Causal Relationships between Homocysteine and the Polycystic Ovary Syndrome: A Mendelian Randomization Analysis

Lin, Xianping; Jin, Yaojuan; Hong, Shihao

doi:https://doi.org/10.1155/2024/3090797

International Journal of Endocrinology

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Abstract Introduction Materials and Methods Results Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 3090797 | https://doi.org/10.1155/2024/3090797

Causal Relationships between Homocysteine and the Polycystic Ovary Syndrome: A Mendelian Randomization Analysis

Xianping Lin,¹Yaojuan Jin,²and Shihao Hong³

Academic Editor: Faustino R. Perez-Lopez

Received08 Jan 2024

Revised20 Apr 2024

Accepted26 Apr 2024

Published07 May 2024

Abstract

Background. Polycystic ovary syndrome (PCOS) is an endocrine disease attributed to multiple genetic variants and environmental factors. We aimed to find the causal association of homocysteine (Hcy) with PCOS. Methods. A two-sample Mendelian randomization (MR) analysis was performed. We selected 14 single-nucleotide polymorphisms (SNPs) as instrumental variables to predict the risk of PCOS from genome-wide association studies (GWAS). The summary statistics of PCOS were obtained from 3 large genome-wide association studies in the European population, involving 4,138 cases and 20,129 controls, 3,609 cases and 229,788 controls, 994 cases and 165,817 controls, separately. Results. The IVM analyses revealed that plasma Hcy levels were not causally associated with the risk of PCOS in the meta-analysis (combined effect = 1.032, 95% confidence interval (CI): 0.885–1.203, ). Conclusions. There was no sufficient evidence to support the causal association of the Hcy with the risk of PCOS.

1. Introduction

Polycystic ovary syndrome (PCOS) is a commonly encountered endocrinopathy in women of reproductive age, arising from the interaction between multiple genetic variants and environmental factors [1]. At present, there are three distinct diagnostic criteria for PCOS, namely, the National Institutes of Health (NIH) criteria, the Rotterdam criteria, and the Androgen Excess and PCOS (AE-PCOS) Society criteria, each with a prevalence of 6%, 10%, and 10% for PCOS, respectively [2–4]. The Rotterdam diagnostic criteria, predominantly adopted by experts worldwide, requires the presence of two out of three traits: [1] oligo- and/or anovulation, [2] hyperandrogenism, and [3] polycystic ovaries [3, 5]. PCOS is associated with an elevated risk of developing obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease [6].

Homocysteine (Hcy), an intermediary amino acid formed during methionine metabolism, is metabolized by remethylation into methionine or by transsulfuration into cysteine [7]. Disruption of these metabolic pathways results in hyperhomocysteinemia, influenced by genetic factors such as MTHFR polymorphism and vitamin deficiencies such as folate, vitamin B6, or vitamin B12 [8]. Emerging evidence suggests that an elevated plasma Hcy level is a risk factor for cardiovascular disease, stroke, obesity, and diabetes [9–12]. At the same time, numerous studies have documented that hyperhomocysteinemia is associated with PCOS [13–22], although inconsistent findings have also been reported in other studies [23–27]. Nevertheless, the interpretation of these observational studies is limited by confounding variables (e.g., age, BMI, and smoking status) and reverse-causality bias.

To gain deeper insights into the nature of the observed associations, Mendelian randomization (MR) is recommended for advanced study, utilizing genetic variations as instrumental variables (IVs) for exposures. MR is an evolving methodological approach that employs IVs, including single-nucleotide polymorphisms (SNPs) identified through genome-wide association studies (GWAS), to delineate causal relationships between exposures and disease outcomes. It is less susceptible to the aforementioned shortcomings, given that genetic variations are randomly allocated and remain steady throughout life, owing to Mendel’s first and second laws of heritability [28, 29]. However, MR studies rely on the following assumptions: [1] selected IVs must be reliably associated with the exposure factor; [2] selected IVs can only affect the outcome through the exposure; and [3] selected IVs cannot be associated with confounding factors. In recent years, MR methods have been widely applied to elucidate the etiology and consequences of PCOS using GWAS data [30], but no relevant studies for Hcy and PCOS have been undertaken so far. In the current study, a two-sample MR was performed to evaluate the causal role of hyperhomocysteinemia on PCOS using SNPs as IVs, Hcy as the exposure, and PCOS as the outcome in European populations. A brief overview of the MR study design is illustrated in Figure 1.

2. Materials and Methods

2.1. IVs Selection

We obtained instrumental variables (IVs) from a genome-wide association study (GWAS) comprising 10 European population cohorts with a total of 44,147 white individuals [31] and 18 SNPs from 13 loci were found to be associated with plasma Hcy levels at a genome-wide significance (). Using the European population of 1000 Genomes as a reference panel, linkage disequilibrium was estimated across SNPs ( < 0.05), and three SNPs (rs12134663, rs957140, and rs12921383) were removed from our study to make the remaining SNPs independent. To ensure that the specified SNPs only influenced the outcome through the exposure, we used the PhenoScanner V2 website (https://www.phenoscanner.medschl.cam.ac.uk) to kick out the SNP associated with any potential confounders of PCOS and rs548987 was removed because of pleiotropic effect on BMI. Three [32] SNPs (rs7422339, rs234709, and rs2851391) were not available in the FinnGen datasets and did not get a replacement with proxy SNPs ( > 0.8). Lastly, a total of 14 (11 in FinnGen) SNPs were selected (Table 1), explaining 5.9% of the variation in the Hcy plasma levels [31], and these SNPs were selected by the previous studies to predict the serum level of Hcy [32–34]. In advance, the F statistic was calculated to detect the strength of the IVs, and F < 10 meant a weak IV scenario [35].

2.2. Study Outcomes

Genetic associations with PCOS were gathered from 3 large genome-wide association studies (GWAS) in the European population. The study conducted by Day et al. included 4,138 cases of PCOS based on NIH or Rotterdam criteria and 20,129 controls collected from six cohorts (self-report-based data from 23andMe was excluded) [36]. Using the ICD code of PCOS (ICD-10 code E28.2, ICD-9 code 256.4, or ICD-8 code 256.90), the study performed by Tyrmi et al. comprised 3,609 cases and 229,788 controls from Finland and Estonia [37], whilst the FinnGen study release 7 (R7) consisted of 994 cases and 165,817 controls (https://finngen.gitbook.io/documentation/v/r7/) [38]. A description of all data sources is summarized in Table 1. Written consent was provided by participants, and the relevant ethical review boards approved all the studies involved in the MR analysis. Considering that the data used herein were previously published, ethics committee approval was waived.

2.3. Statistical Analyses

The inverse-variance-weighted (IVW) method was applied to evaluate the association between plasma Hcy levels and the risk of PCOS. A p value of <0.05 was considered a statistically significant difference [39]. For sensitive analysis, the weighted median estimator, which can yield consistent results even when half of the instrumental variables are invalid, was used [40]. The MR-Egger regression method and the MR-PRESSO method were also used to evaluate the directional pleiotropy by calculating the intercept of the association between Hcy and PCOS [41]. The MR-Steiger filtering was used to estimate potential reverse causal impact.

Leave-one-out sensitivity analysis was performed for the heterogeneity test to explore the influence of specific SNPs on the association. All statistical analyses were conducted using the R Studio (version 4.0.2) and the package “MendelianRandomization.”

3. Results

3.1. Causal Associations with PCOS

The F statistics of all the selected SNPs exceeded 10 (Table 1), indicating they were not weak instruments.

As depicted in Figure 2, the IVM analyses revealed that plasma Hcy level was not causally associated with the risk of PCOS in the meta-analysis (combined effect = 1.032, 95% confidence interval (CI): 0.885–1.203, ). As anticipated, consistent results were observed in the FinnGen study (effect = 1.163, 95% CI: 0.705–1.919, ), the study of Day et al. (effect = 1.093, 95% CI: 0.817–1.464, ), and that of Tyrmi et al. (effect = 0.988, 95% CI: 0.814–1.200, ). The forest plots and scatter plots for Hcy and PCOS are displayed in Figures 3–5.

3.2. Sensitivity Analyses

The results of the weighted median method and MR-Egger regression were in line with the aforementioned findings (Table 2). The MR-Egger intercept analysis, assessing the pleiotropy of the IVW model, delineated no evidence of directional pleiotropy in the FinnGen study (MR-Egger intercept = −0.004, ), the study of Day et al. (MR-Egger intercept = −0.024, ), or that of Tyrmi et al. (MR-Egger intercept = −0.005, ). Pleiotropy tests using MR-PRESSO did not identify any pleiotropic SNPs, suggesting the absence of bias in our study. Taken together, these results suggested that pleiotropy did not influence our estimated association. The MR-Steiger test results supported the absence of reverse causality.

4. Discussion

To the best of our knowledge, this is the first study based on MR methods involving adequate sample sizes to examine the correlation between plasma Hcy levels and PCOS, and our result did not identify plasma Hcy levels as a risk factor for PCOS. The selection of homocysteine (Hcy) as a biomarker for assessing the risk of polycystic ovary syndrome (PCOS) as an outcome disease warrants careful consideration of potential confounders. While our study aimed to elucidate the relationship between Hcy levels and PCOS risk, it is imperative to acknowledge the multifactorial nature of PCOS etiology. Factors such as age, body mass index (BMI), hormonal imbalances, insulin resistance, and lifestyle variables can significantly influence both Hcy levels and PCOS development [42]. For instance, higher BMI has been associated with elevated Hcy levels and increased PCOS risk [43]. Furthermore, hormonal imbalances, particularly elevated androgen levels and insulin resistance, are common features of PCOS and may independently impact Hcy metabolism. In addition, dietary factors rich in folate, vitamin B6, and vitamin B12 have been linked to lower Hcy levels and may potentially mitigate the risk of developing PCOS [44]. Therefore, future studies investigating the association between Hcy and PCOS should comprehensively account for these confounding variables to accurately assess the true relationship between Hcy levels and PCOS risk.

Accumulating evidence uncovered these years suggests that higher Hcy levels may play an instrumental role in the etiology of PCOS. A recent meta-analysis, enrolling 1718 PCOS cases and 1399 controls, demonstrated that PCOS patients had statistically significantly higher Hcy levels after individually adjusting for obesity, insulin resistance, and testosterone level [13]. However, heterogeneity was significant in this meta-analysis, and subgroup analyses for these confounders were not performed [13]. Another meta-analysis performed by Murri et al., including 2090 cases and 1421 women without PCOS, found that Hcy levels were 23% higher in PCOS women than in healthy controls [14]. Interestingly, the observed difference between the Hcy levels and PCOS in this study was not significant. This inconsistency may be ascribed to several factors as follows: (1) both studies exhibited considerable heterogeneity, thereby compromising the reliability of their results; (2) publication bias is also an indispensable factor. Publishers are prone to accepting studies with statistically significant results. Negative results regarding the association between Hcy levels and PCOS were identified in limited articles in PubMed and other databases; (3) a large number of past studies have been restrained by small sample sizes, with the biggest meta-analysis merely involving 2090 cases, which is significantly lower than our study population; and (4) Hcy levels are correlated with many confounding variables, encompassing age, BMI, smoking status, concomitant subclinical inflammatory diseases, and insulin resistance [11, 18, 45], which may have influenced the results of the studies. Obesity, a prevalent feature among women with PCOS, has been reported to increase Hcy levels through various pathways, including impaired methylation and increased oxidative stress [46]. Moreover, insulin resistance, a hallmark of PCOS, may contribute to elevated Hcy levels by altering the activity of enzymes involved in Hcy metabolism.

Earlier studies have provided evidence that obesity is a risk factor for PCOS [36]. However, Hcy levels are typically elevated in obese patients [11], indicating that a high-fat diet might aggravate hepatic enzyme activity involved in homocysteine metabolism [47]. While some confounding variables were adjusted in observational studies, they may not fully account for reverse causation. Elevated Hcy levels may stem from various factors associated with obesity, such as dietary patterns and metabolic dysregulation. Indeed, the consumption of high-fat diets among obese individuals exacerbates the activity of hepatic enzymes involved in Hcy metabolism, resulting in elevated Hcy levels [48]. However, it is critical to recognize that altered liver enzyme activity is a direct consequence of obesity. Our study explored their potential impact on diseases such as polycystic ovary syndrome (PCOS), thereby elucidating the complex interplay between metabolic disorders and Hcy metabolism. The robust evidence generated by the MR methodological approach strongly validates this finding.

Nevertheless, some limitations in our study should not be overlooked. First, PCOS patients were not stratified by the abovementioned diagnostic criteria, given that Tyrmi et al. and the FinnGen (R7) study [37, 38] exclusively provided information based on ICD codes without detailed diagnostic information. Besides, the study of Tyrmi et al. contained data from FinnGen data freeze release 6 (R6) and Estonian Biobank, leading to a partial overlap of the outcome data with the FinnGen (R7) study. Second, our study only enrolled individuals of European ancestry to mitigate population structure bias; consequently, our conclusions may not be generalizable to a global population. Finally, PhenoScanner was used to identify potential pleiotropy of the IVs, but the possibility of missing potential pleiotropic effects cannot be excluded, considering that various phenotypes associated with genetic variants are still being explored.

5. Conclusion

Our two-sample MR analysis found that plasma Hcy levels were not causally associated with PCOS, which will ultimately help improve prevention and management strategies for this common gynecological condition.

Data Availability

All data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

XPL and YJJ designed the study and performed the data analysis. SHH revised the manuscript and was responsible for the submitted manuscript. All authors read and approved the final manuscript. Xianping Lin and Yaojuan Jin contributed equally to this work and share first authorship.

Acknowledgments

The authors would like to thank Home for Researchers (https://www.home-for-researchers.com) who designed Figure 1 using Figdraw in this paper.

References

J. Zhou, Z. Jiang, L. Fu et al., “Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome,” PLoS One, vol. 18, no. 3, Article ID e0282292, p. e0282292, 2023.
View at: Publisher Site | Google Scholar
R. Azziz, E. Carmina, D. Dewailly et al., “The Androgen Excess and PCOS Society criteria for the polycystic ovary syndrome: the complete task force report,” Fertility and Sterility, vol. 91, no. 2, pp. 456–488, 2009.
View at: Publisher Site | Google Scholar
P. C. O. S. consensus workshop group The Rotterdam Eshre/Asrm-sponsored, “Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS),” Human Reproduction, vol. 19, no. 1, pp. 41–47, 2004.
View at: Publisher Site | Google Scholar
J. Zawadzki and A. Dunaif, “Diagnostic criteria for polycystic ovary syndrome: toward a rational approach,” in Polycystic Ovary Syndrome, A. Dunaif, J. R. Givens, F. P. Haseltine, and G. R. Merriam, Eds., pp. 377–384, Blackwell Scientific, Boston, MA, USA, 1992.
View at: Google Scholar
H. J. Teede, M. L. Misso, M. F. Costello et al., “Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome,” Fertility and Sterility, vol. 110, no. 3, pp. 364–379, 2018.
View at: Publisher Site | Google Scholar
F. Orio, G. Muscogiuri, C. Nese et al., “Obesity, type 2 diabetes mellitus and cardiovascular disease risk: an uptodate in the management of polycystic ovary syndrome,” European Journal of Obstetrics & Gynecology and Reproductive Biology, vol. 207, pp. 214–219, 2016.
View at: Publisher Site | Google Scholar
E. Grodnitskaya and M. Kurtser, “Homocysteine metabolism in polycystic ovary syndrome,” Gynecological Endocrinology, vol. 28, no. 3, pp. 186–189, 2012.
View at: Publisher Site | Google Scholar
T. Forges, P. Monnier-Barbarino, J. M. Alberto, R. M. Guéant-Rodriguez, J. L. Daval, and J. L. Guéant, “Impact of folate and homocysteine metabolism on human reproductive health,” Human Reproduction Update, vol. 13, no. 3, pp. 225–238, 2007.
View at: Publisher Site | Google Scholar
J. W. Eikelboom, E. Lonn, J. Genest, G. Hankey, and S. Yusuf, “Homocyst(e)ine and cardiovascular disease: a critical review of the epidemiologic evidence,” Annals of Internal Medicine, vol. 131, no. 5, pp. 363–375, 1999.
View at: Publisher Site | Google Scholar
J. P. Casas, L. E. Bautista, L. Smeeth, P. Sharma, and A. D. Hingorani, “Homocysteine and stroke: evidence on a causal link from mendelian randomisation,” The Lancet, vol. 365, no. 9455, pp. 224–232, 2005.
View at: Publisher Site | Google Scholar
J. Wang, D. You, H. Wang et al., “Association between homocysteine and obesity: a meta-analysis,” Journal of Evidence-Based Medicine, vol. 14, no. 3, pp. 208–217, 2021.
View at: Publisher Site | Google Scholar
M. T. Mursleen and S. Riaz, “Implication of homocysteine in diabetes and impact of folate and vitamin B12 in diabetic population,” Diabetes & Metabolic Syndrome: Clinical Research Reviews, vol. 11, no. Suppl 1, pp. S141–s146, 2017.
View at: Publisher Site | Google Scholar
Y. Meng, X. Chen, Z. Peng, X. Liu, Y. Sun, and S. Dai, “Association between high serum homocysteine levels and biochemical characteristics in women with polycystic ovarian syndrome: a systematic review and meta-analysis,” PLoS One, vol. 11, no. 6, Article ID e0157389, 2016.
View at: Publisher Site | Google Scholar
M. Murri, M. Luque-Ramírez, M. Insenser, M. Ojeda-Ojeda, and H. F. Escobar-Morreale, “Circulating markers of oxidative stress and polycystic ovary syndrome (PCOS): a systematic review and meta-analysis,” Human Reproduction Update, vol. 19, no. 3, pp. 268–288, 2013.
View at: Publisher Site | Google Scholar
A. Diwaker and D. Kishore, “Evaluation of plasma homocysteine levels in patients of PCOS,” Journal of the Association of Physicians of India, vol. 66, no. 10, pp. 17–20, 2018.
View at: Google Scholar
E. Elci, C. Kaya, N. Cim, R. Yildizhan, and G. G. Elci, “Evaluation of cardiac risk marker levels in obese and non-obese patients with polycystic ovaries,” Gynecological Endocrinology, vol. 33, no. 1, pp. 43–47, 2017.
View at: Publisher Site | Google Scholar
P. Maleedhu, V. M, S. B. S. S, P. K. Kodumuri, and V. Devi D, “Status of homocysteine in polycystic ovary syndrome (PCOS),” Journal of Clinical and Diagnostic Research, vol. 8, no. 2, pp. 31–33, 2014.
View at: Publisher Site | Google Scholar
K. Toulis, D. Goulis, G. Mintziori et al., “Meta-analysis of cardiovascular disease risk markers in women with polycystic ovary syndrome,” Human Reproduction Update, vol. 17, no. 6, pp. 741–760, 2011.
View at: Publisher Site | Google Scholar
S. Salehpour, O. Manzor-Al-Ajdad, E. N. Samani, and A. Abadi, “Evaluation of homocysteine levels in patients with polycystic ovarian syndrome,” International Journal of Fertility and Sterility, vol. 4, pp. 168–171, 2011.
View at: Google Scholar
T. Hemati, N. Moghadami-Tabrizi, F. Davari-Tanha, B. Salmanian, and P. Javadian, “High plasma homocysteine and insulin resistance in patients with polycystic ovarian syndrome,” Iranian Journal of Reproductive Medicine, vol. 9, no. 3, pp. 223–228, 2011.
View at: Google Scholar
T. Altuğ Şen, R. Köken, A. Narcı, and M. Yılmazer, “Homocysteine and ghrelin link with polcystic ovary syndrome in relation to obesity,” Journal of Pediatric and Adolescent Gynecology, vol. 24, no. 4, pp. 211–217, 2011.
View at: Publisher Site | Google Scholar
A. Mohamadin, F. Habib, and A. Al-Saggaf, “Cardiovascular disease markers in women with polycystic ovary syndrome with emphasis on asymmetric dimethylarginine and homocysteine,” Annals of Saudi Medicine, vol. 30, no. 4, pp. 278–283, 2010.
View at: Publisher Site | Google Scholar
M. Gözüküçük, A. Y. Gürsoy, E. Destegül, S. Taşkın, and H. Şatıroğlu, “Homocysteine and C-reactive protein levels in women with polycystic ovary syndrome,” Gynecology and Minimally Invasive Therapy, vol. 10, no. 4, pp. 210–214, 2021.
View at: Publisher Site | Google Scholar
G. M. Soares, C. S. Vieira, W. P. Martins et al., “Increased arterial stiffness in nonobese women with polycystic ovary syndrome (PCOS) without comorbidities: one more characteristic inherent to the syndrome?” Clinical Endocrinology, vol. 71, no. 3, pp. 406–411, 2009.
View at: Publisher Site | Google Scholar
A. Harmanci, N. Cinar, M. Bayraktar, and B. O. Yildiz, “Oral contraceptive plus antiandrogen therapy and cardiometabolic risk in polycystic ovary syndrome,” Clinical Endocrinology, vol. 78, no. 1, pp. 120–125, 2013.
View at: Publisher Site | Google Scholar
S. Arikan, M. Bahceci, A. Tuzcu, E. Kale, and D. Gökalp, “Serum resistin and adiponectin levels in young non-obese women with polycystic ovary syndrome,” Gynecological Endocrinology, vol. 26, no. 3, pp. 161–166, 2010.
View at: Publisher Site | Google Scholar
R. Deveer, Y. Engin-Üstün, S. Uysal et al., “Serum brain natriuretic peptide and C-reactive protein levels in adolescent with polycystic ovary syndrome,” Gynecological Endocrinology, vol. 28, no. 8, pp. 602–605, 2012.
View at: Publisher Site | Google Scholar
H. Gala and I. Tomlinson, “The use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations,” The Journal of Pathology, vol. 250, no. 5, pp. 541–554, 2020.
View at: Publisher Site | Google Scholar
A. Latvala and M. Ollikainen, “Mendelian randomization in (epi)genetic epidemiology: an effective tool to be handled with care,” Genome Biology, vol. 17, no. 1, p. 156, 2016.
View at: Publisher Site | Google Scholar
T. Zhu and M. O. Goodarzi, “Causes and consequences of polycystic ovary syndrome: insights from mendelian randomization,” Journal of Clinical Endocrinology & Metabolism, vol. 107, no. 3, pp. e899–e911, 2022.
View at: Publisher Site | Google Scholar
J. B. van Meurs, G. Pare, S. M. Schwartz et al., “Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease,” The American Journal of Clinical Nutrition, vol. 98, no. 3, pp. 668–676, 2013.
View at: Publisher Site | Google Scholar
Q. He, Z. Yang, Y. Sun et al., “The impact of homocysteine on the risk of hormone-related cancers: a mendelian randomization study,” Frontiers in Nutrition, vol. 8, Article ID 645371, 2021.
View at: Publisher Site | Google Scholar
P. Chen, Z. Yang, L. Guo, Y. Huang, J. Li, and X. Chen, “Effects of homocysteine on nonalcoholic fatty liver related disease: a mendelian randomization study,” Frontiers in Molecular Biosciences, vol. 9, Article ID 1083855, 2022.
View at: Publisher Site | Google Scholar
S. Yuan, A. M. Mason, P. Carter, S. Burgess, and S. C. Larsson, “Homocysteine, B vitamins, and cardiovascular disease: a Mendelian randomization study,” BMC Medicine, vol. 19, no. 1, p. 97, 2021.
View at: Publisher Site | Google Scholar
S. Burgess, N. M. Davies, and S. G. Thompson, “Bias due to participant overlap in two-sample Mendelian randomization,” Genetic Epidemiology, vol. 40, no. 7, pp. 597–608, 2016.
View at: Publisher Site | Google Scholar
F. Day, T. Karaderi, M. R. Jones et al., “Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria,” PLoS Genetics, vol. 14, no. 12, Article ID e1007813, 2018.
View at: Publisher Site | Google Scholar
J. S. Tyrmi, R. K. Arffman, N. Pujol-Gualdo et al., “Leveraging Northern European population history: novel low-frequency variants for polycystic ovary syndrome,” Human Reproduction, vol. 37, no. 2, pp. 352–365, 2022.
View at: Publisher Site | Google Scholar
M. I. Kurki, J. Karjalainen, P. Palta et al., “FinnGen: unique genetic insights from combining isolated population and national health register data,” medRxiv, 2022.
View at: Publisher Site | Google Scholar
S. Burgess, A. Butterworth, and S. G. Thompson, “Mendelian randomization analysis with multiple genetic variants using summarized data,” Genetic Epidemiology, vol. 37, no. 7, pp. 658–665, 2013.
View at: Publisher Site | Google Scholar
J. Bowden, G. Davey Smith, P. C. Haycock, and S. Burgess, “Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator,” Genetic Epidemiology, vol. 40, no. 4, pp. 304–314, 2016.
View at: Publisher Site | Google Scholar
J. Bowden, G. Davey Smith, and S. Burgess, “Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression,” International Journal of Epidemiology, vol. 44, no. 2, pp. 512–525, 2015.
View at: Publisher Site | Google Scholar
L. Boer-Martins, V. N. Figueiredo, C. Demacq et al., “Relationship of autonomic imbalance and circadian disruption with obesity and type 2 diabetes in resistant hypertensive patients,” Cardiovascular Diabetology, vol. 10, no. 1, p. 24, 2011.
View at: Publisher Site | Google Scholar
H. J. Teede, M. L. Misso, A. A. Deeks et al., “Assessment and management of polycystic ovary syndrome: summary of an evidence-based guideline,” Medical Journal of Australia, vol. 195, no. 10, pp. 1–112, 2011.
View at: Publisher Site | Google Scholar
L. J. Moran, S. K. Hutchison, R. J. Norman, and H. J. Teede, “Lifestyle changes in women with polycystic ovary syndrome,” Cochrane Database of Systematic Reviews, vol. 16, no. 2, p. CD007506, 2011.
View at: Publisher Site | Google Scholar
V. Kondapaneni, S. D. Gutlapalli, S. Poudel, M. Zeb, I. A. Toulassi, and I. Cancarevic, “Significance of homocysteine levels in the management of polycystic ovarian syndrome: a literature review,” Cureus, vol. 12, no. 10, Article ID e11110, 2020.
View at: Publisher Site | Google Scholar
A. Vaya, L. Rivera, A. Hernandez-Mijares et al., “Homocysteine levels in morbidly obese patients: its association with waist circumference and insulin resistance,” Clinical Hemorheology and Microcirculation, vol. 52, no. 1, pp. 49–56, 2012.
View at: Publisher Site | Google Scholar
E. Bravo, S. Palleschi, P. Aspichueta et al., “High fat diet-induced non alcoholic fatty liver disease in rats is associated with hyperhomocysteinemia caused by down regulation of the transsulphuration pathway,” Lipids in Health and Disease, vol. 10, no. 1, p. 60, 2011.
View at: Publisher Site | Google Scholar
Y. Zheng, Y. Li, Q. Qi et al., “Cumulative consumption of branched-chain amino acids and incidence of type 2 diabetes,” International Journal of Epidemiology, vol. 45, no. 5, pp. 1482–1492, 2016.
View at: Publisher Site | Google Scholar

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Copyright © 2024 Xianping Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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