Elevated Body Mass Index And associated Factors Among Women with Infertility Undergoing Assisted Reproductive Technology (ART) Treatment in a low-income setting

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Appiah, Promise E. Sefogah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4756260/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Elevated Body Mass Index in infertile women has important implications for medically assisted reproduction. The prevalence and impact of elevated BMI on assisted reproductive technologies treatment outcomes in low-income settings remains under-studied and little unknown. This study investigated the prevalence of elevated BMI and associated socio-demographic characteristics among infertile women in Ghana. Retrospective analysis of five-years data of 3,660 infertile women attending clinic in Ghana for assisted conception treatment was carried out. The data was analysed using the SPSS ( 22 ). Descriptive statistics performed and chi square was used to assess associations between categorical variables with p-value below 0.05 considered statistically significant. Overall, 76.83% of women with infertility had elevated BMI, of whom 39.56% were obese and 37.27% were overweight. Majority of participants with elevated BMI was aged between 30–49years.(p < 0.000) Infertility prevalence and BMI increased with increasing level of education.(p < 0.003) Secondary infertility was more common among overweight or obese women. Traders had the highest prevalence of overweight and obesity followed by civil servants and health workers. Elevated BMI was highly prevalent among women seeking infertility care in Ghana, particularly so among those with secondary infertility. Traders had the highest prevalence of elevated BMI, probably reflecting their predominantly sedentary lifestyles. Health sciences/Health care Health sciences/Health care/Weight management Prevalence body mass index overweight obesity infertility women assisted reproductive technology Figures Figure 1 Figure 2 INTRODUCTION Obesity has become a major global public health challenge over the past few decades because of its established health risks and its rising prevalence worldwide. 1,2 Previously considered a problem of only high-income countries, overweight and obesity have more recently, from studies carried out in low and middle income countries, have shown to be attaining a global epidemic proportions affecting nearly all countries. 2 According to the World Obesity Atlas 2023 report, 38% of the global population are currently either overweight or obese, with an alarming projection that over half of the global adult population would be affected by 2035. 3 Elevated BMI levels co-exist with various degrees of underweight have been demonstrated in several Africa countries including Nigeria and others in the Sub-Saharan African region, giving rise to the double burden of malnutrition. 4,5 In the case of Ghana, a similar trend of increasing levels of overweight and obesity has been reported in recent years. 6 – 9 The health risks of overweight and obesity have been established and include but not limited to the following: hypertension, diabetes mellitus, dyslipidaemias, increased risk of stroke and heart attacks, increased risk malignancies (breast, endometrial and colon), and an overall escalated risk of early death 10 . Additionally, overweight and obesity are associated with poor reproductive health outcomes including infertility, delayed time conception, increased risk of miscarriage and increased risk of macrosomia leading to increased risk of caesarean section. Among women with infertility, the elevated BMI, adversely impacts their health and the infertility treatment outcomes including Assisted Reproductive Technologies (ARTs).Obese and overweight women often require higher doses of gonadotropins for a longer durations 11 – 13 due to poorer response to ovarian stimulation; 14 Women with elevated BMI produce poorer quality oocytes and embryos, 14–17 due to higher prevalence of oocyte morphological abnormalities 18,19 resulting in which significantly lower the number of embryos available for transfer. Further, they have lower pregnancy rates with increased rates of miscarriages compared to non-obese controls. 15,20 Studies on the prevalence of elevated BMI among infertile women attending ART facilities is limited globally. Our literature search revealed very scanty data available on the prevalence on the subject in globally, with one such study among this clinical cohort among 60 purposefully selected Indian infertile women 21 . Despite its importance, prevalence of abnormal BMI among women seeking infertility treatment in Sub-Saharan Africa (SSA) has not received the needed research attention. This study sought document this and fill the knowledge gap on the subject in the Ghanaian context. METHOD This retrospective study involved the review and analysis of a five-year archived data of patients from 2012–2017 who attended the Ruma Fertility and Specialist Hospital in Kumasi, Ghana, to seek treatment for infertility. The data was retrieved from patients’ case notes from the hospital’s main database and included data of all females 18years and above. The study protocol was approved, and permission granted by the management of Ruma Hospital (RUMA/PT/OL/019/030) before data collection commenced. Ethical clearance for the study was granted by the Committee on Human Research, Publication and Ethics (CHRPE) of the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. (Ref No. CHRPE/AP/135/20). The research methodology, data collection, analysis, and reporting processes have been carried out with full compliance with relevant guidelines and regulations. All necessary approvals and informed consents from participants have been obtained, and the rights and confidentiality of all participants have been safeguarded. Patients who visited the hospital with complaints other than infertility were excluded from the study, as well as those with missing BMI records. Data obtained focussed on patients’ socio-demographic characteristics; patients’ body mass index (BMI) in kilogram per square meter; type and duration of infertility in years. The sociodemographic characteristics included patients’ age, occupation, educational level, religion, and marital status. Infertility was categorized as ‘primary’ if the woman had never been pregnant, and ‘secondary’ if she had ever been pregnant irrespective of its outcome. The patients’ BMI were categorised under underweight (< 18.5kg/m 2 ); normal weight (18.5kg/m 2 – 24.99kg/m 2 ); overweight (25.0kg/m 2 – 29.99kg/m 2 ) and obese (≥ 30kg/m 2 ). The weight, height and the BMI were calculated with an electronic scale (JENIX® height, weight, and BMI measuring system – model: DS-103-South Korea). The electronic scale simultaneously measured the three afore-mentioned indices automatically and displayed results digitally. The resulting data was entered in Microsoft Excel spreadsheet, cleaned and exported into SPSS (version 22; Chicago, IL) for analysis. Data was summarized as frequencies and proportions. Chi-square test was used to test for associations between demographic characteristics and BMI measurement categories. All reported p-values were considered statistically significant at a level of p-value below 0.05. RESULTS A total of 3,660 patients reported for infertility treatment over the study period. The ages of the patients ranged between 18 to 68 years, the mean age was 36years, and the standard deviation was 7 years (36 + 7) . Majority of the participants 51.94% (1,898/3,660) seeking infertility treatment were in the 30–39 years age category, whilst 2.20% (80/3,660) of the women were aged 50years and above. (Table 1 ). Approximately half, 47.24% (1,729/3,660) of the respondents had completed secondary education, 45.90% (1,680/3660) completed tertiary education, 5.82% (213/3660) completed primary education and, 1.04% (38/3660) had no formal education. Christianity was the dominant religion practised by 90.98.% (3,330/3660) of the participants and Islam by 9.02% (330/3,660). (Table 1 ) Table 1 Socio-demographic characteristics of participants VARIABLES FREQUENCY PERCENTAGE (%) Age 18–29 741 20.28 30–39 1,898 51.94 40–49 935 25.59 50 and above 80 2.19 Level of Education No Formal Education 38 1.04 Primary 213 5.82 Secondary 1,729 47.24 Tertiary 1,680 45.9 Religion Christianity 3,330 90.98 Islamic 330 9.02 Occupation Artisans 462 12.62 Civil Servants 890 24.32 Corporate Workers 454 12.4 Health Workers 398 10.87 Student 102 2.79 Trading 1,354 36.99 Marital Status Divorced 8 0.22 In Relationship 170 4.64 Married 3,361 91.83 Single 118 3.22 Widowed 3 0.08 BMI Range Normal Weight 818 22.35 Obese 1,448 39.56 Overweight 1,364 37.27 Underweight 30 0.82 Table 2 Comparison of age categories and BMI categories of participants AGE CATEGORY (YEARS) BMI CATEGORY UNDERWEIGHT NORMAL WEIGHT OVERWEIGHT OBESE Total 18–29 12 (40.00%) 259 (31.66%) 293 (21.48%) 181 (12.50%) 741 30–39 11 (36.66%) 411 (50.24%) 702 (51.47%) 775 (53.52%) 1,898 40–49 7 (23.33%) 135 (16.50%) 343 (25.15%) 450 (31.08%) 935 50 and above 0 (0%) 12 (1.47%) 26 (1.91%) 42 (2.90%) 80 Total 30 (100%) 818 (100%) 1,364 (100%) 1,448 (100%) 3,660 Of the participants, 91.83% (3,361/3660) were married, 4.64% (170/3660) were in relationships, 3.22% (118/3660) were single, while 0.08% (3/3,660) and 0.22% (8/3,660) were divorced or widowed respectively. Overall, the average weight and height of the participants were 75kg ± 14.8 and 1.6m ± 0.1 respectively. Regarding participants’ BMI, 39.56% (1,446/3,660) and 37.27% (1,363/3,660) of participants were obese and overweight respectively; whilst 22.35% (818/3,660) and 0.82% (30/3,660) had normal weight and underweight respectively. (Table 1 ) Of participants who were overweight, 51.47% (702/1,364) of them were aged between 30–39years, 25.15% (343/1,364) were within 40–49years, whilst 21.48% (293/1,364) were 18–29years (Table 2 ). Of the obese participants, 53.52% (775/1,448) of them were between 30–39 years, 31.08% (450/1,448) were between 40–49years, whilst 12.50% (181/1,448) were between ages 18–29years, and 2.90% (42/1,448) were at least 50 years old. (Table 2 ). Comparing the educational levels of participants and their BMI categories, it was found out that of the participants who were overweight, 47.87% (653/1,364) of them had secondary education whilst 45.60% (622/1,364) had tertiary education. Only 5.57% (76/1,364) of the overweight participants had primary education with 0.95% (13/1,364) having no formal education. Table 3 Comparing participants’ educational levels with BMI categories LEVEL OF EDUCATION UNDERWEIGHT NORMAL WEIGHT OVERWEIGHT OBESE Total No Formal Education 1 (3.33%) 10 (1.22%) 13 (0.95%) 14 (0.96%) 38 Primary 1 (3.33%) 51 (6.23%) 76 (5.57%) 85 (5.87%) 213 Secondary 12 (40.00%) 331 (40.46%) 653 (47.87%) 733 (50.62%) 1,729 Tertiary 16 (53.33%) 426 (52.08%) 622 (45.60%) 616 (42.54%) 1,680 Total 30 818 1,364 1,448 3,660 Table 4 Type of infertility and participants’ BMI categories TYPE OF INFERTILITY BMI CATEGORY UNDERWEIGHT NORMAL WEIGHT OVERWEIGHT OBESE Total Primary 13 (43.33%) 301 (16.56%) 504 (36.95%) 533 (36.81%) 1,351 Secondary 17 (56.67%) 517 (28.445) 860 (63.05%) 915(63.19%) 2,309 Total 30 1,818 1,364 1,448 3,660 Of the participants who were obese, the majority, 50.62% (733/1,448) had attained secondary education and 42.54% (616/1,448) had tertiary education. Only 5.87% (85/1,448) of the participants had primary education whilst 0.96% (14/1,448) had no formal education (Table 3 ). As indicated in Table 4 , 63.05% (860/1,364) of the patients who were having secondary infertility were overweight with the remaining being primary infertility patients. A similar trend was discovered in those with obesity as 63.19% (915/1,448) of the patients who were obese were suffering from secondary infertility. Analysis of type of infertility in relation to the BMI category revealed that, majority of the women with both primary 36.95% (504/1,364) and secondary infertility 63.05% (860/1,364) were overweight. Similarly, most of the participants who were obese had primary 36.81% (533/1448) and secondary 63.19% (915/1,448). This analysis clearly illustrates an increase prevalence of both primary and secondary amongst the overweight and obese BMI categories. (Table 4 ). Table 5 Associations between participants’ sociodemographic characteristics and their BMI categories Variables Calculated value P-value Age category 0.000 p < 0.05 Level of Education 0.003 p 0.05 Occupation 0.523 p > 0.05 Duration of infertility 0.173 p > 0.05 Marital status 0.884 p > 0.05 Type of infertility 0.909 p > 0.05 Significantly higher number of clients who were overweight (457) and obese (526) had infertility duration of 2–5 years compared with those who were normal weight (295). Similarly, higher number of clients who were overweight (464) and obese (498) had infertility duration of 6–10 years compared with those who were normal weight. This study clearly showed that in all BMI categories, the duration of infertility was longer in overweight and obese clients than in clients with normal weight. (Fig. 1 ) This study revealed that the highest occurrence of overweight and obesity was amongst traders (508 and 533 respectively) followed by civil servants (328 and 345 respectively). The least occurrence of overweight and obesity was amongst students and health workers. (Fig. 2 ) As indicated in Table 5 , only two parameters - age and level of education of participants had statistically significant associations (p < 0.05). The remaining characteristics showed no association with BMI scale. DISCUSSION This study reviewed the body mass index distribution of women seeking infertility treatment in Ghana. From this study, the mean age and BMI of women seeking infertility were 35.50years and 29.08 kg/m 2 respectively. This is comparable to findings reported from similar study by Owiredu WKBA, et al, on the association of anthropometric indices with hormonal imbalance among women with infertility who sought treatment at Komfo Anokye Teaching Hospital in Kumasi, Ghana 8 . Overall, 76.83% of the women with infertility at the Ruma Fertility and Specialist Hospital were obese (39.56%) and overweight (37.27%), with just over one-fifth (22.35%) having normal BMI. The prevalence of overweight and obesity from our study appears much higher than from other similar study by Shanthakumari K, et al 21 that reported 35% to be overweight and obese in India. The proportions of normal and underweight were comparable from these two studies. 21 Again, the prevalence of overweight and obesity from our study is higher than that reported from a systematic review on the epidemic of obesity in Ghana in the same year of our study 7 and among participants of the earlier Women’s Health Study of Accra, Ghana 9 . Admittedly, while our current study was among clinical cohort of women with infertility, the other two studies were among women in the general population and not necessarily those with infertility. As a result, and seeing the availability of evidence that suggest a link between elevated BMI and infertility, our study population may have demonstrated such a high prevalence due the inherent selection bias of our population of women with infertility. 22 Our findings are also consistent with the research evidence that indicates the prevalence of infertility in obese women is on the ascendancy 15 , and that increasingly larger numbers of women seeking medical intervention to achieve pregnancy are reportedly overweight or obese. 23,24 Similarly, infertility has been reported to be three-fold higher inn obese women than non-obese, with reduced overall pregnancy probability with the woman’s BMI exceeds 29kg/m 2 , 15,25 Our study found that a significant majority of women with elevated BMI were between 30 to 49years, in keeping with existing evidence that BMI generally increases with age among urban women 29,28 with women within the 35 and 44 year age bracket reportedly having the highest odds of being overweight and obese, according Agbeko et al 30 . The rural – urban distribution of elevated BMI was not included in this retrospective review. Our current study also found that infertility and BMI increase with participants’ educational level. These findings are consistent with previous studies which revealed that higher education was associated with twice the tendency of becoming overweight or obese in women, compared with women with lower education. 30,39 It also agrees with evidence that women with tertiary education had the highest prevalence of obesity compared with less literate and illiterate women. 29 However, this finding appears to contrast other researchers’ reports of a higher prevalence of overweight and obesity among women with no or low education compared with those with secondary education or higher in seven Sub-Saharan African countries. 4 The disparity may probably be explained by the fact that our review focused on clinical cohort of women with infertility, and differs from the continent-wide non-clinical based health survey among seven African countries. Another important finding in this current study is that many more obese and overweight women presented with secondary than primary infertility, suggesting the possibility that elevated BMI plays a major role in secondary infertility. A plausible explanation could be the effect of retained pregnancy-related weight gain from their previous pregnancies, but this may not account of early loss of previous pregnancies. This is consistent with research finding which suggest that childbearing is a risk factor for BMI elevation, 40 and that for every childbirth there is a risk of 7% increase in BMI, 41 as women usually struggle to lose all the weight gained during pregnancy. 30,42 In terms of occupation, the risk of being obese and overweight was greatest among traders, followed closely by civil servants, artisans, health workers and students. This finding reflects the national narrative by Ofori-Asenso et al., that, in Ghana, women tend to settle for more sedentary occupations such as table-top trading, resulting in lower levels of physical activity among Ghanaian women than men. 36 Further, the elevated BMI risk among health workers agrees with findings of high level of physical inactivity and compromised dietary habits such as late night eating as contributory factors to overweight and obesity among Nurses and Midwives in Ghana. 46 Occupation dictates an individual’s physical or sedentary activity level involved, and together with behavioural lifestyle and socioeconomic characteristics, may potentially influence the propensity for elevated BMI. 43,44,45 Overall, only two among the seven main sociodemographic characteristics, namely age and level of education had significant associations with elevated BMI. This finding is in line with findings from a previous Health Survey data by Agbeko et al 30 and a large Demographic and Health Survey (DHS) involving 32 Sub-Saharan African countries by Neupane et al 47 who observed a positive significant association between age, education, wealth, and overweight and obesity. However, findings from a similar prevalence study done among clinical cohorts in Mangalore, India by Shanthakumari 21 in which they found no such associations. This may possibly be explained by their limited sample size of only 60 infertile women in their study. Strengths and Limitations of Study To the best of our information, this work has been the first such research to analyse elevated BMI and associated sociodemographic parameters among women with infertility in Ghana. Additionally, data collection was done by highly trained research assistants comprising Nurses and Health Information Technologists in this reputable fertility hospital with very stringent internal data quality assurance mechanisms in place. The results of this research must be interpreted with cautions because of the limitation of retrospective archival research methodology. Notably among these limitations, our findings cannot assign causality of infertility as due to elevated BMI from our work. Also, the inherent disadvantages of the tendency of selection bias needs to be considered. Finally, generalizability of the present study is limited because it was based on data from a single ART service provider in Ghana. There is therefore the need for an expanded prospective follow-up study involving a representative sample of women attending multiple fertility hospitals in Ghana to give a wider and more representative picture of the prevalence of elevated BMI among this clinical cohort. CONCLUSION Overall, 76.83% of the women with infertility at Ruma, in Ghana had elevated BMI, made up of 39.56% obese and 37.27% overweight; while 22.35% have normal BMI. The participants’ age and level of education were significantly associated with elevated BMI. Elevated BMI was more prevalent among women with secondary infertility. Increased awareness creation on the prevalence, impact and prevention of elevated BMI among women needs to be prioritized in Ghana. Recommendations for further Research On the basis of our findings, we recommend a more rigorous prospective, multi-center research design to further examine the impact of elevated BMI on fertility and pregnancy outcomes in our Ghanaian context to inform public health policy and interventions. Declarations Data Availability Statement The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Authors Contributions CA and RKA co-designed the study and data extraction form. CA and EKA participated in proposal writing, data collection and analysis. RKA supervised proposal writing and data collection. CA, EKA and PES drafted the first version of the manuscript and PES is responsible for correspondence. All authors participated in manuscript writing and review. All authors read and approved the final manuscript for submission. Ethical Approval This study was approved by the university of Ghana Ethical and Protocol Review Committee. The study was conducted in accordance with the fundamental ethical principles outlined in the University of Ghana Ethics Policy, which encompasses the Declaration of Helsinki (1996), International Conference on Harmonization Good Clinical Practice (ICH GCP E6) Guidelines, Council for International Organizations of Medical Sciences (CIOMS) principles, the Belmont Report, and applicable laws and statutory regulations of Ghana and the University. By adhering to these esteemed guidelines, we ensured the highest ethical standards in the design, implementation, and reporting of our research. Human Ethics And Consent To Participate: The research methodology, data collection, analysis, and reporting processes have been carried out with full compliance to the above ethical standards. All necessary approvals and consents have been obtained, and the rights and confidentiality of all participants have been safeguarded. Conflict of interest All authors have no conflict of interest to declare. Acknowledgement Authors are grateful to the Management and Staff of the Ruma Specialist Hospital and Fertility for their immense support to make this study very successful. References Amiri P, Jalali-Farahani S, Rezaei M, Cheraghi L, Hosseinpanah F and Azizi F. Which obesity phenotypes predict poor health-related quality of life in adult men and women? Tehran Lipid and glucose study. PLoS One [Internet]. 2018;13(9):1–14. 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Available from: https://pubmed.ncbi.nlm.nih.gov/17321584/ Yaya S and Ghose B. Trend in overweight and obesity among women of reproductive age in Uganda: 1995–2016. Obes Sci Pract. 2019;5(4):312–23. Wolfe WS. Parity-associated body weight: Modification by sociodemographic and behavioral factors. Obes Res. 1997;5(2):131–41. Martorell R, Kettel Khan L, Hughes ML and Grummer-Strawn LM. Obesity in women from developing countries. Eur J Clin Nutr [Internet]. 2000 [cited 2022 Mar 24];54(3):247–52. Available from: www.nature.com/ejcn Abubakari AR, Lauder W, Agyemang C, Jones M, Kirk A and Bhopal RS. Prevalence and time trends in obesity among adult West African populations: A meta-analysis. Obes Rev. 2008;9(4):297–311. Allman-Farinelli MA, Chey T, Merom D and Bauman AE. Occupational risk of overweight and obesity: An analysis of the Australian Health Survey. J Occup Med Toxicol. 2010;5(1):1–9. Barlin H and Mercan MA. Occupation and Obesity: Effect of Working Hours on Obesity by Occupation Groups. Appl Econ Financ. 2016;3(2). Kajitani S. Which is worse for your long-term health, a white-collar or a blue-collar job? J Jpn Int Econ. 2015;38:228–43. Duodu C. Assessment of Overweight and Obesity Prevalence Among Practicing Nurses and Midwives in the Hohoe Municipality of the Volta Region, Ghana. Sci J Public Heal. 2015;3(6):842. Neupane S, Prakash KC and Doku DT. Overweight and obesity among women: Analysis of demographic and health survey data from 32 Sub-Saharan African Countries. BMC Public Health [Internet]. 2016;16(1). Available from: http://dx.doi.org/10.1186/s12889-016-2698-5 Bolboacă SD, Jäntschi L, Sestraş AF, Sestraş RE and Pamfil DC. Pearson-fisher chi-square statistic revisited. Inf. 2011;2(3):528–45. Van Der Steeg JW, Steures P, Eijkemans MJC, Habbema JDF, Hompes PGA, Burggraaff JM, Oosterhuis, GJE, Bossuyt PMM, Veen FVD and Mol BWJ. Obesity affects spontaneous pregnancy chances in subfertile, ovulatory women. Hum Reprod. 2008;23(2):324–8. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 26 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviews received at journal 18 Aug, 2024 Reviewers agreed at journal 08 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers invited by journal 07 Aug, 2024 Editor assigned by journal 07 Aug, 2024 Editor invited by journal 24 Jul, 2024 Submission checks completed at journal 23 Jul, 2024 First submitted to journal 17 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. 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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-4756260","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":341645968,"identity":"9b87a97e-bd4d-43c6-ba4d-4010c8130405","order_by":0,"name":"Christian Amoah","email":"","orcid":"","institution":"KNUST","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Amoah","suffix":""},{"id":341645969,"identity":"279df713-ec0f-4fec-a53a-370acc6273c0","order_by":1,"name":"Rudolph Kantum Adageba","email":"","orcid":"","institution":"Ruma Fertility and Specialist Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rudolph","middleName":"Kantum","lastName":"Adageba","suffix":""},{"id":341645970,"identity":"fa23689d-613c-4c25-b8ab-84c1e8e74dac","order_by":2,"name":"Ernest K. Appiah","email":"","orcid":"","institution":"Ruma Fertility and Specialist Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ernest","middleName":"K.","lastName":"Appiah","suffix":""},{"id":341645971,"identity":"e82afa61-0ba3-474c-a38c-bc1bc3ce3d8f","order_by":3,"name":"Promise E. Sefogah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxklEQVRIiWNgGAWjYBADOQMwZWBBvBZjAwZmkBYJ4rUkbgBrYSBCi7n04aMbPlTUpW9n7z+64UeBBAN/e3cCXi2WfWlpN2ecOZy7s+cw280eoMMkzpzdgFeLwRkes9u8bQdyN9xIZrvBA9RiIJFLhJa//+rSDYBabv4hWgtjA3MCSMttomyx7GFLu9lz7LDhhjOHzW7LGEjwEPSLOQ/zsRs/aurkDY43Prv55o+NHH97LwGHoQvw4FWOVcsoGAWjYBSMAgwAAKOUR65wgoWLAAAAAElFTkSuQmCC","orcid":"","institution":"University of Ghana Medical School","correspondingAuthor":true,"prefix":"","firstName":"Promise","middleName":"E.","lastName":"Sefogah","suffix":""}],"badges":[],"createdAt":"2024-07-17 12:32:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4756260/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4756260/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-82818-5","type":"published","date":"2025-02-20T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63281384,"identity":"5019dc71-7725-4821-9c53-99b64ec268a7","added_by":"auto","created_at":"2024-08-26 12:56:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17735,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between BMI categories and duration of infertility\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4756260/v1/90ee7b05f0505aa872a34879.png"},{"id":63279992,"identity":"ae94365f-2c47-4a3e-b237-b9cc4a120871","added_by":"auto","created_at":"2024-08-26 12:48:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27498,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between patients’ body mass index and occupation\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4756260/v1/26f71f57427ef695a4580f77.png"},{"id":77052633,"identity":"b7118bc7-10d6-49eb-9777-bc0edb6ac1a1","added_by":"auto","created_at":"2025-02-24 16:19:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":734456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4756260/v1/068e9f10-32a6-4ed0-8445-cc753a02bcce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Elevated Body Mass Index And associated Factors Among Women with Infertility Undergoing Assisted Reproductive Technology (ART) Treatment in a low-income setting","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eObesity has become a major global public health challenge over the past few decades because of its established health risks and its rising prevalence worldwide.\u003csup\u003e1,2\u003c/sup\u003e Previously considered a problem of only high-income countries, overweight and obesity have more recently, from studies carried out in low and middle income countries, have shown to be attaining a global epidemic proportions affecting nearly all countries.\u003csup\u003e2\u003c/sup\u003e According to the World Obesity Atlas 2023 report, 38% of the global population are currently either overweight or obese, with an alarming projection that over half of the global adult population would be affected by 2035.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eElevated BMI levels co-exist with various degrees of underweight have been demonstrated in several Africa countries including Nigeria and others in the Sub-Saharan African region, giving rise to the double burden of malnutrition.\u003csup\u003e4,5\u003c/sup\u003e In the case of Ghana, a similar trend of increasing levels of overweight and obesity has been reported in recent years.\u003csup\u003e6 \u0026ndash; 9\u003c/sup\u003e The health risks of overweight and obesity have been established and include but not limited to the following: hypertension, diabetes mellitus, dyslipidaemias, increased risk of stroke and heart attacks, increased risk malignancies (breast, endometrial and colon), and an overall escalated risk of early death\u003csup\u003e10\u003c/sup\u003e. Additionally, overweight and obesity are associated with poor reproductive health outcomes including infertility, delayed time conception, increased risk of miscarriage and increased risk of macrosomia leading to increased risk of caesarean section. Among women with infertility, the elevated BMI, adversely impacts their health and the infertility treatment outcomes including Assisted Reproductive Technologies (ARTs).Obese and overweight women often require higher doses of gonadotropins for a longer durations\u003csup\u003e11 \u0026ndash; 13\u003c/sup\u003e due to poorer response to ovarian stimulation;\u003csup\u003e14\u003c/sup\u003e Women with elevated BMI produce poorer quality oocytes and embryos,\u003csup\u003e14\u0026ndash;17\u003c/sup\u003e due to higher prevalence of oocyte morphological abnormalities\u003csup\u003e18,19\u003c/sup\u003e resulting in which significantly lower the number of embryos available for transfer. Further, they have lower pregnancy rates with increased rates of miscarriages compared to non-obese controls.\u003csup\u003e15,20\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eStudies on the prevalence of elevated BMI among infertile women attending ART facilities is limited globally. Our literature search revealed very scanty data available on the prevalence on the subject in globally, with one such study among this clinical cohort among 60 purposefully selected Indian infertile women\u003csup\u003e21\u003c/sup\u003e. Despite its importance, prevalence of abnormal BMI among women seeking infertility treatment in Sub-Saharan Africa (SSA) has not received the needed research attention. This study sought document this and fill the knowledge gap on the subject in the Ghanaian context.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e This retrospective study involved the review and analysis of a five-year archived data of patients from 2012\u0026ndash;2017 who attended the Ruma Fertility and Specialist Hospital in Kumasi, Ghana, to seek treatment for infertility. The data was retrieved from patients\u0026rsquo; case notes from the hospital\u0026rsquo;s main database and included data of all females 18years and above. The study protocol was approved, and permission granted by the management of Ruma Hospital (RUMA/PT/OL/019/030) before data collection commenced. Ethical clearance for the study was granted by the Committee on Human Research, Publication and Ethics (CHRPE) of the Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana. (Ref No. CHRPE/AP/135/20). The research methodology, data collection, analysis, and reporting processes have been carried out with full compliance with relevant guidelines and regulations.\u003c/p\u003e \u003cp\u003e All necessary approvals and informed consents from participants have been obtained, and the rights and confidentiality of all participants have been safeguarded.\u003c/p\u003e \u003cp\u003ePatients who visited the hospital with complaints other than infertility were excluded from the study, as well as those with missing BMI records.\u003c/p\u003e \u003cp\u003eData obtained focussed on patients\u0026rsquo; socio-demographic characteristics; patients\u0026rsquo; body mass index (BMI) in kilogram per square meter; type and duration of infertility in years. The sociodemographic characteristics included patients\u0026rsquo; age, occupation, educational level, religion, and marital status. Infertility was categorized as \u0026lsquo;primary\u0026rsquo; if the woman had never been pregnant, and \u0026lsquo;secondary\u0026rsquo; if she had ever been pregnant irrespective of its outcome. The patients\u0026rsquo; BMI were categorised under underweight (\u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e); normal weight (18.5kg/m\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 24.99kg/m\u003csup\u003e2\u003c/sup\u003e); overweight (25.0kg/m\u003csup\u003e2\u003c/sup\u003e \u0026ndash; 29.99kg/m\u003csup\u003e2\u003c/sup\u003e) and obese (\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e). The weight, height and the BMI were calculated with an electronic scale (JENIX\u0026reg; height, weight, and BMI measuring system \u0026ndash; model: DS-103-South Korea). The electronic scale simultaneously measured the three afore-mentioned indices automatically and displayed results digitally. The resulting data was entered in Microsoft Excel spreadsheet, cleaned and exported into SPSS (version 22; Chicago, IL) for analysis. Data was summarized as frequencies and proportions. Chi-square test was used to test for associations between demographic characteristics and BMI measurement categories. All reported p-values were considered statistically significant at a level of p-value below 0.05.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 3,660 patients reported for infertility treatment over the study period. The ages of the patients ranged between 18 to 68 years, the mean age was 36years, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand\u003c/span\u003e the standard deviation was 7 years (36\u0026thinsp;+\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e7)\u003c/span\u003e. Majority of the participants 51.94% (1,898/3,660) seeking infertility treatment were in the 30\u0026ndash;39 years age category, whilst 2.20% (80/3,660) of the women were aged 50years and above. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApproximately half, 47.24% (1,729/3,660) of the respondents had completed secondary education, 45.90% (1,680/3660) completed tertiary education, 5.82% (213/3660) completed primary education and, 1.04% (38/3660) had no formal education. Christianity was the dominant religion practised by 90.98.% (3,330/3660) of the participants and Islam by 9.02% (330/3,660). (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFREQUENCY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePERCENTAGE (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLevel of Education\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Formal Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristianity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIslamic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArtisans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCivil Servants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorporate Workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth Workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn Relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI Range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of age categories and BMI categories of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAGE CATEGORY (YEARS)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBMI CATEGORY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNDERWEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNORMAL WEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOVERWEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOBESE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (40.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (31.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293 (21.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181 (12.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e741\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (36.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e411 (50.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e702 (51.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e775 (53.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (23.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (16.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343 (25.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e450 (31.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50 and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (1.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26 (1.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (2.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e818 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,364 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,448 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the participants, 91.83% (3,361/3660) were married, 4.64% (170/3660) were in relationships, 3.22% (118/3660) were single, while 0.08% (3/3,660) and 0.22% (8/3,660) were divorced or widowed respectively.\u003c/p\u003e \u003cp\u003eOverall, the average weight and height of the participants were 75kg\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8 and 1.6m\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 respectively. Regarding participants\u0026rsquo; BMI, 39.56% (1,446/3,660) and 37.27% (1,363/3,660) of participants were obese and overweight respectively; whilst 22.35% (818/3,660) and 0.82% (30/3,660) had normal weight and underweight respectively. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eOf participants who were overweight, 51.47% (702/1,364) of them were aged between 30\u0026ndash;39years, 25.15% (343/1,364) were within 40\u0026ndash;49years, whilst 21.48% (293/1,364) were 18\u0026ndash;29years (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf the obese participants, 53.52% (775/1,448) of them were between 30\u0026ndash;39 years, 31.08% (450/1,448) were between 40\u0026ndash;49years, whilst 12.50% (181/1,448) were between ages 18\u0026ndash;29years, and 2.90% (42/1,448) were at least 50 years old. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComparing the educational levels of participants and their BMI categories, it was found out that of the participants who were overweight, 47.87% (653/1,364) of them had secondary education whilst 45.60% (622/1,364) had tertiary education. Only 5.57% (76/1,364) of the overweight participants had primary education with 0.95% (13/1,364) having no formal education.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparing participants\u0026rsquo; educational levels with BMI categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEVEL OF EDUCATION\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNDERWEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNORMAL WEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOVERWEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOBESE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Formal Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (1.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13 (0.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (0.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (3.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (6.23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (5.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85 (5.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (40.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e331 (40.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e653 (47.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e733 (50.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (53.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e426 (52.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e622 (45.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e616 (42.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eType of infertility and participants\u0026rsquo; BMI categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTYPE OF INFERTILITY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eBMI CATEGORY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNDERWEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNORMAL WEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOVERWEIGHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOBESE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (43.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e301 (16.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e504 (36.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e533 (36.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (56.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e517 (28.445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e860 (63.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e915(63.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the participants who were obese, the majority, 50.62% (733/1,448) had attained secondary education and 42.54% (616/1,448) had tertiary education. Only 5.87% (85/1,448) of the participants had primary education whilst 0.96% (14/1,448) had no formal education (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, 63.05% (860/1,364) of the patients who were having secondary infertility were overweight with the remaining being primary infertility patients. A similar trend was discovered in those with obesity as 63.19% (915/1,448) of the patients who were obese were suffering from secondary infertility.\u003c/p\u003e \u003cp\u003eAnalysis of type of infertility in relation to the BMI category revealed that, majority of the women with both primary 36.95% (504/1,364) and secondary infertility 63.05% (860/1,364) were overweight. Similarly, most of the participants who were obese had primary 36.81% (533/1448) and secondary 63.19% (915/1,448). This analysis clearly illustrates an increase prevalence of both primary and secondary amongst the overweight and obese BMI categories. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between participants\u0026rsquo; sociodemographic characteristics and their BMI categories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCalculated value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReligion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType of infertility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificantly higher number of clients who were overweight (457) and obese (526) had infertility duration of 2\u0026ndash;5 years compared with those who were normal weight (295). Similarly, higher number of clients who were overweight (464) and obese (498) had infertility duration of 6\u0026ndash;10 years compared with those who were normal weight. This study clearly showed that in all BMI categories, the duration of infertility was longer in overweight and obese clients than in clients with normal weight. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study revealed that the highest occurrence of overweight and obesity was amongst traders (508 and 533 respectively) followed by civil servants (328 and 345 respectively). The least occurrence of overweight and obesity was amongst students and health workers. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, only two parameters - age and level of education of participants had statistically significant associations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The remaining characteristics showed no association with BMI scale.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study reviewed the body mass index distribution of women seeking infertility treatment in Ghana.\u003c/p\u003e \u003cp\u003eFrom this study, the mean age and BMI of women seeking infertility were 35.50years and 29.08 kg/m\u003csup\u003e2\u003c/sup\u003e respectively. This is comparable to findings reported from similar study by Owiredu WKBA, et al, on the association of anthropometric indices with hormonal imbalance among women with infertility who sought treatment at Komfo Anokye Teaching Hospital in Kumasi, Ghana\u003csup\u003e8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, 76.83% of the women with infertility at the Ruma Fertility and Specialist Hospital were obese (39.56%) and overweight (37.27%), with just over one-fifth (22.35%) having normal BMI. The prevalence of overweight and obesity from our study appears much higher than from other similar study by Shanthakumari K, et al\u003csup\u003e21\u003c/sup\u003e that reported 35% to be overweight and obese in India. The proportions of normal and underweight were comparable from these two studies.\u003csup\u003e21\u003c/sup\u003e Again, the prevalence of overweight and obesity from our study is higher than that reported from a systematic review on the epidemic of obesity in Ghana in the same year of our study\u003csup\u003e7\u003c/sup\u003e and among participants of the earlier Women\u0026rsquo;s Health Study of Accra, Ghana\u003csup\u003e9\u003c/sup\u003e. Admittedly, while our current study was among clinical cohort of women with infertility, the other two studies were among women in the general population and not necessarily those with infertility. As a result, and seeing the availability of evidence that suggest a link between elevated BMI and infertility, our study population may have demonstrated such a high prevalence due the inherent selection bias of our population of women with infertility.\u003csup\u003e22\u003c/sup\u003e Our findings are also consistent with the research evidence that indicates the prevalence of infertility in obese women is on the ascendancy\u003csup\u003e15\u003c/sup\u003e, and that increasingly larger numbers of women seeking medical intervention to achieve pregnancy are reportedly overweight or obese.\u003csup\u003e23,24\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSimilarly, infertility has been reported to be three-fold higher inn obese women than non-obese, with reduced overall pregnancy probability with the woman\u0026rsquo;s BMI exceeds 29kg/m\u003csup\u003e2\u003c/sup\u003e,\u003csup\u003e15,25\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOur study found that a significant majority of women with elevated BMI were between 30 to 49years, in keeping with existing evidence that BMI generally increases with age among urban women\u003csup\u003e29,28\u003c/sup\u003e with women within the 35 and 44 year age bracket reportedly having the highest odds of being overweight and obese, according Agbeko et al\u003csup\u003e30\u003c/sup\u003e. The rural \u0026ndash; urban distribution of elevated BMI was not included in this retrospective review.\u003c/p\u003e \u003cp\u003eOur current study also found that infertility and BMI increase with participants\u0026rsquo; educational level. These findings are consistent with previous studies which revealed that higher education was associated with twice the tendency of becoming overweight or obese in women, compared with women with lower education.\u003csup\u003e30,39\u003c/sup\u003e It also agrees with evidence that women with tertiary education had the highest prevalence of obesity compared with less literate and illiterate women.\u003csup\u003e29\u003c/sup\u003e However, this finding appears to contrast other researchers\u0026rsquo; reports of a higher prevalence of overweight and obesity among women with no or low education compared with those with secondary education or higher in seven Sub-Saharan African countries.\u003csup\u003e4\u003c/sup\u003e The disparity may probably be explained by the fact that our review focused on clinical cohort of women with infertility, and differs from the continent-wide non-clinical based health survey among seven African countries.\u003c/p\u003e \u003cp\u003eAnother important finding in this current study is that many more obese and overweight women presented with secondary than primary infertility, suggesting the possibility that elevated BMI plays a major role in secondary infertility. A plausible explanation could be the effect of retained pregnancy-related weight gain from their previous pregnancies, but this may not account of early loss of previous pregnancies. This is consistent with research finding which suggest that childbearing is a risk factor for BMI elevation,\u003csup\u003e40\u003c/sup\u003e and that for every childbirth there is a risk of 7% increase in BMI,\u003csup\u003e41\u003c/sup\u003e as women usually struggle to lose all the weight gained during pregnancy.\u003csup\u003e30,42\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn terms of occupation, the risk of being obese and overweight was greatest among traders, followed closely by civil servants, artisans, health workers and students. This finding reflects the national narrative by Ofori-Asenso et al., that, in Ghana, women tend to settle for more sedentary occupations such as table-top trading, resulting in lower levels of physical activity among Ghanaian women than men.\u003csup\u003e36\u003c/sup\u003e Further, the elevated BMI risk among health workers agrees with findings of high level of physical inactivity and compromised dietary habits such as late night eating as contributory factors to overweight and obesity among Nurses and Midwives in Ghana.\u003csup\u003e46\u003c/sup\u003e Occupation dictates an individual\u0026rsquo;s physical or sedentary activity level involved, and together with behavioural lifestyle and socioeconomic characteristics, may potentially influence the propensity for elevated BMI.\u003csup\u003e43,44,45\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eOverall, only two among the seven main sociodemographic characteristics, namely age and level of education had significant associations with elevated BMI. This finding is in line with findings from a previous Health Survey data by Agbeko et al\u003csup\u003e30\u003c/sup\u003e and a large Demographic and Health Survey (DHS) involving 32 Sub-Saharan African countries by Neupane et al\u003csup\u003e47\u003c/sup\u003e who observed a positive significant association between age, education, wealth, and overweight and obesity. However, findings from a similar prevalence study done among clinical cohorts in Mangalore, India by Shanthakumari \u003csup\u003e21\u003c/sup\u003e in which they found no such associations. This may possibly be explained by their limited sample size of only 60 infertile women in their study.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations of Study\u003c/h2\u003e \u003cp\u003eTo the best of our information, this work has been the first such research to analyse elevated BMI and associated sociodemographic parameters among women with infertility in Ghana. Additionally, data collection was done by highly trained research assistants comprising Nurses and Health Information Technologists in this reputable fertility hospital with very stringent internal data quality assurance mechanisms in place.\u003c/p\u003e \u003cp\u003eThe results of this research must be interpreted with cautions because of the limitation of retrospective archival research methodology. Notably among these limitations, our findings cannot assign causality of infertility as due to elevated BMI from our work. Also, the inherent disadvantages of the tendency of selection bias needs to be considered.\u003c/p\u003e \u003cp\u003eFinally, generalizability of the present study is limited because it was based on data from a single ART service provider in Ghana.\u003c/p\u003e \u003cp\u003eThere is therefore the need for an expanded prospective follow-up study involving a representative sample of women attending multiple fertility hospitals in Ghana to give a wider and more representative picture of the prevalence of elevated BMI among this clinical cohort.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOverall, 76.83% of the women with infertility at Ruma, in Ghana had elevated BMI, made up of 39.56% obese and 37.27% overweight; while 22.35% have normal BMI. The participants\u0026rsquo; age and level of education were significantly associated with elevated BMI. Elevated BMI was more prevalent among women with secondary infertility.\u003c/p\u003e \u003cp\u003eIncreased awareness creation on the prevalence, impact and prevention of elevated BMI among women needs to be prioritized in Ghana.\u003c/p\u003e\n\u003ch3\u003eRecommendations for further Research\u003c/h3\u003e\n\u003cp\u003eOn the basis of our findings, we recommend a more rigorous prospective, multi-center research design to further examine the impact of elevated BMI on fertility and pregnancy outcomes in our Ghanaian context to inform public health policy and interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCA and RKA co-designed the study and data extraction form. CA and EKA participated in proposal writing, data collection and analysis. RKA supervised proposal writing and data collection. CA, EKA and PES drafted the first version of the manuscript and PES is responsible for correspondence. All authors participated in manuscript writing and review. All authors read and approved the final manuscript for submission. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the university of Ghana Ethical and Protocol Review Committee. The study was conducted in accordance with the fundamental ethical principles outlined in the University of Ghana Ethics Policy, which encompasses the Declaration of Helsinki (1996), International Conference on Harmonization Good Clinical Practice (ICH GCP E6) Guidelines, Council for International Organizations of Medical Sciences (CIOMS) principles, the Belmont Report, and applicable laws and statutory regulations of Ghana and the University.\u003c/p\u003e\n\u003cp\u003eBy adhering to these esteemed guidelines, we ensured the highest ethical standards in the design, implementation, and reporting of our research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics And Consent To Participate:\u003c/strong\u003e The research methodology, data collection, analysis, and reporting processes have been carried out with full compliance to the above ethical standards. \u0026nbsp;All necessary approvals and consents have been obtained, and the rights and confidentiality of all participants have been safeguarded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflict of interest to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are grateful to the Management and Staff of the Ruma Specialist Hospital and Fertility for their immense support to make this study very successful.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmiri P, Jalali-Farahani S, Rezaei M, Cheraghi L, Hosseinpanah F and Azizi F. 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Hum Reprod. 2008;23(2):324\u0026ndash;8. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prevalence, body mass index, overweight, obesity, infertility, women, assisted reproductive technology","lastPublishedDoi":"10.21203/rs.3.rs-4756260/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4756260/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElevated Body Mass Index in infertile women has important implications for medically assisted reproduction. The prevalence and impact of elevated BMI on assisted reproductive technologies treatment outcomes in low-income settings remains under-studied and little unknown. This study investigated the prevalence of elevated BMI and associated socio-demographic characteristics among infertile women in Ghana.\u003c/p\u003e \u003cp\u003eRetrospective analysis of five-years data of 3,660 infertile women attending clinic in Ghana for assisted conception treatment was carried out. The data was analysed using the SPSS (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Descriptive statistics performed and chi square was used to assess associations between categorical variables with p-value below 0.05 considered statistically significant.\u003c/p\u003e \u003cp\u003eOverall, 76.83% of women with infertility had elevated BMI, of whom 39.56% were obese and 37.27% were overweight. Majority of participants with elevated BMI was aged between 30\u0026ndash;49years.(p\u0026thinsp;\u0026lt;\u0026thinsp;0.000)\u003c/p\u003e \u003cp\u003eInfertility prevalence and BMI increased with increasing level of education.(p\u0026thinsp;\u0026lt;\u0026thinsp;0.003) Secondary infertility was more common among overweight or obese women. Traders had the highest prevalence of overweight and obesity followed by civil servants and health workers.\u003c/p\u003e \u003cp\u003eElevated BMI was highly prevalent among women seeking infertility care in Ghana, particularly so among those with secondary infertility. Traders had the highest prevalence of elevated BMI, probably reflecting their predominantly sedentary lifestyles.\u003c/p\u003e","manuscriptTitle":"Elevated Body Mass Index And associated Factors Among Women with Infertility Undergoing Assisted Reproductive Technology (ART) Treatment in a low-income setting","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 12:48:28","doi":"10.21203/rs.3.rs-4756260/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-26T06:58:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T07:52:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-18T08:02:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313016575022406771864939060007721564097","date":"2024-08-08T06:51:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45269343018287649082151601944308821981","date":"2024-08-07T12:59:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-07T09:14:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-07T07:34:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-24T04:11:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-23T05:43:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-17T12:30:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fce19327-33e0-49d8-86da-383071345d3d","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36198434,"name":"Health sciences/Health care"},{"id":36198435,"name":"Health sciences/Health care/Weight management"}],"tags":[],"updatedAt":"2025-02-24T16:02:47+00:00","versionOfRecord":{"articleIdentity":"rs-4756260","link":"https://doi.org/10.1038/s41598-024-82818-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-20 15:57:39","publishedOnDateReadable":"February 20th, 2025"},"versionCreatedAt":"2024-08-26 12:48:28","video":"","vorDoi":"10.1038/s41598-024-82818-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-82818-5","workflowStages":[]},"version":"v1","identity":"rs-4756260","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4756260","identity":"rs-4756260","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}