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Caveolin-1 genetic polymorphism interacts with PUFA to modulate metabolic syndrome risk

Published online by Cambridge University Press:  21 June 2021

Faezeh Abaj
Affiliation:
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
Khadijeh Mirzaei*
Affiliation:
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran
*
*Corresponding author: Khadijeh Mirzaei, email [email protected]
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Abstract

Several studies have reported a significant association between the metabolic syndrome (MetS) and mortality around the world. Caveolin-1 (CAV-1) has been widely studied in dyslipidaemia, and several studies have indicated that CAV-1 genetic variations may correlate with dietary intake of fatty acids. This study aimed to investigate the interaction of CAV-1 rs3807992 with types of dietary fatty acid in the MetS risk. This cross-sectional study was carried out on 404 overweight and obese females. Dietary intake was obtained from a 147-item FFQ. The CAV-1 genotype was measured using the PCR-restriction fragment length polymorphism method. Anthropometric values and serum levels (TC, LDL, HDL, TAG and FBS) were measured by standard methods. It was observed that the (AA + AG) group had significantly higher BMI, waist circumference and DBP (P = 0·02, P = 0·02, and P = 0·01, respectively) and lower serum LDL, HDL and TC (P < 0·05) than the GG group. It was found that A allele carriers were at higher odds of the MetS (P = 0·01), abdominal obesity (P = 0·06), increased TAG concentration (P = 0·01), elevated blood pressure (BP) (P = 0·01), increased glucose concentration (P = 0·45) and decreased HDL-cholesterol concentration (P = 0·03). Moreover, the interaction of CAV-1 and SFA intake was significant in terms of the MetS (P = 0·03), LDL (P = 0·03) and BP (P = 0·01). Additionally, the (AA + AG) group was significantly related to PUFA intake in terms of the MetS (P = 0·04), TAG (P = 0·02), glucose (P = 0·02) and homoeostasis model assessment insulin resistance (P = 0·01). Higher PUFA consumption might attenuate the CAV-1 rs3807992 associations with the MetS, and individuals with greater genetic predisposition appeared to have a higher risk of the MetS, associated with higher SFA consumption.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Nutrition Society

The metabolic syndrome (MetS) is related to metabolic abnormalities including obesity, hyperglycaemia, dyslipidaemia and hypertension and is prevalent worldwide(Reference Beltrán-Sánchez, Harhay and Harhay1). Compared with people without the MetS, people with MetS had a higher overall number of deaths for all causes(Reference Ho2). Particularly, the evidence demonstrates that genetic variations play a main part in the prevention and treatment of various chronic diseases, particularly in the MetS(Reference Katzmarzyk, Pérusse and Rice3Reference Pérusse, Després and Tremblay5). The MetS and CVD also demonstrate the role of genetic and environmental factors in diet-related disorders. The genetic background of metabolic disorders has been shown to relate to death rate over the last few decades(Reference Wang, Dong and Xu6). There is therefore a need to identify the genes that derive MetS and to develop new therapies. Caveolin-1 (CAV-1) is a key protein component of caveolae and has been widely studied in dyslipidaemia and CVD due to signal transduction, trafficking in cholesterol haemostasis and TAG metabolism(Reference Jin, Lee and Minshall7). Mice treated with CAV-1 are resistant to high-fat diets, and they show lipodystrophy, hypertension, insulin resistance and abnormal glucose metabolism(Reference Pavlides, Gutierrez-Pajares and Danilo8). Moreover, several studies have indicated that CAV-1 genetic variations might interact with other risk factors, including dietary intake of fatty acids, suggesting a positive association between CAV-1 and hypercholesterolaemia(Reference Shyu, Chen and Hsieh9). In studies conducted among Caucasian and Hispanic cohorts, the prevalence of CAV-1 gene variant rs926198 is related to higher odds of the MetS risk and low HDL(Reference Baudrand, Goodarzi and Vaidya10,Reference Pojoga, Underwood and Goodarzi11) . Also, CAV-1 overexpression has been observed to relate to higher odds of atherosclerosis in experimental models(Reference Fernandez-Hernando, Yu and Davalos12). The exact mechanisms are unclear, but it seems that CAV-1 is able to regulate several key enzymes in lipid metabolism, such as cholesterol ester transfer protein and phospholipid transfer protein(Reference Frank, Pavlides and Cheung13). While the association between CAV-1 polymorphisms and type 2 diabetes risk has been widely reported in various populations(Reference Haddad, Al Madhoun and Nizam14), these relationships with the MetS have been inconsistent, despite several publications on the association between CAV-1 gene variants and serum lipid profiles(Reference Haddad, Al Madhoun and Nizam14Reference Nizam, Al-Ozairi and Goodson17). To the authors’ knowledge, there has been no study evaluating CAV-1 rs3807992 variant, metabolic risk factor and the interaction of fatty acid intake levels with this SNP. Hence, the aim of the current study was to evaluate the interaction of CAV-1 genetic polymorphism with the types of dietary fatty acids, in terms of the MetS risk factor status.

Materials and methods

Study population

For this cross-sectional study, 404 women in the range of 18–55 years old were recruited from health referral centres. Participants provided written informed consent. The inclusion criteria were: obese or overweight, no alcohol consumption and no smoking. CVD, hypertension, type 2 diabetes, polycystic ovary syndrome, kidney failure, stroke, thyroid disease, liver disease, cancer, inflammatory diseases and those who were on certain therapeutic drugs, weight loss programmes or supplements during the study time were all excluded. Each participant was interviewed in order to obtain demographic data and then referred to the laboratory for blood sampling. Anthropometric measures were taken, including: height (m), weight (kg), waist circumference (cm) measured at the narrowest part of the abdomen, and BMI (kg/m2, calculated by dividing weight by height squared). Blood pressure (BP) was measured with a sphygmomanometer (BP) after 5 min’ rest. The study was approved by the Ethics Committee at the Tehran University of Medical Sciences (97-03-161-41017).

The metabolic syndrome definition

MetS cases were required to meet three or more of the following criteria according to the Adult Treatment Panel III criteria:

  1. 1) Elevated fasting blood glucose ≥ 100 mg/dl.

  2. 2) Hypertriacylglycerolaemia TAG ≥ 150 mg/dl

  3. 3) Elevated BP (≥ 130/85 mmHg)

  4. 4) Low (HDL-cholesterol) < 50 mg/dl in women

  5. 5) Waist circumference cut-off 80 cm (women) was considered as an indicator of abdominal obesity(Reference Hu, Rimm and Stampfer18).

Fig. 1. Adjusted OR (95% CI) for the metabolic syndrome (MetS) and its components according to median of dietary fat intake and CAV-1 rs3807992

Dietary assessment

The FFQ was a useful tool for evaluating dietary intake and included 147 items(Reference Mohammadifard, Sajjadi and Maghroun19). This assessment was carried out by interviewing the occurrence of food items consumed on the basis of a predetermined list of foods. The extracted FFQ values were then changed to g/d. For the evaluation of macro- and micronutrient content, N4 software was used; all measurements were then entered into IBM SPPS.

Physical activity measurement

Physical activity was measured using the International Physical Activity Questionnaire short form. Women’s physical activity was divided into three categories: low (under 600), mild (600–3500) and extreme (over 3500) (MET-h/week). International Physical Activity Questionnaire’s validity and reliability were tested in Iranian people(Reference Moghaddam, Aghdam and Asghari Jafarabadi20).

Genotyping

For genotyping the CAV-1 polymorphisms, DNA was extracted from whole blood via a Mini Columns kit (Type G; Genall; Exgene). The PCR-restriction fragment length polymorphism technique was employed to investigate CAV-1 polymorphisms (rs3807992) in gene fragments (major allele G and minor allele A). PCR was carried out using the following primers: F:3′AGTATTGACCTGATTTGCCATG5′ R:5′GTCTTCTGGAAAAAGCACATGA-3′. PCR were performed in a volume of 20 µl, containing 50 ng extracted DNA, 10 μm Forward primers, 10 μm, Reverse primers,7 µl distilled water and Taq DNA Polymerase Master Mix (2X) in a DNA thermocycler. The DNA templates were denatured at 94°C for 3 min and 40 cycles, including a min denaturation at 94°C, a min annealing at 42–50°C and elongation at 72°C for 2 min. Amplified DNA was digested with Hin1II (NIaIII) (2·5 U) restriction enzyme at 37°C overnight and then separated by electrophoresis on an agarose gel (2·5 %). Fragments concluding three genotypes of the CAV-1 rs3807992 variant were detected: uncut homozygous AA (213bp), cut heterozygous GA (3 bands: 118 & 95 & 213 bp) and cut homozygous GG (2 bands: 118 & 95 bp).

Biochemical analysis

All blood samples were drawn, after 12–14 h of fasting, at the nutrition laboratory of Tehran University of Medical Sciences. Blood samples were centrifuged for 10 min at 3000 rpm to extract serums, then aliquoted into 1 ml tubes and stored at 70°C before analysis. Auto-analyzer BT 1500 (Selectra 2; Vital Scientific) was used to test the samples. Fasting plasma glucose was determined using the Glucose Oxidase Phenol 4-Aminoantipyrine Peroxidase process. TAG kits (Pars Azmoon Inc.) were used to measure serum TAG by colorimetric method tests with Glycerol-3-phosphate oxidase Phenol 4-Aminoantipyrine Peroxidase. The cholesterol oxidase-Phenol 4-Aminoantipyrine Peroxidase was used to calculate total cholesterol, and the direct method and immunoinhibition were used to measure LDL and HDL. Pars Azmoon (Pars Azmoon Inc.) provided all of the kits.

Statistical analysis

Statistical analysis was performed using SPSS version 25 software (SPSS Inc.), and significance level was considered P < 0·05. The Kolmogorov–Smirnov was utilised to test the normality of the data, and all data were expressed as means and standard deviation. The Pearson’s χ 2 test was used to determine the Hardy–Weinberg Equilibrium deviation among allele frequency of CAV-1 G32124A (rs3807992). We used the dominant model for genetic analysis, risk allele carriers (AA, AG) compare with homozygotes major allele (GG). Medians and interquartile ranges were calculated for dietary intakes between CAV-1 genotypes. Mann–Whitney U test and Quade’s ANCOVA were used for associations between dietary and genotypes in crude and adjusted models, respectively. Independent-sample t test was used to evaluate the differences between the two groups. Binary logistic regression was used to estimate interactions between rs3807992 and high and low dietary fat intake on the OR of the MetS component. Interaction analyses were adjusted for variables proven to be related to MetS components (such as age, physical activity, energy intake, BMI, age at onset of obesity, and total SFA and PUFA intake).

Results

Clinical characteristics according to caveolin-1 rs3807992 genotypes

The overall prevalence of rs3807992 genotypes was 38·34% and 61·66 % for the A and G alleles. The genotype distributions had a deviation from HWE (P < 0·05). Table 1 expresses the mean clinical characteristics of all women according to CAV-1 genotypes. It was observed that the (AA + AG) group had significantly higher BMI, waist circumference and DBP (P = 0·02, P = 0·02 and P = 0·01, respectively). Furthermore, women with (AA + AG) allele had significantly lower serum LDL (P = 0·006) and HDL (P < 0·0001) than the GG group. Additionally, no significant differences were detected for age, height, weight, FBG, TAG and SBP.

Table 1. Clinical characteristics of all subjects based on caveolin-1 (CAV-1) rs3807992 genotypes

(Mean values and standard deviations)

WC, waist circumference; FPG, fasting plasma glucose; TC, total cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure.

Comparisons between groups were determined based on independent-samples t test.

Bold values indicate statistical significance (P < 0·05).

Genotype frequencies between the metabolic syndrome and control groups

Distributions of allele frequencies and the effect size of rs3807992 on various genetic models (codominant, dominant, recessive) had significant differences between the MetS and control groups. In the codominant model, the A homozygous allele for the CAV-1 (rs3807992) gene was 52 % higher in the control group than the MetS group (OR 2·52 (1·11, 5·70), P = 0·02). In the dominant model, the A homozygous allele for the CAV-1 (rs3807992) gene was 31% higher in the control group than the MetS group (OR 2·31 (1·16, 4·61), P = 0·01) (Table 2).

Table 2. Caveolin-1 (CAV-1) rs3807992 frequencies between the metabolic syndrome (MetS) and control groups

(Numbers and percentages)

Comparisons between groups were determined based on logistic regression analysis.

Bold values indicate statistical significance (P < 0·05).

Associations between caveolin-1 rs3807992 and the risk of the metabolic syndrome and its components

It was found that (rs3807992) A allele carriers were at higher odds of the MetS (OR 2·31 (1·16, 4·16), P = 0·01), abdominal obesity (OR 1·42 (0·98, 2·06), P = 0·06), increased TAG concentration (OR 2·12 (1·13, 3·95), P = 0·01), elevated BP (OR 7·03 (1·43, 34·44), P = 0·01), increased glucose concentration (OR 0·7 (0·27, 1·78), P = 0·45) and decreased HDL-cholesterol concentration (OR −1·4 (1·02, 1·93) P = 0·03) (Table 3).

Table 3. Associations between caveolin-1 (CAV-1) rs3807992 and the risk of the metabolic syndrome (MetS) and its components

(Odds ratios and 95 % confidence intervals)

BP, blood pressure; FPG, fasting plasma glucose.

Comparisons between groups were determined based on binary logistic regression analysis adjusted by age.

Bold values indicate statistical significance (P < 0·05).

Dietary intake relationships between caveolin-1 rs3807992 gene polymorphisms and their interaction on the metabolic syndrome and its components

The results of dietary nutritional intake are described according to genotype groups, which show no significant differences were found in nutrient consumption values (Table 4).

Table 4. Dietary fat intakes of all subjects based on caveolin-1 (CAV-1) rs3807992 genotypes

(Median values and interquartile ranges (IQR))

Mann–Whitney U test and Quade’s ANCOVA were performed to identify significant differences between CAV-1 rs3807992 genotypes in crude and adjusted model respectively.

* (P value): crude model.

(P value): adjusted model by potential confounding factors (age, energy intake, educational level, DBP).

It was observed that the interaction of CAV-1 rs3807992 gene polymorphism and SFA intake was significant on the MetS (OR 5·60 (1·14, 27·40), P = 0·03) and its components, including LDL (OR 12·95 (1·21, 24·69), P = 0·03), BP (OR 6·52 (1·59, 11·45), P = 0·01) in the multi-adjusted model (adjusted for age, energy intake, BMI, age at onset of obesity and total PUFA intake). There was no further significant interaction between the (AA + AG) group and SFA intake on other biochemical parameters, including HDL and TAG in both the crude and adjusted models (P > 0·05) (Table 5; Fig. 1).

Table 5. Interactions between the caveolin-1 (CAV-1) rs3807992 and SFA intake in relation to the metabolic syndrome (MetS) and its components

(Odd ratios and 95 % confidence intervals)

BP, blood pressure.

Binary logistic was performed.

* P-value: adjusted model by potential confounding factors (age, physical activity, energy intake, BMI, age at onset of obesity, and total PUFA intake).

Interestingly, when CAV-1 (AA + AG) group interaction was analysed by PUFA intake, the relationship was significant in terms of the MetS (OR − 0·207 (0·04, 0·95), P = 0·04) and its components, including TAG (OR − 0·27 (0·05, 0·8), P = 0·02), fasting plasma glucose (OR −0·06 (0·005, 0·75), P = 0·02) and homoeostasis model assessment insulin resistance (OR − 0·22 (0·06, 0·78), P = 0·01) in the multi-adjusted model (adjusted for age, energy intake, BMI, age at onset of obesity and SFA intake) (Table 6; Fig. 1).

Table 6. Interactions between the caveolin-1 (CAV-1) rs3807992 and PUFA intake in relation to the metabolic syndrome (MetS) and its components

(Odds ratios and 95 % confidence intervals)

FPG, fasting plasma glucose; HOMA-IR, homoeostasis model assessment insulin resistance.

Binary logistic was performed.

* P-value: adjusted model by potential confounding factors (age, physical activity, energy intake, BMI, age at onset of obesity and total SFA intake).

Discussion

According to these findings, female A allele carriers had significantly higher BMI, waist circumference and DBP and had lower serum LDL and HDL, compared with GG genotypes. This demonstrates that their clinical parameters are predisposed to the MetS. A allele carriers were at higher odds of the MetS. To the authors’ knowledge, there is little evidence describing an association between CAV-1 and the MetS. It was observed that a genetic variant of CAV-1 (rs3807992) was associated with increased MetS risk, which is consistent with previous studies that revealed a significant association between the minor allele in CAV-1 variations and the odds of metabolic diseases(Reference Baudrand, Goodarzi and Vaidya10,Reference Pojoga, Underwood and Goodarzi11,Reference Chen, Wang and Wang15,Reference Nizam, Al-Ozairi and Goodson17) . Previous studies by our research team have shown association between CAV-1 rs3807992 and metabolic disease(Reference Abaj, Koohdani and Rafiee21,Reference Abaj, Saeedy and Mirzaei22) . Also, compared with other candidate gene studies, no studies had evaluated SNP rs3807992 and MetS risk.

It is proposed that CAV-1 polymorphisms increase MetS risk through altered CAV-1 gene expression, attenuating dyslipidaemia and hypertension, while impairing glucose and insulin homoeostasis(Reference Fernandez-Hernando, Yu and Davalos12Reference Haddad, Al Madhoun and Nizam14,Reference Razani, Woodman and Lisanti23) . CAV-1 regulates signalling molecules, such as IRS1, that have a key role in appropriate insulin responses, protein kinase A, angiotensin II receptors, active BP molecules, binding sites for Ca ions, insulin, lipids and hormone metabolisms; all of these may affect various clinical traits of the MetS(Reference Haddad, Al Madhoun and Nizam14,Reference Wang, Wang and Liu24Reference Czikora, Feher and Lucas26) . Apart from genetic mutations, increasing evidence indicates that epigenetic modifications can provide an upstream regulatory switch to regulate the expression of CAV-1 thus leading to disease conditions(Reference Low and Nicholson27). Most of the recent research focuses on the effects of methylation on CAV1 expression. MiRNA-103 and 107 are up-regulated in obese mice, which can lead to impaired glucose homoeostasis, insulin receptor stabilisation and a receptive insulin signalling system(Reference Nystrom, Chen and Cong28,Reference Trajkovski, Hausser and Soutschek29) . In lipid metabolism, DHA has been shown to modify the transcriptome of miRNA. The expression of miRNA-192 in endothelial cells is greatly increased by DHA, and CAV1 is estimated to be a target for miRNA-192. CAV1 expression is decreased when miRNA-192 is overexpressed(Reference Gil-Zamorano, Martin and Daimiel30).

Most previous intervention studies have shown that environmental factors, in particular dietary fat composition, may alter the risk of the MetS. Based on previous studies, higher intake of SFA was detrimental to maintaining insulin sensitivity, whereas PUFA showed beneficial effects(Reference Iggman and Risérus31). Additionally, nutrigenetic research has indicated that dietary fat background can influence genotype–phenotype relations(Reference Phillips32). In our study, the genetic association between CAV-1 polymorphisms and the MetS by dietary fat intake was reported. High dietary SFA intake (≥25 g) especially accentuated the negative effects of rs3807992 in terms of MetS risk.

The evidence for the CAV1 gene and nutrient is limited so far, and it is mostly based on animal studies. In mice models, the role of dietary fat in the relationship between the CAV1 gene and lipid and glucose metabolism has been investigated previously. CAV-1 KO mice have demonstrated resistance to obesity diet, as well as increased plasma levels of chylomicron/VLDL particles and TAG caused by a defect in lipid droplet formation and hydrolysis(Reference Razani, Combs and Wang33). Under fat diet conditions, CAV-1 null mice have shown white adipose tissue atrophy and TAG deposition in plasma. Besides, CAV-1 knockout mice fed a western-type diet represent a significant rise in VLDL and IDL/LDL(Reference Frank, Lee and Park34).

Besides, CAV-1 has also been observed moving from the plasma membrane to lipid droplets in response to NEFA(Reference Cohen, Razani and Schubert35).CAV-1ʼs function in lipid transport had also been confirmed by CAV-1-deficient mice’s lean phenotype, which had significantly higher serum levels of NEFA and TAG in the postprandial state(Reference Pol, Luetterforst and Lindsay36Reference Pol, Martin and Fernandez38). Accordingly, caveolae are a main centre for several nutrient metabolisms through the cell membrane and play a unique function in the uptake of various lipid and glucose metabolites(Reference Pilch and Liu39). Caveolae are able to uptake fatty acid, TAG and cholesterol in many tissues, which leads to an elevation in caveolae density in obese rats(Reference Grayson, Chadha and Bertrand40,Reference Su and Abumrad41) . In line with previous studies, experimental studies have reported that sphingomyelin is a key phospholipid of caveolae. SFA intake may lead to increases in sphingolipids levels in the cardiac cell membranes, thus disrupting the caveolae contents(Reference Le Lay, Li and Proschogo42,Reference Perona43) . Furthermore, CAV-1 mRNA levels are up-regulated by free cholesterol in human cells(Reference Fielding44). An appropriate level of cholesterol in the cell membrane under dietary fat conditions causes the caveolae activity are disrupted(Reference Thomas and Smart45). Experimental studies suggest that lipid abnormalities can influence caveolae formation and function, thereby disrupting fatty acid metabolism and contributing to the development of the MetS and obesity(Reference Zhang46).

A further novel finding is that high PUFA intake (≥6 % energy) reduced the negative effects of rs3807992 in terms of MetS risk, with the greatest protection achieved by A allele carriers. Caveolae membrane fatty acids (in the internal and external leaflet) are also significantly altered by n-3 PUFA intake and are even able to change the function of the caveolae. In this regard, Chapkin et al. reported that in animal models, n-3 PUFA intake may modulate the function of caveolae proteins/lipid, affecting membrane fusion and cell–cell signalling, and improving insulin signalling(Reference Chapkin, McMurray and Davidson47). Previous studies have indicated that H-Ras and endothelial nitric-oxide synthase are moved from caveolae in n-3 PUFA-supplemented rats, which suppressed the Ras-dependent signalling and reduced BP, thereby lowering the MetS risk(Reference Li, Zhang and Wang48,Reference Ma, Seo and Davidson49) . CAV-1 prevents the production of nitric oxide. These findings demonstrate that PUFA intake affects CAV-1 abundance, which in turn affects endothelial nitric-oxide synthase activity and, as a result, vascular function(Reference Komarova and Malik50,Reference Schubert, Frank and Woodman51) .

Besides, CAV-1 can regulate fatty acid translocase surface accessibility by acting as an intracellular shuttle for long-chain fatty acids to lipid droplets and thus indirectly control long fatty acid uptake(Reference Razani, Combs and Wang33,Reference Ostermeyer, Paci and Zeng37,Reference Pol, Martin and Fernandez38) . Fatty acid translocase is required for long-chain fatty acid uptake. CAV-1 deficiency has previously been linked to a total loss of caveolae, the lack of fatty acid translocase cellular membrane expression and a decrease in fatty acid uptake(Reference Ring, Le Lay and Pohl52).

In this research, we observed that a PUFA can be effective on glucose profile in risk allele carriers. Changes in insulin receptor activity are one of the proposed pathways for the effect of dietary fat and CAV-1 on metabolic markers. According to strong evidence, caveolae are implicated in the pathogenesis of insulin receptor and the MetS(Reference Haddad, Al Madhoun and Nizam14). If animals were fed a high-fat diet, insulin receptor substrate 1 and Protein Kinase B showed reduced activity, indicating insulin resistance. These results point to a mechanism by which a high-cholesterol diet affected CAV-1 expression in vivo, as well as insulin receptor localisation and function(Reference Haddad, Al Madhoun and Nizam14,Reference Cohen, Combs and Scherer53,Reference Hahn-Obercyger, Graeve and Madar54) . It is suggested that a correlation between hyperlipidaemia and insulin receptor through caveolae under dietary fat conditions.

However, another mechanism by which fatty acids can alter the genetic risk posed by CAV-1 polymorphisms may also contribute to the apoA1(Reference Frank, Pavlides and Cheung13). Consistent with our results, CAV-1-deficient animals fed a western-style diet have significantly lower apoA-I plasma levels. Loss of CAV-1 contributes to the development of the MetS, according to apoA-I levels, the key protein marker of HDL(Reference Frank, Cheung and Pavlides55).

The mechanism observed in the present study, by which fatty acids are able to modify the genetic risk posed by CAV-1 polymorphisms, remains unknown; further studies are needed to indicate such gene–diet interventions. Caveolae and its components may become useful sites for further investigation into treating the MetS.

Limitations

Several limitations can also be identified in the current study. Dietary intake was assessed by a FFQ, which is self-reported and thus dependent on patient memory. Due to financial limitations, it was not possible to perform western blot analysis to determine whether rs-3807992 SNP alters the expression of CAV-1. The focus of the current study was on dietary fat composition, but other nutrient components, including carbohydrates or fibre, can also play a role in the progression of the MetS. Furthermore, lipid parameter measurements were taken, while the subjects were fasted, which may have obscured differences in CAV-1 under fed conditions. Given the observational nature of the study, it is not possible to tell whether the associations which were identified in women (but not men) are of a causal nature. Finally, we did not include a normal-weight participant due to financial constraints and instead focused on overweight and obese women as high-risk categories for the MetS(Reference Bradshaw, Monda and Stevens56). However, as the first study in this regard, we recommend that future research concentrate on normal-weight people both men and women.

Conclusion

To the authors’ knowledge, this is the first study presenting the association of a genetic variant of CAV-1 rs3807992 with the risk of the MetS and its components, including TAG, BP and HDL level. However, further studies are needed to determine the strength of this association in a larger population; the contribution of this study is the novel finding that rs3807992 clearly predicts the MetS among obese women. Analyses of the individual components of the MetS confirmed that the rs3807992 variant is related to elevated BP, dyslipidaemia, low HDL-cholesterol and high TAG levels. Also, CAV-1 rs3807992 genotypes are sensitive to dietary SFA and PUFA, which allows individuals to monitor and adjust SFA and PUFA consumption accordingly. Finally, these results can be used in combination with a patient’s genetic history in order to provide more applicable and tailored nutritional advice for preventing or attenuating the MetS in overweight and obese women.

Acknowledgements

We would like to thank all the individuals who participated in this project.

This study was supported by a grant from TUMS (41017).

F. A. contributed to conception, design, data analyses, data interpretation and manuscript drafting. K. H. M. supervised the study. All authors approved the final manuscript for submission.

The authors declare no conflict of interest.

References

Beltrán-Sánchez, H, Harhay, MO, Harhay, MM, et al. (2013) Prevalence and trends of metabolic syndrome in the adult US population, 1999–2010. J Am Coll Cardiol 62, 697703.10.1016/j.jacc.2013.05.064CrossRefGoogle Scholar
Ho, WSHZLSC (2010) Metabolic syndrome and all-cause mortality: a meta-analysis of prospective cohort studies. Eur J Epidemiol 25, 375384.Google Scholar
Katzmarzyk, PT, Pérusse, L, Rice, T, et al. (2001) Familial aggregation of seven-year changes in blood pressure in Canada. Can J Cardiol 17, 12671274.Google ScholarPubMed
Monda, KL, North, KE, Hunt, SC, et al. (2010) The genetics of obesity and the metabolic syndrome. Endocr Metab Immune Disord Drug Target 10, 86108.CrossRefGoogle ScholarPubMed
Pérusse, L, Després, JP, Tremblay, A, et al. (1989) Genetic and environmental determinants of serum lipids and lipoproteins in French Canadian families. Arteriosclerosis 9, 308318.10.1161/01.ATV.9.3.308CrossRefGoogle ScholarPubMed
Wang, H, Dong, S, Xu, H, et al. (2012) Genetic variants in FTO associated with metabolic syndrome: a meta-and gene-based analysis. Mol Biol Rep 39, 56915698.10.1007/s11033-011-1377-yCrossRefGoogle ScholarPubMed
Jin, Y, Lee, S-J, Minshall, RD, et al. (2011) Caveolin-1: a critical regulator of lung injury. J Physiol-Lung Cell Mol Physiol 300, L151L160.10.1152/ajplung.00170.2010CrossRefGoogle ScholarPubMed
Pavlides, S, Gutierrez-Pajares, JL, Danilo, C, et al. (2012) Atherosclerosis, Caveolae and Caveolin-1. Caveolins and Caveolae. Adv Exp Med Biol 729, 127144.CrossRefGoogle ScholarPubMed
Shyu, HY, Chen, MH, Hsieh, YH, et al. (2017) Association of eNOS and Cav-1 gene polymorphisms with susceptibility risk of large artery atherosclerotic stroke. PLoS One 12, e0174110.CrossRefGoogle ScholarPubMed
Baudrand, R, Goodarzi, MO, Vaidya, A, et al. (2015) A prevalent caveolin-1 gene variant is associated with the metabolic syndrome in Caucasians and Hispanics. Metab Clin Exp 64, 16741681.CrossRefGoogle ScholarPubMed
Pojoga, LH, Underwood, PC, Goodarzi, MO, et al. (2011) Variants of the caveolin-1 gene: a translational investigation linking insulin resistance and hypertension. J Clin Endocrinol Metab 96, E1288E1292.10.1210/jc.2010-2738CrossRefGoogle ScholarPubMed
Fernandez-Hernando, C, Yu, J, Davalos, A, et al. (2010) Endothelial-specific overexpression of caveolin-1 accelerates atherosclerosis in apolipoprotein E-deficient mice. Am J Pathol 177, 9981003.CrossRefGoogle ScholarPubMed
Frank, PG, Pavlides, S, Cheung, MW, et al. (2008) Role of caveolin-1 in the regulation of lipoprotein metabolism. Am J Physiol Cell Physiol 295, C242C248.10.1152/ajpcell.00185.2008CrossRefGoogle ScholarPubMed
Haddad, D, Al Madhoun, A, Nizam, R, et al. (2020) Role of Caveolin-1 in diabetes and its complications. Oxid Med Cell Longevity 2020, 9761539.10.1155/2020/9761539CrossRefGoogle ScholarPubMed
Chen, S, Wang, X, Wang, J, et al. (2016) Genomic variant in CAV1 increases susceptibility to coronary artery disease and myocardial infarction. Atherosclerosis 246, 148156.CrossRefGoogle ScholarPubMed
Mora-Garcia, G, Gomez-Camargo, D, Alario, A, et al. (2018) A common variation in the caveolin 1 gene is associated with high serum triglycerides and metabolic syndrome in an admixed latin American population. Metab Syndr Relat Disord 16, 453463.CrossRefGoogle Scholar
Nizam, R, Al-Ozairi, E, Goodson, JM, et al. (2018) Caveolin-1 variant is associated with the metabolic syndrome in Kuwaiti children. Front Genet 9, 689.10.3389/fgene.2018.00689CrossRefGoogle ScholarPubMed
Hu, FB, Rimm, EB, Stampfer, MJ, et al. (2000) Prospective study of major dietary patterns and risk of coronary heart disease in men. Am J Clin Nutr 72, 912921.CrossRefGoogle ScholarPubMed
Mohammadifard, N, Sajjadi, F, Maghroun, M, et al. (2015) Validation of a simplified food frequency questionnaire for the assessment of dietary habits in Iranian adults: isfahan Healthy Heart Program, Iran. ARYA Atherosclerosis 11, 139146.Google ScholarPubMed
Moghaddam, MHB, Aghdam, F, Asghari Jafarabadi, M, et al. (2012) The Iranian Version of International Physical Activity Questionnaire (IPAQ) in Iran: content and construct validity, factor structure, internal consistency and stability. World Appl Sci J 18, 10731080.Google Scholar
Abaj, F, Koohdani, F, Rafiee, M, et al. (2021) Interactions between Caveolin-1 (rs3807992) polymorphism and major dietary patterns on cardio-metabolic risk factors among obese and overweight women. BMC Endocr Disord 21, 138.CrossRefGoogle ScholarPubMed
Abaj, F, Saeedy, SAG & Mirzaei, K (2021) Are caveolin-1 minor alleles more likely to be risk alleles in insulin resistance mechanisms in metabolic diseases? BMC Res Notes 14, 185.10.1186/s13104-021-05597-6CrossRefGoogle ScholarPubMed
Razani, B, Woodman, SE & Lisanti, MP (2002) Caveolae: from cell biology to animal physiology. Pharmacol Rev 54, 431467.CrossRefGoogle ScholarPubMed
Wang, H, Wang, AX, Liu, Z, et al. (2009) The trafficking/interaction of eNOS and caveolin-1 induced by insulin modulates endothelial nitric oxide production. Mol Endocrinol 23, 16131623.CrossRefGoogle ScholarPubMed
Daniel, EE, Eteraf, T, Sommer, B, et al. (2009) The role of caveolae and caveolin 1 in calcium handling in pacing and contraction of mouse intestine. J Cell Mol Med 13, 352364.10.1111/j.1582-4934.2008.00667.xCrossRefGoogle ScholarPubMed
Czikora, I, Feher, A, Lucas, R, et al. (2015) Caveolin-1 prevents sustained angiotensin II-induced resistance artery constriction and obesity-induced high blood pressure. Am J Physiol Heart Circ Physiol 308, H376H385.CrossRefGoogle ScholarPubMed
Low, JY & Nicholson, HD (2015) Epigenetic modifications of caveolae associated proteins in health and disease. BMC Genet 16, 71.10.1186/s12863-015-0231-yCrossRefGoogle ScholarPubMed
Nystrom, FH, Chen, H, Cong, LN, et al. (1999) Caveolin-1 interacts with the insulin receptor and can differentially modulate insulin signaling in transfected Cos-7 cells and rat adipose cells. Mol Endocrinol 13, 20132024.10.1210/mend.13.12.0392CrossRefGoogle ScholarPubMed
Trajkovski, M, Hausser, J, Soutschek, J, et al. (2011) MicroRNAs 103 and 107 regulate insulin sensitivity. Nature 474, 649653.10.1038/nature10112CrossRefGoogle ScholarPubMed
Gil-Zamorano, J, Martin, R, Daimiel, L, et al. (2014) Docosahexaenoic acid modulates the enterocyte Caco-2 cell expression of microRNAs involved in lipid metabolism. J Nutr 144, 575585.CrossRefGoogle ScholarPubMed
Iggman, D & Risérus, U (2011) Role of different dietary saturated fatty acids for cardiometabolic risk. Clin Lipidol 6, 209223.CrossRefGoogle Scholar
Phillips, CM (2013) Nutrigenetics and metabolic disease: current status and implications for personalised nutrition. Nutrients 5, 3257.10.3390/nu5010032CrossRefGoogle ScholarPubMed
Razani, B, Combs, TP, Wang, XB, et al. (2002) Caveolin-1-deficient mice are lean, resistant to diet-induced obesity, and show hypertriglyceridemia with adipocyte abnormalities. J Biol Chem 277, 86358647.CrossRefGoogle ScholarPubMed
Frank, PG, Lee, H, Park, DS, et al. (2004) Genetic ablation of caveolin-1 confers protection against atherosclerosis. Arterioscler Thromb Vasc Biol 24, 98105.CrossRefGoogle ScholarPubMed
Cohen, AW, Razani, B, Schubert, W, et al. (2004) Role of Caveolin-1 in the modulation of lipolysis and lipid droplet formation. Diabetes 53, 12611270.10.2337/diabetes.53.5.1261CrossRefGoogle ScholarPubMed
Pol, A, Luetterforst, R, Lindsay, M, et al. (2001) A caveolin dominant negative mutant associates with lipid bodies and induces intracellular cholesterol imbalance. J Cell Biol 152, 10571070.CrossRefGoogle ScholarPubMed
Ostermeyer, AG, Paci, JM, Zeng, Y, et al. (2001) Accumulation of caveolin in the endoplasmic reticulum redirects the protein to lipid storage droplets. J Cell Biol 152, 10711078.10.1083/jcb.152.5.1071CrossRefGoogle ScholarPubMed
Pol, A, Martin, S, Fernandez, MA, et al. (2004) Dynamic and regulated association of caveolin with lipid bodies: modulation of lipid body motility and function by a dominant negative mutant. Mol Biol Cell 15, 99110.CrossRefGoogle ScholarPubMed
Pilch, PF & Liu, L (2011) Fat caves: caveolae, lipid trafficking and lipid metabolism in adipocytes. Trend Endocrinol Metab 22, 318324.10.1016/j.tem.2011.04.001CrossRefGoogle ScholarPubMed
Grayson, TH, Chadha, PS, Bertrand, PP, et al. (2013) Increased caveolae density and caveolin-1 expression accompany impaired NO-mediated vasorelaxation in diet-induced obesity. Histochem Cell Biol 139, 309321.10.1007/s00418-012-1032-2CrossRefGoogle ScholarPubMed
Su, X & Abumrad, NA (2009) Cellular fatty acid uptake: a pathway under construction. Trend Endocrinol Metab 20, 7277.10.1016/j.tem.2008.11.001CrossRefGoogle ScholarPubMed
Le Lay, S, Li, Q, Proschogo, N, et al. (2009) Caveolin-1-dependent and -independent membrane domains. J Lipid Res 50, 16091620.10.1194/jlr.M800601-JLR200CrossRefGoogle ScholarPubMed
Perona, JS (2017) Membrane lipid alterations in the metabolic syndrome and the role of dietary oils. Biochim Biophys Acta Biomembr 1859, 16901703.10.1016/j.bbamem.2017.04.015CrossRefGoogle ScholarPubMed
Fielding, CJ (1997) Caveolin mRNA levels are up-regulated by free cholesterol and down-regulated by oxysterols in fibroblast monolayers. Natl Acad Sci. USA, 0027-8424y97y943753.10.1073/pnas.94.8.3753CrossRefGoogle ScholarPubMed
Thomas, CM & Smart, EJ (2008) Caveolae structure and function. J Cell Mol Med 12, 796809.10.1111/j.1582-4934.2008.00295.xCrossRefGoogle ScholarPubMed
Zhang, WZ (2014) An association of metabolic syndrome constellation with cellular membrane caveolae. Pathobiol Aging Age Relat Dis 4, 23866.10.3402/pba.v4.23866CrossRefGoogle ScholarPubMed
Chapkin, RS, McMurray, DN, Davidson, LA, et al. (2008) Bioactive dietary long-chain fatty acids: emerging mechanisms of action. Br J Nutr 100, 11521157.10.1017/S0007114508992576CrossRefGoogle ScholarPubMed
Li, Q, Zhang, Q, Wang, M, et al. (2007) Docosahexaenoic acid affects endothelial nitric oxide synthase in caveolae. Arch Biochem Biophys 466, 250259.CrossRefGoogle ScholarPubMed
Ma, DW, Seo, J, Davidson, LA, et al. (2004) n-3 PUFA alter caveolae lipid composition and resident protein localization in mouse colon. Faseb J 18, 10401042.10.1096/fj.03-1430fjeCrossRefGoogle ScholarPubMed
Komarova, Y & Malik, AB (2010) Regulation of endothelial permeability via paracellular and transcellular transport pathways. Ann Rev Physiol 72, 463493.CrossRefGoogle ScholarPubMed
Schubert, W, Frank, PG, Woodman, SE, et al. (2002) Microvascular hyperpermeability in caveolin-1 (-/-) knock-out mice. Treatment with a specific nitric-oxide synthase inhibitor, L-NAME, restores normal microvascular permeability in Cav-1 null mice. J Biol Chem 277, 4009140098.10.1074/jbc.M205948200CrossRefGoogle ScholarPubMed
Ring, A, Le Lay, S, Pohl, J, et al. (2006) Caveolin-1 is required for fatty acid translocase (FAT/CD36) localization and function at the plasma membrane of mouse embryonic fibroblasts. BBA 1761, 416423.Google ScholarPubMed
Cohen, AW, Combs, TP, Scherer, PE, et al. (2003) Role of caveolin and caveolae in insulin signaling and diabetes. Am J Physiol Endocrinol Metab 285, E1151E1160.10.1152/ajpendo.00324.2003CrossRefGoogle ScholarPubMed
Hahn-Obercyger, M, Graeve, L & Madar, Z (2009) A high-cholesterol diet increases the association between caveolae and insulin receptors in rat liver. J Lipid Res 50, 98107.CrossRefGoogle ScholarPubMed
Frank, PG, Cheung, MW-C, Pavlides, S, et al. (2006) Caveolin-1 and regulation of cellular cholesterol homeostasis. Am J Physiol-Heart Circ Physiol 291, H677H686.CrossRefGoogle ScholarPubMed
Bradshaw, PT, Monda, KL & Stevens, J (2012) Metabolic syndrome in healthy obese, overweight, and normal weight individuals: the atherosclerosis risk in communities study. Obesity.CrossRefGoogle Scholar
Figure 0

Fig. 1. Adjusted OR (95% CI) for the metabolic syndrome (MetS) and its components according to median of dietary fat intake and CAV-1 rs3807992

Figure 1

Table 1. Clinical characteristics of all subjects based on caveolin-1 (CAV-1) rs3807992 genotypes(Mean values and standard deviations)

Figure 2

Table 2. Caveolin-1 (CAV-1) rs3807992 frequencies between the metabolic syndrome (MetS) and control groups(Numbers and percentages)

Figure 3

Table 3. Associations between caveolin-1 (CAV-1) rs3807992 and the risk of the metabolic syndrome (MetS) and its components(Odds ratios and 95 % confidence intervals)

Figure 4

Table 4. Dietary fat intakes of all subjects based on caveolin-1 (CAV-1) rs3807992 genotypes(Median values and interquartile ranges (IQR))

Figure 5

Table 5. Interactions between the caveolin-1 (CAV-1) rs3807992 and SFA intake in relation to the metabolic syndrome (MetS) and its components(Odd ratios and 95 % confidence intervals)

Figure 6

Table 6. Interactions between the caveolin-1 (CAV-1) rs3807992 and PUFA intake in relation to the metabolic syndrome (MetS) and its components(Odds ratios and 95 % confidence intervals)