Introduction
Polycystic ovary syndrome (PCOS) is a common endocrine disorder among women of reproductive age and, based on the Rotterdam criteria, has a prevalence of approximately 10% [1]. Currently, the heterogeneity of this endocrinopathy is emphasised, and PCOS is therefore divided into 4 phenotypes: the first is characterised by hyperandrogenism, oligo-/anovulation, and polycystic ovarian morphology (phenotype A); the second by hyperandrogenism and oligo-/anovulation (phenotype B); the third by hyperandrogenism and polycystic ovarian morphology (phenotype C); and the fourth by oligo-/anovulation and polycystic ovarian morphology (phenotype D) [2]. All these phenotypes may be associated with different rates of metabolic complications and life expectancy [2]. Studies to date have shown an association between polycystic ovary syndrome and the risk of non-alcoholic fatty liver disease — NAFLD (now metabolic dysfunction-associated steatotic liver disease — MASLD [3]) [4]. This was confirmed by a study using Fibroscan to assess the liver [5]. Other non-invasive indicators that may be used in the future as a screening tool for secondary prevention of MASLD are the fibrosis index based on 4 factors (Fibrosis-4; FIB-4) and BAAT [body mass index (BMI), age, alanine aminotransferase (ALT), triglycerides (TG)] calculators [6, 7]. They are used to assess the risk of liver fibrosis; FIB-4 includes age, ALT and aspartate aminotransferase (AST) enzyme activity, and platelet count [8]. BAAT, on the other hand, is calculated based on BMI, age, ALT activity, and serum TG levels [9]. Previous studies have shown higher FIB-4 and BAAT scores in patients with PCOS [10]. Despite all this information, in the current international recommendations for the evaluation and treatment of PCOS, it is futile to search for guidelines relating to the diagnosis of MASLD in these patients [11]. In our study, we used FIB-4 and BAAT calculators to screen for liver disease in PCOS patients, which we categorised according to phenotypes. In addition, the study highlighted other parameters that may be useful in assessing for MASLD in PCOS patients, i.e. anthropometric measurements, androgen levels, and Homeostatic Model Assessment — Insulin Resistance (HOMA-IR) index.
Material and methods
The study included 242 female patients, aged 18–35 years, hospitalised at the Department of Gynaecological Endocrinology of the K. Gibinski University Clinical Centre of the Silesian Medical University in Katowice, who were diagnosed with polycystic ovary syndrome on the basis of the Rotterdam criteria. The patients were assigned to the appropriate phenotype according to the accepted classification: phenotype A — 141 patients, phenotype B — 31 patients, phenotype C — 40 patients, or phenotype D — 30 patients. The diagnosis of PCOS was established after a prior medical history review, gynaecological examination, determination of ovarian and adrenal androgen levels, and ultrasound examination of the reproductive organs using a Voluson 730 Expert device. An interview and physical examination including measurement of body weight, height, waist circumference, and hip circumference was carried out in all study patients, in the morning, on an empty stomach, 12 hours after the last meal. In addition, body weight was assessed based on BMI according to WHO criteria. Colorimetry [AU 680 analyser with reagents from Beckman Coulter (Brea, California, USA)] was used for lipid profile and glucose determinations. Alanine and aspartate aminotransferase enzyme activities and blood counts were determined using a Cobas Pro biochemical analyser (Roche, Switzerland). After obtaining the above results, FIB-4 and BAAT indices were calculated. Determinations of oestradiol, folliculotropin (FSH), lutropin (LH), total and free testosterone, 17-hydroxyprogesterone (17-OH-P), androstenedione, cortisol, dehydroepiandrosterone (DHEAS), sex hormone binding globulin (SHBG), anti-müllerian hormone (AMH), and insulin were made by immunochemical method using microparticles and chemiluminescent marker (CMIA) and reagents according to Abbott (Architect i2000SR; Chicago, IL, USA). Insulin resistance was assessed by an indirect method using the HOMA-IR ratio (HOMA-IR = fasting serum insulin concentration [uIU/mL] × fasting serum glucose concentration [mmol/l]/22.5). Insulin resistance was diagnosed with HOMA-IR values ≥ 2.5. The free androgen index (FAI) was calculated with the formula: FAI = (total testosterone/ SHBG) × 100%, and FAI < 5% was taken as normal values.
Statistical analysis
Values of continuous variables were presented as mean with standard deviation and median with interquartile range (lower and upper quartiles). The Shapiro-Wilk test was used to assess the distribution of continuous variables, while the differences between groups were assessed using the Kruskal-Wallis test (due to the lack of normal distribution of assessed variables in specific groups). The post hoc Dunn test was used to assess the difference between continuous variables according to phenotypes. The chi-square test was used to compare the frequency of the feature in groups/subgroups. Statistical significance was set at p < 0.05. Statistical analysis was performed using Statistica 13.0 PL (Tibco, Kraków, Poland).
Results
In the conducted study, anthropometric variables were assessed among all patients: body weight, height, BMI, circumference measured at the waist, hip circumference, and waist-to-ratio (WHR) index. There were no significant differences between groups for these analysed variables.
In the next step, biochemical variables were analysed, including an assessment of the content of selected hormones. The highest value of 17-hydroxyprogesterone (17-OH-P) was found in phenotype A and was significantly different in comparison to C phenotype. The lowest level of free testosterone was found in phenotype D and was significantly different from its values in women with phenotypes A and B. Same results were found for total testosterone. For androstenedione levels, significant differences were found between phenotype C and D. In relation to DHEAS the lowest values were found in phenotype D, and significant differences were found against phenotypes A, B, and C. The lowest levels of AMH were found in phenotype B, and they were significantly different from phenotypes A and D. The lowest levels of insulin were noted in phenotype C, and it was significantly different from levels of insulin in phenotype B. The multiple comparisons for levels of glucose and HOMA-IR were not significantly different, in contrast with Kruskal-Wallis test results; however, Dunn’s test had different features. Detailed results, along with the division into PCOS phenotypes of the patients included in the study are presented in Tables 1 and 2.
|
Variable |
Group |
p-value |
|||
|
A |
B |
C |
D |
||
|
17-OH-P [ng/mL] |
1.73 ± 8.43 |
1.12 ± 0.95 |
1.54 ± 0.80 |
1.22 ± 0.71 |
0.001 |
|
Free Testosterone [pg/mL] |
2.24 ± 1.64 |
1.89 ± 1.13 |
1.80 ± 1.17 |
1.08 ± 0.67 |
0.001 |
|
Total Testosterone [ng/mL] |
0.41 ± 0.17 |
0.35 ± 0.12 |
0.39 ± 0.18 |
0.25 ± 0.11 |
0.001 |
|
Androstenedione [ng/mL] |
1.77 ± 0.71 |
1.82 ± 1.69 |
2.49 ± 1.67 |
1.59 ± 0.76 |
0.01 |
|
DHEAS [µg/dL] |
323.80 ± 118.47 |
336.06 ± 95.01 |
363.96 ± 166.95 |
229.65 ± 73.63 |
0.001 |
|
AMH [ng/mL] |
7.56 ± 6.50 |
4.05 ± 3.07 |
5.44 ± 3.14 |
6.21 ± 3.52 |
0.0001 |
|
Insulin 0’ [µU/mL] |
10.92 ± 14.32 |
16.92 ± 24.36 |
7.03 ± 5.05 |
8.40 ± 7.50 |
0.01 |
|
Glucose 0’ [mg/dL] |
85.82 ± 6.27 |
91.80 ± 13.06 |
85.02 ± 6.40 |
86.32 ± 6.42 |
0.04 |
|
HOMA-IR [–] |
2.37 ± 3.34 |
3.39 ± 2.01 |
1.50 ± 0.95 |
1.80 ± 1.43 |
0.01 |
|
Variable |
The level of statistical significance between groups [p] |
|||||
|
A vs. B |
A vs. C |
A vs. D |
B vs. C |
B vs. D |
C vs. D |
|
|
17-OH-P [ng/mL] |
0.2 |
0.001 |
1.0 |
1.0 |
1.0 |
0.4 |
|
Free testosterone [pg/mL] |
1.0 |
1.0 |
0.0001 |
1.0 |
0.02 |
0.05 |
|
Total testosterone [ng/mL] |
0.8 |
1.0 |
0.0001 |
1.0 |
0.02 |
0.001 |
|
Androstenedione [ng/mL] |
0.4 |
0.6 |
0.4 |
0.05 |
1.0 |
0.04 |
|
DHEAS [µg/dL] |
1.0 |
1.0 |
0.0002 |
1.0 |
0.0005 |
0.0001 |
|
AMH [ng/mL] |
0.00003 |
0.1 |
1.0 |
0.2 |
0.03 |
1.0 |
|
Insulin 0’ [µU/mL] |
1.0 |
0.08 |
0.9 |
0.02 |
0.2 |
1.0 |
|
Glucose 0’ [mg/dL] |
0.05 |
1.0 |
1.0 |
0.06 |
0.6 |
1.0 |
|
HOMA-IR [–] |
1.0 |
0.1 |
1.0 |
0.05 |
0.4 |
1.0 |
The study analysed the results in terms of median FIB-4 index values. A significant difference in FIB-4 was noted only between phenotypes A and B (p = 0.01). The median FIB-4 index among patients with phenotype B was Me: –0.51; interquartile range (IQR): 0.22. The median of FIB-4 index among patients with phenotype A was Me: –0.41; IQR: 0.18. Post hoc (Dunn’s test) P values controlled for multiple comparisons. Detailed results for the FIB-4 index median are presented in Figure 1.
The study assessed the occurrence of fibrosis according to the BAAT scale and non-occurrence of fibrosis according to the BAAT scale in the studied patients, with a breakdown by PCOS phenotype. The results of these groups are similar, with a difference that proved statistically insignificant (p = 0.3). Detailed results are presented in Figure 2.
Discussion
Considering the safety of patients, there is a constant search for new, non-invasive diagnostic methods that can be used in practice to better detect certain known disease entities. This approach is necessary due to increasing incidence of liver disease and other conditions, and the resulting increase in healthcare costs [12]. Studies show that the most common liver condition is fatty liver disease associated with metabolic disorders, which in turn is associated with an increased risk of death from cardiovascular causes, malignant tumours, and liver diseases [13]. A meta-analysis that included 32 studies with a total of 145,131 patients showed a significant association between PCOS and an increased risk of MASLD [4]. Moreover, the authors of the aforementioned meta-analysis emphasise that early and correct detection of this liver disease is crucial for patients with PCOS. The 2 diseases share many common risk factors, including insulin resistance, hyperandrogenaemia (increased levels of total and free testosterone), and chronic low-grade inflammation [14]. It is therefore worth considering what can be done to detect MASLD as early as possible in patients with PCOS.
In a meta-analysis including 13,046 patients, which compared the effectiveness of non-invasive methods in diagnosing the severity of liver fibrosis, NAFLD fibrosis score and FIB-4 were found to be the most effective [15]. When using FIB-4, it is worth noting the different cut-off points depending on the age of patients; according to a multicentre study by Japanese FIB-4 researchers in the youngest age group, i.e. under 49 years old, cut-off points of 1.05 to 1.2 were proposed, which increase with age [16]. Other studies have also shown that the FIB-4 calculator, in addition to assessing liver fibrosis, is a significant predictor of mortality from cardiovascular causes, non-liver cancers, liver disease, and diabetes in patients with MASLD [17–20]. Previously published studies have shown that the FIB-4 index was higher in PCOS patients compared to the control group; however, due to their young age and small number of comorbidities, the results were relatively low, and further studies are needed to confirm the indicators of liver damage10. Considering the heterogeneity of PCOS, in our study we divided the patients into phenotypes according to the accepted classification [2]. Statistical analysis performed in the study showed the highest median FIB-4 index for the B phenotype; however, it should be noted that the probability of liver fibrosis according to FIB-4 was low in our study regardless of the PCOS phenotype. In this context, it is worth noting that in this study anthropometric parameters were assessed: body mass index (BMI) and WHR in PCOS patients categorised according to phenotypes, without showing statistical significance, which does not confirm the influence of the above-mentioned parameters on increased FIB-4. This may be confirmed by previous studies in which liver fibrosis defined by FIB-4 was independently associated with the risk of cardiovascular events in patients with MASLD, even after adjusting for other risk factors such as age, gender, obesity, hypertension, and diabetes [21–23].
In our study, the laboratory parameter that was statistically significantly elevated in phenotype B was an insulin. It is therefore worth discussing whether HOMA-IR can serve as an indicator for screening for liver disease in patients with PCOS. Studies show that HOMA-IR values ≥ 2.0 or 2.5 correlate with an increased risk of MASLD [24]. Furthermore, previous studies on potential early indicators of MASLD detection have shown that HOMA-IR (≥ 2.5) has a better ability to distinguish MASLD from liver fibrosis in patients with chronic liver disease with or without steatosis and in the absence the absence of excessive alcohol consumption [25]. In turn, other studies have shown an increased risk of MASLD in patients with hyperandrogenic phenotypes of PCOS, regardless of insulin resistance [26].
With these concerns in mind, in our study we also included the MASLD assessment index, which, in addition to assessing liver enzyme activity and age, also includes a parameter for determining body weight (BMI) and triglyceride (TG) levels. For this purpose, we used the BAAT calculator, in which one point is awarded for BMI ≥ 28.0 kg/m2, age ≥ 50 years, ALT ≥ 2 times the upper limit of normal, and TG concentration ≥ 1.7 mmol/L (≥ 150 mg/dL) [9]. A Korean study involving 3634 patients showed a significant correlation between MASLD and BAAT score [7]. Moreover, a study of 314 PCOS patients showed higher BAAT scores compared to controls [10]. Both independent studies suggest the usefulness of the BAAT index for screening for liver disease in PCOS patients.
The FIB-4 and BAAT indices have been used together in many publications on the impact of liver fibrosis on many diseases. One of them assessed the predictive value of the above-mentioned liver fibrosis indices for the risk of cardiovascular disease in a hypertensive population. This study, with a mean follow-up time of 4.66 years, demonstrated an association of FIB-4 and BAAT, among others, with cardiovascular disease in this population, and further suggested that the above-mentioned liver fibrosis markers could be a new tool for identifying patients at high risk of primary cardiovascular disease in the hypertensive population [27]. Another study using FIB-4 and BAAT attempted to clarify the relationship between liver fibrosis calculators and chronic kidney disease (CKD). It was observed that FIB-4 and BAAT were higher in populations with CKD compared to populations without CKD [28]. The same authors evaluated markers of liver fibrosis in relation to stroke risk; they found that increased FIB-4 and BAAT were associated with an increased likelihood of stroke. Furthermore, this study suggests that the aforementioned markers of liver fibrosis can be used as a risk assessment tool to predict stroke [29]. The cited studies show that liver fibrosis, which has previously been shown to be more common in patients with PCOS [10], may be associated with several complications that increase mortality.
Conclusions
In summary, the probability of liver fibrosis in PCOS patients examined on the basis of FIB-4 indices is low, which is probably due to the young age of the subjects. Higher FIB-4 index results were obtained in the group of patients with phenotype B compared to the group with phenotype A, and the group with phenotype B was similar to the groups with phenotypes C and D. Moreover, based on our results, we demonstrated lower level of insulin in phenotype C compared to the group with phenotype B. The BAAT index result proved to be statistically insignificant in the studied patients, with a breakdown by PCOS phenotype.
Considering the above, further studies are needed to assess the usefulness of non-invasive methods for diagnosing MASLD, especially in patients in younger age groups, including those with PCOS.
Data availability statement:
The data used to support the findings of this research are available upon request from the corresponding author, Maciej Migacz: maciek.migacz@gmail.com.
Ethics statement
A bioethics committee was not required.
Author contributions
Conceptualisation: M.M., D.P.; methodology: M.M., D.P.; formal analysis: K.B., M.M., D.P.; investigation: M.M., D.P.; resources: M.M., D.P., B.K.; writing — original draft preparation: M.M, D.P., K.B.; writing — review and editing: M.H, P.M.; visualization: K.B.; supervision: P.M.; project administration: M.M.
Funding
Funded by Medical University of Silesia.
Conflict of interest
No potential conflict of interest was reported by the authors.
