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Introduction: Affective disorders are a major global burden, with approximately 15% of people worldwide suffering from some form of affective disorder. In patients experiencing their first depressive episode, in most cases it cannot be distinguished whether this is due to bipolar disorder (BD) or major depressive disorder (MDD). Valid fluid biomarkers able to discriminate between the two disorders in a clinical setting are not yet available.
Material and Methods: Seventy depressed patients suffering from BD (bipolar I and II subtypes) and 42 patients with major MDD were recruited and blood samples were taken for proteomic analyses after 8 h fasting. Proteomic profiles were analyzed using the Multiplex Immunoassay platform from Myriad Rules Based Medicine (Myriad RBM; Austin, Texas, USA). Human DiscoveryMAPTM was used to measure the concentration of various proteins, peptides, and small molecules. A multivariate predictive model was consequently constructed to differentiate between BD and MDD.
Results: Based on the various proteomic profiles, the algorithm could discriminate depressed BD patients from MDD patients with an accuracy of 67%.
Discussion: The results of this preliminary study suggest that future discrimination between bipolar and unipolar depression in a single case could be possible, using predictive biomarker models based on blood proteomic profiling.
Evidence gained from recent studies has generated increasing interest in the role of vitamin D in extraskeletal functions such as inflammation and immunoregulation. Although vitamin D deficiency has been implicated in the pathophysiology of inflammatory diseases including inflammatory bowel disease (IBD), evidence as to whether vitamin D supplementation may cure or prevent chronic disease is inconsistent. Since 25OH-vitamin D (25OHD) has been suggested to be an acute-phase protein, its utility as a vitamin D status marker is therefore questionable. In this study, possible interactions of vitamin D and inflammation were studied in 188 patients with IBD, with high-sensitivity C-reactive protein (hsCRP) levels ≥ 5 mg/dL and/or fecal calprotectin ≥ 250 µg/g defined as biochemical evidence of inflammatory activity. Levels of 25OHD and vitamin D-binding protein (VDBP) were determined by ELISA, and 1,25-dihydroxyvitamin D (1,25OHD) and dihydroxycholecalciferol (24,25OHD) by LC-MS/MS. Free and bioavailable vitamin D levels were calculated with the validated formula of Bikle. Serum 1,25OH2D and vitamin D binding protein (VDBP) levels were shown to differ between the inflammatory and noninflammatory groups: patients with inflammatory disease activity had significantly higher serum concentrations of 1,25OH2D (35.0 (16.4–67.3) vs. 18.5 (1.2–51.0) pg/mL, p < 0.001) and VDBP (351.2 (252.2–530.6) vs. 330.8 (183.5–560.3) mg/dL, p < 0.05) than patients without active inflammation. Serum 24,25OH2D levels were negatively correlated with erythrocyte sedimentation rate (ESR) (−0.155, p = 0.049) while concentrations of serum 1,25OH2D correlated positively with hsCRP (0.157, p = 0.036). Correlations with serum VDBP levels were found for ESR (0.150, p = 0.049), transferrin (0.160, p = 0.037) and hsCRP (0.261, p < 0.001). Levels of serum free and bioavailable 25OHD showed a negative correlation with ESR (−0.165, p = 0.031, −0.205, p < 0.001, respectively) and hsCRP (−0.164, p = 0.032, −0.208, p < 0.001 respectively), and a moderate negative correlation with fecal calprotectin (−0.377, p = 0.028, −0.409, p < 0.016, respectively). Serum total 25OHD concentration was the only vitamin D parameter found to have no specific correlation with any of the inflammatory markers. According to these results, the traditional parameter, total 25OHD, still appears to be the best marker of vitamin D status in patients with inflammatory bowel disease regardless of the presence of inflammation.
Background: Definitive chemoradiotherapy (CRT) is the primary treatment for non-metastatic anal squamous cell carcinoma (ASCC). Despite favorable treatment outcomes in general, failure rates up to 40% occur in locally advanced disease. For treatment escalation or de-escalation strategies easily assessable and valid biomarkers are needed.
Methods: We identified 125 patients with ASCC treated with standard CRT at our department. C-reactive protein (CRP) to albumin ratio (CAR) was calculated dividing baseline CRP by baseline albumin levels. We used maximally selected rank statistics to dichotomize patients to high and low risk groups. Associations of CAR with clinicopathologic parameters were evaluated and the prognostic impact was tested using univariate and multivariate cox regression analysis. In a subset of 78 patients, pretreatment tumor tissue was available and CD8+ tumor infiltrating lymphocytes (TILs) and p16INK4a status were scored by immunohistochemistry and correlated with CAR.
Results: Advanced T-stage and male gender were significantly associated with higher baseline CAR. Using the calculated cutoff of 0.117, a high baseline CAR was also associated with worse locoregional control (p = 0.002), distant metastasis-free survival (p = 0.01), disease-free survival (DFS, p = 0.002) and overall survival (OS, p < 0.001). A combined risk score incorporating N-stage and CAR, termed N-CAR score, was associated with worse outcome across all endpoints and in multivariate analysis independent of T-stage and Gender (HR 4.27, p = 0.003). In the subset of 78 patients, a strong infiltration with intratumoral CD8+ TIL was associated with a significantly lower CAR (p = 0.007). CAR is an easily accessible biomarker that is associated with DFS. Our study revealed a possible link between chronic systemic inflammation and an impaired intratumoral immune response.
Based on accumulating evidence of a role of lipid signaling in many physiological and pathophysiological processes including psychiatric diseases, the present data driven analysis was designed to gather information needed to develop a prospective biomarker, using a targeted lipidomics approach covering different lipid mediators. Using unsupervised methods of data structure detection, implemented as hierarchal clustering, emergent self-organizing maps of neuronal networks, and principal component analysis, a cluster structure was found in the input data space comprising plasma concentrations of d = 35 different lipid-markers of various classes acquired in n = 94 subjects with the clinical diagnoses depression, bipolar disorder, ADHD, dementia, or in healthy controls. The structure separated patients with dementia from the other clinical groups, indicating that dementia is associated with a distinct lipid mediator plasma concentrations pattern possibly providing a basis for a future biomarker. This hypothesis was subsequently assessed using supervised machine-learning methods, implemented as random forests or principal component analysis followed by computed ABC analysis used for feature selection, and as random forests, k-nearest neighbors, support vector machines, multilayer perceptron, and naïve Bayesian classifiers to estimate whether the selected lipid mediators provide sufficient information that the diagnosis of dementia can be established at a higher accuracy than by guessing. This succeeded using a set of d = 7 markers comprising GluCerC16:0, Cer24:0, Cer20:0, Cer16:0, Cer24:1, C16 sphinganine, and LacCerC16:0, at an accuracy of 77%. By contrast, using random lipid markers reduced the diagnostic accuracy to values of 65% or less, whereas training the algorithms with randomly permuted data was followed by complete failure to diagnose dementia, emphasizing that the selected lipid mediators were display a particular pattern in this disease possibly qualifying as biomarkers.