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Relationship between depressive disorders and biochemical indicators in adult men and women

Abstract

Background

Depression is a psychiatric disorder with global public health concerns. Although a number of risk factors have been identified for depression, there is no clear relationship between biochemistry and depression. In this study, we assessed whether depressive disorders are significantly associated with biochemical indicators.

Methods

Our study included 17,561 adults (age ≥ 18 years) participating in the 2009-2018 National Health and Nutrition Examination Survey (NHANES). The relationship between depression and biochemical and obesity indicators was analyzed by logistic regression.

Results

As compared to the control group, men with depression showed significantly higher levels of gamma-glutamyl transferase, glucose, and triglycerides, and lower levels of albumin and total bilirubin. The depressed group had higher levels of alkaline phosphatase, bicarbonate, and sodium than the control group.

Conclusion

Several biochemical and anthropometric indices were associated with depression in this study. It would be interesting to further analyze their cause-effect relationship.

Limitations

This study is a cross-sectional study. The population is less restricted and does not exclude people with diabetes, pregnancy, etc., so it is less significant for a specific population. Dietary information was not included, as diet plays an important role in many indicators.

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Introduction

There is widespread public health concern about depression, especially in developing countries [1]. It is expected that depression will contribute the most to the burden of disease by 2030 [2]. There are several diseases associated with depression, including suicide, obesity, hypertension and stroke, cardiovascular disease, and Alzheimer’s disease [2,3,4]. The main known risk factors for depression are gender (most common in women), low education level, low income, smoking, nicotine dependence symptoms, alcohol consumption, body mass index (BMI), waist circumference, triglycerides, glucose, total cholesterol, blood urea nitrogen (BUN), genetic factors, etc. Several studies have reported a correlation between gender and body mass index and depression [5, 6]. But the correlation between depression and biochemically related indices is not clear, for example, triglyceride levels are strongly associated with depression [7], but not with depression [8]. Therefore, this study aimed to examine whether depressive disorders are significantly associated with biochemical indicators in men and women separately in a large cross-sectional study and to determine whether multiple biochemical markers can discriminate between depressed patients. It may be possible to gain a better understanding of depressive disorders in the US population based on our findings.

Methods

Study population

Those participating in the study were from the National Health and Nutrition Examination Survey (NHANES), which aimed to assess the health and nutrition status of adults and children in the United States. In this cross-sectional study, we used data from NHANES 2009-2018. Participants were selected from 49,693 surveyed residents based on selection criteria. Finally, our study analyzed 17,561 participants after the exclusion criteria: 1) lack of information on depression (Fig. 1).

Fig. 1
figure 1

Sample selection procedure

Measurements

Depression

The Patient Health Questionnaire-9 (PHQ-9) was used as an independent variable in this study to measure depression symptoms in the past 2 weeks [9]. The PHQ-9 items range from 0 (not at all) to 3 (almost every day) and the total score ranges from 0 to 27. A meta-analysis found that defining depression based on a score of 10 maximized composite sensitivity and specificity [10]. Additionally, patients with major depressive disorder rarely score below 10 [9]. The participants classified as depressed were those who gained a total score of 10 or above.

Covariates

In the demographic questionnaire, age, gender, race, education level, marital status, and family poverty income ratio, body mass index, smoking status, alcohol intake status, whether to drink too much alcohol and waist circumference were included among the demographic characteristics. Based on the questionnaire responses, alcohol intake status was classified as never drinking, former drinking, and whether to drink too much alcohol [11].

Statistical analysis

All analyses were performed using R 3.3.2(http://www.R-project.org, R base) and Free Statistics version 1.5. Data were compared using the Mann-Whitney test for continuous variables and the chi-square test for categorical variables. The chi-square test was used to compare categorical variables between the control and depressive disorder groups. Binary logistic regression was used for standardized transformed correlations (mean = 0, standard deviation = 1) between the control group and the depressive disorder group with two covariates (age and race) for the crude model. Model 1 included the covariates from the crude model plus income, education, marital status, whether alcohol was used, and female drinking status. Model 2 included the covariates of model 1, plus BMI and waist circumference. Ratio ratios are expressed as 95% confidence intervals (ci), and p values < 0.05 were considered significant.

Results

The sociodemographic characteristics of the study sample are shown in Table 1. Out of 49,693 participants in NHANES 2009-2018, 17,561 were included in the analysis. The study population included 6857 men and 7931 women in the control group, 1059 men and 1714 women in the depression group (Fig. 1). Age, race, income, education, marital status, BMI, waist circumference, and alcohol abuse were significantly associated with depression in men (Table 2). Drinking status was associated with depression significantly more in women than in men, and the rest was associated with depression significantly in men as well (Table 3).

Table 1 Gender differences in the full sample
Table 2 Sociodemographic characteristics of the male study sample
Table 3 Sociodemographic characteristics of the female study sample

Table 4 describes the relationship between biochemical indicators of depressive disorders in men. In the crude model, alanineamino transferase (ALT), alkaline phosphatase (AKP), bicarbonate, and chloride were all significantly linked to depression, yet neither model 1 nor model 2 exhibited these same associations. Albumin and total bilirubin (Tbil) levels were lower in the depressed group than in the control group in all models (p <  0.05). Gamma-glutamyl transferase (GGT), glucose, and triglycerides levels were higher in the depressed group than in the control group in all models (p <  0.05).

Table 4 Relationship between depressive disorders and biochemical indicators in men

Table 5 shows the relationship between biochemical indicators of depression in women. Female models 1, and 2 were adjusted for all confounding factors in male models 1, and 2 plus alcohol consumption status. ALT, AKP, aspartate aminotransferase (AST), creatinine, GGT, glucose, iron, lactate dehydrogenase (LDH), Tbil, triglycerides, and globulin were significantly associated with depression in the crude model, but none of these indicators were significantly associated with depression in both model 1 and model 2. Although glucose and depression were strongly linked in models 1 and 2, sodium and depression were not connected even in the crude model. In contrast to the crude model and model 1, model 2 discovered a strong link between bicarbonate levels and depression. ALP, bicarbonate, and sodium levels were higher in the depressed group than in the control group in all models (p <  0.05).

Table 5 Relationship between depressive disorders and biochemical indicators in women

The biochemical indicator variables were included in stepwise logistic regression analysis and the regression coefficients of these biochemical indicators were used to calculate logit equations for assessing depressed male and female patients. In male patients, the logarithm of odds = − 0.112 − 0.042 (Albumin) + 0.002 (GGT) + 0.046(Glucose) − 0.027 (Tbil) (Table 6). In female patients, the logarithm of odds = − 1.629 -0.018 (Albumin) + 0.002 (GGT) + 0.033(Glucose) + 0.003 (AKP) + 0.002(Creatinine) + 0.125(Triglycerides) (Table 7). Based on the results of the Hosmer-Lemeshow test (p = 0.059, Chi-square = 15.032), this computational model was evaluated in women. But this computational model wasn’t evaluated in men (p = 0.017, Chi-square = 18.631). The sensitivity, specificity, and area under the curve (AUC) of these biomarker combinations were calculated separately in depressed patients (Tables 8 and 9; Figs. 2 and 3). The combined AUC was 0.592 (95% CI: 0.57-0.61) in men, indicating that they were more effective than all single markers in identifying depressed patients.

Table 6 Logistic regression analysis of variables associated with depression in men
Table 7 Logistic regression analysis of variables associated with depression in women
Table 8 Estimated performances of all single markers and combined markers by ROC curve in men
Table 9 Estimated performances of all single markers and combined markers by ROC curve in women
Fig. 2
figure 2

ROC curves for all single and combined markers in men

Fig. 3
figure 3

ROC curves for all single and combined markers in women

Conclusion and directions for future research

In this large cross-sectional study, men in the depressed group had significantly higher levels of GGT, glucose, and triglycerides. Depressed men had lower albumin and total bilirubin levels than control men. As compared to the control group, women with depression had higher levels of AKP, Bicarbonate, and Sodium. For combined markers in men, the area under the curve was around 59.16%. The area under the curve for combined markers in women was 58.19%.

There has been a conflicting relationship between biochemical indicators and depression found in many studies to date. To provide some strong support, some large cross-sectional studies are lacking.

In the liver, kidney, and pancreas, GGT is primarily observed. Currently, GGT is the most sensitive enzyme indicator for liver diseases and is used to diagnose and monitor hepatobiliary diseases. Huang et al. reported that GGT was higher in NAFLD patients with depression than in patients without depression [12]. A positive association was also found between GGT levels and depression in men.

Kahn reported that fasting blood glucose levels were higher in depressed patients and these levels were significantly associated with depression scores [13]. Our findings are consistent with this. Loss of appetite is a common feature of depression, which can adversely affect blood glucose levels. In depressed patients, this difference may be caused by defects in glucose metabolism in brain regions such as the amygdala, prefrontal cortex, and hippocampus [14, 15].

We found that higher triglycerides was associated with depressive symptoms, and higher triglycerides subjects had higher levels of depression than normal subjects [16]. Triglycerides levels were significantly higher in the depressed group of men. In addition to lowering cholesterol and increasing triglycerides, interleukin-2 inhibits melatonin release, which reduces brain serotonin, resulting in depression and suicidal tendencies [17].

According to Pascoe MC, a low serum albumin level after stroke was associated with long-term depression symptoms in elderly Swedish patients [18]. We also found that serum albumin was negatively associated with depression.

Peng YF found correlations between BUN, fasting blood-glucose (FBG), TBil, and MDD in a Chinese Han population [8]. Bilirubin is an endogenous antioxidant, and low blood bilirubin levels are associated with seasonal depression, according to Shcherbinina MB [19]. Our results are further confirmed by this.

Recently, sodium was shown to modulate oxidative stress and inflammation, alter the autonomic nervous system, and cause innate and adaptive immune dysfunction [20]. .Many studies have shown that high sodium and chloride are directly associated with depression [21]. Women in the depressed group in our study tended to have higher levels of Sodium, which is more consistent with previous studies.

A measure of bone production, bone-specific alkaline phosphatase, was shown by Cizza G to be significantly greater in women with MDD than in controls [22]. Tissue non-specific alkaline phosphatase (TNAP), a globally expressed enzyme, is known for its activity in bone mineralization. Vitamin B6 molecules are calcified and transportable when this enzyme metabolizes phosphate compounds. Hypophosphatemia (HPP) is an uncommon metabolic disorder caused by hereditary loss-of-function mutations in the ALPL gene. In addition to decreased mineralization of bones and teeth, this systemic illness is also associated with anxiety disorders, seizures, and depression [23].

The study has some limitations. First, although though this study had a high sample size, the study group was only composed of Americans, and our findings might not be applicable to other nations due to variations in socio-demographic traits. Our findings also do not suggest a cause-and-effect link because this study was cross-sectional in nature. Finally, health status at the time of blood collection may affect biomarker results, and therefore the effect of certain disease information not obtained during the survey on biomarkers cannot be excluded. Despite these limitations of our results, the statistical results and findings in this study are robust due to the large-scale data.

In this study, a large sample size was analyzed, and a combined marker was constructed for both males and females, and the combined marker had a higher diagnostic value compared to the individual markers. Although similar studies have been conducted previously, the sample size was small, or the data collection was incomplete. This study provides a comprehensive analysis of 17,561 depressed patients from the NHANES database and provides some insight into the lack of laboratory indicators for depression diagnosis.

Availability of data and materials

The datasets generated and analyzed during the current study are available on the NHANES website: https://www.cdc.gov/nchs/nhanes/index.htm

References

  1. Wittchen HU, Jacobi F, Rehm J, Gustavsson A, Svensson M, Jönsson B, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655–79.

    Article  CAS  Google Scholar 

  2. Friedrich MJ. Depression is the leading cause of disability around the world. Jama. 2017;317(15):1517.

    Google Scholar 

  3. Ferrari AJ, Charlson FJ, Norman RE, Patten SB, Freedman G, Murray CJ, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10(11):e1001547.

    Article  Google Scholar 

  4. Tsuno N, Homma A. What is the association between depression and Alzheimer's disease? Expert Rev Neurother. 2009;9(11):1667–76.

    Article  Google Scholar 

  5. Ormonde do Carmo MB, Mendes-Ribeiro AC, Matsuura C, Pinto VL, Mury WV, Pinto NO, et al. Major depression induces oxidative stress and platelet hyperaggregability. J Psychiatr Res. 2015;61:19–24.

    Article  Google Scholar 

  6. Wei YG, Cai DB, Liu J, Liu RX, Wang SB, Tang YQ, et al. Cholesterol and triglyceride levels in first-episode patients with major depressive disorder: a meta-analysis of case-control studies. J Affect Disord. 2020;266:465–72.

    Article  CAS  Google Scholar 

  7. Wu H, Li H, Ding Y, Jiang J, Guo P, Wang C, et al. Is triglyceride associated with adult depressive symptoms? A big sample cross-sectional study from the rural areas of central China. J Affect Disord. 2020;273:8–15.

    Article  CAS  Google Scholar 

  8. Peng YF, Xiang Y, Wei YS. The significance of routine biochemical markers in patients with major depressive disorder. Sci Rep. 2016;6:34402.

    Article  CAS  Google Scholar 

  9. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    Article  CAS  Google Scholar 

  10. Levis B, Benedetti A, Thombs BD. Accuracy of Patient Health Questionnaire-9 (PHQ-9) for screening to detect major depression: individual participant data meta-analysis. Bmj. 2019;365:l1476.

    Article  Google Scholar 

  11. Taylor AL, Denniston MM, Klevens RM, McKnight-Eily LR, Jiles RB. Association of hepatitis C virus with alcohol use among U.S. adults: NHANES 2003-2010. Am J Prev Med. 2016;51(2):206–15.

    Article  Google Scholar 

  12. Huang X, Liu X, Yu Y. Depression and chronic liver diseases: are there shared underlying mechanisms? Front Mol Neurosci. 2017;10:134.

    Article  Google Scholar 

  13. Kahn LS, McIntyre RS, Rafalson L, Berdine DE, Fox CH. Fasting blood glucose and depressive mood among patients with mental illness in a medicaid managed care program. Depress Res Treat. 2011;2011:862708.

    Google Scholar 

  14. Drevets WC, Price JL, Bardgett ME, Reich T, Todd RD, Raichle ME. Glucose metabolism in the amygdala in depression: relationship to diagnostic subtype and plasma cortisol levels. Pharmacol Biochem Behav. 2002;71(3):431–47.

    Article  CAS  Google Scholar 

  15. Song X, Zhang Z, Zhang R, Wang M, Lin D, Li T, et al. Predictive markers of depression in hypertension. Medicine (Baltimore). 2018;97(32):e11768.

    Article  CAS  Google Scholar 

  16. Tyrovolas S, Lionis C, Zeimbekis A, Bountziouka V, Micheli M, Katsarou A, et al. Increased body mass and depressive symptomatology are associated with hypercholesterolemia, among elderly individuals; results from the MEDIS study. Lipids Health Dis. 2009;8:10.

    Article  Google Scholar 

  17. Ergün UG, Uguz S, Bozdemir N, Güzel R, Burgut R, Saatçi E, et al. The relationship between cholesterol levels and depression in the elderly. Int J Geriatr Psychiatry. 2004;19(3):291–6.

    Article  Google Scholar 

  18. Pascoe MC, Skoog I, Blomstrand C, Linden T. Albumin and depression in elderly stroke survivors: An observational cohort study. Psychiatry Res. 2015;230(2):658–63.

    Article  CAS  Google Scholar 

  19. Shcherbinina MB. Low blood bilirubin level: possible diagnostic and prognostic importance. Klin Med (Mosk). 2007;85(10):10–4.

    CAS  Google Scholar 

  20. Das UN. Molecular biochemical aspects of salt (sodium chloride) in inflammation and immune response with reference to hypertension and type 2 diabetes mellitus. Lipids Health Dis. 2021;20(1):83.

    Article  CAS  Google Scholar 

  21. Jayedi A, Soltani S, Abdolshahi A, Shab-Bidar S. Healthy and unhealthy dietary patterns and the risk of chronic disease: an umbrella review of meta-analyses of prospective cohort studies. Br J Nutr. 2020;124(11):1133–44.

    Article  CAS  Google Scholar 

  22. Cizza G, Mistry S, Nguyen VT, Eskandari F, Martinez P, Torvik S, et al. Do premenopausal women with major depression have low bone mineral density? A 36-month prospective study. PLoS One. 2012;7(7):e40894.

    Article  CAS  Google Scholar 

  23. Liedtke D, Hofmann C, Jakob F, Klopocki E, Graser S. Tissue-Nonspecific Alkaline Phosphatase-A Gatekeeper of Physiological Conditions in Health and a Modulator of Biological Environments in Disease. Biomolecules. 2020;10(12):1648.

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Acknowledgements

The authors acknowledge the efforts of the US National Center for Health Statistics (NCHS) for the creation of the National Health and Nutrition Examination Survey Data.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

Xinyuan Li analyzed the data. Yafei Mao, Shumin Zhu, Jin Ma, Shichao Gao, Xiuyu jin, Zishuan Wei and Yulan Geng made significant contributions to the data analysis and were the primary contributors to writing the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yulan Geng.

Ethics declarations

Ethics approval and consent to participate

The survey protocol was approved by the Research Ethics Review Board of the National Center for Health Statistics (https://www.cdc.gov/nchs/nhanes/irba98.htm). The NCHS Research Ethics Review Board reviewed and approved NHANES, and all survey participants provided written informed consent. Therefore, no further ethical approval and informed consent were required.

NHANES 2017-2018

Protocol #2018-01 (Effective beginning October 26, 2017)

Continuation of Protocol #2011-17 (Effective through October 26, 2017)

NHANES 2015-2016

Continuation of Protocol #2011-17

NHANES 2013-2014

Continuation of Protocol #2011-17

NHANES 2011-2012

Protocol #2011-17

NHANES 2009-2010

Continuation of Protocol #2005-06

Consent for publication

Since all NHANES data were de-identified, the study did not require the approval of the institutional board review committee or the informed consent of participants.

Competing interests

The authors declare that they have no conflict of interest.

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Li, X., Mao, Y., Zhu, S. et al. Relationship between depressive disorders and biochemical indicators in adult men and women. BMC Psychiatry 23, 49 (2023). https://doi.org/10.1186/s12888-023-04536-y

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Keywords

  • Depressive disorder
  • Biochemistry profile