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Down syndrome (DS) is a segmental progeroid genetic disorder associated with multi-systemic precocious aging phenotypes, which are particularly evident in the immune and nervous systems. Accordingly, people with DS show an increased biological age as measured by epigenetic clocks. The Ts65Dn trisomic mouse, which harbors extra-numerary copies of chromosome 21 (Hsa21)-syntenic regions, was shown to recapitulate several progeroid features of DS, but no biomarkers of age have been applied to it so far. In this pilot study, we used a mouse-specific epigenetic clock to measure the epigenetic age of hippocampi from Ts65Dn and euploid mice at 20 weeks. Ts65Dn mice showed an increased epigenetic age in comparison with controls, and the observed changes in DNA methylation partially recapitulated those observed in hippocampi from people with DS. Collectively, our results support the use of the Ts65Dn model to decipher the molecular mechanisms underlying the progeroid DS phenotypes.

Almost all individuals with Down’s syndrome (DS) show the characteristic neuropathological features of Alzheimer’s disease (AD) by the age of 40, yet not every individual with DS experiences symptoms of AD later in life. Similar to neurotypical developing subjects, AD in people with DS lasts for a long preclinical phase in which biomarkers follow a predictable order of changes. Hence, a prolonged asymptomatic period precedes the onset of dementia, underscoring the importance of identifying new biomarkers for the early detection and monitoring of cognitive decline in individuals with DS. Blood-based biomarkers may offer an alternative non-invasive strategy for the detection of peripheral biological alterations paralleling nervous system pathology in an early phase of the AD continuum. In the last few years, a strong neurobiological link has been demonstrated between the deficit of transforming growth factor-β1 (TGF-β1) levels, an anti-inflammatory cytokine endowed with neuroprotective activity, and early pro-inflammatory processes in the AD brain. In this clinical prospective observational study, we found significant lower plasma TGF-β1 concentrations at the first neuropsychological evaluation (baseline = T0) both in young adult DS individuals (19–35 years) and older DS subjects without AD (35–60 years) compared to age- and sex-matched healthy controls. Interestingly, we found that the lower TGF-β1 plasma concentrations at T0 were strongly correlated with the following cognitive decline at 12 months. In addition, in young individuals with DS, we found, for the first time, a negative correlation between low TGF-β1 concentrations and high TNF-α plasma concentrations, a pro-inflammatory cytokine that is known to be associated with cognitive impairment in DS individuals with AD. Finally, adopting an ex vivo approach, we found that TGF-β1 concentrations were reduced in parallel both in the plasma and in the peripheral blood mononuclear cells (PBMCs) of DS subjects, and interestingly, therapeutic concentrations of fluoxetine (FLX) applied to cultured PBMCs (1 µM for 24 h) were able to rescue TGF-β1 concentrations in the culture media from DS PBMCs, suggesting that FLX, a selective serotonin reuptake inhibitor (SSRI) endowed with neuroprotective activity, might rescue TGF-β1 concentrations in DS subjects at higher risk to develop cognitive decline.

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense.Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set.Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when—unsurprisingly—the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR.Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Abstract Parkinson’s disease is increasingly prevalent. It progresses from the pre-motor stage (characterised by non-motor symptoms like REM sleep behaviour disorder), to the disabling motor stage. We need objective biomarkers for early/pre-motor disease stages to be able to intervene and slow the underlying neurodegenerative process. Here, we validate a targeted multiplexed mass spectrometry assay for blood samples from recently diagnosed motor Parkinson’s patients (n = 99), pre-motor individuals with isolated REM sleep behaviour disorder (two cohorts: n = 18 and n = 54 longitudinally), and healthy controls (n = 36). Our machine-learning model accurately identifies all Parkinson patients and classifies 79% of the pre-motor individuals up to 7 years before motor onset by analysing the expression of eight proteins—Granulin precursor, Mannan-binding-lectin-serine-peptidase-2, Endoplasmatic-reticulum-chaperone-BiP, Prostaglaindin-H2-D-isomaerase, Interceullular-adhesion-molecule-1, Complement C3, Dickkopf-WNT-signalling pathway-inhibitor-3, and Plasma-protease-C1-inhibitor. Many of these biomarkers correlate with symptom severity. This specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson’s disease.