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Record W4404606345 · doi:10.1016/s2666-5247(24)00162-9

Altered IL-6 signalling and risk of tuberculosis: a multi-ancestry mendelian randomisation study

2024· review· en· W4404606345 on OpenAlexaff
Fergus Hamilton, Haiko Schurz, Tom Yates, James J. Gilchrist, Marlo Möller, Vivek Naranbhai, Peter Ghazal, Nicholas J. Timpson

Bibliographic record

VenueThe Lancet Microbe · 2024
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsInstitute of Infection and Immunity
FundersFaculty of Medicine and Health, University of SydneyMedical Research CouncilSouth African Medical Research CouncilUniversity College LondonNational Institute for Health and Care ResearchEuropean Regional Development FundUK Research and InnovationUniversity of BristolDepartment of Health and Social CareUniversiteit StellenboschCancer Research UKWellcome Trust
KeywordsMendelian inheritanceTuberculosisMendelian randomizationBiologySignallingMedicineGeneticsGeneGenotypePathologyCell biology

Abstract

fetched live from OpenAlex

BACKGROUND: The role of IL-6 responses in human tuberculosis risk is unknown. IL-6 signalling inhibitors, such as tocilizumab, are thought to increase the risk of progression to tuberculosis, and screening for latent Mycobacterium tuberculosis infection before using these drugs is widely recommended. We used single nucleotide polymorphisms (SNPs) in and near the IL-6 receptor gene (IL6R), including the non-synonymous variant, rs2228145, for which the C allele contributes to reduced classic (cis) IL-6 signalling activity, to test the hypothesis that altered IL-6 signalling is causally associated with the risk of developing tuberculosis. METHODS: We performed a meta-analysis of genome-wide association studies (GWAS) published in English from database inception to Jan 1, 2024. GWAS were identified from the European Bioinformatics Institute, MRC Integrative Epidemiology Unit catalogues, and MEDLINE, selecting publicly available studies for which tuberculosis was an outcome and that included the IL6R rs2228145 SNP. Using each study's population-level summary statistics, effect estimates were extracted for each additional copy of the C allele of rs2228145. We used these estimates to perform multi-ancestry, two-sample mendelian randomisation analyses to estimate the causal effect of reduced IL-6 signalling on tuberculosis. Our primary analyses used rs2228145-C as a genetic instrument, weighted on C-reactive protein (CRP) reduction as a measure of the effect on IL-6 signalling. We also took an alternative, ancestry-specific, multiple SNP approach using IL-6 receptor plasma protein as an exposure. Additionally, we compared the effects of rs2228145 in tuberculosis with those in critical COVID-19, rheumatoid arthritis, Crohn's disease, and coronary artery disease using the summary statistics extracted from GWAS. FINDINGS: ). The protective effects on tuberculosis seen with rs2228145-C were similar in size and direction to those observed in critical COVID-19 (0·66 [0·50-0·86]), Crohn's disease (0·57 [0·44-0·74]), and rheumatoid arthritis (0·45 [0·36-0·58]), all of which benefit from the therapeutic effects of IL-6 antagonism. INTERPRETATION: Our findings propose a causal relationship between reduced IL-6 signalling and lower risk of tuberculosis, akin to the effect seen in other IL-6 mediated diseases. This study suggests that IL-6 antagonists do not increase the risk of tuberculosis but rather should be investigated as therapeutic adjuncts in its treatment. FUNDING: UK National Institute for Health and Care Research, Wellcome Trust, EU European Regional Development Fund, the Welsh Government, and UK Research and Innovation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.005
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.152
GPT teacher head0.422
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2024
Admission routes1
Has abstractyes

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