Literature DB >> 32715836

Rising occurrence of hypocitraturia and hyperoxaluria associated with increasing prevalence of stone disease in calcium kidney stone formers.

Ramy F Youssef1, Jeremy W Martin1, Khashayar Sakhaee2, John Poindexter2, Sharmin Dianatnejad1, Charles D Scales3, Glenn M Preminger3, Michael E Lipkin3.   

Abstract

OBJECTIVE: To evaluate metabolic risk factors in calcium kidney stone formers from two different decades, comparing changes in metabolic profiles over time.
METHODS: A retrospective analysis was performed of calcium kidney stone formers who underwent metabolic evaluation of urolithiasis with 24-hour urine collections at a single institution. There were 309 patients evaluated from 1988 to 1994 (Group A), and 229 patients from 2007 to 2010 (Group B). A comparison between both groups was performed to assess changes in demographics and in metabolic stone profiles.
RESULTS: Comparing Group A to Group B, the percentage of females increased from 43 to 56%, obese patients (BMI ≥ 30) increased from 22 to 35%, and patients ≥ 50 years increased from 29 to 47% (all p < 0.005). A greater percentage of patients had hypocitraturia in the recent cohort (46-60%, p = 0.001), with hypocitraturia significantly more frequent in obese patients (p = 0.005). Hyperoxaluria was also increased in Group B compared to Group A (23-30% p = 0.07), a finding that was significant in males (32-53%, p = 0.001).
CONCLUSIONS: Urolithiasis has increased in females, obese, and older patients, consistent with population-based studies. We report a rising incidence of hypocitraturia and hyperoxaluria in the contemporary cohort, particularly in obese patients and in males, respectively. Further studies are needed to better characterize the metabolic changes corresponding to the increase in stone disease.

Entities:  

Keywords:  Kidney calculi; calcium stones; hyperoxaluria; hypocitraturia; stone disease

Mesh:

Substances:

Year:  2020        PMID: 32715836     DOI: 10.1080/21681805.2020.1794955

Source DB:  PubMed          Journal:  Scand J Urol        ISSN: 2168-1805            Impact factor:   1.612


  2 in total

1.  Machine Learning Prediction of Kidney Stone Composition Using Electronic Health Record-Derived Features.

Authors:  Abin Abraham; Nicholas L Kavoussi; Wilson Sui; Cosmin Bejan; John A Capra; Ryan Hsi
Journal:  J Endourol       Date:  2022-02       Impact factor: 2.942

2.  Editorial: Immunity and Inflammatory Response in Kidney Stone Disease.

Authors:  Visith Thongboonkerd; Takahiro Yasui; Saeed R Khan
Journal:  Front Immunol       Date:  2021-11-01       Impact factor: 7.561

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.