ABSTRACT
Background:
Polycystic ovary syndrome (PCOS) is one of the most common endocrinopathies affecting women in reproductive age group. The interrelationship of serum homocysteine, homoeostatic assessment of insulin resistance (HOMA-IR) and body mass index amongst overweight, obese and non-obese PCOS patients is not fully established.
Aims:
We aimed to study the correlation of serum homocysteine levels and hyperinsulinaemia with body mass index (BMI) in PCOS patients.
Study Setting and Design:
This was a case–control study in which 35 women with PCOS and 35 non-PCOS women acting as controls were enrolled.
Materials and Methods:
Cases were identified by Rotterdam's criteria. (IR) indices, HOMA determination and serum homocysteine levels were determined and their correlation with BMI was studied.
Statistical Analysis Used:
Student's t-test and analysis of variance test were used for statistical analysis. The Pearson correlation coefficient was then used to estimate the correlation.
Results:
On overall evaluation, a significant positive correlation of fasting insulin, HOMA-IR and serum homocysteine) was observed (P < 0.05), however, on evaluating the correlation of these markers independently in cases and controls, only fasting insulin and HOMA-IR showed a significant correlation. In a multivariate model where PCOS was considered a dependent variable with age, fasting glucose, HOMA-IR, serum homocysteine and body mass index as the independent variables, only serum homocysteine levels were found to be significantly associated with the dependent variable (odds ratio = 1.172; 95% confidence interval = 1.032–1.330).
Conclusion:
PCOS women had significantly higher mean fasting glucose, fasting insulin, HOMA-IR and homocysteine levels as compared to non-PCOS controls. Mean HOMA-IR, homocysteine and fasting insulin levels showed a significant incremental trend with increasing BMI category in overall evaluation as well as in cases and controls independently.
KEYWORDS: Body mass index, homocysteine, insulin resistance, polycystic ovary syndrome
INTRODUCTION
Polycystic ovary syndrome (PCOS) is one of the most common endocrinopathies affecting women in reproductive age group and is a significant cause of stress.[1] It affects nearly 5%–10% of women of reproductive age. PCOS is characterised by oligo- or anovulation, clinical or biochemical hyperandrogenaemia and/or polycystic ovaries on ultrasonography.[2] It has been shown to be associated with type 2 diabetes mellitus, increased risk of cardiovascular disease[3,4] and endometrial cancer.[5] In view of the absence of an ideal explanatory model for pathogenesis of PCOS, a number of possible factors responsible for pathogenesis have been suggested. These include genetic factors, intrauterine exposures, environmental/lifestyle factors and obesity.[6]
PCOS is characterised by hyperandrogenaemia and hyperinsulinaemia.[7] The high levels of androgens lead to chronic anovulation, menstrual disturbances and hirsutism. These patients typically have insulin resistance (IR). The hyperandrogenism is closely tied with the IR, and the decline in insulin levels leads to decreased androgen production.[8,9,10] The associated IR leads to increased risk for developing glucose intolerance, type 2 diabetes mellitus and gestational diabetes mellitus.[11,12]
Clinical manifestations of PCOS symptoms are often preceded by a history of weight gain.[13] Overweight and obese women have been reported to have a high prevalence of PCOS.[14] Between 40% and 80% of women with this condition are reported to be overweight or obese.[15] Nearly 40% of women with PCOS are expected to have impaired glucose tolerance or overt type 2 diabetes, a finding that is consistently seen across several geographic areas and ethnic groups.[16] Moreover, women with PCOS are more likely to have IR,[17] central adiposity, dyslipidaemia and hypertension.[18] Other markers of cardiovascular disease such as C-reactive protein[19] and homocysteine[20,21] have also been found to be elevated in women with PCOS.
Homocysteine, a sulfhydryl-containing amino acid, is an intermediate product in the normal biosynthesis of the amino acids methionine and cysteine.[22] Elevated levels of circulating homocysteine increase the risk for developing atherothrombotic coronary artery disease, peripheral vascular disease, myocardial infarction (MI) and stroke.[23] Interestingly, obesity, hyperinsulinaemia and hyperhomocysteinaemia have been shown to be associated with an increased risk of cardiovascular disease. Moreover, studies have shown a significant association between obesity and IR,[24,25,26] obesity and homocysteine levels[27,28] and insulin and homocysteine levels.[29,30] However, whether these three cardiovascular risk factors having a high prevalence in PCOS are also interrelated is not established fully. It is also of interest to study the relationship between insulin and homocysteine levels with respect to body mass index (BMI) status of the PCOS women. Hence, the present study was planned to evaluate the correlation of serum homocysteine levels and hyperinsulinaemia with BMI in PCOS patients visiting a facility in North India.
MATERIALS AND METHODS
It was a case–control study done in the Department of Gynaecology, from June 2019 to May 2020, approved by the Institutional Ethical Committee (RCell EC/2019/112 dated 15 May 2019).
Inclusion criteria
Cases
The patients were included on the basis of Rotterdam diagnostic criteria of PCOS.
Two of the following three criteria are required:
Oligo/anovulation,
hyperandrogenism-clinical (hirsutism or less commonly male pattern Alopecia) or biochemical (Raised testosterone)
Polycystic ovaries on Ultrasound i.e presence of at least 12 follicles of 2-9 mm diameter and/or increased ovarian volume 10ml.
Controls
Age-matched women without PCOS were included in the study.
Exclusion criteria
Patients with pregnancy, hyperprolactenaemia, thyroid dysfunction, hypertension, gastrectomy, on medications for treatment of cardiovascular and coronary heart disease, patients on folate antagonist (methotrexate), phenytoin, carbamazepine and those on anti-obesity 27 drugs, contraceptive pills, smokers and chronic 28 alcoholics were excluded from the study.
Sample size calculation
Sample size was calculated on the basis of variation in serum homocysteine, plasma insulin levels and BMI to be studied as described by Esmaielzadeh et al.[31] Thus, a total of 70 participants – 35 each in case and control groups, respectively – were enrolled in the study.
All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional and/or National Research Committee and with the Helsinki Declaration (2013).
Written informed consent was obtained from all individual participants included in the study. The cases and controls underwent thorough clinical and biochemical investigations necessary to ascertain their fulfilment of sampling criteria. Age of women was noted and their weight and height were measured. The body mass index was calculated using the following formula:
BMI = weight in kg/height in m2
On the basis of body mass index, the women were categorised into the following categories using the definitions proposed by the WHO for Asian population.[32] Fasting glucose, insulin and homocysteine levels were measured by direct chemiluminescence immunoassay. IR was measured in terms of homoeostatic assessment of insulin resistance (HOMA-IR), which was calculated using the following formula:
HOMA-IR = fasting insulin (microU/L) × fasting glucose (nmol/L)/22.5.
For homocysteine and fasting insulin, the normal range was taken as 4.44–13.56 μmol/L and 2.60–24.90 μU/mL, respectively.[33] Homocysteine value <4.44 μmol/L was considered hypohomocysteinaemia, whereas a value >13.56 μmol/L was considered hyperhomocysteinaemia. For fasting insulin, a value <2.60 μU/mL was considered hypoinsulinaemia and a value >13.56 μU/mL was considered hyperinsulinaemia. IR was considered at HOMA-IR value >2.5.[23]
Statistical analysis
The statistical analysis was done using SPSS (Statistical Package for the Social Sciences) version 21.0 Statistical Analysis Software. The values were represented in number (%) and mean ± standard deviation. Student's t-test was used to test the significance of two means and to compare the scores of each of the measures and some of the parameter data between the groups. Analysis of variance test was used to compare the within-group and between-group variances amongst the study groups. The Pearson correlation coefficient was then used to estimate of the correlation between paired data sets and its value ranges from –1 to + 1, with +1 indicating perfect positive correlation and –1 indicating perfect negative correlation. A P value of ≤0.05 was considered statistically significant.
RESULTS
Cases had significantly higher mean fasting glucose and insulin levels (94.06 mg/dl ± 10.27 mg/dl and 23.35 μU/ml ± 33.35 μU/ml, respectively) as compared to that of controls (81.77 mg/dl ± 8.82 mg/dl and 11.09 μU/ml ± 12.05 μU/ml, respectively) (P < 0.05). Mean HOMA-IR and homocysteine levels were 5.61 ± 8.56 and 17.93 μmol/L ± 9.86 μmol/L, respectively, in cases and 2.29 ± 2.63 and 10.32 μmol/L ± 5.95 μmol/L, respectively, in controls; both the values were significantly higher in cases as compared to that in controls. The prevalence of hyperinsulinaemia, IR and hyperhomocysteinaemia was 11.4%, 65.7% and 62.9%, respectively, in cases as compared to 5.7%, 28.6% and 20%, respectively, in controls. A statistically significant difference between the two groups was observed for IR and hyperhomocysteinaemia, respectively (P < 0.05) [Table 1].
Table 1.
Comparison of characteristics of cases and controls
| Characteristic | Cases (n=35) | Controls (n=35) | Significance of difference |
|---|---|---|---|
| Mean age±SD (years) | 23.94±4.45 | 23.83±4.64 | t=0.105; P=0.917 |
| Mean BMI±SD (kg/m2) | 27.61±5.74 | 25.37±4.35 | t=1.842; P=0.070 |
| BMI category (kg/m2), n (%) | |||
| Normal weight (18.5-22.9) | 9 (25.7) | 10 (28.6) | χ2=0.545; P=0.762 |
| Overweight (23-24.9) | 10 (28.6) | 12 (34.3) | |
| Obese (>25.0) | 16 (45.7) | 13 (37.1) | |
| Mean fasting glucose±SD (mg/dl) | 94.06±10.27 | 81.77±8.82 | t=5.369; P<0.001 |
| Mean fasting insulin±SD (µmol/L) | 23.35±33.35 | 11.09±12.05 | t=2.034; P=0.046 |
| Hyperinsulinaemia, n (%) | 4 (11.4) | 2 (5.7) | χ2=0.729; P=0.393 |
| Mean HOMA-IR±SD | 5.61±8.56 | 2.29±2.63 | t=2.191; P=0.032 |
| Insulin resistance, n (%) | 23 (65.7) | 10 (28.6) | χ2=9.689; P=0.002 |
| Mean HCy±SD (µmol/L) | 17.93±9.86 | 10.32±5.95 | t=3.911; P<0.001 |
| Hyperhomocysteinaemia, n (%) | 22 (62.9) | 7 (20.0) | χ2=13.25; P<0.001 |
SD=Standard deviation, BMI=Body mass index, HOMA-IR=Homoeostatic assessment of insulin resistance
On overall evaluation, there was a significant incremental trend of IR with increasing body mass index (P < 0.001). On comparing the cases and controls for prevalence of IR amongst different BMI categories, although the prevalence of IR was found to be higher in cases as compared to controls in all the BMI categories, this difference was significant statistically for overweight and obese categories (P < 0.05) but not for normal weight category (P = 0.115). The prevalence of hyperinsulinaemia showed a significant difference amongst different BMI categories (P = 0.010). Hyperhomocysteinaemia was found to be higher in cases as compared to that in controls in all the BMI categories, however, the difference was significant statistically only for overweight and obese categories (P < 0.05) [Table 2]. On evaluating the correlation of BMI with different markers, on overall evaluation, a significant positive correlation of all the four markers (fasting glucose, fasting insulin, HOMA-IR and serum homocysteine) was observed (P < 0.05), however, on evaluating the correlation of these markers independently in cases and controls, the only fasting insulin and HOMA-IR showed a significant correlation [Table 3]. In a multivariate model where PCOS was considered a dependent variable with age, fasting glucose, HOMA-IR, serum homocysteine and body mass index as the independent variables, only serum homocysteine levels were found to be significantly associated with the dependent variable (odds ratio = 1.172; 95% confidence interval = 1.032–1.330). The predicted model had a high classifying ability with sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 82.9%, 80%, 80.6%, 77.8% and 81.4%, respectively [Table 4].
Table 2.
Comparison of insulin resistance, hyperinsulinaemia and hyperhomocysteinaemia in relation to different body mass index categories
| Insulin resistance (HOMA-IR >2.5) | |||||||
|---|---|---|---|---|---|---|---|
|
| |||||||
| Weight category | Overall | Cases | Controls | Statistical significance | |||
|
|
|
|
|
||||
| Total | IR (%) | Total | IR (%) | Total | IR (%) | χ2-test (cases vs. controls) | |
| Normal | 19 | 2 (10.5) | 9 | 2 (22.2) | 10 | 0 | χ2=2.484; P=0.115 |
| Overweight | 22 | 6 (27.3) | 10 | 5 (50.0) | 12 | 1 (8.3) | χ2=4.774; P=0.029 |
| Obese | 29 | 25 (86.2) | 16 | 16 (100) | 13 | 9 (69.2) | χ2=5.711; P=0.017 |
| Inter-BMI category statistical significance | χ2=31.47; P<0.001 | χ2=17.0; P<0.001 | χ2=16.939; P<0.001 | ||||
|
| |||||||
| Hyperinsulinaemia | |||||||
|
| |||||||
| Weight category | Overall | Cases | Controls | Statistical significance | |||
|
|
|
|
|
||||
| Total | Hyper insulin | Total | Hyper insulin | Total | Hyper insulin | Cases versus controls (Student’s t-test) | |
|
| |||||||
| Normal | 19 | 0 | 9 | 0 | 10 | 0 | - |
| Overweight | 22 | 0 | 10 | 0 | 12 | 0 | - |
| Obese | 29 | 6 (20.7%) | 16 | 4 (25.0%) | 13 | 2 (15.4%) | χ2=0.404; P=0.525 |
| Inter-BMI category statistical significance | χ2=9.278; P=0.010 | χ2=5.363; P=0.068 | χ2=3.590; P=0.166 | ||||
|
| |||||||
| Hyperhomocysteinaemia | |||||||
|
| |||||||
| Weight category | Overall | Cases | Controls | Cases versus controls | |||
|
|
|
|
|||||
| Total | Hyper HCy (%) | Total | Hyper HCy (%) | Total | Hyper HCy (%) | ||
|
| |||||||
| Normal | 19 | 2 (10.5) | 9 | 1 (11.1) | 10 | 1 (10.0) | χ2=0.006; P=0.937 |
| Overweight | 22 | 10 (45.5) | 10 | 7 (70.0) | 12 | 3 (25.0) | χ2=4.455; P=0.035 |
| Obese | 29 | 17 (58.6) | 16 | 14 (87.5) | 13 | 3 (23.1) | χ2=12.272; P<0.001 |
| Inter-BMI category statistical significance | χ2=11.157; P=0.004 | χ2=14.702; P=0.001 | χ2=0.889; P=0.641 | ||||
BMI=Body mass index, HOMA-IR=Homoeostatic assessment of insulin resistance, IR=Insulin resistance
Table 3.
Correlation of body mass index with fasting glucose, fasting insulin, homoeostatic assessment of insulin resistance and serum homocysteine levels
| Parameter | Overall (n=70) | Cases (n=35) | Controls (n=35) | |||
|---|---|---|---|---|---|---|
|
|
|
|
||||
| r | P | r | P | r | P | |
| Fasting glucose | 0.283 | 0.018 | 0.190 | 0.274 | 0.192 | 0.268 |
| Fasting insulin | 0.381 | 0.001 | 0.353 | 0.038 | 0.372 | 0.028 |
| HOMA-IR | 0.373 | 0.001 | 0.348 | 0.040 | 0.360 | 0.034 |
| Serum Homocysteine | 0.356 | 0.002 | 0.296 | 0.084 | 0.281 | 0.102 |
HOMA-IR=Homoeostatic assessment of insulin resistance
Table 4.
Multivariate binary logistic regression analysis showing polycystic ovary syndrome as a dependent variable on independent variables age, body mass index, serum fasting glucose, serum fasting insulin and serum homocysteine levels
| Variables | B | SE | Wald statistic | P | OR | 95.0% CI for OR | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Lower | Upper | ||||||
| Age | 0.044 | 0.068 | 0.407 | 0.524 | 1.045 | 0.914 | 1.194 |
| Fasting glucose | 0.093 | 0.060 | 2.411 | 0.120 | 1.098 | 0.976 | 1.234 |
| Fasting insulin | −0.107 | 0.353 | 0.091 | 0.763 | 0.899 | 0.450 | 1.796 |
| HOMA-IR | 0.522 | 1.594 | 0.107 | 0.743 | 1.685 | 0.074 | 38.340 |
| Serum homocysteine | 0.158 | 0.065 | 5.977 | 0.014 | 1.172 | 1.032 | 1.330 |
| BMI | −0.032 | 0.079 | 0.159 | 0.690 | 0.969 | 0.829 | 1.132 |
| Constant | −10.626 | 5.612 | 3.585 | 0.058 | <0.001 | ||
|
| |||||||
| Classification table based on binary logistic regression | |||||||
| Projected group | Group | Total | |||||
|
| |||||||
| PCOS | Controls | ||||||
|
| |||||||
| PCOS | 29 | 7 | 36 | ||||
| Controls | 6 | 28 | 34 | ||||
|
| |||||||
| Classifying efficacy of predictive model | |||||||
|
| |||||||
| Sensitivity | Specificity | PPV | NPV | Accuracy | |||
|
| |||||||
| 82.9 | 80.0 | 80.6 | 77.8 | 81.4 | |||
BMI=Body mass index, SE=Standard error, OR=Odds ratio, CI=Confidence interval, HOMA-IR=Homoeostatic assessment of insulin resistance, PCOS=Polycystic ovary syndrome, PPV=Positive predictive value, NPV=Negative predictive value
DISCUSSION
In the present study, the purpose was to study the relationship of serum homocysteine, IR and serum homocysteine levels in PCOS patients. Three major considerations were kept in mind while planning the study – (i) whether insulin and homocysteine levels in PCOS women are different from age- and BMI-matched non-PCOS women, (ii) whether insulin and homocysteine levels show a relationship with BMI and (iii) how does presence of PCOS affect the relationship of BMI, homocysteine and IR. For these considerations, a stratified sampling design with adequate representation of PCOS women in different body mass index categories was the best choice,[34,35] however, owing to practical difficulties, such a design could not be adopted, and within PCOS women, the representation of different body mass index categories was governed randomly. A study of body mass index, IR and homocysteine levels amongst PCOS women has been done amongst a diverse profile of patients in different studies. Table 5 highlights the findings of some of the contemporary studies and compares them with the present study. An overview of Table 5 shows that the age of cases ranges from as low as 23.13 ± 4.28 years to as high as 31.09 ± 8.9 years[36] while the mean BMI of cases ranged from 22.31 kg/m ± 5.04 kg/m2 to 29.95 kg/m ± 4.91 kg/m2 (Esmailzadeh et al.[31]). In studies having matched age and BMI as well as in studies having matched age but higher BMI of PCOS women, the serum homocysteine levels were found to be significantly higher in PCOS women as compared to controls, thus indicating that presence of PCOS itself was a strong factor associated with increased homocysteine levels and thus increased homocysteine levels in PCOS women cannot be attributed just to the increased BMI. Only one study, Bagheri et al.[35] despite having cases with significantly higher BMI as compared to controls, did not find a significant difference in mean homocysteine levels. Most of the studies reported above have also evaluated fasting insulin and HOMA-IR between two groups and have reported these values to be significantly higher in the case group as compared to controls as observed in the present study,[31,37,39] thus implying that irrespective of the BMI status, it is primarily the PCOS status that affects the homocysteine levels in PCOS. Esmailzadeh et al.[31] also showed that there was a tendency towards hyperhomocysteinaemia and hyperinsulinaemia in PCOS patients, with as high as 76.7% of cases being insulin resistant, thus indicating a relationship between hyperhomocysteinaemia and IR. All these findings indicate that the trends of IR and homocysteinaemia are affected by PCOS presence. We have found that mean HOMA-IR, homocysteine and fasting insulin levels showed a significant incremental trend with increasing BMI category in overall evaluation as well as in cases and controls, underlining the two facts – first, presence of PCOS itself was a strong factor affecting IR and homocysteine levels, and second, BMI does have some relationship with IR and homocysteine levels. Moreover, an existence of relationship between BMI, IR and homocysteine independently in cases as well as controls showed that this relationship is universal irrespective of the PCOS status. This implies that obese patients with PCOS are at a multiplied risk of developing hyperhomocysteinaemia and IR and thus in turn cardiovascular disease. The relationship of obesity with IR and homocysteine levels extends beyond PCOS, as have also been shown by previous studies.[34,35] The findings of the present study replicate these previous studies with respect to their relationship with homocysteine levels between cases and controls in different BMI categories.
Table 5.
Body mass index, insulin resistance and homocysteine in polycystic ovary syndrome as observed in different contemporary studies compared to the present study
| Author (year), location | Sample size and study design | Characteristic | Cases | Controls |
|---|---|---|---|---|
| Bagheri et al. (2015), Iran[35] | Case-control (52 cases, 104 controls) | Age (years) | 24.27±3.75 | 25.62±4.32 |
| BMI (kg/m2) | 25.80±6.67* | 22.60±2.87 | ||
| Insulin (µIU/ml) | 16.62±7.45* | 12.04±4.24 | ||
| HOMA | 33.72±14.74* | 44.56±16.94 | ||
| Homocysteine (µmol/l) | 12.21±4.55 | 13.68±4.31 | ||
| Al-Gareeb et al. (2016), Iraq[36] | Case-control (101 cases, 106 controls) | Age (years) | 24.86 | 25.41 |
| BMI (kg/m2) | 60% >25* | 22.6% >25 | ||
| Homocysteine (µmol/l) | 11.5±5.41* | 8.10±1.89 | ||
| Esmaielzadeh et al. (2017), Iraq[31] | Case-control (60 cases, 20 controls) | Age (years) | 23.65±5.08 | 23.73±3.85 |
| BMI (kg/m2) | 29.95±4.91* | 26.09±4.80 | ||
| Insulin (µU/dl) | 17.80±8.02* | 11.55±6.81 | ||
| HOMA-IR | 3.91±1.86* | 2.49±1.63 | ||
| Homocysteine (µmol/l) | 11.68±2.92* | 9.31±2.92 | ||
| Saadeh et al. (2018), Iraq[37] | Case-control (154 cases, 151 controls) | Age (years) | 23.85±5.12 | 24.35±5.10 |
| BMI (kg/m2) | 26.57±5.98 | 26.23±5.82 | ||
| Homocysteine (µmol/l) | 29.03±11.80* | 14.23±5.53 | ||
| Suleiman and Sulaiman (2018), Jordan[38] | Case-control (50 cases, 40 controls) | Age (years) | 26.88±6.76 | 27.17±5.24 |
| BMI (kg/m2) | 27.17±5.24* | 24.24±3.39 | ||
| Homocysteine (µmol/l) | 17.00±4.00* | 9.00±2.15 | ||
| Present study (2020), Lucknow, India | Case-control (35 cases, 35 controls) | Age (years) | 23.94±4.45 | 23.83±4.64 |
| BMI (kg/m2) | 27.61±5.74 | 25.37±4.35 | ||
| Insulin (µU/dl) | 23.35±33.35* | 11.09±12.05 | ||
| HOMA-IR | 5.61±8.56* | 2.29±2.63 | ||
| Homocysteine (µmol/l) | 17.93±9.86* | 10.32±5.95 |
*A significant difference between cases and controls, BMI=Body mass index, HOMA-IR=Homoeostatic assessment of insulin resistance
Some of the previous studies have also evaluated relationship of BMI with these parameters in linear terms. Guzelmeric et al.[40] in their study also found a significant mild positive correlation of homocysteine with BMI (r = 0.349) which is close to the value obtained in the present study (r = 0.356, overall, r = 0.296 PCOS group). In another study, Esmailzadeh et al.[31] also observed a near-moderate (r = 0.46) correlation of BMI with HOMA-IR. In the present study, we found this correlation to be 0.373 in overall and 0.348 in PCOS women. In the present study, we also found a significant correlation of BMI with fasting insulin (r = 0.381 – overall, r = 0.353 – PCOS).
The present study had certain limitations; owing to limitation of sample size, the stratification of different BMI categories could not be done at a level at which the differences based on BMI could be elaborated statistically in a more convincing manner. Moreover, the analytical case–control nature of the study was a barrier in exploring the multifactorial relationship of PCOS, BMI, IR and hyperhomocysteinaemia. Consecutively, dietary factors and nutritional deficiencies also have a role in determination of PCOS and Vitamin B12 levels. Inclusion of such parameters could have helped in understanding the relationship of PCOS, BMI, IR and hyperhomocysteinaemia in a better way. Nevertheless, within these limitations, the findings of the present study showed that both BMI and presence of PCOS were linked with IR as well as hyperhomocysteinaemia.
CONCLUSION
PCOS women had significantly higher mean fasting glucose, fasting insulin, HOMA-IR and homocysteine levels as compared to non-PCOS controls. The prevalence of IR and hyperhomocysteinaemia was significantly higher in PCOS women as compared to non-PCOS controls. Mean HOMA-IR, homocysteine and fasting insulin levels showed a significant incremental trend with increasing BMI category in overall evaluation as well as in cases and controls independently. Fasting insulin and HOMA-IR also showed a significantly mild positive correlation with BMI in cases and controls when evaluated individually.
Data availability statement
All the data are available with the corresponding author and will be willingly shared on reasonable requests.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
REFERENCES
- 1.Zacur HA. Epidemiology, clinical manifestations and pathophysiology of polycystic ovary syndrome. Adv Stud Med. 2003;3:S733–9. [Google Scholar]
- 2.Zadehmodarres S, Heidar Z, Razzaghi Z, Ebrahimi L, Soltanzadeh K, Abed F. Anti-mullerian hormon level and polycystic ovarian syndrome diagnosis. Iran J Reprod Med. 2015;13:227–30. [PMC free article] [PubMed] [Google Scholar]
- 3.Amowitz LL, Sobel BE. Cardiovascular consequences of polycystic ovary syndrome. Endocrinol Metab Clin North Am. 1999;28:439–58. doi: 10.1016/s0889-8529(05)70079-7. viii. [DOI] [PubMed] [Google Scholar]
- 4.Dokras A. Cardiovascular disease risk factors in polycystic ovary syndrome. Semin Reprod Med. 2008;26:39–44. doi: 10.1055/s-2007-992923. [DOI] [PubMed] [Google Scholar]
- 5.Dahlgren E, Friberg LG, Johansson S, Lindström B, Odén A, Samsioe G, et al. Endometrial carcinoma; ovarian dysfunction – A risk factor in young women. Eur J Obstet Gynecol Reprod Biol. 1991;41:143–50. doi: 10.1016/0028-2243(91)90092-y. [DOI] [PubMed] [Google Scholar]
- 6.King J. Polycystic ovary syndrome. J Midwifery Womens Health. 2006;51:415–22. doi: 10.1016/j.jmwh.2006.01.008. [DOI] [PubMed] [Google Scholar]
- 7.Stadtmauer LA, Wong BC, Oehninger S. Should patients with polycystic ovary syndrome be treated with metformin? Benefits of insulin sensitizing drugs in polycystic ovary syndrome-beyond ovulation induction. Hum Reprod. 2002;17:3016–26. doi: 10.1093/humrep/17.12.3016. [DOI] [PubMed] [Google Scholar]
- 8.Barbieri RL, Smith S, Ryan KJ. The role of hyperinsulinemia in the pathogenesis of ovarian hyperandrogenism. Fertil Steril. 1988;50:197–212. doi: 10.1016/s0015-0282(16)60060-2. [DOI] [PubMed] [Google Scholar]
- 9.Dunaif A, Graf M, Mandeli J, Laumas V, Dobrjansky A. Characterization of groups of hyperandrogenic women with acanthosis nigricans, impaired glucose tolerance, and/or hyperinsulinemia. J Clin Endocrinol Metab. 1987;65:499–507. doi: 10.1210/jcem-65-3-499. [DOI] [PubMed] [Google Scholar]
- 10.Franks S. Polycystic ovary syndrome: A changing perspective. Clin Endocrinol (Oxf) 1989;31:87–120. doi: 10.1111/j.1365-2265.1989.tb00457.x. [DOI] [PubMed] [Google Scholar]
- 11.Utiger RD. Insulin and the polycystic ovary syndrome. N Engl J Med. 1996;335:657–8. doi: 10.1056/NEJM199608293350909. [DOI] [PubMed] [Google Scholar]
- 12.Dahlgren E, Janson PO, Johansson S, Lapidus L, Odén A. Polycystic ovary syndrome and risk for myocardial infarction. Evaluated from a risk factor model based on a prospective population study of women. Acta Obstet Gynecol Scand. 1992;71:599–604. doi: 10.3109/00016349209006227. [DOI] [PubMed] [Google Scholar]
- 13.Legro RS, Kunselman AR, Dodson WC, Dunaif A. Prevalence and predictors of risk for type 2 diabetes mellitus and impaired glucose tolerance in polycystic ovary syndrome: A prospective, controlled study in 254 affected women. J Clin Endocrinol Metab. 1999;84:165–9. doi: 10.1210/jcem.84.1.5393. [DOI] [PubMed] [Google Scholar]
- 14.Isikoglu M, Berkkanoglu M, Cemal H, Ozgur K. Polycystic ovary syndrome: What is the role of obesity? In: Allahbadia GN, Agrawal R, editors. Polycystic Ovary Syndrome. Kent, UK: Anshan, Ltd; 2007. pp. 157–63. [Google Scholar]
- 15.Alvarez-Blasco F, Botella-Carretero JI, San Millán JL, Escobar-Morreale HF. Prevalence and characteristics of the polycystic ovary syndrome in overweight and obese women. Arch Intern Med. 2006;166:2081–6. doi: 10.1001/archinte.166.19.2081. [DOI] [PubMed] [Google Scholar]
- 16.Sam S. Obesity and polycystic ovary syndrome. Obes Manag. 2007;3:69–73. doi: 10.1089/obe.2007.0019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Reaven GM. Role of insulin resistance in human disease (syndrome X): An expanded definition. Annu Rev Med. 1993;44:121–31. doi: 10.1146/annurev.me.44.020193.001005. [DOI] [PubMed] [Google Scholar]
- 18.Dunaif A, Segal KR, Futterweit W, Dobrjansky A. Profound peripheral insulin resistance, independent of obesity, in polycystic ovary syndrome. Diabetes. 1989;38:1165–74. doi: 10.2337/diab.38.9.1165. [DOI] [PubMed] [Google Scholar]
- 19.Talbott E, Guzick D, Clerici A, Berga S, Detre K, Weimer K, et al. Coronary heart disease risk factors in women with polycystic ovary syndrome. Arterioscler Thromb Vasc Biol. 1995;15:821–6. doi: 10.1161/01.atv.15.7.821. [DOI] [PubMed] [Google Scholar]
- 20.Kelly CC, Lyall H, Petrie JR, Gould GW, Connell JM, Sattar N. Low grade chronic inflammation in women with polycystic ovarian syndrome. J Clin Endocrinol Metab. 2001;86:2453–5. doi: 10.1210/jcem.86.6.7580. [DOI] [PubMed] [Google Scholar]
- 21.Schachter M, Raziel A, Friedler S, Strassburger D, Bern O, Ron-El R. Insulin resistance in patients with polycystic ovary syndrome is associated with elevated plasma homocysteine. Hum Reprod. 2003;18:721–7. doi: 10.1093/humrep/deg190. [DOI] [PubMed] [Google Scholar]
- 22.Loverro G, Lorusso F, Mei L, Depalo R, Cormio G, Selvaggi L. The plasma homocysteine levels are increased in polycystic ovary syndrome. Gynecol Obstet Invest. 2002;53:157–62. doi: 10.1159/000058367. [DOI] [PubMed] [Google Scholar]
- 23.Faeh D, Chiolero A, Paccaud F. Homocysteine as a risk factor for cardiovascular disease: Should we (still) worry about? Swiss Med Wkly. 2006;136:745–56. doi: 10.4414/smw.2006.11283. [DOI] [PubMed] [Google Scholar]
- 24.Loscalzo J, Handy DE. Epigenetic modifications: Basic mechanisms and role in cardiovascular disease (2013 Grover Conference series) Pulm Circ. 2014;4:169–74. doi: 10.1086/675979. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Sachan R, Patel M L, Gupta P, Diwakar A, Sachan P, Natu S M. Correlation between elevated homocysteine levels and insulin resistance in infertile women with or without polycystic ovary syndrome in North Indian population. Int J Med Med Sci. 2013;5:116–23. [Google Scholar]
- 26.World Health Organization (WHO). International Association for the Study of Obesity (IASO) and International Obesity Task Force (IOTF). The Asia-Pacific Perspective: Redefining Obesity and its Treatment. Geneva: World Health Organization; 2000. pp. 378–420. [Google Scholar]
- 27.Geloneze B, Vasques AC, Stabe CF, Pareja JC, Rosado LE, Queiroz EC, et al. HOMA1-IR and HOMA2-IR indexes in identifying insulin resistance and metabolic syndrome: Brazilian Metabolic Syndrome Study (BRAMS) Arq Bras Endocrinol Metabol. 2009;53:281–7. doi: 10.1590/s0004-27302009000200020. [DOI] [PubMed] [Google Scholar]
- 28.Hankey GJ, Eikelboom JW. Homocysteine and vascular disease. Lancet. 1999;354:407–13. doi: 10.1016/S0140-6736(98)11058-9. [DOI] [PubMed] [Google Scholar]
- 29.Lanzone A, Fulghesu AM, Andreani CL, Apa R, Fortini A, Caruso A, et al. Insulin secretion in polycystic ovarian disease: Effect of ovarian suppression by GnRH agonist. Hum Reprod. 1990;5:143–9. doi: 10.1093/oxfordjournals.humrep.a137058. [DOI] [PubMed] [Google Scholar]
- 30.Kaya C, Cengiz SD, Satiroğlu H. Obesity and insulin resistance associated with lower plasma vitamin B12 in PCOS. Reprod Biomed Online. 2009;19:721–6. doi: 10.1016/j.rbmo.2009.06.005. [DOI] [PubMed] [Google Scholar]
- 31.Esmaielzadeh S, Tahmasbpour E, Gholinezhad-Chari M. Hyperhomocysteinemia, insulin resistance and body mass index in Iranian young women with polycystic ovary syndrome. Middle East Fertil Soc J. 2017;22:149–55. [Google Scholar]
- 32.Salehpour S, Manzor-Al-Ajdad O, Samani EN, Abadi A. Evaluation of homocysteine levels in patients with polycystic ovarian syndrome. Int J Fertil Steril. 2011;4:168–71. [PMC free article] [PubMed] [Google Scholar]
- 33.Hemati T, Moghadami-Tabrizi N, Davari-Tanha F, Salmanian B, Javadian P. High plasma homocysteine and insulin resistance in patients with polycystic ovarian syndrome. Iran J Reprod Med. 2011;9:223–8. [PMC free article] [PubMed] [Google Scholar]
- 34.Gupta MM, Chari S, Chandankhede M, Ghike S. Insulin resistance and homocysteine levels in patients with polycystic ovarian syndrome. J South Asian Fed Obstet Gynaecol. 2013;5:49–51. [Google Scholar]
- 35.Bagheri M, Sohrabvand F, Lankarani M, Zandieh Z, Haghollahi F, Shariat M. Comparison of biomedical variables in PCOS patients with normal Iranian Women. J Family Reprod Health. 2015;9:5–11. [PMC free article] [PubMed] [Google Scholar]
- 36.Al-Gareeb AI, Abd Al-Amieer WS, Alkuraishy HM, Al-Mayahi TJ. Effect of body weight on serum homocysteine level in patients with polycystic ovarian syndrome: A case control study. Int J Reprod Biomed. 2016;14:81–8. [PMC free article] [PubMed] [Google Scholar]
- 37.Saadeh N, Alfaqih MA, Mansour H, Khader YS, Saadeh R, Al-Dwairi A, et al. Serum homocysteine is associated with polycystic ovarian syndrome in Jordan. Biomed Rep. 2018;9:439–45. doi: 10.3892/br.2018.1149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Suleiman RR, Sulaiman DM. Studying levels of homocysteine in diagnosed cases of polycystic ovary syndrome. Med J Babylon. 2018;15:291–4. [Google Scholar]
- 39.Li D, Liu HX, Fang YY, Huo JN, Wu QJ, Wang TR, et al. Hyperhomocysteinemia in polycystic ovary syndrome: Decreased betaine-homocysteine methyltransferase and cystathionine β-synthase-mediated homocysteine metabolism. Reprod Biomed Online. 2018;37:234–41. doi: 10.1016/j.rbmo.2018.05.008. [DOI] [PubMed] [Google Scholar]
- 40.Guzelmeric K, Alkan N, Pirimoglu M, Unal O, Turan C. Chronic inflammation and elevated homocysteine levels are associated with increased body mass index in women with polycystic ovary syndrome. Gynecol Endocrinol. 2007;23:505–10. doi: 10.1080/09513590701554306. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All the data are available with the corresponding author and will be willingly shared on reasonable requests.