Joseph J Crivelli1, David T Redden2, Robert D Johnson3, Lucia D Juarez4, Naim M Maalouf5, Amy E Hughes6, Kyle D Wood7, G Assimos7, Gabriela R Oates8. 1. Department of Urology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama. Electronic address: crivelli@uab.edu. 2. Department of Biostatistics, School of Public Health, The University of Alabama at Birmingham, Birmingham, Alabama. 3. Informatics Institute, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama. 4. Division of Preventive Medicine, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama. 5. Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas; Charles and Jane Pak Center for Mineral Metabolism and Clinical Research, University of Texas Southwestern Medical Center, Dallas, Texas. 6. Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas. 7. Department of Urology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama. 8. Department of Pediatrics, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama.
Abstract
INTRODUCTION: Obesity is associated with kidney stone disease, but it is unknown whether this association differs by SES. This study assessed the extent to which obesity and neighborhood characteristics jointly contribute to urinary risk factors for kidney stone disease. METHODS: This was a retrospective analysis of adult patients with kidney stone disease evaluated with 24-hour urine collection (2001-2020). Neighborhood-level socioeconomic data were obtained for a principal component analysis, which identified 3 linearly independent factors. Associations between these factors and 24-hour urine measurements were assessed using linear regression as well as groupings of 24-hour urine results using multivariable logistic regression. Finally, multiplicative interactions were assessed testing effect modification by obesity, and analyses stratified by obesity were performed. Analyses were performed in 2021. RESULTS: In total, 1,264 patients met the study criteria. Factors retained on principal component analysis represented SES, family structure, and housing characteristics. On linear regression, there was a significant inverse correlation between SES and 24-hour urine sodium (p=0.0002). On multivariable logistic regression, obesity was associated with increased odds of multiple stone risk factors (OR=1.61; 95% CI=1.15, 2.26) and multiple dietary factors (OR=1.33; 95% CI=1.06, 1.67). No significant and consistent multiplicative interactions were observed between obesity and quartiles of neighborhood SES, family structure, or housing characteristics. CONCLUSIONS: Obesity was associated with the presence of multiple stone risk factors and multiple dietary factors; however, the strength and magnitude of these associations did not vary significantly by neighborhood SES, family structure, and housing characteristics.
INTRODUCTION: Obesity is associated with kidney stone disease, but it is unknown whether this association differs by SES. This study assessed the extent to which obesity and neighborhood characteristics jointly contribute to urinary risk factors for kidney stone disease. METHODS: This was a retrospective analysis of adult patients with kidney stone disease evaluated with 24-hour urine collection (2001-2020). Neighborhood-level socioeconomic data were obtained for a principal component analysis, which identified 3 linearly independent factors. Associations between these factors and 24-hour urine measurements were assessed using linear regression as well as groupings of 24-hour urine results using multivariable logistic regression. Finally, multiplicative interactions were assessed testing effect modification by obesity, and analyses stratified by obesity were performed. Analyses were performed in 2021. RESULTS: In total, 1,264 patients met the study criteria. Factors retained on principal component analysis represented SES, family structure, and housing characteristics. On linear regression, there was a significant inverse correlation between SES and 24-hour urine sodium (p=0.0002). On multivariable logistic regression, obesity was associated with increased odds of multiple stone risk factors (OR=1.61; 95% CI=1.15, 2.26) and multiple dietary factors (OR=1.33; 95% CI=1.06, 1.67). No significant and consistent multiplicative interactions were observed between obesity and quartiles of neighborhood SES, family structure, or housing characteristics. CONCLUSIONS: Obesity was associated with the presence of multiple stone risk factors and multiple dietary factors; however, the strength and magnitude of these associations did not vary significantly by neighborhood SES, family structure, and housing characteristics.
Authors: Margaret S Pearle; David S Goldfarb; Dean G Assimos; Gary Curhan; Cynthia J Denu-Ciocca; Brian R Matlaga; Manoj Monga; Kristina L Penniston; Glenn M Preminger; Thomas M T Turk; James R White Journal: J Urol Date: 2014-05-20 Impact factor: 7.450
Authors: Chika Vera Anekwe; Amber R Jarrell; Matthew J Townsend; Gabriela I Gaudier; Julia M Hiserodt; Fatima Cody Stanford Journal: Curr Obes Rep Date: 2020-09
Authors: Joseph J Crivelli; Naim M Maalouf; Henry J Paiste; Kyle D Wood; Amy E Hughes; Gabriela R Oates; Dean G Assimos Journal: J Urol Date: 2021-04-27 Impact factor: 7.450
Authors: Maria I Creatore; Richard H Glazier; Rahim Moineddin; Ghazal S Fazli; Ashley Johns; Peter Gozdyra; Flora I Matheson; Vered Kaufman-Shriqui; Laura C Rosella; Doug G Manuel; Gillian L Booth Journal: JAMA Date: 2016 May 24-31 Impact factor: 56.272