Towards a classification of biomarkers of neuropsychiatric disease: from encompass to compass

There is currently considerable imprecision in the nosology of biomarkers used in the study of neuropsychiatric disease. The neuropsychiatric field lags behind others such as oncology, wherein, rather than using 'biomarker' as a blanket term for a diverse range of clinical phenomena, biomarkers have been actively classified into separate categories, including prognostic and predictive tests. A similar taxonomy is proposed for neuropsychiatric diseases in which the core biology remains relatively unknown. This paper divides potential biomarkers into those of (1) risk, (2) diagnosis/trait, (3) state or acuity, (4) stage, (5) treatment response and (6) prognosis, and provides illustrative exemplars. Of course, biomarkers rely on available technology and, as we learn more about the neurobiological correlates of neuropsychiatric disorders, we will realize that the classification of biomarkers across these six categories can change, and some markers may fit into more than one category.Molecular Psychiatry advance online publication, 28 October 2014; doi:10.1038/mp.2014.139.

Adipokines as emerging depression biomarkers: a systematic review and meta-analysis

Adiponectin, leptin and resistin may play a role in the pathophysiology of major depressive disorder (MDD). However, differences in peripheral levels of these hormones are inconsistent across diagnostic and intervention studies. Therefore, we performed meta-analyses of diagnostic studies (i.e., MDD subjects versus healthy controls) and intervention investigations (i.e., pre-vs. post-antidepressant treatment) in MDD. Adiponectin (N=1278; Hedge's g=-0.35; P=0.16) and leptin (N=893; Hedge's g=-0.018; P=0.93) did not differ across diagnostic studies. Meta-regression analyses revealed that gender and depression severity explained the heterogeneity observed in adiponectin diagnostic studies, while BMI and the difference in BMI between MDD individuals and controls explained the heterogeneity of leptin diagnostic studies. Subgroup analyses revealed that adiponectin peripheral levels were significantly lower in MDD participants compared to controls when assayed with RIA, but not ELISA. Leptin levels were significantly higher in individuals with mild/moderate depression versus controls. Resistin serum levels were lower in MDD individuals compared to healthy controls (N=298; Hedge's g=-0.25; P=0.03). Leptin serum levels did not change after antidepressant treatment. However, heterogeneity was significant and sample size was low (N=108); consequently meta-regression analysis could not be performed. Intervention meta-analyses could not be performed for adiponectin and resistin (i.e., few studies met inclusion criteria). In conclusion, this systematic review and meta-analysis underscored that relevant moderators/confounders (e.g., BMI, depression severity and type of assay) should be controlled for when considering the role of leptin and adiponectin as putative MDD diagnostic biomarkers.

Biomarkers and clinical staging in psychiatry

Personalized medicine is rapidly becoming a reality in today's physical medicine. However, as yet this is largely an aspirational goal in psychiatry, despite significant advances in our understanding of the biochemical, genetic and neurobiological processes underlying major mental disorders. Preventive medicine relies on the availability of predictive tools; in psychiatry we still largely lack these. Furthermore, our current diagnostic systems, with their focus on well-established, largely chronic illness, do not support a pre-emptive, let alone a preventive, approach, since it is during the early stages of a disorder that interventions have the potential to offer the greatest benefit. Here, we present a clinical staging model for severe mental disorders and discuss examples of biological markers that have already undergone some systematic evaluation and that could be integrated into such a framework. The advantage of this model is that it explicitly considers the evolution of psychopathology during the development of a mental illness and emphasizes that progression of illness is by no means inevitable, but can be altered by providing appropriate interventions that target individual modifiable risk and protective factors. The specific goals of therapeutic intervention are therefore broadened to include the prevention of illness onset or progression, and to minimize the risk of harm associated with more complex treatment regimens. The staging model also facilitates the integration of new data on the biological, social and environmental factors that influence mental illness into our clinical and diagnostic infrastructure, which will provide a major step forward in the development of a truly pre-emptive psychiatry.

Validation of DNA methylation biomarkers for diagnosis of acute lymphoblastic leukemia

DNA methylation biomarkers capable of diagnosis and subtyping have been found for many cancers. Fifteen such markers have previously been identified for pediatric acute lymphoblastic leukemia (ALL). Validation of these markers is necessary to assess their clinical utility for molecular diagnostics. Substantial efficiencies could be achieved with these DNA methylation markers for disease tracking with potential to replace patient-specific genetic testing.

MicroRNAs: biogenesis, roles for carcinogenesis and as potential biomarkers for cancer diagnosis and prognosis

MicroRNAs (miRNAs) are short non-coding RNAs of 20-24 nucleotides that play important roles in carcinogenesis. Accordingly, miRNAs control numerous cancer-relevant biological events such as cell proliferation, cell cycle control, metabolism and apoptosis. In this review, we summarize the current knowledge and concepts concerning the biogenesis of miRNAs, miRNA roles in cancer and their potential as biomarkers for cancer diagnosis and prognosis including the regulation of key cancer-related pathways, such as cell cycle control and miRNA dysregulation. Moreover, microRNA molecules are already receiving the attention of world researchers as therapeutic targets and agents. Therefore, in-depth knowledge of microRNAs has the potential not only to identify their roles in cancer, but also to exploit them as potential biomarkers for cancer diagnosis and identify therapeutic targets for new drug discovery.

Urbanicity and lifestyle risk factors for cardiometabolic diseases in rural Uganda: a cross-sectional study

Urban living is associated with unhealthy lifestyles that can increase the risk of cardiometabolic diseases. In sub-Saharan Africa (SSA), where the majority of people live in rural areas, it is still unclear if there is a corresponding increase in unhealthy lifestyles as rural areas adopt urban characteristics. This study examines the distribution of urban characteristics across rural communities in Uganda and their associations with lifestyle risk factors for chronic diseases.

Effects of a multivitamin, mineral and herbal supplement on cognition and blood biomarkers in older men: a randomised, placebo-controlled trial

OBJECTIVE: Nutritional and vitamin status may be related to cognitive function and decline in older adults. The aim of this study was to investigate the effects of nutritional supplementation on cognition in older men. METHOD: The current study was an 8-week, placebo-controlled, double-blind investigation into the effects of a multivitamin, mineral and herbal supplement (Swisse Men's Ultivite®, Swisse Vitamins Pty Ltd, Melbourne, Australia) on cognitive performance in older men. Participants were 51 male individuals aged between 50 and 74 years, with a sedentary lifestyle. Cognitive performance was assessed at baseline and post-treatment using a computerised battery of cognitive tasks, enabling the measurement of a range of attentional and memory processes. Blood measures of vitamin B(12) , folate and homocysteine were collected prior to and after supplementation. RESULTS: The results of this study revealed that contextual recognition memory performance was significantly improved following multivitamin supplementation (p < 0.05). Performance on other cognitive tasks did not change. Levels of vitamin B(12) and folate were significantly increased with a concomitant decrease in homocysteine, indicating that relatively short-term supplementation with a multivitamin can benefit these risk factors for cognitive decline. CONCLUSION: Findings from this study indicate that daily multivitamin supplementation may improve episodic memory in older men at risk of cognitive decline.

A dietary guideline adherence score is positively associated with dietary biomarkers but not lipid profile in healthy children

BACKGROUND: Whether dietary indexes are associated with biomarkers of children's dietary intake is unclear. OBJECTIVE: The study aim was to examine the relations between diet quality and selected plasma biomarkers of dietary intake and serum lipid profile. METHODS: The study sample consisted of 130 children aged 4-13 y (mean ± SD: 8.6 ± 2.9 y) derived by using baseline data from an intervention study. The Dietary Guideline Index for Children and Adolescents (DGI-CA) comprises the following 11 components with age-specific criteria: 5 core food groups, whole-grain bread, reduced-fat dairy foods, discretionary foods (nutrient poor; high in saturated fat, salt, and added sugar), healthy fats/oils, water, and diet variety (possible score of 100). A higher score reflects greater compliance with dietary guidelines. Venous blood was collected for measurements of serum lipids, fatty acid composition, plasma carotenoids, lutein, lycopene, and α-tocopherol. Linear regression was used to examine the relation between DGI-CA score (independent variable) and concentrations of biomarkers by using the log-transformed variable (outcome), controlling for confounders. RESULTS: DGI-CA score was positively associated (P < 0.05) with plasma concentrations of lutein (standardized β = 0.17), α-carotene (standardized β = 0.28), β-carotene (standardized β = 0.26), and n-3 (ω-3) fatty acids (standardized β = 0.51) and inversely associated with plasma concentrations of lycopene (standardized β = -0.23) and stearic acid (18:0) (standardized β = -0.22). No association was observed between diet quality and α-tocopherol, n-6 fatty acids, or serum lipid profile (all P > 0.05). CONCLUSION: Diet quality, conceptualized as adherence to national dietary guidelines, is cross-sectionally associated with plasma biomarkers of dietary exposure but not serum lipid profile. This trial was registered with the Australia New Zealand Clinical Trial Registry (www.anztr.org.au) as ACTRN12609000453280.

Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have promise in the analysis of large epidemiological datasets. This study illustrates the use of a hybrid methodology for variable selection that took account of missing data and complex survey design to identify key biomarkers associated with depression from a large epidemiological study.

METHODS: The study used a three-step methodology amalgamating multiple imputation, a machine learning boosted regression algorithm and logistic regression, to identify key biomarkers associated with depression in the National Health and Nutrition Examination Study (2009-2010). Depression was measured using the Patient Health Questionnaire-9 and 67 biomarkers were analysed. Covariates in this study included gender, age, race, smoking, food security, Poverty Income Ratio, Body Mass Index, physical activity, alcohol use, medical conditions and medications. The final imputed weighted multiple logistic regression model included possible confounders and moderators.

RESULTS: After the creation of 20 imputation data sets from multiple chained regression sequences, machine learning boosted regression initially identified 21 biomarkers associated with depression. Using traditional logistic regression methods, including controlling for possible confounders and moderators, a final set of three biomarkers were selected. The final three biomarkers from the novel hybrid variable selection methodology were red cell distribution width (OR 1.15; 95% CI 1.01, 1.30), serum glucose (OR 1.01; 95% CI 1.00, 1.01) and total bilirubin (OR 0.12; 95% CI 0.05, 0.28). Significant interactions were found between total bilirubin with Mexican American/Hispanic group (p = 0.016), and current smokers (p<0.001).

CONCLUSION: The systematic use of a hybrid methodology for variable selection, fusing data mining techniques using a machine learning algorithm with traditional statistical modelling, accounted for missing data and complex survey sampling methodology and was demonstrated to be a useful tool for detecting three biomarkers associated with depression for future hypothesis generation: red cell distribution width, serum glucose and total bilirubin.

Investigation of urinary volatile organic metabolites as potential cancer biomarkers by solid-phase microextraction in combination with gas chromatography-mass spectrometry

BACKGROUND: Non-invasive diagnostic strategies aimed at identifying biomarkers of cancer are of great interest for early cancer detection. Urine is potentially a rich source of volatile organic metabolites (VOMs) that can be used as potential cancer biomarkers. Our aim was to develop a generally reliable, rapid, sensitive, and robust analytical method for screening large numbers of urine samples, resulting in a broad spectrum of native VOMs, as a tool to evaluate the potential of these metabolites in the early diagnosis of cancer. METHODS: To investigate urinary volatile metabolites as potential cancer biomarkers, urine samples from 33 cancer patients (oncological group: 14 leukaemia, 12 colorectal and 7 lymphoma) and 21 healthy (control group, cancer-free) individuals were qualitatively and quantitatively analysed. Dynamic solid-phase microextraction in headspace mode (dHS-SPME) using a carboxenpolydimethylsiloxane (CAR/PDMS) sorbent in combination with GC-qMS-based metabolomics was applied to isolate and identify the volatile metabolites. This method provides a potential non-invasive method for early cancer diagnosis as a first approach. To fulfil this objective, three important dHS-SPME experimental parameters that influence extraction efficiency (fibre coating, extraction time and temperature of sampling) were optimised using a univariate optimisation design. The highest extraction efficiency was obtained when sampling was performed at 501C for 60min using samples with high ionic strengths (17% sodium chloride, wv 1) and under agitation. RESULTS: A total of 82 volatile metabolites belonging to distinct chemical classes were identified in the control and oncological groups. Benzene derivatives, terpenoids and phenols were the most common classes for the oncological group, whereas ketones and sulphur compounds were the main classes that were isolated from the urine headspace of healthy subjects. The results demonstrate that compound concentrations were dramatically different between cancer patients and healthy volunteers. The positive rates of 16 patients among the 82 identified were found to be statistically different (Po0.05). A significant increase in the peak area of 2-methyl3-phenyl-2-propenal, p-cymene, anisole, 4-methyl-phenol and 1,2-dihydro-1,1,6-trimethyl-naphthalene in cancer patients was observed. On average, statistically significant lower abundances of dimethyl disulphide were found in cancer patients. CONCLUSIONS: Gas chromatographic peak areas were submitted to multivariate analysis (principal component analysis and supervised linear discriminant analysis) to visualise clusters within cases and to detect the volatile metabolites that are able to differentiate cancer patients from healthy individuals. Very good discrimination within cancer groups and between cancer and control groups was achieved.

Solid phase microextraction, mass spectrometry and metabolomic approaches for detection of potential urinary cancer biomarkers: a powerful strategy for breast cancer diagnosis

A sensitive assay to identify volatile organic metabolites (VOMs) as biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. Therefore the aim of this study was to establish the urinary metabolomic profile of breast cancer patients and healthy individuals (control group) and to explore the VOMs as potential biomarkers in breast cancer diagnosis at early stage. Solid-phase microextraction (SPME) using CAR/PDMS sorbent combined with gas chromatography–mass spectrometry was applied to obtain metabolomic information patterns of 26 breast cancer patients and 21 healthy individuals (controls). A total of seventy-nine VOMs, belonging to distinct chemical classes, were detected and identified in control and breast cancer groups. Ketones and sulfur compounds were the chemical classes with highest contribution for both groups. Results showed that excretion values of 6 VOMs among the total of 79 detected were found to be statistically different (p < 0.05). A significant increase in the peak area of (−)-4-carene, 3-heptanone, 1,2,4-trimethylbenzene, 2-methoxythiophene and phenol, in VOMs of cancer patients relatively to controls was observed. Statiscally significant lower abundances of dimethyl disulfide were found in cancer patients. Bioanalytical data were submitted to multivariate statistics [principal component analysis (PCA)], in order to visualize clusters of cases and to detect the VOMs that are able to differentiate cancer patients from healthy individuals. Very good discrimination within breast cancer and control groups was achieved. Nevertheless, a deep study using a larger number of patients must be carried out to confirm the results.

Use of novel biomarkers (Homocysteine, vitamin B6, B9 and B12) on the assessing the progression of cardiovascular disease

A large number of evidences correlate elevated levels of homocysteine (Hcys) with a higher cardiovascular diseases (CVDs) risk, especially, atherosclerosis. Similarly, abnormal low levels of the vitamins B6, B9 and B12 are associated to an instability in the methionine cycle with an over production of Hcys. Thus, biomedical sciences are looking forward for a cheaper, faster, precise and accurate analytical methodology to quantify these compounds in a suitable format for the clinical environment. Therefore the objective of this study was the development of a simple, inexpensive and appropriate methodology to use at the clinical level. To achieve this goal, a procedure integrating a digitally controlled (eVol®) microextraction by packed sorbent (MEPS) and an ultra performance liquid chromatography (UPLC) coupled to a photodiode array detector (PDA) was developed to identify and quantify Hcys vitamins B6, B9 and B12. Although different conditions were assayed, we were not able to combine Hcys with the vitamins in the same analytical procedure, and so we proceeded to the optimization of two methods differing only in the composition of the gradient of the mobile phase and the injected volume. It was found that MEPS did not bring any benefit to the quantification of the Hcys in the plasma. Therefore, we developed and validate an alternative method that uses the direct injection of treated plasma (reduced and precipitated). This same method was evaluated in terms of selectivity, linearity, limit of detection (LOD), limit of quantification (LOQ), matrix effect and precision (intra-and inter-day) and applied to the determination of Hcys in a group composed by patients presenting augmented CVD risk. Good results in terms of selectivity and linearity (R2> 0.9968) were obtained, being the values of LOD and LOQ 0.007 and 0.21 mol / L, respectively. The intra-day precision (1.23-3.32%), inter-day precision (5.43-6.99%) and the recovery rate (82.5 to 93.1%) of this method were satisfactory. The matrix effect (>120%) was, however, higher than we were waiting for. Using this methodology it was possible to determine the amount of Hcys in real plasma samples from individuals presenting augmented CVD risk. Regarding the methodology developed for vitamins, despite the optimization of the extraction technique and the chromatographic conditions, it was found that the levels usually present in plasma are far below the sensitivity we obtained. Therefore, further optimizations of the methodology developed are needed. As conclusion, part of the objectives of this study was achieved with the development of a quick, simple and cheaper method for the quantification of Hcys.