Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles.

OBJECTIVE: Polycystic ovary syndrome (PCOS) has been associated with an increased risk of metabolic disturbances and cardiovascular disease. Intima-media thickness of the common carotid artery (CIMT) represents a valid surrogate marker of early systemic atherosclerosis. This study aimed to investigate if CIMT is increased in PCOS patients compared to healthy controls and if there is an association with hormone and metabolic profiles.
METHODS: In this prospective cross-sectional study, past medical history, anthropometrical measurements and hormonal, lipidemic and glycemic parameters were obtained in 41 PCOS patients and 43 age-matched healthy controls of similar body mass index (BMI) and frequency of smokers. B-mode ultrasound enabled CIMT measurement at the far wall of the left and right common carotid artery.
RESULTS: Patients with PCOS showed significantly increased CIMT values compared to healthy controls (0.49±0.04mm vs. 0.37±0.04mm respectively, P<0.001). They featured a generally increased cardiovascular risk profile. Correlation analysis showed a positive association between CIMT and the adverse metabolic risk profile. The diagnosis of PCOS was the strongest predictor of CIMT, even after multiple adjustments for BMI, age and smoking status (β = 0.797, P<0.001, R2 = 0.73). A model among oligomenorrhoic patients revealed a relationship between CIMT and the suspected duration of disease (β = 0.373, P = 0.021, R2 = 0.14).
CONCLUSIONS: PCOS patients are likely to feature signs of premature systemic atherosclerosis at a young age. Early exposure to adverse cardiovascular risk factors may possibly have long-term consequences on the vascular system. An early vessel screening might thus already be beneficial in these patients at a younger age.

Introduction

Polycystic ovary syndrome (PCOS) represents one of the most common endocrinopathies in women of reproductive age, affecting approximately 4 to 7% of all women [1]. Clinical features include hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology on ultrasound (PCOM) [2,3]. The fundamental underlying defect in PCOS still remains unclear. However, PCOS seems to be a complex state of multifactorial origin, resulting from genetic components that interact with environmental factors. Intrinsic ovarian dysfunction, leading to dysregulated ovarian steroidogenesis with higher androgen production, neuroendocrine abnormalities and hyperinsulinemia are thought to play significant interactive roles [4,5]. In fact, PCOS seems to be closely associated with a metabolic disorder linked to insulin resistance (IR) [6], as 50–70% of PCOS patients show IR with compensatory hyperinsulinemia [7]. This state is characterized by IR in skeletal muscle, adipose tissue and liver, due to a post-binding defect in insulin receptor signaling caused by increased serine phosphorylation of the insulin receptor and the insulin receptor substrate-1 (IRS-1), but ovarian hypersensitivity to insulin [6,8]. Further clinical manifestations of PCOS include metabolic disturbances, such as abdominal obesity, dyslipidemia, impaired glucose tolerance, type II diabetes mellitus (DM), arterial hypertension and the metabolic syndrome [7,9,10]. IR being a key component of the pathophysiology of PCOS, treatment options with insulin-sensitizers, such as metformin or inositol isoforms [11], are increasingly being used due to their beneficial effects on metabolic and hormonal parameters.

The above-mentioned characteristics represent typical risk factors for early atherosclerosis [12], leading to an increased risk of cardiovascular disease [13]. In fact, a 2-fold increased likelihood of coronary heart disease or stroke [14] and higher prevalence of coronary artery and aortic calcification have been reported among PCOS patients [15]. Measurement of intima-media thickness of the common carotid artery (CIMT) using brightness mode ultrasound (B-mode) represents a valid surrogate marker of early systemic atherosclerosis [16,17,18]. This sensitive and noninvasive method thus enables screening for atherosclerosis and cardiovascular risk assessment. CIMT has been associated with an increased risk of coronary heart disease [19] and permits to predict the likelihood of future cardiovascular events, such as stroke and myocardial infarction [17,18,19].

There is evidence that CIMT is elevated in women suffering from PCOS [20,21], especially when they have passed forty [22,23]. However, CIMT increase can already be seen at a young age, such as in adolescence [24], although discrepant findings have been reported [21,25]. Previous studies have also shown an association between CIMT and lipid profile [26], as well as a relationship with hyperinsulinemia [26] and endogenous androgen levels [20], even though differing results have been reported. In fact, hyperandrogenism is said to lead to an increase in CIMT through its proatherogenic effect [20], but numerous studies have also demonstrated an inverse correlation of CIMT with androgen levels in women [27,28,29,30]. On the other hand, estrogen and sex hormone-binding globulin (SHBG) are found to have beneficial effects on lipid profile, thus leading to a reduced CIMT [29].

Despite increasing knowledge about PCOS in the past years, the findings about CIMT and its predictors in PCOS are still very controversial and little is known about the true impact of the disorder. The aim of the present study was therefore to investigate whether CIMT is increased in PCOS patients compared to healthy control subjects, as well as to find out, if this cardiovascular risk factor is associated with hormone and metabolic profiles.

Materials and methods

Study participants

In this prospective cross-sectional study, 41 patients with PCOS between 18 and 34 years of age were consecutively recruited as they attended the endocrine outpatient department of Vienna General Hospital for medical consultation (Department of Gynecology, Division of Gynecological Endocrinology and Reproductive Medicine, Medical University of Vienna) between July 1st and September 30th 2015. Diagnosis of PCOS was made according to the Rotterdam ESHRE/ASRM criteria [3] if two out of the three following features were present: oligoovulation or anovulation, quantified by oligomenorrhea (≤6 menstrual periods in the past year [24]); clinical and/or biochemical signs of hyperandrogenism; PCOM, i.e. at least one ovary with presence of ≥12 follicles measuring 2–9 mm in diameter and/or increased ovarian volume (>10 ml) [3]. In this way, 4 different PCOS phenotypes were defined, i.e. phenotype 1 (hyperandrogenism, oligo-anovulation, PCOM), phenotype 2 (hyperandrogenism with oligo-anovulation), phenotype 3 (hyperandrogenism with PCOM) and phenotype 4 (oligo-anovulation with PCOM). Clinical hyperandrogenism included hirsutism, determined as Ferriman-Gallwey score (FGS) ≥8, and/or acne [24]. Biochemical hyperandrogenism was defined according to the local laboratory’s normal reference ranges as at least one of the following conditions: dehydroepiandrosterone sulfate (DHEAS) >3.7 μg/mL, free testosterone >0.22 ng/mL, total testosterone >0.48 ng/mL, androstenedione >4.1 ng/mL. Hyperandrogenemia was considered mandatory in order to fulfill the criterion of hyperandrogenism in this study. Other etiologies of hyperandrogenism (e.g. congenital adrenal hyperplasia, androgen-secreting tumors, Cushing’s syndrome, non-classic congenital adrenal hyperplasia using 17-hydroxyprogesterone (17-OHP) <2 ng/mL as surrogate parameter [3]) or secondary amenorrhea (anorexia, hyperprolactinemia, i.e. prolactin ≥31 ng/mL after polyethylene glycol immunoprecipitation [31]) were excluded. Thus, 26 newly diagnosed women with PCOS and 15 already diagnosed patients (1 month to 12 years ago) under no treatment for at least 3 months were included in this study.

43 age-matched, healthy and regularly menstruating female volunteers, drawn from the normal community and of similar body mass index (BMI) and frequency of smokers served as control subjects. PCOS was ruled out in all controls according to the Rotterdam ESHRE/ASRM criteria [3], as transvaginal ultrasound was performed between the third and the fifth day of their menstrual cycle in case of hyperandrogenism to exclude PCOS phenotype 3. Metabolic or cardiovascular diseases, e.g. diabetes mellitus, hyperlipidemia, arterial hypertension, coronary heart disease, thyroid dysfunction (i.e. thyroid-stimulating hormone (TSH) <0.44 μU/mL or ≥4.9 μU/mL) or BMI <17 kg/m2 or >50 kg/m2, lactation or pregnancy (beta-human chorionic gonadotropin (beta-hCG) levels >0.1 mU/mL), ongoing medication or hormonal contraception, current psychiatric disorders, heavy consumption of alcohol, caffeine or smoking (>20 cigarettes per day) were further ruled out in all subjects.

The study was approved by the Ethics Committee of the Medical University of Vienna (EK-Nr. 1197/2015). It was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all recruited subjects.

Clinical and biochemical measurements

Initial physical examination included past medical history, age, BMI (BMI = weight[kg]/height[m]2), waist-hip ratio (WHR = waist circumference (WC)[cm]/hip circumference (HC)[cm]), FGS and acne. We further defined the suspected starting point of disease in PCOS patients as the onset of oligomenorrhea. Therefore, when oligomenorrhea started right at menarche, it was noted as starting 0 years after menarche, meaning that 0 years equals menarche. After a 5-minute-rest in a sitting position, resting heart rate, systolic (SBP) and diastolic (DBP) blood pressure were taken with a sphygmomanometer, SBP and DBP being defined by the means of two consecutive measurements on each arm. Pelvic ultrasound was further performed in PCOS patients and controls with hyperandrogenism.

Blood samples were obtained in the morning after overnight fasting in subjects with a normal carbohydrate diet, between the third and the fifth day of their menstrual cycle or randomly in PCOS cases with an irregular cycle for the measurement of hormonal, lipidemic and glycemic parameters, 25-hydroxy-vitamine D and C-reactive protein. Free androgen index (FAI) was calculated as follows: (total testosterone[nmol/L]/SHBG[nmol/L])x100 [32]. Insulin sensitivity was estimated with homeostasis model assessment of insulin resistance (HOMA-IR = (glucose[mmol/L]x insulin[μU/mL])/22.5) [7].

Biochemical and hormonal parameters were acquired by enzyme-linked immuno assays (ELISA). All analyses were performed at the routine hormone laboratory of Vienna General Hospital, the Department of Medical and Chemical Laboratory Diagnostics (http://www.kimcl.at).

CIMT measurement

B-mode ultrasound images of the common carotid artery (CCA) were obtained within the next couple of days, using a 6- to 8-MHz high-resolution linear ultrasound probe for vascular/small parts (GE Medical Systems Ultrasound LOGIQ 9), following a standardized protocol under optimal adjustment of depth, gain and focus and controlled temperature and light conditions. After a resting period of 10 minutes to enable heart rate and blood pressure stabilization, subjects were examined in supine position, with a 35-degree extension and a slight right- or left-turn of the head, depending on the explored side. Left and right CCA were scanned in different transversal and longitudinal planes by the same trained sonographer, who was sitting at the top end of the examination bed behind the patient [33]. Interference of CIMT estimation with a possible plaque or thrombus was thus excluded. After freezing a gray-scale image in a longitudinal plane, CIMT was measured directly during diastole at the far wall of both distal CCAs, as the distance between the lumen-intima and media-adventitia interfaces [16,20,33]. Three consecutive measurements of CIMT were conducted on each side at a distance of 5 millimeters, starting off 1 centimeter prior to the common carotid bulb [20,33] (Fig 1). This resulted in an average value for each left and right CCA. The outcome variable “CIMT” was defined within a single subject as the mean value calculated from left and right CIMT [25], the average from both sides being a more stable representation of CIMT [21]. The intraclass correlation coefficient, measured in 21 subjects, was 0.98 for the right and 0.99 for the left CIMT.

Fig 1

CIMT measurement in PCOS patients and control subjects.

CIMT measurement at the far wall of the distal right (A) and left (B) common carotid artery as the distance between the lumen-intima and media-adventitia interfaces. (A) CIMT in a control subject. (B) CIMT in a PCOS patient.

Statistical analysis

Deviation of continuous data from a normal distribution was assessed through visual inspection of histograms and box and whisker plots. Continuous scaled variables were expressed as means ± standard deviations (SD) if normally distributed, or medians and interquartile ranges (IQR: Q1-Q3). Categorical variables were presented as numbers and percentages (%). Age matching was achieved by ensuring that overall age distribution in terms of mean and standard deviation in both groups is roughly the same.

Continuous parameters were compared between PCOS patients and controls using unpaired two-tailed Student’s t-test when following a Gaussian distribution, or Mann-Whitney U test in case of skewed data. Nominal data were analyzed using χ2 test or Fisher’s exact test. Associations between CIMT and different continuous variables were assessed by Spearman’s rank correlation analysis (rho). The effect of nominal variables on CIMT was determined by Student’s t-test.

Subsequently, multiple linear regression analysis was performed in order to identify independent factors that predict CIMT as the dependent continuous variable. PCOS status, BMI, age and smoking status were therefore entered sequentially as independent factors into the regression model, in order to assess the effect of PCOS and the potential confounding effect of the above-mentioned parameters. They were followed by factors found to be significantly associated with CIMT in individual bivariate correlation. First, the independent variable best correlated with CIMT was included, then the one with the next highest correlation, checking for multicollinearity and normal distribution of the residuals. Nominal variables, i.e. PCOS and smoking status, were defined as dummy variables, coded as 1 for “yes” and 0 for “no”. Models were evaluated with the coefficient of determination R2, which expresses the proportion of variability in the dependent variable, i.e. CIMT, explained by the model. Regression analysis was also done for the oligomenorrhoic PCOS group.

All data analyses were performed using the Statistical Package for the Social Sciences software (IBM SPSS Statistics) version 24.0. Two-sided P-values <0.05 were considered statistically significant. Sample size calculation was performed using “Java applets for power and sample size” (http://homepage.stat.uiowa.edu/~rlenth/Power/) with a significance α of 0.05 and a statistical power of 90% set to find a mean difference in CIMT of 0.08 mm [20,32].

Results

Subject characteristics

Baseline demographic and anthropometrical parameters and clinical characteristics of the study population are exposed in Table 1. Females with PCOS showed increased BMI (P = 0.001) and higher prevalence of smokers (P = 0.007), metabolic syndrome according to the NCEP ATP III criteria [7] (P = 0.011) and parental history of metabolic disorders (i.e. maternal PCOS or DM or paternal DM, P = 0.001) compared to controls, who were slightly older and had higher prevalence of alcohol consumption (P = 0.001). PCOS patients also exhibited preponderance of abdominal obesity, as revealed by significantly increased WC, WHR (P<0.001) and HC (P = 0.019) and elevated prevalence of WC>80 cm (P = 0.001). As expected [3], they further featured significantly higher prevalence of ovulatory dysfunction, hirsutism and hyperandrogenemia (P<0.001) and increased age at menarche (P = 0.010). Accordingly, 29 (70.7%) met the criteria of classic PCOS (phenotype 1), 3 (7.3%) exhibited ovulatory PCOS (phenotype 3) and 9 (22%) had non-hyperandrogenic PCOS (phenotype 4). None showed phenotype 2. In oligomenorrhoic patients (phenotypes 1 and 4), onset of oligomenorrhea had mostly occurred at menarche, resulting in a suspected duration of disease since onset of 9.1 ± 4.8 years.

Table 1

Baseline demographic and anthropometrical parameters and clinical and biochemical diagnostic criteria of the polycystic ovary syndrome among the study population.

Variables	PCOS (n = 41)	Controls (n = 43)	P-value
Age (years)	24 ± 4	25 ± 4	0.296a
Age at menarche (years)	14 (12–14)i	12 (12–13)i	0.010b
Single	16 (39.0)	24 (55.8)	0.124c
Nulligravida	33 (80.5)	38 (88.4)	0.318c
BMI (kg‎/m2)	26.33 ± 7.30	21.91 ± 3.22	0.001a
WC (cm)	91.2 ± 18.0	78.4 ± 9.8	< 0.001a
WC > 80cm	27 (65.9)	13 (30.2)	0.001c
HC (cm)	104 ± 14	98 ± 9	0.019a
WHR	0.87 ± 0.07	0.80 ± 0.06	< 0.001a
Metabolic syndrome (NCEP)ii	6 (14.6)	0	0.011d
Current smokers	15 (36.6)	5 (11.6)	0.007c
Alcohol consumption	16 (39.0)	32 (74.4)	0.001c
Caffeine consumption	32 (78.0)	33 (76.7)	0.886c
Parental history of metabolic disordersiii	17 (41.5)	4 (9.3)	0.001c
Oligomenorrhea/amenorrhea	38 (92.7)	0	< 0.001c
Menstrual periods per year	4 (2–6)i	12±1	< 0.001b
Hirsutism (FGS ≥ 8)	29 (70.7)	8 (18.6)	< 0.001c
FGS	11 (7–23)i	4 (2–7)i	< 0.001b
Acne	32 (78.0)	21 (48.8)	0.006c
Hyperandrogenemiaiv	32 (78.0)	6 (14.0)	< 0.001c
*DHEAS > 3.7μg/mL	19 (46.3)	4 (9.3)	< 0.001c
*fT > 0.22 ng/mL	17 (41.5)	0	< 0.001c
*TT > 0.48 ng/mL	28 (68.3)	3 (7.0)	< 0.001c
*A > 4.1 ng/mL	14 (35.0)	0	< 0.001c
PCOM	41 (100)	-	-
Oligomenorrhea onset (years after menarche)v	0.0 (0.0–3.0)i	-	-
Suspected duration of disease since onset (years)v	9.1 ± 4.8	-	-

Values are presented as means ± standard deviations, numbers and percentages (%) or imedians and interquartile ranges (IQR).

P-values < 0.05 were considered statistically significant and marked in bold.

aStudent’s t-test

bMann-Whitney U test

cχ2 test

dFisher’s exact test.

iiMetabolic syndrome according to the NCEP ATP III criteria [7].

iiiMother with polycystic ovary syndrome or diabetes mellitus or father with diabetes mellitus.

ivDefined as the increase of at least one androgen (DHEAS, fT, TT, A).

vIn PCOS patients with phenotypes 1 and 4 (n = 38).

BMI: body mass index; WC: waist circumference; HC: hip circumference; WHR: waist-hip ratio; FGS: Ferriman-Gallwey score; DHEAS: dehydroepiandrosterone sulfate; fT: free testosterone; TT: total testosterone; A: androstenedione; PCOM: polycystic ovarian morphology on ultrasound.

Carotid intima-media thickness

CIMT values were significantly higher in PCOS patients than controls (0.49 ± 0.04 mm vs. 0.37 ± 0.04 mm respectively, P<0.001, Table 2), resulting in a mean difference of 0.12 mm between both study groups. They ranged from 0.40 to 0.63 mm in women with PCOS (n = 41) and from 0.31 to 0.46 mm in control subjects (n = 43, Fig 2). Key exploratory endpoints, i.e. right and left CIMT and maximal and minimal CIMT, were also significantly higher in PCOS patients (P<0.001).

Table 2

Cardiovascular parameters.

Variables	PCOS (n = 41)	Controls (n = 43)	P-value
CIMT (mm)	0.49 ± 0.04	0.37 ± 0.04	< 0.001a
Right CIMT (mm)	0.49 ± 0.05	0.37 ± 0.04	< 0.001a
Left CIMT (mm)	0.50 ± 0.05	0.37 ± 0.04	< 0.001a
CIMTmax (mm)	0.56 ± 0.06	0.41 ± 0.04	< 0.001a
CIMTmin (mm)	0.42 ± 0.04	0.33 ± 0.04	< 0.001a
SBP (mmHg)	113 ± 13	109 ± 10	0.088a
DBP (mmHg)	72 ± 9	71 ± 8	0.533a
Heart rate (bpm)	70 ± 11	71 ± 10	0.484a
hs-CRP (mg/dL)	0.07 (0.05–0.25)	0.07 (0.04–0.17)	0.343b

Values are presented as means ± standard deviations or medians and interquartile ranges (IQR).

P-values < 0.05 were considered statistically significant and marked in bold.

aStudent’s t-test

bMann-Whitney U test.

CIMT: carotid intima-media thickness; SBP: systolic blood pressure; DBP: diastolic blood pressure; hs-CRP: high-sensitivity C-reactive protein.

Fig 2

Box and whisker plots of CIMT.

Among each group of interest (i.e. PCOS and controls). Box and whisker plots enable to summarize descriptive analysis of CIMT. They comprise the median (bold horizontal line) and the interquartile range (IQR), represented by a box, the first and third quartile being the borders of this box. Whiskers show values that are within 1.5 IQRs (vertical lines extending from the box). The small circle indicates an outlier (over 1.5 IQRs).

Cardiovascular, metabolic and hormone profiles

Cardiovascular, metabolic and hormonal parameters are provided in Tables 2 and 3. In comparison to controls, PCOS patients showed higher levels of low-density lipoprotein cholesterol (LDL-C), total cholesterol/high-density lipoprotein cholesterol ratio (TC/HDL-C ratio) and apolipoprotein B (P<0.001), triglycerides (P = 0.001) and TC (P = 0.005), as well as lower levels of HDL-C (P = 0.044), revealing an adverse lipid profile. Increased levels of C-peptide and fasting insulin (P<0.05) also indicated higher endogenous insulin secretion. PCOS patients further presented with significantly higher levels of all measured androgens, FAI, 17-OHP, luteinizing hormone (LH), LH/FSH (follicle-stimulating hormone) ratio and Anti-Müllerian hormone (AMH) (P≤0.001), and lower levels of SHBG (P = 0.006) and FSH (P = 0.008).

Table 3

Metabolic parameters and hormone profile.

Variables	PCOS (n = 41)	Controls (n = 43)	P-value
Metabolic parameters
Triglycerides (mg/dL)	81 ± 48	54 ± 17	0.001a
Total cholesterol (mg/dL)	175 ± 27	175 ± 27	0.005a
LDL-cholesterol (mg/dL)	98.6 ± 24.4	79.5 ± 17.5	< 0.001a
HDL-cholesterol (mg/dL)	60 (45–73)	68 (58–77)	0.044b
TC/HDL-C ratio	3.19 ± 1.13	2.39 ± 0.48	< 0.001a
TC/HDL-C ratio > 4	9 (22.0)i	0i	0.001c
Apolipoprotein B (mg/dL)	85 ± 20	70 ± 11	< 0.001a
Apolipoprotein A1 (mg/dL)	147 ± 30	157 ± 27	0.093a
Lipoprotein(a) (mg/dL)	14 (7–58)	11 (7–33)	0.644b
Fasting glucose (mg/dL)	84 ± 6	82 ± 6	0.338a
HbA1c (%)	5.1 ± 0.3	5.1 ± 0.2	0.931a
C-peptide (ng/mL)	2.3 ± 1.1	1.6 ± 0.5	0.001a
Fasting insulin (μU/mL)	7.8 (5.5–16.0)	6.3 (4.8–9.2)	0.047b
HOMA-IR	1.56 (1.06–3.24)	1.38 (0.94–1.84)	0.051b
25-OH-Vit D (nmol/L)	57.9 (29.1–80.9)	63.6 (51.5–77.5)	0.080b
Hormone profile
Total testosterone (ng/mL)	0.56 ± 0.20	0.29 ± 0.15	< 0.001a
Free testosterone (ng/mL)	0.21 ± 0.13	0.08 ± 0.04	< 0.001a
Androstenedione (ng/mL)	3.50 ± 1.29	2.04 ± 0.82	< 0.001a
DHEAS (μg/mL)	3.40 ± 1.29	2.55 ± 1.03	0.001a
SHBG (nmol/L)	53.8 ± 31.2	73.3 ± 32.0	0.006a
FAI	3.90 (2.26–6.34)	1.61 (0.81–2.22)	< 0.001b
17-OHP (ng/mL)	1.00 ± 0.56	0.61 ± 0.37	< 0.001a
LH (mU/mL)	12.9 ± 7.6	6.3 ± 2.2	< 0.001a
FSH (mU/mL)	5.6 ± 1.6	6.8 ± 2.4	0.008a
LH/FSH	2.39 ± 1.38	1.01 ± 0.45	< 0.001a
AMH (ng/mL)	12.30 (10.20–17.70)	5.81 (2.55–9.11)	< 0.001b
Prolactin (ng/mL)	12.5 ± 7.1	13.7 ± 5.3	0.368a
TSH (μU/mL)	1.78 ± 0.85	1.96 ± 0.92	0.347a
Free T4 (ng/dL)	1.25 ± 0.19	1.24 ± 0.17	0.798a

Values are presented as means ± standard deviations (SD), medians and interquartile ranges (IQR) or inumbers and percentages (%).

P-values < 0.05 were considered statistically significant and marked in bold.

aStudent’s t-test

bMann-Whitney U test

cFisher’s exact test.

LDL-cholesterol: low-density lipoprotein cholesterol; HDL-cholesterol: high-density lipoprotein cholesterol; TC/HDL-C ratio: total cholesterol/HDL-cholesterol ratio; HbA1c: glycosylated hemoglobin; HOMA-IR: homeostasis model assessment of insulin resistance; 25-OH-Vit D: 25-hydroxy-vitamine D; DHEAS: dehydroepiandrosterone sulfate; SHBG: sex hormone-binding globulin; FAI: free androgen index; 17-OHP: 17-hydroxyprogesterone; LH: luteinizing hormone; FSH: follicle-stimulating hormone; AMH: anti-Müllerian hormone; TSH: thyroid-stimulating hormone; T4: thyroxine.

Correlation and regression analysis

Several cardiovascular risk factors and hormonal parameters were found to be significantly positively correlated with CIMT in our subjects (Table 4), including total testosterone, free testosterone, androstenedione, FAI, AMH, LH/FSH ratio, WC, WHR, BMI, FGS and apolipoprotein B (P<0.001), as well as 17-OHP, LDL-C, smoking, TC, triglycerides, TC/HDL-C ratio, DHEAS and SBP (P<0.05). Besides, SHBG showed a significant negative relationship with CIMT (P = 0.023). When carried out separately in PCOS patients, correlation analysis revealed a significant positive association of CIMT with the suspected duration of disease (P = 0.012). Additionally, unpaired two-tailed Student’s t-test showed significantly higher CIMT values among the entire study population in case of hyperandrogenemia (0.47 ± 0.06 (n = 38) vs. 0.40 ± 0.07 (n = 46), P<0.001) and parental history of metabolic disorders (0.48 ± 0.05 (n = 21) vs. 0.41 ± 0.07 (n = 63), P<0.001).

Table 4

Correlation analysis with CIMT and baseline demographic characteristics, metabolic and hormone profiles with Spearman’s correlation coefficient (rho).

Variables	Rho	P-value
In all study subjects (n = 84)p:
Total testosterone	0.528	< 0.001
Free testosterone	0.505	< 0.001
Androstenedione	0.493	< 0.001
FAI	0.466	< 0.001
DHEAS	0.241	0.027
SHBG	-0.247	0.023
17-OHP	0.357	0.001
LH/FSH	0.436	< 0.001
AMH	0.458	< 0.001
Triglycerides	0.291	0.007
Total cholesterol	0.301	0.005
LDL-cholesterol	0.345	0.001
HDL-cholesterol	-0.112	0.310
TC/HDL-C ratio	0.286	0.008
Apolipoprotein B	0.375	< 0.001
Fasting insulin	0.178	0.105
Age	0.053	0.630
BMI	0.398	< 0.001
Waist circumference	0.425	< 0.001
Waist-hip ratio	0.420	< 0.001
SBP	0.222	0.043
Ferriman-Gallwey score	0.394	< 0.001
Smoking (py)	0.306	0.005
In oligomenorrhoic PCOS subjectsa (n = 38):
Suspected duration of disease	0.403	0.012

P-values < 0.05 were considered statistically significant and marked in bold.

aPCOS patients with phenotypes 1 and 4.

FAI: free androgen index; DHEAS: dehydroepiandrosterone sulfate; SHBG: sex hormone-binding globulin; 17-OHP: 17-hydroxyprogesterone; LH/FSH: luteinizing hormone/follicle-stimulating hormone; AMH: anti-Müllerian hormone; LDL-cholesterol: low-density lipoprotein cholesterol; HDL-cholesterol: high-density lipoprotein cholesterol; TC/HDL-C ratio: total cholesterol/HDL-cholesterol ratio; BMI: body mass index; SBP: systolic blood pressure; Py: pack years.

Multiple linear regression analysis was carried out in order to identify independent factors that predict CIMT as the dependent continuous variable (Table 5). When considering PCOS patients and controls as a group, the diagnosis of PCOS was the strongest predictor of CIMT (β = 0.836, P<0.001), explaining 70% of its variability (P<0.001, model 1). In order to assess the potential confounding effect of obesity, age and smoking on CIMT, BMI, age and smoking status were entered sequentially as independent factors into the regression model. Only BMI represented an independent positive predictor of CIMT (β = 0.145, P = 0.025) among these parameters, raising the predicted variability in CIMT to 72% (P<0.001, model 2). However, PCOS status remained the primary predictor of CIMT, even after multiple adjustments for BMI, age and smoking status (P<0.001, R2 = 0.73, models 3 and 4), therefore indicating an independent effect of PCOS on CIMT (β = 0.797, P<0.001). No statistical significance was found for further hormonal and cardiovascular risk factors, probably due to multicollinearity with the PCOS status. When performed separately in oligomenorrhoic PCOS patients (n = 38), regression analysis revealed suspected duration of disease as an independent positive predictor of CIMT (β = 0.373, P = 0.021), accounting for 14% of the variability in CIMT (P = 0.021, model 5).

Table 5

Multiple linear regression models of CIMT as dependent variable.

Predictors	Ba	SEb	βc	P-value
In all study subjects (n = 84):
Model 1 (R2 = 0.70)d
PCOS	0.122	0.009	0.836	< 0.001
Constant	0.371	0.006		< 0.001
Model 2 (R2 = 0.72)d
PCOS	0.114	0.009	0.783	< 0.001
BMI	0.002	0.001	0.145	0.025
Constant	0.332	0.018		< 0.001
Model 3 (R2 = 0.73)d
PCOS	0.115	0.009	0.793	< 0.001
BMI	0.002	0.001	0.152	0.018
Age	0.002	0.001	0.111	0.061
Constant	0.281	0.032		< 0.001
Model 4 (R2 = 0.73)d
PCOS	0.116	0.009	0.797	< 0.001
BMI	0.002	0.001	0.159	0.018
Age	0.002	0.001		0.065
Smoking	-0.004	0.011	0.110	0.705
Constant	0.280	0.032	-0.024	< 0.001
In oligomenorrhoic PCOS subjectsi (n = 38):
Model 5 (R2 = 0.14)e
Suspected duration of disease	0.003	0.001	0.373	0.021
Constant	0.460	0.015		< 0.001

P-values < 0.05 were considered statistically significant and marked in bold.

aUnstandardized coefficients for determining the regression equation

bStandard error

cStandardized coefficient.

P-value of the model:

d< 0.001

e0.021.

iPCOS patients with phenotypes 1 and 4.

BMI: Body mass index.

Discussion

The present study aimed to assess evidence of early systemic atherosclerosis in PCOS with measurement of CIMT and its possible association with hormone and metabolic profiles. Our results revealed significantly increased CIMT in PCOS patients when compared to healthy controls. This increase was further found to be independent of BMI, age and smoking status, given that PCOS status remained the primary predictor of CIMT after covariate adjustment. These findings suggest that the disorder itself is playing a causative role in CIMT increase.

As described in former studies, measurement of CIMT using B-mode ultrasound represents a valid surrogate marker of subclinical systemic atherosclerosis and enables cardiovascular risk stratification [17,18]. There is evidence that women with PCOS feature an increased CIMT compared to healthy controls [20,21,34], with reported mean differences in CIMT ranging from 0.06 mm [35] to 0.14 mm [24,36]. The mean difference of 0.12 mm found in our study is therefore in accordance with literature, and so are our measured CIMT values among each group of interest, given that a meta-analysis [21] indicates a mean CIMT ranging from 0.41 to 0.75 mm in PCOS and from 0.33 to 0.74 mm in controls in previous studies. Differences in the prevalence of PCOS phenotypes and thus cardiovascular risk profiles between studies can explain discrepant findings regarding elevated CIMT in literature [21,25]. In fact, the prevalence of total and abdominal obesity, metabolic disturbances and cardiovascular risk factors is found to decrease from phenotype 1 to 4, just like the severity of hyperandrogenism and menstrual disturbances [3,10,37,38].

In the present study, several cardiovascular risk factors and hormonal parameters were found to be significantly associated with CIMT, i.e. visceral obesity, dyslipidemia, hyperandrogenemia, AMH, smoking, SBP and parental history of metabolic disorders.

Obesity is a common finding in PCOS and has been linked to increased CIMT in young and middle-aged women [39]. As a matter of fact, women with PCOS are found to exhibit central body fat distribution regardless of BMI, related to hyperandrogenemia and hyperinsulinemia [40]. However, adipose tissue is metabolically active and increased visceral fat thus enhances secretion of free fatty acids (FFA), adipokines, i.e. adiponectin and leptin, growth factors and inflammatory cytokines, e.g. TNF-α [41]. Those substances have a crucial impact on metabolism and cardiovascular system. Visceral adipose tissue is therefore a source of inflammation, the latter representing a key component of atherosclerosis development [42]. Markers of premature atherosclerosis, such as C-reactive protein (CRP), homocysteine and endothelial dysfunction were shown to be involved in CIMT increase [27,32,36]. Visceral obesity is further associated with dyslipidemia and IR, consequently leading to increased risk of DM and cardiovascular disease [40,43,44]. A positive relationship has thus been described with early signs of vascular damage and CIMT increase [32,45]. The higher prevalence of WC >80 cm in our PCOS patients, as defined in European women by the WHO [46], therefore indicates an increased risk of metabolic and cardiovascular complications, WC being a better predictor than WHR or BMI [43]. Metabolic syndrome according to the NCEP ATP III [7] being also more prevalent in our patients and former studies [24] further contributes to dyslipidemia and IR, leading to enhanced atherosclerosis and eventually CIMT increase.

Consistent with literature, PCOS patients featured an adverse lipid profile, including a higher TC/HDL-C ratio, which represents a risk factor for coronary heart disease [24,47]. Dyslipidemia is believed to be the consequence of increased visceral fat [32] and androgen levels [48,49]. Previous studies further confirmed the association found between CIMT and an adverse lipid profile, CIMT being positively associated with the atherogenic LDL-C, TC and triglycerides and negatively correlated with the antiatherogenic HDL-C [26]. As a result, women with PCOS are at increased risk of early systemic atherosclerosis [47].

The positive association of androgen levels with CIMT and higher CIMT values found in case of hyperandrogenemia are in accordance with previous findings [20], suggesting that CIMT increase in PCOS is being caused by hyperandrogenism. High levels of androgens are believed to lead to an increased CIMT through a direct effect on the vascular system. In fact, testosterone has proatherogenic effects on macrophage function by facilitating the uptake of modified lipoproteins [50] and enhancing monocyte adhesion to vascular endothelial cells [51]. Furthermore, androgens regulate lipolysis and lipogenesis in women, having an impact on lipid metabolism and adipocyte production [52]. Testosterone has been found to inhibit catecholamine-stimulated lipolysis in subcutaneous fat cells through decreased expression of hormone-sensitive lipase (HSL) and β2-adrenoceptors [53]. However, it seems to exert the opposite effect on visceral fat, leading to an increased sensitivity to catecholamines through upregulation of lipolytic β3-adrenergic receptors and HSL [49,54] and a reduced sensitivity to the antilipolytic effect of insulin [40,55]. As a consequence, the elevated release of FFA affects liver functioning and insulin signaling, leading to dyslipidemia, hyperinsulinemia, glucose intolerance and IR. It also reduces hepatic SHBG production, amplifying hyperandrogenemia [56]. Androgens further lead to an adverse lipid profile through inhibition of lipoprotein lipase (LPL) in adipose tissue [48,49]. Moreover, serum testosterone has been associated with advanced glycation end products (AGEs) [57]. These proinflammatory and oxidant mediators result from non-enzymatic glycation of proteins or lipids and are believed to play a contributive role in PCOS development [58,59], given that elevated levels have been reported [10,38,57,58]. AGEs further exert a proatherogenic effect on the vascular system [60]. They have been related to tissue damaging in the framework of atherosclerosis and may therefore contribute to the elevated cardiovascular risk in PCOS [38,57]. Besides, the role of testosterone as crucial determinant of CIMT increase is further emphasized by the positive relationship of CIMT seen with FAI, which represents a good reflection of testosterone action. Furthermore, the observed association between CIMT and hirsutism demonstrates the impact played by dihydrotestosterone (DHT), which leads to similar regional differences concerning lipolysis as testosterone [48,53]. The positive relationship of CIMT found with AMH further underlines the impact of hyperandrogenemia. In fact, granulosa cells of growing ovarian follicles in PCOS secrete elevated levels of AMH, which inhibit aromatase activity, thus contributing to androgen excess [61].

Smoking and SBP were also positively associated with CIMT. Indeed, smoking enhances the progression of extracoronary atherosclerosis [12] and hypertension is involved in the pathogenesis of vascular damage and thus atherosclerosis [62]. Moreover, higher CIMT values were seen in case of parental history of metabolic disorders, putting an emphasis on genetic predisposition in PCOS. Despite showing no correlation with CIMT in our study, higher endogenous insulin secretion, as found in our patients, is thought to contribute to CIMT increase. In fact, insulin exerts an atherogenic effect by increasing cholesterol transport into arteriolar smooth muscle cells. In this way, it stimulates their proliferation and endogenous cholesterol and collagen synthesis, eventually leading to elevated formation of lipid plaques [63].

Finally, our results from multiple regression analysis are in accordance with previous studies, in which the diagnosis of PCOS and BMI were found to predict CIMT [24,27], with PCOS status being the strongest predictor, independent of BMI [27]. Furthermore, our study revealed suspected duration of disease as predictor of CIMT, which is a new finding that has not been reported so far to our knowledge. Therefore, oligomenorrhoic patients seem to feature higher CIMT the earlier onset of disease occurs. Although suspected duration of disease is related to the subjects’ age, it also depends on the age of menarche that varies in each patient. Besides, PCOS patients have later menarche than healthy controls [30,64,65], therefore it does not totally equate to the age. The observed relationship between suspected duration of disease and CIMT in oligomenorrhoic patients thus suggests even more that PCOS itself contributes to the enhancement of atherosclerosis, probably due to hyperandrogenism, an adverse lipid profile and hyperinsulinemia.

However, although women with PCOS feature increased CIMT and metabolic and cardiovascular risk at a younger age, there is evidence suggesting that they do not show higher prevalence of cardiovascular events than controls after menopause [30,64,65]. Possible explanations could be a protective effect of delayed menopause with a consequently prolonged estrogen exposure in PCOS [30,64,65] or even hyperandrogenism itself [64,65] in peri- and postmenopausal PCOS patients, as indicated by previous studies [27,28,29], probably mainly due to enzymatic conversion to estrogen. Moreover, differences in cardiovascular risk factors (e.g. diabetes, abdominal obesity) between PCOS and controls seem to be less preponderant in aging women, explaining the similar cardiovascular morbidity and mortality later in life [65].

Strengths of this study include the comparable sample size and age in both groups of interest. Controls were slightly older than PCOS patients, enabling to exclude even more a potential confounding effect of age on CIMT. All subjects further benefited from a thorough hormone, metabolic and cardiovascular assessment. Regarding limitations of the current study, even though most patients featured the classic PCOS phenotype, 29.3% belonged to phenotypes 3 or 4, with lesser severity of clinical and metabolic manifestations [3]. This could explain the only mild metabolic disturbances seen in our patients. Given that 9 PCOS patients without hyperandrogenism (i.e. phenotype 4) and 6 controls with hyperandrogenemia were included in the study, the impact of androgen excess on CIMT may have been underestimated and thus no significant association was found in multiple regression analysis. Besides, the presence of acne was quantified subjectively, thus no major focus should be put on its prevalence. Moreover, estrogen and progesterone effect on CIMT could not be examined in our study, given that blood tests were not always performed during follicular phase in oligomenorrhoic PCOS patients. Analyses should therefore be conducted in these women after progestin-induced withdrawal bleeding. Further studies with a larger sample size, especially among each PCOS phenotype, are required in order to unravel the exact underlying pathogenetic mechanisms in PCOS and to identify which components of this disorder have the greatest effect on CIMT.

Conclusions

The present study indicates that patients with PCOS are likely to feature signs of premature systemic atherosclerosis already at a young age, as shown by their elevated CIMT values. Our findings further suggest that early exposure to adverse cardiovascular risk factors in the framework of this disorder may possibly have long-term consequences on the vascular system, given that the duration of disease seems to have a predictive impact on the extent of CIMT increase. In fact, metabolic disturbances, including visceral obesity, IR with compensatory hyperinsulinemia and dyslipidemia were found to be involved. Moreover, our results revealed that hyperandrogenism, a central feature of PCOS, represents a crucial determinant of CIMT elevation in women with this disorder and is thus implicated in the enhancement of atherosclerosis. However, there seems to exist a still unexplained independent effect of PCOS. A possible contributing factor to the elevated cardiovascular risk could be the proatherogenic role of AGEs, leading to tissue damage in the framework of atherosclerosis. PCOS patients thus seem to be at greater risk for early systemic atherosclerosis. This emphasizes the importance of thorough metabolic and cardiovascular evaluation in all women with PCOS. An early vessel screening might therefore already be beneficial in these patients at a younger age.

18 Dec 2019

PONE-D-19-30548

Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles

PLOS ONE

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Reviewer #1: GENERAL COMMENTS

The manuscript entitled “Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles” refers to a topic on which there seems to be quite a consensus that CITM is greater in women with PCOS. In addition, numerous antecedents associate this parameter with cardiovascular risk and alterations of the lipid profile. It has also been pointed out a relationship between CIMT and the levels of androgens, one of the fundamental features of PCOS, so it is difficult for me to rescue the novelty of the article. I think it is essential that you can emphasize this aspect. Could it be that "oligomenorrheic patients revealed a relationship between CIMT and the suspected duration of disease" is a new finding?

SPECIFIC COMMENTS

Other articles have reported higher CIMT in young PCOS women, however most studies have failed to demonstrate greater cardiovascular risk in perimenopausal women with PCOS despite having a higher prevalence of metabolic disorders at an early age. It will be possible that when women approaching menopause, the control women worsen this parameter and the PCOS remain the same, equating CIMT at this stage? In other words, is it likely that the damage appears earlier, but not that there are major alterations in the vascular system? If there is background on this, I suggest adding and discussing.

Materials and methods (study participants), I would you like to know if they had a range of BMI for the recruitment of women (PCOS and controls), nothing is pointed out about it. The authors only mention that the control women had a BMI similar to PCOS.

In the selection of the control group, the authors mention that it was healthy and regularly menstruating female volunteers, it is striking that they did not rule out hyperandrogenism (at least clinical). In fact, table 1 shows that there is 18.6% of hirsutism and 14% of hyperandrogenemia in the control group. In addition, they do not mention whether the control group had ovarian ultrasound. Finally, it would be appropriate to be able to confirm that the percentage of control women with hyperandrogenism have ovaries without polycystic morphology. Otherwise the question remains whether they are pure hyperandrogenic or if they have the C phenotype of Rotterdam?

In this article, the authors diagnose PCOS according to the Rotterdam criteria, giving the frequency of each phenotype, however in the results they do not report this data associated with CIMT. What was the reason for reporting those frequencies by phenotype? On the other hand, this idea is retaken in the discussion (lines 292-295), but without mentioning the results of the present study.

The way that the results are organized in tables 1-3 is confusing. The data does not appear to be grouped in the proper order, to simplify reading and interpretation. I think the anthropometric, clinical and demographic data should go in the first table, since they are the ones obtained in the first instance; then hormonal, metabolic and CIMT measurement. The processed data (applying cut-off or classification values) should be shown in the final table or at the end of their respective tables (raw data). I suggest including the free androgen index, as it is a good reflection of hyperandrogenism (testosterone action).

In table 1, draws attention to the high percentage of acne, both in the PCOS and in the control group. Is there any bias in the selection of the groups? How was this parameter quantified (presence or absence)? How could these values be explained?

The most comparable results with this study would be those of early reproductive age women with PCOS, such as those suggested below and that have not been included in the discussion.

J Clin Endocrinol Metab. 2018;103(4):1622-1630. Meun C et al.

Menopause. 2012; 19(1):10–15. Munir JA et al.

Indian J Endocrinol Metab. 2016;20(5):662-666. Garg N et al.

Gynecol Endocrinol. 2015;31(6):477-82. Yilmaz SA et al.

Int J Prev Med. 2013;4(11):1266-70. Allameh Z et al.

Reviewer #2: Comments to the Author

This study aimed to investigate if CIMT is increased in PCOS patients compared to healthy controls and if there is an association with hormone and metabolic profiles.

This study presents interesting findings, but there are considerable concerns related to the study design and data presenting.

Specific comments are as follows:

1. Please explain the definition of biochemical hyperandrogenism. How is the local reference defined?

2. In abstract and method, age matched and similar BMI controls were enrolled. Please describe the criterion for matching in detail in the manuscript.

3. What is oligomenorrhea onset (years after menarche) as 0.0 (0.0-3.0) in Table 1?

Is “Suspected duration of disease since onset” not directly related to the age of the subjects?

4. Why did not the authors compare of CIMT between the control group and hyperandrogenic and non-hyperandrogenic PCOS patients?

5. The authors need to explain the table 5 in detail. They stated that multiple linear regression analysis was carried out in order to identify independent factors that predict CIMT as the dependent continuous variable. What is the dependent variable? If CIMT is the dependent variable, the authors should analyze using univariate linear regression analysis with CIMT as the dependent variable.

Reviewer #3: I was pleased to revise the manuscript entitled “Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles” (Manuscript Number: PONE-D-19-30548).

The study was approved by the Ethics Committee of the Medical University of Vienna (EK-Nr. 1197/2015). In general, this manuscript was aimed to investigate if the intima-media thickness of the common carotid artery is increased in PCOS patients compared to healthy controls and if there is an association with hormone and metabolic profiles, and in my opinion this study is interesting for the readers. Nevertheless, methodology is not accurate, and conclusions are not completely supported by the reported data. Authors should clarify some point and improve the results and discussion.

In general, the Manuscript may benefit from several major revisions, as suggested below:

1. Results and statistical methods. I would suggest investigating the multicollinearity between PCOS and cardiovascular risk factors. The strong association between them and PCOS may explain the cardiovascular risk reported in these patients. Age, BMI, and smoking status are not the only possible confounders in the association between PCOS and cardiovascular risk.

2. Methods. It is not clear why the Authors used the correlation coefficient instead of univariate linear regression.

3. Discussion. Lines 282. This point is unclear. The PCOS is a complex disorder and it is probably that specific included metabolic factors are the cause of increased CIMT in PCOS women. It is of paramount importance to identify these elements as possible target of preventive treatments.

4. Conclusion. Lines 391-393. Based in the results, the role of hyperandrogenism as crucial determinant of CIMT is not demonstrated.

5. Conclusion. Lines 394. This statement is not supported by results. A complete multivariate regression analysis was not performed, the collinearity needs to be better investigated and assessed. A backward method could be better with an appropriate evaluation of collinearity by the use of variance inflation factor.

6. I would suggest improving the introduction reporting about the role of insulin resistance, that is one of the most important mechanisms of PCOS pathogenesis. For this reason, the use of insulin-sensitizers, such an inositol isoform, gained increasing attention due to their safety profile and effectiveness. Authors may better discuss this point, taking to account these recent articles: PMID: 30270194.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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4 Feb 2020

Please find below our response to the points raised by the reviewers. The mentioned lines of the manuscript correspond to those of the document “Revised_Manuscript_With_Track_Changes_Rhea_Jabbour”, as they differ from the ones in the Manuscript without tracked changes.

Reviewer #1:

GENERAL COMMENTS

The manuscript entitled “Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles” refers to a topic on which there seems to be quite a consensus that CITM is greater in women with PCOS. In addition, numerous antecedents associate this parameter with cardiovascular risk and alterations of the lipid profile. It has also been pointed out a relationship between CIMT and the levels of androgens, one of the fundamental features of PCOS, so it is difficult for me to rescue the novelty of the article. I think it is essential that you can emphasize this aspect. Could it be that "oligomenorrheic patients revealed a relationship between CIMT and the suspected duration of disease" is a new finding?

Reply: It is true that there is already a lot of data about CIMT and PCOS. However, discrepant findings have been reported regarding CIMT increase in PCOS, as described in our Introduction. The published results concerning predictors of CIMT are also still very controversial. Therefore, our aim was to verify those findings and see how it is in the Austrian population, given that there is no literature about it to our knowledge yet. Indeed, we found that CIMT is increased in PCOS and given the scope of PLOS ONE to contribute to the base of scientific knowledge by also publishing confirmatory studies, we were hoping that you would consider our manuscript for publication.

On top of that, you are correct. To our knowledge, the fact that suspected duration of disease was found to be a predictor of CIMT in oligomenorrhoic patients is a new finding. We have therefore emphasized this aspect in the Discussion:

l.576-591: “Furthermore, our study revealed suspected duration of disease as predictor of CIMT, which is a new finding that has not been reported so far to our knowledge. Therefore, oligomenorrhoic patients seem to feature higher CIMT the earlier onset of disease occurs. Although suspected duration of disease is related to the subjects’ age, it also depends on the age of menarche that varies in each patient. Besides, PCOS patients have later menarche than healthy controls[30,64,65], therefore it does not totally equate to the age. The observed relationship between suspected duration of disease and CIMT in oligomenorrhoic patients thus suggests even more that PCOS itself contributes to the enhancement of atherosclerosis, probably due to hyperandrogenism, an adverse lipid profile and hyperinsulinemia.”

SPECIFIC COMMENTS

Other articles have reported higher CIMT in young PCOS women, however most studies have failed to demonstrate greater cardiovascular risk in perimenopausal women with PCOS despite having a higher prevalence of metabolic disorders at an early age. It will be possible that when women approaching menopause, the control women worsen this parameter and the PCOS remain the same, equating CIMT at this stage? In other words, is it likely that the damage appears earlier, but not that there are major alterations in the vascular system? If there is background on this, I suggest adding and discussing.

Reply: This is a very interesting point, thank you for your valuable input. We have found several studies and have added two of them in the Discussion l.592-599:

-Journal of Clinical Endocrinology and Metabolism. 2011. p. 3675–7. Fauser BCJM et al. [64]

-Climacteric. Taylor and Francis Ltd; 2017. p. 222–7. Gunning MN et al. [65]

We have also added your suggested reference:

-J Clin Endocrinol Metab. 2018;103(4):1622-1630. Meun C et al. [30]

l.592-599: “However, although PCOS patients feature increased CIMT and metabolic and cardiovascular risk at a younger age, there is evidence suggesting that they do not show higher prevalence of cardiovascular events than controls after menopause [30,64,65]. Possible explanations could be a protective effect of hyperandrogenism, especially DHEAS, in menopause, or delayed menopause in PCOS[30,64,65]. Moreover, differences in cardiovascular risk factors (e.g. diabetes, abdominal obesity) between PCOS and controls seem to be less preponderant in aging women, explaining the similar cardiovascular morbidity and mortality later in life[65].”

Materials and methods (study participants), I would you like to know if they had a range of BMI for the recruitment of women (PCOS and controls), nothing is pointed out about it. The authors only mention that the control women had a BMI similar to PCOS.

In the selection of the control group, the authors mention that it was healthy and regularly menstruating female volunteers, it is striking that they did not rule out hyperandrogenism (at least clinical). In fact, table 1 shows that there is 18.6% of hirsutism and 14% of hyperandrogenemia in the control group. In addition, they do not mention whether the control group had ovarian ultrasound. Finally, it would be appropriate to be able to confirm that the percentage of control women with hyperandrogenism have ovaries without polycystic morphology. Otherwise the question remains whether they are pure hyperandrogenic or if they have the C phenotype of Rotterdam?

Reply: Yes, we had a specific range of BMI for the recruitment of our study subjects. It is reported in the Materials and Methods as exclusion criteria in l.153, i.e. “BMI <17 kg/m2 or >50 kg/m2”.

Actually, we did rule out PCOS in controls with hyperandrogenism. In fact, 8 women of the control group showed hirsutism and 6 hyperandrogenemia. However, hyperandrogenemia was considered mandatory in order to fulfill the criterion of hyperandrogenism in this study (l.139-140). Therefore, in these 6 regularly-cycling women, we performed transvaginal ultrasound to make sure that they did not feature polycystic ovaries and did not belong to phenotype 3 of Rotterdam, as you correctly pointed out. Given that ultrasound was normal in these 6 patients, we then included them in our study. We did not mention this approach in the manuscript for the sake of space, but we have added it now in the Materials and Methods:

l.149-151: “PCOS was ruled out in all controls according to the Rotterdam ESHRE/ASRM criteria[3], as transvaginal ultrasound was performed in case of hyperandrogenism to exclude PCOS phenotype 3.”

l.170-171 “Pelvic ultrasound was further performed in PCOS patients and controls with hyperandrogenism. ”

In this article, the authors diagnose PCOS according to the Rotterdam criteria, giving the frequency of each phenotype, however in the results they do not report this data associated with CIMT. What was the reason for reporting those frequencies by phenotype? On the other hand, this idea is retaken in the discussion (lines 292-295), but without mentioning the results of the present study.

Reply: The frequency of each phenotype was only given in a descriptive manner, in order to characterize our study subjects in the most precise manner. As pointed out in the Discussion (l.621-623), the sample size was too small in order to be able to perform statistical analyses among each phenotype. Therefore, we did not mention the results of the present study concerning cardiovascular risk profile according to phenotypes in the Discussion either.

The way that the results are organized in tables 1-3 is confusing. The data does not appear to be grouped in the proper order, to simplify reading and interpretation. I think the anthropometric, clinical and demographic data should go in the first table, since they are the ones obtained in the first instance; then hormonal, metabolic and CIMT measurement. The processed data (applying cut-off or classification values) should be shown in the final table or at the end of their respective tables (raw data). I suggest including the free androgen index, as it is a good reflection of hyperandrogenism (testosterone action).

In table 1, draws attention to the high percentage of acne, both in the PCOS and in the control group. Is there any bias in the selection of the groups? How was this parameter quantified (presence or absence)? How could these values be explained?

Reply: Thank you for your comment. In fact, given that we have a lot of data, we tried to dispose it as clearly as possible in as few tables as possible. As you suggested, we have added anthropometric data, i.e. WC, HC and WHR, in Table 1 and TC/HDL > 4 in Table 3. However, in our opinion, merging hormonal, metabolic and CIMT measurements in one table would lead to a table that is too long and unclear, so we would prefer to keep two tables, i.e. Table 2 and 3. Moreover, cut-off values for hyperandrogenism were not added in Table 3, as this would make the table too long as well. Besides, there are part of the baseline classification of the study population and should therefore be presented in Table 1 in our opinion. Perhaps the added subclassification with “*” makes it more clear. We have therefore modified the following sentence and tables:

l.251-252: “Baseline demographic and anthropometrical parameters and clinical characteristics of the study population are exposed in Table 1.”

l.256-260: “PCOS patients also exhibited preponderance of abdominal obesity, as revealed by significantly increased WC, WHR (P<0.001) and HC (P=0.019) and elevated prevalence of WC>80 cm (P=0.001). As expected[3], they further featured significantly higher prevalence of ovulatory dysfunction, hirsutism and hyperandrogenemia (P<0.001) and increased age at menarche (P=0.010).”

Regarding the free androgen index, thank you for your suggestion, we included it into our analyses now. It is exposed in Tables 3 and 4, as well as in the text as follows:

l.175-176: “Free androgen index (FAI) was calculated as follows: (total testosterone[nmol/L]/SHBG[nmol/L])x100[32].”

l.390-394: “PCOS patients further presented with significantly higher levels of all measured androgens, FAI, 17-OHP, luteinizing hormone (LH), LH/FSH (follicle-stimulating hormone) ratio and Anti-Müllerian hormone (AMH) (P≤0.001), and lower levels of SHBG (P=0.006) and FSH (P=0.008).”

l.408-412: “Several cardiovascular risk factors and hormonal parameters were found to be significantly positively correlated with CIMT in our subjects (Table 4), including total testosterone, free testosterone, androstenedione, FAI, AMH, LH/FSH ratio, WC, WHR, BMI, FGS and apolipoprotein B (P<0.001), as well as 17-OHP, LDL-C, smoking, TC, triglycerides, TC/HDL-C ratio, DHEAS and SBP (P<0.05).”

l.540-558: “Besides, the role of testosterone as crucial determinant of CIMT increase is further emphasized by the positive relationship of CIMT seen with FAI, which represents a good reflection of testosterone action.”

Concerning acne, its presence was quantified subjectively, due to patient reports or when clinically visible. In fact, the high prevalence could be explained by the fact that we included a lot of young subjects in our study. On top of that, acne is not a reliable reflection of hyperandrogenemia, therefore hyperandrogenemia was considered mandatory in order to fulfill the criterion of hyperandrogenism in this study, as explained in l. 139-140. We have added the following sentence in the Discussion:

l.609-617: “Besides, the presence of acne was quantified subjectively, thus no major focus should be put on its prevalence.”

The most comparable results with this study would be those of early reproductive age women with PCOS, such as those suggested below and that have not been included in the discussion.

J Clin Endocrinol Metab. 2018;103(4):1622-1630. Meun C et al.

Menopause. 2012; 19(1):10–15. Munir JA et al.

Indian J Endocrinol Metab. 2016;20(5):662-666. Garg N et al.

Gynecol Endocrinol. 2015;31(6):477-82. Yilmaz SA et al.

Int J Prev Med. 2013;4(11):1266-70. Allameh Z et al.

Reply: Thank you very much for your suggestion, we have already included a lot of references in our manuscript, so we cannot cite all of them. We had a look at the five papers you have mentioned and decided to include the following ones:

-J Clin Endocrinol Metab. 2018;103(4):1622-1630. Meun C et al. [30]

l. 89-91: In fact, hyperandrogenism is said to lead to an increase in CIMT through its proatherogenic effect[20], but numerous studies have also demonstrated an inverse correlation of CIMT with androgen levels in women[27,28,29,30].

l. 576-588: “Furthermore, our study revealed suspected duration of disease as predictor of CIMT, which is a new finding that has not been reported so far to our knowledge. Therefore, oligomenorrhoic patients seem to feature higher CIMT the earlier onset of disease occurs. Although suspected duration of disease is related to the subjects’ age, it also depends on the age of menarche that varies in each patient. Besides, PCOS patients have later menarche than healthy controls[30,64,65], therefore it does not totally equate to the age.”

l.592-596: “However, although PCOS patients feature increased CIMT and metabolic and cardiovascular risk at a younger age, there is evidence suggesting that they do not show higher prevalence of cardiovascular events than controls after menopause [30,64,65]. Possible explanations could be a protective effect of hyperandrogenism, especially DHEAS, in menopause, or delayed menopause in PCOS[30,64,65].

-Indian J Endocrinol Metab. 2016;20(5):662-666. Garg N et al. [34]

l.449-451:“There is evidence that women with PCOS feature an increased CIMT compared to healthy controls[20,21,34], with reported mean differences in CIMT ranging from 0.06 mm[35] to 0.14 mm[24,36].”

However, we did not include the following ones and hope that this is okay for the reviewer:

-Menopause. 2012; 19(1):10–15. Munir JA et al.: In fact, Munir et al. examined perimenopausal women, with a mean age of 50 years, whereas we had women of reproductive age between 18 and 34 years.

-Gynecol Endocrinol. 2015;31(6):477-82. Yilmaz SA et al.: In fact, authors examined radial IMT and not carotid IMT.

-Int J Prev Med. 2013;4(11):1266-70. Allameh Z et al.: In fact, Allameh et al. examined women between 35 and 50 years, with a mean age of 38 years, which might also include perimenopausal women, whereas we had an age-range of 18 to 34 years.

If the reviewer still wants us to include these references, we shall be happy to do so in a future revision process.

Reviewer #2:

1. Please explain the definition of biochemical hyperandrogenism. How is the local reference defined?

Reply: The definition of biochemical hyperandrogenism is exposed in the Materials and Methods l.136-139. We have added the word “normal” in l.137 to make it more clear.

l.136-139: “Biochemical hyperandrogenism was defined according to the local laboratory’s normal reference ranges as at least one of the following conditions: dehydroepiandrosterone sulfate (DHEAS) >3.7 μg/mL, free testosterone >0.22 ng/mL, total testosterone >0.48 ng/mL, androstenedione >4.1 ng/mL.”

2. In abstract and method, age matched and similar BMI controls were enrolled. Please describe the criterion for matching in detail in the manuscript.

Reply: Thank you for pointing that out. We have added this information in the Materials and Methods l.215-223:

l.215-223:“Age matching was achieved by ensuring that overall age distribution in terms of mean and standard deviation in both groups is roughly the same.”

3. What is oligomenorrhea onset (years after menarche) as 0.0 (0.0-3.0) in Table 1?

Is “Suspected duration of disease since onset” not directly related to the age of the subjects?

Reply: We have added the following explanation in the Materials and Methods l.164-166:

l.164-166: “We further defined the suspected starting point of disease in PCOS patients as the onset of oligomenorrhea. Therefore, when oligomenorrhea started right at menarche, it was noted as starting 0 years after menarche, meaning that 0 years equals menarche.”

Concerning the relation of “suspected duration of disease” and “age”, we have added the following explanation in the Discussion l.576-588:

l.576-588: “Furthermore, our study revealed suspected duration of disease as predictor of CIMT, which is a new finding that has not been reported so far to our knowledge. Therefore, oligomenorrhoic patients seem to feature higher CIMT the earlier onset of disease occurs. Although suspected duration of disease is related to the subjects’ age, it also depends on the age of menarche that varies in each patient. Besides, PCOS patients have later menarche than healthy controls[30,64,65], therefore it does not totally equate to the age.”

4. Why did not the authors compare of CIMT between the control group and hyperandrogenic and non-hyperandrogenic PCOS patients?

Reply: Our sample size was too small to be able to compare CIMT between controls and hyperandrogenic and non-hyperandrogenic PCOS patients. A larger sample size would be needed in order to yield statistical significance, as described in the Discussion l.603-609 and l.621-623.

5. The authors need to explain the table 5 in detail. They stated that multiple linear regression analysis was carried out in order to identify independent factors that predict CIMT as the dependent continuous variable. What is the dependent variable? If CIMT is the dependent variable, the authors should analyze using univariate linear regression analysis with CIMT as the dependent variable.

Reply: In fact, as explained in the Results l.419-420, as well as in the title of Table 5, CIMT is the dependent variable. As stated in l.420-422, model 1 revealed that the diagnosis of PCOS was the strongest predictor of CIMT when considering PCOS patients and controls as a group, explaining 70% of its variability, as quantified by the coefficient of determination R2, which expresses the proportion of variability in the dependent variable, i.e. CIMT, explained by the model (see definition l.238-240). In order to show that PCOS remains the primary predictor of CIMT, independent of BMI, age and smoking status, multiple adjustments had to be made for these 3 variables (l.422-424). This required a multiple linear regression analysis and not univariate linear regression analysis.

Reviewer #3:

Nevertheless, methodology is not accurate, and conclusions are not completely supported by the reported data. Authors should clarify some point and improve the results and discussion.

Reply: Thank you for your feedback. We have improved the Results and Discussion by modifying Tables 1-4 (see above), including the free androgen index (FAI) into our analyses and adding the following elements:

Results:

l.251-252: “Baseline demographic and anthropometrical parameters and clinical characteristics of the study population are exposed in Table 1.”

l.256-260: “PCOS patients also exhibited preponderance of abdominal obesity, as revealed by significantly increased WC, WHR (P<0.001) and HC (P=0.019) and elevated prevalence of WC>80 cm (P=0.001). As expected[3], they further featured significantly higher prevalence of ovulatory dysfunction, hirsutism and hyperandrogenemia (P<0.001) and increased age at menarche (P=0.010).”

l.390-394: “PCOS patients further presented with significantly higher levels of all measured androgens, FAI, 17-OHP, luteinizing hormone (LH), LH/FSH (follicle-stimulating hormone) ratio and Anti-Müllerian hormone (AMH) (P≤0.001), and lower levels of SHBG (P=0.006) and FSH (P=0.008).”

l.408-412: “Several cardiovascular risk factors and hormonal parameters were found to be significantly positively correlated with CIMT in our subjects (Table 4), including total testosterone, free testosterone, androstenedione, FAI, AMH, LH/FSH ratio, WC, WHR, BMI, FGS and apolipoprotein B (P<0.001), as well as 17-OHP, LDL-C, smoking, TC, triglycerides, TC/HDL-C ratio, DHEAS and SBP (P<0.05).”

Discussion:

l.540-558: “Besides, the role of testosterone as crucial determinant of CIMT increase is further emphasized by the positive relationship of CIMT seen with FAI, which represents a good reflection of testosterone action.”

l.576-591: “Furthermore, our study revealed suspected duration of disease as predictor of CIMT, which is a new finding that has not been reported so far to our knowledge. Therefore, oligomenorrhoic patients seem to feature higher CIMT the earlier onset of disease occurs. Although suspected duration of disease is related to the subjects’ age, it also depends on the age of menarche that varies in each patient. Besides, PCOS patients have later menarche than healthy controls[30,64,65], therefore it does not totally equate to the age. The observed relationship between suspected duration of disease and CIMT in oligomenorrhoic patients thus suggests even more that PCOS itself contributes to the enhancement of atherosclerosis, probably due to hyperandrogenism, an adverse lipid profile and hyperinsulinemia.”

l.592-599: “However, although PCOS patients feature increased CIMT and metabolic and cardiovascular risk at a younger age, there is evidence suggesting that they do not show higher prevalence of cardiovascular events than controls after menopause [30,64,65]. Possible explanations could be a protective effect of hyperandrogenism, especially DHEAS, in menopause, or delayed menopause in PCOS[30,64,65]. Moreover, differences in cardiovascular risk factors (e.g. diabetes, abdominal obesity) between PCOS and controls seem to be less preponderant in aging women, explaining the similar cardiovascular morbidity and mortality later in life[65].”

l.609-617: “Besides, the presence of acne was quantified subjectively, thus no major focus should be put on its prevalence.”

1. Results and statistical methods. I would suggest investigating the multicollinearity between PCOS and cardiovascular risk factors. The strong association between them and PCOS may explain the cardiovascular risk reported in these patients. Age, BMI, and smoking status are not the only possible confounders in the association between PCOS and cardiovascular risk.

Reply: Our primary hypothesis was to demonstrate that there is a difference in CIMT between PCOS and controls. We succeeded in showing this in our study. However, in order to analyze the exact factors that play a role in CIMT increase in PCOS, our sample size was too small. Therefore, we limited ourselves to a model showing that even when taking the most known confounders into account, i.e. age, BMI and smoking status, PCOS was still the primary predictor of CIMT.

However, we did explore multicollinearity between PCOS and cardiovascular risk factors. In fact, as described in the Materials and Methods l.230-241, after adjusting for BMI, age and smoking status, factors found to be significantly associated with CIMT in individual bivariate correlation were entered sequentially as independent factors into the regression model. First, the independent variable best correlated with CIMT was included, then the one with the next highest correlation, checking for multicollinearity and normal distribution of the residuals. However, no statistical significance was found for further hormonal and cardiovascular risk factors, i.e. androgens or lipidemic parameters, probably due to multicollinearity with the PCOS status (l.430-432). They were therefore excluded from the predictive model.

Due to high multicollinearity and the small sample size of the study, we thus decided not to publish these results or investigate further, given that the small sample size could lead to a bias. We have therefore only created a model with age, BMI and smoking status in order to subtract their potential confounding effect on CIMT (see Table 5). As pointed out in the Discussion (l.621-623), a larger sample size is needed in order to investigate the effect of other cardiovascular risk factors found to be significantly associated with CIMT in correlation analysis, like hyperandrogenism for example.

2. Methods. It is not clear why the Authors used the correlation coefficient instead of univariate linear regression.

Reply: Univariate linear regression analysis would not add any new information to the one obtained with Spearman’s rank correlation analysis (rho). That is why solely correlation and multiple linear regression analyses were performed in this study. Besides, unlike univariate linear regression analysis, Spearman’s rank coefficient does not assume linear dependencies and it is also more robust to outliers. Therefore, it is in our opinion a more adequate statistical tool for analysis of our data.

3. Discussion. Lines 282. This point is unclear. The PCOS is a complex disorder and it is probably that specific included metabolic factors are the cause of increased CIMT in PCOS women. It is of paramount importance to identify these elements as possible target of preventive treatments.

Reply: The primary aim of our study was to demonstrate that there is a difference in CIMT between PCOS and controls. We succeeded in showing this in our research. In order to analyze the exact factors that play a role in CIMT increase in PCOS, our sample size was too small. However, with our results from correlation and multiple regression analysis, we tried to identify those elements in an explorative manner. There are exposed in the Discussion l. 460-591. In fact, the sentence “These findings suggest that the disorder itself is playing a causative role in CIMT increase” was only supposed to serve as an introduction for the following detailed analysis of those parameters.

4. Conclusion. Lines 391-393. Based in the results, the role of hyperandrogenism as crucial determinant of CIMT is not demonstrated.

Reply: It is true that we did not include hyperandrogenemia into our regression model, due to multicollinearity with the PCOS status. However, in correlation analysis, markers of hyperandrogenemia, i.e. total testosterone, free testosterone and androstenedione showed the strongest positive correlation with CIMT (P<0.001) among all analyzed cardiovascular risk factors. Besides, unpaired two-tailed Student’s t-test revealed significantly higher CIMT values among the entire study population in case of hyperandrogenemia (0.47 ± 0.06 (n=38) vs. 0.40 ± 0.07 (n=46), P<0.001). Therefore, the role of hyperandrogenism as crucial determinant of CIMT is demonstrated in our opinion. In order to emphasize our results, we have now included the free androgen index (FAI) into our analyses, given that it represents a good reflection of testosterone action. In fact, FAI was significantly higher in PCOS patients (P<0.001, see Table 3) and it was also significantly positively correlated with CIMT (P<0.001, see Table 4). We have therefore added the following information into our manuscript:

l.175-176: “Free androgen index (FAI) was calculated as follows: (total testosterone[nmol/L]/SHBG[nmol/L])x100[32].”

l.390-394: “PCOS patients further presented with significantly higher levels of all measured androgens, FAI, 17-OHP, luteinizing hormone (LH), LH/FSH (follicle-stimulating hormone) ratio and Anti-Müllerian hormone (AMH) (P≤0.001), and lower levels of SHBG (P=0.006) and FSH (P=0.008).”

l.408-412: “Several cardiovascular risk factors and hormonal parameters were found to be significantly positively correlated with CIMT in our subjects (Table 4), including total testosterone, free testosterone, androstenedione, FAI, AMH, LH/FSH ratio, WC, WHR, BMI, FGS and apolipoprotein B (P<0.001), as well as 17-OHP, LDL-C, smoking, TC, triglycerides, TC/HDL-C ratio, DHEAS and SBP (P<0.05).”

l.540-558: “Besides, the role of testosterone as crucial determinant of CIMT increase is further emphasized by the positive relationship of CIMT seen with FAI, which represents a good reflection of testosterone action.”

5. Conclusion. Lines 394. This statement is not supported by results. A complete multivariate regression analysis was not performed, the collinearity needs to be better investigated and assessed. A backward method could be better with an appropriate evaluation of collinearity by the use of variance inflation factor.

Reply: We would kindly refer the reviewer to our reply to comment 1, as we can give the same response to this comment as well.

6. I would suggest improving the introduction reporting about the role of insulin resistance, that is one of the most important mechanisms of PCOS pathogenesis. For this reason, the use of insulin-sensitizers, such an inositol isoform, gained increasing attention due to their safety profile and effectiveness. Authors may better discuss this point, taking to account these recent articles: PMID: 30270194.

Reply: Thank you for your valuable comment, we have added the suggested reference in the Introduction l.60-62:

l.60-62: “IR being a key component of the pathophysiology of PCOS, treatment options with insulin-sensitizers, such as metformin or inositol isoforms[11], are increasingly being used due to their beneficial effects on metabolic and hormonal parameters.”

Submitted filename: Response_To_Reviewers_Rhea_Jabbour.docx

Click here for additional data file.

13 Mar 2020

PONE-D-19-30548R1

Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles

PLOS ONE

Dear Dr. Jabbour,

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Reviewer #1: The authors have addressed all the comments of the reviewers and the manuscript has been improved. However, I have additional minor comments:

-In the sentence (592-599): “Possible explanations could be a protective effect of hyperandrogenism, especially DHEAS, in menopause, or delayed menopause in PCOS.

I disagree with this statement and I find it contradictory to what is stated in the article. It seems to me that it is unlikely that hyperandrogenism (DHEAS) has a protective effect. Probably to be an estrogenic effect, as some articles show. “Meun et al. hypothesize that later menopause with prolonged estrogen exposure may have a protective role against CVD. (Meun, C., J Clin Endocrinol Metab. 2018; 103: 1622–1630).

Although some articles have reported that PCOS women have lower basal estradiol level, it has also been described that this value would be comparable to control women. Moreover, a study has showed higher peak estradiol level in PCOS patients (Eur J Obstet Gynecol Reprod Biol. 2013 Sep;170(1):165-70). Which could favor the testosterone/estradiol ratio towards estradiol effect as women with PCOS are older and androgens decreased.

-The range of BMI that the authors report is quite wide (17-50 kg/m2). Atypical outcomes are usually observed in women with BMI> 40. Did you observe any particularity in them or behave within the average? Remember the contribution of steroids in the adipose tissue of women with high BMI.

- Regarding transvaginal ultrasound that was performed in 6 regularly-cycling women in order to exclude PCOS phenotype. How long after the initial study was performed? Remember that the ovarian image may vary over time. I suggest indicating in materials and methods so that the interpretation is at the reader criteria

Reviewer #2: The authors revised the manuscript generally well according to my comments.

However, I still have a question. Have the authors analyzed if there is a difference in CIMT between obese PCOS patients and non-obese PCOS patients? It is recommended to present the results of this subgroup analysis.

Reviewer #3: The Authors clarified different points of the analysis improving the overall value of the manuscript that can be considered for publication.

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10 Apr 2020

Please find below our response to the points raised by the reviewers. The mentioned lines of the manuscript correspond to those of the document “Revised_Manuscript_With_Track_Changes_Rhea_Jabbour”, as they differ from the ones in the Manuscript without tracked changes.

Reviewer #1:

The authors have addressed all the comments of the reviewers and the manuscript has been improved. However, I have additional minor comments:

-In the sentence (592-599): “Possible explanations could be a protective effect of hyperandrogenism, especially DHEAS, in menopause, or delayed menopause in PCOS.” I disagree with this statement and I find it contradictory to what is stated in the article. It seems to me that it is unlikely that hyperandrogenism (DHEAS) has a protective effect. Probably to be an estrogenic effect, as some articles show. “Meun et al. hypothesize that later menopause with prolonged estrogen exposure may have a protective role against CVD. (Meun, C., J Clin Endocrinol Metab. 2018; 103: 1622–1630).

Although some articles have reported that PCOS women have lower basal estradiol level, it has also been described that this value would be comparable to control women. Moreover, a study has showed higher peak estradiol level in PCOS patients (Eur J Obstet Gynecol Reprod Biol. 2013 Sep;170(1):165-70). Which could favor the testosterone/estradiol ratio towards estradiol effect as women with PCOS are older and androgens decreased.

Reply: Thank you very much for your valuable comment. We have clarified this point as exposed below.

l.388-396: “However, although women with PCOS feature increased CIMT and metabolic and cardiovascular risk at a younger age, there is evidence suggesting that they do not show higher prevalence of cardiovascular events than controls after menopause[30,64,65]. Possible explanations could be a protective effect of delayed menopause with a consequently prolonged estrogen exposure in PCOS[30,64,65] or even hyperandrogenism itself[64,65] in peri- and postmenopausal PCOS patients, as suggested by previous studies[27,28,29], probably mainly due to enzymatic conversion to estrogen. Moreover, differences in cardiovascular risk factors (e.g. diabetes, abdominal obesity) between PCOS and controls seem to be less preponderant in aging women, explaining the similar cardiovascular morbidity and mortality later in life[65].”

-The range of BMI that the authors report is quite wide (17-50 kg/m2). Atypical outcomes are usually observed in women with BMI> 40. Did you observe any particularity in them or behave within the average? Remember the contribution of steroids in the adipose tissue of women with high BMI.

Reply: We have only included 4 PCOS patients and no controls with a BMI > 40 kg/m2 in our study. Therefore, the sample size was too small in order to be able to analyze any atypical outcomes in these subjects.

-Regarding transvaginal ultrasound that was performed in 6 regularly-cycling women in order to exclude PCOS phenotype. How long after the initial study was performed? Remember that the ovarian image may vary over time. I suggest indicating in materials and methods so that the interpretation is at the reader criteria

Reply: Thank you very much for your suggestion. In fact, transvaginal ultrasound was performed either on the same day of blood tests, if past medical history suggested hyperandrogenemia, or the following day, once laboratory results were obtained. It was therefore performed between the third and the fifth day of the subjects’ menstrual cycle. We have included this precision in the Materials and Methods l.121-123:

l.121-123: “PCOS was ruled out in all controls according to the Rotterdam ESHRE/ASRM criteria[3], as transvaginal ultrasound was performed between the third and the fifth day of their menstrual cycle in case of hyperandrogenism to exclude PCOS phenotype 3.”

Reviewer #2:

The authors revised the manuscript generally well according to my comments.

However, I still have a question. Have the authors analyzed if there is a difference in CIMT between obese PCOS patients and non-obese PCOS patients? It is recommended to present the results of this subgroup analysis.

Reply: Thank you very much for your feedback. Our sample size was too small in order to be able to compare CIMT between obese and non-obese PCOS patients. A larger sample size would be needed in order to yield statistical significance, as described in the Discussion l.418-420.

However, the impact of obesity on CIMT was analyzed in this study among the entire study population. Our results showed that visceral obesity was positively associated with CIMT, both in correlation analysis (see l.258-259) and multiple linear regression analysis (see l.274-275), as BMI represented an independent positive predictor of CIMT according to model 2. However, as stated in l.276-278, “PCOS status remained the primary predictor of CIMT, even after multiple adjustments for BMI, age and smoking status (P<0.001, R2=0.73, models 3 and 4), therefore indicating an independent effect of PCOS on CIMT (β=0.797, P<0.001)”.

Reviewer #3:

The Authors clarified different points of the analysis improving the overall value of the manuscript that can be considered for publication.

Reply: Thank you very much for your feedback and your positive recommendation.

Submitted filename: Response_To_Reviewers_Rhea_Jabbour.docx

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13 Apr 2020

Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles

PONE-D-19-30548R2

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Authors performed the required corrections. I am pleased to accept this paper for publication.

Reviewers' comments:

15 Apr 2020

PONE-D-19-30548R2

Carotid intima-media thickness in polycystic ovary syndrome and its association with hormone and lipid profiles

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