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.

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.

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.

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.

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.

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.

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.

Chronic defensiveness and neuroendocrine dysfunction reflect a novel cardiac troponin T cut point: The SABPA study.

BACKGROUND: Sympatho-adrenal responses are activated as an innate defense coping (DefS) mechanism during emotional stress. Whether these sympatho-adrenal responses drive cardiac troponin T (cTnT) increases are unknown. Therefore, associations between cTnT and sympatho-adrenal responses were assessed. METHODS: A prospective bi-ethnic cohort, excluding atrial fibrillation, myocardial infarction and stroke cases, was followed for 3 years (N=342; 45.6±9.0years). We obtained serum high-sensitive cTnT and exposure measures [Coping-Strategy-Indicator, depression/Patient-Health-Questionnarie-9, 24h BP, 24h heart-rate-variability (HRV) and 24h urinary catecholamines]. RESULTS: Blacks showed moderate depression (45% vs. 16%) and 24h hypertension (67% vs. 42%) prevalence compared to Whites. A receiver-operating-characteristics cTnT cut-point 4.2ng/L predicting hypertension in Blacks was used as binary outcome measure in relation to exposure measures [AUC 0.68 (95% CI 0.60-0.76); sensitivity/specificity 63/70%; P≤0.001]. Bi-ethnic cTnT-incidence was similar (Blacks=27%, Whites=25%) with cTnT-recovery better in Blacks (9%) compared to Whites (5%), P=0.001. In cross-sectional analyses, elevated cTnT was related to DefS [OR 1.08 (95% CI 0.99-1.16); P=0.06]; 24h BP [OR 1.03-1.04 (95% CI 1.01-1.08); P≤0.02] and depressed HRV [OR 2.19 (95% CI 1.09-4.41); P=0.03] in Blacks, but not in Whites. At 3year follow-up, elevated cTnT was related to attenuated urine norepinephrine:creatinine ratio in Blacks [OR 1.46 (95% CI 1.01-2.10); P=0.04]. In Whites, a cut point of 5.6ng/L cTnT predicting hypertension was not associated with exposure measures. CONCLUSION: Central neural control systems exemplified a brain-heart stress pathway. Desensitization of sympatho-adrenal responses occurred with initial neural- (HRV) followed by neuroendocrine dysfunction (norepinephrine:creatinine) in relation to elevated cTnT. Chronic defensiveness may thus drive the desensitization or physiological depression, reflecting ischemic heart disease risk at a novel 4.2ng/L cTnT cut-point in Blacks.

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.

Identification of sex-specific urinary biomarkers for major depressive disorder by combined application of NMR- and GC-MS-based metabonomics.

Women are more vulnerable to major depressive disorder (MDD) than men. However, molecular biomarkers of sex differences are limited. Here we combined gas chromatography-mass spectrometry (GC-MS)- and nuclear magnetic resonance (NMR)-based metabonomics to investigate sex differences of urinary metabolite markers in MDD, and further explore their potential of diagnosing MDD. Consequently, the metabolite signatures of women and men MDD subjects were significantly different from of that in their respective healthy controls (HCs). Twenty seven women and 36 men related differentially expressed metabolites were identified in MDD. Fourteen metabolites were changed in both women and men MDD subjects. Significantly, the women-specific (m-Hydroxyphenylacetate, malonate, glycolate, hypoxanthine, isobutyrate and azelaic acid) and men-specific (tyrosine, N-acetyl-d-glucosamine, N-methylnicotinamide, indoxyl sulfate, citrate and succinate) marker panels were further identified, which could differentiate men and women MDD patients from their respective HCs with higher accuracy than previously reported sex-nonspecific marker panels. Our findings demonstrate that men and women MDD patients have distinct metabonomic signatures and sex-specific biomarkers have promising values in diagnosing MDD.

Identification and validation of argininosuccinate synthase as a candidate urinary biomarker for major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a debilitating psychiatric mood disorder. However, no objective laboratory-based test is yet available to aid in the diagnosis of this disorder. METHODS: In order to identify urinary protein biomarker candidates for MDD, the differential proteomic analysis of urine samples from first-episode drug-naïve MDD subjects and healthy controls (HC) was carried out by using two-dimensional gel electrophoresis separation followed by MALDI-TOF/TOF-MS/MS identification. Then, the differential expression levels of some candidate proteins were further validated by immunoblot analysis. RESULTS: Through mass spectrometry and database searching, a total of 27 differential proteins were identified, primarily including enzymes, plasma proteins, serpins, and adhesion molecules. Five proteins were selected for subsequent validation by Western blotting. One arginine recycling enzyme - argininosuccinate synthase (ASS1) - was further confirmed to be significantly downregulated in the urine of 30 depressed subjects while remaining unchanged in the plasma. Importantly, receiver-operator curve analyses revealed that ASS1 displayed strong efficacy in distinguishing MDD subjects from HC. CONCLUSION: The present study provides a range of urinary protein biomarker candidates for MDD, and further demonstrates that ASS1 has a potential for clinical diagnosis of this disorder.

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.

A simple LC-MS/MS method for the determination of cortisol, cortisone and tetrahydro-metabolites in human urine: assay development, validation and application in depression patients.

Chronic stress as well as major depressive disorders is associated with cortisol metabolism. Two enzymes modulate cortisol (F) and cortisone (E) interconversion: 11ß-hydroxysteroid dehydrogenase type 1 and type 2 (11ß-HSD1 and 11ß-HSD2). Furthermore, F and E were inactivated by 5α and 5ß reductases to their tetrahydro-metabolites: tetrahydrocortisol (THF), allo-tetrahydrocortisol (5α-THF) and tetrahydrocortisone (THE). To better understand depression a LC-MS/MS method for simultaneous determination of F, E THF, 5α-THF and THE in human urine has been developed and validated. The quantification range was 0.1-160 ng mL(-1) for F and E, and 0.2-160 ng mL(-1) for the tetrahydro-metabolites, with >86.1% recovery for all analytes. The nocturnal urine concentrations of F, E and tetrahydro-metabolites in 12 apparently healthy male adult volunteers and 12 drug-free male patients (age range, 20-50 years) with a diagnosis of depression were analyzed. A series of significant changes in glucocorticoid metabolism can be detected: F/E ratios and (THF+5α-THF)/THE ratios as well as F and THF concentrations were significantly higher in depression patients than in healthy subjects (p<0.05); 5α-THF/F ratios, 5α-THF/THF ratios as well as 5α-THF concentrations were significantly lower in depression patients (p<0.05). The results pointed to the decreased 11ß-HSD2 activity and a dysfunction in the 5α-reductase pathway in depressed patients. This method allows the assessment of 11ß-HSD1/2 and 5α/ß-reductase activities in a single analytical run providing an innovative tool to explain the potential etiology of depression.

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.

The McGill Geriatric Lithium-Induced Diabetes Insipidus Clinical Study (McGLIDICS).

OBJECTIVE: Despite being a common and potentially serious condition, nephrogenic diabetes insipidus (NDI) remains poorly understood in older lithium users. Our main objective was to compare the prevalence of NDI symptoms and decreased urine osmolality ([UOsm] < 300 milli-Osmoles [mOsm/kg]) among geriatric and adult lithium users. We also assessed NDI symptoms, serum sodium (Na+), and urine specific gravity (USG) as possible surrogate measures of decreased UOsm, and ascertained whether potential etiologic factors independently correlated with decreased UOsm. METHOD: This was a cross-sectional study of 100 consecutive outpatients treated with lithium from 6 tertiary care clinics, of which 45 were geriatric (aged 65 years and older) and 55 adult (aged 18 to 64 years). Patients completed a symptom questionnaire and underwent laboratory tests, including UOsm, serum Na+, and USG. RESULTS: Geriatric and adult lithium users had similar rates of decreased UOsm (12.5%, compared with 17.9%, P = 0.74), but geriatric patients reported less symptoms (P < 0.05). Although UOsm did not correlate with symptoms or current serum Na+, USG of less than 1.010 was suggestive of UOsm of less than 300 mOsm/kg. Age, lithium duration, and serum lithium level were independently associated with UOsm. CONCLUSIONS: The prevalence of decreased UOsm is similar in geriatric and adult lithium users, but older patients are less likely to report urinary and thirst symptoms. Although subjective symptoms do not correlate with UOsm, USG may be a cost-efficient clinical surrogate measure for UOsm. We suggest clinicians increase their vigilance for decreased UOsm, especially in lithium users with advanced age, longer duration of lithium exposure, and higher lithium levels. This may potentially prevent lithium intoxication, falls, hypernatremic events, and renal dysfunction.

Urinary peptidomics identifies potential biomarkers for major depressive disorder.

Major depressive disorder (MDD) is a debilitating psychiatric illness with no available objective laboratory-based diagnostic test. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based peptidomics was applied to identify potential urinary diagnostic biomarkers for MDD. A training set of 42 first-episode drug-naive MDD patients and 28 age- and gender-matched healthy controls (HC) was used to develop a peptide diagnostic pattern. Then, the diagnostic efficacy of this pattern was assessed in an independent blinded test set consisting of 24 MDD patients and 13 age- and gender-matched HC. A combination of five potential biomarkers was identified, yielding a sensitivity of 91.7% and specificity of 84.6% in the test set. Moreover, the protein precursors of four of the five peptides were identified by tandem mass spectrometric analysis: serum albumin, apolipoprotein A-I, protein AMBP, and basement membrane-specific heparan sulfate proteoglycan core protein. Taken together, the peptide pattern may be valuable for establishing an objective laboratory-based diagnostic test for 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.

Peripheral biomarkers in animal models of major depressive disorder.

Investigations of preclinical biomarkers for major depressive disorder (MDD) encompass the quantification of proteins, peptides, mRNAs, or small molecules in blood or urine of animal models. Most studies aim at characterising the animal model by including the assessment of analytes or hormones affected in depressive patients. The ultimate objective is to validate the model to better understand the neurobiological basis of MDD. Stress hormones or inflammation-related analytes associated with MDD are frequently measured. In contrast, other investigators evaluate peripheral analytes in preclinical models to translate the results in clinical settings afterwards. Large-scale, hypothesis-free studies are performed in MDD models to identify candidate biomarkers. Other studies wish to propose new targets for drug discovery. Animal models endowed with predictive validity are investigated, and the assessment of peripheral analytes, such as stress hormones or immune molecules, is comprised to increase the confidence in the target. Finally, since the mechanism of action of antidepressants is incompletely understood, studies investigating molecular alterations associated with antidepressant treatment may include peripheral analyte levels. In conclusion, preclinical biomarker studies aid the identification of new candidate analytes to be tested in clinical trials. They also increase our understanding of MDD pathophysiology and help to identify new pharmacological targets.