Association of estimated glomerular filtration rate with muscle function in older persons who have fallen

Association of estimated glomerular filtration rate with muscle function in older persons who... Abstract Background studies suggest that estimated glomerular filtration rate (eGFR) is less reliable in older persons and that a low serum-creatinine might reflect reduced muscle mass rather than high kidney function. This study investigates the possible relationship between eGFR and multiple elements of physical performance in older fallers. Methods baseline data of the IMPROveFALL-study were examined in participants ≥65 years. Serum-creatinine based eGFR was classified as normal (≥90 ml/min), mildly reduced (60–89 ml/min) or moderately–severely reduced (<60 ml/min). Timed-Up-and-Go-test and Five-Times-Sit-to-Stand-test were used to assess mobility; calf circumference and handgrip strength to assess muscle status. Ancova models adjusted for age, sex, Charlson comorbidity index and body mass index were performed. Results a total of 578 participants were included. Participants with a normal eGFR had lower handgrip strength than those with a mildly reduced eGFR (−9.5%, P < 0.001) and those with a moderately–severely reduced eGFR (−6.3%, P = 0.033) with mean strengths of 23.4, 25.8 and 24.9 kg, respectively. Participants with a normal eGFR had a smaller calf circumference than those with a mildly reduced eGFR (35.5 versus 36.5 cm, P = 0.006). Mean time to complete the mobility tests did not differ. Conclusions in this study we found that older fallers with an eGFR ≥ 90 ml/min had smaller calf circumference and up to 10% lower handgrip strength than those with a reduced eGFR. This lower muscle mass is likely to lead to an overestimation of kidney function. This outcome therefore supports the search for biomarkers independent of muscle mass to estimate kidney function in older persons. estimated glomerular filtration rate, mobility, muscle strength, muscle mass, ageing, older people Introduction The prevalence of chronic kidney disease (CKD) is rising, due to increased ageing of the population and an increase in the number of risk factors, such as obesity, diabetes and hypertension [1]. The prevalence of CKD, defined as a glomerular filtration rate (GFR) <60 ml/min/1.73 m2 [2], rises in older persons with a prevalence from <1% in persons under 35 years to more than 33% in persons aged 75 years and over [1, 3, 4]. Additionally, CKD increases the risk of morbidity and mortality [5, 6] and negatively affects multiple aspects of functionality and daily performance [7–9]. In clinical practice, estimation of GFR (eGFR) is commonly used as a quick evaluation of kidney function [10, 11]. For estimating GFR, serum-creatinine (s-creatinine) is used as marker, despite the fact that it may be misleading in older persons due to a reduction in muscle mass with ageing [12]. Recent studies have focussed on evaluation of new and independent filtration markers, such as serum cystatin C, ß-trace protein and ß2-microglobuline, to assess renal function and to improve risk prediction related to decreased GFR [13]. However, in clinical practice s-creatinine based eGFR is still daily used. Therefore, it seems relevant to take a closer look at the accuracy of the s-creatinine based eGFR in older persons, since this estimation of renal function may be greatly influenced by muscle mass. Previous studies showed that low s-creatinine, resulting in higher eGFR values, partly reflects muscle atrophy and ‘frailty’ in older persons [14, 15]. In the oldest old community-dwelling persons ≥90 years, where the prevalence of sarcopenia is higher [12], a U-shaped relationship was found between eGFR and handgrip strength and mortality [14]. These findings generate the hypothesis that sarcopenic persons, having low s-creatinine, might group in categories with high eGFR values. In this study we investigated whether s-creatinine based eGFR is associated with physical performance in persons aged 65 years and over who have fallen. Methods Data collection Baseline data from the Improving Medication Prescribing to reduce Risk Of FALLs (IMPROveFALL) study were analysed. The IMPROveFALL-study is a randomised multicentre trial investigating the effect of withdrawal of fall-risk increasing drugs versus ‘care as usual’ on reducing falls in community-dwelling persons. A detailed description of the methods can be found elsewhere [16]. In summary, individuals aged ≥65 years who visited the Emergency Department (ED) because of a fall were asked to participate. Individuals had a Mini-Mental State Examination (MMSE) score of at least 21 out of 30 points [17]. The baseline visit took place within 2 months after ED attendance. The ethical committee of the Erasmus MC approved the study and all participants signed informed consent. Covariates Demographic data were collected from ED records. Medical history, use of medications, lifestyle factors and number of comorbidities were documented and Charlson comorbidity index (CCI) was determined. Height and weight were measured and body mass index (BMI) was calculated. Biochemistry S-creatinine, sex and age were used to calculate the eGFR in ml/min/1.73 m2 with the Modification of Diet in Renal Disease (MDRD) Study formula widely used: 175 × (s-creatinine (μmol/l) × 0.0113)−1.154 × age−0.203 (years) × 0.742 (if female). GFR was also estimated with the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula, because of the increasing use of this formula in general practice [18]. Kidney function was classified as proposed by the National Kidney Foundation [2] and slightly merged into the following three categories: normal (eGFR ≥ 90 ml/min/1.73 m2), mildly reduced (eGFR 60–89 ml/min/1.73 m2), moderately–severely reduced (eGFR 15–59 ml/min/1.73 m2). Physical performance The Timed-Up-and-Go test (TUG) and the Five-Time-Sit-to-Stand test (FTSS) were used to assess mobility [19, 20]. Handgrip strength was measured to reflect muscle strength and calf circumference to reflect muscle mass. Handgrip strength is reported as a validated indicator for global muscle strength [21] and was measured with a hand-held dynamometer (Takei TKK 540, Takei Scientific Instruments Co., Ltd., Tokyo, Japan) in standing position with arms beside the body, using the maximum strength from four alternating (right–left) attempts. For measuring calf circumference participants sat with a knee angle of 90° and the sole on the ground. The thickest part of both calves was measured with a measuring tape and the mean value was documented. Statistical analyses Statistical analyses were performed using SPSS statistical package 21.0 for Windows. Descriptive data for continues variables were presented as median and interquartile range (IQR). Numbers and percent prevalences were presented for dichotomous variables. Spearman’s correlation analyses were used to investigate the relation of age with s-creatinine, eGFR (MDRD and CKD-EPI formula) and physical performance. Missing performance test measures, mostly due to injuries following fall, were excluded from all related analyses. Because of a right-skewed distribution, outcomes of physical performance were log transformed using the natural log. In analyses of covariance we compared mean (natural log transformed) scores of physical performance tests across three categories of eGFR (eGFR ≥ 90 ml/min, eGFR 60–89, eGFR < 60 ml/min). All models were adjusted for sex, age and CCI. Models investigating muscle mass and strength were also adjusted for BMI. We back transformed the results to the original scale. A two-tailed P < 0.05 was defined as statistically significant. Results Overall, 616 participants were enroled in the IMPROveFALL-study; information on s-creatinine was obtained from 578 participants. Median age was 76.3 years (IQR: 70.2–81.6), 39% were men, 50% lived with a partner, 72% were ADL independent and 58% were iADL independent. The 20% of the participants had diabetes mellitus, 21% had a history of coronary heart disease and 18% had a history of cancer. Using the MDRD-formula 115 participants (19.9%) had a normal eGFR, 314 participants (54.3%) had a mildly reduced eGFR and 149 participants (25.8%) had a moderately–severely reduced eGFR. Participants in the lowest category had a median eGFR of 50 ml/min (IQR: 41–55). When GFR was estimated with the CKD-EPI formula, the categories were different with 11.4, 62.5 and 26.1% from highest to lowest eGFR, respectively. Median s-creatinine was 75.0 μmol/l (IQR: 65.0–93.0), median eGFR was 72.0 ml/min (IQR: 59.0–85.0) using the MDRD-formula and 73.7 ml/min (IQR: 59.2–84.9) using the CKD-EPI formula. Median time to complete the TUG was 10.0 s (IQR: 8.0–14.0) and 15.0 s (IQR: 12.0–20.0) for the FTSS. Median handgrip strength was 24.7 kg (IQR: 19.9–32.3) and median calf circumference was 36.3 cm (IQR: 34.0–38.8). Characteristics of the participants are shown in Table 1. Table 1. Characteristics of the participants (n = 578) Age in years, median (IQR)  76.3 (70.2–81.6)  Men, n (%)  226 (39.1)  Education     ≤6 years, n (%)  161 (27.9)   6–10 years, n (%)  214 (37.0)   >10 years, n (%)  203 (35.1)  Living with partner, n (%)  288 (49.8)  Widow(er), n (%)  194 (33.6)  Current smoker, n (%)  64 (11.1)  Former smoker, n (%)  201 (34.8)  ADL independent, n (%)  414 (72.0)  iADL independent, n (%)  331 (57.6)  BMI in kg/m2, median (IQR) (n = 564)  27.1 (24.4–30.1)  MMSE in points, median (IQR)  27.0 (25.0–29.0)  CCI score, median (IQR)  2.0 (1.0–3.0)   CCI 0, n (%)  113 (19.6)   CCI 1–2, n (%)  291 (50.3)   CCI ≥3, n (%)  174 (30.1)  CHD, n (%)  120 (20.8)  Diabetes mellitus, n (%)  118 (20.4)  Cancer, n (%)  106 (18.3)  Use of NSAIDs, n (%)  13 (2.2)  Use of diuretics, n (%)  56 (9.7)  Use of inhibitors of RAS, n (%)  73 (12.6)  S-creatinine in μmol/l, median (IQR)  75.0 (65.0–93.0)  eGFR, MDRD in ml/min, median (IQR)  72.0 (59.0–85.0)  eGFR, CKD-EPI in ml/min, median (IQR)  73.7 (59.2–84.9)   eGFR ≥ 90 ml/min, n (%)  115 (19.9)/66 (11.4)a   eGFR 60–89 ml/min, n (%)  314 (54.3)/361 (62.5)a   eGFR < 60 ml/min, n (%)  149 (25.8)/151 (26.1)a  TUG in seconds, median (IQR) (n = 520)  10.0 (8.0–14.0)  FTSS in seconds, median (IQR) (n = 479)  15.0 (12.0–20.0)  Handgrip strength in kg, median (IQR) (n = 570)  24.7 (19.9–32.3)  Calf circumference in cm, median (IQR) (n = 566)  36.3 (34.0–38.8)  Age in years, median (IQR)  76.3 (70.2–81.6)  Men, n (%)  226 (39.1)  Education     ≤6 years, n (%)  161 (27.9)   6–10 years, n (%)  214 (37.0)   >10 years, n (%)  203 (35.1)  Living with partner, n (%)  288 (49.8)  Widow(er), n (%)  194 (33.6)  Current smoker, n (%)  64 (11.1)  Former smoker, n (%)  201 (34.8)  ADL independent, n (%)  414 (72.0)  iADL independent, n (%)  331 (57.6)  BMI in kg/m2, median (IQR) (n = 564)  27.1 (24.4–30.1)  MMSE in points, median (IQR)  27.0 (25.0–29.0)  CCI score, median (IQR)  2.0 (1.0–3.0)   CCI 0, n (%)  113 (19.6)   CCI 1–2, n (%)  291 (50.3)   CCI ≥3, n (%)  174 (30.1)  CHD, n (%)  120 (20.8)  Diabetes mellitus, n (%)  118 (20.4)  Cancer, n (%)  106 (18.3)  Use of NSAIDs, n (%)  13 (2.2)  Use of diuretics, n (%)  56 (9.7)  Use of inhibitors of RAS, n (%)  73 (12.6)  S-creatinine in μmol/l, median (IQR)  75.0 (65.0–93.0)  eGFR, MDRD in ml/min, median (IQR)  72.0 (59.0–85.0)  eGFR, CKD-EPI in ml/min, median (IQR)  73.7 (59.2–84.9)   eGFR ≥ 90 ml/min, n (%)  115 (19.9)/66 (11.4)a   eGFR 60–89 ml/min, n (%)  314 (54.3)/361 (62.5)a   eGFR < 60 ml/min, n (%)  149 (25.8)/151 (26.1)a  TUG in seconds, median (IQR) (n = 520)  10.0 (8.0–14.0)  FTSS in seconds, median (IQR) (n = 479)  15.0 (12.0–20.0)  Handgrip strength in kg, median (IQR) (n = 570)  24.7 (19.9–32.3)  Calf circumference in cm, median (IQR) (n = 566)  36.3 (34.0–38.8)  Continues values are expressed as median and IQR. Dichotomous variables are expressed as number and percentage. ADL, activities of daily living; iADL, instrumental activities of daily living; CHD, coronary heart disease; NSAIDs, non-steroidal anti-inflammatory drugs; RAS, renin–angiotensin system. aData on eGFR categories are expressed as number and percentage using MDRD-formula and CKD-EPI formula, respectively. Age was associated with s-creatinine level (Spearman’s r = 0.190, P < 0.001), whereas an inverse association was found between age and eGFR using both MDRD and CKD-EPI formula (Spearman’s r = −0.274, P < 0.001 and Spearman’s r = −0.411, P < 0.001, respectively). MDRD eGFR and CKD-EPI eGFR were strongly correlated (Spearman’s r = 0.979, P < 0.001). Age was also associated with reduced physical performance. An association was found between age and both mobility tests (TUG Spearman’s r = 0.333, P < 0.001; FTSS Spearman’s r = 0.203, P < 0.001). An inverse association was found between age and handgrip strength (Spearman’s r = −0.316, P < 0.001) and calf circumference (Spearman’s r = −0.337, P < 0.001). Results are shown in Figure 1. Figure 1. View largeDownload slide Correlations of age with renal function and physical performance. S-Creatinine, serum-Creatinine; eGFR, estimated Glomerular Filtration Rate; MDRD, Modification of Diet in Renal Disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; TUG, Timed Up and Go test; FTSS, Five-Times-Sit-to-Stand test. Figure 1. View largeDownload slide Correlations of age with renal function and physical performance. S-Creatinine, serum-Creatinine; eGFR, estimated Glomerular Filtration Rate; MDRD, Modification of Diet in Renal Disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; TUG, Timed Up and Go test; FTSS, Five-Times-Sit-to-Stand test. Mean time to complete the mobility tests did not differ between categories. Mean time in seconds for the TUG, from lowest to highest category, was 11.4 (95% CI: 10.5–12.3), 10.4 (95% CI: 9.9–10.9) and 11.0 (95% CI: 10.1–11.9), respectively. Mean time in seconds for the FTSS was 16.2 (95% CI: 15.0–17.5), 16.0 (95% CI: 15.3–16.8) and 16.8 (95% CI: 15.5–18.2), respectively. Mean handgrip strength was 9.5% lower in participants with a normal eGFR (23.4 kg, 95% CI: 22.4–24.4) than those with a mildly reduced eGFR (25.8 kg, 95% CI: 25.2–26.5) and 6.3% lower than those with a moderately–severely reduced eGFR (24.9 kg, 95% CI: 24.0–25.9). Mean calf circumference was 1.0 cm smaller in participants with a normal eGFR (35.5 cm, 95% CI: 34.9–36.1) than those with a mildly reduced eGFR (36.5 cm, 95% CI: 36.1–36.8). Mean calf circumference of participants with a moderately–severely reduced eGFR did not differ from those in other categories (36.0 cm, 95% CI: 35.5–36.6). Results of physical measures across categories of eGFR are shown in Figure 2. Figure 2. View largeDownload slide Mean values of (a) Timed-Up-and-Go test (TUG), (b) Five-Times-Sit-to-Stand test (FTTS), (c) Handgrip strength and d. Calf circumference across categories of eGFR using the MDRD-formula (in ml/min). Dots represent mean values of back-transformed data, bars represent 95% confidence intervals. Reported estimates are the mean differences of the higher category compared to the lower category expressed in percentage (with 95% confidence interval of the difference). All analyses are adjusted for age, sex and Charlson comorbidity index. Analyses in (c, d) are also adjusted for body mass index. *P-value < 0.05; **P-value < 0.01; ***P-value < 0.001. Figure 2. View largeDownload slide Mean values of (a) Timed-Up-and-Go test (TUG), (b) Five-Times-Sit-to-Stand test (FTTS), (c) Handgrip strength and d. Calf circumference across categories of eGFR using the MDRD-formula (in ml/min). Dots represent mean values of back-transformed data, bars represent 95% confidence intervals. Reported estimates are the mean differences of the higher category compared to the lower category expressed in percentage (with 95% confidence interval of the difference). All analyses are adjusted for age, sex and Charlson comorbidity index. Analyses in (c, d) are also adjusted for body mass index. *P-value < 0.05; **P-value < 0.01; ***P-value < 0.001. Estimates in models where the CKD-EPI formula was used, were slightly different. However, the shape of the results remained unchanged. Mean handgrip strength in participants with an eGFR ≥ 90 ml/min was 8.7% lower (23.2 kg, 95% CI: 21.9–24.7) than those with a mildly reduced eGFR (25.5 kg, 95% CI: 24.9–26.1). A mean difference of 7.2% and a tendency towards significance was found when compared to those with a moderately–severely reduced eGFR (25.0 kg, 95% CI: 24.1–26.1). No differences were found in mean calf circumference, whereas the shape of the results were the same as in models where the MDRD-formula was used (Appendix 1 in the Supplementary data). Discussion In this study we showed that older fallers with an eGFR ≥ 90 ml/min had less muscle mass and strength than older fallers with a reduced eGFR. Handgrip strength was found to be 10% lower in participants with a normal eGFR than those with a reduced eGFR. No association was found between eGFR and mobility. The consequences of kidney disease in older persons are frequently reported in studies [5–9]. However, few studies focussed on the significance of low s-creatinine and associated high eGFR. The implications of different eGFR values were investigated in a large study population of community-dwelling persons over the age of 50 in England [22]. Cox et al. found both low and high eGFR, assessed by the MDRD-formula, to predict mortality. Cardiovascular diseases were the leading causes of mortality in persons with reduced eGFR. Interestingly, persons in high eGFR groups died of respiratory and neoplastic causes. The hypothesis was that respiratory and neoplastic diseases cause cachexia and sarcopenia and the associated low s-creatinine values resulting in a high eGFR. These outcomes support our findings that high eGFR is associated with reduced muscle strength and mass. In a large study population of community-dwelling persons in Italy, a U-shaped relation was found between eGFR and physical performance in the oldest old group of persons ≥90 years [14]. Subjects in the highest eGFR band had the lowest handgrip strength and ADL values comparable with subjects with the lowest eGFR. In our study, a similar trend was found across a wider and also younger age range. However, due to the fact that slightly different methods were used in our study, the results cannot be fully compared to the study of Montesanto et al. There are several possible explanations for our results. In participants with higher eGFR, the observed smaller calf circumference and lesser handgrip strength, are likely manifestations of sarcopenia. Therefore, low s-creatinine resulting in high eGFR values might represent participants with less lean muscle mass and not necessarily with good filtration of s-creatinine in the kidney. Comparable results were also found using the CKD-EPI formula, although with less power most likely due to small shifts in numbers within eGFR categories. It should be noted that the MDRD-formula [23], like most other eGFR formulae [24], is validated in persons until the age of 70 years. Therefore, the MDRD-formula could be less reliable in older persons. This loss of reliability is the effect of changes in body composition with ageing, where, in general, muscle mass decreases [25]. We did not find any relation between eGFR and mobility; this might be due to the fact that mobility, although dependent on muscle mass, is determined by many factors such as balance, reaction time, eyesight and pain [26, 27], whereas handgrip strength and calf circumference are largely determined by muscle mass only [21, 28]. Since the highest eGFR values, i.e. low s-creatinine values, are associated with the lowest muscle mass and strength, the accuracy of estimating kidney function in older persons with formulae based on s-creatinine should be seriously questioned. The present study has several limitations. First, the cross-sectional design of the study limits the ability to draw conclusions about causality. Second, around 25 formulae are used for estimating GFR. We chose to use the MDRD and CKD-EPI-formula, the most used formulae nowadays [18, 29]. It cannot be excluded that the use of another formula would have yielded (slightly) different results. Third, all participants attended the ED because of an accidental fall. Therefore, considering this characteristic, extrapolation to the general population is not possible. Our study also has strengths. First, we used data from a multicentre study, representing a heterogeneous group of older fallers throughout The Netherlands. Second, more than 500 participants in a relatively wide range of age were included in analyses. Third, since s-creatinine based eGFR is the most common way to evaluate kidney function in clinical practice, showing the implications of using this eGFR in older fallers, makes our outcomes clinically relevant. In conclusion, we found that older fallers with a normal eGFR had smaller calf circumference and up to 10% less handgrip strength than those with a reduced eGFR. This lower muscle mass is likely to lead to an overestimation of kidney function in such subjects. The findings of this study therefore support the current search for biomarkers independent of muscle mass to estimate kidney function in older persons. In clinical practice awareness of functional consequences of low s-creatinine and high eGFR values might be appropriate. Key points Muscle mass and muscle strength decrease with ageing. The current estimation of kidney function based on serum-creatinine is questionable in older persons. In older fallers high estimated glomerular filtration rate values is associated with low muscle mass and muscle strength. These findings support the search for biomarkers independent of muscle mass to estimate kidney function. Supplementary Data Supplementary data mentioned in the text are available to subscribers in Age and Ageing online. Funding The study is funded by a grant from The Netherlands Organization for Health Research and Development (ZonMw) (project number 170.885.607). The funding body has no role in study design, data collection and analysis, decision to publish, or preparation of the article. Conflicts of interest None. Ethics Committee Approval The Medical Ethics review board (METC) of the Erasmus University Medical Center in Rotterdam, the Netherlands. The trial is registered in the Netherlands Trial Register (NTR1593). Acknowledgements IMPROveFALL trial collaborators. Erasmus MC, University Medical Center Rotterdam, Rotterdam: T.J.M. van der Cammen, P. Patka, E.F. van Beeck, N. van der Velde, E.M.M. van Lieshout, S. Polinder, F.U.S. Mattace-Raso, K.A. Hartholt, N.D.A. Boyé, C.W.N. Looman. VU University Medical Center, Amsterdam: P. Lips, O.J. de Vries. Sint Franciscus Gasthuis, Rotterdam: A.J.H. Kerver. References 1 Coresh J, Selvin E, Stevens LA et al.  . Prevalence of chronic kidney disease in the United States. J Am Med Assoc  2007; 298: 2038– 47. Google Scholar CrossRef Search ADS   2 National Kidney Foundation. 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Association of estimated glomerular filtration rate with muscle function in older persons who have fallen

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Abstract

Abstract Background studies suggest that estimated glomerular filtration rate (eGFR) is less reliable in older persons and that a low serum-creatinine might reflect reduced muscle mass rather than high kidney function. This study investigates the possible relationship between eGFR and multiple elements of physical performance in older fallers. Methods baseline data of the IMPROveFALL-study were examined in participants ≥65 years. Serum-creatinine based eGFR was classified as normal (≥90 ml/min), mildly reduced (60–89 ml/min) or moderately–severely reduced (<60 ml/min). Timed-Up-and-Go-test and Five-Times-Sit-to-Stand-test were used to assess mobility; calf circumference and handgrip strength to assess muscle status. Ancova models adjusted for age, sex, Charlson comorbidity index and body mass index were performed. Results a total of 578 participants were included. Participants with a normal eGFR had lower handgrip strength than those with a mildly reduced eGFR (−9.5%, P < 0.001) and those with a moderately–severely reduced eGFR (−6.3%, P = 0.033) with mean strengths of 23.4, 25.8 and 24.9 kg, respectively. Participants with a normal eGFR had a smaller calf circumference than those with a mildly reduced eGFR (35.5 versus 36.5 cm, P = 0.006). Mean time to complete the mobility tests did not differ. Conclusions in this study we found that older fallers with an eGFR ≥ 90 ml/min had smaller calf circumference and up to 10% lower handgrip strength than those with a reduced eGFR. This lower muscle mass is likely to lead to an overestimation of kidney function. This outcome therefore supports the search for biomarkers independent of muscle mass to estimate kidney function in older persons. estimated glomerular filtration rate, mobility, muscle strength, muscle mass, ageing, older people Introduction The prevalence of chronic kidney disease (CKD) is rising, due to increased ageing of the population and an increase in the number of risk factors, such as obesity, diabetes and hypertension [1]. The prevalence of CKD, defined as a glomerular filtration rate (GFR) <60 ml/min/1.73 m2 [2], rises in older persons with a prevalence from <1% in persons under 35 years to more than 33% in persons aged 75 years and over [1, 3, 4]. Additionally, CKD increases the risk of morbidity and mortality [5, 6] and negatively affects multiple aspects of functionality and daily performance [7–9]. In clinical practice, estimation of GFR (eGFR) is commonly used as a quick evaluation of kidney function [10, 11]. For estimating GFR, serum-creatinine (s-creatinine) is used as marker, despite the fact that it may be misleading in older persons due to a reduction in muscle mass with ageing [12]. Recent studies have focussed on evaluation of new and independent filtration markers, such as serum cystatin C, ß-trace protein and ß2-microglobuline, to assess renal function and to improve risk prediction related to decreased GFR [13]. However, in clinical practice s-creatinine based eGFR is still daily used. Therefore, it seems relevant to take a closer look at the accuracy of the s-creatinine based eGFR in older persons, since this estimation of renal function may be greatly influenced by muscle mass. Previous studies showed that low s-creatinine, resulting in higher eGFR values, partly reflects muscle atrophy and ‘frailty’ in older persons [14, 15]. In the oldest old community-dwelling persons ≥90 years, where the prevalence of sarcopenia is higher [12], a U-shaped relationship was found between eGFR and handgrip strength and mortality [14]. These findings generate the hypothesis that sarcopenic persons, having low s-creatinine, might group in categories with high eGFR values. In this study we investigated whether s-creatinine based eGFR is associated with physical performance in persons aged 65 years and over who have fallen. Methods Data collection Baseline data from the Improving Medication Prescribing to reduce Risk Of FALLs (IMPROveFALL) study were analysed. The IMPROveFALL-study is a randomised multicentre trial investigating the effect of withdrawal of fall-risk increasing drugs versus ‘care as usual’ on reducing falls in community-dwelling persons. A detailed description of the methods can be found elsewhere [16]. In summary, individuals aged ≥65 years who visited the Emergency Department (ED) because of a fall were asked to participate. Individuals had a Mini-Mental State Examination (MMSE) score of at least 21 out of 30 points [17]. The baseline visit took place within 2 months after ED attendance. The ethical committee of the Erasmus MC approved the study and all participants signed informed consent. Covariates Demographic data were collected from ED records. Medical history, use of medications, lifestyle factors and number of comorbidities were documented and Charlson comorbidity index (CCI) was determined. Height and weight were measured and body mass index (BMI) was calculated. Biochemistry S-creatinine, sex and age were used to calculate the eGFR in ml/min/1.73 m2 with the Modification of Diet in Renal Disease (MDRD) Study formula widely used: 175 × (s-creatinine (μmol/l) × 0.0113)−1.154 × age−0.203 (years) × 0.742 (if female). GFR was also estimated with the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula, because of the increasing use of this formula in general practice [18]. Kidney function was classified as proposed by the National Kidney Foundation [2] and slightly merged into the following three categories: normal (eGFR ≥ 90 ml/min/1.73 m2), mildly reduced (eGFR 60–89 ml/min/1.73 m2), moderately–severely reduced (eGFR 15–59 ml/min/1.73 m2). Physical performance The Timed-Up-and-Go test (TUG) and the Five-Time-Sit-to-Stand test (FTSS) were used to assess mobility [19, 20]. Handgrip strength was measured to reflect muscle strength and calf circumference to reflect muscle mass. Handgrip strength is reported as a validated indicator for global muscle strength [21] and was measured with a hand-held dynamometer (Takei TKK 540, Takei Scientific Instruments Co., Ltd., Tokyo, Japan) in standing position with arms beside the body, using the maximum strength from four alternating (right–left) attempts. For measuring calf circumference participants sat with a knee angle of 90° and the sole on the ground. The thickest part of both calves was measured with a measuring tape and the mean value was documented. Statistical analyses Statistical analyses were performed using SPSS statistical package 21.0 for Windows. Descriptive data for continues variables were presented as median and interquartile range (IQR). Numbers and percent prevalences were presented for dichotomous variables. Spearman’s correlation analyses were used to investigate the relation of age with s-creatinine, eGFR (MDRD and CKD-EPI formula) and physical performance. Missing performance test measures, mostly due to injuries following fall, were excluded from all related analyses. Because of a right-skewed distribution, outcomes of physical performance were log transformed using the natural log. In analyses of covariance we compared mean (natural log transformed) scores of physical performance tests across three categories of eGFR (eGFR ≥ 90 ml/min, eGFR 60–89, eGFR < 60 ml/min). All models were adjusted for sex, age and CCI. Models investigating muscle mass and strength were also adjusted for BMI. We back transformed the results to the original scale. A two-tailed P < 0.05 was defined as statistically significant. Results Overall, 616 participants were enroled in the IMPROveFALL-study; information on s-creatinine was obtained from 578 participants. Median age was 76.3 years (IQR: 70.2–81.6), 39% were men, 50% lived with a partner, 72% were ADL independent and 58% were iADL independent. The 20% of the participants had diabetes mellitus, 21% had a history of coronary heart disease and 18% had a history of cancer. Using the MDRD-formula 115 participants (19.9%) had a normal eGFR, 314 participants (54.3%) had a mildly reduced eGFR and 149 participants (25.8%) had a moderately–severely reduced eGFR. Participants in the lowest category had a median eGFR of 50 ml/min (IQR: 41–55). When GFR was estimated with the CKD-EPI formula, the categories were different with 11.4, 62.5 and 26.1% from highest to lowest eGFR, respectively. Median s-creatinine was 75.0 μmol/l (IQR: 65.0–93.0), median eGFR was 72.0 ml/min (IQR: 59.0–85.0) using the MDRD-formula and 73.7 ml/min (IQR: 59.2–84.9) using the CKD-EPI formula. Median time to complete the TUG was 10.0 s (IQR: 8.0–14.0) and 15.0 s (IQR: 12.0–20.0) for the FTSS. Median handgrip strength was 24.7 kg (IQR: 19.9–32.3) and median calf circumference was 36.3 cm (IQR: 34.0–38.8). Characteristics of the participants are shown in Table 1. Table 1. Characteristics of the participants (n = 578) Age in years, median (IQR)  76.3 (70.2–81.6)  Men, n (%)  226 (39.1)  Education     ≤6 years, n (%)  161 (27.9)   6–10 years, n (%)  214 (37.0)   >10 years, n (%)  203 (35.1)  Living with partner, n (%)  288 (49.8)  Widow(er), n (%)  194 (33.6)  Current smoker, n (%)  64 (11.1)  Former smoker, n (%)  201 (34.8)  ADL independent, n (%)  414 (72.0)  iADL independent, n (%)  331 (57.6)  BMI in kg/m2, median (IQR) (n = 564)  27.1 (24.4–30.1)  MMSE in points, median (IQR)  27.0 (25.0–29.0)  CCI score, median (IQR)  2.0 (1.0–3.0)   CCI 0, n (%)  113 (19.6)   CCI 1–2, n (%)  291 (50.3)   CCI ≥3, n (%)  174 (30.1)  CHD, n (%)  120 (20.8)  Diabetes mellitus, n (%)  118 (20.4)  Cancer, n (%)  106 (18.3)  Use of NSAIDs, n (%)  13 (2.2)  Use of diuretics, n (%)  56 (9.7)  Use of inhibitors of RAS, n (%)  73 (12.6)  S-creatinine in μmol/l, median (IQR)  75.0 (65.0–93.0)  eGFR, MDRD in ml/min, median (IQR)  72.0 (59.0–85.0)  eGFR, CKD-EPI in ml/min, median (IQR)  73.7 (59.2–84.9)   eGFR ≥ 90 ml/min, n (%)  115 (19.9)/66 (11.4)a   eGFR 60–89 ml/min, n (%)  314 (54.3)/361 (62.5)a   eGFR < 60 ml/min, n (%)  149 (25.8)/151 (26.1)a  TUG in seconds, median (IQR) (n = 520)  10.0 (8.0–14.0)  FTSS in seconds, median (IQR) (n = 479)  15.0 (12.0–20.0)  Handgrip strength in kg, median (IQR) (n = 570)  24.7 (19.9–32.3)  Calf circumference in cm, median (IQR) (n = 566)  36.3 (34.0–38.8)  Age in years, median (IQR)  76.3 (70.2–81.6)  Men, n (%)  226 (39.1)  Education     ≤6 years, n (%)  161 (27.9)   6–10 years, n (%)  214 (37.0)   >10 years, n (%)  203 (35.1)  Living with partner, n (%)  288 (49.8)  Widow(er), n (%)  194 (33.6)  Current smoker, n (%)  64 (11.1)  Former smoker, n (%)  201 (34.8)  ADL independent, n (%)  414 (72.0)  iADL independent, n (%)  331 (57.6)  BMI in kg/m2, median (IQR) (n = 564)  27.1 (24.4–30.1)  MMSE in points, median (IQR)  27.0 (25.0–29.0)  CCI score, median (IQR)  2.0 (1.0–3.0)   CCI 0, n (%)  113 (19.6)   CCI 1–2, n (%)  291 (50.3)   CCI ≥3, n (%)  174 (30.1)  CHD, n (%)  120 (20.8)  Diabetes mellitus, n (%)  118 (20.4)  Cancer, n (%)  106 (18.3)  Use of NSAIDs, n (%)  13 (2.2)  Use of diuretics, n (%)  56 (9.7)  Use of inhibitors of RAS, n (%)  73 (12.6)  S-creatinine in μmol/l, median (IQR)  75.0 (65.0–93.0)  eGFR, MDRD in ml/min, median (IQR)  72.0 (59.0–85.0)  eGFR, CKD-EPI in ml/min, median (IQR)  73.7 (59.2–84.9)   eGFR ≥ 90 ml/min, n (%)  115 (19.9)/66 (11.4)a   eGFR 60–89 ml/min, n (%)  314 (54.3)/361 (62.5)a   eGFR < 60 ml/min, n (%)  149 (25.8)/151 (26.1)a  TUG in seconds, median (IQR) (n = 520)  10.0 (8.0–14.0)  FTSS in seconds, median (IQR) (n = 479)  15.0 (12.0–20.0)  Handgrip strength in kg, median (IQR) (n = 570)  24.7 (19.9–32.3)  Calf circumference in cm, median (IQR) (n = 566)  36.3 (34.0–38.8)  Continues values are expressed as median and IQR. Dichotomous variables are expressed as number and percentage. ADL, activities of daily living; iADL, instrumental activities of daily living; CHD, coronary heart disease; NSAIDs, non-steroidal anti-inflammatory drugs; RAS, renin–angiotensin system. aData on eGFR categories are expressed as number and percentage using MDRD-formula and CKD-EPI formula, respectively. Age was associated with s-creatinine level (Spearman’s r = 0.190, P < 0.001), whereas an inverse association was found between age and eGFR using both MDRD and CKD-EPI formula (Spearman’s r = −0.274, P < 0.001 and Spearman’s r = −0.411, P < 0.001, respectively). MDRD eGFR and CKD-EPI eGFR were strongly correlated (Spearman’s r = 0.979, P < 0.001). Age was also associated with reduced physical performance. An association was found between age and both mobility tests (TUG Spearman’s r = 0.333, P < 0.001; FTSS Spearman’s r = 0.203, P < 0.001). An inverse association was found between age and handgrip strength (Spearman’s r = −0.316, P < 0.001) and calf circumference (Spearman’s r = −0.337, P < 0.001). Results are shown in Figure 1. Figure 1. View largeDownload slide Correlations of age with renal function and physical performance. S-Creatinine, serum-Creatinine; eGFR, estimated Glomerular Filtration Rate; MDRD, Modification of Diet in Renal Disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; TUG, Timed Up and Go test; FTSS, Five-Times-Sit-to-Stand test. Figure 1. View largeDownload slide Correlations of age with renal function and physical performance. S-Creatinine, serum-Creatinine; eGFR, estimated Glomerular Filtration Rate; MDRD, Modification of Diet in Renal Disease; CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; TUG, Timed Up and Go test; FTSS, Five-Times-Sit-to-Stand test. Mean time to complete the mobility tests did not differ between categories. Mean time in seconds for the TUG, from lowest to highest category, was 11.4 (95% CI: 10.5–12.3), 10.4 (95% CI: 9.9–10.9) and 11.0 (95% CI: 10.1–11.9), respectively. Mean time in seconds for the FTSS was 16.2 (95% CI: 15.0–17.5), 16.0 (95% CI: 15.3–16.8) and 16.8 (95% CI: 15.5–18.2), respectively. Mean handgrip strength was 9.5% lower in participants with a normal eGFR (23.4 kg, 95% CI: 22.4–24.4) than those with a mildly reduced eGFR (25.8 kg, 95% CI: 25.2–26.5) and 6.3% lower than those with a moderately–severely reduced eGFR (24.9 kg, 95% CI: 24.0–25.9). Mean calf circumference was 1.0 cm smaller in participants with a normal eGFR (35.5 cm, 95% CI: 34.9–36.1) than those with a mildly reduced eGFR (36.5 cm, 95% CI: 36.1–36.8). Mean calf circumference of participants with a moderately–severely reduced eGFR did not differ from those in other categories (36.0 cm, 95% CI: 35.5–36.6). Results of physical measures across categories of eGFR are shown in Figure 2. Figure 2. View largeDownload slide Mean values of (a) Timed-Up-and-Go test (TUG), (b) Five-Times-Sit-to-Stand test (FTTS), (c) Handgrip strength and d. Calf circumference across categories of eGFR using the MDRD-formula (in ml/min). Dots represent mean values of back-transformed data, bars represent 95% confidence intervals. Reported estimates are the mean differences of the higher category compared to the lower category expressed in percentage (with 95% confidence interval of the difference). All analyses are adjusted for age, sex and Charlson comorbidity index. Analyses in (c, d) are also adjusted for body mass index. *P-value < 0.05; **P-value < 0.01; ***P-value < 0.001. Figure 2. View largeDownload slide Mean values of (a) Timed-Up-and-Go test (TUG), (b) Five-Times-Sit-to-Stand test (FTTS), (c) Handgrip strength and d. Calf circumference across categories of eGFR using the MDRD-formula (in ml/min). Dots represent mean values of back-transformed data, bars represent 95% confidence intervals. Reported estimates are the mean differences of the higher category compared to the lower category expressed in percentage (with 95% confidence interval of the difference). All analyses are adjusted for age, sex and Charlson comorbidity index. Analyses in (c, d) are also adjusted for body mass index. *P-value < 0.05; **P-value < 0.01; ***P-value < 0.001. Estimates in models where the CKD-EPI formula was used, were slightly different. However, the shape of the results remained unchanged. Mean handgrip strength in participants with an eGFR ≥ 90 ml/min was 8.7% lower (23.2 kg, 95% CI: 21.9–24.7) than those with a mildly reduced eGFR (25.5 kg, 95% CI: 24.9–26.1). A mean difference of 7.2% and a tendency towards significance was found when compared to those with a moderately–severely reduced eGFR (25.0 kg, 95% CI: 24.1–26.1). No differences were found in mean calf circumference, whereas the shape of the results were the same as in models where the MDRD-formula was used (Appendix 1 in the Supplementary data). Discussion In this study we showed that older fallers with an eGFR ≥ 90 ml/min had less muscle mass and strength than older fallers with a reduced eGFR. Handgrip strength was found to be 10% lower in participants with a normal eGFR than those with a reduced eGFR. No association was found between eGFR and mobility. The consequences of kidney disease in older persons are frequently reported in studies [5–9]. However, few studies focussed on the significance of low s-creatinine and associated high eGFR. The implications of different eGFR values were investigated in a large study population of community-dwelling persons over the age of 50 in England [22]. Cox et al. found both low and high eGFR, assessed by the MDRD-formula, to predict mortality. Cardiovascular diseases were the leading causes of mortality in persons with reduced eGFR. Interestingly, persons in high eGFR groups died of respiratory and neoplastic causes. The hypothesis was that respiratory and neoplastic diseases cause cachexia and sarcopenia and the associated low s-creatinine values resulting in a high eGFR. These outcomes support our findings that high eGFR is associated with reduced muscle strength and mass. In a large study population of community-dwelling persons in Italy, a U-shaped relation was found between eGFR and physical performance in the oldest old group of persons ≥90 years [14]. Subjects in the highest eGFR band had the lowest handgrip strength and ADL values comparable with subjects with the lowest eGFR. In our study, a similar trend was found across a wider and also younger age range. However, due to the fact that slightly different methods were used in our study, the results cannot be fully compared to the study of Montesanto et al. There are several possible explanations for our results. In participants with higher eGFR, the observed smaller calf circumference and lesser handgrip strength, are likely manifestations of sarcopenia. Therefore, low s-creatinine resulting in high eGFR values might represent participants with less lean muscle mass and not necessarily with good filtration of s-creatinine in the kidney. Comparable results were also found using the CKD-EPI formula, although with less power most likely due to small shifts in numbers within eGFR categories. It should be noted that the MDRD-formula [23], like most other eGFR formulae [24], is validated in persons until the age of 70 years. Therefore, the MDRD-formula could be less reliable in older persons. This loss of reliability is the effect of changes in body composition with ageing, where, in general, muscle mass decreases [25]. We did not find any relation between eGFR and mobility; this might be due to the fact that mobility, although dependent on muscle mass, is determined by many factors such as balance, reaction time, eyesight and pain [26, 27], whereas handgrip strength and calf circumference are largely determined by muscle mass only [21, 28]. Since the highest eGFR values, i.e. low s-creatinine values, are associated with the lowest muscle mass and strength, the accuracy of estimating kidney function in older persons with formulae based on s-creatinine should be seriously questioned. The present study has several limitations. First, the cross-sectional design of the study limits the ability to draw conclusions about causality. Second, around 25 formulae are used for estimating GFR. We chose to use the MDRD and CKD-EPI-formula, the most used formulae nowadays [18, 29]. It cannot be excluded that the use of another formula would have yielded (slightly) different results. Third, all participants attended the ED because of an accidental fall. Therefore, considering this characteristic, extrapolation to the general population is not possible. Our study also has strengths. First, we used data from a multicentre study, representing a heterogeneous group of older fallers throughout The Netherlands. Second, more than 500 participants in a relatively wide range of age were included in analyses. Third, since s-creatinine based eGFR is the most common way to evaluate kidney function in clinical practice, showing the implications of using this eGFR in older fallers, makes our outcomes clinically relevant. In conclusion, we found that older fallers with a normal eGFR had smaller calf circumference and up to 10% less handgrip strength than those with a reduced eGFR. This lower muscle mass is likely to lead to an overestimation of kidney function in such subjects. The findings of this study therefore support the current search for biomarkers independent of muscle mass to estimate kidney function in older persons. In clinical practice awareness of functional consequences of low s-creatinine and high eGFR values might be appropriate. Key points Muscle mass and muscle strength decrease with ageing. The current estimation of kidney function based on serum-creatinine is questionable in older persons. In older fallers high estimated glomerular filtration rate values is associated with low muscle mass and muscle strength. These findings support the search for biomarkers independent of muscle mass to estimate kidney function. Supplementary Data Supplementary data mentioned in the text are available to subscribers in Age and Ageing online. Funding The study is funded by a grant from The Netherlands Organization for Health Research and Development (ZonMw) (project number 170.885.607). The funding body has no role in study design, data collection and analysis, decision to publish, or preparation of the article. Conflicts of interest None. Ethics Committee Approval The Medical Ethics review board (METC) of the Erasmus University Medical Center in Rotterdam, the Netherlands. The trial is registered in the Netherlands Trial Register (NTR1593). Acknowledgements IMPROveFALL trial collaborators. Erasmus MC, University Medical Center Rotterdam, Rotterdam: T.J.M. van der Cammen, P. Patka, E.F. van Beeck, N. van der Velde, E.M.M. van Lieshout, S. Polinder, F.U.S. Mattace-Raso, K.A. Hartholt, N.D.A. Boyé, C.W.N. Looman. VU University Medical Center, Amsterdam: P. Lips, O.J. de Vries. Sint Franciscus Gasthuis, Rotterdam: A.J.H. Kerver. References 1 Coresh J, Selvin E, Stevens LA et al.  . Prevalence of chronic kidney disease in the United States. J Am Med Assoc  2007; 298: 2038– 47. Google Scholar CrossRef Search ADS   2 National Kidney Foundation. 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Age and AgeingOxford University Press

Published: Mar 1, 2018

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