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Blood-flow restriction resistance training improves skeletal muscle mitochondrial oxidative capacity and cardiovascular risk factors in type 2 diabetes

GSE312780 Homo sapiens (human) RNA-seq (gene expression) 34 samples Submitted 2026/03/20 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 34 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
34 samples: 34 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Blood-flow restriction resistance training improves skeletal muscle mitochondrial capacity and cardiovascular risk factors in type 2 diabetesCell metabolism · 2026
Summary (written by the submitters)
Impaired muscle strength and mitochondrial functionality are hallmarks of type 2 diabetes (T2D). Conventional combined resistance/endurance exercise training has limited efficacy to simultaneously improve muscle function and metabolism. We examined whether low-load blood flow restriction training (BFRT) increases both muscle strength and mitochondrial oxidative capacity in T2D.
Read the full summary ↓
Over 12 weeks, BFRT and conventional resistance training (CREST) similarly improved muscle strength despite lower workload in BFRT. Uniquely, BFRT enhanced muscle and adipose tissue oxidative capacity and increased muscle mitochondrial content. Transcriptomic profiling revealed more pronounced changes, particularly in angiogenesis-linked pathways, upon BFRT. BFRT also preferentially led to reductions in visceral adipose tissue volume and waist circumference, whereas CREST more effectively decreased subcutaneous adipose tissue volume. Both interventions lowered resting heart rate and diastolic blood pressure. These findings position BFRT as promising low-load exercising strategy to simultaneously improve mitochondrial oxidative capacity, muscle strength and body composition in individuals with T2D.
Published in
Blood-flow restriction resistance training improves skeletal muscle mitochondrial capacity and cardiovascular risk factors in type 2 diabetes
Trinks N, Gancheva S, Pützer J et al. · Cell metabolism 2026 · PMID 41610852 · doi:10.1016/j.cmet.2025.12.016
This dataset
Open GSE312780 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE312780_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1375866 and SRA study SRP651879. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41610852) ↗
Study design
34 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 34 sample titles: 34 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.