{"paper_id":"70ce8e5a-6b07-4479-ade4-96e09e4e03b9","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 /.\nYang et al. BMC Women's Health          (2025) 25:478 \nhttps://doi.org/10.1186/s12905-025-04003-8\nBMC Women's Health\n*Correspondence:\nNing Wang\nnonaware@sina.com\n1Dalian Medical University, Dalian, China\n2Dandong Central Hospital, Dandong, China\n3Fudan University Shanghai Cancer center, Shanghai, China\n4Second Hospital of Dalian Medical University, No. 467 Zhongshan Road, \nShahekou District, Dalian, Liaoning Province 116000, China\nAbstract\nBackground Hysterectomy is a common surgery for women, where nutrition, inflammation, and depression impact \nrecovery. These factors are interconnected. Although previous studies have explored the changes in nutrition and \ninflammation after hysterectomy, there is still no reliable biomarker to predict adverse postoperative outcomes. \nAdditionally, the role of depression in postoperative recovery should not be overlooked. This study aims to fill this \ngap by identifying the advanced lung cancer inflammation index (ALI) as a potential indicator of poor postoperative \nprognosis following hysterectomy. It also seeks to examine the combined effect of ALI and depression on \npostoperative mortality.\nMethods This study uses NHANES data (2005–2023) and employs multivariable Cox proportional hazards regression \nmodels, restricted cubic spline plots, subgroup analysis, threshold analysis, and mediation analysis to evaluate \nthe independent and combined effects of ALI and PHQ-9 depression scores on postoperative mortality following \nhysterectomy.\nResults Over 18 years, 620 all-cause and 150 cardiovascular-related deaths were recorded. Multivariable-adjusted \nanalysis showed that high ALI was significantly linked to a lower risk of both all-cause and cardiovascular mortality. \nIn contrast, women with PHQ-9 scores ≥ 10 had a significantly higher risk of death. Combined analysis showed that \nwomen with high ALI and no depression had the lowest mortality risk. Further analysis confirmed that ALI was \nnegatively correlated with mortality, while depression scores increased the risk.\nConclusion This study identifies ALI as a biomarker for poor postoperative prognosis and highlights the \ncombined effects of nutrition, inflammation, and depression. Proper control of these factors reduces mortality risk \npost-hysterectomy.\nKeywords Hysterectomy, Postoperative, The advanced lung cancer inflammation index (ALI), The Patient Health \nQuestionnaire-9(PHQ-9), Nutrition, Inflammation, Depression, Mortality, NHANES\nCombined impact of nutritional \nand inflammatory status and depressive \nsymptoms on mortality following \nhysterectomy\nYing Yang1,2, Yazhou Liu1,2, Xiaohang Lu3, Wei Sun2, Haiyan Chen2 and Ning Wang4*\n\nPage 2 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nIntroduction\nHysterectomy is a common and widely used procedure in \ngynecology, primarily performed to treat uterine fibroids, \nuterine cancer, uterine prolapse, and other severe gyne -\ncological conditions [ 1]. This procedure not only effec -\ntively alleviates or cures these conditions but also \nsignificantly improves patients’ quality of life, relieves \nsymptoms, and restores normal function. However, \ndespite its effectiveness in treating gynecological condi -\ntions, hysterectomy can lead to various long-term conse -\nquences, particularly affecting both physical and mental \nhealth. An increased risk of postoperative depression is a \nmajor concern [2]. The psychological stress from the sur-\ngery, along with hormonal changes and the loss of repro -\nductive capacity, may significantly raise the incidence of \ndepressive symptoms. Since the introduction of the term \n“post-hysterectomy syndrome” in the 1970 s, many stud -\nies have shown that women who undergo hysterectomy \nare more likely to experience psychological issues such \nas insomnia, anxiety, and depression compared to those \nwho do not undergo the procedure [3– 5].\nFurthermore, the postoperative effects of surgery can \nhave significant physiological impacts on the body [ 6], \nparticularly in terms of immune function, nutrition, and \ninflammatory responses [ 7]. After hysterectomy, sev -\neral factors, including hormonal imbalances, changes in \nmetabolism, and alterations in gastrointestinal function, \ncan affect the body’s nutritional status [8]. These changes \nmay lead to deficiencies in essential nutrients, which, \nin turn, can increase systemic inflammation. Chronic \ninflammation is linked to the development of many \nnon-communicable diseases, such as obesity-related \nmetabolic syndrome, cardiovascular diseases, neurode -\ngenerative disorders, certain cancers, and even increased \nmortality [ 9– 12]. Therefore, the interaction between \nnutrition and inflammation in women after hysterectomy \nraises overall health risks and significantly increases mor-\ntality risk in this population.\nThe relationship between depression and inflamma -\ntion is complex and multifaceted [ 13, 14]. Some epide -\nmiological studies suggest that depression can affect \ninflammation levels, while other research indicates \nthat inflammation may contribute to the development \nof depression [ 15, 16]. Furthermore, some researchers \nhave proposed a bidirectional link between inflamma -\ntion and depression [ 17]. This relationship may worsen \nhealth issues in women after hysterectomy. Additionally, \nchronic inflammation linked to depression may impair \nimmune function, increasing the risk of other comorbidi-\nties [18, 19].\nAlthough many studies have explored the links between \ndepression, inflammation, and nutritional health, their \ncombined impact on mortality in women after hysterec -\ntomy remains understudied. The challenges these women \nface extend beyond physical health to include mental \nhealth and nutrition, both of which require attention. \nThis study aims to identify key indicators of inflamma -\ntion and nutrition while also examining how depressive \nstates affect survival rates in this population. Addition -\nally, we analyze the combined effects of inflammatory \nnutrition and depression on mortality risk. The goal is to \nprovide new insights into long-term health outcomes and \nidentify potential interventions to improve quality of life \nand survival.\nMethods\nStudy design and data collection\nThis study uses a retrospective cohort design and ana -\nlyzes data from NHANES collected between 2005 \nand 2023. NHANES is organized and managed by the \nNational Center for Health Statistics (NCHS), which \nuses a nationally representative, stratified, multistage \nprobability sampling method [ 20]. More details about \nthe project are available on the website:  h t t p  : / /  w w w .  c d  c . \ng  o v /  n c h s  / n  h a n e s. The database is maintained by NCHS, \nand all participants gave written informed consent. The \nstudy received approval from the NCHS Institutional \nReview Board (IRB). Since NHANES is a public database \nwith anonymous data, no additional ethical approval or \ninformed consent was needed for this study. It strictly \nfollowed the guidelines set by the relevant institutions \nand data administrators to protect the safety and privacy \nof participants.\nStudy population and inclusion/exclusion criteria\nThis study investigates women who have undergone hys -\nterectomy, with the cohort including both women who \nhave had hysterectomy procedures and a general female \npopulation for comparison. The following inclusion and \nexclusion criteria were applied to ensure the validity and \nreliability of the study findings:\nInclusion Criteria: (1) Women who have undergone \nhysterectomy. (2) Participants who have complete medi -\ncal and demographic data available for analysis, includ -\ning depression questionnaire data and hematological test \nresults.\nExclusion Criteria: (1) Women with incomplete hyster-\nectomy records, which prevent accurate classification of \nthe surgical procedure performed. (2) Individuals miss -\ning depression questionnaire data or whose depression \nstatus could not be determined. (3) Participants without \navailable hematological test results, as these are essential \nfor assessing inflammatory and nutritional status, both \nof which are key to our analysis. (4) Individuals lacking \nessential covariate data, which are necessary to control \nfor confounding variables in our analyses. (5) Partici -\npants without mortality data, as the primary outcome of \ninterest is postoperative mortality risk. (6) Individuals for \n\nPage 3 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nwhom sample weights are unavailable, as the study uti -\nlizes survey data that requires appropriate weighting for \naccurate population representation.\nDefinition of a hysterectomy\nHysterectomy data were collected from the reproduc -\ntive health section of the NHANES questionnaire. These \ninterviews took place at the Mobile Examination Center \n(MEC). Each participant’s hysterectomy status was deter-\nmined by their response to the question, “Have you ever \nhad a hysterectomy, that is, the removal of your uterus?” \n(coded as RHD280). Participants who answered “yes” \nwere classified as having had a hysterectomy.\nAssessment of depressive symptoms\nThe Patient Health Questionnaire-9 (PHQ-9) was used \nto diagnose and assess the severity of depressive symp -\ntoms in the hysterectomy population. The PHQ-9 con -\ntains nine questions, with each question scored from 0 \nto 3. This results in a total score ranging from 0 to 27. A \nhigher score indicates more severe depressive symptoms. \nBased on the PHQ-9 scoring criteria, patients are catego -\nrized into three groups: no depression (0–4 points), mild \ndepression (5–9 points), and moderate to severe depres -\nsion (≥ 10 points). Furthermore, extensive research on the \nvalidity of the PHQ-9 defines patients with a score of ≥ 10 \nas having clinically significant depression [21].\nMeasurement of ALI\nThe hematological laboratory data for this study were \nobtained from the NHANES laboratory database. The \ncomplete blood count was performed using the Beckman \nCoulter method, while the white blood cell differential \ncount was measured using flow cytometry. The advanced \nlung cancer inflammation index (ALI) was calculated by \nmeasuring serum albumin levels (Alb), neutrophil count, \nlymphocyte count, and body mass index (BMI). The for -\nmula used is: ALI = BMI × Alb/NLR [ 22]. All laboratory \nmeasurements were carried out in strict accordance with \nstandardized certification procedures.\nAscertainment of mortality\nThe mortality rate of the follow-up population was deter-\nmined by linking NHANES data with the National Death \nIndex (NDI) mortality file, which was publicly available \nuntil December 31, 2019. This linkage was performed \nusing a probabilistic matching algorithm. The algorithm \nmatches records from the two datasets based on the \npatient’s Social Security Number (SSN), name, date of \nbirth, and other identifying information. In the event of \ndeath, the time between the NHANES examination and \nthe subject’s death (in months) was recorded. Addition -\nally, disease-specific mortality was classified using the \nInternational Statistical Classification of Diseases, 10th \nRevision (ICD-10). For this study, the NCHS classified \ndeaths due to heart disease (codes 054–064) and all other \ncauses (code 010) [23].\nCovariates\nThis study included independent risk factor covariates \nassociated with women who had undergone hysterec -\ntomy. These covariates were selected based on previous \nresearch. The specific factors considered were age, race, \nincome-to-poverty ratio, education level, BMI, smoking \nstatus, alcohol consumption, self-reported history of dia -\nbetes, self-reported history of hypertension, and medica -\ntion use. Medication variables included female hormone \nuse, antidepressants use and treatment for sleep disor -\nders. The study aimed to minimize confounding bias.\nTrained interviewers collected demographic informa -\ntion, including age, race, income-to-poverty ratio, and \neducation level, through household and sample popu -\nlation surveys using the Computer-Assisted Personal \nInterviewing (CAPI) system. Health technicians from \nMEC conducted physical measurements. BMI was calcu -\nlated as weight (in kilograms) divided by height squared \n(in meters).\nSmoking status was categorized based on participants’ \nresponses to survey questions (SMQ020: whether they \nhad ever smoked at least 100 cigarettes; SMQ040: current \nsmoking status). The categories included never smok -\ners, former smokers, and current smokers. Never smok -\ners were defined as individuals who had never smoked \n100 cigarettes in their lifetime and were not currently \nsmoking. Current smokers were those who had smoked \nat least 100 cigarettes and continued to smoke. Former \nsmokers were individuals who had smoked at least 100 \ncigarettes but had quit. Alcohol consumption was catego-\nrized based on self-reported drinking frequency. Catego -\nries included heavy, moderate, light, and never drinkers. \nHeavy drinkers were those who consumed four or more \ndrinks per day, while moderate drinkers consumed three \nor fewer drinks per day. Light drinkers had consumed \nalcohol previously but had fewer than 12 drinking occa -\nsions in the past year. Never drinkers were individuals \nwho reported never having consumed alcohol.\nThe diagnosis of diabetes and hypertension was con -\nfirmed using both survey data and laboratory results \nto ensure accurate findings. Relevant survey questions \nincluded: “Has a doctor ever told you that you have dia -\nbetes?” “Do you use insulin?” “Do you use oral hypo -\nglycemic agents?” The laboratory criteria for diabetes \nincluded fasting blood glucose levels ≥ 7.0 mmol/L, \nHbA1c ≥ 6.5%, and an oral glucose tolerance test (OGTT) \nwith blood glucose ≥ 11.1 mmol/L. Similarly, the diagno -\nsis of hypertension was based on multiple blood pressure \nreadings ≥ 130/80 mmHg or self-reported hypertension \nconfirmed by a doctor.\n\nPage 4 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nMedication use, including the use of female hormones, \nwas identified through a self-reported question in the \nreproductive health questionnaire. The question asked, \n“Have you ever used female hormones such as estrogen \nand progesterone?” (coded as RHQ540). Information \non the use of antidepressants was extracted from the \nNHANES prescription data file, as detailed in Supple -\nmentary Table 1.\nThe status of sleep disorders was assessed using the \nSLQ060 and SLQ050 question modules from NHANES. \nThe questions included: “Has a doctor or other health -\ncare professional ever told you that you have a sleep \ndisorder?” and “Have you ever reported any sleep prob -\nlems?” Individuals who answered “Yes” were classified as \nhaving a sleep disorder and were included in further anal-\nysis. Additionally, the SLQ070 question module included \nself-reported symptoms of sleep disorders, such as sleep \napnea, insomnia, and restless leg syndrome. Individuals \nwho answered “Yes” to these questions were also classi -\nfied as having a sleep disorder.\nStatistical analysis\nTo ensure the national representativeness of the sam -\nple, we followed the NHANES weighting guidelines (  h \nt t p  s : /  / w w w  . c  d c .  g o v  / n c h  s /  n h a  n e s  / t u t  o r  i a l s / w e i g h t i n g . \na s p x) and applied MEC weights in the sampling design. \nWe used time-dependent receiver operating charac -\nteristic (timeROC) curves to identify the most effective \nnutrition/inflammation biomarker for ALI in NHANES. \nWe also described the baseline characteristics of differ -\nent levels of ALI and depressive symptoms. Continuous \nvariables were expressed as weighted means ± standard \nerrors, while categorical variables were presented as \nfrequency and weighted proportions. To explore the \nrelationship between ALI, depressive symptoms, and \nmortality, we performed multivariable Cox proportional \nhazards regression analysis. Model 2 adjusted for demo -\ngraphic characteristics, while Model 3 controlled for all \ncovariates. The results were quantified by hazard ratios \n(HR) and 95% confidence intervals (CIs). To examine the \ncombined effects, we grouped participants by ALI and \ndepressive symptoms. We then used multivariable Cox \nproportional hazards regression models, adjusting for the \nsame set of covariates, to assess mortality risk.\nAdditionally, we will conduct a threshold analysis to \nfurther explore the relationship between ALI, depressive \nsymptoms, and mortality. The restricted cubic splines \n(RCS) method in Cox proportional hazards regression \nmodels will be used to describe the linear and nonlinear \nassociations between ALI or PHQ-9 scores and mortal -\nity. We also performed subgroup analyses to assess the \nimpact of other potential factors on the relationship \nbetween ALI, depressive symptoms, and mortality, aim -\ning to verify the robustness of the results. Finally, we \nconducted a mediation analysis to examine how ALI and \nPHQ-9 scores mediate the relationship between hyster -\nectomy and mortality outcomes.\nAll statistical tests were two-sided, with a significance \nlevel set at P < 0.05. Data analysis for this study was per -\nformed using IBM SPSS Statistics 25.0 and R version \n4.4.1.\nResults\nBetween 2005 and 2023, a total of 97,683 participants \nwere enrolled in NHANES. After excluding individu -\nals who did not meet the study criteria or lacked neces -\nsary data, the final cohort included 3,703 women who \nhad undergone hysterectomy, with a mean age of 63 ± 12 \nyears. The baseline characteristics for the hysterectomy \nsubgroup are shown in Table 1. Additionally, 11,883 \nhealthy female controls were included as a comparison \ngroup, primarily for subsequent mediation analysis. The \nhealthy controls were not directly involved in the statisti-\ncal modeling of the hysterectomized subgroup. A detailed \nparticipant selection flowchart is provided in Supplemen-\ntary Fig. 1. In the tertile-based analysis of ALI, significant \ndifferences were observed across groups in variables such \nas age, race, hypertension, and BMI ( p < 0.05). Among \nthe participants, 179 women were identified as having \nboth high ALI and high PHQ-9 scores, which indicates \na subgroup with elevated systemic inflammation and \nsignificant depressive symptoms. During the 18-year \nfollow-up period, 620 all-cause deaths and 150 cardio -\nvascular-related deaths were recorded. Additionally, we \ncompared the ability of ALI and common inflamma -\ntory biomarkers to predict mortality in hysterectomized \npatients using ROC curves. As shown in Fig. 1, ALI \ndemonstrated superior predictive performance for both \nall-cause and cardiovascular mortality, providing a com -\nprehensive reflection of metabolic status.\nProportional hazards regression analysis was con -\nducted to examine the relationship between ALI, \ndepressive symptoms, and mortality. After adjusting \nfor covariates, when ALI was considered as a continu -\nous variable, the results showed that ALI was negatively \nassociated with both all-cause and cardiovascular mor -\ntality, with HRs of 0.51 (0.43, 0.59) and 0.51 (0.37, 0.69), \nrespectively. Compared to low ALI levels, high ALI levels \nwere linked to lower all-cause and cardiovascular mor -\ntality, with HRs of 0.46 (0.37, 0.58) and 0.45 (0.28, 0.71), \nrespectively. These results suggest that higher ALI levels \nare independently associated with a reduced risk of both \nall-cause and cardiovascular mortality in patients who \nhave undergone hysterectomy. In contrast, patients with \nPHQ-9 scores ≥ 10 had a higher risk of all-cause mortal -\nity [HR, 1.48(1.14,1.93)] compared to those with PHQ-9 \nscores between 0 and 4 (Table 2).\n\nPage 5 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nTable 1 Baseline characteristics of the study cohort\nStudy variables Total\n(n = 3703)\nNo. of participants by ALI P value\nQ1 < 6.26\n(n = 1234)\nQ2: 6.26–6.70\n(n = 1233)\nQ3 > 6.70\n(n = 1236)\nAge, years 63.28 ± 12.26 65.87 ± 13.25 63.15 ± 11.75 60.83 ± 11.18 < 0.001\nRace < 0.001\n Mexican 421 (11.37%) 113 (9.16%) 170 (13.78%) 138 (11.17%)\n Hispanic 319 (8.61%) 83 (6.73%) 110 (8.91%) 126 (10.20%)\n Non-Hispanic white 1878 (50.72%) 801 (64.91%) 640 (51.86%) 437 (35.38%)\n Non-Hispanic black 868 (23.44%) 165 (13.37%) 242 (19.61%) 461 (37.33%)\n Other/multiracial 217 (5.86%) 72 (5.83%) 72 (5.83%) 73 (5.91%)\nEducation level, n (%) 0.628\n Never attended high school 933 (25.20%) 301 (24.39%) 310 (25.12%) 322 (26.07%)\n High school and above 2770 (74.80%) 933 (75.61%) 924 (74.88%) 913 (73.93%)\nPoverty-to-income ratio, n (%) 0.073\n Poor (≤ 1) 659 (17.80%) 224 (18.15%) 196 (15.88%) 239 (19.35%)\n Not poor (> 1) 3044 (82.20%) 1010 (81.85%) 1038 (84.12%) 996 (80.65%)\nSmoking status, n (%) 0.058\n Never 71 (1.92%) 34 (2.76%) 23 (1.86%) 14 (1.13%)\n Former 2320 (62.65%) 763 (61.83%) 768 (62.24%) 789 (63.89%)\n Current smoker 1312 (35.43%) 437 (35.41%) 443 (35.90%) 432 (34.98%)\nAlcohol use, n (%) 0.665\n Never 753 (20.33%) 257 (20.83%) 246 (19.94%) 250 (20.24%)\n Mild 686 (18.53%) 235 (19.04%) 220 (17.83%) 231 (18.70%)\n Moderate 2058 (55.58%) 664 (53.81%) 706 (57.21%) 688 (55.71%)\n Heavy 206 (5.56%) 78 (6.32%) 62 (5.02%) 66 (5.34%)\nHypertension, n (%) 2272 (61.36%) 743 (60.21%) 735 (59.56%) 794 (64.29%) 0.033\nDiabetes mellitus, n (%) 815 (22.01%) 272 (22.04%) 263 (21.31%) 280 (22.67%) 0.717\nHormone use, n (%) 1840 (49.69%) 638 (51.70%) 614 (49.76%) 588 (47.61%) 0.127\nAntidepressants use, n (%) 146 (3.94%) 52 (4.21%) 54 (4.38%) 40 (3.24%) 0.288\nBMI, kg/m2 30.65 ± 7.18 27.30 ± 5.80 30.90 ± 6.78 33.76 ± 7.36 < 0.001\nSleep disorders 1513 (40.86%) 520 (42.14%) 496 (40.19%) 497 (40.24%) 0.5\nPHQ-9 score (%) 0.551\n 0–4 2450 (66.16%) 817 (66.21%) 808 (65.48%) 825 (66.80%)\n 5–9 746 (20.15%) 250 (20.26%) 264 (21.39%) 232 (18.79%)\n ≥ 10 507 (13.69%) 167 (13.53%) 162 (13.13%) 178 (14.41%)\nAbbreviations: ALI advanced lung cancer inflammation index, BMI body mass index, PHQ-9 score Patient Health Questionnaire-9\nFig. 1 The time-dependent ROC of inflammation and nutrition-relative indicators for diagnosing overall survival in US women after hysterectomy. Ab -\nbreviations: ALI, advanced lung cancer inflammation index; SII, systemic immune-inflammation index; NLR, neutrophil-to-lymphocyte ratio; SIRI, systemic \ninflammatory response index\n \n\nPage 6 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nIn the combined analysis, these findings remained \nconsistent after adjusting for various covariates (Models \n2 and 3). Specifically, a higher ALI level combined with \na lower PHQ-9 score was significantly associated with a \nreduced risk of all-cause mortality (Table 3). Compared \nto survivors with PHQ-9 scores ≥ 10 and low ALI levels, \nsurvivors with PHQ-9 scores < 10 and high ALI levels had \na significantly lower risk of all-cause mortality, with an \nHR of 0.34(0.25,0.46).\nAs shown in Fig. 2, after adjusting for multiple poten -\ntial confounding factors, the RCS analysis revealed a \nnegative relationship between ALI and both all-cause and \ncardiovascular mortality. As ALI increased, the HRs for \nboth all-cause and cardiovascular mortality significantly \ndecreased. In contrast, the PHQ-9 score showed a posi -\ntive relationship with all-cause mortality and a U-shaped \nrelationship with cardiovascular mortality (Fig. 3). To \nfurther explore this relationship, we performed a thresh -\nold analysis. The results showed a non-linear relation -\nship between ALI and all-cause mortality. Specifically, \nwhen ALI was less than 6.76, the protective effect of ALI \nincreased as its levels rose, with an HR of 0.42 (0.35–\n0.51). In contrast, the relationship with cardiovascular \nmortality was consistently negative. The PHQ-9 score, \nhowever, showed a linear positive relationship with both \nall-cause and cardiovascular mortality (Table 4).\nAdditionally, subgroup analyses examined the interac -\ntion between other factors and ALI/PHQ-9 scores in rela-\ntion to mortality (Figs. 4 and 5). The association between \nhigher ALI levels and lower cardiovascular mortality was \nstronger in patients aged 65 to 85 years. No statistically \nsignificant interactions were found for other outcomes \n(all interaction p-values > 0.05). Finally, the mediation \nanalysis revealed limited evidence for biological media -\ntion by ALI or PHQ-9 scores. While ALI exhibited a sta -\ntistically significant indirect effect for all-cause mortality \nin adjusted models (β = 0.001, 95% CI: 0.0005–0.002; \nP = 0.002), the effect size was clinically negligible, accom -\npanied by an implausible negative mediation propor -\ntion (PM = −33.2%). Depression scores (PHQ-9) showed \nno significant mediation for cardiovascular mortality \n(P = 0.052), with inconsistent effects for all-cause mor -\ntality. Collectively, these findings do not support ALI or \ndepression as substantial mediators of the hysterectomy-\nmortality association (Table 5).\nTable 2 HRs (95% CI) for all-cause mortality and cardiovascular mortality among U.S. Patients who have undergone hysterectomy in \nNHANES (2005–2023) based on ALI and PHQ-9 scores\nModel1 Model2 Model3\nHR (95% CI) p value HR (95% CI) p value HR (95% CI) p value\nAll-cause mortality\nALI\nContinuous data 0.36(0.31,0.42) < 0.0001 0.50(0.43,0.59) < 0.0001 0.51(0.43,0.59) < 0.0001\nQuartiles Q1 Reference Reference Reference\nQ2 0.50(0.41,0.60) < 0.0001 0.63(0.52,0.76) < 0.0001 0.63(0.52,0.76) < 0.0001\nQ3 0.33(0.27,0.41) < 0.0001 0.46(0.37,0.58) < 0.0001 0.46(0.37,0.58) < 0.0001\nPHQ-9 score\n0–4 Reference Reference Reference\n5–9 1.11(0.91,1.35) 0.3194 1.31(1.08,1.61) 0.0074 1.24(1.01,1.52) 0.0385\n≥ 10 0.99(0.78,1.26) 0.953 1.77(1.38,2.26) < 0.0001 1.48(1.14,1.93) 0.0029\nCardiovascular mortality\nALI\nContinuous data 0.36(0.27,0.48) < 0.0001 0.51(0.38,0.69) < 0.0001 0.51(0.37,0.69) < 0.0001\nQuartiles Q1 Reference Reference Reference\nQ2 0.52(0.36,0.75) 0.0005 0.69(0.48,1.01) 0.0556 0.68(0.46,0.98) 0.0414\nQ3 0.32(0.20,0.49) < 0.0001 0.44(0.28,0.70) 0.0005 0.45(0.28,0.71) 0.0006\nPHQ-9 score\n0–4 Reference Reference Reference\n5–9 1.39(0.95,2.03) 0.0871 1.69(1.15,2.47) 0.0071 1.48(0.99,2.18) 0.0510\n≥ 10 0.88(0.52,1.49) 0.6308 1.70(0.99,2.90) 0.0527 1.32(0.75,2.32) 0.3282\nModel 1: we did not adjust other covariant\nModel 2: we adjusted age and race\nModel 3 on ALI: we adjusted age, race, education, poverty-to-income ratio, hypertension, diabetes, alcohol use, smoking, hormone use, antidepressants use and \nsleep disorders\nModel 3 on Depression: we adjusted age, race, education, poverty-to-income ratio, hypertension, diabetes, alcohol use, smoking, hormone use, antidepressants \nuse, BMI and sleep disorders\n\nPage 7 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nDiscussion\nIn this study, we performed a retrospective cohort anal -\nysis using a nationally representative sample to assess \nthe impact of inflammation, nutritional status, and \ndepressive symptoms on mortality after hysterectomy in \nwomen. The results indicated that patients with higher \nALI levels and no depressive symptoms had a signifi -\ncantly lower risk of all-cause and cardiovascular mortal -\nity compared to those with lower ALI levels or significant \ndepressive symptoms.\nPrevious research has demonstrated that hysterec -\ntomy is frequently associated with distressing symptoms. \nApproximately 70% of patients experience depression \nfollowing the procedure, while about half report symp -\ntoms such as headaches, dizziness, or insomnia—symp -\ntoms that are less prevalent in individuals undergoing \nother types of surgery [ 3]. Hysterectomy not only affects \nwomen’s physical health but may also alter their sense of \nself-identity. Many patients no longer view themselves \nas complete women, which can negatively affect their \nTable 3 Combined association of ALI and PHQ-9 scores with all-cause mortality and cardiovascular mortality in patients who have \nundergone hysterectomy in the united states, NHANES, 2005–2023\nModel1 Model2 Model3\nMortality outcome ALI HR (95% CI) p value HR (95% CI) p value HR (95% CI) p value\nAll cause\nPHQ-9 score ≥ 10 Low Reference Reference Reference\nIntermediate 0.50(0.29,0.85) 0.0106 0.58(0.34,0.999) 0.0495 0.55(0.39,0.77) 0.0004\nHigh 0.42(0.24,0.72) 0.0018 0.48(0.27,0.82) 0.008 0.41(0.28,0.60) < 0.0001\nPHQ-9 score < 10 Low 1.08(0.78,1.52) 0.634 0.58(0.42,0.82) 0.002 0.70(0.55,0.89) 0.0039\nIntermediate 0.54(0.38,0.76) 0.0005 0.38(0.26,0.53) < 0.0001 0.46(0.36,0.60) < 0.0001\nHigh 0.35(0.24,0.50) < 0.0001 0.27(0.18,0.39) < 0.0001 0.34(0.25,0.46) < 0.0001\nCardiovascular\nPHQ-9 score ≥ 10 Low Reference Reference Reference\nIntermediate 0.69(0.22,2.17) 0.5239 0.94(0.30,3.00) 0.921 0.87(0.27,2.80) 0.8196\nHigh 0.49(0.14,1.68) 0.2589 0.59(0.17,2.03) 0.403 0.61(0.18,2.11) 0.4395\nPHQ-9 score < 10 Low 1.50(0.69,3.26) 0.3074 0.79(0.36,1.74) 0.5593 1.01(0.45,2.25) 0.9863\nIntermediate 0.76(0.34,1.69) 0.4943 0.54(0.24,1.20) 0.1289 0.66(0.29,1.50) 0.3249\nHigh 0.45(0.19,1.04) 0.0613 0.33(0.14,0.77) 0.0104 0.43(0.18,1.02) 0.0542\nModel 1: we did not adjust other covariant\nModel 2: we adjusted age and race\nModel 3: we adjusted age, race, education, poverty-to-income ratio, hypertension, diabetes, alcohol use, smoking, hormone use, antidepressants use and sleep \ndisorders\nFig. 2 The association between ALI and all-cause and cardiovascular mortality in women after hysterectomy was adjusted for age, race, education, \npoverty-to-income ratio, hypertension, diabetes mellitus, alcohol consumption, smoking, hormone use, antidepressants use and sleep disorders. Shaded \nareas represent 95% CI\n \n\nPage 8 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nself-confidence and self-esteem [24, 25]. Additionally, the \nsudden drop in estrogen levels after the surgery can have \nharmful effects on the neuroendocrine system, worsen -\ning depressive symptoms [ 2]. Together, these physical \nand psychological factors contribute to greater mental \nhealth challenges for women after surgery. Depression \nis often linked with increased inflammation, including \nhigher levels of pro-inflammatory cytokines and acute-\nphase proteins [ 26, 27]. This, in turn, raises the risk of \ncardiovascular diseases, diabetes, and death [ 28, 29]. \nAdditionally, depression significantly affects patients’ \nquality of life, impairing disease management, health \nmonitoring, and treatment adherence [30, 31]. These fac-\ntors contribute to the progression of disease and a higher \nrisk of death. In our study, about 13.69% of women who \nunderwent hysterectomy showed elevated depressive \nsymptoms based on PHQ-9 scores. Women with PHQ-9 \nscores between 0 and 4 had a 48% lower risk of all-cause \nmortality compared to those with scores above 10. Our \nfindings suggest that depression may increase the risk of \nmortality [32].\nA review of the literature on nutrition, inflammation, \nand mortality emphasizes the role of chronic inflamma -\ntion and the influence of diet on the immune system. \nChronic inflammation is considered a key factor in the \ndevelopment of various chronic diseases [ 33– 35], as \nit increases the risk of mortality through mechanisms \nsuch as immune dysfunction and exacerbation of meta -\nbolic disorders. In women after hysterectomy, these \neffects are particularly complex and pronounced. Previ -\nous studies have shown that reduced ovarian blood sup -\nply following hysterectomy leads to a decline in ovarian \nhormone secretion, which further decreases blood flow \nto the ovaries, creating a vicious cycle [ 36]. The decline \nin ovarian function and hormonal fluctuations can also \ncause changes in the immune system, leading to higher \nlevels of chronic inflammation [ 37]. Furthermore, Diet \nis closely linked to the immune system. A balanced diet \nenhances immune responses, regulates inflammation, \nand modulates oxidative stress processes [38– 43]. On the \nother hand, an unbalanced diet can trigger inflammatory \nresponses in the body [ 44, 45], weaken immune func -\ntion, and disrupt various physiological processes, such as \nhormone regulation [ 46], metabolism [ 47, 48], circadian \nrhythms [49], and nutrient utilization [50].\nIn summary, women undergoing hysterectomy often \nexperience multiple comorbidities, including metabolic \ndisorders, inflammatory responses, immune deficiencies, \nand malnutrition. Therefore, a comprehensive assessment \nof nutritional status and inflammation-related markers is \nessential [ 51]. Unlike traditional inflammatory markers, \nthe ALI score integrates both nutritional and inflam -\nmatory factors, offering a more complete evaluation of \noverall health. As shown in our analysis (Fig. 2), its area \nunder the curve (AUC) is relatively high, indicating that \nit is more reliable than other markers in predicting post -\noperative outcomes. Previous research has emphasized \nthe importance of predictive biomarkers, particularly in \nenhancing perioperative safety, which is crucial, espe -\ncially in the context of oncological hysterectomy [ 52, 53]. \nInitially, the ALI score was used to assess the systemic \ninflammatory response in patients with metastatic non-\nsmall cell lung cancer (NSCLC), and it has since proven \nto be an effective predictive marker for adverse events in \nFig. 3 Association between depression index and all-cause and cardiovascular mortality in women after hysterectomy adjusted for age, race, education, \npoverty-to-income ratio, BMI, hypertension, diabetes mellitus, alcohol consumption, cigarette smoking, hormone use, antidepressants use and sleep \ndisorders. Shaded areas represent 95% CI\n \n\nPage 9 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nvarious cancers, Crohn’s disease, and heart failure [ 22, \n54– 57]. Our study further suggests that in women post-\nhysterectomy, a high ALI score is significantly associated \nwith lower long-term mortality. Additionally, our sup -\nplementary analysis shows that, compared to the 20–64 \nage group, the relationship between ALI levels and car -\ndiovascular mortality is stronger in the 65–85 age group. \nThis may be due to the elderly population’s decreased \nability to manage chronic inflammation and malnutrition \n[58], combined with the higher prevalence of cardiovas -\ncular diseases in older individuals [ 59]. These findings \nsuggest that elevated ALI levels may significantly reduce \nthe incidence and mortality of cardiovascular diseases by \nimproving inflammatory responses and promoting better \nnutritional status. Moreover, integrating structured clas -\nsification systems and predictive modeling into periop -\nerative care could improve patient management [ 60– 63]. \nPredictive models, such as those incorporating ALI, can \ninform clinical decision-making and facilitate personal -\nized treatment plans that address both inflammatory and \nnutritional needs.\nIn exploring the potential biological mechanisms \nbehind the reduced mortality risk in women after hys -\nterectomy, ALI provides insights into the inflammatory \nand nutritional status of the body. ALI integrates sev -\neral components, including the Neutrophil-to-Lympho -\ncyte Ratio (NLR), albumin, and BMI, which collectively \nreflect systemic inflammation and nutritional status—\nboth of which have significant implications for recovery \ntrajectories.\nNLR is a well-established marker of systemic inflam -\nmation, which can impair immune function and hinder \npostoperative recovery. Elevated NLR has been associ -\nated with poor clinical outcomes across a range of con -\nditions, including cancer, cardiovascular diseases, and \npostoperative complications. Chronic inflammation, \nas evidenced by high NLR levels, can suppress immune \nresponses, delay wound healing, and increase susceptibil-\nity to postoperative complications [ 64]. Moreover, NLR \nhas been linked to depression progression, as inflam -\nmatory cytokines play a role in the pathophysiology of \ndepression [65].\nSerum albumin is a key marker of nutritional sta -\ntus, with low levels typically indicating inflammation \nand malnutrition. Albumin is essential for maintaining \noncotic pressure and supporting tissue repair processes. \nLow albumin levels reflect a compromised nutritional \nstate, which can impair immune function and tissue \nhealing. Additionally, albumin possesses antioxidant \nproperties that protect tissues from oxidative stress and \ninflammatory damage, both of which are critical for \npost-surgical recovery [ 66]. Furthermore, low albumin \nlevels have been associated with poorer mental health \noutcomes, including depression, suggesting a complex \nTable 4 Threshold analysis of ALI index and depression score \nfor all-cause and cardiovascular mortality in patients who have \nundergone hysterectomy\nAdjusted \nHR (95% \nCI)\nP value P for \nLog-like-\nlihood \nratio†\nAll-cause mortality\nALI Fitting by the stan-\ndard linear model\n0.51(0.43–\n0.60)\n< 0.0001\nInflection \npoint:6.76\n< 0.0001\nFitting by the two-\npiecewise linear \nmodel\nALI index < 6.76 0.42(0.35–\n0.51)\n< 0.0001\nALI index > 6.76 1.15(0.73–\n1.81)\n0.5366\nCardiovascular mortality\nALI Fitting by the stan-\ndard linear model\n0.46(0.33–\n0.64)\n< 0.0001\nInflection \npoint:5.57\n0.145\nFitting by the two-\npiecewise linear \nmodel\nALI index < 5.57 0.22(0.08–\n0.57)\n0.0019\nALI index > 5.57 0.54(0.37–\n0.80)\n0.0018\nAll-cause mortality\nDepression Fitting by the stan-\ndard linear model\n1.03(1.01–\n1.05)\n0.0011\nInflection point:14 0.189\nFitting by the two-\npiecewise linear \nmodel\nALI index < 14 1.04(1.02–\n1.06)\n0.0006\nALI index > 14 0.98(0.89–\n1.07)\n0.5957\nCardiovascular mortality\nDepression Fitting by the stan-\ndard linear model\n1.03(0.99–\n1.07)\n0.1503\nInflection point:1 0.084\nFitting by the two-\npiecewise linear \nmodel\nALI index < 1 1.50(0.97–\n2.32)\n0.0703\nALI index > 1 1.01(0.97–\n1.06)\n0.6467\n *Loglikelihood ratio is used to assess whether there is a statistical difference \nbetween two segmented linear models\n\nPage 10 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \ninterplay between nutritional status and emotional well-\nbeing [67].\nBMI is commonly used to assess nutritional status and \nis associated with both chronic inflammation and recov -\nery outcomes. A low BMI is linked to increased mortality \nrisk, often reflecting inadequate nutritional intake and \npoor body function. Conversely, a high BMI can lead to \nchronic low-grade inflammation, which contributes to \nmetabolic diseases and complicates recovery, particu -\nlarly in the presence of depression [ 68]. Both extremes \nFig. 5 Subgroup analysis of the associations between PHQ-9 scores and all-cause and cardiovascular mortality, adjusted for age, race, education, poverty-\nto-income ratio, hypertension, diabetes, alcohol use, smoking, BMI, hormone use, antidepressants use and sleep disorders\n \nFig. 4 Subgroup analysis of the associations between ALI and all-cause and cardiovascular mortality, adjusted for age, race, education, poverty-to-\nincome ratio, hypertension, diabetes, alcohol use, smoking, hormone use, antidepressants use and sleep disorders\n \n\nPage 11 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nof BMI—low and high—are associated with worse health \noutcomes, highlighting the importance of maintaining an \noptimal weight for recovery and long-term health.\nIn conclusion, the components of ALI—NLR, albu -\nmin, and BMI—serve as vital indicators of the body’s \ninflammatory and nutritional status, which are criti -\ncal for recovery after surgery. Maintaining a healthy \nBMI, optimal serum albumin levels, and a low NLR may \nimprove ALI levels and lead to better clinical outcomes. \nHormone replacement therapy (HRT) has anti-inflam -\nmatory effects, potentially improving recovery after hys -\nterectomy [69]. Additionally, healthcare disparities linked \nto socioeconomic status can hinder timely recovery and \npsychological well-being, particularly in underprivileged \ngroups [ 70]. Therefore, individualized postoperative \nmanagement strategies should account for these factors \nto improve patient outcomes.\nAlthough our study identifies ALI as a promising bio -\nmarker associated with postoperative outcomes, its \nclinical applications require further exploration. In this \ncontext, we propose two potential translational applica -\ntions for surgical and gynecologic practitioners: (1) Pre -\noperative ALI Screening: Given the association between \nALI and postoperative mortality risk, preoperative ALI \nscreening could help identify high-risk patients. By \nevaluating ALI components, such as NLR, albumin lev -\nels, and BMI, clinicians could better stratify patients \nand tailor interventions to optimize recovery. (2) Nutri -\ntional Support Protocols: ALI’s reflection of nutritional \nstatus suggests its potential use in developing preopera -\ntive nutritional support protocols. Patients with low ALI \nlevels could benefit from early nutritional interventions \naimed at improving immune function and supporting \npostoperative recovery. These recommendations high -\nlight the potential for ALI to be integrated into preopera-\ntive screening and nutritional management to improve \npostoperative outcomes. However, further prospective \nstudies are required to validate these clinical applications.\nLawes was among the first researchers to examine how \ninflammation and depression together affect mortality \nrisk. A combined analysis found that men with depres -\nsive symptoms and high C-reactive protein (CRP) levels \nhad a 140% higher risk of death compared to those with -\nout depressive symptoms and with normal CRP levels \n[71]. Later studies on cancer patients ALI and depression \nreported similar results. These studies showed that can -\ncer survivors with low ALI levels and depression faced a \nhigher risk of death, while those with high ALI levels and \ngood mental health had a 60% lower risk [ 72]. To date, \nno research has investigated the link between ALI and \ndepressive symptoms in women after hysterectomy. Our \nstudy indicates that women with higher ALI levels and \ngood mental health (PHQ-9: 0–4) have a 66% lower risk \nof all-cause mortality compared to those with depressive \nsymptoms and low ALI levels. This finding addresses a \ngap in the current literature.\nThe main strength of this study is its pioneering \napproach, using large cohort data from NHANES, which \nallows for broader generalization of the findings to vari -\nous populations. This study is the first to identify ALI as \na potential biomarker for adverse outcomes following \nTable 5 Mediation analysis of associations between \nhysterectomy populations and risk of all-cause mortality and \ncardiovascular mortality using ALI and depression as mediators\nNon-adjusted β \n(95%CI)P-value\nAdjust II \nβ(95%CI) P-value\nALI\nAll-cause mortality\nDirect effect −0.102 (−0.115, \n−0.089) < 0.0001\n−0.006 (−0.013, \n0.003) 0.228\nIndirect effect 0.004 (0.003, 0.006) < 0.0001 0.001 (0.0005, \n0.002) 0.002\nTotal effect −0.098 (−0.110, \n−0.085) < 0.0001\n−0.004 (−0.012, \n0.005) 0.348\nPM, % −4.3 −33.2\nP-value < 0.0001 0.35\nCardiovascular mortality\nDirect effect −0.024 (−0.03, −0.017) < 0.0001 0.001 (−0.003, \n0.006) 0.626\nIndirect effect 0.001 (0.0007, 0.002) < 0.0001 0.0003 (0.0001, \n0.0006) 0.002\nTotal effect −0.023 (−0.030, \n−0.016) < 0.0001\n0.001 (−0.003, \n0.006) 0.496\nPM, % −5.6 26.2\nP-value < 0.0001 0.498\nDepression\nAll-cause mortality\nDirect effect −0.99 (−0.111, −0.086) < 0.0001 −0.004 (−0.012, \n0.004) 0.314\nIndirect effect −0.0003 (−0.001, 0.0005) 0.456 −0.0007 (−0.001, \n−0.0002) < 0.0001\nTotal effect −0.0997 (−0.112, \n−0.086) < 0.0001\n−0.005 (−0.013, \n0.004) 0.256\nPM, % 0.3 13.6\nP-value 0.456 0.256\nCardiovascular mortality\nDirect effect −0.023 (−0.030, \n−0.017) < 0.0001\n0.002 (−0.003, \n0.006) 0.462\nIndirect effect −0.002 (−0.0006, 0.0001) 0.234 −0.002 (−0.0005, \n0.0000002) 0.052\nTotal effect −0.023 (−0.030, \n−0.017) < 0.0001\n0.001 (−0.003, \n0.006) 0.52\nPM, % 0.86 −14.2\nP-value 0.234 0.536\nCrude model: we did not adjust other covariant.\nModel II on ALI: we adjusted age, race, education, poverty-to-income ratio, \nhypertension, diabetes, alcohol use, smoking, hormone use, antidepressants \nuse and sleep disorders.\nModel II on Depression: we adjusted age, race, education, poverty-to-\nincome ratio, hypertension, diabetes, alcohol use, smoking, hormone use, \nantidepressants use, BMI and sleep disorders.\n\nPage 12 of 14\nYang et al. BMC Women's Health          (2025) 25:478 \nhysterectomy. It also examines the relationships between \nnutrition, inflammation, depression, and both overall and \ncardiovascular mortality in women after hysterectomy.\nLimitations\nDespite the significant findings of this study, its limita -\ntions should be carefully considered. First, it relies on \ncross-sectional NHANES laboratory data, limiting our \nability to assess temporal changes and long-term inter -\nvention effects. Given the dynamic nature of inflam -\nmation and nutritional status, especially post-surgery, \nincorporating longitudinal biomarker data or repeated \nALI measurements would provide deeper insights into \nhow these factors evolve and affect patient outcomes. \nThis would improve the prognostic value and clinical rel-\nevance of our findings. Second, depression was assessed \nusing self-reports with the PHQ-9 scale, which may not \nfully reflect an individual’s depressive condition. Third, \nthe dataset did not distinguish between the types of hys -\nterectomy (e.g., laparoscopic, robotic, or abdominal), \nwhich may introduce heterogeneity in outcomes due to \ndifferences in surgical techniques, perioperative manage -\nment, and postoperative recovery. Future studies should \nstratify outcomes by surgical approach to more precisely \nassess these relationships. Lastly, the potential confound-\ning effects of the underlying indications for hysterectomy, \nsuch as whether the surgery was performed for benign or \nmalignant conditions, were not addressed in this study. \nFuture research with more detailed clinical data could \nfurther investigate the potential impact of these factors \non depressive symptoms and nutritional-inflammatory \nprofiles in relation to mortality outcomes.\nConclusions\nThis study identifies a nonlinear negative correlation \nbetween ALI and mortality risk in women after hyster -\nectomy, along with a linear positive correlation between \nPHQ-9 scores and mortality risk. It highlights the impor -\ntance of maintaining adequate nutrition, controlling \ninflammation, and addressing depressive symptoms. \nThese findings establish a theoretical basis for person -\nalized assessment and management of postoperative \npatients. They also provide essential scientific support \nfor improving long-term outcomes and offer guidance \nfor early intervention and targeted treatment in clinical \npractice.\nAbbreviations\nNHANES  National Health and Nutrition Examination Survey\nALI  The advanced lung cancer inflammation index\nPHQ-9  The Patient Health Questionnaire-9\nBMI  Body Mass Index\nNCHS  National Center for Health Statistics\nSupplementary Information\nThe online version contains supplementary material available at  h t t p  s : /  / d o i  . o  r \ng /  1 0 .  1 1 8 6  / s  1 2 9 0 5 - 0 2 5 - 0 4 0 0 3 - 8.\nSupplementary Material 1.\nSupplementary Material 2.\nAcknowledgements\nNot applicable.\nAuthors’ contributions\nYing Yang: Conceptualization, Data Curation, Formal Analysis, Investigation, \nMethodology, Visualization, Writing-Original Draft, Writing-Review & Editing; \nYazhou Liu: Data Curation, Formal Analysis, Visualization; Xiaohang Lu: Data \nCuration, Methodology; Wei Sun: Supervision, Validation; Haiyan Chen: \nSupervision, Validation; Ning Wang: Conceptualization, Funding Acquisition, \nSupervision, Validation, Writing-Original Draft, Writing-Review &Editing.\nFunding\nThis research was jointly supported by the “1 + X” Research Project of The \nSecond Hospital of Dalian Medical University (YJ2024001202), the “1 + X” \nClinical Technology Enhancement Project on Ovarian Cancer Ultra Radical \nSurgery (2022LCJSZD04), and the “Xingliao Talent Plan” Medical Expert Project \n(YXMJ-QN-17).\nData availability\nThe datasets used and/or analysed during the current study available from the \ncorresponding author on reasonable request.\nDeclarations\nEthics approval and consent to participate\nAll participants provided written informed consent before undergoing the \nNHANES survey, and the survey received approval from the NCHS IRB, as \ndetailed at  h t t p  s : /  / w w w  . c  d c .  g o v  / n c h  s /  n h a n e s / i r b a 9 8 . h t m. 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