
==== Front
Transl Psychiatry
Transl Psychiatry
Translational Psychiatry
2158-3188
Nature Publishing Group UK London

39231933
3063
10.1038/s41398-024-03063-y
Article
Eight-year total, cognitive-affective, and somatic depressive symptoms trajectories and risks of cardiac events
Lu Xinyi 1
Liu Chunxiao 2
Lu Hong 3
Qian Xiaoyu 1
Wang Congdi 1
Jia Cunxian 1
http://orcid.org/0000-0002-5553-7433
Jia Feifei feifeijia@sdu.edu.cn

1
1 https://ror.org/0207yh398 grid.27255.37 0000 0004 1761 1174 Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong China
2 https://ror.org/056ef9489 grid.452402.5 0000 0004 1808 3430 Department of Cardiovascular Surgery, Qilu Hospital of Shandong University, Jinan, Shandong China
3 https://ror.org/00p1jee13 grid.440277.2 Department of Pharmacy, Yucheng City People’s Hospital, Dezhou, Shandong China
4 9 2024
4 9 2024
2024
14 3569 6 2024
20 8 2024
22 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
In this study, we analyzed pooled data from two prospective population-based cohorts—the Health Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA)—to explore the association between trajectories of depressive symptoms and the risk of cardiac events. Depressive symptoms were assessed using the 8-item CES-D scale and categorized into somatic and cognitive-affective subtypes. Trajectories were tracked for four surveys from baseline. Heart disease was identified based on self-reported physician-diagnosed conditions. Hazard ratios and 95% confidence intervals were calculated with Cox proportional risk models that adjusted for potential confounders. In total, 17,787 subjects (59.7% female, median age 63 years) were enrolled at baseline. During a 10-year follow-up, 2409 cases of heart disease were identified. Participants with fluctuating (HR = 1.13, 95% CI: 1.06–1.20), increasing (HR = 1.43, 95% CI: 1.25–1.64), and consistently high (HR = 1.64, 95% CI: 1.45–1.84) depressive symptom trajectories exhibited an increased risk of heart disease compared to those with consistently low depressive symptoms, while a decreasing (HR = 1.07, 95% CI: 0.96–1.19) depressive symptom trajectory did not significantly affect the risk of heart disease. Moreover, the association between heart disease and somatic depressive symptoms was found to be stronger than with cognitive-affective symptoms. These findings suggest a significant link between depressive symptom trajectories and heart disease, with particular emphasis on stronger associations with somatic symptoms. It is recommended that the identification and management of depressive symptoms be incorporated into heart disease prevention strategies.

Subject terms

Scientific community
Depression
issue-copyright-statement© Springer Nature Limited 2024
==== Body
pmcIntroduction

Heart disease remains the leading cause of death and disability worldwide [1]. Although the global risk of death from cardiovascular disease has shown a declining trend [2], the absolute number of deaths continues to rise due to population growth, aging, and increased life expectancy. Identification of risk factors for heart disease that are amenable to intervention is essential for the development of strategies to prevent heart disease and its associated morbidity. While many classical risk factors have been identified, such as smoking [3, 4], hypertension [5, 6], and abnormal cholesterol levels [7, 8], these do not fully explain the observed risk. Consequently, there is a growing interest in exploring additional modifiable factors, particularly psychological ones. Among these, depressive symptoms have been identified as a critical area of focus. Depressive symptoms are distinct from clinical depression, which is a mental disorder characterized by persistent low mood and loss of enjoyment that requires a clinical diagnosis. Depressive symptoms are a set of symptoms that may be associated with a depressive disorder but are not sufficient to constitute a complete clinical diagnosis and may significantly contribute to the risk of developing heart disease [9, 10].

This study focuses on depressive symptoms, which are dynamic and may fluctuate over time with varying patterns. However, the relationship between trajectories of change in depressive symptoms over time, such as increasing, fluctuating, and decreasing, and heart disease remains underexplored. Whether getting relief from depressive symptoms has the potential to reduce the risk of heart disease remains an area of uncertainty. Understanding these trajectories could reveal underlying mechanisms that associate depressive symptoms with heart disease, thereby improving causal inferences.

Moreover, depressive symptoms include both cognitive-affective and somatic dimensions [11]. However, the relative contribution of depressive symptoms’ different dimensions to heart disease risk remains controversial. Some studies have reported that both somatic and cognitive-affective depressive symptoms are risk factors for cardiac disease progression [12–14], while others have shown that somatic depressive symptoms are more strongly correlated with cardiovascular events than cognitive-affective symptoms [15]. Somatic, but not cognitive-affective, depressive symptoms are associated with myocardial infarction severity and cardiovascular prognosis [16–18]. While these studies have deepened our understanding of how different dimensions of depressive symptoms affect heart disease, they have focused primarily on static analyses of different dimensions of depressive symptoms at a single point in time, and our study seeks to address this limitation by examining the dynamic trajectories of these different dimensions of depressive symptoms over time and their relationship to heart disease.

Utilizing data from the Health and Retirement Study (HRS) and the English Longitudinal Study on Ageing (ELSA), this study aims to provide the first estimates of the 8-year trajectories of total, cognitive-affective, and somatic depressive symptoms and their association with heart disease over a subsequent 10-year follow-up period. We hypothesize that individuals with high levels of depressive symptoms across multiple time points will be at greater risk for developing heart disease and that both cognitive-affective and somatic depressive symptom trajectories will be correlated with heart disease risk.

Methods

Study population

The present study utilizes data from two important longitudinal datasets, the HRS and the ELSA, which are representative of the national aging population in the United States and the United Kingdom, respectively. Both datasets are based on adults over the age of 50 who are interviewed every two years using standardized questionnaires and similar measurement tools to assess economic, physical, and mental health aspects, thus establishing strong comparability between the two cohorts in terms of depressive symptoms and their trajectories over time, and in terms of assessments of heart disease.

We pooled data from waves 4 to 12 of the HRS (1998–2014), and waves 1 to 9 of the ELSA (2002–2018). Wave 4 in HRS (1998) and Wave 1 in ELSA (2002) were considered as baseline. The dynamic trajectory of depressive symptoms was assessed from these baseline points through the next three survey waves: up to Wave 7 for HRS and Wave 4 for ELSA. Subsequent surveys tracked heart disease outcomes until the final waves—Wave 12 for HRS and Wave 9 for ELSA.

Exclusion criteria for this analysis included (1) age less than 50 years, (2) lack of a complete trajectory of depressive symptoms during the exposure period, (3) presence of heart disease and stroke before and during the exposure period, and (4) missing information on heart disease during follow-up. Participants younger than 50 years were excluded to focus the analysis on an aging population, which is more susceptible to the onset of new heart disease and depressive symptoms due to age-related physiological and social changes. Additionally, individuals with pre-existing heart disease or stroke were excluded to ensure that our analysis more accurately assesses the impact of depressive symptoms on the incidence of new heart disease events. A detailed flowchart of the sample selection process is shown in Fig. 1.Fig. 1 Flow chart for the study population.

The flowchart illustrates the inclusion and exclusion process for this study population.

Primary exposures

Depressive symptoms were assessed using a modified version of the Centre for Epidemiological Studies Depression Scale (CES-D) validated 8-item scale [19], and it has been found to be reliable in assessing depressive symptoms in older adults [20]. At the biennial follow-up survey, participants were asked whether they had experienced each of the eight symptoms in the past week. By summing the number of “yes” responses to each of the eight items (with two positive items reverse scored), a total score ranging from 0–8. Previous validation studies have shown that a score of 3 or higher is effective in differentiating clinically significant depressive symptoms in the elderly population, and therefore we chose this threshold to identify significant total depressive symptoms [21, 22]. Two domains of depressive symptoms were identified: cognitive-affective and somatic. The cognitive-affective domain, comprising symptoms such as ‘Feeling depressed,’ ‘Feeling lonely,’ and ‘Feeling sad,’ reflects core emotional disturbances associated with depression. In contrast, the somatic domain includes ‘Everything was an effort,’ ‘Restless sleep,’ and ‘Could not get going,’ which are indicative of the physical expressions of depressive states commonly experienced by the elderly [23]. The bifactorial model of depressive symptoms, which was validated in a previous study using confirmatory factor analysis (CFA), effectively differentiates between two distinct domains: cognitive-affective and somatic symptoms. This two-factor models had sufficient discriminant validity [24]. Scores for both cognitive-affective and somatic domains were dichotomized using the upper tertiles as cut-offs, with CES-D ≥ 2 indicating significant symptoms for each domain [25].

We constructed five trajectories of depressive symptoms (persistently low, decreasing, fluctuating, increasing, and persistently high) [21] based on changes in CES-D scores in waves 4–7 of the HRS and waves 1–4 of the ELSA. Persistently low was defined as no elevation of depressive symptoms at any of the four time points. Decreasing was defined as (1) elevated depressive symptoms at time point 1 only, followed by decreasing depressive symptoms at the next 3 time points, or (2) elevated depressive symptoms at the first 2 time points, followed by decreasing depressive symptoms consistently over the next 2 time points. Increasing was defined as depressive symptoms not elevated only at time point 1 and elevated at all subsequent time points; or (2) depressive symptoms not elevated at the first 2 time points but consistently elevated at both subsequent time points. Persistently high was defined as elevated depressive symptoms at all 4 time points throughout the assessment period. Fluctuating encompassed other depressive symptom trajectories that did not fit the above categorization (Supplementary Table 1). Respondents with persistent low depressive symptoms served as a reference group for the other depressive symptoms trajectories. The somatic depressive symptoms trajectories and cognitive-affective depressive symptoms trajectories were also categorized in this way.

Heart disease outcomes

Heart disease was assessed at waves 8–12 in HRS and waves 5–9 in ELSA. In both HRS and ELSA, heart disease was determined on the basis of self-reported physician diagnosis based on self-reported diagnosis during the follow-up period. At each wave of both cohorts’ participants were asked “Has your doctor ever told you that you have a heart condition (including angina, heart attack, congestive heart failure and other heart problems)?” Heart disease was considered to have occurred if the participant self-reported a diagnosed heart condition during the follow-up period. In the next wave, participants were asked to confirm the presence or absence of heart disease if they had reported having a heart disease in the previous wave. If a participant disputed a self-reported heart condition in previous waves, it was retrospectively corrected. Regarding self-reported heart disease, ELSA researchers confirmed 77.5% of self-reported coronary heart disease (defined as angina and heart attack) [26]. Self-reported health conditions in the HRS is strongly consistent with medical record data, and previous studies using the HRS have shown that self-reported health conditions measures have good external validity [27].

Covariates

Covariates were carefully selected based on their potential confounding effects on the relationship between depressive symptoms and the risk of heart disease. These include self-reported demographic and socio-economic variables such as age, sex (male or female), highest level of education (less than high school, high school graduate/general educational development (GED), some colleges or 4-year colleges and above, other), marital status (married/partnered, separated/divorced/widowed, or unmarried). Additionally, self-reported measures of health behavior and conditions were considered, including drinking status (ever drank, never drank), and smoking status (ever smoked, never smoked). Also included were self-reported, physician-diagnosed health conditions such as diabetes and high blood pressure. Including these covariates helps control for their confounding influence when assessing the direct impact of depressive symptoms trajectories on heart disease.

Ethical approval

Both the HRS and the ELSA were conducted in strict accordance with the ethical principles of the Declaration of Helsinki. The HRS was approved by the Institute for Social Research and Survey Research Center of the University of Michigan. Written informed consent was obtained from all participants or their guardians. The ELSA received ethical approval for all waves from the London Multicentre Research Ethics Committee, with informed consent obtained from all participants.

Statistical analysis

The baseline demographics of the study population was statistically described according to different trajectories of depressive symptoms. Continuous variables were expressed using mean [standard deviation (SD)] or median [interquartile range (IQR)], and categorical variables were expressed as frequency (percentage).

To estimate the association between different trajectories of depressive symptoms and the risk of developing heart disease, Cox proportional risk models were used to calculate hazard ratios (HR) and their 95% confidence intervals (95% CI), providing a measure of the relative risk over time. The ‘time’ variable was defined by the duration until the first occurrence of heart disease or the end of follow-up, whichever occurred first. Follow-up time ended at the last survey completed by each participant. Three models were fitted for Cox regression using the persistent low trajectory of depressive symptoms as a reference. Model 1 was unadjusted, with depressive symptoms trajectories being the only variable. Progressing to model 2, we additionally adjusted for the potential demographic confounders of age, gender, education level, and marital status. In Model 3, we extended the adjustments to include health behaviors (smoking, alcohol consumption) and health conditions (hypertension, diabetes), building upon the adjustments made in Model 2. The proportional risk assumptions of these Cox regression models were confirmed by Schoenfeld residuals. Missing data for covariates were handled using chained-equation multiple imputation via the R package ‘mice’. We conducted five rounds of imputation to generate five datasets, setting the maximum number of iterations to 50 for each. The first complete dataset from these was selected and merged to form a complete analyzed dataset.

We performed three sensitivity analyses using HRS data to assess the robustness of our findings. These analyses were sequentially repeated, each time increasing the number of follow-up years by extending the cardiac event endpoint to waves 13, 14, and 15, respectively. Model 3 was used for all analyses, and sections containing missing values were removed.

Statistical analysis procedures were all performed using R software (version 4.3.2). Statistical tests were performed using two-sided tests, and differences were considered statistically significant at P < 0.05.

Results

Our study investigated the relationship between the trajectory of depressive symptoms and the occurrence of heart disease in 2409 participants over a 10-year follow-up period. The findings shed light on the impact of different dimensions and multiple trajectories of depressive symptoms on heart disease.

Table 1 describes the demographic and health characteristics of the 17,787 pooled sample grouped at baseline according to the overall depressive symptom trajectory. The median age of the participants was 63.0 years, with a majority being female (59.7%), had a high school or GED (52.0%). Lifestyle factors showed 59.4% were former smokers, and 35.5% had never consumed alcohol. Most notably, 57.8% exhibited a consistently low trajectory of depressive symptoms throughout the study period. Participants with a consistently low trajectory of depressive symptoms were more likely to be married or in a partnership (76.2%). In contrast, the majority of participants (50.6%) with a persistently high trajectory of depressive symptoms had less than high school education, and 55.3% of participants with a persistently high trajectory of depressive symptoms had high blood pressure and 17.2% had diabetes.Table 1 Baseline characteristics of participants for baseline depressive symptom trajectory analyses.

Variables		Depressive symptoms trajectory group	
	Overall sample	Consistently low	Decreasing	Fluctuating	Increasing	Consistently high	
Number (%)	17787	10277 (57.8)	1238 (7.0)	4499 (25.3)	788 (4.4)	985 (5.5)	
Age, M (QR), years	63. 0[57.0; 70.0]	62.0 [57.0; 70.0]	62.0 [56.0; 70.0]	63.0 [57.0; 71.0]	66.0 [58.0; 73.0]	63.0 [57.0; 71.0]	
Sex, n (%)	
Male	7174 (40.3)	4757 (46.3)	431 (34.8)	1502 (33.4)	237 (30.1)	247 (25.1)	
Female	10613 (59.7)	5520 (53.7)	807 (65.2)	2997 (66.6)	551 (69.9)	738 (74.9)	
Education, n (%)	
Below high school	4818 (27.1)	2136 (20.8)	408 (33.0)	1491 (33.1)	285 (36.2)	498 (50.6)	
High school	9250 (52.0)	5545 (54.0)	621 (50.2)	2273 (50.5)	399 (50.6)	412 (41.8)	
College or above	3219 (18.1)	2309 (22.5)	183 (14.8)	605 (13.4)	76 (9.6)	46 (4.7)	
Other	500 (2.8)	287 (2.8)	26 (2.1)	130 (2.9)	28 (3.6)	29 (2.9)	
Marital status, n (%)	
Married or partnered	12551 (70.6)	7828 (76.2)	750 (60.6)	2963 (65.9)	505 (64.1)	505 (51.3)	
Separated/divorced/ Widowed	4629 (26.0)	2115 (20.6)	449 (36.3)	1377 (30.6)	257 (32.6)	431 (43.8)	
Never married	607 (3.4)	334 (3.2)	39 (3.2)	159 (3.5)	26 (3.3)	49 (5.0)	
Smoking status, n (%)	
Never smokers	7227 (40.6)	4336 (42.2)	504 (40.7)	1754 (39.0)	277 (35.2)	356 (36.1)	
Ever smokers	10560 (59.4)	5941 (57.8)	734 (59.3)	2745 (61.0)	511 (64.8)	629 (63.9)	
Drinking status, n (%)	
Never drinkers	6307 (35.5)	3159 (30.7)	457 (36.9)	1802 (40.1)	365 (46.3)	524 (53.2)	
Ever drinkers	11480 (64.5)	7118 (69.3)	781 (63.1)	2697 (59.9)	423 (53.7)	461 (46.8)	
Hypertension (yes/no), n (%)	7379 (41.5)	3829 (37.3)	553 (44.7)	2057 (45.7)	395 (50.1)	545 (55.3)	
Diabetes (yes/no), n (%)	1796 (10.1)	812 (7.9)	139 (11.2)	572 (12.7)	104 (13.2)	169 (17.2)	
A total of 17,787 participants were included in the Baseline.

Quantitative data are shown as median and quartiles and categorical variables are presented as counts and percentages.

This table presents the baseline characteristics of the participants included in the analysis of depressive symptom trajectories. It summarizes variables such as age, gender, socioeconomic status, and initial depressive symptom scores, providing an overview of the participants’ demographic profiles at the start of the study.

Table 2 and Figs. 2–4 shows the longitudinal associations of overall depressive symptom trajectories, somatic depressive symptom trajectories, and cognitive-affective depressive symptom trajectories with heart disease for the pooled sample. In Model 3, i.e., after adjusting for age, gender, marital status, education level, health behaviors, and health conditions, individuals with consistently high depressive symptoms had a higher risk for incident heart disease compared with those with consistently low depressive symptoms (reference) (HR = 1.64, 95% CI: 1.45–1.84). Similarly, individuals with increasing (HR = 1.43, 95% CI: 1.25–1.64) and fluctuating (HR = 1.13, 95% CI: 1.06–1.20) depressive symptoms also had a higher risk, compared with the reference group. However, the effect estimates for incident heart disease were not significantly different for individuals with decreasing depressive symptoms (HR = 1.07, 95% CI: 0.96–1.19), when compared with the reference group.Table 2 Cox proportional hazard ratios for the association of depressive symptom trajectories with heart disease over a 10-year follow-up period.

	Model 1	Model 2	Model 3	
	HR (95% CI)	P-value	HR (95% CI)	P-value	HR (95% CI)	P-value	
Consistently low	Reference		Reference		Reference		
Total depressive symptom trajectory	
Decreasing	1.04 (0.94–1.16)	0.459	1.10 (0.99–1.22)	0.084	1.07 (0.96–1.19)	0.224	
Fluctuating	1.13 (1.07–1.21)	<0.001	1.18 (1.11–1.26)	<0.001	1.13 (1.06–1.20)	<0.001	
Increasing	1.46 (1.28–1.67)	<0.001	1.53 (1.33–1.75)	<0.001	1.43 (1.25–1.64)	<0.001	
Consistently high	1.65 (1.47–1.85)	<0.001	1.76 (1.57–1.98)	<0.001	1.64 (1.45–1.84)	<0.001	
Somatic trajectory of depressive symptom	
Decreasing	0.92 (0.83–1.03)	0.161	0.98 (0.87–1.09)	0.671	0.94 (0.84–1.05)	0.259	
Fluctuating	1.33 (1.25–1.41)	<0.001	1.39 (1.31–1.48)	<0.001	1.33 (1.25–1.41)	<0.001	
Increasing	1.38 (1.20–1.59)	<0.001	1.41 (1.23–1.62)	<0.001	1.31 (1.14–1.50)	<0.001	
Consistently high	1.93 (1.71–2.18)	<0.001	2.10 (1.85–2.38)	<0.001	1.93 (1.70–2.19)	<0.001	
Cognitive-affective trajectory of depressive symptom	
Decreasing	1.00 (0.90–1.12)	0.938	1.06 (0.95–1.18)	0.290	1.03 (0.92–1.14)	0.627	
Fluctuating	1.10 (1.03–1.17)	0.005	1.14 (1.07–1.21)	<0.001	1.10 (1.04–1.18)	0.002	
Increasing	1.32 (1.15–1.53)	<0.001	1.38 (1.19–1.59)	<0.001	1.31 (1.13–1.51)	<0.001	
Consistently high	1.65 (1.45–1.88)	<0.001	1.72 (1.51–1.97)	<0.001	1.64 (1.43–1.87)	<0.001	
A total of 11,725 participants were included in the analysis.

Results of the Cox regression model are reported as HR and 95% CI.

Consistently low depressive symptoms are used as the reference category.

Model 1 was unadjusted.

Model 2 additionally adjusts for sociodemographics (age and sex, education, marital status).

Model 3 additionally adjusts for health behaviors and conditions (smoking, alcohol consumption, hypertension and diabetes).

This table displays the Cox proportional hazard ratios, which quantify the association between different trajectories of depressive symptoms and the incidence of heart disease during a 10-year follow-up period. The table includes hazard ratios, 95% confidence intervals, and p-values, providing a comprehensive statistical overview of how depressive symptoms relate to heart disease risk over time.

Fig. 2 Forest plot visualization of Model 1 results from Table 2.

This forest plot visually represents the hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable in Model 1 from Table 2. Each horizontal line corresponds to a different variable, with the square indicating the point estimate (HR) and the horizontal line representing the 95% confidence interval. The vertical dashed line denotes the null value (HR = 1), indicating no effect of the variable.

Fig. 3 Forest plot visualization of Model 2 results from Table 2.

This forest plot visually represents the hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable in Model 2 from Table 2. Each horizontal line corresponds to a different variable, with the square indicating the point estimate (HR) and the horizontal line representing the 95% confidence interval. The vertical dashed line denotes the null value (HR = 1), indicating no effect of the variable.

Fig. 4 Forest plot visualization of Model 3 results from Table 2.

This forest plot visually represents the hazard ratios (HRs) and 95% confidence intervals (CIs) for each variable in Model 3 from Table 2. Each horizontal line corresponds to a different variable, with the square indicating the point estimate (HR) and the horizontal line representing the 95% confidence interval. The vertical dashed line denotes the null value (HR = 1), indicating no effect of the variable.

The somatic depressive symptoms trajectories resulted in a stronger risk of heart disease than the total depressive symptoms trajectories. Using participants with consistently low somatic depressive symptoms as a reference, individuals with consistently high somatic depressive symptoms had a significantly higher risk for incident heart disease in model 3 (HR = 1.93, 95% CI: 1.70–2.19). The risks of heart disease for individuals with fluctuating somatic depressive symptoms and increasing somatic depressive symptoms were 1.33 (95% CI: 1.25–1.41) and 1.31 (95%CI: 1.14–1.50), respectively. Similarly, the association between decreasing somatic depressive symptoms and heart disease was not statistically significant (HR = 0.94, 95% CI: 0.84–1.05).

The pattern of association between cognitive-affective depressive symptoms trajectories and the risk of developing heart disease was similar to that observed with total depressive symptoms trajectories and somatic depressive symptoms. In contrast, while cognitive-affective symptoms trajectories are linked to an increased risk of heart disease, they had less impact on heart disease development compared to the effects of total and somatic depressive symptoms trajectories. After adjusting for demographic characteristics and health behaviors and conditions (model 3), the association between decreasing depressive symptoms and heart disease remained statistically non-significant (HR = 1.03, 95% CI: 0.92–1.14). Fluctuating depressive symptoms were associated with a 10% higher risk of heart disease (HR = 1.10, 95% CI: 1.04–1.18), and increasing depressive symptoms were associated with a 31% higher risk of heart disease (HR = 1.31, 95% CI: 1.13–1.51). Persistent high level of depressive symptoms trajectories remained the strongest positive association with heart disease (HR = 1.64, 95% CI: 1.43–1.87).

Sensitivity analyses were consistent with the main analysis results. When follow-up years were extended to 12, 14, and 16 years, using persistently low depressive symptoms as a reference, no statistically significant association was found between decreasing depressive symptoms and the risk of heart disease. In contrast, fluctuating, increasing, and persistently high depressive symptoms all demonstrated a significant correlation with heart disease risk, with persistently high depressive symptoms associated with the highest risk. Similarly, somatic depressive symptoms trajectories also increased the risk of heart disease more than the overall depressive symptoms trajectories and the cognitive-affective depressive symptoms trajectories. (The results of sensitivity analyses are presented in the Supplementary Material).

Discussion

In this study of middle-aged and older adults in the United States and the United Kingdom, we analyzed the relationship between heart disease and long-term depressive symptom trajectories: persistently low, decreasing, fluctuating, increasing, and persistently high. Our findings indicate that there was no significant correlation between heart disease risk and the decreasing trajectory among the five trajectories, whether total, somatic, or cognitive-affective. In contrast, trajectories characterized by increasing, fluctuating, and persistently high depressive symptoms were identified as risk factors for heart disease, with the persistently high trajectories showing the strongest correlations.

Furthermore, somatic symptom trajectories—whether fluctuating, increasing, or persistently high—demonstrated greater predictive power for heart disease risk compared to the overall and cognitive-affective symptom trajectories. These conclusions remain robust after adjusting for multiple potential confounders and covariates that could influence the relationship between depressive symptom trajectories and heart disease risk. Moreover, the robustness of the results persists across varying durations of follow-up. To our knowledge, it is the first study to measure the relationship between somatic depressive symptoms trajectories and cognitive-affective symptoms trajectories and heart disease.

Depressive symptoms can be long-lasting and dynamic, so repeated measures of depressive symptoms mapping the trajectory of the illness are more useful in predicting the impact of depressive symptoms on long-term heart health. By analysing different trajectories of depressive symptoms, the present study found that persistently high levels of total depressive symptoms were most strongly associated with the risk of heart disease, possibly due to cumulative cardiovascular damage over time. In contrast, the association between the group with decreasing trajectory of depressive symptoms and heart disease was not statistically significant [28]. This result suggests that with sufficient recovery time (i.e., a sustained period of low depressive symptoms), there may not be an increased risk of adverse outcomes such as heart disease, even in people who experience high depressive symptoms. It has been found that improving depressive symptoms by administering psychotherapy leads to a reduced risk of new-onset cardiovascular disease [29]. This may suggest that reducing or mitigating the progression of depressive states is a potential way to reduce the risk of heart disease, and intervening in the trajectory of depressive symptoms may be an effective heart disease prevention strategy.

The potential biological mechanisms that may underlie this finding may be that it has been found [30] in the presence of persistent depressive symptoms, both the somatic and cognitive-affective symptom dimensions may influence heart health by affecting an individual’s behavioral patterns [31]. Cognitive- affective depressive symptoms (e.g., mood disorders, anxiety, and emotional stress) involve changes in mood, socialization, and other areas. These changes are thought to be associated with an increased risk of heart disease. Emotional factors play an important role in heart health [32], and prolonged depression may lead to imbalanced hormone levels in the body, affecting cardiovascular function. In addition, cognitive- affective depressive symptoms may contribute to heart disease through multiple biopsychosocial pathways. These symptoms can lead to poor health behaviors, resulting in individuals being unable to effectively manage their health and exhibiting poor lifestyles, such as reduced physical activity, unhealthy eating habits, and non-compliance with medical advice. In addition, depressive symptoms may cause feelings of loneliness and decreased socialization, all of which may increase the risk of heart disease [33]. At the same time, lack of well-being and socialization opportunities may lead to reduced physical activity, decreased treatment adherence, and worsened dietary habits, all of which may further increase the risk of heart disease.

Somatic depressive symptoms have a stronger predictive power. It involves a range of physical discomforts, such as depletion of a lot of energy, sleep disturbances, and behavioral delays. These somatic symptoms may increase the risk of heart disease through several pathways. Firstly, somatic complaints may lead to adverse lifestyle changes such as physical inactivity [34–36], poor diet [37, 38], smoking [39–41], and alcohol abuse [42–44], which are important influences on heart health. Secondly, persistent somatic symptoms may trigger a stress response, leading to a highly stressed cardiovascular system that exhibits an imbalance in the autonomic nervous system [45], in particular a disruption of the balance between sympathetic and parasympathetic nerves. Excessive activation of the sympathetic nervous system can lead to an increase in heart rate and blood pressure [46], and in the long term, this state increases the risk of cardiovascular disease. It has also been found that the somatic depressive symptoms are associated with dysregulated levels of inflammation in the body. Inflammatory markers such as C-reactive protein (CRP) [11, 47, 48] and interleukin-6 (IL-6) [49, 50] are inflammatory factors that can damage the walls of blood vessels and contribute to the development of atherosclerosis [51, 52], which can increase the risk of heart disease [53]. There is also the possibility that hypothalamic-pituitary-adrenal (HPA) axis hyperactivity may be a key pathophysiological mechanism for depressive symptoms [54, 55]. Multiple factors may collectively mediate the risk that somatic depressive symptoms promote the development of heart disease.

The strength of this study is the large sample size, pooling two large cohorts from nationally representative, prospective study designs in the United States and the United Kingdom, which enhances the generalizability of the results. Secondly, repeated measures and long-term assessments of depressive symptoms dynamically mapped the trajectories of depressive symptoms, capturing the nuances of how depressive symptoms trajectories differentially affect heart disease risk, and finding that reducing depressive symptoms was not associated with increased risk of heart disease. The correlation between somatic and cognitive-affective depressive symptoms and heart disease, respectively, was also analyzed, leading to the hypothesis that increased intervention for somatic depressive symptoms may be more effective in preventing heart disease. Additionally, participants were not selected on the basis of mental health issues and are therefore more representative of the general population. There are a number of limitations to our study. Although we used a more appropriate classification of somatic symptoms for the 8-item CES-D, this may be inconsistent with the definition of somatic depressive symptoms in the literature and the types of items characterizing this symptom cluster in different depression scales [56]. Secondly, the study population was over 50 years of age in the United States and the United Kingdom, and the results may not be generalizable to other populations. Given that the data collection relies on self-reported questionnaires, which subjects may not accurately recall or may report in socially desirable ways, there is a risk of recall bias, which can affect the accuracy of responses. And the results of this study should be interpreted with caution as they may be subject to the effects of confounding variables not accounted for in the analysis. In addition, the nature of observational studies limits the identification of causal relationships.

Based on the results of the current study, we recommend that the identification and management of depressive symptoms be incorporated into heart disease prevention strategies. This includes assessing the psychological status of all patients during routine health checkups, especially those who exhibit somatic depressive symptoms that require special attention. A comprehensive intervention plan that includes medication, psychotherapy, and other supportive interventions is essential, tailored to each patient’s individual situation. At the same time, a comprehensive mental health and heart health assessment should be conducted regularly, and the treatment plan should be adjusted according to the results of the assessment to effectively reduce or alleviate depressive symptoms and thus reduce the risk of heart disease.

Conclusion

We found significant associations between different trajectories (fluctuating, increasing, and persistently high depressive symptoms) and the increased risk of heart disease. However, a trajectory with depressive symptoms that started high but decreased over time was not associated with higher heart disease risk. The somatic symptoms have a stronger association with heart disease incident. Further studies are needed to explore the underlying overall and trajectory-specific mechanisms of these associations.

Supplementary information

Supplementary table

Supplementary information

The online version contains supplementary material available at 10.1038/s41398-024-03063-y.

Acknowledgements

All authors thank the original data collectors, depositors, copyright holders, and funders of English Longitudinal Study of Ageing, and Health and Retirement Study.

Author contributions

XL: Formal analysis, Writing—original draft. CL: Methodology, Software, Writing—review & editing. HL: Software, Writing—review & editing. XQ: Visualization, Writing—review & editing. CW: Writing—review & editing. CJ: Supervision, Project administration. FJ*: Conceptualization, funding acquisition, Supervision, Writing—review & editing, Final approval of the paper.

Funding

Feifei jia is supported by Natural Science Foundation of Shandong Province (Grant No. ZR2021QH176).

Data availability

The databases used in this study are available at https://hrs.isr.umich.edu and www.elsa-project.ac.uk, respectively.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Roth GA Mensah GA Johnson CO Addolorato G Ammirati E Baddour LM Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: update from the GBD 2019 study J Am Coll Cardiol 2020 76 2982 3021 10.1016/j.jacc.2020.11.010 33309175
Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76:2982–3021.33309175 10.1016/j.jacc.2020.11.010
2. Roth GA Mensah GA Fuster V The Global Burden of Cardiovascular Diseases and Risks: a compass for global action J Am Coll Cardiol 2020 76 2980 81 10.1016/j.jacc.2020.11.021 33309174
Roth GA, Mensah GA, Fuster V. The Global Burden of Cardiovascular Diseases and Risks: a compass for global action. J Am Coll Cardiol. 2020;76:2980–81.33309174 10.1016/j.jacc.2020.11.021
3. Tsao Aday CW Almarzooq AW Anderson CAM ZI Arora P Avery CL Heart Disease and Stroke Statistics-2023 Update: a report from the American Heart Association Circulation 2023 147 e93 e621 10.1161/CIR.0000000000001123 36695182
Tsao, Aday CW, Almarzooq AW, Anderson CAM ZI, Arora P, Avery CL, et al. Heart Disease and Stroke Statistics-2023 Update: a report from the American Heart Association. Circulation. 2023;147:e93–e621.36695182 10.1161/CIR.0000000000001123
4. Hodson R Smoking: an avoidable health disaster explained Nature 2023 618 S2 s3 10.1038/d41586-023-01837-w 37286656
Hodson R. Smoking: an avoidable health disaster explained. Nature. 2023;618:S2–s3.37286656 10.1038/d41586-023-01837-w
5. Tian F Chen L Qian ZM Xia H Zhang Z Zhang J Ranking age-specific modifiable risk factors for cardiovascular disease and mortality: evidence from a population-based longitudinal study EClinicalMedicine 2023 64 102230 10.1016/j.eclinm.2023.102230 37936651
Tian F, Chen L, Qian ZM, Xia H, Zhang Z, Zhang J, et al. Ranking age-specific modifiable risk factors for cardiovascular disease and mortality: evidence from a population-based longitudinal study. EClinicalMedicine. 2023;64:102230.37936651 10.1016/j.eclinm.2023.102230
6. Vaduganathan M Mensah GA Turco JV Fuster V Roth GA The Global Burden of Cardiovascular Diseases and Risk: a compass for future health J Am Coll Cardiol 2022 80 2361 71 10.1016/j.jacc.2022.11.005 36368511
Vaduganathan M, Mensah GA, Turco JV, Fuster V, Roth GA. The Global Burden of Cardiovascular Diseases and Risk: a compass for future health. J Am Coll Cardiol. 2022;80:2361–71.36368511 10.1016/j.jacc.2022.11.005
7. Wadström BN Pedersen KM Wulff AB Nordestgaard BG Elevated remnant cholesterol, plasma triglycerides, and cardiovascular and non-cardiovascular mortality Eur Heart J 2023 44 1432 45 10.1093/eurheartj/ehac822 36631967
Wadström BN, Pedersen KM, Wulff AB, Nordestgaard BG. Elevated remnant cholesterol, plasma triglycerides, and cardiovascular and non-cardiovascular mortality. Eur Heart J. 2023;44:1432–45.36631967 10.1093/eurheartj/ehac822
8. Mortensen MB Dzaye O Bøtker HE Jensen JM Maeng M Bentzon JF Low-density lipoprotein cholesterol is predominantly associated with atherosclerotic cardiovascular disease events in patients with evidence of coronary atherosclerosis: the Western Denmark Heart Registry Circulation 2023 147 1053 63 10.1161/CIRCULATIONAHA.122.061010 36621817
Mortensen MB, Dzaye O, Bøtker HE, Jensen JM, Maeng M, Bentzon JF, et al. Low-density lipoprotein cholesterol is predominantly associated with atherosclerotic cardiovascular disease events in patients with evidence of coronary atherosclerosis: the Western Denmark Heart Registry. Circulation. 2023;147:1053–63.36621817 10.1161/CIRCULATIONAHA.122.061010
9. Moise N Khodneva Y Richman J Shimbo D Kronish I Safford MM Elucidating the association between depressive symptoms, coronary heart disease, and stroke in black and white adults: The REasons for Geographic And Racial Differences in Stroke (REGARDS) Study J Am Heart Assoc 2016 5 e003767 10.1161/JAHA.116.003767 27521153
Moise N, Khodneva Y, Richman J, Shimbo D, Kronish I, Safford MM. Elucidating the association between depressive symptoms, coronary heart disease, and stroke in black and white adults: The REasons for Geographic And Racial Differences in Stroke (REGARDS) Study. J Am Heart Assoc. 2016;5:e003767.27521153 10.1161/JAHA.116.003767
10. Deschênes SS Burns RJ Graham E Schmitz N Depressive symptoms and sleep problems as risk factors for heart disease: a prospective community study Epidemiol Psychiatr Sci 2019 29 e50 10.1017/S2045796019000441 31426879
Deschênes SS, Burns RJ, Graham E, Schmitz N. Depressive symptoms and sleep problems as risk factors for heart disease: a prospective community study. Epidemiol Psychiatr Sci. 2019;29:e50.31426879 10.1017/S2045796019000441
11. Iob E Kirschbaum C Steptoe A Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms Mol Psychiatry 2020 25 1130 40 10.1038/s41380-019-0501-6 31435001
Iob E, Kirschbaum C, Steptoe A. Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: the role of cognitive-affective and somatic symptoms. Mol Psychiatry. 2020;25:1130–40.31435001 10.1038/s41380-019-0501-6
12. Hoen PW Conradi HJ Denollet J Martens EJ de Jonge P Interview-based ratings of somatic and cognitive symptoms of depression and their impact on cardiovascular prognosis Psychother Psychosom 2010 79 319 20 10.1159/000319528 20689349
Hoen PW, Conradi HJ, Denollet J, Martens EJ, de Jonge P. Interview-based ratings of somatic and cognitive symptoms of depression and their impact on cardiovascular prognosis. Psychother Psychosom. 2010;79:319–20.20689349 10.1159/000319528
13. Norton J Pastore M Ancelin M Hotopf M Tylee A Mann A Time-dependent cognitive and somatic symptoms of depression as predictors of new cardiac-related events in at-risk patients: the UPBEAT-UK cohort Psychol Med 2021 51 1271 78 10.1017/S0033291719004082 31996279
Norton J, Pastore M, Ancelin M, Hotopf M, Tylee A, Mann A, et al. Time-dependent cognitive and somatic symptoms of depression as predictors of new cardiac-related events in at-risk patients: the UPBEAT-UK cohort. Psychol Med. 2021;51:1271–78.31996279 10.1017/S0033291719004082
14. Wium-Andersen MK Wium-Andersen IK Jørgensen MB McGue M Jørgensen TSH Christensen K The association between depressive mood and ischemic heart disease: a twin study Acta Psychiatr Scand 2019 140 265 74 10.1111/acps.13072 31306494
Wium-Andersen MK, Wium-Andersen IK, Jørgensen MB, McGue M, Jørgensen TSH, Christensen K, et al. The association between depressive mood and ischemic heart disease: a twin study. Acta Psychiatr Scand. 2019;140:265–74.31306494 10.1111/acps.13072
15. de Miranda Azevedo R Roest AM Hoen PW de Jonge P Cognitive/affective and somatic/affective symptoms of depression in patients with heart disease and their association with cardiovascular prognosis: a meta-analysis Psychol Med 2014 44 2689 703 10.1017/S0033291714000063 24467963
de Miranda Azevedo R, Roest AM, Hoen PW, de Jonge P. Cognitive/affective and somatic/affective symptoms of depression in patients with heart disease and their association with cardiovascular prognosis: a meta-analysis. Psychol Med. 2014;44:2689–703.24467963 10.1017/S0033291714000063
16. Martens EJ Hoen PW Mittelhaeuser M de Jonge P Denollet J Symptom dimensions of post-myocardial infarction depression, disease severity and cardiac prognosis Psychol Med 2010 40 807 14 10.1017/S0033291709990997 19691872
Martens EJ, Hoen PW, Mittelhaeuser M, de Jonge P, Denollet J. Symptom dimensions of post-myocardial infarction depression, disease severity and cardiac prognosis. Psychol Med. 2010;40:807–14.19691872 10.1017/S0033291709990997
17. Meijer A Conradi HJ Bos EH Thombs BD van Melle JP de Jonge P Prognostic association of depression following myocardial infarction with mortality and cardiovascular events: a meta-analysis of 25 years of research Gen Hosp Psychiatry 2011 33 203 16 10.1016/j.genhosppsych.2011.02.007 21601716
Meijer A, Conradi HJ, Bos EH, Thombs BD, van Melle JP, de Jonge P. Prognostic association of depression following myocardial infarction with mortality and cardiovascular events: a meta-analysis of 25 years of research. Gen Hosp Psychiatry. 2011;33:203–16.21601716 10.1016/j.genhosppsych.2011.02.007
18. Hoen PW Whooley MA Martens EJ Na B van Melle JP de Jonge P Differential associations between specific depressive symptoms and cardiovascular prognosis in patients with stable coronary heart disease J Am Coll Cardiol 2010 56 838 44 10.1016/j.jacc.2010.03.080 20813281
Hoen PW, Whooley MA, Martens EJ, Na B, van Melle JP, de Jonge P. Differential associations between specific depressive symptoms and cardiovascular prognosis in patients with stable coronary heart disease. J Am Coll Cardiol. 2010;56:838–44.20813281 10.1016/j.jacc.2010.03.080
19. Schlechter P Ford TJ Neufeld SAS The Eight-Item Center for Epidemiological Studies Depression Scale in the English Longitudinal Study of Aging: Longitudinal and Gender Invariance, Sum Score Models, and External Associations Assessment 2023 30 2146 61 10.1177/10731911221138930 36511122
Schlechter P, Ford TJ, Neufeld SAS. The Eight-Item Center for Epidemiological Studies Depression Scale in the English Longitudinal Study of Aging: Longitudinal and Gender Invariance, Sum Score Models, and External Associations. Assessment. 2023;30:2146–61.36511122 10.1177/10731911221138930
20. Karim J Weisz R Bibi Z Ur Rehman S Validation of the Eight-Item Center for Epidemiologic Studies Depression Scale (CES-D) Among Older Adults Curr Psychol 2015 34 681 92 10.1007/s12144-014-9281-y
Karim J, Weisz R, Bibi Z, Ur Rehman S. Validation of the Eight-Item Center for Epidemiologic Studies Depression Scale (CES-D) Among Older Adults. Curr Psychol. 2015;34:681–92.10.1007/s12144-014-9281-y
21. Soh Y Tiemeier H Kawachi I Berkman LF Kubzansky LD Eight-year depressive symptom trajectories and incident stroke: a 10-year follow-up of the HRS (Health and Retirement Study) Stroke 2022 53 2569 76 10.1161/STROKEAHA.121.037768 35603598
Soh Y, Tiemeier H, Kawachi I, Berkman LF, Kubzansky LD. Eight-year depressive symptom trajectories and incident stroke: a 10-year follow-up of the HRS (Health and Retirement Study). Stroke. 2022;53:2569–76.35603598 10.1161/STROKEAHA.121.037768
22. Lee SL Pearce E Ajnakina O Johnson S Lewis G Mann F The association between loneliness and depressive symptoms among adults aged 50 years and older: a 12-year population-based cohort study Lancet Psychiatry 2021 8 48 57 10.1016/S2215-0366(20)30383-7 33181096
Lee SL, Pearce E, Ajnakina O, Johnson S, Lewis G, Mann F, et al. The association between loneliness and depressive symptoms among adults aged 50 years and older: a 12-year population-based cohort study. Lancet Psychiatry. 2021;8:48–57.33181096 10.1016/S2215-0366(20)30383-7
23. White J Kivimäki M Jokela M Batty GD Association of inflammation with specific symptoms of depression in a general population of older people: the English Longitudinal Study of Ageing Brain Behav Immun 2017 61 27 30 10.1016/j.bbi.2016.08.012 27562420
White J, Kivimäki M, Jokela M, Batty GD. Association of inflammation with specific symptoms of depression in a general population of older people: the English Longitudinal Study of Ageing. Brain Behav Immun. 2017;61:27–30.27562420 10.1016/j.bbi.2016.08.012
24. Frank P Ajnakina O Steptoe A Cadar D Genetic susceptibility, inflammation and specific types of depressive symptoms: evidence from the English Longitudinal Study of Ageing Transl Psychiatry 2020 10 140 10.1038/s41398-020-0815-9 32398645
Frank P, Ajnakina O, Steptoe A, Cadar D. Genetic susceptibility, inflammation and specific types of depressive symptoms: evidence from the English Longitudinal Study of Ageing. Transl Psychiatry. 2020;10:140.32398645 10.1038/s41398-020-0815-9
25. Chu K Cadar D Iob E Frank P Excess body weight and specific types of depressive symptoms: is there a mediating role of systemic low-grade inflammation? Brain Behav Immun 2023 108 233 44 10.1016/j.bbi.2022.11.016 36462595
Chu K, Cadar D, Iob E, Frank P. Excess body weight and specific types of depressive symptoms: is there a mediating role of systemic low-grade inflammation? Brain Behav Immun. 2023;108:233–44.36462595 10.1016/j.bbi.2022.11.016
26. Xie W Zheng F Yan L Zhong B Cognitive decline before and after incident coronary events J Am Coll Cardiol 2019 73 3041 50 10.1016/j.jacc.2019.04.019 31221251
Xie W, Zheng F, Yan L, Zhong B. Cognitive decline before and after incident coronary events. J Am Coll Cardiol. 2019;73:3041–50.31221251 10.1016/j.jacc.2019.04.019
27. Wallace RB Herzog AR Overview of the Health Measures in the Health and Retirement Study J Hum Resour 1995 30 S84 S107 10.2307/146279
Wallace RB, Herzog AR. Overview of the Health Measures in the Health and Retirement Study. J Hum Resour. 1995;30:S84–S107.10.2307/146279
28. Han L Shen S Wu Y Zhong C Zheng X Trajectories of depressive symptoms and risk of cardiovascular disease: evidence from the China Health and Retirement Longitudinal Study J Psychiatr Res 2021 145 137 43 10.1016/j.jpsychires.2021.12.017 34922097
Han L, Shen S, Wu Y, Zhong C, Zheng X. Trajectories of depressive symptoms and risk of cardiovascular disease: evidence from the China Health and Retirement Longitudinal Study. J Psychiatr Res. 2021;145:137–43.34922097 10.1016/j.jpsychires.2021.12.017
29. El Baou C Desai R Cooper C Marchant NL Pilling S Richards M Psychological therapies for depression and cardiovascular risk: evidence from national healthcare records in England Eur Heart J 2023 44 1650 62 10.1093/eurheartj/ehad188 37072130
El Baou C, Desai R, Cooper C, Marchant NL, Pilling S, Richards M, et al. Psychological therapies for depression and cardiovascular risk: evidence from national healthcare records in England. Eur Heart J. 2023;44:1650–62.37072130 10.1093/eurheartj/ehad188
30. Ormel J de Jonge P Unipolar depression and the progression of coronary artery disease: toward an integrative model Psychother Psychosom 2011 80 264 74 10.1159/000323165 21646821
Ormel J, de Jonge P. Unipolar depression and the progression of coronary artery disease: toward an integrative model. Psychother Psychosom. 2011;80:264–74.21646821 10.1159/000323165
31. Whooley MA de Jonge P Vittinghoff E Otte C Moos R Carney RM Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease J Am Med Assoc 2008 300 2379 88 10.1001/jama.2008.711
Whooley MA, de Jonge P, Vittinghoff E, Otte C, Moos R, Carney RM, et al. Depressive symptoms, health behaviors, and risk of cardiovascular events in patients with coronary heart disease. J Am Med Assoc. 2008;300:2379–88.10.1001/jama.2008.711
32. Prossin AR Koch AE Campbell PL Barichello T Zalcman SS Zubieta JK Acute experimental changes in mood state regulate immune function in relation to central opioid neurotransmission: a model of human CNS-peripheral inflammatory interaction Mol Psychiatry 2016 21 243 51 10.1038/mp.2015.110 26283642
Prossin AR, Koch AE, Campbell PL, Barichello T, Zalcman SS, Zubieta JK. Acute experimental changes in mood state regulate immune function in relation to central opioid neurotransmission: a model of human CNS-peripheral inflammatory interaction. Mol Psychiatry. 2016;21:243–51.26283642 10.1038/mp.2015.110
33. Valtorta NK Kanaan M Gilbody S Ronzi S Hanratty B Loneliness and social isolation as risk factors for coronary heart disease and stroke: systematic review and meta-analysis of longitudinal observational studies Heart 2016 102 1009 16 10.1136/heartjnl-2015-308790 27091846
Valtorta NK, Kanaan M, Gilbody S, Ronzi S, Hanratty B. Loneliness and social isolation as risk factors for coronary heart disease and stroke: systematic review and meta-analysis of longitudinal observational studies. Heart. 2016;102:1009–16.27091846 10.1136/heartjnl-2015-308790
34. Werneck AO Vancampfort D Stubbs B Silva DR Cucato GG Christofaro DGD Prospective associations between multiple lifestyle behaviors and depressive symptoms J Affect Disord 2022 301 233 39 10.1016/j.jad.2021.12.131 34986379
Werneck AO, Vancampfort D, Stubbs B, Silva DR, Cucato GG, Christofaro DGD, et al. Prospective associations between multiple lifestyle behaviors and depressive symptoms. J Affect Disord. 2022;301:233–39.34986379 10.1016/j.jad.2021.12.131
35. López-Bueno R Ahmadi M Stamatakis E Yang L Del Pozo Cruz B Prospective associations of different combinations of aerobic and muscle-strengthening activity with all-cause, cardiovascular, and cancer mortality JAMA Intern Med 2023 183 982 90 10.1001/jamainternmed.2023.3093 37548973
López-Bueno R, Ahmadi M, Stamatakis E, Yang L, Del Pozo Cruz B. Prospective associations of different combinations of aerobic and muscle-strengthening activity with all-cause, cardiovascular, and cancer mortality. JAMA Intern Med. 2023;183:982–90.37548973 10.1001/jamainternmed.2023.3093
36. Ballantyne CM Davis JW Exercise and mortality in heart disease cohorts: meta-analysis to augment available evidence J Am Coll Cardiol 2022 79 1701 03 10.1016/j.jacc.2022.02.037 35483758
Ballantyne CM, Davis JW. Exercise and mortality in heart disease cohorts: meta-analysis to augment available evidence. J Am Coll Cardiol. 2022;79:1701–03.35483758 10.1016/j.jacc.2022.02.037
37. Li J Lee DH Hu J Tabung FK Li Y Bhupathiraju SN Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S J Am Coll Cardiol 2020 76 2181 93 10.1016/j.jacc.2020.09.535 33153576
Li J, Lee DH, Hu J, Tabung FK, Li Y, Bhupathiraju SN, et al. Dietary inflammatory potential and risk of cardiovascular disease among men and women in the U.S. J Am Coll Cardiol. 2020;76:2181–93.33153576 10.1016/j.jacc.2020.09.535
38. Micha R Peñalvo JL Cudhea F Imamura F Rehm CD Mozaffarian D Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States J Am Med Assoc 2017 317 912 24 10.1001/jama.2017.0947
Micha R, Peñalvo JL, Cudhea F, Imamura F, Rehm CD, Mozaffarian D. Association between dietary factors and mortality from heart disease, stroke, and type 2 diabetes in the United States. J Am Med Assoc. 2017;317:912–24.10.1001/jama.2017.0947
39. Ding N Sang Y Chen J Ballew SH Kalbaugh CA Salameh MJ Cigarette smoking, smoking cessation, and long-term risk of 3 major atherosclerotic diseases J Am Coll Cardiol 2019 74 498 507 10.1016/j.jacc.2019.05.049 31345423
Ding N, Sang Y, Chen J, Ballew SH, Kalbaugh CA, Salameh MJ, et al. Cigarette smoking, smoking cessation, and long-term risk of 3 major atherosclerotic diseases. J Am Coll Cardiol. 2019;74:498–507.31345423 10.1016/j.jacc.2019.05.049
40. Middlekauff HR Park J Moheimani RS Adverse effects of cigarette and noncigarette smoke exposure on the autonomic nervous system: mechanisms and implications for cardiovascular risk J Am Coll Cardiol 2014 64 1740 50 10.1016/j.jacc.2014.06.1201 25323263
Middlekauff HR, Park J, Moheimani RS. Adverse effects of cigarette and noncigarette smoke exposure on the autonomic nervous system: mechanisms and implications for cardiovascular risk. J Am Coll Cardiol. 2014;64:1740–50.25323263 10.1016/j.jacc.2014.06.1201
41. Ambrose JA Barua RS The pathophysiology of cigarette smoking and cardiovascular disease: an update J Am Coll Cardiol 2004 43 1731 7 10.1016/j.jacc.2003.12.047 15145091
Ambrose JA, Barua RS. The pathophysiology of cigarette smoking and cardiovascular disease: an update. J Am Coll Cardiol. 2004;43:1731–7.15145091 10.1016/j.jacc.2003.12.047
42. Whitman IR Agarwal V Nah G Dukes JW Vittinghoff E Dewland TA Alcohol abuse and cardiac disease J Am Coll Cardiol 2017 69 13 24 10.1016/j.jacc.2016.10.048 28057245
Whitman IR, Agarwal V, Nah G, Dukes JW, Vittinghoff E, Dewland TA, et al. Alcohol abuse and cardiac disease. J Am Coll Cardiol. 2017;69:13–24.28057245 10.1016/j.jacc.2016.10.048
43. Csengeri D Sprünker NA Di Castelnuovo A Niiranen T Vishram-Nielsen JK Costanzo S Alcohol consumption, cardiac biomarkers, and risk of atrial fibrillation and adverse outcomes Eur Heart J 2021 42 1170 77 10.1093/eurheartj/ehaa953 33438022
Csengeri D, Sprünker NA, Di Castelnuovo A, Niiranen T, Vishram-Nielsen JK, Costanzo S, et al. Alcohol consumption, cardiac biomarkers, and risk of atrial fibrillation and adverse outcomes. Eur Heart J. 2021;42:1170–77.33438022 10.1093/eurheartj/ehaa953
44. Biddinger KJ Emdin CA Haas ME Wang M Hindy G Ellinor PT Association of habitual alcohol intake with risk of cardiovascular disease JAMA Netw Open 2022 5 e223849 10.1001/jamanetworkopen.2022.3849 35333364
Biddinger KJ, Emdin CA, Haas ME, Wang M, Hindy G, Ellinor PT, et al. Association of habitual alcohol intake with risk of cardiovascular disease. JAMA Netw Open. 2022;5:e223849.35333364 10.1001/jamanetworkopen.2022.3849
45. Penninx BW Depression and cardiovascular disease: epidemiological evidence on their linking mechanisms Neurosci Biobehav Rev 2017 74 277 86 10.1016/j.neubiorev.2016.07.003 27461915
Penninx BW. Depression and cardiovascular disease: epidemiological evidence on their linking mechanisms. Neurosci Biobehav Rev. 2017;74:277–86.27461915 10.1016/j.neubiorev.2016.07.003
46. Kivimäki M Steptoe A Effects of stress on the development and progression of cardiovascular disease Nat Rev Cardiol 2018 15 215 29 10.1038/nrcardio.2017.189 29213140
Kivimäki M, Steptoe A. Effects of stress on the development and progression of cardiovascular disease. Nat Rev Cardiol. 2018;15:215–29.29213140 10.1038/nrcardio.2017.189
47. Kappelmann N Arloth J Georgakis MK Czamara D Rost N Ligthart S Dissecting the association between inflammation, metabolic dysregulation, and specific depressive symptoms: a genetic correlation and 2-sample mendelian randomization study JAMA Psychiatry 2021 78 161 70 10.1001/jamapsychiatry.2020.3436 33079133
Kappelmann N, Arloth J, Georgakis MK, Czamara D, Rost N, Ligthart S, et al. Dissecting the association between inflammation, metabolic dysregulation, and specific depressive symptoms: a genetic correlation and 2-sample mendelian randomization study. JAMA Psychiatry. 2021;78:161–70.33079133 10.1001/jamapsychiatry.2020.3436
48. Köhler-Forsberg O Buttenschøn HN Tansey KE Maier W Hauser J Dernovsek MZ Association between C-reactive protein (CRP) with depression symptom severity and specific depressive symptoms in major depression Brain Behav Immun 2017 62 344 50 10.1016/j.bbi.2017.02.020 28257825
Köhler-Forsberg O, Buttenschøn HN, Tansey KE, Maier W, Hauser J, Dernovsek MZ, et al. Association between C-reactive protein (CRP) with depression symptom severity and specific depressive symptoms in major depression. Brain Behav Immun. 2017;62:344–50.28257825 10.1016/j.bbi.2017.02.020
49. Milaneschi Y Kappelmann N Ye Z Lamers F Moser S Jones PB Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts Mol Psychiatry 2021 26 7393 402 10.1038/s41380-021-01188-w 34135474
Milaneschi Y, Kappelmann N, Ye Z, Lamers F, Moser S, Jones PB, et al. Association of inflammation with depression and anxiety: evidence for symptom-specificity and potential causality from UK Biobank and NESDA cohorts. Mol Psychiatry. 2021;26:7393–402.34135474 10.1038/s41380-021-01188-w
50. Kelly KM Smith JA Mezuk B Depression and interleukin-6 signaling: a Mendelian Randomization study Brain Behav Immun 2021 95 106 14 10.1016/j.bbi.2021.02.019 33631287
Kelly KM, Smith JA, Mezuk B. Depression and interleukin-6 signaling: a Mendelian Randomization study. Brain Behav Immun. 2021;95:106–14.33631287 10.1016/j.bbi.2021.02.019
51. Kong P Cui ZY Huang XF Zhang DD Guo RJ Han M Inflammation and atherosclerosis: signaling pathways and therapeutic intervention Signal Transduct Target Ther 2022 7 131 10.1038/s41392-022-00955-7 35459215
Kong P, Cui ZY, Huang XF, Zhang DD, Guo RJ, Han M. Inflammation and atherosclerosis: signaling pathways and therapeutic intervention. Signal Transduct Target Ther. 2022;7:131.35459215 10.1038/s41392-022-00955-7
52. Soehnlein O Libby P Targeting inflammation in atherosclerosis—from experimental insights to the clinic Nat Rev Drug Discov 2021 20 589 610 10.1038/s41573-021-00198-1 33976384
Soehnlein O, Libby P. Targeting inflammation in atherosclerosis—from experimental insights to the clinic. Nat Rev Drug Discov. 2021;20:589–610.33976384 10.1038/s41573-021-00198-1
53. Fioranelli M Bottaccioli AG Bottaccioli F Bianchi M Rovesti M Roccia MG Stress and inflammation in coronary artery disease: a review psychoneuroendocrineimmunology-based Front Immunol 2018 9 2031 10.3389/fimmu.2018.02031 30237802
Fioranelli M, Bottaccioli AG, Bottaccioli F, Bianchi M, Rovesti M, Roccia MG. Stress and inflammation in coronary artery disease: a review psychoneuroendocrineimmunology-based. Front Immunol. 2018;9:2031.30237802 10.3389/fimmu.2018.02031
54. Eachus H, Cunliffe VT. Chapter 12—biological embedding of psychosocial stress over the life course. In: Moskalev A, Vaiserman AM, editors. Epigenetics of aging and longevity. Boston: Academic Press; 2018:251–70.
55. Penninx BW Milaneschi Y Lamers F Vogelzangs N Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile BMC Med 2013 11 129 10.1186/1741-7015-11-129 23672628
Penninx BW, Milaneschi Y, Lamers F, Vogelzangs N. Understanding the somatic consequences of depression: biological mechanisms and the role of depression symptom profile. BMC Med. 2013;11:129.23672628 10.1186/1741-7015-11-129
56. Shafer AB Meta-analysis of the factor structures of four depression questionnaires: beck, CES-D, Hamilton, and Zung J Clin Psychol 2006 62 123 46 10.1002/jclp.20213 16287149
Shafer AB. Meta-analysis of the factor structures of four depression questionnaires: beck, CES-D, Hamilton, and Zung. J Clin Psychol. 2006;62:123–46.16287149 10.1002/jclp.20213
