Abstract
This study aims to evaluate the association between hysterectomy with bilateral salpingo-oophorectomy (HBSO) and depressive symptoms, exploring the impact of different surgical approaches on the severity of depression. Data from the 2006–2017 National Health and Nutrition Examination Survey (NHANES) were used to analyze the relationship between surgical methods and depressive symptoms.This study analyzed data from 10,780 women aged 20–80 years, with a diverse racial composition: 44.2% non-Hispanic White, 20.4% non-Hispanic Black, 14.7% Mexican American, 11.0% Other Hispanic, and 9.7% Other Race.The Patient Health Questionnaire-9 (PHQ-9), a validated depression screening tool, was utilized to assess depressive symptoms. Multivariable linear regression and binary logistic regression analyses were conducted to evaluate the association between surgical approaches and depressive symptoms, with results presented as odds ratios (OR) and their 95% confidence intervals (CI). Subgroup analyses employed stratified regression models to investigate interactions between baseline characteristics and surgical methods. Demographic analysis showed differences in age, marital status, education, income, smoking, BMI, and chronic disease prevalence between the depressive and non-depressive groups. HBSO was significantly associated with higher PHQ-9 scores and a higher likelihood of significant depressive symptoms (PHQ-9 ≥ 10). Hysterectomy was also associated with depressive symptoms, but to a lesser extent. Further analysis revealed that hysterectomy was significantly associated with higher depressive scores, particularly in the PHQ-9 ≥ 20 group. Subgroup analysis indicated significant interaction effects between surgical types and factors such as BMI, Income-to-Poverty Ratio (IPR), smoking, and alcohol consumption. The findings suggest a significant association between hysterectomy, particularly HBSO, and the severity of depressive symptoms. Lifestyle and behavioral factors, such as BMI, smoking, and alcohol consumption, significantly influence the occurrence of postoperative depression. Thorough evaluation of patients’ psychological health and related factors is essential when considering gynecological surgery.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-83675-y.
Keywords: Hysterectomy, Bilateral oophorectomy, PHQ-9, Depression, NHANES
Subject terms: Psychology, Health care, Medical research, Outcomes research
Introduction
Depression is a prevalent and multifaceted mental health disorder with etiological factors spanning biological, psychological, and social dimensions. As one of the leading contributors to the global disease burden, depression profoundly impacts patients’ emotional and cognitive functions and markedly diminishes their quality of life1,2. Data from the Global Burden of Disease study in 2019 reveal that mental disorders remain among the top ten global disease burdens, with no evidence of a reduction in this burden since 19903. Clinically, depression poses significant challenges for detection, diagnosis, and management due to its diverse manifestations, unpredictable course and prognosis, and variable response to treatment. Moreover, the prevalence of depression is notably higher in women compared to men, with rates approximately twice as high in females4,5. Epidemiological studies suggest that the elevated incidence of depression in women may be associated with a range of factors, including physiological changes, social role stressors, and psychological influences6.
Hysterectomy and bilateral oophorectomy are commonly performed procedures in the field of gynecology, typically used to address a range of benign and malignant conditions such as uterine fibroids, abnormal uterine bleeding, cervical cancer, endometrial cancer, benign ovarian tumors, and ovarian cancer7,8. Hysterectomy is the most common gynecological surgical procedure in the United States, with over 600,000 procedures conducted each year9. It is estimated that around 20 million women have undergone a hysterectomy for various obstetric and gynecological reasons10.Traditionally, it has been believed that the ovaries are crucial for hormone secretion, and thus, oophorectomy might lead to a sudden drop in hormone levels due to surgical menopause. This hormonal shift could result in symptoms associated with menopause, such as anxiety, insomnia, and emotional disturbances, potentially progressing to depression11–14. This risk is particularly pronounced in younger, premenopausal women, where the abrupt hormonal changes triggered by surgery can significantly increase the likelihood of depression. Consequently, bilateral oophorectomy has been widely considered to be more likely to lead to depression15. However, recent studies have indicated that the probability of depressive symptoms also increases significantly in women following hysterectomy, especially when both hysterectomy and bilateral oophorectomy are performed together. In contrast, bilateral oophorectomy alone does not appear to significantly elevate the incidence of depression16–18. This emerging evidence has prompted further investigation into the relationship between different surgical procedures and the risk of developing depression.
This study analyzes data from the 2006–2017 National Health and Nutrition Examination Survey (NHANES) to categorize four surgical procedures: (1) No surgery; (2) Hysterectomy; (3) Bilateral Oophorectomy; and (4) Hysterectomy with Bilateral Salpingo-Oophorectomy (HBSO). The objective is to assess the association between these procedures and depression, using Patient Health Questionnaire-9 (PHQ-9) scores to measure incidence and severity. Key questions include whether hysterectomy or oophorectomy significantly affects depression risk and how surgical type correlates with depression severity. The study aims to provide evidence for improving mental health management in women post-gynecological surgery.
Materials and methods
Study population
This study utilizes a cross-sectional design, drawing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2006 and 2017. NHANES, organized by the Centers for Disease Control and Prevention (CDC), is an ongoing survey to evaluate the health and nutritional status of the adult population in the United States. The survey employs a stratified, multistage, probability sampling design, ensuring national representativeness.Our study population reflects the diverse demographic composition of the United States, with 69.8% non-Hispanic white, 11.1% non-Hispanic black, 12.5% Hispanic (including 7.2% Mexican American and 5.3% Other Hispanic), and 6.6% other racial/ethnic groups.For this research, NHANES data from 2006 to 2017 were screened and processed. Figure 1 illustrates the data selection and inclusion/exclusion criteria, detailing the selection process of study participants. As shown in Fig. 1, the initial dataset included all individuals participating in NHANES .After applying multiple exclusion criteria—such as missing records on HBSO, incomplete PHQ-9 depression assessments, and missing general information like BMI, Income-to-Poverty Ratio (IPR), marital status, smoking, drinking, hypertension, and diabetes—the final sample comprised 10,780 women who met the study criteria.
Fig. 1.
Flow diagram of study selection.
Definition of depression
Depressive symptoms were assessed using the PHQ-9, a widely utilized tool for screening depression. The PHQ-9 has been validated across diverse populations and demonstrates strong reliability and validity in previous NHANES studies19.The PHQ-9 consists of 9 items, each scored from 0 (not at all) to 3 (nearly every day), with a total possible score ranging from 0 to 27. Depression was analyzed using three complementary approaches to provide a comprehensive understanding of the relationship between surgical procedures and depression:
Continuous Variable: The total PHQ-9 score was treated as a continuous variable to evaluate the severity of depressive symptoms, allowing for detection of subtle variations in symptom severity.
Binary Variable: Depression was defined based on a PHQ-9 score ≥ 10, indicating clinically significant depressive symptoms20,21, which provides a clinically relevant cutoff point for identifying significant depression.
Ordered Categorical Variable: Depression was categorized based on PHQ-9 scores to capture the full spectrum of depression severity as follows to < 5: No depression;≥5 to < 10: Mild depression;≥10 to < 15: Moderate depression;≥15 to < 20: Moderately severe depression;≥20: Severe depressions22,23.
Definition of surgical procedures
Data were obtained from the reproductive health section of the NHANES questionnaire during the Mobile Examination Center (MEC) interview. Each participant’s hysterectomy status was determined by her response to the question, “Have you ever had a hysterectomy, which is the removal of the uterus?” (coded as RHD280). Responses to questions about bilateral oophorectomy (coded as RHQ305) and HBSO (hysterectomy plus bilateral salpingo-oophorectomy) were also recorded. Participants who answered “yes” to these questions were classified accordingly. The primary independent variable, surgical type, was categorized into four groups based on the interview data: None (no hysterectomy or oophorectomy), Hysterectomy, Bilateral Oophorectomy, and HBSO (Hysterectomy with Bilateral Salpingo-Oophorectomy).
Determination of covariates
Covariates that might confound the results were selected based on literature and widely accepted academic standards. Age was categorized into two groups (< 50 and ≥ 50 years) to account for the physiological transition of menopause, which significantly alters hormonal dynamics and potentially modifies the impact of surgical interventions.These covariates included race/ethnicity (non-Hispanic white, non-Hispanic black, other Hispanic, and Mexican American/other), body mass index (BMI, kg/m², categorized into < 25.0, 25.0–29.9, and ≥ 30.0 kg/m²), marital status (widowed/divorced/separated/never married/living with a partner), education level ( less than 9th Grade, 9-11th Grade, High School Grad/GED or Equivalent, Some College or AA degree, College Graduate or above), and the income-poverty ratio (IPR, ≤ 1.3,1.3< IPR ≤ 3.5, > 3.5 ) and systemic inflammation index. Additionally, smoking status (ever smoked 100 cigarettes in a lifetime), alcohol consumption (less than 12 drinks per year vs. 12 or more drinks per year), and self-reported chronic conditions such as hypertension, diabetes, stroke, coronary heart disease, and cancer (yes/no), as well as hormone use (yes/no), were included. These covariates were used as adjustment factors to minimize their potential influence on the relationship between the primary independent and dependent variables.
Statistical analysis
Descriptive statistics were presented as count (percentage) for categorical variables and mean ± standard deviation (SD) for continuous variables.Between-group comparisons were conducted using Chi-square tests or Fisher’s exact tests as appropriate.Multiple linear and binary logistic regression analyses were performed to assess the association between surgical procedures and depressive symptoms. The following models were constructed: Model 1: Unadjusted crude model. Model 2: Adjusted for age and race/ethnicity to control these potential confounding factors.Model 3: Further adjusted for multiple social and health factors, including education level, marital status, BMI, smoking and alcohol consumption status, chronic diseases, and hormone use, to comprehensively evaluate the independent association between surgical procedures and depressive symptoms. For the analysis of depression severity, multinomial logistic regression models were utilized to examine the risk of depression across different PHQ-9 score ranges for various surgical procedures. Results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs) and p-values.
Subgroup analyses were conducted using stratified regression models to explore potential interactions between different baseline characteristics (such as BMI, income-to-poverty ratio, smoking and alcohol consumption status, hypertension, diabetes, etc.) and the relationship between surgical procedures and depressive symptoms.
All statistical analyses were weighted according to NHANES guidelines using the MEC weights, specifically “WTMEC2YR/5,” to ensure nationally representative estimates from the five NHANES cycles (2007–2016). Data analysis and processing were performed using R software (version 4.2.2) and EmpowerStats software. A two-tailed p-value of < 0.05 was considered statistically significant.
Results
Comparative analysis of surgical methods, depressive symptoms, and baseline characteristics
In the study sample, Table 1 compares the depressive symptom group (PHQ-9 score ≥ 10) and the non-depressive symptom group. The results indicate that the depressive symptom group had a significantly higher proportion of individuals younger than 50 years (57.4% vs. 53.4%, P = 0.0353) and a lower proportion of married individuals (36.6% vs. 54.8%, P < 0.0001). Lower educational attainment and income were more prevalent in the depressive symptom group, with significant differences observed (P < 0.0001). Additionally, the depressive symptom group exhibited a higher smoking rate (59.1% vs. 37.2%, P < 0.0001), a greater proportion of individuals with a BMI > 30, and a significantly increased prevalence of various chronic diseases (all P < 0.0001). Laboratory data also revealed higher inflammatory markers in the depressive symptom group (P < 0.0001). These findings suggest significant differences in multiple characteristics between the depressive and non-depressive groups.
Table 1.
Baseline characteristics of participants with and without depressive symptoms.
| Characteristic | Total mean(95%CI)/%(95%CI) | Depressive Group(PHQ-9 score < 10) | Non-Depressive Group(PHQ-9 score ≥ 10) | P-value |
|---|---|---|---|---|
| Age(years),% | 0.256 | |||
| <50 | 5526 (51.3%) | 4870 (51.1%) | 656 (52.8%) | |
| ≥50 | 5254 (48.7%) | 4667 (48.9%) | 587 (47.2%) | |
| Race,% | < 0.001 | |||
| Mexican American | 1585 (14.7%) | 1399 (14.7%) | 186 (15.0%) | |
| Other Hispanic | 1190 (11.0%) | 1008 (10.6%) | 182 (14.6%) | |
| Non-Hispanic White | 4766 (44.2%) | 4232 (44.4%) | 534 (43.0%) | |
| Non-Hispanic Black | 2194 (20.4%) | 1928 (20.2%) | 266 (21.4%) | |
| Other Race | 1045 (9.7%) | 970 (10.2%) | 75 (6.0%) | |
| Marital status,% | < 0.001 | |||
| Married | 5064 (47.0%) | 4651 (48.8%) | 413 (33.2%) | |
| Widowed | 1173 (10.9%) | 1041 (10.9%) | 132 (10.6%) | |
| Divorced | 1439 (13.3%) | 1198 (12.6%) | 241 (19.4%) | |
| Separated | 415 (3.8%) | 316 (3.3%) | 99 (8.0%) | |
| Never married | 1908 (17.7%) | 1666 (17.5%) | 242 (19.5%) | |
| Living with partner | 781 (7.2%) | 665 (7.0%) | 116 (9.3%) | |
| Education level,% | < 0.001 | |||
| Less Than 9th Grade | 966 (9.0%) | 799 (8.4%) | 167 (13.4%) | |
| 9-11th Grade | 1473 (13.7%) | 1208 (12.7%) | 265 (21.3%) | |
| High School Grad/GED or Equivalent | 2308 (21.4%) | 2037 (21.4%) | 271 (21.8%) | |
| Some College or AA degree | 3484 (32.3%) | 3086 (32.4%) | 398 (32.0%) | |
| College Graduate or above | 2549 (23.6%) | 2407 (25.2%) | 142 (11.4%) | |
| Income-to-poverty ratio,% | < 0.001 | |||
| ≤1.3 | 3664 (34.0%) | 2975 (31.2%) | 689 (55.4%) | |
| >1.3,≤3.5 | 3994 (37.1%) | 3609 (37.8%) | 385 (31.0%) | |
| >3.5 | 3122 (29.0%) | 2953 (31.0%) | 169 (13.6%) | |
| BMI(kg/m2),% | < 0.001 | |||
| ≤25 | 3248 (30.1%) | 2974 (31.2%) | 274 (22.0%) | |
| >25, ≤ 30 | 3049 (28.3%) | 2761 (29.0%) | 288 (23.2%) | |
| >30 | 4483 (41.6%) | 3802 (39.9%) | 681 (54.8%) | |
| Smoking,% | < 0.001 | |||
| ≥ 100 cigarettes in life | 3965 (36.8%) | 3272 (34.3%) | 693 (55.8%) | |
| < 100 cigarettes in life | 6815 (63.2%) | 6265 (65.7%) | 550 (44.2%) | |
| Alcohol, % | < 0.001 | |||
| ≥ 12 drinks/year | 6651 (61.7%) | 5824 (61.1%) | 827 (66.5%) | |
| < 12 drinks/year | 4129 (38.3%) | 3713 (38.9%) | 416 (33.5%) | |
| Hypertension,% | < 0.001 | |||
| yes | 3971 (36.8%) | 3383 (35.5%) | 588 (47.3%) | |
| no | 6809 (63.2%) | 6154 (64.5%) | 655 (52.7%) | |
| Diabetes,% | < 0.001 | |||
| yes | 1318 (12.2%) | 1067 (11.2%) | 251 (20.2%) | |
| no | 9218 (85.5%) | 8258 (86.6%) | 960 (77.2%) | |
| borderline | 244 (2.3%) | 212 (2.2%) | 32 (2.6%) | |
| Stroke, % | < 0.001 | |||
| yes | 382 (3.5%) | 295 (3.1%) | 87 (7.0%) | |
| no | 10,398 (96.5%) | 9242 (96.9%) | 1156 (93.0%) | |
| Coronary heart disease,% | < 0.001 | |||
| yes | 265 (2.5%) | 208 (2.2%) | 57 (4.6%) | |
| no | 10,515 (97.5%) | 9329 (97.8%) | 1186 (95.4%) | |
| Cancer, % | 0.027 | |||
| yes | 1067 (9.9%) | 922 (9.7%) | 145 (11.7%) | |
| no | 9713 (90.1%) | 8615 (90.3%) | 1098 (88.3%) | |
| Surgery,% | < 0.001 | |||
| None | 8348 (77.4%) | 7453 (78.1%) | 895 (72.0%) | |
| Hysterectomy | 1146 (10.6%) | 987 (10.3%) | 159 (12.8%) | |
| Bilateral Oophorectomy | 33 (0.3%) | 31 (0.3%) | 2 (0.2%) | |
| HBSO | 1253 (11.6%) | 1066 (11.2%) | 187 (15.0%) | |
| Female hormones,% | 0.335 | |||
| yes | 2043 (19.0%) | 1803 (18.9%) | 240 (19.3%) | |
| no | 8699 (80.7%) | 7697 (80.8%) | 1002 (80.6%) | |
| unknown | 31 (0.3%) | 30 (0.3%) | 1 (0.1%) | |
| Laboratory data, (109/L), mean | ||||
| Platelet count | 258.6 ± 67.0 | 257.7 ± 66.2 | 265.9 ± 72.3 | < 0.001 |
| Neutrophils number | 4.3 ± 1.7 | 4.2 ± 1.7 | 4.6 ± 2.0 | < 0.001 |
| Lymphocyte number | 2.2 ± 0.9 | 2.2 ± 0.9 | 2.3 ± 0.9 | < 0.001 |
| NLR | 2.1 ± 1.1 | 2.1 ± 1.1 | 2.2 ± 1.2 | 0.008 |
| SII | 547.4 ± 337.5 | 541.4 ± 324.5 | 593.7 ± 421.5 | < 0.001 |
Data in the Table: Mean ± SD / N (%), P-values: For continuous variables, P-values are derived using the Kruskal-Wallis rank sum test. For count variables with theoretical cell counts < 10, P-values are derived using Fisher’s exact test.
None: No Hysterectomy or Oophorectomy, HBSO hysterectomy with bilateral.
Salpingo-Oophorectomy.
Table 2 compares the baseline characteristics of participants across different surgical methods, including no surgery, hysterectomy, bilateral oophorectomy, and HBSO. The results show that the proportion of individuals younger than 50 years was highest in the no-surgery group (63.1%), whereas the proportion of individuals aged ≥ 50 years significantly increased in the HBSO group (86.2%). The highest percentage of non-Hispanic whites was observed in the HBSO group (81.0%). Significant differences in marital status, educational attainment, BMI, smoking rates, and the prevalence of chronic diseases were observed across the groups, with the HBSO group exhibiting the highest prevalence of hypertension, diabetes, stroke, and coronary artery disease. The PHQ-9 scores indicated the highest occurrence of depressive symptoms in the HBSO group (14.4%). These findings highlight the potential associations between surgical methods and health and psychological status.
Table 2.
Baseline characteristics of participants with different surgery.
| Characteristic | Total | Surgery | P-value | |||
|---|---|---|---|---|---|---|
| Mean(95%CI)/%(95%CI) | None | Hysterectomy | Bilateral Oophorectomy | H-BSO | ||
| 8348 | 1146 | 33 | 1253 | |||
| Age(years),% | < 0.001 | |||||
| <50 | 5526 (51.3%) | 5102 (61.1%) | 276 (24.1%) | 6 (18.2%) | 142 (11.3%) | |
| ≥50 | 5254 (48.7%) | 3246 (38.9%) | 870 (75.9%) | 27 (81.8%) | 1111 (88.7%) | |
| Race,% | < 0.001 | |||||
| Mexican American | 1585 (14.7%) | 1320 (15.8%) | 136 (11.9%) | 4 (12.1%) | 125 (10.0%) | |
| Other Hispanic | 1190 (11.0%) | 960 (11.5%) | 129 (11.3%) | 3 (9.1%) | 98 (7.8%) | |
| Non-Hispanic White | 4766 (44.2%) | 3524 (42.2%) | 521 (45.5%) | 15 (45.5%) | 706 (56.3%) | |
| Non-Hispanic Black | 2194 (20.4%) | 1631 (19.5%) | 303 (26.4%) | 5 (15.2%) | 255 (20.4%) | |
| Other Race | 1045 (9.7%) | 913 (10.9%) | 57 (5.0%) | 6 (18.2%) | 69 (5.5%) | |
| Marital status,% | < 0.001 | |||||
| Married | 5064 (47.0%) | 3870 (46.4%) | 557 (48.6%) | 13 (39.4%) | 624 (49.8%) | |
| Widowed | 1173 (10.9%) | 676 (8.1%) | 209 (18.2%) | 9 (27.3%) | 279 (22.3%) | |
| Divorced | 1439 (13.3%) | 990 (11.9%) | 231 (20.2%) | 2 (6.1%) | 216 (17.2%) | |
| Separated | 415 (3.8%) | 335 (4.0%) | 40 (3.5%) | 2 (6.1%) | 38 (3.0%) | |
| Never married | 1908 (17.7%) | 1775 (21.3%) | 71 (6.2%) | 3 (9.1%) | 59 (4.7%) | |
| Living with partner | 781 (7.2%) | 702 (8.4%) | 38 (3.3%) | 4 (12.1%) | 37 (3.0%) | |
| Education level,% | < 0.001 | |||||
| Less Than 9th Grade | 966 (9.0%) | 729 (8.7%) | 112 (9.8%) | 2 (6.1%) | 123 (9.8%) | |
| 9-11th Grade | 1473 (13.7%) | 1100 (13.2%) | 199 (17.4%) | 6 (18.2%) | 168 (13.4%) | |
| High School Grad/GED or Equivalent | 2308 (21.4%) | 1692 (20.3%) | 279 (24.3%) | 8 (24.2%) | 329 (26.3%) | |
| Some College or AA degree | 3484 (32.3%) | 2684 (32.2%) | 378 (33.0%) | 11 (33.3%) | 411 (32.8%) | |
| College Graduate or above | 2549 (23.6%) | 2143 (25.7%) | 178 (15.5%) | 6 (18.2%) | 222 (17.7%) | |
| Income-to-poverty ratio,% | < 0.001 | |||||
| ≤1.3 | 3664 (34.0%) | 2916 (34.9%) | 367 (32.0%) | 15 (45.5%) | 366 (29.2%) | |
| >1.3,≤3.5 | 3994 (37.1%) | 2996 (35.9%) | 466 (40.7%) | 11 (33.3%) | 521 (41.6%) | |
| >3.5 | 3122 (29.0%) | 2436 (29.2%) | 313 (27.3%) | 7 (21.2%) | 366 (29.2%) | |
| BMI(kg/m2),% | < 0.001 | |||||
| ≤25 | 3248 (30.1%) | 2721 (32.6%) | 240 (20.9%) | 12 (36.4%) | 275 (21.9%) | |
| >25, ≤ 30 | 3049 (28.3%) | 2305 (27.6%) | 341 (29.8%) | 7 (21.2%) | 396 (31.6%) | |
| >30 | 4483 (41.6%) | 3322 (39.8%) | 565 (49.3%) | 14 (42.4%) | 582 (46.4%) | |
| Smoking, % | < 0.001 | |||||
| ≥ 100 cigarettes in life | 3965 (36.8%) | 2907 (34.8%) | 487 (42.5%) | 17 (51.5%) | 554 (44.2%) | |
| < 100 cigarettes in life | 6815 (63.2%) | 5441 (65.2%) | 659 (57.5%) | 16 (48.5%) | 699 (55.8%) | |
| Alcohol, % | < 0.001 | |||||
| ≥ 12 drinks/year | 6651 (61.7%) | 5272 (63.2%) | 658 (57.4%) | 21 (63.6%) | 700 (55.9%) | |
| < 12 drinks/year | 4129 (38.3%) | 3076 (36.8%) | 488 (42.6%) | 12 (36.4%) | 553 (44.1%) | |
| Hypertension,% | < 0.001 | |||||
| yes | 3971 (36.8%) | 2482 (29.7%) | 659 (57.5%) | 16 (48.5%) | 814 (65.0%) | |
| no | 6809 (63.2%) | 5866 (70.3%) | 487 (42.5%) | 17 (51.5%) | 439 (35.0%) | |
| Diabetes,% | < 0.001 | |||||
| yes | 1318 (12.2%) | 805 (9.6%) | 238 (20.8%) | 5 (15.2%) | 270 (21.5%) | |
| no | 9218 (85.5%) | 7392 (88.5%) | 871 (76.0%) | 28 (84.8%) | 927 (74.0%) | |
| borderline | 244 (2.3%) | 151 (1.8%) | 37 (3.2%) | 0 (0.0%) | 56 (4.5%) | |
| Stroke, % | < 0.001 | |||||
| yes | 382 (3.5%) | 200 (2.4%) | 72 (6.3%) | 2 (6.1%) | 108 (8.6%) | |
| no | 10,398 (96.5%) | 8148 (97.6%) | 1074 (93.7%) | 31 (93.9%) | 1145 (91.4%) | |
| Coronary heart disease,% | < 0.001 | |||||
| yes | 265 (2.5%) | 132 (1.6%) | 59 (5.1%) | 3 (9.1%) | 71 (5.7%) | |
| no | 10,515 (97.5%) | 8216 (98.4%) | 1087 (94.9%) | 30 (90.9%) | 1182 (94.3%) | |
| Cancer, % | < 0.001 | |||||
| yes | 1067 (9.9%) | 556 (6.7%) | 209 (18.2%) | 5 (15.2%) | 297 (23.7%) | |
| no | 9713 (90.1%) | 7792 (93.3%) | 937 (81.8%) | 28 (84.8%) | 956 (76.3%) | |
| Female hormones,% | < 0.001 | |||||
| yes | 2043 (19.0%) | 842 (10.1%) | 372 (32.5%) | 12 (36.4%) | 817 (65.3%) | |
| no | 8699 (80.7%) | 7480 (89.7%) | 771 (67.3%) | 20 (60.6%) | 428 (34.2%) | |
| unknown | 31 (0.3%) | 21 (0.3%) | 2 (0.2%) | 1 (3.0%) | 7 (0.6%) | |
| Laboratory data, (109/L), mean | ||||||
| Platelet count | 258.6 ± 67.0 | 260.5 ± 66.3 | 254.6 ± 67.0 | 269.5 ± 123.5 | 249.7 ± 68.2 | < 0.001 |
| Neutrophils number | 4.3 ± 1.7 | 4.3 ± 1.7 | 4.2 ± 1.7 | 4.4 ± 1.6 | 4.3 ± 1.8 | 0.118 |
| Lymphocyte number | 2.2 ± 0.9 | 2.2 ± 0.9 | 2.2 ± 1.2 | 2.3 ± 0.8 | 2.2 ± 0.9 | 0.377 |
| NLR | 2.1 ± 1.1 | 2.1 ± 1.1 | 2.1 ± 1.0 | 2.1 ± 0.9 | 2.2 ± 1.3 | 0.392 |
| SII | 547.4 ± 337.5 | 550.3 ± 333.0 | 530.2 ± 315.4 | 560.8 ± 333.8 | 543.1 ± 383.7 | 0.001 |
| PHQ-9 score, mean | 3.8 ± 4.6 | 3.6 ± 4.5 | 4.4 ± 5.0 | 3.5 ± 3.1 | 4.4 ± 5.1 | < 0.001 |
| PHQ-9 score Binary classification,% | < 0.001 | |||||
| < 10 | 9537 (88.5%) | 7453 (89.3%) | 987 (86.1%) | 31 (93.9%) | 1066 (85.1%) | |
| ≥ 10 | 1243 (11.5%) | 895 (10.7%) | 159 (13.9%) | 2 (6.1%) | 187 (14.9%) | |
| PHQ-9 score Ordered multicategory classification ,% | < 0.001 | |||||
| < 5 | 7559 (70.1%) | 5970 (71.5%) | 734 (64.0%) | 24 (72.7%) | 831 (66.3%) | |
| ≥ 5, < 10 | 1978 (18.3%) | 1483 (17.8%) | 253 (22.1%) | 7 (21.2%) | 235 (18.8%) | |
| ≥ 10, < 15 | 759 (7.0%) | 556 (6.7%) | 94 (8.2%) | 2 (6.1%) | 107 (8.5%) | |
| ≥ 15, < 20 | 350 (3.2%) | 250 (3.0%) | 42 (3.7%) | 0 (0.0%) | 58 (4.6%) | |
| ≥ 20 | 134 (1.2%) | 89 (1.1%) | 23 (2.0%) | 0 (0.0%) | 22 (1.8%) | |
Data in the Table: Mean ± SD / N (%), P-values: For continuous variables, P-values are derived using the Kruskal-Wallis rank sum test. For count variables with theoretical cell counts < 10, P-values are derived using Fisher’s exact test.
None: No Hysterectomy or Oophorectomy,
H-BSO hysterectomy with bilateral salpingo-oophorectomy.
Association between surgical methods and depression
The results from the linear regression and binary logistic regression analyses are presented in Table 3.Hysterectomy was significantly associated with depressive symptoms across all three models. In the unadjusted Model 1, the β coefficient for the hysterectomy group was 0.79 (p = 0.001). This association remained significant in the age- and race-adjusted Model 2 (β = 1.00, p < 0.001) and the fully adjusted Model 3 (β = 0.73, p = 0.002).In the binary logistic regression for PHQ-9 score ≥ 10, the hysterectomy group also showed significantly elevated odds ratios of 1.39 (p = 0.019), 1.57 (p = 0.003), and 1.35 (p = 0.047) in Models 1, 2, and 3, respectively.Compared to the non-surgical group, the HBSO group demonstrated a stronger association with higher PHQ-9 scores, with β coefficients of 0.91, 1.26, and 0.98 across the three models (all p < 0.001). The HBSO group also had significantly higher odds of severe depressive symptoms, with ORs of 1.66, 2.04, and 1.77 in Models 1, 2, and 3 (all p < 0.001).In contrast, bilateral oophorectomy alone did not show a significant association with depressive outcomes in any of the models (p > 0.05). This may be due to the small sample size, leading to unstable results.
Table 3.
Association between surgical methods and depressive symptoms.
| Model 1 | Model 2 | Model 3 | ||
|---|---|---|---|---|
| Outcome: PHQ-9 score, continuous | N | β (95%CI) P-value | β (95%CI) P-value | β (95%CI) P-value |
| Surgery | ||||
| None | 8348 | Ref. | Ref. | Ref. |
| Hysterectomy | 1146 | 0.79 (0.36, 1.21) 0.001 | 1.00 (0.56, 1.44) < 0.001 | 0.73 (0.29, 1.17) 0.002 |
| Bilateral Oophorectomy | 33 | 0.20 (-1.73, 2.12) 0.842 | 0.53 (-1.36, 2.43) 0.583 | 0.12 (-1.52, 1.76) 0.885 |
| HBSO | 1253 | 0.91 (0.54, 1.28) < 0.001 | 1.26 (0.87, 1.64) < 0.001 | 0.98 (0.62, 1.34) < 0.001 |
| Outcome: PHQ-9 score ≥ 10, Binary | OR (95%CI) P-value | OR (95%CI) P-value | OR (95%CI) P-value | |
|---|---|---|---|---|
| Surgery | ||||
| None | 8348 | Ref. | Ref. | Ref. |
| Hysterectomy | 1146 | 1.39 (1.06, 1.81) 0.019 | 1.57 (1.18, 2.09) 0.003 | 1.35 (1.01, 1.81) 0.047 |
| Bilateral Oophorectomy | 33 | 1.14 (0.17, 7.58) 0.896 | 1.36 (0.20, 9.45) 0.754 | 0.99 (0.13, 7.57) 0.992 |
| H-BSO | 1253 | 1.66 (1.32, 2.09) < 0.001 | 2.04 (1.56, 2.67) < 0.001 | 1.77 (1.34, 2.34) < 0.001 |
None: No Hysterectomy or Oophorectomy,
H-BSO hysterectomy with bilateral salpingo-oophorectomy.
For PHQ-9 score as continuous estimated results were expressed as β (95% CI), and for PHQ-9 score ≥ 10, Binary estimated results were expressed as OR (95% CI); β, Partial regression coefficient; OR, Odds Ratio; CI, Confidence Interval.
Model 1: Covariates were not adjusted at all.
Model 2: Adjusted for age and race.
Model 3: Adjusted for age, race, marital status, education level, ratio of family income to poverty, BMI, smoking, alcohol consumption, hypertension, diabetes, and systemic inflammation index (SII).
Relationship between surgical methods and depression severity
In multiple logistic regression analyses (Table 4), the distribution of different severity levels of depression across surgical methods was examined. The results showed that hysterectomy was significantly associated with higher depressive scores, particularly in the PHQ-9 ≥ 20 group, with an OR of 2.10 (P = 0.002), indicating a significant association between hysterectomy and severe depression. HBSO surgery also demonstrated a similar positive association, especially in the PHQ-9 score groups of 10 ≤ PHQ-9 < 15 and PHQ-9 ≥ 20, with ORs of 1.38 (P = 0.004) and 1.78 (P = 0.017), respectively, further supporting the increased risk of depression following surgery. The association between bilateral oophorectomy and depression severity was unclear. The small sample size for bilateral oophorectomy (n = 33) made it difficult to draw reliable conclusions about the relationship between this surgical method and the severity of depressive symptoms. Overall, the data support the association between hysterectomy, HBSO surgery, and the severity of depressive symptoms.
Table 4.
Multinomial Logistic Regression analysis of PHQ-9 scores (ordered multi-category classification).
| PHQ-9 < 5 (Reference) | 5 ≤ PHQ-9 < 10 | 10 ≤ PHQ-9 < 15 | 15 ≤ PHQ-9 < 20 | PHQ-9 ≥ 20 | |
|---|---|---|---|---|---|
| OR (95%CI) P-value | OR (95%CI) P-value | OR (95%CI) P-value | OR (95%CI) P-value | OR (95%CI) P-value | |
| (Intercept) | 1.0 (ref.) | 0.25 (0.23, 0.26) < 0.001 | 0.09 (0.09, 0.10) < 0.001 | 0.04 (0.04, 0.05) < 0.001 | 0.01 (0.01, 0.02) < 0.001 |
| Hysterectomy | 1.0 (ref.) | 1.39 (1.19, 1.62) < 0.001 | 1.38 (1.09, 1.73) 0.007 | 1.37 (0.98, 1.91) 0.068 | 2.10 (1.32, 3.35) 0.002 |
| Bilateral Oophorectomy | 1.0 (ref.) | 1.17 (0.51, 2.73) 0.709 | 0.89 (0.21, 3.80) 0.880 | 0.00 (0.00, 0.00) < 0.001 | 0.01 (0.00, inf.) 0.959 |
| H-BSO | 1.0 (ref.) | 1.14 (0.97, 1.33) 0.102 | 1.38 (1.11, 1.72) 0.004 | 1.67 (1.24, 2.24) 0.001 | 1.78 (1.11, 2.85) 0.017 |
None: No Hysterectomy or Oophorectomy,
H-BSO hysterectomy with bilateral salpingo-oophorectomy.
Supplementary Tables 1 and Fig. 2 illustrate the predicted probabilities of depressive symptoms (as measured by PHQ-9 scores) across different surgical methods. Among patients who did not undergo surgery (None group), most exhibited mild depressive symptoms, with the highest probability of a PHQ-9 score < 5 (71.5%) and the lowest probability of severe depressive symptoms (PHQ-9 ≥ 20) at 1.1%. In contrast, patients undergoing hysterectomy (Hysterectomy group) and HBSO surgery were more likely to experience moderate to severe depressive symptoms, with probabilities of PHQ-9 scores ≥ 20 at 2.0% and 1.8%, respectively. The relationship for bilateral oophorectomy remained unclear.
Fig. 2.
Predicted probabilities of depressive symptoms (PHQ-9 scores) across different surgical methods.
The results of the ordinary logistic regression for depressive symptoms by different surgeries are presented in Supplementary Table 2.The analysis shows that hysterectomy (OR = 1.40, 95% CI: 1.23 to 1.59, adjusted P < 0.017) is significantly associated with increased odds of depressive symptoms. Similarly, the combined procedure of hysterectomy and bilateral salpingo-oophorectomy (H-BSO) (OR = 1.31, 95% CI: 1.16 to 1.48, adjusted P < 0.017) also shows a significant association. However, bilateral oophorectomy alone (OR = 0.88, 95% CI: 0.42 to 1.87, adjusted P = 0.747) does not show a significant association with depressive symptoms, likely due to the limited sample size for this specific procedure.
Subgroup analysis
The results of the subgroup analysis on the relationship between surgical methods and depression are presented in the form of a forest plot (Fig. 3). The analysis indicated significant differences in the association between different surgical types (particularly HBSO) and depressive symptoms across various subgroups, a trend validated in the overall population. Specifically, hysterectomy was significantly associated with depressive symptoms, with the addition of oophorectomy further enhancing this association, whereas bilateral oophorectomy alone was not significantly associated with depressive symptoms. Moreover, significant interactions with BMI, INR, smoking, and alcohol consumption were observed (all interactions P < 0.0001), suggesting that these factors played a moderating role in the relationship between surgery and depressive symptoms. In contrast, interactions with age, hypertension, and diabetes were insignificant (interaction P values: 0.572, 0.154, and 0.752, respectively).
Fig. 3.
Forest plot of subgroup analysis on the relationship between surgical methods and depression.
Discussion
This study conducted an in-depth analysis of the association between various surgical procedures and depressive symptoms. The results indicate that hysterectomy is associated with a higher prevalence of depressive symptoms, even after adjusting for multiple social and health factors. When hysterectomy is accompanied by bilateral salpingo-oophorectomy (HBSO), the prevalence of depressive symptoms is further increased (OR = 1.77, 95%CI 1.34–2.34, P = 0.0002). These findings underscore the psychological impact of hysterectomy on women, particularly in cases where both ovaries are removed, leading to a higher prevalence of depressive symptoms.The results of this study suggest the following key considerations for patients undergoing inevitable hysterectomy or hysterectomy with bilateral salpingo-oophorectomy (HBSO): Comprehensive preoperative mental health screening, active psychological support during the perioperative period, referral to mental health professionals for patients with prior risk factors, individualized psychological support plans, hormone replacement therapy to alleviate the psychological impact of bilateral salpingo-oophorectomy.
This outcome aligns with previous research. For example, one study reported that hysterectomy alone is already associated with a higher prevalence of depression, but when combined with oophorectomy, the association with depressive symptoms becomes even stronger16. Another study emphasized that women who undergo both hysterectomy and oophorectomy, especially younger women, have a significantly higher prevalence of developing depression18. Additionally, some research suggests that bilateral oophorectomy may be associated with a reduced prevalence of postoperative depressive symptoms in women without baseline depressive symptoms24. However, the small sample size in our bilateral oophorectomy group (n = 33) limits our ability to draw conclusive findings, and further investigation is needed to confirm these relationships.
To understand the reasons behind the heightened depressive symptoms in patients undergoing hysterectomy or HBSO, it is crucial first to consider the profound psychological impact of hysterectomy itself. The uterus, as a vital reproductive organ, represents not just a physiological entity but also a significant psychological one. Many women may experience psychological stress related to the loss of reproductive ability or the symbolic representation of femininity following a hysterectomy, which could directly cause or exacerbate depressive symptoms25,26. Even in cases where the ovaries are not removed, this psychological stress alone may lead to the onset of depressive symptoms27.
When an oophorectomy is performed in addition to a hysterectomy, the prevalence of depressive symptoms significantly increases. This may be attributed to the sharp decline in estrogen and progesterone levels following oophorectomy, which further diminishes the neuroprotective effects within the female body28,29. The deficiency of estrogen may lead to neurotransmitter imbalances, such as those involving serotonin and dopamine, thereby being associated with an increased prevalence of depressive symptoms30. Furthermore, oophorectomy may affect the stability of the HPA axis, exacerbating psychological health issues29,30. Thus, the worsening of depressive symptoms following HBSO may stem from a combination of psychological trauma from hysterectomy and hormonal imbalances after oophorectomy29.However, our study did not conduct a mediation analysis to confirm these mechanistic pathways, and further research is needed to explore these potential mechanisms.
Additionally, the trauma of surgery and issues related to pain management during postoperative recovery may contribute significantly to the increase in depressive symptoms. Patients who undergo hysterectomy may require long-term management of pelvic pain, which has a bidirectional relationship with depressive symptoms31,32. Inadequate pain management or persistent chronic pain post-surgery could be a critical factor in the onset of depressive symptoms33,34. Moreover, systemic inflammatory responses triggered by surgery could lead to elevated levels of inflammatory markers such as interleukin-6 (IL-6) and C-reactive protein (CRP), which have been shown to be significantly associated with depression35–38.This study could not adjust for inflammatory markers such as interleukin-6 (IL-6) due to its absence in the nhance database. We used the SII as a surrogate marker for systemic inflammation, but future research should further investigate the role of inflammation in this process.
The modulatory effects of baseline characteristics such as high BMI, smoking, and alcohol consumption further support our conclusions. These factors are closely associated with the increase in depressive symptoms following hysterectomy, indicating that metabolic, lifestyle, and behavioral factors play a critical role in the onset of postoperative depression. For example, high BMI may negatively impact mental health through social pressure and reduced self-esteem39,40. Patients who smoke or drink alcohol tend to have poorer postoperative recovery, with these unhealthy behaviors further exacerbating depressive symptoms41.
Our study population demonstrated notable racial and ethnic diversity, comprising 69.8% non-Hispanic white, 11.1% non-Hispanic black, 12.5% Hispanic (7.2% Mexican American and 5.3% Other Hispanic), and 6.6% other racial/ethnic groups (including Asian Americans, Pacific Islanders, and Native Americans). This distribution generally aligns with the U.S. demographic composition, though with some variations. Importantly, we observed differences in surgery prevalence across ethnic groups, with 71.6% of non-Hispanic white women having undergone hysterectomy compared to 14.1% of non-Hispanic black women and 9.4% of Hispanic women (5.0% Mexican American and 4.4% Other Hispanic). These variations might reflect underlying disparities in healthcare access, cultural attitudes toward gynecological surgery, or differences in the prevalence of conditions requiring these procedures42 .
Several limitations of our study should be acknowledged. First, the cross-sectional nature of NHANES data prevents us from establishing causal relationships between surgical procedures and depression. Second, we lack information about the timing of surgery relative to depression onset, which could influence the interpretation of our results. Third, while PHQ-9 is a validated screening tool, cultural differences in depression expression and reporting might affect its accuracy across different ethnic groups. Fourth, the relatively small sample size in certain subgroups, particularly in the bilateral oophorectomy group (n = 33), limits our ability to draw definitive conclusions about some associations. Finally, we were unable to account for certain potential confounders such as pre-existing mental health conditions, detailed hormone replacement therapy regimens, or specific surgical indications, which might influence the relationship between surgery and depression.
In summary, this study elucidates the complex relationship between hysterectomy, HBSO, and the development of depressive symptoms. This relationship is influenced not only by hormonal changes but also by psychological trauma, chronic pain, inflammatory responses, and individual lifestyle and behavioral factors.Furthermore, the observed racial and ethnic differences in surgery prevalence and outcomes suggest the need for culturally sensitive approaches in both research and clinical practice. Future research should continue to explore the interplay of these factors and their long-term impact on women’s mental health to provide more comprehensive evidence for clinical decision-making.
Conclusion
Hysterectomy is significantly associated with the onset of depressive symptoms, and the association is further increased when accompanied by oophorectomy. However, the underlying mechanisms, including the roles of psychological trauma, chronic pain, and inflammatory responses, were not fully explored in this cross-sectional analysis. These findings underscore the importance of preoperative mental health assessment and postoperative psychological support to reduce depression and enhance patient well-being.Future research is needed to elucidate the specific pathways linking these surgical procedures to mental health outcomes.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
We would like to acknowledge all the participants.
Author contributions
C.X.: Writing – original draft, Visualization, Validation, Conceptualization. G. Z.:Software, Methodology, Formal analysis, Visualization, Conceptualization.W. Y.: Writing – review & editing, Supervision, Methodology, Formal analysis.Y.Z. : Writing – review & editing, Software, Supervision, Project administration.
Funding
None.
Data availability
Data availabilityThe datasets analyzed in this study are available in the National Health and Nutrition Examination Survey (NHANES) repository and are openly accessible online (www.cdc.gov/nchs/nhanes/).
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
The NHANES project was approved by the National Ethical Review Board for Health Statistics Research, and the data are publicly available on the project website (https://wwwn.cdc.gov/nchs/nhanes). The patients’ information was anonymized, and thus, the need for informed consent was waived for this study.
Consent for publication
All authors approved the final manuscript and the submission to this journal.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Prince, M. et al. (2007). Lancet(Lancet (London, England)), 370 (9590): 859–877 . [DOI] [PubMed]
- 2.Malhi, G. S. & Mann, J. J. Lancet(Lancet (London, England)), 392 (10161): 2299–2312. (2018). [DOI] [PubMed]
- 3.Lancet Psychiatry(The lancet. Psychiatry), 9 (2): 137–150. (2022).35026139 [Google Scholar]
- 4.Kuehner, C. Lancet Psychiatry(the lancet. Psychiatry)4 (2), 146–158 (2017). [DOI] [PubMed] [Google Scholar]
- 5.Sassarini, D. J. Maturitas(Maturitas), 94: 149–154. (2016). [DOI] [PubMed] [Google Scholar]
- 6.Perlis, R. H. et al. Depress. Anxiety(Depression Anxiety), 38 (10): 1026–1033. (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Harvey, S. V., Pfeiffer, R. M., Landy, R., Wentzensen, N. & Clarke, M. A. Am. J. Obstet. Gynecol(American J. Obstet. Gynecology), 227 (4): 611. (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Marsh, E. E., Wegienka, G. & Williams, D. R. JAMA(JAMA), 331 (17): 1492–1493. (2024). [DOI] [PubMed] [Google Scholar]
- 9.Simms, K. T. et al. Gynecol. Oncol(Gynecologic Oncology), 158 (3): 710–718. (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Broni, E. K. et al. Am. J. Obstet. Gynecol(American J. Obstet. Gynecology), 231 (4): 441–448. (2024). [Google Scholar]
- 11.Kim, H. et al. J. Affect. Disord(Journal Affect. Disorders), 285: 47–54. (2021). [DOI] [PubMed] [Google Scholar]
- 12.Bräuner, E. V. et al. (2022). Menopause(Menopause (New York, N.Y.)), 29 (3):276–283 . [DOI] [PubMed]
- 13.Lin, K. Y. et al. J. Obstet. Gynecol(Taiwanese J. Obstet. Gynecology), 59 (6): 899–905. (2020). [DOI] [PubMed] [Google Scholar]
- 14.Hickey, M. et al. Gynecol. Oncol(Gynecologic Oncology), 161 (2): 527–534. (2021). [DOI] [PubMed] [Google Scholar]
- 15.Rocca, W. A. et al. (2018). Menopause(Menopause (New York, N.Y.)), 25 (11): 1275–1285 . [DOI] [PubMed]
- 16.Yang, Y. et al. Sci. Rep(Scientific Reports), 13 (1): 9744. (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Richards, D. H. Lancet(Lancet (London, England)), 2 (7826): 430–433. (1973). [DOI] [PubMed]
- 18.Hassan, H. et al. Am. J. Obstet. Gynecol(American J. Obstet. Gynecology), 230 (1): 44–57. (2024). [DOI] [PubMed] [Google Scholar]
- 19.Liang, J. et al. JMIR Public. Health Surveill(JMIR Public. Health Surveillance), 9: e45776. (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Ettman, C. K. et al. JAMA Netw. Open(JAMA Netw. open), 3 (9): e2019686. (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Costantini, L. et al. J. Affect. Disord(Journal Affect. Disorders), 279: 473–483. (2021). [DOI] [PubMed] [Google Scholar]
- 22.de Oliveira, R. D. et al. JAMA Netw. Open(JAMA Netw. open), 6 (11): e2345138. (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Kroenke, K., Spitzer, R. L. & Williams, J. B. J. Gen. Intern. Med(Journal Gen. Intern. Medicine), 16 (9): 606–613. (2001). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Rohl, J., Kjerulff, K., Langenberg, P. & Steege, J. Am. J. Obstet. Gynecol(American J. Obstet. Gynecology), 199 (1): 21–22. (2008). [DOI] [PubMed] [Google Scholar]
- 25.Choi, H. G., Rhim, C. C., Yoon, J. Y. & Lee, S. W. (2020). Menopause(Menopause (New York, N.Y.)), 27 (5): 543–549 . [DOI] [PubMed]
- 26.Bahri, N. et al. Iran. Red Crescent Med. J(Iranian Red Crescent Med. Journal), 18 (1): e30493. (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ielmini, M. et al. J. Clin. Med(Journal Clin. Medicine), 13 (11). (2024).
- 28.Farquhar, C. M., Harvey, S. A., Yu, Y., Sadler, L. & Stewart, A. W. Am. J. Obstet. Gynecol(American J. Obstet. Gynecology), 194 (3): 711–717. (2006). [DOI] [PubMed] [Google Scholar]
- 29.Wise, P. M., Dubal, D. B., Wilson, M. E., Rau, S. W. & Böttner, M. Endocrinology(Endocrinology), 142 (3): 969–973. (2001). [DOI] [PubMed] [Google Scholar]
- 30.Herrera-Pérez, J. J. et al. Front. Psychiatry(Frontiers Psychiatry), 15: 1408878. (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Poleshuck, E. L. et al. Pain med(Pain medicine (Mass)), 14 (5): 686–691. (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lirk, P., Thiry, J., Bonnet, M. P., Joshi, G. P. & Bonnet, F. Reg anesth pain med(Regional anesthesia and pain medicine), 44 (4): 425–436. (2019). [DOI] [PubMed]
- 33.Bougie, O., Blom, J., Zhou, G., Murji, A. & Thurston, J. Best pract res clin Obstet Gynaecol(best practice & research. Clin. Obstet. Gynaecology). 85 (Pt B), 23–34 (2022). [DOI] [PubMed] [Google Scholar]
- 34.Rawal, N. Best Pract Res Clin Anaesthesiol(best practice & research. Clin. Anaesthesiol.37 (2), 123–132 (2023). [DOI] [PubMed] [Google Scholar]
- 35.Ting, E. Y., Yang, A. C. & Tsai, S. J. Int. J. Mol. Sci(International J. Mol. Sciences), 21 (6). (2020). [DOI] [PMC free article] [PubMed]
- 36.Beurel, E., Toups, M. & Nemeroff, C. B. Neuron(Neuron), 107 (2): 234–256. (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Harsanyi, S., Kupcova, I., Danisovic, L. & Klein, M. Int. J. Mol. Sci(International J. Mol. Sciences), 24 (1). (2022). [DOI] [PMC free article] [PubMed]
- 38.Kofod, J., Elfving, B., Nielsen, E. H., Mors, O. & Köhler-Forsberg, O. Eur. Neuropsychopharmacol(European Neuropsychopharmacology: J. Eur. Coll. Neuropsychopharmacology), 54 :116–125. (2022). [DOI] [PubMed] [Google Scholar]
- 39.Karageorgiou, V. et al. BMC Med(BMC Medicine), 21 (1): 355. (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Scott, K. M., Mcgee, M. A., Wells, J. E. & Oakley, B. M. J. Psychosom. Res(Journal Psychosom. Research), 64 (1): 97–105. (2008). [DOI] [PubMed] [Google Scholar]
- 41.Yonek, J. C., Meacham, M. C., Shumway, M., Tolou-Shams, M. & Satre, D. D. Drug alcohol depend(Drug and alcohol dependence), 227: 108922. (2021). [DOI] [PMC free article] [PubMed]
- 42.Jacoby, V. L., Fujimoto, V. Y., Giudice, L. C., Kuppermann, M. & Washington, A. E. Am. J. Obstet. Gynecol(American J. Obstet. Gynecology), 202 (6): 514–521. (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data availabilityThe datasets analyzed in this study are available in the National Health and Nutrition Examination Survey (NHANES) repository and are openly accessible online (www.cdc.gov/nchs/nhanes/).