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Record W4285020331 · doi:10.3389/fragi.2022.931331

Pathobiology of the Klotho Antiaging Protein and Therapeutic Considerations

2022· review· en· W4285020331 on OpenAlexafffund
Gérald J. Prud’homme, Mervé Kurt, Qinghua Wang

Bibliographic record

VenueFrontiers in Aging · 2022
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of ChinaJuvenile Diabetes Research Foundation InternationalServierSt. Michael's Hospital FoundationCanadian Diabetes AssociationNational Science Foundation
KeywordsKlothoFibroblast growth factor 23EndocrinologyInternal medicineHyperphosphatemiaMedicineFibrosisKidney diseaseKidneyParathyroid hormoneCalcium

Abstract

fetched live from OpenAlex

). These mice have a shortened lifespan, stunted growth, renal disease, hyperphosphatemia, hypercalcemia, vascular calcification, cardiac hypertrophy, hypertension, pulmonary disease, cognitive impairment, multi-organ atrophy and fibrosis. Overexpression of Klotho has opposite effects, extending lifespan. In humans, Klotho levels decline with age, chronic kidney disease, diabetes, Alzheimer's disease and other conditions. Low Klotho levels correlate with an increase in the death rate from all causes. Klotho acts either as an obligate coreceptor for fibroblast growth factor 23 (FGF23), or as a soluble pleiotropic endocrine hormone (s-Klotho). It is mainly produced in the kidneys, but also in the brain, pancreas and other tissues. On renal tubular-cell membranes, it associates with FGF receptors to bind FGF23. Produced in bones, FGF23 regulates renal excretion of phosphate (phosphaturic effect) and vitamin D metabolism. Lack of Klotho or FGF23 results in hyperphosphatemia and hypervitaminosis D. With age, human renal function often deteriorates, lowering Klotho levels. This appears to promote age-related pathology. Remarkably, Klotho inhibits four pathways that have been linked to aging in various ways: Transforming growth factor β (TGF-β), insulin-like growth factor 1 (IGF-1), Wnt and NF-κB. These can induce cellular senescence, apoptosis, inflammation, immune dysfunction, fibrosis and neoplasia. Furthermore, Klotho increases cell-protective antioxidant enzymes through Nrf2 and FoxO. In accord, preclinical Klotho therapy ameliorated renal, cardiovascular, diabetes-related and neurodegenerative diseases, as well as cancer. s-Klotho protein injection was effective, but requires further investigation. Several drugs enhance circulating Klotho levels, and some cross the blood-brain barrier to potentially act in the brain. In clinical trials, increased Klotho was noted with renin-angiotensin system inhibitors (losartan, valsartan), a statin (fluvastatin), mTOR inhibitors (rapamycin, everolimus), vitamin D and pentoxifylline. In preclinical work, antidiabetic drugs (metformin, GLP-1-based, GABA, PPAR-γ agonists) also enhanced Klotho. Several traditional medicines and/or nutraceuticals increased Klotho in rodents, including astaxanthin, curcumin, ginseng, ligustilide and resveratrol. Notably, exercise and sport activity increased Klotho. This review addresses molecular, physiological and therapeutic aspects of Klotho.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.327
Teacher spread0.278 · 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 designNot applicable
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

Citations208
Published2022
Admission routes2
Has abstractyes

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