Gender Differences in Developing Biomarker-Based Major Depressive Disorder Diagnostics.

The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such as gender. We first performed a cross-sectional study on the discriminative power of biomarkers for MDD by investigating gender differences in biomarker levels. Out of 28 biomarkers, 21 biomarkers were significantly different between genders. Second, a novel statistical approach was applied to investigate the effect of gender on MDD disease classification using a panel of biomarkers. Eleven biomarkers were identified in men and eight in women, three of which were active in both genders. Gender stratification caused a (non-significant) increase of Area Under Curve (AUC) for men (AUC = 0.806) and women (AUC = 0.807) compared to non-stratification (AUC = 0.739). In conclusion, we have shown that there are differences in biomarker levels between men and women which may impact accurate disease classification of MDD when gender is not taken into account.

Association of functioning and quality of life with objective and subjective measures of sleep and biological rhythms in major depressive and bipolar disorder.

OBJECTIVE: Disruptions in biological rhythms and sleep are a core aspect of mood disorders, with sleep and rhythm changes frequently occurring prior to and during mood episodes. Wrist-worn actigraphs are increasingly utilized to measure ambulatory activity rhythm and sleep patterns. METHODS: A comprehensive study using subjective and objective measures of sleep and biological rhythms was conducted in 111 participants (40 healthy volunteers [HC], 38 with major depressive disorder [MDD] and 33 with bipolar disorder [BD]). Participants completed 15-day actigraphy and first-morning urine samples to measure 6-sulfatoxymelatonin levels. Sleep and biological rhythm questionnaires were administered: Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN), Munich Chronotype Questionnaire (MCTQ), Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). Actigraph data were analyzed for sleep and daily activity rhythms, light exposure and likelihood of transitioning between rest and activity states. RESULTS: Mood groups had worse subjective sleep quality (PSQI) and biological rhythm disruption (BRIAN) and higher objective mean nighttime activity than controls. Participants with BD had longer total sleep time, higher circadian quotient and lower 6-sulfatoxymelatonin levels than HC group. The MDD group had longer sleep onset latency and higher daytime probability of transitioning from rest to activity than HCs. Mood groups displayed later mean timing of light exposure. Multiple linear regression analysis with BRIAN scores, circadian quotient, mean nighttime activity during rest and daytime probability of transitioning from activity to rest explained 43% of variance in quality-of-life scores. BRIAN scores, total sleep time and probability of transitioning from activity to rest explained 52% of variance in functioning (all p < 0.05). CONCLUSIONS: Disruption in biological rhythms is associated with poorer functioning and quality of life in bipolar and MDD. Investigating biological rhythms and sleep using actigraphy variables, urinary 6-sulfatoxymelatonin and subjective measures provide evidence of widespread sleep and circadian system disruptions in mood disorders.

Divergent Urinary Metabolic Phenotypes between Major Depressive Disorder and Bipolar Disorder Identified by a Combined GC-MS and NMR Spectroscopic Metabonomic Approach.

Bipolar disorder (BD) is a complex debilitating mental disorder that is often misdiagnosed as major depressive disorder (MDD). Therefore, a large percentage of BD subjects are incorrectly treated with antidepressants in clinical practice. To address this challenge, objective laboratory-based tests are needed to discriminate BD from MDD patients. Here, a combined gas chromatography-mass spectrometry (GC-MS)-based and nuclear magnetic resonance (NMR) spectroscopic-based metabonomic approach was performed to profile urine samples from 76 MDD and 43 BD subjects (training set) to identify the differential metabolites. Samples from 126 healthy controls were included as metabolic controls. A candidate biomarker panel was identified by further analyzing these differential metabolites. A testing set of, 50 MDD and 28 BD subjects was then used to independently validate the diagnostic efficacy of the identified panel using an area under the receiver operating characteristic curve (AUC). A total of 20 differential metabolites responsible for the discrimination between MDD and BD subjects were identified. A panel consisting of six candidate urinary metabolite biomarkers (propionate, formate, (R*,S*)2,3-dihydroxybutanoic acid, 2,4-dihydroxypyrimidine, phenylalanine, and ß-alanine) was identified. This panel could distinguish BD from MDD subjects with an AUC of 0.913 and 0.896 in the training and testing sets, respectively. These results reveal divergent urinary metabolic phenotypes between MDD and BD. The identified urinary biomarkers can aid in the future development of an objective laboratory-based diagnostic test for distinguishing BD from MDD patients.

Identification and validation of urinary metabolite biomarkers for major depressive disorder.

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy-based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers-malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine-was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.

Arginine and asymmetric dimethylarginine in pregnant women with major depression.

OBJECTIVE: Depression has been associated with vascular dysfunction, which may be of particular relevance in pregnancy. Asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), and L-arginine play a critical role in vascular function. The objective of this study was to investigate differences in ADMA, SDMA, and L-arginine among pregnant women with major depression compared with pregnant women without depression. METHODS: A case-control study was conducted in 21 depressed pregnant women and 42 matched controls. Maternal plasma ADMA, SDMA, and L-arginine were quantified, as well as C-reactive protein and urine excretion of ADMA, SDMA, L-arginine, and Arginase I. RESULTS: Plasma L-arginine and ADMA levels were significantly lower in the first trimester in women with depression (mean [standard deviation = 37.0 [9.2] and 0.298 [0.06] µM, respectively) compared with matched controls (42.1 [11.4] and 0.336 [0.08] µM, p = .004 and p = .002, respectively) and across pregnancies (p < .001 both). Depressed pregnant women had higher levels of plasma C-reactive protein (7.5 [3.7] versus 5.1 [4.0] µg/ml, p = .027), but no differences in urine excretion of ADMA, SDMA, or L-arginine, or plasma levels of Arginase I (p > .10). CONCLUSIONS: Pregnant women with depression show lower plasma levels of L-arginine and ADMA. These differences are not explained by urinary excretion or Arginase I levels. The mechanism responsible for the observed differences in depressed pregnant women requires further research.

Association between serum and urinary environmental metal levels and major depressive disorder: a study based on logistic regression and quantile regression.

Background: Major depressive disorder (MDD) is a prevalent mental disorder globally. Increasing evidence suggests that Environmental Metal (EM) play a crucial role in MDD. Therefore, this study investigated the roles of barium (Ba), cesium (Cs), nickel (Ni), manganese (Mn), lead (Pb), mercury (Hg), cadmium (Cd), and tin (Sn) in the etiology of MDD. Methods: The study included 72 MDD patients and 75 healthy controls (HCs) from the Second People's Hospital of Zhumadian, China. Inductively coupled plasma mass spectrometer (ICP-MS) measured the metal levels in serum and urine samples from both groups. Results: Significant differences in serum and urine levels of EMs were observed between MDD patients and HCs. After adjusting for age, gender, and BMI, logistic regression and quantile regression models revealed significant associations between EMs and MDD. In serum samples, higher Sn levels (OR = 1.22, p = 0.044) increased MDD risk, whereas higher Cs levels (OR = 0.02, p < 0.001), Cd (OR = 0.06, p = 0.047), and Mn (OR = 0.54, p = 0.016) decreased MDD risk. In urine samples, higher Ba levels (OR = 0.94, p = 0.015), Ni (OR = 0.87, p = 0.0024), Sn (OR = 1.62, p < 0.001), and Mn (OR = 0.77, p = 0.037) were significantly associated with MDD. Sn significantly positively predicted HAMD-24 scores at the 0.50 and 0.75 quantiles (ß = 0.96, p = 0.018; ß = 1.25, p = 0.008) as did Pb (ß = 5.15, p = 0.001; ß = 4.19, p = 0.004). Ba positively predicted depressive symptoms across all quantiles (all p < 0.05). Hg positively predicted HAMD-24 scores at the 0.50 quantile (ß = 9.20, p = 0.050). Conclusion: These findings underscore EMs' importance in depression, aiding in targeted interventions for varying degrees of depression and necessitating future studies to clarify causality and mechanisms.

Evaluation of metabolomics-based urinary biomarker models for recognizing major depression disorder and bipolar disorder.

BACKGROUND: Major depressive disorder (MDD) and bipolar disorder (BD) are psychiatric disorders with overlapping symptoms, leading to high rates of misdiagnosis due to the lack of biomarkers for differentiation. This study aimed to identify metabolic biomarkers in urine samples for diagnosing MDD and BD, as well as to establish unbiased differential diagnostic models. METHODS: We utilized a metabolomics approach employing ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) to analyze the metabolic profiles of urine samples from individuals with MDD (n = 50), BD (n = 12), and healthy controls (n = 50). The identification of urine metabolites was verified using MS data analysis tools and online metabolite databases. RESULTS: Two diagnostic panels consisting of a combination of metabolites and clinical indicators were identified-one for MDD and another for BD. The discriminative capacity of these panels was assessed using the area under the receiver operating characteristic (ROC) curve, yielding an area under the curve (AUC) of 0.9084 for MDD and an AUC value of 0.9017 for BD. CONCLUSIONS: High-resolution mass spectrometry-based assays show promise in identifying urinary biomarkers for depressive disorders. The combination of urine metabolites and clinical indicators is effective in differentiating healthy controls from individuals with MDD and BD. The metabolic pathway indicating oxidative stress is seen to significantly contribute to depressive disorders.

Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study.

RATIONALE: The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied. OBJECTIVES: We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures. METHODS: Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel. RESULTS: In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD. CONCLUSIONS: Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.

A novel urinary metabolite signature for diagnosing major depressive disorder.

Major depressive disorder (MDD) is a prevalent and debilitating mental disorder. Yet, there are no objective biomarkers available to support diagnostic laboratory testing for this disease. Here, gas chromatography-mass spectrometry was applied to urine metabolic profiling of 126 MDD and 134 control subjects. Orthogonal partial least-squares discriminant analysis (OPLS-DA) was used to identify the differential metabolites in MDD subjects relative to healthy controls. The OPLS-DA analysis of data from training samples (82 first-episode, drug-naïve MDD subjects and 82 well-matched healthy controls) showed that the depressed group was significantly distinguishable from the control group. Totally, 23 differential urinary metabolites responsible for the discrimination between the two groups were identified. Postanalysis, 6 of the 23 metabolites (sorbitol, uric acid, azelaic acid, quinolinic acid, hippuric acid, and tyrosine) were defined as candidate diagnostic biomarkers for MDD. Receiver operating characteristic analysis of combined levels of these six biomarkers yielded an area under the receiver operating characteristic curve (AUC) of 0.905 in distinguishing training samples; this simplified metabolite signature classified blinded test samples (44 MDD subjects and 52 healthy controls) with an AUC of 0.837. Furthermore, a composite panel by the addition of previously identified urine biomarker (N-methylnicotinamide) to this biomarker panel achieved a more satisfactory accuracy, yielding an AUC of 0.909 in the training samples and 0.917 in the test samples. Taken together, these results suggest this composite urinary metabolite signature should facilitate development of a urine-based diagnostic test for MDD.

Urine catecholamine levels are not influenced by electroconvulsive therapy in depression or schizophrenia over the long term.

AIM: Change in catecholamine seems to be associated with not only effects of electroconvulsive therapy (ECT), but also adverse events associated with ECT. Our aim in this study was to investigate whether or not ECT influences the concentration of catecholamine over the long term. Patients with a major depressive episode or schizophrenia, diagnosed according to DSM-IV criteria, who were newly admitted to Juntendo University Hospital to receive ECT, were recruited. METHODS: Urine was collected during the 24 h before the first ECT treatment, during the 24 h after the first ECT treatment, during the 24 h after the final ECT treatment and during the 24 h 1 week after the final ECT treatment. Heart rate, the Hamilton Rating Scale for Depression and the Positive and Negative Syndrome Scale were assessed before and after ECT. RESULTS: Twenty-four patients were included in the final sample, which consisted of 14 patients with major depressive episodes and 10 patients with schizophrenia. Abnormal electrocardiograms were indicated in four patients with depression during the ECT operation but all recovered naturally. There were no significant differences in the levels of dopamine, adrenaline or noradrenaline the day before the first ECT, a day after the first ECT, a day after the final ECT and a week after the final ECT. CONCLUSION: These results suggest that ECT does not alter urine catecholamine levels after ECT over the long term. Further studies will be required to confirm these findings in a larger sample of patients.

Cortisol metabolism in depressed patients and healthy controls.

BACKGROUND: Chronic stress as well as major depressive disorders are associated with hypercortisolemia and impaired hypothalamic-pituitary-adrenocortical axis functioning. The aim of this study was to determine whether in major depression changes in the activity patterns of local modulators of glucocorticoid action might contribute to an increase in cortisol bioavailability and if they change during antidepressant treatment and clinical response. METHODS: Concentrations of urinary total cortisol (UFF), urinary total cortisone (UFE), tetrahydrocortisone (THE), tetrahydrocortisol (THF) and allo-THF (5alpha-THF) were measured in 10-hour nocturnal urine samples of 19 depressed patients and 15 healthy controls. The activity of 11beta-hydroxysteroid dehydrogenases (11beta-HSD) as well as 5alpha- and 5beta-reductases was assessed by calculating the ratios of glucocorticoid metabolites. Patients were treated for 28 days with either mirtazapine or venlafaxine. Enzyme activity was observed during the course of treatment and compared to healthy controls. Responders to treatment were selected for this analysis. RESULTS: Depressed patients showed reduced 5alpha-reductase activity manifested as a significantly lower amount of 5alpha-THF (102.8 +/- 167.2 vs. 194.6 +/- 165.8 microg, p = 0.019). The increase in the UFF/UFE ratio (0.73 +/- 0.32 vs. 0.29 +/- 0.13, p < 0.0001) indicates reduced activity of renal 11beta-hydroxysteroid dehydrogenase type 2 (11beta-HSD2). During pharmacological treatment, 5alpha-reductase activity in patients returned to the level of the control group, while the decrease in 11beta-HSD2 activity persisted until day 28. CONCLUSIONS: Our results show changes in activity of intracellular modulators of steroid action in major depressive disorders, particularly a reduced activity of the intracellular cortisol-deactivating enzymes 5alpha-reductase and 11beta-HSD2. These changes suggest an increase in cortisol bioavailability within tissues.

Age-specific urinary metabolite signatures and functions in patients with major depressive disorder.

Major depressive disorder (MDD) patients in different age ranges might have different urinary metabolic phenotypes, because age could significantly affect the physiological and psychological status of person. Therefore, it was very important to take age into consideration when studying MDD. Here, a dual platform metabolomic approach was performed to profile urine samples from young and middle-aged MDD patients. In total, 18 and 15 differential metabolites that separately discriminated young and middle-aged MDD patients, respectively, from their respective HC were identified. Only ten metabolites were significantly disturbed in both young and middle-aged MDD patients. Meanwhile, two different biomarker panels for diagnosing young and middle-aged MDD patients, respectively, were identified. Additionally, the TCA cycle was significantly affected in both young and middle-aged MDD patients, but the Glyoxylate and dicarboxylate metabolism and phenylalanine metabolism were only significantly affected in young and middle-aged MDD patients, respectively. Our results would be helpful for developing age-specific diagnostic method for MDD and further investigating the pathogenesis of this disease.

Major depressive disorder is associated with changes in a cluster of serum and urine biomarkers.

Major Depressive Disorder (MDD) is a heterogeneous disorder with a considerable symptomatic overlap with other psychiatric and somatic disorders. This study aims at providing evidence for association of a set of serum and urine biomarkers with MDD. We analyzed urine and serum samples of 40 MDD patients and 47 age- and sex-matched controls using 40 potential MDD biomarkers (21 serum biomarkers and 19 urine biomarkers). All participants were of Caucasian origin. We developed an algorithm to combine the heterogeneity at biomarker level. This method enabled the identification of correlating biomarkers based on differences in variation and distribution between groups, combined the outcome of the selected biomarkers, and calculated depression probability scores (the "bio depression score"). Phenotype permutation analysis showed a significant discrimination between MDD and euthymic (control) subjects for biomarkers in urine (P < .001), in serum (P = .02) and in the combined serum plus urine result (P < .001). Based on this algorithm, a combination of 8 urine biomarkers and 9 serum biomarkers were identified to correlate with MDD, enabling an area under the curve (AUC) of 0.955 in a Receiver Operating Characteristic (ROC) analysis. Selection of either urine biomarkers or serum biomarkers resulted in AUC values of 0.907 and 0.853, respectively. Internal cross-validation (5-fold) confirmed the association of this set of biomarkers with MDD.

Differentiating Medicated Patients Suffering from Major Depressive Disorder from Healthy Controls by Spot Urine Measurement of Monoamines and Steroid Hormones.

Introduction: Major Depressive Disorder (MDD) is a common psychiatric disorder. Currently, there is no objective, cost-effective and non-invasive method to measure biological markers related to the pathogenesis of MDD. Previous studies primarily focused on urinary metabolite markers which are not proximal to the pathogenesis of MDD. Herein, we compare urinary monoamines, steroid hormones and the derived ratios amongst MDD when compared to healthy controls. Methods: Morning urine samples of medicated patients suffering from MDD (n = 47) and healthy controls (n = 41) were collected. Enzyme-linked immunosorbent assay (ELISA) was performed to measure five biomarkers: cortisol, dopamine, noradrenaline, serotonin and sulphate derivative of dehydroepiandrosterone (DHEAS). The mean urinary levels and derived ratios of monoamines and steroid hormones were compared between patients and controls to identify potential biomarkers. The receiver operative characteristic curve (ROC) analysis was conducted to evaluate the diagnostic performance of potential biomarkers. Results: Medicated patients with MDD showed significantly higher spot urine ratio of DHEAS/serotonin (1.56 vs. 1.19, p = 0.004) and lower ratio of serotonin/dopamine (599.71 vs. 888.60, p = 0.008) than healthy controls. A spot urine serotonin/dopamine ratio cut-off of >667.38 had a sensitivity of 73.2% and specificity of 51.1%. Conclusions: Our results suggest that spot urine serotonin/dopamine ratio can be used as an objective diagnostic method for adults with MDD.

Uric acid in major depressive and anxiety disorders.

BACKGROUND: Uric acid has neuroprotective effects, owing to its antioxidant properties. Lowered antioxidant capacity, causing increased oxidative stress, may be involved in affective disorders and might be altered by antidepressants. This study investigated the association of plasma uric acid, the greatest contributor to blood antioxidant capacity, with major depressive disorder (MDD) and anxiety disorders. METHODS: Data were from the Netherlands Study of Depression and Anxiety including patients with current (N = 1648), remitted (N = 609) MDD and/or anxiety disorders (of which N = 710 antidepressant users) and 618 controls. Diagnoses were established with the Composite International Diagnostic Interview. Symptom severity was assessed with the Inventory of Depressive Symptoms-Self Report, Beck Anxiety Inventory and Fear Questionnaire. Uric acid was measured in plasma. Analyses were adjusted for sociodemographic, health and lifestyle variables. RESULTS: Plasma uric acid adjusted mean levels were lower in current MDD and/or anxiety disorder(s) (289µmol/l) compared to remitted disorders (298µmol/l, p < .001) and controls (299µmol/l, p < .001; Cohen's d .10). This finding was independent of antidepressant use. Depressive (ß-.05, p = .0012), anxiety (ß-.04, p = .009) and phobic (ß-.03, p = .036) symptom severity, and symptom duration (ß-.04, p = .009) were negatively associated with uric acid. LIMITATIONS: Limitations include the lack of data on dietary intake which could be a potential confounding factor. From these cross-sectional findings, the association between uric acid and psychopathology cannot be inferred to be causal. CONCLUSION: This large scale study finds plasma uric acid levels are lower in current, but not remitted, MDD and/or anxiety disorders, according to a dose-response gradient. This suggests the involvement of decreased antioxidant status in affective disorders, and points to their potential as an avenue for treatment.

Performance of the biological rhythms interview for assessment in neuropsychiatry: An item response theory and actigraphy analysis.

BACKGROUND: Biological rhythm disturbances are widely associated with the pathophysiology of mood disorders. The Biological Rhythms Interview for Assessment in Neuropsychiatry (BRIAN) is a self-report that indexes rhythm disturbance in sleep, activity, social and eating patterns. The aim of this study was to perform an Item Response Theory (IRT) analysis of the BRIAN and investigate its associations with objective sleep and rhythm disturbance measures. METHODS: 103 subjects (31 bipolar, 32 major depression and 40 healthy volunteers) wore an actiwatch for fifteen days, and completed a first morning urine sample and the BRIAN on day 15. IRT analysis assessed individual BRIAN items and their relationship to total score. Individual actiwatch records were processed to produce a sequence of transitions between rest/activity, and a likelihood of transitioning between states was calculated to investigate sleep-wake dynamics. Cosinor analysis produced daily activity rhythms (DARs). Spearman correlations were used to assess the association between sleep/DAR variables and the BRIAN. RESULTS: IRT analyses showed that 11 of 18 BRIAN items displayed a high level of discrimination between item options across a range of BRIAN total scores. Total BRIAN score correlated with wake after sleep onset, total activity count during sleep, and urinary 6-sulphatoxymelatonin. BRIAN Activity domain correlated with the daytime transition probability from rest to activity. LIMITATIONS: The sample size may have been underpowered for the graded-response model employed in IRT. The study lacked an objective comparison for BRIAN eating and social domain. CONCLUSION: The present study reveals the BRIAN displays promising external validity compared to objective parameters of circadian rhythmicity.

Metabolomics of Major Depressive Disorder and Bipolar Disorder: Overview and Future Perspective.

Major depressive disorder (MDD) and bipolar disorder (BD) are the most common mood disorders. They are etiologically related, but clinically distinct psychiatric illnesses. Their shared clinical features result in high rates of misdiagnosis due to a lack of biomarkers that allow their differentiation. BD is more frequently misdiagnosed as MDD because of overlapping symptomology, often later onset of mania, and frequent occurrence of depressive episodes in patients with BD. Misdiagnosis is also increased when patients with BD present symptoms indicative of a clinically significant depressive episode, but are premorbid for manic symptoms, or previous manic states not recognized. Therefore, the development of specific biomarkers for these disorders would be invaluable for establishing the correct diagnosis and treatment of MDD and BD. This chapter presents an overview and future perspective of the identification of biomarkers for mood disorders using metabolomics.

Association of a serotonin transporter polymorphism (5-HTTLPR) with depression, perceived stress, and norepinephrine in patients with coronary disease: the Heart and Soul Study.

OBJECTIVE: The short allele of a functional polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR) has been shown to interact with stressful life events to predict depression in otherwise healthy individuals. Whether the short allele increases risk for depression associated with the stress of a chronic illness has not been established. METHOD: In a cross-sectional genetic association study, the authors examined the association of 5-HTTLPR with current depression (measured by the Computerized Diagnostic Interview Schedule), perceived stress (measured by the Perceived Stress Scale), and 24-hour urinary norepinephrine excretion in 557 outpatients with chronic coronary disease. RESULTS: Among individuals carrying an s allele, 25% (97 of 383) had current depression, compared with 17% (29 of 174) of l/l homozygotes. The unadjusted odds ratio was 1.6, with a 95% confidence interval (CI) of 1.0-2.6; the age- and gender-adjusted odds ratio was also 1.6 (95% CI=1.0-2.5). Participants carrying an s allele had a higher mean score for perceived stress than l/l homozygotes (5.4 versus 4.7) and a higher rate of moderate or high perceived stress (adjusted odds ratio=1.6, 95% CI=1.1-2.3). Mean 24-hour norepinephrine excretion was higher in s allele carriers (55.6 versus 50.2 mg/day), who were more likely to have norepinephrine values in the highest quartile (adjusted odds ratio=1.7, 95% CI=1.0-3.0). CONCLUSIONS: Among patients with chronic illness, carriers of the s allele of 5-HTTLPR are more vulnerable to depression, perceived stress, and high norepinephrine secretion. These factors may contribute to worse cardiovascular outcomes in these patients.

Differential urinary metabolites related with the severity of major depressive disorder.

Major depressive disorder (MDD) is a common mental disorder that affects a person's general health. However, there is still no objective laboratory test for diagnosing MDD. Here, an integrated analysis of data from our previous studies was performed to identify the differential metabolites in the urine of moderate and severe MDD patients. A dual platform approach (NMR spectroscopy and GC-MS) was used. Consequently, 14 and 22 differential metabolites responsible for separating moderate and severe MDD patients, respectively, from their respective healthy controls (HCs) were identified. Meanwhile, the moderate MDD-specific panel (N-Methylnicotinamide, Acetone, Choline, Citrate, vanillic acid and azelaic acid) and severe MDD-specific panel (indoxyl sulphate, Taurine, Citrate, 3-hydroxyphenylacetic acid, palmitic acid and Lactate) could discriminate moderate and severe MDD patients, respectively, from their respective HCs with high accuracy. Moreover, the differential metabolites in severe MDD were significantly involved in three metabolic pathways and some biofunctions. These results showed that there were divergent urinary metabolic phenotypes in moderate and severe MDD patients, and the identified potential urinary biomarkers might be useful for future developing objective diagnostic tests for MDD diagnosis. Our results could also be helpful for researchers to study the pathogenesis of MDD.