The significance of routine biochemical markers in patients with major depressive disorder

Peng, You‐Fan; Xiang, Yang; Wei, Ye‐Sheng

doi:10.1038/srep34402

Cited by 54 publications

(56 citation statements)

References 37 publications

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“…We observed lower concentrations of total protein in patients with both diabetes mellitus and depression compared to patients with diabetes mellitus alone, the result is consistent with the research by Peng et al [27]. The above results suggested that blood biochemical parameters, including urea nitrogen, lactate dehydrogenase, alanine transaminase, uric acid, and total protein, were significantly different between depression patients and healthy controls, and that multiple biochemical parameters in combination may improve the diagnostic effectiveness of depression and the comprehensive management for depressive patients.…”

Section: Resultssupporting

confidence: 93%

“…Recent studies have shown that oxidative stress may enhance induction of HO-1 expression, which may result in insulin resistance and insufficiency [26,27]. It is clear that increased oxidative stress may lead to insulin resistance and impose an impact on insulin secretion in patients having depressive disorder [27]. One study demonstrated that reducing inflammation through nondrug treatments such as psychological interventions, physical exercises, and meditation can play a role in preventing depression [28].…”

Section: Resultsmentioning

confidence: 99%

“…in blood are not specific to depression and may be present in other psychiatric disorders such as eating disorders [47], schizophrenia [49,50], and / or bipolar disorder [51,52]. Researchers suggested that a single biomarker often lacks in sensitivity and specificity [27] and thus may not well distinguish depression from other diseases. Monitoring changes in multiple factor levels will provide a more comprehensive and accurate assessment, which can help us better understand the disease status and characteristics of specific diseases.…”

Section: Resultsmentioning

confidence: 99%

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Potential biomarkers for predicting of depression in diabetes mellitus

Song¹,

Zheng

Zhang³

et al. 2020

Preprint

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Background To identify the potential biomarkers for predicting depression in diabetes mellitus using the support vector machine technique to analyze routine biochemical tests and vital signs between two groups: subjects with both diabetes mellitus and depression, and subjects with diabetes mellitus alone. Methods Electronic medical records upon admission and biochemical tests and vital signs of 135 patients with both diabetes mellitus and depression and 178 patients with diabetes mellitus alone were identified for this retrospective study. After the covariate regression analysis on age and sex, the two groups were classified by the recursive feature elimination-based support vector machine and the biomarkers were also identified by 10-fold cross validation. Specifically, the training data, evaluation data, and testing data were split for ranking the parameters, determine the optimal parameters, and assess classification performance. Results The experimental results identified 12 predictive biomarkers with classification accuracy of 74%. The 12 biomarkers are hydroxybutyrate, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, total protein, high-density lipoprotein cholesterol, cholesterol, absolute value of the lymphocyte, blood urea nitrogen, chlorine, platelet count, and glutamyltranspeptidase. Receiver operating characteristic curve analysis was also used with area under the curve being 0.79. Conclusions Some biochemical parameters may be potential biomarkers to predict depression among the subjects with diabetes mellitus.

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Section: Resultssupporting

confidence: 93%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Potential biomarkers for predicting of depression in diabetes mellitus

Song¹,

Zheng

Zhang³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…They considered that depression symptoms were usually associated with poor appetite and poor nutrition in hemodialysis patients with depression. [ 72 ] Another study by Peng et al [ 73 ] showed that biochemical parameters, such as urea nitrogen, alanine transaminase, uric acid, lactate dehydrogenase, and total protein, were significantly different between depression patients and healthy controls, and that multiple biochemical parameters in combination may improve the diagnostic effectiveness of depression and the complete management for depressive patients.…”

Section: Discussionmentioning

confidence: 99%

Predictive markers of depression in hypertension

Song

Zhang

et al. 2018

Medicine

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Hypertension and depression, as 2 major public health issues, are closely related. For patients having hypertension, in particular, depression is a risk factor for mortality and jeopardizes their wellbeing. The aim of the study is to apply support vector machine (SVM) learning to blood tests and vital signs to classify patients having hypertension complicated by depression and patients having hypertension alone for the identification of novel markers.Data on patients having both hypertension and depression (n = 147) and patients having hypertension alone (n = 147) were obtained from electronic medical records of admissions containing the records on blood tests and vital signs. Using SVM, we distinguished patients having both hypertension and depression from gender- and age-matched patients having hypertension alone.SVM-based classification achieved 73.5% accuracy by 10-fold cross-validation between patients having both hypertension and depression and those having hypertension alone. Twelve features were selected to compose the optimal feature sets, including body temperature (T), glucose (GLU), creatine kinase (CK), albumin (ALB), hydroxybutyrate dehydrogenase (HBDH), blood urea nitrogen (BUN), uric Acid (UA), creatinine (Crea), cholesterol (TC), total protein (TP), pulse (P), and respiration (R).SVM can be used to distinguish patients having both hypertension and depression from those having hypertension alone. A significant association was identified between depression and blood tests and vital signs. This approach can be helpful for clinical diagnosis of depression, but further studies are needed to verify the role of these candidate markers for depression diagnosis.

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“…This is not to say that they are less important, rather there is a smaller pool of evidence supporting their role in depression. Furthermore, while the serum concentrations of single lipid species have been linked to MDD, it may be more useful to consider the entire lipid profile composed of several lipid species of interest, potentially alongside the more traditional peptide and metabolite biomarkers [146]. …”

Section: Other Lipids Biomarkers and Mddmentioning

confidence: 99%

The Role of Lipid Biomarkers in Major Depression

et al. 2017

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In the UK, the lifetime-documented prevalence of major depressive disorder (MDD) is currently 10%. Despite its increasing prevalence and devastating impact on quality of life, the pathophysiological mechanisms underpinning MDD remain to be fully elucidated. Current theories of neurobiological components remain incomplete and protein-centric, rendering pharmacological treatment options suboptimal. In this review, we highlight the pivotal role of lipids in intra- and inter-neuronal functioning, emphasising the potential use of lipids as biomarkers for MDD. The latter has significant implications for improving our understanding of MDD at the cellular and circuit level. There is particular focus on cholesterol (high and low density lipoprotein), omega-3, and omega-6 polyunsaturated fatty acids due to established evidence in the literature of a link between atherosclerotic disease and major depression. We argue that there is significant potential scope for the use of such peripheral biomarkers in the diagnosis, stratification and treatment of MDD.

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The significance of routine biochemical markers in patients with major depressive disorder

Cited by 54 publications

References 37 publications

Potential biomarkers for predicting of depression in diabetes mellitus

Potential biomarkers for predicting of depression in diabetes mellitus

Predictive markers of depression in hypertension

The Role of Lipid Biomarkers in Major Depression

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