Association between underweight and menstrual pain severity, irregular menstruation in a young Japanese population

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Abnormal body mass index (BMI), including underweight and obesity, increases the risk of developing chronic diseases. However, the association between abnormal BMI and dysmenorrhea remains inconsistent. In this study, 4,999 female students were enrolled. Information on lifestyle and menstrual status (pain severity, irregularity, and medication) was collected through self-reported questionnaires. Underweight and overweight were defined as BMI < 18.5 and BMI ≥ 25, respectively. The prevalences of underweight, overweight, severe pain, and irregular menstruation were 17.1%, 6.8%, 15.2%, and 13.9%, respectively. Underweight was significantly positively associated with severe menstrual pain (adjusted OR: 1.28, 95% CI: 1.05–1.56; p for trend = 0.047), medication for menstrual pain (adjusted OR: 1.39, 95% CI: 1.13–1.70; p for trend = 0.006), and irregular menstruation (adjusted odds ratio [OR]: 1.32, 95% confidence interval [CI]: 1.08–1.61; p for trend = 0.04). However, no association between being overweight and dysmenorrhea was found. In a young Japanese population, underweight—but not overweight—was independently and positively associated with dysmenorrhea. Health sciences/Diseases Health sciences/Risk factors dysmenorrhea menstrual irregularities body mass index underweight overweight Introduction Dysmenorrhea and irregular menstrual cycles are the most common symptoms experienced by women [ 1 ]. Dysmenorrhea, in particular, is highly prevalent among women of reproductive age and constitutes a significant global public health concern, primarily due to its adverse effects on women’s quality of life [ 2 – 6 ]. Obesity is associated with numerous diseases, including cardiovascular diseases, type 2 diabetes mellitus, cancer, and mental disorders [ 7 – 9 ]. Conversely, underweight is linked to increased mortality following infections or cardiovascular diseases, as well as respiratory diseases, osteoporosis, and sarcopenia [ 10 – 14 ]. While the association between dysmenorrhea, irregular menstrual cycles, and abnormal body mass index (BMI)—including underweight and obesity—has been previously reported, the evidence regarding this issue remains inconsistent [ 3 , 15 , 16 ]. Underweight is notably prevalent among women in developed Asian countries, with Japanese women showing particularly high and increasing rates [ 17 – 19 ]. Despite growing societal interest in underweight, few studies have explored the relationship between underweight and the prevalence and severity of dysmenorrhea. This study aims to investigate the association between dysmenorrhea, irregular menstruation, and abnormal BMI, including underweight and overweight, in a young Japanese population. Materials and methods 2.1 Study population This study included 4,999 female students who underwent health examinations at Ehime University (Ehime, Japan) between April 2015 and May 2017. After excluding 50 participants with incomplete data, a total of 4,949 participants were included in the final analysis. Informed consent was obtained from all participants with the option to withdraw from the study. The study protocol was designed in accordance with the ethical guidelines of the Declaration of Helsinki. This study was approved by the Ethics Committee of the Ehime University Graduate School of Medicine (approval no. 1610012). 2.2 Measurements Information on menstrual issues, along with daily lifestyle habits such as alcohol consumption, smoking, and exercise habits, was collected using self-reported questionnaires. Current drinking was defined as positive if a study participant reported a habit of consuming alcoholic beverages, while current smoking was defined as positive if a participant reported smoking at least one cigarette per day. Having a habit of exercising was defined as maintaining a routine of exercising at least once per week. 2.3 Assessment of menstrual issues Menstrual issues were assessed using the following questions regarding severity of menstrual pain, medication for menstrual pain, and menstrual regularity: “How strong is your menstrual pain, if any?” with response options of “None,” “Light,” “Moderate,” and “Severe”; “How often do you use medication for menstrual pain?” with response options of “Never,” “Sometimes,” and “Often.”; and “Which of the following best describes your menstrual cycle?” with response options of “No cycle for three months or more,” “Irregular,” and “Mainly regular.” 2.4 Body mass index and definitions of underweight and overweight Body mass index (BMI) was calculated by dividing body weight in kilograms by the square of height in meters. Height was measured to the nearest millimeter using a stadiometer, with participants standing in an upright posture. Weight was measured while participants were wearing light clothing to ensure consistency. Underweight was defined as a BMI of less than 18.5, while overweight was classified as a BMI of 25.0 or greater. 2.5 Statistical methods Crude odds ratios (ORs) and their 95% confidence intervals (CIs) for BMI in relation to menstrual pain severity, medication for menstrual pain, and menstrual regularity were estimated using logistic regression analysis. Multiple logistic regression analyses were performed to adjust for potential confounding factors. Age, alcohol consumption, smoking status, and exercise habits were selected as potential confounders. Statistical analyses were primarily conducted using the SAS software package, version 9.4 (SAS Institute Inc., Cary, NC, USA). All statistical tests were two-tailed, and a p -value of less than 0.05 was considered statistically significant. Results The characteristics of the participants are listed in Table 1 . The total number of participants was 4,949. The mean age and BMI were 19.9 years and 20.9 kg/m 2 , respectively. The proportions of underweight (BMI < 18.5), normal weight (≤ 18.5 BMI < 25.0), and overweight (BMI ≥ 25.0) were 17.1%, 76.1%, and 6.8%, respectively. Among menstrual pain severity, the proportions of no pain, light pain, moderate pain, and severe pain were 16.3%, 29.2%, 39.4%, and 15.2%, respectively. The percentage of participants using medication for menstrual pain sometimes and often was 34.6% and 13.7%, respectively. The proportions of regular, irregular, and amenorrhea in menstrual regularity were 85.0%, 13.9%, and 1.2%, respectively. Table 1 Clinical characteristics of 4,949 study participants Variables Female (N = 4,949) Age, years, mean ± SD 19.9 ± 3.3 BMI, kg/m 2 , mean ± SD 20.9 ± 2.8 BMI < 18.5 kg/m 2 847 (17.1) 18.5 kg/m 2 ≤ BMI < 25.0 kg/m 2 3,768 (76.1) 25.0 kg/m 2 ≤ BMI 334 (6.8) Smoking, n, (%) 52 (1.1) Drinking, n, (%) 309 (6.2) Exercise habit, n, (%) 1,548 (31.3) Menstrual pain severity None, n (%) 805 (16.3) Light, n (%) 1,444 (29.2) Moderate, n (%) 1,948 (39.4) Heavy, n (%) 752 (15.2) Medication for menstrual pain Never, n (%) 2,561 (51.8) Sometimes, n (%) 1,711 (34.6) Often, n (%) 677 (13.7) Menstrual regularity Mainly regular, n (%) 4,204 (85.0) Irregular, n (%) 687 (13.9) Amenorrhea, n (%) 58 (1.2) BMI, body mass index; SD, standard deviation Table 2 presents the crude and adjusted ORs and 95% CIs for severe menstrual pain in relation to BMI. The prevalences of severe menstrual pain in underweight, normal weight, and overweight participants were 18.1%, 14.6%, and 14.7%, respectively. Underweight, but not overweight, was significantly positively associated with severe menstrual pain (adjusted OR: 1.28, 95% CI: 1.05–1.56; p for trend = 0.047). Table 2 Crude and adjusted odds ratios and 95% confidence intervals for severe menstrual pain in relation to BMI Variable Prevalence (%) Crude OR (95% CI) Adjusted OR (95% CI) BMI Underweight (18.5 < BMI) 153/847 (18.1) 1.29 (1.06–1.57) 1.28 (1.05–1.56) Normal weight 550/3,768 (14.6) 1.00 1.00 Overweight (BMI ≥ 25.0) 49/334 (14.7) 1.01 (0.73–1.37) 1.03 (0.74–1.41) p for trend 0.047 BMI, body mass index; OR, odds ratio; CI, confidence interval. Odd ratios were adjusted for age, drinking, smoking, and exercise habits. The association between the use of medication for menstrual pain and BMI is shown in Table 3 . Underweight was independently and positively associated with medication for menstrual pain (adjusted OR: 1.39, 95% CI: 1.13–1.70; p for trend = 0.006). On the other hand, no association between being overweight and medication for menstrual pain was observed. Table 3 Crude and adjusted odds ratios and 95% confidence intervals for medication for menstrual pain in relation to BMI Variable Prevalence (%) Crude OR (95% CI) Adjusted OR (95% CI) BMI Underweight (18.5 < BMI) 146/847 (17.2) 1.40 (1.14–1.71) 1.39 (1.13–1.70) Normal weight 488/3,768 (13.0) 1.00 1.00 Overweight (BMI ≥ 25.0) 43/334 (12.9) 0.99 (0.70–1.37) 1.00 (0.71–1.38) p for trend 0.006 BMI, body mass index; OR, odds ratio; CI, confidence interval. Odd ratios were adjusted for age, sex, drinking, smoking, and exercise habits. Table 4 shows the crude and adjusted ORs and 95% CIs for irregular menstruation in relation to BMI. The percentage of irregular menstrual regularity in underweight, normal weight, and overweight participants was 18.1%, 14.3%, and 15.6%, respectively. Underweight was significantly positively associated with irregular menstruation (adjusted OR: 1.32, 95% CI: 1.08–1.61; p for trend = 0.04). However, no significant association was observed between being overweight and irregular menstruation. Table 4 Crude and adjusted odds ratios and 95% confidence intervals for irregular menstruation in relation to BMI Variable Prevalence (%) Crude OR (95% CI) Adjusted OR (95% CI) BMI Underweight (18.5 < BMI) 153/847 (18.1) 1.32 (1.08–1.60) 1.32 (1.08–1.61) Normal weight 540/3,768 (14.3) 1.00 1.00 Overweight (BMI ≥ 25.0) 52/334 (15.6) 1.10 (0.80–1.49) 1.09 (0.89–1.47) p for trend 0.040 BMI, body mass index; OR, odds ratio; CI, confidence interval. Odd ratios were adjusted for age, drinking, smoking, and exercise habits. Discussion In this study of a young Japanese population, underweight was independently and positively associated with severe menstrual pain, the use of medication for menstrual pain, and irregular menstruation, while no significant associations were observed between overweight and severe menstrual pain, medication use for menstrual pain, or irregular menstruation. This is the first study to identify a close association between underweight and dysmenorrhea among Japanese university students. Several studies have explored the association between BMI and the severity of dysmenorrhea. A Chinese study of 450 women aged 18 to 45 found a significant association between high BMI (≥ 24) and the severity of menstrual pain [ 20 ]. Another study of 2,805 premenopausal women in Japan reported a positive correlation between BMI and dysmenorrhea severity [ 21 ]. Conversely, a study of 210 adolescents in Kazakhstan aged 12 to 18 showed a U-shaped relationship between BMI and the severity of dysmenorrhea [ 22 ], while a study of 630 Indonesians aged 19 to 21 indicated an inverse correlation between BMI and the severity of dysmenorrhea [ 23 ]. Thus, previous studies on the association between BMI and the severity of dysmenorrhea are inconsistent. Similarly, study findings on BMI and menstrual cycles have varied, and their conclusions remain inconsistent. In an Australian study of 10,618 women aged 22 to 27, the prevalence of menstrual irregularities in overweight and obese participants was higher compared to those with underweight or normal BMI [ 24 ]. On the other hand, studies conducted on 200 participants in India and 436 participants aged 24 to 45 in the United States identified a U-shaped relationship between BMI and menstrual irregularities [ 25 – 26 ]. However, a study of 264 Japanese university students identified a significant relationship between low BMI and menstrual irregularities, while another study involving 848 Portuguese adolescents aged 12 to 18 reported no association between BMI and the menstrual cycle [ 27 ]. The findings of this study were partially consistent with the results of the Kazakhstan study regarding underweight and the severity of dysmenorrhea [ 22 ]. The results concerning menstrual irregularities were consistent with a previous Japanese study [ 27 ]. The discrepancies regarding the severity of dysmenorrhea and menstrual cycles between this study and previous studies may reflect differences in sample size, the percentage of overweight participants, ethnic diversity, or population age. Notably, the findings align with those of a study of Japanese university students, providing further support for this hypothesis. The underlying mechanisms between BMI, the severity of dysmenorrhea and irregular menstruation remain unclear. However, several hypotheses have been considered. The activation of prostaglandin-related pathways is thought to contribute to the severity of dysmenorrhea [ 28 – 30 ]. While hormonal imbalances caused by obesity or underweight may influence menstrual irregularities and menstrual pain [ 31 , 32 ], alterations in the secretion patterns of luteinizing hormone and follicle-stimulating hormone may also contribute to a less predictable menstrual cycle [ 25 ]. This study has several limitations. First, as this was a cross-sectional study, causal relationships could not be evaluated. Second, the data were assessed using questionnaires, which may have introduced recall bias. Third, the study focused on a limited population of Japanese university students, making it difficult to generalize the results. Fourth, nutrition information was unavailable in this study. Finally, diseases associated with dysmenorrhea, such as endometriosis, were not considered due to the lack of access to medical records. Conclusions This study showed that underweight was independently and positively associated with the severity of dysmenorrhea, medication for menstrual pain, and menstrual irregularities in a young Japanese population. Further research on the association between BMI (especially underweight) and dysmenorrhea and menstrual irregularities is needed in the future. Declarations Acknowledgments We would like to thank the entire team at the Health Services Center for their support and encouragement. Author contributions Conceptualization: SF; Methodology: SF; Formal analysis: SF; Investigation: SF, AK (Kato), and KK; Data curation: SF, AK (Kato), and KK; Writing-original draft: SI; Writing-review & editing: SI, SF, TM, OY, YM, AK (Kanamoto), MM, AS, HN, HS, KM, MK, YN, MK, TK, BM, YH; Visualization: SI and SF; Supervision: SF; Project administration: SF. Data availability statement The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Declaration of conflicting interests The authors state that they have no competing interests to disclose. Funding None. References Marques, P., Madeira, T. & Gama, A. Menstrual cycle among adolescents: Girls’ awareness and influence of age at menarche and overweight. Rev. Paul. Pediatr. 40 , e2020494 (2022). Liu, J., Wang, Y., Wu, L., Wang, L. & Fang, H. Study on the influencing factors of primary dysmenorrhea in female college students: Systematic review and meta-analysis. Med. (Baltim.) 103 , e40906 (2024). Dixon, S. et al. What is known about adolescent dysmenorrhoea in (and for) community health settings? Front. Reprod. Health 6 , 1394978 (2024). Chen, C. X. et al. Social determinants of health and dysmenorrhea: A systematic review. J. Pain 25 , 104574 (2024). Moreno Gómez, A., Guo, P., de la Llave Rincón, A. 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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-6575224","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":468162988,"identity":"2fb23973-3d3d-4c61-9297-6ba1ad953e39","order_by":0,"name":"Sho Ishikawa","email":"","orcid":"","institution":"Ehime University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Sho","middleName":"","lastName":"Ishikawa","suffix":""},{"id":468162989,"identity":"e7f2d8cf-50e3-4b80-961c-341d3be87b1e","order_by":1,"name":"Teruki Miyake","email":"","orcid":"","institution":"Ehime University Graduate School of 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Medicine","correspondingAuthor":false,"prefix":"","firstName":"Bunzo","middleName":"","lastName":"Matsuura","suffix":""},{"id":468163006,"identity":"fe876a4e-943c-4176-b1d4-d7e78812d1f6","order_by":18,"name":"Yoichi Hiasa","email":"","orcid":"","institution":"Ehime University Graduate School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yoichi","middleName":"","lastName":"Hiasa","suffix":""}],"badges":[],"createdAt":"2025-05-02 04:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6575224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6575224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102890062,"identity":"ade75a76-5f4b-4528-8254-63bfdc125533","added_by":"auto","created_at":"2026-02-18 04:25:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":750429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6575224/v1/127d0044-b130-4140-98e5-b8606178af39.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between underweight and menstrual pain severity, irregular menstruation in a young Japanese population","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDysmenorrhea and irregular menstrual cycles are the most common symptoms experienced by women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Dysmenorrhea, in particular, is highly prevalent among women of reproductive age and constitutes a significant global public health concern, primarily due to its adverse effects on women\u0026rsquo;s quality of life [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity is associated with numerous diseases, including cardiovascular diseases, type 2 diabetes mellitus, cancer, and mental disorders [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Conversely, underweight is linked to increased mortality following infections or cardiovascular diseases, as well as respiratory diseases, osteoporosis, and sarcopenia [\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. While the association between dysmenorrhea, irregular menstrual cycles, and abnormal body mass index (BMI)\u0026mdash;including underweight and obesity\u0026mdash;has been previously reported, the evidence regarding this issue remains inconsistent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Underweight is notably prevalent among women in developed Asian countries, with Japanese women showing particularly high and increasing rates [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Despite growing societal interest in underweight, few studies have explored the relationship between underweight and the prevalence and severity of dysmenorrhea.\u003c/p\u003e \u003cp\u003eThis study aims to investigate the association between dysmenorrhea, irregular menstruation, and abnormal BMI, including underweight and overweight, in a young Japanese population.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThis study included 4,999 female students who underwent health examinations at Ehime University (Ehime, Japan) between April 2015 and May 2017. After excluding 50 participants with incomplete data, a total of 4,949 participants were included in the final analysis. Informed consent was obtained from all participants with the option to withdraw from the study. The study protocol was designed in accordance with the ethical guidelines of the Declaration of Helsinki. This study was approved by the Ethics Committee of the Ehime University Graduate School of Medicine (approval no. 1610012).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurements\u003c/h2\u003e \u003cp\u003eInformation on menstrual issues, along with daily lifestyle habits such as alcohol consumption, smoking, and exercise habits, was collected using self-reported questionnaires. Current drinking was defined as positive if a study participant reported a habit of consuming alcoholic beverages, while current smoking was defined as positive if a participant reported smoking at least one cigarette per day. Having a habit of exercising was defined as maintaining a routine of exercising at least once per week.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of menstrual issues\u003c/h2\u003e \u003cp\u003eMenstrual issues were assessed using the following questions regarding severity of menstrual pain, medication for menstrual pain, and menstrual regularity: \u0026ldquo;How strong is your menstrual pain, if any?\u0026rdquo; with response options of \u0026ldquo;None,\u0026rdquo; \u0026ldquo;Light,\u0026rdquo; \u0026ldquo;Moderate,\u0026rdquo; and \u0026ldquo;Severe\u0026rdquo;; \u0026ldquo;How often do you use medication for menstrual pain?\u0026rdquo; with response options of \u0026ldquo;Never,\u0026rdquo; \u0026ldquo;Sometimes,\u0026rdquo; and \u0026ldquo;Often.\u0026rdquo;; and \u0026ldquo;Which of the following best describes your menstrual cycle?\u0026rdquo; with response options of \u0026ldquo;No cycle for three months or more,\u0026rdquo; \u0026ldquo;Irregular,\u0026rdquo; and \u0026ldquo;Mainly regular.\u0026rdquo;\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Body mass index and definitions of underweight and overweight\u003c/h2\u003e \u003cp\u003eBody mass index (BMI) was calculated by dividing body weight in kilograms by the square of height in meters. Height was measured to the nearest millimeter using a stadiometer, with participants standing in an upright posture. Weight was measured while participants were wearing light clothing to ensure consistency. Underweight was defined as a BMI of less than 18.5, while overweight was classified as a BMI of 25.0 or greater.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical methods\u003c/h2\u003e \u003cp\u003eCrude odds ratios (ORs) and their 95% confidence intervals (CIs) for BMI in relation to menstrual pain severity, medication for menstrual pain, and menstrual regularity were estimated using logistic regression analysis. Multiple logistic regression analyses were performed to adjust for potential confounding factors. Age, alcohol consumption, smoking status, and exercise habits were selected as potential confounders. Statistical analyses were primarily conducted using the SAS software package, version 9.4 (SAS Institute Inc., Cary, NC, USA). All statistical tests were two-tailed, and a \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe characteristics of the participants are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The total number of participants was 4,949. The mean age and BMI were 19.9 years and 20.9 kg/m\u003csup\u003e2\u003c/sup\u003e, respectively. The proportions of underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal weight (\u0026le;\u0026thinsp;18.5 BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0), and overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.0) were 17.1%, 76.1%, and 6.8%, respectively. Among menstrual pain severity, the proportions of no pain, light pain, moderate pain, and severe pain were 16.3%, 29.2%, 39.4%, and 15.2%, respectively. The percentage of participants using medication for menstrual pain sometimes and often was 34.6% and 13.7%, respectively. The proportions of regular, irregular, and amenorrhea in menstrual regularity were 85.0%, 13.9%, and 1.2%, respectively.\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\u003eClinical characteristics of 4,949 study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \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\u003eFemale (N\u0026thinsp;=\u0026thinsp;4,949)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e847 (17.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,768 (76.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0 kg/m\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026le;\u0026thinsp;BMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e334 (6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e309 (6.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise habit, n, (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,548 (31.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual pain severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e805 (16.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,444 (29.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,948 (39.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e752 (15.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedication for menstrual pain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,561 (51.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSometimes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,711 (34.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOften, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e677 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenstrual regularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMainly regular, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,204 (85.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIrregular, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e687 (13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmenorrhea, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eBMI, body mass index; SD, standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the crude and adjusted ORs and 95% CIs for severe menstrual pain in relation to BMI. The prevalences of severe menstrual pain in underweight, normal weight, and overweight participants were 18.1%, 14.6%, and 14.7%, respectively. Underweight, but not overweight, was significantly positively associated with severe menstrual pain (adjusted OR: 1.28, 95% CI: 1.05\u0026ndash;1.56; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.047).\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\u003eCrude and adjusted odds ratios and 95% confidence intervals for severe menstrual pain in relation to BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (18.5\u0026thinsp;\u0026lt;\u0026thinsp;BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153/847 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29 (1.06\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.28 (1.05\u0026ndash;1.56)\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e550/3,768 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49/334 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.73\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03 (0.74\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep for trend\u003c/em\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, body mass index; OR, odds ratio; CI, confidence interval. Odd ratios were adjusted for age, drinking, smoking, and exercise habits.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe association between the use of medication for menstrual pain and BMI is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Underweight was independently and positively associated with medication for menstrual pain (adjusted OR: 1.39, 95% CI: 1.13\u0026ndash;1.70; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.006). On the other hand, no association between being overweight and medication for menstrual pain was observed.\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\u003eCrude and adjusted odds ratios and 95% confidence intervals for medication for menstrual pain in relation to BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (18.5\u0026thinsp;\u0026lt;\u0026thinsp;BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e146/847 (17.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.40 (1.14\u0026ndash;1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.39 (1.13\u0026ndash;1.70)\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e488/3,768 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43/334 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.70\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00 (0.71\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep for trend\u003c/em\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, body mass index; OR, odds ratio; CI, confidence interval. Odd ratios were adjusted for age, sex, drinking, smoking, and exercise habits.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the crude and adjusted ORs and 95% CIs for irregular menstruation in relation to BMI. The percentage of irregular menstrual regularity in underweight, normal weight, and overweight participants was 18.1%, 14.3%, and 15.6%, respectively. Underweight was significantly positively associated with irregular menstruation (adjusted OR: 1.32, 95% CI: 1.08\u0026ndash;1.61; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.04). However, no significant association was observed between being overweight and irregular menstruation.\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\u003eCrude and adjusted odds ratios and 95% confidence intervals for irregular menstruation in relation to BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrevalence (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrude OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdjusted OR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (18.5\u0026thinsp;\u0026lt;\u0026thinsp;BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153/847 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.32 (1.08\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.32 (1.08\u0026ndash;1.61)\u003c/p\u003e \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=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e540/3,768 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52/334 (15.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10 (0.80\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.09 (0.89\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ep for trend\u003c/em\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 \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eBMI, body mass index; OR, odds ratio; CI, confidence interval. Odd ratios were adjusted for age, drinking, smoking, and exercise habits.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eIn this study of a young Japanese population, underweight was independently and positively associated with severe menstrual pain, the use of medication for menstrual pain, and irregular menstruation, while no significant associations were observed between overweight and severe menstrual pain, medication use for menstrual pain, or irregular menstruation. This is the first study to identify a close association between underweight and dysmenorrhea among Japanese university students.\u003c/p\u003e \u003cp\u003eSeveral studies have explored the association between BMI and the severity of dysmenorrhea. A Chinese study of 450 women aged 18 to 45 found a significant association between high BMI (\u0026ge;\u0026thinsp;24) and the severity of menstrual pain [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Another study of 2,805 premenopausal women in Japan reported a positive correlation between BMI and dysmenorrhea severity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Conversely, a study of 210 adolescents in Kazakhstan aged 12 to 18 showed a U-shaped relationship between BMI and the severity of dysmenorrhea [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], while a study of 630 Indonesians aged 19 to 21 indicated an inverse correlation between BMI and the severity of dysmenorrhea [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Thus, previous studies on the association between BMI and the severity of dysmenorrhea are inconsistent.\u003c/p\u003e \u003cp\u003eSimilarly, study findings on BMI and menstrual cycles have varied, and their conclusions remain inconsistent. In an Australian study of 10,618 women aged 22 to 27, the prevalence of menstrual irregularities in overweight and obese participants was higher compared to those with underweight or normal BMI [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. On the other hand, studies conducted on 200 participants in India and 436 participants aged 24 to 45 in the United States identified a U-shaped relationship between BMI and menstrual irregularities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, a study of 264 Japanese university students identified a significant relationship between low BMI and menstrual irregularities, while another study involving 848 Portuguese adolescents aged 12 to 18 reported no association between BMI and the menstrual cycle [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe findings of this study were partially consistent with the results of the Kazakhstan study regarding underweight and the severity of dysmenorrhea [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The results concerning menstrual irregularities were consistent with a previous Japanese study [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The discrepancies regarding the severity of dysmenorrhea and menstrual cycles between this study and previous studies may reflect differences in sample size, the percentage of overweight participants, ethnic diversity, or population age. Notably, the findings align with those of a study of Japanese university students, providing further support for this hypothesis.\u003c/p\u003e \u003cp\u003eThe underlying mechanisms between BMI, the severity of dysmenorrhea and irregular menstruation remain unclear. However, several hypotheses have been considered. The activation of prostaglandin-related pathways is thought to contribute to the severity of dysmenorrhea [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. While hormonal imbalances caused by obesity or underweight may influence menstrual irregularities and menstrual pain [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], alterations in the secretion patterns of luteinizing hormone and follicle-stimulating hormone may also contribute to a less predictable menstrual cycle [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, as this was a cross-sectional study, causal relationships could not be evaluated. Second, the data were assessed using questionnaires, which may have introduced recall bias. Third, the study focused on a limited population of Japanese university students, making it difficult to generalize the results. Fourth, nutrition information was unavailable in this study. Finally, diseases associated with dysmenorrhea, such as endometriosis, were not considered due to the lack of access to medical records.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThis study showed that underweight was independently and positively associated with the severity of dysmenorrhea, medication for menstrual pain, and menstrual irregularities in a young Japanese population. Further research on the association between BMI (especially underweight) and dysmenorrhea and menstrual irregularities is needed in the future.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the entire team at the Health Services Center for their support and encouragement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: SF; Methodology: SF; Formal analysis: SF; Investigation: SF, AK (Kato), and KK; Data curation: SF, AK (Kato), and KK; Writing-original draft: SI; Writing-review \u0026amp; editing: SI, SF, TM, OY, YM, AK (Kanamoto), MM, AS, HN, HS, KM, MK, YN, MK, TK, BM, YH; Visualization: SI and SF; Supervision: SF; Project administration: SF.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they have no competing interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarques, P., Madeira, T. \u0026amp; Gama, A. 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Prostaglandin E\u003csub\u003e2\u003c/sub\u003e-EP4 axis promotes lipolysis and fibrosis in adipose tissue leading to ectopic fat deposition and insulin resistance. \u003cem\u003eCell Rep.\u003c/em\u003e \u003cstrong\u003e33\u003c/strong\u003e, 108265 (2020).\u003c/li\u003e\n\u003cli\u003eBjune, J. I., Str\u0026oslash;mland, P. P., Jersin, R. \u0026Aring;., Mellgren, G. \u0026amp; Dankel, S. N. Metabolic and epigenetic regulation by estrogen in adipocytes. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 828780 (2022).\u003c/li\u003e\n\u003cli\u003eChauhan, M. \u0026amp; Kala, J. Relation between dysmenorrhea and body mass index in adolescents with rural versus urban variation. \u003cem\u003eJ. Obstet. Gynaecol. India\u003c/em\u003e \u003cstrong\u003e62\u003c/strong\u003e, 442-445 (2012).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"dysmenorrhea, menstrual irregularities, body mass index, underweight, overweight","lastPublishedDoi":"10.21203/rs.3.rs-6575224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6575224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDysmenorrhea is closely linked to quality of life in females. Abnormal body mass index (BMI), including underweight and obesity, increases the risk of developing chronic diseases. However, the association between abnormal BMI and dysmenorrhea remains inconsistent. In this study, 4,999 female students were enrolled. Information on lifestyle and menstrual status (pain severity, irregularity, and medication) was collected through self-reported questionnaires. Underweight and overweight were defined as BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 and BMI\u0026thinsp;\u0026ge;\u0026thinsp;25, respectively. The prevalences of underweight, overweight, severe pain, and irregular menstruation were 17.1%, 6.8%, 15.2%, and 13.9%, respectively. Underweight was significantly positively associated with severe menstrual pain (adjusted OR: 1.28, 95% CI: 1.05\u0026ndash;1.56; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.047), medication for menstrual pain (adjusted OR: 1.39, 95% CI: 1.13\u0026ndash;1.70; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.006), and irregular menstruation (adjusted odds ratio [OR]: 1.32, 95% confidence interval [CI]: 1.08\u0026ndash;1.61; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.04). However, no association between being overweight and dysmenorrhea was found. In a young Japanese population, underweight\u0026mdash;but not overweight\u0026mdash;was independently and positively associated with dysmenorrhea.\u003c/p\u003e","manuscriptTitle":"Association between underweight and menstrual pain severity, irregular menstruation in a young Japanese population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 02:08:08","doi":"10.21203/rs.3.rs-6575224/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9a2d1b17-b8c3-4dea-bb94-7a14ea597cf4","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49704570,"name":"Health sciences/Diseases"},{"id":49704571,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-07T17:10:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 02:08:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6575224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6575224","identity":"rs-6575224","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}