{"paper_id":"a09f9aca-1f05-43f5-a118-f4385660749b","body_text":"Menstruation is a cyclic endometrial desquamation resulting from the interactions of\nhormones produced by the hypothalamic-pituitary-ovarian axis. The integrity of the\nhypothalamic-pituitary-ovarian axis is essential for ovulation and regular menstrual\ncycles, and external factors may influence this complex feedback system ( Speroff & Fritz, 2005 ). Since the primary\nfunction of the menstrual cycle is to promote a woman's reproductive capacity,\nchanges in this cycle impact on female fertility.  Rogers & Mitchell (1952)  demonstrated an association between\nmenstrual changes, excess weight, infertility, and recurrent miscarriages.\nTherefore, studies on the association of menstrual irregularity and obesity are\nlong-standing; however, many of these studies have been limited because of their\nsmall sample size and the participants have gynecological problems or are\nparticipating in weight reduction programs.\nObesity is one of the factors that can influence the menstrual cycle. It can be\ndefined as a disease characterized by excessive accumulation of body fat, due\npositive energy balance, causing health effects, with significant loss in quality\nand lifetime. One of the classifications of obesity was proposed by the World Health\nOrganization ( WHO, 2015 ). Several\npathophysiological disorders are caused by obesity, especially in people with Body\nMass Indexes (BMI) above 30 kg/m 2 . Cardiovascular disorders, endocrine\nand metabolic disorders, respiratory disorders, gastrointestinal disorders,\ndermatological disorders, musculoskeletal disorders, neoplasia psychosocial\ndisorders, increased surgical and anesthetic risk, decreased physical agility and\ndisorders in pregnancy and fertility, as absence of ovulation ( Tavares  et al ., 2010 ). In the United States,\nabout 68% of the adult population is overweight, and about 34.9% are obese ( Ogden  et al ., 2014 ). According\nto the Brazilian Ministry of Health, in the adult population in Brazilian capitals,\noverweight prevalence is of 49.1% among women. In Goiânia, the state capital\nof Goiás (Brazil), 48.1% of women are overweight and 16.3% are obese ( Brazil-Ministério da Saúde,\n2015 ).\nSome studies have used self-reported height and weight; and the definitions of\nmenstrual regularity vary according to each study. Much of this research is limited\nto overweight and obese patients associated with Polycystic Ovarian Syndrome (PCOS).\nDue to the increasing prevalence of overweight and obesity, it is important to\ninvestigate its effects on the reproductive health of women, and to better quantify\nthe strength of associations with menstrual irregularity ( Wei  et al ., 2009 ). The early onset of obesity,\nespecially in adolescence, favors the development of irregular cycles, oligo/chronic\nanovulation and infertility in adults ( Pasquali\n& Gambineri, 2006 ). In addition to the deleterious effects of obesity\non female reproductive function, such as delay in spontaneous conception, higher\nprevalence of infertility and natural abortions, there is still a worse response to\ninfertility treatments and a higher prevalence of obstetric complications ( Pasquali & Gambineri, 2006 ;  Nelson & Fleming, 2007 ).\nThe subject of this study was the ecograph assessment of ovulation and menstrual\ncycle parameters, associating them to higher BMI in infertile patients. The problem\nis the high prevalence of overweight and obese women in the world, in Brazil and\nalso in Goiânia, and the repercussions on their health, especially in\nrelation to their reproductive health. There is also an increase in childhood\nobesity, and there may be an increase in sub and infertility in the future. This\nstudy is justified, because although there are several worldwide studies on\nmenstrual irregularity and obesity, there are few with hospital samples involving\nassisted reproductive clinics accompanying the cycle and ovulation of these patients\nthrough the monitoring of ovulation, and the great majority is patients with obesity\nand PCOS concomitantly.\nDue to the increasing prevalence of overweight and obesity, it is important to\ninvestigate their effects on the reproductive health of women and quantify the\nstrength of association with ovulation and menstrual irregularity ( Wei  et al ., 2009 ). Since\nmenstrual regularity is closely associated with endocrine physiology and ovulation,\nthe assessment of the association of these factors with body mass index (BMI) will\nhelp on this quantification. Various researches invariably associate obesity with\npolycystic ovaries thus creating a bias. We reinforce that patients with polycystic\novaries are not included; demonstrating that obesity alone is an association factor\nto anovulation. The objective of this study is to assess whether there is an\nassociation between the presence or absence of ovulation and the presence of\noverweight/obesity calculated by BMI in infertile patients without Polycystic Ovary\nSyndrome, in a university service.\n\nThis was a case-control study performed at the Human Reproduction Laboratory of the\nUniversity Hospital (Lab Rep HC/UFG). We evaluated 1,356 monitoring of basal\novulation, without stimulation with ovulation inducers, performed between January\n2011 and December 2015. For \"n\" calculation, with 80% power test, the number of\ncases and controls needed was 105 each.\nAfter collecting the data from the ovulation monitoring form, we selected the cases\nas the anovulatory patients in the cycle. The study included patients with\ninfertility for at least one year, aged 18 to 38 years, no more than 10 mm follicle\non 1 st  examination, antral follicle count between 3 and 12 in each ovary\ncounted between the 2 nd  and the 5 th  day of the cycle, absence\nof prior cystectomy or oophorectomy, and endometriomas absence. We selected 148\ncases.\nThe control group, consisting of reports of patients who ovulate on the monitored\ncycle, was randomly selected, and we applied the same inclusion criteria used for\nthe group of cases. Ovulation in the control group was considered only in those\ncycles in which the follicle reached at least 16 mm in diameter. The disappearance\nor decrease of at least 70% of the diameter was considered follicular collapse. We\nselected 154 controls.\nLater, we assessed the medical records of patients. The inclusion and exclusion\ncriteria were based on the fact that they are factors that influence the menstrual\ncycle and fertility of women (confounders). After applying the exclusion criteria,\nwe ended up having 110 cases (patients who did not ovulate on the monitored cycle),\nand 118 controls (patients who ovulated on the monitored cycle). The exclusion\ncriteria were patients with low body weight (BMI <18.5 kg/m 2 );\nfollicle stimulating hormone (FSH) above 10IU/L; stages III and IV endometriosis\ndiagnosis, Thyroid Stimulant Hormone (TSH) lower than 0.4 or higher than 4.5mU/L;\nprolactinaemia above 20 ng/ml; diagnostic Polycystic Ovary Syndrome and smokers. At\nthis point we had access to BMI data of patients, checking for the presence or\nabsence of exposure factors (overweight/obesity). Henceforth, the presence of\noverweight or obese patients will be considered \"BMI above normal\".\nThe ovulatory status of the study variables was: absence or presence of a monitored\novulation in the cycle. The exposure variables were: normal BMI or patients\nclassified with a BMI above normal ( Figure\n1 ).\nFigure 1 Flow chart assessing the presence of ovulation in overweight and normal\nindividuals. Lab Rep HC-UFG 2016\nFlow chart assessing the presence of ovulation in overweight and normal\nindividuals. Lab Rep HC-UFG 2016\nThe patients were classified according to BMI, following the WHO’s definition of body\nmass (18.5 to 24 kg/m 2 ), and above normal (>25 kg/m 2 )\n( WHO, 2015 ).\nThe patients were subjected to biometric examination, assessing height and weight on\na WELMY, W110H model scale. Ovulation monitoring was performed on the 2 nd \nto the 5 th  day of the cycle, with new measurements from the\n10 th  day of the cycle until ovulation occurs, or until day 16 if\nthere is no dominant developing follicle(s) or follicular collapse. Antral follicles\nwere counted following the technical recommendations of  Broekmans  et al . (2010)  between the\n2 nd  and the 4 th  day of the spontaneous menstrual cycle.\nThe ultrasound equipment used was the LOGIQ P6 model, manufactured by General\nElectrics (GE). The examinations were performed by physicians from the Human\nReproduction Laboratory HC-UFG.\nComparability between the case and control groups was confirmed by checking the\npairing of the following variables: age (years); age at first menstruation\n(menarche); number of pregnancies; number of children born up to 22 weeks of\ngestational age (Parity); number of abortions; the number of antral follicles\nbetween the 2 nd  and 5 th  day of the cycle; having bilateral\ntubal ligation (BTL)and the laboratory test values of FSH (IU/L), prolactin (mcg/L)\nand TSH (mU/L).\nIn order to compare the mean values between the case and control groups, because they\nwere two unrelated groups, we chose the t-test for independent samples as a\nparametric test for the variables with normal distribution and the Mann-Whitney test\nas a non-parametric test for variables with non-normal distribution ( Mann & Whitney, 1947 ).\nTo assess whether the patients in the case group and the control group had a\ndifferent BMI on the WHO classification between normal weight or overweight, we used\nthe Pearson’s performed chi-square statistical test (χ 2 ) and\ncalculated the Odds Ratio. For the statistical analysis we used the SPSS 22.0\nsoftware.\nIn the study, we used the database of medical records of patients from the Human\nReproduction Laboratory of the Clinica's Hospital, Federal University of\nGoiás. No informed consent of patients was needed. The study was approved by\nthe Ethics Committee of the Clinica's Hospital, Federal University of Goiás\nunder number 1,235,590.\n\nAfter assessing the statistical tests of case and control groups to see if they were\ncomparable, we found out that the variables matched, so the groups were paired;\nthus, there was no need for adjustments ( Table\n1 ).\nCharacteristics of the two study groups. Lab Rep HC/UFG 2016\nLab Rep HC/UFG= Human Reproduction Laboratory of the Clinica's Hospital;\nn=number;\nMann-Whitney test;\nt-Test\nThe height of anovulatory patients ranged between 1.40 and 1.80 m\n( x =1.60±0.07), the weight between 41.5 and 105kg\n( x =65.60Kg±12.52) and BMI ranged between 18.11 and\n38.96kg/m 2 \n( x =25.64±4.24Kg/m 2 ). In the group of\novulatory patients, height was between 1.45 to 1.73m\n( x =1.60±0.063), weight between 45.2 and 97.5Kg\n( x =63,29Kg±10.77) and BMI between 18.25 and 36.40\nkg/m 2  ( x =24,76Kg/m 2 ±3.81).\nTable 2  and  Figure 2  depict the distribution of patients, according to ovulation,\ncategorized by BMI. From a total of 228 patients monitored, 110 were anovulatory and\n118 were ovulatory. Among the anovulatory patients, 57 (51.82%) were overweight;\nwhile among ovulatory patients, 44 (37.29%) were in this same BMI category. The odds\nratio was 1.8087, with a significant  p  value\n( p <0.05).\nDistribution of 228 patients according to ovulation, categorized according to\nBMI. Lab Rep HC/UFG 2016.\nLab Rep HC/UFG= Human Reproduction Laboratory of the University Hospital;\nn= number;\nX 2 = 4.871, Pearson’s test; OR=  Odds\nRatio ; CI= Confidence Interval.\nFigure 2 Distribution of 228 patients according to ovulation categorized by BMI.\nLab Rep HC/UFG 2016\nDistribution of 228 patients according to ovulation categorized by BMI.\nLab Rep HC/UFG 2016\n\nInfertility is a disease of the reproductive system, defined by the inability to\nachieve clinical pregnancy after 12 months or more of regular and unprotected sexual\nintercourse ( ASRM, 2013 ). WHO estimates that\n48.5 million couples worldwide are infertile. Ovulatory infertility can reach up to\na quarter of infertility etiologies. Since obesity affects ovarian function,\nevaluating body mass parameters are very relevant for infertile patients ( Mascarenhas  et al ., 2012 ;\n Nardo & Chouliaras, 2015 ). Ovulation\nsets the cycle regularity, and both int raovarian factors regulate folliculogenesis,\nand there should be a balance between them. Any imbalance between the extra and\nintra ovarian factors may result in abnormal folliculogenesis ( NICE, 2013 ). However, the clinical evaluation of menstrual\nregularity, may not be a reliable parameter for the diagnosis of ovulation. The\nassessment of ovulation by ultrasound provides for a more accurate diagnosis of\novulation ( Frank  et al .,\n2008 ).\nObesity is a complex and multifactorial disease, developed by the interaction between\nthe person's genotype and the environment ( Malcolm\n& Cumming, 2003 ;  NHI, 2000 ).\nThe most obese women do not have fertility disorders, but obesity may negatively\ninfluence their menstrual cycle and fertility; and one mechanism, is the absence of\novulation ( ASRM, 2008 ). Beyond its risks\nduring pregnancy, obesity on puerperium is associated with the occurrence of\ninfectious complications in the postpartum period, such as infection of surgical\nwounds, urinary tract infection and need for antibiotics ( Chin  et al ., 2014 ).\nThe study was carefully concocted, and the selection bias was mitigated by the\nstrictness of the inclusion and exclusion criteria. Group comparability was ensured\nby the assessment of pairing the possible confounding variables. The observer bias\nwas mitigated because there was blinding about the exposure factor in both the study\ngroup and the control group, avoiding bias in information collection. Although most\nobese women do not develop infertility, when obesity influences cycle control and\novulation, it can have a negative impact on female fertility ( Paiva  et al. , 2012 ). With the increasing\nprevalence of obese and overweight children and women, the interference of those\nwith menstrual regularity, and consequently on fertility, acquires greater\nimportance. There are many studies involving infertile patients with overweight and\nobesity, but the samples in most cases include or are limited to patients with\npolycystic ovaries. In this study, the lack or absence of ovulation was assessed by\nanalyzing the body mass index of patients, excluding those with polycystic\novaries.\nOur results were coincident with many studies in the literature, such as  Kuchenbecker  et al . (2010) , who\nreported an association between anovulation and overweight. It also corroborated by\nother studies that indicate an increased risk of oligo and anovulation in obese\nwomen, such as those from  Brewer & Balen\n(2010)  and  Oliveira & Lemos\n(2010) , even among those women who have regular menstrual cycles. Obesity\nwas also associated with oligomenorrhea and anovulation by  Yilmaz  et al . (2009) . The chance of patients\nwith higher-than-normal BMI not ovulating is about 1.8 times greater than among\nnormal BMI individuals.\nIt is possible that both the excess weight and infertility be symptoms of the same\npathology. Although obesity decreases fertility, it is unclear how much weight loss\ncould increase it in overweight patients ( Koning\n et al.,  2010 ). The fertility treatment in subfertile\nwomen with overweight and obesity differs between countries and treatment centers.\nIn some centers in the Netherlands, overweight women are not treated at all. At\nother fertility centers, overweight and obese women are treated regardless of their\nBMI. The British Fertility Society inform women with a BMI above 30 kg/m 2 \nthat they tend to take longer to get pregnant, and if they are not ovulating weight\nloss increases the chances of conception. While there is enough convincing evidence\nthat weight reduction eventually leads to more spontaneous pregnancies, the main\ngoal is to reduce the complications of excess weight during pregnancy ( NICE, 2013 ).\nThe American Society for Reproductive Medicine and the American College of Obstetrics\ndid not publish guidelines for clinical management of obese infertile patients\n( Vahratian & Smith, 2009 ). The\ninstitutions that restrict the treatment of overweight patients justify the\nrestrictions because of high costs and limited funding for the procedures,\ndecreasing success and increasing risk of complications during pregnancy ( Koning  et al.,  2010 ).\nRestrictions on fertility treatments in patients with high BMI often do not address\nissues of justice and may violate a woman's right to autonomy. Those patients with\nincreased BMI can be discriminated and without access to treatment.\nOur study indicates that patients with excess body mass, without other diseases, were\nassociated with anovulation. This is a contribution to clarify these issues. We\nconsidered as a limiting factor, the fact that in our study, monitoring of ovulation\nis performed in a single cycle, and there may be variations between the follicular\ngrowth cycles in the same woman. However, monitoring during one cycle only is the\nprocedure adopted by most human reproduction services ( Mikolajczyk  et al ., 2008 ).\n\nThere was an association between the lack of ovulation upon ultrasound monitoring of\nthe ovulation cycle and increased body mass index, with higher anovulation risk in\npatients above normal weight, even if they did not have polycystic ovaries.","source_license":"CC-BY-4.0","license_restricted":false}