Evidence map›Paper›PMID 42907900›Full record

ArticleGenome medicine2026

Spatial mixed models for assessing environmental exposure effects on the microbiome.

Sooran Kim, Chan Wang, Soyoung Kwak, Fares Darawshy, Alexander Bain, Leopoldo N Segal, Jiyoung Ahn, Huilin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome medicine, 2026. 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Sooran KimDivision of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA.
Chan WangDivision of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA.
Soyoung KwakSchool of Dentistry, Department of Microbiology and Immunology, Kyungpook National University, Daegu, Korea.
Fares DarawshyDivision of Pulmonary, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
Alexander BainDivision of Pulmonary, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
Leopoldo N SegalDivision of Pulmonary, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
Jiyoung AhnDepartment of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
Huilin LiDivision of Biostatistics, Department of Population Health, Grossman School of Medicine, New York University, 180 Madison Avenue, 4th Floor, New York, NY, 10016, USA. Huilin.Li@nyulangone.org.

Funding

National Institutes of Health R01LM014085National Institutes of Health R33 GM147800National Institutes of Health U24ES036002
6 · The paper itself

Abstract

Environmental exposures such as air pollution may influence human health partly through microbiome, but statistical modeling is challenged by spatial dependence across regions and ecological correlations among taxa. We propose SpaMixed, a novel spatial mixed modeling framework for microbiome count data that incorporates both sources of dependence using conditional autoregressive priors. In simulations, SpaMixed achieved high feature selection power while maintaining low false positive rates and reduced estimation error relative to existing methods. Applied to two real studies with fine particulate matter (PM

Indexed as

Environmental ExposureMicrobiotaModels, StatisticalComputer SimulationHumansParticulate MatterParticulate MatterEnvironmental exposureHierarchical modelingMicrobiomeSpatial mixed modelZero-inflated Poisson model

Identifiers

PMID42907900
PMCPMC13685090

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.