Association of anthropometric indices with menstrual abnormalities among female patients attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi

Association of anthropometric indices with menstrual abnormalities among female patients attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi | 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 Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of anthropometric indices with menstrual abnormalities among female patients attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi Darlington Onyejike, Ifeoma Okwuonu, Anita Chukwuma, Albert Nwamaradi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4668292/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Menstrual abnormalities encompass a range of menstrual cycle disturbances, such as changes in the frequency, duration, or amount of bleeding. This study evaluated the association between menstrual abnormalities such as oligomenorrhea, menorrhagia and amenorrhea with anthropometric parameters such as BMI, WHR and WHtR among young women attending the Fertility Clinic of the Obstetrics and Gynaecology unit of NAUTH, Nigeria. Methods Random sampling technique was employed to select 200 women aged between 18–40 years, without any known medical condition that may affect menstrual function. Data were collected via questionnaires which composed of demographic information concerning menstruation, menstrual cycle and anthropometric parameters. The anthropometric parameters include height, weight, waist circumference and hip circumference. Results There was a statistically significant positive correlation between oligomenorrhea and BMI (r = .445, p = .001), WHR (r = .207, p = .003) and WHtR (r = .440, p = .001). There was a statistically non-significant negative correlation between menorrhagia and BMI (r = − .035, p = .618), WHR (r = − .010, p = .890) and WHtR (r = − .008, p = .912). There was a statistically non-significant very weak positive correlation between age and oligomenorrhea (p = .084) and menorrhagia (p = .104). Results from this study showed that there was no prevalence of amenorrhea among the study subjects. Conclusions Findings from this study indicate that obesity is a predisposing factor for oligomenorrhea. Age is not a predisposing factor for menorrhagia and oligomenorrhea. Amenorrhea is not a common menstrual disorder among young women in Anambra state, Nigeria. This study highlights the need for healthy body weight among young women. Amenorrhea BMI Infertility Menorrhagia Oligomenorrhea WHR WHtR BACKGROUND A significant proportion (approximately 20–40%) of women of reproductive / childbearing ages suffer from menstrual disorders (Ansong et al., 2019 ; Igbokwe and John-Akinola, 2021 ; Ju et al., 2014 ). Menstrual abnormalities refer to any changes or irregularities in a woman’s menstrual cycle, such as changes in the frequency, duration, or amount of bleeding. There are several types of menstrual abnormalities, including amenorrhea (absence of menstruation), oligomenorrhea (infrequent menstruation), menorrhagia (excessive bleeding during menstruation), and metrorrhagia (irregular bleeding during periods) as well as the presence of pain and other symptoms. A normal menstrual cycle is typically between 21 and 35 days (NHS, 2023 ). However, it is important to note that there is variability in the length and regularity of menstrual cycles between individuals, and some women may have cycles that are shorter or longer than this range (Grieger and Norman, 2020 ). Menstrual cycle is a complex, highly regulated physiological process that makes conception and pregnancy possible. From the start of menstruation (menarche) to its cessation(menopause), menstrual bleeding(menses) is regulated by pituitary and hypothalamic hormones and if these hormones are out of balance, the cycle will be disrupted (Amboss, 2023 ; Popat et al., 2008 ). The disorder of menstrual cycle indicates major disruptions such as functional impairment in the endocrine system, reproductive system, organic disorders, and polycystic ovarian syndrome (PCOS) (Rasquin Leon et al., 2023 ). In addition to PCOS, other menstrual abnormalities have been associated with anthropometric indices (Abdolahian et al., 2020 ; Dhar et al., 2023 ; Thathapudi et al., 2014 ). Anthropometric indices are measurements of different parameters of the human body or physical characteristics, such as height, weight, body mass index (BMI), and waist circumference (Casadei and Kiel, 2022 ). These indices are used to assess a person’s overall health and risk for certain diseases. Waist circumference and waist-to-hip ratio are measures of abdominal and central obesity and are markers of visceral fat (Gadekar et al., 2020 ). BMI is a measure of body fat which can be determined using some anthropometric parameters such as weight and height. BMI is derived by a person’s weight in kilograms (or pounds) divided by the square of height in meters (or feet) i.e weight/height 2 (kg/m 2 ) (Casadei and Kiel, 2022 ). BMI classifications are: Underweight (18.5%), Healthy weight (18.5 to < 24.9%), Overweight (25.0 to < 29.9%) and Obesity (30.0% or higher) (CDCP, 2022). Obesity was categorized by World Health Organization into 3 classes. Class 1 is referred to moderately obese group with a BMI of 30 to < 34.9; class 2 is referred to severely obese group with a BMI of 35 to < 39.9; and class 3 is referred to very severe or morbidly obese group with a BMI of 40 or higher (WHO, 2010). In Nigeria, the prevalence of overweight ranges between 20.3% and 35.1%, while obesity ranges between 8.1% -22.2% (Chukwuonye et al., 2013 ). The association between anthropometric indices and menstrual abnormalities has been the subject of extensive research in recent years. Several studies have reported that anthropometric indices have been found to be associated with menstrual irregularities (Bradley et al., 2023 ; Entringer et al., 2012 ; Fluitman et al., 2017 ; Romieu et al., 2017 ). This is because BMI is closely related to energy balance, and both undernutrition and overnutrition can disrupt normal menstrual function. High waist circumference, particularly when combined with high BMI, has been associated with an increased risk of menstrual abnormalities, including oligomenorrhea and anovulatory cycles (Itriyeva, 2022 ; Lentscher and Decherney, 2021 ). This may be because excess abdominal fat can disrupt the balance of hormones involved in menstrual regulation. Overall, the association of menstrual abnormalities and anthropometric indices is likely to be multifactorial, with multiple physiological and psychological mechanisms contributing to the observed association. Therefore, this study was aimed at investigating the association of anthropometric variables such as BMI, waist-hip ratio and waist-height ratio with menstrual abnormalities including oligomenorrhea, amenorrhea and menorrhagia for female students of College of Health Sciences of Nnamdi Azikiwe University, Nigeria. METHODS Study Population The study population was drawn from patients between 18 and 40 years old that visited the Fertility Clinic within the study duration. Patients that had known medical conditions that could affect menstrual function were excluded from the study. These medical conditions include pregnant women, breastfeeding mothers, perimenopausal women, patients with a history of hormonal contraception or hormonal therapy, and patients with a history of gynecological surgery or reproductive disorders. Patients with chronic medical conditions that may affect menstrual function were also excluded from the study. In addition, patients that were unable to provide informed consent were excluded from the study. Sampling This study design was a cross-sectional study. The sample size constituted 200 patients out of over 2000 patients attending the fertility clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria within the study period. They were aged between 18–40 years. Experimental Protocol Informed Consent: the procedure was explained to the participants and their informed consent was obtained before the commencement of the measurement. Consent was obtained from each subject by oral means. The participants were screened in line with the exclusion criteria. Questionnaire: A structured questionnaire was used to collect demographic information and menstrual history of participants. This was used to determine menstrual abnormalities, such as irregular cycles, heavy or prolonged bleeding, or absence of menstruation, based on self-report. Measurement Parameters Height (cm): this parameter was measured using a steel tape fixed to the wall and recorded to the nearest 0.1 decimal place. The participants were measured bare-footed (Balogun et al., 1994) while standing in an erect position against the wall with the back of the head, shoulder blades, buttocks and heels touching the steel tape. The measurement was read from the level of the vertex of the scalp. Weight (kg): this was measured using a digital weight scale and recorded to the nearest o.1 decimal place. The participant was measured bare-footed standing at the center of the scale with minimal clothing and weight distributed evenly across both feet. The scale was placed on a hard floor to minimize error. Waist Circumference (cm): using a flexible measuring tape, measurement was taken in standing position, the narrowest point between the last palpable rib and the ridge of the hip were determined and measured at the level of the navel at the end of normal exhalation. The tape was snug but not compressing the skin. Hip Circumference (cm): using a flexible measuring tape, measurement was taken at the level of the widest part of the buttocks. Body Mass Index (BMI) (kg/m 2 ): BMI was calculated from the measured height and weight respectively using the following relation: Menstrual History Menstrual Cycle Length: participants were asked to report the number of days between the start of one menstrual period and the start of the next. Menstrual Flow: participants were asked about their average duration and intensity of menstrual flow, including heavy or prolonged bleeding. Menstrual Regularity: participants were asked to report menstrual irregularities such as skipped periods or fluctuations in cycle length. Menstrual Symptoms: information on presence of menstrual symptoms like pain (dysmenorrhea) was assessed. Research Instruments The instruments used in this research include: Weight scale: This was used to measure the weight of the participants Steel tape: This was used to measure the height of the participants Stretch-resistant measuring tape: This was used to evaluate the waist and hip circumferences of the participants Questionnaire: A structured questionnaire was used to obtain the personal and health characteristics and menstrual patterns of the participants. Validity of the Instruments The weighing scale was capable of measuring weight to the nearest kilogram. The meter rule was capable of measuring height to the nearest 0.1 centimeter. The stretch-resistant measuring tape was capable of measuring waist and hip circumferences to the nearest 0.1 centimeter. Data Analysis Data obtained from this study was analyzed using Statistical Package for Social Sciences (SPSS) IBM series version 25. Pearson’s correlation was used to assess the relationship between the anthropometric indices and the menstrual abnormalities. Results The result of this study showed that out of 200 subjects 40 suffered from menorrhagia, 36 suffered from oligomenorrhea and none suffered from amenorrhea (Table 1). Hence the relationship between amenorrhea, and body mass index (BMI), waist-hip ratio and waist-height ratio could not be calculated (Table 2). The mean age of the participants for this study was between 23 and 24 years (Table 1). The relationship between oligomenorrhea and anthropometric parameters such as body mass index (BMI), waist-hip ratio and waist-height ratio was tested using Pearson’s correlation. The test showed that there was a statistically significant moderate positive correlation (r = .445, n = 200, p = .001) between oligomenorrhea and BMI; a statistically significant weak positive correlation (r = .207, n = 200, p = .003) with waist-hip ratio; and a statistically significant moderate positive correlation (r = .440, n = 200, p = .001) with waist-height ratio (Table 2). Result also showed that there was a statistically non-significant very weak positive correlation (r = .112, n = 200, p = .084) between oligomenorrhea and age (Table 2). The relationship between menorrhagia and anthropometric parameters such as body mass index (BMI), waist-hip ratio and waist-height ratio was tested using Pearson’s correlation. The test showed that there was a statistically non-significant very weak negative correlation (r = -.035, n = 200, p = .618) between menorrhagia and BMI; a statistically non-significant very weak negative correlation (r = -.010, n = 200, p = .890) with waist-hip ratio; and a statistically non-significant very weak negative correlation (r = -.008, n = 200, p = .912) with waist-height ratio (Table 2). Result also showed that there was a statistically non-significant very weak positive correlation (r = .115, n = 200, p = .104) between menorrhagia and age (Table 2). Discussion Menstruation is a unique phenomenon which represents the beginning and end of reproductive age (Castillo-Martinez et al., 2003). In addition, menstruation is considered an indicator of women’s health (Attia et al., 2023). It is important to note that there is a certain amount of body fat to maintain normal menstrual cycle, and this means that more or less body fat could cause reproductive health disorders (Itriyeva, 2022; Kanellakis et al., 2023). There are several known mechanisms on the effect of adipose tissue on menstrual cycle. Therefore, women in their reproductive ages should understand their patterns of menstruation and the factors that affect it. The result from this study showed that the anthropometric parameters that predispose a woman to oligomenorrhea are body mass index, waist-hip ratio and waist-height ratio. The result showed that increase in body mass index, waist-hip ratio and waist-height ratio is directly proportional to increase in oligomenorrhea. This means that people with high body mass index, waist-hip ratio and waist-height ratio are at high risk of suffering from oligomenorrhea. This report has also been corroborated in a study by Amgain et al. (2022) which reported that increase in BMI is associated with abnormalities in menstrual cycle length, oligomenorrhea, and menorrhagia; while increase in WHR was associated with menorrhagia only; and increased WHtR was associated with abnormal cycle length and oligomenorrhea only. In addition, similar findings have also been reported that obesity predisposes one to oligomenorrhea (Itriyeva, 2022; Tang et al., 2020; Tayebi et al., 2018). Findings from this study have shown that BMI, WHR and WHtR do not significantly affect menorrhagia. This could be as a result of low prevalence of menorrhagia among the subjects. This is because a similar study by Dhar et al. (2023) has also reported that menorrhagia had a very low prevalence among the study population of adolescent and young women of West Bengal, India when compared to other menstrual disorders such as amenorrhea and oligomenorrhea. However, the result from this study on the association of menorrhagia and obesity correlates with a study by Amgain et al. (2022) which noted that increased WHtR does not predispose an individual to menorrhagia. In addition, Agrawal et al. (2023) noted that high BMI was associated with oligomenorrhoea, hypomenorrhoea, polymenorrhoea, premenstrual symptoms and irregular menstrual cycles, whereas low BMI was associated with dysmenorrhea and menorrhagia. Conclusions There is evidence to suggest that anthropometric indices such as body mass index, waist-hip ratio and waist-height ratio are associated with oligomenorrhea among young women attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi. Findings from this study also showed that amenorrhea was not prevalent in this region. In addition, age is not a factor that predisposes young women between age 23 and 24 to menstrual disorders. Anthropometric markers of obesity such as BMI, WHR and WHtR were not associated to menorrhagia in this study. Therefore, this study highlights the importance of maintaining healthy weight for optimal reproductive health and overall well-being. Monitoring menstrual regularity can help identify potential concerns and help to facilitate timely intervention and appropriate care. Abbreviations BMI: Body mass index CDCP: Centers for Disease Control and Prevention NHS: National Health Service PCOS: Polycystic ovarian syndrome WHO: World Health Organization WHR: Waist-hip ratio WHtR: Waist-height ratio Declarations Ethical Approval The ethical approval for this research was obtained from the Research Ethics Committee of Faculty of Basic Medical Sciences, College of Health Sciences, Nnamdi Azikiwe University, Nnewi Campus. The approval number is NAU/CHS/NC/FMBS/496, dated11 th April, 2023. Consent for publication Authors enlisted in this manuscript have given full consent for review and publication in the Middle East Fertility Society Journal. Availability of data and materials All analyzed data can be found within the text. The datasets generated during the study are available at: https://www.researchgate.net/publication/373266014_Association_of_anthropometric_indices_with_menstrual_abnormalities_among_female_patients_attending_the_fertility_clinic_of_Nnamdi_Azikiwe_University_Teaching_Nnewi Competing interests Authors have declared that no competing interests exist. Funding Not applicable. Authors’ contributions This work was carried out in collaboration of all authors; and all authors read and approved the final manuscript. Author ODN supervised the data collection and wrote the first draft of the manuscript. Author OIF designed the study and wrote the protocol. Author CAK conceptualized the study and carried out the data collection. Author NAT analyzed the data. Author ACB managed the literature searches. Author OEC assisted author EDE to review the draft manuscript. Author AAE curated the data. Author OSN assisted author ODN to draft the manuscript. Author OCG assisted author CAK to collect the data. Author EDE reviewed the draft manuscript. Acknowledgement We appreciate the warm support of Prof George Uchenna Eleje in helping us obtain data from the fertility clinic. References Abdolahian S, Tehrani FR, Amiri M, Ghodsi D, Yarandi RB, Jafari M, et al. (2020) Effect of lifestyle modifications on anthropometric, clinical, and biochemical parameters in adolescent girls with polycystic ovary syndrome: a systematic review and meta-analysis. BMC EndocrDisord. 20: 71. Agrawal M, Goyal A, Gupta P, Agrawal A (2023) Int J Reprod Contracept Obstet Gynecol. 12 (5): 1399-1404. Amboss (2023) The menstrual cycle and menstrual cycle abnormalities https://www.amboss.com/us/knowledge/the-menstrual-cycle-and-menstrual-cycle-abnormalities/. Accessed 20 June, 2023. Amgain K, Subedi P, Yadav GK, Neupane S, Khadka S, Sapkota SD (2022) Association of Anthropometric Indices with Menstrual Abnormality among Nursing Students of Nepal: A Cross-Sectional Study. J Obes. 2022:6755436. doi: 10.1155/2022/6755436. Ansong E, Arhin SK, Cai Y, Xu X, Wu X (2019) Menstrual characteristics, disorders and associated risk factors among female international students in Zhejiang Province, China: a cross-sectional survey. BMC Women's Health. 19: 35. Attia GM, Alharbi OA, Aljohani RM (2023) The Impact of Irregular Menstruation on Health: A Review of the Literature. Cureus. 15(11):e49146. doi: 10.7759/cureus.49146. Bradley M, Melchor J, Carr R, Karjoo S (2023) Obesity and malnutrition in children and adults: A clinical review. Obes Pillars. 8: 100087. doi: 10.1016/j.obpill.2023.100087. Casadei K, Kiel J (2022) Anthropometric Measurement. In: StatPearls (Online) Treasure Island (FL). StatPearls Publishing. https://pubmed.ncbi.nlm.nih.gov/30726000/. Accessed 15 January, 2024. Castillo-Martínez L, López-Alvarenga JC, Villa AR, González-Barranco J (2003) Menstrual cycle length disorders in 18- to 40-y-old obese women. Nutrition. 19(4):317-20. Centers for Disease Control and Prevention – CDCP (2022) Defining Adult Overweight & Obesity.https://www.cdc.gov/obesity/basics/adult-defining.html. Accessed 15 January, 2024. Chukwuonye II, Chuku A, John C, Ohagwu KA, Imoh ME, Isa SE, et al. (2013) Prevalence of overweight and obesity in adult Nigerians - a systematic review. Diabetes MetabSyndrObes. 6:43-7. doi: 10.2147/DMSO.S38626. Dhar S, Mondal KK, Bhattacharjee P (2023) Influence of lifestyle factors with the outcome of menstrual disorders among adolescents and young women in West Bengal, India. Sci Rep. 13: 12476. Entringer S, Buss C, Swanson JM, Cooper DM, Wing DA, Waffarn F, et al. (2012) Fetal programming of body composition, obesity, and metabolic function: the role of intrauterine stress and stress biology. J NutrMetab. 2012:632548. doi: 10.1155/2012/632548. Fluitman KS, De Clercq NC, Keijser BJF, Visser M, Nieuwdorp M, IJzerman RG (2017) The intestinal microbiota, energy balance, and malnutrition: emphasis on the role of short-chain fatty acids. Expert Rev Endocrinol Metab. 12 (3): 215-226. doi: 10.1080/17446651.2017.1318060. Gadekar T, Dudeja P, Basu I, Vashisht S, Mukherji S (2020) Correlation of visceral body fat with waist-hip ratio, waist circumference and body mass index in healthy adults: A cross sectional study. Med J Armed Forces India.76(1):41-46. doi: 10.1016/j.mjafi.2017.12.001. Grieger JA, Norman RJ (2020) Menstrual Cycle Length and Patterns in a Global Cohort of Women Using a Mobile Phone App: Retrospective Cohort Study. J Med Internet Res. 22 (6): e17109. doi: 10.2196/17109. Igbokwe UC, John-Akinola YO (2021) Knowledge of menstrual disorders and health seeking behavior among female undergraduate students of University of Ibadan, Nigeria. Ann Ib Postgrad Med.19(1):40-48. Itriyeva K (2022)The effects of obesity on the menstrual cycle. CurrProblPediatrAdolesc Health Care. 52(8):101241. doi: 10.1016/j.cppeds.2022.101241. Kanellakis S, Skoufas E, Simitsopoulou E, Migdanis A, Migdanis I, Prelorentzou T, et al . (2023) Changes in body weight and body composition during the menstrual cycle. Am J Hum Biol. 35: e23951. Ju H, Jones M, Mishra G (2014) The Prevalence and Risk Factors of Dysmenorrhea, Epidemiol Rev. 36 (1): 104–113. Lentscher JA, Decherney AH (2021) Clinical Presentation and Diagnosis of Polycystic Ovarian Syndrome. Clin Obstet Gynecol. 64(1):3-11. doi: 10.1097/GRF.0000000000000563. NHS (2023) Periods and fertility in the menstrual cycle. https://www.nhs.uk/conditions/periods/fertility-in-the-menstrual-cycle/#:~:text=The%20length%20of%20the%20menstrual,day%20before%20her%20next%20period. Accessed 20 June, 2023. Popat VB, Prodanov T, Calis KA, Nelson LM (2008) The menstrual cycle: a biological marker of general health in adolescents. Ann N Y Acad Sci. 1135: 43-51. doi: 10.1196/annals.1429.040. Rasquin Leon LI, Anastasopoulou C, Mayrin JV (2023) Polycystic Ovarian Disease. In: StatPearls (Online) Treasure Island (FL). StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK459251/. Accessed 20 June, 2023. Romieu I, Dossus L, Barquera S, Blottière HM, Franks PW, Gunter M, et al. (2017) IARC working group on Energy Balance and Obesity. Energy balance and obesity: what are the main drivers? Cancer Causes Control. 28(3):247-258. doi: 10.1007/s10552-017-0869-z. Tayebi N, Yazdanpanahi Z, Yektatalab S, Pourahmad S, Akbarzadeh M (2018) The relationship between body mass index (BMI) and menstrual disorders at different ages of menarche and sex hormones. J Natl Med Assoc. 110:440–447. Tang Y, Chen Y, Feng H, Zhu C, Tong M, Chen Q (2020) Is body mass index associated with irregular menstruation: a questionnaire study? BMC Womens Health. 20:226. Thathapudi S, Kodati V, Erukkambattu J, Katragadda A, Addepally U, Hasan Q (2014) Anthropometric and Biochemical Characteristics of Polycystic Ovarian Syndrome in South Indian Women Using AES-2006 Criteria. Int J Endocrinol Metab. 12 (1): e12470. doi: 10.5812/ijem.12470. World Health Organization – WHO (2010) A healthy lifestyle - WHO recommendations https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations. Accessed 15 January, 2024. Tables Table 1: Descriptive Statistics Mean Std. Deviation N AGE 23.8950 5.09350 200 AMENORRHEA>6 MONTHS - - 200 MENORRHAGIA 40.0 .41185 200 OLIGOMENORRHEA 36.0 .38515 200 Table 2: Relationship between Amenorrhea, Oligomenorrhea and Menorrhagia, and Body Mass Index (BMI), Waist-hip ratio (WHR), and Waist-Height ratio (WHtR) AGE BMI WHR WHtR Amenorrhea Pearson Correlation . b . b . b . b Sig. (2-tailed) . . . . N 200 200 200 200 Oligomenorrhea Pearson Correlation .122 .445 ** .207 ** .440 ** Sig. (2-tailed) .084 .000 .003 .000 N 200 200 200 200 Menorrhagia Pearson Correlation .115 -.035 .010 -.008 Sig. (2-tailed) .104 .618 .890 .912 N 200 200 200 200 *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). b. Cannot be computed because at least one of the variables is constant. KEY TO QUALITY OF RELATIONSHIP 0.80 – 1.00 Very strong positive 0.60 – 0.79 Strong Positive 0.40 – 0.59 Moderate positive 0.20 – 0.39 Weak positive 0.00 – 0.19 Very weak positive Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Reject before peer review 31 Aug, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers invited by journal 08 Aug, 2024 Editor invited by journal 08 Jul, 2024 Editor assigned by journal 07 Jul, 2024 First submitted to journal 04 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-4668292","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337707372,"identity":"a633ecc8-1159-4e4d-94df-d1b2a62ece8d","order_by":0,"name":"Darlington Onyejike","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEElEQVRIiWNgGAWjYDCCAxCKmY2HgfEBA4MEA5DBwMBDpBZmA5K0gFSxSUAZ+LXw3W5gfFzYZsPOx3P4WDXvDgt7Pp4DjA/etjHkyTtg1yJ55wCz8cy2NGY23ra027xnJICMBmbDuW0MxYYHsGsxuJHAJs3bdpiZjZ/H7DZvmwQbGz8DSIQhcWMDXi3/gVr4vxUDtfAAtbD/JkLLAaB7etiYgVokgA4DMRgS5+PwvuSNxGZjnnPJwEA+Ziw5t03CgI3nYLPknHMSiRtwhdiN5IOPecrskuV7kh9+eNtWZw9kHPzwpswmcT4OhzEwMDYwMLIxJKOKgOLU4AAuLSDwh8EOU1Aepy2jYBSMglEwwgAAlIRL8XOpbKIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6725-0245","institution":"Nnamdi Azikiwe University","correspondingAuthor":true,"prefix":"","firstName":"Darlington","middleName":"","lastName":"Onyejike","suffix":""},{"id":337707373,"identity":"4ca106c3-f584-438f-9a2b-ed3d2e72be46","order_by":1,"name":"Ifeoma Okwuonu","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Ifeoma","middleName":"","lastName":"Okwuonu","suffix":""},{"id":337707374,"identity":"f2182887-33ae-4948-879a-f824c430c79f","order_by":2,"name":"Anita Chukwuma","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"","lastName":"Chukwuma","suffix":""},{"id":337707375,"identity":"54208697-13ec-48e9-826f-154cf0bd8ea7","order_by":3,"name":"Albert Nwamaradi","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Albert","middleName":"","lastName":"Nwamaradi","suffix":""},{"id":337707376,"identity":"3586690e-75f6-45d5-9204-13c3c6b71af9","order_by":4,"name":"Chinenye Amaonye","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Chinenye","middleName":"","lastName":"Amaonye","suffix":""},{"id":337707377,"identity":"b53f29a1-82c0-4bd8-89fd-166c971f3aef","order_by":5,"name":"Emeka Okafor","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Emeka","middleName":"","lastName":"Okafor","suffix":""},{"id":337707378,"identity":"8ff72e02-45be-442e-a833-084165d7f5a2","order_by":6,"name":"Ambrose Agulanna","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Ambrose","middleName":"","lastName":"Agulanna","suffix":""},{"id":337707379,"identity":"eb54cb03-a5a1-401d-aacd-c13b6a884cf0","order_by":7,"name":"Somadina Okeke","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Somadina","middleName":"","lastName":"Okeke","suffix":""},{"id":337707380,"identity":"be6743d3-523c-4eff-8dd2-c64d822fabec","order_by":8,"name":"Chinenye Ojemeni","email":"","orcid":"","institution":"Nnamdi Azikiwe University","correspondingAuthor":false,"prefix":"","firstName":"Chinenye","middleName":"","lastName":"Ojemeni","suffix":""},{"id":337707381,"identity":"3f31ae00-681a-4690-afa9-e1f8e8832910","order_by":9,"name":"Dominic Ejiofor","email":"","orcid":"","institution":"David Umahi Federal University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Ejiofor","suffix":""}],"badges":[],"createdAt":"2024-07-01 13:11:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4668292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4668292/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63866015,"identity":"b37e8058-bb57-4dd4-99c4-8d99c33f29b6","added_by":"auto","created_at":"2024-09-03 07:21:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":449073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4668292/v1/3efe911c-2986-494f-a1c0-1605917d399a.pdf"}],"financialInterests":"","formattedTitle":"Association of anthropometric indices with menstrual abnormalities among female patients attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eA significant proportion (approximately 20\u0026ndash;40%) of women of reproductive / childbearing ages suffer from menstrual disorders (Ansong et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Igbokwe and John-Akinola, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ju et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Menstrual abnormalities refer to any changes or irregularities in a woman\u0026rsquo;s menstrual cycle, such as changes in the frequency, duration, or amount of bleeding. There are several types of menstrual abnormalities, including amenorrhea (absence of menstruation), oligomenorrhea (infrequent menstruation), menorrhagia (excessive bleeding during menstruation), and metrorrhagia (irregular bleeding during periods) as well as the presence of pain and other symptoms. A normal menstrual cycle is typically between 21 and 35 days (NHS, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, it is important to note that there is variability in the length and regularity of menstrual cycles between individuals, and some women may have cycles that are shorter or longer than this range (Grieger and Norman, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Menstrual cycle is a complex, highly regulated physiological process that makes conception and pregnancy possible. From the start of menstruation (menarche) to its cessation(menopause), menstrual bleeding(menses) is regulated by pituitary and hypothalamic hormones and if these hormones are out of balance, the cycle will be disrupted (Amboss, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Popat et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The disorder of menstrual cycle indicates major disruptions such as functional impairment in the endocrine system, reproductive system, organic disorders, and polycystic ovarian syndrome (PCOS) (Rasquin Leon et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition to PCOS, other menstrual abnormalities have been associated with anthropometric indices (Abdolahian et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Dhar et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thathapudi et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnthropometric indices are measurements of different parameters of the human body or physical characteristics, such as height, weight, body mass index (BMI), and waist circumference (Casadei and Kiel, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These indices are used to assess a person\u0026rsquo;s overall health and risk for certain diseases. Waist circumference and waist-to-hip ratio are measures of abdominal and central obesity and are markers of visceral fat (Gadekar et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). BMI is a measure of body fat which can be determined using some anthropometric parameters such as weight and height. BMI is derived by a person\u0026rsquo;s weight in kilograms (or pounds) divided by the square of height in meters (or feet) i.e weight/height\u003csup\u003e2\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e) (Casadei and Kiel, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). BMI classifications are: Underweight (18.5%), Healthy weight (18.5 to \u0026lt;\u0026thinsp;24.9%), Overweight (25.0 to \u0026lt;\u0026thinsp;29.9%) and Obesity (30.0% or higher) (CDCP, 2022). Obesity was categorized by World Health Organization into 3 classes. Class 1 is referred to moderately obese group with a BMI of 30 to \u0026lt;\u0026thinsp;34.9; class 2 is referred to severely obese group with a BMI of 35 to \u0026lt;\u0026thinsp;39.9; and class 3 is referred to very severe or morbidly obese group with a BMI of 40 or higher (WHO, 2010). In Nigeria, the prevalence of overweight ranges between 20.3% and 35.1%, while obesity ranges between 8.1% -22.2% (Chukwuonye et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe association between anthropometric indices and menstrual abnormalities has been the subject of extensive research in recent years. Several studies have reported that anthropometric indices have been found to be associated with menstrual irregularities (Bradley et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Entringer et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Fluitman et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Romieu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This is because BMI is closely related to energy balance, and both undernutrition and overnutrition can disrupt normal menstrual function. High waist circumference, particularly when combined with high BMI, has been associated with an increased risk of menstrual abnormalities, including oligomenorrhea and anovulatory cycles (Itriyeva, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Lentscher and Decherney, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This may be because excess abdominal fat can disrupt the balance of hormones involved in menstrual regulation. Overall, the association of menstrual abnormalities and anthropometric indices is likely to be multifactorial, with multiple physiological and psychological mechanisms contributing to the observed association. Therefore, this study was aimed at investigating the association of anthropometric variables such as BMI, waist-hip ratio and waist-height ratio with menstrual abnormalities including oligomenorrhea, amenorrhea and menorrhagia for female students of College of Health Sciences of Nnamdi Azikiwe University, Nigeria.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eThe study population was drawn from patients between 18 and 40 years old that visited the Fertility Clinic within the study duration. Patients that had known medical conditions that could affect menstrual function were excluded from the study. These medical conditions include pregnant women, breastfeeding mothers, perimenopausal women, patients with a history of hormonal contraception or hormonal therapy, and patients with a history of gynecological surgery or reproductive disorders. Patients with chronic medical conditions that may affect menstrual function were also excluded from the study. In addition, patients that were unable to provide informed consent were excluded from the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSampling\u003c/h2\u003e \u003cp\u003eThis study design was a cross-sectional study. The sample size constituted 200 patients out of over 2000 patients attending the fertility clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi, Nigeria within the study period. They were aged between 18\u0026ndash;40 years.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExperimental Protocol\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e\u003col start=\"1\"\u003e\n \u003cli\u003eInformed Consent: the procedure was explained to the participants and their informed consent was obtained before the commencement of the measurement. Consent was obtained from each subject by oral means. The participants were screened in line with the exclusion criteria.\u003c/li\u003e\n \u003cli\u003eQuestionnaire: A structured questionnaire was used to collect demographic information and menstrual history of participants. This was used to determine menstrual abnormalities, such as irregular cycles, heavy or prolonged bleeding, or absence of menstruation, based on self-report.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\"\u003e\n \u003cli\u003eHeight (cm): this parameter was measured using a steel tape fixed to the wall and recorded to the nearest 0.1 decimal place. The participants were measured bare-footed (Balogun \u003cem\u003eet al.,\u003c/em\u003e 1994) while standing in an erect position against the wall with the back of the head, shoulder blades, buttocks and heels touching the steel tape. The measurement was read from the level of the vertex of the scalp.\u003c/li\u003e\n \u003cli\u003eWeight (kg): this was measured using a digital weight scale and recorded to the nearest o.1 decimal place. The participant was measured bare-footed standing at the center of the scale with minimal clothing and weight distributed evenly across both feet. The scale was placed on a hard floor to minimize error.\u003c/li\u003e\n \u003cli\u003eWaist Circumference (cm): using a flexible measuring tape, measurement was taken in standing position, the narrowest point between the last palpable rib and the ridge of the hip were determined and measured at the level of the navel at the end of normal exhalation. The tape was snug but not compressing the skin.\u003c/li\u003e\n \u003cli\u003eHip Circumference (cm): using a flexible measuring tape, measurement was taken at the level of the widest part of the buttocks.\u003c/li\u003e\n \u003cli\u003eBody Mass Index (BMI) (kg/m\u003csup\u003e2\u003c/sup\u003e): BMI was calculated from the measured height and weight respectively using the following relation:\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1725347132.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMenstrual History\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\"\u003e\n \u003cli\u003eMenstrual Cycle Length: participants were asked to report the number of days between the start of one menstrual period and the start of the next.\u003c/li\u003e\n \u003cli\u003eMenstrual Flow: participants were asked about their average duration and intensity of menstrual flow, including heavy or prolonged bleeding.\u003c/li\u003e\n \u003cli\u003eMenstrual Regularity: participants were asked to report menstrual irregularities such as skipped periods or fluctuations in cycle length.\u003c/li\u003e\n \u003cli\u003eMenstrual Symptoms: information on presence of menstrual symptoms like pain (dysmenorrhea) was assessed.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe instruments used in this research include:\u003c/p\u003e\n\u003col start=\"1\"\u003e\n \u003cli\u003eWeight scale: This was used to measure the weight of the participants\u003c/li\u003e\n \u003cli\u003eSteel tape: This was used to measure the height of the participants\u003c/li\u003e\n \u003cli\u003eStretch-resistant measuring tape: This was used to evaluate the waist and hip circumferences of the participants\u003c/li\u003e\n \u003cli\u003eQuestionnaire: A structured questionnaire was used to obtain the personal and health characteristics and menstrual patterns of the participants.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eValidity of the Instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe weighing scale was capable of measuring weight to the nearest kilogram. The meter rule was capable of measuring height to the nearest 0.1 centimeter. The stretch-resistant measuring tape was capable of measuring waist and hip circumferences to the nearest 0.1 centimeter.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData obtained from this study was analyzed using Statistical Package for Social Sciences (SPSS) IBM series version 25. Pearson\u0026rsquo;s correlation was used to assess the relationship between the anthropometric indices and the menstrual abnormalities.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe result of this study showed that out of 200 subjects 40 suffered from menorrhagia, 36 suffered from oligomenorrhea and none suffered from amenorrhea (Table 1). Hence the relationship between amenorrhea, and body mass index (BMI), waist-hip ratio and waist-height ratio could not be calculated (Table 2). The mean age of the participants for this study was between 23 and 24 years (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe relationship between oligomenorrhea and anthropometric parameters such as body mass index (BMI), waist-hip ratio and waist-height ratio was tested using\u0026nbsp;Pearson\u0026rsquo;s correlation. The test showed that there was a statistically significant moderate positive correlation (r = .445, n = 200, p = .001) between oligomenorrhea and BMI; a statistically significant weak positive correlation (r = .207, n = 200, p = .003) with waist-hip ratio; and a statistically significant moderate positive correlation (r = .440, n = 200, p = .001) with waist-height ratio (Table 2). Result also showed that there was a statistically non-significant very weak positive correlation (r = .112, n = 200, p = .084) between oligomenorrhea and age (Table 2).\u003c/p\u003e\n\u003cp\u003eThe relationship between menorrhagia and anthropometric parameters such as body mass index (BMI), waist-hip ratio and waist-height ratio was tested using Pearson\u0026rsquo;s correlation. The test showed that there was a statistically non-significant very weak negative correlation (r = -.035, n = 200, p = .618) between menorrhagia and BMI; a statistically non-significant very weak negative correlation (r = -.010, n = 200, p = .890) with waist-hip ratio; and a statistically non-significant very weak negative correlation (r = -.008, n = 200, p = .912) with waist-height ratio (Table 2). Result also showed that there was a statistically non-significant very weak positive correlation (r = .115, n = 200, p = .104) between menorrhagia and age (Table 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMenstruation is a unique phenomenon which represents the beginning and end of reproductive age (Castillo-Martinez \u003cem\u003eet al.,\u003c/em\u003e 2003). \u0026nbsp;In addition, menstruation is considered an indicator of women\u0026rsquo;s health (Attia \u003cem\u003eet al.,\u003c/em\u003e 2023). It is important to note that there is a certain amount of body fat to maintain normal menstrual cycle, and this means that more or less body fat could cause reproductive health disorders (Itriyeva, 2022; Kanellakis \u003cem\u003eet al.,\u003c/em\u003e 2023). There are several known mechanisms on the effect of adipose tissue on menstrual cycle. Therefore, women in their reproductive ages should understand their patterns of menstruation and the factors that affect it.\u003c/p\u003e\n\u003cp\u003eThe result from this study showed that the anthropometric parameters that predispose a woman to oligomenorrhea are body mass index, waist-hip ratio and waist-height ratio. The result showed that increase in body mass index, waist-hip ratio and waist-height ratio is directly proportional to increase in oligomenorrhea. This means that people with high body mass index, waist-hip ratio and waist-height ratio are at high risk of suffering from oligomenorrhea. This report has also been corroborated in a study by Amgain \u003cem\u003eet al.\u003c/em\u003e (2022) which reported that increase in BMI is associated with abnormalities in menstrual cycle length, oligomenorrhea, and menorrhagia; while increase in WHR was associated with menorrhagia only; and increased WHtR was associated with abnormal cycle length and oligomenorrhea only. In addition, similar findings have also been reported that obesity predisposes one to oligomenorrhea (Itriyeva, 2022; Tang \u003cem\u003eet al.,\u003c/em\u003e 2020; Tayebi \u003cem\u003eet al.,\u003c/em\u003e 2018).\u003c/p\u003e\n\u003cp\u003eFindings from this study have shown that BMI, WHR and WHtR do not significantly affect menorrhagia. This could be as a result of low prevalence of menorrhagia among the subjects. This is because a similar study by Dhar \u003cem\u003eet al.\u003c/em\u003e (2023) has also reported that menorrhagia had a very low prevalence among the study population of adolescent and young women of West Bengal, India when compared to other menstrual disorders such as amenorrhea and oligomenorrhea. However, the result from this study on the association of menorrhagia and obesity correlates with a study by Amgain \u003cem\u003eet al.\u003c/em\u003e (2022) which noted that increased WHtR does not predispose an individual to menorrhagia. In addition, Agrawal \u003cem\u003eet al.\u003c/em\u003e (2023) noted that high BMI was associated with oligomenorrhoea, hypomenorrhoea, polymenorrhoea, premenstrual symptoms and irregular menstrual cycles, whereas low BMI was associated with dysmenorrhea and menorrhagia.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThere is evidence to suggest that anthropometric indices such as body mass index, waist-hip ratio and waist-height ratio are associated with oligomenorrhea among young women attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi. Findings from this study also showed that amenorrhea was not prevalent in this region. In addition, age is not a factor that predisposes young women between age 23 and 24 to menstrual disorders. Anthropometric markers of obesity such as BMI, WHR and WHtR were not associated to menorrhagia in this study. Therefore, this study highlights the importance of maintaining healthy weight for optimal reproductive health and overall well-being. Monitoring menstrual regularity can help identify potential concerns and help to facilitate timely intervention and appropriate care.\u003c/p\u003e\n"},{"header":"Abbreviations","content":"\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eCDCP:\u0026nbsp;Centers for Disease Control and Prevention\u003c/p\u003e\n\u003cp\u003eNHS: National Health Service\u003c/p\u003e\n\u003cp\u003ePCOS: Polycystic ovarian syndrome\u003c/p\u003e\n\u003cp\u003eWHO:\u0026nbsp;World Health Organization\u003c/p\u003e\n\u003cp\u003eWHR: Waist-hip ratio\u003c/p\u003e\n\u003cp\u003eWHtR: Waist-height ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethical approval for this research was obtained from the Research Ethics Committee of Faculty of Basic Medical Sciences, College of Health Sciences, Nnamdi Azikiwe University, Nnewi Campus. The approval number is NAU/CHS/NC/FMBS/496, dated11\u003csup\u003eth\u003c/sup\u003eApril, 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors enlisted in this manuscript have given full consent for review and publication in the Middle East Fertility Society Journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyzed data can be found within the text. The datasets generated during the study are available at: https://www.researchgate.net/publication/373266014_Association_of_anthropometric_indices_with_menstrual_abnormalities_among_female_patients_attending_the_fertility_clinic_of_Nnamdi_Azikiwe_University_Teaching_Nnewi\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was carried out in collaboration of all authors; and all authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAuthor ODN supervised the data collection and wrote the first draft of the manuscript. Author OIF designed the study and wrote the protocol. Author CAK conceptualized the study and carried out the data collection. Author NAT analyzed the data. Author ACB managed the literature searches. Author OEC assisted author EDE to review the draft manuscript. Author AAE curated the data. Author OSN assisted author ODN to draft the manuscript. Author OCG assisted author CAK to collect the data. Author EDE reviewed the draft manuscript. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate the warm support of Prof George Uchenna Eleje in helping us obtain data from the fertility clinic.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdolahian S, Tehrani FR, Amiri M, Ghodsi D, Yarandi RB, Jafari M, \u003cem\u003eet al.\u003c/em\u003e (2020) Effect of lifestyle modifications on anthropometric, clinical, and biochemical parameters in adolescent girls with polycystic ovary syndrome: a systematic review and meta-analysis. BMC EndocrDisord. 20: 71.\u003c/li\u003e\n\u003cli\u003eAgrawal M, Goyal A, Gupta P, Agrawal A (2023)\u003cem\u003e \u003c/em\u003eInt J Reprod Contracept Obstet Gynecol. 12 (5): 1399-1404.\u003c/li\u003e\n\u003cli\u003eAmboss (2023) The menstrual cycle and menstrual cycle abnormalities\u003c/li\u003e\n\u003cli\u003ehttps://www.amboss.com/us/knowledge/the-menstrual-cycle-and-menstrual-cycle-abnormalities/. Accessed 20 June, 2023.\u003c/li\u003e\n\u003cli\u003eAmgain K, Subedi P, Yadav GK, Neupane S, Khadka S, Sapkota SD (2022) Association of Anthropometric Indices with Menstrual Abnormality among Nursing Students of Nepal: A Cross-Sectional Study. J Obes. 2022:6755436. doi: 10.1155/2022/6755436.\u003c/li\u003e\n\u003cli\u003eAnsong E, Arhin SK, Cai Y, Xu X, Wu X (2019) Menstrual characteristics, disorders and associated risk factors among female international students in Zhejiang Province, China: a cross-sectional survey. BMC Women\u0026apos;s Health. 19: 35.\u003c/li\u003e\n\u003cli\u003eAttia GM, Alharbi OA, Aljohani RM (2023) The Impact of Irregular Menstruation on Health: A Review of the Literature. Cureus. 15(11):e49146. doi: 10.7759/cureus.49146.\u003c/li\u003e\n\u003cli\u003eBradley M, Melchor J, Carr R, Karjoo S (2023) Obesity and malnutrition in children and adults: A clinical review. Obes Pillars. 8: 100087. doi: 10.1016/j.obpill.2023.100087.\u003c/li\u003e\n\u003cli\u003eCasadei K, Kiel J (2022) Anthropometric Measurement. In: StatPearls (Online) Treasure Island (FL). StatPearls Publishing. https://pubmed.ncbi.nlm.nih.gov/30726000/. Accessed 15 January, 2024.\u003c/li\u003e\n\u003cli\u003eCastillo-Mart\u0026iacute;nez L, L\u0026oacute;pez-Alvarenga JC, Villa AR, Gonz\u0026aacute;lez-Barranco J (2003) Menstrual cycle length disorders in 18- to 40-y-old obese women. Nutrition. 19(4):317-20.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention \u0026ndash; CDCP (2022) Defining Adult Overweight \u0026amp; Obesity.https://www.cdc.gov/obesity/basics/adult-defining.html. Accessed 15 January, 2024.\u003c/li\u003e\n\u003cli\u003eChukwuonye II, Chuku A, John C, Ohagwu KA, Imoh ME, Isa SE, \u003cem\u003eet al.\u003c/em\u003e (2013) Prevalence of overweight and obesity in adult Nigerians - a systematic review. Diabetes MetabSyndrObes. 6:43-7. doi: 10.2147/DMSO.S38626.\u003c/li\u003e\n\u003cli\u003eDhar S, Mondal KK, Bhattacharjee P (2023) Influence of lifestyle factors with the outcome of menstrual disorders among adolescents and young women in West Bengal, India. Sci Rep. 13: 12476.\u003c/li\u003e\n\u003cli\u003eEntringer S, Buss C, Swanson JM, Cooper DM, Wing DA, Waffarn F, \u003cem\u003eet al. \u003c/em\u003e(2012) Fetal programming of body composition, obesity, and metabolic function: the role of intrauterine stress and stress biology. J NutrMetab. 2012:632548. doi: 10.1155/2012/632548.\u003c/li\u003e\n\u003cli\u003eFluitman KS, De Clercq NC, Keijser BJF, Visser M, Nieuwdorp M, IJzerman RG (2017) The intestinal microbiota, energy balance, and malnutrition: emphasis on the role of short-chain fatty acids. Expert Rev Endocrinol Metab. 12 (3): 215-226. doi: 10.1080/17446651.2017.1318060.\u003c/li\u003e\n\u003cli\u003eGadekar T, Dudeja P, Basu I, Vashisht S, Mukherji S (2020) Correlation of visceral body fat with waist-hip ratio, waist circumference and body mass index in healthy adults: A cross sectional study. Med J Armed Forces India.76(1):41-46. doi: 10.1016/j.mjafi.2017.12.001.\u003c/li\u003e\n\u003cli\u003eGrieger JA, Norman RJ (2020) Menstrual Cycle Length and Patterns in a Global Cohort of Women Using a Mobile Phone App: Retrospective Cohort Study. J Med Internet Res. 22 (6): e17109. doi: 10.2196/17109.\u003c/li\u003e\n\u003cli\u003eIgbokwe UC, John-Akinola YO (2021) Knowledge of menstrual disorders and health seeking behavior among female undergraduate students of University of Ibadan, Nigeria. Ann Ib Postgrad Med.19(1):40-48.\u003c/li\u003e\n\u003cli\u003eItriyeva K (2022)The effects of obesity on the menstrual cycle. CurrProblPediatrAdolesc Health Care. 52(8):101241. doi: 10.1016/j.cppeds.2022.101241.\u003c/li\u003e\n\u003cli\u003eKanellakis S, Skoufas E, Simitsopoulou E, Migdanis A, Migdanis I, Prelorentzou T, \u003cem\u003eet al\u003c/em\u003e. (2023) Changes in body weight and body composition during the menstrual cycle. Am J Hum Biol. 35: e23951.\u003c/li\u003e\n\u003cli\u003eJu H, Jones M, Mishra G (2014) The Prevalence and Risk Factors of Dysmenorrhea, Epidemiol Rev. 36 (1): 104\u0026ndash;113.\u003c/li\u003e\n\u003cli\u003eLentscher JA, Decherney AH (2021) Clinical Presentation and Diagnosis of Polycystic Ovarian Syndrome. Clin Obstet Gynecol. 64(1):3-11. doi: 10.1097/GRF.0000000000000563.\u003c/li\u003e\n\u003cli\u003eNHS (2023) Periods and fertility in the menstrual cycle. https://www.nhs.uk/conditions/periods/fertility-in-the-menstrual-cycle/#:~:text=The%20length%20of%20the%20menstrual,day%20before%20her%20next%20period. Accessed 20 June, 2023.\u003c/li\u003e\n\u003cli\u003ePopat VB, Prodanov T, Calis KA, Nelson LM (2008) The menstrual cycle: a biological marker of general health in adolescents. Ann N Y Acad Sci. 1135: 43-51. doi: 10.1196/annals.1429.040.\u003c/li\u003e\n\u003cli\u003eRasquin Leon LI, Anastasopoulou C, Mayrin JV (2023) Polycystic Ovarian Disease. In: StatPearls (Online) Treasure Island (FL). StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK459251/. Accessed 20 June, 2023.\u003c/li\u003e\n\u003cli\u003eRomieu I, Dossus L, Barquera S, Blotti\u0026egrave;re HM, Franks PW, Gunter M, \u003cem\u003eet al.\u003c/em\u003e (2017) IARC working group on Energy Balance and Obesity. Energy balance and obesity: what are the main drivers? Cancer Causes Control. 28(3):247-258. doi: 10.1007/s10552-017-0869-z.\u003c/li\u003e\n\u003cli\u003eTayebi N, Yazdanpanahi Z, Yektatalab S, Pourahmad S, Akbarzadeh M (2018) The relationship between body mass index (BMI) and menstrual disorders at different ages of menarche and sex hormones. J Natl Med Assoc.\u003cem\u003e \u003c/em\u003e110:440\u0026ndash;447.\u003c/li\u003e\n\u003cli\u003eTang Y, Chen Y, Feng H, Zhu C, Tong M, Chen Q (2020) Is body mass index associated with irregular menstruation: a questionnaire study? BMC Womens Health.\u003cem\u003e \u003c/em\u003e20:226.\u003c/li\u003e\n\u003cli\u003eThathapudi S, Kodati V, Erukkambattu J, Katragadda A, Addepally U, Hasan Q (2014) Anthropometric and Biochemical Characteristics of Polycystic Ovarian Syndrome in South Indian Women Using AES-2006 Criteria. Int J Endocrinol Metab. 12 (1): e12470. doi: 10.5812/ijem.12470.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization \u0026ndash; WHO (2010) A healthy lifestyle - WHO recommendations https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations. Accessed 15 January, 2024.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1: Descriptive Statistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"bottom\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"bottom\"\u003e\n \u003cp\u003eStd. Deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"bottom\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e23.8950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e5.09350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eAMENORRHEA\u0026gt;6 MONTHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eMENORRHAGIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e.41185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.714285714285715%\" valign=\"top\"\u003e\n \u003cp\u003eOLIGOMENORRHEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\" valign=\"top\"\u003e\n \u003cp\u003e36.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.46938775510204%\" valign=\"top\"\u003e\n \u003cp\u003e.38515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\" valign=\"top\"\u003e\n \u003cp\u003e200\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\u003eTable 2: Relationship between Amenorrhea, Oligomenorrhea and Menorrhagia, and Body Mass Index (BMI), Waist-hip ratio (WHR), and Waist-Height ratio (WHtR)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"723\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.85477178423236%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.78838174273859%\" valign=\"bottom\"\u003e\n \u003cp\u003eAGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.618257261410788%\" valign=\"bottom\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.107883817427386%\" valign=\"bottom\"\u003e\n \u003cp\u003eWHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"bottom\"\u003e\n \u003cp\u003eWHtR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.203319502074688%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.42738589211618%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAmenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"top\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.78838174273859%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.618257261410788%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.107883817427386%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.203319502074688%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.06532663316583%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07035175879397%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.08542713567839%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003e.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5678391959799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.06532663316583%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07035175879397%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.08542713567839%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5678391959799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.42738589211618%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eOligomenorrhea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"top\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.78838174273859%\" valign=\"top\"\u003e\n \u003cp\u003e.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.618257261410788%\" valign=\"top\"\u003e\n \u003cp\u003e.445\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.107883817427386%\" valign=\"top\"\u003e\n \u003cp\u003e.207\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"top\"\u003e\n \u003cp\u003e.440\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.203319502074688%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.06532663316583%\" valign=\"top\"\u003e\n \u003cp\u003e.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07035175879397%\" valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.08542713567839%\" valign=\"top\"\u003e\n \u003cp\u003e.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5678391959799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.06532663316583%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07035175879397%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.08542713567839%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5678391959799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.42738589211618%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMenorrhagia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"top\"\u003e\n \u003cp\u003ePearson Correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.78838174273859%\" valign=\"top\"\u003e\n \u003cp\u003e.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.618257261410788%\" valign=\"top\"\u003e\n \u003cp\u003e-.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.107883817427386%\" valign=\"top\"\u003e\n \u003cp\u003e.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.42738589211618%\" valign=\"top\"\u003e\n \u003cp\u003e-.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.203319502074688%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003eSig. (2-tailed)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.06532663316583%\" valign=\"top\"\u003e\n \u003cp\u003e.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07035175879397%\" valign=\"top\"\u003e\n \u003cp\u003e.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.08542713567839%\" valign=\"top\"\u003e\n \u003cp\u003e.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003e.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5678391959799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.06532663316583%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.07035175879397%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.08542713567839%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.105527638190956%\" valign=\"top\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.5678391959799%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e*. Correlation is significant at the 0.05 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003e**. Correlation is significant at the 0.01 level (2-tailed).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"7\" valign=\"top\"\u003e\n \u003cp\u003eb. Cannot be computed because at least one of the variables is constant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEY TO QUALITY OF RELATIONSHIP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e0.80 \u0026ndash; 1.00 Very strong positive\u003c/p\u003e\n\u003cp\u003e0.60 \u0026ndash; 0.79 Strong Positive\u003c/p\u003e\n\u003cp\u003e0.40 \u0026ndash; 0.59 Moderate positive\u003c/p\u003e\n\u003cp\u003e0.20 \u0026ndash; 0.39 Weak positive\u003c/p\u003e\n\u003cp\u003e0.00 \u0026ndash; 0.19 Very weak positive\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"middle-east-fertility-society-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mefj","sideBox":"Learn more about [High Temperature Corrosion of Materials](https://www.springer.com/journal/43043)","snPcode":"43043","submissionUrl":"https://submission.nature.com/new-submission/43043/3","title":"Middle East Fertility Society Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Amenorrhea, BMI, Infertility, Menorrhagia, Oligomenorrhea, WHR, WHtR","lastPublishedDoi":"10.21203/rs.3.rs-4668292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4668292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMenstrual abnormalities encompass a range of menstrual cycle disturbances, such as changes in the frequency, duration, or amount of bleeding. This study evaluated the association between menstrual abnormalities such as oligomenorrhea, menorrhagia and amenorrhea with anthropometric parameters such as BMI, WHR and WHtR among young women attending the Fertility Clinic of the Obstetrics and Gynaecology unit of NAUTH, Nigeria.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRandom sampling technique was employed to select 200 women aged between 18\u0026ndash;40 years, without any known medical condition that may affect menstrual function. Data were collected via questionnaires which composed of demographic information concerning menstruation, menstrual cycle and anthropometric parameters. The anthropometric parameters include height, weight, waist circumference and hip circumference.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThere was a statistically significant positive correlation between oligomenorrhea and BMI (r\u0026thinsp;=\u0026thinsp;.445, p\u0026thinsp;=\u0026thinsp;.001), WHR (r\u0026thinsp;=\u0026thinsp;.207, p\u0026thinsp;=\u0026thinsp;.003) and WHtR (r\u0026thinsp;=\u0026thinsp;.440, p\u0026thinsp;=\u0026thinsp;.001). There was a statistically non-significant negative correlation between menorrhagia and BMI (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.035, p\u0026thinsp;=\u0026thinsp;.618), WHR (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.010, p\u0026thinsp;=\u0026thinsp;.890) and WHtR (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.008, p\u0026thinsp;=\u0026thinsp;.912). There was a statistically non-significant very weak positive correlation between age and oligomenorrhea (p\u0026thinsp;=\u0026thinsp;.084) and menorrhagia (p\u0026thinsp;=\u0026thinsp;.104). Results from this study showed that there was no prevalence of amenorrhea among the study subjects.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eFindings from this study indicate that obesity is a predisposing factor for oligomenorrhea. Age is not a predisposing factor for menorrhagia and oligomenorrhea. Amenorrhea is not a common menstrual disorder among young women in Anambra state, Nigeria. This study highlights the need for healthy body weight among young women.\u003c/p\u003e","manuscriptTitle":"Association of anthropometric indices with menstrual abnormalities among female patients attending the Fertility Clinic of Nnamdi Azikiwe University Teaching Hospital, Nnewi","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-03 07:13:40","doi":"10.21203/rs.3.rs-4668292/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Reject before peer review","date":"2024-08-31T14:18:59+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-08-09T08:17:48+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-08T13:05:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Middle East Fertility Society Journal","date":"2024-07-08T06:24:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-08T03:00:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Middle East Fertility Society Journal","date":"2024-07-04T23:32:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"middle-east-fertility-society-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mefj","sideBox":"Learn more about [High Temperature Corrosion of Materials](https://www.springer.com/journal/43043)","snPcode":"43043","submissionUrl":"https://submission.nature.com/new-submission/43043/3","title":"Middle East Fertility Society Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e47f8b74-4c39-41b0-9124-0d52e5a7df74","owner":[],"postedDate":"September 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-03T07:13:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-03 07:13:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4668292","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4668292","identity":"rs-4668292","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}