A New Equation to Estimate Muscle Mass from Creatinine and Cystatin C
A New Equation to Estimate Muscle Mass from Creatinine and Cystatin C
Sun-wook Kim 0 1
Hee-Won Jung 0 1
Cheol-Ho Kim 0 1
Kwang-il Kim 0 1
Ho Jun Chin 0 1
Hajeong Lee 0 1
0 1 Department of Internal Medicine, Seoul National University Bundang Hospital , Seongnam , Republic of Korea, 2 Seoul National University College of Medicine, Seoul, Republic of Korea, 3 Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea, 4 Department of Internal Medicine, Seoul National University Hospital , Seoul , Republic of Korea
1 Editor: Cordula M. Stover, University of Leicester , UNITED KINGDOM
Data Availability Statement; All relevant data are within the paper
Competing Interests: The authors have declared
that no competing interests exist.
With evaluation for physical performance, measuring muscle mass is an important step in detecting sarcopenia. However, there are no methods to estimate muscle mass from blood sampling.
To develop a new equation to estimate total-body muscle mass with serum creatinine and
cystatin C level, we designed a cross-sectional study with separate derivation and validation
cohorts. Total body muscle mass and fat mass were measured using dual-energy x-ray
absorptiometry (DXA) in 214 adults aged 25 to 84 years who underwent physical checkups
from 2010 to 2013 in a single tertiary hospital. Serum creatinine and cystatin C levels were
Serum creatinine was correlated with muscle mass (P < .001), and serum cystatin C was
correlated with body fat mass (P < .001) after adjusting glomerular filtration rate (GFR). After
eliminating GFR, an equation to estimate total-body muscle mass was generated and
coefficients were calculated in the derivation cohort. There was an agreement between muscle
mass calculated by the novel equation and measured by DXA in both the derivation and
validation cohort (P < .001, adjusted R2 = 0.829, β = 0.95, P < .001, adjusted R2 = 0.856, β =
The new equation based on serum creatinine and cystatin C levels can be used to estimate total-body muscle mass.
Aging leads to biological and physical changes in the structure and function of skeletal muscle.
Sarcopenia has been defined as a phenomenon of age-related progressive decline in skeletal
muscle mass and function that may result in decreased strength and low physical performance.
It has been known that sarcopenia is associated with functional impairment, increased risk of
] and consequently with decreased quality of life. Therefore, sarcopenia is assumed
to be a major factor of geriatric syndromes and cycle of frailty. Moreover, sarcopenia is related
to metabolic diseases (e.g. diabetes mellitus, dyslipidemia), major adverse cardiovascular
events, and mortality.[
In detecting sarcopenia, algorithms required measuring physical performance or muscle
strength and muscle mass.[
] To date, many methods have been developed to measure
muscle mass and diagnose sarcopenia. One of classical methods for estimating muscle mass is
calculating 24-hour urinary creatinine excretion. However, the reliability of this test is largely
dependent on the subject’s compliance.[
] With technical improvement, methods for
estimating muscle mass with computed tomography (CT) or magnetic resonance imaging (MRI) have
been established, and currently they are considered the gold standards in research.[
Recently, dual-energy x-ray absorptiometry (DXA) and bioimpedance analysis (BIA) have
been often used to estimate muscle mass in routine practice.[
] Although imaging
modalities like MRI, CT, and DXA are considered to produce precise results, these methods have
caveats in terms of cost, possible radiation exposure, and limited accessibility for primary care
and field studies. Furthermore, these tests cannot be performed using archived samples of
serum in large scale cohorts.[
] On the other hand, BIA has its weakness in low precision and
reproducibility especially in patients who have chronic illness or extreme body height or
Assessment of glomerular filtration rate (GFR) is essential for clinical practice, research, and
serum creatinine has been widely used for estimating GFR.[
] However, creatinine-based
GFR estimation is largely influenced by physiological and clinical conditions that affect muscle
mass. The serum creatinine levels of sarcopenic elderly are usually low or below normal
ranges; therefore, estimated GFR (eGFR) calculated with serum creatinine level, usually
overestimates their real kidney function.[
] Recently, cystatin C has been the focus of a new marker
for GFR which is a low molecular weight protein produced with a stable production rate and
filtered by the glomerulus freely.[
] Because it is not influenced by dietary factor or muscle
mass, cystatin C-based eGFR is more appropriate for the elderly who are susceptible to
We focused on the fact that cystatin C is independent of muscle mass, and hypothesized
that discrepancy between creatinine and cystatin C-based GFR can be explained by the muscle
mass. Therefore, we aimed to develop a novel equation to estimate total-body muscle mass
(TBMM) with serum creatinine and cystatin C levels.
The current study is a retrospective cross-sectional study. We studied community-dwelling
participants aged 25 years who visited the health promotion center of a single tertiary
hospital for health screening from January 2010 to June 2013. Data were collected through an
electronic medical record system. A total of 303 people who had done DXA as well as those who
had undergone measurement of serum creatinine and serum cystatin C were screened. We
excluded 85 people who underwent these tests over a month of period and 4 people who had
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metal prosthesis which interfere with measuring muscle mass with DXA. Finally, records of
214 people were included for the analysis, and none of included people had been diagnosed
with chronic kidney disease. Each person was assigned 3-digit random number electronically,
and categorized into two groups with 1:1 distribution by the random number. The first group
served as an equation-derivation cohort and the second group as an equation-validation
cohort. The study protocol was reviewed and approved by the institutional review board of the
Seoul National University Hospital, which waived the requirement for informed consent.
Previous medical history was acquired, and clinical variables including height, weight were
measured. All people included in the analysis underwent DXA, measurement of serum
creatinine level, and serum cystatin C level within 30 days. TBMM and total-body fat mass were
estimated using DXA (Lunar prodigy advance, GE healthcare, Fairfield, CT) with standardized
protocols. TBMM was considered equivalent to the value of total-body lean mass minus
totalbody bone mass, assuming that nonfat and nonbone tissue was muscle.[
] Assays for serum
creatinine and cystatin C were performed at the Seoul National University Hospital
immediately after sampling. Serum cystatin C concentration was determined by using a
particleenhanced immunoturbidimetric assay (MODULAR P analyzer, Roche/Hitachi, Indianapolis,
Construction and Validation of the New Equation
We assumed that the serum creatinine level is proportional to the TBMM and inversely
correlated to the eGFR. We also presumed, based on a previous study, that the serum cystatin C
level is proportional to the total-body fat percent and inversely correlated to the eGFR.[
made a linear equation based on those suppositions then determined the coefficient K in the
derivation cohort (Fig 1A). Total-body bone mass accounts for less than 5% of total-body
muscle mass; therefore, we eliminated bone mass from the equation (Fig 1B).[
] After elimination
of the eGFR and total-body fat percent, a final equation to estimate TBMM was developed
(Fig 1C). Confirmation of the final equation was done in the validation cohort and
performance was reported.
Continuous variables were expressed as mean (SD) and discrete variables were expressed as
counts (percentages). Linear regression analysis was used to evaluate the association between
cystatin C and total-body fat percent. The association between creatinine and TBMM was
assessed. Performance of the novel equation was described by the R2 value and plotted in a
scatter plot. We compared calculated TBMM and TBMM measured with DXA minus
calculated TBMM with a Bland-Altman plot graphically.[
] To assess the relationships between
TBMM by DXA and TBMM by the novel equation, we calculated intraclass correlation
coefficients (ICC). We logged and analyzed data using PASW Statistics 18.0 (SPSS Inc., Chicago, IL)
and MedCalc (Medcalc Software, Acacialaan 22, Ostend, Belgium). All records from
participants were de-identified and analyzed anonymously.
One hundred and seven people were assigned to the derivation cohort and 107 were assigned
to the validation cohort using a random number table. The baseline characteristics of the
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Fig 1. Development of the novel equation to estimate muscle mass.
derivation cohort (37 men and 70 women) and validation cohort (34 men and 73 women) are
shown in Table 1. There was no significant difference between two groups.
To verify hypotheses, we evaluated the relationship between the creatinine level and TBMM
in the derivation cohort. There was a linear correlation between serum creatinine level and
TBMM after adjusting for CKD-EPI creatinine equation eGFR (P < .001, R2 = 0.804). We also
examined the relationship between the cystatin C level and total-body fat percent. After
correction of CKD-EPI creatinine equation eGFR, there was a linear correlation between the serum
cystatin C level and total-body fat percent (P < .001, R2 = 0.475).
With serum creatinine level, serum cystatin C level, DXA measured total-body fat percent,
and TBMM, we determined the coefficient K value in the derivation cohort (Fig 1A). We
choose the mean value of the coefficient K for men and women separately, considering possible
influence of sex on metabolisms of creatinine and cystatin C.[
] The coefficient values were
0.00675 for men and 0.01006 for women. We calculated individual muscle mass with the novel
equation and determined coefficient values (Fig 1B), and the calculated TBMM explained
82.9% of the between-subject variance in DXA measured TBMM (P < .001, adjusted R2 =
0.829) (Fig 2A). We verified the equation and coefficient K in the validation cohort. There was
a linear correlation between the calculated TBMM and DXA measured TBMM (P < .001,
adjusted R2 = 0.856, β = 1.03) (Fig 2B). By comparison, the calculated TBMM by the novel
equation could explain DXA measured TBMM by 85.6%, although individual body weight
could explain TBMM by 69.6%. We graphically compared the calculated TBMM and DXA
measured TBMM with a Bland-Altman plot (Fig 3), and agreements between the two
techniques were measured by intraclass correlation coefficients (ICC). ICC in validation cohort
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Men (n = 34)
*Mean ± SD (all such values).
†Estimated by the CKD-EPI creatinine equation
Abbreviation: BMI-Body mass index, DXA-Dual-energy x-ray absorptiometry, eGFR-Estimated glomerular filtration rate
was 0.93 (P < .001) and ICC in derivation cohort was 0.91 (P < .001) respectively, which
showed statistical agreements between two methods in both cohorts (Fig 3).
Fig 2. Scatter plots of muscle mass measured with DXA and the novel equation. Thick line indicates mean trend line and dotted line indicates 95%
confidence interval. Legend: DXA- Dual-energy x-ray absorptiometry.
PLOS ONE | DOI:10.1371/journal.pone.0148495
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Fig 3. Bland-Altman plots of muscle mass measured with DXA and the novel equation. Legend: DXA- Dual-energy x-ray absorptiometry, SD-Standard
In the current study, eGFR adjusted serum creatinine level was proportional to the TBMM,
and eGFR adjusted serum cystatin C level was proportional to total-body fat percent. We then
made a novel equation to estimate TBMM and determined the coefficient of the equation in
the derivation cohort. We performed verification in the validation cohort, and found that the
novel equation can estimate TBMM with statistical significance. Furthermore, the errors of the
equation were statistically acceptable.
Cystatin C is anticipated as a potential replacement for serum creatinine in GFR estimation.
Cystatin C may be more useful to estimate GFR in older adults with decreased muscle mass,
because previously existing formulas that predict GFR take into account gender, age, and
weight, but not muscle mass. On the other hand, the cystatin C level is useful to assess the renal
function in individuals with higher muscle mass as well; therefore, muscle mass can account
for a large portion of the error in creatinine-based GFR estimation.[
In our equation, a higher cystatin C level was associated with lower muscle mass and it
connotes a large fat percent. Many studies reported that there is a graded association between
higher BMI and elevated serum cystatin C.[
] Moreover, cystatin C gene expression and
secretion by adipose tissue were increased two- to threefold in obese individuals, and it was
confirmed that increased production of cystatin C was contributed from enlarged adipose
tissue in vitro study.[
] Hypertension, coronary heart disease, increased inflammatory marker,
and low functional status were correlated with high cystatin C levels and these morbidities
were also associated with the sarcopenia.[
] Furthermore, higher cystatin C was associated
with frailty and poor physical function in the elderly and the association was separate from
] In the Health ABC study, there was an inverse correlation between eGFR and
physical performance in the subgroup whose eGFR by creatinine was above 60mL/min/1.73m2.[
Additionally, cystatin C was a strong independent risk factor for mortality in that study.[
Therefore, we can make inferences that decreased muscle mass and increased fat mass elevate
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serum cystatin C level, and high cystatin C levels are associated with functional decline,
morbidity, and mortality in sarcopenic patients.
Our study has several strengths. To the best of our knowledge, it is the first study to measure
body muscle mass using serum creatinine and cystatin C levels. We proposed a novel equation
to estimate TBMM, which can predict TBMM with statistical significance. Our novel equation
could be used to measure body muscle mass with less cost compared to MRI, CT, and DXA
without radiation hazard. Unlike MRI, CT and DXA, blood sampling can be done anywhere,
and a lot of samples can be handled at the same time, therefore it can ease off the pressure on
time and space. Moreover, TBMM can be calculated using archived samples of serum in large
scale cohorts so we can estimate participant’s muscle status in the past. And it is useful to check
patient’s temporal change by repetitive measurements. Furthermore, considering that dosing
of some medications by a patient’s muscle mass might be helpful, and serum cystatin C level
can predict serum levels of drugs such as vancomycin better than serum creatinine, our
equation can be used in adjusting drug doses which mainly distribute in the muscle.[
However, our study has several limitations. First, our estimation is limited to TBMM while
sarcopenia has been diagnosed with decreased appendicular muscle mass. As serum creatinine
and cystatin C are affected by TBMM and total-body fat mass, calculated TBMM with these
markers includes non-appendicular muscle mass. However, it has been known that TBMM has a
strong correlation with appendicular skeletal muscle mass.[
] Second, we regarded the TBMM
measured with DXA as a reference standard. DXA cannot detect intramuscular fat infiltration
which is known to affect quality of skeletal muscle, consequently we assumed in this study that
muscle mass is bone mass subtracted from lean mass. Third, the correlation between cystatin C
and body fat percent is lower than that of creatinine and TBMM. Previous studies showed that
various clinical factors including diabetes, thyroid function, and hemoglobin.[
we could not collect whole participants’ clinical factors and they can be confounders of that
relationship, statistical significance of the correlation between cystatin C and body fat percent was
remained after adjustment of eGFR. Finally, our models were developed in a retrospective
crosssectional dataset from a single hospital in Korea and the measurements were done at only one
point in time. Furthermore, people included in this study were ambulatory, who can visit hospital
to take health examination. However, the present study is rather hypothesis generating and
following studies in other ethnic groups, sick or frail patients, and longitudinal cohorts with larger
scale are warranted for the generalization of findings in this study.
In conclusion, a new equation using serum creatinine and cystatin C levels can estimate
TBMM in community-dwelling Koreans, without radiation hazard. This equation may be used
to calculate muscle mass in various setting including retrospective cohort in which performing
other measurement methods for muscle mass are impossible.
We want to thank Soyeon Ahn, PhD (Medical Research Collaborating Center, Seoul National
University Bundang Hospital) who helped with statistical analysis and manuscript revision,
without financial compensation
The authors’ responsibilities were as follows—S-WK and H-WJ: designed research,
analyzed data and wrote of the manuscript equally as co-first authors; K-IK, C-HK, and HJC:
analyzed and interpreted data; HL: conducted research and had primary responsibility for final
content; and all authors: read and approved the final manuscript. None of the authors reported
a conflict of interest related to the study.
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Conceived and designed the experiments: S-wK H-WJ. Performed the experiments: S-wK
HWJ HL. Analyzed the data: S-wK H-WJ K-iK C-HK HJC HL. Wrote the paper: S-wK H-WJ
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1. Inouye SK , Studenski S , Tinetti ME , Kuchel GA . Geriatric syndromes: clinical, research, and policy implications of a core geriatric concept . J Am Geriatr Soc 2007 ; 55 ( 5 ): 780 - 91 . doi: 10 .1111/j.1532- 5415 . 2007 . 01156 . x PMID : 17493201 .
2. Cawthon PM , Marshall LM , Michael Y , Dam TT , Ensrud KE , Barrett-Connor E , et al. Frailty in older men: prevalence, progression, and relationship with mortality . J Am Geriatr Soc 2007 ; 55 ( 8 ): 1216 - 23 . doi: 10 .1111/j.1532- 5415 . 2007 . 01259 . x PMID : 17661960 .
3. Oterdoom LH , Gansevoort RT , Schouten JP , de Jong PE , Gans RO , Bakker SJ . 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 ( 2 ): 534 - 40 . doi: 10 .1016/j. atherosclerosis. 2009 . 05 .010 PMID: 19535078 .
4. Srikanthan P , Karlamangla AS . Muscle mass index as a predictor of longevity in older adults . Am J Med 2014 ; 127 ( 6 ): 547 - 53 . doi: 10 .1016/j.amjmed. 2014 . 02 .007 PMID: 24561114 .
5. Cruz-Jentoft AJ , Baeyens JP , Bauer JM , Boirie Y , Cederholm T , Landi F , et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age ageing . 2010 ; 39 ( 4 ): 412 - 23 . doi: 10 .1093/ageing/afq034 PMID: 20392703 .
6. Chen LK , Liu LK , Woo J , Assantachai P , Auyeung TW , Bahyah KS , et al. Sarcopenia in Asia: consensus report of the Asian Working Group for Sarcopenia. J Am Med Dir Assoc 2014 ; 15 ( 2 ): 95 - 101 . doi: 10 .1016/j.jamda. 2013 . 11 .025 PMID: 24461239 .
7. Heymsfield SB , Arteaga C , McManus C , Smith J , Moffitt S. Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method . Am J Clin Nutr 1983 ; 37 ( 3 ): 478 - 94 . PMID: 6829490 .
8. Proctor DN , O'Brien PC , Atkinson EJ , Nair KS . Comparison of techniques to estimate total body skeletal muscle mass in people of different age groups . Am J Physiol 1999 ; 277 ( 3 ): E489 - 95 . PMID: 10484361 .
9. Engstrom CM , Loeb GE , Reid JG , Forrest WJ , Avruch L . Morphometry of the human thigh muscles. A comparison between anatomical sections and computer tomographic and magnetic resonance images . J Anat 1991 ; 176 : 139 - 56 . PMID: 1917669 .
10. Mitsiopoulos N , Baumgartner RN , Heymsfield SB , Lyons W , Gallagher D , Ross R . Cadaver validation of skeletal muscle measurement by magnetic resonance imaging and computerized tomography . J Appl Physiol 1998 ; 85 ( 1 ): 115 - 22 . PMID: 9655763 .
11. Heymsfield SB , Smith R , Aulet M , Bensen B , Lichtman S , Wang J , et al. Appendicular skeletal muscle mass: measurement by dual-photon absorptiometry . Am J Clin Nutr 1990 ; 52 ( 2 ): 214 - 8 . PMID: 2375286 .
12. Janssen I , Heymsfield SB , Baumgartner RN , Ross R . Estimation of skeletal muscle mass by bioelectrical impedance analysis . J Appl Physiol 2000 ; 89 ( 2 ): 465 - 71 . PMID: 10926627 .
13. Chien M-Y , Huang T-Y , Wu Y-T. Prevalence of Sarcopenia Estimated Using a Bioelectrical Impedance Analysis Prediction Equation in Community-Dwelling Elderly People in Taiwan . J Am Geriatr Soc 2008 ; 56 ( 9 ): 1710 - 5 . doi: 10 .1111/j.1532- 5415 . 2008 . 01854 .x PMID: 18691288 .
14. Kyle UG , Bosaeus I , De Lorenzo AD , Deurenberg P , Elia M , Manuel Gomez J , et al. Bioelectrical impedance analysis-part II: utilization in clinical practice . Clin Nutr 2004 ; 23 ( 6 ): 1430 - 53 . doi: 10 .1016/j. clnu. 2004 . 09 .012 PMID: 15556267 .
15. Trutschnigg B , Kilgour RD , Reinglas J , Rosenthall L , Hornby L , Morais JA , et al. Precision and reliability of strength (Jamar vs. Biodex handgrip) and body composition (dual-energy X-ray absorptiometry vs. bioimpedance analysis) measurements in advanced cancer patients . Appl Physiol Nutr Metab 2008 ; 33 ( 6 ): 1232 - 9 . doi: 10 .1139/H08-122 PMID: 19088782.
16. Levey AS , Bosch JP , Lewis JB , Greene T , Rogers N , Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation . Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999 ; 130 ( 6 ): 461 - 70 . doi: 10 .7326/ 0003 -4819-130-6- 199903160 -00002 PMID: 10075613 .
17. Levey AS , Stevens LA , Schmid CH , Zhang YL , Castro AF 3rd, Feldman HI , et al. A new equation to estimate glomerular filtration rate . Ann Intern Med 2009 ; 150 ( 9 ): 604 - 12 . doi: 10 .7326/ 0003 -4819-150-9- 200905050 -00006 PMID: 19414839 .
18. Stevens LA , Schmid CH , Greene T , Li L , Beck GJ , Joffe MM , et al. Factors other than glomerular filtration rate affect serum cystatin C levels . Kidney Int 2009 ; 75 ( 6 ): 652 - 60 . doi: 10 .1038/ki. 2008 .638 PMID: 19119287 .
19. Goldberg TH , Finkelstein MS . Difficulties in estimating glomerular filtration rate in the elderly . Arch Intern Med 1987 ; 147 ( 8 ): 1430 - 3 . doi: 10 .1001/archinte. 1987 .00370080066014 PMID: 3453695 .
20. Coll E , Botey A , Alvarez L , Poch E , Quinto L , Saurina A , et al. Serum cystatin C as a new marker for noninvasive estimation of glomerular filtration rate and as a marker for early renal impairment . Am J kidney Dis 2000 ; 36 ( 1 ): 29 - 34 . doi: 10 .1053/ajkd. 2000 .8237 PMID: 10873868 .
21. Schaeffner ES , Ebert N , Delanaye P , Frei U , Gaedeke J , Jakob O , et al. Two novel equations to estimate kidney function in persons aged 70 years or older . Ann Intern Med 2012 ; 157 ( 7 ): 471 - 81 . doi: 10 . 7326/ 0003 -4819-157-7- 201210020 -00003 PMID: 23027318 .
22. Dharnidharka VR , Kwon C , Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis . Am J kidney Dis 2002 ; 40 ( 2 ): 221 - 6 . doi: 10 .1053/ajkd. 2002 .34487 PMID: 12148093 .
23. Kim J , Wang Z , Heymsfield SB , Baumgartner RN , Gallagher D . Total-body skeletal muscle mass: estimation by a new dual-energy X-ray absorptiometry method . Am J Clin Nutr 2002 ; 76 ( 2 ): 378 - 83 . PMID: 12145010 .
24. Chew-Harris JS , Florkowski CM , George PM , Elmslie JL , Endre ZH . The relative effects of fat versus muscle mass on cystatin C and estimates of renal function in healthy young men . Ann Clin Biochem 2013 ; 50 (Pt 1): 39 - 46 . doi: 10 .1258/acb. 2012 .011241 PMID: 23129724 .
25. Horber FF , Gruber B , Thomi F , Jensen EX , Jaeger P . Effect of sex and age on bone mass, body composition and fuel metabolism in humans . Nutrition 1997 ; 13 ( 6 ): 524 - 34 . doi: 10 .1016/S0899- 9007 ( 97 ) 00031 - 2 PMID: 9263233 .
26. Bland JM , Altman DG . Statistical methods for assessing agreement between two methods of clinical measurement . Lancet 1986 ; 1 ( 8476 ): 307 - 10 . doi: 10 .1016/S0140- 6736 ( 86 ) 90837 - 8 PMID: 2868172 .
27. Inker LA , Schmid CH , Tighiouart H , Eckfeldt JH , Feldman HI , Greene T , et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012 ; 367 ( 1 ): 20 - 9 . doi: 10 .1056/ NEJMoa1114248 PMID: 22762315 .
28. Baxmann AC , Ahmed MS , Marques NC , Menon VB , Pereira AB , Kirsztajn GM , et al. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol 2008 ; 3 ( 2 ): 348 - 54 . doi: 10 .2215/CJN.02870707 PMID: 18235143 .
29. Muntner P , Winston J , Uribarri J , Mann D , Fox CS . Overweight, obesity, and elevated serum cystatin C levels in adults in the United States . Am J Med 2008 ; 121 ( 4 ): 341 - 8 . doi: 10 .1016/j.amjmed. 2008 . 01 . 003 PMID: 18374694 .
30. Knight EL , Verhave JC , Spiegelman D , Hillege HL , de Zeeuw D , Curhan GC , et al. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement . Kidney Int 2004 ; 65 ( 4 ): 1416 - 21 . doi: 10 .1111/j.1523- 1755 . 2004 . 00517 . x PMID : 15086483 .
31. Kottgen A , Selvin E , Stevens LA , Levey AS , Van Lente F , Coresh J . Serum cystatin C in the United States: the Third National Health and Nutrition Examination Survey (NHANES III) . Am J kidney Dis 2008 ; 51 ( 3 ): 385 - 94 . doi: 10 .1053/j.ajkd. 2007 . 11 .019 PMID: 18295054 .
32. Naour N , Fellahi S , Renucci JF , Poitou C , Rouault C , Basdevant A , et al. Potential contribution of adipose tissue to elevated serum cystatin C in human obesity . Obesity 2009 ; 17 ( 12 ): 2121 - 6 . doi: 10 .1038/ oby. 2009 .96 PMID: 19360013 .
33. Wasén E , Isoaho R , Mattila K , Vahlberg T , Kivelä S-L , Irjala K. Serum cystatin C in the aged: relationships with health status . Am J Kidney Dis 2003 ; 42 ( 1 ): 36 - 43 . doi: 10 .1016/s0272- 6386 ( 03 ) 00406 - 2 PMID: 12830454 .
34. Hart A , Paudel ML , Taylor BC , Ishani A , Orwoll ES , Cawthon PM , et al. Cystatin C and frailty in older men . J Am Geriatr Soc 2013 ; 61 ( 9 ): 1530 - 6 . doi: 10 .1111/jgs.12413 PMID: 24001352 .
35. Odden MC , Chertow GM , Fried LF , Newman AB , Connelly S , Angleman S , et al. Cystatin C and measures of physical function in elderly adults: the Health, Aging, and Body Composition (HABC) Study . Am J Epidemiol 2006 ; 164 ( 12 ): 1180 - 9 . doi: 10 .1093/aje/kwj333 PMID: 17035344 .
36. Shlipak MG , Wassel Fyr CL , Chertow GM , Harris TB , Kritchevsky SB , Tylavsky FA , et al. Cystatin C and mortality risk in the elderly: the health, aging, and body composition study . J Am Soc Nephrol 2006 ; 17 ( 1 ): 254 - 61 . doi: 10 .1681/ASN.2005050545 PMID: 16267155 .
37. Lake KD , Peterson CD . A Simplified Dosing Method for Initiating Vancomycin Therapy . Pharmacotherapy 1985 ; 5 ( 6 ): 340 - 4 . doi: 10 .1002/j.1875- 9114 . 1985 .tb03441. x PMID: 4080569.
38. Frazee EN , Rule AD , Herrmann SM , Kashani KB , Leung N , Virk A , et al. Serum cystatin C predicts vancomycin trough levels better than serum creatinine in hospitalized patients: a cohort study . Crit Care 2014 ; 18 ( 3 ):R110. doi: 10 .1186/cc13899 PMID: 24887089 .