Bidirectional Associations Between Obesity and Depressive Symptoms: Results From the Multiethnic Postmenopausal Cohort of the Women’s Health Initiative Study

Nicole P Yuan; Hamza Butt; Jordan F Karp; Eniola Idowu; Chengcheng Hu; Aladdin H Shadyab; Julie C Weitlauf; Nazmus Saquib; Jennifer W Bea; Phyllis A Richey; Shawna Follis; Mace Coday; Tamar Jacobsohn; Zhao Chen

doi:10.1016/j.osep.2025.10.002

. Author manuscript; available in PMC: 2025 Dec 31.

Published in final edited form as: Am J Geriatr Psychiatry Open Sci Educ Pract. 2025 Nov 26;8:63–74. doi: 10.1016/j.osep.2025.10.002

Bidirectional Associations Between Obesity and Depressive Symptoms: Results From the Multiethnic Postmenopausal Cohort of the Women’s Health Initiative Study

Nicole P Yuan ¹, Hamza Butt ¹, Jordan F Karp ¹, Eniola Idowu ¹, Chengcheng Hu ¹, Aladdin H Shadyab ¹, Julie C Weitlauf ¹, Nazmus Saquib ¹, Jennifer W Bea ¹, Phyllis A Richey ¹, Shawna Follis ¹, Mace Coday ¹, Tamar Jacobsohn ¹, Zhao Chen ¹

¹Department of Health Promotion Sciences (NPY, EI, JWB), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ; Department of Epidemiology and Biostatistics (HB, CH, ZC), Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ; Department of Psychiatry (JFK), College of Medicine, University of Arizona, Tucson, AZ; Herbert Wertheim School of Public Health and Human Longevity Science and Division of Geriatrics (AHS), Gerontology, and Palliative Care, Department of Medicine, University of California San Diego, La Jolla, CA; VA Palo Alto Health Care System (JCW), Palo Alto, CA; Department of Psychiatry and Behavioral Sciences, and, by Courtesy, Obstetrics and Gynecology (JCW), Stanford University School of Medicine, Stanford CA; Clinical Sciences Department (NS), College of Medicine, Sulaiman AlRajhi University, Al-Qassim, Saudi Arabia; Department of Preventive Medicine (PAR, MC), The University of Tennessee Health Science Center, Memphis, TN; Department of Epidemiology and Population Health (SF), School of Medicine, Stanford University, Stanford, CA; and the Mount Sinai - Morningside/West Psychiatry Residency (TJ), Icahn School of Medicine at Mount Sinai, New York, NY. Send correspondence and reprint requests to: Nicole P. Yuan, PhD, MPH, Department of Health Promotion Sciences, University of Arizona, 1295 North Martin Avenue, PO Box 245219, Tucson, AZ 85724.

^✉

Email: nyuan@arizona.edu

AUTHOR CONTRIBUTION

Nicole P. Yuan: Conceptualization, Methodology, Supervision, Writing – original draft, Writing – review & editing, Project administration. Hamza Butt: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft. Jordan F Karp: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. Eniola Idowu: Conceptualization, Writing – original draft, Writing – review & editing. Chengcheng Hu: Formal analysis, Methodology. Aladdin H. Shadyab: Writing – review & editing. Julie C. Weitlauf: Writing – review & editing. Nazmus Saquib: Writing – review & editing. Jennifer W. Bea: Writing – review & editing. Phyllis A. Richey: Writing – review & editing. Shawna Follis: Writing – review & editing. Mace Coday: Writing – review & editing. Tamar Jacobsohn: Writing – review & editing. Zhao Chen: Conceptualization, Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing.

PMCID: PMC12753003 NIHMSID: NIHMS2128205 PMID: 41477444

Abstract

Objectives:

Although obesity and depression are prevalent among postmenopausal women, few cohort studies have examined the association between obesity and depression among this population. We examined longitudinal and bidirectional associations between obesity and depressive symptoms among U. S. postmenopausal women.

Design:

We analyzed data from Women’s Health Initiative (WHI) study.

Participants:

Sample consisted of 95,238 postmenopausal women, aged 50-79, from the WHI study who had obesity and depression data at baseline and 3-year follow-up.

Measurements:

The dataset included anthropometric measurements of height and weight and the Burnam self-report screening instrument for depression. We conducted logistic regression analyses to assess the bidirectional association between obesity and depressive symptoms, adjusting for confounding factors, including age, race, ethnicity, years since menopause, marital status, education, employment status, and family income.

Results:

At baseline, 11.3% of the women reported depressive symptoms and 25% were categorized as obese (body mass index ≥ 30 kg/m²). Women who were obese at baseline were significantly more likely to report depressive symptoms at 3-year follow-up (OR = 1.26, 95% CI: 1.19–1.34) compared to women who were not obese. Women who reported depressive symptoms at baseline had higher odds of being obese at 3-year follow-up (OR = 1.33, 95% CI: 1.20–1.46) compared to women who did not. Age, race, ethnicity, and years since menopause did not modify the associations.

Conclusions:

Our findings of longitudinal and bidirectional associations between obesity and depressive symptoms highlight the importance of addressing both diseases among postmenopausal women in the U.S.

Keywords: Depressive symptoms, longitudinal research, obesity, postmenopausal women

OBJECTIVE

Obesity is a public health concern in the United States (U.S.) and is defined as a body mass index (BMI) of greater than 30kg/m².¹ Elevated BMI is a risk factor for several health conditions, including cardiovascular disease,² diabetes mellitus,³ some types of cancers,⁴ and knee and hip osteoarthritis.^5,6 Obesity is also associated with frailty in older adults.⁷ A significant relationship between obesity and mental illness, particularly depressive symptoms and major depressive disorder, was documented by meta-analysis studies.^8,9 Co-occurrence of obesity and depression worsens health outcomes with increased risks of disability, mortality, and morbidity.¹⁰

Women are at higher risk for obesity than men, and the prevalence rate of obesity in women is increasing.¹¹ Prospective studies have documented significant gender differences, with obese women experiencing higher risks of depression.⁹ Theories to explain this gender difference in the co-occurrence of obesity and depression are related to pressures on women to be thin,¹² and women’s hormonal fluctuations¹³ and body composition changes during menarche, pregnancy, postpartum, and menopause.

While the bidirectional association between depression and obesity has been described, there is inconsistent evidence with some research lacking significant findings,¹⁴ and some documenting a negative association between depression and obesity.¹⁵ There also are mixed findings on the magnitude of the obesity-to-depression and depression-to-obesity pathways. For example, 1 meta-analysis described a stronger relationship for depression being a risk factor for obesity compared to obesity being a risk factor for depression.¹⁶ Another study described the magnitude of the obesity-to-depression pathway to be greater.¹⁷ Understanding the nuances of the association between obesity and depression is compromised by inconsistent inclusion of potential modifiers in study analyses.^8,9 A review article documented that the range of covariates used in models was from 2 to 13 across studies.⁹

Postmenopausal women are a population of interest because of high prevalence rates of obesity and depression. From 1999–2000 through 2017–2018, the prevalence of obesity in older U.S. women increased from 30.5% to 42.4%.¹⁸ Findings from a meta-analysis documented that the prevalence of postmenopausal depression was 28%.¹⁹ There is evidence of increased rates of depression among women. From 2005 to 2015, the prevalence of past-year depression increased significantly for U.S. women from 8.6% to 9.7% and adults who were 50 years and older from 4.3% to 4.8%.²⁰

Using data from the Women’s Health Initiative (WHI), the largest prospective cohort of U.S. postmenopausal women, we aimed to clarify the bidirectional association between obesity and depression by: 1) examining the direction and magnitude of the association of obesity being a risk factor for depressive symptoms; 2) examining the direction and magnitude of the association of depressive symptoms being a risk factor for obesity; and 3) identifying demographic characteristics that modify the association between obesity and depressive symptoms.

METHODS

Sample and Study Design

The WHI study is a large, clinical investigation of prevention and intervention strategies for the common causes of mortality and morbidity among U.S. postmenopausal women.²¹ The WHI study began in 1992 with enrollment of women, aged 50-79, in 1 of 40 WHI clinical centers across the country. Women were enrolled in a clinical trial or an observational study. Details about the design of the WHI clinical trial and observational study were reported previously.^21,22 Our study included 97,957 WHI participants, consisting of 93,676 women who participated in the observational study and a subset of 4,281 women from the clinical trial.

Measures

Depressive symptoms

Depressive symptoms were measured at baseline and 3-year follow-up using the 8-item Burnam self-report screening instrument for depressive symptoms and mood disorders.²³ The Burnam instrument includes 6 items from the Center for Epidemiological Studies Depression Scale (CES-D;²⁴) and 2 items from the Diagnostic Interview Schedule (DIS;²⁵) The full CES-D scale consists of twenty items that assess the frequency of twenty symptoms in the past week. The responses were scored from 0 (rarely or none) to 3 (most or all). The 2 items from the DIS were “In the past year, have you had 2 weeks or more during which you felt sad, blue, or depressed, or lost pleasure in things that you usually cared about or enjoyed?” and “Have you had 2 years or more in your life when you felt depressed or sad most days, even if you felt okay sometimes?” The responses were scored 0 (no) or 1 (yes). The Burnam instrument was scored using an equation to determine the probability of being depressed and developing a scaled score for each respondent.²³ Similar to past research,²⁶ we used a cutoff of ≥ 0.06 to dichotomize the score.

Obesity classification and body composition

Anthropometric measurements were measured by trained staff at baseline and 3-year follow-up.^21,22 Height was measured using a stadiometer to the nearest 0.1 cm. Weight was measured using a balance-beam scale to the nearest 0.1 kg. Body Mass Index (BMI) was calculated by dividing weight (kg) by height (meter) squared. Consistent with past research on the bidirectional association between depression and obesity,¹⁶ we organized the BMI data into 4 categories based on the Centers for Disease Control and Prevention definitions: <18.5 (underweight); 18.5–24.9 (normal); 25.0–29.9 (overweight); ≥ 30 (obese;²⁷).

Covariates

Demographic, health, and behavioral covariates were assessed at baseline using self-report questionnaires. We recoded continuous age in 5-year increments. Race was self-reported and consisted of the following categories: White, Black/African-American, American Indian/Alaska Native (AIAN), Asian, Native Hawaiian/Pacific Islander (NHPI), and more than 1 race. Ethnicity was assessed by asking participants if they were Hispanic/Spanish/Latino (Hispanic). Years since menopause was calculated as the difference between age at screening and age at menopause. Other demographic variables included marital status (never married, divorced or separated, widowed, presently married, living in a marriage-like relationship), education (didn’t go to school, grade school (1–4 years), grade school (5–8 years), some high school (9–11 years), high school diploma or G.E. D., vocational or training school after high school graduation, some college or Associate Degree, college graduate or Baccalaureate Degree, some college or professional school after college graduation, Master’s degree, and Doctoral degree (Ph.D., M.D., J.D., etc.), employment (full-time or part-time), and family income (less than $10,000, $10,000-19,999, $20,000-34,999, $35,000-49,999, $50,000-74,999, $75,000-99,999, $100,000-149,999, $150,000 or more). Health history questions included: history of female hormone use (never used, past use, current use), history of depression (yes, no), history of diabetes (yes, no), and history of breast, colorectal (colon, rectum, bowel or intestinal), endometrial (lining of the uterus or womb), skin cancer, melanoma cancer, and any other cancers in the past 10 years (yes, no). Physical functioning was assessed using the Rand 36-item Health Survey.²⁸ The physical functioning subscale consists of 10 items with 3 response options (1, 2 or 3). The response options were recorded (0, 50 or 100) and then averaged to obtain a physical functioning score. The scores ranged from 0 to 100 with a higher score indicating more favorable physical functioning.

Physical activity was measured as total energy expended from recreational activity.²⁹ Total metabolic equivalent of task (MET)-hours per week were calculated based on responses to 9 items about recreational physical activity that included walking, mild, moderate, and strenuous physical activity in kcal/week/kg. A total dietary quality score was assessed using the Healthy Eating Index-2015.³⁰ This was the sum of 13 types of food with ranges from 0 to 100. An overall score of 100 showed perfect alignment with the key dietary recommendations and dietary patterns recommended in the Dietary Guidelines for Americans.³¹ The thirteen components included total vegetables, greens and beans, total fruits, whole fruits, whole grains, dairy, total protein foods, seafood and plant proteins, fatty acids, sodium, refined grains, saturated fats, and added sugars. Higher scores indicated conformance to the 2015 dietary guidance for the U.S. population.

Statistical Analysis

We performed all analyses using SAS 9.4. We computed summary statistics (mean, standard deviation, frequency) for demographic and baseline measures, stratified by depressive symptoms at baseline. We conducted logistic regression models to examine cross-sectional and longitudinal associations between obesity and depressive symptoms. For models with depressive symptoms as the outcome, we dichotomized BMI category with a cutoff value of ≥ 30 for obesity. For models with obesity as the outcome, we used 4 BMI categories: underweight (<18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25.0-29.9 kg/m2), and obese (≥30 kg/m2; 24).

We examined the cross-sectional relationship between obesity and depressive symptoms at either the baseline or at the 3-year follow-up using logistic regression analysis. The models included depressive symptoms as exposure and BMI category as outcome, or obesity as exposure and depressive symptoms as outcome. To assess the longitudinal association of obesity with depressive symptoms, we conducted a binary logistic regression with baseline obesity as the exposure and depressive symptoms at 3-year follow-up as the outcome, adjusting for baseline depressive symptoms. To examine the longitudinal association of depressive symptoms with BMI category, we performed a multinomial logistic regression analysis with depressive symptoms at baseline as the exposure and BMI category at 3-year follow-up as the outcome, adjusting for baseline obesity. For BMI categories, we used normal weight as the reference category.

Guided by published research, we included covariates in adjusted models. The minimally adjusted model included age (per 5 years), race, ethnicity, and postmenopausal status (measured by years since menopause). The further adjusted model included additional variables consisting of marital status, education, employment status, and family income. Statistical significance was based on 95% confidence interval (CI) of the odds ratio and hence is equivalent to that from a 2-tailed test with significance level of 0.05. We assessed interaction terms between exposure and covariates for modification of the relationship between the exposure and outcome.

Imputation Analysis to Address Missing Data at Year 3

The amount of missingness was not considerable for BMI (1.15%) and depression (2.78%) at baseline. At the year 3 follow-up measurements, the percentage of missingness was above 10% for each outcome (17.2% for BMI and 13.5% for depression). Due to the amount of missingness, we assessed if the missingness depended on any baseline covariates included in the regression models. To address missing data for 3-year follow-up covariates, we employed multiple imputations using the fully conditional specification (FCS) method to generate 10 imputed datasets per model. Imputed variables for depressive symptoms at 3-year follow-up outcome model included years since menopause, depressive symptoms at baseline, obesity at baseline, and BMI category at 3-year follow-up. Imputed variables for outcome models featuring the BMI category at 3-year follow-up included years since menopause, obesity at baseline, and depressive symptoms at baseline. We combined and compared estimates from the logistic regression models on the imputed datasets to the original models to evaluate the robustness of the findings.

RESULTS

Characteristics of Participants

Baseline participant characteristics (n = 95,238) are presented in Table 1. The mean age was 63.5 years (SD = 7.3). Most participants were White (85%), 8.4% were Black, 3.0% were Asian, 0.4% were AIAN, and 0.1% were NHPI. A small percentage (4.4%) of women were Hispanic. Most participants were currently married (60.5%). More than half of the sample had at least some college education (68.8%). Many participants were unemployed (63.9%) and had a family income of ≤ $50,000 (57.9 %).

TABLE 1.

Sample Characteristics at Baseline by Depressive Symptoms

Variables	Total Sample (N = 95,238) N (%)	Sample With Depressive Symptoms^a at Baseline (N = 10,781) N (%)	Sample With No Depressive Symptoms at Baseline (N = 84,457) N (%)
Age, Mean (SD)	63.5 (7.3)	62.0 (7.5)	63.7 (7.3)
Years since menopause, Mean (SD)	15.3 (9.4)	15.0 (9.7)	15.4 (9.3)
Race
White	80,951(85.0)	8,697(80.7)	72,254 (85.6)
Black, African-American, or Negro	7,988 (8.4)	1,168 (10.8)	6,820 (8.1)
Asian	2,816 (3.0)	184 (1.7)	2,632 (3.1)
American Indian/Alaska Native	363 (0.4)	84 (0.8)	279 (0.3)
Native Hawaiians/Pacific Islander	69 (0.1)	7 (0.1)	62 (0.1)
More than 1 race	1,021 (1.1)	133 (1.2)	888 (1.1)
Unknown/not reported	2,030 (2.1)	508 (4.7)	1,522 (1.8)
Ethnicity
Spanish/Hispanic/Latino	4,169 (4.4)	893 (8.3)	3276 (3.9)
Not Spanish/Hispanic/Latino	90,116 (94.6)	9,719 (90.2)	80,397 (95.2)
Unknown/not reported	953 (1.0)	169 (1.6)	784 (0.9)
Marital Status
Presently married	57,388 (60.5)	5,303 (49.5)	52,085 (61.9)
Divorced or separated	15,000 (15.8)	2,444 (22.8)	12,556 (14.9)
Widowed	16,418 (17.3)	2,255 (21.1)	14,163 (16.8)
Never married	4,441 (4.7)	513 (4.8)	3,928 (4.7)
Living in a marriage-like relationship	1,546 (1.6)	195 (1.8)	1,351 (1.6)
Employment status (full-time, part-time)
Yes	33,335 (36.1)	3,833 (36.7)	29,502 (36.1)
No	58,910 (63.9)	6,605 (63.3)	52,305 (63.9)
Family Income
< $10,000	3,890 (4.3)	979 (9.5)	2,911 (3.6)
$10,000-19,999	10,279 (11.3)	1,732(16.8)	8,547 (10.6)
$20,000-34,999	20,706 (22.7)	2,441 (23.6)	18,265 (22.6)
$35,000-49,999	17,838 (19.6)	1,835 (17.8)	16,003 (19.8)
$50,000-74,999	17,882 (19.6)	1,664 (16.1)	16,218 (20.1)
$75,000-99,999	8340 (9.1)	625 (6.1)	7,715 (9.5)
$100,000-149,999	6,098 (6.7)	438 (4.2)	5,660 (7.0)
≥$150,000	3,404 (3.7)	216 (2.1)	3188 (3.9)
Don’t know	2,766 (3.0)	406 (3.9)	2,360 (2.9)
Education
Didn’t go to school	77 (0.1)	13 (0.1)	64 (0.1)
Grade school (1-4 years)	312 (0.33)	94 (0.9)	218 (0.3)
Grade school (5-8 years)	1,088 (1.2)	274 (2.6)	814 (1.0)
Some high school (9-11 years)	3,320 (3.5)	663 (6.2)	2,657(3.2)
High school diploma or GED	15,448 (16.4)	1,981 (18.6)	13467 (16.1)
Vocational or training school	9,310 (9.9)	1,225 (11.5)	8,085(9.7)
Some college or associate degree	25,339 (26.8)	3,024 (28.3)	22,315 (26.6)
College graduate or baccalaureate degree	10,836 (11.5)	874 (8.2)	9,962 (11.9)
Some postgraduate or professional	11,144 (11.8)	1,009 (9.5)	10,135 (12.1)
Master’s degree	14,985 (15.9)	1, 258(11.8)	13,727 (16.4)
Doctoral degree	2,630 (2.8)	258 (2.4)	2,372 (2.8)
History of female hormone use
Never used hormones	28,930 (30.4)	3,030 (28.1)	25,900 (30.7)
Past hormone use	20,141 (21.2)	2,500 (23.2)	17,641 (20.9)
Current hormone use	44,441 (46.7)	5,066 (47.0)	39,375 (46.6)
Unknown/not reported	1,726 (1.8)	185 (1.7)	1541 (1.8)
BMI Category
Underweight	1092 (1.2)	112 (1.0)	980(1.2)
Normal weight	36,857 (38.7)	3,450 (32.0)	33,407(39.6)
Overweight	32,002 (33.6)	3,448 (32.0)	28,554(33.8)
Obese	24,201 (25.4)	3,647 (33.8)	20,554(24.3)
Unknown/not reported	1086 (1.1)	124 (1.2)	962(1.1)
Physical functioning score, Mean (SD)	81.1 (20.3)	72.1 (24.8)	82.3 (19.4)
Diet quality score, Mean (SD)	66.8 (10.4)	64.0 (10.9)	67.2 (10.2)
MET-hours per week of energy expenditure resulting from recreational physical activity, Mean (SD)	13.6 (14.3)	10.6 (13.0)	13.9 (14.4)
History of diabetes
Yes	5,400 (5.7)	937(8.7)	4,463(5.3)
No	89,751 (94.2)	9,834(91.2)	79,917(94.6)
Unknown/not reported	87(0.1)	10(0.1)	77(0.1)
History of breast cancer
Yes	4,879 (5.1)	592(5.5)	4,287(5.1)
No	90,244 (94.8)	10,169(94.3)	80,075(94.8)
Unknown/not reported	115 (0.1)	20 (0.2)	95 (0.1)
History of colorectal cancer
Yes	832 (0.9)	98 (0.9)	734 (0.9)
No	94,168 (98.9)	10,640 (98.7)	83,528 (98.9)
Unknown/not reported	238 (0.3)	43 (0.4)	195 (0.2)
History of endometrial cancer
Yes	1695 (1.8)	254 (2.4)	1441 (1.7)
No	93,428(98.1)	10,508 (97.5)	82,920 (98.2)
Unknown/not reported	115 (0.1)	19 (0.2)	96 (0.1)
History of skin cancer
Yes	11,027 (11.6)	1,092 (10.1)	9,935 (11.8)
No	84,004 (88.2)	9,670 (89.7)	74,334 (88.0)
Unknown/not reported	207 (0.2)	19 (0.2)	188 (0.2)
History of melanoma cancer
Yes	1,635 (1.7)	215 (2.0)	1,420 (1.7)
No	93,308 (98.0)	10,536 (97.7)	82,772 (98.0)
Unknown/not reported	295 (0.3)	30 (0.3)	265 (0.3)
Any other cancers in past 10 years
Yes	1,460 (1.5)	187 (1.7)	1,273 (1.5)
No	92,107 (96.7)	10,422 (96.7)	81,685 (96.7)
Unknown/not reported	1,671 (1.8)	172 (1.6)	1,499 (1.8)

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Note. BMI: body mass index; Underweight (BMI<18.5); Normal Weight (18.5 ≤ BMI<25.0); Overweight (25.0 ≤ BMI< 30.0); Obese (BMI≥30.0).

Determined by the Burnam screening instrument (≥0.06 indicates depressive symptoms)

The average years since menopause was 15.3 (SD = 9.4). Over 46% of the women reported current female hormone use and 21.2% reported using hormones in the past. Based on BMI categories, 25.4% of the women were obese, 33.6% were overweight, and 1.3% were underweight. Over 5% of women reported a history of diabetes. The most frequently reported types of lifetime cancer were skin (11.6%), breast (5.1%), endometrial (1.8%), melanoma (1.7%), and colorectal (0.9%). The average physical functioning score was 81 out of 100 (SD = 20.3). The average diet quality score was 66.8 out of 100 (SD = 10.4). The average MET-hours per week of energy expenditure resulting from recreational physical activity was 13.6 (SD = 4.3).

At baseline, 10,781 participants (11.3%) reported depressive symptoms based on the Burnam score (≥ 0.06; Table 1), and those women were slightly younger (62.0 ± 7.5) than women who did not report depressive symptoms at baseline (63.7 ± 7.3). Depression was more common among Hispanic women (8.3%) compared to non-Hispanic women (3.9%). Depression was more common among women who were obese (33.8%) compared to those who were not obese (24.3%). Lifetime rates of cancer were similar between women who had depressive symptoms at baseline and those who did not. Physical functioning score and MET-hours per week of energy expenditure from recreational physical activity were lower among women who reported depressive symptoms at baseline (72.1 ± 24.8 and 10.6 ± 13.0, respectively) compared to women who did not (82.3 ± 19.4 and 13.9 ± 14.4, respectively). Diet quality scores were lower among women who reported depressive symptoms at baseline (64.0 ± 10.9) compared to those who did not (67.2 ± 10.2).

Associations Between Obesity and Depressive Symptoms

The cross-sectional associations between obesity and depressive symptoms at baseline, and the longitudinal relationship between obesity at baseline and depression at the 3-year follow-up are presented in Table 2. We examined the associations in crude models, minimally adjusted (adjusted for age, race, ethnicity, and years since menopause) models, and then in further adjusted models (minimal adjustment plus marital status, education, employment status, and family income). In comparison to women who were not obese, women who were obese at baseline were significantly more likely to report depressive symptoms at baseline as indicated by the results from the crude model (OR = 1.59,95% CI: 1.53–1.66), the minimally adjusted model (OR = 1.48, 95% CI: 1.42–1.55). and the further adjusted model (OR = 1.34, 95% CI: 1.28–1.41).

TABLE 2.

Logistic Regression of Depression at Baseline and 3-Year Follow-Up With Obesity at Baseline as Exposure

Variables	Crude and Adjusted OR (95%CI)
Variables	Sample With Depressive Symptoms at Baseline^a (N = 94,152)	Sample With Depressive Symptoms at 3-Year Follow-Up^a (N = 81,825)
	Crude	Crude
Obesity at baseline	1.59 (1.53,1.66)	1.39 (1.32,1.46)
	Minimally adjusted^b	Minimally adjusted^c
Obesity at baseline	1.48 (1.42,1.55)	1.32 (1.24,1.39)
	Further adjusted^d	Further adjusted^e
Obesity at baseline	1.34 (1.28,1.41)	1.26 (1.19,1.34)

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Note. OR: odds ratio.

Determined by the Burnam screening instrument (≥0.06 indicates depressive symptoms).

Age, race, ethnicity, and years since menopause were included in the models.

Depressive symptoms at baseline, age, race, ethnicity, and years since menopause were included in the models.

Age, race, ethnicity, years since menopause, marital status, education, employment status, and family income were included in the models.

Depressive symptoms at baseline, age, race, ethnicity, years since menopause, marital status, education, employment status, and family income were included in the models.

Women who were obese at baseline were significantly more likely to report depressive symptoms at 3-year follow-up (OR = 1.39, 95% CI: 1.32–1.46) compared to women who were not obese at baseline, using the crude model. This association was slightly attenuated with the minimally adjusted model (adjusted OR = 1.32, 95% CI: 1.24–1.39), and with the further adjusted model (adjusted OR = 1.26, 95% CI: 1.19–1.34). The results from analyses with the complete case analysis were consistent with those from analyses with imputed datasets to address missing values (data not shown).

Table 3 shows the associations of depressive symptoms at baseline with the BMI category at baseline and 3-year follow-up. We used normal weight as the reference group for the BMI category. For the cross-sectional models, depressive symptoms at baseline were significantly associated with higher odds of being obese (OR = 1.72, 95% CI: 1.64–1.81) or overweight (OR = 1.17, 95% CI: 1.11–1.23) at baseline. The associations were similar with the minimally adjusted model (OR = 1.58, 95% CI: 1.50–1.67 for obese; OR = 1.13, 95% CI 1.07–1.19 for overweight) and the further adjusted model (OR = 1.41, 95% CI: 1.33–1.49 for obese; OR = 1.09, 95% CI 1.03–1.15 for overweight). The association between depressive symptoms and underweight was not statistically significant in all the crude and adjusted cross-sectional models.

TABLE 3.

Logistic Regression of Obesity at Baseline and 3-year Follow-Up With Depressive Symptoms at Baseline as Exposure

Variables	Crude and Adjusted OR (95% CI)
	Baseline			3-Year Follow-Up
	UW^b (n = 1,092)	OW^b (n = 32,002)	Obese^b (n = 24,201)	UW^b (n = 938)	OW^b (n = 27,120)	Obese^b (n = 20,661)
	Crude			Crude
Depressive symptoms at baseline^a	1.11 (0.91,1.35)	1.17 (1.11,1.23)	1.72 (1.64,1.81)	1.29 (1.04,1.59)	1.20 (1.13,1.27)	1.49 (1.37,1.63)
	Minimally Adjusted^c			Minimally Adjusted^d
Depressive symptoms at baseline^a	1.20 (0.97,1.48)	1.13 (1.07,1.19)	1.58 (1.50,1.67)	1.40 (1.12,1.74)	1.16 (1.09,1.23)	1.38 (1.25,1.51)
	Further Adjusted^e			Further Adjusted^f
Depressive symptoms at baseline^a	1.14 (0.92,1.43)	1.09 (1.03,1.15)	1.41 (1.33,1.49)	1.39 (1.10,1.75)	1.14 (1.07,1.21)	1.33 (1.20,1.46)

Open in a new tab

Note. UW: underweight (BMI < 18.5); OW: overweight (BMI range: 25.0–29.9); Obese (BMI ≥ 30). OR: odds ratio.

Determined by the Burnam screening instrument (≥0.06 indicates depressive symptoms).

Reference was normal weight (BMI range: 18.5–24.9).

Age, race, ethnicity, and years since menopause were included in the models.

Obesity at baseline, age, race, ethnicity, and years since menopause were included in the models.

Age, race, ethnicity, years since menopause, marital status, education, employment status, and family income were included in the models.

Obesity at baseline, age, race, ethnicity, years since menopause, marital status, education, employment status, and family income were included in the models.

Using the longitudinal crude models, depressive symptoms at baseline were significantly associated with higher odds of being obese (OR = 1.49, 95% CI: 1.37–1.63), overweight (OR = 1.20, 95% CI: 1.13–1.27), and underweight (OR = 1.29, 95% CI: 1.04–1.59) at 3-year follow-up. The associations remained significant with the minimally adjusted models (OR = 1.38, 95% CI: 1.25–1.51 for obesity; OR = 1.16, 95% CI = 1.09–1.23 for overweight; and OR = 1.40, 95% CI = 1.12–1.74 for underweight) and further adjusted models (OR = 1.33, 95% CI: 1.20–1.46 for obesity; OR = 1.14, 95% CI: 1.07–1.21 for overweight; and OR = 1.39, 95% CI: 1.10–1.75 for underweight). The same models that were fit on imputed datasets were consistent with those from the complete case analysis in Table 3 (data not shown).

Interaction Tests

To assess potential modifiers, we added interaction terms of age, race, ethnicity, and years since menopause with depressive symptoms or obesity in corresponding models. The interaction terms were not statistically significant in the longitudinal models with baseline and 3-year follow-up.

CONCLUSIONS

We observed a bidirectional association between obesity and depressive symptoms among a large cohort of U.S. postmenopausal women from the WHI study. Our findings were consistent with a systematic review¹⁶ and cohort studies with older adults.^17,32 Our study found that the associations between obesity and depressive symptoms were positive and similar in magnitude in both directions among U.S. postmenopausal women. The risk was slightly higher for depression-to-obesity (OR = 1.49) compared to obesity-to-depression (OR = 1.39) which was consistent with some past research. A systematic review of studies with adult men and women in Europe and the U.S showed that the strength of the association was larger from depression-to-obesity compared to the obesity-to-depression (37% versus 18%; 16). In contrast, other studies documented a higher risk of obesity-to-depression compared to depression-to-obesity,⁹ including an investigation with older adults, aged 50-96, who participated in the Swedish Twin Registry.¹⁷ A review study of 25 articles found that 80% of the studies indicated significant obesity-to-depression pathways and 53% of the studies indicated significant depression-to-obesity pathways.⁹

Researchers have several hypotheses about the greater magnitude of the association of depression-to-obesity compared to obesity-to-depression. Some hypotheses might apply to older women. For example, the hypothesis that depression-to-obesity might be explained by associations between depression and disordered eating¹⁶ might apply to older women because there is evidence that eating pathology exists in older women.³³ Depression is significantly related to eating pathology in this population.³³ Another explanation for the high risk of depression-to-obesity is that individuals who experience depression might engage in unhealthy behaviors.¹⁶ Research with middle-aged and older Australian women with hypertension or heart disease found that depression was a barrier to healthy behaviors.³⁴ Women with heart disease and depression were 65% less likely to be highly physically active compared to women without depression. Future studies on obesity and depression among older women should examine underlying mechanisms tied to disordered eating and unhealthy lifestyle behaviors which were not the focus of our investigation.

Our study tested age, race, ethnicity, and years since menopause as potential modifiers of the association between obesity and depression among the cohort of postmenopausal women. We found that none of the variables significantly modified the associations between obesity and depression. Our findings contribute to the body of literature of mixed findings on mediators and moderators. An example of inconsistent findings is related to race moderation. A review article documented that race moderation was significant among only 1 out of 5 studies.⁹ The 1 study found that the association between BMI and subsequent depressive symptoms was greater for African American older adults than White older adults, particularly for African-American older adults who had less education.³⁵ The findings contrasted with those from a study with U.S. middle-aged and older adults that documented that an unhealthy body weight status had a greater association with onset of depressive symptoms among non-Hispanic, White adults compared to non-Hispanic African American and Hispanic adults.³⁶

The inconsistency of findings across investigations, including our study, might be due to diversity in study samples, study designs, research methods, measures, and follow-up time periods.³⁶ Our study had some limitations that might have affected the findings. The WHI study is one of the most diverse and largest cohort studies of U.S. women³⁷; however, future studies need to increase sample sizes of each ethnic group to obtain more statistical power to examine a moderating or mediating effect of race and ethnicity. Another limitation was that the WHI dataset only had 2 waves of data collection of obesity and depression variables. We were unable to examine nonlinear trends in the associations between obesity and depression. Researchers recommend longitudinal modeling of repeated assessments with samples of individuals who have a range of levels of BMI and depression.¹⁷

The mixed findings on mediators and moderators of the association between obesity and depression highlight the complexity of this body of research. Considering our lack of significant findings on modifiers, we recommend further investigation into the pathway that has the greatest risk and identification of modifying and moderating factors of each pathway. Future research should examine modifiable factors like dietary behaviors, physical activity, body image, maladaptive eating behaviors, and avoidance of physical activity.³⁸ Identification of modifiable factors might inform the development of prevention and intervention strategies appropriate for postmenopausal women in the U.S.

Our study had the unique opportunity to analyze the WHI study dataset of 95,328 postmenopausal women with high percentages of obesity and depressive symptoms at baseline and 3-year follow-up. Our findings of bidirectional associations between obesity and depressive symptoms further highlights the links between both diseases among U.S. postmenopausal women. Our research supports the assessment of the risk or presence of 1 disease when the other 1 is detected as a critical strategy to promote the physical and mental health of older women in the U.S.

The following is a short list of WHI Investigators. For a list of all the investigators who have contributed to WHI science, go to: https://s3-us-west-2.amazonaws.com/www-whi-org/wp-content/uploads/WHI-Investigator-Long-List.pdf.

Program Office: (National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Joan McGowan, Leslie Ford, and Nancy Geller. Clinical Coordinating Center: (Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg. Investigators and Academic Centers: (Brigham and Women’s Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski-Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Jennifer Robinson; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston-Salem, NC) Sally Shumaker; (University of Nevada, Reno, NV) Robert Brunner. Women’s Health Initiative Memory Study: (Wake Forest University School of Medicine, Winston-Salem, NC) Mark Espeland.

Highlights.

What are the primary questions addressed by this study?

We examined whether there are longitudinal and bidirectional associations between obesity and depressive symptoms among postmenopausal women in the U.S. We examined the direction and magnitude of each pathway.
What is the main finding of this study?

We found bidirectional associations between obesity and depressive symptoms among a racially and ethnically diverse cohort of postmenopausal women in the United States. The associations between obesity and depressive symptoms were similar across age, race, ethnicity, and years since menopause.
What is the meaning of the finding?

Our results highlight the links between obesity and depressive symptoms among U.S. postmenopausal women. The presence of 1 health condition support efforts to assess for the risk or presence of the other condition as a critical strategy to promote the health of older women in the U.S.

Acknowledgments

The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, 75N92021D00005. This study was supported by a Dr. Avi Lane Friedlich Scholarship that was awarded to Eniola Idowu. We appreciated the contributions of the WHI Investigators and staff at the clinical centers, clinical coordinating center, and project office.

DISCLOSURES

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: S. Follis reports financial support was provided by National Heart, Lung, and Blood Institute (K99HL169908). J Bea reports a relationship with Women’s Health Initiative that includes: consulting or advisory. J Bea reports a relationship with Global Health and Body Composition Institute (2019-2023) that includes: board membership. J Karp reports a relationship with National Institutes of Health that includes: funding grants. J Karp reports a relationship with Patient-Centered Outcomes Research Institute that includes: funding grants. J Karp reports a relationship with Johnson and Johnson Neuroscience that includes: funding grants. J Karp reports a relationship with Johnson and Johnson Neuroscience that includes: consulting or advisory. J Karp reports a relationship with Otsuka that includes: consulting or advisory. J Karp reports a relationship with American Association for Geriatric Psychiatry that includes: board membership. J Karp reports a relationship with Institute for Mental Health Research that includes: board membership. The remaining authors (N. Yuan, E. Idowu, C. Hu, A. Shadyab, J. Weitlauf, N Saquib, P. Richey, M. Coday, T. Jacobsohn, Z. Chen) reported no conflicts with any product or concept mentioned in this article. Conflicts of interest were not reported for H. Butt because he is deceased. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

DATA SHARING STATEMENT

The data from the Women’s Health Initiative is publicly available. Individuals who are interested in developing and publishing a manuscript that uses existing data from the Women’s Health Initiative (WHI) should review information on the WHI website: https://www.whi.org/propose-a-paper.

The data has not been previously presented orally or by poster at scientific meetings.

References

1.National Institutes of Health: Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: the Evidence Report. NIH Publication No. 984083. Bethesda, MD: National Heart, Lung, and Blood Institute, 1998 [Google Scholar]
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3.Cameron NA, Petito LC, McCabe M, et al. : Quantifying the sex-race/ethnicity-specific burden of obesity on incident diabetes mellitus in the United States, 2001 to 2016: MESA and NHANES. J Am Heart Assoc 2021; 10(4):e018799. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Renehan AG, Tyson M, Egger M, et al. : Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008; 371:569–578 [DOI] [PubMed] [Google Scholar]
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8.Luppino FS, de Wit LM, Bouvy PF, et al. : Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67:220–229 [DOI] [PubMed] [Google Scholar]
9.Faith MS, Butryn M, Wadden TA, et al. : Evidence for prospective associations among depression and obesity in population-based studies. Obes Rev 2011; 12:e438–e453 [DOI] [PubMed] [Google Scholar]
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13.Lokuge S, Frey BN, Foster JA, et al. : Depression in women: windows of vulnerability and new insights into the link between estrogen and serotonin. J Clin Psychiatry 2011; 72(11):3297 [Google Scholar]
14.Konttinen H, van Strien T, Männistö S, et al. : Depression, emotional eating and long-term weight changes: a population-based prospective study. Int J Behav Nutr Phys Act 2019; 16(1):28. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Kim J, Noh J-W, Park J, et al. : Body mass index and depressive symptoms in older adults: a cross-lagged panel analysis. PLoS One 2014; 9(12):e114891. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Mannan M, Mamun A, Doi S, et al. : Is there a bi-directional relationship between depression and obesity among adult men and women? Systematic review and bias-adjusted meta-analysis. Asian J Psychiatry 2016; 21:51–66 [Google Scholar]
17.Emery CF, Finkel D, Gatz M, et al. : Evidence of bidirectional associations between depressive symptoms and body mass among older adults. J Gerontol B Psychol Sci Soc Sci 2020; 75:1689–1698 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Hales CM, Carroll MD, Fryar CD, Ogden CL: Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS data brief, no 288. Hyattsville, MD: National Center for Health Statistics, 2017 [Google Scholar]
19.Li J, Liu F, Liu Z, et al. : Prevalence and associated factors of depression in postmenopausal women: a systematic review and meta-analysis. BMC Psychiatry 2024; 24(1):431. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Weinberger AH, Gbedemah M, Martinez AM, et al. : Trends in depression prevalence in the USA from 2005 to 2015: widening disparities in vulnerable groups. Psychol Med 2018; 48(8):1308–1315 [DOI] [PubMed] [Google Scholar]
21.The Women’s Health Initiative Study Group: design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials 1998; 19(1):61–109 [DOI] [PubMed] [Google Scholar]
22.Anderson GL, Manson J, Wallace R, et al. : Implementation of the Women’s Health Initiative study design. Ann Epidemiol 2003; 13 (9 Suppl):S5–17 [DOI] [PubMed] [Google Scholar]
23.Burman M, Wells K, Leake B, et al. : Development of a brief screening instrument for detecting depressive disorders. Med Care 1988; 26(8):775–789 [DOI] [PubMed] [Google Scholar]
24.Radloff LS: The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas 1977; 1(3):385–401 [Google Scholar]
25.Robins LN, Helzer JE, Croughan J, et al. : National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics, and validity. Arch Gen Psychiatry 1981; 38(4):381–389 [DOI] [PubMed] [Google Scholar]
26.Tuunainen A, Langer RD, Klauber MR, et al. : Short version of the CES-D (Burnam screen) for depression in reference to the structured psychiatric interview. Psychiatry Res 2001; 103:261–270 [DOI] [PubMed] [Google Scholar]
27.Centers for Disease Control and Prevention: Defining adult overweight & obesity [Centers for Disease Control and Prevention Website] Available at https://www.cdc.gov/obesity/basics/adult-defining.html; 2022. Accessed June 12, 2023 [Google Scholar]
28.Hays RD, Sherbourne CD, Mazel RM: The RAND 36-item Health Survey 1.0. Health Econ 1993; 2(3):217–222 [DOI] [PubMed] [Google Scholar]
29.Bea JW, Thomson CA, Wallace RB, et al. : Changes in physical activity, sedentary time, and risk of falling: The Women’s Health Initiative Observational Study. Prevent Med 2017; 95:103–109 [Google Scholar]
30.Krebs-Smith SM, Pannucci TE, Subar AF, et al. : Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet 2018; 118 (9):1591–1602 [Google Scholar]
31.Dietary Guidelines for Americans: Make every bite count with the Dietary Guidelines [Dietary Guidelines for Americans website]. Available at: https://www.dietaryguidelines.gov/. Accessed May 4, 2024. [Google Scholar]
32.Pan A, Sun Q, Czernichow S, et al. : Bidirectional association between depression and obesity in middle-aged and older women. Int J Obes (Lond) 2012; 36(4):595–602 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Midlarsky E, Marotta AK, Pirutinsky S, et al. : Psychological predictors of eating pathology in older adult women. J Women Aging 2018; 30(2):145–157 [DOI] [PubMed] [Google Scholar]
34.Cramer H, Lauche R, Adams J, et al. : Is depression associated with unhealthy behaviors among middle-aged and older women with hypertension or heart disease? Womens Health Issues 2020; 30(1):35–40 [DOI] [PubMed] [Google Scholar]
35.Sachs-Ericsson N, Burns AB, Gordon KH, et al. : Body mass index and depressive symptoms in older adults: the moderating roles of race, sex, and socioeconomic status. Am Journal Geriatr Psychiatry 2007; 15(9):815–825 [DOI] [PubMed] [Google Scholar]
36.Xiang X, An R: Obesity and onset of depression among US middle-aged and older adults. J Psychosom Res 2015; 78:242–248 [DOI] [PubMed] [Google Scholar]
37.LaMonte MJ, Manson JE, Anderson GL, et al. : Contributions of the Women’s Health Initiative to cardiovascular research: JACC state-of-the-art review. J Am Coll Cardiol 2022; 80(3):256–275 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Markowitz S, Friedman MA, Arent SM: Understanding the relation between obesity and depression: causal mechanisms and implications for treatment. Clin Psychol Sci Pract 2008; 15:1–20 [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data has not been previously presented orally or by poster at scientific meetings.

[R1] 1.National Institutes of Health: Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults: the Evidence Report. NIH Publication No. 984083. Bethesda, MD: National Heart, Lung, and Blood Institute, 1998 [Google Scholar]

[R2] 2.Khan SS, Ning H, Wilkins JT, et al. : Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity. JAMA Cardiol 2018; 3(4):280–287 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Cameron NA, Petito LC, McCabe M, et al. : Quantifying the sex-race/ethnicity-specific burden of obesity on incident diabetes mellitus in the United States, 2001 to 2016: MESA and NHANES. J Am Heart Assoc 2021; 10(4):e018799. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Renehan AG, Tyson M, Egger M, et al. : Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008; 371:569–578 [DOI] [PubMed] [Google Scholar]

[R5] 5.Jiang L, Tian W, Wang Y, et al. : Body mass index and susceptibility to knee osteoarthritis: a systematic review and meta-analysis. Joint Bone Spine 2012; 79:291–297 [DOI] [PubMed] [Google Scholar]

[R6] 6.Jiang L, Rong J, Wang Y, et al. : The relationship between body mass index and hip osteoarthritis: a systematic review and meta-analysis. Joint Bone Spine 2011; 78:150–155 [DOI] [PubMed] [Google Scholar]

[R7] 7.Jayanama K, Theou O, Godin J, et al. : Relationship of body mass index with frailty and all-cause mortality among middle-aged and older adults. BMC Med 2022; 20(1):404. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Luppino FS, de Wit LM, Bouvy PF, et al. : Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Arch Gen Psychiatry 2010; 67:220–229 [DOI] [PubMed] [Google Scholar]

[R9] 9.Faith MS, Butryn M, Wadden TA, et al. : Evidence for prospective associations among depression and obesity in population-based studies. Obes Rev 2011; 12:e438–e453 [DOI] [PubMed] [Google Scholar]

[R10] 10.Avila C, Holloway AC, Hahn MK, et al. : An overview of links between obesity and mental health. Curr Obes Rep 2015; 4:303–310 [DOI] [PubMed] [Google Scholar]

[R11] 11.Wang Y, Beydoun MA, Min J, et al. : Has the prevalence of overweight, obesity and central obesity levelled off in the United States? Trends, patterns, disparities, and future projections for the obesity epidemic. Int J Epidemiol 2020; 49(3):810–823 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Fardouly J, Diedrichs PC, Vartanian LR, et al. : Social comparisons on social media: the impact of Facebook on young women’s body image concerns and mood. Body Image 2015; 13:38–45 [DOI] [PubMed] [Google Scholar]

[R13] 13.Lokuge S, Frey BN, Foster JA, et al. : Depression in women: windows of vulnerability and new insights into the link between estrogen and serotonin. J Clin Psychiatry 2011; 72(11):3297 [Google Scholar]

[R14] 14.Konttinen H, van Strien T, Männistö S, et al. : Depression, emotional eating and long-term weight changes: a population-based prospective study. Int J Behav Nutr Phys Act 2019; 16(1):28. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Kim J, Noh J-W, Park J, et al. : Body mass index and depressive symptoms in older adults: a cross-lagged panel analysis. PLoS One 2014; 9(12):e114891. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Mannan M, Mamun A, Doi S, et al. : Is there a bi-directional relationship between depression and obesity among adult men and women? Systematic review and bias-adjusted meta-analysis. Asian J Psychiatry 2016; 21:51–66 [Google Scholar]

[R17] 17.Emery CF, Finkel D, Gatz M, et al. : Evidence of bidirectional associations between depressive symptoms and body mass among older adults. J Gerontol B Psychol Sci Soc Sci 2020; 75:1689–1698 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Hales CM, Carroll MD, Fryar CD, Ogden CL: Prevalence of obesity among adults and youth: United States, 2015–2016. NCHS data brief, no 288. Hyattsville, MD: National Center for Health Statistics, 2017 [Google Scholar]

[R19] 19.Li J, Liu F, Liu Z, et al. : Prevalence and associated factors of depression in postmenopausal women: a systematic review and meta-analysis. BMC Psychiatry 2024; 24(1):431. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Weinberger AH, Gbedemah M, Martinez AM, et al. : Trends in depression prevalence in the USA from 2005 to 2015: widening disparities in vulnerable groups. Psychol Med 2018; 48(8):1308–1315 [DOI] [PubMed] [Google Scholar]

[R21] 21.The Women’s Health Initiative Study Group: design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials 1998; 19(1):61–109 [DOI] [PubMed] [Google Scholar]

[R22] 22.Anderson GL, Manson J, Wallace R, et al. : Implementation of the Women’s Health Initiative study design. Ann Epidemiol 2003; 13 (9 Suppl):S5–17 [DOI] [PubMed] [Google Scholar]

[R23] 23.Burman M, Wells K, Leake B, et al. : Development of a brief screening instrument for detecting depressive disorders. Med Care 1988; 26(8):775–789 [DOI] [PubMed] [Google Scholar]

[R24] 24.Radloff LS: The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas 1977; 1(3):385–401 [Google Scholar]

[R25] 25.Robins LN, Helzer JE, Croughan J, et al. : National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics, and validity. Arch Gen Psychiatry 1981; 38(4):381–389 [DOI] [PubMed] [Google Scholar]

[R26] 26.Tuunainen A, Langer RD, Klauber MR, et al. : Short version of the CES-D (Burnam screen) for depression in reference to the structured psychiatric interview. Psychiatry Res 2001; 103:261–270 [DOI] [PubMed] [Google Scholar]

[R27] 27.Centers for Disease Control and Prevention: Defining adult overweight & obesity [Centers for Disease Control and Prevention Website] Available at https://www.cdc.gov/obesity/basics/adult-defining.html; 2022. Accessed June 12, 2023 [Google Scholar]

[R28] 28.Hays RD, Sherbourne CD, Mazel RM: The RAND 36-item Health Survey 1.0. Health Econ 1993; 2(3):217–222 [DOI] [PubMed] [Google Scholar]

[R29] 29.Bea JW, Thomson CA, Wallace RB, et al. : Changes in physical activity, sedentary time, and risk of falling: The Women’s Health Initiative Observational Study. Prevent Med 2017; 95:103–109 [Google Scholar]

[R30] 30.Krebs-Smith SM, Pannucci TE, Subar AF, et al. : Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet 2018; 118 (9):1591–1602 [Google Scholar]

[R31] 31.Dietary Guidelines for Americans: Make every bite count with the Dietary Guidelines [Dietary Guidelines for Americans website]. Available at: https://www.dietaryguidelines.gov/. Accessed May 4, 2024. [Google Scholar]

[R32] 32.Pan A, Sun Q, Czernichow S, et al. : Bidirectional association between depression and obesity in middle-aged and older women. Int J Obes (Lond) 2012; 36(4):595–602 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Midlarsky E, Marotta AK, Pirutinsky S, et al. : Psychological predictors of eating pathology in older adult women. J Women Aging 2018; 30(2):145–157 [DOI] [PubMed] [Google Scholar]

[R34] 34.Cramer H, Lauche R, Adams J, et al. : Is depression associated with unhealthy behaviors among middle-aged and older women with hypertension or heart disease? Womens Health Issues 2020; 30(1):35–40 [DOI] [PubMed] [Google Scholar]

[R35] 35.Sachs-Ericsson N, Burns AB, Gordon KH, et al. : Body mass index and depressive symptoms in older adults: the moderating roles of race, sex, and socioeconomic status. Am Journal Geriatr Psychiatry 2007; 15(9):815–825 [DOI] [PubMed] [Google Scholar]

[R36] 36.Xiang X, An R: Obesity and onset of depression among US middle-aged and older adults. J Psychosom Res 2015; 78:242–248 [DOI] [PubMed] [Google Scholar]

[R37] 37.LaMonte MJ, Manson JE, Anderson GL, et al. : Contributions of the Women’s Health Initiative to cardiovascular research: JACC state-of-the-art review. J Am Coll Cardiol 2022; 80(3):256–275 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Markowitz S, Friedman MA, Arent SM: Understanding the relation between obesity and depression: causal mechanisms and implications for treatment. Clin Psychol Sci Pract 2008; 15:1–20 [Google Scholar]

PERMALINK

Bidirectional Associations Between Obesity and Depressive Symptoms: Results From the Multiethnic Postmenopausal Cohort of the Women’s Health Initiative Study

Nicole P Yuan, Ph.D.

Hamza Butt, Ph.D.

Jordan F Karp, M.D.

Eniola Idowu, M.P.H.

Chengcheng Hu, Ph.D.

Aladdin H Shadyab, Ph.D.

Julie C Weitlauf, Ph.D.

Nazmus Saquib, Ph.D.

Jennifer W Bea, Ph.D.

Phyllis A Richey, Ph.D.

Shawna Follis, Ph.D.

Mace Coday, Ph.D.

Tamar Jacobsohn, M.D.

Zhao Chen, Ph.D.

Abstract

Objectives:

Design:

Participants:

Measurements:

Results:

Conclusions:

OBJECTIVE

METHODS

Sample and Study Design

Measures

Depressive symptoms

Obesity classification and body composition

Covariates

Statistical Analysis

Imputation Analysis to Address Missing Data at Year 3

RESULTS

Characteristics of Participants

TABLE 1.

Associations Between Obesity and Depressive Symptoms

TABLE 2.

TABLE 3.

Interaction Tests

CONCLUSIONS

Highlights.

Acknowledgments

DISCLOSURES

DATA SHARING STATEMENT

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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