Skip to main content
Download PDF

Micronutrients intake and genetic variants associated with premature ovarian insufficiency; MASHAD cohort study

BMC Women's Health volume 24, Article number: 91 (2024) Cite this article

Abstract

Background and aim

premature ovarian insufficiency (POI) is defined as the menopause before 40 years of age, and its prevalence is reported to be two-fold higher in Iranian women than the average for woman globally. POI is associated with several cardio/cerebrovascular complications as well as an increased overall mortality. Genetic factors, and serum levels of minerals and vitamin D, have been reported to be related to the prevalence of POI. We have investigated the association between some POI -related genotypes with the serum levels of some important micronutrients.

Methods

One hundred and seventeen women with POI and 183 controls without any renal, hepatic, and thyroid abnormalities were recruited as part of the MASHAD study. Demographic and anthropometric features were recorded and blood samples were collected and processed. DNA was extracted from the buffy coat of blood samples from all participants and 8 POI-related single nucleotide polymorphisms (SNPs) were determined using ASO-PCR or Tetra ARMS-PCR. Serum minerals and vitamin D concentrations were measured using routine methods.

Results

In women with POI, serum copper, phosphate, and calcium were significantly different for those with rs244715, rs16991615, and rs4806660 genotypes, respectively. In our control population, significant differences were also found in serum copper concentrations between different genotypes of rs4806660, rs7246479, rs1046089, and rs2303369. After adjusting for all confounding factors, the women with POI carrying TC genotype (rs4806660) had a lower risk to have serum copper levels < 80 (µg/dL) than those carrying a TT genotype. Furthermore, women with POI carrying GG genotype (rs244715) had a 6-fold higher risk to have serum copper levels > 155 than those carrying AA genotype.

Conclusion

The C and G alleles of the rs4806660 and rs244715 polymorphisms respectively are independently associated with serum copper in women with POI. Further studies are necessary to investigate the association of serum copper and other micronutrients in women and other POI -related polymorphisms.

Peer Review reports

Introduction

Natural menopause is defined as the cessation of menstruation and the menstrual cycle for 12 continuous months during a women’s lifecycle and that happens without any identified psychological or pathological cause [1, 2]. This condition occurs naturally as a result of deficient serum estrogen due to ovarian ageing and follicular depletion at the average age of 48.78 years [3,4,5]. Premature menopause (PM), also identified as primary ovarian insufficiency (POI), is defined as menopause arising before the age of 40 years, that occurs naturally, or due to previous history of reproductive system surgery [6, 7]. A recently published meta-analysis estimated 3.60% of women experienced POI [3], whilst, an epidemiological study reported 5.94% of Iranian women develop POI [8] which is 65% higher than the worldwide estimate of POI prevalence. This difference in prevalence of POI may be due to the lower age of menarche in Iran compared to the world mean, which is one of the risk factors for POI [9,10,11,12]. Also, the age of development of ovarian cancer is considerably lower in Iranian women than in the developed countries, and treatments such as chemotherapy and other therapeutic interventions may lead to POI [13, 14].

Genome-wide association studies (GWAS) have shown that a polymorphism (rs16991615) of the minichromosome maintenance 8 homologous recombination repair factor (MCM8) gene was concluded to play a significant role on the age at which natural menopause occurs, as a result of aminoacid change (Glu to Lys) in the synthetized protein [15]. Moreover, one of the most significant associations was found in the rs1046089 single nucleotide polymorphism (SNP) in Proline Rich Coiled-Coil 2 A (PRRC2A) gene. As this SNP is responsible for modifying human leukocyte antigen in monocytes (HLA-DRB4) and lymphoblastoid cells (HLA-DQA1), it may be a potential proinflammatory component which causes oocyte depletion and subsequently, an earlier menopause [16]. In addition, the study of Weedon et al. reported that the rs4806660 polymorphism, transmembrane (TMEM) gene, is associated with a higher risk of developing an early menopause [17, 18]. Furthermore, there are several more variants and polymorphisms significantly related to an earlier age of the menopause mentioned in the GWAS [19, 20].

Micronutrients are found to have significant impact on the hormones involved in female reproduction [21]. Vitamin D, via its receptors that are present on the cells of ovary and uterus, might be responsible for female reproductive tract disorders, or effects the regulation of anti-Müllerian hormone [22]. A cohort study of registered nurses from across US, found women who consumed higher amounts of vitamin D and calcium from dietary dairy products, had a lower risk of premature ovarian insufficiency or POI [23]. Furthermore, Kebapcilar et al. in a cross-sectional analysis comparing healthy women and women with non-surgical POI, found lower serum vitamin D and zinc and higher copper in the cases [24]. According to these studies, it appears to be necessary to assess the association between POI -related genotypes and micronutrients, that appear to be related to the function of female reproductive organs.

To the best of our knowledge, there have been no studies addressing the question of whether there is any association between serum minerals/vitamin D and genotypes known to be related to PM/POI. Therefore, we aim to investigate this relationship in a sample of Iranian people who participated in Mashhad Stroke and Heart Atherosclerotic Disorder (MASHAD) study.

Methods

Study participants

In this cross-sectional study, 117 women with POI and 183 healthy women were recruited as the case and control groups, respectively. Theses participants were enrolled as part of the MASHAD study that is a 10-year cohort study (2010–2020) and includes a total of 9704 people aged 35–65 years [25]. According to the questionnaire obtained from the baseline study, women who: (1) entered the menopause at 40 and < 40 years of age, (2) had 12 consecutive months without menstrual bleeding, or (3) had a serum FSH > 30 IU/L (repeated 3 times at four-week intervals) were considered as having POI [7]. Women > 40 years old, those with a history of diseases, syndromes, surgeries, and taking drugs that may have affected menstruation, were excluded. Vacutainer® tubes were used for blood sample collection. All participants were on 14-hour overnight fast. Blood samples were centrifuged at 4o C in 5000 rpm for 15 min and the serum separated and stored at -80oC for future analysis. Measurement of the anthropometric characteristics: weight, height, body mass index (BMI) hip and mid-upper arm circumference, waist-hip and waist-height ratio were performed using standardized methods [25, 26]. All participants had normal kidney, liver, and thyroid functions.

DNA extraction and quality controls

Whole DNA of all participants’ genome from 200 µl of their buffy coat samples were extracted by DNA extraction kit (Pars Tous Biotechnology, Mashhad, Iran). Qualitative and quantitative quality controls were done using agarose gel electrophoresis (Pars Tous Biotechnology, Mashhad, Iran) and Nano drop 2000 (Thermo Fisher Scientific, USA) in 280 and 260 nm wavelengths, respectively.

Allele-specific oligonucleotide polymerase chain reaction (ASO-PCR)

We added ASO-PCR in a total 15 µl reaction volume which consisted of the followings: 2 µl genomic DNA, 1.5 µl water, 1 µl of each primer, and 7.5 µl master mix (Pars Tous Biotechnology, Mashhad, Iran). The PCR conditions were as follows: at 95 °C, we carried out one cycle of denaturation for 7 min, followed by 35 cycles consisting of the following: 30 s for denaturation at 95 °C, annealing for 30 s at 60 °C, and 30s of extension at 72 °C. Final extension was performed for 7 min the same as initial denaturation.

Tetra amplification refractory mutation system PCR (ARMS-PCR)

Tetra ARMS PCR was carried out on the same composition of 15 µl reaction volume and method of performing PCR which were addressed above for ASO-PCR. Primer1 software [27] was used for designing the primers.

Minerals and vitamin D measurement

Zinc (Women: 70–114 µg/dL, Men: 73–127 µg/dL), copper (Women: 80–155 µg/dL, Men: 70–140 µg/dL), phosphate (2.5–4.5 mg/dL), calcium (Children: 8.8–10.8 mg/dL. Adults: 9-10.5 mg/dL), magnesium (Infants: 1.4-2.0 mEq/L, children: 1.4–1.7 mEq/L, adults: 1.3–2.1 mEq/L), vitamin D (adults: 10–30 ng/ml) were measured in the blood serum of all study participants. Flame atomic absorption spectrometry (Perkin Elmer model 3030, USA, 1980) was used to measure serum levels of copper and zinc, as previously described [28]. Using Pars Azmun kits (Pars Azmun, Tehran, Iran) and a BT3000 auto-analyzer along with photometric methods, we measured serum calcium and phosphate [29]. According to intensity of purple color radiated originally from calcium and cresolphthalein complexone interaction. We measured serum phosphate by accessing the intensity of color produced by chemical reaction of phosphate, ammonium molybdate, and sulfuric acid [29]. Xylidyl blue photometric method, BT3000 autoanalyzer (Biotechnica, Rome, Italy), and Pars Azmun kits (Pars Azmun, Tehran, Iran) were applied for measurement of magnesium. An enzyme linked immunoassay (ELISA) method was applied to measure 25-hydroxy vitamin D [30, 31].

Ethics

All participants were made aware of study protocol and signed an informed consent form. Mashhad University of Medical Sciences (MUMS) ethics committee approved the methodology of this project.

Statistical analysis

Values in this study are shown as frequencies along with the percentage or mean and standard deviation. Chi-square test was used for comparing SNPs’ genotype frequencies between POI cases and healthy controls. Kolmogorov-Smirnoff test used for assessment of normal distribution in the quantitative data. Two-independent sample t-test, one-way analysis of variance (ANOVA) analyses and Tukey tests were performed to compare quantity of normally distributed values between the two subgroups. Moreover, we employed multivariate logistic regression in order not to allow confounders, impact our results. All tests were performed by Statistical Package for Social Sciences software, known as SPSS (IBM Corp. Released 2016. IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.), and P < 0.05 was treated as significant.

Results

The participants’ demographic data are summarized in Table 1. This study the mean age of the participants was 54 years. Moreover, details on POI-related polymorphisms and genes that were investigated in our study are shown in Table 2.

Table 1 Demographic features and characteristics of the study population
Full size table
Table 2 Characteristics of polymorphisms associated with POI in which were included in this study
Full size table

Association of polymorphisms with POI

Of the study population, 117 were confirmed to have a POI. We identified the association between the occurrence of the eight variants and POI in patients and control groups, also the Hardy-Weinberg equilibrium calculated for each SNP. It showed that society is in Hardy-Weinberg equilibrium, rs4806660, rs451417, rs16991615, rs7246479, rs244715, rs1046089, rs2303369, and rs10183486 with a P-value of 0.2, 0.1, 0.2, 0.2, 0.3, 0.1, 0.1, and 0.2, respectively (Table 3). As shown from this table, 8 SNPs were selected for this study: rs16991615 (p = 0.002), rs244715 (p = 0.006), rs451417 (p = 0.001), rs7246479 (p = 0.002), rs4806660 (p = 0.02), and rs10183486 (p = 0.02) and were significantly associated with POI. Also, the distribution of genotypes is listed in Table 3. As an example, TT, TC, CC genotypes frequencies of TMEM variants rs4806660 were estimated 34.7% and 65.3%, 39.3% and 60.7%, 66.7% and 33.3% for the POI and control group, respectively. The POI group showed the following BR Serine/Threonine Kinase 1 (BRSK1) gene rs7246479 genotypic distribution; 28% TT, 43% TA and 63% AA, which was significantly different from the controls (71%, 58% and 36% respectively) (p = 0.002). Analysis of two polymorphisms at the MCM8 gene locus showed that the rs16991615 and rs451417 variants were similar in frequency for the AA genotypes; 61.75 & 62.35 for POI and 38.3% & 37.7% for control. Furthermore, the rs244715 genotyping showed 172 (57.3%) for TT, 110 (36.3%) for TA, and 18 (6%) for AA in total population. The tousled like kinase 1 (TLK1) gene rs10183486 were significantly associated with POI so that, 33.6% CC, 40.4% CT and 62.5% TT for POI group and 66.4% CC, 59.6% CT and 37.5% TT for control. Allele T and C frequencies for the TMEM rs4806660 were estimated as 65.3% and 34.6%, respectively for the POI group and 73.7% and 26.2%, respectively, for the control group.

Table 3 Genotype frequencies in all study participants
Full size table

Association analysis between genotypes and serum vitamin D, calcium, copper, magnesium, zinc, phosphate levels in patient with POI and control

Serum mineral and vitamin D of individuals based on the presence or absence of POI across the genotypes are illustrated in Tables 4 and 5, respectively. Analysis showed that there was significantly difference between calcium level of serum and TMEM variants rs4806660 in participants with POI, although this did not attain significance in control. Conversely, significant difference was shown between the same variant and copper serum level in control. In rs7246479, significant variance with copper level was detected by comparing A/A to T/A and A/A to T/T in control group, whereas for the rs244715 SNP there were significant differences were between the G/G compared with the A/G and the G/G compared to the A/G individuals in the POI group. There was a significant difference between serum phosphate level and MCM8 variants rs451417 only in the patients’ group (CC = 4.3 ± 0.06, CA = 4.2 ± 0.1, AA = 4.7 ± 0.1, p = 0.01). Also, there was significant difference between copper serum level and PRRC2A & Fibronectin Type III Domain Containing 4 (FNDC4) variants rs1046089 & rs2303369 respectively, only in the control’ group (p = 0.01 for both). No significant differences were found between studied minerals and vitamin D and MCM8 & TLK1 gene variants rs16991615 & rs10183486, respectively.

Table 4 Association of genotypes with serum mineral concentrations in the patients with POI (N = 117)
Full size table
Table 5 Association of genotypes with serum minerals in the control participants (N = 183)
Full size table

The significant association between SNPs associated with POI and copper

Multinomial regression analysis was used to predict the association of 5 variants with serum Cu (µg/dL) in patient and control groups (Table 6). We categorized serum copper levels into three categories; low < 80 µg/dL, moderate 80–155 µg/dL, high > 155 µg/dL. The adjusted model included traditional risk factors for POI. We found that participants with POI who carried the C allele of rs4806660 variant had lower serum copper than those with TT genotypes (OR = 0.22 (0.11–0.81), p = 0.03), although this did not attain significance in individual without POI (p = 0.3). According to analysis of the BRSK1 gene polymorphism, rs7246479, healthy individuals with A/A genotype are more likely to have high copper level (OR = 41.08 (3.7-450.8), p = 0.002) than patients with T/T genotype. This indicates that the A allele could be a promoting allele for a high serum copper in healthy people. Whilst, the rs244715 polymorphism, patients with the G/G genotype were more likely to have higher serum copper levels (OR = 6.7 (1.3–24.3), p = 0.02) than patients with the A/A genotype. This means that the effect of G allele on serum copper level may be in the homozygous state among patient with POI. After regression analysis, two SNPs (rs1046089, rs2303369) were not in significant associations with serum copper concentration among person with and without POI (Table 6).

Table 6 Multivariable logistic regression analysis; Most common genotypes significantly associated with the minerals
Full size table

Discussion

In this study we selected several SNPs that have previously been shown to be associated with POI, including: rs16991615, rs244715, rs451417, rs7246479, rs4806660, rs10183486, rs2303369 and rs1046089. All of these SNPs were found to be associated with POI apart from the rs2303369 and rs1046089 polymorphisms and POI. These SNPs are located on different loci in the genome for example, rs1046089, rs2303369, rs10183486 and rs244715 are located on PRRC2A, Fibronectin Type III Domain Containing 4 (FNDC4), TLK1 and Zinc Finger Protein 346 (ZNF346), respectively while rs16991615 and rs451417 are located on MCM8. In addition to rs7246479 and rs4806660 are both located on TMEM150B. It was also similar to GWAS population due to the proximity of MAF of us to the GWAS. We investigated the genotype frequency for the POI and the control groups, and evaluated the association of genotypes with serum minerals in the two groups. To our knowledge, for the first time in this study, we have investigated the relationship between minerals with vitamin D and variants associated with POI.

Various studies have investigated the role of genetic variants in the occurrence of POI. Approximately 30–85% of the onset menopausal age is related to heredity, and remarkable amount (15–30%) of POF cases are familial [46]. The rs4806660 is located on chromosome 19 containing TMEM150B, which encodes the transmembrane protein 150B, also known damage-regulated autophagy modulator 3 (DRAM3). This protein is involved in autophagy and apoptosis [47, 48]. Expression of TMEM150B leads to accumulation of autophagosomes in basal conditions and increases the autophagy flux, while removal of TMEM150B interrupts the autophagy flux and confirms that it modulates macro autophagy [47]. Our finding indicated that there was significantly difference between serum calcium and TMEM variants rs4806660 in participants with POI, although this did not attain significance in control. Also, there was no significant difference between serum minerals and vitamin D and MCM8 & TLK1 gene variants rs16991615 and rs10183486, respectively. Calcium and vitamin D have been implicated in several obstetric diseases, including polycystic ovary syndrome, endometriosis, and premenstrual syndrome, and appear to be involved in fertility [49]. Carwile et al. [50] did not find any relationship between calcium intake and age at menopause. Purdue-Smithe et al. found that high doses calcium intake and vitamin D were associated with lower risk of early menopause and they may play a role in reducing the risk of early menopause [49]. Also analysis showed a significant difference between phosphate serum level and MCM8 variants rs451417 in the POI group. MCM8 is located on chromosome 19 that coding DNA replication licensing factor MCM8 and acts in DNA repair and also MCM8 play role in gametogenesis [51, 52]. Although Kebapcillar et al. showed that serum zinc was significantly lower in POI cases than controls [53], we found no significant relationship between serum zinc and the POI-related genetic variants.

Another SNP is rs7246479 which is located in BRSK1, a gene is most commonly expressed in the brain and to a lesser extent in the ovaries and BRSK1 is thought to stimulate gonadotropin-releasing hormone secretion, leading to POI [47]. We observed significant variance with copper level was detected by comparing A/A to T/A and A/A to T/T in control group, whiles in rs244715 significant differences between copper with G/G compared with A/G and G/G compared to A/G only in POI group. The rs244715 is located in ZNF346 that encodes a double-stranded RNA binding protein Which is probably involved in apoptosis regulation [54, 55]. Furthermore, we observed a significant difference between copper serum level and PRRC2A & FNDC4 variants rs1046089 & rs2303369 respectively, only in the control group.

We also examined the association between SNPs associated with POI and copper, calcium, phosphate including rs4806660, rs7246479, rs244715, rs1046089, rs2303369, and rs451417. We found that the C allele of rs4806660 variant was associated with lower serum copper values than those with TT genotypes in the POI group, while this did not achieve significance in the control group. In rs7246479 that is located in BRSK1 gene, participants who have a normal menopause with A/A genotype are more likely to have high copper level than patients with T/T genotype. This suggests that the A allele may be involved in raising serum copper in healthy individuals. Besides, in rs244715 the analysis indicated that in the POI group with the G/G genotype have higher serum copper than patients with the A/A genotype. This implies the effect of G allele on copper level must be in the homozygous condition in POI group in two SNPs including rs1046089, rs2303369, we did not find significant associations with serum copper among POI or the control group. Also, in regression analysis we did not find any associations between serum calcium and phosphate and the SNPs (rs4806660, rs451417).

A possible explanation for this might be that these SNPs, which are associated with POI, cause hormonal changes that leads to altered metabolism [1, 56]. Menopausal changes in trace mineral status may affect the pathology of premature menopausal diseases [56]. Bednarek-Tupikowska et al. conducted a study on women during sex hormone therapy that measured the level of different minerals in pre- and postmenopausal women. They showed that estrogen administration in postmenopausal women tended to increase serum copper levels [56]. Berg et al. found that oral contraceptive administration is associated with increased serum copper levels [57]. However, in another study, no significant relationship was observed between estrogen administration and copper levels [58]. In another study, serum copper levels were significantly associated with reproductive health problems in women [59]. Women with POF had significantly higher copper levels than a control group [24]. Ferdous et al. stated that serum copper levels were significantly higher in postmenopausal women than in the premenopausal [60]. In contrast, Mutlu et al. did not find any significant difference between copper levels in pre- and postmenopausal women [61].

Copper plays an important role in various metabolic functions [59]. It is also involved in the development and maintenance of the immune system [24]. The relationship between copper and menopause was also expressed in studies. These results suggest that, in general, hormonal changes may be related to serum copper levels. Therefore, some variants associated with POI and POI associated with hormonal changes [62]. Hormonal changes can be linked to variation of micronutrient levels. However, further studies are needed on the association of serum levels of various minerals with POI.

There are some limitations to this study. First, it was a cross sectional study that includes groups of case and control and therefore we cannot attribute causality. Second, we did not consider the other SNPs that is possible associated with POI. Third dietary mineral intake was not considered, which could affect the results. We recommend future researches conduct longitudinal studies such as case-control or cohort with larger sample size and consider other associated SNPs. Also they can investigate the effect of nutritional interventions on these variants and POI in randomized clinical trials.

In conclusion, serum calcium, phosphate and copper in POI patients were significantly different in women with different rs4806660, rs451417, rs244715, SNPs respectively. Also, for the rs4806660, rs7246479, rs1046089, rs2303369, SNPs there was a significant difference between serum copper and SNPs in the control group. Regression analysis showed that the patients with C allele of rs4806660 had lower serum copper than patients with T/T genotype. Furthermore, the healthy participants with A/A genotype of rs7246479 and POI individuals with the G/G genotype of rs244715 probably have higher copper than individuals with the T/T genotype and the A/A genotype, respectively. This study suggests a potential mechanism in the pathogenesis of gene-related cases of POI. Further studies of the mechanisms involved in POI will require longitudinal studies.

Data Availability

The datasets generated during the current study are available from the corresponding author upon reasonable request. Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Anagnostis P, Christou K, Artzouchaltzi A-M, Gkekas NK, Kosmidou N, Siolos P, et al. Early menopause and premature ovarian insufficiency are associated with increased risk of type 2 Diabetes: a systematic review and meta-analysis. Eur J Endocrinol. 2019;180(1):41–50.

    Article  CAS  PubMed  Google Scholar 

  2. Hernández-Angeles C, Castelo-Branco C. Early menopause: a hazard to a woman’s health. Indian J Med Res. 2016;143(4):420.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Golezar S, Ramezani Tehrani F, Khazaei S, Ebadi A, Keshavarz Z. The global prevalence of primary ovarian insufficiency and early menopause: a meta-analysis. Climacteric. 2019;22(4):403–11.

    Article  CAS  PubMed  Google Scholar 

  4. Panagiotis A, Konstantinos C, Aikaterini-Maria A, Nifon KG, Nikoletta K, Pavlos S, et al. Early menopause and premature ovarian insufficiency are associated with increased risk of type 2 Diabetes: a systematic review and meta-analysis. Eur J Endocrinol. 2019;180(1):41–50.

    Article  Google Scholar 

  5. Schoenaker DA, Jackson CA, Rowlands JV, Mishra GD. Socioeconomic position, lifestyle factors and age at natural menopause: a systematic review and meta-analyses of studies across six continents. Int J Epidemiol. 2014;43(5):1542–62.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Honigberg MC, Zekavat SM, Aragam K, Finneran P, Klarin D, Bhatt DL, et al. Association of Premature Natural and Surgical Menopause With Incident Cardiovascular Disease. JAMA. 2019;322(24):2411–21.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Mishra GD, Chung H-F, Cano A, Chedraui P, Goulis DG, Lopes P, et al. EMAS position statement: predictors of premature and early natural menopause. Maturitas. 2019;123:82–8.

    Article  PubMed  Google Scholar 

  8. Delavar MA, Hajiahmadi M. Age at menopause and measuring symptoms at midlife in a community in Babol. Iran. 2011;18(11):1213–8.

    Google Scholar 

  9. Bahrami N, Soleimani MA, Chan YH, Ghojazadeh M, Mirmiran P. Menarche age in Iran: a meta-analysis. Iran J Nurs Midwifery Res. 2014;19(5):444–50.

    PubMed  PubMed Central  Google Scholar 

  10. Wang M, Gong W-W, Hu R-Y, Wang H, Guo Y, Bian Z, et al. Age at natural menopause and associated factors in adult women: findings from the China Kadoorie Biobank study in Zhejiang rural area. PLoS ONE. 2018;13(4):e0195658.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Whitcomb BW, Purdue-Smithe A, Hankinson SE, Manson JE, Rosner BA, Bertone-Johnson ER. Menstrual cycle characteristics in adolescence and early adulthood are Associated with risk of early natural menopause. J Clin Endocrinol Metabolism. 2018;103(10):3909–18.

    Article  Google Scholar 

  12. Thomas F, Renaud F, Benefice E, De Meeüs T, Guegan J-FJH. International variability of ages at menarche and menopause: patterns and main determinants. 2001:271 – 90.

  13. Arab M, Khayamzadeh M, Tehranian A, Tabatabaeefar M, Hosseini M, Anbiaee R, et al. Incidence rate of Ovarian cancer in Iran in comparison with developed countries. Indian J Cancer. 2010;47(3):322.

    Article  CAS  PubMed  Google Scholar 

  14. De Bruin ML, Huisbrink J, Hauptmann M, Kuenen MA, Ouwens GM, van’t Veer MB, et al. Treatment-related risk factors for premature menopause following Hodgkin Lymphoma. Blood the Journal of the American Society of Hematology. 2008;111(1):101–8.

    Google Scholar 

  15. He C, Kraft P, Chen C, Buring JE, Paré G, Hankinson SE, et al. Genome-wide association studies identify loci associated with age at menarche and age at natural menopause. Nat Genet. 2009;41(6):724–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Stolk L, Perry JR, Chasman DI, He C, Mangino M, Sulem P, et al. Meta-analyses identify 13 loci associated with age at menopause and highlight DNA repair and immune pathways. Nat Genet. 2012;44(3):260–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Murray A, Bennett C, Perry J, Weedon M, Jacobs P, Bodicoat D, et al. Common genetic variants are significant risk factors for early menopause: results from the breakthrough generations study. Hum Mol Genet. 2010;20:186–92.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Javan R, Feyzabadi Z, KIANI MA. Management of infantile colic; based on traditional Iranian medicine. 2015.

  19. Laven JSE. Genetics of Early and normal menopause. Semin Reprod Med. 2015;33(06):377–83.

    Article  PubMed  Google Scholar 

  20. Roshan-Milani S, Seyyedabadi B, Saboory E, Parsamanesh N, Mehranfard N. Prenatal stress and increased susceptibility to anxiety-like behaviors: role of neuroinflammation and balance between GABAergic and glutamatergic transmission. Stress. 2021;24(5):481–95.

    Article  CAS  PubMed  Google Scholar 

  21. Hosseini B, Eslamian G. Association of Micronutrient Intakes with Female Infertility: review of recent evidence. Thrita. 2015;4(1):e25586.

    Article  Google Scholar 

  22. Ebru Ersoy AUAOEAUGYAUUBAUATAUNY. Vitamin D levels in patients with premature ovarian failure. Vitamin D levels in patients with. Premature Ovarian Failure. 2016;87(1):32–6.

    Google Scholar 

  23. Purdue-Smithe AC, Whitcomb BW, Szegda KL, Boutot ME, Manson JE, Hankinson SE, et al. Vitamin D and calcium intake and risk of early menopause. Am J Clin Nutr. 2017;105(6):1493–501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kebapcilar AG, Kulaksizoglu M, Kebapcilar L, Gonen MS, Ünlü A, Topcu A, et al. Is there a link between premature ovarian failure and serum concentrations of vitamin D, zinc, and copper? Menopause. 2013;20(1):94–9.

    Article  PubMed  Google Scholar 

  25. Ghayour-Mobarhan M, Moohebati M, Esmaily H, Ebrahimi M, Parizadeh SMR, Heidari-Bakavoli AR, et al. Mashhad Stroke and heart atherosclerotic disorder (MASHAD) study: design, baseline characteristics and 10-year cardiovascular risk estimation. Int J Public Health. 2015;60(5):561–72.

    Article  PubMed  Google Scholar 

  26. Ghazizadeh H, Mirinezhad SMR, Asadi Z, Parizadeh SM, Zare-Feyzabadi R, Shabani N, et al. Association between obesity categories with Cardiovascular Disease and its related risk factors in the MASHAD cohort study population. J Clin Lab Anal. 2020;34(5):e23160.

    Article  CAS  PubMed  Google Scholar 

  27. Collins A, Ke XJTOBJ. Primer1: primer design web service for tetra-primer ARMS-PCR. 2012;6(1).

  28. Ghayour-Mobarhan M, Shapouri-Moghaddam A, Azimi-Nezhad M, Esmaeili H, Parizadeh SMR, Safarian M, et al. The relationship between established coronary risk factors and serum copper and zinc concentrations in a large persian cohort. J Trace Elem Med Biol. 2009;23(3):167–75.

    Article  CAS  PubMed  Google Scholar 

  29. Jafari-Giv Z, Avan A, Hamidi F, Tayefi M, Ghazizadeh H, Ghasemi F, et al. Association of body mass index with serum calcium and phosphate levels. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019;13(2):975–80.

    Article  Google Scholar 

  30. Holick MF. The vitamin D deficiency pandemic: approaches for diagnosis, treatment and prevention. Reviews in Endocrine and Metabolic Disorders. 2017;18(2):153–65.

    Article  CAS  PubMed  Google Scholar 

  31. Ghazizadeh H, Mirinezhad MR, Seyedi SMR, Sadabadi F, Ahmadnezhad M, Jaberi N, et al. Prognostic factors associating with pro-oxidant-antioxidant balance; neutrophils to lymphocytes ratio, vitamin D, heat shock protein 27, and red cell distribution width. Archives of Medical Research; 2020.

  32. Coignet MV, Zirpoli GR, Roberts MR, Khoury T, Bandera EV, Zhu Q, et al. Genetic variations, reproductive aging, and Breast cancer risk in African American and European American women: the women’s Circle of Health Study. PLoS ONE. 2017;12(10):e0187205.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chen CT, Fernandez-Rhodes L, Brzyski RG, Carlson CS, Chen Z, Heiss G, et al. Replication of loci influencing ages at menarche and menopause in hispanic women: the women’s Health Initiative SHARe Study. Hum Mol Genet. 2012;21(6):1419–32.

    Article  CAS  PubMed  Google Scholar 

  34. Murray A, Bennett CE, Perry JR, Weedon MN, Consortium R, Jacobs PA, et al. Common genetic variants are significant risk factors for early menopause: results from the breakthrough generations study. Hum Mol Genet. 2011;20(1):186–92.

    Article  CAS  PubMed  Google Scholar 

  35. Ruth KS, Murray A. Lessons from Genome-Wide Association Studies in Reproductive Medicine: Menopause. Semin Reprod Med. 2016;34(04):215–23.

    Article  PubMed  Google Scholar 

  36. Ruth KS, Soares ALG, Borges M-C, Eliassen AH, Hankinson SE, Jones ME, et al. Genome-wide association study of anti-Müllerian hormone levels in pre-menopausal women of late reproductive age and relationship with genetic determinants of reproductive lifespan. Hum Mol Genet. 2019;28(8):1392–401.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bae H, Lunetta KL, Murabito JM, Andersen SL, Schupf N, Perls T, et al. Genetic associations with age of menopause in familial longevity. Menopause. 2019;26(10):1204–12.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Moron FJ, Ruiz A, Galan JJ. Genetic and genomic insights into age at natural menopause. Genome Med. 2009;1(8):1–4.

    Article  Google Scholar 

  39. Qin Y, Sun M, You L, Wei D, Sun J, Liang X, et al. ESR1, HK3 and BRSK1 gene variants are associated with both age at natural menopause and premature ovarian failure. Orphanet J Rare Dis. 2012;7(1):5.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Perry JR, Hsu Y-H, Chasman DI, Johnson AD, Elks C, Albrecht E, et al. DNA mismatch repair gene MSH6 implicated in determining age at natural menopause. Hum Mol Genet. 2014;23(9):2490–7.

    Article  CAS  PubMed  Google Scholar 

  41. Diakite M, Clark TG, Auburn S, Campino S, Fry AE, Green A, et al. A genetic association study in the Gambia using tagging polymorphisms in the major histocompatibility complex class III region implicates a HLA-B associated transcript 2 polymorphism in severe Malaria susceptibility. Hum Genet. 2009;125(1):105–9.

    Article  CAS  PubMed  Google Scholar 

  42. He C, Hamon S, Li D, Barral-Rodriguez S, Ott J, Consortium DG. MHC fine mapping of human type 1 Diabetes using the T1DGC data. Diabetes Obes Metabolism. 2009;11:53–9.

    Article  Google Scholar 

  43. Perry JR, Corre T, Esko T, Chasman DI, Fischer K, Franceschini N, et al. A genome-wide association study of early menopause and the combined impact of identified variants. Hum Mol Genet. 2013;22(7):1465–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Spencer KL, Malinowski J, Carty CL, Franceschini N, Fernández-Rhodes L, Young A, et al. Genetic variation and reproductive timing: African American women from the Population Architecture using Genomics and Epidemiology (PAGE) study. PLoS ONE. 2013;8(2):e55258.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. He C, Chasman DI, Dreyfus J, Hwang S-J, Ruiter R, Sanna S, et al. Reproductive aging-associated common genetic variants and the risk of Breast cancer. Breast Cancer Res. 2012;14(2):R54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Murray A, Bennett CE, Perry JRB, Weedon MN, Consortium R, Jacobs PA, et al. Common genetic variants are significant risk factors for early menopause: results from the breakthrough generations study. Hum Mol Genet. 2011;20(1):186–92.

    Article  CAS  PubMed  Google Scholar 

  47. Fu X, Wang H, Zhang X. Genetic aspects of early menopause. J Bio-X Res. 2019;2(3):105–11.

    Google Scholar 

  48. Lee W, Yun S, Choi GH, Jung TW. Fibronectin type III domain containing 4 attenuates hyperlipidemia-induced insulin resistance via suppression of inflammation and ER stress through HO-1 expression in adipocytes. Biochem Biophys Res Commun. 2018;502(1):129–36.

    Article  CAS  PubMed  Google Scholar 

  49. Whitcomb BW, Purdue-Smithe AC, Szegda KL, Boutot ME, Hankinson SE, Manson JE, et al. Cigarette Smoking and risk of early natural menopause. Am J Epidemiol. 2017;187(4):696–704.

    Article  PubMed Central  Google Scholar 

  50. Carwile JL, Willett WC, Michels KB. Consumption of low-fat dairy products may delay natural menopause. J Nutr. 2013;143(10):1642–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. AlAsiri S, Basit S, Wood-Trageser MA, Yatsenko SA, Jeffries EP, Surti U, et al. Exome sequencing reveals MCM8 mutation underlies ovarian failure and chromosomal instability. J Clin Investig. 2015;125(1):258–62.

    Article  PubMed  Google Scholar 

  52. Heddar A, Beckers D, Fouquet B, Roland D, Misrahi M. A novel phenotype combining primary ovarian insufficiency growth retardation and pilomatricomas with MCM8 mutation. J Clin Endocrinol Metabolism. 2020;105(6):dgaa155.

    Article  Google Scholar 

  53. Kebapcilar AG, Kulaksizoglu M, Kebapcilar L, Gonen MS, Ünlü A, Topcu A et al. Is there a link between premature ovarian failure and serum concentrations of vitamin D, zinc, and copper? Menopause. 2013;20(1).

  54. Adoue V, Schiavi A, Light N, Almlöf JC, Lundmark P, Ge B, et al. Allelic expression mapping across cellular lineages to establish impact of non-coding SNP s. Mol Syst Biol. 2014;10(10):754.

    Article  PubMed  Google Scholar 

  55. Mrschtik M, Ryan KM. Another DRAM involved in autophagy and cell death. Autophagy. 2016;12(3):603–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Bednarek-Tupikowska G, Jodkowska A, Antonowicz-Juchniewicz J, Zinc. Cooper, Manganese, and Selenium Status in Pre-and Postmenopausal women during sex hormone therapy Stężenia Cynku, miedzi, manganu i selenu u kobiet menopauzalnych przyjmujących terapię hormonalną. Adv Clin Experimental Med. 2010;19(3):337–45.

    Google Scholar 

  57. Berg G, Kohlmeier L, Brenner H. Effect of oral contraceptive progestins on serum copper concentration. Eur J Clin Nutr. 1998;52(10):711–5.

    Article  CAS  PubMed  Google Scholar 

  58. Lopes PA, Santos MC, Vicente L, Rodrigues MO, Pavao ML, Nève J, et al. Trace element status (Se, Cu, Zn) in healthy Portuguese subjects of Lisbon population. Biol Trace Elem Res. 2004;101(1):1–17.

    Article  CAS  PubMed  Google Scholar 

  59. Skalnaya MG, Tinkov AA, Lobanova YN, Chang J-S, Skalny AV. Serum levels of copper, iron, and manganese in women with pregnancy, miscarriage, and primary infertility. J Trace Elem Med Biol. 2019;56:124–30.

    Article  CAS  PubMed  Google Scholar 

  60. Ferdous N, Mishu FA, Haque SMT, Khan AAR, Siddique S. Estimation of serum copper in post-menopausal women attending in a Tertiary Care Hospital, Bangladesh. Anwer Khan Modern Medical College Journal. 2019;10(2):110–3.

    Article  Google Scholar 

  61. Mutlu M, Argun M, Kilic E, Saraymen R, Yazar S. Magnesium, zinc and copper status in osteoporotic, osteopenic and normal post-menopausal women. J Int Med Res. 2007;35(5):692–5.

    Article  CAS  PubMed  Google Scholar 

  62. Valizadeh M, Aghasizadeh M, Nemati M, Hashemi M, Aghaee-Bakhtiari SH, Zare-Feyzabadi R, et al. The association between a fatty acid binding protein 1 (FABP1) gene polymorphism and serum lipid abnormalities in the MASHAD cohort study. Prostaglandins Leukot Essent Fatty Acids. 2021;172:102324.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank Mashhad University of Medical Sciences Research Council for their financial supports (grant No:85134).

We gratefully acknowledge the contributions of the data collection team and the individuals who participated in this study. This project was implemented in collaboration with the Dr. Majid Ghayour Mobarhan (Iranian UNESCO center of excellence for human nutrition) for guidance in our project.

Funding

This work was supported by a grant from Mashhad University of Medical Science, Iran.

Author information

Author notes

  1. Mohammad Reza Mirinezhad, Maliheh Aghsizadeh and Hamideh Ghazizadeh contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Genetics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

    Mohammad Reza Mirinezhad, Tayebeh Hamzehloei & Alireza Pasdar

  2. International UNESCO Center for Health-Related Basic Sciences and Human Nutrition, Mashhad University of Medical Sciences, Mashhad, Iran

    Maliheh Aghsizadeh, Hamideh Ghazizadeh, Sahar Ghoflchi & Majid Ghayour-Mobarhan

  3. Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran

    Maliheh Aghsizadeh, Hamideh Ghazizadeh, Mohammad Zamiri Bidary & Alireza Naghipour

  4. Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex, BN1 9PH, UK

    Gordon A. Ferns

  5. Division of Applied Medicine, Medical School, University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK

    Alireza Pasdar

  6. Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, 99199-91766, Iran

    Alireza Pasdar & Majid Ghayour-Mobarhan

Authors
  1. Mohammad Reza Mirinezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maliheh Aghsizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hamideh Ghazizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sahar Ghoflchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohammad Zamiri Bidary
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Alireza Naghipour
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Gordon A. Ferns
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Tayebeh Hamzehloei
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alireza Pasdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Majid Ghayour-Mobarhan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mohammad Reza Mirinezhad: writing paper, implementing model, Coders, statistical analyzer. Maliheh Aghsizadeh: revisor of manuscript, model strategist. Hamideh Ghazizadeh, Sahar Ghoflchi, Mohammad Zamiri Bidary, and Alireza Naghipour: writing paper, the revisor of manuscript, statistical analysis. Tayebeh Hamzehloei and Alireza Pasdar: statistical analysis. Gordon Ferns: revisor of manuscript. Majid Ghayour-Mobarhan: revisor of manuscript, model strategist.

Corresponding authors

Correspondence to Tayebeh Hamzehloei, Alireza Pasdar or Majid Ghayour-Mobarhan.

Ethics declarations

Ethics approval and consent to participate

All steps and procedures involving human subjects of the study were approved by the Mashhad University of Medical Sciences (MUMS) Ethics Committee (Code: IR.MUMS.MEDICAL.REC.1398.658), and all methods were carried out in accordance with relevant guidelines and regulations. We confirm that informed consent was obtained from all subjects or their legal guardian(s).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mirinezhad, M.R., Aghsizadeh, M., Ghazizadeh, H. et al. Micronutrients intake and genetic variants associated with premature ovarian insufficiency; MASHAD cohort study. BMC Women's Health 24, 91 (2024). https://doi.org/10.1186/s12905-023-02865-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12905-023-02865-4

Keywords

BMC Women's Health

ISSN: 1472-6874

Contact us