Relationship between hematologic parameters related to systemic inflammation and insulin resistance-associated metabolic parameters in women with polycystic ovary syndrome

Minkyung Cho; Suji Kim; Sungwook Chun

doi:10.5653/cerm.2023.05932

Clin Exp Reprod Med > Volume 50(3); 2023 > Article

Cho, Kim, and Chun: Relationship between hematologic parameters related to systemic inflammation and insulin resistance-associated metabolic parameters in women with polycystic ovary syndrome

Original Article

Clinical and Experimental Reproductive Medicine 2023;50(3):206-212.

Published online: May 19, 2023

DOI: https://doi.org/10.5653/cerm.2023.05932

Relationship between hematologic parameters related to systemic inflammation and insulin resistance-associated metabolic parameters in women with polycystic ovary syndrome

Minkyung Cho

, Suji Kim, Sungwook Chun

Department of Obstetrics and Gynecology, Inje University Haeundae Paik Hospital, College of Medicine, Inje University, Busan, Republic of Korea

Corresponding author: Sungwook Chun Department of Obstetrics and Gynecology, Inje University Haeundae Paik Hospital, College of Medicine, Inje University, 875 Haeun-daero, Haeundae-gu, Busan 48108, Republic of Korea
Tel: +82-51-797-2020 Fax: +82-51-797-2030 E-mail: wooki1974@empal.com

Received January 30, 2023 Revised March 24, 2023 Accepted April 25, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Objective

The aim of the present study was to evaluate the associations between hematologic parameters related to systemic inflammation and insulin resistance-associated metabolic parameters in women with polycystic ovary syndrome (PCOS).

Methods

Eighty-two women between the ages of 18 and 35 years who were diagnosed with PCOS were included in this study. A 2-hour 75-g oral glucose tolerance test (OGTT) was administered to all study participants; fasting and postprandial glucose and insulin levels were measured simultaneously during the 2-hour OGTT. Hematologic parameters were derived from a standard complete blood count and a differential count of fasting-state blood samples. The correlations between hematologic parameters and insulin resistance-associated clinical and metabolic parameters were evaluated using the Spearman rank correlation and partial correlation coefficients. Hematologic parameters related to systemic inflammation were compared between the two groups, categorized by the presence or absence of insulin resistance.

Results

Significant differences in the absolute neutrophil count, absolute monocyte count, platelet count, and neutrophil-lymphocyte ratio were found between the insulin-resistant group and insulin-nonresistant group. Correlation analysis found that all hematological parameters, except for the platelet-lymphocyte ratio, were associated with at least one insulin resistance-associated metabolic parameter. However, these significant correlations between hematological and metabolic parameters were attenuated after controlling for the effects of other covariates using partial correlation analysis.

Conclusion

The association between hematologic parameters indicative of systemic inflammation and insulin resistance-associated metabolic parameters seems to be strongly influenced by other anthropometric covariates in women with PCOS.

Keywords: Hematologic parameters; Inflammation; Insulin resistance; Polycystic ovary syndrome

Introduction

Polycystic ovary syndrome (PCOS) is the most common endocrine disease and affects between 5% and 10% of reproductive-age women [1]. PCOS is a complex disease associated with both reproductive problems and metabolic disturbances. Insulin resistance is the cardinal component involved in the pathogenesis of metabolic disorders such as impaired glucose tolerance (IGT), type 2 diabetes mellitus (T2DM), dyslipidemia, and cardiovascular disease in PCOS [2,3]. Although the pathophysiology of insulin resistance has not been clearly established, it is known that systemic inflammation is an important factor in triggering insulin resistance [4-6].

Systemic inflammation is associated with the pathogenesis of a variety of chronic diseases. C-reactive protein (CRP) is a very useful and commonly used parameter for evaluating systemic inflammation [7,8]. Hematologic parameters derived from the complete blood count and differential count have recently emerged as indicators of systemic inflammation due to their cost-effectiveness and convenience [9,10].

Several studies have confirmed the relationship between hematologic parameters associated with systemic inflammation and metabolic parameters indicating insulin resistance [11-13]. However, most studies have been conducted among patients who have metabolic diseases, such as coronary heart disease or T2DM. To our best knowledge, studies on insulin resistance and inflammation in women with PCOS are lacking or inconclusive [10,14-16].

Therefore, the purpose of this study was to evaluate the association between hematologic parameters indicative of systemic inflammation and insulin resistance-associated metabolic parameters in women with PCOS.

Methods

1. Participants

Korean women between the ages of 18 and 35 years who were newly diagnosed with PCOS at Inje University Haeundae Paik Hospital from January 2010 to December 2013 were recruited for this study. Among patients previously diagnosed with PCOS according to the 2003 Rotterdam criteria, those who met the diagnostic criteria of the recently revised international consensus guidelines for PCOS were included in this study, after the exclusion of patients with other etiologies (congenital adrenal hyperplasias, androgen-secreting tumors, and Cushing's syndrome) [17,18]. Irregular menstrual cycles were defined as menstrual cycles longer than 35 days or fewer than 21 days, or longer than 90 days for any one cycle, or less than eight cycles per year in women with PCOS [18]. Primary amenorrhea by age 15 or >3 years post-thelarche was also included in the category of irregular menstrual cycles. Clinical hyperandrogenism was defined by the presence of hirsutism (modified Ferriman-Gallwey score >6) [18], and biochemical hyperandrogenism was defined as an elevated serum androgen level beyond the 95% confidence limits defined in controls in a study conducted on Korean women with PCOS (total testosterone >0.68 ng/mL and/or free testosterone >1.72 pg/mL) [19]. Patients who were previously or newly diagnosed with diabetes, thyroid disease, or hyperprolactinemia; those with a history of ovarian surgery, use of a medication known to affect sex hormone or gonadotropin levels within 6 months of enrollment in the study (e.g., oral contraceptives, ovulation induction agents, glucocorticoids, or antiandrogens); and those who were taking antidiabetic drugs, including insulin sensitizers, were excluded from this study [20-23]. This retrospective study was approved by the Institutional Review Board of Inje University Haeundae Paik Hospital (IRB No. 129792-2014-035), which waived the requirement for patient informed consent in the present study.

2. Measurement of anthropometric parameters and ultrasound examinations

Clinical variables such as age, parity, height, body weight, body mass index (BMI), waist circumference, hip circumference, and waist-to-hip ratio (WHR) were evaluated for all study participants when they first visited the outpatient department. Pelvic ultrasound examinations (transvaginal or transrectal [24]) were conducted in the early follicular phase using a Voluson LOGIQ S7 (GE Ultrasound Korea Ltd.) equipped with a microconvex intracavitary probe with a frequency range of 3.6 to 9.0 MHz. Transvaginal ultrasound was performed in all patients except for eight patients who underwent transrectal ultrasound because they were virgins without coital history. Polycystic ovarian morphology was defined as the presence of over 20 follicles (2 to 9 mm in size) and/or an ovarian volume of over 10 mL [17,18,24]. All ultrasound examinations were performed by the same expert in ultrasound for reproductive endocrinology based on the international consensus on ultrasound assessment of PCOS [25].

3. Biochemical measurements and determination of hyperglycemia

This study was conducted according to the guidelines of the Declaration of Helsinki. Blood samples for laboratory analyses were taken from all subjects in the early follicular phase after overnight fasting. Hematologic parameters, including the white blood cell count, absolute neutrophil count (ANC), absolute lymphocyte count, absolute monocyte count (AMC), and platelet count (PC), were derived from a standard complete blood count and a differential count of fasting-state blood samples. The hematologic parameters were calculated from a combination of these parameters, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio. Serum glucose and insulin levels were analyzed using an L-Type GluI device (Wako) and an Elecsys Insulin assay (Roche), respectively. Cholesterol and triglyceride levels were measured using Pureauto S (Sekisui), and serum high-density lipoprotein and low-density lipoprotein levels were measured using Cholestest (Sekisui). Both intra- and inter-assay coefficients of variation for all assays were below 8%. Postprandial glucose and insulin levels were measured 60 and 120 minutes after glucose ingestion during a 2-hour 75-g oral glucose tolerance test.

In the present study, hyperglycemia, including prediabetes (high fasting glucose or IGT) and diabetes, was diagnosed based on the American Diabetes Association criteria of fasting glucose ≥100 mg/dL or 2-hour postload glucose ≥140 mg/dL [23,26].

4. Assessment of insulin sensitivity and determination of insulin resistance

Insulin sensitivity assessment indices (ISAIs) were calculated for all study participants. Established fasting ISAIs derived from a combination of fasting insulin and glucose levels were calculated as follows [20-23]: homeostatic model assessment of insulin resistance (HOMA-IR) was calculated as the glucose level (mg/dL)×insulin level (μU/mL)/405; the glucose-to-insulin ratio (GIR) was calculated by dividing the glucose level (mg/dL) by the insulin level (μU/mL); and quantitative insulin sensitivity check index (QUICKI) was calculated as 1/{log[insulin level (μU/mL)]+log[glucose level (mg/dL)]}. Patients with PCOS who showed abnormal levels for at least one of the established ISAI criteria in previous studies were defined as having abnormal insulin sensitivity: fasting insulin ≥15 µIU/mL [27], HOMA-IR ≥2.64 [28], GIR ≤10.7, or QUICKI ≤0.34 [29]; and postprandial 2-hour insulin ≥45 µIU/mL [30]. In the present study, insulin resistance was determined as the presence of abnormal insulin sensitivity or hyperglycemia [23].

5. Statistical analyses

Values are expressed as the mean±standard deviation. The correlations between hematologic parameters and insulin resistance-associated clinical and metabolic parameters were evaluated using Spearman rank correlation coefficients and linear regression analysis, with partial correlations used to control for the effects of other confounding covariates such as age, BMI, and WHR. The Mann-Whitney U test was used to compare hematologic parameters between the two groups categorized according to the presence or absence of insulin resistance. All statistical analyses were conducted using SPSS version 25.0 (IBM Corp.), with p<0.05 considered to indicate statistical significance.

Results

In total, 82 patients with PCOS were enrolled in the present study. Table 1 shows the baseline clinical and biochemical characteristics of the study participants. Table 2 shows a comparison of hematologic parameters between those with insulin resistance and those without insulin resistance among patients with PCOS. ANC, AMC, PC, and NLR were significantly higher in the insulin-resistant group than in the insulin-nonresistant group.

In the correlation analysis, all hematological parameters except for the PLR were associated with at least one insulin resistance-associated biochemical metabolic parameter (Table 3). In particular, ANC showed significant positive correlations with fasting and postprandial glucose and insulin levels. Additionally, ANC showed significant correlations with all fasting-state ISAIs, including a positive correlation with HOMA-IR and negative correlations with GIR and QUICKI. However, these significant correlations between hematological and metabolic parameters were attenuated after controlling for the effects of other covariates, including age, BMI, and WHR, using partial correlation analysis (Table 4).

Discussion

PCOS is not only an endocrine disorder but also a metabolic disorder that consequently contributes to lifetime health risks. Inflammation and insulin resistance are cardinal components in the pathogenesis of PCOS [30]. The association between these two factors has been proven through several studies in patients with metabolic diseases such as metabolic syndrome and T2DM [4,11-13]. To date, however, few studies have been conducted on the relationship between these two factors in patients with PCOS [16]. Therefore, we aimed to evaluate the relationship between hematologic parameters related to inflammation and insulin resistance in patients with PCOS. In the present study, ANC, AMC, PC, and NLR were significantly higher in the insulin-resistant group than in the insulin-nonresistant group, and most systemic inflammation-related hematologic parameters were significantly associated with at least one insulin resistance-associated metabolic parameter in women with PCOS. However, most of these strong associations were substantially attenuated after controlling for covariates such as age, BMI, and WHR.

As previously mentioned, the association between insulin resistance and inflammation has been demonstrated in several studies in patients with metabolic diseases such as T2DM and glucose intolerance [4,11-13,31,33]. Lou et al. [12] found a significant positive correlation between the NLR value and insulin resistance in patients who were newly diagnosed with T2DM, and they suggested that the NLR value can be a predictive and prognostic marker for insulin resistance.

To date, few studies have investigated the relationship between insulin resistance and chronic systemic inflammation in women with PCOS, and the results of those studies have been inconsistent [9,10,11,14-16]. Ozay and Ozay [10] compared metabolic and hormonal factors and inflammatory markers between 110 PCOS patients and 135 healthy women. They noted that the neutrophil count and PC were significantly higher in patients with PCOS, and these two parameters were also significantly correlated with BMI and WHR, similar to the findings of our study. In the study of Yilmaz et al. [34], the NLR and neutrophil count were found to be significantly higher in patients with PCOS. In a subgroup analysis, the obese PCOS group had higher insulin and HOMA-IR levels than the controls, and the NLR was positively correlated with HOMA-IR, high-sensitive CRP, BMI, waist circumference, and insulin levels [34].

Although the association between inflammatory markers and insulin resistance has been demonstrated in PCOS patients, whether obesity is an independent factor in this association remains a matter of debate. In our study, the significant correlation between systemic inflammation-related markers and insulin resistance-associated metabolic parameters in women with PCOS was strongly attenuated after controlling for the effects of age and other anthropometric parameters. Pergialiotis et al. [16] conducted a study of inflammatory markers in 266 PCOS patients. Consistent with our study, significant positive correlations were found between metabolic parameters and hematologic parameters such as the NLR and PLR. However, contradictory to our results, these associations did not change after adjusting for confounding effects due to BMI. Cakiroglu et al. [9] also suggested that the NLR and PLR were significantly elevated in all PCOS subjects, but this increase was independent of the effect of obesity, which is not in agreement with our findings. The discrepancy in the results between these two studies and ours remains difficult to explain, and whether the impact of a chronic systemic inflammatory state on the phenotypic features of PCOS is due to its own pathophysiology or due to factors such as comorbid obesity or age remains unclear. One possible explanation is that serum hormones such as androgens have an additional effect on the confounding effect of age and body weight. Blood androgen levels have been noted to be significantly correlated with inflammatory markers in PCOS patients. For example, a study suggested that androgens trigger inflammatory cells and initiate the inflammatory process [14]. Zeng et al. [35] suggested that hyperandrogenism, insulin resistance, and obesity form a vicious cycle to promote PCOS development. In women with PCOS, the prevalence of hyperandrogenism differed according to the ethnicity of the study participants [19], and it is possible that these differences in hormonal patterns of study participants resulted in the discrepancy in the confounding effects of age and BMI between other studies and ours.

De Luca and Olefsky [4] reported the mechanism by which systemic inflammation leads to insulin resistance and suggested that obesity is an important factor involved in inflammation, which strongly supports our results.

CRP is a very useful and commonly used parameter for evaluating systemic inflammation. In studies of PCOS patients, CRP has also been widely used as a parameter reflecting systemic inflammation. However, a CRP test is comparatively expensive, so it is not routinely measured in the general population. In the present study, we used systemic inflammatory markers derived from the complete blood count, which is less expensive and much more commonly assessed than CRP.

Our study has several limitations, including a retrospective study design and a relatively small sample size. When the sample size was calculated with reference to a previous study [11], the sample size was calculated as 41 people in each group (82 people in total) with an effect size=0.629, a significance level α=0.05 and a power of (1-β)=0.80.

As mentioned in the ‘discussion,’ this study did not separately evaluate hormonal effects on the confounding effects of covariates such as age, BMI, and WHR, which could be another drawback of the study

In conclusion, the association between hematologic parameters indicating chronic systemic inflammation and insulin resistance-associated metabolic parameters seems to be strongly influenced by other anthropometric covariates in women with PCOS. Further prospective large-scale trials on the relationship between insulin resistance and inflammation with additional analyses including hormonal factors are needed to clarify these preliminary findings.

Conflict of interest

No potential conflict of interest relevant to this article was reported.

Author contributions

Conceptualization: MC, SK, SC. Data curation: SC. Formal analysis: SC. Methodology: MC, SC. Project administration: SC. Visualization: MC, SC. Writing-original draft: MC. Writing-review & editing: MC, SK, SC.

Table 1.

Baseline clinical and biochemical characteristics of the study participants

Characteristic	Participants (n=82)
Age (yr)	24.39±4.42
Height (cm)	162.73±5.61
Body weight (kg)	60.86±14.64
Body mass index (kg/m²)	22.94±5.13
Waist-to-hip ratio	0.80±0.07
Fasting glucose (mg/dL)	91.77±8.00
2-hr PG (mg/dL)	110.80±26.66
Fasting insulin (μIU/mL)	8.00 (1.90–49.60)
2-hr insulin (μIU/mL)	61.15 (11.30–320.00)
HOMA-IR (fasting)	1.78 (0.36–12.86)
GIR (fasting)	13.57±9.91
QUICKI (fasting)	0.35±0.04
Cholesterol (mg/dL)	183.44±33.38
Triglyceride (mg/dL)	76.50 (22.00–238.00)
HDL (mg/dL)	61.66±17.77
LDL (mg/dL)	107.73±29.62
ANC (/µL)	3,730.33±1,499.15
ALC (/µL)	2,180.91±735.41
AMC (/µL)	415.11±160.26
AEC (/µL)	129.38±88.02
Platelet (×10³/µL)	260.51±65.99
NLR	1.82±0.89
PLR	126.72±37.65
LMR	5.69±1.89

Values are presented as mean±standard deviation ormedian (range).

2-hr PG, postprandial glucose at 2 hours; HOMA-IR, homeostasis model assessment of insulin resistance; GIR, glucose-to-insulin ratio; QUICKI, quantitative insulin sensitivity check index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; ANC, absolute neutrophil count; ALC, absolute lymphocyte count; AMC, absolute monocyte count; AEC, absolute eosinophil count; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio.

Table 2.

Comparisons of hematologic parameters derived from the white blood cell count and differential count between those with insulin resistance and those without insulin resistance among patients with polycystic ovary syndrome

Variable	Group 1 (n=23)	Group 2 (n=59)	p-value
ANC (/µL)	2,884.22±1,069.73	4,060.17±1,519.89	0.001^a)
ALC (/µL)	1,938.37±345.75	2,275.46±823.13	0.122
AMC (/µL)	334.45±102.50	446.55±168.21	0.003^a)
AEC (/µL)	121.80±76.26	132.34±92.64	0.897
PC (×10³/µL)	235.65±64.01	270.20±64.71	0.004^a)
NLR	1.51±0.54	1.95±0.97	0.047^b)
PLR	124.09±36.66	127.74±38.29	0.546
LMR	6.33±2.18	5.45±1.73	0.104

Values are presented as mean±standard deviation. p-values were obtained by the Mann-Whitney U test. Group 1: patients without insulin resistance; Group 2: patients with insulin resistance.

^a)p<0.01; ^b)p<0.05.

Table 3.

Correlations between hematologic parameters and clinical and biochemical metabolic parameters

Variable	Value	ANC	ALC	AMC	AEC	PC	NLR	PLR	LMR
Fasting glucose (mg/dL)	r	0.320^a)	0.212	0.125	0.126	0.320^a)	0.163	0.011	–0.013
Fasting glucose (mg/dL)	p	0.003	0.056	0.261	0.258	0.003	0.145	0.924	0.909
2-hr PG (mg/dL)	r	0.378^a)	0.254^b)	0.292^a)	0.026	0.138	0.189	–0.126	–0.076
2-hr PG (mg/dL)	p	<0.001	0.021	0.008	0.819	0.216	0.089	0.260	0.497
Fasting insulin (μIU/mL)	r	0.505^a)	0.420^a)	0.547^a)	0.180	0.382^a)	0.225	–0.111	–0.254^b)
Fasting insulin (μIU/mL)	p	<0.001	<0.001	<0.001	0.106	<0.001	0.043^b)	0.321	0.021
2-hr Insulin (μIU/mL)	r	0.386^a)	0.416^a)	0.425^a)	0.096	0.311^a)	0.113	–0.175	–0.103
2-hr Insulin (μIU/mL)	p	<0.001	<0.001	<0.001	0.391	0.004	0.311	0.116	0.355
HOMA-IR	r	0.511^a)	0.426^a)	0.525^a)	0.193	0.390^a)	0.228^b)	–0.112	–0.230^b)
HOMA-IR	p	<0.001	<0.001	<0.001	0.083	<0.001	0.039	0.318	0.038
GIR	r	–0.485^a)	–0.419^a)	–0.559^a)	–0.174	–0.362^a)	–0.204	0.122	0.264^b)
GIR	p	<0.001	<0.001	<0.001	0.119	0.001	0.066	0.274	0.016
QUICKI	r	–0.515^a)	–0.426^a)	–0.529^a)	–0.194	–0.389^a)	–0.232^b)	0.112	0.233^b)
QUICKI	p	<0.001	<0.001	<0.001	0.080	<0.001	0.036	0.316	0.035
Cholesterol (mg/dL)	r	–0.077	0.109	–0.010	0.084	0.322^a)	–0.155	0.096	0.058
Cholesterol (mg/dL)	p	0.491	0.329	0.932	0.453	0.003	0.166	0.391	0.604
Triglyceride (mg/dL)	r	0.310^a)	0.356^a)	0.328^a)	0.312^a)	0.349^a)	0.034	–0.118	–0.046
Triglyceride (mg/dL)	p	0.005	0.001	0.003	0.004	0.001	0.758	0.292	0.681
HDL (mg/dL)	r	–0.272^b)	–0.134^b)	–0.161	–0.033	–0.207	–0.196	0.026	0.070
HDL (mg/dL)	p	0.013	0.299	0.149	0.766	0.063	0.078	0.814	0.530
LDL (mg/dL)	r	0.020	0.099	–0.006	0.082	0.301^a)	–0.034	0.117	0.036
LDL (mg/dL)	p	0.857	0.377	0.955	0.467	0.006	0.759	0.296	0.746

ANC, absolute neutrophil count; ALC, absolute lymphocyte count; AMC, absolute monocyte count; AEC, absolute eosinophil count; PC, platelet count; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; r, Spearman rank correlation coefficient; 2-hr PG, postprandial glucose at 2 hours; HOMA-IR, homeostasis model assessment of insulin resistance; GIR, glucose-to-insulin ratio; QUICKI, quantitative insulin sensitivity check index; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

^a)p<0.01; ^b)p<0.05.

Table 4.

Correlations between hematologic parameters and clinical and biochemical metabolic parameters after adjustment for other covariates

Variable	Value	ANC	ALC	AMC	AEC	PC	NLR	PLR	LMR
Fasting glucose (mg/dL)	r^a)	0.224^b)	0.102	0.020	0.042	0.197	0.128	0.112	–0.004
Fasting glucose (mg/dL)	p	0.047	0.370	0.861	0.713	0.082	0.261	0.325	–0.012
2-hr PG (mg/dL)	r^a)	0.135	0.093	0.118	0.015	0.098	0.109	0.031	–0.919
2-hr PG (mg/dL)	p	0.234	0.414	0.300	0.897	0.392	0.340	0.784	0.468
Fasting insulin (μIU/mL)	r^a)	0.260^b)	0.097	0.171	–0.017	0.007	0.132	–0.047	–0.116
Fasting insulin (μIU/mL)	p	0.021	0.393	0.132	0.884	0.951	0.245	0.682	0.310
2-hr Insulin (μIU/mL)	r^a)	0.053	0.103	0.100	0.099	0.023	0.030	–0.049	–0.026
2-hr Insulin (μIU/mL)	p	0.642	0.368	0.383	0.386	0.838	0.794	0.667	0.823
HOMA-IR	r^a)	0.274^b)	0.129	0.172	0.005	0.024	0.129	–0.050	–0.096
HOMA-IR	p	0.014	0.258	0.130	0.967	0.833	0.259	0.663	0.401
GIR	r^a)	–0.179	–0.061	–0.257^b)	–0.075	–0.105	–0.153	–0.040	0.250^b)
GIR	p	0.114	0.596	0.022	0.513	0.358	0.178	0.729	0.026
QUICKI	r^a)	–0.267^b)	–0.080	–0.254^b)	–0.046	–0.138	–0.199	–0.062	0.245^b)
QUICKI	p	0.017	0.481	0.024	0.687	0.224	0.079	0.586	0.029
Cholesterol (mg/dL)	r^a)	–0.089	0.152	–0.018	–0.030	0.284^b)	–0.170	0.110	0.129
Cholesterol (mg/dL)	p	0.436	0.182	0.878	0.790	0.011	0.133	0.334	0.258
Triglyceride (mg/dL)	r^a)	0.102	0.307^c)	0.155	0.222	0.112	–0.015	–0.065	0.023
Triglyceride (mg/dL)	p	0.370	0.006	0.172	0.05	0.326	0.898	0.570	0.838
HDL (mg/dL)	r^a)	–0.010	0.155	0.141	0.072	–0.033	–0.075	–0.125	0.012
HDL (mg/dL)	p	0.931	0.173	0.214	0.526	0.773	0.510	0.273	0.917
LDL (mg/dL)	r^a)	–0.163	–0.191	–0.011	–0.057	0.244^b)	–0.118	0.225^b)	0.125
LDL (mg/dL)	p	0.152	0.092	0.897	0.620	0.030	0.301	0.045	0.274

^a)Partial correlation coefficient adjusted by age, body mass index, and waist-to-hip ratio; ^b)p<0.05; ^c)p<0.01.

References

1. Kuzbari O, Doralis J, Peterson CM. Endocrine disorders. In: Berek JS, editor. Berek & Novak’s gynecology. 15th ed. Lippincott Williams & Wilkins; 2012. p. 1075-80.

2. Diamanti-Kandarakis E, Papavassiliou AG. Molecular mechanisms of insulin resistance in polycystic ovary syndrome. Trends Mol Med 2006;12:324-32.

3. Diamanti-Kandarakis E, Dunaif A. Insulin resistance and the polycystic ovary syndrome revisited: an update on mechanisms and implications. Endocr Rev 2012;33:981-1030.

4. de Luca C, Olefsky JM. Inflammation and insulin resistance. FEBS Lett 2008;582:97-105.

5. Deligeoroglou E, Vrachnis N, Athanasopoulos N, Iliodromiti Z, Sifakis S, Iliodromiti S, et al. Mediators of chronic inflammation in polycystic ovarian syndrome. Gynecol Endocrinol 2012;28:974-8.

6. Dandona P, Aljada A, Chaudhuri A, Bandyopadhyay A. The potential influence of inflammation and insulin resistance on the pathogenesis and treatment of atherosclerosis-related complications in type 2 diabetes. J Clin Endocrinol Metab 2003;88:2422-9.

7. Kahal H, Aburima A, Ungvari T, Rigby AS, Dawson AJ, Coady AM, et al. Polycystic ovary syndrome has no independent effect on vascular, inflammatory or thrombotic markers when matched or obesity. Clin Endocrinol (Oxf) 2013;79:252-8.

8. Kim JW, Han JE, Kim YS, Won HJ, Yoon TK, Lee WS. High sensitivity C-reactive protein and its relationship with impaired glucose regulation in lean patients with polycystic ovary syndrome. Gynecol Endocrinol 2012;28:259-63.

9. Cakiroglu Y, Vural F, Vural B. The inflammatory markers in polycystic ovary syndrome: association with obesity and IVF outcomes. J Endocrinol Invest 2016;39:899-907.

10. Özay AC, Özay OE. The importance of inflammation markers in polycystic ovary syndrome. Rev Assoc Med Bras (1992) 2021;67:411-7.

11. Shiny A, Bibin YS, Shanthirani CS, Regin BS, Anjana RM, Balasubramanyam M, et al. Association of neutrophil-lymphocyte ratio with glucose intolerance: an indicator of systemic inflammation in patients with type 2 diabetes. Diabetes Technol Ther 2014;16:524-30.

12. Lou M, Luo P, Tang R, Peng Y, Yu S, Huang W, et al. Relationship between neutrophil-lymphocyte ratio and insulin resistance in newly diagnosed type 2 diabetes mellitus patients. BMC Endocr Disord 2015;15:9.

13. Hussain M, Babar MZ, Akhtar L, Hussain MS. Neutrophil lymphocyte ratio (NLR): a well assessment tool of glycemic control in type 2 diabetic patients. Pak J Med Sci 2017;33:1366-70.

14. Gonzalez F. Inflammation in polycystic ovary syndrome: underpinning of insulin resistance and ovarian dysfunction. Steroids 2012;77:300-5.

15. Keskin Kurt R, Okyay AG, Hakverdi AU, Gungoren A, Dolapcioglu KS, Karateke A, et al. The effect of obesity on inflammatory markers in patients with PCOS: a BMI-matched case-control study. Arch Gynecol Obstet 2014;290:315-9.

16. Pergialiotis V, Trakakis E, Parthenis C, Hatziagelaki E, Chrelias C, Thomakos N, et al. Correlation of platelet to lymphocyte and neutrophil to lymphocyte ratio with hormonal and metabolic parameters in women with PCOS. Horm Mol Biol Clin Investig 2018;34:20170073.

17. Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertil Steril 2004;81:19-25.

18. Teede HJ, Misso ML, Costello MF, Dokras A, Laven J, Moran L, et al. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum Reprod 2018;33:1602-18.

19. Chae SJ, Kim JJ, Choi YM, Hwang KR, Jee BC, Ku SY, et al. Clinical and biochemical characteristics of polycystic ovary syndrome in Korean women. Hum Reprod 2008;23:1924-31.

20. Chun S. 1-h Postprandial glucose level is related to the serum anti-Müllerian hormone level in women with polycystic ovary syndrome. Gynecol Endocrinol 2015;31:815-8.

21. Park CH, Chun S. Association between serum gonadotropin level and insulin resistance-related parameters in Korean women with polycystic ovary syndrome. Obstet Gynecol Sci 2016;59:498-505.

22. Kim N, Chun S. Association between the serum estrone-to-estradiol ratio and parameters related to glucose metabolism and insulin resistance in women with polycystic ovary syndrome. Clin Exp Reprod Med 2021;48:374-9.

23. Chun S, Lee S. Optimal cutoff value of 1-hour postload glucose to identify insulin resistance in women with polycystic ovary syndrome. Clin Exp Obstet Gynecol 2022;49:219.

24. The Korean Society of Gynecologic Endocrinology. The Korean Society of Gynecologic Endocrinology clinical guideline 2016. The Korean Society of Gynecologic Endocrinology; 2016. p.27.

25. Balen AH, Laven JS, Tan SL, Dewailly D. Ultrasound assessment of the polycystic ovary: international consensus definitions. Hum Reprod Update 2003;9:505-14.

26. American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2018. Diabetes Care 2018;41(Suppl 1): S13-27.

27. Negishi H, Nakao K, Kimura M, Takenaka H, Horikawa M. Insulin resistance in nonobese Japanese women with polycystic ovary syndrome is associated with poorer glucose tolerance, delayed insulin secretion, and enhanced insulin response. Reprod Med Biol 2015;14:123-9.

28. Kim JJ, Hwang KR, Oh SH, Chae SJ, Yoon SH, Choi YM. Prevalence of insulin resistance in Korean women with polycystic ovary syndrome according to various homeostasis model assessment for insulin resistance cutoff values. Fertil Steril 2019;112:959-66.

29. Chen X, Yang D, Li L, Feng S, Wang L. Abnormal glucose tolerance in Chinese women with polycystic ovary syndrome. Hum Reprod 2006;21:2027-32.

30. Pina-Aguero MI, Zaldivar-Delgado A, Salas-Fernandez A, Martinez-Basila A, Bernabe-Garcia M, Maldonado-Hernandez J. Optimal cut-off points of fasting and post-glucose stimulus surrogates of insulin resistance as predictors of metabolic syndrome in adolescents according to several definitions. J Clin Res Pediatr Endocrinol 2018;10:139-46.

31. Taylor, Hugh S, Seli E. Speroff's clinical gynecologic endocrinology and infertility. 9th ed. Wolters Kluwer; 2020. p. 395-441.

32. Akboga MK, Canpolat U, Yuksel M, Yayla C, Yilmaz S, Turak O, et al. Platelet to lymphocyte ratio as a novel indicator of inflammation is correlated with the severity of metabolic syndrome: a single center large-scale study. Platelets 2016;27:178-83.

33. Demirtas L, Degirmenci H, Akbas EM, Ozcicek A, Timuroglu A, Gurel A, et al. Association of hematological indicies with diabetes, impaired glucose regulation and microvascular complications of diabetes. Int J Clin Exp Med 2015;8:11420-7.

34. Yilmaz MA, Duran C, Basaran M. The mean platelet volume and neutrophil to lymphocyte ratio in obese and lean patients with polycystic ovary syndrome. J Endocrinol Invest 2016;39:45-53.

35. Zeng X, Xie YJ, Liu YT, Long SL, Mo ZC. Polycystic ovarian syndrome: correlation between hyperandrogenism, insulin resistance and obesity. Clin Chim Acta 2020;502:214-21.