{"paper_id":"326dcec5-c1d3-41c1-bfff-6541da2601ad","body_text":"RESEARCH Open Access\n© The Author(s) 2025. Open Access  This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 \nInternational License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you \ngive appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the \nlicensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or \nother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the \nmaterial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or \nexceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit  h t t p  : / /  c r e a  t i  \nv e c  o m m  o n s .  o r  g / l  i c e  n s e s  / b  y - n c - n d / 4 . 0 /.\nHu and He BMC Psychology          (2025) 13:514 \nhttps://doi.org/10.1186/s40359-025-02853-3\nBMC Psychology\n*Correspondence:\nLijuan He\n15181993200@163.com\n1Department of Gynecology, The Affiliated Hospital, Southwest Medical \nUniversity, 25 Taiping Street, Jiangyang District, Luzhou 646000, Sichuan, \nChina\n2Department of Health Management Center, The Affiliated Hospital, \nSouthwest Medical University, 25 Taiping Street, Jiangyang District, \nLuzhou 646000, Sichuan, China\nAbstract\nObjective To investigate factors associated with anxiety and depression in perimenopausal women experiencing \nabnormal uterine bleeding (AUB), with a focus on endocrine markers and lifestyle factors.\nMethods This retrospective cohort study analyzed 1,234 perimenopausal women with AUB treated at a tertiary \nhospital from January 2023 to January 2024. Participants were classified based on DSM-5 diagnoses of anxiety and \ndepression. Data collected included demographics, lifestyle habits, comorbidities, psychiatric history, and endocrine \nlevels (estradiol, follicle-stimulating hormone [FSH], luteinizing hormone [LH], cortisol, prolactin, testosterone, and \nthyroid-stimulating hormone [TSH]). Logistic regression models identified independent predictors, with interaction \nand stratified analyses conducted by age group (< 50 and ≥ 50 years).\nResults Factors associated with anxiety and depression included higher BMI (OR 1.08, P = 0.008), longer AUB duration \n(OR 1.12, P = 0.001), single/divorced/widowed marital status (OR 1.54, P = 0.015), and lower education levels (OR \n1.62, P < 0.001). Smoking history (OR 2.84, P < 0.001) and psychiatric history (OR 3.11, P < 0.001) emerged as strong \npredictors, while regular exercise was protective (OR 0.64, P = 0.001). Hormonal factors, including lower estradiol and \nelevated levels of FSH, LH, and cortisol, were significantly linked to increased odds of psychological distress (P < 0.01). \nInteraction analyses revealed that smoking and elevated cortisol exacerbated risks, whereas regular exercise mitigated \nthe adverse effects of elevated FSH and LH. These associations were consistent across age groups.\nConclusions Anxiety and depression in perimenopausal women with AUB are influenced by a combination of \ndemographic, lifestyle, clinical, and endocrine factors. Addressing modifiable risk factors, such as smoking cessation \nand increased physical activity, may alleviate psychological distress. Further research is needed to elucidate the \nhormonal pathways connecting endocrine changes to mental health.\nKeywords Anxiety, Depression, Abnormal uterine bleeding, Perimenopause, Endocrine markers\nFactors associated with anxiety \nand depression in perimenopausal \nwomen with abnormal uterine bleeding: A \nretrospective cohort study\nJun Hu1 and Lijuan He2*\n\nPage 2 of 10\nHu and He BMC Psychology          (2025) 13:514 \nIntroduction\nAnxiety and depression are common psychological \nconditions that significantly affect women during the \nperimenopausal period, a transitional phase marked by \nhormonal fluctuations and changes in menstrual pat -\nterns. The perimenopausal period has been associated \nwith a higher prevalence of mental health issues, with \nstudies suggesting that fluctuations in gonadal hormones, \nparticularly estradiol and follicle-stimulating hormone \n(FSH), may contribute to mood disturbances [ 1– 2]. In \naddition, alterations in stress-related hormones such as \ncortisol—though not necessarily fluctuating in a cyclical \nmanner—have also been linked to anxiety and depres -\nsion during midlife, potentially due to dysregulation \nof the hypothalamic-pituitary-adrenal (HPA) axis [ 3]. \nAbnormal uterine bleeding (AUB), which is also com -\nmon during this period, can exacerbate emotional stress, \nparticularly in women experiencing prolonged or heavy \nbleeding [ 4]. However, the interplay between AUB, hor -\nmonal changes, and psychological distress remains \nunderexplored.\nHormonal changes in perimenopausal women, includ -\ning declining levels of estradiol and increased FSH and \nluteinizing hormone (LH), have been implicated in both \nthe onset and severity of anxiety and depression [ 5– 6]. \nEstradiol, in particular, has been shown to have neuro -\nprotective effects, influencing mood regulation through \nits actions on neurotransmitters such as serotonin and \ndopamine [ 7]. Cortisol, the body’s primary stress hor -\nmone, has also been linked to psychological disorders, \nwith elevated cortisol levels being associated with an \nincreased risk of anxiety and depression [ 8– 9]. These \nhormonal imbalances may be further compounded \nby lifestyle factors, such as smoking, physical inactiv -\nity, and poor sleep quality, all of which are prevalent in \nperimenopausal women and have been associated with \nadverse mental health outcomes [10– 12].\nPrevious research has highlighted the role of demo -\ngraphic and lifestyle factors in influencing the psycholog -\nical well-being of perimenopausal women. For instance, \nwomen with lower education levels, those who are single, \ndivorced, or widowed, and those with a history of psy -\nchiatric illness are more likely to experience anxiety and \ndepression during the perimenopausal period [ 13– 14]. \nAdditionally, smoking has been identified as a significant \nrisk factor for depression, while regular physical activ -\nity has been shown to have a protective effect on mental \nhealth [ 15– 16]. Despite these findings, there is a pau -\ncity of data on how these factors interact with hormonal \nchanges to influence the risk of anxiety and depression \nspecifically in women with abnormal uterine bleeding \nduring the perimenopausal period.\nThe present study aims to explore the associations \nbetween demographic, lifestyle, and endocrine factors \nwith anxiety and depression in perimenopausal women \nwith AUB. We hypothesize that lifestyle factors (such as \nsmoking and physical inactivity), medical and psychiatric \ncomorbidities, as well as hormonal imbalances, contrib -\nute to the increased risk of anxiety and depression in this \npopulation. By identifying these associations, this study \nseeks to provide a better understanding of the psycho -\nlogical challenges faced by perimenopausal women with \nAUB and to inform targeted interventions for improving \nmental health outcomes.\nMethods\nStudy design and population\nThis cross-sectional study, based on a retrospective \nreview of medical records, was conducted to investigate \nthe factors associated with anxiety and depression in \nperimenopausal women with abnormal uterine bleeding \n(AUB). Data were collected from perimenopausal women \ndiagnosed with AUB who visited the gynecology out -\npatient department at a tertiary hospital between Janu -\nary 2023 and January 2024. Perimenopause was defined \nbased on the STRAW + 10 criteria, as the transitional \nperiod characterized by changes in menstrual cycle regu -\nlarity—such as a cycle length variation of ≥ 7 days or ≥ 60 \ndays of amenorrhea—accompanied by typical meno -\npausal symptoms. These symptoms included hot flashes, \nnight sweats, mood disturbances, or sleep disorders. \nWomen with lifelong menstrual irregularities (e.g., due \nto polycystic ovary syndrome) were excluded. The study \nincluded women aged 40 to 55 years. For women within \n12 months after their final menstrual period, abnormal \nuterine bleeding (AUB) was included if the episode was \nconsistent with perimenopausal hormonal patterns and \nnot attributable to postmenopausal pathology. AUB was \ndiagnosed based on clinical evaluation and transvaginal \nultrasound.\nPatients with missing data for any key variables, includ-\ning hormone measurements or psychiatric evaluation \nresults, were excluded from the analysis. As such, the \nfinal sample only included individuals with complete \nclinical and laboratory data. No imputation was applied \nfor missing values.\nData collection\nData were extracted from the patients’ medical records, \nincluding demographic and lifestyle characteristics (age, \nBMI, marital status, education level, smoking history, \nalcohol use, and exercise frequency), clinical factors \n(duration of abnormal uterine bleeding, comorbidities \nsuch as hypertension and diabetes, psychiatric history), \nand laboratory results of endocrine markers (estradiol, \nfollicle-stimulating hormone [FSH], luteinizing hormone \n[LH], cortisol, prolactin, testosterone, and thyroid-stimu-\nlating hormone [TSH]).\n\nPage 3 of 10\nHu and He BMC Psychology          (2025) 13:514 \nAnxiety and depression status was assessed through \nclinical psychiatric evaluations conducted during outpa -\ntient visits, based on the Diagnostic and Statistical Man -\nual of Mental Disorders, Fifth Edition (DSM-5) criteria. \nThese evaluations were performed by qualified mental \nhealth professionals and documented in the electronic \nmedical records. Psychiatric history, including any prior \ndiagnoses of anxiety, depression, or other mental disor -\nders, was also extracted. Patients with major psychiatric \ndisorders other than anxiety or depression (e.g., schizo -\nphrenia or bipolar disorder) were excluded from the \nanalysis.\nStatistical analysis\nContinuous variables were expressed as mean ± stan-\ndard deviation for normally distributed variables or \nmedian (interquartile range) for skewed variables. Cat -\negorical variables were presented as frequencies and \npercentages. Comparisons between the anxiety and non-\nanxiety groups, as well as between the depression and \nnon-depression groups, were made using independent \nt-tests for normally distributed variables, Mann-Whitney \nU tests for skewed variables, and Chi-square tests for cat-\negorical variables. A two-sided P-value < 0.05 was consid-\nered statistically significant.\nUnivariate and multivariate logistic regression analy -\nses were performed to identify factors associated with \nanxiety and depression in perimenopausal women with \nAUB. The odds ratios (OR) and corresponding 95% con -\nfidence intervals (CI) were calculated. All variables with \nP-values < 0.05 in univariate analysis were included in the \nmultivariate logistic regression models. The multivariate \nmodels were adjusted for age, BMI, duration of abnormal \nuterine bleeding, marital status, education level, smoking \nhistory, exercise frequency, psychiatric history, endocrine \nmarkers, and sleep quality.\nFor further analysis, interaction effects between \nendocrine markers and lifestyle factors (e.g., smoking \nand exercise) were examined using multivariate logis -\ntic regression models, with interaction terms included. \nStratified analyses by age group (< 50 vs. ≥50 years) were \nconducted to explore whether age modified the associa -\ntions between risk factors and psychological outcomes. \nHowever, interaction terms with age were not statistically \nsignificant, and the effect estimates remained consistent \nacross both groups. Therefore, detailed stratified results \nare not shown but are available upon request. Endocrine \nmarkers such as estradiol, FSH, LH, cortisol, prolactin, \ntestosterone, and TSH were initially screened for inclu -\nsion in the regression models. TSH was excluded from \nthe final models due to lack of independent significance \nin multivariate analysis and to avoid redundancy among \ncorrelated hormonal variables. All statistical analyses \nwere performed using IBM SPSS Statistics version 28.0 \n(IBM Corp., Armonk, NY, USA). P-values were calcu -\nlated using two-tailed tests, and a significance level of \nP < 0.05 was considered statistically significant for all \nanalyses.\nResults\nBaseline characteristics of perimenopausal women with \nabnormal uterine bleeding (n = 1234)\nTables 1 and 2 present the baseline characteristics of the \nstudy population according to anxiety and depression \nstatus, respectively. Age was similar across groups. How -\never, women with either anxiety or depression were more \nlikely to have higher BMI, longer duration of abnormal \nuterine bleeding, lower education levels, and be unmar -\nried (all P < 0.05). Smoking history and physical inactivity \nwere significantly more common in both the anxiety and \ndepression groups ( P < 0.001). Additionally, participants \nin both groups exhibited lower estradiol levels and higher \nlevels of FSH, LH, and cortisol compared to those with -\nout anxiety or depression (all P < 0.01). Poor sleep qual -\nity, as indicated by significantly elevated PSQI scores, was \nalso strongly associated with both psychological condi -\ntions (P < 0.001). These findings highlight consistent pat -\nterns of demographic, lifestyle, and endocrine differences \nin women experiencing anxiety or depression.\nUnivariate and multivariate logistic regression analysis for \nfactors associated with anxiety in perimenopausal women \nwith abnormal uterine bleeding (n = 1234)\nIn multivariate analysis (Table  3), anxiety in perimeno -\npausal women with AUB was significantly associated \nwith higher BMI, longer duration of abnormal uterine \nbleeding, being single/divorced/widowed, and lower \neducation levels. Lifestyle factors such as smoking and \nphysical inactivity were strong predictors, while regu -\nlar exercise appeared protective. Notably, elevated PSQI \nscores and endocrine markers including lower estradiol \nand higher FSH, LH, and cortisol levels were also linked \nto increased anxiety risk. These findings suggest that anx-\niety is influenced by an interplay of demographic, behav -\nioral, and hormonal factors.\nUnivariate and multivariate logistic regression analysis \nfor factors associated with depression in perimenopausal \nwomen with abnormal uterine bleeding (n = 1234)\nAs shown in Table  4, depression was significantly asso -\nciated with higher BMI, longer duration of abnormal \nuterine bleeding, unmarried status, and lower educa -\ntion. A history of smoking and prior psychiatric disorders \nemerged as particularly strong predictors. Lower estra -\ndiol and elevated FSH, LH, and cortisol levels were also \nsignificantly associated with increased depression risk. \nAs with anxiety, poor sleep quality and lack of regular \nphysical activity were linked to higher odds of depression. \n\nPage 4 of 10\nHu and He BMC Psychology          (2025) 13:514 \nThese results support a multifactorial model, in which \ndemographic, behavioral, clinical, and hormonal factors \ncollectively contribute to depression in this population.\nComparison of clinical and hormonal characteristics \namong women with anxiety only, depression only, \ncomorbid anxiety and depression, and neither condition\nOf the 1,234 participants, 298 (24.1%) had anxiety only, \n250 (20.3%) had depression only, 418 (33.9%) had both \nanxiety and depression, and 268 (21.7%) had neither con-\ndition. As shown in Tables  5 and 6, women with comor -\nbid anxiety and depression exhibited more pronounced \nclinical and hormonal dysregulation compared to those \nwithout psychological symptoms. This group had the \nhighest BMI and longest duration of AUB, as well as the \nhighest rates of smoking, psychiatric history, and poor \nsleep quality. Endocrine disturbances were also most \nevident in the comorbid group, with significantly lower \nestradiol and elevated FSH, LH, and cortisol levels (all \nP < 0.001), suggesting activation of both gonadotropic and \nstress axes. Prolactin, testosterone, and TSH levels were \nalso elevated. Although TSH was significantly higher in \nthe comorbid group, it was not retained in multivari -\nate models, indicating a lack of independent predictive \nvalue after adjustment for other clinical and hormonal \nvariables. These findings support the hypothesis that \ncomorbid anxiety and depression in perimenopausal \nwomen with AUB are associated with a distinct pattern \nof physiological dysregulation.\nMultivariate logistic regression analysis for factors \nassociated with both anxiety and depression in \nperimenopausal women with abnormal uterine bleeding \n(n = 1234)\nAs shown in Table  7, the presence of both anxiety and \ndepression was independently associated with higher \nBMI, longer duration of abnormal uterine bleeding, \nunmarried status, lower education level, smoking history, \nand poor sleep quality. A psychiatric history emerged as \nthe strongest predictor. Regular physical activity was pro-\ntective. Hormonal factors including lower estradiol and \nelevated FSH, LH, and cortisol levels were also signifi -\ncantly associated with comorbidity. These findings rein -\nforce the multifactorial nature of psychological distress \nin perimenopausal women with AUB, involving demo -\ngraphic, behavioral, clinical, and endocrine dimensions.\nTable 1 Baseline characteristics of perimenopausal women with abnormal uterine bleeding (n = 1234)\nCharacteristic Total (n = 1234) Anxiety Group \n(n = 652)\nNon-Anxiety Group \n(n = 582)\nP-value\nAge (years), Mean ± SD 48.7 ± 3.5 48.9 ± 3.4 48.5 ± 3.6 0.182\nBMI (kg/m²), Mean ± SD 25.8 ± 4.1 26.1 ± 4.2 25.5 ± 3.9 0.045*\nDuration of Abnormal Uterine Bleeding (months), \nMedian (IQR)\n8 (3–12) 10 (5–13) 7 (3–11) 0.003**\nMarital Status, n (%)\n- Married 954 (77.3) 482 (73.9) 472 (81.1) 0.008**\n- Single/Divorced/Widowed 280 (22.7) 170 (26.1) 110 (18.9)\nEducation Level, n (%)\n- Primary/Secondary 645 (52.3) 380 (58.3) 265 (45.5) < 0.001***\n- College/University 589 (47.7) 272 (41.7) 317 (54.5)\nSmoking History, n (%) 98 (7.9) 74 (11.4) 24 (4.1) < 0.001***\nAlcohol Use, n (%) 147 (11.9) 89 (13.7) 58 (10.0) 0.052\nExercise Regularly (≥ 3 times/week), n (%) 546 (44.3) 232 (35.6) 314 (53.9) < 0.001***\nComorbidities, n (%)\n- Hypertension 398 (32.2) 220 (33.7) 178 (30.6) 0.253\n- Diabetes 148 (12.0) 89 (13.6) 59 (10.1) 0.052\nPsychiatric History, n (%) 256 (20.7) 183 (28.1) 73 (12.5) < 0.001***\nEndocrine Levels, Mean ± SD\n- Estradiol (pg/mL) 62.5 ± 22.4 60.2 ± 21.8 64.9 ± 23.0 0.012*\n- FSH (mIU/mL) 25.6 ± 8.9 27.4 ± 8.6 23.7 ± 8.8 < 0.001***\n- LH (mIU/mL) 21.8 ± 6.7 22.9 ± 6.5 20.7 ± 6.8 < 0.001***\n- Cortisol (µg/dL) 14.2 ± 5.3 15.6 ± 5.1 12.7 ± 5.5 < 0.001***\n- Prolactin (ng/mL) 18.9 ± 6.3 19.5 ± 6.5 18.2 ± 6.0 0.002**\nSleep Quality (PSQI Score), Median (IQR) 7 (5–10) 9 (6–12) 6 (4–8) < 0.001***\nNote: Data are presented as mean ± standard deviation for normally distributed variables, median (interquartile range) for skewed variables, and frequency \n(percentage) for categorical variables. P-values were calculated using t-tests, Mann-Whitney U tests, or Chi-square tests as appropriate.*Significance levels: * P < 0.05, \n**P < 0.01, ***P < 0.001\n\nPage 5 of 10\nHu and He BMC Psychology          (2025) 13:514 \nInteraction effects between endocrine markers and \nlifestyle and clinical factors on anxiety and depression in \nperimenopausal women with abnormal uterine bleeding \n(n = 1234)\nInteraction terms between endocrine markers (estra -\ndiol, FSH, LH, cortisol, prolactin, testosterone, and TSH) \nand lifestyle or clinical factors (smoking, regular physi -\ncal activity, and psychiatric history) were tested using \nmultivariate logistic regression. As shown in Table  8, \nonly statistically significant interactions ( P < 0.05) are \nreported; non-significant results are omitted for clarity \nbut are available upon request. Notably, smoking ampli -\nfied the adverse effects of lower estradiol and higher cor -\ntisol on anxiety and depression, while regular exercise \nmitigated the psychological risk associated with elevated \nFSH and LH levels. Additionally, higher TSH levels were \nTable 2 Baseline characteristics of perimenopausal women with abnormal uterine bleeding by depression status (n = 1234)\nCharacteristic Total (n = 1234) Depression Group (n = 604) Non-Depression Group (n = 630) P-value\nAge (years), Mean ± SD 48.7 ± 3.5 48.9 ± 3.4 48.6 ± 3.6 0.109\nBMI (kg/m²), Mean ± SD 25.8 ± 4.1 26.0 ± 4.2 25.4 ± 3.9 0.033*\nDuration of AUB (months), Median (IQR) 8 (3–12) 10 (5–13) 7 (3–11) 0.005**\nMarital Status, n (%) 0.019*\n– Married 954 (77.3) 455 (75.3) 499 (79.2)\n– Single/Divorced/Widowed 280 (22.7) 149 (24.7) 131 (20.8)\nEducation Level, n (%) < 0.001***\n– Primary/Secondary 645 (52.3) 360 (59.6) 285 (45.2)\n– College/University 589 (47.7) 244 (40.4) 345 (54.8)\nSmoking History, n (%) 98 (7.9) 69 (11.4) 29 (4.6) < 0.001***\nAlcohol Use, n (%) 147 (11.9) 83 (13.7) 64 (10.2) 0.058\nExercise Regularly (≥ 3 times/week), n (%) 546 (44.3) 218 (36.1) 328 (52.1) < 0.001***\nComorbidities, n (%)\n– Hypertension 398 (32.2) 207 (34.3) 191 (30.3) 0.174\n– Diabetes 148 (12.0) 82 (13.6) 66 (10.5) 0.072\nPsychiatric History, n (%) 256 (20.7) 170 (28.1) 86 (13.7) < 0.001***\nEndocrine Levels, Mean ± SD\n– Estradiol (pg/mL) 62.5 ± 22.4 59.3 ± 22.1 65.7 ± 22.6 0.003**\n– FSH (mIU/mL) 25.6 ± 8.9 27.1 ± 8.5 24.2 ± 8.9 < 0.001***\n– LH (mIU/mL) 21.8 ± 6.7 22.7 ± 6.6 20.9 ± 6.7 < 0.001***\n– Cortisol (µg/dL) 14.2 ± 5.3 15.3 ± 5.1 13.2 ± 5.4 < 0.001***\n– Prolactin (ng/mL) 18.9 ± 6.3 19.4 ± 6.5 18.3 ± 6.0 0.004**\nSleep Quality (PSQI Score), Median (IQR) 7 (5–10) 9 (6–12) 6 (4–8) < 0.001***\nNote: Data are presented as mean ± standard deviation for normally distributed variables, median (interquartile range) for skewed variables, and frequency \n(percentage) for categorical variables. P-values were calculated using t-tests, Mann-Whitney U tests, or Chi-square tests as appropriate. *Significance levels: * P < 0.05, \n**P < 0.01, ***P < 0.001\nTable 3 Univariate and multivariate logistic regression analysis for factors associated with anxiety in perimenopausal women with \nabnormal uterine bleeding (n = 1234)\nVariable Univariate OR (95% CI) P-value Multivariate OR (95% CI) P-value\nAge (years) 1.03 (0.98–1.07) 0.182 1.02 (0.97–1.06) 0.215\nBMI (kg/m²) 1.07 (1.02–1.12) 0.041* 1.06 (1.01–1.11) 0.049*\nDuration of Abnormal Uterine Bleeding (months) 1.09 (1.03–1.15) 0.003** 1.08 (1.02–1.14) 0.006**\nMarital Status (Single/Divorced/Widowed vs. Married) 1.56 (1.11–2.18) 0.010* 1.42 (1.01-2.00) 0.042*\nEducation Level (Primary/Secondary vs. College/University) 1.71 (1.34–2.19) < 0.001*** 1.58 (1.22–2.05) < 0.001***\nSmoking History (Yes vs. No) 3.07 (1.89–4.98) < 0.001*** 2.91 (1.78–4.76) < 0.001***\nExercise Regularly (≥ 3 times/week) (Yes vs. No) 0.53 (0.42–0.68) < 0.001*** 0.61 (0.47–0.79) < 0.001***\nPsychiatric History (Yes vs. No) 2.71 (1.94–3.79) < 0.001*** 2.44 (1.72–3.47) < 0.001***\nEstradiol (pg/mL) 0.98 (0.96–0.99) 0.014* 0.98 (0.96–0.99) 0.018*\nFSH (mIU/mL) 1.06 (1.04–1.09) < 0.001*** 1.05 (1.03–1.08) < 0.001***\nLH (mIU/mL) 1.04 (1.02–1.07) < 0.001*** 1.03 (1.01–1.06) 0.002**\nCortisol (µg/dL) 1.09 (1.04–1.14) < 0.001*** 1.07 (1.02–1.13) 0.005**\nSleep Quality (PSQI Score) 1.26 (1.19–1.33) < 0.001*** 1.24 (1.17–1.32) < 0.001***\nNote: OR = Odds Ratio; CI = Confidence Interval. P-values were calculated using univariate and multivariate logistic regression models. Multivariate models adjusted \nfor all variables listed in the table.*Significance levels: * P < 0.05, **P < 0.01, ***P < 0.001\n\nPage 6 of 10\nHu and He BMC Psychology          (2025) 13:514 \nassociated with a reduced risk among women who exer -\ncised regularly. The interaction between cortisol and \npsychiatric history further intensified psychological vul -\nnerability. These findings underscore the moderating role \nof behavioral and clinical history factors on the mental \nhealth impact of endocrine dysregulation in perimeno -\npausal women.\nDiscussion\nThis study sought to explore the factors associated with \nanxiety and depression in perimenopausal women with \nabnormal uterine bleeding (AUB), focusing on the roles \nof demographic, lifestyle, and endocrine factors. Our \nfindings provide critical insights into the multifacto -\nrial nature of psychological distress in this population, \ndemonstrating the combined effects of hormonal imbal -\nances, social factors, and lifestyle habits on mental health \noutcomes.\nTable 4 Univariate and multivariate logistic regression analysis for factors associated with depression in perimenopausal women with \nabnormal uterine bleeding (n = 1234)\nVariable Univariate OR (95% CI) P-value Multivariate OR (95% CI) P-value\nAge (years) 1.04 (0.99–1.08) 0.085 1.03 (0.98–1.07) 0.109\nBMI (kg/m²) 1.08 (1.03–1.13) 0.027* 1.07 (1.02–1.12) 0.033*\nDuration of Abnormal Uterine Bleeding (months) 1.12 (1.05–1.18) 0.002** 1.09 (1.03–1.15) 0.005**\nMarital Status (Single/Divorced/Widowed vs. Married) 1.62 (1.18–2.24) 0.007** 1.49 (1.06–2.09) 0.019*\nEducation Level (Primary/Secondary vs. College/University) 1.84 (1.45–2.33) < 0.001*** 1.66 (1.28–2.17) < 0.001***\nSmoking History (Yes vs. No) 2.94 (1.83–4.72) < 0.001*** 2.72 (1.69–4.40) < 0.001***\nExercise Regularly (≥ 3 times/week) (Yes vs. No) 0.59 (0.45–0.78) < 0.001*** 0.68 (0.52–0.89) 0.006**\nPsychiatric History (Yes vs. No) 3.02 (2.14–4.26) < 0.001*** 2.67 (1.88–3.78) < 0.001***\nEstradiol (pg/mL) 0.96 (0.94–0.98) < 0.001*** 0.97 (0.95–0.99) 0.003**\nFSH (mIU/mL) 1.05 (1.03–1.08) < 0.001*** 1.04 (1.02–1.07) < 0.001***\nLH (mIU/mL) 1.03 (1.01–1.06) 0.004** 1.02 (1.00-1.05) 0.012*\nCortisol (µg/dL) 1.11 (1.06–1.16) < 0.001*** 1.09 (1.04–1.15) 0.002**\nSleep Quality (PSQI Score) 1.32 (1.25–1.39) < 0.001*** 1.29 (1.22–1.36) < 0.001***\nNote: OR = Odds Ratio; CI = Confidence Interval. P-values were calculated using univariate and multivariate logistic regression models. Multivariate models adjusted \nfor all variables listed in the table. *Significance levels: * P < 0.05, **P < 0.01, ***P < 0.001\nTable 5 Comparison of endocrine levels between women with and without anxiety and depression (n = 1234)\nEndocrine Marker Anxiety & Depression Group (n = 418) Non-Anxiety & Non-Depression Group (n = 416) P-value\nEstradiol (pg/mL), Mean ± SD 58.7 ± 22.0 67.5 ± 23.5 < 0.001***\nFSH (mIU/mL), Mean ± SD 27.9 ± 8.4 22.5 ± 8.6 < 0.001***\nLH (mIU/mL), Mean ± SD 23.5 ± 6.9 19.8 ± 6.7 < 0.001***\nCortisol (µg/dL), Mean ± SD 15.8 ± 5.2 13.1 ± 5.4 < 0.001***\nProlactin (ng/mL), Mean ± SD 19.6 ± 6.1 17.8 ± 6.0 0.002**\nTestosterone (ng/dL), Mean ± SD 0.52 ± 0.15 0.49 ± 0.13 0.034*\nTSH (mIU/L), Mean ± SD 2.71 ± 1.14 2.41 ± 1.10 0.012*\nNote: Data are presented as mean ± standard deviation. P-values were calculated using t-tests to compare the mean endocrine levels between the anxiety & \ndepression group and the non-anxiety & non-depression group. *Significance levels: * P < 0.05, **P < 0.01, ***P < 0.001\nTable 6 Comparison of key characteristics among four mental health subgroups\nVariable Anxiety Only \n(n = 298)\nDepression Only \n(n = 250)\nComorbid A&D \n(n = 418)\nNeither (n = 268) P-value\nAge (years), Mean ± SD 48.8 ± 3.3 48.9 ± 3.4 49.0 ± 3.5 48.5 ± 3.6 0.217\nBMI (kg/m²), Mean ± SD 25.9 ± 4.1 25.8 ± 4.0 26.3 ± 4.2 25.2 ± 3.8 0.019*\nDuration of AUB (months), Median (IQR) 9 (5–12) 9 (5–13) 10 (6–14) 6 (3–10) < 0.001***\nSmoking History (%) 10.1% 9.6% 11.5% 2.6% < 0.001***\nPsychiatric History (%) 22.1% 23.6% 34.5% 9.3% < 0.001***\nEstradiol (pg/mL) 61.8 ± 22.3 60.5 ± 22.5 58.7 ± 22.0 67.5 ± 23.5 < 0.001***\nFSH (mIU/mL) 26.5 ± 8.7 26.7 ± 8.4 27.9 ± 8.4 22.5 ± 8.6 < 0.001***\nCortisol (µg/dL) 14.8 ± 5.1 14.9 ± 5.2 15.8 ± 5.2 13.1 ± 5.4 < 0.001***\nSleep Quality (PSQI), Median (IQR) 8 (5–10) 8 (5–11) 9 (6–12) 5 (3–7) < 0.001***\nNote: Comparison across four mental health subgroups. P-values derived using ANOVA, Kruskal-Wallis, or Chi-square tests as appropriate. A&D = Anxiety and \nDepression. *Significance levels: * P < 0.05, **P < 0.01, ***P < 0.001\n\nPage 7 of 10\nHu and He BMC Psychology          (2025) 13:514 \nConsistent with previous studies, we found that higher \nBMI was significantly associated with increased odds \nof anxiety and depression in perimenopausal women. \nBarghandan et al. (2021) reported that postmenopausal \nwomen with elevated BMI and body fat mass (BFM) \nexperienced more severe menopausal symptoms and \nhigher levels of trait anxiety, highlighting a clear link \nbetween excess adiposity and psychological distress \nduring this transitional period [ 17]. Obesity is widely \nrecognized as a risk factor for mental health disorders, \nprimarily due to the metabolic and inflammatory dis -\nturbances associated with excess body fat. In particular, \ncentral obesity has been linked to disruptions in neuro -\nendocrine signaling, increased systemic inflammation, \nand insulin resistance, all of which contribute to mood \ndysregulation and heightened vulnerability to anxiety and \ndepression [18]. Moreover, Barghandan et al. also empha-\nsized that negative body image perceptions related to \nphysiological changes in menopause may further amplify \nemotional distress, reinforcing the psychological burden \nin women with higher BMI during the perimenopausal \nand postmenopausal phases. Research shows that meta -\nbolic abnormalities, such as alterations in glucocorti -\ncoids, insulin resistance, and increased inflammatory \nsignaling from dysfunctional adipose tissue, may signifi -\ncantly impact mood regulation and emotional control, \nthereby contributing to the higher incidence of depres -\nsion in obese individual [ 19]. Additionally, the pro -\nlonged duration of abnormal uterine bleeding (AUB) was \nanother significant predictor, underscoring the emotional \nburden imposed by persistent bleeding. Women with \nlonger durations of AUB frequently experience height -\nened anxiety and depression due to the ongoing uncer -\ntainty and physical discomfort associated with irregular \nmenstrual cycles. As Lebduska et al. (2023) note, AUB \noften presents with variations in frequency, duration, and \nvolume of bleeding, contributing to psychological stress \nand discomfort in affected women [ 20]. This prolonged \ndisruption of daily life can exacerbate feelings of anxiety \nand emotional distress, further impacting mental health \noutcomes.\nMarital status also played a significant role in men -\ntal health outcomes, with single, divorced, or widowed \nwomen being more likely to experience anxiety and \ndepression compared to their married counterparts. This \nobservation is supported by studies highlighting the pro -\ntective role of social support against mental health dis -\norders. Li et al. (2023), in their longitudinal study, found \nTable 7 Multivariate logistic regression analysis for factors associated with both anxiety and depression in perimenopausal women \nwith abnormal uterine bleeding (n = 1234)\nVariable Multivariate OR (95% CI) P-value\nAge (years) 1.01 (0.97–1.05) 0.268\nBMI (kg/m²) 1.08 (1.02–1.14) 0.008**\nDuration of Abnormal Uterine Bleeding (months) 1.12 (1.05–1.18) 0.001***\nMarital Status (Single/Divorced/Widowed vs. Married) 1.54 (1.09–2.18) 0.015*\nEducation Level (Primary/Secondary vs. College/University) 1.62 (1.24–2.12) < 0.001***\nSmoking History (Yes vs. No) 2.84 (1.79–4.51) < 0.001***\nExercise Regularly (≥ 3 times/week) (Yes vs. No) 0.64 (0.49–0.84) 0.001***\nPsychiatric History (Yes vs. No) 3.11 (2.16–4.48) < 0.001***\nEstradiol (pg/mL) 0.96 (0.94–0.98) < 0.001***\nFSH (mIU/mL) 1.05 (1.03–1.08) < 0.001***\nLH (mIU/mL) 1.03 (1.01–1.06) 0.006**\nCortisol (µg/dL) 1.10 (1.04–1.16) < 0.001***\nSleep Quality (PSQI Score) 1.30 (1.22–1.38) < 0.001***\nNote: OR = Odds Ratio; CI = Confidence Interval. P-values were calculated using multivariate logistic regression analysis. *Significance levels: * P < 0.05, **P < 0.01, \n***P < 0.001\nTable 8 Interaction effects between endocrine markers and lifestyle and clinical factors on anxiety and depression in perimenopausal \nwomen with abnormal uterine bleeding (n = 1234)\nInteraction Term Multivariate OR (95% CI) P-value\nEstradiol (pg/mL) × Smoking History 1.08 (1.02–1.14) 0.006**\nFSH (mIU/mL) × Exercise Regularly (≥ 3 times/week) 0.92 (0.88–0.97) < 0.001***\nCortisol (µg/dL) × Smoking History 1.12 (1.05–1.18) < 0.001***\nLH (mIU/mL) × Exercise Regularly (≥ 3 times/week) 0.95 (0.91–0.99) 0.015*\nTSH (mIU/L) × Exercise Regularly (≥ 3 times/week) 0.89 (0.82–0.97) 0.005**\nCortisol (µg/dL) × Psychiatric History 1.14 (1.06–1.23) < 0.001***\nNote: OR = Odds Ratio; CI = Confidence Interval. P-values were calculated using multivariate logistic regression models that included interaction terms between \nendocrine markers and lifestyle factors (smoking, exercise) and clinical history (psychiatric history). *Significance levels: * P < 0.05, **P < 0.01, ***P < 0.001\n\nPage 8 of 10\nHu and He BMC Psychology          (2025) 13:514 \nthat individuals with greater social support, such as \nclose confidants or access to practical help, had reduced \ndepressive symptoms [ 21]. Their research indicated that \nsocial support can buffer against the effects of loneli -\nness, thus mitigating depressive symptoms, especially in \nmen. Similarly, Gariépy et al. (2016) conducted a system -\natic review and meta-analysis and confirmed that social \nsupport, particularly from spouses and family members, \nserves as a protective factor against depression across \nvarious life stages [ 22]. They found that support from a \nspouse had the strongest protective effect in adulthood \nand older age. These findings underline the critical role of \nmarital status and close social connections in maintain -\ning mental well-being, especially during vulnerable peri -\nods such as perimenopause.\nLower education levels were similarly associated with \nhigher odds of anxiety and depression, which aligns with \nprevious research linking lower socioeconomic status \nto poorer mental health outcomes. Cohen et al. (2020) \nfound that individuals with lower educational attain -\nment had a higher likelihood of experiencing depression \nin midlife, emphasizing that education shapes access to \nhealth resources and coping mechanisms across the lifes-\npan [ 23]. Similarly, Hoebel et al. (2017) demonstrated \nthat lower education, as a core dimension of socioeco -\nnomic status, was significantly associated with depressive \nsymptoms, as education is closely related to cognitive \nabilities and health-related behaviors [ 24]. Furthermore, \na meta-analysis by Lorant et al. (2003) revealed that indi -\nviduals with lower education levels had higher odds of \nboth new episodes and persistent depression, confirming \nthe dose-response relationship between education and \nmental health outcomes [ 25]. Education not only pro -\nvides access to better resources but also improves health \nliteracy and enhances coping mechanisms, all of which \ncan mitigate the psychological effects of perimenopausal \nsymptoms.\nLifestyle factors, including smoking and physical \ninactivity, further compounded the risk of anxiety and \ndepression in this cohort. Smoking has been identified as \na significant modifiable risk factor for depression, likely \ndue to its impact on neurochemistry and its association \nwith unhealthy coping mechanisms [26]. Conversely, reg-\nular physical activity was found to have a protective effect \non mental health, supporting the well-established ben -\nefits of exercise on mood regulation and stress reduction \n[27]. These findings underscore the importance of life -\nstyle interventions in mitigating the psychological burden \nof perimenopausal symptoms.\nAlthough our analysis revealed a statistically significant \nassociation between lower estradiol levels and anxiety, \nthe effect size was relatively small. Therefore, estradiol \nmay play a contributory rather than a central role in the \ndevelopment of anxiety in this population. Estradiol, \nknown for its neuroprotective properties, plays a critical \nrole in mood regulation by modulating neurotransmit -\nters such as serotonin and dopamine. As estradiol levels \ndecline during perimenopause, women become more \nsusceptible to mood disturbances. Raglan et al. (2020) \nemphasized that hormonal fluctuations, particularly \ndeclining estradiol, are closely linked to the increased \nincidence of depression during perimenopause, a period \nof heightened vulnerability for mood disorders [ 5]. Simi-\nlarly, Freeman (2015) reviewed the accumulating evi -\ndence linking the changing hormonal milieu, including \nincreased FSH and cortisol levels, to depressive symp -\ntoms in the menopause transition [ 6]. Elevated cortisol \nlevels, indicative of heightened stress responses, have \nbeen consistently associated with anxiety and depres -\nsion during this life stage. Additionally, the interaction \nbetween endocrine markers and lifestyle factors, such as \nsmoking and physical activity, suggests that these hor -\nmonal imbalances may be either exacerbated or mitigated \nby behavioral factors. Higher cortisol levels in smokers, \nfor example, have been shown to amplify the risk of anxi -\nety and depression, whereas regular physical activity can \nmoderate the effects of elevated FSH and LH levels, pro -\nviding a protective effect against mood disturbances dur -\ning perimenopause. Although elevated TSH levels were \nassociated with anxiety and depression, it is important to \nnote that TSH levels may naturally increase with age, and \nage could act as a potential confounder in this relation -\nship. Future studies with age-stratified thyroid function \nanalysis are warranted to clarify this association.\nOur study also highlighted the critical role of sleep \nquality, with poor sleep being a significant contributor \nto anxiety and depression. This is particularly evident in \nperimenopausal women, who are vulnerable due to hor -\nmonal fluctuations, often leading to night sweats and \ninsomnia. Zhou et al. (2021) found a strong relationship \nbetween hot flashes, sweating, and poor sleep quality, \nwith anxiety and depression mediating this relationship \n[28]. Their study revealed that anxiety accounted for \n17.86% of the mediating effect, while depression contrib -\nuted 5.36%, indicating that both symptoms play crucial \nroles in worsening sleep quality for women in this transi -\ntional period. These findings support the notion that hor-\nmonal imbalances during perimenopause significantly \nimpact sleep, which, in turn, exacerbates mental health \nissues like anxiety and depression.\nOne of the strengths of this study is the comprehensive \nassessment of both hormonal and lifestyle factors, which \nprovides a more holistic understanding of the contribu -\ntors to anxiety and depression in perimenopausal women \nwith abnormal uterine bleeding (AUB). However, sev -\neral limitations should be noted. First, the retrospective \ndesign may introduce recall bias, particularly regarding \nself-reported lifestyle factors such as smoking, physical \n\nPage 9 of 10\nHu and He BMC Psychology          (2025) 13:514 \nactivity, and sleep quality. Second, the study population \nwas drawn from a single tertiary care gynecology outpa -\ntient clinic, which may limit the generalizability of the \nfindings to the broader population of perimenopausal \nwomen. Third, hormone levels were measured only \nonce, which does not account for the natural circadian \nand menstrual cycle-related fluctuations of key endo -\ncrine markers such as estradiol, FSH, LH, and cortisol. \nAlthough blood samples were collected under standard -\nized conditions in the early morning (between 7:30 and \n9:30 AM) following an overnight fast to minimize diur -\nnal variation, the phase of the menstrual cycle at the time \nof sampling was not recorded. This may have introduced \nvariability in hormone measurements and limits the \ninterpretation of their associations with psychological \nsymptoms. Future research should incorporate longi -\ntudinal and repeated hormone measurements, include \na more diverse and representative sample, and consider \nmenstrual cycle phase to more accurately assess the tem -\nporal and causal relationships between hormonal fluctua-\ntions, lifestyle factors, and mental health outcomes in this \npopulation.\nThe findings of this study have important clinical impli-\ncations. Given the identified associations between demo -\ngraphic, lifestyle, and hormonal factors with anxiety and \ndepression, clinicians should consider systematically \nevaluating these risk factors during the initial assessment \nof perimenopausal women with AUB. Early identification \nof high-risk individuals—particularly those with elevated \nBMI, a history of smoking or psychiatric illness, poor \nsleep quality, or abnormal hormone levels—may enable \ntimely mental health screening and intervention. Inte -\ngrating psychological evaluation into routine gynecologic \ncare could significantly improve the holistic management \nand quality of life of these patients.\nIn conclusion, our findings highlight the complex inter-\nplay between hormonal, demographic, and lifestyle fac -\ntors in the development of anxiety and depression in \nperimenopausal women with AUB. Targeted interven -\ntions addressing modifiable risk factors, such as smok -\ning cessation, physical activity promotion, and sleep \nimprovement, may offer substantial benefits in alleviat -\ning psychological distress during this vulnerable period. \nFurther research is needed to explore the mechanisms \nunderlying these associations and to develop tailored \ninterventions for women at higher risk of mental health \ndisorders during perimenopause.\nAcknowledgements\nThe authors wish to thank the staff and clinicians at the Department of \nGynecology, The Affiliated Hospital, Southwest Medical University, for their \nsupport during data collection and analysis. Additionally, we extend our \ngratitude to any individuals or institutions who contributed to the study but \ndo not meet the criteria for authorship.\nAuthor contributions\nJun Hu and Lijuan He contributed equally to the conceptualization and design \nof the study. Jun Hu was primarily responsible for data collection, statistical \nanalysis, and drafting the initial manuscript. Lijuan He supervised the study, \nprovided critical revisions to the manuscript, and ensured the scientific rigor \nof the analysis. Both authors reviewed and approved the final manuscript for \nsubmission.\nFunding\nThis research received no external funding.\nData availability\nThe datasets analyzed during the current study are available from the \ncorresponding author on reasonable request.\nDeclarations\nEthical statement\nThis study was conducted in accordance with the ethical principles outlined in \nthe Declaration of Helsinki and was approved by the Ethics Committee of The \nAffiliated Hospital, Southwest Medical University (Ethics Approval Number: \nKY2024498). Due to the retrospective nature of the study, the requirement for \ninformed consent to participate was formally waived by the ethics committee. \nAll patient data were anonymized and handled in strict confidentiality, in \ncompliance with applicable data protection regulations.\nPatient consent for publication\nNot applicable.\nCompeting interests\nThe authors declare no competing interests.\nReceived: 19 December 2024 / Accepted: 8 May 2025\nReferences\n1. Turek J, Gąsior Ł. Estrogen fluctuations during the menopausal transition are \na risk factor for depressive disorders. Pharmacol Rep. 2023;75(1):32–43.  h t t p  s : /  \n/ d o i  . o  r g /  1 0 .  1 0 0 7  / s  4 3 4 4 0 - 0 2 2 - 0 0 4 4 4 - 2.\n2. Joffe H, de Wit A, Coborn J, et al. Impact of estradiol variability and progester-\none on mood in perimenopausal women with depressive symptoms. J Clin \nEndocrinol Metab. 2020;105(3):e642–50.  h t t p  s : /  / d o i  . o  r g /  1 0 .  1 2 1 0  / c  l i n e m / d g z 1 \n8 1.\n3. 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