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• W2593204562 abstract "IntroductionFrailty and muscle wasting, a component of frailty, are common in advanced stage chronic kidney disease (CKD). Whether frailty is associated with low urinary creatinine excretion (UCrE) as a measure of muscle mass in this population is unknown. Furthermore, reference values of UCrE are lacking. We first defined low UCrE and studied correlates of low UCrE, and subsequently studied cross-sectional associations of frailty with low UCrE in patients with advanced CKD.MethodsA total of 2748 healthy individuals of the general population-based PREVEND study were included to define low UCrE (UCrE indexed for height, below the age- and sex-specific 5th percentile of the distribution). Frailty was defined using a modification of the Fried frailty phenotype. In a CKD population that included 320 and 967 participants of the PREPARE-2 and NECOSAD studies, respectively, cross-sectional associations of self-reported frailty, the individual components that define self-reported frailty, and frailty-associated variables with low UCrE were evaluated using multivariate logistic and linear regression models.ResultsLow UCrE was found in 38% of the CKD patients. A lower glomerular filtration rate was strongly associated with low UCrE. Self-reported frailty (adjusted odds ratio: 2.19; 95% confidence interval: 1.28−3.77) and the individual components were associated with low UCrE, independent of comorbidities. The frailty-associated variables hemoglobin and albumin were inversely associated with low UCrE, and parathyroid hormone was positively associated with low UCrE.DiscussionLower kidney function is a strong correlate of low UCrE and self-reported frailty, and the individual frailty components are associated with low UCrE as well, independent of comorbidities. Frailty and muscle wasting, a component of frailty, are common in advanced stage chronic kidney disease (CKD). Whether frailty is associated with low urinary creatinine excretion (UCrE) as a measure of muscle mass in this population is unknown. Furthermore, reference values of UCrE are lacking. We first defined low UCrE and studied correlates of low UCrE, and subsequently studied cross-sectional associations of frailty with low UCrE in patients with advanced CKD. A total of 2748 healthy individuals of the general population-based PREVEND study were included to define low UCrE (UCrE indexed for height, below the age- and sex-specific 5th percentile of the distribution). Frailty was defined using a modification of the Fried frailty phenotype. In a CKD population that included 320 and 967 participants of the PREPARE-2 and NECOSAD studies, respectively, cross-sectional associations of self-reported frailty, the individual components that define self-reported frailty, and frailty-associated variables with low UCrE were evaluated using multivariate logistic and linear regression models. Low UCrE was found in 38% of the CKD patients. A lower glomerular filtration rate was strongly associated with low UCrE. Self-reported frailty (adjusted odds ratio: 2.19; 95% confidence interval: 1.28−3.77) and the individual components were associated with low UCrE, independent of comorbidities. The frailty-associated variables hemoglobin and albumin were inversely associated with low UCrE, and parathyroid hormone was positively associated with low UCrE. Lower kidney function is a strong correlate of low UCrE and self-reported frailty, and the individual frailty components are associated with low UCrE as well, independent of comorbidities. Urinary creatinine excretion (UCrE), measured by 24-hour urine sampling, is an established marker of muscle mass in individuals at steady state.1Heymsfield S.B. Arteaga C. McManus C. et al.Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method.Am J Clin Nutr. 1983; 37: 478-494Crossref PubMed Scopus (655) Google Scholar, 2Proctor D.N. O'Brien P.C. Atkinson E.J. Nair K.S. Comparison of techniques to estimate total body skeletal muscle mass in people of different age groups.Am J Physiol. 1999; 277: E489-E495PubMed Google Scholar, 3Wang Z.M. Gallagher D. Nelson M.E. et al.Total-body skeletal muscle mass: evaluation of 24-h urinary creatinine excretion by computerized axial tomography.Am J Clin Nutr. 1996; 63: 863-869Crossref PubMed Scopus (117) Google Scholar, 4Welle S. Thornton C. Totterman S. Forbes G. Utility of creatinine excretion in body-composition studies of healthy men and women older than 60 y.Am J Clin Nutr. 1996; 63: 151-156Crossref PubMed Scopus (77) Google Scholar, 5Keshaviah P.R. Nolph K.D. Moore H.L. et al.Lean body mass estimation by creatinine kinetics.J Am Soc Nephrol. 1994; 4: 1475-1485Crossref PubMed Google Scholar, 6Wyss M. Kaddurah-Daouk R. Creatine and creatinine metabolism.Physiol Rev. 2000; 80: 1107-1213Crossref PubMed Scopus (1923) Google Scholar, 7Wilson F.P. Xie D. Anderson A.H. et al.Urinary creatinine excretion, bioelectrical impedance analysis, and clinical outcomes in patients with CKD: the CRIC study.Clin J Am Soc Nephrol. 2014; 9: 2095-2103Crossref PubMed Scopus (54) Google Scholar Low UCrE has been recognized as a predictor of mortality and adverse health outcomes in patients with stage 3 to 5 chronic kidney disease (CKD) and various other populations.7Wilson F.P. Xie D. Anderson A.H. et al.Urinary creatinine excretion, bioelectrical impedance analysis, and clinical outcomes in patients with CKD: the CRIC study.Clin J Am Soc Nephrol. 2014; 9: 2095-2103Crossref PubMed Scopus (54) Google Scholar, 8Di Micco L. Quinn R.R. Ronksley P.E. et al.Urine creatinine excretion and clinical outcomes in CKD.Clin J Am Soc Nephrol. 2013; 8: 1877-1883Crossref PubMed Scopus (38) Google Scholar, 9Ix J.H. de Boer I.H. Wassel C.L. et al.Urinary creatinine excretion rate and mortality in persons with coronary artery disease: the Heart and Soul Study.Circulation. 2010; 121: 1295-1303Crossref PubMed Scopus (95) Google Scholar, 10Oterdoom L.H. Gansevoort R.T. Schouten J.P. et al.Urinary creatinine excretion, an indirect measure of muscle mass, is an independent predictor of cardiovascular disease and mortality in the general population.Atherosclerosis. 2009; 207: 534-540Abstract Full Text Full Text PDF PubMed Scopus (140) Google Scholar, 11ter Maaten J.M. Damman K. Hillege H.L. et al.Creatinine excretion rate, a marker of muscle mass, is related to clinical outcome in patients with chronic systolic heart failure.Clin Res Cardiol. 2014; 103: 976-983Crossref PubMed Scopus (28) Google Scholar, 12Sinkeler S.J. Kwakernaak A.J. Bakker S.J. et al.Creatinine excretion rate and mortality in type 2 diabetes and nephropathy.Diabetes Care. 2013; 36: 1489-1494Crossref PubMed Scopus (30) Google Scholar, 13Tynkevich E. Flamant M. Haymann J.P. et al.Urinary creatinine excretion, measured glomerular filtration rate and CKD outcomes.Nephrol Dial Transplant. 2015; 30: 1386-1394Crossref PubMed Scopus (15) Google Scholar However, the link between low muscle mass and adverse health outcomes remains unclear. Explanations include that low UCrE reflects worse muscle health, or is related to chronic low-grade inflammation, insulin resistance, or protein caloric malnutrition. We hypothesize that frailty is related to low UCrE because low muscle mass is an important component of frailty, and especially of the physical frailty definition.14Fried L.P. Tangen C.M. Walston J. et al.Frailty in older adults: evidence for a phenotype.J Gerontol A Biol Sci Med Sci. 2001; 56: M146-M156Crossref PubMed Google Scholar Frailty is common in patients with advanced CKD and has been associated with earlier need for dialysis initiation, lower quality of life, and increased mortality risk.15Dalrymple L.S. Katz R. Rifkin D.E. et al.Kidney function and prevalent and incident frailty.Clin J Am Soc Nephrol. 2013; 8: 2091-2099Crossref PubMed Scopus (87) Google Scholar, 16Reese P.P. Cappola A.R. Shults J. et al.Physical performance and frailty in chronic kidney disease.Am J Nephrol. 2013; 38: 307-315Crossref PubMed Scopus (106) Google Scholar, 17Roshanravan B. Khatri M. Robinson-Cohen C. et al.A prospective study of frailty in nephrology-referred patients with CKD.Am J Kidney Dis. 2012; 60: 912-921Abstract Full Text Full Text PDF PubMed Scopus (193) Google Scholar, 18Shlipak M.G. Stehman-Breen C. Fried L.F. et al.The presence of frailty in elderly persons with chronic renal insufficiency.Am J Kidney Dis. 2004; 43: 861-867Abstract Full Text Full Text PDF PubMed Scopus (282) Google Scholar, 19Wilhelm-Leen E.R. Hall Y.N. K Tamura M Chertow G.M. Frailty and chronic kidney disease: the Third National Health and Nutrition Evaluation Survey.Am J Med. 2009; 122: 664-671.e2Abstract Full Text Full Text PDF PubMed Scopus (229) Google Scholar, 20Johansen K.L. Dalrymple L.S. Delgado C. et al.Comparison of self-report-based and physical performance-based frailty definitions among patients receiving maintenance hemodialysis.Am J Kidney Dis. 2014; 64: 600-607Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar CKD has been hypothesized as an accelerator of decline of physical function that leads to frailty.21Fried L.F. Boudreau R. Lee J.S. et al.Kidney function as a predictor of loss of lean mass in older adults: health, aging and body composition study.J Am Geriatr Soc. 2007; 55: 1578-1584Crossref PubMed Scopus (28) Google Scholar, 22Wang X.H. Mitch W.E. Mechanisms of muscle wasting in chronic kidney disease.Nat Rev Nephrol. 2014; 10: 504-516Crossref PubMed Scopus (358) Google Scholar, 23Anand S. Johansen K.L. Kurella Tamura M. Aging and chronic kidney disease: the impact on physical function and cognition.J Gerontol A Biol Sci Med Sci. 2014; 69: 315-322Crossref PubMed Scopus (78) Google Scholar However, whether frailty is associated with a low UCrE in advanced CKD has not yet been studied. Furthermore, reference values of UCrE are lacking. Therefore, we first aimed to determine low UCrE by using the UCrE distribution of a healthy population and subsequently evaluate correlates of low UCrE. Second, we aimed to evaluate associations of self-reported frailty, the individual components that define self-reported frailty, and frailty-associated variables with low UCrE in a cohort of patients with advanced CKD. To define low UCrE values, we included as a healthy population a subsample representative of the general population (n = 3432) of the Prevention of Renal and Vascular End-Stage Disease (PREVEND) study. This prospective, population-based cohort study investigated the natural course of urinary albumin excretion and its relation to renal and cardiovascular disease. Detailed information on the design of the PREVEND study has been published previously.24Lambers Heerspink H.J. Brantsma A.H. de Zeeuw D. et al.Albuminuria assessed from first-morning-void urine samples versus 24-hour urine collections as a predictor of cardiovascular morbidity and mortality.Am J Epidemiol. 2008; 168: 897-905Crossref PubMed Scopus (188) Google Scholar In summary, all inhabitants of the city of Groningen aged 28 to 75 years were sent a questionnaire and a vial to collect a first-morning urine sample. We excluded pregnant women and subjects with diabetes mellitus type 1 from the 40,856 respondents, and 2 cohorts were formed based on urinary albumin concentration. From these 2 cohorts, a subsample of 3432 subjects was derived that was representative of the general population. From this subsample, we excluded participants with no UCrE available, no serum creatinine and/or height available, those with comorbid conditions, or those aged younger than 25 years, which left 2892 participants for the present study. Participants with missing UCrE values did not differ significantly from participants for whom UCrE values were available. The PREVEND study was approved by the institutional review board, and all participants gave written informed consent. The CKD population included participants of the multicenter observational PREdialysis PAtients REcords-study (PREPARE-2) and NEtherlands COoperative Study on the Adequacy of Dialysis (NECOSAD) studies. PREPARE-2 included 502 patients with stage 4 CKD aged 18 years or older who were treated by a nephrologist and had recently been referred to a specialized predialysis outpatient clinic. All patients had to be suitable for renal replacement therapy. Patients with chronic transplantation dysfunction were excluded from the study if the transplant was within the previous year. NECOSAD included 2051 patients with stage 5 CKD who were starting dialysis. To be eligible for inclusion in NECOSAD, adult patients (age 18 years or older) had to start with dialysis as their first renal replacement therapy. No other inclusion or exclusion criteria were applied. The institutional review boards of all participating hospitals approved the studies. All patients gave written informed consent. Detailed information on the design of the PREPARE-2 and NECOSAD studies has been published previously.25Suttorp M.M. Hoekstra T. Mittelman M. et al.Effect of erythropoiesis-stimulating agents on blood pressure in pre-dialysis patients.PLoS One. 2013; 8: e84848Crossref PubMed Scopus (5) Google Scholar, 26de Jager D.J. Halbesma N. Krediet R.T. et al.Is the decline of renal function different before and after the start of dialysis?.Nephrol Dial Transplant. 2013; 28: 698-705Crossref PubMed Scopus (17) Google Scholar For this present study, we included 340 and 1055 participants of PREPARE-2 and NECOSAD respectively, aged between 25 years or older and 85 years and younger, for whom height and UCrE were available, and UCrE was collected before dialysis initiation (NECOSAD). In PREPARE-2, patients with missing UCrE values had a higher estimated glomerular filtration rate (eGFR), according to the Modification of Diet in Renal Disease Study equation, compared with patients for whom UCrE values were available (16.2 ml/min/1.73 m2 vs. 13.9 ml/min/1.73 m2, respectively; P = 0.03), a lower median albumin (39 g/L vs. 42 g/L, respectively; P < 0.001), and were less likely to have low physical performance (53% vs. 65%; P = 0.03). In NECOSAD, patients with missing UCrE values were slightly older (median age 64.4 years vs. 61.9 years; P = 0.004), had lower hemoglobin values (median 6.3 mmol/L vs. 6.4 mmol/L; P = 0.02), higher eGFR Modification of Diet in Renal Disease Study values (median 7.0 ml/min/1.73 m2 vs. 6.7 ml/min/1.73 m2; P = 0.02), and lower albumin values (median 35 g/L vs. 37 g/L; P < 0.001). Self-reported frailty was defined similarly to a frequently used modification of Fried’s criteria for frailty developed by Woods et al.27Woods N.F. LaCroix A.Z. Gray S.L. et al.Frailty: emergence and consequences in women aged 65 and older in the Women's Health Initiative Observational Study.J Am Geriatr Soc. 2005; 53: 1321-1330Crossref PubMed Scopus (769) Google Scholar and Johansen et al.27Woods N.F. LaCroix A.Z. Gray S.L. et al.Frailty: emergence and consequences in women aged 65 and older in the Women's Health Initiative Observational Study.J Am Geriatr Soc. 2005; 53: 1321-1330Crossref PubMed Scopus (769) Google Scholar, 28Delgado C. Shieh S. Grimes B. et al.Association of self-reported frailty with falls and fractures among patients new to dialysis.Am J Nephrol. 2015; 42: 134-140Crossref PubMed Scopus (74) Google Scholar, 29Bao Y. Dalrymple L. Chertow G.M. et al.Frailty, dialysis initiation, and mortality in end-stage renal disease.Arch Intern Med. 2012; 172: 1071-1077Crossref PubMed Scopus (224) Google Scholar, 30Johansen K.L. Chertow G.M. Jin C. Kutner N.G. Significance of frailty among dialysis patients.J Am Soc Nephrol. 2007; 18: 2960-2967Crossref PubMed Scopus (450) Google Scholar, 31Johansen K.L. Dalrymple L.S. Glidden D. et al.Association of performance-based and self-reported function-based definitions of frailty with mortality among patients receiving hemodialysis.Clin J Am Soc Nephrol. 2016; 11: 626-632Crossref PubMed Scopus (62) Google Scholar Physical weakness and slowness was defined as a score <75 on the physical functioning scale of the Short Form of Health Survey-36. Exhaustion was defined as a score <55 on the vitality scale of the Short Form of Health Survey-36. A body mass index (BMI) <18.5 kg/m2 was used as a substitute for unintentional weight loss.19Wilhelm-Leen E.R. Hall Y.N. K Tamura M Chertow G.M. Frailty and chronic kidney disease: the Third National Health and Nutrition Evaluation Survey.Am J Med. 2009; 122: 664-671.e2Abstract Full Text Full Text PDF PubMed Scopus (229) Google Scholar Because underweight is a more accurate description of this modified criterion, the unintentional weight loss criterion is used as “underweight” hereafter. Physical inactivity was defined as the combination of self-reported moderate or extreme walking problems, with moderate or extreme usual activities limitations according the EuroQol 5-dimensional (EQ-5D) questionnaire.32Szende A. Oppe M. Devlin N. EQ-5D Value Sets: Inventory, Comparative Review and User Guide. Springer, Dordrecht, The Netherlands2007Crossref Google Scholar A total of 5 points was possible, with 2 points for low physical functioning and 1 point for each of the other criteria. Patients scoring ≥3 were defined as frail according to the literature.14Fried L.P. Tangen C.M. Walston J. et al.Frailty in older adults: evidence for a phenotype.J Gerontol A Biol Sci Med Sci. 2001; 56: M146-M156Crossref PubMed Google Scholar, 27Woods N.F. LaCroix A.Z. Gray S.L. et al.Frailty: emergence and consequences in women aged 65 and older in the Women's Health Initiative Observational Study.J Am Geriatr Soc. 2005; 53: 1321-1330Crossref PubMed Scopus (769) Google Scholar, 30Johansen K.L. Chertow G.M. Jin C. Kutner N.G. Significance of frailty among dialysis patients.J Am Soc Nephrol. 2007; 18: 2960-2967Crossref PubMed Scopus (450) Google Scholar For NECOSAD patients, the Short Form of Health Survey-36 and EQ-5D questionnaire had to be completed before dialysis initiation or within 7 days from the start of dialysis. In an additional analysis, we studied the association of prefrailty (a self-reported frailty score of 1 or 2) with low UCrE. Patients in whom all frailty data were available were slightly different from those patients in whom not all frailty data were available. Therefore, patients in whom frailty was available had higher mean levels of UCrE (9.6 mmol/24 hours vs. 8.4 mmol/24 hours; P < 0.001), a higher BMI (26.4 kg/m2 vs. 25.2 kg/m2; P < 0.001), and higher levels of GFR (12.5 ml/min vs. 9.5 ml/min; P < 0.001), hemoglobin (7.5 mmol/L vs. 6.6 mmol/L; P < 0.001), and albumin (40 g/L vs. 37 g/L; P < 0.001). Frailty-associated variables included cigarette smoking, albumin, parathyroid hormone, hemoglobin, protein-energy wasting according to the Subjective Global Assessment total score, and the Charlson Comorbidity Index. The Charlson Comorbidity Index was divided into 3 tertiles: 1: 0 to 2; 2: 3 to 5; and 3: 6 to 10. Standard laboratory techniques were used in the different centers participating in the PREPARE-2, NECOSAD, and PREVEND studies. GFR was calculated as the mean of urea and creatinine clearance, measured from 24-hour urine collections. The abbreviated Modification of Diet in Renal Disease Study equation was used to measure eGFR. Educational levels were categorized according to the International Standard Classification of Education as bachelor, master or doctorate graduate (level 1), postsecondary or nontertiary or short-cycle tertiary education (level 2), upper secondary education (level 3), lower secondary education (level 4), and primary or less than primary education (level 5).33UNESCO Institute for Statistics: International Standard Classification of Education, ISCED 2011. UNESCO, Paris2011Google Scholar Malignancy (n = 82) was defined as a history of (83%) or active (treated or untreated) malignancy (17%). The skin tumors squamous cell carcinoma and basal cell carcinoma were excluded for the definition of malignancy. Differences between patients with low UCrE versus normal range UCrE were tested for statistical significance using Student’s t-test, Mann-Whitney test, or χ2 test, as appropriate. Low UCrE was defined stratified by sex, then indexed by height (UCrE/height); we subsequently calculated the 5th and 95th percentiles per 5-year age category. These values were plotted, and a third-order polynomial regression line was chosen, because this model yielded the highest R2 values. UCrE was indexed by height, because muscle mass is highly dependent on body size. All height-indexed UCrE values in patients with CKD that were below the 5th percentile of the healthy population were defined as low. Logistic regression was used to identify correlates of low UCrE, presented in (i) crude analyses; (ii) analyses adjusted for age, race, sex, and height; and (iii) analyses with all variables added in 1 model. Furthermore, logistic regression was used to evaluate the associations of the frailty variables with low UCrE. These models are presented as (i) crude, and then adjusted for (ii) comorbidities, and (iii) GFR, using both the linear and quadratic function of GFR to allow for nonlinear associations. Adjustment for GFR was performed to model the effect of kidney function on both frailty and low UCrE. A subsidiary multivariate linear regression analysis was performed with UCrE treated as a continuous variable. Subjects with UCrE values that were biologically implausible (UCrE <3.09 or >30.9 mmol/day) were excluded.34Ix J.H. Wassel C.L. Stevens L.A. et al.Equations to estimate creatinine excretion rate: the CKD epidemiology collaboration.Clin J Am Soc Nephrol. 2011; 6: 184-191Crossref PubMed Scopus (142) Google Scholar Furthermore, patients with the 5% greatest differences between measured UCrE and calculated UCrE were excluded. For this purpose, we calculated the estimated UCrE by multiplying creatinine clearance (according to the Cockroft-Gault formula) with plasma creatinine. Missing values of variables that were used for adjustment were imputed with standard multiple imputation techniques using 10 repetitions. Information on chronic lung disease and malignancy was missing in 31% of cases due to availability in the NECOSAD study only. Information on urea clearance was missing in 42% of cases. The multiple imputation model included the characteristics described in Table 1.Table 1Patient characteristics according urinary creatinine excretion levelCharacteristicsAll CKD patientsN = 1287Low UCrE38%Normal UCrE62%UCrE in men (mmol/24 h)9.4 (7.6−11.4)7.5 (6.3−8.5)11.0aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (9.7−12.4)UCrE in women (mmol/24 h)6.9 (5.7−8.4)5.2 (4.7−6.1)8.0aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (6.8−9.2)Demographics Age (yr)63.1 (51.9−72.4)62.5 (51.6−70.4)64.2bP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (52−74.1) Men (%)636760cP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. Non-Caucasian race (%)897Primary kidney disease (%) Glomerulonephritis131015 Diabetes Mellitus151814 Renal vascular disease191818 Other535353Educational level (%) Level 1556 Level 2999 Level 3181917 Level 4424342 Level 5262526Smoking (%)273224bP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases.Anthropometry Body mass index (kg/m2)24.8 (22.5−27.9)23.6 (21.5−26.1)25.7aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (23.3−28.7) Length (cm)171.3 ± 9.7171.4 ± 9.3171.3 ± 9.9 Weight (kg)74.0 (65−84)70.7 (62−79)76.9aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (67−87)Comorbidities (%) Myocardial infarction131512 Heart failure121510cP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. DM232622 Peripheral vascular disease161814 CVA1099 Malignancy9109 Chronic lung disease796Charlson Comorbidity Index Class 1323433 Class 2323032 Class 3363635Laboratory results eGFR (ml/min/1.73 m2)8.0 (5.8−11.4)7.1 (5.5−10.0)8.3aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (6.1−12.5) GFR (ml/min)9.5 (7.3−12.3)7.4 (5.6−9.2)11.2aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (8.9−14.0) Hemoglobin (mmol/L)6.8 ± 1.16.5 ± 1.17.0 ± 1.2aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. Albumin (g/L)38 (34−42)36 (31−40)39aP < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. (35−43) PTH (pmol/L)17.8 (10−28.1)18.3 (8.3−46.9)16.9 (10.0−27.1)SGA total score (%) 1−5 = severe to moderate PEW9179 6−7 = normal nutritional status918392CVA, cerebrovascular accident; DM, diabetes mellitus; GFR, glomerular filtration rate; PEW, protein-energy wasting; PTH, parathyroid hormone; SGA, subjective global assessment of nutritional status; UCrE, urinary creatinine excretion.Data are given as mean ± SD or median (interquartile range).Charlson Comorbidity Index: class 3 indicating the highest comorbidity burden.Educational level: level 1 = university, level 5 = primary school or less.a P < 0.001; bP < 0.01; cP < 0.05, cases versus noncases. Open table in a new tab CVA, cerebrovascular accident; DM, diabetes mellitus; GFR, glomerular filtration rate; PEW, protein-energy wasting; PTH, parathyroid hormone; SGA, subjective global assessment of nutritional status; UCrE, urinary creatinine excretion. Data are given as mean ± SD or median (interquartile range). Charlson Comorbidity Index: class 3 indicating the highest comorbidity burden. Educational level: level 1 = university, level 5 = primary school or less. Several sensitivity analyses were performed. First, we repeated the analyses without excluding subjects with UCrE values <3.09 or >30.9 mmol/day. Second, we repeated the analyses in complete cases. Third, we tested potential interactions of the frailty variables with the original study cohort. Subsequently, we repeated the analyses in the PREPARE-2 and NECOSAD cohorts separately. A P value < 0.05 was considered statistically significant. All analyses were performed in SPSS (version 22.0; IBM, Armonk, New York). The healthy control group included 2748 PREVEND participants, after excluding subjects with UCrE values that were biologically implausible (UCrE <3.09 or >30.9 mmol/day) and excluding patients with the 5% highest differences between measured UCrE and calculated UCrE. The CKD population finally included 320 PREPARE-2 and 967 NECOSAD participants, after applying the same exclusion methods as the healthy cohort to exclude possibly incorrectly measured UCrE values. Patient characteristics according to study population are shown in Supplementary Table 1. The regression equation of the 5th percentile of UCrE values was modeled as follows:UCrE/height (m) 5th percentile=(−0.0338age3+4.8107age2−240.54age+8462.7)/1000 (women)(−0.111 age3+17.185 age2−869.31 age+19,838)/1000 (men)The fit of the regression model and the fits of the other models that were tested are shown in Supplementary Table 2, and the fit of the regression model is further visualized in Supplementary Figures 1a and 1b. Of the CKD patients, 38% had a low UCrE value according to the 5th percentile of the healthy population (Figures 1a and 1b). Of the patients with low UCrE, 88% originated from the NECOSAD study. Patients with low UCrE had a lower BMI, lower albumin, hemoglobin, and eGFR levels, compared with patients with a normal UCrE (Table 1). Crude odds of low UCrE were significantly higher in men, smokers, patients with heart failure, patients with peripheral vascular disease, and patients with lower GFR (Table 2). Excluding patients with peripheral vascular disease defined as an amputation (n = 14) yielded similar results (model 3: odds ratio [OR]: 1.88; 95% confidence interval [CI]: 1.13−3.12; P = 0.01). Remarkably, patients in the oldest age quintile showed significant lower odds of low UCrE compared with the youngest age quintile (OR: 0.31; 95% CI: 0.17−0.57; P < 0.001). A low GFR was the strongest correlate of low UCrE in model 3. Compared with patients with glomerulonephritis (GN) as a primary cause of renal disease, patients with other primary causes of renal disease (e.g., diabetes mellitus, hypertension) were more likely to have low UCrE.Table 2Potential correlates of low urinary creatinine excretionModel 1Model 2Model 3OR95% CIP valueOR95% CIP valueOR95% CIP valueAge (yr) <48.4 (ref) 48.5−59.31.260.88−1.790.211.240.87−1.770.241.200.73−1.970.48 59.4−67.351.431.002−2.030.051.330.93−1.910.121.400.84−2.340.20 67.36−74.071.300.91−1.860.141.230.85−1.760.270.880.51−1.510.63 ≥74.10.500.34−0.73<0.0010.450.30−0.67<0.0010.310.17−0.57<0.001Male gender1.311.03−1.660.031.661.21−2.270.0022.071.32−3.250.002Race Caucasian (ref) Non−Caucasian1.240.83−1.860.301.070.70−1.640.750.970.52−1.800.92Educational level Level 1 Level 20.960.53−1.740.890.800.43−1.500.480.880.40−1.930.75 Level 31.070.67−1.730.770.900.54−1.500.691.060.56−2.010.86 Level 41.170.80−1.710.411.070.71−1.610.761.670.98−2.830.06 Level 51.050.77−1.420.780.950.70−1.320.771.000.66−1.510.99Primary kidney disease Glomerulonephritis (ref) DM2.161.39−3.350.0012.301.46−3.62<0.0013.021.28−7.090.01 Renovascular disease1.581.04−2.410.031.771.14−2.750.012.271.22−4.240.01 Other1.561.08−2.260.021.751.20−2.550.0041.941.18−3.210.009Smoking1.381.06−1.780.021.331.02−1.740.041.360.95−1.960.10GFR quartiles (ml/min) 1st (1.6−5.3)25.816.0−41.8<0.00133.220.0−55.1<0.00141.422.5−76.2<0.001 2nd (5.3−7.2)6.153.95−9.55<0.0017.134.50−11.3<0.0019.035.21−15.7<0.001 3rd (7.2−9.6)2.261.36−3.780.0022.291.35−3.870.0022.351.26−4.380.008 4th (9.6−32.4) (ref)Myocardial infarction1.330.94−1.880.101.290.90−1.850.171.340.79−2.290.28Heart failure1.471.04−2.080.031.631.13−2.370.0091.290.74−2.250.36DM1.250.95−1.640.111.280.96−1.700.091.140.59−2.210.70Peripheral vascular disease1.371.00−1.870.051.451.04−2.010.031.731.06−2.830.03CVA0.900.61−1.340.610.900.60−1.350.611.060.61−1.850.84Malignancy1.040.67−1.620.861.090.68−1.750.721.110.60−2.070.73Chronic lung disease1.470.85−2.540.171.480.85−2.580.172.061.03−4.140.04CI, confidence interval; CVA, cerebr" @default.
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• W2593204562 title "Low Urinary Creatinine Excretion Is Associated With Self-Reported Frailty in Patients With Advanced Chronic Kidney Disease" @default.
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