Evidence map›Paper›PMID 39006431›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID.

Maik Pietzner, Spiros Denaxas, Summaira Yasmeen, Maria A Ulmer, Tomoko Nakanishi, Matthias Arnold, Gabi Kastenmüller, Harry Hemingway, Claudia Langenberg

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Maik PietznerComputational Medicine, Berlin Institute of Health at Charité - Universitatsmedizin Berlin, Berlin, Germany.ORCID 0000-0003-3437-9963
Spiros DenaxasInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0001-9612-7791
Summaira YasmeenComputational Medicine, Berlin Institute of Health at Charité - Universitatsmedizin Berlin, Berlin, Germany.
Maria A UlmerInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
Tomoko NakanishiPrecision Healthcare University Research Institute, Queen Mary University of London, London, UK.ORCID 0000-0001-9510-5646
Matthias ArnoldInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID 0000-0002-4666-0923
Gabi KastenmüllerInstitute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.ORCID 0000-0002-2368-7322
Harry HemingwayInstitute of Health Informatics, University College London, London, UK.ORCID 0000-0003-2279-0624
Claudia LangenbergComputational Medicine, Berlin Institute of Health at Charité - Universitatsmedizin Berlin, Berlin, Germany.

Funding

Project 4 - Mechanistic studies on the role of the gut microbiome in models for Alzheimer's diseaseU19AG063744 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ROB KNIGHT, Rima F Kaddurah-Daouk · 2019 to 2026
$54.1M
Peripheral and Central Biomarkers of Alzheimer's Disease in Diverse CohortsU19AG074879 · NIA · MAYO CLINIC JACKSONVILLE · PI Minerva Maria Carrasquillo · 2023 to 2026
$42.0M
Metabolomic Signatures for Disease Sub-classification and Target Prioritization in AMP-ADU01AG061359 · NIA · DUKE UNIVERSITY · PI KADDURAH-DAOUK, RIMA F, KASTENMULLER, GABI · 2018 to 2022
$10.0M
Metabolic Networks and Pathways Predictive of Sex Differences in AD Risk and Responsiveness to TreatmentRF1AG059093 · NIA · DUKE UNIVERSITY · PI BRINTON, ROBERTA EILEEN, CHANG, RUI · 2018 to 2018
$5.9M
TargetAD: A systems multi-omics approach to drug repositioning in Alzheimer's diseaseR01AG069901 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI Matthias Arnold, Jan Krumsiek · 2021 to 2026
$3.7M
Metabolic age to define influences of the lipidome on brain aging in Alzheimer's diseaseR01AG081322 · NIA · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Matthias Arnold, Rima F Kaddurah-Daouk · 2023 to 2026
$2.6M
Medical Research Council MC_PC_20059NIA NIH HHS R01 AG069901NIA NIH HHS R01 AG081322NIA NIH HHS RF1 AG059093NIA NIH HHS U01 AG061359NIA NIH HHS U19 AG063744NIA NIH HHS U19 AG074879
6 · The paper itself

Abstract

Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses, including common, but non-fatal diseases are scarce, but may help to understand severe COVID-19 among patients at supposedly low risk. Here, we systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID. We identified a total of 679 diseases associated with an increased risk for severe COVID-19 (n=672) and/or Long COVID (n=72) that spanned almost all clinical specialties and were strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we established consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This included a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observed partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis (e.g.,

Identifiers

PMID39006431
PMCPMC11245059

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.