Evidence map›Paper›PMID 42835133›Full record

ArticleiScience2026

Inverse directions of association of higher physical activity and higher insulin resistance with human skeletal muscle fiber-type gene expression.

Dan L Ciotlos, Sarah C Hanks, Arushi Varshney, Michael R Erdos, Nandini Manickam, Heather M Stringham, Peter Orchard, Erin M Hill-Burns, Narisu Narisu, Lori L Bonnycastle and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

21 authors.

Dan L CiotlosDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Sarah C HanksDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Arushi VarshneyGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Michael R ErdosCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Nandini ManickamGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Heather M StringhamDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Peter OrchardGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
Erin M Hill-BurnsDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Narisu NarisuCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Lori L BonnycastleCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Michael D SweeneyDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Jouko SaramiesSouth Karelia Social and Health Care District, Wellbeing Services County of South Karelia, South Karelia, Finland.
Markku LaaksoInstitute of Clinical Medicine, Internal Medicine, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland.
Jaakko TuomilehtoDepartment of Public Health, University of Helsinki, Helsinki, Finland.
Timo A LakkaInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.
Karen L MohlkeDepartment of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA.
Michael BoehnkeDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Francis S CollinsCenter for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
Heikki A KoistinenUniversity of Helsinki, Faculty of Medicine, Research Programs Unit, Clinical and Molecular Metabolism (CAMM), Helsinki, Finland.
Stephen C J ParkerDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
Laura J ScottDepartment of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

University of Michigan Training Program in Genomic ScienceT32HG000040 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Sebastian Zoellner · 1995 to 2026
$16.4M
Identifying Genes for Type 2 Diabetes: FUSIONU01DK062370 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L, SCOTT, LAURA J. · 2009 to 2021
$11.6M
Targeted Genetic Analysis of T2D and Quantitative TraitsR01DK072193 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI KAREN L. MOHLKE · 2005 to 2026
$11.3M
Bridging the gap between type 2 diabetes GWAS and therapeutic targetsUM1DK126185 · NIDDK · UNIV OF NORTH CAROLINA CHAPEL HILL · PI CLAUSSNITZER, MELINA C, GLOYN, ANNA LOUISE · 2020 to 2024
$9.5M
Structure, Composition, & Histology Core - Core BP30AR069620 · NIAMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KARL J JEPSEN · 2016 to 2026
$8.4M
Context-specific and combinatorial genetic regulatory grammars in diabetesR01DK117960 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Stephen CJ Parker · 2018 to 2026
$2.8M
Design and Analysis of Human Gene Mapping StudiesR01HG009976 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOEHNKE, MICHAEL L · 2018 to 2022
$2.5M
NHGRI NIH HHS R01 HG009976NHGRI NIH HHS T32 HG000040NIAMS NIH HHS P30 AR069620NIDDK NIH HHS R01 DK072193NIDDK NIH HHS R01 DK117960NIDDK NIH HHS U01 DK062370NIDDK NIH HHS UM1 DK126185
6 · The paper itself

Abstract

To investigate the interplay between physical activity and cardiometabolic traits in human skeletal muscle, we characterized gene expression and chromatin accessibility across skeletal muscle cell types in 263 Finnish individuals from the FUSION Tissue Biopsy Study. We analyzed single-nucleus RNA-seq data (168,309 nuclei), single-nucleus ATAC-seq data (242,069 nuclei), and bulk RNA-seq data. Lower insulin resistance (HOMA-IR) and higher total physical activity were both associated with higher proportions of Type 1 nuclei and lower proportions of Type 2x nuclei. Traits typically associated with better health-lower trait values of cardiometabolic traits such as HOMA-IR and higher physical activity levels-were associated with higher expression of energy metabolism genes and lower expression of signaling pathway genes across muscle fiber types, total pseudobulk, and to some extent in bulk tissue. HOMA-IR and physical activity appeared to have inverse and partially independent directions of association when adjusting for both traits in the same model.

Indexed as

chromatin accessibilitygene expressioninsulin resistancephysical activitysingle nucleusskeletal musclesnATAC-seqsnRNA-Seqtype 2 diabetes

Identifiers

PMID42835133
PMCPMC13635265

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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.