Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients

Body composition monitoring in children and adolescents: reproducibility and reference values

There is an increasing need for suitable tools to evaluate body composition in paediatrics. The Body Composition Monitor (BCM) shows promise as a method, but reference values in children are lacking. Twenty children were included and measured twice by 4 different raters to asses inter- and intra-rater reproducibility of the BCM. Reliability was assessed using the Bland-Altman method and by calculating intraclass correlation coefficients (ICCs). The intra-rater ICCs were high (≥ 0.97) for all parameters measured by BCM as were the inter-rater ICCs for all parameters (≥ 0.98) except for overhydration (0.76). Consequently, a study was set up in which BCM measurements were performed in 2058 healthy children aged 3–18.5 years. The age- and gender-specific percentile values and reference curves for body composition (BMI, waist circumference, fat mass and lean tissue mass) and fluid status (extracellular and intracellular water and total body water) relative to age were produced using the GAMLSS method for growth curves. Conclusion: A high reproducibility of BCM measurements was found for fat mass, lean tissue mass, extracellular water and total body water. Reference values for these BCM parameters were calculated in over 2000 children and adolescents aged 3 to 18 years.What is Known• The 4-compartment model is regarded as the ‘gold standard’ of body composition methods, but is inappropriate for regular follow-up or screening of large groups, because of associated limitations.• Body Composition Monitor® is an inexpensive field method that has the potential to be an adequate monitoring tool.What is New• Good reproducibility of BCM measurements in children provides evidence to use the device in longitudinal follow-up, multicentre and comparative studies.• Paediatric reference values relative to age and sex for the various compartments of the body are provided

Education as Risk Factor of Mild Cognitive Impairment: The Link to the Gut Microbiome

peer reviewedBackground: With differences apparent in the gut microbiome in mild cognitive impairment (MCI) and dementia, and risk factors of dementia linked to alterations of the gut microbiome, the question remains if gut microbiome characteristics may mediate associations of education with MCI. Objectives: We sought to examine potential mediation of the association of education and MCI by gut microbiome diversity or composition. Design: Cross-sectional study. Setting: Luxembourg, the Greater Region (surrounding areas in Belgium, France, Germany). Participants: Control participants of the Luxembourg Parkinson’s Study. Measurements: Gut microbiome composition, ascertained with 16S rRNA gene amplicon sequencing. Differential abundance, assessed across education groups (0–10, 11–16, 16+ years of education). Alpha diversity (Chao1, Shannon and inverse Simpson indices). Mediation analysis with effect decomposition was conducted with education as exposure, MCI as outcome and gut microbiome metrics as mediators. Results: After exclusion of participants below 50, or with missing data, n=258 participants (n=58 MCI) were included (M [SD] Age=64.6 [8.3] years). Higher education (16+ years) was associated with MCI (Odds ratio natural direct effect=0.35 [95% CI 0.15–0.81]. Streptococcus and Lachnospiraceae-UCG-001 genera were more abundant in higher education. Conclusions: Education is associated with gut microbiome composition and MCI risk without clear evidence for mediation. However, our results suggest signatures of the gut microbiome that have been identified previously in AD and MCI to be reflected in lower education and suggest education as important covariate in microbiome studies.MCI-BIOME_20193. Good health and well-bein