Effect modification of luteinizing hormone chorionic gonadotropin hormone receptor gene variant (rs2293275) on clinical and biochemical profile, and levels of luteinizing hormone in polycystic ovary syndrome patients

Effect modification of luteinizing hormone chorionic gonadotropin hormone receptor gene variant (rs2293275) on clinical and biochemical profile, and levels of luteinizing hormone in polycystic ovary syndrome patients | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect modification of luteinizing hormone chorionic gonadotropin hormone receptor gene variant (rs2293275) on clinical and biochemical profile, and levels of luteinizing hormone in polycystic ovary syndrome patients Mudassir Jan Makhdoomi, IdreesA. Shah, Rabiya Rashid, Aafia Rashid, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2004110/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jan, 2023 Read the published version in Biochemical Genetics → Version 1 posted 7 You are reading this latest preprint version Abstract Background Polycystic ovary syndrome (PCOS) is a common multifaceted endocrine disorder among reproductive women. Deranged luteinizing hormone levels and associated downstream signalling cascade mediated by its receptor luteinizing hormone chorionic gonadotropin receptor ( LHCGR ) are pivotal in the etiopathogenesis of PCOS. Genetic variations in the LHCGR have been associated with PCOS risk, however, the results are inconclusive. We evaluated association of LHCGR rs2293275 polymorphic variant with PCOS risk and its impact on clinicobiochemical features of PCOS. Methods 120 confirmed PCOS cases and an equal number of age-matched controls were subjected to clinical, biochemical and hormonal investigations. Genotyping for rs2293275 was performed using polymerase chain reaction restriction fragment length polymorphism. Logistic regression models were used to calculate odds ratios (OR) at 95%confidence intervals (95%CIs). Results PCOS cases reported lower annual menstrual cyclicity, significantly higher BMI and Ferriman Galway score (p < 0.01). Levels of serum testosterone, TSH, FSH and indicators of glucose homeostasis were significantly deranged in cases than controls. Higher risk of developing PCOS was noted in GA (OR = 10.4, P < 0.0001) or AA (OR = 7.73, P = 0.02) genotype carriers and risk persisted in the dominant model (GA + AA) as well (OR = 10.29, P = 0.01). On stratification, a higher risk of developing PCOS was observed in variant genotype carriers who had a family history of either T2DM (OR = 117;p < 0.0001) or hirsutism (OR = 79;p < 0.0001). We also found a significant linear increase in the serum LH levels in the subjects carrying GA and AA genotypes. Conclusion In the present study, we report a significant association ofthe LHCGR rs2293275 variant with the PCOS risk. LHCGR Luteinizing hormone PCOS Gene polymorphism PCR-RFLP SNP Figures Figure 1 Introduction Worldwide, polycystic ovary syndrome (PCOS) is the most prevalent female endocrinopathy(Castillo-Higuera, Alarcón-Granados et al. 2021 ). Although the disorder starts early in life and affects significantly in the reproductive phase of women’s lives, it has a lifelong impact on their metabolic health in the form of different comorbidities like type 2 diabetes mellitus(T2DM), cardiovascular diseases, etc(Aversa, La Vignera et al. 2020 ). Phenotypically, PCOS is characterized by hyperandrogenism, polycystic ovarian morphology, oligo-anovulation, irregular menstrual cyclicity, infertility, alopecia, acne, hirsutism, and metabolic derangements including insulin resistance(IR), hyperinsulinemia, etc. Although, these symptoms are non-uniform and follow a heterogeneous pattern, but do impact the physical, psycho-sexual, emotional, and financial health of the women(Hiam, Moreno-Asso et al. 2019 ). The global prevalence of PCOS ranges between 2-22.5% depending on the diagnostic criteria used varied prevalence among different ethnicities across the globe(Castillo-Higuera, Alarcón-Granados et al. 2021 ).However, limited prevalence data from India suggests it to be a growing epidemic with 19–23% prevalence and is parallel to that of Type 2 Diabetes Mellitus (T2DM) (Stratigopoulos, Padilla et al. 2008 , Tan, Adya et al. 2010 ). The heterogenous aetio-pathophysiology of PCOS is a multifaceted, and poorly understood orchestration of gene-gene and gene-environment interactions. Elevated androgen levels are central in the clinical phenotypes of PCOS patients, and are aggravated due to other conditions like obesity and IR. Furthermore, instigating endocrine abnormalities include aberrantly rapid gonadotropin-releasing hormone(GnRH), pulsatile secretion, elevated luteinizing hormone (LH), sub-optimal levels of follicle-stimulating hormone(FSH), and subsequent hyperandrogenism leads to ovarian dysfunction and improper folliculogenesis, both implicated in the pathophysiology of PCOS.Thus, normal ovarian function and follicular growth are a consequence of complementary activities of FSH and LH, and any deviation of the ratio of LH to FSH from unity is an indication of abnormal ovarian function.The LH-mediated downstream cellular functions are transduced upon binding of LH to its receptor, the luteinizinghormonechorio-gonadotrophin receptor ( LHCGR ).This G-protein coupled receptor LHGCR is expressed on the ovarian theca cells and regulates the action of both LH and choriogonadotropin in steroid biogenesis.The bindingof these ligands to the high-affinity receptor ( LHCGR ) induces a conformational shift, leading to its activation and subsequent mediation of this signal via second messenger (cAMP)and specific kinases in turn regulating the expression of the genes involved in the steroid biogenesis(Cesta, Månsson et al. 2016 ).Any genetic variations altering the LHCGR protein structure or function would directly impact ovarian function and associated diseases including PCOS. The gene encoding LHCGR is located on Chromosome 2 (Gromoll, Gudermann et al. 1992 , Ulloa-Aguirre, Reiter et al. 2014 ) and is highly polymorphic with more than 300 single-nucleotide polymorphisms (SNPs) having been reported and extensively studied so far.Studies have persistently studied the association of these polymorphic variants with PCOS and the results are inconclusive.The variation in these results has been attributed to ethnic variations and study designs. Kashmir valley, a north Indian state represents an ethnically distinct population with a conserved genetic pool where consanguineous marriages are common. The prevalence of PCOS has been reported to be as high as ~ 28.9% by NIH criteria and 34.3% by AE-PCOS criteria (Ganie, Rashid et al. 2020 ).Given these observations, the Kashmiri population offers a unique setting for evaluating the genetic predisposition of PCOS.Therefore, we conducted a case-control study to evaluate the association of the LHCGR polymorphic variant (rs2293275) with the PCOS risk and its effect modification on the disease phenotypes. Materials And Methods Study Subjects, their clinical assessment, and anthropometric assessment. We invited 137 women aged between 18-40 years for the current prospective case-control study visiting the PCOS clinic at the Department of Endocrinology Sher-i-Kashmir Institute of Medical Sciences (SKIMS) Srinagar, Kashmir from January 2018 to January 2021. The subjects who agreed to participate underwent a complete clinical examination and anthropometric measurements like measurement of height, weight, waist-hip circumference ratio, BMI (BMI = weight (kg)/Height (m2). The clinical history included hypertension, age of menarche, presence of acne, alopecia, menstrual history, and hirsutism assessment was done by using a modified Ferriman-Gallwey score by counting nine specified body areas. A score of > 8 out of a total of 36 was taken as significant.The Rotterdam criteria were used for the diagnosis of PCOS which states that 2 out of three features need to be present to make the diagnosis of PCOS. These features include (1) Oligo- or anovulation (< eight menstrual cycles in the presenting year) (2) Clinical and/or biochemical signs of hyperandrogenism and (3) Polycystic ovaries (either 12 or more follicles measuring 2-9 mm in diameter, or an ovarian volume of >10 mL or 12), However, women suffering from any endocrinological abnormality like adrenal hyperplasia, Cushing’s syndrome, androgen-secreting neoplasms, androgenic/anabolic drug use or abuse, syndromes of severe insulin resistance, hyperprolactinemia. and thyroid dysfunction was excluded from the study. Of all the invited subjects, 11 refused to participate and 6 were ineligible. We also recruited an equal numberof (n=120) apparently healthy subjects matched for age having regular menstrual cycles (21-35d), displaying no evidence of clinical/biochemical hyperandrogenism, and having normal ovarian morphology on trans-abdominal ultrasonography from various medical camps organized across various colleges and universities in Kashmir valley. The study protocol was approved by the institutional ethics committee (IEC No.RP55/19), SKIMS.Written informed consent was collected from all the subjects. Sample collection Five milliliters of peripheral blood were collected from all the subjects after 8-12 hours of overnight fasting for analysing various biochemical and hormonal parameters.Two milliliters(2ml) of the collected blood were transferred into an EDTA vial and 3ml was transferred into the red top vial and centrifuged for the separation of serum.The blood and sera samples are stored at stored at-20°C till further processing. Biochemical and hormonal Analysis All the PCOS cases and controls were subjected to biochemical analysis and the following parameters were undertaken-fasting blood glucose, oral glucose tolerance test(OGTT), triglycerides(TG), low-density lipoprotein(LDL) high-density lipoprotein(HDL), total cholesterol, uric acid, urea, creatinine ALT, AST, ALP, bilirubin, albumin and total protein using a fully auto biochemistry analyser(Response 910, Diasys), and standard commercially available kits following manufacturer’s instructions. The immuno-chemical measurement of hormones including fasting insulin, FSH, LH, Testosterone, Prolactin, TSH, and T4 was analysed by Electrochemiluminescence using Cobas e 411 (Roche diagnostics) and Insulin resistance was evaluated in three different ways—Homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check-index (QUICKI), and fasting glucose to fasting insulin ratio(FGIR). The HOMA index was calculated as [fasting serum insulin (μIU/mL) x fasting glucose (mg/dL)]/405. The QUICKI was calculated as 1/ [log fasting insulin (μIU/ mL) + log fasting glucose (mg/dL)]. High HOMA-IR, low QUICKI, and low FGIR scores denote insulin resistance (low insulin sensitivity). Body mass index (BMI) was calculated as body weight (kg) divided by body height squared (m 2 ). DNA Isolation and Genotype analysis Genomic DNA was isolated from the peripheral blood of all PCOS cases and controls by using standard phenol-chloroform/Isoamyl-alcohol method.The quality and quantity were determined by measuring A 260 /A 280 in a Nano-drop(JenwayNano-drop, model Genova nano) and by running the samples on 1% agarose gel. DNA was stored at -20°C until processing. The targeted DNA fragment was amplified by PCR, using specific forward primer 5´-CCTCTTCTCTTTCAGACAGA-3´ and reverse primer 5-´CATGCAAATACTTACAGTGTTTTGGTA-3´as per the published literature(Thathapudi, Kodati et al. 2015) PCR was performed in three steps using Sure-thermocycler 8800 (Agilent Technologies) . Briefly, the PCR conditions included an initial denaturation at 95°C for 5 min, followed by 35 cycles of denaturation at 95°C for 1 min, annealing at 58.5°C for 30 seconds, extension at 72°C for 45 seconds, and a final extension at 72°C for 5 min. The 111 bp amplified PCR product was then digested with RsaI Restriction enzyme at 37°C for 2 hours and was electrophoresed on 3% agarose gel. The banding pattern demonstrated an undigested 111 bp in the case of GG (homozygous wild) genotype, 111/86/25 bp in the case of GA (heterozygous) genotype, and 86/25 bp in the case of AA (homozygous mutant) genotype. Statistical Analysis All the categorical variables as numbers and percentages while the continuous variables were presented as mean standard deviation. The clinical, anthropometric, hormonal, and metabolic variables were compared between PCOS and controls by unpaired student t -test and categorical variables were compared by chi-square test. The odds ratio (OR) was calculated as an estimate of risk at 95% confidence interval(CI). The two-tailed p-value of magnitude < 0.05 was considered statistically significant. All the statistical calculations were performed by using STATA Software, version16(STATA Corp., College Station, TX, USA). Power Calculations Power calculations were carried out using the GAS power calculator (csg.sph.umich.edu /abecasis /gas_power _calculator). Using the dominant model, post-hoc power analysis revealed that the study is significantly powered to detect any associations (power of the study:1-β= 87% at the significance level α= 0.01). Results Clinical, and biochemical profile of the study Subjects The anthropometric, clinical, and biochemical parameters of PCOS cases and healthy controls are given in Table 1 .The mean age of the case and controls was 22.72 ± 4.53 and 23.37 ± 3.03 respectively. The number of menstrual cycles/years was significantly lower in cases than in controls (P < 0.0001). Unlike controls, the BMI and FG score was significantly higher in cases. The hormone levels including testosterone, TSH, insulin, and LH to FSH ratio were higher in cases and controls (p < 0.001). The indices of glycaemic control including fasting blood glucose (mg/dL) and fasting insulin levels (µIU/mL) were considerably higher in cases compared to controls (87.47 ± 8.58 vs. 83.67 ± 9.23; P = 0.001 and 18.99 ± 15.20 vs. 6.26 ± 3.25; P < 0.0001). Likewise, Insulin resistance assessed by HOMA-IR, QUICKI, and FGIR was significantly higher in PCOS patients when compared to controls (P < 0.0001). Similarly, significantly higher levels of the alkaline transferase, total protein, renal function parameters, uric,acid, and lipid parameters were observed in PCOS cases compared to controls (P < 0.0001) Table 1 . Table 1 Anthropometric, clinical and biochemical parameters in PCOS cases and healthy controls Parameters PCOS (N = 120) Mean (± SD) Controls(N = 120) Mean (± SD) P-value Mean age (years) 22.72(± 4.53) 23.37 (± 3.03) 0.193 Age at Menarche (years) 13.12 (± 1.75) 13.49 (± 1.23) 0.061 Menstrual cycles /year 7.51 (± 3.25) 11.95 (± .20) 0.000 BMI(Kg/m 2 ) 24.58 (± 4.02) 21.98 (± 3.72) 0.000 Systolic blood pressure(mm/Hg) 118.46 (± 13.51) 113.51 (± 10.75) 0.002 Diastolic Blood pressure(mm/Hg) 79.30 (± 11.25) 77.94 (± 8.47) 0.309 Height(cm) 157.4 (± 6.01) 156.4 (± 5.39) 0.176 Weight(kg) 61.23(± 10.73) 53.82(± 9.49) 0.000 FG Score 12.04(± 4.45) 5.96(± 0.99) 0.000 T 4 (µg/dL) 8.52 (± 1.96) 8.13 (± 1.70) 0.146 TSH(µI/mL) 3.65 (± 2.01) 3.02(± 1.48) 0.008 FSH(IU/L) 7.74 (± 6.76) 6.66 (± 2.50) 0.163 Testosterone(ng/mL) 66.96 (± 34.04) 24.79(± 9.95) 0.000 LH:FSH 1.66(± 1.25) 1.10 (± .87) 0.000 Prolactin(ng/mL) 21.40(± 14.90) 13.34 (± 8.70) 0.146 Serum Fasting Blood Glucose(mg/dl) 87.47(± 8.58) 83.67 (± 9.23) 0.001 LH(IU/L) 10.96 (± 7.50) 6.09(± 3.23) 0.000 Fasting Insulin(µIU/mL) 18.99 (± 15.20) 6.26 (± 3.25) 0.000 HOMA IR 3.84(± 2.96) 1.32 (± .724) 0.000 QUICKI .327(± .04) .378(± .03) 0.000 FGIR 9.25 (± 14.57) 17.13 (± 8.92) 0.000 SerumAST/OT (IU/L) 25.86 (± 14.99) 25.66(± 10.14) 0.908 SerumALT/PT (IU/L) 28.79(± 25.33) 23.20 (± 15.09) 0.044 Serum billrubin(mg/dL) 0.78(± .97) 0.62(± .31) 0.127 SerumALP (IU/L) 95.37(± 30.22) 88.71 (± 35.46) 0.141 Serum Total Protein(g/dl) 7.73(± .60) 7.15(± 1.57) 0.000 Serum Albumin (gm/L) 4.49(± .49) 4.53(± .96) 0.680 Serum urea(mg/dL) 20.96(± 6.50) 27.92(± 22.78) 0.002 Serum Creatinine(mg/dL) 0.82(± .17) 1.02(± .85) 0.017 Serum Uric Acid(mg/dL) 4.84(± 1.28) 4.26(± .93) 0.001 Serum Total Cholesterol(mg/dL) 170.5(± 31.16) 156.89(± 151.58) 0.000 Serum Triglyceride(mg/dL) 120.63(± 54.79) 118.67(± 46.26) 0.769 Serum HDL(mg/dL) 50.14(± 15.23) 44.65(± 12.20) 0.005 Serum LDL (mg/dL) 91.34(± 19.35) 83.51(± 20.34) 0.009 bmi, body mass index; fgir, fasting glucose insulin ratio; fg score, ferrimen gallwey score; fsh, follicular stimulating hormone; hdl, high density lipoprotein; homa−ir, homeostasis model assessment insulin resistance index; ldl, low density lipoprotein; lh, luteinizing hormone; quicki, quantitative insulin sensitivity index. students t−test was used to calculate p values. Distribution Of Lhcgrrs2293275genotypes And Alleles The genotypic and allele frequencies of rs2293275 LHCGR c.G935A (Ser312Asn) in PCOS cases and controls are presented in Table 2 .The frequency ofheterozygous (GA) and homozygous (AA) genotypes in cases was significantly higher in cases than in controls. The variant allele (A) was significantly overrepresented in cases than the respective controls (p < 0.0001). We observed a higher risk of developing PCOS in the subjects who harbored either GA (OR = 10.4, P < 0.0001)or AA (OR = 7.73, P = 0.02) genotype. The risk persisted in the dominant model (GA + AA) as well (OR = 10.29, P = 0.01),(Table 2 ). Table 2 Genotype and allele frequencies of LHCGR G935A SNP in PCOS cases and controls. LHCGR (rs2293275) PCOS (N = 120) Control (N = 120) OR (95% CI) Pvalue Frequency of Genotypes GG GA AA GA + AA 10(8.3%) 106(88.3%) 04(3.3%) 110(91.6%) 58(48.3%) 59(49.1%) 03(2.5%) 62(51.6%) Ref. 10.4(4.95–21.9) 7.73(1.49–39.8) 10.29(4.96–20.60) < 0.0001 0.02 0.001 Frequency of Allele types G A 126 (52.5%) 114(47.5%) 175(72.9% ) 65 (27.1%) Ref. 2.43(1.66–3.56) < 0.0001 Inheritance Models Dominant GG GA + AA 10(8.3%) 110(91.6%) 58(48.3%) 61(50.8%) Ref. 10.45(4.98–21.9) < 0.0001 Recessive GG + GA AA 116(96.6%) 04(3.3%) 117(97.5%) 03(2.5%) Ref. 1.34(0.29–6.14) 0.99 genotypic and allelic frequency of lhcgr gene in pcos as compared with control women. data is presented as number (%) of pcos and controls. gg gaaa, and are genotypes in pcos and controls, pcos polycystic ovary syndrome, g and a are alleles for polymorphism, or odds ratio, ci confidence interval, significant p value < 0.05. Risk modification by the variant genotype of rs2293275 LHCGR in presence of other PCOS modulators. Like earlier reports, we also found a significant association betweenthe family history of T2DM and hirsutism with the PCOS risk (Table 3 ).On further stratification of the participants, we observed a higher risk of developing PCOS in the subjects who harboured the variant genotype and had a family history of either T2DM (OR = 117;p < 0.0001) or hirsutism (OR = 79;p < 0.0001) when compared to the wildtype carriers who did not have any above-mentioned family histories. We observed a synergistic effect modification by the variant genotype ofrs2293275 in the subjects whose BMI was ≥ 24(OR = 204;p < 0.0001), albeit with wider confidence intervals due to low numbers in the model. Table 3 Stratification of subjects based on LHCGRG935A genotypes and PCOS phenotypes Phenotype PCOS (N = 120) Control (N = 120) OR(95%CI) p-value Family History of T2DM Absent 38(33.93) 89(74.79) Referent Present 74(66.07) 30(25.21) 5.78 (3.29–10.30) 0.000 Family History of T2DM F/H DM − + Wildtype 02(1.79) 39(32.77) Referent F/H DM − + Variant 08(7.14) 19(15.97) 8.21(1.62–40.07) 0.010 F/H DM ++ + Wildtype 36(32.14) 50(42.02) 14.04(3.48–61.04) 0.000 F/H DM ++ + Variant 66(58.93) 11(9.24 ) 117.0(27.0–511. 9) 0.000 Family History of Hirsutism Absent 73(68.22) 113(94.17) Referent Present 34(31.78) 07 (5.83) 7.52 (3.13–18.77) 0.000 Family History of Hirsutism F/H Hirsutism − + Wildtype 07(6.54) 54 (45.00) Referent F/H Hirsutism − + Variant 03(2.80) 04 (3.33) 5.79(1.21–25.52) 0.059 F/H Hirsutism ++ + Wildtype 66(61.68) 59 (49.17) 8.63(3.64–21.44) 0.000 F/H Hirsutism ++ + Variant 31(28.97) 3 (2.50) 79.71(19.65–273.5) 0.000 LH level (mIU/ml) LH ≤ 12.5 + Wildtype 08(6.67) 43 (35.83) Referent LH ≥ 12.5 + Wildtype 02(1.67) 15 (12.50) 0.71(0.14–3.75) 0.999 LH ≤ 12.5 + Variant 76(63.33) 50 (41.67) 8.17(3.52–17.58) < 0.0001 LH ≥ 12.5 + Variant 34 (28.33) 12 (10.00) 15.23(5.43–39.06) < 0.0001 BMI (Kg/m 2 ) BMI < 24 +Wildtype 03(2.50) 41(34.17) Referent BMI ≤ 24 + variant 39(32.50) 49(40.83) 28.75(3.48–237.3) 0.002 BMI ≥ 24 + Wildtype 07(5.83) 17(14.17) 7.32(0. 72–73.93) 0.091 BMI ≥ 24 + variant 71(59.17) 13(10.83) 204.9(21.95-1912.6) 0.000 ++present−absent n: number of individuals . f/h: family history; dm: diabetes mellitus, lh: luteinizing hormone, bmi: basal metabolic index, combined variant: ga + aa To evaluate the correlationbetween serum LH levels and the genotype of LHCGR rs2293275, we categorized all the subjects based on their genotype. Compared to GG carriers, we observed a significant linear increase in the serum LH levels in the subjects that harbored GA genotypes that were further increased in the AA carriers (Fig. 1). Besides, we found a strong risk of developing PCOS in the subjects who have LH levels ≥ 12.5 (mIU/mL) and harbored the variant genotype of the rs2293275(Table 3 ). Moreover, we also found a synergistic effect modification of the PCOS risk in the subjects carrying the variant genotype and presented either alopecia (OR = 34.29; p < 0.0001), acne (OR = 9.42; p < 0.0001) or acanthosis (OR = 16.8; p < 0.0001) (Supplementary table S 1). Discussion The present case-control study evaluated the association of a polymorphic variant rs2293275 of LHCGR p.S312N with the PCOS risk and its correlation with the clinical and biochemical indices. We found a significant association of rs2293275 with the PCOS risk and linearly increased LH levels in the subjects harboring heterozygous (GA and the mutant (AA) genotype when compared to the wild-type (GG) genotype carriers. Our results demonstrated a significant difference in genotypic as well as allelic frequencies of rs2293275 LHCGR gene between PCOS women and controls, indicating that women with GA and AA genotypes are at higher risk for developing PCOS. The higher frequency of the A allele found in PCOS cases revealed a > 2-fold increased risk of PCOS in our study. These findings are in agreement with the earlier studies (Capalbo, Sagnella et al. 2012 , Bassiouny, Rabie et al. 2014 , Ha, Shi et al. 2015 , El-Shal, Zidan et al. 2016 ),reporting a positive association between various ethnicities. However, no significant association of this variant with the risk of PCOS was reported in Caucasian and Bahraini populations respectively(Valkenburg, Uitterlinden et al. 2009 , Almawi, Hubail et al. 2015 ).On the contrary, Thathapudi et al. revealed that the GG(major allele) genotype, rather than AA, conferred a significant risk of developing PCOS in South Indian women(3.36-fold)(Thathapudi, Kodati et al. 2015 ), while a recent meta-analysis reported a 4.1 risk increase of developing PCOS for carriers of the AA (minor allele) genotype of LHCGR (Zou, Wu et al. 2019 ). These conflicting results among the studies might be explained by differences in sample size, non-uniform diagnostic criteria, ethnic background, and study design. LH is an associated member of the glycoprotein family that stimulates follicular development, steroid biogenesis, and the formation of the corpus luteum(Dufau 1998 ),and ovulation(Ascoli, Fanelli et al. 2002 )acts by binding with its high-affinity receptor, LHCGR )(Dufau 1998 )and transducing luteinizing hormone-mediated signals that play a vital role in the ovulation process(Qiao and Han 2019 ). LHCGR gene is one of the few candidate genes recognized susceptibility loci consistently associated with the risk of PCOS in diverse ethnicities.Abnormal LH signaling is believed to play a crucial role in augmenting ovarian androgen production in PCOS and leading to anovulation(Balen 1993 , Norman, Dewailly et al. 2007 ). Evidence from the study conducted by Zhihua et al showed that mutation in LHCGR causes abnormal LHCGR glycosylation, decreased LHCGR protein level, effects on subcellular localization, and reduced cellular ATP consumption, which indicate the signal transduction may be affected and leads to the cause of abnormal ovulation(Zhang, Wu et al. 2020 ).Reports have shown that enhanced expression or overactivation of LHCGR might contribute to the development of PCOS(Kanamarlapudi, Gordon et al. 2016 ). In addition to the two-cell, two-gonadotrophin theory, LH modulates multiple genes' mRNA levels in granulose cells through the LHCGR receptor, which can aid in the growth of follicles(Sasson, Rimon et al. 2004 , Lindeberg, Carlström et al. 2007 ).The secretion of androgen hormones by ovarian theca cells promotes by LH, which may result in follicular maturation arrest.Consequently, the variation that occurred at the LH level may potentially influence the reproductive process that it leads to and is associated with menstruation dysfunction and infertility,thereby orchestrating the risk of PCOS(Laven, Imani et al. 2002 ).The genetic variants of LHCGR p.S312N, which falls within exon 10 of the LHCGR gene, and is next to the glycosylation signals of the protein, might affect the trafficking and stability of the receptor, resulting in an increased risk of developing polycystic ovary syndrome (PCOS) in women (Thathapudi, Kodati et al. 2015 ). An earlier study reported that mutant homozygous or heterozygous inactivating gene variants of the LHCGR cause gonadal resistance to LH thereby increasing the LH level and subsequent feedback to the pituitary resulting in the further elevation of LH levels leads to the anovulation(Segaloff 2009 ).Moreover, a recent study showed a strong association of LHCGR rs2293275 polymorphism with high LH levels and LH/FSH ratio in PCOS women contributes to enhancing the risk of PCOS development. The study suggested that high serum LH levels in PCOS subjects are important for PCOS diagnosis and may be useful as a molecular marker for early detection of high risk for PCOS(Atoum, Alajlouni et al. 2022 ).Given the important pivotal role of LH in androgen metabolism and ovulation, can be a plausible explanation for the enhanced PCOS risk in the women that harbored the variant genotype of LHCGR in our study.However,further mechanistic studies are warranted to elucidate LHCGR (rs2293275) mediated PCOS etiology. On stratification analysis, similar to earlier reports, we found a significantly increased serum LH level in subjects with PCOS who harbored variant genotypes when compared to healthy controls(Piersma, Berns et al. 2006 , El-Shal, Zidan et al. 2016 ). Maternal family history is considered a risk factor for PCOS in daughters. PCOS is thought to be a heritable disorder based on familial case clustering(Rosenfield and Ehrmann 2016 ). The significant frequency of PCOS or its clinical manifestations, such as hyperandrogenism, hirsutism, infertility, and polycystic ovaries, among first-degree relatives suggests that genetic and familial factors play a role in the disorder(Bruni, Capozzi et al. 2021 ).We found an enhanced risk in the subjects who had a family history of T2DM or hirsutism and harbored the variant genotype of rs2293275 suggesting the heritability associated with the later onset in PCOS women.Although a direct correlation of LHCGR genotypes with a positive family history of T2DM has not been evaluated as before, a positive family history of T2DM has been previously associated with the development of PCOS(Kulshreshtha, Singh et al. 2013 , Yilmaz, Vellanki et al. 2018 ).Vrbikovaet al. reported that defective early beta cell function was characteristic of only patients with PCOS and a positive family history of T2DM(Vrbikova, Bendlova et al. 2009 ),also reported a significant difference in glucose and lipid metabolism between PCOS patients with and without a family history of T2DM(Wang, Gao et al. 2021 ). A literature survey suggests that T2DM appears to be an important factor in predicting the risks of metabolic abnormalities in women with PCOS(Ehrmann, Kasza et al. 2005 , Vrbíková, Grimmichová et al. 2008 , Lerchbaum, Schwetz et al. 2014 ). However, further replicative, and mechanistic studies are required to validate and unveil the underlying role. In the present study, we found an enhanced risk of PCOS in subjects with PCOS who had alopecia, acne, or Acanthosis nigricans compared to healthy controls and harbored variant genotypes of LHCGR (rs2293275).Hyperandrogenism is a major characteristic in women with PCOS, the hallmark feature of PCOS 58–82% of hyperandrogenic women have PCOS(Pinola, Puukka et al. 2017 ).Elevated LH levels or increased testosterone production from polycystic ovaries may cause hyperandrogenaemia(Ashraf, Nabi et al. 2019 ).Elevated insulin levels may also trigger increased testosterone levels in women and thus modulate the risk of PCOS(Nestler, Jakubowicz et al. 1998 ).Consequently, the resulting Androgen excess(hyperandrogenism) acts as the main promoting factor inducing anovulation and follicular arrest, suggesting decreased oocyte development and maturation(Qiao and Feng 2011 ). Obesity is a common finding in PCOS that worsens its phenotype and is considered one of the most crucial pathophysiological features in PCOS. It also aggravates menstrual irregularity and increases serum total testosterone levels(Xita and Tsatsoulis 2006 , Baldani, Skrgatić et al. 2013 ).This excessive amount of androgen in turn can affect the follicle growth and metabolic process and also trigger insulin levels, which further enhances the risk of PCOS development in obese women.Furthermore, a recent study that showed increased testosterone level promotes visceral fat accumulation and insulin resistance by inhibiting lipolysis and promoting lipogenesis(Rosenfield and Ehrmann 2016 ), which in turnis associatedwith suppressed ovulation and high LH levels(Roth, Allshouse et al. 2014 ).In this study,We found a synergistic effect on the PCOS risk in the subjects,carrying the LHCGR variant genotype(GA + AA) having BMI greater than ≥ 24 in subjects with PCOS women, albeit with wider CI’s due to low numbers in the model. Our findings are consistent with previous studies that found BMI to be statistically significant and highlight the contribution of LHCGR polymorphism to PCOS phenotypes, particularly BMI(Thathapudi, Kodati et al. 2015 , Atoum, Alajlouni et al. 2022 )While as the study was statistically powered to detect any associations, however, the low number in the subsequent stratification analysis might be a concern of the present study. Conclusion The present study indicated the potential influence of LHCGR G935A (rs2293275) polymorphism on the development and clinical course of PCOS. More replicative studies are warranted to substantiate our findings. Declarations Acknowledgments: The authors thank all the participants for volunteering in the study. The authors also thank the Multi-disciplinary Research Unit, SKIMS, Srinagar funded by the Department of Health Research, Govt of India, for providing necessary research facilities for carrying out this study. Funding “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Competing Interests “The author has no relevant financial and non- financial interests to disclose.” Author contribution “MAG conceived the study; MAG, ZAS, and SS designed the study. MJM and AR collected the data, and MJM and RR performed experiments. IAS, MJM, and MAG analyzed and interpreted the data. MJM, IAS, and MAG wrote the first draft of the manuscript.All the authors reviewed and approved the final draft of the manuscript.” Data Availability “Data will be available to anyone on proper request to the corresponding author.” Ethics Approval “This Study was approved by local institutional ethics committee of SKIMS (SKIMS-IEC) under protocol number RP 55/19. Consent to participate “Informed consent was obtained from all individual participant including in this study.” Consent to Publish “No object or image was obtained or copied from any publication. The images used in this manuscript are my own” References Almawi, W. Y., B. Hubail, D. Z. Arekat, S. M. Al-Farsi, S. K. Al-Kindi, M. R. Arekat, N. Mahmood and S. Madan (2015). "Leutinizing hormone/choriogonadotropin receptor and follicle stimulating hormone receptor gene variants in polycystic ovary syndrome." J Assist Reprod Genet 32 (4): 607–614. Ascoli, M., F. Fanelli and D. L. Segaloff (2002). "The lutropin/choriogonadotropin receptor, a 2002 perspective." Endocr Rev 23 (2): 141–174. Ashraf, S., M. Nabi, S. u. A. Rasool, F. Rashid and S. Amin (2019). "Hyperandrogenism in polycystic ovarian syndrome and role of CYP gene variants: a review." Egyptian Journal of Medical Human Genetics 20 (1): 25. Atoum, M. F., M. M. Alajlouni and F. Alzoughool (2022). "A Case-Control Study of the Luteinizing Hormone Level in Luteinizing Hormone Receptor Gene (rs2293275) Polymorphism in Polycystic Ovarian Syndrome Females." Public Health Genomics 25 (3–4): 89–97. Aversa, A., S. La Vignera, R. Rago, A. 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"Insulin Stimulates Testosterone Biosynthesis by Human Thecal Cells from Women with Polycystic Ovary Syndrome by Activating Its Own Receptor and Using Inositolglycan Mediators as the Signal Transduction System1." The Journal of Clinical Endocrinology & Metabolism 83 (6): 2001–2005. Norman, R. J., D. Dewailly, R. S. Legro and T. E. Hickey (2007). "Polycystic ovary syndrome." Lancet (London, England) 370 (9588): 685–697. Piersma, D., E. M. Berns, M. Verhoef-Post, A. G. Uitterlinden, I. Braakman, H. A. Pols and A. P. Themmen (2006). "A common polymorphism renders the luteinizing hormone receptor protein more active by improving signal peptide function and predicts adverse outcome in breast cancer patients." J Clin Endocrinol Metab 91 (4): 1470–1476. Pinola, P., K. Puukka, T. T. Piltonen, J. Puurunen, E. Vanky, I. Sundström-Poromaa, E. Stener-Victorin, A. Lindén Hirschberg, P. Ravn, M. Skovsager Andersen, D. Glintborg, J. R. Mellembakken, A. Ruokonen, J. S. Tapanainen and L. C. 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Diabetes 59 (12): 3023–3031. Thathapudi, S., V. Kodati, J. Erukkambattu, U. Addepally and H. Qurratulain (2015). "Association of luteinizing hormone chorionic gonadotropin receptor gene polymorphism (rs2293275) with polycystic ovarian syndrome." Genet Test Mol Biomarkers 19 (3): 128–132. Ulloa-Aguirre, A., E. Reiter, G. Bousfield, J. A. Dias and I. Huhtaniemi (2014). "Constitutive activity in gonadotropin receptors." Adv Pharmacol 70 : 37–80. Valkenburg, O., A. G. Uitterlinden, D. Piersma, A. Hofman, A. P. Themmen, F. H. de Jong, B. C. Fauser and J. S. Laven (2009). "Genetic polymorphisms of GnRH and gonadotrophic hormone receptors affect the phenotype of polycystic ovary syndrome." Hum Reprod 24 (8): 2014–2022. Vrbikova, J., B. Bendlova, M. Vankova, K. Dvorakova, T. Grimmichova, K. Vondra and G. Pacini (2009). "Beta cell function and insulin sensitivity in women with polycystic ovary syndrome: influence of the family history of type 2 diabetes mellitus." Gynecol Endocrinol 25 (9): 597–602. Vrbíková, J., T. Grimmichová, K. Dvořáková, M. Hill, S. Stanická and K. Vondra (2008). "Family history of diabetes mellitus determines insulin sensitivity and beta cell function in polycystic ovary syndrome." Physiol Res 57 (4): 547–553. Wang, Y., H. Gao, W. Di and Z. Gu (2021). "Endocrinological and metabolic characteristics in patients who are non-obese and have polycystic ovary syndrome and different types of a family history of type 2 diabetes mellitus." J Int Med Res 49 (5): 3000605211016672. Xita, N. and A. Tsatsoulis (2006). "Review: fetal programming of polycystic ovary syndrome by androgen excess: evidence from experimental, clinical, and genetic association studies." J Clin Endocrinol Metab 91 (5): 1660–1666. Yilmaz, B., P. Vellanki, B. Ata and B. O. Yildiz (2018). "Diabetes mellitus and insulin resistance in mothers, fathers, sisters, and brothers of women with polycystic ovary syndrome: a systematic review and meta-analysis." Fertil Steril 110 (3): 523–533.e514. Zhang, Z., L. Wu, F. Diao, B. Chen, J. Fu, X. Mao, Z. Yan, B. Li, J. Mu, Z. Zhou, W. Wang, L. Zhao, J. Dong, Y. Zeng, J. Du, Y. Kuang, X. Sun, L. He, Q. Sang and L. Wang (2020). "Novel mutations in LHCGR (luteinizing hormone/choriogonadotropin receptor): expanding the spectrum of mutations responsible for human empty follicle syndrome." J Assist Reprod Genet 37 (11): 2861–2868. Zou, J., D. Wu, Y. Liu and S. Tan (2019). "Association of luteinizing hormone/choriogonadotropin receptor gene polymorphisms with polycystic ovary syndrome risk: a meta-analysis." Gynecol Endocrinol 35 (1): 81–85. Statement and Decleration Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-2004110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132645547,"identity":"863e1dac-60e5-44d9-bc7a-97182dbb136b","order_by":0,"name":"Mudassir Jan Makhdoomi","email":"","orcid":"","institution":"Jaipur National University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mudassir","middleName":"Jan","lastName":"Makhdoomi","suffix":""},{"id":132645549,"identity":"4adebdd0-69e3-4807-a0b5-d27f51418eb5","order_by":1,"name":"IdreesA. Shah","email":"","orcid":"","institution":"Sher-i-Kashmir Institute of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"IdreesA.","middleName":"","lastName":"Shah","suffix":""},{"id":132645550,"identity":"c0d5f500-af41-4cac-9fad-0c5124b19379","order_by":2,"name":"Rabiya Rashid","email":"","orcid":"","institution":"Sher-i-Kashmir Institute of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rabiya","middleName":"","lastName":"Rashid","suffix":""},{"id":132645551,"identity":"c896a3e0-e72a-44ad-99c7-ef38683b9537","order_by":3,"name":"Aafia Rashid","email":"","orcid":"","institution":"Sher-i-Kashmir Institute of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aafia","middleName":"","lastName":"Rashid","suffix":""},{"id":132645552,"identity":"249f165d-eaf3-4033-93fe-572d0f4bf12e","order_by":4,"name":"Saurabh singh","email":"","orcid":"","institution":"Jaipur National University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Saurabh","middleName":"","lastName":"singh","suffix":""},{"id":132645553,"identity":"4d600251-c34c-4db6-a3cb-6aca72161783","order_by":5,"name":"Zaffar Amin Shah","email":"","orcid":"","institution":"Sher-i-Kashmir Institute of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zaffar","middleName":"Amin","lastName":"Shah","suffix":""},{"id":132645554,"identity":"b9f52c53-f036-49f6-8bcd-685fad5a93df","order_by":6,"name":"Mohd Ashraf Ganie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFACHiC2ATESGB+AuHzEaUkDa2E2AHHZSNHCJgGiCGrh7z977MOPBAY5c/bkY5Vfc+xk2BiYHz66gUeLxI285Jk9CQzGlj3P0m7LbksGOozN2DgHnzU3eIwZeH8wJG64kWN2W3IbM1ALD5s0Pi3y588YM/5JYKgHaSmW3FZPWIvBgRxjZp4EhgQDoBbGj9sOE9ZieAOoRSZBwnDDmWfJ0ozbjvOwMRPwixzIYW8SbOQNjicf/PhzW7U9P3vzw8d4vQ8B4BhhYOYBk4SVIwDjD1JUj4JRMApGwYgBADOFQjjH2PqbAAAAAElFTkSuQmCC","orcid":"","institution":"Sher-i-Kashmir Institute of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohd","middleName":"Ashraf","lastName":"Ganie","suffix":""}],"badges":[],"createdAt":"2022-08-27 09:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2004110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2004110/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10528-022-10327-z","type":"published","date":"2023-01-12T18:17:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25941855,"identity":"3e951cf5-c9e9-4bb9-b0b5-0a538f3bf5f6","added_by":"auto","created_at":"2022-09-01 16:46:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5209,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of LH levels on the basis of\u0026nbsp;genotypes of\u0026nbsp;rs2293275 of \u003cem\u003eLHCGR gene\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2004110/v1/1f08d824568cd19e0c394add.png"},{"id":44716328,"identity":"75d51e7b-0232-44a4-a54c-c56271b3c35e","added_by":"auto","created_at":"2023-10-16 18:25:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":614583,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2004110/v1/d362b1f7-fdd6-4ea5-9014-36575f5381ee.pdf"},{"id":25941854,"identity":"dbafde9a-cae9-4d54-8bb7-283fc1f1a527","added_by":"auto","created_at":"2022-09-01 16:46:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15175,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-2004110/v1/8ec217b09dc16b3c82a65afa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect modification of luteinizing hormone chorionic gonadotropin hormone receptor gene variant (rs2293275) on clinical and biochemical profile, and levels of luteinizing hormone in polycystic ovary syndrome patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWorldwide, polycystic ovary syndrome (PCOS) is the most prevalent female endocrinopathy(Castillo-Higuera, Alarc\u0026oacute;n-Granados et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although the disorder starts early in life and affects significantly in the reproductive phase of women\u0026rsquo;s lives, it has a lifelong impact on their metabolic health in the form of different comorbidities like type 2 diabetes mellitus(T2DM), cardiovascular diseases, etc(Aversa, La Vignera et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Phenotypically, PCOS is characterized by hyperandrogenism, polycystic ovarian morphology, oligo-anovulation, irregular menstrual cyclicity, infertility, alopecia, acne, hirsutism, and metabolic derangements including insulin resistance(IR), hyperinsulinemia, etc. Although, these symptoms are non-uniform and follow a heterogeneous pattern, but do impact the physical, psycho-sexual, emotional, and financial health of the women(Hiam, Moreno-Asso et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The global prevalence of PCOS ranges between 2-22.5% depending on the diagnostic criteria used varied prevalence among different ethnicities across the globe(Castillo-Higuera, Alarc\u0026oacute;n-Granados et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).However, limited prevalence data from India suggests it to be a growing epidemic with 19\u0026ndash;23% prevalence and is parallel to that of Type 2 Diabetes Mellitus (T2DM) (Stratigopoulos, Padilla et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Tan, Adya et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe heterogenous aetio-pathophysiology of PCOS is a multifaceted, and poorly understood orchestration of gene-gene and gene-environment interactions. Elevated androgen levels are central in the clinical phenotypes of PCOS patients, and are aggravated due to other conditions like obesity and IR. Furthermore, instigating endocrine abnormalities include aberrantly rapid gonadotropin-releasing hormone(GnRH), pulsatile secretion, elevated luteinizing hormone (LH), sub-optimal levels of follicle-stimulating hormone(FSH), and subsequent hyperandrogenism leads to ovarian dysfunction and improper folliculogenesis, both implicated in the pathophysiology of PCOS.Thus, normal ovarian function and follicular growth are a consequence of complementary activities of FSH and LH, and any deviation of the ratio of LH to FSH from unity is an indication of abnormal ovarian function.The LH-mediated downstream cellular functions are transduced upon binding of LH to its receptor, the luteinizinghormonechorio-gonadotrophin receptor (\u003cem\u003eLHCGR\u003c/em\u003e).This G-protein coupled receptor LHGCR is expressed on the ovarian theca cells and regulates the action of both LH and choriogonadotropin in steroid biogenesis.The bindingof these ligands to the high-affinity receptor (\u003cem\u003eLHCGR\u003c/em\u003e) induces a conformational shift, leading to its activation and subsequent mediation of this signal via second messenger (cAMP)and specific kinases in turn regulating the expression of the genes involved in the steroid biogenesis(Cesta, M\u0026aring;nsson et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).Any genetic variations altering the \u003cem\u003eLHCGR\u003c/em\u003e protein structure or function would directly impact ovarian function and associated diseases including PCOS.\u003c/p\u003e \u003cp\u003eThe gene encoding \u003cem\u003eLHCGR\u003c/em\u003e is located on Chromosome 2 (Gromoll, Gudermann et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1992\u003c/span\u003e, Ulloa-Aguirre, Reiter et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and is highly polymorphic with more than 300 single-nucleotide polymorphisms (SNPs) having been reported and extensively studied so far.Studies have persistently studied the association of these polymorphic variants with PCOS and the results are inconclusive.The variation in these results has been attributed to ethnic variations and study designs. Kashmir valley, a north Indian state represents an ethnically distinct population with a conserved genetic pool where consanguineous marriages are common. The prevalence of PCOS has been reported to be as high as ~\u0026thinsp;28.9% by NIH criteria and 34.3% by AE-PCOS criteria (Ganie, Rashid et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).Given these observations, the Kashmiri population offers a unique setting for evaluating the genetic predisposition of PCOS.Therefore, we conducted a case-control study to evaluate the association of the \u003cem\u003eLHCGR\u003c/em\u003e polymorphic variant (rs2293275) with the PCOS risk and its effect modification on the disease phenotypes.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Subjects, their clinical assessment, and anthropometric assessment.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe invited 137 women aged between 18-40 years for the current prospective case-control study visiting the PCOS clinic at the Department of Endocrinology Sher-i-Kashmir Institute of Medical Sciences (SKIMS) Srinagar, Kashmir from January 2018 to January 2021. The subjects who agreed to participate underwent a complete clinical examination and anthropometric measurements like measurement of height, weight, waist-hip circumference ratio, BMI (BMI = weight (kg)/Height (m2). The clinical history included hypertension, age of menarche, presence of acne, alopecia, menstrual history, and hirsutism assessment was done by using a modified Ferriman-Gallwey score\u0026nbsp;by counting nine specified body areas. A score of \u0026gt; 8 out of a total of 36 was taken as significant.The Rotterdam criteria were used for the diagnosis of PCOS which states that 2 out of three features need to be present to make the diagnosis of PCOS. These features include (1) Oligo- or anovulation (\u0026lt; eight menstrual cycles in the presenting year) (2) Clinical and/or biochemical signs of hyperandrogenism and (3) Polycystic ovaries (either 12 or more follicles measuring 2-9 mm in diameter, or an ovarian volume of \u0026gt;10 mL or 12), However, women suffering from any endocrinological abnormality like adrenal hyperplasia, Cushing\u0026rsquo;s syndrome, androgen-secreting neoplasms, androgenic/anabolic drug use or abuse, syndromes of severe insulin resistance, hyperprolactinemia. and thyroid dysfunction was excluded from the study. Of all the invited subjects, 11 refused to participate and 6 were ineligible. We also recruited an equal numberof (n=120) apparently healthy subjects matched for age having regular menstrual cycles (21-35d), displaying no evidence of clinical/biochemical hyperandrogenism, and having normal ovarian morphology on trans-abdominal ultrasonography from various medical camps organized across various colleges and universities in Kashmir valley. The study protocol was approved by the institutional ethics committee (IEC No.RP55/19), SKIMS.Written informed consent was collected from all the subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSample collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive milliliters of peripheral blood were collected from all the subjects after 8-12 hours of overnight fasting for analysing various biochemical and hormonal parameters.Two milliliters(2ml) of the collected blood were transferred into an EDTA vial and 3ml was transferred into the red top vial and centrifuged for the separation of serum.The blood and sera samples are stored at stored at-20\u0026deg;C till further processing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical and hormonal Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the PCOS cases and controls were subjected to biochemical analysis and the following parameters were undertaken-fasting blood glucose, oral glucose tolerance test(OGTT), triglycerides(TG), low-density lipoprotein(LDL) high-density lipoprotein(HDL), total cholesterol, uric acid, urea, creatinine ALT, AST, ALP, bilirubin, albumin and total protein using a fully auto biochemistry analyser(Response 910, Diasys), and standard commercially available kits following manufacturer\u0026rsquo;s instructions. The immuno-chemical measurement of hormones including fasting insulin, FSH, LH, Testosterone, Prolactin, TSH, and T4 was analysed by Electrochemiluminescence using Cobas e 411 (Roche diagnostics) and Insulin resistance was evaluated in three different ways\u0026mdash;Homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check-index (QUICKI), and fasting glucose to fasting insulin ratio(FGIR). The HOMA index was calculated as [fasting serum insulin (\u0026mu;IU/mL) x fasting glucose (mg/dL)]/405. The QUICKI was calculated as 1/ [log fasting insulin (\u0026mu;IU/ mL) + log fasting glucose (mg/dL)]. High HOMA-IR, low QUICKI, and low FGIR scores denote insulin resistance (low insulin sensitivity). Body mass index (BMI) was calculated as body weight (kg) divided by body height squared\u0026nbsp;(m\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA Isolation and Genotype analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenomic DNA was isolated from the peripheral blood of all PCOS cases and controls by using standard phenol-chloroform/Isoamyl-alcohol method.The quality and quantity were determined by measuring A\u003csub\u003e260\u003c/sub\u003e/A\u003csub\u003e280\u003c/sub\u003e in a\u0026nbsp;Nano-drop(JenwayNano-drop, model Genova nano)\u0026nbsp;and by running the samples on 1% agarose gel.\u0026nbsp;DNA was stored at -20\u0026deg;C until processing. The targeted DNA fragment was amplified by PCR, using specific forward primer 5\u0026acute;-CCTCTTCTCTTTCAGACAGA-3\u0026acute; and reverse primer 5-\u0026acute;CATGCAAATACTTACAGTGTTTTGGTA-3\u0026acute;as per the published literature(Thathapudi, Kodati et al. 2015)\u0026nbsp;PCR was performed in three steps using \u003cem\u003eSure-thermocycler 8800\u003c/em\u003e (Agilent Technologies)\u003cem\u003e.\u0026nbsp;\u003c/em\u003eBriefly, the PCR conditions included an initial denaturation at 95\u0026deg;C for 5 min, followed by 35 cycles of denaturation at 95\u0026deg;C for 1 min, annealing at 58.5\u0026deg;C for 30 seconds, extension at 72\u0026deg;C for 45 seconds, and a final extension at 72\u0026deg;C for 5 min. The 111 bp amplified PCR product was then digested with \u003cem\u003eRsaI\u003c/em\u003eRestriction enzyme at 37\u0026deg;C for 2 hours and was electrophoresed on 3% agarose gel. The banding pattern demonstrated an undigested 111 bp in the case of GG (homozygous wild) genotype, 111/86/25 bp in the case of GA (heterozygous) genotype, and 86/25 bp in the case of AA (homozygous mutant) genotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the categorical variables as numbers and percentages while the continuous variables were presented as mean\u0026nbsp;\u0026nbsp;standard deviation. The clinical, anthropometric, hormonal, and metabolic variables were compared between PCOS and controls by unpaired student \u003cem\u003et\u003c/em\u003e-test and categorical variables were compared by chi-square test. The odds ratio (OR) was calculated as an estimate of risk at 95% confidence interval(CI). The two-tailed p-value of magnitude \u0026lt; 0.05 was considered statistically significant. All the statistical calculations were performed by using STATA Software, version16(STATA Corp., College Station, TX, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePower Calculations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePower calculations were carried out using the GAS power calculator (csg.sph.umich.edu /abecasis /gas_power _calculator). Using the dominant model, post-hoc power analysis revealed that the study is significantly powered to detect any associations (power of the study:1-\u0026beta;= 87% at the significance level \u0026alpha;= 0.01).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eClinical, and biochemical profile of the study Subjects\u003c/h2\u003e\n \u003cp\u003eThe anthropometric, clinical, and biochemical parameters of PCOS cases and healthy controls are given in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.The mean age of the case and controls was 22.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53 and 23.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03 respectively. The number of menstrual cycles/years was significantly lower in cases than in controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Unlike controls, the BMI and FG score was significantly higher in cases. The hormone levels including testosterone, TSH, insulin, and LH to FSH ratio were higher in cases and controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The indices of glycaemic control including fasting blood glucose (mg/dL) and fasting insulin levels (\u0026micro;IU/mL) were considerably higher in cases compared to controls (87.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.58 vs. 83.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.23; P\u0026thinsp;=\u0026thinsp;0.001 and 18.99\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20 vs. 6.26\u0026thinsp;\u0026plusmn;\u0026thinsp;3.25; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Likewise, Insulin resistance assessed by HOMA-IR, QUICKI, and FGIR was significantly higher in PCOS patients when compared to controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, significantly higher levels of the alkaline transferase, total protein, renal function parameters, uric,acid, and lipid parameters were observed in PCOS cases compared to controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnthropometric, clinical and biochemical parameters in PCOS cases and healthy controls\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCOS (N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControls(N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\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\u003eMean age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.72(\u0026plusmn;\u0026thinsp;4.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.37 (\u0026plusmn;\u0026thinsp;3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge at Menarche (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.12 (\u0026plusmn;\u0026thinsp;1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.49 (\u0026plusmn;\u0026thinsp;1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMenstrual cycles /year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.51 (\u0026plusmn;\u0026thinsp;3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.95 (\u0026plusmn;\u0026thinsp;.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.58 (\u0026plusmn;\u0026thinsp;4.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.98 (\u0026plusmn;\u0026thinsp;3.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystolic blood pressure(mm/Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.46 (\u0026plusmn;\u0026thinsp;13.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.51 (\u0026plusmn;\u0026thinsp;10.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiastolic Blood pressure(mm/Hg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.30 (\u0026plusmn;\u0026thinsp;11.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.94 (\u0026plusmn;\u0026thinsp;8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeight(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157.4 (\u0026plusmn;\u0026thinsp;6.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156.4 (\u0026plusmn;\u0026thinsp;5.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.23(\u0026plusmn;\u0026thinsp;10.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.82(\u0026plusmn;\u0026thinsp;9.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFG Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.04(\u0026plusmn;\u0026thinsp;4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.96(\u0026plusmn;\u0026thinsp;0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e(\u0026micro;g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.52 (\u0026plusmn;\u0026thinsp;1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.13 (\u0026plusmn;\u0026thinsp;1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTSH(\u0026micro;I/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.65 (\u0026plusmn;\u0026thinsp;2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02(\u0026plusmn;\u0026thinsp;1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFSH(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.74 (\u0026plusmn;\u0026thinsp;6.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.66 (\u0026plusmn;\u0026thinsp;2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTestosterone(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.96 (\u0026plusmn;\u0026thinsp;34.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.79(\u0026plusmn;\u0026thinsp;9.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH:FSH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66(\u0026plusmn;\u0026thinsp;1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10 (\u0026plusmn;\u0026thinsp;.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProlactin(ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.40(\u0026plusmn;\u0026thinsp;14.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.34 (\u0026plusmn;\u0026thinsp;8.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Fasting Blood Glucose(mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.47(\u0026plusmn;\u0026thinsp;8.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.67 (\u0026plusmn;\u0026thinsp;9.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH(IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.96 (\u0026plusmn;\u0026thinsp;7.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.09(\u0026plusmn;\u0026thinsp;3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFasting Insulin(\u0026micro;IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.99 (\u0026plusmn;\u0026thinsp;15.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.26 (\u0026plusmn;\u0026thinsp;3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMA IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84(\u0026plusmn;\u0026thinsp;2.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32 (\u0026plusmn;\u0026thinsp;.724)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQUICKI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.327(\u0026plusmn;\u0026thinsp;.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.378(\u0026plusmn;\u0026thinsp;.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFGIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.25 (\u0026plusmn;\u0026thinsp;14.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.13 (\u0026plusmn;\u0026thinsp;8.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerumAST/OT (IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.86 (\u0026plusmn;\u0026thinsp;14.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.66(\u0026plusmn;\u0026thinsp;10.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerumALT/PT (IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.79(\u0026plusmn;\u0026thinsp;25.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.20 (\u0026plusmn;\u0026thinsp;15.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.044\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum billrubin(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78(\u0026plusmn;\u0026thinsp;.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62(\u0026plusmn;\u0026thinsp;.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerumALP (IU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.37(\u0026plusmn;\u0026thinsp;30.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.71 (\u0026plusmn;\u0026thinsp;35.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Total Protein(g/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.73(\u0026plusmn;\u0026thinsp;.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.15(\u0026plusmn;\u0026thinsp;1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Albumin (gm/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.49(\u0026plusmn;\u0026thinsp;.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.53(\u0026plusmn;\u0026thinsp;.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum urea(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.96(\u0026plusmn;\u0026thinsp;6.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.92(\u0026plusmn;\u0026thinsp;22.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Creatinine(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82(\u0026plusmn;\u0026thinsp;.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02(\u0026plusmn;\u0026thinsp;.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Uric Acid(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.84(\u0026plusmn;\u0026thinsp;1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.26(\u0026plusmn;\u0026thinsp;.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Total Cholesterol(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170.5(\u0026plusmn;\u0026thinsp;31.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e156.89(\u0026plusmn;\u0026thinsp;151.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum Triglyceride(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120.63(\u0026plusmn;\u0026thinsp;54.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.67(\u0026plusmn;\u0026thinsp;46.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum HDL(mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.14(\u0026plusmn;\u0026thinsp;15.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.65(\u0026plusmn;\u0026thinsp;12.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum LDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.34(\u0026plusmn;\u0026thinsp;19.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.51(\u0026plusmn;\u0026thinsp;20.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ebmi, body mass index; fgir, fasting glucose insulin ratio; fg score, ferrimen gallwey score; fsh, follicular stimulating hormone; hdl, high density lipoprotein; homa\u0026minus;ir, homeostasis model assessment insulin resistance index; ldl, low density lipoprotein; lh, luteinizing hormone; quicki, quantitative insulin sensitivity index. students t\u0026minus;test was used to calculate p values.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003eDistribution Of Lhcgrrs2293275genotypes And Alleles\u003c/h2\u003e\n\u003cp\u003eThe genotypic and allele frequencies of rs2293275 \u003cem\u003eLHCGR\u003c/em\u003ec.G935A (Ser312Asn) in PCOS cases and controls are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.The frequency ofheterozygous (GA) and homozygous (AA) genotypes in cases was significantly higher in cases than in controls. The variant allele (A) was significantly overrepresented in cases than the respective controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). We observed a higher risk of developing PCOS in the subjects who harbored either GA (OR\u0026thinsp;=\u0026thinsp;10.4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001)or AA (OR\u0026thinsp;=\u0026thinsp;7.73, P\u0026thinsp;=\u0026thinsp;0.02) genotype. The risk persisted in the dominant model (GA\u0026thinsp;+\u0026thinsp;AA) as well (OR\u0026thinsp;=\u0026thinsp;10.29, P\u0026thinsp;=\u0026thinsp;0.01),(Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenotype and allele frequencies of LHCGR G935A SNP in PCOS cases and controls.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLHCGR\u003c/em\u003e (rs2293275)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePvalue\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\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency of Genotypes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003cp\u003eGA\u003c/p\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(8.3%)\u003c/p\u003e\n \u003cp\u003e106(88.3%)\u003c/p\u003e\n \u003cp\u003e04(3.3%)\u003c/p\u003e\n \u003cp\u003e110(91.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(48.3%)\u003c/p\u003e\n \u003cp\u003e59(49.1%)\u003c/p\u003e\n \u003cp\u003e03(2.5%)\u003c/p\u003e\n \u003cp\u003e62(51.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e10.4(4.95\u0026ndash;21.9)\u003c/p\u003e\n \u003cp\u003e7.73(1.49\u0026ndash;39.8)\u003c/p\u003e\n \u003cp\u003e10.29(4.96\u0026ndash;20.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency of Allele types\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (52.5%)\u003c/p\u003e\n \u003cp\u003e114(47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175(72.9%\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e65 (27.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e2.43(1.66\u0026ndash;3.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eInheritance Models\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eDominant\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eGG\u003c/p\u003e\n \u003cp\u003eGA\u0026thinsp;+\u0026thinsp;AA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(8.3%)\u003c/p\u003e\n \u003cp\u003e110(91.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(48.3%)\u003c/p\u003e\n \u003cp\u003e61(50.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e10.45(4.98\u0026ndash;21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003eRecessive\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eGG\u0026thinsp;+\u0026thinsp;GA\u003c/p\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116(96.6%)\u003c/p\u003e\n \u003cp\u003e04(3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117(97.5%)\u003c/p\u003e\n \u003cp\u003e03(2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003cp\u003e1.34(0.29\u0026ndash;6.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003egenotypic and allelic frequency of \u003cem\u003elhcgr\u003c/em\u003e gene in pcos as compared with control women. data is presented as number (%) of pcos and controls. gg gaaa, and are genotypes in pcos and controls, pcos polycystic ovary syndrome, g and a are alleles for polymorphism, or odds ratio, ci confidence interval, significant p value \u0026lt; 0.05.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRisk modification by the variant genotype of rs2293275 LHCGR in presence of other PCOS modulators.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLike earlier reports, we also found a significant association betweenthe family history of T2DM and hirsutism with the PCOS risk (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).On further stratification of the participants, we observed a higher risk of developing PCOS in the subjects who harboured the variant genotype and had a family history of either T2DM (OR\u0026thinsp;=\u0026thinsp;117;p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or hirsutism (OR\u0026thinsp;=\u0026thinsp;79;p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) when compared to the wildtype carriers who did not have any above-mentioned family histories. We observed a synergistic effect modification by the variant genotype ofrs2293275 in the subjects whose BMI was \u0026ge;\u0026thinsp;24(OR\u0026thinsp;=\u0026thinsp;204;p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), albeit with wider confidence intervals due to low numbers in the model.\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStratification of subjects based on LHCGRG935A genotypes and PCOS phenotypes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePhenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCOS (N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl (N\u0026thinsp;=\u0026thinsp;120)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\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\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily History of T2DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(33.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(74.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(66.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(25.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.78 (3.29\u0026ndash;10.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily History of T2DM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H DM\u003csup\u003e\u0026minus;\u003c/sup\u003e + Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02(1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(32.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H DM\u003csup\u003e\u0026minus;\u003c/sup\u003e + Variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08(7.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(15.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21(1.62\u0026ndash;40.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H DM\u003csup\u003e++\u003c/sup\u003e + Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(32.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50(42.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.04(3.48\u0026ndash;61.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H DM\u003csup\u003e++\u003c/sup\u003e + Variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(58.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(9.24 )\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.0(27.0\u0026ndash;511. 9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily History of Hirsutism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbsent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73(68.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113(94.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePresent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(31.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07 (5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.52 (3.13\u0026ndash;18.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily History of Hirsutism\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H Hirsutism\u003csup\u003e\u0026minus;\u003c/sup\u003e + Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07(6.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (45.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H Hirsutism\u003csup\u003e\u0026minus;\u003c/sup\u003e + Variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03(2.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e04 (3.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.79(1.21\u0026ndash;25.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.059\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H Hirsutism\u003csup\u003e++\u003c/sup\u003e + Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(61.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (49.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.63(3.64\u0026ndash;21.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF/H Hirsutism\u003csup\u003e++\u003c/sup\u003e + Variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(28.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79.71(19.65\u0026ndash;273.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eLH level (mIU/ml)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH\u0026thinsp;\u0026le;\u0026thinsp;12.5\u0026thinsp;+\u0026thinsp;Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e08(6.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (35.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH\u0026thinsp;\u0026ge;\u0026thinsp;12.5\u0026thinsp;+\u0026thinsp;Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e02(1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (12.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71(0.14\u0026ndash;3.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH\u0026thinsp;\u0026le;\u0026thinsp;12.5\u0026thinsp;+\u0026thinsp;Variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(63.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (41.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.17(3.52\u0026ndash;17.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLH\u0026thinsp;\u0026ge;\u0026thinsp;12.5\u0026thinsp;+\u0026thinsp;Variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (28.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.23(5.43\u0026ndash;39.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (Kg/m\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;24 +Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e03(2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(34.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReferent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026le;\u0026thinsp;24\u0026thinsp;+\u0026thinsp;variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(32.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(40.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.75(3.48\u0026ndash;237.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;24\u0026thinsp;+\u0026thinsp;Wildtype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e07(5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(14.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.32(0. 72\u0026ndash;73.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;24\u0026thinsp;+\u0026thinsp;variant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71(59.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(10.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e204.9(21.95-1912.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e++present\u0026minus;absent \u003cem\u003en: number of individuals\u003c/em\u003e. \u003cem\u003ef/h: family history; dm: diabetes mellitus, lh: luteinizing hormone, bmi: basal metabolic index, combined variant: ga + aa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the correlationbetween serum LH levels and the genotype of \u003cem\u003eLHCGR\u003c/em\u003e rs2293275, we categorized all the subjects based on their genotype. Compared to GG carriers, we observed a significant linear increase in the serum LH levels in the subjects that harbored GA genotypes that were further increased in the AA carriers (Fig.\u0026nbsp;1). Besides, we found a strong risk of developing PCOS in the subjects who have LH levels\u0026thinsp;\u0026ge;\u0026thinsp;12.5 (mIU/mL) and harbored the variant genotype of the rs2293275(Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Moreover, we also found a synergistic effect modification of the PCOS risk in the subjects carrying the variant genotype and presented either alopecia (OR\u0026thinsp;=\u0026thinsp;34.29; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), acne (OR\u0026thinsp;=\u0026thinsp;9.42; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or acanthosis (OR\u0026thinsp;=\u0026thinsp;16.8; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Supplementary table S 1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present case-control study evaluated the association of a polymorphic variant rs2293275 of \u003cem\u003eLHCGR\u003c/em\u003e p.S312N with the PCOS risk and its correlation with the clinical and biochemical indices. We found a significant association of rs2293275 with the PCOS risk and linearly increased LH levels in the subjects harboring heterozygous (GA and the mutant (AA) genotype when compared to the wild-type (GG) genotype carriers.\u003c/p\u003e \u003cp\u003eOur results demonstrated a significant difference in genotypic as well as allelic frequencies of rs2293275 \u003cem\u003eLHCGR\u003c/em\u003e gene between PCOS women and controls, indicating that women with GA and AA genotypes are at higher risk for developing PCOS. The higher frequency of the A allele found in PCOS cases revealed a\u0026thinsp;\u0026gt;\u0026thinsp;2-fold increased risk of PCOS in our study. These findings are in agreement with the earlier studies (Capalbo, Sagnella et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Bassiouny, Rabie et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Ha, Shi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, El-Shal, Zidan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e),reporting a positive association between various ethnicities. However, no significant association of this variant with the risk of PCOS was reported in Caucasian and Bahraini populations respectively(Valkenburg, Uitterlinden et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Almawi, Hubail et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).On the contrary, Thathapudi et al. revealed that the GG(major allele) genotype, rather than AA, conferred a significant risk of developing PCOS in South Indian women(3.36-fold)(Thathapudi, Kodati et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while a recent meta-analysis reported a 4.1 risk increase of developing PCOS for carriers of the AA (minor allele) genotype of \u003cem\u003eLHCGR\u003c/em\u003e(Zou, Wu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These conflicting results among the studies might be explained by differences in sample size, non-uniform diagnostic criteria, ethnic background, and study design.\u003c/p\u003e \u003cp\u003eLH is an associated member of the glycoprotein family that stimulates follicular development, steroid biogenesis, and the formation of the corpus luteum(Dufau \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e),and ovulation(Ascoli, Fanelli et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)acts by binding with its high-affinity receptor, \u003cem\u003eLHCGR\u003c/em\u003e)(Dufau \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)and transducing luteinizing hormone-mediated signals that play a vital role in the ovulation process(Qiao and Han \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003cem\u003eLHCGR\u003c/em\u003e gene is one of the few candidate genes recognized susceptibility loci consistently associated with the risk of PCOS in diverse ethnicities.Abnormal LH signaling is believed to play a crucial role in augmenting ovarian androgen production in PCOS and leading to anovulation(Balen \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1993\u003c/span\u003e, Norman, Dewailly et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Evidence from the study conducted by Zhihua\u003cem\u003eet al\u003c/em\u003e showed that mutation in \u003cem\u003eLHCGR\u003c/em\u003e causes abnormal \u003cem\u003eLHCGR\u003c/em\u003e glycosylation, decreased \u003cem\u003eLHCGR\u003c/em\u003e protein level, effects on subcellular localization, and reduced cellular ATP consumption, which indicate the signal transduction may be affected and leads to the cause of abnormal ovulation(Zhang, Wu et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).Reports have shown that enhanced expression or overactivation of \u003cem\u003eLHCGR\u003c/em\u003e might contribute to the development of PCOS(Kanamarlapudi, Gordon et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition to the two-cell, two-gonadotrophin theory, LH modulates multiple genes' mRNA levels in granulose cells through the \u003cem\u003eLHCGR\u003c/em\u003e receptor, which can aid in the growth of follicles(Sasson, Rimon et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Lindeberg, Carlstr\u0026ouml;m et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).The secretion of androgen hormones by ovarian theca cells promotes by LH, which may result in follicular maturation arrest.Consequently, the variation that occurred at the LH level may potentially influence the reproductive process that it leads to and is associated with menstruation dysfunction and infertility,thereby orchestrating the risk of PCOS(Laven, Imani et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).The genetic variants of \u003cem\u003eLHCGR\u003c/em\u003e p.S312N, which falls within exon 10 of the \u003cem\u003eLHCGR\u003c/em\u003e gene, and is next to the glycosylation signals of the protein, might affect the trafficking and stability of the receptor, resulting in an increased risk of developing polycystic ovary syndrome (PCOS) in women (Thathapudi, Kodati et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). An earlier study reported that mutant homozygous or heterozygous inactivating gene variants of the \u003cem\u003eLHCGR\u003c/em\u003e cause gonadal resistance to LH thereby increasing the LH level and subsequent feedback to the pituitary resulting in the further elevation of LH levels leads to the anovulation(Segaloff \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).Moreover, a recent study showed a strong association of \u003cem\u003eLHCGR\u003c/em\u003e rs2293275 polymorphism with high LH levels and LH/FSH ratio in PCOS women contributes to enhancing the risk of PCOS development. The study suggested that high serum LH levels in PCOS subjects are important for PCOS diagnosis and may be useful as a molecular marker for early detection of high risk for PCOS(Atoum, Alajlouni et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).Given the important pivotal role of LH in androgen metabolism and ovulation, can be a plausible explanation for the enhanced PCOS risk in the women that harbored the variant genotype of \u003cem\u003eLHCGR\u003c/em\u003e in our study.However,further mechanistic studies are warranted to elucidate \u003cem\u003eLHCGR\u003c/em\u003e(rs2293275) mediated PCOS etiology. On stratification analysis, similar to earlier reports, we found a significantly increased serum LH level in subjects with PCOS who harbored variant genotypes when compared to healthy controls(Piersma, Berns et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, El-Shal, Zidan et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMaternal family history is considered a risk factor for PCOS in daughters. PCOS is thought to be a heritable disorder based on familial case clustering(Rosenfield and Ehrmann \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The significant frequency of PCOS or its clinical manifestations, such as hyperandrogenism, hirsutism, infertility, and polycystic ovaries, among first-degree relatives suggests that genetic and familial factors play a role in the disorder(Bruni, Capozzi et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).We found an enhanced risk in the subjects who had a family history of T2DM or hirsutism and harbored the variant genotype of rs2293275 suggesting the heritability associated with the later onset in PCOS women.Although a direct correlation of \u003cem\u003eLHCGR\u003c/em\u003e genotypes with a positive family history of T2DM has not been evaluated as before, a positive family history of T2DM has been previously associated with the development of PCOS(Kulshreshtha, Singh et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Yilmaz, Vellanki et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Vrbikovaet al. reported that defective early beta cell function was characteristic of only patients with PCOS and a positive family history of T2DM(Vrbikova, Bendlova et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2009\u003c/span\u003e),also reported a significant difference in glucose and lipid metabolism between PCOS patients with and without a family history of T2DM(Wang, Gao et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A literature survey suggests that T2DM appears to be an important factor in predicting the risks of metabolic abnormalities in women with PCOS(Ehrmann, Kasza et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Vrb\u0026iacute;kov\u0026aacute;, Grimmichov\u0026aacute; et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Lerchbaum, Schwetz et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, further replicative, and mechanistic studies are required to validate and unveil the underlying role.\u003c/p\u003e \u003cp\u003eIn the present study, we found an enhanced risk of PCOS in subjects with PCOS who had alopecia, acne, or \u003cem\u003eAcanthosis nigricans\u003c/em\u003e compared to healthy controls and harbored variant genotypes of \u003cem\u003eLHCGR\u003c/em\u003e (rs2293275).Hyperandrogenism is a major characteristic in women with PCOS, the hallmark feature of PCOS 58\u0026ndash;82% of hyperandrogenic women have PCOS(Pinola, Puukka et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).Elevated LH levels or increased testosterone production from polycystic ovaries may cause hyperandrogenaemia(Ashraf, Nabi et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Elevated insulin levels may also trigger increased testosterone levels in women and thus modulate the risk of PCOS(Nestler, Jakubowicz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).Consequently, the resulting Androgen excess(hyperandrogenism) acts as the main promoting factor inducing anovulation and follicular arrest, suggesting decreased oocyte development and maturation(Qiao and Feng \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eObesity is a common finding in PCOS that worsens its phenotype and is considered one of the most crucial pathophysiological features in PCOS. It also aggravates menstrual irregularity and increases serum total testosterone levels(Xita and Tsatsoulis \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Baldani, Skrgatić et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).This excessive amount of androgen in turn can affect the follicle growth and metabolic process and also trigger insulin levels, which further enhances the risk of PCOS development in obese women.Furthermore, a recent study that showed increased testosterone level promotes visceral fat accumulation and insulin resistance by inhibiting lipolysis and promoting lipogenesis(Rosenfield and Ehrmann \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), which in turnis associatedwith suppressed ovulation and high LH levels(Roth, Allshouse et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).In this study,We found a synergistic effect on the PCOS risk in the subjects,carrying the \u003cem\u003eLHCGR\u003c/em\u003e variant genotype(GA\u0026thinsp;+\u0026thinsp;AA) having BMI greater than \u0026ge;\u0026thinsp;24 in subjects with PCOS women, albeit with wider CI\u0026rsquo;s due to low numbers in the model. Our findings are consistent with previous studies that found BMI to be statistically significant and highlight the contribution of \u003cem\u003eLHCGR\u003c/em\u003e polymorphism to PCOS phenotypes, particularly BMI(Thathapudi, Kodati et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Atoum, Alajlouni et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)While as the study was statistically powered to detect any associations, however, the low number in the subsequent stratification analysis might be a concern of the present study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study indicated the potential influence of \u003cem\u003eLHCGR\u003c/em\u003e G935A (rs2293275) polymorphism on the development and clinical course of PCOS. More replicative studies are warranted to substantiate our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments: \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the participants for volunteering in the study. The authors also thank the Multi-disciplinary Research Unit, SKIMS, Srinagar funded by the Department of Health Research, Govt of India, for providing necessary research facilities for carrying out this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;The author has no relevant \u0026nbsp;financial and non- financial \u0026nbsp;interests to disclose.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;MAG conceived the study; MAG, ZAS, and SS designed the study. MJM and AR collected the data, and MJM and RR performed experiments. IAS, MJM, and MAG analyzed and interpreted the data. MJM, IAS, and MAG wrote the first draft of the manuscript.All the authors reviewed and approved the final draft of the manuscript.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Data will be available to anyone on proper request to the corresponding author.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;This Study was approved by local institutional ethics committee of SKIMS (SKIMS-IEC) under protocol number RP 55/19.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;Informed consent was obtained from all individual participant including in this study.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;No object or image was obtained or copied from any publication. The images used in this manuscript are my own\u0026rdquo;\u003c/em\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlmawi, W. Y., B. Hubail, D. Z. Arekat, S. M. Al-Farsi, S. K. Al-Kindi, M. R. Arekat, N. Mahmood and S. Madan (2015). \"Leutinizing hormone/choriogonadotropin receptor and follicle stimulating hormone receptor gene variants in polycystic ovary syndrome.\" J Assist Reprod Genet \u003cb\u003e32\u003c/b\u003e(4): 607\u0026ndash;614.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAscoli, M., F. Fanelli and D. L. Segaloff (2002). \"The lutropin/choriogonadotropin receptor, a 2002 perspective.\" Endocr Rev \u003cb\u003e23\u003c/b\u003e(2): 141\u0026ndash;174.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAshraf, S., M. Nabi, S. u. A. Rasool, F. Rashid and S. 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Yildiz (2018). \"Diabetes mellitus and insulin resistance in mothers, fathers, sisters, and brothers of women with polycystic ovary syndrome: a systematic review and meta-analysis.\" Fertil Steril \u003cb\u003e110\u003c/b\u003e(3): 523\u0026ndash;533.e514.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Z., L. Wu, F. Diao, B. Chen, J. Fu, X. Mao, Z. Yan, B. Li, J. Mu, Z. Zhou, W. Wang, L. Zhao, J. Dong, Y. Zeng, J. Du, Y. Kuang, X. Sun, L. He, Q. Sang and L. Wang (2020). \"Novel mutations in LHCGR (luteinizing hormone/choriogonadotropin receptor): expanding the spectrum of mutations responsible for human empty follicle syndrome.\" J Assist Reprod Genet \u003cb\u003e37\u003c/b\u003e(11): 2861\u0026ndash;2868.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou, J., D. Wu, Y. Liu and S. Tan (2019). \"Association of luteinizing hormone/choriogonadotropin receptor gene polymorphisms with polycystic ovary syndrome risk: a meta-analysis.\" Gynecol Endocrinol \u003cb\u003e35\u003c/b\u003e(1): 81\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cb\u003eStatement and Decleration\u003c/b\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biochemical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigi","sideBox":"Learn more about [Biochemical Genetics](http://link.springer.com/journal/10528)","snPcode":"10528","submissionUrl":"https://submission.nature.com/new-submission/10528/3","title":"Biochemical Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"LHCGR, Luteinizing hormone, PCOS, Gene polymorphism, PCR-RFLP, SNP","lastPublishedDoi":"10.21203/rs.3.rs-2004110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2004110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePolycystic ovary syndrome (PCOS) is a common multifaceted endocrine disorder among reproductive women. Deranged luteinizing hormone levels and associated downstream signalling cascade mediated by its receptor luteinizing hormone chorionic gonadotropin receptor (\u003cem\u003eLHCGR\u003c/em\u003e) are pivotal in the etiopathogenesis of PCOS. Genetic variations in the \u003cem\u003eLHCGR\u003c/em\u003e have been associated with PCOS risk, however, the results are inconclusive. We evaluated association of \u003cem\u003eLHCGR\u003c/em\u003e rs2293275 polymorphic variant with PCOS risk and its impact on clinicobiochemical features of PCOS.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e120 confirmed PCOS cases and an equal number of age-matched controls were subjected to clinical, biochemical and hormonal investigations. Genotyping for rs2293275 was performed using polymerase chain reaction restriction fragment length polymorphism. Logistic regression models were used to calculate odds ratios (OR) at 95%confidence intervals (95%CIs).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePCOS cases reported lower annual menstrual cyclicity, significantly higher BMI and Ferriman Galway score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Levels of serum testosterone, TSH, FSH and indicators of glucose homeostasis were significantly deranged in cases than controls. Higher risk of developing PCOS was noted in GA (OR\u0026thinsp;=\u0026thinsp;10.4, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or AA (OR\u0026thinsp;=\u0026thinsp;7.73, P\u0026thinsp;=\u0026thinsp;0.02) genotype carriers and risk persisted in the dominant model (GA\u0026thinsp;+\u0026thinsp;AA) as well (OR\u0026thinsp;=\u0026thinsp;10.29, P\u0026thinsp;=\u0026thinsp;0.01). On stratification, a higher risk of developing PCOS was observed in variant genotype carriers who had a family history of either T2DM (OR\u0026thinsp;=\u0026thinsp;117;p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) or hirsutism (OR\u0026thinsp;=\u0026thinsp;79;p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). We also found a significant linear increase in the serum LH levels in the subjects carrying GA and AA genotypes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn the present study, we report a significant association ofthe \u003cem\u003eLHCGR\u003c/em\u003e rs2293275 variant with the PCOS risk.\u003c/p\u003e","manuscriptTitle":"Effect modification of luteinizing hormone chorionic gonadotropin hormone receptor gene variant (rs2293275) on clinical and biochemical profile, and levels of luteinizing hormone in polycystic ovary syndrome patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-01 16:46:53","doi":"10.21203/rs.3.rs-2004110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-11-19T14:22:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-10-24T01:57:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"131b213b-ab66-4078-ac12-3771b050718d","date":"2022-10-13T11:30:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-09-05T04:37:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-30T01:21:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-30T01:21:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biochemical Genetics","date":"2022-08-27T09:37:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biochemical-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bigi","sideBox":"Learn more about [Biochemical Genetics](http://link.springer.com/journal/10528)","snPcode":"10528","submissionUrl":"https://submission.nature.com/new-submission/10528/3","title":"Biochemical Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"22d28539-a74c-494f-b0c3-63f541540863","owner":[],"postedDate":"September 1st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:21:45+00:00","versionOfRecord":{"articleIdentity":"rs-2004110","link":"https://doi.org/10.1007/s10528-022-10327-z","journal":{"identity":"biochemical-genetics","isVorOnly":false,"title":"Biochemical Genetics"},"publishedOn":"2023-01-12 18:17:21","publishedOnDateReadable":"January 12th, 2023"},"versionCreatedAt":"2022-09-01 16:46:53","video":"","vorDoi":"10.1007/s10528-022-10327-z","vorDoiUrl":"https://doi.org/10.1007/s10528-022-10327-z","workflowStages":[]},"version":"v1","identity":"rs-2004110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2004110","identity":"rs-2004110","version":["v1"]},"buildId":"zQwnuV7TCBrMSSSToR1PI","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}