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Record W4392744919 · doi:10.1093/braincomms/fcae088

<i>Helicobacter pylori</i>, persistent infection burden and structural brain imaging markers

2024· article· en· W4392744919 on OpenAlexaff
May A. Beydoun, Hind A. Beydoun, Yi‐Han Hu, Ziad W. El‐Hajj, Michael F. Georgescu, Nicole Noren Hooten, Chi Kong Li, Jordan Weiss, Donald M. Lyall, Shari R. Waldstein, Shawn D. Gale, Lenore J. Launer, Michele K. Evans, Alan B. Zonderman

Bibliographic record

VenueBrain Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsMcGill University
FundersNational Institute on AgingNational Institutes of HealthUniversity of Pennsylvania
KeywordsHelicobacter pyloriMedicineHelicobacter pylori infectionNeuroimagingImmunologyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract Persistent infections, whether viral, bacterial or parasitic, including Helicobacter pylori infection, have been implicated in non-communicable diseases, including dementia and other neurodegenerative diseases. In this cross-sectional study, data on 635 cognitively normal participants from the UK Biobank study (2006–21, age range: 40–70 years) were used to examine whether H. pylori seropositivity (e.g. presence of antibodies), serointensities of five H. pylori antigens and a measure of total persistent infection burden were associated with selected brain volumetric structural MRI (total, white, grey matter, frontal grey matter (left/right), white matter hyperintensity as percent intracranial volume and bi-lateral sub-cortical volumes) and diffusion-weighted MRI measures (global and tract-specific bi-lateral fractional anisotropy and mean diffusivity), after an average 9–10 years of lag time. Persistent infection burden was calculated as a cumulative score of seropositivity for over 20 different pathogens. Multivariable-adjusted linear regression analyses were conducted, whereby selected potential confounders (all measures) and intracranial volume (sub-cortical volumes) were adjusted, with stratification by Alzheimer’s disease polygenic risk score tertile when exposures were H. pylori antigen serointensities. Type I error was adjusted to 0.007. We report little evidence of an association between H. pylori seropositivity and persistent infection burden with various volumetric outcomes (P &amp;gt; 0.007, from multivariable regression models), unlike previously reported in past research. However, H. pylori antigen serointensities, particularly immunoglobulin G against the vacuolating cytotoxin A, GroEL and outer membrane protein antigens, were associated with poorer tract-specific white matter integrity (P &amp;lt; 0.007), with outer membrane protein serointensity linked to worse outcomes in cognition-related tracts such as the external capsule, the anterior limb of the internal capsule and the cingulum, specifically at low Alzheimer’s disease polygenic risk. Vacuolating cytotoxin A serointensity was associated with greater white matter hyperintensity volume among individuals with mid-level Alzheimer’s disease polygenic risk, while among individuals with the highest Alzheimer’s disease polygenic risk, the urease serointensity was consistently associated with reduced bi-lateral caudate volumes and the vacuolating cytotoxin A serointensity was linked to reduced right putamen volume (P &amp;lt; 0.007). Outer membrane protein and urease were associated with larger sub-cortical volumes (e.g. left putamen and right nucleus accumbens) at middle Alzheimer’s disease polygenic risk levels (P &amp;lt; 0.007). Our results shed light on the relationship between H. pylori seropositivity, H. pylori antigen levels and persistent infection burden with brain volumetric structural measures. These data are important given the links between infectious agents and neurodegenerative diseases, including Alzheimer’s disease, and can be used for the development of drugs and preventive interventions that would reduce the burden of those diseases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.296
Teacher spread0.274 · 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 teacher head, not a consensus.

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

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

Citations9
Published2024
Admission routes1
Has abstractyes

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