Background

Oxidative stress (OS) refers to an imbalance between the oxidative and antioxidant systems in the body [1, 2]. Common biomarkers of OS damage include enzymes, such as superoxide dismutase (SOD), glutathione S-transferase (GST), glutathione peroxidase (GPX), and catalase (CAT), and non-enzymes, such as uric acid (UA), zinc, tocopherol, ascorbic acid, retinol, albumin, and total bilirubin (TBIL) [3,4,5]. A balanced OS system is essential for maintaining normal body functions. Increased OS can lead to oocyte ageing and can affect the development of polycystic ovary syndrome (PCOS) and other female reproductive system diseases [6].

PCOS is one of the most common endocrine diseases in women of reproductive age [7]. In PCOS, OS levels are often increased [1, 8]. Serum malondialdehyde (MDA) levels, total oxidant status (TOS) and OS index (OSI) were reported to be higher in patients with PCOS than in the control group. Compared with the non-hyperandrogenism-PCOS subgroup, the hyperandrogenism-PCOS subgroup had higher levels of serum MDA, TOS, and OSI [9, 10], and more severe impairment of the antioxidant function of high-density lipoproteins [11]. Increasing circulating androgen levels can sensitise leukocytes, increase the expression of glucose-induced NADPH oxidase and production of oxidation-active molecules, and promote the occurrence of OS [12, 13]. Compared with non-obese patients with PCOS, patients with obesity and PCOS had higher TOS levels; however, there were no significant differences in OSI and MDA levels [9, 10]. The severity of OS was positively correlated with the hirsutism score, androgen level, blood glucose, and lipid levels [9,10,11].

Several oxidative stress-related enzyme gene variants included platelet-activating factor acetyl hydrolase (PAF-AH) G994→T and paraoxonase (PON) 1 Q192→R, superoxide dismutase 2 (SOD2) V16→A, glutathione peroxidase 1 (GPX1) P198→L, myeloperoxidase (MPO) G-463→A, cytochrome P450 2E1 (CYP2E1) C-1054→T variants are genetic risk factors for PCOS [14,15,16,17,18,19]. The GCLC gene C-129→T variant is a protective factor for the development of hyperandrogenism-PCOS [20]. These studies indicate that patients with PCOS have increased genetic susceptibility to OS and that patients with hyperandrogenism-PCOS have more severe OS than those without hyperandrogenism-PCOS. However, whether PCOS can lead to increased OS and whether OS can increase the occurrence of PCOS remain unknown. Additionally, observational studies often include potential confounding factors and reverse causality; therefore, no clear causal relationship can be obtained [21, 22].

Mendelian randomisation (MR) is an instrumental variable (IV) analysis that detects and quantifies causality using genetic variation as an IV [23]. Because of its ability to overcome potential confounding factors and reverse causality, MR has been increasingly used in observational studies in recent years [24,25,26]. Therefore, this study aimed to clarify the causal association between PCOS, its characteristics, and OS using a two-sample MR study.

Methods

Study design

Two-sample MR design was used to detect the causal effects of PCOS and 11 OS injury biomarkers and the characteristic indices of PCOS and 11 OS indices (Fig. 1). It was based on the three hypotheses of MR: (1) Single nucleotide polymorphisms (SNPs) from genome-wide association studies (GWAS) were used as IVs, and the selected IVs were strongly correlated with exposure ; (2) IVs were not associated with confounding factors; (3) IVs affected outcomes (11 OS markers/PCOS/11 OS markers) only by exposure (PCOS/11 OS markers/ characteristic indices of PCOS) [27].

Fig. 1
figure 1

Flow chart of the Two-sample MR study design. Step 1, A two-sample bidirectional Mendelian randomisation study for PCOS and 11 oxidative stress indices; Step 2, Some two-sample Mendelian randomisation studies for characteristics indices of PCOS and 11 oxidative stress indices. IVs, instrumental variables; PCOS, polycystic ovary syndrome; GST, glutathione S-transferase; CAT, catalase; SOD, superoxide dismutase; GPX, glutathione peroxidase; UA, uric acid; SNP, single nucleotide polymorphism;T, testosterone; LDL, low-density lipoprotein; HDL, high-density lipoprotein; SHBG, sex hormone-binding globulin; BMI, body mass index; TAG, triacylglycerol

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Selection of GWAS and IVs

The GWAS of PCOS included 10,074 PCOS cases and 103,164 controls, all of whom were of European descent [28]. Fourteen independent SNPs were used according to a previous article [29]. The GWAS sources of 11 OS markers, which consisted of SOD, GST, GPX, CAT, UA, zinc, alpha-tocopherol, ascorbate, retinol, albumin, and TBIL, were used according to the previously published article [30], and the details are shown in Table 1. The participants were of European descent. The criterions of selection of IVs related to exposures were as follows (unless otherwise stated): independent SNPs (r2 < 0.001 and clumping distance > 10,000 kb); P value < 5 × 10− 8; the F statistics of all SNPs included in the MR analysis were evaluated using mRnd (an online tool named, https://shiny.cnsgenomics.com/mRnd/), all the F statistics of the included SNPs were more than 10.

Table 1 The GWAS sources of oxidative stress markers
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Statistical analysis

Random effects inverse variance weighting (IVW) was used as the main analytical method to evaluate the causal relationships among PCOS, characteristic indices of PCOS, and OS. MR-Egger, weighted median, simple mode, and weighted mode were used to verify the association. Then, the MR-Egger intercept and P values were used to evaluate horizontal and vertical pleiotropy. The MR-Egger and IVW Q and P values were used to evaluate the heterogeneity. Funnel plots were constructed to determine the presence of outlier SNPs. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to express the causal effects of PCOS on the OS injury biomarkers, characteristic indices of PCOS, and OS indices. All analyses were performed using the R software (version 4.2.1) two-sample MR package. A P value of less than 0.05 was considered as evidence of statistically significant causality.

Results

Causal association between PCOS and various OS markers: based on IVW method

As shown in Table 2, PCOS did not show a causal relationship with the 11 OS indices (based on different IVs, OR values, and 95% CI; all P values were > 0.05). Detailed information on PCOS IVs is provided in the Supplementary Materials: PCOS IVs (14SNPs). For alpha-tocopherol, nine SNPs served as IVs because the nine SNPs were found in the outcome (rs2349415, rs2178575, rs11031005, rs1784692, rs1795379, rs13164856, rs2271194, rs9696009, rs804279). For zinc, the nine SNPs (rs2349415, rs2178575, rs1795379,, rs1784692, rs13164856, rs2271194, rs804279, rs11031005, rs9696009) were also found in the outcome. When data of PCOS and zinc was harmonised, rs2271194 and rs804279 were removed as palindromic variants with intermediate allele frequencies. Therefore, seven SNPs served as IVs.

Table 2 Causal association between PCOS and various oxidative stress markers: based on IVW method
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Causal association between PCOS and OS markers: heterogeneity and pleiotropy

As shown in Table 3, there was no pleiotropy according to the MR-Egger intercept and P value. Meanwhile, there was no heterogeneity except for GPX and UA.

Table 3 Causal association between PCOS and oxidative stress markers: heterogeneity and pleiotropy
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Causal association between PCOS and SOD according to five methods

As shown in Table 4, PCOS did not show a causal relationship with SOD according to the five methods. MR sizes for PCOS on SOD, scatter plots, leave-one-out, and funnel plots are shown in Figs. 2, 3 and 4, and 5, respectively.

Table 4 Causal association between PCOS and SOD.
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Causal association between PCOS and GST /GPX /CAT /UA /zinc /alpha-tocopherol /ascorbic acid /retinol /albumin /TBIL according to five methods

PCOS did not show causal relationship with GST (Supplementary Materials: Table S1) /GPX (Table S2) /CAT (Table S3) /UA (Table S4) /zinc (Table S5) / alpha-tocopherol (Table S6) /ascorbic acid (Table S7) /retinol (Table S8) /albumin (Table S9) /TBIL (Table S10) according to five methods. The MR effect size, scatter plot, leave-one-out, and funnel plots are shown in Supplementary Materials Figure S1-4 /S5-8 /S9-12 /S13-16 /S17-20 /S21-24 /S25-28 /S29-32 /S33-36 /S37-40.

Causal association between various OS markers and PCOS

As shown in Table 5, most OS indices did not show a causal relationship with PCOS (based on different IVs, OR values, and 95% CI; all P values were > 0.05), except for tocopherol (MR-Egger, OR: 3.74, 95% CI: 1.297–10.783, P = 0.035) and ascorbate (IVW, OR: 2.112, 95% CI: 1.257–3.549, P = 0.005).

Table 5 The associations between genetically predicted oxidative stress indices and the risk of PCOS.
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Causal association between various characteristics indices of PCOS and OS markers: based on IVW method

As shown in Table 6, the characteristic indices of PCOS showed a causal relationship with most OS indices (based on different IVs, OR values, and 95% CI; all P values were less than 0.05).

Table 6 The associations between genetically predicted characteristics indices of PCOS and the risk of oxidative stress
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Discussion

To the best of our knowledge, this is the first study exploring the causal effects of polycystic ovary syndrome and characteristic indices of PCOS on OS. In this study, phenotypic GWAS data were analysed using two-sample MR, and no evidence of a causal relationship between PCOS and OS markers was found. However, there was a causal relationship between OS index, ascorbate, and PCOS. This revealed that PCOS itself could not increase OS, ascorbate could increase the occurrence of PCOS, and the increase in the oxidative level of PCOS was related to other potential factors, such as testosterone, low-density lipoprotein, high-density lipoprotein, sex hormone-binding globulin, body mass index, triacylglycerol, and age at menarche, which may act as characteristic indices of PCOS. An observational study has emphasised the association between PCOS and OS [31]. However, relevant MR studies regarding this association are lacking. In addition to observational studies, relevant mechanistic studies have been conducted on OS and PCOS. A study pointed out that OS contributed to insulin resistance in the skeletal muscles of mice with dehydroepiandrosterone-induced PCOS [32]. Salidroside alleviates OS and apoptosis via AMPK/Nrf2 pathway in dihydrotestosterone-induced human granulosa cell line KGN [33].

A meta-analysis has indicated that circulating markers of OS are abnormal in patients with PCOS [1]. OS in patients with PCOS may be associated with several diseases [34, 35]. A few antioxidants can ameliorate PCOS through reducing OS, such as Tempol [36], Kelulut honey [37], Standardised Aronia melanocarpa [38], astaxanthin [39], resveratrol [40], and N-acetyl cysteine [41]. Besides, silibinin [42] and vitamin E supplementation [43] as well as melatonin and/or magnesium supplementation [44] also ameliorate PCOS by reducing the level of OS.

This study included 11 different markers of OS injury, 10 characteristic indices of PCOS, and large-sample PCOS GWAS data from the same race- European ancestor. The proposed method has several advantages. First, it included a two-sample bidirectional MR. Hence, a causal association between OS and PCOS can be proven in reverse. In addition, PCOS itself does not increase OS; therefore, characteristic indices of PCOS were used to explore the causal effects on OS. Some indices related to PCOS characteristics have causal effects on OS. PCOS is a heterogeneous endocrine disorder. Patients with PCOS often present with hyperandrogenemia, glucose and lipid metabolism disorders, obesity, waist-to-hip ratio imbalance, menstrual disorders, ovulation abnormalities, and other symptoms. This study provides evidence for the need to regulate glycaemic and lipid metabolism, control body weight, reduce hyperandrogenemia, and replenish ascorbate and tocopherol in patients with polycystic ovary syndrome, with the aim to reduce the levels of OS or the occurrence of PCOS.

Meanwhile, this study has some limitations. First, GWAS data were obtained from a European ancestor, and whether this conclusion is true for other races needs to be studied. In addition, some analyses used a small number of SNPs (less than 10), and some analyses were not pleiotropic but heterogeneous, such as GPX and UA, which may lead to inaccurate results and compromise confidence. With the continuous update and release of PCOS GWAS data [28, 45,46,47,48], we are likely to overcome these limitations. Finally, the conclusion may be more accurate if the measures of OS included only women.

Conclusions

In summary, this two-sample MR study indicated that genetically predicted PCOS was not significantly associated with oxidative stress; however, the OS index, ascorbate, was significantly associated with PCOS. PCOS itself does not lead to an increase in OS levels, and the increase in OS levels in PCOS is related to other potential factors, such as hyperandrogenism, low-density lipoprotein, high-density lipoprotein, sex hormone-binding globulin, body mass index, triacylglycerol, and age at menarche. It is necessary to regulate glycaemic and lipid metabolism, control body weight, reduce hyperandrogenemia, and replenish ascorbate and tocopherol to reduce the levels of OS or the occurrence of PCOS. Further scientific studies are needed to uncover the mechanisms underlying the increased levels of OS in PCOS.

Fig. 2
figure 2

MR effect size for PCOS on SOD

PCOS, polycystic ovary syndrome; SOD, superoxide dismutase

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Fig. 3
figure 3

Scatter plot of the MR analysis of PCOS on SOD

PCOS, polycystic ovary syndrome; SOD, superoxide dismutase

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Fig. 4
figure 4

Leave-one-out regression analysis of PCOS on SOD

PCOS, polycystic ovary syndrome; SOD, superoxide dismutase

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Fig. 5
figure 5

Funnel plot of the MR analysis of PCOS on SOD

PCOS, polycystic ovary syndrome; SOD, superoxide dismutase

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