Evidence map›Paper›PMID 36348107›Full record

ArticleResults and problems in cell differentiation2022

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics.

Sreemol Gokuladhas, Roan E Zaied, William Schierding, Sophie Farrow, Tayaza Fadason, Justin M O'Sullivan

Abstract read
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In one paragraph

Article in Results and problems in cell differentiation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

6 authors.

Sreemol GokuladhasLiggins Institute, The University of Auckland, Auckland, New Zealand.
Roan E ZaiedLiggins Institute, The University of Auckland, Auckland, New Zealand.
William SchierdingLiggins Institute, The University of Auckland, Auckland, New Zealand.
Sophie FarrowLiggins Institute, The University of Auckland, Auckland, New Zealand.
Tayaza FadasonLiggins Institute, The University of Auckland, Auckland, New Zealand.
Justin M O'SullivanLiggins Institute, The University of Auckland, Auckland, New Zealand. justin.osullivan@auckland.ac.nz.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.

Indexed as

GenomicsMultimorbidityGene Expression RegulationHumansMultimorbidityMulti-omicsMulti-tissueSpatial genomicsThree-dimensional genomeTissue networking

Identifiers

PMID36348107

What OpenQuestion holds

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Registered trials

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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.