Association between urinary free light chains and progression to end stage renal disease in chronic kidney disease
Association between urinary free light chains and progression to end stage renal disease in chronic kidney disease
Anthony Fenton 0 1 2 3
Mark D. Jesky 0 1 2 3
Rachel Webster 0 1 3
Stephanie J. Stringer 0 1 2 3
Punit Yadav 0 1 2 3
Iain Chapple 0 1 3
Indranil Dasgupta 0 1 3 5
Stephen J. Harding 0 1 3 4
Charles J. Ferro 0 1 2 3
Paul Cockwell 0 1 2 3
0 Funding: The Jabbs Foundation provided funding towards the RIISC study. The Jabbs Foundation
1 Data Availability Statement: Due to ethical restrictions by the National Research Ethics Service (NRES) Committee West Midlands-South Birmingham, data cannot be shared publicly and is subject to adherence to existing ethics approval. Interested, qualified researchers can request the data by contacting Penelope Gregory, REC Manager , at NRESCommittee.westmidlands-
2 Department of Renal Medicine, University Hospitals Birmingham NHS Foundation Trust , Birmingham , United Kingdom , 2 Institute of Inflammation and Ageing, University of Birmingham , Birmingham , United Kingdom , 3 Department of Biochemistry, University Hospitals Birmingham NHS Foundation Trust , Birmingham , United Kingdom , 4 Periodontal Research Group, Institute of Clinical Sciences, University of Birmingham and Birmingham Community Healthcare Foundation Trust , Birmingham , United Kingdom
3 Editor: Petter Bjornstad, University of Colorado Denver School of Medicine , UNITED STATES
4 The Binding Site , Edgbaston, Birmingham , United Kingdom
5 Department of Renal Medicine, Heart of England NHS Foundation Trust , Birmingham , United Kingdom
Patients with chronic kidney disease (CKD) are at an increased risk of developing end-stage renal disease (ESRD). We assessed for the first time whether urinary free light chains (FLC) are independently associated with risk of ESRD in patients with CKD, and whether they offer incremental value in risk stratification.
Materials and methods
We measured urinary FLCs in 556 patients with CKD from a prospective cohort study. The
association between urinary kappa/creatinine (KCR) and lambda/creatinine (LCR) ratios
and development of ESRD was assessed by competing-risks regression (to account for the
competing risk of death). The change in C-statistic and integrated discrimination
improvement were used to assess the incremental value of adding KCR or LCR to the Kidney Failure
Risk Equation (KFRE).
136 participants developed ESRD during a median follow-up time of 51 months. Significant
associations between KCR and LCR and risk of ESRD became non-significant after
adjustment for estimated glomerular filtration rate (eGFR) and albumin/creatinine ratio (ACR),
although having a KCR or LCR >75th centile remained independently associated with risk of
ESRD. Neither KCR nor LCR as continuous or categorical variables provided incremental
value when added to the KFRE for estimating risk of ESRD at two years.
had no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript. The Binding Site, Birmingham,
provided support in the form of salaries for author
SJH and the measurement of urinary and serum
free light chains for the cohort. SJH played a role in
study conceptualization and revision of the draft,
but did not receive fees, bonuses, or other benefits
for the work described in the manuscript. The
Binding Site did not have any additional role in the
study design, data collection and analysis, decision
to publish, or preparation of the manuscript.
Urinary FLCs have an association with progression to ESRD in patients with CKD which
appears to be explained to a degree by their correlation with eGFR and ACR. Levels above
the 75th centile do have an independent association with ESRD, but do not improve upon a
current model for risk stratification.
Patients with chronic kidney disease (CKD) are at an increased risk of adverse health outcomes
including progression to end-stage renal disease (ESRD) and early mortality [1±6]. Risk
prediction models can be used to estimate an individual's risk of ESRD, incorporating prognostic
factors such as age, gender, glomerular filtration rate (GFR), and level of albuminuria [7±9].
There is significant interest in identifying further prognostic factors, including urinary
proteins in addition to albumin, with a view to enhancing prognostic models and identifying
potential targets for novel interventions.
During the synthesis of intact immunoglobulins, light chains are produced in excess of
heavy chains, resulting in the release into the circulation of approximately 500 mg per day of
unbound free light chains (FLC) [
]. As monomeric FLCs (usually kappa) weigh ~25 kDa,
and dimeric FLCs (usually lambda) weigh ~50 kDa, they are filtered at the glomerulus and
then reabsorbed and metabolised in the proximal tubule [
]. Although small amounts of FLC
may be present from mucosal secretion from the urinary tract, the presence of significant FLC
in the urine implies either concentrations in the proximal tubule greater than can be
reabsorbed, as may occur with excess monoclonal FLC production in plasma cell dyscrasias, or
renal disease with glomerular hyperfiltration and/or tubular dysfunction [
Previous work has demonstrated that urinary FLC concentration increases as estimated
GFR (eGFR) decreases, and urinary FLC may be more sensitive than albuminuria as a marker
of early CKD [
]. In patients with type 2 diabetes mellitus (DM), raised urinary
concentrations of FLC are detectable prior to the development of increased albuminuria [
Urinary FLC levels have also been shown to correlate with disease activity in IgA nephropathy
and lupus nephritis [
Our objectives were to assess, for the first time, whether urinary FLC are independently
associated with risk of ESRD in patients with CKD, and whether measuring urinary FLC
improves upon an established model for risk stratification.
We used data and samples from the Renal Impairment in Secondary Care (RIISC) Study
(ClinicalTrials.gov: NCT01722383). The RIISC study is a prospective cohort study of patients with
CKD and the detailed methodology has been published previously [
In brief, patients in nephrology clinics at two renal centres were invited to participate if they
had high-risk CKD, defined as an eGFR < 30 mL/min/1.73m2, or an eGFR 30±59 mL/min/
1.73m2 with a decline of 5 mL/min/1.73m2 over a year or 10 mL/min/1.73m2 over 5 years
or an ACR 70 mg/mmol on three occasions. Patients were excluded if they had received
immunosuppression for immune-mediated renal disease or if they had started renal
replacement therapy (RRT, i.e. dialysis or kidney transplant). For this analysis, we also excluded
patients with a monoclonal gammopathy i.e. monoclonal gammopathy of undetermined
2 / 17
significance (MGUS), including patients with an abnormal serum FLC isotype and a serum
kappa/lambda (κ/λ) FLC ratio outside of the renal reference range (0.37±3.1) [
], or a
known diagnosis of myeloma, AL amyloidosis, or other monoclonal gammopathy of renal
significance. Participants were recruited between October 2010 and December 2015 and followed
up until they started RRT or died. The primary outcome of interest was progression to ESRD,
which was defined as the initiation of RRT and was captured using both centres' local
databases of patients who have started dialysis or received a kidney transplant. Outcomes were
captured up to 31 January 2017 and patients who had not reached a study end-point were
censored on this date. Ethical approval was granted by the South Birmingham Research Ethics
Committee (reference: 10/H1207/6). All patients provided written consent, and the study was
conducted in accordance with the Declaration of Helsinki.
Serum and urine were processed immediately after collection according to pre-defined
standard operating procedures and stored at -80ÊC until analysis.
Urinary FLCs were measured by turbidimetry on a Roche Modular P analyser using the
Freelite™ immunoassay (The Binding Site Group Ltd, Birmingham, UK). To correct for
variations in urine concentration, urinary FLCs were divided by urine creatinine concentration to
give urinary FLC/creatinine (FLC/Cr) ratios in mg/mmol, i.e. a kappa/creatinine ratio (KCR)
and a lambda/creatinine ratio (LCR). Serum creatinine measurements were performed on a
Roche Modular analyser using a rate-blanked and compensated Jaffe reaction, and eGFR was
calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI)
]. Serum kappa (κ) and lambda (λ) FLC concentrations were measured by
nephelometry on a Dade-Behring BN™ II System (Siemens AG, Erlangen, Germany) using the Freelite™
assay. Urine ACR was measured using a Roche Hitachi 702 analyser. Other biochemistry
testing was performed by the local clinical laboratories in accordance with the current standard of
Blood pressure (BP) was measured using the BpTRU automated device (BpTRU Medical
Devices, Coquitlam, BC, Canada) which obtains six BP readings after a five-minute rest period.
The systolic (SBP) and diastolic BP (DBP) are derived from the mean of the second to sixth
readings, and this method has been reported to be comparable to the mean daytime BP from
24-hour ambulatory BP monitoring [
]. Mean arterial pressure (MAP) was calculated as
((2 × DBP) + SBP) 3.
Statistical analyses were performed using SPSS Statistics 24 (Armonk, NY: IBM Corp, 2016)
and Stata 15 (College Station, TX: StataCorp, 2017). Baseline characteristics are presented as
a frequency and percentage for categorical variables, and a median and interquartile range
(IQR) for continuous variables.
Urinary FLC/Cr ratios were log-transformed prior to parametric testing. Their associations
with other continuous baseline variables were examined by scatter plots and Pearson's
correlation coefficients. Correlation coefficients of 0.2, 0.5, and 0.8 were considered weak, moderate,
and strong, respectively [
]. To assess their association with categorical variables,
betweengroup differences were tested for using the Mann-Whitney U (two groups) and Kruskal-Wallis
(three or more groups) tests. Multiple linear regression was used to further assess the influence
3 / 17
of serum FLC on urinary FLC/Cr ratios after adjustment for other significant determinants of
urinary FLC/Cr ratios (eGFR, ACR, DM, and renal diagnosis).
The associations between urinary FLC/Cr and progression to ESRD were assessed in
univariable and multivariable regression models. As 75 (13.5%) participants died without ESRD,
we used competing-risks regression based on the proportional subhazards model of Fine and
], which allowed us to handle mortality as a competing event (simply censoring at
death in standard Cox regression models would have made the incorrect assumption that the
censored deceased participants remained at risk of developing ESRD). In the multivariable
models, we adjusted for confounding factors known to be associated with CKD progression:
eGFR (model 1); ACR (model 2); eGFR and ACR (model 3); age, gender, ethnicity, renal
diagnosis, MAP, eGFR, and ACR (model 4). Subhazard ratios (SHR) are presented with 95%
confidence intervals (CI), which for continuous variables represented the risk of ESRD associated
with an increase of one standard deviation (SD). Urinary FLC/Cr ratios were analysed as both
full-range continuous variables and as categorical variables (comparing above versus below the
75th centiles). Competing-risks models were also built to include restricted cubic splines of
urinary FLC/Cr ratios to assess for non-linear associations between urinary FLC/Cr ratios and
risk of ESRD. Kaplan-Meier curves were plotted to show the cumulative incidence of ESRD by
quartiles of urinary FLC/Cr.
To examine whether urinary FLC/Cr provide any incremental value in risk stratification,
we chose the four-variable `Kidney Failure Risk Equation' (KFRE) [
] as the baseline model for
comparison, which estimates an individuals' risk of ESRD at two and five years. Binary logistic
regression models were fitted for the outcome of ESRD at two years. The baseline model
contained only the KFRE-calculated two-year risk of ESRD, calculated as:
age=10 7:036 0:2467
male 0:5642 0:5567
eGFR=5 7:222 0:4510
(the four-variable, non-North America, two-year risk equation from eAppendix 2 of [
ACR was converted to mg/g before being entered into the model by dividing by 0.113).
KCR and LCR (separately) were added as continuous and categorical predictors to the
baseline KFRE model and the models compared. Overall model performance was estimated by
R2 (Nagelkerke, a version of the Cox & Snell R2 adjusted to cover the full range from 0 to 1
]), discrimination was assessed by the C-statistic, and calibration by the Hosmer-Lemeshow
goodness-of-fit test. The incremental value of adding KCR or LCR to the baseline model was
assessed by the change in C-statistic and by the reclassification measure Integrated
Discrimination Index (IDI). The IDI is a measure of the extent to which adding a new marker to a model
correctly revises upward the predicted risk of individuals who experience an event and correctly
revises downward the predicted risk of individuals who do not experience an event [
Urinary FLCs were measured in 636 participants of the RIISC study. We excluded 41 patients
with a monoclonal gammopathy (21 with a serum κ/λ FLC ratio outside the renal reference
range, 15 with MGUS, and 5 with multiple myeloma), and also excluded 39 patients with
urinary FLC or creatinine results above or below the limits of detection so that accurate FLC/Cr
ratios could be calculated. Therefore, 556 participants were included for analysis with a median
follow-up time of 51 (IQR 46±60) months.
The baseline characteristics of the study population are shown in Table 1. The cohort had a
median age of 64 (IQR 51±76) years, were 63% male, and 68% were of White ethnicity. The
4 / 17
Completeness of data (%)
Continuous variables are expressed as a median and interquartile range, and categorical variables are expressed as a frequency and percentage.
Abbreviations: ACR = albumin/creatinine ratio; COPD = chronic obstructive pulmonary disease; FLC = free light chains; GFR = glomerular filtration rate;
IQR = interquartile range; KCR = kappa/creatinine ratio; LCR = lambda/creatinine ratio.
most common aetiologies of CKD were ischaemic/hypertensive nephropathy (28.9%),
glomerulonephritis (14.3%), and diabetic kidney disease (12.9%). Median eGFR was 25 (IQR 19±34)
mL/min/1.73m2 and median ACR was 28.1 (IQR 5.8±103.2) mg/mmol. Median urinary KCR
was 14.6 (IQR 7.1±27.7) mg/mmol and median LCR was 2.1 (IQR 1.0±5.1) mg/mmol.
Urinary FLC/Cr associations
The associations between urinary KCR and LCR with other baseline variables are shown in
Table 2. For comparison, the associations with ACR are also given. Urinary FLC/Cr ratios had
moderate positive correlations with serum FLC (sFLC), weak-to-moderate positive
correlations with ACR, and weak negative correlations with eGFR. The urinary FLC/Cr ratios, unlike
ACR, increased significantly with worsening CKD stage.
5 / 17
Associations with continuous variables are expressed as Pearson's r (after log transformation of both variables) and its corresponding P. For categorical variables,
median and interquartile ranges are shown with between-group differences assessed using the Mann-Whitney U or Kruskal-Wallis tests.
Abbreviations: ACR = albumin/creatinine ratio; CKD = chronic kidney disease; FLC = free light chains; GFR = glomerular filtration rate; IQR = interquartile range;
KCR = kappa/creatinine ratio; LCR = lambda/creatinine ratio.
6 / 17
Urinary FLC/Cr ratios were significantly higher in males, those of South Asian ethnicity,
and those with diabetic kidney disease, and significantly lower in those with
glomerulonephritis and polycystic kidney disease. However, these associations were no longer significant after
adjustment for ACR and eGFR. Patients with DM as a comorbidity had significantly higher
urinary FLC/Cr ratios, even after adjustment for eGFR, ACR, and serum FLC.
Scatter plots of the relationship between urinary FLC/Cr ratios with eGFR, ACR, and
serum FLC, are shown in Figs 1, 2 and 3, respectively. Urinary FLC/Cr ratios were most
strongly correlated with serum FLC; after adjustment for eGFR, ACR, DM, and renal
diagnosis, a 10% higher serum kappa was associated with a 4.8% (3.4±6.2%) higher urinary KCR, and
a 10% higher serum lambda was associated with a 7.5% (5.8±9.3%) higher urinary LCR.
Progression to ESRD
During follow-up, 136 (24.5%) participants progressed to ESRD. A higher KCR and LCR were
both associated with a significantly increased risk of ESRD. The association between urinary
FLC/Cr ratios and risk of ESRD approximated a linear relationship, although there was a
smaller increase in risk per unit increase in urinary FLC/Cr ratio at the lower and upper
extremes of the range, as shown in Fig 4. A one SD higher KCR was associated with a HR of
1.58 (1.40±1.79) and a one SD higher LCR was associated with a HR of 1.47 (1.31±1.66). A
urinary FLC/Cr ratio above the 75th centile was associated with HRs of 3.18 (2.26±4.47) for KCR
and 3.67 (2.62±5.16) for LCR. The cumulative incidence of ESRD by quartiles of urinary FLC/
Cr are shown in Fig 5. The univariable associations between other baseline variables and risk
of ESRD are shown in Table 3.
Multivariable models incorporating urinary FLC/Cr ratios are shown in Table 4. Urinary
KCR and LCR both remained significantly associated with risk of ESRD after adjustment for
either eGFR (model 1) or ACR (model 2). However, after adjustment for both eGFR and ACR
(model 3), including models incorporating age, sex, ethnicity, renal diagnosis, MAP, eGFR,
and ACR (model 4), urinary KCR and LCR as continuous or spline predictor variables were
not significantly associated with risk of ESRD. The significant association between urinary
FLC/Cr ratios above the 75th centile and risk of ESRD did however remain in models 3 and 4.
When these analyses were repeated in participants with a urinary ACR < 30 mg/mmol
(N = 265), we found that higher urinary FLC/Cr ratios, as either continuous or categorical
variables, were associated with a significantly increased risk of ESRD on univariable analysis, but
the associations became non-significant after adjustment for eGFR.
Urinary FLC/Cr in risk stratification
After excluding those who died without ESRD within two years (N = 34) and those with less
than two years of follow-up (N = 58), 464 participants had data on ESRD at two years, of
whom 60 (12.9%) had reached ESRD. Measures of model performance for the prediction of
ESRD at two years are shown in Table 5, including the incremental value of adding urinary
FLC/Cr ratios to KFRE. The baseline model containing only KFRE had a strong predictive
ability for ESRD at two years (C-statistic 0.89 [95% CI 0.84±0.94]) and was well calibrated
(Hosmer-Lemeshow statistic 12.19, P = 0.14). None of the KFRE + KCR or KFRE + LCR
models had a significant change in the C-statistic, suggesting no improvement in discrimination
between those who did and did not develop ESRD, nor any significant improvement in
reclassification of risk based on the IDI. Similarly, the addition of splines of KCR and LCR to the
KFRE model did not improve the C-statistic or show any improvement in discrimination
based on the IDI.
7 / 17
Fig 1. Scatter plots of urinary kappa/creatinine ratio (a) and lambda/creatinine ratio (b) by estimated glomerular
filtration rate. Scales are logarithmic. Abbreviations: GFR = glomerular filtration rate.
There is significant interest in identifying bioclinical prognostic factors in CKD that might
enhance our current models for risk stratification and potentially identify novel therapeutic
targets. This is to the best of our knowledge the first study to evaluate whether there is an
8 / 17
Fig 2. Scatter plots of urinary kappa/creatinine ratio (a) and lambda/creatinine ratio (b) by urinary albumin/
creatinine ratio. Scales are logarithmic.
independent association between urinary FLCs and risk of ESRD in patients with CKD. We
utilised a cohort of patients who were prospectively recruited with (i) a high risk of CKD
progression; (ii) extended follow-up; (iii) a hard end-point; and (iv) a high proportion progressing
to the end-point.
9 / 17
Fig 3. Scatter plots of urinary kappa/creatinine ratio by serum kappa (a) and urinary lambda/creatinine ratio by
serum lambda (b). Scales are logarithmic.
There was a negative correlation between eGFR and urinary FLC/Cr ratios, i.e. FLC/Cr
ratios increase as GFR falls, consistent with previously published data [
]. We hypothesise
that in patients with CKD, as nephrons are lost, there is hyperfiltration of the remaining
functional glomeruli and increased concentrations of FLC in the glomerular filtrate that exceeds
10 / 17
Fig 4. Restricted cubic splines of the subhazard ratio (solid line, with 95% confidence intervals shown as
interrupted lines) of end stage renal disease by urinary kappa/creatinine ratio (a) and urinary lambda/creatinine
ratio (b), adjusted for the competing risk of death. Percentiles are shown as vertical dotted lines. Abbreviations:
P = percentile.
11 / 17
Fig 5. Cumulative incidence of end stage renal disease by quartiles of urinary kappa/creatinine ratio (a) and
lambda/creatinine ratio (b). Abbreviations: ESRD = end stage renal disease; KCR = kappa/creatinine ratio;
LCR = lambda/creatinine ratio; Q = quartile.
the capacity of the proximal tubule to reabsorb and metabolise them. Tubular dysfunction
with decreased uptake of FLC may also contribute to increasing urinary FLC/Cr ratios.
There was also a positive correlation between urinary FLC/Cr ratios and ACR. Regardless
of the underlying cause of CKD, glomerular damage (associated with albuminuria and possibly
12 / 17
Urinary KCR and LCR were analysed as both a continuous variable (per +1 SD) and a categorical variable (above
versus below the 75th centile). Subhazard ratios for continuous variables represent the risk associated with an increase
of one standard deviation.
Abbreviations: ACR = albumin/creatinine ratio; CI = confidence interval; COPD = chronic obstructive pulmonary
disease; FLC = free light chains; GFR = glomerular filtration rate; KCR = kappa/creatinine ratio; LCR = lambda/
creatinine ratio; SD = standard deviation; SHR = subhazard ratio
13 / 17
with increased filtration of FLCs) often coexists with tubulointerstitial fibrosis (and thus
possibly reduced tubular FLC reabsorption). The correlation between urinary albumin and FLC is
therefore not unexpected.
Adjustment for the confounding relationships with eGFR and ACR significantly attenuated
the association between FLC/Cr ratios as continuous predictor variables and risk of ESRD,
which became non-significant. The lack of an independent association may reflect their lack of
specificity for renal damage: urinary FLC/Cr ratios correlated most strongly with serum FLC,
suggesting that systemic inflammation may be an important determinant of urinary levels, and
urinary FLC concentration may also be influenced by mucosal secretion in the urinary tract,
although data do not exist on how much this pathway contributes.
Despite the lack of an independent association between FLC/Cr ratios and progression to
ESRD when analysed as full-range continuous variables, our multivariable analyses did show
Models were fitted with LCR and KCR as both continuous predictors (per +1 SD) and with a categorical cut-off above the 75th centile.
Abbreviations: CI = confidence interval; IDI = integrated discrimination improvement; KCR = kappa/creatinine ratio; KFRE = kidney failure risk equation;
LCR = lambda/creatinine ratio; SD = standard deviation.
14 / 17
that urinary FLC/Cr ratios above the 75th centile were independently associated with a
significantly increased risk of ESRD. It is possible that this is due to a nephrotoxic effect of FLCs, i.e.
those with the highest levels in the urine may be more likely to sustain FLC-induced tubular
damage, thus increasing their risk of CKD progression and ESRD. A nephrotoxic effect of
FLCs has been proposed as a possible explanation for an independent association between
serum FLC levels and progression to ESRD in patients with CKD, although the published data
on the relationship between serum FLC and progression to ESRD are inconsistent [
Alternatively, the highest levels may be present in patients who have glomerular hyperfiltration
and/or tubular dysfunction, both factors that are associated with accelerated progression of
CKD. It is also possible that the association reflects a residual confounding effect of kidney
function that has not been fully accounted for by adjustment for creatinine-based eGFR.
Adding urinary FLC/Cr ratios to an established model for the estimation of two-year risk of
ESRD did not improve model performance in our cohort of patients with advanced kidney
disease. However, having previously been shown to be detectable prior to the development of
albuminuria, the use of urinary FLCs to stratify risk in early CKD could be examined in other
CKD cohorts with populations made up of less severe CKD.
The strength of this study is that it included a well-characterised cohort of patients with
prospective follow-up and a significant number of outcome events. Limitations were that it
was an observational study without mechanistic data, and we lacked a separate cohort to
validate our findings.
In conclusion, urinary FLCs have an association with progression to ESRD in patients with
CKD which appears to be explained largely by their correlation with eGFR and ACR, and the
current evidence suggests that measuring urinary FLC does not improve upon current models
for risk stratification.
AF would like to acknowledge The Binding Site Ltd (Birmingham, UK) for the funding of his
Conceptualization: Iain Chapple, Indranil Dasgupta, Stephen J. Harding, Charles J. Ferro,
Data curation: Anthony Fenton, Mark D. Jesky, Rachel Webster, Stephanie J. Stringer, Punit
Yadav, Indranil Dasgupta, Stephen J. Harding, Charles J. Ferro, Paul Cockwell.
Formal analysis: Anthony Fenton.
Funding acquisition: Stephen J. Harding, Charles J. Ferro, Paul Cockwell.
Investigation: Anthony Fenton, Mark D. Jesky, Rachel Webster, Stephanie J. Stringer, Indranil
Dasgupta, Stephen J. Harding, Charles J. Ferro, Paul Cockwell.
Methodology: Anthony Fenton, Mark D. Jesky, Stephanie J. Stringer, Iain Chapple, Indranil
Dasgupta, Stephen J. Harding, Charles J. Ferro, Paul Cockwell.
Project administration: Anthony Fenton, Mark D. Jesky, Stephanie J. Stringer, Punit Yadav,
Indranil Dasgupta, Charles J. Ferro, Paul Cockwell.
Resources: Indranil Dasgupta, Stephen J. Harding, Charles J. Ferro, Paul Cockwell.
Supervision: Indranil Dasgupta, Charles J. Ferro, Paul Cockwell.
15 / 17
Writing ± original draft: Anthony Fenton, Paul Cockwell.
Writing ± review & editing: Anthony Fenton, Mark D. Jesky, Rachel Webster, Stephanie J.
Stringer, Punit Yadav, Iain Chapple, Indranil Dasgupta, Stephen J. Harding, Charles J.
Ferro, Paul Cockwell.
16 / 17
Clinical journal of the American Society of Nephrology: CJASN. 2013; 8(7):1115±25. https://doi.org/10.
2215/CJN.05950612 PMID: 23599406.
1. Turin TC , Tonelli M , Manns BJ , Ahmed SB , Ravani P , James M , et al. Lifetime risk of ESRD . Journal of the American Society of Nephrology: JASN . 2012 ; 23 ( 9 ): 1569 ± 78 . https://doi.org/10.1681/ASN. 2012020164 PMID: 22904351 .
2. Levey AS , de Jong PE , Coresh J , El Nahas M , Astor BC , Matsushita K , et al. The definition, classification, and prognosis of chronic kidney disease: a KDIGO Controversies Conference report . Kidney Int . 2011 ; 80 ( 1 ): 17 ± 28 .
3. Gansevoort RT , Matsushita K , van der Velde M , Astor BC , Woodward M , Levey AS , et al. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts . Kidney Int . 2011 ; 80 ( 1 ): 93 ± 104 .
4. Astor BC , Matsushita K , Gansevoort RT , van der Velde M , Woodward M , Levey AS , et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with mortality and end-stage renal disease. A collaborative meta-analysis of kidney disease population cohorts . Kidney Int . 2011 ; 79 ( 12 ): 1331 ± 40 .
5. Chronic Kidney Disease Prognosis C , Matsushita K, van der Velde M , Astor BC , Woodward M , Levey AS , et al. Association of estimated glomerular filtration rate and albuminuria with all-cause and cardiovascular mortality in general population cohorts: a collaborative meta-analysis . Lancet . 2010 ; 375 ( 9731 ): 2073 ± 81 .
6. van der Velde M , Matsushita K , Coresh J , Astor BC , Woodward M , Levey A , et al. Lower estimated glomerular filtration rate and higher albuminuria are associated with all-cause and cardiovascular mortality. A collaborative meta-analysis of high-risk population cohorts . Kidney Int . 2011 ; 79 ( 12 ): 1341 ± 52 .
7. Tangri N , Kitsios GD , Inker LA , Griffith J , Naimark DM , Walker S , et al. Risk prediction models for patients with chronic kidney disease: a systematic review . Ann Intern Med . 2013 ; 158 ( 8 ): 596 ± 603 . https://doi.org/10.7326/ 0003 -4819-158-8- 201304160 -00004 PMID: 23588748 .
8. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group . KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease . Kidney International Supplements . 2013 ; 3 ( 1 ):1± 150 .
9. Tangri N , Stevens LA , Griffith J , Tighiouart H , Djurdjev O , Naimark D , et al. A predictive model for progression of chronic kidney disease to kidney failure . JAMA . 2011 ; 305 ( 15 ): 1553 ±9. https://doi.org/10. 1001/jama. 2011 .451 PMID: 21482743 .
10. Waldmann TA , Strober W , Mogielnicki RP . The renal handling of low molecular weight proteins. II. Disorders of serum protein catabolism in patients with tubular proteinuria, the nephrotic syndrome, or uremia . J Clin Invest . 1972 ; 51 ( 8 ): 2162 ± 74 . https://doi.org/10.1172/JCI107023 PMID: 5054468 .
11. Maack T , Johnson V , Kau ST , Figueiredo J , Sigulem D. Renal filtration, transport, and metabolism of low-molecular-weight proteins: a review . Kidney Int . 1979 ; 16 ( 3 ): 251 ± 70 . PMID: 393891 .
12. Solomon A . Light chains of human immunoglobulins . Methods Enzymol . 1985 ; 116 : 101 ± 21 . PMID: 3937021 .
13. Hutchison CA , Harding S , Hewins P , Mead GP , Townsend J , Bradwell AR , et al. Quantitative assessment of serum and urinary polyclonal free light chains in patients with chronic kidney disease . Clin J Am Soc Nephrol . 2008 ; 3 ( 6 ): 1684 ± 90 . https://doi.org/10.2215/CJN.02290508 PMID: 18945993 .
14. Hassan SB , Hanna MO . Urinary kappa and lambda immunoglobulin light chains in normoalbuminuric type 2 diabetes mellitus patients . J Clin Lab Anal . 2011 ; 25 ( 4 ): 229 ± 32 . https://doi.org/10.1002/jcla. 20463 PMID: 21786323 .
15. Hutchison CA , Cockwell P , Harding S , Mead GP , Bradwell AR , Barnett AH . Quantitative assessment of serum and urinary polyclonal free light chains in patients with type II diabetes: an early marker of diabetic kidney disease? Expert Opin Ther Targets . 2008 ; 12 ( 6 ): 667 ± 76 . https://doi.org/10.1517/ 1472822.214.171.1247 PMID: 18479214 .
16. Hanaoka M , Gono T , Kawaguchi Y , Uchida K , Koseki Y , Katsumata Y , et al. Urinary free light chain is a potential biomarker for ISN/RPS class III/IV lupus nephritis . Rheumatology . 2013 ; 52 ( 12 ): 2149 ± 57 . https://doi.org/10.1093/rheumatology/ket108 PMID: 23463805 .
17. Rocchetti MT , Papale M , d'Apollo AM , Suriano IV , Di Palma AM , Vocino G , et al. Association of urinary laminin G-like 3 and free K light chains with disease activity and histological injury in IgA nephropathy .
18. Stringer S , Sharma P , Dutton M , Jesky M , Ng K , Kaur O , et al. The natural history of, and risk factors for, progressive chronic kidney disease (CKD): the Renal Impairment in Secondary care (RIISC) study; rationale and protocol . BMC Nephrol . 2013 ; 14 : 95 . https://doi.org/10.1186/ 1471 -2369-14-95 PMID: 23617441 .
19. Hutchison CA , Plant T , Drayson M , Cockwell P , Kountouri M , Basnayake K , et al. Serum free light chain measurement aids the diagnosis of myeloma in patients with severe renal failure . BMC Nephrol . 2008 ; 9 : 11 . https://doi.org/10.1186/ 1471 -2369-9-11 PMID: 18808676 .
20. 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 . PMID: 19414839 .
21. Brothwell S , Dutton M , Ferro C , Stringer S , Cockwell P. Optimising the accuracy of blood pressure monitoring in chronic kidney disease: the utility of BpTRU . BMC Nephrol . 2013 ; 14 : 218 . https://doi.org/10. 1186/ 1471 -2369-14-218 PMID: 24112304 .
22. Zou KH , Tuncali K , Silverman SG . Correlation and simple linear regression . Radiology . 2003 ; 227 ( 3 ): 617 ± 22 . https://doi.org/10.1148/radiol.2273011499 PMID: 12773666 .
23. Fine JP , Gray RJ . A Proportional Hazards Model for the Subdistribution of a Competing Risk . Journal of the American Statistical Association . 1999 ; 94 ( 446 ): 496 ± 509 . https://doi.org/10.2307/2670170
24. Tangri N , Grams ME , Levey AS , Coresh J , Appel LJ , Astor BC , et al. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure: A Meta-analysis . JAMA . 2016 ; 315 ( 2 ): 164 ± 74 . https://doi.org/10.1001/jama. 2015 .18202 PMID: 26757465 .
25. Nagelkerke NJD . A note on a general definition of the coefficient of determination . Biometrika . 1991 ; 78 ( 3 ): 691 ±2. https://doi.org/10.1093/biomet/78.3. 691
26. Pencina MJ , D'Agostino RB Sr. , D'Agostino RB Jr., Vasan RS . Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond . Stat Med . 2008 ; 27 ( 2 ): 157 ±72; discussion 207±12. https://doi.org/10.1002/sim.2929 PMID: 17569110 .
27. Hlatky MA , Greenland P , Arnett DK , Ballantyne CM , Criqui MH , Elkind MS , et al. Criteria for evaluation of novel markers of cardiovascular risk: a scientific statement from the American Heart Association . Circulation. 2009 ; 119 ( 17 ): 2408 ± 16 . https://doi.org/10.1161/CIRCULATIONAHA.109.192278 PMID: 19364974 .
28. Ritchie J , Assi LK , Burmeister A , Hoefield R , Cockwell P , Kalra PA . Association of Serum Ig Free Light Chains with Mortality and ESRD among Patients with Nondialysis-Dependent CKD . Clin J Am Soc Nephrol . 2015 ; 10 ( 5 ): 740 ±9. https://doi.org/10.2215/CJN.09660914 PMID: 25825483 .
29. Haynes R , Hutchison CA , Emberson J , Dasgupta T , Wheeler DC , Townend JN , et al. Serum free light chains and the risk of ESRD and death in CKD . Clin J Am Soc Nephrol . 2011 ; 6 ( 12 ): 2829 ± 37 . https:// doi.org/10.2215/CJN.03350411 PMID: 22034503 .