Cardiovascular Risk in Reproductive-aged Women With Polycystic Ovary Syndrome According to Body Mass Index: a Systematic Review and Meta-analysis

Cardiovascular Risk in Reproductive-aged Women With Polycystic Ovary Syndrome According to Body Mass Index: a Systematic Review and Meta-analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Cardiovascular Risk in Reproductive-aged Women With Polycystic Ovary Syndrome According to Body Mass Index: a Systematic Review and Meta-analysis Chenchen Zhuang, Qiongying Wang, Wenjuan Wang, Runmin Sun, Xiaofang Zhang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-230193/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: To evaluate the association between cardiovascular risk and reproductive-aged women with polycystic ovary syndrome (PCOS) according to body mass index (BMI) categories. Methods: A literature search was conducted in MEDLINE, EMBASE, Cochrane Library and PubMed. Blood pressure (BP) [including systolic BP (SBP), diastolic BP (DBP) and hypertension prevalence] and plasma lipids [triglyceride, high-density lipoprotein (HDL)-cholesterol, nonHDL-cholesterol and low-density lipoprotein (LDL)-cholesterol] were calculated at different BMI levels. Results: SBP [MD (95% CI): 2.96 mmHg (1.62, 4.30), P < 0.001] and DBP [MD (95% CI): 1.64 mmHg (0.03, 3.24), P = 0.046] increased in reproductive-aged women with PCOS, however, hypertension prevalence had no difference [OR (95% CI): 1.56 (0.76, 3.06), P = 0.198]. HDL-cholesterol were [SMD (95% CI): -0.24 (-0.39, -0.09), P = 0.002] lower and triglyceride [SMD (95% CI): 0.37 (0.26, 0.48), P < 0.001] higher in reproductive PCOS women. Besides, LDL-cholesterol [SMD (95% CI): 0.25 (0.17, 0.34), P < 0.001] and nonHDL-cholesterol [SMD (95% CI): 0.38 (0.29, 0.47), P < 0.001] were higher in reproductive-aged PCOS. In the subgroup analysis, SBP and LDL-cholesterol were increased in BMI of < 25 kg/m 2 and BMI of ≥ 30 kg/m 2 , but had no difference in BMI of 25-< 30 kg/m 2 . Triglyceride and nonHDL-cholesterol increased in all BMI categories. However, DBP and hypertension prevalence had no difference in all BMI categories. Conclusion: High baseline BP and dyslipidemia is represented in reproductive PCOS. Besides, reproductive PCOS women are significantly associated with increased cardiovascular risk independent of BMI categories. Health Economics & Outcomes Research PCOS cardiovascular risk reproductive-age dyslipidemia blood pressure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Polycystic ovary syndrome (PCOS) is one of the most common female endocrinopathies, and is a highly prevalent disorder affecting about 7–14% of reproductive women [ 1 , 2 ]. The clinical manifestations of PCOS are heterogeneous, however, the hallmarks of the syndrome remain anovulation, insulin resistance and androgen excess. Moreover, each of these features is responsible for the promotion of cardiovascular risk in this population. The American Society for Reproductive Medicine Practice Committee reported cardiovascular risk was increased in women with PCOS [ 3 ]. The current studies showed that cardiovascular risk factors were more frequent among women with PCOS compared to non-PCOS women [ 4 , 5 ]. This set of major risk factors includes age, sex, body mass index (BMI) category, low-density lipoprotein cholesterol (LDL-C) or triglyceride (TG), low high-density lipoprotein cholesterol (HDL-C), high systolic blood pressure (SBP) or diastolic blood pressure (DBP), diabetes mellitus, tobacco smoking and family history of early cardiovascular disease (CVD). Apart from the deleterious effects of PCOS per se, individual risk markers including hypertension and lipid profile are important mediators of the further cardiovascular outcomes. Blood pressure and lipid levels are nearly always included in risk assessment algorithms because of their established independent contributions to cardiovascular risk prediction [ 6 ]. The studies of early cardiovascular epidemiology focused on risk factors related to atherosclerosis, which begins at a young age. Detection of traditional cardiovascular risk factors and alternative markers of atherosclerosis in women with PCOS at a young age increase the risk for symptomatic cardiovascular risk [ 7 , 8 ]. Therefore, PCOS may represent an important key to lipid alternations starting during adolescence or fertile age. Dyslipidemia is common in young adult women with PCOS, and the disorder may be the most common cause of dyslipidemia in women before the age of 40 years [ 9 , 10 ]. Moreover, atherosclerotic appears early life during life, this confirms the need to assess and eventually treat altered lipid profile in young women with PCOS [ 7 ]. LDL-C is considered to be the primary target to reduce cardiovascular risk, prevalence metabolic syndrome in women with PCOS has concentrated attention on TG and HDL-C, with relatively little attention to other lipid changes [ 11 , 12 ]. In addition, Rotterdam guidelines suggested evaluation for metabolic syndrome and indirectly indicated the need to measure only HDL-C and TG with relatively little attention to other lipid parameters [ 13 ]. The Androgen Excess and PCOS Society guidelines have reported that different LDL-C cutoff values depends on the degree of cardiovascular risk of women with PCOS [ 7 ]. However, many clinicians do not measure lipid levels in PCOS or continue measuring only HDL-C and TG. The effect of obesity on PCOS and PCOS on obesity is complex. Although it is agreed that the prevalence of increased BMI is high in PCOS [ 14 , 15 ]. A previous meta-analysis showed that obesity was more prevalent in women with PCOS than in women without PCOS [ 16 ]. PCOS occurs both in obese and lean women. However, studies of cardiovascular risk in PCOS have not distinguished effects of BMI categories from those of PCOS. There is little consensus that the possible increased cardiovascular risk, is merely related to obesity. In the fact, lipid profile may differ in their association with lipid profile and PCOS potentially. Based on BMI categories, our meta-analysis classified as normal weight (18.5-<25 kg/m 2 ), overweight (25-<30 kg/m 2 ), and obesity (≥ 30 kg/m 2 ) [ 17 ]. However, there is no met studies to report underweight (< 18.5 kg/m 2 ) women with PCOS. These standard categories have been increasingly used in published studies of BMI levels and PCOS, but the literature reporting these results has not been systematically reviewed. Therefore, the present meta-analysis of surrogate markers (i.e. blood pressure, lipids and hypertension prevalence) regarding cardiovascular risk factors shows the effect of different BMI levels in reproductive-aged women with PCOS. To reduce biases that may be present in all meta-analyses, only studies with BMI matching and having sufficient numbers of subjects were included. To our knowledge, this is the first meta-analysis to investigate the association between reproductive-aged women with PCOS and cardiovascular risk factors according to BMI levels. Methods The present study was approved by the Ethics Committee Board of Lanzhou University Second Hospital (D2019-098) and conducted in accordance with the Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 18 ]. Search strategy We searched Cochrane Library, PubMed, EMBASE and MEDLINE databases to 9 November 2020 for full text articles in English describing trials and prospective cohort studies in reproductive-aged women that assessed the association between cardiovascular risk factors with PCOS according to BMI categories. The search terms were used as follows: (polycystic ovary syndrome or PCOS) and (cardiovascular risk). The search strategy was limited to English language papers. Study selection We used a mix of medical subject heading (MeSH) and keywords including polycystic ovary syndrome, Stein Leventhal, cardiovascular risk, blood pressure, hypertension, lipoprotein, dyslipidemia, hyperlipidemia. A manual search of the list of references of all identified studies and review articles was performed for additional relevant studies. The selection criteria of the retrieved articles in our meta-analysis were as follows: 1) case-control studies or cohort studies; 2) reproductive-aged women with PCOS by the National Institutes of Health or Rotterdam criteria; 4) no comorbidities; 5) no evidence of an androgen-secreting tumor, congenital adrenal hyperplasia, or medication altering androgen metabolism or lipids; 6) each article with BMI matching. Each study had to have evaluated ≥ 30 adults PCOS women ≥ 18 years but ≤ 45 years (to avoid the perimenopause transition). Control subjects of the same catchment area, and found to be without PCOS. The exclusion criteria of the meta-analysis were as follows: 1) case-only studies; 2) studies with incomplete data; 3) meta-analysis, letters, reviews, and editorial articles. Nonpatient-examined community control studies were excluded, recognizing that PCOS occurs infrequently and that some phenotypic elements of PCOS can occur in regularly menstruating women. Assessment of study quality Included studies were assessed for quality using the Newcastle-Ottawa Scale (NOS), a validated scale for meta-analysis of observational studies [19]. We scored (max, nine points) the following items important for risk of bias assessment in non-randomized cohort studies: representativeness of the exposed cohort; adequate selection of controls; adequate definition of the outcome; adequacy of follow-up; comparability of exposed and non-exposed women (two points). Data extraction Authors of studies eligible for inclusion in present meta-analysis were invited to join our study and share their data. When not reported in the individual study, nonHDL-C was calculated as total cholesterol minus HDL-C. Data extraction was independently performed by three investigators (C.C.Z., Q.Y.W., W.J.W). Disagreement was resolved by consensus. Statistical analysis The dichotomous outcome measure was the proportion of women with hypertension prevalence. Data were presented as odds ratio (OR, 95% confidence interval [CI]). Continuous normally distributed data were summarized with a mean and standard deviation (SD), and if non-normally distributes a median and interquartile range (IQR) were used. If the outcome was measured on the same scale, we used weighted mean difference (MD) and 95% CI. Otherwise, standardized mean difference (SMD) and 95% CI were calculated. Heterogeneity between the studies was assessed using I 2 tests ( I 2 ＞50% was considered substantial heterogeneity). When the I 2 statistic was lower than 30% and P < 0.10, a fixed-effect model was used; otherwise, a random effects model was used. We assessed publication bias through Egger’s regression test. Analyses were performed by STATA statistical software version 14.0 (StataCorp, College Station, TX, USA). p -Value < 0.05 were considered statistically significant for all analyses except heterogeneity tests. Results Study design and analysis characteristics The search yielded 593 potential reports as shown in Fig. 1 . After removal of duplicates, 556 records remained. Initial screening of the title and abstract resulted in the exclusion of 400 references and 156 studies proceeded to detailed evaluation. After further examination, 34 studies met the inclusion criteria and were included in the meta-analysis [ 8 , 20 – 52 ]. The basic characteristics of each study were summarized in Table 1 . A total of 34 studies including 12711 subjects were included in the present meta-analysis. The present meta-analysis was assessed blood pressure, prevalence of hypertension, TG, HDL-C, nonHDL-C and LDL-C according to BMI categories. Table 1 Characteristics of included studies in the meta-analysis Author/year Country Design Participants age (years) PCOS vs. control (n) BMI level (kg/m 2 ) Outcomes Adali (2010) Turkey PC PCOS: 24.73 ± 2.91 Control: 13.04 ± 1.11 26 vs. 25 PCOS: 24.4 ± 4.23 Control: 23.9 ± 3.95 BP, HDL-C, LDL-C, TG Akram (2010) Pakistan RC PCOS: 20–39 Control: 20–39 50 vs. 30 PCOS: 23.6 ± 0.50 Control: 23.5 ± 0.71 HDL-C, LDL-C, TG Alexandraki (2006) Greece PC PCOS: 25.41 ± 0.80 Control: 27.33 ± 0.83 27 vs. 27 PCOS: 27.42 ± 1.12 Control: 25.05 ± 1.19 BP, HDL-C, LDL-C, TG Arikan (2007) Turkey PC PCOS: 22.82 ± 5.53 Control: 24.64 ± 4.22 39 vs. 30 PCOS: 21.48 ± 6.50 Control: 20.90 ± 6.04 HDL-C, LDL-C, TG Berneis (2006) Italy PC PCOS: 25.1 ± 4.2 Control: 25.5 ± 3 30 vs. 24 PCOS: 28.4 ± 5.8 Control: 28 ± 4.4 HDL-C, LDL-C, TG Cascella (2006) Italy PC PCOS: 21.9 ± 2.7 Control: 22.2 ± 2.8 50 vs. 50 PCOS: 24.6 ± 2.5 Control: 24.4 ± 2.8 BP, HDL-C, LDL-C, TG Cetinakalp (2009) Turkey PC PCOS: 24.58 ± 4.61 Control: 25.48 ± 3.38 129 vs. 91 PCOS: 24.47 ± 4.64 Control: 24.2 ± 3.31 HDL-C, LDL-C, TG Cussons (2009) Australia PC PCOS: 30.4 ± 5.54 Control: 34.44 ± 7.8 19 vs. 19 PCOS: 24.1 ± 2.9 Control: 22.9 ± 3.2 BP, HDL-C, LDL-C, TG Diamanti-Kandarakis (2006) Greece PC PCOS: 25.64 ± 0.86 Control: 27.52 ± 1.02 25 vs. 25 PCOS: 29.08 ± 1.43 Control: 26.22 ± 1.16 BP, HDL-C El-Kannishy (2009) Egypt PC PCOS: 25.2 ± 3.6 Control: 24.4 ± 4.07 14 vs. 10 PCOS: 22.8 ± 2.1 Control: 21.9 ± 2.97 HDL-C, LDL-C, TG Erdogan (2007) Turkey PC PCOS: 24.27 ± 5.44 Control: 26.41 ± 5.65 68 vs. 26 PCOS: 24.41 ± 5.43 Control: 23.35 ± 5.04 HDL-C, LDL-C, TG Erdogan (2009) Turkey PC PCOS: 24.07 ± 1.32 Control: 25.01 ± 2.05 88 vs. 119 PCOS: 24.38 ± 4.13 Control: 23.47 ± 4.12 HDL-C, LDL-C, TG Joham (2014) Australia RC 28–33 183 vs. 4638 < 25 Prevalence of hypertension Joham (2014) Australia RC 28–33 117 vs. 1742 25.0–29.9 Prevalence of hypertension Joham (2014) Australia RC 28–33 149 vs. 1181 ≥ 30 Prevalence of hypertension Kargili (2010) Turkey RC PCOS: 25.7 ± 5.5 Control: 26.1 ± 5.4 168 vs. 52 PCOS: 26.8 ± 3.4 Control: 25.4 ± 2.8 HDL-C, LDL-C, TG Ketel (2010) Netherland RC PCOS: 28.6 ± 4.5 Control: 27.7 ± 5.3 22 vs.14 PCOS: 22.0 ± 2.2 Control: 22.2 ± 1.7 HDL-C, LDL-C, TG Ketel (2010) Netherland RC PCOS: 30.3 ± 4.2 Control: 28.6 ± 5.3 18 vs. 13 PCOS: 36.2 ± 5.9 Control: 40.5 ± 7.0 HDL-C, LDL-C, TG Legro (2001) USA PC PCOS: 25 ± 6 Control: 29 ± 7 42 vs. 27 PCOS: 23.1 ± 2.4 Control: 23.0 ± 1.8 BP, HDL-C, LDL-C, TG Legro (2001) USA PC PCOS: 28 ± 5 Control: 32 ± 7 153 vs. 35 PCOS: 37.0 ± 6.9 Control: 37.7 ± 6.4 BP, HDL-C, LDL-C, TG Liang (2012) Taiwan PC PCOS: 27 ± 6.4 Control: 29.0 ± 5.1 110 vs. 20 PCOS: 31.1 ± 3.9 Control: 30.4 ± 3.7 Prevalence of hypertension, HDL-C, LDL-C, TG Liang (2012) Taiwan PC PCOS: 26.8 ± 5.1 Control: 28.1 ± 4.2 110 vs. 50 PCOS: 20.6 ± 2.0 Control: 20.4 ± 2.0 Prevalence of hypertension, HDL-C, LDL-C, TG Luque-Ramirez (2007) Spain PC PCOS: 23.0 ± 5.4 Control: 24.8 ± 6.0 11 vs. 8 PCOS: 22.2 ± 2.0 Control: 21.3 ± 1.3 HDL-C, LDL-C, TG Luque-Ramirez (2007) Spain PC PCOS: 23.6 ± 4.6 Control: 29.3 ± 10.3 13 vs. 4 PCOS: 27.5 ± 1.8 Control: 27.4 ± 1.5 HDL-C, LDL-C, TG Luque-Ramirez (2007) Spain PC PCOS: 26.3 ± 6.7 Control: 28.5 ± 5.8 16 vs. 8 PCOS: 35.8 ± 3.9 Control: 35.5 ± 3.2 HDL-C, LDL-C, TG Macut (2008) Serbia PC PCOS: 23.1 ± 5.1 Control: 24.6 ± 4.1 75 vs. 51 PCOS: 24.9 ± 4.7 Control: 23.7 ± 4.0 HDL-C, LDL-C, TG Moran (2009) Australia PC PCOS: 34.1 ± 6.9 Control: 33.8 ± 6.8 80 vs. 27 PCOS: 36.0 ± 6.6 Control: 37.4 ± 5.6 HDL-C, LDL-C, TG Mayer (2005) Australia PC PCOS: 32.7 ± 1.8 Control: 33.2 ± 2.3 100 vs. 20 PCOS: 37.3 ± 2.43 Control: 36.7 ± 1.28 HDL-C, LDL-C, TG Ni (2009) China PC PCOS: 27 Control: 28 578 vs. 281 PCOS: 21.9 Control: 21.9 BP, TG Oral (2008) Turkey PC PCOS: 23.9 ± 3.3 Control: 24.2 ± 3.9 48 vs. 43 PCOS: 24.1 ± 2.9 Control: 24.0 ± 1.9 HDL-C, LDL-C, TG Orio (2004) Italy PC PCOS: 22.2 ± 2.5 Control: 22.6 ± 2.3 30 vs. 30 PCOS: 22.4 ± 2.1 Control: 22.1 ± 1.8 BP, HDL-C, LDL-C, TG Philbois (2018) Brazil PC PCOS: 28.5 ± 5.2 Control: 31.2 ± 6.6 30 vs. 30 PCOS: 22.9 ± 1.6 Control: 23.5 ± 3 BP Rizzo (2011) Italy PC PCOS: 24 ± 5 Control: 24 ± 3 350 vs. 90 PCOS: 27 ± 7 Control: 27 ± 4 HDL-C, LDL-C, TG, nonHDL-C Sasaki (2011) Japan PC PCOS: 30.2 ± 3.9 Control: 31.5 ± 4.4 54 vs. 24 PCOS: 24.3 ± 5.7 Control: 22.2 ± 3.4 BP, HDL-C, LDL-C, TG Shroff (2007) USA PC PCOS: 32 ± 6.5 Control: 36 ± 7.2 24 vs. 24 PCOS: 36 ± 5.4 Control: 35 ± 3.3 Prevalence of hypertension, HDL-C, LDL-C, TG Soares (2009) Brazil PC PCOS: 24.5 ± 3.8 Control: 24.5 ± 5.1 40 vs. 50 PCOS: 22.7 ± 3.3 Control: 23.1 ± 3.2 BP, HDL-C, LDL-C, TG Sterling (2015) Canada RC PCOS: 33 Control: 35 71 vs. 323 PCOS: 24.6 Control: 23.6 Prevalence of hypertension Tarkun (2004) Turkey PC PCOS: 23.45 ± 4.3 Control: 24.4 ± 4.07 37 vs. 25 PCOS: 23.85 ± 3.26 Control: 22.9 ± 2.97 HDL-C, LDL-C, TG Tiras (1999) Turkey PC PCOS: 24.5 ± 6.0 Control: 23.6 ± 3.9 35 vs. 35 PCOS: 22.9 ± 4.2 Control: 22.0 ± 1.8 HDL-C, LDL-C, TG Vryonidou (2005) Greece PC PCOS: 23.9 ± 5.4 Control: 24.7 ± 5.3 75 vs. 55 PCOS: 27.3 ± 7.0 Control: 26.3 ± 7.7 SBP, HDL-C, LDL-C, TG Yildiz (2002) Turkey PC PCOS: 22.9 ± 4.4 Control: 24.8 ± 4.2 59 vs. 23 PCOS: 23.0 ± 2.4 Control: 22.1 ± 2.2 HDL-C, TG PCOS, polycystic ovary syndrome; BMI, body mass index; PC, prospectively cohort study; RC, retrospectively cohort study, BP, blood pressure (including systolic and diastolic blood pressure); SBP, systolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides. Risk of bias and quality assessment The 34 studies included for systematic review were then reviewed with the NOS tool (Table 2 ). Some studies did not report on allocation concealment. Binding was impossible. One study scored seven out of nine points and one study scored six points, indicating high-quality. Three studies scored four points or less. Table 2 Assessment of methodological quality (based on Newcastle-Ottawa Scale) Selection (max 4 stars) Comparability (max 2 stars) Exposure (max 3 stars) Overall quality Adali (2010) *** ** ** Good Akram (2010) ** ** ** Fair Alexandraki (2006) *** ** ** Good Arikan (2007) *** ** ** Good Berneis (2006) **** ** ** Good Cascella (2006) **** ** ** Good Cetinakalp (2009) ** ** ** Fair Cussons (2009) ** ** * Fair Diamanti-Kandarakis (2006) ** ** * Fair El-Kannishy (2009) **** ** ** Good Erdogan (2007) ** * ** Fair Erdogan (2009) **** ** ** Good Joham (2014) ** ** ** Fair Kargili (2010) ** ** ** Fair Ketel (2010) **** ** ** Good Legro (2001) *** * ** Good Liang (2012) *** * ** Good Luque-Ramirez (2007) *** ** ** Good Macut (2008) ** ** ** Fair Moran (2009) *** ** ** Good Mayer (2005) **** ** ** Good Ni (2009) ** * ** Fair Oral (2008) ** * ** Fair Orio (2004) *** ** ** Good Philbois (2018) ** ** ** Fair Rizzo (2011) ** ** ** Fair Sasaki (2011) *** ** ** Good Shroff (2007) *** ** ** Good Soares (2009) ** ** ** Fair Sterling (2015) ** * ** Fair Tarkun (2004) ** ** ** Fair Tiras (1999) ** ** ** Fair Vryonidou (2005) **** ** ** Good Yildiz (2002) ** * ** Fair SELECTION (1) Is the case definition adequate? (a) yes, with independent validation*, (b) yes, e.g. record linkage or based on self-reports, (c) no description (2) Representativeness of the cases: (a) consecutive or obviously representative series of cases*, (b) potential for selection biases or not stated (3) Selection of controls: (a) community controls*, (b) hospital controls, (c) no description (4) Definition of controls: (a) no history of disease (end-point)*, (b) no description of source COMPARABILITY Comparability of cases and controls on basis of design or analysis: (a) study controls for ___ (most important factor)*, (b) study controls for any additional factor* (could be modified to indicate specific control for a second factor) EXPOSURE (1) Ascertainment of exposure: (a) secure record (e.g. surgical record)*, (b) structured interview where blind to case/control status*, (c) interview not blinded to case/control status, (d) written self-report or medical record only, (e) no description. (2) Same method of ascertainment for cases and controls: (a) yes*, (b) no. (3) Non-response rate: (a) same rate for both groups*, (b) non-respondents describe, (c) rate different and no designation. OVERALL QUALITY Good quality: 3 or 4 stars in selection domain AND 1 or 2 stars in comparability domain AND 2 or 3 stars in exposure domain. Fair quality: 2 stars in selection domain AND 1 or 2 stars in comparability domain AND 2 or 3 stars in exposure domain. Poor quality: 0 or 1 star in selection domain OR 0 star in comparability domain OR 0 or 1 star in exposure domain. Blood pressure Figures 2 – 4 are forest plots summarizing comparison of SBP, DBP and prevalence of hypertension, respectively. Because the aim was assessed the effect of BMI categories on cardiovascular risk in reproductive women with PCOS, we analyzed the outcomes based on BMI categories. As shown in Figs. 2 and 3 , SBP [MD (95% CI): 2.96 mmHg (1.62, 4.30), P < 0.001] and DBP [MD (95% CI): 1.64 mmHg (0.03, 3.24), P = 0.046] increased in reproductive-aged women with PCOS. In the subgroup analysis of SBP, BMI of < 25 kg/m 2 [MD (95% CI): 3.02 mmHg (1.11, 4.94), P < 0.002] and BMI of ≥ 30 kg/m 2 [MD (95% CI): 4.27 mmHg (0.50, 8.04), P < 0.026] showed significant results, while BMI of 25-< 30 kg/m 2 [MD (95% CI): 1.56 mmHg (-0.60, 3.71), P = 0.157] did not show positive result. However, in the subgroup analysis, DBP did not increase in BMI of < 25 kg/m 2 [MD (95% CI): 1.75 mmHg (-0.23, 3.75), P = 0.083], BMI of ≥ 30 kg/m 2 [MD (95% CI): -0.24 mmHg (-3.16, 2.69), P = 0.873], and BMI of 25-< 30 kg/m 2 [MD (95% CI): 3.13 mmHg (-0.98, 7.24), P = 0.136]. As shown in Fig. 4 , prevalence of hypertension had non-significant difference in reproductive-aged women with PCOS [OR (95% CI): 1.56 (0.76, 3.06), P = 0.198]. Besides, there was no significant difference in BMI of < 25 kg/m 2 [OR (95% CI): 1.33 (0.12, 15.14), P = 0.819], BMI of ≥ 30 kg/m 2 [OR (95% CI): 1.53 (0.90, 2.61), P = 0.116], and BMI of 25-< 30 kg/m 2 [OR (95% CI): 1.85 (0.80, 4.28), P = 0.153]. Lipid profiles Figure 5-8 are forest plots summarizing comparison of HDL-C, TG, nonHDL-C and LDL-C, respectively. As shown in Figure 5 and 6, reproductive-aged women with PCOS had significantly difference on HDL-C [SMD (95% CI): -0.24 (-0.39, -0.09), P = 0.002] and TG [SMD (95% CI): 0.37 (0.26, 0.48), P < 0.001]. The subgroup analysis of TG, BMI of < 25 kg/m 2 [SMD (95% CI): 0.34 (0.20, 0.49), P < 0.001], BMI of ≥ 30 kg/m 2 [MD (95% CI): 0.54 (0.31, 0.78), P < 0.001], and BMI of 25-< 30 kg/m 2 [MD (95% CI): 0.25 (0.06, 0.43), P = 0.008] were increased in reproductive-aged women with PCOS. Meanwhile, the subgroup analysis showed that HDL-C significantly increased in BMI of < 25 kg/m 2 [SMD (95% CI): -0.30 (-0.50, -0.11), P = 0.002], and BMI of 25-< 30 kg/m 2 [MD (95% CI): -0.33 (-0.65, -0.01), P = 0.041]. However, BMI of ≥ 30 kg/m 2 had no significant difference [MD (95% CI): 0.07 (-0.26, 0.40), P = 0.664]. Figure 7 and 8 are forest plots summarizing comparison of nonHDL-C and LDL-C, respectively. As shown in Figure 7 and 8, reproductive-aged women with PCOS had significant difference on nonHDL-C [SMD (95% CI): 0.38 (0.29, 0.47), P < 0.001] and LDL-C [SMD (95% CI): 0.25 (0.17, 0.34), P < 0.001]. The subgroup analysis showed that nonHDL-C increased in BMI of < 25 kg/m 2 [SMD (95% CI): 0.37 (0.24, 0.50), P < 0.001], BMI of ≥ 30 kg/m 2 [MD (95% CI): 0.49 (0.30, 0.69), P < 0.001], and BMI of 25-< 30 kg/m 2 [MD (95% CI): 0.33 (0.24, 0.50), P < 0.001]. In addition, LDL-C increased in BMI of < 25 kg/m 2 [SMD (95% CI): 0.28 (0.15, 0.41), P < 0.001] and BMI of ≥ 30 kg/m 2 [MD (95% CI): 0.25 (0.06, 0.44), P = 0.010]. However, LDL-C did not increase in BMI of 25-< 30 kg/m 2 [MD (95% CI): 0.19 (-0.01, 0.39), P = 0.066]. Publication bias There was no publication bias for SBP (asymmetry test P = 0.510), DBP (asymmetry test P = 0.092), HDL (asymmetry test P = 0.405), TG (asymmetry test P = 0.633), nonHDL-C (asymmetry test P = 0.938), and LDL (asymmetry test P = 0.628) according to the results of Egger’s regression test. Discussion The novelty of this systematic review from 34 observational studies reports the correlation between cardiovascular risk and BMI categories for reproductive-aged women with PCOS. To the best of our knowledge, it is the first time that BMI categories were evaluated cardiovascular risk factors in reproductive-aged women with PCOS. There are four main results in the present meta-analysis: (1) baseline blood pressure increased in reproductive-aged women with PCOS, but there was non-significant difference on hypertension prevalence; (2) increased LDL-C, low HDL-C, and increased TG + increased nonHDL-C were seen in reproductive-aged women with PCOS; (3) increased cardiovascular risk was seen in reproductive-aged women with PCOS independent of BMI categories; (4) DBP and hypertension prevalence had no difference in all BMI categories. In present meta-analysis, increased baseline blood pressure was seen in reproductive PCOS women, but there was no significant difference on hypertension prevalence. There was no relationship between blood pressure and BMI categories in reproductive-aged women with PCOS. The study of Zimmermann et al. reported that BMI failed to reveal an association between reproductive-aged women with PCOS and hypertension [ 53 ]. Hudecova et al. reported that after adjusting for BMI, reproductive-aged women with PCOS had higher daytime systolic blood pressure, mean arterial values of blood pressure, and an increased pulse rate irrespectively of clinically verified hypertension [ 54 ]. Moreover, a study from Australia reported that hypertension in reproductive PCOS women was not associated with BMI, giving further information that the cardiometabolic abnormalities may be independent of weight in PCOS [ 32 ]. However, previous studies showed that the prevalence of hypertension increased in premenopausal [ 55 ] and perimenopausal PCOS women [ 56 ]. Schmidt et al. showed that hypertension and hypertriglyceridemia were the only cardiovascular risk factor that persisted more frequently in postmenopausal PCOS women. Therefore, hypertension is more prevalent in PCOS women from the fourth decade of life in comparison to respective controls [ 55 ]. There are some mainly factors for increased blood pressure in PCOS women, including endothelial dysfunction and decreased vascular compliance [ 57 ]. The increased risk of hypertension was explained by insulin resistance and hyperinsulinemia that exert hypertrophy of vascular muscle wall and reduce compliance by interference with endothelium-dependent vasodilatation mechanisms [ 58 ]. Hyperinsulinemia advocated endothelin 1 (ET-1) production, meanwhile, it also influences the hypertrophic effect on vascular endothelial and smooth muscle cells [ 57 ]. Besides, ET-1 is commonly chronically increased in women with PCOS, which regulates endothelial function. In weight-matched studies, our results showed that increased LDL-C, low HDL-C, and increased TG + increased nonHDL-C were seen in reproductive-aged women with PCOS. Moreover, dyslipidemia in reproductive PCOS women had no relationship with body weight. The outcomes consistent with that of the previous studies [ 9 , 59 ]. PCOS women younger than 40 appears to have dyslipidemia [ 59 ]. Therefore, PCOS per se increases lipid levels, though the absolute value and the related cardiovascular risk may be different between individual patients. This is an important finding that lipid pattern assessment for decreased cardiovascular risk in reproductive PCOS women at all BMI categories. The present meta-analysis reported that higher LDL-C was found in normal-weight (BMI of < 25 kg/m 2 ) and obese reproductive-aged women with PCOS (BMI of ≥ 30 kg/m 2 ), and low HDL-C was seen in normal-weight and overweight (BMI of 25-< 30 kg/m 2 ) subjects. Moreover, reproductive PCOS women at all BMI categories increased TG and nonHDL-C levels, including normal-weight, overweight and obesity. The subset analyses from a meta-analysis showed higher LDL-C was found in women with higher BMI categories [ 60 ]. However, our outcomes of reproductive PCOS women showed that increased LDL-C was seen in normal-weight and obese subjects. Meanwhile, nonHDL-C increased in all BMI categories. Because the measurement of LDL-C can be influenced by increased TG in cardiovascular risk assessment [ 61 ], an appropriate way to estimate the amount of apoB-containing lipoprotein is the determination of nonHDL-C [ 62 ]. The level of nonHDL-C has a comparable prognostic relevance, similar to LDL-C [ 63 , 64 ]. It is necessary to detect the nonHDL-C level in reproductive PCOS women. Higher nonHDL-C levels were generally found in this population whose mean BMI was higher, but differences were also clearly found with PCOS even the BMI was in the normal-weight and overweight categories. The increasing level of nonHDL-C predict long-term cardiovascular risk, particularly at a young age. The suggests that obesity or overweight is not the absolute factor for reproductive PCOS women. Because, atherosclerotic process starts early life during life, this confirms the need to assess and eventually treat altered lipid profile in young women with PCOS [ 7 ]. Although obesity is an important confounder in relationship between PCOS and cardiovascular risk, non-obese subjects also have a high dyslipidemia risk [ 57 , 65 ]. In a meta-analysis of approximately 15000 women from 35 studies, women with PCOS had an increased prevalence of overweight, obesity and central obesity [ 57 ]. In addition to the reproductive and psychological characteristics of PCOS, the metabolic aberrations of PCOS worsen with obesity [ 66 ]. However, non-obese women with PCOS are also at an increased risk of similar cardiometabolic aberrations. Therefore, it is necessary to screened lipid levels for reproductive PCOS women with all BMI categories. Limitation Several limitations should also be noted. First, PCOS is a heterogeneous disease and the phenotypes are unable to exhibit in this meta-analysis. Second, there was a significant clinical and statistical heterogeneity in the pooled analysis. This could be due to confounding effects related to factors such as age, BMI, study quality, ethnicity, PCOS phenotypes and other clinical features. Besides, the use of country regions as a proxy for ethnicity due to lack of reporting of ethnic compositions in most studies. Further studies should include ethnicity of participants to allow for better understanding on the differential effects of this on PCOS or the metabolic syndrome. Third, the Egger’s test has low power when there are less than 10 studies in the meta-analysis [ 67 ]. Our analyses included more than 10 studies for blood pressure and lipid profile, but less than ten studies for hypertension prevalence, which may have resulted in too low power to detect asymmetry. Conclusion In conclusion, our meta-analysis suggests that baseline blood pressure increases in reproductive-aged women with PCOS, but prevalence of hypertension has non-significantly difference. Besides, dyslipidemia is common in reproductive-aged women with PCOS and is characterized not only by low HDL-C and increased TG but also by high LDL-C and nonHDL-C. These lipid profiles are worse in reproductive-aged women with PCOS compared with controls, independent of BMI categories. Declarations Consent for publication The present study was approved by the Ethics Committee Board of Lanzhou University Second Hospital (D2019-098). Competing interests No potential conflict of interest was reported by the authors. Funding This work was supported by the National Natural Science Foundation of China (81960086). Author Contributions Chenchen Zhuang and Jing Yu conceived the manuscript. Chenchen Zhuang designed the study and wrote the initial draft of manuscript. Chenchen Zhuang, Qiongying Wang, Wenjuan Wang and Runmin Sun analyzed the data. Qiongying Wang and Wenjuan Wang collected the data. All authors reviewed the manuscript. Acknowledgements We are grateful to Associate Ph.D. Jumo Zhu (University Medical Center of Groningen) and Ph.D. Ziyi Guo (University of New South Wales) for his assistance in the field. We also acknowledge Prof. Yuanhang Cheng (City University of Hong Kong) for constructive suggestions. Extremely careful and thoughtful reviews by Associate Editor and Reviewer improved this manuscript greatly. References Cooney LG, Dokras A. Beyond fertility: polycystic ovary syndrome and long-term health. Fertility and sterility. 2018;110(5):794-809. Bozdag G, Mumusoglu S, Zengin D, Karabulut E, Yildiz BO. The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction (Oxford, England). 2016;31(12):2841-55. Obesity and reproduction: an educational bulletin. Fertility and sterility. 2008;90(5 Suppl):S21-9. Behboudi-Gandevani S, Ramezani Tehrani F, Hosseinpanah F, Khalili D, Cheraghi L, Kazemijaliseh H, et al. Cardiometabolic risks in polycystic ovary syndrome: long-term population-based follow-up study. Fertility and sterility. 2018;110(7):1377-86. Sirmans SM, Pate KA. Epidemiology, diagnosis, and management of polycystic ovary syndrome. Clinical epidemiology. 2013;6:1-13. Pencina MJ, Navar AM, Wojdyla D, Sanchez RJ, Khan I, Elassal J, et al. Quantifying Importance of Major Risk Factors for Coronary Heart Disease. Circulation. 2019;139(13):1603-11. Wild RA, Carmina E, Diamanti-Kandarakis E, Dokras A, Escobar-Morreale HF, Futterweit W, et al. Assessment of cardiovascular risk and prevention of cardiovascular disease in women with the polycystic ovary syndrome: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome (AE-PCOS) Society. The Journal of clinical endocrinology and metabolism. 2010;95(5):2038-49. Shroff R, Kerchner A, Maifeld M, Van Beek EJ, Jagasia D, Dokras A. Young obese women with polycystic ovary syndrome have evidence of early coronary atherosclerosis. The Journal of clinical endocrinology and metabolism. 2007;92(12):4609-14. Wild RA, Painter PC, Coulson PB, Carruth KB, Ranney GB. Lipoprotein lipid concentrations and cardiovascular risk in women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 1985;61(5):946-51. Carmina E. Cardiovascular risk and events in polycystic ovary syndrome. Climacteric : the journal of the International Menopause Society. 2009;12 Suppl 1:22-5. Moran LJ, Misso ML, Wild RA, Norman RJ. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction update. 2010;16(4):347-63. Dokras A, Bochner M, Hollinrake E, Markham S, Vanvoorhis B, Jagasia DH. Screening women with polycystic ovary syndrome for metabolic syndrome. Obstetrics and gynecology. 2005;106(1):131-7. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertility and sterility. 2004;81(1):19-25. Glueck CJ, Goldenberg N. Characteristics of obesity in polycystic ovary syndrome: Etiology, treatment, and genetics. Metabolism: clinical and experimental. 2019;92:108-20. Jeanes YM, Reeves S. Metabolic consequences of obesity and insulin resistance in polycystic ovary syndrome: diagnostic and methodological challenges. Nutrition research reviews. 2017;30(1):97-105. Lim SS, Davies MJ, Norman RJ, Moran LJ. Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction update. 2012;18(6):618-37. Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary. Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. The American journal of clinical nutrition. 1998;68(4):899-917. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine. 2009;6(7):e1000097. Wells G, Shea B, O'Connell D, Peterson j, Welch V, Losos M, et al. The Newcastle–Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis. ᅟ. 2000;ᅟ. Adali E, Yildizhan R, Kurdoglu M, Bugdayci G, Kolusari A, Sahin HG. Increased plasma thrombin-activatable fibrinolysis inhibitor levels in young obese women with polycystic ovary syndrome. Fertility and sterility. 2010;94(2):666-72. Akram T, Hasan S, Imran M, Karim A, Arslan M. Association of polycystic ovary syndrome with cardiovascular risk factors. Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology. 2010;26(1):47-53. Alexandraki K, Protogerou AD, Papaioannou TG, Piperi C, Mastorakos G, Lekakis J, et al. Early microvascular and macrovascular dysfunction is not accompanied by structural arterial injury in polycystic ovary syndrome. Hormones (Athens, Greece). 2006;5(2):126-36. Arikan S, Akay H, Bahceci M, Tuzcu A, Gokalp D. The evaluation of endothelial function with flow-mediated dilatation and carotid intima media thickness in young nonobese polycystic ovary syndrome patients; existence of insulin resistance alone may not represent an adequate condition for deterioration of endothelial function. Fertility and sterility. 2009;91(2):450-5. Berneis K, Rizzo M, Lazzarini V, Fruzzetti F, Carmina E. Atherogenic lipoprotein phenotype and low-density lipoproteins size and subclasses in women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 2007;92(1):186-9. Cascella T, Palomba S, Tauchmanovà L, Manguso F, Di Biase S, Labella D, et al. Serum aldosterone concentration and cardiovascular risk in women with polycystic ovarian syndrome. The Journal of clinical endocrinology and metabolism. 2006;91(11):4395-400. Cetinkalp S, Karadeniz M, Erdogan M, Zengi A, Cetintas V, Tetik A, et al. Apolipoprotein E gene polymorphism and polycystic ovary syndrome patients in Western Anatolia, Turkey. Journal of assisted reproduction and genetics. 2009;26(1):1-6. Cussons AJ, Watts GF, Stuckey BG. Dissociation of endothelial function and arterial stiffness in nonobese women with polycystic ovary syndrome (PCOS). Clinical endocrinology. 2009;71(6):808-14. Diamanti-Kandarakis E, Alexandraki K, Piperi C, Protogerou A, Katsikis I, Paterakis T, et al. Inflammatory and endothelial markers in women with polycystic ovary syndrome. European journal of clinical investigation. 2006;36(10):691-7. El-Kannishy G, Kamal S, Mousa A, Saleh O, Badrawy AE, Farahaty RE, et al. Endothelial function in young women with polycystic ovary syndrome (PCOS): Implications of body mass index (BMI) and insulin resistance. Obesity research & clinical practice. 2010;4(1):e1-e82. Erdoğan M, Karadeniz M, Alper GE, Tamsel S, Uluer H, Cağlayan O, et al. Thrombin-activatable fibrinolysis inhibitor and cardiovascular risk factors in polycystic ovary syndrome. Experimental and clinical endocrinology & diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association. 2008;116(3):143-7. Erdogan M, Karadeniz M, Berdeli A, Tamsel S, Yilmaz C. The relationship of the interleukin-6 -174 G>C gene polymorphism with cardiovascular risk factors in Turkish polycystic ovary syndrome patients. International journal of immunogenetics. 2009;36(5):283-8. Joham AE, Boyle JA, Zoungas S, Teede HJ. Hypertension in Reproductive-Aged Women With Polycystic Ovary Syndrome and Association With Obesity. American journal of hypertension. 2015;28(7):847-51. Kargili A, Karakurt F, Kasapoglu B, Derbent A, Koca C, Selcoki Y. Association of polycystic ovary syndrome and a non-dipping blood pressure pattern in young women. Clinics (Sao Paulo, Brazil). 2010;65(5):475-9. Ketel IJ, Stehouwer CD, Henry RM, Serné EH, Hompes P, Homburg R, et al. Greater arterial stiffness in polycystic ovary syndrome (PCOS) is an obesity--but not a PCOS-associated phenomenon. The Journal of clinical endocrinology and metabolism. 2010;95(10):4566-75. Legro RS, Kunselman AR, Dunaif A. Prevalence and predictors of dyslipidemia in women with polycystic ovary syndrome. The American journal of medicine. 2001;111(8):607-13. Liang SJ, Liou TH, Lin HW, Hsu CS, Tzeng CR, Hsu MI. Obesity is the predominant predictor of impaired glucose tolerance and metabolic disturbance in polycystic ovary syndrome. Acta obstetricia et gynecologica Scandinavica. 2012;91(10):1167-72. Luque-Ramírez M, Mendieta-Azcona C, Alvarez-Blasco F, Escobar-Morreale HF. Androgen excess is associated with the increased carotid intima-media thickness observed in young women with polycystic ovary syndrome. Human reproduction (Oxford, England). 2007;22(12):3197-203. Macut D, Panidis D, Glisić B, Spanos N, Petakov M, Bjekić J, et al. Lipid and lipoprotein profile in women with polycystic ovary syndrome. Canadian journal of physiology and pharmacology. 2008;86(4):199-204. Meyer C, McGrath BP, Teede HJ. Overweight women with polycystic ovary syndrome have evidence of subclinical cardiovascular disease. The Journal of clinical endocrinology and metabolism. 2005;90(10):5711-6. Moran LJ, Hutchison SK, Meyer C, Zoungas S, Teede HJ. A comprehensive assessment of endothelial function in overweight women with and without polycystic ovary syndrome. Clinical science (London, England : 1979). 2009;116(10):761-70. Ni RM, Mo Y, Chen X, Zhong J, Liu W, Yang D. Low prevalence of the metabolic syndrome but high occurrence of various metabolic disorders in Chinese women with polycystic ovary syndrome. European journal of endocrinology. 2009;161(3):411-8. Oral B, Mermi B, Dilek M, Alanoğlu G, Sütçü R. Thrombin activatable fibrinolysis inhibitor and other hemostatic parameters in patients with polycystic ovary syndrome. Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology. 2009;25(2):110-6. Orio F, Jr., Palomba S, Cascella T, De Simone B, Di Biase S, Russo T, et al. Early impairment of endothelial structure and function in young normal-weight women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 2004;89(9):4588-93. Philbois SV, Gastaldi AC, Facioli TP, Felix ACS, Reis RMD, Fares TH, et al. Women with Polycystic Ovarian Syndrome Exhibit Reduced Baroreflex Sensitivity That May Be Associated with Increased Body Fat. Arquivos brasileiros de cardiologia. 2019;112(4):424-9. Rizzo M, Longo RA, Guastella E, Rini GB, Carmina E. Assessing cardiovascular risk in Mediterranean women with polycystic ovary syndrome. Journal of endocrinological investigation. 2011;34(6):422-6. Sasaki A, Emi Y, Matsuda M, Sharula, Kamada Y, Chekir C, et al. Increased arterial stiffness in mildly-hypertensive women with polycystic ovary syndrome. The journal of obstetrics and gynaecology research. 2011;37(5):402-11. Soares GM, Vieira CS, Martins WP, Franceschini SA, dos Reis RM, Silva de Sá MF, et al. Increased arterial stiffness in nonobese women with polycystic ovary syndrome (PCOS) without comorbidities: one more characteristic inherent to the syndrome? Clinical endocrinology. 2009;71(3):406-11. Sterling L, Liu J, Okun N, Sakhuja A, Sierra S, Greenblatt E. Pregnancy outcomes in women with polycystic ovary syndrome undergoing in vitro fertilization. Fertility and sterility. 2016;105(3):791-7.e2. Tarkun I, Arslan BC, Cantürk Z, Türemen E, Sahin T, Duman C. Endothelial dysfunction in young women with polycystic ovary syndrome: relationship with insulin resistance and low-grade chronic inflammation. The Journal of clinical endocrinology and metabolism. 2004;89(11):5592-6. Tíras MB, Yalcìn R, Noyan V, Maral I, Yìldìrìm M, Dörtlemez O, et al. Alterations in cardiac flow parameters in patients with polycystic ovarian syndrome. Human reproduction (Oxford, England). 1999;14(8):1949-52. Vryonidou A, Papatheodorou A, Tavridou A, Terzi T, Loi V, Vatalas IA, et al. Association of hyperandrogenemic and metabolic phenotype with carotid intima-media thickness in young women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 2005;90(5):2740-6. Yildiz BO, Haznedaroğlu IC, Kirazli S, Bayraktar M. Global fibrinolytic capacity is decreased in polycystic ovary syndrome, suggesting a prothrombotic state. The Journal of clinical endocrinology and metabolism. 2002;87(8):3871-5. Zimmermann S, Phillips RA, Dunaif A, Finegood DT, Wilkenfeld C, Ardeljan M, et al. Polycystic ovary syndrome: lack of hypertension despite profound insulin resistance. The Journal of clinical endocrinology and metabolism. 1992;75(2):508-13. Hudecova M, Holte J, Olovsson M, Larsson A, Berne C, Sundstrom-Poromaa I. Prevalence of the metabolic syndrome in women with a previous diagnosis of polycystic ovary syndrome: long-term follow-up. Fertility and sterility. 2011;96(5):1271-4. Holte J, Gennarelli G, Berne C, Bergh T, Lithell H. Elevated ambulatory day-time blood pressure in women with polycystic ovary syndrome: a sign of a pre-hypertensive state? Human reproduction (Oxford, England). 1996;11(1):23-8. Dahlgren E, Johansson S, Lindstedt G, Knutsson F, Oden A, Janson PO, et al. Reprint of: Women with polycystic ovary syndrome wedge resected in 1956 to 1965: a long-term follow-up focusing on natural history and circulating hormones. Fertility and sterility. 2019;112(4 Suppl1):e162-e70. Lim SS, Kakoly NS, Tan JWJ, Fitzgerald G, Bahri Khomami M, Joham AE, et al. Metabolic syndrome in polycystic ovary syndrome: a systematic review, meta-analysis and meta-regression. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2019;20(2):339-52. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Jama. 2001;285(19):2486-97. Talbott E, Clerici A, Berga SL, Kuller L, Guzick D, Detre K, et al. Adverse lipid and coronary heart disease risk profiles in young women with polycystic ovary syndrome: results of a case-control study. Journal of clinical epidemiology. 1998;51(5):415-22. Wild RA, Rizzo M, Clifton S, Carmina E. Lipid levels in polycystic ovary syndrome: systematic review and meta-analysis. Fertility and sterility. 2011;95(3):1073-9.e1-11. Martin SS, Blaha MJ, Elshazly MB, Brinton EA, Toth PP, McEvoy JW, et al. Friedewald-estimated versus directly measured low-density lipoprotein cholesterol and treatment implications. Journal of the American College of Cardiology. 2013;62(8):732-9. Di Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, et al. Major lipids, apolipoproteins, and risk of vascular disease. Jama. 2009;302(18):1993-2000. Palazón-Bru A, Carbayo-Herencia JA, Simarro-Rueda M, Artigao-Ródenas LM, Divisón-Garrote JA, Molina-Escribano F, et al. Comparison Between Non-High-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol to Estimate Cardiovascular Risk Using a Multivariate Model. The Journal of cardiovascular nursing. 2018;33(6):E17-e23. Palazón-Bru A, Folgado-de la Rosa DM, Cortés-Castell E, López-Cascales MT, Gil-Guillén VF. Sample size calculation to externally validate scoring systems based on logistic regression models. PloS one. 2017;12(5):e0176726. Ma X, Hayes E, Prizant H, Srivastava RK, Hammes SR, Sen A. Leptin-Induced CART (Cocaine- and Amphetamine-Regulated Transcript) Is a Novel Intraovarian Mediator of Obesity-Related Infertility in Females. Endocrinology. 2016;157(3):1248-57. Liou TH, Yang JH, Hsieh CH, Lee CY, Hsu CS, Hsu MI. Clinical and biochemical presentations of polycystic ovary syndrome among obese and nonobese women. Fertility and sterility. 2009;92(6):1960-5. Pustejovsky JE, Rodgers MA. Testing for funnel plot asymmetry of standardized mean differences. Research synthesis methods. 2019;10(1):57-71. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-230193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":12792334,"identity":"17247fd1-d8a1-4287-8b70-4f26e527c196","order_by":0,"name":"Chenchen Zhuang","email":"","orcid":"https://orcid.org/0000-0003-1746-6731","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chenchen","middleName":"","lastName":"Zhuang","suffix":""},{"id":12792335,"identity":"8e0c312b-32ae-47e8-9f76-6c0ddde3a706","order_by":1,"name":"Qiongying Wang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiongying","middleName":"","lastName":"Wang","suffix":""},{"id":12792336,"identity":"53cf1687-9e1e-42c1-bc9d-771640078807","order_by":2,"name":"Wenjuan Wang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenjuan","middleName":"","lastName":"Wang","suffix":""},{"id":12792337,"identity":"3ed169c2-dba4-43ce-bef2-16ed1c94394b","order_by":3,"name":"Runmin Sun","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Runmin","middleName":"","lastName":"Sun","suffix":""},{"id":12792338,"identity":"dd1edf28-5ba3-4567-80e4-32d2bbaa59a4","order_by":4,"name":"Xiaofang Zhang","email":"","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaofang","middleName":"","lastName":"Zhang","suffix":""},{"id":12792339,"identity":"9e31fcf0-0ec9-4d72-9adf-6842a7c2d955","order_by":5,"name":"Jing Yu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyklEQVRIie3RsQrCMBCA4SuB6BDNmiLoK5w4iHTwVeqURaFTZ6dMxbngS+gbVLKGzg4OfYSOggoG7dymm2B+OLjhvukAfL5fTQBMx5+N9iAL2o/YNsqZ8IlBWCktFbdLnWrgx307CQ9bhFDpnQKDQV5qELeinaBhDQkyJCOlAUXcTtYNkZQwJC8XguxLYkotCVyIMDQBUcq5YjS5ZKVk4tpBeEZORKTRjHN9ru5pNOV5BwEYLh9h847CDuu6tw2qoH463Pl8Pt//9gYCbzcsIiRJuQAAAABJRU5ErkJggg==","orcid":"","institution":"Lanzhou University Second Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2021-02-10 23:52:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-230193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-230193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6276631,"identity":"987d2919-5a09-43cb-80d6-fc547687aba3","added_by":"auto","created_at":"2021-02-23 21:17:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22518,"visible":true,"origin":"","legend":"Flow chart of the study selection procedure.","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/e8a37690430dd353b64d56f4.png"},{"id":6276808,"identity":"672c6aec-84c9-44b7-b654-e7da9f88efed","added_by":"auto","created_at":"2021-02-23 21:20:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157154,"visible":true,"origin":"","legend":"Forest plot showing the effect of systolic blood pressure in reproductive women with polycystic ovary syndrome (PCOS) (mmHg). Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/2cdc6cd6aa81a9b8a767ccc9.png"},{"id":6276629,"identity":"73de85d6-99af-4970-baa7-0d7bc30d2a85","added_by":"auto","created_at":"2021-02-23 21:17:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":150591,"visible":true,"origin":"","legend":"Forest plot showing the effect of diastolic blood pressure in reproductive women with polycystic ovary syndrome (PCOS) (mmHg). Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/953eabf9b45d9dfd31d7566c.png"},{"id":6276643,"identity":"bedfea35-a945-4cbb-9421-50ed1aa36609","added_by":"auto","created_at":"2021-02-23 21:17:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104161,"visible":true,"origin":"","legend":"Forest plot of the association between reproductive polycystic ovary syndrome (PCOS) and hypertension prevalence. Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/21347cb8c2cce5ad05689b41.png"},{"id":6276637,"identity":"307dbd99-1aab-49f6-8f11-c77028b36269","added_by":"auto","created_at":"2021-02-23 21:17:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":335842,"visible":true,"origin":"","legend":"Forest plot showing the effect of high-density lipoprotein-cholesterol (HDL-C) in reproductive women with polycystic ovary syndrome (PCOS). Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/766a419e950cf36a68dd632e.png"},{"id":6276806,"identity":"644392e0-7dde-4c28-9dc8-a489814323b3","added_by":"auto","created_at":"2021-02-23 21:20:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":325250,"visible":true,"origin":"","legend":"Forest plot showing the effect of triglyceride in reproductive women with polycystic ovary syndrome (PCOS). Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure6.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/465f6b659dc4edf0da19fa63.png"},{"id":6276638,"identity":"91682fb6-72a8-4987-b682-99524c083be8","added_by":"auto","created_at":"2021-02-23 21:17:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":332319,"visible":true,"origin":"","legend":"Forest plot showing the effect of nonhigh-density lipoprotein-cholesterol (nonHDL-C) in reproductive women with polycystic ovary syndrome (PCOS). Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure7.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/f28ab7475bc504d3dd9777ab.png"},{"id":6276626,"identity":"62a28406-d9ff-45a5-a3fd-cc7b875b5ffe","added_by":"auto","created_at":"2021-02-23 21:17:41","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":316084,"visible":true,"origin":"","legend":"Forest plot showing the effect of low-density lipoprotein-cholesterol (LDL-C) in reproductive women with polycystic ovary syndrome (PCOS). Studies are classified by different body mass index (BMI) categories (BMI of \u003c 25 kg/m2, BMI of ≥ 30 kg/m2 and BMI of 25-\u003c 30 kg/m2).","description":"","filename":"OnlineFigure8.png","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/7d1c89673e2bb7bdee310e34.png"},{"id":13669554,"identity":"c2dfe33a-83e6-4092-9bb3-e6a03bfedaa5","added_by":"auto","created_at":"2021-09-17 11:01:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2629882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-230193/v1/68b9aee2-8bed-4719-9b13-2c75804b77e8.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCardiovascular Risk in Reproductive-aged Women With Polycystic Ovary Syndrome According to Body Mass Index: a Systematic Review and Meta-analysis\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003ePolycystic ovary syndrome (PCOS) is one of the most common female endocrinopathies, and is a highly prevalent disorder affecting about 7\u0026ndash;14% of reproductive women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical manifestations of PCOS are heterogeneous, however, the hallmarks of the syndrome remain anovulation, insulin resistance and androgen excess. Moreover, each of these features is responsible for the promotion of cardiovascular risk in this population.\u003c/p\u003e \u003cp\u003eThe American Society for Reproductive Medicine Practice Committee reported cardiovascular risk was increased in women with PCOS [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The current studies showed that cardiovascular risk factors were more frequent among women with PCOS compared to non-PCOS women [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This set of major risk factors includes age, sex, body mass index (BMI) category, low-density lipoprotein cholesterol (LDL-C) or triglyceride (TG), low high-density lipoprotein cholesterol (HDL-C), high systolic blood pressure (SBP) or diastolic blood pressure (DBP), diabetes mellitus, tobacco smoking and family history of early cardiovascular disease (CVD). Apart from the deleterious effects of PCOS per se, individual risk markers including hypertension and lipid profile are important mediators of the further cardiovascular outcomes. Blood pressure and lipid levels are nearly always included in risk assessment algorithms because of their established independent contributions to cardiovascular risk prediction [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The studies of early cardiovascular epidemiology focused on risk factors related to atherosclerosis, which begins at a young age. Detection of traditional cardiovascular risk factors and alternative markers of atherosclerosis in women with PCOS at a young age increase the risk for symptomatic cardiovascular risk [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, PCOS may represent an important key to lipid alternations starting during adolescence or fertile age.\u003c/p\u003e \u003cp\u003eDyslipidemia is common in young adult women with PCOS, and the disorder may be the most common cause of dyslipidemia in women before the age of 40 years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Moreover, atherosclerotic appears early life during life, this confirms the need to assess and eventually treat altered lipid profile in young women with PCOS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. LDL-C is considered to be the primary target to reduce cardiovascular risk, prevalence metabolic syndrome in women with PCOS has concentrated attention on TG and HDL-C, with relatively little attention to other lipid changes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In addition, Rotterdam guidelines suggested evaluation for metabolic syndrome and indirectly indicated the need to measure only HDL-C and TG with relatively little attention to other lipid parameters [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The Androgen Excess and PCOS Society guidelines have reported that different LDL-C cutoff values depends on the degree of cardiovascular risk of women with PCOS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, many clinicians do not measure lipid levels in PCOS or continue measuring only HDL-C and TG.\u003c/p\u003e \u003cp\u003eThe effect of obesity on PCOS and PCOS on obesity is complex. Although it is agreed that the prevalence of increased BMI is high in PCOS [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A previous meta-analysis showed that obesity was more prevalent in women with PCOS than in women without PCOS [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. PCOS occurs both in obese and lean women. However, studies of cardiovascular risk in PCOS have not distinguished effects of BMI categories from those of PCOS. There is little consensus that the possible increased cardiovascular risk, is merely related to obesity. In the fact, lipid profile may differ in their association with lipid profile and PCOS potentially. Based on BMI categories, our meta-analysis classified as normal weight (18.5-\u0026lt;25 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25-\u0026lt;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and obesity (\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there is no met studies to report underweight (\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e) women with PCOS. These standard categories have been increasingly used in published studies of BMI levels and PCOS, but the literature reporting these results has not been systematically reviewed.\u003c/p\u003e \u003cp\u003eTherefore, the present meta-analysis of surrogate markers (i.e. blood pressure, lipids and hypertension prevalence) regarding cardiovascular risk factors shows the effect of different BMI levels in reproductive-aged women with PCOS. To reduce biases that may be present in all meta-analyses, only studies with BMI matching and having sufficient numbers of subjects were included. To our knowledge, this is the first meta-analysis to investigate the association between reproductive-aged women with PCOS and cardiovascular risk factors according to BMI levels.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eThe present study was approved by the Ethics Committee Board of Lanzhou University Second Hospital (D2019-098) and conducted in accordance with the Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eSearch strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe searched Cochrane Library, PubMed, EMBASE and MEDLINE databases to 9 November 2020 for full text articles in English describing trials and prospective cohort studies in reproductive-aged women that assessed the association between cardiovascular risk factors with PCOS according to BMI categories. The search terms were used as follows: (polycystic ovary syndrome or PCOS) and (cardiovascular risk). The search strategy was limited to English language papers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used a mix of medical subject heading (MeSH) and keywords including polycystic ovary syndrome, Stein Leventhal, cardiovascular risk, blood pressure, hypertension, lipoprotein, dyslipidemia, hyperlipidemia. A manual search of the list of references of all identified studies and review articles was performed for additional relevant studies.\u003c/p\u003e\n\u003cp\u003eThe selection criteria of the retrieved articles in our meta-analysis were as follows: 1) case-control studies or cohort studies; 2) reproductive-aged women with PCOS by the National Institutes of Health or Rotterdam criteria; 4) no comorbidities; 5) no evidence of an androgen-secreting tumor, congenital adrenal hyperplasia, or medication altering androgen metabolism or lipids; 6) each article with BMI matching. Each study had to have evaluated \u0026ge; 30 adults PCOS women \u0026ge; 18 years but \u0026le; 45 years (to avoid the perimenopause transition). Control subjects of the same catchment area, and found to be without PCOS.\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria of the meta-analysis were as follows: 1) case-only studies; 2) studies with incomplete data; 3) meta-analysis, letters, reviews, and editorial articles. Nonpatient-examined community control studies were excluded, recognizing that PCOS occurs infrequently and that some phenotypic elements of PCOS can occur in regularly menstruating women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of study quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncluded studies were assessed for quality using the Newcastle-Ottawa Scale (NOS), a validated scale for meta-analysis of observational studies [19]. We scored (max, nine points) the following items important for risk of bias assessment in non-randomized cohort studies: representativeness of the exposed cohort; adequate selection of controls; adequate definition of the outcome; adequacy of follow-up; comparability of exposed and non-exposed women (two points).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors of studies eligible for inclusion in present meta-analysis were invited to join our study and share their data. When not reported in the individual study, nonHDL-C was calculated as total cholesterol minus HDL-C. Data extraction was independently performed by three investigators (C.C.Z., Q.Y.W., W.J.W). Disagreement was resolved by consensus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dichotomous outcome measure was the proportion of women with hypertension prevalence. Data were presented as odds ratio (OR, 95% confidence interval [CI]). Continuous normally distributed data were summarized with a mean and standard deviation (SD), and if non-normally distributes a median and interquartile range (IQR) were used. If the outcome was measured on the same scale, we used weighted mean difference (MD) and 95% CI. Otherwise, standardized mean difference (SMD) and 95% CI were calculated. Heterogeneity between the studies was assessed using \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e tests (\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e ＞50% was considered substantial heterogeneity). When the \u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistic was lower than 30% and P \u0026lt; 0.10, a fixed-effect model was used; otherwise, a random effects model was used. We assessed publication bias through Egger\u0026rsquo;s regression test. Analyses were performed by STATA statistical software version 14.0 (StataCorp, College Station, TX, USA). \u003cem\u003ep\u003c/em\u003e-Value \u0026lt; 0.05 were considered statistically significant for all analyses except heterogeneity tests.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy design and analysis characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe search yielded 593 potential reports as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. After removal of duplicates, 556 records remained. Initial screening of the title and abstract resulted in the exclusion of 400 references and 156 studies proceeded to detailed evaluation. After further examination, 34 studies met the inclusion criteria and were included in the meta-analysis [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe basic characteristics of each study were summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 34 studies including 12711 subjects were included in the present meta-analysis. The present meta-analysis was assessed blood pressure, prevalence of hypertension, TG, HDL-C, nonHDL-C and LDL-C according to BMI categories.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of included studies in the meta-analysis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAuthor/year\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDesign\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParticipants age (years)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePCOS vs. control (n)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eBMI level (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOutcomes\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdali (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.91\u003c/p\u003e\n\u003cp\u003eControl: 13.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26 vs. 25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.23\u003c/p\u003e\n\u003cp\u003eControl: 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAkram (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePakistan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 20\u0026ndash;39\u003c/p\u003e\n\u003cp\u003eControl: 20\u0026ndash;39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 vs. 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e\n\u003cp\u003eControl: 23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlexandraki (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreece\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 25.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\n\u003cp\u003eControl: 27.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 vs. 27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 27.42\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e\n\u003cp\u003eControl: 25.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArikan (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.82\u0026thinsp;\u0026plusmn;\u0026thinsp;5.53\u003c/p\u003e\n\u003cp\u003eControl: 24.64\u0026thinsp;\u0026plusmn;\u0026thinsp;4.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39 vs. 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 21.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.50\u003c/p\u003e\n\u003cp\u003eControl: 20.90\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBerneis (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n\u003cp\u003eControl: 25.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 vs. 24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 28.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\n\u003cp\u003eControl: 28\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCascella (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\n\u003cp\u003eControl: 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 vs. 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n\u003cp\u003eControl: 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCetinakalp (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.58\u0026thinsp;\u0026plusmn;\u0026thinsp;4.61\u003c/p\u003e\n\u003cp\u003eControl: 25.48\u0026thinsp;\u0026plusmn;\u0026thinsp;3.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 vs. 91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.64\u003c/p\u003e\n\u003cp\u003eControl: 24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCussons (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.54\u003c/p\u003e\n\u003cp\u003eControl: 34.44\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 vs. 19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n\u003cp\u003eControl: 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiamanti-Kandarakis (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreece\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 25.64\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n\u003cp\u003eControl: 27.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25 vs. 25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 29.08\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43\u003c/p\u003e\n\u003cp\u003eControl: 26.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEl-Kannishy (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEgypt\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 25.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\n\u003cp\u003eControl: 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 vs. 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n\u003cp\u003eControl: 21.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eErdogan (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.27\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44\u003c/p\u003e\n\u003cp\u003eControl: 26.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68 vs. 26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.41\u0026thinsp;\u0026plusmn;\u0026thinsp;5.43\u003c/p\u003e\n\u003cp\u003eControl: 23.35\u0026thinsp;\u0026plusmn;\u0026thinsp;5.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eErdogan (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\n\u003cp\u003eControl: 25.01\u0026thinsp;\u0026plusmn;\u0026thinsp;2.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e88 vs. 119\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.13\u003c/p\u003e\n\u003cp\u003eControl: 23.47\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJoham (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u0026ndash;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e183 vs. 4638\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJoham (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u0026ndash;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117 vs. 1742\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.0\u0026ndash;29.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJoham (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u0026ndash;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149 vs. 1181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKargili (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 25.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e\n\u003cp\u003eControl: 26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e168 vs. 52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\n\u003cp\u003eControl: 25.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKetel (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNetherland\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e\n\u003cp\u003eControl: 27.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 vs.14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003cp\u003eControl: 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKetel (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNetherland\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 30.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n\u003cp\u003eControl: 28.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 vs. 13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 36.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\n\u003cp\u003eControl: 40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLegro (2001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUSA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 25\u0026thinsp;\u0026plusmn;\u0026thinsp;6\u003c/p\u003e\n\u003cp\u003eControl: 29\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42 vs. 27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n\u003cp\u003eControl: 23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLegro (2001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUSA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 28\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\n\u003cp\u003eControl: 32\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153 vs. 35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 37.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n\u003cp\u003eControl: 37.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiang (2012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTaiwan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 27\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e\n\u003cp\u003eControl: 29.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 vs. 20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 31.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n\u003cp\u003eControl: 30.4\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiang (2012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTaiwan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 26.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n\u003cp\u003eControl: 28.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110 vs. 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n\u003cp\u003eControl: 20.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLuque-Ramirez (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n\u003cp\u003eControl: 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 vs. 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e\n\u003cp\u003eControl: 21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLuque-Ramirez (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e\n\u003cp\u003eControl: 29.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 vs. 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 27.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003cp\u003eControl: 27.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLuque-Ramirez (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\n\u003cp\u003eControl: 28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 vs. 8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 35.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n\u003cp\u003eControl: 35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMacut (2008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSerbia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n\u003cp\u003eControl: 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 vs. 51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e\n\u003cp\u003eControl: 23.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMoran (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 34.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\n\u003cp\u003eControl: 33.8\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80 vs. 27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 36.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n\u003cp\u003eControl: 37.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMayer (2005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAustralia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 32.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003cp\u003eControl: 33.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 vs. 20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 37.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.43\u003c/p\u003e\n\u003cp\u003eControl: 36.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNi (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 27\u003c/p\u003e\n\u003cp\u003eControl: 28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e578 vs. 281\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 21.9\u003c/p\u003e\n\u003cp\u003eControl: 21.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOral (2008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n\u003cp\u003eControl: 24.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 vs. 43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e\n\u003cp\u003eControl: 24.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrio (2004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.5\u003c/p\u003e\n\u003cp\u003eControl: 22.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 vs. 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e\n\u003cp\u003eControl: 22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhilbois (2018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBrazil\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\n\u003cp\u003eControl: 31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30 vs. 30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n\u003cp\u003eControl: 23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRizzo (2011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItaly\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24\u0026thinsp;\u0026plusmn;\u0026thinsp;5\u003c/p\u003e\n\u003cp\u003eControl: 24\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e350 vs. 90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 27\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e\n\u003cp\u003eControl: 27\u0026thinsp;\u0026plusmn;\u0026thinsp;4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG, nonHDL-C\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSasaki (2011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJapan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 30.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n\u003cp\u003eControl: 31.5\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54 vs. 24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e\n\u003cp\u003eControl: 22.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShroff (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUSA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 32\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\n\u003cp\u003eControl: 36\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24 vs. 24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n\u003cp\u003eControl: 35\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSoares (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBrazil\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\n\u003cp\u003eControl: 24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40 vs. 50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e\n\u003cp\u003eControl: 23.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSterling (2015)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCanada\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 33\u003c/p\u003e\n\u003cp\u003eControl: 35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71 vs. 323\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.6\u003c/p\u003e\n\u003cp\u003eControl: 23.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrevalence of hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTarkun (2004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.45\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\n\u003cp\u003eControl: 24.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37 vs. 25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.85\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e\n\u003cp\u003eControl: 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTiras (1999)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 24.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e\n\u003cp\u003eControl: 23.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 vs. 35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n\u003cp\u003eControl: 22.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVryonidou (2005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGreece\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n\u003cp\u003eControl: 24.7\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75 vs. 55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 27.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.0\u003c/p\u003e\n\u003cp\u003eControl: 26.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSBP, HDL-C, LDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYildiz (2002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTurkey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 22.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\n\u003cp\u003eControl: 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59 vs. 23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCOS: 23.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e\n\u003cp\u003eControl: 22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHDL-C, TG\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"7\"\u003ePCOS, polycystic ovary syndrome; BMI, body mass index; PC, prospectively cohort study; RC, retrospectively cohort study, BP, blood pressure (including systolic and diastolic blood pressure); SBP, systolic blood pressure; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglycerides.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk of bias and quality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 34 studies included for systematic review were then reviewed with the NOS tool (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Some studies did not report on allocation concealment. Binding was impossible. One study scored seven out of nine points and one study scored six points, indicating high-quality. Three studies scored four points or less.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssessment of methodological quality (based on Newcastle-Ottawa Scale)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSelection (max 4 stars)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComparability (max 2 stars)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eExposure (max 3 stars)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall quality\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdali (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAkram (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlexandraki (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eArikan (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBerneis (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCascella (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCetinakalp (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCussons (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiamanti-Kandarakis (2006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEl-Kannishy (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eErdogan (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eErdogan (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJoham (2014)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKargili (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eKetel (2010)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLegro (2001)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLiang (2012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLuque-Ramirez (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMacut (2008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMoran (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMayer (2005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNi (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOral (2008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrio (2004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhilbois (2018)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRizzo (2011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSasaki (2011)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShroff (2007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSoares (2009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSterling (2015)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTarkun (2004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTiras (1999)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVryonidou (2005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e****\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGood\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYildiz (2002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFair\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eSELECTION\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(1) Is the case definition adequate? (a) yes, with independent validation*, (b) yes, e.g. record linkage or based on self-reports, (c) no description\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(2) Representativeness of the cases: (a) consecutive or obviously representative series of cases*, (b) potential for selection biases or not stated\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(3) Selection of controls: (a) community controls*, (b) hospital controls, (c) no description\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(4) Definition of controls: (a) no history of disease (end-point)*, (b) no description of source\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eCOMPARABILITY\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eComparability of cases and controls on basis of design or analysis: (a) study controls for ___ (most important factor)*, (b) study controls for any additional factor* (could be modified to indicate specific control for a second factor)\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eEXPOSURE\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(1) Ascertainment of exposure: (a) secure record (e.g. surgical record)*, (b) structured interview where blind to case/control status*, (c) interview not blinded to case/control status, (d) written self-report or medical record only, (e) no description.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(2) Same method of ascertainment for cases and controls: (a) yes*, (b) no.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e(3) Non-response rate: (a) same rate for both groups*, (b) non-respondents describe, (c) rate different and no designation.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eOVERALL QUALITY\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eGood quality: 3 or 4 stars in selection domain AND 1 or 2 stars in comparability domain AND 2 or 3 stars in exposure domain.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eFair quality: 2 stars in selection domain AND 1 or 2 stars in comparability domain AND 2 or 3 stars in exposure domain.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003ePoor quality: 0 or 1 star in selection domain OR 0 star in comparability domain OR 0 or 1 star in exposure domain.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlood pressure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e are forest plots summarizing comparison of SBP, DBP and prevalence of hypertension, respectively. Because the aim was assessed the effect of BMI categories on cardiovascular risk in reproductive women with PCOS, we analyzed the outcomes based on BMI categories.\u003c/p\u003e\n\u003cp\u003eAs shown in Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, SBP [MD (95% CI): 2.96 mmHg (1.62, 4.30), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001] and DBP [MD (95% CI): 1.64 mmHg (0.03, 3.24), P\u0026thinsp;=\u0026thinsp;0.046] increased in reproductive-aged women with PCOS. In the subgroup analysis of SBP, BMI of \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 3.02 mmHg (1.11, 4.94), P\u0026thinsp;\u0026lt;\u0026thinsp;0.002] and BMI of \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 4.27 mmHg (0.50, 8.04), P\u0026thinsp;\u0026lt;\u0026thinsp;0.026] showed significant results, while BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 1.56 mmHg (-0.60, 3.71), P\u0026thinsp;=\u0026thinsp;0.157] did not show positive result. However, in the subgroup analysis, DBP did not increase in BMI of \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 1.75 mmHg (-0.23, 3.75), P\u0026thinsp;=\u0026thinsp;0.083], BMI of \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): -0.24 mmHg (-3.16, 2.69), P\u0026thinsp;=\u0026thinsp;0.873], and BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 3.13 mmHg (-0.98, 7.24), P\u0026thinsp;=\u0026thinsp;0.136].\u003c/p\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, prevalence of hypertension had non-significant difference in reproductive-aged women with PCOS [OR (95% CI): 1.56 (0.76, 3.06), P\u0026thinsp;=\u0026thinsp;0.198]. Besides, there was no significant difference in BMI of \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e [OR (95% CI): 1.33 (0.12, 15.14), P\u0026thinsp;=\u0026thinsp;0.819], BMI of \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e [OR (95% CI): 1.53 (0.90, 2.61), P\u0026thinsp;=\u0026thinsp;0.116], and BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [OR (95% CI): 1.85 (0.80, 4.28), P\u0026thinsp;=\u0026thinsp;0.153].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipid profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 5-8 are forest plots summarizing comparison of HDL-C, TG, nonHDL-C and LDL-C, respectively. As shown in Figure 5 and 6, reproductive-aged women with PCOS had significantly difference on HDL-C [SMD (95% CI): -0.24 (-0.39, -0.09), P = 0.002] and TG [SMD (95% CI): 0.37 (0.26, 0.48), P \u0026lt; 0.001]. The subgroup analysis of TG, BMI of \u0026lt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e [SMD (95% CI): 0.34 (0.20, 0.49), P \u0026lt; 0.001], BMI of \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 0.54 (0.31, 0.78), P \u0026lt; 0.001], and BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 0.25 (0.06, 0.43), P = 0.008] were increased in reproductive-aged women with PCOS. Meanwhile, the subgroup analysis showed that HDL-C significantly increased in BMI of \u0026lt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e [SMD (95% CI): -0.30 (-0.50, -0.11), P = 0.002], and BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): -0.33 (-0.65, -0.01), P = 0.041]. However, BMI of \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e had no significant difference [MD (95% CI): 0.07 (-0.26, 0.40), P = 0.664].\u003c/p\u003e\n\u003cp\u003eFigure 7 and 8 are forest plots summarizing comparison of nonHDL-C and LDL-C, respectively. As shown in Figure 7 and 8, reproductive-aged women with PCOS had significant difference on nonHDL-C [SMD (95% CI): 0.38 (0.29, 0.47), P \u0026lt; 0.001] and LDL-C [SMD (95% CI): 0.25 (0.17, 0.34), P \u0026lt; 0.001]. The subgroup analysis showed that nonHDL-C increased in BMI of \u0026lt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e [SMD (95% CI): 0.37 (0.24, 0.50), P \u0026lt; 0.001], BMI of \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 0.49 (0.30, 0.69), P \u0026lt; 0.001], and BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 0.33 (0.24, 0.50), P \u0026lt; 0.001]. In addition, LDL-C increased in BMI of \u0026lt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e [SMD (95% CI): 0.28 (0.15, 0.41), P \u0026lt; 0.001] and BMI of \u0026ge; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 0.25 (0.06, 0.44), P = 0.010]. However, LDL-C did not increase in BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e [MD (95% CI): 0.19 (-0.01, 0.39), P = 0.066].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no publication bias for SBP (asymmetry test P\u0026thinsp;=\u0026thinsp;0.510), DBP (asymmetry test P\u0026thinsp;=\u0026thinsp;0.092), HDL (asymmetry test P\u0026thinsp;=\u0026thinsp;0.405), TG (asymmetry test P\u0026thinsp;=\u0026thinsp;0.633), nonHDL-C (asymmetry test P\u0026thinsp;=\u0026thinsp;0.938), and LDL (asymmetry test P\u0026thinsp;=\u0026thinsp;0.628) according to the results of Egger\u0026rsquo;s regression test.\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eThe novelty of this systematic review from 34 observational studies reports the correlation between cardiovascular risk and BMI categories for reproductive-aged women with PCOS. To the best of our knowledge, it is the first time that BMI categories were evaluated cardiovascular risk factors in reproductive-aged women with PCOS. There are four main results in the present meta-analysis: (1) baseline blood pressure increased in reproductive-aged women with PCOS, but there was non-significant difference on hypertension prevalence; (2) increased LDL-C, low HDL-C, and increased TG\u0026thinsp;+\u0026thinsp;increased nonHDL-C were seen in reproductive-aged women with PCOS; (3) increased cardiovascular risk was seen in reproductive-aged women with PCOS independent of BMI categories; (4) DBP and hypertension prevalence had no difference in all BMI categories.\u003c/p\u003e \u003cp\u003eIn present meta-analysis, increased baseline blood pressure was seen in reproductive PCOS women, but there was no significant difference on hypertension prevalence. There was no relationship between blood pressure and BMI categories in reproductive-aged women with PCOS.\u003c/p\u003e \u003cp\u003eThe study of Zimmermann et al. reported that BMI failed to reveal an association between reproductive-aged women with PCOS and hypertension [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Hudecova et al. reported that after adjusting for BMI, reproductive-aged women with PCOS had higher daytime systolic blood pressure, mean arterial values of blood pressure, and an increased pulse rate irrespectively of clinically verified hypertension [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Moreover, a study from Australia reported that hypertension in reproductive PCOS women was not associated with BMI, giving further information that the cardiometabolic abnormalities may be independent of weight in PCOS [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, previous studies showed that the prevalence of hypertension increased in premenopausal [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and perimenopausal PCOS women [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Schmidt et al. showed that hypertension and hypertriglyceridemia were the only cardiovascular risk factor that persisted more frequently in postmenopausal PCOS women. Therefore, hypertension is more prevalent in PCOS women from the fourth decade of life in comparison to respective controls [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere are some mainly factors for increased blood pressure in PCOS women, including endothelial dysfunction and decreased vascular compliance [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The increased risk of hypertension was explained by insulin resistance and hyperinsulinemia that exert hypertrophy of vascular muscle wall and reduce compliance by interference with endothelium-dependent vasodilatation mechanisms [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Hyperinsulinemia advocated endothelin 1 (ET-1) production, meanwhile, it also influences the hypertrophic effect on vascular endothelial and smooth muscle cells [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Besides, ET-1 is commonly chronically increased in women with PCOS, which regulates endothelial function.\u003c/p\u003e \u003cp\u003eIn weight-matched studies, our results showed that increased LDL-C, low HDL-C, and increased TG\u0026thinsp;+\u0026thinsp;increased nonHDL-C were seen in reproductive-aged women with PCOS. Moreover, dyslipidemia in reproductive PCOS women had no relationship with body weight. The outcomes consistent with that of the previous studies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. PCOS women younger than 40 appears to have dyslipidemia [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Therefore, PCOS per se increases lipid levels, though the absolute value and the related cardiovascular risk may be different between individual patients. This is an important finding that lipid pattern assessment for decreased cardiovascular risk in reproductive PCOS women at all BMI categories.\u003c/p\u003e \u003cp\u003eThe present meta-analysis reported that higher LDL-C was found in normal-weight (BMI of \u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) and obese reproductive-aged women with PCOS (BMI of \u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), and low HDL-C was seen in normal-weight and overweight (BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e) subjects. Moreover, reproductive PCOS women at all BMI categories increased TG and nonHDL-C levels, including normal-weight, overweight and obesity. The subset analyses from a meta-analysis showed higher LDL-C was found in women with higher BMI categories [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. However, our outcomes of reproductive PCOS women showed that increased LDL-C was seen in normal-weight and obese subjects. Meanwhile, nonHDL-C increased in all BMI categories. Because the measurement of LDL-C can be influenced by increased TG in cardiovascular risk assessment [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], an appropriate way to estimate the amount of apoB-containing lipoprotein is the determination of nonHDL-C [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe level of nonHDL-C has a comparable prognostic relevance, similar to LDL-C [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. It is necessary to detect the nonHDL-C level in reproductive PCOS women. Higher nonHDL-C levels were generally found in this population whose mean BMI was higher, but differences were also clearly found with PCOS even the BMI was in the normal-weight and overweight categories. The increasing level of nonHDL-C predict long-term cardiovascular risk, particularly at a young age. The suggests that obesity or overweight is not the absolute factor for reproductive PCOS women. Because, atherosclerotic process starts early life during life, this confirms the need to assess and eventually treat altered lipid profile in young women with PCOS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough obesity is an important confounder in relationship between PCOS and cardiovascular risk, non-obese subjects also have a high dyslipidemia risk [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. In a meta-analysis of approximately 15000 women from 35 studies, women with PCOS had an increased prevalence of overweight, obesity and central obesity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In addition to the reproductive and psychological characteristics of PCOS, the metabolic aberrations of PCOS worsen with obesity [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, non-obese women with PCOS are also at an increased risk of similar cardiometabolic aberrations. Therefore, it is necessary to screened lipid levels for reproductive PCOS women with all BMI categories.\u003c/p\u003e "},{"header":"Limitation","content":" \u003cp\u003eSeveral limitations should also be noted. First, PCOS is a heterogeneous disease and the phenotypes are unable to exhibit in this meta-analysis. Second, there was a significant clinical and statistical heterogeneity in the pooled analysis. This could be due to confounding effects related to factors such as age, BMI, study quality, ethnicity, PCOS phenotypes and other clinical features. Besides, the use of country regions as a proxy for ethnicity due to lack of reporting of ethnic compositions in most studies. Further studies should include ethnicity of participants to allow for better understanding on the differential effects of this on PCOS or the metabolic syndrome. Third, the Egger\u0026rsquo;s test has low power when there are less than 10 studies in the meta-analysis [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Our analyses included more than 10 studies for blood pressure and lipid profile, but less than ten studies for hypertension prevalence, which may have resulted in too low power to detect asymmetry.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eIn conclusion, our meta-analysis suggests that baseline blood pressure increases in reproductive-aged women with PCOS, but prevalence of hypertension has non-significantly difference. Besides, dyslipidemia is common in reproductive-aged women with PCOS and is characterized not only by low HDL-C and increased TG but also by high LDL-C and nonHDL-C. These lipid profiles are worse in reproductive-aged women with PCOS compared with controls, independent of BMI categories.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was approved by the Ethics Committee Board of Lanzhou University Second Hospital (D2019-098).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China (81960086).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eChenchen Zhuang and Jing Yu conceived the manuscript. Chenchen Zhuang designed the study and wrote the initial draft of manuscript. Chenchen Zhuang, Qiongying Wang, Wenjuan Wang and Runmin Sun analyzed the data. Qiongying Wang and Wenjuan Wang collected the data. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to Associate Ph.D. Jumo Zhu (University Medical Center of Groningen) and Ph.D. Ziyi Guo (University of New South Wales) for his assistance in the field. We also acknowledge Prof. Yuanhang Cheng (City University of Hong Kong) for constructive suggestions. Extremely careful and thoughtful reviews by Associate Editor and Reviewer improved this manuscript greatly.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCooney LG, Dokras A. Beyond fertility: polycystic ovary syndrome and long-term health. Fertility and sterility. 2018;110(5):794-809.\u003c/li\u003e\n\u003cli\u003eBozdag G, Mumusoglu S, Zengin D, Karabulut E, Yildiz BO. The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction (Oxford, England). 2016;31(12):2841-55.\u003c/li\u003e\n\u003cli\u003eObesity and reproduction: an educational bulletin. Fertility and sterility. 2008;90(5 Suppl):S21-9.\u003c/li\u003e\n\u003cli\u003eBehboudi-Gandevani S, Ramezani Tehrani F, Hosseinpanah F, Khalili D, Cheraghi L, Kazemijaliseh H, et al. Cardiometabolic risks in polycystic ovary syndrome: long-term population-based follow-up study. Fertility and sterility. 2018;110(7):1377-86.\u003c/li\u003e\n\u003cli\u003eSirmans SM, Pate KA. Epidemiology, diagnosis, and management of polycystic ovary syndrome. Clinical epidemiology. 2013;6:1-13.\u003c/li\u003e\n\u003cli\u003ePencina MJ, Navar AM, Wojdyla D, Sanchez RJ, Khan I, Elassal J, et al. Quantifying Importance of Major Risk Factors for Coronary Heart Disease. Circulation. 2019;139(13):1603-11.\u003c/li\u003e\n\u003cli\u003eWild RA, Carmina E, Diamanti-Kandarakis E, Dokras A, Escobar-Morreale HF, Futterweit W, et al. Assessment of cardiovascular risk and prevention of cardiovascular disease in women with the polycystic ovary syndrome: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome (AE-PCOS) Society. The Journal of clinical endocrinology and metabolism. 2010;95(5):2038-49.\u003c/li\u003e\n\u003cli\u003eShroff R, Kerchner A, Maifeld M, Van Beek EJ, Jagasia D, Dokras A. Young obese women with polycystic ovary syndrome have evidence of early coronary atherosclerosis. The Journal of clinical endocrinology and metabolism. 2007;92(12):4609-14.\u003c/li\u003e\n\u003cli\u003eWild RA, Painter PC, Coulson PB, Carruth KB, Ranney GB. Lipoprotein lipid concentrations and cardiovascular risk in women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 1985;61(5):946-51.\u003c/li\u003e\n\u003cli\u003eCarmina E. Cardiovascular risk and events in polycystic ovary syndrome. Climacteric : the journal of the International Menopause Society. 2009;12 Suppl 1:22-5.\u003c/li\u003e\n\u003cli\u003eMoran LJ, Misso ML, Wild RA, Norman RJ. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction update. 2010;16(4):347-63.\u003c/li\u003e\n\u003cli\u003eDokras A, Bochner M, Hollinrake E, Markham S, Vanvoorhis B, Jagasia DH. Screening women with polycystic ovary syndrome for metabolic syndrome. Obstetrics and gynecology. 2005;106(1):131-7.\u003c/li\u003e\n\u003cli\u003eRevised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertility and sterility. 2004;81(1):19-25.\u003c/li\u003e\n\u003cli\u003eGlueck CJ, Goldenberg N. Characteristics of obesity in polycystic ovary syndrome: Etiology, treatment, and genetics. Metabolism: clinical and experimental. 2019;92:108-20.\u003c/li\u003e\n\u003cli\u003eJeanes YM, Reeves S. Metabolic consequences of obesity and insulin resistance in polycystic ovary syndrome: diagnostic and methodological challenges. Nutrition research reviews. 2017;30(1):97-105.\u003c/li\u003e\n\u003cli\u003eLim SS, Davies MJ, Norman RJ, Moran LJ. Overweight, obesity and central obesity in women with polycystic ovary syndrome: a systematic review and meta-analysis. Human reproduction update. 2012;18(6):618-37.\u003c/li\u003e\n\u003cli\u003eClinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: executive summary. Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. The American journal of clinical nutrition. 1998;68(4):899-917.\u003c/li\u003e\n\u003cli\u003eMoher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS medicine. 2009;6(7):e1000097.\u003c/li\u003e\n\u003cli\u003eWells G, Shea B, O'Connell D, Peterson j, Welch V, Losos M, et al. The Newcastle\u0026ndash;Ottawa Scale (NOS) for Assessing the Quality of Non-Randomized Studies in Meta-Analysis. ᅟ. 2000;ᅟ.\u003c/li\u003e\n\u003cli\u003eAdali E, Yildizhan R, Kurdoglu M, Bugdayci G, Kolusari A, Sahin HG. Increased plasma thrombin-activatable fibrinolysis inhibitor levels in young obese women with polycystic ovary syndrome. Fertility and sterility. 2010;94(2):666-72.\u003c/li\u003e\n\u003cli\u003eAkram T, Hasan S, Imran M, Karim A, Arslan M. Association of polycystic ovary syndrome with cardiovascular risk factors. Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology. 2010;26(1):47-53.\u003c/li\u003e\n\u003cli\u003eAlexandraki K, Protogerou AD, Papaioannou TG, Piperi C, Mastorakos G, Lekakis J, et al. Early microvascular and macrovascular dysfunction is not accompanied by structural arterial injury in polycystic ovary syndrome. Hormones (Athens, Greece). 2006;5(2):126-36.\u003c/li\u003e\n\u003cli\u003eArikan S, Akay H, Bahceci M, Tuzcu A, Gokalp D. The evaluation of endothelial function with flow-mediated dilatation and carotid intima media thickness in young nonobese polycystic ovary syndrome patients; existence of insulin resistance alone may not represent an adequate condition for deterioration of endothelial function. Fertility and sterility. 2009;91(2):450-5.\u003c/li\u003e\n\u003cli\u003eBerneis K, Rizzo M, Lazzarini V, Fruzzetti F, Carmina E. Atherogenic lipoprotein phenotype and low-density lipoproteins size and subclasses in women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 2007;92(1):186-9.\u003c/li\u003e\n\u003cli\u003eCascella T, Palomba S, Tauchmanov\u0026agrave; L, Manguso F, Di Biase S, Labella D, et al. Serum aldosterone concentration and cardiovascular risk in women with polycystic ovarian syndrome. The Journal of clinical endocrinology and metabolism. 2006;91(11):4395-400.\u003c/li\u003e\n\u003cli\u003eCetinkalp S, Karadeniz M, Erdogan M, Zengi A, Cetintas V, Tetik A, et al. Apolipoprotein E gene polymorphism and polycystic ovary syndrome patients in Western Anatolia, Turkey. Journal of assisted reproduction and genetics. 2009;26(1):1-6.\u003c/li\u003e\n\u003cli\u003eCussons AJ, Watts GF, Stuckey BG. Dissociation of endothelial function and arterial stiffness in nonobese women with polycystic ovary syndrome (PCOS). Clinical endocrinology. 2009;71(6):808-14.\u003c/li\u003e\n\u003cli\u003eDiamanti-Kandarakis E, Alexandraki K, Piperi C, Protogerou A, Katsikis I, Paterakis T, et al. Inflammatory and endothelial markers in women with polycystic ovary syndrome. European journal of clinical investigation. 2006;36(10):691-7.\u003c/li\u003e\n\u003cli\u003eEl-Kannishy G, Kamal S, Mousa A, Saleh O, Badrawy AE, Farahaty RE, et al. Endothelial function in young women with polycystic ovary syndrome (PCOS): Implications of body mass index (BMI) and insulin resistance. Obesity research \u0026amp; clinical practice. 2010;4(1):e1-e82.\u003c/li\u003e\n\u003cli\u003eErdoğan M, Karadeniz M, Alper GE, Tamsel S, Uluer H, Cağlayan O, et al. Thrombin-activatable fibrinolysis inhibitor and cardiovascular risk factors in polycystic ovary syndrome. Experimental and clinical endocrinology \u0026amp; diabetes : official journal, German Society of Endocrinology [and] German Diabetes Association. 2008;116(3):143-7.\u003c/li\u003e\n\u003cli\u003eErdogan M, Karadeniz M, Berdeli A, Tamsel S, Yilmaz C. The relationship of the interleukin-6 -174 G\u0026gt;C gene polymorphism with cardiovascular risk factors in Turkish polycystic ovary syndrome patients. International journal of immunogenetics. 2009;36(5):283-8.\u003c/li\u003e\n\u003cli\u003eJoham AE, Boyle JA, Zoungas S, Teede HJ. Hypertension in Reproductive-Aged Women With Polycystic Ovary Syndrome and Association With Obesity. American journal of hypertension. 2015;28(7):847-51.\u003c/li\u003e\n\u003cli\u003eKargili A, Karakurt F, Kasapoglu B, Derbent A, Koca C, Selcoki Y. Association of polycystic ovary syndrome and a non-dipping blood pressure pattern in young women. Clinics (Sao Paulo, Brazil). 2010;65(5):475-9.\u003c/li\u003e\n\u003cli\u003eKetel IJ, Stehouwer CD, Henry RM, Sern\u0026eacute; EH, Hompes P, Homburg R, et al. Greater arterial stiffness in polycystic ovary syndrome (PCOS) is an obesity--but not a PCOS-associated phenomenon. The Journal of clinical endocrinology and metabolism. 2010;95(10):4566-75.\u003c/li\u003e\n\u003cli\u003eLegro RS, Kunselman AR, Dunaif A. Prevalence and predictors of dyslipidemia in women with polycystic ovary syndrome. The American journal of medicine. 2001;111(8):607-13.\u003c/li\u003e\n\u003cli\u003eLiang SJ, Liou TH, Lin HW, Hsu CS, Tzeng CR, Hsu MI. Obesity is the predominant predictor of impaired glucose tolerance and metabolic disturbance in polycystic ovary syndrome. Acta obstetricia et gynecologica Scandinavica. 2012;91(10):1167-72.\u003c/li\u003e\n\u003cli\u003eLuque-Ram\u0026iacute;rez M, Mendieta-Azcona C, Alvarez-Blasco F, Escobar-Morreale HF. Androgen excess is associated with the increased carotid intima-media thickness observed in young women with polycystic ovary syndrome. Human reproduction (Oxford, England). 2007;22(12):3197-203.\u003c/li\u003e\n\u003cli\u003eMacut D, Panidis D, Glisić B, Spanos N, Petakov M, Bjekić J, et al. Lipid and lipoprotein profile in women with polycystic ovary syndrome. Canadian journal of physiology and pharmacology. 2008;86(4):199-204.\u003c/li\u003e\n\u003cli\u003eMeyer C, McGrath BP, Teede HJ. Overweight women with polycystic ovary syndrome have evidence of subclinical cardiovascular disease. The Journal of clinical endocrinology and metabolism. 2005;90(10):5711-6.\u003c/li\u003e\n\u003cli\u003eMoran LJ, Hutchison SK, Meyer C, Zoungas S, Teede HJ. A comprehensive assessment of endothelial function in overweight women with and without polycystic ovary syndrome. Clinical science (London, England : 1979). 2009;116(10):761-70.\u003c/li\u003e\n\u003cli\u003eNi RM, Mo Y, Chen X, Zhong J, Liu W, Yang D. Low prevalence of the metabolic syndrome but high occurrence of various metabolic disorders in Chinese women with polycystic ovary syndrome. European journal of endocrinology. 2009;161(3):411-8.\u003c/li\u003e\n\u003cli\u003eOral B, Mermi B, Dilek M, Alanoğlu G, S\u0026uuml;t\u0026ccedil;\u0026uuml; R. Thrombin activatable fibrinolysis inhibitor and other hemostatic parameters in patients with polycystic ovary syndrome. Gynecological endocrinology : the official journal of the International Society of Gynecological Endocrinology. 2009;25(2):110-6.\u003c/li\u003e\n\u003cli\u003eOrio F, Jr., Palomba S, Cascella T, De Simone B, Di Biase S, Russo T, et al. Early impairment of endothelial structure and function in young normal-weight women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 2004;89(9):4588-93.\u003c/li\u003e\n\u003cli\u003ePhilbois SV, Gastaldi AC, Facioli TP, Felix ACS, Reis RMD, Fares TH, et al. Women with Polycystic Ovarian Syndrome Exhibit Reduced Baroreflex Sensitivity That May Be Associated with Increased Body Fat. Arquivos brasileiros de cardiologia. 2019;112(4):424-9.\u003c/li\u003e\n\u003cli\u003eRizzo M, Longo RA, Guastella E, Rini GB, Carmina E. Assessing cardiovascular risk in Mediterranean women with polycystic ovary syndrome. Journal of endocrinological investigation. 2011;34(6):422-6.\u003c/li\u003e\n\u003cli\u003eSasaki A, Emi Y, Matsuda M, Sharula, Kamada Y, Chekir C, et al. Increased arterial stiffness in mildly-hypertensive women with polycystic ovary syndrome. The journal of obstetrics and gynaecology research. 2011;37(5):402-11.\u003c/li\u003e\n\u003cli\u003eSoares GM, Vieira CS, Martins WP, Franceschini SA, dos Reis RM, Silva de S\u0026aacute; MF, et al. Increased arterial stiffness in nonobese women with polycystic ovary syndrome (PCOS) without comorbidities: one more characteristic inherent to the syndrome? Clinical endocrinology. 2009;71(3):406-11.\u003c/li\u003e\n\u003cli\u003eSterling L, Liu J, Okun N, Sakhuja A, Sierra S, Greenblatt E. Pregnancy outcomes in women with polycystic ovary syndrome undergoing in vitro fertilization. Fertility and sterility. 2016;105(3):791-7.e2.\u003c/li\u003e\n\u003cli\u003eTarkun I, Arslan BC, Cant\u0026uuml;rk Z, T\u0026uuml;remen E, Sahin T, Duman C. Endothelial dysfunction in young women with polycystic ovary syndrome: relationship with insulin resistance and low-grade chronic inflammation. The Journal of clinical endocrinology and metabolism. 2004;89(11):5592-6.\u003c/li\u003e\n\u003cli\u003eT\u0026iacute;ras MB, Yalc\u0026igrave;n R, Noyan V, Maral I, Y\u0026igrave;ld\u0026igrave;r\u0026igrave;m M, D\u0026ouml;rtlemez O, et al. Alterations in cardiac flow parameters in patients with polycystic ovarian syndrome. Human reproduction (Oxford, England). 1999;14(8):1949-52.\u003c/li\u003e\n\u003cli\u003eVryonidou A, Papatheodorou A, Tavridou A, Terzi T, Loi V, Vatalas IA, et al. Association of hyperandrogenemic and metabolic phenotype with carotid intima-media thickness in young women with polycystic ovary syndrome. The Journal of clinical endocrinology and metabolism. 2005;90(5):2740-6.\u003c/li\u003e\n\u003cli\u003eYildiz BO, Haznedaroğlu IC, Kirazli S, Bayraktar M. Global fibrinolytic capacity is decreased in polycystic ovary syndrome, suggesting a prothrombotic state. The Journal of clinical endocrinology and metabolism. 2002;87(8):3871-5.\u003c/li\u003e\n\u003cli\u003eZimmermann S, Phillips RA, Dunaif A, Finegood DT, Wilkenfeld C, Ardeljan M, et al. Polycystic ovary syndrome: lack of hypertension despite profound insulin resistance. The Journal of clinical endocrinology and metabolism. 1992;75(2):508-13.\u003c/li\u003e\n\u003cli\u003eHudecova M, Holte J, Olovsson M, Larsson A, Berne C, Sundstrom-Poromaa I. Prevalence of the metabolic syndrome in women with a previous diagnosis of polycystic ovary syndrome: long-term follow-up. Fertility and sterility. 2011;96(5):1271-4.\u003c/li\u003e\n\u003cli\u003eHolte J, Gennarelli G, Berne C, Bergh T, Lithell H. Elevated ambulatory day-time blood pressure in women with polycystic ovary syndrome: a sign of a pre-hypertensive state? Human reproduction (Oxford, England). 1996;11(1):23-8.\u003c/li\u003e\n\u003cli\u003eDahlgren E, Johansson S, Lindstedt G, Knutsson F, Oden A, Janson PO, et al. Reprint of: Women with polycystic ovary syndrome wedge resected in 1956 to 1965: a long-term follow-up focusing on natural history and circulating hormones. Fertility and sterility. 2019;112(4 Suppl1):e162-e70.\u003c/li\u003e\n\u003cli\u003eLim SS, Kakoly NS, Tan JWJ, Fitzgerald G, Bahri Khomami M, Joham AE, et al. Metabolic syndrome in polycystic ovary syndrome: a systematic review, meta-analysis and meta-regression. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2019;20(2):339-52.\u003c/li\u003e\n\u003cli\u003eExecutive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Jama. 2001;285(19):2486-97.\u003c/li\u003e\n\u003cli\u003eTalbott E, Clerici A, Berga SL, Kuller L, Guzick D, Detre K, et al. Adverse lipid and coronary heart disease risk profiles in young women with polycystic ovary syndrome: results of a case-control study. Journal of clinical epidemiology. 1998;51(5):415-22.\u003c/li\u003e\n\u003cli\u003eWild RA, Rizzo M, Clifton S, Carmina E. Lipid levels in polycystic ovary syndrome: systematic review and meta-analysis. Fertility and sterility. 2011;95(3):1073-9.e1-11.\u003c/li\u003e\n\u003cli\u003eMartin SS, Blaha MJ, Elshazly MB, Brinton EA, Toth PP, McEvoy JW, et al. Friedewald-estimated versus directly measured low-density lipoprotein cholesterol and treatment implications. Journal of the American College of Cardiology. 2013;62(8):732-9.\u003c/li\u003e\n\u003cli\u003eDi Angelantonio E, Sarwar N, Perry P, Kaptoge S, Ray KK, Thompson A, et al. Major lipids, apolipoproteins, and risk of vascular disease. Jama. 2009;302(18):1993-2000.\u003c/li\u003e\n\u003cli\u003ePalaz\u0026oacute;n-Bru A, Carbayo-Herencia JA, Simarro-Rueda M, Artigao-R\u0026oacute;denas LM, Divis\u0026oacute;n-Garrote JA, Molina-Escribano F, et al. Comparison Between Non-High-Density Lipoprotein Cholesterol and Low-Density Lipoprotein Cholesterol to Estimate Cardiovascular Risk Using a Multivariate Model. The Journal of cardiovascular nursing. 2018;33(6):E17-e23.\u003c/li\u003e\n\u003cli\u003ePalaz\u0026oacute;n-Bru A, Folgado-de la Rosa DM, Cort\u0026eacute;s-Castell E, L\u0026oacute;pez-Cascales MT, Gil-Guill\u0026eacute;n VF. Sample size calculation to externally validate scoring systems based on logistic regression models. PloS one. 2017;12(5):e0176726.\u003c/li\u003e\n\u003cli\u003eMa X, Hayes E, Prizant H, Srivastava RK, Hammes SR, Sen A. Leptin-Induced CART (Cocaine- and Amphetamine-Regulated Transcript) Is a Novel Intraovarian Mediator of Obesity-Related Infertility in Females. Endocrinology. 2016;157(3):1248-57.\u003c/li\u003e\n\u003cli\u003eLiou TH, Yang JH, Hsieh CH, Lee CY, Hsu CS, Hsu MI. Clinical and biochemical presentations of polycystic ovary syndrome among obese and nonobese women. Fertility and sterility. 2009;92(6):1960-5.\u003c/li\u003e\n\u003cli\u003ePustejovsky JE, Rodgers MA. Testing for funnel plot asymmetry of standardized mean differences. Research synthesis methods. 2019;10(1):57-71.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PCOS, cardiovascular risk, reproductive-age, dyslipidemia, blood pressure","lastPublishedDoi":"10.21203/rs.3.rs-230193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-230193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTo evaluate the association between cardiovascular risk and reproductive-aged women with polycystic ovary syndrome (PCOS) according to body mass index (BMI) categories.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A literature search was conducted in MEDLINE, EMBASE, Cochrane Library and PubMed. Blood pressure (BP) [including systolic BP (SBP), diastolic BP (DBP) and hypertension prevalence] and plasma lipids [triglyceride, high-density lipoprotein (HDL)-cholesterol, nonHDL-cholesterol and low-density lipoprotein (LDL)-cholesterol] were calculated at different BMI levels.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e SBP [MD (95% CI): 2.96 mmHg (1.62, 4.30), P \u0026lt; 0.001] and DBP [MD (95% CI): 1.64 mmHg (0.03, 3.24), P = 0.046] increased in reproductive-aged women with PCOS, however, hypertension prevalence had no difference [OR (95% CI): 1.56 (0.76, 3.06), P = 0.198]. HDL-cholesterol were [SMD (95% CI): -0.24 (-0.39, -0.09), P = 0.002] lower and triglyceride [SMD (95% CI): 0.37 (0.26, 0.48), P \u0026lt; 0.001] higher in reproductive PCOS women. Besides, LDL-cholesterol [SMD (95% CI): 0.25 (0.17, 0.34), P \u0026lt; 0.001] and nonHDL-cholesterol [SMD (95% CI): 0.38 (0.29, 0.47), P \u0026lt; 0.001] were higher in reproductive-aged PCOS. In the subgroup analysis, SBP and LDL-cholesterol were increased in BMI of \u0026lt; 25 kg/m\u003csup\u003e2\u003c/sup\u003e and BMI of ≥ 30 kg/m\u003csup\u003e2\u003c/sup\u003e, but had no difference in BMI of 25-\u0026lt; 30 kg/m\u003csup\u003e2\u003c/sup\u003e. Triglyceride and nonHDL-cholesterol increased in all BMI categories. However, DBP and hypertension prevalence had no difference in all BMI categories.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHigh baseline BP and dyslipidemia is represented in reproductive PCOS. Besides, reproductive PCOS women are significantly associated with increased cardiovascular risk independent of BMI categories.\u003c/p\u003e","manuscriptTitle":"Cardiovascular Risk in Reproductive-aged Women With Polycystic Ovary Syndrome According to Body Mass Index: a Systematic Review and Meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-23 21:16:22","doi":"10.21203/rs.3.rs-230193/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21b4435f-9ad6-412e-92d4-fd7d354b2e9b","owner":[],"postedDate":"February 23rd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2576255,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2021-05-24T14:32:03+00:00","versionOfRecord":[],"versionCreatedAt":"2021-02-23 21:16:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-230193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-230193","identity":"rs-230193","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}