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Specific biological processes associated with T2D-induced left ventricular dysfunction in patients with aortic stenosis pressure overload

GSE236191 Homo sapiens (human) RNA-seq (gene expression) 47 samples Submitted 2026/06/26 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 47 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
47 samples: 30 × Non-diabetic, myocardial biopsy from patient, biol vs 17 × Type 2 diabetic, myocardial biopsy from patient, biolGroups 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.
Summary (written by the submitters)
Background: Type 2 diabetes (T2D) is increasingly prevalent and increases the risk of developing heart failure. T2D is frequently associated with left ventricular (LV) pressure overload, such as encountered in hypertension and aortic stenosis (AS), and few studies suggested an aggravating effect of T2D. We aimed to explore if the deleterious impact of T2D on LV remodeling and function in patients with AS is associated with specific biological processes.
Read the full summary ↓
Methods: We combined a profound clinical and echocardiography phenotype of patients with severe AS+/-T2D (without overt heart disease) referred for a valve replacement with histolog-ical and transcriptomic analyses of an intra-operative myocardial LV biopsy and ELISA measurement of circulating levels of different biomarkers. Results: 84 patients, including 34 with T2D, were included with median age (67 YO), and 38% were females. Hypertension (p<0.01) and dyslipidemia (p<0.01) coexisted more frequently in T2D patients. T2D patients had greater BMI (p<0.01) and BSA (p=0.034). T2D patients had similar AS severity but displayed worse cardiac remodeling, systolic and diastolic function than non-diabetics. At the tissue level, T2D was associated with worse cardiomyocyte hypertrophy and apoptosis with a structural reticulum-mitochondria Ca2+ uncoupling. Regarding metabolic biomarkers, leptin and FABP3 were significantly increased in T2D patients compared to non-diabetics, correlating respectively with diastolic dysfunction and hypertrophy. Endothelin-1 was also significantly increased in the T2D group and associated with hypertrophy and systolic dysfunction. Importantly, in agreement with the myocardial analyses, inflammatory and fibrotic circulating markers were not different between groups. Regarding the transcriptomic analysis, the upregulated pathways in T2D gathered regulation of inflammation, extracellular matrix organization, endothelial function/angiogenesis, and adaptation to cardiac hypertrophy and remained T2D-specific despite comorbidity adjustment. Gene sets downregulated in T2D patients were related to mitochondrial respiratory chain organization/function, mitochondrial organization, amino acid metabolism, and regulation of muscle contraction. The mitochondrial respiratory chain process and mitochondrial organization were independently associated with T2D, while the other downregulated pathways were no longer significantly associated with T2D after adjusting for covariates. Conclusion: Increased adverse LV remodeling and dysfunction in T2D with severe AS is associated with specific processes which may lead to potential therapeutic targets.
This dataset
Open GSE236191 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 GSE236191_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 PRJNA989227 and SRA study SRP446616. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
30 × Non-diabetic, myocardial biopsy from patient, biol vs 17 × Type 2 diabetic, myocardial biopsy from patient, biol

Supports a between-group comparison across 47 samples.

How we read this: 2 replicated groups read from 47 sample titles; they account for 47 of them. 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

+ 7 more — browse all 47 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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