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Record W4386257250 · doi:10.21203/rs.3.rs-3292082/v1

Characteristics of plasma exosomal RNA profile in obesity-related knee osteoarthritis

2023· preprint· en· W4386257250 on OpenAlexaboutno aff
Tao Wai Lun, Bin Zhang, Song Li, Daibo Feng, Yunquan Gong, Wei Xiang, Tong‐Yi Zhang, Bo Huang, Yan Xiong, Zhenhong Ni

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsnot available
FundersCentre Scientifique et Technique du BâtimentNatural Science Foundation of ChongqingArmy Medical UniversityNational Natural Science Foundation of China
KeywordsOsteoarthritisObesityWOMACBody mass indexMedicineMicrovesiclesInternal medicineBiomarkerBioinformaticsmicroRNAExosomeTranscriptomeOncologyGene expressionBiologyGenePathologyGenetics

Abstract

fetched live from OpenAlex

Abstract Background: As the most important risk factors of knee osteoarthritis (OA), obesity is closely related to the clinical symptoms and OA progression of patients. The purpose of this study was to explore the characteristics of exosomal RNAs in plasma of knee OA patients with obesity and discussed their potential diagnostic and therapeutic value in obese knee OA. Methods: The 101 participants with knee OA patients were divided into three groups according to BMI class. The corresponding clinical information was recorded and the correlation with obesity was analyzed. Next, we extracted the plasma exosomes from three OA patients with obesity (BMI≥30kg/m2) and three OA patients without obesity (BMI 18.5-24kg/m2). Then, quantitative sequencing of the whole transcriptome exosomal RNAs, including mRNAs, lncRNAs and circRNAs, was performed and the differential expression of the exosomal RNAs were analyzed. At last, the function of differential RNAs in plasma exosomes between the two groups were discussed via GO enrichment, KEGG pathways and interaction Analysis. Results: There was a negative relationship between BMI and HSS (Hospital for special surgery) score and a positive relationship between BMI and WOMAC (The Western Ontario and McMaster Universities Osteoarthritis) index in 101 participants with knee OA. There were 334 mRNAs and 29 lncRNAs showing significant differential expression between obesity OA group and non-obesity OA group, including 189 up-regulated mRNAs, 145 down-regulated mRNAs, 15 up-regulated lncRNAs and 14 down-regulated lncRNAs. Signal pathway analysis showed that metabolism-related changes including metabolism and organismal system, fatty acid metabolism, positive regulation of fatty acid oxidation, adipocytokine signaling pathway, insulin resistance were enriched in obesity-related OA group. Furthermore, 7 differentially expressed lncRNAs related to lipid metabolism process were screened out, including lnc-TAL1-3-2, NONHSAT209148.1, lnc-DLEU2, Inc00969, lnc-CABP4-2, lnc-CHD1L-5 and lnc-ERICH1-19. However, there was no differential expression of cirRNAs between two groups. Conclusion: Knee OA patients with obesity had more serious clinical symptoms and signs. Compared to the control group, there was obviously differential expression of mRNAs and lncRNAs in plasma exosomes of knee OA patients with obesity. The differential mRNAs and lncRNAs in plasma exosomes may potentially affect synovial inflammation of joint and participate in the pathological injury of OA. Our data suggested that plasma exosomal RNA may be a potential diagnostic and intervention target for OA patients with obesity in the future.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.329
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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