Associations of physical activity, sedentary time, and diet quality with biomarkers of inflammation in children

We investigated the associations of physical activity (PA), sedentary time (ST), and diet quality with biomarkers of inflammation in 390 children (192 girls, 198 boys) aged 6–8 years. PA energy expenditure (PAEE), light PA, moderate PA (MPA), vigorous PA (VPA), moderate-to-vigorous PA (MVPA), and ST were assessed by combined movement and heart rate sensor. Finnish Children Healthy Eating Index was calculated using data from 4 d food records. Body fat percentage (BF%) was measured by dual-energy X-ray absorptiometry. High-sensitivity C-reactive protein (Hs-CRP), leptin, interleukin-6 (IL-6), adiponectin, tumour necrosis factor-α, and glycoprotein acetyls were measured from fasting blood samples. PAEE, MPA, VPA, and MVPA were inversely associated with hs-CRP (β=−191 to −139, 95% CI=−0.294 to −0.024), leptin (β=−0.409 to −0.301, 95% CI=−0.499 to −0.107), IL-6 (β=−0.136 to −0.104, 95% CI=−0.240 to −0.001) and PAEE, MPA, and MVPA were inversely associated with glycoprotein acetyls (β=−0.117 to −0.103, 95% CI=−0.213 to −0.001). ST was directly associated with hs-CRP (β=0.170, 95% CI=0.070–0.269), leptin (β=0.355, 95% CI=0.265–0.445), and IL-6 (β=0.105, 95% CI=0.005–0.205). VPA was inversely associated with hs-CRP, leptin, and IL-6 in children with higher BF% (β=−0.344 to −0.181, 95% CI=−0.477 to −0.033) but not among children with lower BF% (β=−0.007–0.033, 95% CI=−0.183–0.184). In conclusion, PA was inversely and ST directly associated with circulating levels of biomarkers of inflammation among children. Furthermore, we observed that PA was inversely associated with these biomarkers for inflammation in children with a higher BF%. Highlights Systemic inflammation, as indicated by increased circulating concentrations of biomarkers for inflammation, may be important in causal pathways leading to insulin resistance, sub-clinical atherosclerosis, and eventually clinical manifestations of cardiovascular diseases. Higher levels of physical activity and lower levels of sedentary time were associated with more favourable inflammatory profile. Body fat percentage modified these associations and especially vigorous intensity physical activity was inversely associated with biomarkers of inflammation on children with higher body fat percentage but not in children with lower body fat percentage.

Early stage litter decomposition across biomes

peer reviewedThrough litter decomposition enormous amounts of carbon is emitted to the atmosphere. Numerous large-scale decomposition experiments have been conducted focusing on this fundamental soil process in order to understand the controls on the terrestrial carbon transfer to the atmosphere. However, previous studies were mostly based on site-specific litter and methodologies, adding major uncertainty to syntheses, comparisons and metaanalyses across different experiments and sites. In the TeaComposition initiative, the potential litter decomposition is investigated by using standardized substrates (Rooibos and Green tea) for comparison of litter mass loss at 336 sites (ranging from −9 to +26 °C MAT and from 60 to 3113mm MAP) across different ecosystems. In this study we tested the effect of climate (temperature and moisture), litter type and land-use on early stage decomposition (3 months) across nine biomes. We show that litter quality was the predominant controlling factor in early stage litter decomposition, which explained about 65% of the variability in litter decomposition at a global scale. The effect of climate, on the other hand, was not litter specific and explained b0.5% of the variation for Green tea and 5% for Rooibos tea, and was of significance only under unfavorable decomposition conditions (i.e. xeric versus mesic environments).When the data were aggregated at the biome scale, climate played a significant role on decomposition of both litter types (explaining 64% of the variation for Green tea and 72% for Rooibos tea).No significant effect of land-use on early stage litter decompositionwas notedwithin the temperate biome. Our results indicate that multiple drivers are affecting early stage littermass loss with litter quality being dominant. In order to be able to quantify the relative importance of the different drivers over time, long-term studies combined with experimental trials are needed

A global metagenomic map of urban microbiomes and antimicrobial resistance

We present a global atlas of 4,728 metagenomic samples from mass-transit systems in 60 cities over 3 years, representing the first systematic, worldwide catalog of the urban microbial ecosystem. This atlas provides an annotated, geospatial profile of microbial strains, functional characteristics, antimicrobial resistance (AMR) markers, and genetic elements, including 10,928 viruses, 1,302 bacteria, 2 archaea, and 838,532 CRISPR arrays not found in reference databases. We identified 4,246 known species of urban microorganisms and a consistent set of 31 species found in 97% of samples that were distinct from human commensal organisms. Profiles of AMR genes varied widely in type and density across cities. Cities showed distinct microbial taxonomic signatures that were driven by climate and geographic differences. These results constitute a high-resolution global metagenomic atlas that enables discovery of organisms and genes, highlights potential public health and forensic applications, and provides a culture-independent view of AMR burden in cities.Funding: the Tri-I Program in Computational Biology and Medicine (CBM) funded by NIH grant 1T32GM083937; GitHub; Philip Blood and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1548562 and NSF award number ACI-1445606; NASA (NNX14AH50G, NNX17AB26G), the NIH (R01AI151059, R25EB020393, R21AI129851, R35GM138152, U01DA053941); STARR Foundation (I13- 0052); LLS (MCL7001-18, LLS 9238-16, LLS-MCL7001-18); the NSF (1840275); the Bill and Melinda Gates Foundation (OPP1151054); the Alfred P. Sloan Foundation (G-2015-13964); Swiss National Science Foundation grant number 407540_167331; NIH award number UL1TR000457; the US Department of Energy Joint Genome Institute under contract number DE-AC02-05CH11231; the National Energy Research Scientific Computing Center, supported by the Office of Science of the US Department of Energy; Stockholm Health Authority grant SLL 20160933; the Institut Pasteur Korea; an NRF Korea grant (NRF-2014K1A4A7A01074645, 2017M3A9G6068246); the CONICYT Fondecyt Iniciación grants 11140666 and 11160905; Keio University Funds for Individual Research; funds from the Yamagata prefectural government and the city of Tsuruoka; JSPS KAKENHI grant number 20K10436; the bilateral AT-UA collaboration fund (WTZ:UA 02/2019; Ministry of Education and Science of Ukraine, UA:M/84-2019, M/126-2020); Kyiv Academic Univeristy; Ministry of Education and Science of Ukraine project numbers 0118U100290 and 0120U101734; Centro de Excelencia Severo Ochoa 2013–2017; the CERCA Programme / Generalitat de Catalunya; the CRG-Novartis-Africa mobility program 2016; research funds from National Cheng Kung University and the Ministry of Science and Technology; Taiwan (MOST grant number 106-2321-B-006-016); we thank all the volunteers who made sampling NYC possible, Minciencias (project no. 639677758300), CNPq (EDN - 309973/2015-5), the Open Research Fund of Key Laboratory of Advanced Theory and Application in Statistics and Data Science – MOE, ECNU, the Research Grants Council of Hong Kong through project 11215017, National Key RD Project of China (2018YFE0201603), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01) (L.S.