807992 is linked to visceral fat in our study, the influence of this polymorphism on MetS is not via total fat. If replicated, this suggested pathway has the potential to have an important impact on our understanding of CAV1-linked MetS.

Data availability

The data are not publicly available due to containing private information of participants. Data are however available from the authors upon reasonable request and with permission of Khadijeh Mirzaei.

Abbreviations

BIA:

Bioelectrical impedance analysis

BMI:

Body Mass Index

BP:

Base pair

BFM:

Body fat mass

BF:

Body fat

CVDs:

Cardiovascular diseases

DBP:

Diastolic blood pressure

FBS:

Fasting blood sugar

FD:

Fat distribution

FMI:

Fat Mass Index

FFMI:

Fat-free Mass Index

GWAS:

Genome-wide Association Studies

HDL:

High-density Lipoprotein Cholesterol

IR:

Insulin resistance

LDL:

Low-density Lipoprotein

MA:

Minor allele

MAP:

Mean arterial pressure

PCR:

Polymerase Chain Reaction

RFLP:

Restriction Fragment Length Polymorphism

SBP:

Systolic blood pressure

SNP:

Single nucleotide polymorphism

SMM:

Skeletal muscle mass

SLM:

Soft lean mass

TG:

Triglyceride

TBF:

Total body fat

VAT:

Visceral adipose tissue

VFL:

Visceral fat level

WC:

Waist circumference

WHR:

Waist-Hip ratio

References

  1. Ford ES. Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence. Diabetes Care. 2005;28(7):1769–78.

    Article  PubMed  Google Scholar 

  2. Ahrens W, Moreno LA, Mårild S, Molnár D, Siani A, De Henauw S, Böhmann J, Günther K, Hadjigeorgiou C, Iacoviello L, et al. Metabolic syndrome in young children: definitions and results of the IDEFICS study. Int J Obes. 2005;2014(38 Suppl 2):S4-14.

    Google Scholar 

  3. Wilson PW, D’Agostino RB, Parise H, Sullivan L, Meigs JB. Metabolic syndrome as a precursor of cardiovascular disease and type 2 diabetes mellitus. Circulation. 2005;112(20):3066–72.

    Article  CAS  PubMed  Google Scholar 

  4. Jung KJ, Jee YH, Jee SH. Metabolic risk score and vascular mortality among korean adults: the korean metabolic syndrome mortality study. Asia Pacific J Public Health. 2017;29(2):122–31.

    Article  Google Scholar 

  5. Soldatovic I, Vukovic R, Culafic D, Gajic M, Dimitrijevic-Sreckovic V. siMS score: simple method for quantifying metabolic syndrome. PLoS ONE. 2016;11(1):e0146143.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Jung KJ, Jee YH, Jee SH. Metabolic risk score and vascular mortality among Korean adults. Asia Pac J Public Health. 2017;29(2):122–31.

    Article  PubMed  Google Scholar 

  7. Low S, Khoo KCJ, Wang J, Irwan B, Sum CF, Subramaniam T, Lim SC, Wong TKM. Development of a metabolic syndrome severity score and its association with incident diabetes in an Asian population-results from a longitudinal cohort in Singapore. Endocrine. 2019;65(1):73–80.

    Article  CAS  PubMed  Google Scholar 

  8. Jensen MD. Role of body fat distribution and the metabolic complications of obesity. J Clin Endocrinol Metab. 2008;93(11 Suppl 1):S57-63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Garg A, Peshock RM, Fleckenstein JL. Adipose tissue distribution pattern in patients with familial partial lipodystrophy (Dunnigan variety). J Clin Endocrinol Metab. 1999;84(1):170–4.

    CAS  PubMed  Google Scholar 

  10. Schleinitz D, Böttcher Y, Blüher M, Kovacs P. The genetics of fat distribution. Diabetologia. 2014;57(7):1276–86.

    Article  CAS  PubMed  Google Scholar 

  11. Shackleton S, Lloyd DJ, Jackson SN, Evans R, Niermeijer MF, Singh BM, Schmidt H, Brabant G, Kumar S, Durrington PN. LMNA, encoding lamin A/C, is mutated in partial lipodystrophy. Nat Genet. 2000;24(2):153–6.

    Article  CAS  PubMed  Google Scholar 

  12. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, Yang J. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lind L. Genome-wide association study of the metabolic syndrome in UK Biobank. Metab Syndr Relat Disord. 2019;17(10):505–11.

    Article  CAS  PubMed  Google Scholar 

  14. Justice AE, Karaderi T, Highland HM, Young KL, Graff M, Lu Y, Turcot V, Auer PL, Fine RS, Guo X, et al. Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution. Nat Genet. 2019;51(3):452–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Austin ED, Ma L, LeDuc C, Berman Rosenzweig E, Borczuk A, Phillips JA 3rd, Palomero T, Sumazin P, Kim HR, Talati MH, et al. Whole exome sequencing to identify a novel gene (caveolin-1) associated with human pulmonary arterial hypertension. Circ Cardiovasc Genet. 2012;5(3):336–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Pojoga LH, Underwood PC, Goodarzi MO, Williams JS, Adler GK, Jeunemaitre X, Hopkins PN, Raby BA, Lasky-Su J, Sun B, et al. Variants of the caveolin-1 gene: a translational investigation linking insulin resistance and hypertension. J Clin Endocrinol Metab. 2011;96(8):E1288-1292.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Fernández-Real JM, Catalán V, Moreno-Navarrete JM, Gómez-Ambrosi J, Ortega FJ, Rodriguez-Hermosa JI, Ricart W, Frühbeck G. Study of caveolin-1 gene expression in whole adipose tissue and its subfractions and during differentiation of human adipocytes. Nutr Metab (Lond). 2010;7:20.

    Article  CAS  Google Scholar 

  18. Catalán V, Gómez-Ambrosi J, Rodríguez A, Silva C, Rotellar F, Gil MJ, Cienfuegos JA, Salvador J, Frühbeck G. Expression of caveolin-1 in human adipose tissue is upregulated in obesity and obesity-associated type 2 diabetes mellitus and related to inflammation. Clin Endocrinol. 2008;68(2):213–9.

    Google Scholar 

  19. Rask-Andersen M, Karlsson T, Ek WE, Johansson Å. Genome-wide association study of body fat distribution identifies adiposity loci and sex-specific genetic effects. Nat Commun. 2019;10(1):339.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, Strawbridge RJ, Pers TH, Fischer K, Justice AE, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518(7538):187–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Briand N, Prado C, Mabilleau G, Lasnier F, Le Lièpvre X, Covington JD, Ravussin E, Le Lay S, Dugail I. Caveolin-1 expression and cavin stability regulate caveolae dynamics in adipocyte lipid store fluctuation. Diabetes. 2014;63(12):4032–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Blüher M. Adipose tissue dysfunction in obesity. Exp Clin Endocrinol Diabetes. 2009;117(6):241–50.

    Article  PubMed  CAS  Google Scholar 

  23. Abaj F, Koohdani F, Rafiee M, Alvandi E, Yekaninejad MS, Mirzaei K. Interactions between Caveolin-1 (rs3807992) polymorphism and major dietary patterns on cardio-metabolic risk factors among obese and overweight women. BMC Endocr Disord. 2021;21(1):138. https://doi.org/10.1186/s12902-021-00800-y.

  24. Grilo A, Fernandez ML, Beltrán M, et al. Genetic analysis of CAV1 gene in hypertension and metabolic syndrome. Thromb Haemost. 2006;95(4):696–701.

  25. Muntner P, Shimbo D, Carey RM, Charleston JB, Gaillard T, Misra S, Myers MG, Ogedegbe G, Schwartz JE, Townsend RR, et al. Measurement of blood pressure in humans: a scientific statement from the American Heart Association. Hypertension (Dallas, Tex: 1979). 2019;73(5):e35–66.

    CAS  Google Scholar 

  26. McLester C, Nickerson B, Kliszczewicz B, McLester J. Reliability and agreement of various inbody body composition analyzers as compared to dual-energy X-ray absorptiometry in healthy men and women. J Clin Densitom. 2018;23:443–50.

    Article  PubMed  Google Scholar 

  27. Abaj F, Mirzaei K. Caveolin-1 genetic polymorphism interacts with PUFA to modulate metabolic syndrome risk. Br J Nutr. 2021;1–8. https://doi.org/10.1017/S0007114521002221.

  28. Preacher KJ, Hayes AF. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–91.

    Article  PubMed  Google Scholar 

  29. Baudrand R, Goodarzi MO, Vaidya A, et al. A prevalent caveolin-1 gene variant is associated with the metabolic syndrome in Caucasians and Hispanics. Metabolism. 2015;64(12):1674–81. https://doi.org/10.1016/j.metabol.2015.09.005.

  30. Mora-Garcia GJ, Ruiz-Diaz MS, Gomez-Camargo DE, Gomez-Alegria CJ. Frequency of common polymorphisms in Caveolin 1 ( CAV1) gene in adults with high serum triglycerides from Colombian Caribbean Coast. Colomb Med (Cali). 2017;48(4):167–73.

    PubMed  PubMed Central  Google Scholar 

  31. Chen S, Wang X, Wang J, Zhao Y, Wang D, Tan C, Fa J, Zhang R, Wang F, Xu C, et al. Genomic variant in CAV1 increases susceptibility to coronary artery disease and myocardial infarction. Atherosclerosis. 2016;246:148–56.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Baumgartner R. Body composition in elderly persons: a critical review of needs and methods. Prog Food Nutr Sci. 1993;17(3):223–60.

    CAS  PubMed  Google Scholar 

  33. Björntorp P. Différence métabolique entre graisse viscérale et graisse abdominale sous-cutanée [Metabolic difference between visceral fat and subcutaneous abdominal fat]. Diabetes Metab. 2000;26 Suppl 3:10–12.

  34. Bjørndal B, Burri L, Staalesen V, Skorve J, Berge RK. Different adipose depots: their role in the development of metabolic syndrome and mitochondrial response to hypolipidemic agents. J Obes. 2011;2011:490650.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ormazabal V, Nair S, Elfeky O, Aguayo C, Salomon C, Zuñiga FA. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc Diabetol. 2018;17(1):122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Viljanen AP, Lautamäki R, Järvisalo M, Parkkola R, Huupponen R, Lehtimäki T, Rönnemaa T, Raitakari OT, Iozzo P, Nuutila P. Effects of weight loss on visceral and abdominal subcutaneous adipose tissue blood-flow and insulin-mediated glucose uptake in healthy obese subjects. Ann Med. 2009;41(2):152–60.

    Article  CAS  PubMed  Google Scholar 

  37. Després JP, Lemieux I, Bergeron J, Pibarot P, Mathieu P, Larose E, Rodés-Cabau J, Bertrand OF, Poirier P. Abdominal obesity and the metabolic syndrome: contribution to global cardiometabolic risk. Arterioscler Thromb Vasc Biol. 2008;28(6):1039–49.

    Article  PubMed  CAS  Google Scholar 

  38. Després JP, Lemieux I. Abdominal obesity and metabolic syndrome. Nature. 2006;444(7121):881–7.

    Article  PubMed  CAS  Google Scholar 

  39. Ross R, Aru J, Freeman J, Hudson R, Janssen I. Abdominal adiposity and insulin resistance in obese men. Am J Physiol Endocrinol Metab. 2002;282(3):E657-663.

    Article  CAS  PubMed  Google Scholar 

  40. Li X, Qi L. Gene-environment interactions on body fat distribution. Int J Mol Sci. 2019;20(15):3690.

    Article  CAS  PubMed Central  Google Scholar 

  41. Abaj F, Saeedy SAG, Mirzaei K. Are caveolin-1 minor alleles more likely to be risk alleles in insulin resistance mechanisms in metabolic diseases? BMC Res Notes. 2021;14(1):185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Lightbourne M, Brown RJ. Genetics of lipodystrophy. Endocrinol Metab Clin North Am. 2017;46(2):539–54.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Garg A, Agarwal AK. Caveolin-1: a new locus for human lipodystrophy. J Clin Endocrinol Metab. 2008;93(4):1183–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Schrauwen I, Szelinger S, Siniard AL, Kurdoglu A, Corneveaux JJ, Malenica I, Richholt R, Van Camp G, De Both M, Swaminathan S, et al. A Frame-shift mutation in CAV1 is associated with a severe neonatal progeroid and lipodystrophy syndrome. PLoS ONE. 2015;10(7):e0131797.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Razani B, Combs TP, Wang XB, Frank PG, Park DS, Russell RG, Li M, Tang B, Jelicks LA, Scherer PE, et al. Caveolin-1-deficient mice are lean, resistant to diet-induced obesity, and show hypertriglyceridemia with adipocyte abnormalities. J Biol Chem. 2002;277(10):8635–47.

    Article  CAS  PubMed  Google Scholar 

  46. Otis JP, Shen MC, Quinlivan V, Anderson JL, Farber SA. Intestinal epithelial cell caveolin 1 regulates fatty acid and lipoprotein cholesterol plasma levels. Dis Model Mech. 2017;10(3):283–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Baudrand R, Gupta N, Garza AE, Vaidya A, Leopold JA, Hopkins PN, Jeunemaitre X, Ferri C, Romero JR, Williams J, et al. Caveolin 1 modulates aldosterone-mediated pathways of glucose and lipid homeostasis. J Am Heart Assoc. 2016;5(10):e003845.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Cao H, Alston L, Ruschman J, Hegele RA. Heterozygous CAV1 frameshift mutations (MIM 601047) in patients with atypical partial lipodystrophy and hypertriglyceridemia. Lipids Health Dis. 2008;7(1):3.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Catalan V, Gomez-Ambrosi J, Rodriguez A, Silva C, Rotellar F, Gil MJ, Cienfuegos JA, Salvador J, Fruhbeck G. Expression of caveolin-1 in human adipose tissue is upregulated in obesity and obesity-associated type 2 diabetes mellitus and related to inflammation. Clin Endocrinol. 2008;68(2):213–9.

    CAS  Google Scholar 

  50. Engel D, Beckers L, Wijnands E, Seijkens T, Lievens D, Drechsler M, Gerdes N, Soehnlein O, Daemen MJ, Stan RV, et al. Caveolin-1 deficiency decreases atherosclerosis by hampering leukocyte influx into the arterial wall and generating a regulatory T-cell response. FASEB J. 2011;25(11):3838–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Fernandez-Rojo MA, Gongora M, Fitzsimmons RL, Martel N, Martin SD, Nixon SJ, Brooks AJ, Ikonomopoulou MP, Martin S, Lo HP, et al. Caveolin-1 is necessary for hepatic oxidative lipid metabolism: evidence for crosstalk between caveolin-1 and bile acid signaling. Cell Rep. 2013;4(2):238–47.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the participants in this research.

Funding

This study was supported by a grant from the Tehran University of Medical Sciences (41017). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and Affiliations

  1. Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences (TUMS), P.O. Box 14155-6117, Tehran, Iran

    Faezeh Abaj & Khadijeh Mirzaei

  2. Department of Paraclinic, School of Medicine, Herat University, Herat, Afghanistan

    Said Abdul Ghafour Saeedy

Authors
  1. Faezeh Abaj
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Said Abdul Ghafour Saeedy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Khadijeh Mirzaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

FA: Conceived and designed the study, Collected the data; Contributed data or analysis tools, Performed the analysis, Wrote the paper. FA & SAS: Writing—review & editing. KM: Supervision; Validation; Project administration. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Khadijeh Mirzaei.

Ethics declarations

Ethics approval and consent to participate

The protocol of the study was approved by the ethics committee of TUMS (Ethics number: 97-03-161-41017). All participants completed a written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abaj, F., Saeedy, S.A.G. & Mirzaei, K. Mediation role of body fat distribution (FD) on the relationship between CAV1 rs3807992 polymorphism and metabolic syndrome in overweight and obese women. BMC Med Genomics 14, 202 (2021). https://doi.org/10.1186/s12920-021-01050-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12920-021-01050-6

Keywords

BMC Medical Genomics

ISSN: 1755-8794

Contact us