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Record W2075475582 · doi:10.4239/wjd.v3.i1.1

Renal hyperfiltration related to diabetes mellitus and obesity in human disease

2012· article· en· W2075475582 on OpenAlexafffund
Alexa N. Sasson

Bibliographic record

VenueWorld Journal of Diabetes · 2012
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsUniversity Health Network
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicineGlomerular hyperfiltrationEndocrinologyInternal medicineTubuloglomerular feedbackDiabetes mellitusAfferent arteriolesAngiotensin IIKidneyBlood pressureDiabetic nephropathy

Abstract

fetched live from OpenAlex

High intraglomerular pressure is associated with renal hyperfiltration, leading to the initiation and progression of kidney disease in experimental models of diabetes mellitus (DM). In humans, hyperfiltration is observed in patients with type 1 and type 2 DM, and is also seen in patients with pre-diabetic conditions, such as the metabolic syndrome. From a mechanistic perspective, both vascular and tubular factors likely contribute to the pathogenesis of hyperfiltration. Until now, human studies have primarily focused on the use of medications that inhibit the renin angiotensin system to reduce efferent vasoconstriction and thereby improve hyperfiltration. More recent advances in the development of investigational adenosine antagonists and inhibitors of sodium glucose co-transport may help to elucidate tubular factors that contribute to afferent vasodilatation. In this review, we summarize available data from experimental and human studies of type 1 and type 2 DM and obesity to provide an overview of factors that contribute to the hyperfiltration state. We have focused on the renin angiotensin system, cyclooxygenase-2 system, nitric oxide, protein kinase C and endothelin as vascular determinants of hyperfiltration. We also discuss relevant tubular factors, since experimental models have suggested that inhibition of sodium-glucose cotransport may be renoprotective.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.017
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.010
GPT teacher head0.260
Teacher spread0.250 · 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.

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

Citations163
Published2012
Admission routes2
Has abstractyes

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