@article {6546, title = {Dietary protein intake and change in estimated GFR in the Cardiovascular Health Study.}, journal = {Nutrition}, volume = {30}, year = {2014}, month = {2014 Jul-Aug}, pages = {794-9}, abstract = {

OBJECTIVE: With aging, kidney function declines, as evidenced by reduced glomerular filtration rate. It is controversial whether or not high protein intake accelerates this decline. The aim of this study was to determine whether high protein intake was associated with declines in kidney function among older patients.

METHODS: We examined whether dietary protein is associated with change in kidney function (mean follow-up 6.4 y [SD = 1.4, range = 2.5-7.9] in the Cardiovascular Health Study (N = 3623). We estimated protein intake using a food frequency questionnaire and estimated glomerular filtration rate from cystatin C. Associations between protein intake and kidney function were determined by linear and logistic regression models.

RESULTS: Average protein intake was 19\% of energy intake (SD = 5\%). Twenty-seven percent (n = 963) of study participants had rapid decline in kidney function, as defined by (ΔeGFRcysC > 3 mL{\textbullet}min{\textbullet}1.73 m(2)). Protein intake (characterized as g/d and \% energy/d), was not associated with change in estimated glomerular filtration rate (P > 0.05 for all comparisons). There were also no significant associations when protein intake was separated by source (animal and vegetable).

CONCLUSION: These data suggest that higher protein intake does not have a major effect on kidney function decline among elderly men and women.

}, keywords = {Aged, Aging, Diet, Dietary Proteins, Feeding Behavior, Female, Glomerular Filtration Rate, Health, Humans, Kidney, Male, Regression Analysis, Surveys and Questionnaires}, issn = {1873-1244}, doi = {10.1016/j.nut.2013.12.006}, author = {Beasley, Jeannette M and Katz, Ronit and Shlipak, Michael and Rifkin, Dena E and Siscovick, David and Kaplan, Robert} } @article {6659, title = {Development and validation of a model to predict 5-year risk of death without ESRD among older adults with CKD.}, journal = {Clin J Am Soc Nephrol}, volume = {10}, year = {2015}, month = {2015 Mar 6}, pages = {363-71}, abstract = {

BACKGROUND AND OBJECTIVES: CKD is associated with mortality. Accurate prediction tools for mortality may guide clinical decision-making, particularly among elderly persons with CKD.

DESIGN, SETTING, PARTICIPANTS, \& MEASUREMENTS: A prediction equation was developed for 5-year risk of mortality among participants with CKD in the Cardiovascular Health Study. Sixteen candidate predictor variables were explored, which included demographics, physical examination measures, comorbidity, medication use, and kidney function measures (eGFR calculated from serum creatinine and the CKD Epidemiology Collaboration equation and the urine albumin-to-creatinine ratio). Models were developed using Cox regression and evaluated using c statistics. A final parsimonious model was externally validated in an independent cohort of community-living elders with CKD in the Health, Aging, and Body Composition Study.

RESULTS: The development cohort included 828 participants who had a mean age of 80 ({\textpm}5.6) years and an eGFR of 47 ({\textpm}11) ml/min per 1.73 m(2), and median albumin-to-creatinine ratio of 13 (interquartile range 6-51) mg/g. The validation cohort included 789 participants who had a mean age of 74 ({\textpm}2.8) years and an eGFR of 50 ({\textpm}9) ml/min per 1.73 m(2), and median albumin-to-creatinine ratio of 13 (interquartile range 6-42) mg/g. The final model for 5-year mortality risk included age, sex, race, eGFR, urine albumin-to-creatinine ratio, smoking, diabetes mellitus, and history of heart failure and stroke (c statistic=0.72; 95\% confidence interval, 0.68 to 0.74). When a point-based system was assigned for each of nine variables in the equation, the estimated risk of death within 5 years ranged from 3.8\% among participants with the lowest scores to 83.6\% among participants with nine points. The model performed fair in external validation (c statistic=0.69; 95\% confidence interval, 0.64 to 0.74).

CONCLUSIONS: A simple prediction tool using nine readily available clinical variables can assist in predicting 5-year mortality risk in elderly patients with CKD, which may be useful in counseling patients and guiding clinical decision making.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Albuminuria, Continental Population Groups, Creatinine, Diabetes Mellitus, Female, Glomerular Filtration Rate, Heart Failure, Humans, Male, Proportional Hazards Models, Regression Analysis, Renal Insufficiency, Chronic, Risk Factors, Sex Factors, Smoking, Stroke}, issn = {1555-905X}, doi = {10.2215/CJN.04650514}, author = {Bansal, Nisha and Katz, Ronit and de Boer, Ian H and Peralta, Carmen A and Fried, Linda F and Siscovick, David S and Rifkin, Dena E and Hirsch, Calvin and Cummings, Steven R and Harris, Tamara B and Kritchevsky, Stephen B and Sarnak, Mark J and Shlipak, Michael G and Ix, Joachim H} } @article {8404, title = {The Difference Between Cystatin C and Creatinine-Based Estimated GFR and Incident Frailty: An Analysis of the Cardiovascular Health Study (CHS).}, journal = {Am J Kidney Dis}, year = {2020}, month = {2020 Jul 09}, issn = {1523-6838}, doi = {10.1053/j.ajkd.2020.05.018}, author = {Potok, O Alison and Phil, Ronit Katz D and Bansal, Nisha and Siscovick, David S and Odden, Michelle and Ix, Joachim H and Shlipak, Michael G and Rifkin, Dena E} }