Differences in genome-wide gene expression response in peripheral blood mononuclear cells between young and old men upon caloric restriction

Background: Caloric restriction (CR) is considered to increase lifespan and to prevent various age-related diseases in different nonhuman organisms. Only a limited number of CR studies have been performed on humans, and results put CR as a beneficial tool to decrease risk factors in several age-related diseases. The question remains at what age CR should be implemented to be most effective with respect to healthy aging. The aim of our study was to elucidate the role of age in the transcriptional response to a completely controlled 30 % CR diet on immune cells, as immune response is affected during aging. Ten healthy young men, aged 20–28, and nine healthy old men, aged 64–85, were subjected to a 2-week weight maintenance diet, followed by 3 weeks of 30 % CR. Before and after 30 % CR, the whole genome gene expression in peripheral blood mononuclear cells (PBMCs) was assessed. Results: Expression of 554 genes showed a different response between young and old men upon CR. Gene set enrichment analysis revealed a downregulation of gene sets involved in the immune response in young but not in old men. At baseline, immune response-related genes were higher expressed in old compared to young men. Upstream regulator analyses revealed that most potential regulators were controlling the immune response. Conclusions: Based on the gene expression data, we theorise that a short period of CR is not effective in old men regarding immune-related pathways while it is effective in young men

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

OBJECTIVE: To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. DESIGN: Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. DATA SOURCES: PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. STUDY SELECTION: Studies that developed or validated a multivariable covid-19 related prediction model. DATA EXTRACTION: At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). RESULTS: 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. CONCLUSION: Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. SYSTEMATIC REVIEW REGISTRATION: Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. READERS' NOTE: This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity

The impact of protein quantity during energy restriction on genome-wide gene expression analysis in adipose tissue of obese humans

BACKGROUND: Overweight and obesity is a growing health problem worldwide. The most effective strategy to reduce weight is energy restriction (ER). ER has been shown to be beneficial in disease prevention and it reduces chronic inflammation. Recent studies suggest that reducing the protein quantity of a diet contributes to the beneficial effects by ER. The organ most extensively affected during ER is white adipose tissue (WAT). OBJECTIVE: The first objective was to assess changes in gene expression between a high protein diet and a normal protein diet during ER. Secondly, the total effect of ER on changes in gene expression in WAT was assessed. METHODS: In a parallel double-blinded controlled study, overweight older participants adhered to a 25% ER diet, either combined with high protein intake (HP-ER, 1.7 g/kg per day), or with normal protein intake (NP-ER, 0.9 g/kg per 40 day) for 12 weeks. From 10 HP-ER participants and 12 NP-ER participants subcutaneous WAT biopsies were collected before and after the diet intervention. Adipose tissue was used to isolate total RNA and to evaluate whole genome gene expression changes upon a HP-ER and NP-ER diet. RESULTS: A different gene expression response between HP-ER and NP-ER was observed for 530 genes. After NP-ER a downregulation in expression of genes linked to immune cell infiltration, adaptive immune response, and inflammasome was found whereas no such effect was found after HP-ER. HP-ER resulted in upregulation in expression of genes linked to cell cycle, GPCR signalling, olfactory signalling and nitrogen metabolism. Upon 25% ER, gene sets related to energy metabolism and immune response were decreased. CONCLUSIONS: Based on gen e expression changes, we concluded that consumption of normal protein quantity compared to high protein quantity during ER has a more beneficial effect on inflammation-related gene expression in WAT

The influence of maternal cortisol and emotional state during pregnancy on fetal intrauterine growth

Background:This exploratory study investigates the influence of maternal cortisol and emotional state during pregnancy on fetal intra-uterine growth (IUG). We expected higher basal cortisol levels, or more depressive and anxious complaints during pregnancy, to be associated with slower IUG and lower birth weight.Methods:91 pregnant women were recruited from the antenatal clinic and were seen once each trimester. Next to psychological assessments, a diurnal cortisol profile was derived from saliva samples. IUG was evaluated using ultrasound.Results:In mid-pregnancy (T2) basal cortisol levels significantly predicted the variance of weight (Proportion of variance in growth variable explained (PVE)=11.6%) and body mass index (BMI) at birth (PVE=6.8%). In late-pregnancy (T3) emotional state, particularly depressive symptoms (BMI at Birth: PVE=6.9%; Ponderal Index (PI) at birth: PVE=8.2%; head circumference at T3: PVE=10.3%; head circumference at birth PVE=9.1%) and attachment (BMI at Birth: PVE=6.9%; PI at birth: PVE=7.2%) had an influence on growth.Analysis of growth between T2 and T3 showed that attachment and cortisol in T3 had an influence on the variation in increase in estimated fetal weight (PVE=12.5% - PVE=8.6%).Conclusion:These data indicate basal cortisol levels were more important in T2. Whereas emotional state was more important in T3.status: publishe