Circulating cell-free DNA level predicts all-cause mortality independent of other predictors in the Health 2000 survey

Increased levels of circulating cell-free DNA (cf-DNA) are associated with and predict poor health outcomes. However, its predictive ability for mortality in population-based samples remains understudied. We analysed the capability of cf-DNA to predict all-cause mortality and assessed whether it adds predictive value on top of the other risk factors in the Health 2000 survey (n = 1,257, 46-76 years of age, 15-years-follow-up, 18% deceased). When analysed in a multivariate model with the other factors that independently predicted mortality in the sample (age, gender, self-rated health, smoking and plasma levels of glucose and adiponectin), increases in cf-DNA levels were associated with increased risk of mortality (hazard ratio [HR] for 0.1 mu g increase in cf-DNA: 1.017, 95% confidence interval [CI] 1.008-1.026, p = 0.0003). Inclusion of cf-DNA in the model improved the model fit and discrimination. Stratifying the analysis by cardiovascular disease (CVD) status indicated that cf-DNA predicted mortality equally well in individuals with (HR 1.018, 95% CI 1.008-1.026, p = 0.002) and without (HR 1.018, 95% CI 1.001-1.035, p = 0.033) CVD. In conclusion, our study indicates that cf-DNA level predicts mortality in middle-aged and older individuals, also among those with established CVD, and adds significant value to mortality prediction. Our results thus underscore the role of cf-DNA as a viable marker of health

Fatal Outcome in Bacteremia is Characterized by High Plasma Cell Free DNA Concentration and Apoptotic DNA Fragmentation: A Prospective Cohort Study

INTRODUCTION: Recent studies have shown that apoptosis plays a critical role in the pathogenesis of sepsis. High plasma cell free DNA (cf-DNA) concentrations have been shown to be associated with sepsis outcome. The origin of cf-DNA is unclear. METHODS: Total plasma cf-DNA was quantified directly in plasma and the amplifiable cf-DNA assessed using quantitative PCR in 132 patients with bacteremia caused by Staphylococcus aureus, Streptococcus pneumoniae, ß-hemolytic streptococcae or Escherichia coli. The quality of cf-DNA was analyzed with a DNA Chip assay performed on 8 survivors and 8 nonsurvivors. Values were measured on days 1-4 after positive blood culture, on day 5-17 and on recovery. RESULTS: The maximum cf-DNA values on days 1-4 (n = 132) were markedly higher in nonsurvivors compared to survivors (2.03 vs 1.26 ug/ml, p<0.001) and the AUCROC in the prediction of case fatality was 0.81 (95% CI 0.69-0.94). cf-DNA at a cut-off level of 1.52 ug/ml showed 83% sensitivity and 79% specificity for fatal disease. High cf-DNA (>1.52 ug/ml) remained an independent risk factor for case fatality in a logistic regression model. Qualitative analysis of cf-DNA showed that cf-DNA displayed a predominating low-molecular-weight cf-DNA band (150-200 bp) in nonsurvivors, corresponding to the size of the apoptotic nucleosomal DNA. cf-DNA concentration showed a significant positive correlation with visually graded apoptotic band intensity (R = 0.822, p<0.001). CONCLUSIONS: Plasma cf-DNA concentration proved to be a specific independent prognostic biomarker in bacteremia. cf-DNA displayed a predominating low-molecular-weight cf-DNA band in nonsurvivors corresponding to the size of apoptotic nucleosomal DNA

Body mass index and Mini Nutritional Assessment-Short Form as predictors of in-geriatric hospital mortality in older adults with COVID-19

Background & aims: Overweight and obesity have been consistently reported to carry an increased risk for poorer outcomes in coronavirus disease 2019 (COVID-19) in adults. Existing reports mainly focus on in-hospital and intensive care unit mortality in patient cohorts usually not representative of the population with the highest mortality, i.e. the very old and frail patients. Accordingly, little is known about the risk patterns related to body mass and nutrition in very old patients. Our aim was to assess the relationship between body mass index (BMI), nutritional status and in-geriatric hospital mortality among geriatric patients treated for COVID-19. As a reference, the analyses were performed also in patients treated for other diagnoses than COVID-19. Methods: We analyzed up to 10,031 geriatric patients with a median age of 83 years of which 1409 (14%) were hospitalized for COVID-19 and 8622 (86%) for other diagnoses in seven geriatric hospitals in the Stockholm region, Sweden during March 2020–January 2021. Data were available in electronic hospital records. The associations between 1) BMI and 2) nutritional status, assessed using the Mini-Nutritional Assessment - Short Form (MNA-SF) scale, and short-term in-geriatric hospital mortality were analyzed using logistic regression. Results: After adjusting for age, sex, comorbidity, polypharmacy, frailty and the wave of the pandemic (first vs. second), underweight defined as BMI<18.5 increased the risk of in-hospital mortality in COVID-19 patients (odds ratio [OR] = 2.30; confidence interval [CI] = 1.17–4.31). Overweight and obesity were not associated with in-hospital mortality. Malnutrition; i.e. MNA-SF 0–7 points, increased the risk of in-hospital mortality in patients treated for COVID-19 (OR = 2.03; CI = 1.16–3.68) and other causes (OR = 6.01; CI = 2.73–15.91). Conclusions: Our results indicate that obesity is not a risk factor for very old patients with COVID-19, but emphasize the role of underweight and malnutrition for in-hospital mortality in geriatric patients with COVID-19.publishedVersionPeer reviewe

Methylation status of VTRNA2-1/nc886 is stable across populations, monozygotic twin pairs and in majority of tissues

Aims & methods: The aim of this study was to characterize the methylation level of a polymorphically imprinted gene, VTRNA2-1/nc886, in human populations and somatic tissues.48 datasets, consisting of more than 30 tissues and >30,000 individuals, were used. Results: nc886 methylation status is associated with twin status and ethnic background, but the variation between populations is limited. Monozygotic twin pairs present concordant methylation, whereas similar to 30% of dizygotic twin pairs present discordant methylation in the nc886 locus. The methylation levels of nc886 are uniform across somatic tissues, except in cerebellum and skeletal muscle. Conclusion: The nc886 imprint may be established in the oocyte, and, after implantation, the methylation status is stable, excluding a few specific tissues.Tweetable abstract Methylation status of a polymorphically imprinted gene, VTRNA2-1/nc886, is stable in human populations (48 cohorts, n > 30,000) and in somatic tissues, except in cerebellum and skeletal muscle. Twin data suggest it may already be established in the oocyte.</p

The poly-omics of ageing through individual-based metabolic modelling

Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells

Establishment of a Transgenic Mouse Model Specifically Expressing Human Serum Amyloid A in Adipose Tissue

Obesity and obesity co-morbidities are associated with a low grade inflammation and elevated serum levels of acute phase proteins, including serum amyloid A (SAA). In the non-acute phase in humans, adipocytes are major producers of SAA but the function of adipocyte-derived SAA is unknown. To clarify the role of adipocyte-derived SAA, a transgenic mouse model expressing human SAA1 (hSAA) in adipocytes was established. hSAA expression was analysed using real-time PCR analysis. Male animals were challenged with a high fat (HF) diet. Plasma samples were subjected to fast protein liquid chromatography (FPLC) separation. hSAA, cholesterol and triglyceride content were measured in plasma and in FPLC fractions. Real-time PCR analysis confirmed an adipose tissue-specific hSAA gene expression. Moreover, the hSAA gene expression was not influenced by HF diet. However, hSAA plasma levels in HF fed animals (37.7±4.0 µg/mL, n = 7) were increased compared to those in normal chow fed animals (4.8±0.5 µg/mL, n = 10; p<0.001), and plasma levels in the two groups were in the same ranges as in obese and lean human subjects, respectively. In FPLC separated plasma samples, the concentration of hSAA peaked in high-density lipoprotein (HDL) containing fractions. In addition, cholesterol distribution over the different lipoprotein subfractions as assessed by FPLC analysis was similar within the two experimental groups. The established transgenic mouse model demonstrates that adipose tissue produced hSAA enters the circulation, resulting in elevated plasma levels of hSAA. This new model will enable further studies of metabolic effects of adipose tissue-derived SAA

Metabolite and lipoprotein profiles reveal sex-related oxidative stress imbalance in de novo drug-naive Parkinson's disease patients

Parkinson’s disease (PD) is the neurological disorder showing the greatest rise in prevalence from 1990 to 2016. Despite clinical definition criteria and a tremendous effort to develop objective biomarkers, precise diagnosis of PD is still unavailable at early stage. In recent years, an increasing number of studies have used omic methods to unveil the molecular basis of PD, providing a detailed characterization of potentially pathological alterations in various biological specimens. Metabolomics could provide useful insights to deepen our knowledge of PD aetiopathogenesis, to identify signatures that distinguish groups of patients and uncover responsive biomarkers of PD that may be significant in early detection and in tracking the disease progression and drug treatment efficacy. The present work is the first large metabolomic study based on nuclear magnetic resonance (NMR) with an independent validation cohort aiming at the serum characterization of de novo drug-naive PD patients. Here, NMR is applied to sera from large training and independent validation cohorts of German subjects. Multivariate and univariate approaches are used to infer metabolic differences that characterize the metabolite and the lipoprotein profiles of newly diagnosed de novo drug-naive PD patients also in relation to the biological sex of the subjects in the study, evidencing a more pronounced fingerprint of the pathology in male patients. The presence of a validation cohort allowed us to confirm altered levels of acetone and cholesterol in male PD patients. By comparing the metabolites and lipoproteins levels among de novo drug-naive PD patients, age- and sex-matched healthy controls, and a group of advanced PD patients, we detected several descriptors of stronger oxidative stress

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts

Early downregulation of hsa-miR-144-3p in serum from drug-naïve Parkinson’s disease patients

Advanced age represents one of the major risk factors for Parkinson’s Disease. Recent biomedical studies posit a role for microRNAs, also known to be remodelled during ageing. However, the relationship between microRNA remodelling and ageing in Parkinson’s Disease, has not been fully elucidated. Therefore, the aim of the present study is to unravel the relevance of microRNAs as biomarkers of Parkinson’s Disease within the ageing framework. We employed Next Generation Sequencing to profile serum microRNAs from samples informative for Parkinson’s Disease (recently diagnosed, drug-naïve) and healthy ageing (centenarians) plus healthy controls, age-matched with Parkinson’s Disease patients. Potential microRNA candidates markers, emerging from the combination of differential expression and network analyses, were further validated in an independent cohort including both drug-naïve and advanced Parkinson’s Disease patients, and healthy siblings of Parkinson’s Disease patients at higher genetic risk for developing the disease. While we did not find evidences of microRNAs co-regulated in Parkinson’s Disease and ageing, we report that hsa-miR-144-3p is consistently down-regulated in early Parkinson’s Disease patients. Moreover, interestingly, functional analysis revealed that hsa-miR-144-3p is involved in the regulation of coagulation, a process known to be altered in Parkinson’s Disease. Our results consistently show the down-regulation of hsa-mir144-3p in early Parkinson’s Disease, robustly confirmed across a variety of analytical and experimental analyses. These promising results ask for further research to unveil the functional details of the involvement of hsa-mir144-3p in Parkinson’s Disease

DNA methylation signatures of aggression and closely related constructs : A meta-analysis of epigenome-wide studies across the lifespan

DNA methylation profiles of aggressive behavior may capture lifetime cumulative effects of genetic, stochastic, and environmental influences associated with aggression. Here, we report the first large meta-analysis of epigenome-wide association studies (EWAS) of aggressive behavior (N = 15,324 participants). In peripheral blood samples of 14,434 participants from 18 cohorts with mean ages ranging from 7 to 68 years, 13 methylation sites were significantly associated with aggression (alpha = 1.2 x 10(-7); Bonferroni correction). In cord blood samples of 2425 children from five cohorts with aggression assessed at mean ages ranging from 4 to 7 years, 83% of these sites showed the same direction of association with childhood aggression (r = 0.74, p = 0.006) but no epigenome-wide significant sites were found. Top-sites (48 at a false discovery rate of 5% in the peripheral blood meta-analysis or in a combined meta-analysis of peripheral blood and cord blood) have been associated with chemical exposures, smoking, cognition, metabolic traits, and genetic variation (mQTLs). Three genes whose expression levels were associated with top-sites were previously linked to schizophrenia and general risk tolerance. At six CpGs, DNA methylation variation in blood mirrors variation in the brain. On average 44% (range = 3-82%) of the aggression-methylation association was explained by current and former smoking and BMI. These findings point at loci that are sensitive to chemical exposures with potential implications for neuronal functions. We hope these results to be a starting point for studies leading to applications as peripheral biomarkers and to reveal causal relationships with aggression and related traits.Peer reviewe