Sage Journals: Discover world-class research

Abstract

Background & Aims

Depression and osteoporosis are common among middle-aged and older adults, both impacting morbidity and quality of life. Their shared risk factors suggest a potential link, but this relationship remains underexplored. This study aimed to assess the association between depressive symptoms and osteoporosis in two large cohorts: the National Health and Nutrition Examination Survey (NHANES, 2005-2010) and Health and Retirement Study (HRS, 2012).

Methods

We analyzed data from adults aged ≥50 years in NHANES (n = 3612) and HRS (n = 4307). Depressive symptoms were measured using PHQ-9 in NHANES and CES-D 8 in HRS, while osteoporosis was defined by self-reported diagnosis and medication use. Generalized linear models were used to evaluate the association, adjusting for demographic, lifestyle, and comorbidity factors. Subgroup analyses and sensitivity tests were conducted to explore effect modifiers and result robustness.

Results

Depressive symptoms were positively associated with osteoporosis in both cohorts (NHANES: adjusted OR = 1.061, 95% CI: 1.034-1.088; HRS: adjusted OR = 1.063, 95% CI: 1.014-1.115). Significant associations were observed across subgroups, with stronger effects in individuals with diabetes and arthritis. Sex differences showed higher odds ratios for men in HRS. The relationship exhibited a linear trend, with increasing risk as depressive severity intensified.

Conclusion

Depressive symptoms (PHQ-9/CES-D8) significantly associate with higher osteoporosis risk in older adults, with consistent cross-sectional findings across populations. While causality remains unclear, results support clinical bone density monitoring in depression care and depression screening in osteoporosis management. Future longitudinal studies should clarify mechanisms, while public health strategies should integrate mental-bone health prevention approaches.

Keywords

osteoporosis depressive symptoms NHANES HRS subgroup analysis

Introduction

In recent years, the global trend of population aging has become increasingly severe. An epidemiological study revealed that over 11% of the population is currently aged 60 years or older, and this proportion is expected to double by 2050.¹ The middle-aged and older adults suffering from osteoporosis is rapidly growing. Osteoporosis is primarily characterized by reduced bone mass and diminished bone strength, making affected individuals more susceptible to fractures in various parts of the body. This condition significantly increases the risk of disability and mortality, imposing a substantial burden on both individuals and society. Currently, treatment strategies for osteoporosis include the use of bisphosphonates, calcitonin, prophylactic vitamin D supplementation, and increased calcium intake.² Given its high prevalence and mortality rates, identifying potential risk factors and facilitating early diagnosis of osteoporosis are urgent priorities.

Major depressive disorder (MDD) is one of the most common mental health conditions, affecting approximately 16% of adults in the United States.³ As depression progresses, patients often experience sleep disturbances, physical exhaustion, and abnormal limb sensations, which can lead to a range of complications, including cardiovascular and endocrine disorders, and in severe cases, suicidal behaviors. Middle-aged and older adults, being a vulnerable population both physically and mentally as well as socially, are at a heightened risk of depression due to factors such as chronic illnesses and cognitive impairments.⁴ Current treatments for depression in the older adults primarily involve the use of antidepressant medications, coupled with enhanced social support and attention to their psychological well-being. Early recognition of depressive symptoms can aid in predicting the severity of the condition and the development of potential complications.

Previous studies have identified a correlation between depressive symptoms and osteoporosis, and this relationship has gained medical consensus among clinicians.⁵ This association may extend to broader psychological conditions, as evidenced by Godde et al⁶ using multi-wave HRS data, which established that a history of psychological disorders was significantly associated with higher odds of osteoporosis diagnosis in middle-aged and older Americans. Globally, numerous cohort studies have observed a significant association between depression and osteoporosis, noting that the risk of osteoporosis increases with the severity of depression and the use of certain antidepressant medications.⁵ Current research on the mechanisms linking depressive symptoms to osteoporosis primarily focuses on three pathways: First, the neuro-endocrine pathway, where chronic stress in depression activates the hypothalamic-pituitary-adrenal (HPA) axis, leading to sustained elevation of glucocorticoids that directly suppress osteoblast activity⁷; second, inflammatory mechanisms wherein depression-associated proinflammatory cytokines upregulate osteoclast differentiation⁸; and third, behavioral mediation pathways (e.g., physical inactivity, smoking) affecting the bone microenvironment.⁹ However, most of these studies have been conducted on relatively small sample sizes, highlighting the need for large-scale cohort studies to further explore the relationship between depressive symptoms and osteoporosis. Building upon the “Stress-Bone Integration Model” theoretical framework, this study employs large-scale cohort data to examine how chronic psychological stress influences bone homeostasis through multi-target neuro-immune-endocrine mechanisms, while exploring the modifying effects of individual differences such as age and sex.

The National Health and Nutrition Examination Survey (NHANES) is a large-scale ongoing study, while the Health and Retirement Study (HRS) is characterized by its cohort-based design, both offering valuable opportunities to investigate the relationship between depression severity and osteoporosis. These public databases include depression questionnaire scores and osteoporosis survey data, benefiting from the advantage of large sample sizes. Through statistical analysis and computation of these two extensive datasets, we aim to provide robust evidence for the association between depressive symptoms and osteoporosis.

Methods

Study Population

This dual-database study adheres to the ethical standards of the relevant national human experimentation committees and the principles of the Declaration of Helsinki (revised in 2013). The data were derived from a national cross-sectional study (NHANES 2005-2006, 2007-2008, and 2009-2010 cycles; protocol details available at: https://www.cdc.gov/nchs/nhanes/about/erb.html?CDC_AAref_Val=https://www.cdc.gov/nchs/nhanes/irba98.htm) and a cross-sectional analysis of the 2012 wave from the Health and Retirement Study (HRS) cohort (institutional review documentation: https://hrs.isr.umich.edu/sites/default/files/biblio/HRS_IRB_Information-10-2017.pdf), with the latter treated as cross-sectional data to ensure methodological consistency in comparing depression-osteoporosis associations between datasets.The Institutional Review Board (IRB) of the National Center for Health Statistics (NCHS) approved the collection of NHANES data, and the establishment of HRS was authorized by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration. Analysis of these de-identified public datasets qualifies as non-human subjects research under 45 CFR 46.104(d)(4). Since all analyzed data were de-identified and publicly available, this study was exempt from additional institutional review board approval.

This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (Supplemental Material).

This study analyzed participants aged ≥50 years from both datasets, applying this age threshold to NHANES (2005-2010) to align with HRS’s (2012) design as a population-based study specifically targeting adults aged 50 and older. We selected the 2012 HRS wave for methodological consistency with NHANES’ cross-sectional design and because it contained the most complete CES-D 8 and osteoporosis data among recent waves. Included participants had complete data on depression (PHQ-9 for NHANES; CES-D 8 for HRS), osteoporosis status, and key covariates (demographics, behaviors, comorbidities). After excluding individuals with missing data, the analytic samples comprised 3612 from NHANES (from 31 034 initially eligible) and 4307 from HRS (from 20 395 initially eligible) (Figure 1).

Figure 1.

The Flow Chart of Study Population Selection

Depressive Symptoms

In NHANES and HRS, depressive symptoms were assessed using the PHQ-9 and CES-D 8 depression scoring scales, respectively, which evaluate a series of emotions experienced by participants in daily life. The total scores for PHQ-9 and CES-D 8 are 27 and 8, respectively. A score of 10 or higher on the PHQ-9 and 4 or higher on the CES-D 8 was used to assign a value of 1 to the variable. The accuracy of these two depression scales in identifying depressive symptoms has been validated by related studies.^10,11

Osteoporosis

The osteoporosis data in both NHANES and HRS were derived from identical self-reported questionnaire items that served as the primary outcome measure in this study. Specifically, the 2012 HRS wave - which first incorporated osteoporosis assessment - and NHANES used parallel questions: (1) “Has a doctor ever told you that you have osteoporosis?” followed by (2) “Are you currently taking medication to treat osteoporosis?” Participants affirming either question were classified as having osteoporosis. This approach for outcome ascertainment reflects primary care screening practices where initial assessment relies on clinical history before confirmatory testing.

Covariates

Continuous variables included age and waist circumference (WC), which were analyzed as continuous in all primary analyses (baseline descriptions and generalized linear models [GLMs] of depression-osteoporosis relationships). For subgroup analyses only, age and WC were converted to categorical variables. WC was classified according to International Diabetes Federation (IDF) criteria, with ≥90 cm defining abdominal obesity.¹² Other demographic factors encompassed sex (male/female), marital status (living alone/cohabiting), and educational level (high school dropout/high school graduate/college or above). Lifestyle variables were categorized into smoking status (currently smoking or not), alcohol consumption (ever consumed alcohol or not), and daily physical activity, which was classified as active or inactive based on engagement in moderate- or high-intensity physical activity at least once per month. Comorbidities included hypertension, diabetes, and arthritis. Hypertension was defined based on self-reported responses to “Has a doctor ever told you that you have high blood pressure?” and “Are you currently taking antihypertensive medication?” as well as an average systolic blood pressure >140 mmHg or diastolic blood pressure >90 mmHg from three measurements. Diabetes and arthritis were determined based on related self-reports.

Statistical Analysis

Statistical analyses were conducted using R software (version 4.4.1; R Foundation for Statistical Computing).13Cross-sectional data from NHANES were analyzed to identify associations and observations, followed by a comparison using cohort data from HRS. Descriptive statistics included means ± SD (normality verified by Shapiro-Wilk tests) and frequencies (%). Group comparisons used t-tests (with Levene’s test for equal variances) and χ² tests (with Fisher’s exact test when expected counts <5). All statistical assumptions were explicitly tested and met.

After excluding participants with missing data on key variables (depressive symptoms, osteoporosis diagnosis, or covariates), we utilized GLMs to investigate the association between depressive symptoms and osteoporosis in NHANES and HRS data. We assumed a binomial distribution for the dependent variable with a logit link function. Despite using different depression scales (PHQ-9 in NHANES, CES-D8 in HRS), methodological consistency was maintained through uniform adjustment for all specified covariates and application of the same GLM framework to both datasets. Model diagnostics included examination of residuals, Cook’s distances, and variance inflation factors (all VIFs < 2), confirming no substantial outliers, multicollinearity, or model misspecification. Had overdispersion been detected, we would have employed quasibinomial regression to adjust standard errors. Covariate adjustments were performed in both datasets using three models. Model 1 included no adjustments. Model 2 adjusted for age, sex, education level, and marital status. Model 3 further included lifestyle factors (smoking, alcohol consumption, and physical activity) and comorbid conditions such as hypertension, diabetes, and arthritis. The selection of these covariates was primarily based on their established associations with both depression and osteoporosis in prior epidemiological studies.^14,15Based on these models, depressive symptoms were dichotomized as present (PHQ-9 ≥10 in NHANES; CES-D-8 ≥4 in HRS) or absent, serving as the independent variable, and their association with osteoporosis (binary: yes/no) as the dependent variable was assessed using GLMs.

Additionally, we hypothesized that the association of depressive symptoms with osteoporosis might vary by individual characteristics. To test this, we conducted pre-specified subgroup analyses stratified by sex, age (<65 vs ≥65 years), diabetes status (yes/no), arthritis status (present/absent), smoking status (current/former/never), physical activity level (low/moderate/high), and WC (<90 vs ≥90 cm),¹² as these factors may modify the depression-osteoporosis relationship. In all interaction analyses, the GLMs included both main effects (depressive symptoms and each subgroup variable) and their multiplicative interaction terms (depressive symptoms × subgroup variable) to ensure proper model specification. This approach allows the assessment of whether the association between depressive symptoms and osteoporosis differs across subgroups while accounting for baseline effects of each variable.Statistical significance was defined as P < .05 for all analyses, with interaction terms interpreted in the context of their constituent main effects.

Results

The baseline characteristics of middle-aged and older adults with osteoporosis are presented in Table 1. In the NHANES cohort (n = 3612), participants had a mean age of 65.1 years (SD = 9.57), with 62.1% being male. The prevalence of osteoporosis was 9.6% (346/3612), and participants with osteoporosis showed significantly higher PHQ-9 depression scores compared to those without osteoporosis (4.53 ± 5.35 vs 3.04 ± 4.33, P < .001). In the HRS cohort (n = 4307), the mean age was 66.7 years (SD = 10.3) with balanced sex distribution (50.4% female). The osteoporosis prevalence was 12.0% (518/4307), and similarly, the osteoporosis group demonstrated higher CES-D 8 scores (1.97 ± 2.27 vs 1.56 ± 2.06, P < .001).

Table 1.

Characteristics of Participants in the NHANES(2005-2010) and HRS(2012)

Characteristics	NHANES				HRS
	Total (n = 3612)	Having osteoporosis			Total (n = 4307)	Having osteoporosis
		No	Yes	P-value		No	Yes	P-value
		(n = 3266)	(n = 346)	P-value		(n = 3789)	(n = 518)	P-value
Age,mean(SD)	65.1 (9.57)	64.7 (9.60)	68.1 (8.78)	<.001	66.7 (10.3)	66.3 (10.3)	69.6 (10.0)	<.001
Sex,n(%)				<.001				<.001
Female	1369 (37.9)	1101 (33.7)	268 (77.5)		2172 (50.4)	1726 (45.6)	446 (86.1)
Male	2243 (62.1)	2165 (66.3)	78 (22.5)		2135 (49.6)	2063 (54.4)	72 (13.9)
Marital status,n(%)				<.001				<.001
Not living alone	2233 (61.8)	2053 (62.9)	180 (52.0)		2689 (62.4)	2430 (64.1)	259 (50.0)
Living alone	1379 (38.2)	1213 (37.1)	166 (48.0)		1618 (37.6)	1359 (35.9)	259 (50.0)
Education,n(%)				.237				.001
<High school	547 (15.1)	505 (15.5)	42 (12.1)		497 (11.5)	458 (12.1)	39 (7.5)
Completed HighSchool	644 (17.8)	583 (17.9)	61 (17.6)		1844 (42.8)	1592 (42.0)	252 (48.6)
>High school	2421 (67.0)	2178 (66.7)	243 (70.2)		1966 (45.6)	1739 (45.9)	227 (43.8)
Current smoke,n(%)				.055				.600
Not current smoke	2482 (68.7)	2302 (68.2)	267 (76.9)		3155 (73.3)	2781 (73.4)	374 (72.2)
Current smoke	1130 (31.3)	1064 (31.8)	95 (23.1)		1152 (26.7)	1008 (26.6)	144 (27.8)
Former drink,n(%)				<.001				<.001
No	707 (19.6)	603 (18.5)	104 (30.1)		1632 (37.9)	1392 (36.7)	240 (46.3)
Yes	2905 (80.4)	2663 (81.5)	242 (69.9)		2675 (62.1)	2397 (63.3)	278 (53.7)
Activity,n(%)				.719				<.001
Inactive	2175 (59.7)	1954 (59.8)	217 (58.7)		831 (19.3)	701 (18.5)	130 (25.1)
Active	1478 (40.3)	1333 (40.2)	145 (41.3)		3476 (80.7)	3088 (81.5)	388 (74.9)
WC,mean(SD)	103.0 (15.3)	103.0 (15.1)	98.0 (16.1)	<.001	103.0 (15.7)	103.0 (15.6)	98.0 (15.8)	<.001
Hypertension,n(%)				.016				1.000
No	1445 (40.0)	1328 (40.7)	117 (33..8)		1385 (32.2)	1218 (32.1)	167 (32.2)
Yes	2167 (60.0)	1938 (59.3)	229 (66.2)		2922 (67.8)	2571 (67.9)	351 (67.8)
Diabete,n(%)				.677				.091
No	2814 (77.9)	2548 (78.0)	266 (76.9)		3268 (75.9)	2859 (75.5)	409 (79.0)
Yes	798 (22.1)	718 (22.0)	80 (23.1)		1039 (24.1)	930 (24.5)	109 (21.0)
Arthritis,n(%)				<.001				<.001
No	1911 (52.9)	1823 (55.8)	88 (25.4)		1730 (40.2)	1641 (43.3)	89 (17.2)
Yes	1701 (47.1)	1443 (44.2)	258 (74.6)		2577 (59.8)	2148 (56.7)	429 (82.8)
Depressive symptoms,mean(SD)	3.18 (4.46)	3.04 (4.33)	4.53 (5.35)	<.001	1.61 (2.09)	1.56 (2.06)	1.97 (2.27)	<.001

Abbreviations: WC, waist circumference;SD, standard deviation.

Continuous variables (e.g., age, waist circumference): Mean (SD) reported; group comparisons by two-tailed Student’s t-test (P-values shown). Categorical variables (e.g., sex, marital status): Frequency (%); group comparisons by Chi-square test (P-values shown).

Cross-cohort comparisons revealed descriptive differences: NHANES participants were younger on average (65.1 years) than HRS participants (66.7 years), had a higher proportion with education beyond high school (67.0% vs 45.6%), and reported lower arthritis prevalence (47.1% vs 59.8%). It should be explicitly noted that these cross-cohort comparisons are based on unadjusted descriptive data without formal statistical testing between cohorts, and therefore require cautious interpretation.

Table 2 illustrates the significant association between depressive symptoms and osteoporosis in both study populations. GLM analyses confirmed this relationship. In the NHANES cohort, depressive symptoms were positively associated with osteoporosis across all three models (Model 1: OR = 1.062, 95% CI: 1.041-1.084, P < .001; Model 2: OR = 1.070, 95% CI: 1.045-1.095, P < .001; Model 3: OR = 1.061, 95% CI: 1.034-1.088, P < .001). Similarly, in the HRS cohort, the severity of depressive symptoms was found to increase the risk of osteoporosis (Model 1: OR = 1.091, 95% CI: 1.048-1.136, P < .001; Model 2: OR = 1.093, 95% CI: 1.045-1.143, P < .001; Model 3: OR = 1.063, 95% CI: 1.014-1.115, P = .011).When depressive symptoms were dichotomized, the association between depression and osteoporosis remained positive across all three models (Table 3). Quartile transformation of the depression scores was performed for trend analysis, and the results indicated a linear trend in the risk of osteoporosis with increasing depressive symptoms in the adjusted Model 3. Notably, in the NHANES cohort, when PHQ-9 scores reached 8, there was a significant upward trend in osteoporosis risk. (Table 4).

Table 2.

Stratified Generalized Linear Models of Depressive Symptom Scores and Osteoporosis Associations

	NHANES			HRS
	OR	95%CI	P-value	OR	95%CI	P-value
Model 1
Depressive symptoms	1.062	(1.041,1.084)	<0.001	1.091	(1.048,1.136)	<.001
Model 2
Depressive symptoms	1.070	(1.045,1.095)	<0.001	1.093	(1.045,1.143)	<.001
Model 3
Depressive symptoms	1.061	(1.034,1.088)	<0.001	1.063	(1.014,1.115)	.011

Abbreviations: OR,odds ratio; CI,confidence intervals.

Model adjustments:Model 1,no covariate was adjusted. Model 2,age,sex,marital status,education were adjusted.Model 3,age,sex,marital status,education,drinking status,smoking status,waist circumference,combination conditions(hypertension, diabetes, arthritis) and physical activity were adjusted.

Statistical tests: Two-tailed Wald tests were used to calculate P-values for ORs. Exact P-values are reported (e.g., P < .001).

Table 3.

Stratified Generalized Linear Models of Depression Diagnosis and Osteoporosis Associations

	NHANES			HRS
	OR	95%CI	P-value	OR	95%CI	P-value
Model 1
Depression	1.815	(1.323,2.490)	<0.001	1.394	(1.110,1.751)	.004
Model 2
Depression	2.041	(1.442,2.886)	<0.001	1.322	(1.033,1.692)	.027
Model 3
Depression	1.833	(1.271,2.642)	0.001	1.141	(0.881,1.477)	.317

Abbreviations: OR, odds ratio; CI, confidence intervals.

Model adjustments: Model 1, no covariate was adjusted. Model 2, age,sex,marital status,education were adjusted.Model 3, age, sex, marital status, education, drinking status, smoking status, waist circumference,combination conditions(hypertension, diabetes, arthritis) and physical activity were adjusted.

Statistical tests: Two-tailed Wald tests were used to calculate P-values for ORs. Exact P-values are reported (e.g., P < .001).

Table 4.

Generalized Linear Model of Associations Between Depressive Symptom Quartiles and Osteoporosis

Q	Quartile of PHQ-9 scores and CES-D 8 scores
Group	Q1	Q2	Q3	Q4	OR for trend	P for trend
NHANES
Median	0	1	3	8
Model 1	1.00(Ref)	1.301(0.901,1.887)	1.936(1.377,2.750)	2.503(1.802,3.519)	1.105(1.068,1.143)	<.001
Model 2	1.00(Ref)	1.171(0.797,1.726)	1.613(1.128,2.329)	2.183(1.536,3.136)	1.095(1.055,1.136)	<.001
Model 3	1.00(Ref)	1.101(0.744,1.635)	1.397(0.966,2.035)	1.823(1.262,2.658)	1.074(1.033,1.117)	<.001
HRS
Median	0	0	1	4
Model 1	1.00(Ref)	0.673(0.511,0.885)	0.861(0.663,1.117)	1.206(0.945,1.541)	1.099(1.042,1.159)	<.001
Model 2	1.00(Ref)	0.935(0.694,1.256)	1.144(0.863,1.516)	1.478(1.123,1.948)	1.109(1.046,1.175)	<.001
Model 3	1.00(Ref)	0.914(0.675,1.234)	1.102(0.826,1.469)	1.292(0.969,1.723)	1.075(1.012,1.143)	.021

Abbreviations: OR, odds ratio;CI, confidence interval.

Model adjustments: Model 1, no covariate was adjusted. Model 2, age, sex, marital status, education were adjusted.Model 3, age, sex, marital status, education, drinking status,smoking status,waist circumference,combination conditions(hypertension, diabetes, arthritis) and physical activity were adjusted.

Statistical tests: Trend P-values were derived from two-tailed Wald tests for linear trend in logistic regression (quartiles assigned median values: NHANES-PHQ-9 [0,1,3,8]; HRS-CES-D8 [0,0,1,4]).ORs and 95% CIs were calculated via logistic regression.

The results of the subgroup analyses are presented in Figure 2. In the NHANES cohort, the relationship between depressive symptoms and osteoporosis demonstrated a broad positive correlation across subgroups such as age, sex, and diabetes status. When stratified by age, the <65 age group showed a significant increase in osteoporosis prevalence with worsening depressive symptoms (OR = 1.062, 95% CI:1.028-1.097). Stratification by hypertension status revealed a significant increase in the odds ratio for individuals with hypertension (OR = 1.074, 95% CI:1.041-1.107). Additionally, individuals with arthritis exhibited a significantly higher prevalence of osteoporosis (OR = 1.056, 95% CI:1.026-1.086).In the HRS cohort, similar widespread positive correlations were observed in the subgroup analyses. Stratification by diabetes status revealed that among individuals with diabetes, the prevalence of osteoporosis increased with greater depressive severity (OR = 1.113, 95% CI:1.016-1.219).The interaction analyses presented in Figure 2 indicate no statistically significant effect modifications in either the NHANES or HRS cohorts.

Figure 2.

Forest Plot for Subgroup Analysis of Depressive Symptoms and Osteoporosis. Abbreviations: WC,waist circumference;OR,odds ratio;CI,confidence Interval. Model adjustments:Age,sex,marital status,education,drinking status,smoking status,waist circumference,combination conditions(hypertension, Diabetes, Arthritis) and Physical Activity were Adjusted. Error Bars: 95% Confidence Intervals for Adjusted ORs.Sample Sizes: NHANES (n = 3612), HRS (n = 4307).Statistics:Subgroup ORs and P-Values Calculated by Two-Tailed Wald Tests (e.g., in the NHANES Male Subgroup: OR = 1.076,95% CI:1.026-1.124, P = .002).The P-Value for Interaction (sex × Depressive Symptoms) was 0.749 (Tested by Likelihood Ratio Test Comparing Models with/Without Interaction Terms)

Discussions

The association between depressive symptoms and osteoporosis requires further validation. In this study, we quantified depressive symptoms using the PHQ-9 and CES-D 8 depression scoring scales to explore their relationship with osteoporosis. While previous studies have examined depression-osteoporosis relationships using NHANES data,¹⁶ our study offers the following strengths: (1) implementing harmonized analytical protocols across both NHANES and HRS cohorts to enhance comparability, and (2) conducting pre-specified, theory-driven evaluations of potential moderators including key comorbidities (diabetes, arthritis) and demographic factors. Previous studies using HRS and NHANES data have developed predictive models for osteoporosis diagnosis,¹⁷ identifying mental health issues as predictors associated with higher osteoporosis risk - a finding consistent with our results. However, our study specifically focuses on elucidating the depression-osteoporosis association and investigating how various factors modify this relationship, rather than simply identifying osteoporosis-related factors.Our findings reveal a positive correlation between depressive symptoms and osteoporosis. When depressive symptoms were dichotomized, the association between depression and osteoporosis remained positive. Trend analysis demonstrated a linear relationship between depressive symptoms and osteoporosis. Subgroup analyses further confirmed the widespread association across different populations and explained the impact of specific stratifications on this relationship.

Osteoporosis signifies a reduction in bone density, which can primarily be attributed to three factors: (1) inadequate bone acquisition, (2) abnormal increases in bone resorption, and (3) decreased efficiency in bone remodeling.⁵ Depression may lead to functional abnormalities in inflammatory mediators, as evidenced by elevated levels of pro-inflammatory factors or inflammatory markers such as IL-1β, IL-6, TNF-α, and CRP in depressed patients. These increases can reduce bone density and heighten fracture risk.^18,19In addition to the influence of pro-inflammatory factors, depressed patients often experience neurohormonal imbalances, further disrupting bone metabolism. Increased activation of the HPA axis in depressed individuals results in elevated cortisol secretion, which inhibits osteoblast formation and promotes bone resorption.²⁰ Beyond neuroendocrine mechanisms, reduced secretion of gonadal hormones (estrogen and testosterone) and vitamin D deficiency in depressed patients contribute to bone loss and decreased bone density.^21,22Antidepressant medications may also exacerbate bone loss. In a database study by Rajha, H.E.,²³ the use of conventional antidepressants was associated with a 44% increase in osteoporosis prevalence. Efendioglu, E.M., et al.²⁴ reported that older adults with depression using selective serotonin reuptake inhibitors (SSRIs) showed poorer outcomes with anti-osteoporotic treatments. Furthermore, a meta-analysis highlighted that antidepressant use, particularly widely prescribed SSRIs, increased the risk of hip fractures by 2.5 times.²⁵ Adverse effects of antidepressants, such as balance disorders and dizziness, significantly elevate the risk of falls and fractures among the older adults.

Depressive symptoms are generally associated with osteoporosis prevalence among middle-aged and older adults. The causes of worsening depressive symptoms in middle-aged and older populations are multifaceted.Firstly, during the aging process, serotonin (5-HT) activity undergoes significant changes. The number of 5-HT2A receptors decreases markedly during the transition from youth to middle age.²⁶ Additionally, an animal study found that the binding rate of 5-HT1A receptors declines in older adults, potentially explaining the reduced efficacy of antidepressants in older adults with depression.²⁷Endocrine factors also play a crucial role in both late-life depression and osteoporosis. Beyond the previously mentioned gonadal hormones, alterations in levels of hormones such as growth hormone and parathyroid hormone can influence bone remodeling processes, either synergistically or independently.^28,29Moreover, with advancing age, chronic diseases such as diabetes and arthritis gradually affect overall physical health and mental well-being, exacerbating the severity of depressive symptoms.

Diabetes is one of the most common chronic diseases among middle-aged and older adults. Intriguingly, our stratified analyses across both cohorts demonstrated that depressive symptoms were linked to osteoporosis specifically in individuals with diabetes. Diabetic patients often experience insulin resistance, which is associated with a higher prevalence of mental health issues. An animal study demonstrated that mice lacking insulin receptors in astrocytes exhibited impaired dopamine release mechanisms, leading to depressive-like behaviors.³⁰ While insulin and insulin-like growth factor-1 (IGF-1) may alleviate depressive symptoms, receptor antagonists for these pathways can obstruct these beneficial effects.³¹On the other hand, diabetic patients are often in a metabolically stressed state, with chronic activation of the HPA axis. This leads to hypercortisolism, which contributes to both insulin resistance and depressive symptoms.^32,33 Additionally, structural and functional abnormalities in the brain, as well as systemic inflammation, further link diabetes to depression.³⁴The effects of diabetes on bone density remain inconclusive. While many studies suggest that insulin plays a critical role in bone metabolism, osteoporosis is recognized as a complication of type 1 diabetes, whereas reduced bone density is not typically observed in type 2 diabetes.³⁵ However, Vestergaard, P., et al.³⁶ found that both types of diabetes were associated with a higher risk of fractures compared to healthy individuals. This may indicate that blood glucose levels are a critical factor, although the underlying mechanisms require further investigation.

The impact of arthritis on depressive symptoms and osteoporosis was also significant.Arthritis often negatively affects the mental health and musculoskeletal health of middle-aged and older adults.The immune regulatory mechanisms and pro-inflammatory factors associated with rheumatoid arthritis (RA) may influence relevant neurotransmitters, suggesting that RA and depression may share similar pathogenic mechanisms.³⁷ While the pathophysiological links between osteoarthritis (OA) and depression, beyond immune and inflammatory factors, remain unclear, their association has been well established by numerous studies.^38,39Arthritis, as a musculoskeletal disease, is closely related to osteoporosis through various pathways, including the disease itself, treatment methods such as corticosteroid therapy, and its impact on patients’ lifestyle habits, such as prolonged sedentary behavior or bedrest.^40,41

The sex-specific characteristics observed in the middle-aged and older adults in this study warrant attention. The influence of sex on depression and bone density remains inconclusive. Most studies suggest that postmenopausal women, due to hormonal changes, are more susceptible to both depressive symptoms and osteoporosis compared to men. However, Mussolino, M.E. et al.⁴² reported that severe depression in younger men was significantly associated with reduced bone density, a phenomenon not observed in women. Meanwhile, a prospective cohort study by Whooley, M.A. et al.⁴³ on men aged 50 and older found no association between depression and osteoporosis.Depression may increase osteoporosis risk in older men by reducing bioavailable testosterone - a mechanism supported by documented links between: (1) testosterone-depressive symptoms inverse correlation,⁴⁴ and (2) testosterone-bone density positive association.⁴⁵

These discrepancies across studies may stem from differences in baseline characteristics of the populations studied, bone density measurement methods, and definitions of depression. To understand this sex-specific difference, we analyzed potential mechanisms. From a behavioral perspective, men exhibit high-risk behaviors such as smoking and alcohol consumption, which are more frequent compared to women and are recognized risk factors for osteoporosis.⁴⁶ Biologically, cortisol may play a critical role in this disparity. Given the involvement of the HPA axis in these associations, we draw a comparison to Cushing’s syndrome, which is characterized by elevated cortisol levels. Men with Cushing’s syndrome have a higher prevalence of osteoporosis than women,⁴⁷ and studies have shown that vertebral fractures occur more frequently in men with this condition.⁴⁸ While the mechanisms of depression and Cushing’s syndrome differ, and the degree of cortisol elevation is not identical, these findings offer another perspective on sex differences.Additionally, the role of neuropeptide Y (NPY) warrants consideration. NPY, widely distributed in the nervous system, plays a crucial role in maintaining homeostasis and regulates osteoblasts and chondrocytes through central and peripheral pathways, forming a neuro-bone metabolic axis.⁴⁹ NPY also plays a key role in the pathogenesis of depression,⁵⁰ with Ishida, H. et al finding that NPY injection into the hippocampal CA3 region produced antidepressant effects.⁵¹ Furthermore, a meta-analysis indicated that women taking psychotropic medications exhibited higher levels of NPY compared to men.⁵² This suggests that elevated NPY levels may mitigate the risk of bone density loss in women with depression, providing a possible explanation for the observed sex differences.

This study utilized WC as a clinically superior anthropometric measure for obesity evaluation,¹² particularly valuable in assessing central adiposity and metabolic risks where body mass index (BMI) data availability was limited.We observed a significant positive correlation between depressive symptoms and osteoporosis in populations with larger WC. As a key indicator of abdominal obesity, increased WC was closely associated with higher all-cause mortality and metabolic disease risks in middle-aged and older adults.⁵³Recent genetic studies have demonstrated genomic colocalization between depression-associated loci and obesity-susceptibility genes.⁵⁴ For instance, the neuronal growth regulator 1 gene (NEGR1) influences energy metabolism through hypothalamic appetite regulation networks.⁵⁵ Mechanistically, obesity-related chronic low-grade inflammation promotes the release of proinflammatory cytokines and upregulates NLRP3 inflammasome expression, leading to impaired glucocorticoid receptor function, dysregulated cortisol metabolism, and HPA axis overactivation^56-58 - these neuroendocrine abnormalities have been previously shown to correlate with decreased bone mineral density (BMD).Notably, beyond typical obesity, sarcopenic obesity represents a distinct phenotype where reduced skeletal muscle mass coupled with dysregulated lipid metabolism significantly disrupts bone homeostasis.¹² Currently, precision intervention strategies for such musculoskeletal comorbidities remain to be further explored.

In conclusion, the association between depression and osteoporosis in middle-aged and older adults requires attention to the following aspects:

(1) HPA Axis Dysregulation and Cortisol Abnormalities: The observed abnormalities in cortisol secretion due to HPA axis dysregulation across various factors in this study underscore the critical role of the HPA axis in normal metabolic processes.

(2) Hormonal Imbalances Related to Age, Depression, and Other Diseases: The influence of other hormonal imbalances, such as those involving gonadal hormones, neuropeptide Y (NPY), and insulin, is indispensable and warrants further investigation.

(3) Immune Regulation and Cytokine Effects: Attention should be given to immune regulatory mechanisms and the effects of cytokines such as IL-1β and TNF-α on the body’s inflammatory response, as well as the implications of inflammatory markers across different populations.

These findings highlight the complexity of the interaction between depression and osteoporosis and underscore the importance of a multifaceted approach to understanding and addressing this relationship.

This study included middle-aged and older adults aged ≥50 years from the NHANES and HRS databases, benefiting from a large sample size and confirming the association between depressive symptoms and osteoporosis. The findings contribute to osteoporosis research and prevention by emphasizing the importance of monitoring bone density in middle-aged and older adults with depression. The deepening of depressive symptoms may serve as a predictive factor for osteoporosis. The use of depression scales and self-reported disease diagnoses offers a quick and cost-effective approach. However, this study also has several limitations. First, as a cross-sectional study, it cannot establish a causal relationship between depressive symptoms and osteoporosis.We plan to address this limitation through future longitudinal cohort studies to better characterize temporal associations. Second, the lack of pre-study power analysis may have limited our ability to detect subtle associations. Our variable selection approach was less rigorous than the systematic filtering methods employed by Godde et al. Moreover, the use of different depression scales may have influenced depression severity assessment. Furthermore, the exclusive inclusion of U.S. populations limits generalizability to other healthcare systems and cultural contexts where depression screening practices or osteoporosis risk profiles may differ.⁵⁹ Finally, while NHANES collected BMD measurements, we intentionally used questionnaire-based osteoporosis diagnosis to maintain consistency with HRS, which lacked BMD data. This approach ensured comparability between cohorts but may have reduced diagnostic precision. We hope to strengthen diagnostic accuracy in future research. Therefore, the relationship between depressive symptoms and osteoporosis requires further investigation with more precise and robust evidence.

Conclusion

This study demonstrates a significant association between depressive symptoms (assessed using validated PHQ-9 and CES-D8 scales) and increased osteoporosis risk in middle-aged and older adults, with consistent findings across diverse subpopulations. While the cross-sectional design precludes causal conclusions, the observed dose-response relationship suggests depressive symptoms may adversely affect bone health. These findings highlight three key clinical needs: (1) integrating bone density monitoring into depression management, (2) incorporating depression screening in osteoporosis care, and (3) developing coordinated prevention strategies for these comorbid conditions. At the public health level, our results emphasize the importance of addressing mental health in aging populations through enhanced social support and targeted interventions. Future longitudinal studies should clarify the temporal relationship and potential bidirectional mechanisms between these conditions.

Supplemental Material

Supplemental Material - Depressive Symptoms and Osteoporosis in Middle-Aged and Older Adults: A Cross-Sectional Analysis of NHANES and HRS Data

Supplemental Material for Depressive Symptoms and Osteoporosis in Middle-Aged and Older Adults: A Cross-Sectional Analysis of NHANES and HRS Data by Wen Zhang, Yi Tang, Lei Chen, Zhongyi Zhang, Xinyu Hu, Kai Cheng, Jiaju Zhou, and Peijian Tong in Geriatric Orthopaedic Surgery & Rehabilitation

Footnotes

Author Note

All authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors,and all authors agree with the publication of the manuscript.

ORCID iD

Wen Zhang

Ethical Considerations

Study protocols for NHANES were approved by the NCHS ethnics review board (Protocol #2011-17,https://www.cdc.gov/nchs/nhanes/irba98.htm ). The Health and Retirement Study is a longitudinal project sponsored by the National Institute on Aging (NIA U01AG009740) and the Social Security Administration (

the participants signed the informed consent before participating in the study.

Author Contributions

Wen Zhang: Conceptualization,Methodology,Investigation,Formal Analysis,Writing – Original Draft.

Yi Tang: Formal Analysis,Validation,Writing – Review & Editing.

Lei Chen: Formal Analysis,Data Curation,Writing – Review & Editing.

Zhongyi Zhang & Xinyu Hu: Visualization,Resources (preparation of Figures 1 and

Kai Cheng & Jiaju Zhou: Data Curation,Validation (preparation of Tables 1 –

Peijian Tong: Supervision,Project Administration,Funding Acquisition,Study Design.

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This study was supported by National Natural Science Foundation of China under Grant No.82274547,Zhejiang Provincial Natural Science Foundation of China under Grant No. LD22C060002,Zhejiang Provincial Project of Administration of Chinese Medicine No. 2023ZL369 and 2024 Zhejiang Chinese Medicine University Cultivation Plan for Top Innovative Talents of Post graduates (No:2024YJSBJ011).

Declaration of Conflicting Interests

The author(s) declared the following potential conflicts of interest with respect to the research,authorship,and/or publication of this article: Wen Zhang,Yi Tang,Lei Chen,Zhongyi Zhang,Xinyu Hu,Kai Cheng,Jiaju Zhou and Peijian Tong declare that they have no conflict of interest.

Data Availability Statement

The data used in this study are available on the National Health and Nutrition Examination Survey website: https://www.cdc.gov/nchs/nhanes and Health Retirement and Study website:

Supplemental Material

Supplemental material for this article is available online.

References

Liang

Zheng

Shi

Zhong

Xie

. Association between sleep duration and cognitive decline. JAMA Netw Open. 2020;3(9):e2013573. doi:10.1001/jamanetworkopen.2020.13573

Lane

Russell

Khan

. Osteoporosis. Clin Orthop Relat Res. 2000;372:139-150. doi:10.1097/00003086-200003000-00016

Kessler

Chiu

Demler

Merikangas

Walters

. Prevalence, severity, and comorbidity of 12-month DSM-IV disorders in the national comorbidity survey replication. Arch Gen Psychiatry. 2005;62(6):617-627. doi:10.1001/archpsyc.62.6.617

Alexopoulos

. Depression in the elderly. Lancet. 2005;365(9475):1961-1970. doi:10.1016/s0140-6736(05)66665-2

Mezuk

Eaton

Golden

. Depression and osteoporosis: epidemiology and potential mediating pathways. Osteoporos Int. 2008;19(1):1-12. doi:10.1007/s00198-007-0449-2

Godde

Courtney

Roberts

. Psychological disorders linked to osteoporosis diagnoses in a population-based cohort study of middle and older age United States adults. Gerontol. 2024;64(6):gnae027. doi:10.1093/geront/gnae027

Furlan

Ten Have

Cary

, et al. The role of stress-induced cortisol in the relationship between depression and decreased bone mineral density. Biol Psychiatry. 2005;57(8):911-917. doi:10.1016/j.biopsych.2004.12.033

Eskandari

Martinez

Torvik

, et al. Low bone mass in premenopausal women with depression. Arch Intern Med. 2007;167(21):2329-2336. doi:10.1001/archinte.167.21.2329

Management of osteoporosis in postmenopausal women: 2006 position statement of the North American Menopause Society. Menopause. 2006;13(3):340-367; quiz 368-9. doi:10.1097/01.gme.0000222475.93345.b3

10.

Levis

Benedetti

Thombs

DEPRESsion Screening Data DEPRESSD Collaboration . Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. Bmj. 2019;365:l1476. doi:10.1136/bmj.l1476

11.

Missinne

Vandeviver

Van de Velde

Bracke

. Measurement equivalence of the CES-D 8 depression-scale among the ageing population in eleven European countries. Soc Sci Res. 2014;46:38-47. doi:10.1016/j.ssresearch.2014.02.006

12.

Rubino

Cummings

Eckel

, et al. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025;13(3):221-262. doi:10.1016/s2213-8587(24)00316-4

13.

R Core Team . R: A Language and Environment for Statistical Computing [computer program]. Vienna, Austria: R Foundation for Statistical Computing; 2024. Accessed June 28, 2025. https://www.R-project.org/

14.

Mulsant

Ganguli

. Epidemiology and diagnosis of depression in late life. J Clin Psychiatry. 1999;60(Suppl 20):9-15.

15.

Lane

. Epidemiology, etiology, and diagnosis of osteoporosis. Am J Obstet Gynecol. 2006;194(2 Suppl):S3-S11. doi:10.1016/j.ajog.2005.08.047

16.

Liu

Jia

, et al. The association between depression and bone metabolism: a US nationally representative cross-sectional study. Arch Osteoporos. 2022;17(1):113. doi:10.1007/s11657-022-01154-1

17.

Gough Courtney

Roberts

Godde

. Development of a diverse osteoporosis screening tool for older US adults from the health and retirement study. Heliyon. 2024;10(1):e23806. doi:10.1016/j.heliyon.2023.e23806

18.

Ganesan

Teklehaimanot

Tran

Asuncion

Norris

. Relationship of C-reactive protein and bone mineral density in community-dwelling elderly females. J Natl Med Assoc. 2005;97(3):329-333.

19.

Licinio

Wong

. The role of inflammatory mediators in the biology of major depression: central nervous system cytokines modulate the biological substrate of depressive symptoms, regulate stress-responsive systems, and contribute to neurotoxicity and neuroprotection. Mol Psychiatry. 1999;4(4):317-327. doi:10.1038/sj.mp.4000586

20.

Carroll

Curtis

Davies

Mendels

Sugerman

. Urinary free cortisol excretion in depression. Psychol Med. 1976;6(1):43-50. doi:10.1017/s0033291700007480

21.

Parker

Brotchie

Graham

. Vitamin D and depression. J Affect Disord. 2017;208:56-61. doi:10.1016/j.jad.2016.08.082

22.

Indirli

Lanzi

Arosio

Mantovani

Ferrante

. The association of hypogonadism with depression and its treatments. Front Endocrinol (Lausanne). 2023;14:1198437. doi:10.3389/fendo.2023.1198437

23.

Rajha

Abdelaal

Charfi

, et al. Examining depression, antidepressants use, and class and their potential associations with osteoporosis and fractures in adult women: results from ten NHANES cohorts. J Affect Disord. 2025;369:1223-1232. doi:10.1016/j.jad.2024.10.114

24.

Efendioglu

Cigiloglu

Ozturk

. Bone mineral density response to antiosteoporotic drugs in older depressed adults. Arch Osteoporos. 2023;18(1):31. doi:10.1007/s11657-023-01219-9

25.

Xie

Chen

. Osteoporosis and fracture risk associated with novel antidepressants: a systematic review and meta-analysis. Actas Esp Psiquiatr. 2024;52(3):334-346. doi:10.62641/aep.v52i3.1560

26.

Sheline

Mintun

Moerlein

Snyder

. Greater loss of 5-HT(2A) receptors in midlife than in late life. Am J Psychiatry. 2002;159(3):430-435. doi:10.1176/appi.ajp.159.3.430

27.

Tsukada

Kakiuchi

Nishiyama

Ohba

Harada

. Effects of aging on 5-HT(1A) receptors and their functional response to 5-HT(1a) agonist in the living brain: PET study with [carbonyl-(11)C]WAY-100635 in conscious monkeys. Synapse. 2001;42(4):242-251. doi:10.1002/syn.10011

28.

Hadjidakis

Androulakis

. Bone remodeling. Ann N Y Acad Sci. 2006;1092:385-396. doi:10.1196/annals.1365.035

29.

Blazer

. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003;58(3):249-265. doi:10.1093/gerona/58.3.m249

30.

Cai

Xue

Sakaguchi

, et al. Insulin regulates astrocyte gliotransmission and modulates behavior. J Clin Invest. 2018;128(7):2914-2926. doi:10.1172/jci99366

31.

Mueller

Pritchett

Wiechman

Zharikov

Hajnal

. Antidepressant-like effects of insulin and IGF-1 are mediated by IGF-1 receptors in the brain. Brain Res Bull. 2018;143:27-35. doi:10.1016/j.brainresbull.2018.09.017

32.

Kokkinopoulou

Diakoumi

Moutsatsou

. Glucocorticoid receptor signaling in diabetes. Int J Mol Sci. 2021;22(20):11173. doi:10.3390/ijms222011173

33.

Diz-Chaves

Gil-Lozano

Toba

, et al. Stressing diabetes? The hidden links between insulinotropic peptides and the HPA axis. J Endocrinol. 2016;230(2):R77-R94. doi:10.1530/joe-16-0118

34.

Yang

Zhou

, et al. Neurological and metabolic related pathophysiologies and treatment of comorbid diabetes with depression. CNS Neurosci Ther. 2024;30(4):e14497. doi:10.1111/cns.14497

35.

Thrailkill

Lumpkin

Jr Bunn

Kemp

Fowlkes

. Is insulin an anabolic agent in bone? Dissecting the diabetic bone for clues. Am J Physiol Endocrinol Metab. 2005;289(5):E735-E745. doi:10.1152/ajpendo.00159.2005

36.

Vestergaard

Rejnmark

Mosekilde

. Relative fracture risk in patients with diabetes mellitus, and the impact of insulin and oral antidiabetic medication on relative fracture risk. Diabetologia. 2005;48(7):1292-1299. doi:10.1007/s00125-005-1786-3

37.

Nerurkar

Siebert

McInnes

Cavanagh

. Rheumatoid arthritis and depression: an inflammatory perspective. Lancet Psychiatry. 2019;6(2):164-173. doi:10.1016/s2215-0366(18)30255-4

38.

Wang

. Depression in osteoarthritis: current understanding. Neuropsychiatr Dis Treat. 2022;18:375-389. doi:10.2147/ndt.S346183

39.

Rathbun

Shardell

Ryan

, et al. Association between disease progression and depression onset in persons with radiographic knee osteoarthritis. Rheumatology (Oxford). 2020;59(11):3390-3399. doi:10.1093/rheumatology/keaa141

40.

Henderson

Sambrook

. Relationship between osteoporosis and arthritis and effect of corticosteroids and other drugs on bone. Curr Opin Rheumatol. 1996;8(4):365-369. doi:10.1097/00002281-199607000-00015

41.

Garrison

. Osteoarthritis, osteoporosis, and exercise. Workplace Health Saf. 2012;60(9):381-383. doi:10.1177/216507991206000902

42.

Mussolino

Jonas

Looker

. Depression and bone mineral density in young adults: results from NHANES III. Psychosom Med. 2004;66(4):533-537. doi:10.1097/01.psy.0000132873.50734.7d

43.

Whooley

Cauley

Zmuda

Haney

Glynn

. Depressive symptoms and bone mineral density in older men. J Geriatr Psychiatry Neurol. 2004;17(2):88-92. doi:10.1177/0891988704264537

44.

Barrett-Connor

Von Mühlen

Kritz-Silverstein

. Bioavailable testosterone and depressed mood in older men: the Rancho Bernardo Study. J Clin Endocrinol Metab. 1999;84(2):573-577. doi:10.1210/jcem.84.2.5495

45.

Wong

Lau

Lynn

, et al. Depression and bone mineral density: is there a relationship in elderly Asian men? Results from Mr. Os (Hong Kong). Osteoporos Int. 2005;16(6):610-615. doi:10.1007/s00198-004-1730-2

46.

Aibar-Almazán

Voltes-Martínez

Castellote-Caballero

Afanador-Restrepo

Carcelén-Fraile

MDC

López-Ruiz

. Current status of the diagnosis and management of osteoporosis. Int J Mol Sci. 2022;23(16):9465. doi:10.3390/ijms23169465

47.

Pecori Giraldi

Moro

Cavagnini

Study Group on the Hypothalamo-Pituitary-Adrenal Axis of the Italian Society of Endocrinology . Gender-related differences in the presentation and course of Cushing's disease. J Clin Endocrinol Metab. 2003;88(4):1554-1558. doi:10.1210/jc.2002-021518

48.

Broersen

LHA

van Haalen

Kienitz

, et al. Sex differences in presentation but not in outcome for ACTH-dependent Cushing's syndrome. Front Endocrinol (Lausanne). 2019;10:580. doi:10.3389/fendo.2019.00580

49.

Khor

Baldock

. The NPY system and its neural and neuroendocrine regulation of bone. Curr Osteoporos Rep. 2012;10(2):160-168. doi:10.1007/s11914-012-0102-7

50.

Rana

Behl

Sehgal

, et al. Exploring the role of neuropeptides in depression and anxiety. Prog Neuropsychopharmacol Biol Psychiatry. 2022;114:110478. doi:10.1016/j.pnpbp.2021.110478

51.

Ishida

Shirayama

Iwata

, et al. Infusion of neuropeptide Y into CA3 region of hippocampus produces antidepressant-like effect via Y1 receptor. Hippocampus. 2007;17(4):271-280. doi:10.1002/hipo.20264

52.

Tural

Iosifescu

. Neuropeptide Y in PTSD, MDD, and chronic stress: a systematic review and meta-analysis. J Neurosci Res. 2020;98(5):950-963. doi:10.1002/jnr.24589

53.

Jantaratnotai

Mosikanon

Lee

McIntyre

. The interface of depression and obesity. Obes Res Clin Pract. 2017;11(1):1-10. doi:10.1016/j.orcp.2016.07.003

54.

Milaneschi

Simmons

van Rossum

EFC

Penninx

. Depression and obesity: evidence of shared biological mechanisms. Mol Psychiatry. 2019;24(1):18-33. doi:10.1038/s41380-018-0017-5

55.

Boender

van Rozen

Adan

. Nutritional state affects the expression of the obesity-associated genes Etv5, Faim2, Fto, and Negr1. Obesity. 2012;20(12):2420-2425. doi:10.1038/oby.2012.128

56.

Osborn

Olefsky

. The cellular and signaling networks linking the immune system and metabolism in disease. Nat Med. 2012;18(3):363-374. doi:10.1038/nm.2627

57.

Pace

Miller

. Cytokines and glucocorticoid receptor signaling. Relevance to major depression. Ann N Y Acad Sci. 2009;1179:86-105. doi:10.1111/j.1749-6632.2009.04984.x

58.

Yin

Deng

Peterson

, et al. Transcriptome analysis of human adipocytes implicates the NOD-like receptor pathway in obesity-induced adipose inflammation. Mol Cell Endocrinol. 2014;394(1-2):80-87. doi:10.1016/j.mce.2014.06.018

59.

Kleinman

. Culture and depression. N Engl J Med. 2004;351(10):951-953. doi:10.1056/NEJMp048078

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.10 MB