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Record W4411047907 · doi:10.1016/j.mad.2025.112078

Functional significance of extracellular vesicles as mediators of cardiometabolic and cardiorenal diseases upon aging

2025· review· en· W4411047907 on OpenAlexafffund
Wing Yan Chung, Brooke Pernari, Yoojung Kim, Ssang‐Goo Cho, Dylan Burger, Gary Sweeney

Bibliographic record

VenueMechanisms of Ageing and Development · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsOttawa HospitalYork University
FundersCanadian Institutes of Health ResearchMitacs
KeywordsContext (archaeology)Cardiorenal syndromeDiseaseOxidative stressExtracellular vesiclesDiabetes mellitusMedicineHeart failureType 2 diabetesSenescenceKidney diseaseInsulin resistanceInflammationBiologyInternal medicineEndocrinologyCell biology

Abstract

fetched live from OpenAlex

Aging increases the risk of cardiometabolic and cardiorenal disease and this is associated with cellular dysregulation including oxidative stress, chronic inflammation, insulin resistance and senescence. Extracellular vesicles (EV) facilitate inter-organ communication and are now well established as important pathophysiological mediators in many aging-associated diseases. Our knowledge of EV biosynthesis, cargo composition, cellular targeting and functional effects has expanded significantly over the past decade. Here we provide a comprehensive review on the characteristics and functional significance of EV in cardiometabolic and cardiorenal diseases in the context of aging. Specifically, we focus on heart failure, type 2 diabetes, metabolic dysfunction-associated steatohepatitis (MASH), hypertension, and chronic kidney disease and discuss aging-associated changes in bioactive molecules transferred via EV and how these are associated with healthspan. Furthermore, we summarize current potential therapeutic applications of EV. Overall, this review summarizes current knowledge indicating an important role for EV in aging-related cardiometabolic and cardiorenal diseases, and how insights from basic research can potentially be translated to the clinic in order to combat aging-associated metabolic decline and improve longevity and healthspan.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.921
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.254
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes2
Has abstractyes

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