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Study Protocol

Association between triglyceride and depression: A systematic review and meta-analysis

  • Di-Ru Xu,

    Roles Data curation, Methodology, Writing – original draft

    Affiliation Department of Dermatology, University‑Town Hospital of Chongqing Medical University, Chongqing, China

    ⨯
  • Xi Gao,

    Roles Data curation

    Affiliation Department of Dermatology, University‑Town Hospital of Chongqing Medical University, Chongqing, China

    ⨯
  • Li-Bo Zhao,

    Roles Writing – review & editing

    Affiliations Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China, Chongqing Key Laboratory of Cerebrovascular Disease Research, Chongqing, China

    ⨯
  • Shu-Dong Liu,

    Roles Methodology

    Affiliations Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China, Chongqing Key Laboratory of Cerebrovascular Disease Research, Chongqing, China

    ⨯
  • Ge Tang,

    Roles Formal analysis, Software

    Affiliations Department of Neurology, Yongchuan Hospital of Chongqing Medical University, Chongqing, China, Chongqing Key Laboratory of Cerebrovascular Disease Research, Chongqing, China

    ⨯
  • Chan-Juan Zhou,

    Roles Writing – review & editing

    Affiliation Chongqing Key Laboratory of Cerebrovascular Disease Research, Chongqing, China

    ⨯
  • Yu Chen

    Roles Supervision, Validation, Writing – review & editing

    cheny@hospital.cqmu.edu.cn

    Affiliations Chongqing Key Laboratory of Cerebrovascular Disease Research, Chongqing, China, Department of Neurology, Bishan Hospital of Chongqing Medical University, Chongqing, China

    ⨯

Abstract

Depression is accompanied by dyslipidemia, which may increase the risk of stroke and coronary heart disease. This study sought to quantitatively summarize the clinical data comparing peripheral blood triglyceride (TG) concentrations between patients with major depressive disorder (MDD) and healthy controls (HCs). Studies were searched in PubMed, EMBASE, PsycINFO, and Cochrane Databases up to March 2023. We also reviewed the reference lists of obtained articles. Mean (±SD) for TG concentrations were extracted, combined quantitatively using random-effects meta-analysis, and summarized as a standardized mean difference (SMD). Subgroup analysis and meta-regression was performed to explore the resource of heterogeneity. Thirty-eight studies measuring the concentrations of peripheral blood TG in 2604 patients with MDD and 3272 HCs were included. Meta-analysis results indicated that TG levels were significant higher in patients with MDD than in HCs (SMD = 0.31, 95% confidence interval [CI]: 0.16 to 0.46, Z46 = 4.05, p < 0.01). Heterogeneity was detected (χ2 = 269.97, p < 0.01, I2 = 85%). Subgroup analysis demonstrated significant differences in TG levels between patients with MDD and HCs depended on age, body mass index and drug use (p < 0.05), but no differences between groups. Meta-regression also found no significant variables. TG level was significantly elevated in depression, which may explain the increased risk of cardiovascular and cerebrovascular events in depression.

Citation: Xu D-R, Gao X, Zhao L-B, Liu S-D, Tang G, Zhou C-J, et al. (2024) Association between triglyceride and depression: A systematic review and meta-analysis. PLoS ONE 19(10): e0311625. https://doi.org/10.1371/journal.pone.0311625

Editor: Md. Rabiul Islam, University of Asia Pacific, BANGLADESH

Received: March 13, 2024; Accepted: September 20, 2024; Published: October 4, 2024

Copyright: © 2024 Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files (S1 Dataset).

Funding: National Natural Science Foundation of China (Grant No. 81601207)/. The funder of Professor Li Bo Zhao had proofread the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

MDD is a common disease with a 12-month prevalence of 6.6% and a lifetime prevalence of 16.2%. MDD is a main contributor to years of life lived with disability, characterized by mood disturbances, loss of interest in activities, and deficits in cognitive functions [1, 2]. Accumulating evidence indicates that depression can increase the risk of coronary heart disease and stroke [3–5]; however, the underlying mechanism by which depression elevates the risk of cardiovascular and cerebrovascular diseases is unclear. Although treatment of hyperlipidemia mainly focuses on lowering total plasma cholesterol and low-density lipoprotein cholesterol levels, several recent studies have reported the importance of TG levels in atherosclerotic cardiovascular and cerebrovascular diseases [6, 7].

TG is the most abundant lipid in the peripheral circulation. It is a major source of energy and a critical component of the lipoproteins. However, hypertriglyceridemia has been considered as an independent risk factor for atherosclerosis, which leads to myocardial infarction and ischemic stroke in the long term [4, 8]. Many factors, such as BMI, sex, and age, may increase TG concentration. Our previous studies indicated that certain peripheral metabolites [9, 10], including lipid metabolites [11], can differentiate between patients with MDD and healthy subjects with high sensitivity and specificity. Based on these findings, we suppose there may be a significant alteration in TG levels in depressed patients compared with healthy people. Further investigations are needed to identify potential pathophysiological factors and find alternative strategies for treatment.

TG status is most frequently assessed by measuring serum or plasma levels of TG. Peripheral blood TG concentrations have been measured in numerous studies over the past decades. Most of the previous studies [12–15] suggest that depression might be associated with higher concentrations of TG, but a few studies [16, 17] do not support these results, showing any difference between the two groups [18, 19]. The present meta-analysis was performed to compare TG concentrations of depressed patients with that of healthy subjects and explore the modulatory effect of different factors, such as age, sex, body mass index, and antidepressant drugs, in this relationship.

2. Methods and materials

2.1 Search strategy

The protocol of this study was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched English language studies using MEDLINE, EMBASE, PsycINFO, and Cochrane databases up to March 2023. The search strategy was as follows: (triglyceride) AND (((((("depressive symptom") OR "depressed mood") OR "dysthymia") OR "melancholia") OR "major depressive disorder") OR depression). The reference lists of all relevant studies were also searched for any additional trials.

2.2 Study selection

Inclusion criteria were: 1) measuring overnight fasting serum or plasma TG concentrations; 2) being a case-control or cross-sectional study 3) Studies that patients only diagnosed MDD on any edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM), Chinese Classification and Diagnostic Disorder (CCMD), or International Classification of Disease (ICD); and 4) inclusion of healthy controls defined as those who are not diagnosed with any disease.

Studies were excluded if they reported depressive symptoms in the context of 1) other neuropsychiatric disorders (e.g., schizophrenia, bipolar disorder, autism etc.), 2) medical illnesses before onset of MDD (e.g., hyperlipemia, dyslipidemia, diabetes mellitus, coronary artery disease, cancer, infection, hepatic disease, etc.), 3) Special physiological conditions (e.g., pregnancy, postpartum or menstrual period, trauma, etc.) or 4) accepting medication that may affect TG levels (e.g., statins, niacin et.).

2.3 Data extraction

Each article was separately examined by two independent researchers (Xu and Gao), and any disagreements regarding inclusion were resolved by consensus with a third researcher (Liu). Serum or plasma TG concentrations (mean ± SD) were converted to mg/dL and extracted for depressed and control subjects. Missing data were requested from the corresponding author. Demographic and clinical characteristics (mean age, body mass index, female percentage, and antidepressant use) and study variables (inclusion criteria, publication date, country and diagnosis method) were extracted.

2.4 Quality assessment

The Newcastle-Ottawa Scale (NOS) was used to assess the quality of all studies in this systematic review. Two reviewers (Tang and Zhou) independently assessed the qualities of each study, and the results were compared afterward. Publication bias was assessed using funnel plots and quantitatively measured by Egger’s test.

2.5 Statistical analysis

Following the Cochrane Handbook for systematic reviews, this meta-analysis was carried out using Review Manager software version 5.3 (http://www.cochrane.org) and Stata software version 15.1 (Stata-Corp, College Station, Texas). Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated for TG levels between MDD and HC. Considering the fact that the TG levels may have been affected by confounding factors, a random effects model was used in statistical analysis [20]. The I2 and Q test statistics were calculated to measure within-study heterogeneity, which was considered significant for an I2 > 50% or a P-value < 0.10 in the Q test [21]. Subgroup analysis was performed based on medical condition, age and BMI, to identify the source of heterogeneity. Age was stratified by 45 years, because young patient (< 45 years) with ST-elevation myocardial infarction (STEMI) has significant higher triglyceride level compared to middle-old age patients (> 45) as previous reported [22]. For BMI, stratificational criteria was applied by normal-weight (> 25) and overweight (< 25). Meta-regression analysis was performed based on demographic characteristics (age, sex, and BMI), diagnosis method (DSM vs. ICD-10 vs. CCMD), publication date, country and drug use to detect the sources of heterogeneity.

3. Results

3.1 Characteristics of included studies

We identified 3464 studies (2397 records from MEDLINE, 743 records from EMBASE, 118 records from PsycINFO, and 206 records from Cochrane database). After excluding duplicates, we obtained 2618 studies. Then, title and abstract screening resulted in 146 studies whose full texts were checked. Among these studies, 108 studies were excluded because of data unavailability in 33 studies, presence of other diseases in 15 studies, absence of a control group in 23 studies, inconsistency with the inclusion criteria in 26 studies, and involvement of other research in 11 studies (S1 File). Thirty-eight studies were ultimately included and data was summarized in S1 Dataset [12–19, 23–52]. Fig 1 shows the workflow of the searching and selection processes. Notably, 4 studies separately reported TG levels in males and females. Thus, all 42 items were pooled in the meta-analysis. The characteristics of the included studies are summarized in Table 1. An average 7 scores of The Newcastle-Ottawa Scale (NOS) were obtained for included studies after quality assessment. Of those studies, 6 studies had missing BMI data, and only 6 studies explicitly reported TG values in men and women. The included studies contained a total of 2604 patients with MDD and 3272 HCs.

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Fig 1. Workflow of searching and selection.

Searching strategy was manufactured to obtain target studies as many as possible through four main medical databases (MEDLINE, EMBASE, PsycINFO, Cochrane Database).

https://doi.org/10.1371/journal.pone.0311625.g001

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Table 1. Characteristics of included studies and subjects demography.

https://doi.org/10.1371/journal.pone.0311625.t001

3.2 Triglyceride levels in depressed and healthy subjects

Mean peripheral blood TG concentration was significant higher in patients with MDD compared with HCs (SMD = 0.31, 95% CI: 0.16 to 0.46, Z46 = 4.05, p < 0.01) (Fig 2). Heterogeneity was detected in this comparison (χ2 = 269.97, tau2 = 0.2, p < 0.01, I2 = 85%); therefore, meta-regression and subgroup analysis were used to identify the potential source of heterogeneity.

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Fig 2. Comparison of peripheral triglyceride concentration between MDD and HCs.

A total of 2604 MDD patients and of 3272 HCs were applied to meta-analysis. Results demonstrated higher TG concentration in MDD compared to HC through 38 included studies that containing 42 items. Random effects model generated conservative outcomes for the significant heterogeneity. Effect size of each study had relatively equal weight.

https://doi.org/10.1371/journal.pone.0311625.g002

3.3 Assessment of publication bias

A symmetrical funnel plot was obtained, indicating that there was no publication bias (Fig 3). Similarly, quantitative Egger’s test did not show publication bias for triglyceride (t42 = 0.39, 95% CI: -2.05 to 3.03, p = 0.699). These results revealed that there was no risk of publication bias.

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Fig 3. Funnel plot of publication bias.

The funnel plot was basically symmetrical by visual inspection, following by Egger’s test for quantitative analysis and no significant difference found (p = 0.699).

https://doi.org/10.1371/journal.pone.0311625.g003

3.4 Subgroup analysis and meta-regression

To better understand the results of the meta-analysis, relevant a priori subgroup analyses were conducted. TG levels were significantly higher in patients with MDD compared to HCs in subgroups of age < 45-year (SMD = 0.28, 95% CI: 0.10 to 0.47, I2 = 87%, p < 0.01), age ≥ 45-year (SMD = 0.39, 95% CI: 0.16 to 0.63, I2 = 78%, p < 0.01), BMI < 25 (SMD = 0.27, 95% CI: 0.06 to 0.49, p < 0.01), BMI ≥ 25 (SMD = 0.43, 95% CI: 0.19 to 0.67, p < 0.01), drug-use (SMD = 0.42, 95% CI: 0.22 to 0.63, p < 0.01), and drug-naive (SMD = 0.36, 95% CI: 0.20 to 0.53, I2 =, p < 0.01).Differences between the subgroup studied were also conducted, but had no significant difference (age: χ2 = 0.51, p = 0.46;BMI: χ2 = 0.88, p = 0.35; Drug use: χ2 = 0.41, p = 0.52). Subgroup analysis is shown in Table 2 and Figs 4–6.

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Fig 4. Subgroup analysis on age.

Forest plots of the TG level between MDD and HC according to age.

https://doi.org/10.1371/journal.pone.0311625.g004

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Fig 5. Subgroup analysis on BMI.

Forest plots of the TG level between MDD and HC according to BMI.

https://doi.org/10.1371/journal.pone.0311625.g005

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Fig 6. Subgroup analysis on medication.

Forest plots of the TG level between MDD and HC according to medication.

https://doi.org/10.1371/journal.pone.0311625.g006

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Table 2. Subgroup analyses of TG levels in MDD versus HC.

https://doi.org/10.1371/journal.pone.0311625.t002

Meta-regression was used to investigate the effect of sex, age, BMI, diagnosis method, publication date, country and medication on the results. We pooled these variables into meta-regression, calculating with 1000 permutations. The results showed no significant effect (age: p = 0.47, diagnosis method: p = 0.49, BMI: p = 0.33, medication: p = 0.49, publication date: p = 0.41, country: p = 0.76 and sex: p = 0.50). Restricted maximum likelihood (REML) estimation of between-study variance demonstrates tau2 = 0.41, and residual variation due to heterogeneity I2 = 78.92% (Table 3). Additionally, all seven variables partly explained heterogeneity in TG levels.

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Table 3. Meta-regression analysis.

https://doi.org/10.1371/journal.pone.0311625.t003

4. Discussion

This is the first study to demonstrate high levels of TG in patients with MDD compared to healthy controls, combining meta-regression and subgroup analysis. We included 38 studies with 5876 individuals and demonstrated that TG concentration of patients with MDD was significantly higher than that HCs. Most of the included studies reported that the mean concentrations of TG in patients with MDD and HCs were in the normal range (< 150 mg/dl). However, the TG concentration in the MDD group was often near the upper normal limit. Heterogeneity exploration has been applied to age, sex, BMI, antidepressant use publication date, country and diagnosis method through meta-regression analysis, showing no significant results. All seven variables only explained a part of heterogeneity, and there are other potential confounders worth exploring in future studies. Subgroup analysis consistently show similar significant difference in TG levels between patients with MDD and HC. Publication bias is always a concern in meta-analysis. In our study, there was no potential publication bias in all analyses (Egger’s test p > 0.05).

Differences in TG levels are associated with fat intake, liver synthesis, and TG catabolism [53]. Although hypertriglyceridemia was more prevalent in patients with MDD than in healthy controls, the mechanisms underpinning the association are poorly understood. The blood TG level reflects the concentration of TG-carrying lipoproteins, including very low-density lipoprotein, VLDL, and chylomicrons. Dietary TG are assembled into chylomicrons in the gut. Their interaction with lipoprotein lipase in the luminal surface of capillary endothelial cells leads to the liberation of free fatty acids, which can pass through cell membranes [54]. Depressed people may have increased appetite due to hyperactivation of putative mesocorticolimbic reward circuitry [55]. Except for four studies, most included studies informed that blood samples were collected after at least 8 hours of fasting, implying that the source of TG was mainly endogenous liver synthesis. TG synthesis is controlled by insulin. In depressed subjects, insulin resistance is a major comorbidity. Insulin resistance leads to unrestrained fat mobilization in adipose tissue, increasing plasma-free fatty acid (FFA) levels. An increased FFA flux into the liver stimulates hepatic lipogenesis and promotes VLDL-TG overproduction [56]. Nevertheless, Wagner et al. found that remitted patients with MDD (no depressive episode during the last 12 months) show increased levels of triglyceride [46].

Additionally, the close relationship between depression and cardiovascular disease (CVD) was also clarified in several studies. Robert et al. indicated that non-fasting TG is a marker for CVD risk stratification [57], and Van Marwijk et al. reported that depression increases the risk of CVD [58]. Besides, other studies revealed that depression significantly increases the risk of stroke, and this increase is probably independent of other risk factors, including hypertension and diabetes [5]. In our study, TG had significantly higher levels in moderate and severe MDD. Although TG itself is not a component of arterial plaque, it is believed that cholesterol within TG-rich particles may contribute to plaque development [59]. Elevated TG level in MDD subjects is probably a risk factor for cardiovascular and cerebrovascular diseases.

This study still has some limitations. Our study contained potential heterogeneity in most analyses, although we adopted a random-effects model, subgroup analysis and meta-regression analyses. The reasons for heterogeneity may be that not all studies reported sufficient demographic data, such as sex, drug use, and BMI, or other factors that could potentially influence heterogeneity like diet and food intake. Besides, few studies reported the method of assaying TG; thus, TG concentration largely differed among studies. Furthermore, included studies did not adjust the effect of several confounding factors such as genetic factors, alcohol consumption, and cigarette smoking. Additionally, the results of our meta-analysis cannot support the causal effect of depression on TG. A causal association between TG status and depression is biologically plausible. Thus, a risk comparison meta-analysis between depressed and non-depressed subjects is necessary to establish whether higher TG concentrations predict the future development of depression. Finally, our study was not registered in the International Prospective Register of Systematic Reviews (PROSPERO) database, but the protocol was strictly followed the PRISMA guideline items.

5. Conclusion

This meta-analytic confirm that depression is associated with elevated concentrations of TG in the peripheral blood. These findings indicate that depression increases the risk of cardiovascular and cerebrovascular diseases. These findings suggest the need to investigate the potential roles of TG in the pathogenesis of depression, identify the potential utility of TG and related lipids biomarkers in monitoring MDD and subsequent stroke or myocardial infarction, and measure potential benefits of low-TG diet in MDD patients.

Supporting information

S1 Checklist. PRISMA 2020 checklist.

https://doi.org/10.1371/journal.pone.0311625.s001

(DOCX)

S1 File. List of the studies that were excluded at the full-text assessment.

https://doi.org/10.1371/journal.pone.0311625.s002

(XLSX)

S1 Dataset. Data extracted from included studies and used for all analyses.

https://doi.org/10.1371/journal.pone.0311625.s003

(XLSX)

Acknowledgments

Portion of data in these studies comes from early study “Meta-analysis of peripheral triglyceride of patients with depression”, which as our primary studies published in 2014. Besides, the idea of this study inherits from the first author master’s thesis named “the evaluation of mental health in bromhidrosis or scars patients”.

References

  1. 1. Kupfer DJ, Frank E, Phillips ML. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet (London, England). 2012;379(9820):1045–55. Epub 2011/12/23. pmid:22189047; PubMed Central PMCID: PMC3397431.
  2. 2. Kessler RC, Angermeyer M, Anthony JC, R DEG, Demyttenaere K, Gasquet I, et al. Lifetime prevalence and age-of-onset distributions of mental disorders in the World Health Organization’s World Mental Health Survey Initiative. World Psychiatry. 2007;6(3):168–76. pmid:18188442; PubMed Central PMCID: PMC2174588.
  3. 3. Lemche AV, Chaban OS, Lemche E. Depression contributing to dyslipidemic cardiovascular risk in the metabolic syndrome. Journal of endocrinological investigation. 2017;40(5):539–46. Epub 2016/12/25. pmid:28012071; PubMed Central PMCID: PMC5390000.
  4. 4. Nordestgaard BG, Varbo A. Triglycerides and cardiovascular disease. Lancet (London, England). 2014;384(9943):626–35. pmid:25131982.
  5. 5. Dong JY, Zhang YH, Tong J, Qin LQ. Depression and risk of stroke: a meta-analysis of prospective studies. Stroke. 2012;43(1):32–7. pmid:22020036.
  6. 6. Gowey MA, Khodneva Y, Tison SE, Carson AP, Cherrington AL, Howard VJ, et al. Depressive symptoms, perceived stress, and metabolic health: The REGARDS study. International journal of obesity (2005). 2019;43(3):615–32. Epub 2018/12/07. pmid:30518827; PubMed Central PMCID: PMC6405306.
  7. 7. Egeland GM, Igland J, Sulo G, Nygard O, Ebbing M, Tell GS. Non-fasting triglycerides predict incident acute myocardial infarction among those with favourable HDL-cholesterol: Cohort Norway. European journal of preventive cardiology. 2015;22(7):872–81. Epub 2014/05/13. pmid:24817696.
  8. 8. Sun L, Clarke R, Bennett D, Guo Y, Walters RG, Hill M, et al. Causal associations of blood lipids with risk of ischemic stroke and intracerebral hemorrhage in Chinese adults. Nat Med. 2019;25(4):569–74. pmid:30858617.
  9. 9. Pan JX, Xia JJ, Deng FL, Liang WW, Wu J, Yin BM, et al. Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study. Translational psychiatry. 2018;8(1):130. pmid:29991685; PubMed Central PMCID: PMC6039504.
  10. 10. Zhou X, Liu L, Lan X, Cohen D, Zhang Y, Ravindran AV, et al. Polyunsaturated fatty acids metabolism, purine metabolism and inosine as potential independent diagnostic biomarkers for major depressive disorder in children and adolescents. Molecular psychiatry. 2018. pmid:29679072.
  11. 11. Liu X, Li J, Zheng P, Zhao X, Zhou C, Hu C, et al. Plasma lipidomics reveals potential lipid markers of major depressive disorder. Anal Bioanal Chem. 2016;408(23):6497–507. pmid:27457104.
  12. 12. Cizza G, Eskandari F, Coyle M, Krishnamurthy P, Wright EC, Mistry S, et al. Plasma CRP levels in premenopausal women with major depression: a 12-month controlled study. Hormone and metabolic research = Hormon- und Stoffwechselforschung = Hormones et metabolisme. 2009;41(8):641–8. Epub 2009/05/02. pmid:19408214; PubMed Central PMCID: PMC2782561.
  13. 13. Baghai TC, Varallo-Bedarida G, Born C, Hafner S, Schule C, Eser D, et al. Major depressive disorder is associated with cardiovascular risk factors and low Omega-3 Index. The Journal of clinical psychiatry. 2011;72(9):1242–7. Epub 2011/01/07. pmid:21208589.
  14. 14. Waloszek JM, Byrne ML, Woods MJ, Nicholas CL, Bei B, Murray G, et al. Early physiological markers of cardiovascular risk in community based adolescents with a depressive disorder. Journal of affective disorders. 2015;175:403–10. Epub 2015/02/14. pmid:25678173.
  15. 15. Segoviano-Mendoza M, Cardenas-de la Cruz M, Salas-Pacheco J, Vazquez-Alaniz F, La Llave-Leon O, Castellanos-Juarez F, et al. Hypocholesterolemia is an independent risk factor for depression disorder and suicide attempt in Northern Mexican population. BMC psychiatry. 2018;18(1):7. Epub 2018/01/18. pmid:29334911; PubMed Central PMCID: PMC5769344.
  16. 16. Kuehl LK, Hinkelmann K, Muhtz C, Dettenborn L, Wingenfeld K, Spitzer C, et al. Hair cortisol and cortisol awakening response are associated with criteria of the metabolic syndrome in opposite directions. Psychoneuroendocrinology. 2015;51:365–70. Epub 2014/12/03. pmid:25462908.
  17. 17. Khalid A, Lal N, Trivedi JK, Dalal PK, Asthana OP, Srivastava JS, et al. Serum lipids: new biological markers in depression? Indian journal of psychiatry. 1998;40(3):217–23. pmid:21494476; PubMed Central PMCID: PMC2966595.
  18. 18. Politi P, Brondino N, Emanuele E. Increased proapoptotic serum activity in patients with chronic mood disorders. Archives of medical research. 2008;39(2):242–5. Epub 2008/01/01. pmid:18164972.
  19. 19. Ljubicic R, Jakovac H, Bistrovic IL, Franceski T, Mufic AK, Karlovic D. Prevalence of metabolic syndrome among patients with major depressive disorder—differences between newly diagnosed first episode and recurrent disease. Collegium antropologicum. 2013;37(4):1065–9. Epub 2014/03/13. pmid:24611316.
  20. 20. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. pmid:3802833.
  21. 21. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60. pmid:12958120; PubMed Central PMCID: PMC192859.
  22. 22. Usalp S, Altuntaş E, Bağırtan B, Karabay K. Comparison of serum lipoprotein(a) levels in young and middle-aged patients presenting for the first time with ST-elevation myocardial infarction: a single-centre study. Cardiovasc J Afr. 2023;34:1–5. Epub 20230825. pmid:37656600.
  23. 23. Olusi SO, Fido AA. Serum lipid concentrations in patients with major depressive disorder. Biological psychiatry. 1996;40(11):1128–31. Epub 1996/12/01. pmid:8931915.
  24. 24. Maes M, Smith R, Christophe A, Vandoolaeghe E, Van Gastel A, Neels H, et al. Lower serum high-density lipoprotein cholesterol (HDL-C) in major depression and in depressed men with serious suicidal attempts: relationship with immune-inflammatory markers. Acta psychiatrica Scandinavica. 1997;95(3):212–21. Epub 1997/03/01. pmid:9111854.
  25. 25. Bilici M, Efe H, Koroglu MA, Uydu HA, Bekaroglu M, Deger O. Antioxidative enzyme activities and lipid peroxidation in major depression: alterations by antidepressant treatments. Journal of affective disorders. 2001;64(1):43–51. Epub 2001/04/09. pmid:11292519.
  26. 26. Sevincok L, Buyukozturk A, Dereboy F. Serum lipid concentrations in patients with comorbid generalized anxiety disorder and major depressive disorder. Canadian journal of psychiatry Revue canadienne de psychiatrie. 2001;46(1):68–71. Epub 2001/02/28. pmid:11221492.
  27. 27. Huang T-L, Wu S-C, Chiang Y-S, Chen J-F. Correlation between serum lipid, lipoprotein concentrations and anxious state, depressive state or major depressive disorder. Psychiatry research. 2003;118(2):147–53. 2003-05603-006. pmid:12798979
  28. 28. Wang G, Huang C. The levels of the cholesterol, triglyceride, lipoprotein, apolipoprotein in young depression patients. Medical Journal of Wuhan University. 2003;24(2).
  29. 29. Huang TL, Chen JF. Lipid and lipoprotein levels in depressive disorders with melancholic feature or atypical feature and dysthymia. Psychiatry and clinical neurosciences. 2004;58(3):295–9. pmid:15149297.
  30. 30. Huang TL. Serum lipid profiles in major depression with clinical subtypes, suicide attempts and episodes. Journal of affective disorders. 2005;86(1):75–9. pmid:15820273
  31. 31. Sarandol A, Sarandol E, Eker SS, Karaagac EU, Hizli BZ, Dirican M, et al. Oxidation of apolipoprotein B-containing lipoproteins and serum paraoxonase/arylesterase activities in major depressive disorder. Progress in neuro-psychopharmacology & biological psychiatry. 2006;30(6):1103–8. Epub 2006/05/24. pmid:16716479.
  32. 32. Muhtz C, Zyriax BC, Klahn T, Windler E, Otte C. Depressive symptoms and metabolic risk: effects of cortisol and gender. Psychoneuroendocrinology. 2009;34(7):1004–11. Epub 2009/03/13. pmid:19278789.
  33. 33. Sagud M, Mihaljevic-Peles A, Pivac N, Jakovljevic M, Muck-Seler D. Lipid levels in female patients with affective disorders. Psychiatry research. 2009;168(3):218–21. Epub 20090627. pmid:19560828.
  34. 34. Das PP, Malhotra S, Chakrabarti S, Sharma S. Elevated total cholesterol in severely depressed patients: role in cardiovascular risk? The world journal of biological psychiatry: the official journal of the World Federation of Societies of Biological Psychiatry. 2010;11(2 Pt 2):321–8. Epub 2009/05/23. pmid:19462342.
  35. 35. Lehto SM, Niskanen L, Tolmunen T, Hintikka J, Viinamaki H, Heiskanen T, et al. Low serum HDL-cholesterol levels are associated with long symptom duration in patients with major depressive disorder. Psychiatry and clinical neurosciences. 2010;64(3):279–83. Epub 2010/04/09. pmid:20374538.
  36. 36. Aliyazicioglu R, Deǧer O, Vanizor Kural B, Hocaoǧlu C, Çolak M, Balaban Yücesan F. The relationship between the peroxisome proliferator-activated receptor gamma 2 gene polymorphism, lipids and adipokines in patients with major depression. Turkiye Klinikleri Journal of Medical Sciences. 2011;31(5):1065–72.
  37. 37. Hummel J, Westphal S, Weber-Hamann B, Gilles M, Lederbogen F, Angermeier T, et al. Serum lipoproteins improve after successful pharmacologic antidepressant treatment: a randomized open-label prospective trial. The Journal of clinical psychiatry. 2011;72(7):885–91. pmid:21294998.
  38. 38. Lamers F, Vogelzangs N, Merikangas KR, de Jonge P, Beekman AT, Penninx BW. Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression. Molecular psychiatry. 2013;18(6):692–9. Epub 2012/10/24. pmid:23089630.
  39. 39. Mashele N, Malan L, van Rooyen JM, Harvey BH, Potgieter JC, Hamer M. Depression, cardiometabolic function and left ventricular hypertrophy in African men and women: the SABPA study. Clin Exp Hypertens. 2013;35(3):213–9. pmid:22954159.
  40. 40. Wingenfeld K, Kuehl LK, Boeker A, Schultebraucks K, Schulz A, Stenzel J, et al. Are adverse childhood experiences and depression associated with impaired glucose tolerance in females? An experimental study. Journal of psychiatric research. 2017;95:60–7. Epub 2017/08/08. pmid:28783579.
  41. 41. Ho RCM, Chua AC, Tran BX, Choo CC, Husain SF, Vu GT, et al. Factors associated with the risk of developing coronary artery disease in medicated patients with major depressive disorder. International journal of environmental research and public health. 2018;15(10). pmid:30248896
  42. 42. Skibinska M, Kapelski P, Rajewska-Rager A, Pawlak J, Szczepankiewicz A, Narozna B, et al. Brain-derived neurotrophic factor (BDNF) serum level in women with first-episode depression, correlation with clinical and metabolic parameters. Nordic journal of psychiatry. 2018;72(3):191–6. Epub 2017/12/14. pmid:29235396.
  43. 43. Druzhkova T, Pochigaeva K, Yakovlev A, Kazimirova E, Grishkina M, Chepelev A, et al. Acute stress response to a cognitive task in patients with major depressive disorder: potential metabolic and proinflammatory biomarkers. Metabolic brain disease. 2019;34(2):621–9. Epub 2018/12/20. pmid:30564974.
  44. 44. Eidan AJ, Al-Harmoosh RA, Al-Amarei HM. Estimation of IL-6, INFγ, and Lipid Profile in Suicidal and Nonsuicidal Adults with Major Depressive Disorder. Journal of interferon & cytokine research: the official journal of the International Society for Interferon and Cytokine Research. 2019;39(3):181–9. pmid:30844329.
  45. 45. Péterfalvi Á, Németh N, Herczeg R, Tényi T, Miseta A, Czéh B, et al. Examining the Influence of Early Life Stress on Serum Lipid Profiles and Cognitive Functioning in Depressed Patients. Front Psychol. 2019;10:1798. Epub 20190806. pmid:31447737; PubMed Central PMCID: PMC6691174.
  46. 46. Wagner CJ, Musenbichler C, Bohm L, Farber K, Fischer AI, von Nippold F, et al. LDL cholesterol relates to depression, its severity, and the prospective course. Progress in neuro-psychopharmacology & biological psychiatry. 2019;92:405–11. Epub 2019/02/20. pmid:30779936.
  47. 47. Zhang C, Yang Y, Zhu DM, Zhao W, Zhang Y, Zhang B, et al. Neural correlates of the association between depression and high density lipoprotein cholesterol change. Journal of psychiatric research. 2020;130:9–18. Epub 20200729. pmid:32768711.
  48. 48. Honkalampi K, Virtanen M, Hintsa T, Ruusunen A, Mäntyselkä P, Ali-Sisto T, et al. Comparison of the level of allostatic load between patients with major depression and the general population. Journal of psychosomatic research. 2021;143:110389. Epub 20210215. pmid:33609985.
  49. 49. Liu Y, Tong Y, Huang L, Chen J, Yan S, Yang F. Factors related to retinal nerve fiber layer thickness in bipolar disorder patients and major depression patients. BMC psychiatry. 2021;21(1):301. Epub 20210610. pmid:34112131; PubMed Central PMCID: PMC8191183.
  50. 50. Sánchez-Carro Y, de la Torre-Luque A, Leal-Leturia I, Salvat-Pujol N, Massaneda C, de Arriba-Arnau A, et al. Importance of immunometabolic markers for the classification of patients with major depressive disorder using machine learning. Progress in neuro-psychopharmacology & biological psychiatry. 2022;121:110674. Epub 20221101. pmid:36332700.
  51. 51. Silić A, Vukojević J, Peitl V, De Hert M, Karlović D. Major depressive disorder: a possible typisation according to serotonin, inflammation, and metabolic syndrome. Acta neuropsychiatrica. 2022;34(1):15–23. Epub 20210910. pmid:34503595.
  52. 52. Yang R, Wang L, Cao S, Chen M, Wu CJ, Silva F, et al. Sex difference in lipid levels in first-diagnosed drug-naïve depression patients: A case-control and 12-weeks follow-up study. The world journal of biological psychiatry: the official journal of the World Federation of Societies of Biological Psychiatry. 2022;23(3):228–35. Epub 20210812. pmid:34320901.
  53. 53. Berglund L, Brunzell JD, Goldberg AC, Goldberg IJ, Sacks F, Murad MH, et al. Evaluation and treatment of hypertriglyceridemia: an Endocrine Society clinical practice guideline. The Journal of clinical endocrinology and metabolism. 2012;97(9):2969–89. pmid:22962670; PubMed Central PMCID: PMC3431581.
  54. 54. Tenenbaum A, Klempfner R, Fisman EZ. Hypertriglyceridemia: a too long unfairly neglected major cardiovascular risk factor. Cardiovasc Diabetol. 2014;13:159. pmid:25471221; PubMed Central PMCID: PMC4264548.
  55. 55. Simmons WK, Burrows K, Avery JA, Kerr KL, Bodurka J, Savage CR, et al. Depression-Related Increases and Decreases in Appetite: Dissociable Patterns of Aberrant Activity in Reward and Interoceptive Neurocircuitry. The American journal of psychiatry. 2016;173(4):418–28. Epub 2016/01/26. pmid:26806872; PubMed Central PMCID: PMC4818200.
  56. 56. Kamagate A, Dong HH. FoxO1 integrates insulin signaling to VLDL production. Cell Cycle. 2008;7(20):3162–70. pmid:18927507; PubMed Central PMCID: PMC2664837.
  57. 57. Rosenson RS, Davidson MH, Hirsh BJ, Kathiresan S, Gaudet D. Genetics and causality of triglyceride-rich lipoproteins in atherosclerotic cardiovascular disease. J Am Coll Cardiol. 2014;64(23):2525–40. pmid:25500239.
  58. 58. van Marwijk HW, van der Kooy KG, Stehouwer CD, Beekman AT, van Hout HP. Depression increases the onset of cardiovascular disease over and above other determinants in older primary care patients, a cohort study. BMC Cardiovasc Disord. 2015;15:40. pmid:25962398; PubMed Central PMCID: PMC4493948.
  59. 59. Tabas I, Williams KJ, Boren J. Subendothelial lipoprotein retention as the initiating process in atherosclerosis: update and therapeutic implications. Circulation. 2007;116(16):1832–44. Epub 2007/10/17. pmid:17938300.