Background: The odds of nephrolithiasis increase with more metabolic syndrome (MetS) traits. We evaluated associations of metabolic and dietary factors from urine studies and stone composition with MetS traits in a large cohort of stone-forming patients.
Methods: Patients >18 years old who were evaluated for stones with 24-hour urine collections between July 2009 and December 2018 had their records reviewed retrospectively. Patient factors, laboratory values, and diagnoses were identified within 6 months of urine collection and stone composition within 1 year. Four groups with none, one, two, and three or four MetS traits (hypertension, obesity, dyslipidemia, and diabetes) were evaluated. Trends across groups were tested using linear contrasts in analysis of variance and analysis of covariance.
Results: A total of 1473 patients met the inclusion criteria (835 with stone composition). MetS groups were 684 with no traits, 425 with one trait, 211 with two traits, and 153 with three or four traits. There were no differences among groups for urine volume, calcium, or ammonium excretion. There was a significant trend (<0.001) for more MetS traits being associated with decreasing urine pH, increasing age, calculated dietary protein, urine uric acid (UA), oxalate, citrate, titratable acid phosphate, net acid excretion, and UA supersaturation. The ratio of ammonium to net acid excretion did not differ among the groups. After adjustment for protein intake, the fall in urine pH remained strong, while the upward trend in acid excretion was lost. Calcium oxalate stones were most common, but there was a trend for more UA (<0.001) and fewer calcium phosphate (=0.09) and calcium oxalate stones (=0.01) with more MetS traits.
Conclusions: Stone-forming patients with MetS have a defined pattern of metabolic and dietary risk factors that contribute to an increased risk of stone formation, including higher acid excretion, largely the result of greater protein intake, and lower urine pH.
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http://dx.doi.org/10.34067/KID.0002292021 | DOI Listing |
PLoS One
December 2024
Embrapa Mandioca e Fruticultura, Nugene, Cruz das Almas, Bahia, Brazil.
The variability in genetic variance and covariance due to genotype × environment interaction (G×E) can hinder genotype selection accuracy, especially for complex traits. This study analyzed G×E interactions in cassava to identify stable, high-performing genotypes and predict agronomic performance in untested environments using factor analytic multiplicative mixed models (FAMM) within multi-environment trials (METs). We evaluated 22 cassava genotypes for fresh root yield (FRY), dry root yield (DRY), shoot yield (ShY), and dry matter content (DMC) across 55 Brazilian environments.
View Article and Find Full Text PDFBMC Med
November 2024
Institute for Human Development and Potential (IHDP), Agency for Science, Technology and Research (A*STAR), 30 Medical Drive, Singapore, 117609, Singapore.
Liver Int
November 2024
School of Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK.
Diabetol Metab Syndr
November 2024
Department of Epidemiology and Biostatistics, Key Laboratory of Molecular Cancer Epidemiology in Tianjin, Tianjin's Clinical Research Center for Cancer, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute & Hospital, Tianjin, China.
Objective: To explore the association between the Oxidative Balance Score (OBS), which represents the balance of multiple oxidative stress-related dietary and lifestyle exposures, and the risk of metabolic syndrome (MetS).
Methods: A population-based cross-sectional study design was adopted and 16,850 participants in NHANES database were included in the statistics analysis stage. The OBS was constructed by combining information from 20 a priori selected pro- and antioxidant factors.
Genetics
October 2024
Department of Plant Sciences, University of California Davis, Davis, CA 95616, USA.
Multi-environment trials (METs) are crucial for identifying varieties that perform well across a target population of environments (TPE). However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed.
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