PDW and RDW are new parameters for bipolar episodes and unipolar depression

Electroconvulsive therapy improves hematological inflammatory markers in bipolar disorder

BACKGROUND

Immune dysfunction and inflammation participate in the pathophysiology of bipolar disorder (BD). Abnormal levels of inflammatory markers, namely, red cell distribution width (RDW) and platelet distribution width (PDW), were detected in BD. Electroconvulsive therapy (ECT) for various mental disorders including BD was linked to changes in these inflammatory mediators. Hence, we aimed to assess the impact of ECT on PDW and RDW in patients with BD.

METHODS

Seventy-two patients aged ≥ 16 were enrolled in the current prospective cohort study over 6 months, diagnosed as BD based on DSM-IV and indicated for ECT and complete blood count (CBC) drawn pre-ECT and after four ECT sessions.

RESULTS

By the end of the ECT sessions, we noticed a significant elevation in PDW with lowering in RDW levels. However, no significant differences were detected before and after ECT regarding platelet (PLTs) count, mean platelet volume (MPV), and Plateletcrit (PCT).

CONCLUSION

ECT seems to improve the CBC-derived inflammatory markers (RDW and PDW) subsequently, improving the underlying inflammatory process in BD without disturbing PLT homeostasis which support its anti-inflammatory role in BD.

Depression and Anxiety Symptoms Are Associated with Mean Platelet Volume in Autoimmune Disorders

Platelets are increasingly considered a bridge between mental and immunological disorders. However, data relating to platelet parameters in patients with autoimmune disorders are limited. The aim of the present study was to investigate, for the first time, the association of platelet parameters with the symptoms of affective disorders in patients with autoimmune conditions. In this cross-sectional study, we measured the complete blood count (CBC), the Generalized Anxiety Disorder Scale for anxiety (GAD-7), and the Beck Depression Inventory for depression (BDI) in 121 patients with autoimmune disorders. Mean platelet volume (MPV) was positively correlated with both anxiety and depression. Platelet distribution width (PDW) was negatively correlated with anxiety and depression. Before adjustment for covariates, logistic regression analysis revealed a significant association of MPV with depression and anxiety. After adjustment for covariates, only depression was associated with MPV. The area under the ROC curve of MPV for GAD-7 determined anxiety and BDI determined depression was 0.63. Our study showed that among the CBC hematological parameters, the MPV might be a useful biomarker of depression and anxiety in patients with autoimmune disorders. Further investigations of platelet parameters in controlled prospective studies are warranted to confirm our preliminary results.

Platelet volume indices correlate to severity of heart failure and have prognostic value for both cardiac and thrombotic events in patients with congenital heart disease

Patients with heart failure (HF) reportedly have activated platelets with increased platelet distribution width (PDW) and mean platelet volume (MPV), which lead to thrombotic events. These platelet indices are easily measured by routine blood tests and have been proposed as potential markers of cardiac events. We performed the present study to clarify whether platelet indices correlate to the severity of HF and to the prognosis of patients with congenital heart disease (CHD). We performed a retrospective single-center study including 400 patients with CHD [median age, 34 years (range 12-76); 49% males] hospitalized between 2014 and 2017. We assessed their clinical data, correlation between platelet indices and severity of HF, and prognosis of HF-related hospitalization and thrombus formation. In multivariate analysis, a significant correlation was found between PDW and logBNP (log-transformed brain natriuretic peptide; r = 0.30, p < 0.001), as well as between MPV and logBNP (r = 0.24, p < 0.001). After treatment for heart failure, a significant reduction was found in PDW (average value before treatment: 14.2; after: 13.2, p = 0.017). In multivariate logistic regression analysis, PDW [hazard ratio (HR) 1.365; 95% confidence interval (CI) 1.005-1.768] and MPV (HR 1.472; 95% CI 1.055-2.052) were predictors of HF-related hospitalization. Similarly, PDW (HR 1.998; 95% CI 1.461-2.630) and MPV (HR 1.792; 95% CI 1.155-2.781) were predictors of thrombus formation. Platelet volume indices correlate to severity of heart failure and have prognostic value for both cardiac and thrombotic events in patients with CHD.

Classifying depression using blood biomarkers: A large population study

BACKGROUND

Depression is a common mood disorder characterized by persistent low mood or lack of interest in activities. People with other chronic medical conditions such as obesity and diabetes are at greater risk of depression. Diagnosing depression can be a challenge for primary care providers and others who lack specialized training for these disorders and have insufficient time for in-depth clinical evaluation. We aimed to create a more objective low-cost diagnostic tool based on patients' characteristics and blood biomarkers.

METHODS

Blood biomarker results were obtained from the National Health and Nutrition Examination Survey (NHANES, 2007-2016). A prediction model utilizing random forest (RF) in NHANES (2007-2014) to identify depression was derived and validated internally using out-of-bag technique. Afterwards, the model was validated externally using a validation dataset (NHANES, 2015-2016). We performed four subgroup comparisons (full dataset, overweight and obesity dataset (BMI≥25), diabetes dataset, and metabolic syndrome dataset) then selected features using backward feature selection from RF.

RESULTS

Family income, Gamma-glutamyl transferase (GGT), glucose, Triglyceride, red cell distribution width (RDW), creatinine, Basophils count or percent, Eosinophils count or percent, and Bilirubin were the most important features from four models. In the training set, AUC from full, overweight and obesity, diabetes, and metabolic syndrome datasets were 0.83, 0.80, 0.82, and 0.82, respectively. In the validation set, AUC were 0.69, 0.63, 0.66, and 0.64, respectively.

CONCLUSION

Results of routine blood laboratory tests had good predictive value for distinguishing depression cases from control groups not only in the general population, but also individuals with metabolism-related chronic diseases.

Biological and Clinical Markers in Panic Disorder

OBJECTIVE

Classifying mental disorders on the basis of objective makers might clarify their aetiology, help in making the diagnosis, identify "at risk" individuals, determine the severity of mental illness, and predict the course of the disorder. This study aims to review biological and clinical markers of panic disorder (PD).

METHODS

A computerized search was carried out in PubMed and Science Direct using the key words: "marker/biomarker/clinical marker/neurobiology/staging" combined using Boolean AND operator with "panic." In addition, the reference lists from existing reviews and from the articles retrieved were inspected. Only English language papers published in peer-reviewed journals were included.

RESULTS

Structural changes in the amygdala, hippocampus, cerebral blood level in the left occipital cortex, serotonin 5-TH and noradrenergic systems activation, aberrant respiratory regulation, hearth rate variability, blood cells and peripheral blood stem cells, hypothalamic-pituitary-adrenal axis dysregulation were identified as potential candidate biomarkers of PD. Staging was identified as clinical marker of PD. According to the staging model, PD is described as follows: prodromal phase (stage 1); acute phase (stage 2); panic attacks (stage 3); chronic phase (stage 4).

CONCLUSION

The clinical utility, sensitivity, specificity, and the predictive value of biomarkers for PD is still questionable. The staging model of PD might be a valid susceptibility, diagnostic, prognostic, and predictive marker of PD. A possible longitudinal model of biological and clinical markers of PD is proposed.