Lipid Alterations in Glioma: A Systematic Review.

Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients' survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.

Osteoporosis in Parkinson's Disease: Relevance of Distal Radius Dual-Energy X-Ray Absorptiometry (DXA) and Sarcopenia.

Osteoporotic fractures are common in Parkinson's disease (PD). Standard dual-energy X-ray absorptiometry (DXA) measuring bone mineral density (BMD) at the femoral neck and lumbar spine (central sites) has suboptimal sensitivity in predicting fracture risk in the general population. An association between sarcopenia and osteoporosis in PD has not been studied. We compared BMD and osteoporosis prevalence in PD patients vs controls; determined the osteoporosis detection rates using central alone vs central plus distal radius DXA; and analyzed factors (in particular, sarcopenia) associated with osteoporosis. One hundred and fifty-six subjects (102 patients with PD, 54 spousal/sibling controls) underwent femoral neck-lumbar spine-distal radius DXA. Seventy-three patients and 46 controls were assessed for sarcopenia using whole-body DXA and handgrip strength. Patients underwent clinical and serum biochemical evaluations. PD patients had significantly lower body mass index compared to controls. After adjustment for possible confounders, distal radius BMD and T-scores were significantly lower in PD patients compared to controls, but not at the femoral neck/lumbar spine. With distal radius DXA, an additional 11.0% of patients were diagnosed with osteoporosis (32.0% to 43.0%), vs 3.7% in controls (33.3% to 37.0%) additionally diagnosed; this increase was largely driven by the markedly higher detection rate in female PD patients. Female gender (adjusted odds ratio [ORadjusted] = 11.3, 95% confidence interval [CI]: 2.6-48.6) and sarcopenia (ORadjusted = 8.4, 95% CI: 1.1-64.9) were independent predictors for osteoporosis in PD. Distal radius DXA increased osteoporosis detection, especially in female PD patients, suggesting that diagnostic protocols for osteoporosis in PD could be optimized. A close association between osteoporosis and sarcopenia was documented for the first time in PD, which has important implications for clinical management and future research.

Progressive and accelerated weight and body fat loss in Parkinson's disease: A three-year prospective longitudinal study.

INTRODUCTION: Although weight loss is common in Parkinson's disease (PD), longitudinal studies assessing weight and body composition changes are limited. METHODS: In this three-year longitudinal study, 125 subjects (77 PD patients and 48 spousal/sibling controls) underwent clinical, biochemical and body composition assessments using dual-energy X-ray absorptiometry. RESULTS: Patients were older than controls (65.6 ± 8.9 vs. 62.6 ± 7.1, P = 0.049), with no significant differences in gender, comorbidities, dietary intake and physical activity. Clinically significant weight loss (≥5% from baseline weight) was recorded in 41.6% of patients, with a doubling of cases (6.5 to 13.0%) classified as underweight at study end. Over three years, patients demonstrated greater reductions in BMI (mean -1.2 kg/m2, 95%CI-2.0 to -0.4), whole-body fat percentage (-2.5% points, 95%CI-3.9 to -1.0), fat mass index (FMI) (-0.9 kg/m2, 95%CI-1.4 to -0.4), visceral fat mass (-0.1 kg, 95%CI-0.2 to 0.0), and subcutaneous fat mass (-1.9 kg, 95%CI-3.4 to -0.5) than in controls, with significant group-by-time interactions after adjusting for age and gender. Notably, 31.2% and 53.3% of patients had FMI<3rd (severe fat deficit) and <10th centiles, respectively. Muscle mass indices decreased over time in both groups, without significant group-by-time interactions. Multiple linear regression models showed that loss of body weight and fat mass in patients were associated with age, dyskinesia, psychosis and constipation. CONCLUSIONS: We found progressive loss of weight in PD patients, with greater loss of both visceral and subcutaneous fat, but not muscle, compared to controls. Several associated factors (motor and non-motor disease features) were identified for these changes, providing insights on possible mechanisms and therapeutic targets.

Relapsing encephalopathy with dancing eyes and jerky limbs.

We report a case of relapsing-remitting opsoclonus-myoclonus-ataxia syndrome (OMAS) in a patient with Hashimoto's encephalopathy, diagnosed after comprehensive evaluation. OMAS as a manifestation of Hashimoto's encephalopathy has been reported once previously. It is hoped that recognition of this entity and early initiation of immunotherapy will improve clinical outcomes for patients.

A decision tree for differentiating multiple system atrophy from Parkinson's disease using 3-T MR imaging.

OBJECTIVE: To develop a decision tree based on standard magnetic resonance imaging (MRI) and diffusion tensor imaging to differentiate multiple system atrophy (MSA) from Parkinson's disease (PD). METHODS: 3-T brain MRI and DTI (diffusion tensor imaging) were performed on 26 PD and 13 MSA patients. Regions of interest (ROIs) were the putamen, substantia nigra, pons, middle cerebellar peduncles (MCP) and cerebellum. Linear, volumetry and DTI (fractional anisotropy and mean diffusivity) were measured. A three-node decision tree was formulated, with design goals being 100 % specificity at node 1, 100 % sensitivity at node 2 and highest combined sensitivity and specificity at node 3. RESULTS: Nine parameters (mean width, fractional anisotropy (FA) and mean diffusivity (MD) of MCP; anteroposterior diameter of pons; cerebellar FA and volume; pons and mean putamen volume; mean FA substantia nigra compacta-rostral) showed statistically significant (P < 0.05) differences between MSA and PD with mean MCP width, anteroposterior diameter of pons and mean FA MCP chosen for the decision tree. Threshold values were 14.6 mm, 21.8 mm and 0.55, respectively. Overall performance of the decision tree was 92 % sensitivity, 96 % specificity, 92 % PPV and 96 % NPV. Twelve out of 13 MSA patients were accurately classified. CONCLUSION: Formation of the decision tree using these parameters was both descriptive and predictive in differentiating between MSA and PD. KEY POINTS: • Parkinson's disease and multiple system atrophy can be distinguished on MR imaging. • Combined conventional MRI and diffusion tensor imaging improves the accuracy of diagnosis. • A decision tree is descriptive and predictive in differentiating between clinical entities. • A decision tree can reliably differentiate Parkinson's disease from multiple system atrophy.