Estimated glomerular filtration rate equations in people of self-reported black ethnicity in the United Kingdom: Inappropriate adjustment for ethnicity may lead to reduced access to care

Rouvick M Gama; Amanda Clery; Kathryn Griffiths; Neil Heraghty; Adrien M Peters; Kieran Palmer; Henry Kibble; Royce P Vincent; Claire C Sharpe; Hugh Cairns; Kate Bramham

doi:10.1371/journal.pone.0255869

. 2021 Aug 12;16(8):e0255869. doi: 10.1371/journal.pone.0255869

Estimated glomerular filtration rate equations in people of self-reported black ethnicity in the United Kingdom: Inappropriate adjustment for ethnicity may lead to reduced access to care

Rouvick M Gama ^1,^#, Amanda Clery ^2,^#, Kathryn Griffiths ¹, Neil Heraghty ³, Adrien M Peters ³, Kieran Palmer ¹, Henry Kibble ¹, Royce P Vincent ⁴, Claire C Sharpe ⁵, Hugh Cairns ¹, Kate Bramham ^1,^6,^*

Editor: Pierre Delanaye⁷

PMCID: PMC8360513 PMID: 34383841

Abstract

Assessment in African populations suggest adjustment for ethnicity in estimated glomerular filtration rate (eGFR) equations derived from African Americans lead to overestimation of GFR and failure to determine severity in chronic kidney disease (CKD). However, studies in African Europeans are limited. We aimed to assess accuracy of eGFR equations, with and without ethnicity adjustment compared with measured GFR in people of Black ethnicity in the United Kingdom. Performance of MDRD, CKD-EPI (with and without ethnicity adjustment), Full Age Spectrum (FAS), revised Lund Malmö (LM Revised), and European Kidney Function Consortium (EKFC) eGFR equations were assessed compared to ⁵¹Cr-EDTA GFR studies extracted from hospital databases. Participants with albumin <30g/l, liver disease, <18 years, of non-Black or non-White self-reported ethnicity were excluded. Agreement was assessed by bias, precision and 30%-accuracy and was stratified for ethnicity and GFR. 1888 ⁵¹Cr-EDTA studies were included (Mean age-53.7yrs; 43.6% female; 14.1% Black ethnicity). Compared to White participants, eGFR-MDRD and eGFR-CKD-EPI equations in Black participants significantly overestimated GFR (bias 20.3 and 19.7 ml/min/1.73m² respectively, p<0.001). Disregarding the ethnicity adjustment significantly improved GFR estimates for Black participants (bias 6.7 and 2.4ml/min/1.73m² for eGFR-MDRD and eGFR-CKD-EPI respectively, p<0.001). The LM Revised equation had the smallest bias for both White and Black participants (5.8ml and -1.1ml/min/1.73m² respectively). 30%-accuracy was superior for GFR≥60ml/min/1.73m² compared to <60ml/min/1.73m² using eGFR-CKD-EPI equation for both White and Black participants (p<0.001). Multivariate regression methodology with adjustment for age, sex and log(serum creatinine) in the cohort yielded an ethnicity coefficient of 1.018 (95% CI: 1.009–1.027). Overestimation of measured GFR with eGFR equations using ethnicity adjustment factors may lead to reduced CKD diagnosis and under-recognition of severity in people of Black ethnicity. Our findings suggest that ethnicity adjustment for GFR estimation in non-African Americans may not be appropriate for use in people of Black ethnicity in the UK.

Introduction

Chronic kidney disease (CKD) is a global health problem, with adverse outcomes including end stage kidney disease (ESKD), cardiovascular disease and premature death [1,2]. Gold standard assessment of Glomerular Filtration Rate (GFR) with formal methods is costly, impractical, and not readily available, thus estimated GFR (eGFR) is commonly used in clinical practice. The most widely used equations for estimating GFR are the four variable Modified Diet in Renal Disease (MDRD) and chronic kidney disease epidemiology collaboration (CKD-EPI) equations [3–5]. Both equations, which were derived from cohorts including African Americans, recommend use of an adjustment factor for Black ethnicity (CKD-EPI 1.159; MDRD 1.212) to enhance accuracy [6,7].

Several studies have reported that there are higher rates of production of creatinine in people of Black ethnicity [8–12], which support this practice, and it has been assumed that eGFR adjustment should apply to all of Black ethnicity. However, recent studies from different African countries suggest eGFR equations more accurately reflect measured GFR (mGFR) without ethnicity adjustment [5,6,13–18], but studies of eGFR equations in African Europeans are scarce. Even in the USA, several bodies are recognising the limitations of ‘racial categorisation’, including people with mixed ancestry [19].

People of African or Afro-Caribbean ancestry are at greater risk of CKD [20,21], have more rapid progression of disease, a higher incidence of ESKD and have more advanced disease at presentation compared to Caucasians. Thus accurate assessment of GFR is important for early diagnosis, risk stratification and timely management [22,23]. Early diagnosis of CKD is critical in order to implement preventative strategies, particularly in low-income countries where prevalence of CKD due to non-communicable and infectious diseases is estimated to be high and renal replacement therapy (RRT) may not be easily accessible [6,17,18,24,25]. Inappropriate use of ethnicity adjusted eGFR equations could further contribute to recognised ethnicity related health inequalities in CKD due to delayed diagnosis, preparation for renal replacement therapy and wait-listing for transplantation. To address this, research has been recommended to quantify the benefits and harms of using race in GFR estimation [26], and use of alternative measures of GFR [27].

This study aimed to assess the accuracy of eGFR equations, (MDRD and CKD EPI with and without ethnicity adjustment, Full Age Spectrum (FAS), revised Lund Malmö (LM Revised), and European Kidney Function Consortium (EKFC)) compared with gold standard chromium-51 labelled ethylenediaminetetraacetic acid (⁵¹Cr-EDTA) clearance assays and the impact of eGFR assessment on clinical care in a large population in the United Kingdom.

Methods

We conducted a single-centre observational cross-sectional study in a large tertiary hospital in London, United Kingdom. All ⁵¹Cr-EDTA studies were extracted between 2009–2019 from hospital databases: Laboratory Information Management System (Clinisys) and Sunrise Electronic Patient Records (EPR). Baseline characteristics, including age, gender, self-reported ethnicity, referral specialty, number of ⁵¹Cr-EDTA studies and serum creatinine (IDMS traceable assay) and albumin concentrations taken within one week of ⁵¹Cr-EDTA study were recorded. For individuals with repeated mGFR assessments, only the first mGFR assessment was included in the analysis.

Exclusions were made if: (1) creatinine or albumin measurements were taken more than a week from the ⁵¹Cr-EDTA study, (2) albumin measurements were <30g/L (due to potentially reduced muscle mass), (3) referrals were from liver or rehabilitation services, due to liver disease interference with creatinine levels and likely amputation respectively, (4) patients were under 18 years old, (5) self-reported ethnicity was non-Black or non-White, or mixed ethnicities, and (6) there were any incomplete data in the previous criteria.

Serum creatinine and albumin concentrations were determined using the Siemens clinical chemistry analysers (Advia 2400, Siemens Diagnostics, Frimley, UK) in an UK Accreditation Service (UKAS) accredited laboratory. Creatinine assay was the Jaffe method with an isotope dilution-mass spectrometry (IDMS) traceable calibrator.

mGFR was measured by ⁵¹Cr-EDTA, administered intravenously (10 MBq). Plasma clearance of the tracer was calculated from accurately-timed plasma samples obtained at 120, 180 and 240 min, and corrected for the assumption of a single compartment using the formula of Bröchner-Mortensen [28].

eGFRs were calculated using the following equations: Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), with and without an ethnicity correction factor, Modification of Diet in Renal Disease (MDRD), with and without an ethnicity correction factor, Full Age Spectrum (FAS), revised Lund Malmö (LM Revised), and the European Kidney Function Consortium (EKFC; Table 1).

Table 1. Estimated glomerular filtration rate (eGFR) equations.

CKD-EPI adjusted	141×min(SCr/κ, 1)^α×max(SCr/κ, 1)^-1.209×0.993^Age×1.018[if female]×1.159[if Black] α = -0.329[if female] or -0.411[if male] κ = 0.7[if female] or 0.9[if male] min and max indicate the minimum or maximum of SCr/κ or 1, respectively
CKD-EPI unadjusted	141×min(SCr/κ, 1)^α×max(SCr/κ, 1)^-1.209×0.993^Age×1.018[if female]
MDRD adjusted	175×SCr^-1.154×Age^0.203×0.742[if female]×1.212[if Black]
MDRD unadjusted	1.75×SCr^-1.154×Age^0.203×0.742[if female]
FAS [29]	107.3/(SCr/Q) [if aged 2≤40yr] 107.3/(SCr/Q)×0.988^Age-40 [if age>40yr] Q = median SCr value for age-/sex-specific healthy populations
LM Revised [30]	e^{X-0.0158×Age+0.438×ln(Age)} X = 2.50+0.0121×(150-SCr)[if female and SCr<150] X = 2.50−0.926×ln(SCr/150)[if female and SCr≥150] X = 2.56+0.00968×(180-SCr)[if male and SCr<180] X = 2.56−0.926×ln(SCr/180) [if male and SCr≥180] SCr = μmol/L
EKFC [31]	107.3×(SCr/Q)^-0.322 [if aged 2≤40yr and (SCr/Q)<1] 107.3×(SCr/Q)^-1.132 [if aged 2≤40yr and (SCr/Q)≥1] 107.3×(SCr/Q)^-0.322×0.990^Age-40 [if aged>40yr and (SCr/Q<1] 107.3×(SCr/Q)^-1.132×0.990^Age-40 [if aged>40yr and (SCr/Q)≥1] Q values described in detail elsewhere.

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Statistical methods

All demographic characteristics (age, sex, BMI, referral source and CKD stage) were summarised using counts and percentages for categorical variables and mean with standard deviation for continuous variables (or median and interquartile range for non-normal continuous variables). Characteristics were summarised for the whole cohort as well as stratified by ethnicity. Agreement was tested between mGFR and adjusted and unadjusted eGFR-MDRD and eGFR-CKD-EPI. Agreement was assessed using bias (mean difference between eGFR and mGFR), precision (SD of the bias), limits of agreement (bias +- 2 times precision), and 30% accuracy (proportion of eGFRs that were within 30% of mGFR value). Agreement was also assessed by ethnicity (White and Black) and then by GFR (<60 and ≥60 mL/min/1.73m²).

Post-hoc analyses were conducted to explore agreement further. An ethnicity correction factor in our sample was calculated using multivariable regression analysis between ethnicity and mGFR, adjusting for age, sex and log (SCr). The association between serum creatinine and ethnicity was explored, as well as reasons for high bias between mGFR and eGFR using multivariable linear regression analyses.

To determine the impact of using ethnicity correction factors on current clinical care, the most recent creatinine for all participants of self-reported Black ethnicity attending nephrology services was extracted from the hospital database EPR. Deceased patients and those receiving haemodialysis or peritoneal dialysis were excluded. The number of patients with each CKD stage was calculated according to adjusted and unadjusted eGFR, albuminuria. The proportion of patients categorized by CKD Stage using adjusted and unadjusted eGFR-CKD-EPI and with eGFR <20 mL/min/1.73m² who would be referred for RRT planning according to local policy are summarised using counts and percentages. P-values <0.05 was considered to be significant. Data were analysed using statistical software R, version 3.6.

Data were extracted from King’s College Hospital NHS Foundation Trust, London laboratory databases between Jan 2009 to Dec 2019. All data were fully anonymised prior to access by the researchers. The study was reviewed locally and was not considered to need research ethics committee approval and was registered on the King’s College Hospital NHS Foundation Trust Nephrology Audit Register 2019 (KCH/KKC/2020:003).

Results

After exclusion of participants with predefined confounders ((1) creatinine or albumin measurements were taken more than a week from the ⁵¹Cr-EDTA study, (2) albumin measurements were <30g/L (due to potentially reduced muscle mass), (3) referrals were from liver or rehabilitation services, due to liver disease interference with creatinine levels and likely amputation respectively, (4) patients were under 18 years old, (5) self-reported ethnicity was non-Black or non-White, or mixed ethnicities) which might influence serum creatinine concentration, a total of 1888 ⁵¹Cr-EDTA mGFR studies were identified (266 (14.1%) Black; 1622 White) (Fig 1). Mean age of participants at the time of mGFR test was 53.7 years, 43.6% were female, mean BMI was 26.9 kg/m², and haematology was the most common referral source (71.7% of patients, Table 2).

Table 2. Cohort characteristics, stratified by ethnicity.

	All (N = 1888)	Black (N = 266)	White (N = 1622)
Age in years, mean (SD)	53.7 (12.7)	47.3 (11.5)	54.7 (12.6)
Male, N (%)	1065 (56.4)	141 (53.0)	924 (57.0)
Body Mass Index in kg/m ² , mean (SD)	26.9 (5.3)	27.0 (5.5)	26.9 (5.3)
Referral source, N (%)
Haematology	1353 (71.7)	188 (70.7)	1165 (71.8)
Oncology	35 (1.9)	8 (3.0)	27 (1.7)
Renal	192 (10.2)	19 (7.1)	173 (10.7)
Urology	27 (1.4)	4 (1.5)	23 (1.4)
ICU	2 (0.1)	0 (0.0)	2 (0.1)
Medicine	115 (6.1)	14 (5.3)	101 (6.2)
Paediatrics ^*	65 (3.4)	12 (4.5)	53 (3.3)
Other	54 (2.9)	5 (1.9)	49 (3.0)
Unknown	45 (2.4)	16 (6.0)	29 (1.8)
SCr in μmol/L, median (IQR)	72 (60–86)	76 (64–101)	71 (60–85)
Male	79 (68–93)	93 (75–111)	78 (67–90)
Female	63 (53–74)	65 (56–77)	63 (53–73)
mGFR stage, N (%)
> = 90	518 (27.4)	86 (32.3)	432 (26.6)
60–89	1024 (54.2)	132 (49.6)	892 (55.0)
30–59	304 (16.1)	36 (13.5)	268 (16.5)
<30	42 (2.2)	12 (4.5)	30 (1.8)
GFR in ml/min/1.73m ² , mean (SD)
mGFR	77.0 (21.3)	78.9 (24.1)	76.7 (20.8)
eGFR CKD-EPI adjusted	92.1 (23.1)	99.3 (30.6)	91.0 (21.4)
eGFR CKD-EPI unadjusted	90.2 (22.3)	85.7 (26.4)	91.0 (21.4)
eGFR MDRD adjusted	92.3 (29.7)	98.6 (35.8)	91.2 (28.5)
eGFR MDRD unadjusted	89.8 (28.8)	81.3 (29.6)	91.2 (28.5)
eGFR FAS	91.2 (27.7)	87.1 (29.1)	91.8 (27.5)
eGFR LMRev	81.8 (19.8)	77.8 (23.0)	82.5 (19.2)
eGFR EKFC	85.7 (20.4)	82.6 (24.0)	86.2 (19.7)

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*potential organ donors to paediatric patients who were 18 years or older.

Serum creatinine values were positively skewed. Median values were 71 μmol/L for White patients (IQR: 60–85) and 76 μmol/L (IQR: 64–101) for Black patients, and tended to be higher for Black men than White men. The mean GFR, measured using ⁵¹Cr-EDTA, was 77ml/min/1.73m² and ranged from 4-162ml/min/1.73m² (Fig 2). This was similar for both Black and White patients (Table 2). The eGFR equation with the highest mean was the MDRD using the ethnicity correction factor, 92.3ml/min/1.73m² and the lowest mean was the LM Revised equation, 81.8ml/min/1.73m².

Fig 2 — **(A)** mGFR, **(B)** eGFR FAS equation, **(C)** eGFR LM Revised equation, **(D)** eGFR EKFC equation, **(E)** eGFR CKD-EPI adjusted for ethnicity correction factor, **(F)** eGFR CKD-EPI unadjusted for ethnicity, **(G)** eGFR MDRD adjusted, and **(H)** eGFR MDRD unadjusted.

All eGFR equations tended to overestimate mGFR and the biases fell across an approximately normally distributed range of measurements (Fig 3). For all 1622 White participants, the eGFR FAS equation had the greatest bias of 15.2ml/min/1.73m² followed by MDRD equation with a bias of 14.6ml/min/1.73m² (Table 3). The LM Revised equation had the smallest bias, 5.8ml/min/1.73m². In all 266 Black participants, the CKD-EPI equation adjusted with the ethnicity correction factor had the highest bias (20.3ml/min/1.73m²) and the LM Revised had the smallest (-1.1ml/min/1.73m²). Discounting the ethnicity correction factor, the bias of both CKD-EPI and MDRD equations was significantly reduced (6.7 and 2.4ml/min/1.73m² respectively, p<0.001). This also improved the 30% accuracy of equations for Black participants: from 56.4% to 77.1% (p<0.001) for CKD-EPI and from 56.8% to 75.2% (P<0.001) for MDRD equations.

Table 3. Estimated glomerular filtration rate (eGFR) equations bias, precision and accuracy compared with mGFR, stratified by ethnicity.

			GFR, ml/min/1.73m² Mean (SD)	Median bias, ml/min/1.73m²	Mean bias, ml/min/1.73m²	Mean percentage bias, %	Precision, ml/min/1.73m²	Limits of agreement, ml/min/1.73m²	30% Accuracy (%)
Black	All (N = 266)	mGFR vs:	78.9 (24.1)
		Adj CKD-EPI	99.3 (30.6)	20.0	20.3	29.5	21.8	-23.3 to 63.9	56.4
		Unadj CKD-EPI	85.7 (26.4)	7.0	6.7	11.8	19.4	-32.1 to 45.5	77.1
		Adj MDRD	98.6 (35.8)	16.0	19.7	28.1	27.1	-34.5 to 73.9	56.8
		Unadj MDRD	81.3 (29.6)	1.0	2.4	5.6	22.8	-43.2 to 48	75.2
		FAS	87.1 (29.1)	5.5	8.2	14.1	21.7	-35.2 to 51.6	76.3
		LM Revised	77.8 (23.0)	-1.0	-1.1	2.0	17.6	-36.3 to 34.1	81.6
		EKFC	82.6 (24.0)	4.0	3.7	8.5	18.1	-32.5 to 39.9	80.5
	mGFR <60 ml/min/1.73m ² (N = 48)	mGFR vs:	40.1 (13.9)
		Adj CKD-EPI	57.2 (27.6)	12.0	17.1	44.1	20.4	-23.7 to 57.9	43.8
		Unadj CKD-EPI	49.4 (23.8)	5.0	9.2	24.4	17.3	-25.4 to 43.8	58.3
		Adj MDRD	55.4 (25.4)	10.5	15.2	40.5	18.3	-21.4 to 51.8	50.0
		Unadj MDRD	45.6 (20.9)	2.5	5.5	15.8	14.7	-23.9 to 34.9	64.6
		FAS	51.1 (20.9)	8.5	11.0	32.7	15.0	-19 to 41	58.3
		LM Revised	46.1 (21.0)	3.5	6.0	17.5	14.6	-23.2 to 35.2	66.7
		EKFC	49.1 (22.2)	4.5	9.0	25.3	16.0	-23 to 41	60.4
	mGFR > = 60 ml/min/1.73m ² (N = 218)	mGFR vs:	87.5 (16.1)
		Adj CKD-EPI	108.5 (22.4)	21.0	21.1	26.3	22.0	-22.9 to 65.1	59.2
		Unadj CKD-EPI	93.7 (19.3)	7.0	6.2	9.0	19.9	-33.6 to 46	81.2
		Adj MDRD	108.1 (30.4)	17.5	20.7	25.3	28.6	-36.5 to 77.9	58.3
		Unadj MDRD	89.2 (25.1)	0.5	1.7	3.4	24.2	-46.7 to 50.1	77.5
		FAS	95.0 (24.3)	5.0	7.5	10.0	22.9	-38.3 to 53.3	80.3
		LM Revised	84.8 (16.7)	-2.5	-2.7	-1.3	17.9	-38.5 to 33.1	84.9
		EKFC	90.0 (17.2)	4.0	2.5	4.8	18.3	-34.1 to 39.1	84.9
White	All (N = 1622)	mGFR vs:	76.7 (20.8)
		CKD-EPI	91.0 (21.4)	14.0	14.3	22.3	15.1	-15.9 to 44.5	68.7
		MDRD	91.2 (28.5)	12.0	14.6	21.9	21.9	-29.2 to 58.4	66.0
		FAS	91.8 (27.5)	13.0	15.2	22.8	19.8	-24.4 to 54.8	67.7
		LM Revised	82.5 (19.2)	5.0	5.8	11.3	14.7	-23.6 to 35.2	83.4
		EKFC	86.2 (19.7)	9.0	9.5	16.1	14.5	-19.5 to 38.5	78.6
	mGFR <60 ml/min/1.73m ² (N = 298)	Corrected GFR vs:	46.0 (11.9)
		CKD-EPI	63.7 (22.0)	15.5	17.7	41.3	16.4	-15.1 to 50.5	43.3
		MDRD	60.9 (22.4)	12.0	14.9	35.8	17.8	-20.7 to 50.5	50.0
		FAS	61.8 (20.6)	13.0	15.8	39.3	16.2	-16.6 to 48.2	51.7
		LM Revised	58.9 (19.4)	12.0	12.9	31.3	14.3	-15.7 to 41.5	55.0
		EKFC	61.1 (20.2)	13.0	15.1	36.1	14.9	-14.7 to 44.9	50.3
	mGFR > = 60 ml/min/1.73m ² (N = 1324)	Corrected GFR vs:	83.6 (15.5)
		CKD-EPI	97.1 (15.8)	14.0	13.5	18.1	14.7	-15.9 to 42.9	74.5
		MDRD	98.1 (25.1)	12.0	14.5	18.8	22.7	-30.9 to 59.9	69.6
		FAS	98.6 (24.1)	13.0	15.0	19.0	20.5	-26 to 56	71.3
		LM Revised	87.8 (14.6)	4.0	4.2	6.8	14.4	-24.6 to 33	89.8
		EKFC	91.8 (14.5)	8.0	8.2	11.7	14.1	-20 to 36.4	85.0

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mGFR = ⁵¹Cr-EDTA GFR: chromium-51 labelled ethylenediamine tetraacetic acid glomerular filtration rate corrected for body surface area; adj = adjusted with ethnicity correction factor; unadj = unadjusted with ethnicity correction factor.

All equations had higher 30% accuracy for both Black and White participants in those with an mGFR≥60ml/min/1.73m² compared to <60ml/min/1.73m². In the 48 Black participants with mGFR<60ml/min/1.73m², the LM Revised equation provided the highest 30% accuracy of 66.7%. This equation, along with the EKFC, provided the highest 30% in the 218 Black participants with mGFR≥60ml/min/1.73m² at 84.9%. In White participants, the LM Revised equation also provided the highest 30% accuracy of 55.0% and 89.8% in the <60 and ≥60ml/min/1.73m² groups, respectively (Table 3).

For all 1622 White participants, the eGFR FAS equation had the greatest bias of 15.2ml/min/1.73m² followed by MDRD equation with a bias of 14.6ml/min/1.73m² (Table 3). The LM Revised equation had the smallest bias, 5.8ml/min/1.73m². In all 266 Black participants, the CKD-EPI equation, adjusted with the ethnicity correction factor, had the highest bias (20.3ml/min/1.73m²) and the LM Revised had the smallest bias (-1.1ml/min/1.73m²). Without ethnicity correction factor, bias of both CKD-EPI and MDRD equations was significantly reduced (6.7 and 2.4ml/min/1.73m² respectively, p<0.001). Removal of ethnicity correction factor also improved the 30% accuracy of equations for Black participants: from 56.4% to 77.1% (p<0.001) for CKD-EPI and from 56.8% to 75.2% (P<0.001) for MDRD equations.

Bland-Altman plots showed that for most eGFR equations, the majority of participants fell within 2 standard deviations of the bias, which were evenly distributed (Fig 4). An exception is seen for both adjusted and unadjusted MDRD equations, as well as the FAS equation, where the bias tends to be greater at higher mean GFR values.

Fig 4 — (White, Black) participants: **(A, H)** adjusted CKD-EPI, **(B, I)** unadjusted CKD-EPI, **(C, J)** adjusted MDRD, **(D, K)** unadjusted MDRD, **(E, L)** FAS, **(F, M)** LM Revised, **(G, N)** EKFC. x-axes show the mean between the mGFR and eGFR measurements ([mGFR+eGFR]/2) and y-axes show the differences between the mGFR and eGFR measurements. Units: ml/min/1.73m². The central dashed line represents bias, and the dashed lines above and below represent the 95% limits of agreement.

To explore agreement further, an ethnicity coefficient for our study sample was calculated, which was 1.018 (95% CI: 1.009–1.027; Table 4) after adjustment for age, sex and log(SCr). The association between ethnicity and serum creatinine was also explored. Black participants had a 13.8% higher (95% CI: 10.3–17.3%) SCr value compared to White participants, after adjustment for mGFR, age and sex (Table 5).

Table 4. Regression analysis for the association between ethnicity and mGFR to identify an ethnicity correction factor for our study sample, adjusted for age, sex and log(SCr).

	Odds ratio (95% CI)	P-value
mGFR	1.018 (1.009 to 1.027)	<0.001
Age	0.966 (0.955 to 0.977)	<0.001
Male (vs. female)	0.564 (0.418 to 0.760)	<0.001
Log(SCr)	7.781 (4.529 to 13.595)	<0.001

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Table 5. Linear regression analysis for the association between log(SCr) and ethnicity, adjusted for mGFR, age and sex.

	Regression coefficient (95% CI)	Exponentiated regression coefficient (95% CI)	P-Value
Black (vs. White)	0.129 (0.098 to 0.160)	1.138 (1.103 to 1.173)	<0.001
mGFR	-0.011 (-0.012 to -0.010)	0.989 (0.989 to 0.990)	<0.001
Age	-0.006 (-0.007 to -0.005)	0.994 (0.993 to 0.995)	<0.001
Male (vs. female)	0.231 (0.210 to 0.253)	1.260 (1.234 to 1.287)	<0.001

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Potential explanations for the high bias in the adjusted CKD-EPI and MDRD equations (Table 6) were also investigated in Black and White participants. Analyses were conducted on 1614 out of 1622 White participants, due to 8 participants having missing BMI data. Age was strongly associated with bias in both CKD-EPI and MDRD equations for White participants, but only with the CKD-EPI equation for Black participants. For the CKD-EPI equation, younger participants were associated with higher bias (correlation coefficient -0.1, 95% CI: -0.2 to -0.0 for White and -0.3 (-0.5 to -0.1) for Black), and for the MDRD equation in White participants, older participants were associated with higher bias (correlation coefficient 0.1, 95% CI: 0.0 to 0.2). Higher bias in the MDRD equation was also associated with male sex for White participants (correlation coefficient 3.0, 95% CI: 0.8 to 5.1). There were no associations with bias and BMI or referral source; however, sample size was very small in some categories of referral source, particularly for Black participants.

Table 6. Linear regression analysis for the association between the bias of CKD-EPI and MDRD equations and demographic characteristics in White participants, with mutual adjustment for all other variables in the table.

N = 1614.

	CKD-EPI		MDRD
Characteristic	Linear regression coefficient (95% confidence interval)		Linear regression coefficient (95% confidence interval)
Characteristic	White (N = 1614)	Black (N = 266)	White (N = 1614)	Black (N = 266)
Age	-0.1 (-0.2 to -0.0)	-0.3 (-0.5 to -0.1)	0.1 (0.0 to 0.2)	-0.2 (-0.5 to 0.1)
Male	0.9 (-0.6 to 2.4)	-5.1 (-10.4 to 0.2)	3.0 (0.8 to 5.1)	-4.9 (-11.5 to 1.8)
BMI	0.0 (-0.1 to 0.2)	-0.3 (-0.8 to 0.2)	-0.0 (-0.2 to 0.2)	-0.0 (-0.6 to 0.6)
Referral source
Haematology	Ref	Ref	Ref	Ref
Oncology	-0.6 (-6.3 to 5.2)	2.2 (-13.3 to 17.7)	-1.3 (-9.7 to 7.0)	-2.2 (-21.7 to 17.3)
Renal	-1.4 (-3.8 to 1.0)	-0.3 (-10.4 to 9.9)	-1.8 (-5.2 to 1.7)	-0.3 (-13.1 to 12.5)
Urology	1.6 (-4.6 to 7.8)	-17.8 (-39.2 to 3.5)	0.6 (-8.4 to 9.6)	-21.8 (-48.8 to 5.1)
ICU	-2.9 (-23.9 to 18.1)	No data	14.2 (-16.2 to 44.5)	No data
Medicine	2.2 (-0.9 to 5.3)	5.1 (-6.5 to 16.8)	3.8 (-0.7 to 8.2)	2.3 (-12.4 to 17.1)
Paeds	-1.9 (-6.0 to 2.3)	-8.0 (-20.6 to 4.6)	-3.5 (-9.5 to 2.6)	-6.3 (-22.1 to 9.6)
Other	0.5 (-3.8 to 4.8)	-25.1 (-44.3 to -5.9)	1.1 (-5.2 to 7.3)	-31.1 (-55.3 to -6.9)
Unknown	2.2 (-3.3 to 7.8)	2.0 (-9.0 to 13.0)	2.2 (-5.9 to 10.2)	-1.1 (-15.0 to 12.8)

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After exclusions, 2237 patients of self-reported African or Afro-Caribbean ancestry were known to nephrology services at the time of this study. Characteristics are reported in Table 7. Using adjusted eGFR-CKD-EPI 503/2081 (24.2%) patients were incorrectly classified according to eGFR criteria for CKD Stages but the proportion of patients with incorrect classification tended to decrease with disease severity; 279/2081 (12.5%) had unadjusted eGFR <20 ml/min/1.73m² compared with 205/2081 (9.2%) when adjusted eGFR was used, thus according to local policy 74/279 (26.5%) of patients eligible for RRT planning (which is recommended when eGFR <20 ml/min/1.73m²) may have had their care delayed.

Table 7. Assessment of clinical impact of chronic kidney disease staging according to Kidney Disease Improving Global Outcomes (KDIGO) criteria using adjusted and unadjusted CKD-EPI-GFR equation in a cohort of people of self-reported ‘Black Ethnicity’ receiving nephrology care in the United Kingdom (excluding patients receiving dialysis).

Cohort Characteristics	N = 2237
Age, mean (SD)	57.7 (17.2)
Male, N (%)	1102 (49.3%)
Renal Transplant N (%)	154 (7.0%)
eGFR categories (ml/min/1.73m²) [excluding patients with transplants] N (%) N = 2081	CKD-EPI with ethnicity adjustment	CKD-EPI without ethnicity adjustment	Patients with incorrect classification using ethnicity adjustment
>90	620 (29.7%)	410 (19.7%)	210 (33.9%)
60–89	496 (23.8%)	535 (25.7%)	171 (34.5%)
30–59	582 (27.9%)	662 (32.5%)	91 (15.6%)
15–29	264 (12.7%)	324 (15.6%)	31 (11.7%)
<15	121 (5.8%)	152 (7.3%)	0
eGFR <20mls/min/1.73m ² *(including patients with transplants)*	205 (9.2%)	279 (12.5%)	74 (26.5%)

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CKD: Chronic Kidney Disease; eGFR: estimated Glomerular Filtration Rate.

Discussion

This is one of the largest studies exploring the accuracy and impact of eGFR equations in people of African and Afro-Caribbean ancestry outside of Africa or the USA. We found that both adjusted eGFR-MDRD and eGFR-CKD-EPI equations lead to an overestimation of GFR compared to mGFR, particularly in people of black ethnicity with GFR ≥60 ml/min/1.73m². Removal of ethnicity adjustment for both eGFR-MDRD and eGFR-CKD-EPI significantly reduced bias and improved 30% accuracy of equations. LM Revised equation had the smallest bias for both Black and White participants, including in Black participants with mGFR<60ml/min/1.73m² in which all eGFR equation accuracy tended to be reduced. The best adjustment factor for ethnicity in our cohort was 1.018 i.e. only an increment of approximately 2% is needed to improve eGFR-CKD-EPI accuracy, which is unlikely to be of clinical importance.

Exploration of the clinical impact of use of eGFR-CKD-EPI demonstrated that approximately one in four patients had a more advanced CKD Stage when categorised by unadjusted eGFR-CKD-EPI. Similarly, approximately one in four patients may have had delayed planning for RRT.

However, estimated GFR equation accuracy did not meet required standards in White participants. An important shortcoming of this study is the inclusion of participants who were undergoing measured GFR studies for other indications. Higher bias was seen in younger White participants with CKD-EPI. The majority of these tests were performed prior to commencing chemotherapy, thus reduced muscle mass may be more pronounced in younger participants is possible. However, higher bias was seen in older White participants with MDRD. We excluded those with albumin < 30g/l to attempt to exclude those who were catabolic or malnourished, but acknowledge this approach is imperfect.

Other limitations include the low numbers of participants with CKD and lack of information about hydration and fasting status. In addition, self-reported ancestry was used, and we were unable to explore regional differences in eGFR accuracy. Prospective data collection in controlled settings in people with and without CKD without additional indications for measured GFR assessment (e.g. malignancy) are needed, but will be costly and additional strategies to enhance recruitment of Black participants may be needed [32].

The adjustment factor for ethnicity in our cohort (1.018) was considerably lower than other studies. The MDRD adjustment factor (1.212) was derived from 197 African Americans (8% of total cohort), whereas the eGFR-CKD-EPI adjustment factor (1.159) was developed from pooled datasets including 1737 African Americans (32% of total cohort) and 384 African Americans, South Africans and African Europeans (N = 84) (10% of total cohort) in the validation cohort [33–35]. An adjustment factor of only 1.077 (95% CI, 1.042–1.113) was needed to enhance equation accuracy in an African French study which included 302 African Europeans [11]. In keeping with our findings, numerous studies of healthy adults and patients with CKD in East, Central, West and South Africa report that use of ethnicity correction factors leads to overestimation of GFR; [11,16,36–44] bias and accuracy of estimation equations compared with mGFR are improved without ethnicity correction (Table 8).

Table 8. Studies of measured and estimated glomerular filtration rate in Africa and Europe.

Author	Country	Number of Participants	Cohort	Measured GFR	Median Bias With Ethnicity Correction	Median Bias Without Ethnicity Correction	% estimates within 30% mGFR with ethnicity correction	% estimates within 30% mGFR without ethnicity correcton
*MDRD*
Agoons [45]	Cameroon	51	Type 2 Diabetes Mellitus Median mGFR 69.0 mL/min/1.73m	24-hour creatinine clearance	-13.00%	-0.30%	-	-
Arlet [46]	African French	64	Sickle cell disease patients Median mGFR 112.5 mL/min/1.73m	Iohexol	Median difference in eGFR and mGFR 49.3 [24.7–64.8]	Median difference in eGFR and mGFR 19.9 [4.9–32.9]	-	-
Bukabau [16]	Democratic Republic of Congo	93	Healthy Adults Mean mGFR 92.0 ± 17.2 mL/min/1.73m	Iohexol	Median difference in eGFR and mGFR 13.6 [8.0–19.2]	Median difference in eGFR and mGFR -4.9 [-9.6; -0.2]	79.6%	86.0%
Madala [44]	South Africa		CKD Outpatients 70.3% mGFR <60 mL/min	99m-Tc-DTPA	eGFR <30: 39.2%; 30–59: 5.3%; >60: 19.3%	29.2% 17.1% 38.0%	53.3% 62.5% 35.1%	36.1% 65.2% 68.8%
Moodley [36]	South Africa	188	Nuclear Medicine Studies	99m-Tc-DTPA	-	Mean bias: Female 16.4%; Male 29.1%	-	Mean bias: Female 49.9%; Male 54.3%
Seape [15]	South Africa	97	Black HIV patients	51Cr-EDTA-GFR	38.40%	14.20%	43.30%	59.80%
Van Deventer [38]	South Africa	100	Healthy Adults	51Cr-EDTA-GFR	27%	5%	-	-
Wyatt [47]	Kenya	99	HIV patients	Iohexol	18%	-3%	73%	83%
*CKD-EPI*
Agoons	Cameroon	51	Type 2 Diabetes Mellitus Median mGFR 69.0 mL/min/1.73m	24-hour creatinine clearance	-8.5	1.7	-	-
Arlet	African French	64	Sickle cell disease patients Median mGFR 112.5 mL/min/1.73m	Iohexol	Median difference in eGFR and mGFR 30.5 [16.5–44.3]	Median difference in eGFR and mGFR [-0.7; 24.8]	-	-
Bukabau	Democratic Republic of Congo	93	Healthy Adults Mean mGFR 92.0 ± 17.2 mL/min/1.73m	Iohexol	17.20%	2.30%	73.10%	81.70%
Flamant [11]	African French	302	CKD Patients Mean mGFR 57.6 mL/min/1.73m	51Cr-EDTA-GFR	11.90%	-	74.80%	-
Moodley	South Africa	188	Nuclear Medicine Studies	99m-Tc-DTPA	Female 31.5% Male 39.4%	Femle 13.5% Male 20.2%	Femle 46.7% Male 45.7%	Femle 53.3% Male 54.3%
Seape	South Africa	97	Black HIV patients	51Cr-EDTA-GFR	33.70%	15.30%	41.20%	62.90%
Wyatt	Kenya	99	HIV patients	Iohexol	10%	-4%	82%	85%

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Serum creatinine concentrations are described in people of Black ethnicity with the same mGFR as people of White ethnicity [8,9], and serum creatinine concentrations are reported to increase with higher proportions of genetically determined African ancestry [48]. However, in a UK study of postpartum women there were no differences in serum creatinine concentration with ethnicity, but measured GFR was not performed [49]. Genetic analysis to determine proportion of African ancestry to guide use of ethnicity correction factors has been proposed, but will be impractical in clinical settings [48].

It has been suggested that people of Black ethnicity have a higher muscle mass than people of White ethnicity, but this has not been formally studied [8,9,12]. The mean BMI of Black participants in our cohort (28.2 kg/m²) was higher than an African French cohort (26.0 kg/m²) and African studies (e.g. healthy Africans from Ivory Coast and Democratic Republic of Congo 24 kg/m²) and more comparable to African American cohorts (MDRD study: 28.7 kg/m²; AASK trial: 30.7 kg/m2) [12] suggesting that BMI may be inadequate surrogate for muscle mass. Analysis of the Chronic Renal Insufficiency Cohort (CRIC) Study, (37% Black participants), reported that ‘ethnicity correction’ bias was reduced from 20% to 3.3% when body composition variables were included their CRIC GFR estimating equation [50]. Thus, to enhance accuracy of eGFR equations, it is possible that anthropometric characteristics may also need to be considered.

Dietary protein intake and catabolism has also been proposed to contribute to GFR differences between African and African American studies. Lower dietary protein intake is described in black South Africans compared to both White South Africans and African Americans [51]. However, unlike in African Americans there is evidence that Black British people tend to eat more traditional foods with lower protein, which may account for the lower adjustment factor in our cohort. Other proposed explanations include differences in tubular creatinine secretion [52,53], but these findings are not consistent [11].

Despite the LM Revised equation being derived from a White cohort [30], this equation had the smallest bias in Black participants. Actual body mass was used as ‘estimated lean body mass’ in our cohort, which may have been appropriate for those with other indications for GFR measurement. The FAS equation is based on serum creatinine in healthy populations normalised for age and sex but do not include ethnicity, and high rates of bias identified in our cohort may also reflect their concurrent disease state. EKFC equation estimations were less likely to overestimate GFR than FAS, in keeping with other reports, but validation in people of Black ethnicity is limited [31]. However, unlike other equations, EKFC could potentially be used in children, although was not explored in this study.People of Black ethnicity represent 7.8% of patients requiring RRT in the UK (3.0% of overall UK Black ethnicity) [54,55]. Black patients have earlier onset ESKD (56.5 v 65.8 years) [56], and reduced pre-emptively listing for transplantation compared to White patients (odds ratio, 0.43) [54,55]. However in areas of high RRT uptake, rates of CKD diagnosis are low [57], which has also been reported in the USA, and overestimation due to eGFR equations in African Americans with ≥60 mls/min/1.73m² has been proposed to be contributory in keeping with our findings [37,56,58–61]. In a South African Black cohort approximately one in six patients would be not recognised as having CKD Stage 3 [36], and in a US cohort up to one in three Black patients were reclassified with a more severe CKD if unadjusted eGFR equations were used [60].

The eGFR-CKD-EPI equation is currently recommended for CKD diagnosis and staging in Kidney Disease in Global Outcomes (KDIGO) and National Institute for Health and Care Excellence (NICE) guidelines including use of adjustment for ethnicity in the UK [62]. Identification of early CKD, dependent on eGFR test accuracy, in low and middle-income countries will be critical to reduce the burden of ESKD in low resource settings [63].

KDIGO guidelines highlight potential sources of error in GFR estimation equations due to ‘race/ethnicity other than US and European black and white’ populations [64] but others have advised caution about elimination of adjusted eGFR equations [26,65], However, recently in the USA, there has movement away from race-based medicine. Some institutions have abolished application of adjusted eGFR due to concerns about racial categorisation being used in a non-standard way including for those of mixed ancestry [65]. In 2020, the National Kidney Foundation and American Society of Nephrology established a task force to reassess the inclusion of race eGFR equations in the United States and its final recommendations for future practice are awaited.

Conclusion

Overestimation of measured GFR with eGFR equations using ethnicity adjustment may lead to reduced rates of CKD diagnosis and under-recognition of CKD severity in people of Black ethnicity in the UK. Our findings suggest that ethnicity correction factors for GFR estimation in non-African Americans should no longer be used, until better approaches of assessment are available. Given the consistency with data reported by other groups studying GFR in people of African ancestry outside of the USA, we consider that these findings are generalisable to other UK and European hospitals.

Data Availability

All relevant data are within the manuscript.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0255869.r001

Decision Letter 0

Pierre Delanaye

12 Mar 2021

PONE-D-20-41065

Estimated Glomerular Filtration Rate Equations in Black British people: Inappropriate adjustment for ethnicity may lead to reduced access to care

PLOS ONE

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Additional Editor Comments:

The manuscript is highly topical as the ethnic coefficient is largely debated in the literature. The sample is modest for the comparison of eGFR and mGFR.

Unfortunately, the performance of equation in White people is very low, especially in low GFR range. This is clearly a major limitation for the interpretation of the results. Such a low P30 in Whites is killing the interest of this interesting work. The authors should explain this result (and maybe try to improve): due to selection of patients (with low muscle mass?)? problem with creatinine (not IDMS)? problem with the reference method? Problem with calculation? A matched analysis (based on mGFR, age, weight or BMI, gender, referral) between Blacks and Whites might help. If no significant change is made on this part (comparison eGFR and mGFR), I will recommend the authors to focus only on the second part of the analysis (classification of patients with or without coefficient).

Also I agree with reviewer 1 on the fact that other equations should be tested (LMR and EKFC).

The discussion is too long, although the result section is too brief.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: No

Reviewer #4: Yes

Reviewer #5: I Don't Know

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

Reviewer #5: No

**********

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Reviewer #1: The current article investigates the need to ‘correct’ eGFR-equations for European Blacks, given that such a correction factor is available for African Americans, in two well-known equations: the MDRD Study equation and the CKD-EPI equation.

1. It’s unfortunate that the authors did not take the opportunity to evaluate more recent European equations, like the FAS-equation, the LMREV-equation and the EKFC-equation. Especially, the FAS-equation has been shown to perform better than the CKD-EPI or MDRD equation in African Blacks (see Bukabau J et al, Performance of creatinine- or cystatin C–based equations to estimate glomerular filtration rate in sub-Saharan African populations, Kidney International, 2019). Bukabau et al concluded that: “In conclusion, we showed that both MDRD and CKD-EPI equations perform better in our African population when the African American ethnic factors are omitted, especially in subjects with high GFR values. FAS SCr af has the same performance as FAS SCr. Among creatinine-based equations, FAS SCr and CKD-EPI equations performed similarly, and we suggest that FAS SCr could be slightly better in patients with CKD, but these results need to be confirmed in larger African CKD cohorts.” See also Yayo E et al (your ref 17: Measured (and estimated) glomerular filtration rate: reference values in West AfricaNephrol Dial Transplant (2018) 33: 1176–1180) who concluded: “Regarding eGFR equations, our results showed the best concordance with mGFR for the FAS creatinine equation, confirming the results in Caucasian cohorts but using the Q values adapted for Africans. Regarding the CKD-EPI equation, recognized to be useful in the normal GFR range, a better fit with mGFR percentiles is observed when the ethnic coefficient is not used, confirming prior data in Africans, European Africans and even AAs”.

2. Unlike CKD-EPI and MDRD, the FAS and EKFC equations adjust for differences in Serum Creatinine generation between children and adults and between males and females, and this allowed to develop a full age spectrum equation. No (or little) differences in GFR between children and adults, or between males and females have been shown (for children older than 2 years). This is probably due to the BSA indexation. Also, differences in GFR between ethnicities have never been shown (see Yayo et al, your ref 17). Therefore, it seems to be interesting to have a better look at possible differences between European Blacks and Whites at the creatinine level, not at the GFR-level. Do the authors see systematic differences in creatinine generation between Blacks and Whites? The whole idea of ‘correcting for differences in creatinine generation’ between populations disserves more attention.

3. How was mGFR obtained? Using the full concentration-time decay curve, or using only late samples and correcting for the absence of the early compartment? Please give more details about the measurement method.

4. 11% of 2333 is not equal to 314 Blacks. Do I miss something? Or, is this the difference between self-reported Blacks and what? How did the authors define ‘Black ethnicity’ when it was not self-reported? In figure 1, the % of Blacks is 13.5%. Please check!

5. It is surprising to see the very high bias for MDRD (14.3) and CKD-EPI (14.6) in White participants. MDRD is not really the best equation to estimate GFR in “healthy” participants, because it largely overestimates GFR, but the bias of CKD-EPI is exceptionally high. Can the authors explain this? It is known that CKD-EPI largely overestimates mGFR in young adults (18-30 years), but I cannot imagine that this might be the reason here? Moreover, in patients with low Scr (Scr/k < 1, with k = 0.90 mg/dL for males and 0.70 mg/dL for females), MDRD largely overestimates GFR, which is not really the case for CKD-EPI. So, I would expect to see a larger bias for MDRD than for CKD-EPI. I would advice the authors to check their calculations! See also the articles of Bukabau and Yayo where the bias obtained with CKD-EPI (without adjustment for ethnicity) were not so large, compared to the here reported bias.

6. Also, P30 accuracy is very low for Whites. Could that be due to the Jaffe type assay, although the authors claim that SCr is IDMS equivalent? Other studies that compared eGFR equations did not show large biases for CKD-EPI (see e.g. Pottel et al. An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dialysis Transplant (2016) 31: 798-806 and Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C. Nephrol Dial Transplant (2017) 32: 497–507; and the very recent Development and Validation of a Modified Full Age Spectrum Creatinine-Based Equation to Estimate Glomerular Filtration Rate: A Cross-sectional Analysis of Pooled Data. Ann Intern Med. (2020) doi:10.7326/M20-4366. ) Can the authors explain the large bias (14.6) for CKD-EPI in Whites? I am really concerned about this.

7. The CKD-EPI equation has been developed to overcome the major flaw of the MDRD equation, namely the overestimation of mGFR when mGFR > 60 mL/min/1.73m². Therefore, it was recommended to report the estimated GFR calculated from MDRD as > 60 mL/min/1.73m² instead of reporting the actual value. Thus, the authors should be careful when reporting eGFR and attributing large bias to ethnicity, rather than to the equation itself. However this does not explain the even larger bias in CKD-EPI for Whites (as compared to MDRD). Please check your calculations!

Reviewer #2: Dear Editor,

I read with interest the paper entitled ‘’ Estimated glomerular filtration rate equations in Blacks British people: inappropriate adjustement for ethnicity may led to reduced access to care’’

Ronvick Gama et al studies 2333 participants (314 black and 2019 White) and found that eGFR equations using ethnicity correction in Black British people overestimated mGFR. Although interesting, I have some several concerns.

1. Participants were recruited from hospital database; did they have acute kidney disease or CKD?

2. Authors must clarify which statistical analysis was used to evaluate the performance of equations versus mGFR? I suggest author to perform the Bland and Altman analysis in order to illustrate performance of the different equations in figure.

3. Authors do not explain if mGFR and eGFR were performed at the same time? Additional explanations are necessary for a better understanding of the paper.

4. Also what is the operational definition of Blacks British people? How long they stay in UK? Black people living in UK are they comparable to those living in Africa? After how long time can you expected to see changes in muscle mass?

5. Are the both equations comparable, especially in healthy subjects?

Finally, I think this paper needs major revisions before being published.

Reviewer #3: The present work, performed in a British population, evaluates the impact of taking into account an ethnic factor on the performance of GFR estimation formulae (eGFR), and its consequences on the management of end-stage kidney disease, through the prism of the eligibility for kidney transplantation. The study is based on 2333 EDTA clearances (mGFR) performed over 10 years, of which 334 were obtained in black patients. This work describes a dramatic overestimation of mGFR in patients of African origin when the ethnic factor is taken into account, with a significant improvement in accuracy when the ethnic factor is not taken into account. In a second part of the work, conducted in a cohort of patients of 2237 patients of African origin, the authors evaluate that 26 % of patients had unadjusted eGFR below 20 ml/min/1.73m² and may have been delayed for RRT planning when an ethnic factor is considered. Taken together, the authors conclude that the ethnic factor should not be taken into account when estimating GFR with either MDRD or CKD-EPI in the British black population.

The need to consider an ethnic factor for GFR estimation in patients of African origin other than that which was used to establish the estimation formulas is an undoubtedly major and interesting issue. Unfortunately, this work has many methodological limitations leading the authors to conclude in a totally contradictory way with their data.

Major concerns

. The question underlying this study is the risk of inappropriate management of CKD patients whith the use of an ethnic-adjusted eGFR. While the number of subjects of African origin is significant in this study, the subpopulation of patients with a GFR less than 60ml/min/1.73m² is weak and insufficient to be able to draw any conclusion for the main goal of this work (n=56).

. A major issue of this study is that eGFR dramatically overestimated mGFR both in the white and in the black populations. This overestimation greatly exceeds that observed in all studies that compared the estimators to a reference method, including with the 51CrEDTA tracer. Very importantly, this is not only the case for black patients (when the ethnic factor is incorporated), but also in white patients. This strongly suggests a major issue on the assessment of reference values in this work. This is all the more problematic as all the conclusions of this work are exclusively based on the bias between adjusted-eGFR and mGFR in the black population.

These data appear to strongly support the need for an ethnic factor, in contrast with the conclusion of this work. Indeed, in the whole population, it turns out that although mGFR is 3,5 ml/min higher in the black population than in white patients, non adjusted-CKDEPI is 5 ml lower, which highlights the need for a correction factor. In other words, despited a higher mGFR, black patients have a higher creatininemia (lower eGFR), demonstrating an influence of ethnicity on serum creatinine level, independent of mGFR. Inon-adjusted eGFR assesses the difference in true GFR between the two populations with an error of nearly 8.5ml/min. This error is “only” 5.5ml/min with adjusted CKDEPI (+9ml/min/1.73m² versus +3.5ml/min/1.73m²). Altogether, this seems to call for an intermediate correction factor for ethnicity, but the lack of a correction factor leads to a larger error than the use of the existing factors.

The concern is even worse when considering patients whose GFR is less than 60ml / min / 1.73m². In this subpopulation, the mean biases between adjusted-eGFRs and mGFR in the black population are very close to those obtained in the white population (respectively 15 and 16 ml/min/1.73m² for the CKDEPI equation and 17 and 18 ml/min/1.73m² for the MDRD equation). Consequently, not taking into account the ethnic factor in the black population leads to very important differences in the mean biases between eGFR and mGFR between black and whites.

The conclusions of the authors therefore appear to be in string contradiction with the data, which actually strongly suggest the need to use an ethnic factor in this black population, although the correction factor to be applied seems lower than that proposed for African Americans. Interestingly and not previously described, this factor could be different depending on GFR value.

In a general way, the interest of including an ethnic factor for GFR estimation in a population can only be achieved by comparing the estimates between two populations that differ only by ethnic status, or alternatively by a regression model evaluating whether this status is a factor independently associated with serum creatinine value. In any case, these evaluations must be methodologically independent of the reference value, namely mGFR.

. The part of the study evaluating the risk of misclassification of black patients according to adjusted or non-adjusted eGFR is also a matter of concern, as unadjusted eGFR is considered as the reference method, in relation with the conclusions of the first part of the study. It is also important to note that the impact of the ethnic factor could have been obtained theoretically and independently of any data collection.

Minor concerns

. Bias is defined as mGFR minus mGFR in methods but results discussed in text and implemented in tables seem to indicate otherwise

. Were there repeated GFR measurements in the same patient, which is not uncommon for this type of assessment, especially in transplant patients? The flow chart does not seem to indicate any data exclusion related to repeated measures. In other words, are there 2333 different patients or 2333 different GFR measurements? The number of patients should be indicated if several measurements arise from the same patients, or ideally, only one visit per patient should be kept in the analysis.

. Given the very unusual difference between mGFR and eGFR, it would have been interesting to have some methodological details on the measurement of GFR (Plasma clearance or unirany clearance? Single point method? Equation used for the correction of the plasma clearance...)

Reviewer #4: The authors investigate the accuracy of using the Black race coefficient among Black Europeans versus White Europeans by comparing measured GFR with MDRD and CKD-EPI eGFR estimates. They demonstrate the use of the Black race coefficient significantly overestimates kidney function among Black Europeans compared to White Europeans. The authors suggest that the Black race coefficient should not be used in Europe. This study nicely complements growing evidence globally that the Black race coefficient results in overestimation of kidney function. A few suggestions and clarifications may help strengthen this manuscript:

Results:

Paragraph 5, first sentence: "After exclusion, 37 patients..." It is not clear which type of patients are excluded here. Please clarify.

Paragraph 5, last sentence: It is not clear how delayed RRT planning is determined here. Was this assessed over time after removal of the Black race factor? More details are needed here.

Discussion:

In general, this Discussion is too long and not focused. There is too much background about eGFR studies - it almost read as a review. It would be helpful to explain the findings of overestimation in GFR between Black and White patients including inaccuracies surrounding GFR estimation and measurement more generally. As it reads, the Discussion focuses on why Black individuals may have different genetic characteristics that could explain variability in eGFR (based on muscle mass) compared to other races, however the evidence that supports this is vert poor. There is discussion about socioeconomic differences between Black individuals and other races however this needs to be better organized.

Paragraph 6, 2nd sentence: Udler et al study that is referenced has NOT confirmed association of higher muscle mass based on African ancestry. To my knowledge, no study has done this. Please explain this sentence.

Paragraph 7, 3rd sentence: "Whilst it might be assumed that African Americans and Europeans have similar diet..." Why would it be assumed that African Americans and Europeans have similar diet? More details are needed here.

Paragraph 8, 2nd sentence: "Thus, to enhance accuracy of eGFR equations for people of African and Afro-Caribbean ancestry.." Why would accuracy only be enhanced for people of African and Afro-Caribbean ancestry? What about other races? Please expand here.

Reviewer #5: Thank you for the opportunity to review this manuscript, which is highly topical, and this month the American Society of Nephrology announced they were abandoning racial adjustment for eGFR. Authors should reference this and the informing literature published in CJASN this year (https://cjasn.asnjournals.org/content/early/2021/03/04/CJN.01780221)

This manuscript potentially takes a more precise approach than some of the papers published recently exploring if the adjustment in the two equations should be dropped, not because this improves their accuracy, but rather that their removal would initiate pre-dialysis planning sooner in this group who for a range of reasons do not have equitable access to the best possible healthcare. Indeed removal for race has been cited as inappropriate (https://cjasn.asnjournals.org/content/15/8/1203)

This manuscript does require some additional work to ensure the community gets the most possible from it:

Introduction - please mention creatinine (which one would consider the reason race is being adjusted for although this is explored in the discussion) earlier.

Please acknowledge some of the policy decisions which are being suggested around race adjustment

Methods -

I need to linger on the mean difference and associated precision: as written I was not able to establish if the value used here could only be positive, or could be positive or negative. i.e was this the root mean square error or just the error (presumably eGFR - mGFR as black patients had their kidney function overestimated)? This difference is fairly important, as it give some vital context as to why the SD of the error/bias is so large (same size as the error itself in many circumstances). The SD of the bias is an attempt to give the reader an appreciation of the distribution of the error, so we are saying that 68% of the error data for CKDepi overall lies between -2 (20-21.7) and 42 (20+21.7) if we were using values with a sign I believe? The limits of agreement are effectively the range across which 95% of the data lies, but one cannot put full trust in this without knowing what the original bias value (on which all these numbers rely) was derived on.

Some minor comments on the methods: one would normally have a section specifically on the statistical methods. Data processing would normally come before this.

Results -

Could we please see:

1. Some graphs comparing the data: Histograms of the mGFR and eGFR for the two different race groups for instance? Histograms of the bias ( I believe preferably with pos/neg values rather than RMSE). A lowess smoothing plot of the bias for both ethnic groups against eGFR which might cope a bit better with the small numbers in some CKD categories.

2. Again, understanding reliably the direction of the change is important: for instance when reporting the proportions who change CKD stage, this should probably be divided into % higher and % lower. 1.1ml bias (first line page 11 on my version - why aren't your pages numbered?) is very low - again knowing the RMSE around this would help know if you've improved the error on one size but worsened it on the other.

Discussion -

Clearly a major source of "bias" in the existing equations are the differences in the cohorts which they have been derived and then applied. This is acknowledged as a limitation but not really explored. For instance, what were the BMIs of the seminal papers and how do they compare to your cohort? These are well described.

The discussion is rather long (3.5 pages without line spacing) and probably has elements which could be sacrificed (e.g. you talk about Cockcroft-Gault). A lot of what is mentioned is context and not how your research aligns with existing findings (example: 2nd paragraph page 13 leading with "in the UK, the prevalence" - this could again be sacrificed in the discussion, and mentioned in the introduction). Can I suggest more formally structuring this around: a) Summary of findings b) How findings compare with existing research and any mechanisms you wish to mention c) Strengths and weaknesses d) policy and practice implications e) Recommendations for future research f) Conclusion.

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PLoS One. 2021 Aug 12;16(8):e0255869. doi: 10.1371/journal.pone.0255869.r002

Author response to Decision Letter 0

21 Jun 2021

When submitting your revision, we need you to address these additional requirements.

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Thank you for this recommendation. We have removed the statement from the submission.

Data were extracted by clinical staff, and given a unique study ID, all potential identifiers (name, date of birth and hospital number were removed) prior to analysis by the research team. All Non-clinical staff do not have access to any laboratory records.

Thank you for this recommendation. We have provided the following additional information regarding our statistical analysis.

Tables have now been added as part of the main manuscript.

Editors Comments

The manuscript is highly topical as the ethnic coefficient is largely debated in the literature. The sample is modest for the comparison of eGFR and mGFR.

Thank you for this comment – we have now undertaken a posthoc analayis to explore the bias in White participants, and identified that age was strongly associated with bias for both CKD-EPI and MDRD equations (younger patients higher bias with CKD-EPI, and older patients higher bias with MDRD). These data are now reported on Page XX

Also I agree with reviewer 1 on the fact that other equations should be tested (LMR and EKFC).

Thank you for this helpful suggestion. We have now tested LMR, FAS and EKFC equations. In White patients eGFR FAS equation had the greatest bias of 15.2ml/min/1.73m2. The LM Revised equation had the smallest bias, 5.8ml/min/1.73m2. In Black patients, the LM Revised had the smallest bias (-1.1ml/min/1.73m2).

Page 9 Line 5

The discussion is too long, although the result section is too brief.

Thank you, we have now enhanced the results section and reduced the discussion section as recommended.

Reviewers' comments:

Thank you for this suggestion. We have now revised the analysis to included the FAS-equation, the LMR-equation and the EKFC-equations and found that overall LMR had least bias in both Black and White patients.

Please see Page 9 Line 5.

We have now compared creatinine level in Black and White ethnicities, matched for GFR and found higher creatinine concentrations. We agree this warrants futher focus and have introduced this into the discussion.

mGFR was measured by Cr51-EDTA, administered iv (10 MBq), was used to measure mGFR. Plasma clearance of the tracer was calculated from accurately-timed plasma samples obtained at about 120, 180 and 240 min, and corrected for the assumption of a single compartment using the formula of Brochner-Mortensen

Page 4 Line 10

We have reviewed our calculations and after removal of serial testing, confirm that 266 out of 1888 (14.1%) of the participants were self-reported black ethnicity.

Only self-reported black ethnicity was used. If no ethnicity was reported participants were excluded.

We have reviewed our calculations and confirm that age was strongly associated with bias in both CKD-EPI and MDRD equations for White patients, but only with the CKD-EPI equation for Black patients. For the CKD-EPI equation, younger patients were associated with higher bias (correlation coefficient -0.1, 95% CI: -0.2 to -0.0 for White and -0.3 (-0.5 to -0.1) for Black), and for the MDRD equation in White patients, older patients were associated with higher bias (correlation coefficient 0.1, 95% CI: 0.0 to 0.2). Higher bias in the MDRD equation was also associated with male sex for White patients (correlation coefficient 3.0, 95% CI: 0.8 to 5.1). There were no associations with bias and BMI or referral source; however sample size was very small in some categories of referral source, particularly for Black patients. Page 11 Line 6-16

We have repeated our analysis and confirm that P30 is low, especially in those with CKD, and was explained by a higher bias in younger white patients. This may reflect the comorbidities of patients undergoing mGFR testing, and relative reduction in muscle mass may be more exaggerated in younger patients (e.g. those commencing chemotherapy). The FAS equation appeared to perform better than CKD EPI in those with CKD.

Thank you for these comments – please see response to comment 6.

Reviewer #2: Dear Editor,

I read with interest the paper entitled ‘’ Estimated glomerular filtration rate equations in Blacks British people: inappropriate adjustement for ethnicity may led to reduced access to care’’

1. Participants were recruited from hospital database; did they have acute kidney disease or CKD?

Participants were retrospectively selected from hospital databases. They did not have acute kidney injury but some did have CKD Stages 3-5. We were unable to identify if individuals had CKD Stages 1 or 2 due to incomplete proteinuria/haematuria imaging assessments. This detail has now been clarified in the methods.

Thank you for this suggestion. We have now included further details about the statistical analysis in the methods and included a Bland Altman analysis (Figure 4) (Page 10 Line 11).

3. Authors do not explain if mGFR and eGFR were performed at the same time? Additional explanations are necessary for a better understanding of the paper.

Serum creatinine (IDMS traceable assay) taken within one week of 51Cr-EDTA study were used to calculate eGFR. Methods Page 3 Line 37.

We have changed the title to ‘in People of Self-Reported Black Ethnicity’ to avoid confusion about place of birth and the definition of ‘Black British’. We were unable to assess how long individuals have been living in the UK. Only self-reported ethnicity were available and no additional details about country of birth or ancestry were available, therefore we are unable to address influences of genetics and environment on muscle mass or GFR.

5. Are the both equations comparable, especially in healthy subjects?

Unfortunately we are not able to identify ‘healthy’ subjects in our cohort. However in those with GFR >60mls/min LMR and EKFC were the best performing equations in both White and Black ethnicities, and CKD-EPI performed better than MDRD. (Table 3).

Major concerns

1. The question underlying this study is the risk of inappropriate management of CKD patients with the use of an ethnic-adjusted eGFR. While the number of subjects of African origin is significant in this study, the subpopulation of patients with a GFR less than 60ml/min/1.73m² is weak and insufficient to be able to draw any conclusion for the main goal of this work (n=56).

We recognise the limitation of the small number of patients with CKD in the cohort, which is limited by its retrospective design. However, we feel that exploration of accuracy of eGFR equations remains important in those with eGFR over 60mls/min/1.73m2, as this remains the standard method to diagnose CKD Stage 3 in the UK.

2. A major issue of this study is that eGFR dramatically overestimated mGFR both in the white and in the black populations. This overestimation greatly exceeds that observed in all studies that compared the estimators to a reference method, including with the 51CrEDTA tracer. Very importantly, this is not only the case for black patients (when the ethnic factor is incorporated), but also in white patients. This strongly suggests a major issue on the assessment of reference values in this work. This is all the more problematic as all the conclusions of this work are exclusively based on the bias between adjusted-eGFR and mGFR in the black population.

We recognise that the bias was high for both black and white participants, and have revised our conclusions accordingly.

3. These data appear to strongly support the need for an ethnic factor, in contrast with the conclusion of this work. Indeed, in the whole population, it turns out that although mGFR is 3,5 ml/min higher in the black population than in white patients, non adjusted-CKDEPI is 5 ml lower, which highlights the need for a correction factor. In other words, despited a higher mGFR, black patients have a higher creatininemia (lower eGFR), demonstrating an influence of ethnicity on serum creatinine level, independent of mGFR. Inon-adjusted eGFR assesses the difference in true GFR between the two populations with an error of nearly 8.5ml/min. This error is “only” 5.5ml/min with adjusted CKDEPI (+9ml/min/1.73m² versus +3.5ml/min/1.73m²). Altogether, this seems to call for an intermediate correction factor for ethnicity, but the lack of a correction factor leads to a larger error than the use of the existing factors.

Thank you for this helpful suggestion. We have now undertaken further analysis including identification of a higher ‘creatininemia’ and calculation of a ethnicity correction factor which was only 1.018.

4. The concern is even worse when considering patients whose GFR is less than 60ml / min / 1.73m². In this subpopulation, the mean biases between adjusted-eGFRs and mGFR in the black population are very close to those obtained in the white population (respectively 15 and 16 ml/min/1.73m² for the CKDEPI equation and 17 and 18 ml/min/1.73m² for the MDRD equation). Consequently, not taking into account the ethnic factor in the black population leads to very important differences in the mean biases between eGFR and mGFR between black and whites.

We agree with the reviewer’s comments that performance in considerably worse in both White and Black patients with lower GFR, and have modified the discussion accordingly to encourage recognition of the inaccuracies of equations in advanced CKD.

5. The conclusions of the authors therefore appear to be in string contradiction with the data, which actually strongly suggest the need to use an ethnic factor in this black population, although the correction factor to be applied seems lower than that proposed for African Americans. Interestingly and not previously described, this factor could be different depending on GFR value.

Thank you for this suggestion. We have now undertaken further analysis and confirmed that ethnicity is independently associated with serum creatinine, and reported an ethnicity factor for this cohort. (Page 10 Line 19)

6. The part of the study evaluating the risk of misclassification of black patients according to adjusted or non-adjusted eGFR is also a matter of concern, as unadjusted eGFR is considered as the reference method, in relation with the conclusions of the first part of the study. It is also important to note that the impact of the ethnic factor could have been obtained theoretically and independently of any data collection.

Need to consider re-assessing the clinical impact with new correction factor.

Minor concerns

7. Bias is defined as mGFR minus mGFR in methods but results discussed in text and implemented in tables seem to indicate otherwise

NEED TO CHECK

8. Were there repeated GFR measurements in the same patient, which is not uncommon for this type of assessment, especially in transplant patients? The flow chart does not seem to indicate any data exclusion related to repeated measures. In other words, are there 2333 different patients or 2333 different GFR measurements? The number of patients should be indicated if several measurements arise from the same patients, or ideally, only one visit per patient should be kept in the analysis.

We have now repeated the analysis and only the first mGFR only was used and repeated measurements excluded. This detail is clarified in Methods: Page 3 Line 38.

9. Given the very unusual difference between mGFR and eGFR, it would have been interesting to have some methodological details on the measurement of GFR (Plasma clearance or unirany clearance? Single point method? Equation used for the correction of the plasma clearance...)

Results:

Paragraph 5, first sentence: "After exclusion, 37 patients..." It is not clear which type of patients are excluded here. Please clarify.

Thank you for this suggestion we have now added the following:

After exclusion of participants with predefined confounders which might influence serum creatinine concentration, a total of…… (Page 5 line 34)

Paragraph 5, last sentence: It is not clear how delayed RRT planning is determined here. Was this assessed over time after removal of the Black race factor? More details are needed here.

Thank you for this recommendation we have now clarified as follows:

74/279 (26.5%) had unadjusted eGFR <20 ml/min/1.73m2 and according to local policy may have been delayed for RRT planning (which is recommended when eGFR <20 ml/min/1.73m2)

Discussion:

Thank you for this suggestion. We have now shortened and refocussed the discussion, with removal of the background of eGFR equation derivation.

Thank you for requesting this clarification. This inaccuracy has now been amended to clarify that ‘serum creatinine concentrations are reported to increase with higher proportions of genetically determined African ancestry.’

We have clarified this statement and supported with references outlining difference in diet between African Americans and Europeans.

Whilst it might be assumed that both African Americans and Europeans adopt a local diet; however, unlike in African Americans there is evidence that Black British people tend to eat more traditional foods. Page 14 Line 12.

Thank you for this suggestion. We agree that it is not just for people of African and Afro Caribbean ancestry that could benefit from additional anthropometric assessment to enhance eGFR accuracy and have removed this suggestion.

Thank you for this suggestion. We have now referenced these publications which were not available at the original time of submission.

This manuscript does require some additional work to ensure the community gets the most possible from it:

Introduction - please mention creatinine (which one would consider the reason race is being adjusted for although this is explored in the discussion) earlier.

The issues related to creatinine excretion and race are now highlighted in the introduction as follows.

ADD.

Please acknowledge some of the policy decisions which are being suggested around race adjustment

Thank you for this suggestion. We have now included these policy decisions in both introduction and discussion as follows:

ADD.

Methods -

We confirm that the bias could be positive or negative and have clarified further in the methods and reported in histograms and Bland Altman plots (Figure 3 and 4).

Some minor comments on the methods: one would normally have a section specifically on the statistical methods. Data processing would normally come before this.

The methods session has been expanded to give more details regarding statistical methods (Page 5 Line 3).

Results -

Could we please see:

Thank you for this helpful recommendation, which we have now added as Figures 2 and 3. We felt that the Bland Altmann plots demonstrated bias more clearly but would be happyto add a Lowess smoothing plot if the editor requires.

The proportion of change of CKD Stage refers only those who have a lower CKD stage than previously considered with use of the adjustment factor. We apologise for not having page numbers and have now added.

Discussion -

Need to add

Thank you for this recommendation. The discussion has now been revised and shortened, and we hope focusses more succinctly on the findings, comparison with other research etc. as recommended.

________________________________________

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PLoS One. doi: 10.1371/journal.pone.0255869.r003

Decision Letter 1

Pierre Delanaye

16 Jul 2021

PONE-D-20-41065R1

Estimated Glomerular Filtration Rate Equations in people of self-reported Black ethnicity in the United Kingdom: Inappropriate adjustment for ethnicity may lead to reduced access to care

PLOS ONE

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments (if provided):

The article has been largely improved. The authors should be congratulated for that.

I have three minor comments

1) The authors wrote: "This is the largest study exploring the accuracy and impact of eGFR equations in people of African and Afro-Caribbean ancestry outside of Africa or the USA". This is incorrect. Flamant et al (ref 11) studied more European black subjects (n=302) than in the current analysis (n=266). Please tone down.

2) I agree the authors keep their coefficient, even if I also agree with Reviewer that correction should be at the creatinine level, not the GFR level. However, I question the clinical relevance of the correction 1.018. Is it really significant from a clinical perspective? To be discussed.

3) It must be reminded that the EKFC equation has the advantage to estimate GFR with the same equation in adults and children (even if no children were included in the current analysis). It seems to me that the EKFC equation performs quite good in most of analyses. It could be a bit more discussed.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #4: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Reviewer #1: Thank you for addressing my comments. I would suggest three other minor revisions:

1) in Table 2, serum creatinine should be reported separately for males and females, as the differences between genders are large

2) As the use of a "race coefficient" is highly debated in the US, I would not recommend to calculate yet another "race coefficient" of 1.018 for Black British subjects. As I have said before, the adjustment should be at the creatinine level, not at the GFR-level, which falsely suggests that there are differences in GFR between Black and White people.

3) the authors report "mean" bias in the tables, but this can be largely influenced by outliers (which are always present). In many other studies, "median" bias was the more commonly reported statistic. I would suggest to replace mean bias by median bias (or at least, add median bias).

Reviewer #2: All comments have been well addressed. But, some minors modifications are needed. For example, line 8, page 5 the design of the study is observational cross sectional study (not a cohort study). Line 24, page 18 to refer to the study of Bukabau et al, authors should add in the sentence ''East, Central, West and South Africa report'' that is because the Democratic Republic of the Congo is in Central Africa not in East Africa.

Reviewer #4: The authors have mostly responded to reviewer concerns. One minor point remains:

1. In the first paragraph of the results, please explicitly list "predefined confounders".

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: Yes: Ernest Kiswaya SUMAILI

Reviewer #4: No

PLoS One. 2021 Aug 12;16(8):e0255869. doi: 10.1371/journal.pone.0255869.r004

Author response to Decision Letter 1

24 Jul 2021

The article has been largely improved. The authors should be congratulated for that.

Thank you for the kind comments.

I have three minor comments

We apologise for this error, and have modified to the following. ‘This is one of the largest studies’

We agree with this comment and have added that this is unlikely to have clinical importance Page 17 Line 13.

Thank you for this suggestion. We have added more detail to the discussion – Page 22 Line 20.

Reviewer #1: Thank you for addressing my comments. I would suggest three other minor revisions:

1) in Table 2, serum creatinine should be reported separately for males and females, as the differences between genders are large

This detail has now been added to Table 2.

We completely concur with this comment and have amended the discussion to suggest that it should not be used in clinical practice.

We have added median bias to the tables.

Reviewer #2: All comments have been well addressed. But, some minors modifications are needed.

For example, line 8, page 5 the design of the study is observational cross sectional study (not a cohort study).

Line 24, page 18 to refer to the study of Bukabau et al, authors should add in the sentence ''East, Central, West and South Africa report'' that is because the Democratic Republic of the Congo is in Central Africa not in East Africa.

Thank you for highlighting these inaccuracies which we have now amended.

Reviewer #4: The authors have mostly responded to reviewer concerns. One minor point remains:

1. In the first paragraph of the results, please explicitly list "predefined confounders".

Thank you for this suggestion. We have now added the predefined confounders again in this section.

Attachment

Submitted filename: Ethnicity GFR Study - PLOS1 reviewers comments 7_7_21.docx

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PLoS One. doi: 10.1371/journal.pone.0255869.r005

Decision Letter 2

Pierre Delanaye

27 Jul 2021

Estimated Glomerular Filtration Rate Equations in people of self-reported Black ethnicity in the United Kingdom: Inappropriate adjustment for ethnicity may lead to reduced access to care

PONE-D-20-41065R2

Dear Dr. Bramham,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Pierre Delanaye

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0255869.r006

Acceptance letter

Pierre Delanaye

2 Aug 2021

PONE-D-20-41065R2

Estimated Glomerular Filtration Rate Equations in people of self-reported Black ethnicity in the United Kingdom : Inappropriate adjustment for ethnicity may lead to reduced access to care

Dear Dr. Bramham:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

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Data Availability Statement

All relevant data are within the manuscript.

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PERMALINK

Estimated glomerular filtration rate equations in people of self-reported black ethnicity in the United Kingdom: Inappropriate adjustment for ethnicity may lead to reduced access to care

Rouvick M Gama

Amanda Clery

Kathryn Griffiths

Neil Heraghty

Adrien M Peters

Kieran Palmer

Henry Kibble

Royce P Vincent

Claire C Sharpe

Hugh Cairns

Kate Bramham

Roles

Abstract

Introduction

Methods

Table 1. Estimated glomerular filtration rate (eGFR) equations.

Statistical methods

Results

Fig 1. Flowchart of exclusion criteria.

Table 2. Cohort characteristics, stratified by ethnicity.

Fig 2. Density plots of GFR measurements, stratified by ethnicity.

Fig 3. Density plots of the bias between each eGFR equation and mGFR, stratified by ethnicity.

Table 3. Estimated glomerular filtration rate (eGFR) equations bias, precision and accuracy compared with mGFR, stratified by ethnicity.

Fig 4. Bland-Altman plots for eGFR equations compared to mGFR, stratified by ethnicity.

Table 4. Regression analysis for the association between ethnicity and mGFR to identify an ethnicity correction factor for our study sample, adjusted for age, sex and log(SCr).

Table 5. Linear regression analysis for the association between log(SCr) and ethnicity, adjusted for mGFR, age and sex.

Table 6. Linear regression analysis for the association between the bias of CKD-EPI and MDRD equations and demographic characteristics in White participants, with mutual adjustment for all other variables in the table.

Discussion

Table 8. Studies of measured and estimated glomerular filtration rate in Africa and Europe.

Conclusion

Data Availability

Funding Statement

References

Decision Letter 0

Pierre Delanaye

Roles

Author response to Decision Letter 0

Decision Letter 1

Pierre Delanaye

Roles

Author response to Decision Letter 1

Decision Letter 2

Pierre Delanaye

Roles

Acceptance letter

Pierre Delanaye

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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