Search

BACKGROUNDGut microbiota have been implicated in atherosclerotic disease, but their relation with subclinical coronary atherosclerosis is unclear. This study aimed to identify associations between the gut microbiome and computed tomography-based measures of coronary atherosclerosis, and to explore relevant clinical correlates.METHODSWe conducted a cross-sectional study of 8973 participants aged 50 to 65 without overt atherosclerotic disease from the population-based Swedish Cardiopulmonary BioImage Study (SCAPIS). Coronary atherosclerosis was measured using coronary artery calcium score (CACS) and coronary computed tomography angiography (CCTA). Gut microbiota species abundance and functional potential were assessed with shotgun metagenomics sequencing of stool samples, and their association with coronary atherosclerosis was evaluated with multivariable regression models adjusted for cardiovascular risk factors. Associated species were evaluated for association with inflammatory markers, metabolites, and corresponding species in saliva.RESULTSThe mean age of the study sample was 57.4 years, and 53.7% were female. Coronary artery calcification was detected in 40.3% of participants, and 5.4% had at least one stenosis with more than 50% occlusion. Sixty-four species were associated with CACS independent of cardiovascular risk factors, with the strongest associations observed forStreptococcus anginosusandS. oralissubsp. oralis(P-5). Associations were largely similar across CCTA-based measurements. Out of the 64 species, 19 species, including streptococci and other species commonly found in the oral cavity, were associated with high-sensitivity C-reactive protein plasma concentrations and 16 with neutrophil counts. Oral species in the gut were negatively associated with plasma indole propionate and positively associated with plasma secondary bile acids and imidazole propionate. Five species correlated with the same species in saliva and were associated with worse dental health in the Malmö Offspring Dental Study. Microbial functional potential of dissimilatory nitrate reduction, anaerobic fatty acid beta-oxidation and amino acid degradation was associated with CACS.CONCLUSIONSThis study provides evidence of an association of a gut microbiota composition characterized by increased abundance ofStreptococcusspp. and other species commonly found in the oral cavity with coronary atherosclerosis and systemic inflammation. Further longitudinal and experimental studies are warranted to explore the potential implication of a bacterial component in atherogenesis.CLINICAL PERSPECTIVEWHAT IS NEW?Shotgun metagenomics identified associations between gut species and subclinical atherosclerosis assessed with computed tomography-derived coronary artery calcium score (CACS) in 8973 participants, with an overrepresentation of theStreptococcusandOscillobactergenera.The relative abundance of CACS-associated oral species detected in fecal samples was negatively associated with indole propionate, while positively associated with secondary bile acids and imidazole propionate.GutStreptococcusspp. were positively associated with circulating biomarkers of systemic inflammation and infection response, and with the same species located in the mouth, which were in turn associated with oral pathologies.WHAT ARE THE CLINICAL IMPLICATIONS?We describe the link between gut microbiota composition, especially species commonly found in the mouth, with subclinical coronary atherosclerosis and biomarkers of inflammation in the largest cardiovascular and metagenomics study to date.The effects of gut and oralStreptococcusspp. on risk for coronary artery disease merit further longitudinal and experimental studies.

Summary paragraphThe human gut microbiota produces a variety of small compounds, some of which enter the bloodstream and impact host health. Conversely, various exogenous nutritional and pharmaceutical compounds affect the gut microbiome composition before entering circulation. Characterization of the gut microbiota–host plasma metabolite interactions is an important step towards understanding the effects of the gut microbiota on human health. However, studies involving large and deeply phenotyped cohorts that would reveal such meaningful interactions are scarce. Here, we used deep metagenomic sequencing and ultra-high-performance liquid chromatography linked to mass spectrometry for detailed characterization of the fecal microbiota and plasma metabolome, respectively, of 8,584 participants invited at age 50 to 64 of the Swedish CArdioPulmonary bioImage Study (SCAPIS). After adjusting for multiple comparisons, we identified 1,008 associations between species alpha diversity and plasma metabolites, and 318,944 associations between specific gut metagenomic species and plasma metabolites. The gut microbiota explained up to 50% of the variance of individual plasma metabolites (mean of 4.7%). We present all results as the searchable association atlas “GUTSY” as a rich resource for mining associations, and exemplify the potential of the atlas by presenting novel associations between oral medication and the gut microbiome, and microbiota species strongly associated with levels of the uremic toxin p-cresol sulfate. The association atlas can be used as the basis for targeted studies of perturbation of specific bacteria and for identification of candidate plasma biomarkers of gut flora composition.

BackgroundThe response of the Swedish authorities to the COVID-19 pandemic was less restrictive than in most countries during the first year, with infection and death rates substantially higher than in neighbouring Nordic countries. Because access to PCR testing was limited during the first wave (February to June 2020) and regional data were reported with delay, adequate monitoring of community disease spread was hampered. The app-based COVID Symptom Study was launched in Sweden to disseminate real-time estimates of disease spread and to collect prospective data for research. The aim of this study was to describe the research project, develop models for estimation of COVID-19 prevalence and to evaluate it for prediction of hospital admissions for COVID-19. MethodsWe enrolled 143 531 study participants ([≥]18 years) throughout Sweden, who contributed 10{middle dot}6 million daily symptom reports between April 29, 2020 and February 10, 2021. Data from 19 161 self-reported PCR tests were used to create a symptom-based algorithm to estimate daily prevalence of symptomatic COVID-19. The prediction model was validated using external datasets. We further utilized the model estimates to forecast subsequent new hospital admissions. FindingsA prediction model for symptomatic COVID-19 based on 17 symptoms, age, and sex yielded an area under the ROC curve of 0{middle dot}78 (95% CI 0{middle dot}74-0{middle dot}83) in an external validation dataset of 943 PCR-tested symptomatic individuals. App-based surveillance proved particularly useful for predicting hospital trends in times of insufficient testing capacity and registration delays. During the first wave, our prediction model estimates demonstrated a lower mean error (0{middle dot}38 average new daily hospitalizations per 100 000 inhabitants per week (95% CI 0{middle dot}32, 0{middle dot}45)) for subsequent hospitalizations in the ten most populated counties, than a model based on confirmed case data (0{middle dot}72 (0{middle dot}64, 0{middle dot}81)). The model further correctly identified on average three out of five counties (95% CI 2{middle dot}3, 3{middle dot}7) with the highest rates of hospitalizations the following week during the first wave and four out of five (3{middle dot}0, 4{middle dot}6) during the second wave. InterpretationThe experience of the COVID Symptom Study highlights the important role citizens can play in real-time monitoring of infectious diseases, and how app-based data collection may be used for data-driven rapid responses to public health challenges. FundingSwedish Heart-Lung Foundation (20190470, 20140776), Swedish Research Council (EXODIAB, 2009-1039; 2014-03529), European Commission (ERC-2015-CoG - 681742 NASCENT), and Swedish Foundation for Strategic Research (LUDC-IRC, 15-0067) to MG or PF. European Research Council Starting Grant (801965) to TF. NO was financially supported by the Knut and Alice Wallenberg Foundation as part of the National Bioinformatics Infrastructure Sweden at SciLifeLab. ATC was supported in this work through the Massachusetts Consortium on Pathogen Readiness (MassCPR). ZOE Limited provided in-kind support for all aspects of building, running and supporting the app and service to all users worldwide. Support for this study for KCL researchers was provided by the National Institute for Health Research (NIHR)-funded Biomedical Research Centre based at Guys and St Thomas (GSTT) NHS Foundation Trust. This work was supported by the UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare. Investigators also received support from the Wellcome Trust, Medical Research Council (MRC), British Heart Foundation (BHF), Alzheimers Society, European Union, NIHR, COVID-19 Driver Relief Fund (CDRF) and the NIHR-funded BioResource, Clinical Research Facility and Biomedical Research Centre (BRC) based at GSTT NHS Foundation Trust in partnership with KCL. ZOE Limited developed the app for data collection as a not-for-profit endeavour. None of the funding entities had any role in study design, data analysis, data interpretation, or the writing of this manuscript.

BackgroundDiagnostic testing is essential for disease surveillance and test-trace-isolate efforts. Here, we aimed to investigate if residential area sociodemographic characteristics and test accessibility were associated with COVID-19 testing rates.MethodsWe included information on 421 542 patient-initiated COVID-19 PCR tests from Uppsala County in Sweden from 24 June, 2020 to 9 February, 2022. Using Poisson regression analyses, we investigated whether the Care Need Index (CNI; median 1.0, IQR 0.8, 1.4), a composite measure of sociodemographic factors used in Sweden to allocate primary healthcare resources, was associated with aggregated COVID-19 daily testing rates after adjustments for community transmission. We further assessed if distance to the nearest testing station influenced testing. Lastly, we performed a difference-in-difference analysis of the opening of a testing station targeting a disadvantaged neighbourhood.ResultsWe observed that CNI, i.e. primary healthcare need, was negatively associated with COVID-19 testing rates in inhabitants aged 5-69 years. More pronounced differences were noted across younger age groups and in Uppsala City, with test rate ratios in children (5-14 years) ranging from 0.56 (95% CI 0.47-0.66) to 0.88 (95% CI 0.81-0.95) across the three pandemic waves. Longer distance to testing station was linked to lower testing rates, foremost in less densely populated areas. Furthermore, the opening of the targeted testing station was associated with increased testing, including twice as high testing rates in individuals aged 70-105, supporting an intervention effect.ConclusionsEnsuring accessible testing across all residential areas constitutes a promising tool to decrease differences and inequalities in testing.