Association Between Dietary Atherogenic Index and Risk of Polycystic Ovary Syndrome: A Case-Control Study in Tehranian Women
محورهای موضوعی : Food and Health
Parisa Navidgouei
1
(Department of Nutrition, Electronic Health and Statistics Surveillance Research Center, Science and Research Branch.Islamic Azad University,Tehran,Iran.)
Behnood Abbasi
2
(Department of Nutrition, Electronic Health and Statistics Surveillance Research Center, Science and Research Branch. Islamic Azad University, Tehran, Iran)
Sedigheh Hosseini
3
(Preventive gynecology research center (PGRC), Shahid Beheshti University of Medical Sciences, Tehran,Iran)
کلید واژه: Polycystic Ovary Syndrome, Tehranian women, / Case-control study, Fatty acids, / Atherogenic index,
چکیده مقاله :
Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by impaired glucose metabolism and insulin resistance. Dietary fatty acids may play a role in the progression of PCOS and related metabolic abnormalities. We conducted a case-control study to examine the association between dietary atherogenic index (AI) and PCOS risk. The study included 203 women with PCOS and 291 healthy controls from Taleghenai Hospital in Tehran, Iran. Demographic, anthropometric, and dietary data were collected using a validated food frequency questionnaire. AI was calculated based on dietary fatty acid intake. Participants’ mean ages were 28.98±5.43 and 30.15±6.21 years for cases and controls, respectively. No significant differences in intakes of total fat, cholesterol, saturated, trans, monounsaturated fatty acids, linoleic, or linolenic acids were observed across AI quartiles (p>0.05). However, polyunsaturated fatty acid intake decreased significantly with increasing AI quartiles (p=0.034). In addition, this case-control study found no relationship between the dietary atherogenic index and the risk of PCOS among Iranian women. Further research is needed to validate these findings and elucidate the role of dietary fatty acids in PCOS pathogenesis.
Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder characterized by impaired glucose metabolism and insulin resistance. Dietary fatty acids may play a role in the progression of PCOS and related metabolic abnormalities. We conducted a case-control study to examine the association between dietary atherogenic index (AI) and PCOS risk. The study included 203 women with PCOS and 291 healthy controls from Taleghenai Hospital in Tehran, Iran. Demographic, anthropometric, and dietary data were collected using a validated food frequency questionnaire. AI was calculated based on dietary fatty acid intake. Participants’ mean ages were 28.98±5.43 and 30.15±6.21 years for cases and controls, respectively. No significant differences in intakes of total fat, cholesterol, saturated, trans, monounsaturated fatty acids, linoleic, or linolenic acids were observed across AI quartiles (p>0.05). However, polyunsaturated fatty acid intake decreased significantly with increasing AI quartiles (p=0.034). In addition, this case-control study found no relationship between the dietary atherogenic index and the risk of PCOS among Iranian women. Further research is needed to validate these findings and elucidate the role of dietary fatty acids in PCOS pathogenesis.
Relationship between Atherogenic Index of Diet and Polycystic Ovary Syndrome in a Case Control Study in Tehranian Women.
Abstract:
Background:
Polycystic ovary syndrome is a heterogeneous disorder with various clinical manifestations including impaired glucose metabolism, insulin resistance and dyslipidemia. Since dietary fatty acids might have a remarkable role in progression and development of these disorders, we conducted the present study to evaluate the association between atherogenic index and polycystic ovary syndrome.
Methods:
Our case-control study was performed on 494 individuals including 203 women with polycystic ovary syndrome and 291 healthy women in Taleghenai hospital, Tehran, Iran, in July 2019. Demographic data and anthropometric indices including height, weight and waist circumference gathered by a trained expert. Valid semi-quantitative food-frequency questionnaire was used for dietary intake assessment. Atherogenic index was calculated by empirical formula.
Results:
Participants had mean age of 28.98 ± 5.43 and 30.15 ± 6.21 years in case and control groups. There was no significant trend in total fat, cholesterol, saturated fatty acids, trans fatty acid, MUFA, linoleic, and linolenic fatty acids intake through atherogenic index quartiles (P > 0.05). However, we observed that PUFA intake decreased through atherogenic index quartiles significantly (P= 0.034). In addition, there was no significant association between atherogenic index of diet and polycystic ovary syndrome risk.
Conclusions:
To sum up, we found no relationship between atherogenic index and polycystic ovary syndrome risk. More studies are needed to prove our findings.
Keywords: Polycystic Ovary Syndrome, Atherogenic Index, Fatty Acids, Case-control study, Iran.
Running title: Athrogenic index and PCOS.
1. Introduction
The polycystic ovarian syndrome (PCOS) as a low grade chronic inflammation and the most prevalent endocrine abnormality, affects 5-10 percent of worldwide women in child-bearing age (Kelly et al., 2001, Varanasi et al., 2018, Khan et al., 2019). PCOs is a disorder with imbalance of sex hormones which results in impaired menstrual cycle and blocked ovulation and also, associated with substantial metabolic defects such as obesity, metabolic syndrome, insulin resistance and so on (Patel, 2018, Torchen, 2017). Based on Rotterdam criteria, 7 percent of Iranian women in reproductive age suffer from PCOs (Ghiasi, 2019).
Even though PCOs is a syndrome with an unknown etiology, genetic susceptibility, inflammatory state and oxidative stress are explained as potential mechanisms affects PCOs pathophysiology (Mohammadi, 2019). This heterogeneous disorder has several clinical manifestations including dermatological complications, depression, abdominal obesity, abnormal glucose metabolism, insulin resistance (IR) and dyslipidemia, which might lead to cardiovascular disease and impaired insulin sensitivity eventually (Azziz, 2006, Gilbert et al., 2018, Sha et al., 2019).
Pharmaceutical interventions and lifestyle modification strategies such as increased physical activity and proper nutrition recommendations are proposed to ameliorate PCOs complications (Harrison et al., 2011). Diet has essential role on modifying metabolic disorders and especially insulin resistance (Ostrowska et al., 2013). Calorie restriction, low carbohydrate-ketogenic diet (LCKD), antioxidant supplementation, and desirable dietary fatty acids has been associated with improvements in endocrine characteristics in PCOS (Ostrowska et al., 2013, Panti et al., 2018, Mavropoulos et al., 2005).
Athrogenic index (AI) is an indicator of dietary fat quality and is directly related to AI of plasma. Therefore, it might affect BMI, CVD risk and lipid abnormalities (Javardi et al., 2020). Dietary fatty acids might have remarkable role in progression and development of chronic disorders and metabolic diseases, and in order to calculate AI of diet different fatty acids should be evaluated. In this regard, it should be noted that omega-6 poly unsaturated fatty acids (PUFA) such as linoleic acid (LA) (18:2n – 6) are appreciated for their anti- atherogenic properties (Garaffo et al., 2011). While numerous studies confirmed anti-thrombogenetic action of omega-3 PUFA including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) which regulates platelet adhesion, improves endothelial function, modifies dyslipidemia, decreases inflammation and insulin resistance (Cussons et al., 2009, Nadeem, 2019, Salek et al., 2019, Garaffo et al., 2011). In addition, monounsaturated fatty acid (MUFA) has been recommended due to its role on managing body weight and attenuating pancreatic inflammation and cardiovascular diseases risk (Mashek and Wu, 2015, Ralston et al., 2020, Garaffo et al., 2011). On the other hand, evidences showed that saturated fatty acids (SFA) trigger toll like receptor 4 (TLR4) signaling pathway and promote inflammation by increase the expression of cyclooxygenase 2 (COX2) and nuclear kappa B (NF-κB) (Rocha et al., 2016, Lee et al., 2004).
Since dietary fatty acids may have essential role in pathogenesis of PCOS by affecting inflammation and insulin resistance, and there is limited evidences around this issue, in this context, we aimed to evaluate AI of diet in PCOS and healthy volunteers in a case-control design.
2. Material and methods:
2.1. Study design
This study was conducted in Taleghenai hospital, Tehran, Iran, between July 2019 and December 2019. Due to increase study power, the number of participants in the control group was considered 1.5 times more than the case group, and finally 494 women including 203 women with PCOS and 291 healthy women participated in this case-control study. The study protocol was approved by ethical committee of Islamic Azad University, Tehran, Iran (code: IR.IAU.SRB.REC.13988.028) and all participants filled out the written informed consent.
Patients were selected based on having 2 out 3 indicators of Rotterdam criteria including oligomenorrhea and/or anovulation, biochemical and/or clinical signs of hyperandrogenism and polycystic ovaries, by a gynecologist (Esmaeilinezhad et al., 2019). Women outpatients with PCOS ageing 18 to 45 years were recruited in the study and those with hyperprolactinemia, hypothyroidism, Cushing's syndrome, liver and adrenal malignancies, hormone therapy, birth control drugs intake, smoking and alcohol abuse were not qualified to enter the study. The criteria for selecting participants in control group were as follows: healthy subjects with Freeman-Galloway score < 8 and regular menstruation period.
2.2. Participants characteristics
A detailed questionnaire was prepared to obtain demographic data about age, marital status, educational degree, family history of PCOS and other diseases, anthropometric indices including height, weight and waist circumference by a trained nutritionist. Height was measured with an inelastic tape with an accuracy of 0.1 cm and weight measurement was done by Seca scale made in Germany with an accuracy of 100 grams (Orang et al., 2018). Waist circumference was measured with a flexible tape meter with an accuracy of 0.1 cm, above the iliac crest in the narrowest area (Orang et al., 2018). To calculate body mass index (BMI), weight (kg) was divided upon square of the height (cm) (Orang et al., 2018).
The physical activity assessment was done by IPAQ questionnaire and on this basis physical activity was reported as MET-minute per day (Vasheghani-Farahani et al., 2011).
2.3. Assessment of dietary fatty acid index
A valid semi-quantitative food-frequency questionnaire (FFQ) that included 147 food items was used for dietary intake assessment during the past 12 months on a daily, weekly and monthly basis (Farhangi and Jahangiry, 2018). After recording food frequency, values converted to grams by Nutritionist IV software (First Databank, San Bruno, CA, USA).
Fat quality index was calculated by empirical formula. Atherogenic index formula was represented as a correlation between the saturated and unsaturated fatty acids. Total saturated fatty acids were the sum of Lauric acid (C12:0), Myristic acid (C14:0) and Palmitic acid (C16:0) which divided by unsaturated fatty acids including total amounts of MUFA, Omega-6 and Omega-3 poly unsaturated fatty acids (Javardi et al., 2020).
AI = [(4×C14:0) + C16:0+C18:0] / [ ∑ Mufa + ∑ Pufa – n6 + ∑ Pufa – n3]
2.4. Statistical Methods
Statistical analysis was performed by Statistical Package for the Social Sciences, version 21.0; SSPS Inc., USA. Data were reported as percentages for qualitative variables or mean ± SD for quantitative variables. The reported P-value were calculated using the independent sample T-Test (for quantitative data) or the chi_square test (for qualitative data). The means of different quartiles were assessed using the Analysis of Variance (ANOVA) test. Logistic regression models were used to calculate odds ratios (ORs) and the corresponding 95% confidence interval (CIs) and first quartile of the AI was used as reference in multivariate models. Adjustments of various variables including age (year), BMI (Kg/m2), energy (kcal), body weight (kg), waist circumferences (cm), educational status, marital status, physical activity, taking multivitamin-mineral, folic acid, omega-3 and vitamin D supplement intake was conducted in regression models 1 and 2. P < 0.05 was set statistically significant.
3. Results
Table 1 illustrates demographic, anthropometric and dietary assessments in case and control groups. Participants had mean age of 28.98 ± 5.43 and 30.15 ± 6.21 in case and control groups. Comparing marital an educational status, 26 and 52.6 percent of participants in case and control groups were single, and 54 and 83 percent of individuals in case and control groups had university degree. As shown in table 1, all anthropometric indices including body weight, BMI and WC were significantly higher in case group compare to controls (P < 0.001, P < 0.001 and P = 0.006, respectively). However, subjects of control group had higher physical activity level (1996.65 ± 1258.03 MET-min/d, P < 0.001). Comparable differences were found between case and control groups in carbohydrate and fiber intake, with higher gram per day intake in case group (P = 0.038 and P < 0.001). No remarkable difference was seen in multi vitamin-mineral and NSAID intake between both groups, although high percentage of individuals in control group had omega-3 supplement consumption (P = 0.03).
Table 1. General characteristics of study participants.
Variable | Case (n=203) | Control (n=291) | P-value |
Age (years) | 28.98 ± 5.43 | 30.15 ± 6.21 | 0.029* |
Marital status (single), n (%) | 53 (26.1) | 153 (52.6) | <0.001* |
Educational status (university degree), n (%) | 110 (54.1) | 242 (83.2) | <0.001* |
Body weight (kg) | 67.91 ± 13.14 | 63.79 ± 10.51 | <0.001* |
BMI (kg/m2) | 25.74 ± 5.44 | 23.65 ± 3.90 | <0.001* |
WC (cm) | 87.21 ± 43.38 | 79.92 ± 9.68 | 0.006* |
Physical activity (MET-min/d) | 1638.97 ± 572.95 | 1996.65 ± 1258.03 | <0.001* |
Total energy (kcal/d) | 2500.07 ± 696.19 | 2388.03 ± 657.88 | 0.070 |
Carbohydrate (g/d) | 344.10 ± 95.78 | 326.06 ± 93.83 | 0.038* |
Protein (g/d) | 86.17 ± 28.89 | 88.26 ± 27.96 | 0.421 |
Fat (g/d) | 92.49 ± 36.18 | 86.98 ± 33.15 | 0.081 |
Dietary fiber (g/d) | 44.73 ± 23.47 | 38.01 ± 18.21 | <0.001* |
Multi vitamin-mineral intake, n (%) | 34 (16.7) | 70 (24.1) | 0.050 |
Omega 3 supplement intake, n (%) | 7 (3.4) | 24 (8.2) | 0.030* |
NSAID intake, n (%) | 71 (35.1) | 108 (37.2) | 0.635 |
BMI: body mass index, WC: waist circumferences. Values reported as a mean ± standard deviation for quantitative variables and as number (percentage) for qualitative variables. P< 0.05 considered significant.
As table 2 presents, there is no significant trend in total fat between AI quartiles (P = 0.061). Also, no significant difference was seen in cholesterol, saturated fatty acids, trans fatty acid, MUFA, linoleic and linolenic fatty acids intake by grams as well, when comparing first quartile to fourth quartile (P > 0.05). However, we observed that PUFA intake decreased significantly through AI quartiles (P= 0.034).
Table 2. Dietary intake of fatty acids based on AI quartiles.
| AI | ||||
Variable | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P-value |
Total fat (gr) | 93.81 ± 33.89 | 90.79 ± 27.57 | 91.05 ± 36.91 | 92.37 ± 37.07 | 0.061 |
Cholesterol (mg) | 314.90 ± 121.68 | 321.63 ± 134.70 | 298.63 ± 149.53 | 312.50 ± 123.13 | 0.815 |
Saturated fat (g) | 25.79 ± 9.18 | 26.02 ± 11.85 | 26.82 ± 10.41 | 26.08 ± 14.50 | 0.112 |
Trans fatty acid (g) | 0.0003 ± 0.0007 | 0.0007 ± 0.0002 | 0.0004 ± 0.001 | 0.0005 ± 0.001 | 0.332 |
MUFA (g) | 31.23 ± 14.66 | 30.58 ± 13.95 | 30.60 ± 9.66 | 31.80 ± 11.91 | 0.539 |
PUFA(g) | 25.58 ± 11.81 | 22.30 ± 8.11 | 18.92 ± 5.35 | 16.56 ± 4.79 | 0.034* |
Linoleic fatty acid (g) | 22.47 ± 10.85 | 22.31 ± 7.30 | 21.18 ± 4.65 | 23.89 ± 4.22 | 0.083 |
Linolenic fatty acid (g) | 1.46 ± 0.90 | 1.28 ± 0.65 | 1.25 ± 0.52 | 1.41 ± 0.49 | 0.093 |
AI: atherogenic index. MUFA: mono-unsaturated fatty acids. PUFA: poly-unsaturated fatty acids. Values reported as a mean ± standard deviation. P< 0.05 considered significant.
According to the findings of table 3 and reported odds ratio and 95% confidence intervals, no significant association was seen between AI score and PCOS risk (P > 0.05). Also no significant relationship was seen in regression model 1 and 2 (P = 0.408 and P = 0.380), after adjustment for age, BMI, energy, weight, WC, educational status, marital status, physical activity, taking multivitamin-mineral supplement, folic acid, omega-3 and vitamin D supplement consumption.
Table 3. The association of AI score with the risk of polycystic ovary syndrome.
AI | Quartile 1 | Quartile 2 | Quartile 3 | Quartile 4 | P-value |
Crude | 1 | 2.83 (1.69- 4.75) | 2.74 (1.75- 4.93) | 2.95 (2.13- 5.13) | 0.129 |
Model 1 | 1 | 2.81 (1.64- 4.79) | 2.98 (1.74- 5.11) | 2.97 (2.03- 5.18) | 0.408 |
Model 2 | 1 | 2.35 (1.29- 4.30) | 2.45 (1.37- 4.75) | 2.37 (1.54- 5.33) | 0.380 |
AI: atherogenic index. Data are odds ratio and 95% confidence interval. Logistic regression models were used: Model 1: Adjusted for age (yr) and BMI (Kg/m2) and energy (kcal). Model 2: Additional adjustment for weight, waist circumferences, educational status, marital status, physical activity, taking multivitamin-mineral supplement, folic acid supplement, omega-3 supplement and vitamin D supplement. P< 0.05 considered significant.
4.1. Discussion
There is a lack of literature about AI assessment of diet in PCOS women. In this regard we conducted present study to find if AI pose as an independent risk factor for PCOS. Our case-control study demonstrates that more PUFA is associated with lower AI. However, we didn’t find significant relationship between AI and PCOS risk. Based on evidences women with PCOS had higher risk of cardiovascular diseases and on average, had lower level of HDL-cholesterol, higher non HDL cholesterol and triglyceride levels (Wild, 2012). As Patel et al. indicates in their animal study, high fat diet results in metabolic complications and hormone disturbances similar to changes in PCOS patients including glucose intolerance, hyperinsulinemia, hyperandrogenism, increase thickness of follicular wall and number of cystic follicles (Patel and Shah, 2018).
Although we didn’t find significant results, several studies have demonstrated association of plasma AI with metabolic disturbances. A meta-analysis by Zhu et al. explore the relationship between lipid parameters and risk of type 2 diabetes mellitus (T2DM) (Zhu et al., 2015). Moreover, 2 cross-sectional studies by Cai et al. and Fernández-Macías et al. report a strong relationship between AI and coronary artery disease and CVD events (Cai et al., 2017, Fernández-Macías et al., 2019). Also, Wang et al. believed that AI can be used as an auxiliary tool for non-alcoholic fatty liver disease diagnosis in obese individuals (Qian et al., 2018). In addition, Qin et al. express AI of plasma as a comprehensive indicator for management of dyslipidemia in patients with T2DM (Qin et al., 2020). Although there are various studies available assessing AI of plasma, there is limited number of studies regarding AI of diet on different aspects of health.
We failed to find association between dietary AI and fatty acids intake, except for PUFA. Although the relationship between cardiovascular diseases and cholesterol intake are controversial, some evidences revealed that higher cholesterol intake is related to increasing level of serum cholesterol in susceptible responders. However, dietary cholesterol had no effect on normal individuals (Kapourchali et al., 2016, Fernandez, 2010). In a study by Zhu et al., dietary cholesterol reported as an indicator of dyslipidemia and also remarkably increased cholesterol level of serum (Zhu et al., 2018). Women with PCOS have heightened cardiovascular risks and decrease in SFA intake ameliorate cardiovascular complications in them (Graff et al., 2017). In addition, there is a novel mechanism by which SFA cause insulin signaling impairments due to expression of miRNAs (Min et al., 2018). In addition, SFAs are contributed to de novo lipogenesis in liver and cause insulin resistance (Roumans et al., 2020). Sekar et al. indicates that SFA develop metabolic syndrome (Sekar et al., 2017). It was previously reported by Kasim-Karakas and colleagues that cis fatty acids, compare to trans fatty acids, had improved hormonal and metabolic indicators in women with PCOS (Kasim-Karakas et al., 2004). In line with this finding, trans fatty acids had detrimental effects on ovulation by lowering peroxisome proliferator-activated receptors γ (PPAR) activity (Brown et al., 2003, Szczuko et al., 2016). Since we failed to find significant relationship between fatty acids intake and AI, future well designed studies, with larger samples size are recommended.
Our study has shown an inverse association between PUFA and AI. In a study by Barrea et al., adherence to Mediterranean diet was evaluated in women with PCOS and based on their results individuals with PCOS had higher quantity intake of SFA, and lower intake of PUFA and MUFA compare to controls (Barrea et al., 2019). PUFAs has been proven to be effective in lowering both triglyceride and VLDL concentrations through suppression of sterol regulatory element binding protein 1 (SREBP-1) expression and elevation of fatty acid oxidation by activating peroxisome proliferator-activated receptor (PPARa) (Lombardo et al., 2007). Gonza´lez-Pe´riz findings in an animal study showed that omega-3 PUFAs had beneficial effect in ameliorating hepatic steatosis and obesity-induced insulin resistance (González-Périz et al., 2009). Also, Derosa et al. indicates that 3 grams of omega-3 consumption in overweight and obese participants with impaired fasting glucose reduced hyperglycemia and might have effect in preventing the progression of type 2 diabetes (Derosa et al., 2016).
Furthermore, Yahay and colleagues investigated the effect of canola and olive oils consumption compared to sunflower oil in PCOS women. based on the results, canola oil consumption improves liver function, lipid profile, and HOMA-IR compare to olive and sunflower oils (Yahay et al., 2021). The possible explanation for beneficial effects of canola oil includes high content of MUFAs, ALA, and lower n6/n3 PUFA (Childs et al., 2014). Moreover, a study by Maki et al. showed that corn oil, as a rich source of linoleic acid, improve lipoprotein profile vs olive oil (Maki et al., 2015). Some available data claimed that dietary intake and circulating status of LA negatively correlate with cardiovascular risk factors, mainly based on cholesterol-lowering effect and improving glucose metabolism (Marangoni et al., 2020).
In total, the composition of the macronutrients in the diet especially fatty acids may play an essential role in modifying several aspects of life, so evaluating fatty acids indices including AI should be of interest. As Moussavi Javardi and colleagues proposes, there is a direct association between dietary AI with AI of plasma in overweight/obese volunteers compare to normal participants (Javardi et al., 2020). Clinicians should be aware of these association and consider it in further studies.
The current study is the first to explore the association between AI of diet and PCOS, with a relatively large sample size. In addition, we use multiple logistic regression models with adjustment of several confounding factors, which considers as strengths of our study. However, the present study has potential limitations. We were unable to assess lipid profiles of participants due to financial constraints. Moreover, we were unable to find causal relationship due to design of our study and the risk of recall bias is always probable due to using FFQ.
4.2. Conclusion
To conclude, our findings indicated no significant association between dietary AI and PCOS risk. However, more studies in future are needed to help making dietary recommendations for alleviating this disease complications.
Acknowledgments
Availability of data and materials
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due privacy and ethical considerations.
Consent for publication
The authors give Health and Nutrition full consent to publish. Consent for publication form can be made available upon request.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical Approval
The study protocol was approved by ethical committee of Islamic Azad University, Tehran, Iran (code: IR.IAU.SRB.REC.13988.028) and all participants filled out the written informed consent.
Authors Contribution
“Parisa Navidgoui: contributed to writing, data collection, and data analysis.” “Sedigheh Hosseini: assisted in data collection.” “Behnood Abassi: contributed to the writing, concept, and study design, as well as data analysis.”
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
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