The Correlation between Osteoporosis Occurrences in Both Schizophrenia and Parkinson's Disease

Application of deep learning algorithm on whole genome sequencing data uncovers structural variants associated with multiple mental disorders in African American patients

Mental disorders present a global health concern, while the diagnosis of mental disorders can be challenging. The diagnosis is even harder for patients who have more than one type of mental disorder, especially for young toddlers who are not able to complete questionnaires or standardized rating scales for diagnosis. In the past decade, multiple genomic association signals have been reported for mental disorders, some of which present attractive drug targets. Concurrently, machine learning algorithms, especially deep learning algorithms, have been successful in the diagnosis and/or labeling of complex diseases, such as attention deficit hyperactivity disorder (ADHD) or cancer. In this study, we focused on eight common mental disorders, including ADHD, depression, anxiety, autism, intellectual disabilities, speech/language disorder, delays in developments, and oppositional defiant disorder in the ethnic minority of African Americans. Blood-derived whole genome sequencing data from 4179 individuals were generated, including 1384 patients with the diagnosis of at least one mental disorder. The burden of genomic variants in coding/non-coding regions was applied as feature vectors in the deep learning algorithm. Our model showed ~65% accuracy in differentiating patients from controls. Ability to label patients with multiple disorders was similarly successful, with a hamming loss score less than 0.3, while exact diagnostic matches are around 10%. Genes in genomic regions with the highest weights showed enrichment of biological pathways involved in immune responses, antigen/nucleic acid binding, chemokine signaling pathway, and G-protein receptor activities. A noticeable fact is that variants in non-coding regions (e.g., ncRNA, intronic, and intergenic) performed equally well as variants in coding regions; however, unlike coding region variants, variants in non-coding regions do not express genomic hotspots whereas they carry much more narrow standard deviations, indicating they probably serve as alternative markers.

Comprehensive Profiling of Blood Coagulation and Fibrinolysis Marker Reveals Elevated Plasmin-Antiplasmin Complexes in Parkinson's Disease

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease. Accumulating evidence demonstrates that alpha-synuclein (α-Syn), an apparently predominant neuronal protein, is a major contributor to PD pathology. As α-Syn is also highly abundant in blood, particularly in red blood cells (RBCs) and platelets, this in turn raises the question on the function of presumably dysfunctional α-Syn in "peripheral" cells and its putative effect on the other enclosed constituents. Herein, we detected the internal variance in erythrocytes of PD patients by Raman spectroscopy, but no measurable amount of erythrocytic behavioural change (eryptosis) or any haemoglobin variation was noticed. An elevated level of plasmin-antiplasmin complexes (PAP) was observed in the plasma of PD patients, indicating activation of the fibrinolytic system, but platelet activation after thrombin stimulation was not altered. Sex-specific patterns were noticed for blood coagulation factor XIII and factor XII activity in PD patients. Additionally, the alterations in homocysteine levels which have often been observed in PD patients were found to be independent from L-DOPA usage and PAP levels. Furthermore, a selective gene expression analysis identified subsets of genes related to different blood-associated compartments (RBCs, platelets, coagulation-fibrinolysis) also involved in PD-related pathways.

Effects of Olanzapine on Bone Mineral Density, Glucose, and Lipid Metabolism in Schizophrenia Patients

AIM

To explore whether olanzapine alters bone mineral density (BMD), glucose, and lipid metabolism in schizophrenia patients.

METHODS

This study enrolled 150 patients diagnosed with schizophrenia according to Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), including 101 patients who had over 6-month history of olanzapine use (olanzapine-treated group) and 49 patients who had no history of antipsychotic use (first episode drug-naïve group). 71 subjects with age- and gender-matched healthy volunteers (healthy control group) were also enrolled. All study subjects were from the Chinese Han population recruited in the Second Xiangya Hospital from January 2015 to January 2016. Demographic and physical examination data were collected from all subjects. BMD measurements of the radius+ulna, lumbar spine (L1-4), and left hip were performed via a dual-energy X-ray absorptiometry test. Serum lipid, glucose, and insulin levels were analyzed. Psychopathology profiles in all enrolled schizophrenia patients were assessed by the positive and negative syndrome scale (PANSS).

RESULTS

There was no significant difference in age, gender, activity intensity, smoking, or drinking among the three groups. In the majority of evaluated bone areas, the BMD values in olanzapine-treated or drug-naïve patients were lower than those in the control group. However, BMD values in the drug-naïve group showed no difference or even decreased as compared with those in the olanzapine-treated group. Among the olanzapine-treated group, although not observed in every tested region, a positive correlation was found of BMI or HOMA-IR with BMD. Stepwise multiple linear regression analysis revealed independent predictive factors associated with BMD in groups/subgroups of schizophrenia patients or healthy controls, including gender, TG, BMI, body weight, HOMA-IR, and FBG.

CONCLUSIONS

Schizophrenia, but not the long-term use of olanzapine, correlates with BMD loss in schizophrenia patients. Elevated BMI, TG, FBG, and insulin levels might protect these patients against bone degradation. Our work provides new information to improve the understanding, prevention, and treatment of osteoporosis in schizophrenia patients.

Increased migraine risk in osteoporosis patients: a nationwide population-based study

BACKGROUND

Osteoporosis and migraine are both important public health problems and may have overlapping pathophysiological mechanisms. The aim of this study was to use a Taiwanese population-based dataset to assess migraine risk in osteoporosis patients.

METHODS

The Taiwan National Health Insurance Research Database was used to analyse data for 40,672 patients aged ≥20 years who had been diagnosed with osteoporosis during 1996-2010. An additional 40,672 age-matched patients without osteoporosis were randomly selected as the non-osteoporosis group. The relationship between osteoporosis and migraine risk was estimated using Cox proportional hazard regression models.

RESULTS

During the follow-up period, 1110 patients with osteoporosis and 750 patients without osteoporosis developed migraine. After controlling for covariates, the overall incidence of migraine was 1.37-fold higher in the osteoporosis group than in the non-osteoporosis group (3.72 vs. 1.24 per 1000 person-years, respectively). Migraine risk factors included high Charlson Comorbidity Index score, female gender, hypertension, depression, asthma, allergic rhinitis, obesity, and tobacco use disorder.

CONCLUSIONS

Our results indicate that patients with a history of osteoporosis had a higher risk of migraine.

Possible functional links among brain- and skull-related genes selected in modern humans

The sequencing of the genomes from extinct hominins has revealed that changes in some brain-related genes have been selected after the split between anatomically-modern humans and Neanderthals/Denisovans. To date, no coherent view of these changes has been provided. Following a line of research we initiated in Boeckx and Benítez-Burraco (2014a), we hypothesize functional links among most of these genes and their products, based on the existing literature for each of the gene discussed. The genes we focus on are found mutated in different cognitive disorders affecting modern populations and their products are involved in skull and brain morphology, and neural connectivity. If our hypothesis turns out to be on the right track, it means that the changes affecting most of these proteins resulted in a more globular brain and ultimately brought about modern cognition, with its characteristic generativity and capacity to form and exploit cross-modular concepts, properties most clearly manifested in language.