GEO series

Liver dysfunction in obesity is associated with cerebrovascular health independently of diabetes and hypertension.

GSE249997 Homo sapiens (human) RNA-seq (gene expression) 77 samples Submitted 2024/09/30 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 77 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: 77 samples: 77 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: Metabolic dysfunction-associated steatotic liver disease is associated with effects on cerebral perfusion and white matter integrityHeliyon · 2024

Summary (written by the submitters)

It is unclear whether liver dysfunction is implicated in obesity-related detrimental changes in brain structure and function. This study examineed associations between liver dysfunction and brain health in middle-aged participants with obesity and early-stage metabolic dysfunction-associated steatotic liver disease (MASLD). Using brain magnetic resonance imaging, neuropsychological tests, histological and biochemical characterization of liver biopsies, we showed that specific hallmarks of liver dysfunction (e.g.

Read the full summary ↓

free cholesterol accumulation, early fibrosis) are associated with increased white matter hyperintensities and larger variations in cerebral blood flow indicating poorer cerebrovascular function. These associations were independent of age, sex, body mass index, diabetes and hypertension and corroborated by independent liver RNA-seq and pathway analysis highlighting the role of liver inflammation and cellular stress. Further associations with circulating IL-6 suggest systemic low-grade inflammation as potential mediator between liver and brain. Hence, in obesity at midlife, cerebrovascular health is independently associated with the pathological state of the liver.

Published in

Metabolic dysfunction-associated steatotic liver disease is associated with effects on cerebral perfusion and white matter integrity

Seidel F, Vreeken D, Custers E et al. · Heliyon 2024 · PMID 39391513 · doi:10.1016/j.heliyon.2024.e38516

This dataset

Open GSE249997 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 GSE249997_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 PRJNA1051582 and SRA study SRP477626. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39391513) ↗

Study design

77 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 77 sample titles: 77 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

GSM7968809 Subj_28, liver, middle aged, early MASLD [104019-002-009]
GSM7968810 Subj_44, liver, middle aged, early MASLD [104019-001-026]
GSM7968811 Subj_66, liver, middle aged, early MASLD [104019-002-016]
GSM7968812 Subj_95, liver, middle aged, early MASLD [104019-001-058]
GSM7968813 Subj_108, liver, middle aged, early MASLD [104019-001-068]
GSM7968814 Subj_127, liver, middle aged, early MASLD [104019-001-086]
GSM7968815 Subj_128, liver, middle aged, early MASLD [104019-001-087]
GSM7968816 Subj_136, liver, middle aged, early MASLD [104019-001-092]
GSM7968817 Subj_109, liver, middle aged, early MASLD [104019-001-069]
GSM7968818 Subj_1, liver, middle aged, early MASLD [104019-001-001]
GSM7968819 Subj_7, liver, middle aged, early MASLD [104019-001-005]
GSM7968820 Subj_9, liver, middle aged, early MASLD [104019-001-006]
GSM7968821 Subj_17, liver, middle aged, early MASLD [104019-001-011]
GSM7968822 Subj_19, liver, middle aged, early MASLD [104019-001-012]
GSM7968823 Subj_21, liver, middle aged, early MASLD [104019-001-013]
GSM7968824 Subj_29, liver, middle aged, early MASLD [104019-001-018]
GSM7968825 Subj_40, liver, middle aged, early MASLD [104019-001-023]
GSM7968826 Subj_42, liver, middle aged, early MASLD [104019-001-024]
GSM7968827 Subj_45, liver, middle aged, early MASLD [104019-001-027]
GSM7968828 Subj_47, liver, middle aged, early MASLD [104019-001-028]
GSM7968829 Subj_49, liver, middle aged, early MASLD [104019-001-030]
GSM7968830 Subj_54, liver, middle aged, early MASLD [104019-001-033]
GSM7968831 Subj_55, liver, middle aged, early MASLD [104019-001-034]
GSM7968832 Subj_58, liver, middle aged, early MASLD [104019-001-036]
GSM7968833 Subj_61, liver, middle aged, early MASLD [104019-001-039]
GSM7968834 Subj_69, liver, middle aged, early MASLD [104019-001-041]
GSM7968835 Subj_78, liver, middle aged, early MASLD [104019-001-044]
GSM7968836 Subj_80, liver, middle aged, early MASLD [104019-001-046]
GSM7968837 Subj_81, liver, middle aged, early MASLD [104019-001-047]
GSM7968838 Subj_82, liver, middle aged, early MASLD [104019-001-048]
GSM7968839 Subj_83, liver, middle aged, early MASLD [104019-001-049]
GSM7968840 Subj_97, liver, middle aged, early MASLD [104019-001-059]
GSM7968841 Subj_99, liver, middle aged, early MASLD [104019-001-061]
GSM7968842 Subj_103, liver, middle aged, early MASLD [104019-001-065]
GSM7968843 Subj_105, liver, middle aged, early MASLD [104019-001-066]
GSM7968844 Subj_106, liver, middle aged, early MASLD [104019-001-067]
GSM7968845 Subj_112, liver, middle aged, early MASLD [104019-001-072]
GSM7968846 Subj_115, liver, middle aged, early MASLD [104019-001-075]
GSM7968847 Subj_116, liver, middle aged, early MASLD [104019-001-076]
GSM7968848 Subj_117, liver, middle aged, early MASLD [104019-001-077]

+ 37 more — browse all 77 samples with per-sample file links →

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GSE271223 Prediabetes is associated with musculoskeletal dysfunction independent of total body adiposity 24 samples shares dysfunction, diabetes
GSE233539 A human iPSC-derived multilineage liver organoid model containing Kupffer cells to study human sepsis-associated liver dysfunction 22 samples shares dysfunction, liver

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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.