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Record W2946478947 · doi:10.1681/asn.2018101066

Chlorthalidone Is Superior to Potassium Citrate in Reducing Calcium Phosphate Stones and Increasing Bone Quality in Hypercalciuric Stone-Forming Rats

2019· article· en· W2946478947 on OpenAlexaff
Nancy S. Krieger, John R. Asplin, Ignacio Granja, Felix M. Ramos, Courtney Flotteron, Luojing Chen, Tong Tong Wu, Marc D. Grynpas, David A. Bushinsky

Bibliographic record

VenueJournal of the American Society of Nephrology · 2019
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsLunenfeld-Tanenbaum Research InstituteMount Sinai Hospital
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsChlorthalidonePotassiumHypercalciuriaCalciumChemistryEndocrinologyInternal medicineDiureticMedicine

Abstract

fetched live from OpenAlex

Significance Statement Genetic hypercalciuric stone-forming rats, which universally and spontaneously form calcium phosphate stones, have a pathophysiology resembling that of human idiopathic hypercalciuria. The authors previously demonstrated that chlorthalidone, but not potassium citrate, decreased stone formation in this rat model. In this study, they investigated whether chlorthalidone and potassium citrate combined would reduce calcium phosphate stone formation more than either medication alone. They found that chlorthalidone was more effective than potassium citrate alone or combined with chlorthalidone in reducing stone formation and increasing mechanical strength and bone quality. However, replication of these findings in patients with nephrolithiasis is needed before concluding that chlorthalidone alone is more efficacious in this regard than potassium citrate alone or in combination with chlorthalidone. Background The pathophysiology of genetic hypercalciuric stone-forming rats parallels that of human idiopathic hypercalciuria. In this model, all animals form calcium phosphate stones. We previously found that chlorthalidone, but not potassium citrate, decreased stone formation in these rats. Methods To test whether chlorthalidone and potassium citrate combined would reduce calcium phosphate stone formation more than either medication alone, four groups of rats were fed a fixed amount of a normal calcium and phosphorus diet, supplemented with potassium chloride (as control), potassium citrate, chlorthalidone (with potassium chloride to equalize potassium intake), or potassium citrate plus chlorthalidone. We measured urine every 6 weeks and assessed stone formation and bone quality at 18 weeks. Results Potassium citrate reduced urine calcium compared with controls, chlorthalidone reduced it further, and potassium citrate plus chlorthalidone reduced it even more. Chlorthalidone increased urine citrate and potassium citrate increased it even more; the combination did not increase it further. Potassium citrate, alone or with chlorthalidone, increased urine calcium phosphate supersaturation, but chlorthalidone did not. All control rats formed stones. Potassium citrate did not alter stone formation. No stones formed with chlorthalidone, and rats given potassium citrate plus chlorthalidone had some stones but fewer than controls. Rats given chlorthalidone with or without potassium citrate had higher bone mineral density and better mechanical properties than controls, whereas those given potassium citrate did not. Conclusions In genetic hypercalciuric stone-forming rats, chlorthalidone is superior to potassium citrate alone or combined with chlorthalidone in reducing calcium phosphate stone formation and improving bone quality.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.321
Teacher spread0.295 · 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 designBench or experimental
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

Citations12
Published2019
Admission routes1
Has abstractyes

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