Case Control Study Open Access
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Psychiatry. Aug 19, 2025; 15(8): 106303
Published online Aug 19, 2025. doi: 10.5498/wjp.v15.i8.106303
Novel biomarkers of the Framingham risk score in patients with depression: A cross-sectional study
Li-Na Zhou, Bai-Jia Li, Xian-Cang Ma, Wei Wang, Department of Psychiatry, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, Shaanxi Province, China
Yan Mao, Department of Psychosomatic Medicine, The Fifth Hospital of Yulin, Yulinshi 719000, Shaanxi Province, China
ORCID number: Li-Na Zhou (0000-0003-0688-8162); Wei Wang (0009-0003-1326-5390).
Author contributions: Zhou LN, Mao Y, and Li BJ obtained and interpreted the data, drafted the article, and reviewed the final version; Ma XC, Wang W reflected on the design and recruitment of participants; Zhou LN, Mao Y, Li BJ, Ma XC, and Wang W conducted the conception and design of the research; and all authors thoroughly reviewed and endorsed the final manuscript.
Supported by the National Natural Science Foundation of China, No. 82301737.
Institutional review board statement: This study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University, approval No. XJTUIAF2018 LSK-076.
Informed consent statement: All written informed consent was obtained from all participants.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The data underlying this study are not publicly available.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Li-Na Zhou, Department of Psychiatry, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 West Yanta Street, Xi’an 710061, Shaanxi Province, China. zhoulinapsy@xjtu.edu.cn
Received: February 26, 2025
Revised: May 6, 2025
Accepted: June 23, 2025
Published online: August 19, 2025
Processing time: 164 Days and 1.8 Hours

Abstract
BACKGROUND

The prevalence of coronary heart disease (CHD) is higher in patients with depression than in the general population. Recently, multiple novel biomarkers have been proposed to predict CHD risk, and these factors have been reported to be altered in patients with depression.

AIM

To explore whether these new biomarkers are associated with an increased risk of CHD in patients with depression.

METHODS

We recruited 279 healthy controls and 164 sex- and age-matched patients with depression and collected their clinical characteristics and laboratory values of novel cardiovascular biomarkers. The Framingham CHD risk score was used to assess the CHD risk of all individuals, and the cardiovascular markers related to the CHD risk in patients with depression were analyzed.

RESULTS

Patients with depression had an increased CHD risk of 5.3% (95% confidence interval: 4.470-6.103) and altered novel cardiovascular biomarkers compared to healthy controls, which included lower levels of thyroid stimulating hormone, albumin, total bilirubin, total cholesterol, high-density lipoprotein cholesterol, and higher levels of triglyceride (TG) and uric acid. Further regression analysis showed that illness duration, family history of depression, serum TG, and urea acid levels were significantly correlated with the Framingham risk score in patients with depression.

CONCLUSION

Patients with depression had a higher CHD risk and that their illness duration, family history of depression, serum TG, and uric acid levels could play important roles in predicting CHD risk. Moreover, elevated CHD risk in patients with depression was not only related to physiological changes caused by depression but also to their genetic susceptibility.

Key Words: Depression; Coronary heart disease; Framingham risk score; Cardiovascular risk; Cardiovascular biomarkers

Core Tip: This study evaluated the Framingham risk score of coronary heart disease (CHD) in patients with depression and detected their cardiovascular biomarkers. It was found that the risk of CHD in patients with depression was increased, and the levels of new cardiovascular biomarkers, triglyceride and uric acid, were related to the risk of CHD in patients with depression.
INTRODUCTION

Depression is a mental illness with high incidence and heavy social burden[1,2]. Its clinical manifestation is characterized by a depressed mood and lack of pleasure, interest, and energy. However, its pathogenesis remains unclear. Recent studies have suggested that depression is a disease characterized by multiple systemic changes, including endocrine, metabolic, and oxidative stresses[3]. Coronary heart disease (CHD), which is also a chronic disease with high morbidity and heavy social burden[4], is considered to share significant physiological overlap with depression[5]. Therefore, several researchers have focused on the comorbidities of depression and CHD.

Some studies have reported that the prevalence and mortality of CHD in patients with depression are higher than those in the general population; depression has been identified as an independent risk factor for CHD[6,7]. The Framingham risk score (FRS), which can predict the 10-year CHD risk, has been validated and widely used in different populations. Several studies have reported that more severe depression is related to a higher risk of CHD[6,8] and addition of a depression variable to the Framingham risk equation improves the overall accuracy of the model for predicting 10-year CHD events in women[9]. Another study found a stronger association between CHD and depression prevalence in women than in men[10]. These results support an association between CHD and depression, particularly in women. However, the participants in these studies were from the general population. Furthermore, their depression was assessed using scales, including self-rating and other-rating, rather than diagnosed by professional psychiatrists based on diagnostic criteria. Therefore, exploring CHD risk in patients diagnosed with depression is of great significance.

Some novel cardiovascular biomarkers have been proposed as predictors of CHD risk in recent years, such as: (1) Creatinine and blood urea nitrogen[11,12]; (2) Albumin (Alb)[13], and total bilirubin(TBIL)[14], which are related indicators of oxidative stress; (3) Metabolic related indicators, uric acid (UA)[15]; (4) Endocrine hormones, such as thyroid stimulating hormone (TSH)[16]; and (5) Other indicators of lipid metabolism, for example, triglyceride (TG)[17] might increase the risk of CHD. Changes in these biomarkers have been observed in patients with depression[18-24]. However, few studies have explored whether alterations in these novel cardiovascular biomarkers in patients with depression contribute to an elevated risk of CHD. Based on this background, we sought to use the FRS as a tool to evaluate the CHD risk of inpatients with depression in this study and preliminarily explore biomarkers associated with CHD risk in patients with depression.

MATERIALS AND METHODS
Participants

All inpatients were recruited from the Department of Psychiatry at the First Affiliated Hospital of Xi’an Jiaotong University and diagnosed with major depression according to the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders. The inclusion criteria for patients were: (1) Age between 30 years and 74 years; (2) No severe somatic disease, particularly cardiovascular and cerebrovascular diseases, and diseases that might affect endocrine, metabolic, hepatic, and renal function, except for hypertension and diabetes; (3) No other diseases that met the diagnostic criteria of the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders at present; and (4) The ethnic group was Han. Healthy controls were recruited from the Department of Physical Examination of the First Affiliated Hospital of Xi’an Jiaotong University. The inclusion criteria were the same as those described above. All participants received a detailed introduction to the study and provided written informed consent.

Data collection

All clinical and laboratory data were anonymized and retrospectively collected from the electronic medical system. The clinical information included sex, age, year of education, smoking status, systolic blood pressure (SBP), diastolic blood pressure (DBP), illness duration, number of depressive episodes, medication duration, family history of depression, score of 17-item Hamilton Depression Scale, and complications. Initial clinical data were used for patients with multiple admissions.

Laboratory data, including serum levels of TSH, Alb, TBIL, total cholesterol (TC), TG, high-density lipoprotein cholesterol (HDL-C), creatinine, blood urea nitrogen, and UA, were collected. All fasting blood samples were collected in the morning after the patients were admitted to the hospital and examined at the Laboratory Department of the First Affiliated Hospital of Xi’an Jiaotong University.

FRS assessment

The FRS is a scoring system that uses age, sex, smoking, blood pressure, TC, HDL-C, and diabetes to estimate the CHD risk over 10 years among individuals with no previously diagnosed CHD. In this study, the FRS was assessed using the Framingham Heart Study website (https://www.framinghamheartstudy.org/fhs-risk-functions/coronary-heart-disease-10-year-risk). In addition, we strictly limited the enrollment criteria of the participants to meet the application criteria of the FRS.

Statistical analysis

SPSS Statistics for Windows, version 24 (IBM Corp., Armonk, NY, United States) was used for the statistical analysis. Categorical variables were analyzed using the χ2 test. Continuous variables were analyzed using the Kolmogorov-Smirnov test to determine whether they conformed to a normal distribution. Normally distributed parameters were tested using a two-tailed independent sample t-test, and non-normally distributed parameters were analyzed using a Mann-Whitney U test. Factors correlated with the FRS were calculated using Spearman’s correlation and multiple linear regression. Unstandardized coefficients (B values) with 95% confidence intervals (95%CI) were computed. All statistical tests were two-tailed; differences were considered significant at P < 0.05.

RESULTS
Sociodemographic and clinical characteristics

This study included 279 healthy controls and 164 patients with depression. Table 1 presents sociodemographic characteristics and clinical variables. Compared with healthy controls, more participants with depression were smokers (χ2 = 21.694, P < 0.001), had hypertension (χ2 = 27.203, P < 0.001), diabetes (χ2 = 7.751, P = 0.005), and had higher levels of a high level of education (χ2 = 8.355, P = 0.039), SBP (Z = -9.974, P < 0.001) and DBP (Z = -7.036, P < 0.001). In this study, healthy controls had a mean FRS of 2.3% (95%CI: 1.999-2.503), while patients with depression had a mean of 5.3% (95%CI: 4.470-6.103), which was increased compared with that in healthy controls (Z = -8.538, P < 0.001). All factors related to the FRS assessment of patients with depression were included in the regression model. The results showed that age [B = 0.520, 95%CI: 0.448-0.591, beta = 0.639, t = 14.357, P < 0.001, variance inflation factor (VIF) = 1.201], SBP (B = 0.037, 95%CI: 0.001-0.074, beta = 0.121, t = 2.008, P = 0.046,VIF = 2.204), DBP (B = 0.118, 95%CI: 0.061-0.175, beta = 0.237, t = 4.101, P < 0.001, VIF = 2.027), and diabetes (B = 4.605, 95%CI: 2.639-6.570, beta = 0.193, t = 4.628, P < 0.001, VIF = 1.051) was related to FRS (R2 = 0.738).

Table 1 Sociodemographic and clinical characteristics of healthy controls and patients with depression, n (%).
Characteristics
HC (n = 279)
MD (n = 164)
Statistics
P value
Age (year), mean ± SD44.1 ± 8.044.6 ± 6.7-1.1300.258
Gender
Male98 (35.1)59 (36.0)0.0330.857
Female181 (64.9)105 (64.0)--
Smoking
No7 (2.5)23 (14.0)21.694< 0.001
Yes272 (97.5)141 (86.0)--
SBP (mmHg), mean ± SD105.3 ± 13.9122.0 ± 17.7-9.974< 0.001
DBP (mmHg), mean ± SD72.2 ± 8.379.2 ± 11.0-7.036< 0.001
Hypertension
Yes10 (3.6)30 (18.3)27.203< 0.001
No269 (96.4)134 (81.7)--
Diabetes
Yes3 (1.1)9 (5.5)7.7510.005
No276 (98.9)155 (94.5)--
Illness duration, month, mean ± SD-68.8 ± 75.5--
Medication duration, month, mean ± SD-44.8 ± 57.2--
Attack times, mean ± SD-1.9 ± 1.1--
Family history of depression----
Yes----
No----
FRS (%), mean ± SD2.3 (2.1)5.3 ± 5.3-8.538< 0.001
HC: Healthy controls; MD: Major depression; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FRS: Framingham risk score.
Novel cardiovascular biomarkers and CHD risk

Compared with healthy controls, patients with depression showed lower levels of TSH (Z = -2.861, P = 0.004), Alb (Z = -14.690, P < 0.001), TBIL (Z = -7.512, P < 0.001), TC (Z = -4.947, P < 0.001), and HDL-C (Z = -14.901, P < 0.001) and higher levels of TG (Z = -8.088, P < 0.001) and UA (Z = -3.087, P < 0.001), see in Figure 1. Moreover, the FRS was positively correlated with illness duration (r = 0.173, P = 0.028) and serum TG levels (r = 0.342, P < 0.001) in patients with depression (Figure 2).

Figure 1
Open in New Tab   Full Size Figure   Download Figure
Figure 1 Novel cardiovascular biomarkers between groups. A: Thyroid stimulating hormone; B: Albumin; C: Total bilirubin; D: Total cholesterol; E: Triglyceride; F: High-density lipoprotein cholesterol; G: Creatinine; H: Blood urea nitrogen; I: Uric acid. HC: Healthy controls; MD: Major depression; TSH: Thyroid stimulating hormone; Alb: Albumin; TBIL: Total bilirubin; TC: Total cholesterol; TG: Triglyceride; HDL-C: High-density lipoprotein cholesterol; Cr: Creatinine; BUN: Blood urea nitrogen; UA: Uric acid.
Figure 2
Open in New Tab   Full Size Figure   Download Figure
Figure 2 Correlations among Framingham risk score, illness duration, and serum triglyceride level in patients with depression. A: Framingham risk score and illness duration; B: Framingham risk score and serum triglyceride level. FRS: Framingham risk score; TG: Triglyceride.

To explore the impact of cardiovascular biomarkers and clinical characteristics on the 10-year CHD risk in patients with depression, we performed a multiple linear regression of the FRS; the results are presented in Table 2. The results of patients with depression showed that illness duration (B = 0.018, 95%CI: 0.005-0.032, beta = 0.262, t = 2.628, P = 0.009, VIF = 2.201), family history of depression (B = 2.292, 95%CI: 0.266-4.318, beta = 0.156, t = 2.235, P = 0.005, VIF = 1.078), serum levels of TG (B = 2.625, 95%CI: 1.693-3.558, beta = 0.415, t = 5.560, P < 0.001, VIF = 1.233) and UA (B = 0.010, 95%CI: 0.001-0.020, beta = 0.155, t = 2.105, P = 0.020, VIF = 1.195) had significant correlations with FRS in patients with depression (R2 = 0.658).

Table 2 Significant regression correlations between the Framingham risk score and all the factors in patients with depression.
Characteristics
B (95%CI)
Beta
t
P value
Illness duration0.02 (0.01-0.03)0.2622.6280.009
Medication duration-0.02 (-0.04-0.00)-0.189-1.8750.063
Attack times-0.43 (-1.26-0.40)-0.088-1.0290.305
Family history of psychosis2.29 (0.27-4.32)0.1562.2350.027
TSH0.23 (-0.27-0.74)0.0620.9040.367
Alb0.10 (-0.17-0.38)0.0530.7050.642
TBIL0.03 (-0.11-0.17)0.0350.4660.642
TG2.63 (1.69-3.56)0.4155.560< 0.001
UA0.01 (0.00-0.02)0.1552.1050.037
TSH: Thyroid stimulating hormone; Alb: Albumin; TBIL: Total bilirubin; TG: Triglycerides; UA: Uric acid.
DISCUSSION

To explore the relationship between novel cardiovascular biomarkers and CHD risk in patients with depression, the FRS was used to evaluate CHD risk. We found that patients with depression had an increased risk of CHD and altered novel cardiovascular biomarkers, including TSH, Alb, TBIL, TG, and UA, than did healthy controls. Further regression analysis showed that illness duration, family history of depression, serum TG, and UA levels significantly correlated with FRS in patients with depression. Previous studies have shown that diabetes[25], hypertension[26,27], smoking[28], TC[29], and HDL-C[30], which are traditional biomarkers of CHD, are strongly associated with depression[31,32]. For instance, Casey[33] reported that persons with affective disorders had a high prevalence of risk factors for cardiovascular disease (CVD), such as diabetes and obesity, which were on the order of 1.5 times to 2.0 times higher than those in the general population. In this study, patients with depression had a higher prevalence of cardiovascular risks, including hypertension, diabetes, higher TC, and lower HDL-C. In addition, it was revealed that cardiovascular risks contributed to a higher CHD risk in patients with depression, which presented higher FRS in this study.

Pathophysiological mechanisms that may explain the effect of depression on coronary artery disease include hypercoagulability via platelet activation, hypothalamus-pituitary-adrenal axis and autonomic nervous system dysregulation, and an altered inflammatory response[34]. Serotonin improves platelet aggregation, while serotonin levels are reduced in patients with depression[35]. However, the platelet aggregation ability in patients with depression is enhanced, making them prone to cardiovascular events. Whether antidepressants can reduce CHD risk in patients with depression remains controversial. Some studies indicated that drugs, such as selective serotonin reuptake inhibitors, are uniquely able to block serotonin reuptake and reduce platelet aggregation[36]. Thus, successful treatment of depression would reduce the risk of CHD[37]. Further evidence includes that patients with CHD who respond to treatment of depression are at a lower risk of mortality than are non-responders[38]. These findings suggest that the response to antidepressant treatment in patients with depression is associated with their CHD risk. As we know, the longer the illness duration of depression, the poorer the response of patients to antidepressants[39,40]. Moreover, this study did not find an association between the duration of medication use in patients and the risk of CHD. Therefore, in this study, it was observed that the longer the course of the disease was, the higher the CHD risk was, which indicated that the risk of CHD in patients with depression was more closely related to the depressive disease itself rather than the duration of medication. This prompts us to pay more attention to patients with depression with a long course of disease in clinical practice, even if they may have no medication experience. In addition, studies have identified that both depression and CHD share genetic determinants[41], which reflects that a family history of depression in patients was closely related to CHD risk in this study. Thus, increased CHD risk in patients with depression is associated with both genetic and disease-related factors.

Elevated TG levels represent a diagnostic criterion for metabolic syndrome (MetS). Hypertriglyceridemia is associated with several atherogenic factors, including increased concentrations of TG-rich lipoproteins, an atherogenic lipoprotein phenotype consisting of small, dense low-density lipoprotein particles, and low levels of HDL-C[42]. Thus, MetS and serum TG levels were considered risk factors for CHD. Elevated TG levels[21] and higher prevalence of MetS[8,43] have been widely reported in patients with depression. In details, TG, rather than the other lipid traits, exerted the major risk, contributing 18.3% to the overall CVD risk of 92.7%. The CVD risk was odds ratio of 2.9 in male and odds ratio of 1.3 in female patients, with 30% of MetS prevalence in those with depression[8]. These results suggest that elevated TG levels are a significant predictor of CHD in patients with depression. In this study, the regression analysis revealed that TG was a novel cardiovascular risk factor associated with FRS in patients with depression. Consistent with the results of Wang et al[44], our findings illustrate the critical role of TG in predicting CHD risk in patients with depression.

A large cross-sectional population study in the United States emphasized a significant nonlinear negative correlation between serum uric levels and depressive symptoms[45]. This phenomenon may be related to sex differences in UA, and supporting evidence comes from two sources, Chen et al[46] suggested that higher UA levels were associated with depressive symptoms/depression in postmenopausal women. Moreover, Zhang et al[24] found that high serum UA level may reduce the incidence of depression in men, but in women, it is an independent factor that increases the risk of depression[24]. Although there are sex differences in the relationship between UA and depression, this study found that its impact on the risk of CHD is independent of sex factors. UA plays a protective role in brain disorders. Normally, serum UA can interact with a variety of oxidants, including hydrogen peroxide and hydroxyl radicals, to clear reactive oxygen species, inhibit lipid peroxidation, avoid oxidative damage. However, elevated oxidative stress inevitably leads to vascular endothelial damage, which in turn causes the occurrence of various vascular diseases. That is to say, UA may be involved in the pathological processes of depression and CHD simultaneously through the dual effects of oxidative stress and vascular damage. Sohn et al[47] suggested a significant association between altered white matter connectivity and serum UA levels in patients with major depression, possibly through demyelination. This result may link the biological mechanism of high UA level with increased risk of depression and CHD.

This study has some limitations. First, the FRS has an age limit and strict inclusion criteria in this study, which may have resulted in an inability to assess CHD risk in adolescents with depression. Second, this is a small sample size, single-center, cross-sectional study, did not include patient follow-up; it only used the FRS risk prediction model to predict the risk of CHD in patients with depression. Therefore, no causal conclusions could be drawn. In the future, multi-center and large-sample cohort studies are needed for further in-depth research. Third, although FRS has been used in numerous studies to assess the risk of CHD in Chinese people, some studies have also pointed out that the FRS overestimates the risk of CHD in Chinese people. This result might be related to factors such as race and living environment[48]. We hope to develop a prediction model for CHD risk based on large Chinese samples in the future.

CONCLUSION

In conclusion, this study presented evidence that patients with depression had a higher CHD risk and that their illness duration, family history of depression, serum TG, and UA levels could play important roles in predicting CHD risk. Moreover, elevated CHD risk in patients with depression was not only related to physiological changes caused by depression but also to their genetic susceptibility. The future direction should focus on longitudinal cohort studies to deeply explore the causal relationship between depression and CHD, and to deeply investigate the mechanisms of TG and UA involved.

Footnotes

Provenance and peer review: Unsolicited article; Externally peer reviewed.

Peer-review model: Single blind

Specialty type: Psychiatry

Country of origin: China

Peer-review report’s classification

Scientific Quality: Grade A, Grade B, Grade B, Grade B, Grade D

Novelty: Grade A, Grade B, Grade B, Grade B, Grade C

Creativity or Innovation: Grade A, Grade B, Grade B, Grade C, Grade D

Scientific Significance: Grade A, Grade B, Grade B, Grade B, Grade D

P-Reviewer: Ji KK; Sun PT; Varama A S-Editor: Bai Y L-Editor: A P-Editor: Zhang YL

References
1.  Jiang X, Asmaro R, O'Sullivan DM, Modi J, Budnik E, Schnatz PF. Depression may be a risk factor for coronary heart disease in midlife women <65 years: A 9-year prospective cohort study. Int J Cardiol. 2018;271:8-12.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 10]  [Cited by in RCA: 12]  [Article Influence: 1.7]  [Reference Citation Analysis (0)]
2.  Zhang Y, Jia X, Yang Y, Sun N, Shi S, Wang W. Change in the global burden of depression from 1990-2019 and its prediction for 2030. J Psychiatr Res. 2024;178:16-22.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 15]  [Reference Citation Analysis (0)]
3.  Rimti FH, Shahbaz R, Bhatt K, Xiang A. A review of new insights into existing major depressive disorder biomarkers. Heliyon. 2023;9:e18909.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 3]  [Cited by in RCA: 9]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
4.  Chen WW, Gao RL, Liu LS, Zhu ML, Wang W, Wang YJ, Wu ZS, Li HJ, Gu DF, Yang YJ, Zheng Z, Jiang LX, Hu SS. China cardiovascular diseases report 2015: a summary. J Geriatr Cardiol. 2017;14:1-10.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 144]  [Reference Citation Analysis (1)]
5.  Berecki-Gisolf J, McKenzie SJ, Dobson AJ, McFarlane A, McLaughlin D. A history of comorbid depression and anxiety predicts new onset of heart disease. J Behav Med. 2013;36:347-353.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 6]  [Cited by in RCA: 8]  [Article Influence: 0.6]  [Reference Citation Analysis (0)]
6.  Jang HY, Song YK, Kim JH, Kim MG, Han N, Lee HY, Kim IW, Oh JM. Impact of depression on change in coronary heart disease risk status: the Korean Genome and Epidemiology Study (KoGES). Ther Clin Risk Manag. 2018;14:121-128.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 5]  [Cited by in RCA: 9]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
7.  Cao H, Zhao H, Shen L. Depression increased risk of coronary heart disease: A meta-analysis of prospective cohort studies. Front Cardiovasc Med. 2022;9:913888.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 17]  [Reference Citation Analysis (0)]
8.  Koponen H, Jokelainen J, Keinänen-Kiukaanniemi S, Vanhala M. Depressive symptoms and 10-year risk for cardiovascular morbidity and mortality. World J Biol Psychiatry. 2010;11:834-839.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 37]  [Cited by in RCA: 42]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
9.  O'Neil A, Fisher AJ, Kibbey KJ, Jacka FN, Kotowicz MA, Williams LJ, Stuart AL, Berk M, Lewandowski PA, Atherton JJ, Taylor CB, Pasco JA. The addition of depression to the Framingham Risk Equation model for predicting coronary heart disease risk in women. Prev Med. 2016;87:115-120.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 7]  [Cited by in RCA: 9]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
10.  Rome D, Sales A, Leeds R, Usseglio J, Cornelius T, Monk C, Smolderen KG, Moise N. A Narrative Review of the Association Between Depression and Heart Disease Among Women: Prevalence, Mechanisms of Action, and Treatment. Curr Atheroscler Rep. 2022;24:709-720.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 4]  [Cited by in RCA: 17]  [Article Influence: 5.7]  [Reference Citation Analysis (0)]
11.  Salim A, Tai ES, Tan VY, Welsh AH, Liew R, Naidoo N, Wu Y, Yuan JM, Koh WP, van Dam RM. C-reactive protein and serum creatinine, but not haemoglobin A1c, are independent predictors of coronary heart disease risk in non-diabetic Chinese. Eur J Prev Cardiol. 2016;23:1339-1349.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 10]  [Cited by in RCA: 10]  [Article Influence: 1.1]  [Reference Citation Analysis (0)]
12.  Shen L, Zhang Q, Zhu Z, Huang Z. Genetic insights into blood urea nitrogen as a risk factor for coronary artery disease: a Mendelian randomization study in East Asians. Ren Fail. 2025;47:2477318.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 1]  [Reference Citation Analysis (0)]
13.  Wu CC, Lu YC, Yu TH, Wang CP, Hung WC, Tang WH, Tsai IT, Chung FM, Lee YJ, Hu JJ. Serum albumin level and abnormal corrected QT interval in patients with coronary artery disease and chronic kidney disease. Intern Med J. 2018;48:1242-1251.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 5]  [Article Influence: 0.7]  [Reference Citation Analysis (0)]
14.  Lai X, Fang Q, Yang L, Chen X, Wang H, Ma L, Guo W, Liu M, Yang H, Zhang C, Li X, Min X, Yuan J, He MA, Wu T, Zhang X. Direct, indirect and total bilirubin and risk of incident coronary heart disease in the Dongfeng-Tongji cohort. Ann Med. 2018;50:16-25.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 21]  [Cited by in RCA: 34]  [Article Influence: 4.9]  [Reference Citation Analysis (0)]
15.  Saito Y, Tanaka A, Node K, Kobayashi Y. Uric acid and cardiovascular disease: A clinical review. J Cardiol. 2021;78:51-57.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 29]  [Cited by in RCA: 203]  [Article Influence: 40.6]  [Reference Citation Analysis (0)]
16.  Li H, Cui Y, Zhu Y, Yan H, Xu W. Association of high normal HbA1c and TSH levels with the risk of CHD: a 10-year cohort study and SVM analysis. Sci Rep. 2017;7:45406.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 12]  [Cited by in RCA: 13]  [Article Influence: 1.6]  [Reference Citation Analysis (0)]
17.  Halil M, Yavuz B, Yavuz BB, Cankurtaran M, Dede DS, Ulger Z, Barak A, Karabulut E, Aytemir K, Kabakci G, Ariogul S, Oto A. Novel cardiovascular risk factors in the elderly and their correlation with the Framingham risk score. J Cardiovasc Med (Hagerstown). 2008;9:683-687.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 4]  [Cited by in RCA: 6]  [Article Influence: 0.4]  [Reference Citation Analysis (0)]
18.  Setoyama D, Kato TA, Hashimoto R, Kunugi H, Hattori K, Hayakawa K, Sato-Kasai M, Shimokawa N, Kaneko S, Yoshida S, Goto YI, Yasuda Y, Yamamori H, Ohgidani M, Sagata N, Miura D, Kang D, Kanba S. Plasma Metabolites Predict Severity of Depression and Suicidal Ideation in Psychiatric Patients-A Multicenter Pilot Analysis. PLoS One. 2016;11:e0165267.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 69]  [Cited by in RCA: 96]  [Article Influence: 10.7]  [Reference Citation Analysis (0)]
19.  Song X, Zhang Z, Zhang R, Wang M, Lin D, Li T, Shao J, Ma X. Predictive markers of depression in hypertension. Medicine (Baltimore). 2018;97:e11768.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 16]  [Cited by in RCA: 20]  [Article Influence: 2.9]  [Reference Citation Analysis (0)]
20.  Peng YF, Xiang Y, Wei YS. The significance of routine biochemical markers in patients with major depressive disorder. Sci Rep. 2016;6:34402.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 57]  [Cited by in RCA: 53]  [Article Influence: 5.9]  [Reference Citation Analysis (0)]
21.  Lemche AV, Chaban OS, Lemche E. Depression contributing to dyslipidemic cardiovascular risk in the metabolic syndrome. J Endocrinol Invest. 2017;40:539-546.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 6]  [Cited by in RCA: 11]  [Article Influence: 1.4]  [Reference Citation Analysis (0)]
22.  Lee SG, Lee W, Kim JH, Kwon OH. Gender-specific reference intervals for serum total bilirubin in healthy Korean adults. Clin Biochem. 2012;45:1257-1259.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 6]  [Cited by in RCA: 8]  [Article Influence: 0.6]  [Reference Citation Analysis (0)]
23.  Xu DR, Gao X, Zhao LB, Liu SD, Tang G, Zhou CJ, Chen Y. Association between triglyceride and depression: A systematic review and meta-analysis. PLoS One. 2024;19:e0311625.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 3]  [Reference Citation Analysis (0)]
24.  Zhang E, Li J, Liu Y, Dong Z, Wang L, Zhao C. Associations between serum uric acid and depression in U.S. adults: An analysis of the national health and nutrition examination survey 2007-2016. PLoS One. 2025;20:e0321387.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 1]  [Reference Citation Analysis (0)]
25.  Domingo AK, Asmal L, Seedat S, Esterhuizen TM, Laurence C, Volmink J. Investigating the association between diabetes mellitus, depression and psychological distress in a cohort of South African teachers. S Afr Med J. 2015;105:1057-1060.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 9]  [Cited by in RCA: 13]  [Article Influence: 1.3]  [Reference Citation Analysis (0)]
26.  Park SK, Jung JY, Ryoo JH, Oh CM, Lee JH, Pan Z, Mansur RB, Shekotikhina M, McIntyre RS, Choi JM. The relationship of depression with the level of blood pressure in population-based Kangbuk Samsung Health Study. J Am Soc Hypertens. 2018;12:356-363.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 4]  [Cited by in RCA: 7]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
27.  Meng L, Chen D, Yang Y, Zheng Y, Hui R. Depression increases the risk of hypertension incidence: a meta-analysis of prospective cohort studies. J Hypertens. 2012;30:842-851.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 273]  [Cited by in RCA: 352]  [Article Influence: 27.1]  [Reference Citation Analysis (0)]
28.  Mathew AR, Hogarth L, Leventhal AM, Cook JW, Hitsman B. Cigarette smoking and depression comorbidity: systematic review and proposed theoretical model. Addiction. 2017;112:401-412.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 162]  [Cited by in RCA: 189]  [Article Influence: 23.6]  [Reference Citation Analysis (0)]
29.  Zhang M, Chen J, Yin Z, Wang L, Peng L. The association between depression and metabolic syndrome and its components: a bidirectional two-sample Mendelian randomization study. Transl Psychiatry. 2021;11:633.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 12]  [Cited by in RCA: 87]  [Article Influence: 21.8]  [Reference Citation Analysis (0)]
30.  Enko D, Brandmayr W, Halwachs-Baumann G, Schnedl WJ, Meinitzer A, Kriegshäuser G. Prospective plasma lipid profiling in individuals with and without depression. Lipids Health Dis. 2018;17:149.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 25]  [Cited by in RCA: 49]  [Article Influence: 7.0]  [Reference Citation Analysis (0)]
31.  Cardenas V, Mausbach BT, Sommerfeld D, Jimenez D, von Känel R, Ho JS, Garcia P, Aarons GA. Depression is Associated with Increased Risk for Metabolic Syndrome in Latinos with Type 2 Diabetes. Am J Geriatr Psychiatry. 2017;25:646-653.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 9]  [Cited by in RCA: 12]  [Article Influence: 1.5]  [Reference Citation Analysis (0)]
32.  Ho RCM, Chua AC, Tran BX, Choo CC, Husain SF, Vu GT, McIntyre RS, Ho CSH. Factors Associated with the Risk of Developing Coronary Artery Disease in Medicated Patients with Major Depressive Disorder. Int J Environ Res Public Health. 2018;15:2073.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 25]  [Cited by in RCA: 41]  [Article Influence: 5.9]  [Reference Citation Analysis (0)]
33.  Casey DE. Metabolic issues and cardiovascular disease in patients with psychiatric disorders. Am J Med. 2005;118 Suppl 2:15S-22S.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 20]  [Cited by in RCA: 57]  [Article Influence: 2.9]  [Reference Citation Analysis (0)]
34.  Parissis JT, Fountoulaki K, Filippatos G, Adamopoulos S, Paraskevaidis I, Kremastinos D. Depression in coronary artery disease: novel pathophysiologic mechanisms and therapeutic implications. Int J Cardiol. 2007;116:153-160.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 42]  [Cited by in RCA: 49]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
35.  Bangalore S, Shah R, Pappadopulos E, Deshpande CG, Shelbaya A, Prieto R, Stephens J, McIntyre RS. Cardiovascular hazards of insufficient treatment of depression among patients with known cardiovascular disease: a propensity score adjusted analysis. Eur Heart J Qual Care Clin Outcomes. 2018;4:258-266.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 9]  [Cited by in RCA: 10]  [Article Influence: 1.4]  [Reference Citation Analysis (0)]
36.  McFarlane A, Kamath MV, Fallen EL, Malcolm V, Cherian F, Norman G. Effect of sertraline on the recovery rate of cardiac autonomic function in depressed patients after acute myocardial infarction. Am Heart J. 2001;142:617-623.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 109]  [Cited by in RCA: 102]  [Article Influence: 4.3]  [Reference Citation Analysis (0)]
37.  Rahman I, Humphreys K, Bennet AM, Ingelsson E, Pedersen NL, Magnusson PK. Clinical depression, antidepressant use and risk of future cardiovascular disease. Eur J Epidemiol. 2013;28:589-595.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 33]  [Cited by in RCA: 42]  [Article Influence: 3.5]  [Reference Citation Analysis (0)]
38.  Carney RM, Freedland KE, Steinmeyer BC, Rubin EH, Rich MW. Clinical predictors of depression treatment outcomes in patients with coronary heart disease. J Psychosom Res. 2016;88:36-41.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 8]  [Cited by in RCA: 13]  [Article Influence: 1.4]  [Reference Citation Analysis (0)]
39.  Çiftçi A, Ulaş H, Topuzoğlu A, Tunca Z. Is the Ultimate Treatment Response Predictable with Early Response in Major Depressive Episode? Noro Psikiyatr Ars. 2016;53:245-252.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 2]  [Cited by in RCA: 4]  [Article Influence: 0.5]  [Reference Citation Analysis (0)]
40.  Tunvirachaisakul C, Gould RL, Coulson MC, Ward EV, Reynolds G, Gathercole RL, Grocott H, Supasitthumrong T, Tunvirachaisakul A, Kimona K, Howard RJ. Predictors of treatment outcome in depression in later life: A systematic review and meta-analysis. J Affect Disord. 2018;227:164-182.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 48]  [Cited by in RCA: 75]  [Article Influence: 10.7]  [Reference Citation Analysis (0)]
41.  Skala JA, Freedland KE, Carney RM. Coronary heart disease and depression: a review of recent mechanistic research. Can J Psychiatry. 2006;51:738-745.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in Crossref: 73]  [Cited by in RCA: 68]  [Article Influence: 3.6]  [Reference Citation Analysis (0)]
42.  Blaton V. How is the Metabolic Syndrome Related to the Dyslipidemia? EJIFCC. 2007;18:15-22.  [PubMed]  [DOI]
43.  Zhou LN, Ma XC, Wang W. Incidence and risk factors of depression in patients with metabolic syndrome. World J Psychiatry. 2024;14:245-254.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in RCA: 4]  [Reference Citation Analysis (0)]
44.  Wang Y, Wu CY, Fu HX, Zhang JC. Development and validation of a prediction model for coronary heart disease risk in depressed patients aged 20 years and older using machine learning algorithms. Front Cardiovasc Med. 2024;11:1504957.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 1]  [Reference Citation Analysis (0)]
45.  Wang J, Yang M, Lin H, Wang J. Association between uric acid and the risk of depressive symptoms in US adults: results from NHANES 2005-2018. Sci Rep. 2024;14:24097.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 4]  [Reference Citation Analysis (0)]
46.  Chen J, Zhou W, Huang Y. Association between serum uric acid levels and depressive symptoms according to menopausal status. J Affect Disord. 2024;350:240-246.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Cited by in RCA: 5]  [Reference Citation Analysis (0)]
47.  Sohn H, Kwon MS, Lee SW, Oh J, Kim MK, Lee SH, Lee KS, Kim B. Effects of Uric Acid on the Alterations of White Matter Connectivity in Patients with Major Depression. Psychiatry Investig. 2018;15:743.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 1]  [Cited by in RCA: 3]  [Article Influence: 0.4]  [Reference Citation Analysis (0)]
48.  Damen JA, Pajouheshnia R, Heus P, Moons KGM, Reitsma JB, Scholten RJPM, Hooft L, Debray TPA. Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis. BMC Med. 2019;17:109.  [RCA]  [PubMed]  [DOI]  [Full Text]  [Full Text (PDF)]  [Cited by in Crossref: 83]  [Cited by in RCA: 151]  [Article Influence: 25.2]  [Reference Citation Analysis (0)]