Re-analyzed APOL1 kidney data support new ethics of ‘race’

Authors:  

Cyril O. Burke III^1*, Joshua Ray Tanzer², Leanne M. Burke³ 

¹ Department of Neurology, Brown University Health, Providence, Rhode Island, United  States of America 

² Biostatistics, Epidemiology, Research Design, and Informatics Core, Brown University Health, Providence, Rhode Island, United States of America 

³ Cardiovascular Institute, Brown University Health, Providence, Rhode Island, United  States of America  

^* corresponding author:  
E-mail: cyrilburke@alum.mit.edu (cob3) 

ABSTRACT  

OBJECTIVES  

Persistent US ‘racial’ segregation racializes health, education, employment, and  access. We hypothesized that selective recruitment into US military service, equal  opportunity for military training and jobs, and earned access to Veterans Administration  

Healthcare could mitigate ‘racial’ differences created by racialized early-life deprivations  and harms (i.e., social determinants of health). We examined veteran status and the  starkest ‘racial’ disparity in healthcare—chronic kidney disease.  

METHODS  

First, we defined terms to ensure cross-cultural clarity by orienting international  colleagues to the semantics of socially constructed ‘race’ (in Europe) and overt and  subtle racialization of health (in the US). Second, we removed ‘race correction’ from  estimated glomerular filtration rate and kidney failure under more equal conditions of US  veterans. Third, we used data re-analysis and close examination of social cofactors to  suggest alternative mechanisms for links between socially constructed ‘race’,  apolipoprotein L1 gene variants, and racialized kidney disease.  

RESULTS  

There was no ‘racial’ disparity in kidney failure under more equal social conditions.  Geographically localized “ancestry markers” may proxy for ‘race’. ‘Colorism’ may be  associated with apolipoprotein L1 variants linked to ‘racial’ (e.g., pigmentary) genes.  

CONCLUSIONS  

Under equal conditions, comparable outcomes should be the expected norm for all,  regardless of socially constructed ‘race’, ethnicity, or nationality. Disparity between  socially constructed categories implies disparate social conditions. International  collaboration to discourage misuse of socially constructed ‘race’ in medical care and  research is an actionable Population Health initiative with potentially high impact but low  effort and cost.  

STRENGTHS OF THIS STUDY  

1. Reminds readers of cross-cultural conflicts in socially constructed ‘race’. 

2. Introduces international observers to racialization of health in the US. 

3. Re-analyzes socially constructed ‘race’ under more equal conditions. 

4. Applies new ethics, recommended by the National Academies of Sciences,  Engineering, and Medicine, to older research on socially constructed ‘race’.  

1. INTRODUCTION  

NOTE: An eminent European nephrologist insisted that our study (to reduce US ‘racial’ disparity in kidney disease)  separate discussion of ‘race’ from serial creatinine: “The race part is irrelevant for the key point (race does not  change over time, and thus is not relevant when looking at longitudinal serum creatinine or eGFR) and should be  deleted in the opinion of this reviewer”. After multiple rounds of review, we relented. We produced two research  articles: this re-analysis of kidney disease under more equal conditions, and a proof-of-concept study of ‘race-free’  diagnosis of prechronic kidney disease (preCKD) using within-individual referencing of serial creatinine [1].  

Unchallenged for ‘racial’ inconsistency and selectively including or overlooking its  influence, medical research can unwittingly promote ‘racial’ illusions that advance the  myth of ‘race’ as biology. Hunt et al noted,  

The persistent use of socially constructed ‘race’ in US healthcare may seem  unbelievable to decent, well-meaning international colleagues. That denial creates a  stubborn obstacle to ending misuse of ‘race’. Before our Results and Discussion, we  offer this Introduction (with European and US examples) to orient international readers  to the illusions of socially constructed ‘race’ and ‘ethnicity’ that inspire bad science (BS)  in racialized countries.  

1.1 ‘Race’ and ethnicity  

“Race” and “ethnicity” are commonly used to describe population groups. Yet, their  socially constructed meaning has changed over time and between countries, and  hidden assumptions may limit understanding by international colleagues.  

In “The meaning of racial or ethnic origin in EU law”, the European Commission  noted:  

Because everyone is confident in the local meaning of ‘race’, agreement on socially  constructed ‘race’ between co-located subject and observer reflects ‘shared illusion’  rather than scientific “validity” [4,5,6,7,8,9].  

In 2022, Lu et al summarized contradictions and uncertainties in socially constructed ‘race’ and ethnicity [10]. They noted 1. lack of “consensus definition of race or ethnicity”, 2. inconsistency between subject and observer identification for other than Black or White ‘race’, and 3. “fluidity” of self-identification. Their aspirational recommendations called for “specific definitions” and “justification for collecting and analyzing” data on socially constructed ‘race’ in health research.

Subsequently, in a 2023 report on “Using Population Descriptors in Genetics and Genomics Research”, the National Academies of Sciences, Engineering, and Medicine (NASEM) stated:

1.2 Racialized US healthcare

Although often providing ‘the best healthcare in the world’, US healthcare ranked last among high-income countries [12] and 48th of 50 nations for kidney-related mortality [13], Fig 1.

Fragmented US healthcare spends more per capita than any other nation [14]. In a decade, annual federal spending for chronic kidney disease (CKD) and kidney failure (KF) [15] almost doubled to $122 billion [16], or 1.8% of total federal spending, Fig 2.

1.2.1 ‘Racial’ profiling, hypertension, kidney disease

Despite originating from various continents, studies under similar social conditions are relevant worldwide, regardless of the ‘race’ of the subjects (or lack of contrasting ‘races’). Recruiting ‘racially diverse’ subjects into medical research merely allows tracking of disparate treatment from lingering ‘racial’ segregation and structural racism [17].

In the US, CKD and KF represent the eighth leading cause of death, with lifetime risk varying by socially constructed ‘race’ and ethnicity [18,19], Fig 3, “one of the starkest examples of racial/ethnic disparities in health” [20]. Black people are 12% of the population but 35% of patients on dialysis. “Low-income minorities with bad health had 68% less odds of being insured than high-income Whites with good health” [21]. Ending ‘racial’ disparity in kidney disease for Black patients alone could redirect 20% of funds—$25 billion annually—from late-stage CKD to prevention [22].

Kidney disease mortality rates per 100,000 across the 30 most populous US cities averaged 12.4 for White and 28.2 for Black patients but with marked geographic disparities: White patients, from 2.0 in San Diego, California, to 18.2 in Louisville, Kentucky; Black patients, from 7.9 in New York City, to 45.4 in Charlotte, North Carolina [23].

There are numerous “racially segregated US hospital markets” [24]. At some academic centers, Black patients were more likely to be deemed “teaching cases” for training the medical students, residents, and fellows. In 2003, the National Academy of Medicine reported on “Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care” [25], but 20 years later, “There hasn’t been a lot of progress…. We are still largely seeing what some would call medical apartheid” [26].

However, data collected to monitor and remedy ‘racial’ disparities can worsen them, for example, by misuse in clinical algorithms [27] and popular point-of-care clinical decision-making tools [28,29]. Hunt and Kreiner noted the consequences for primary care:

Word limits of traditional medical publishing can force unintended omissions that hide risk from ‘racial’ disparities. For example, dehydration is prevalent among Black people [31], causes more AKI in low-income areas [32], and increases CKD risk [33]. US treatment guidelines for hypertension in Black patients favor thiazide diuretics [34,35] that can worsen dehydration. A study recommending against diagnosing KF while on thiazides noted thiazide-to-loop-diuretic usage ratios of 1.06 for White and 1.66 for Black subjects but omitted KF by ‘race’ [36]. A study recommending systolic blood pressure control to less than 120 mm Hg (to reduce cardiovascular mortality) reported no benefit in Black subjects [37] but omitted their 100% increase in AKI (until years later [38]). A study recommending a thiazide for poorly controlled hypertension in advanced CKD reported chlorthalidone tripled the number of subjects with acute kidney injury (AKI), and increased AKI episodes per subject by 50%, but omitted AKI by ‘race’ and reliance on a ‘race-corrected’ eGFR [39].

1.2.2 ‘Race correction’

In 1999, the Modification of Diet in Renal Disease (MDRD) equation introduced the first ‘race correction’ for estimating glomerular filtration rate (GFR) [40]. More estimated GFR (eGFR) ‘corrections’ followed—for ethnicity in the US [41] and nationalities in Asia [42,43,44,45]. Laboratory reporting of ‘race corrected’ eGFR results led to questioning [46,47], and the “…majority of studies found that removal of race adjustment improved bias, accuracy, and precision of eGFR equations for Black adults” [48]. Those condemning the “…inappropriate use of race in nephrology” distinguished mistreating individuals from monitoring the effects of racism on a population [49]. However, others hailed “race-free” equations as a “successful compromise” that removed ‘race’ only from the equations [50]. Reports that “race-free” eGFR equations “underestimated measured GFR in Black participants… and overestimated measured GFR in non-Black participants” [51] merely shifted misuse of ‘race’ to pre-test “population weighting” [52,53], potentially delaying care for Black patients [54,55] in a way much harder to detect and stop.

1.2.3 Motivation and outcomes

Kuhn presented ‘science’ as inherently social, demonstrating how the solutions of today (or yesterday) become the problems of tomorrow (or today) [56]. Articles discussing socially constructed ‘race’ as a proxy for social determinants of health (SDOH) are often labeled social ‘Commentary’ [57]. Articles proclaiming the objectivity of numbers [58], and that “…a race stratified eGFRcr (i.e., separate equations for Blacks and non-Blacks) would provide… the best medical treatment for all patients” [59], miss how ‘Confirmation Bias’ influences what numbers we collect, how we understand that data, our confidence in what we are doing now, and which articles get published.

Racialized practices—ongoing despite 100 years of academic articles in Anthropology that ‘race’ is a myth—inspired our data re-analysis (see Results) and examination of socially constructed ‘race’ as a proxy for implied social cofactors (see Discussion). To end misuse of ‘race’ is an overdue Kuhnian paradigm shift [56,60,61].

“With malice toward none, with charity for all” [62], we re-examined ‘race’ in kidney disease. Removing ‘race correction’ from GFR estimates of Black and White veterans showed comparable results. Re-analyzing two studies of gene variants associated with kidney risk in Black veterans—both published before NASEM guidance [11]—gave insights into the influence of socially constructed ‘race’.

2. RESULTS

2.1 Removing ‘race correction’

Removing ‘race correction’ from GFR estimates of an impressive study of KF versus eGFR in 1.70 million White veterans (4% female) and 311,000 Black veterans (6.1% female) [63] tested our hypothesis that differences between arbitrary groups would be less after comparable conditions of selection and opportunity.

2.1.1 Equal outcomes despite ‘race’

Fig 4 shows baseline veteran characteristics and renal comorbidities (all significant to p<0.001), with Black veterans more likely to be younger, female, hypertensive, diabetic, in the highest and lowest eGFR ranges, and living in lower-socioeconomic-status zip codes (left), the latter also shown graphically (right).

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Fig 4.

Left panel: Baseline characteristics of veterans in the primary source by ‘race’, all cofactors with p<0.001. Right panel: Graphic of veteran socioeconomic status, approximated (in the primary source) by zip code.

Mathematically removing the MDRD eGFR ‘race correction’ for Black veterans (dividing by 1.21) shifted rates for KF risk and eGFR prevalence to lower GFRs, Fig 5, also shown graphically in Fig 6. The x-axis ‘race corrections’ (horizontal arrows) sometimes had significant y-axis effects (vertical arrows). The revised KF outcome curves (increasing to the left) overlapped for Black and White veterans (inset). The revised eGFR-prevalence curves (increasing to the right) showed higher prevalence of lower eGFRs in Black veterans. Under similar conditions, the paradox of low prevalence of early CKD yet faster progression at late stages appeared to be a mathematical artifact of ‘race correction’.

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Fig 5.Removing ‘race correction’.

Dividing the upper limit of eGFR range for Black patients by the 1.21 ‘race correction’ numerically removed ‘race correction’, shifting their results to lower numbers on the x axis. In a table, the result is not striking. Note: “end-stage renal disease” (ESRD) is the US equivalent of kidney failure (KF).

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Fig 6.Removing ‘race correction’.

Dividing by 1.21 shifted X-axis values to the left (blue curves). Horizontal and vertical arrows show X- and Y-axis shifts from ‘race correction’.

Curves increasing to the left: kidney failure (KF) curves for Black and White veterans overlap within their 95% confidence intervals (inset).

Curves increasing to the right: ‘race correction’ falsely lowered cumulated Y-axis eGFR percentages of Black veterans.

This visual examination was supported analytically. When estimating magnitude of associations between inverse eGFR and KF rates with and without ‘race correction’, the ratio was exactly 1.21, as expected (‘race corrected’: beta = 11700.74; correction removed, beta = 9670.03; ratio = 1.21). Additionally, as hypothesized, White and Black patients had more similar associations between serum creatinine and GFR after removing the ‘race correction’ (t (10) = 0.91, p = 0.3860) than when ‘race correction’ was maintained (t (10) = 2.36, p = 0.0402). The sample size was small—only 15 data points—because continuous raw data was broken (dichotomized) into five segments, yielding five data points each for White veterans and Black veterans with and without ‘race correction’—therefore, the precision of this result may not hold up with replication. However, it validates concerns raised about an artifact of arithmetic resulting in differences in kidney disease diagnosis. It contradicts notions that Black Americans have less early CKD and need less early care.

2.2 APOL1, AKI, and COVID

Hung et al studied the effects of apolipoprotein L1 (APOL1) risk variant (RV) alleles among 990 participants in the Department of Veterans Affairs Million Veteran Program with “African ancestry” and hospitalized with coronavirus disease 2019 (COVID-19) [64]. Fig 7 shows AKI rates with and without various cofactors, ordered by odds ratios (ORs). They found the relative risk (RR) of AKI was 1.35 times higher for APOL1 “high-risk” (HR) genotype (APOL1 HR = 2 RV copies) than “low-risk” (LR) genotype (APOL1 LR = 0 or 1 RV copy). However, RR of AKI was 2.4 for mechanical ventilation and 2.63 for vasopressor use, both more common in the APOL1 HR group.

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Fig 7.Acute kidney injury rates for various cofactors in veterans hospitalized with COVID-19.

Possible direct or indirect AKI cofactors ordered by odds ratios (ORs), the greatest risk correlating with indicators of poor health.

ORs make the comparison more explicit, Fig 8. We also included data from a similar study of COVID-19 and AKI in the Brazilian general population [65]. Although recalculated confidence intervals are much wider for the Brazilian sample, the ORs between Brazil and the US are close for many cofactors.

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Fig 8.Acute kidney injury (AKI) odds ratios (ORs) for cofactors during hospitalization with COVID-19.

Forest plot of AKI ORs (with 95% confidence intervals) suggests similarity of cofactors between US veterans and Brazilian general population despite differences in genetic origins and socially constructed ‘race’.

Many covariates showed statistically “small” effects (ORs below 1.68), some small enough to suggest random chance. Signs of critical illness showed “large” effects (OR above 6.71). Urine characteristics had ORs primarily toward the upper range, highlighting their importance. The low OR for “dipstick hematuria, year prior” [rows 49 and 61] might reflect two sub-populations: women (some menstruating) and men (hematuria always abnormal).

2.2.1 Bias correction

Mechanical ventilation and vasopressors showed the largest RRs, consistent with findings that AKI in veterans with COVID-19 varied with severity of illness [66]. Verbeek et al recommended correcting bias in observed RR using the “reference confounder” with the largest effect [67]. Unconfounding the APOL1 RR of AKI with vasopressors alone reduced the RR to 1.09, Fig 9.

Fig 9.Bias correction for reference confounders.

Correcting effects of vasopressors and mechanical ventilation on AKI outcomes reveals unconfounded relative risk (RR) of APOL1 high risk versus low risk genotypes. Correlation between mechanical ventilation and vasopressors was not 100 percent, so unconfounded AKI risk would be even closer to unity on adjusting for both.

2.3 APOL1 and N264K

A second important study by Hung et al of 121,000 from the Million Veteran Program with “African ancestry” (13.8% female) compared CKD in patients with APOL1 HR versus LR and with and without N264K (a second-site APOL1 variant) and noted, “presence of a single copy of the APOL1 p.N264K mutation mitigated the increased risk conferred by two APOL1 HR variants” [68]. About 20% of their data came from smaller studies in the general population, but our re-analysis used only 80% of their data, from veterans.

Fig 10 shows the percentage of subjects by kidney outcomes for four combinations of APOL1 HR or LR and N264K alleles present (+) or absent (–) [68]. Black bars, representing APOL1 high-risk genotype without N264K (APOL1 HR, N264K–), show the highest rate for every kidney outcome. Blue bars represent the largest group, APOL1 low-risk genotype without N264K (APOL1 LR, N264K–). Note that presence of the N264K allele (N264K+) in just 3.8% of APOL1 LR and 0.5% of APOL1 HR patients means orange and green bars combined represent less than 5% of subjects, with small absolute numbers (population times rate) in their CKD and outcome groups.

Fig 10.CKD and kidney outcomes in veterans by APOL1 and N264K status.

Ordered by odds ratio (OR) of kidney outcome between APOL1 low-risk versus high-risk without N264K allele (N264K–)—less than 5% of subjects were positive for N264K (N264K+). Note the improvement in outcomes after study “enrollment”.

The chart is ordered by OR between the two largest groups without N264K (N264K–), representing 95% of Black veteran subjects and differing only by APOL1 HR vs LR. Note for APOL1 HR N264K– the association of lower rates of KF and CKD after than before Million Veteran Program “enrollment” [69] and that, after enrollment, rates of CKD were the same for all four groups.

 DISCUSSION

Bioethical oversight of ‘race’ falls under a principle defined after decades of misuse of ‘race’ by the US Public Health Service [70,71], Fig 11. Yet, misuse of ‘race’ as a conscious or unconscious input for clinical care persists, even among those aware of its scientific invalidity [72]. Because ‘race’ is a myth, racialized data can wrongly suggest different outcomes by socially constructed ‘race’. From any continent, studies under equal conditions (e.g., even so-called “single race” studies) should be equally informative. The lowest KF mortality rate (i.e, of the most privileged group in the ideal environment) should be the goal for all—different outcomes reflect social inequality.

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Fig 11.Bioethics: Autonomy, privacy, and justice in United States (US) healthcare.

‘Race’ collected to monitor progress in ‘racial’ disparities can be misused in clinical care. The principal of Justice could inspire oversight.

The Food and Drug Administration’s approval of race-specific medicines “…has not been challenged as a Fourteenth Amendment violation” [73,74,30], and voluntary corrective guidelines “have had little effect on how these concepts are deployed” [75]. Even the comprehensive, scholarly NASEM recommendations are voluntary [11].

A study of 1,000 randomly selected Cochrane Reviews, published between 2000 and 2018, showed that only 14 (1.4%) had planned to include subgroup analysis by ‘race’ [76], and in those 14, there was only a single ‘racial’ finding of statistical significance. Despite centuries of claims of biological differences, ‘race’ was absent under the high standards of the Cochrane Reviews.

To avoid perpetuating false notions of biological ‘race’ [77,78,79], we often label it “socially constructed” and always put ‘race’ or ‘racial’ in quotation marks [80,81].

3.1 Removing ‘race correction’

Our re-analysis found Black and White veterans share the same curve for KF, suggesting cofactors of veteran status minimized ‘racial’ disparities, Fig 6.

3.1.1 Comparable conditions

Implied (but typically unstated) in studies of US veterans are the educational and physical selection standards of the US Armed Services [82]. Long before veterans enrolled in research, servicemembers met written requirements that screen out most of the widespread US functional illiteracy [83], Fig 12, and physical requirements (including urine tests) that precede comparable access to military training, jobs, and healthcare. With rare exceptions, servicemembers earned access to Veterans Administration (VA) Healthcare by completing basic training, entering active duty, and serving honorably. Basic training (e.g., to avoid dehydration) may promote relevant habits in veterans.

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Fig 12.

Late illiteracy: percentage of Black and White 4th grade students reading below basic level.

The European Commission noted that racial or ethnic discrimination “can lead to a cycle of disadvantage which is frequently passed from one generation to the next” [3]. However, US military communities are integrated, and Black service members fared better than Black civilians on numerous metrics [84]. Black and White veterans had similar employment rates [85] and more similar socioeconomic status, Fig 4, in contrast to what Peterson and Krivo showed as the relative advantage/disadvantage of US Black and White neighborhoods [86], Fig 13. The actual economics of Black and White veterans might be closer because socially constructed ‘race’ influences residency options and choices. For example, “…implicit bias in perceptions of crime and disorder”, which Sampson and Bean noted, may be “one of the underappreciated causes of continued racial segregation” [87].

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Fig 13.

Distribution of advantages and disadvantages of US White and Black neighborhoods.

Until confounding factors are characterized and controlled for, analyzing veterans separately from the general population may avoid “Simpson’s Paradox” [88]. Peterson et al showed higher baseline comorbidities and illness severity yet equal or lower mortality among Black veterans in VA healthcare in all but six diseases [89] (or five, after our re-analysis). Despite a higher COVID-19 infection rate among minority women veterans, Tsai et al found equal complications of mortality, cardiovascular events, or onset of heart disease regardless of ‘race’ [90].

3.2 ‘Race’ and genetics

3.2.1 Inconsistency of ‘race’

‘Race’, ‘mixed race’, ‘biracial’, ‘multiracial’: these terms are all scientifically invalid inputs for healthcare [11]. The mechanism of action of socially constructed ‘race’ and APOL1 genotypes in CKD must account for inconsistencies in folk taxonomies of ‘race’ [91,92,93,94], for example in places like South Africa [95], Nigeria [96], and Brazil:

…there appears to be no racial descent rule operational in Brazil and it is possible for two siblings differing in Color to belong to completely diverse racial categories…. asked about their origins (the question admitted multiple responses) <10% of Brazilian black individuals gave Africa as one of their answers [97,98].

‘Racial’ classification in the US dates to a 1662 statute [99] reflected in this segregation-era Census instruction: “A person of mixed white and Negro blood should be returned as a Negro, no matter how small the percentage of Negro blood” [100,101].

3.2.2 Colorism and APOL1

“Colorism” varied the abuse of racism to stratify and stabilize ‘racial’ hierarchies, correlating increasing pigment with worse deprivation.

…the most effective way to segregate black people was to “use the dark skin slaves vs. the light skin slaves and the light skin slaves vs. the dark skin slaves.”.… Subsequently, colorism became an effective strategy in colonizing and dividing people [102].

The odds of inheriting two, one, or zero copies of APOL1 RV alleles vary, for example, with the number of great-grandparents of West African origin. With eight high-risk West African great-grandparents, all with APOL1 HR genotype (i.e., two RV alleles), a subject will inherit two RV alleles. With only one high-risk great-grandparent, the subject “usually looks white or almost so” [103], but the odds of inheriting RV alleles are 75% for zero copies, 25% for one, and zero for two copies.

In contrast, N264K+ is more common in Europeans, found in less than 5% of Black patients, and “…mutually exclusive with the APOL1 G1 allele…. the presence of a single copy of the APOL1 N264K mutation mitigated the increased risk conferred by HR APOL1 genotypes” [104].

With APOL1 RVs more common in West Africa and N264K+ more common in Europe, people with one or the other regional distribution marker likely have different physical appearance and lived experiences. Thus, geographically localized “ancestry markers” can be proxies for ‘race’, and colorism can be a mechanism stratifying subjects even in ostensibly ‘single-race’ studies.

3.2.3 Confusing ‘race’, ethnicity, ancestry, and geography

Commonly used terms for socially constructed population groups are ethnicity in Europe and ‘race’ in the US. ‘Race’, ethnicity, and ancestry are different (albeit similar and overlapping) concepts and all are subject to misuse.

NASEM recommended against conflating ‘race’ with ancestry:

Conclusion 4-2. Using socially constructed groupings indiscriminately in human genetics research can be harmful. Their use reinforces the misconception that differences in social inequities or other factors are caused by innate biological differences and diverts attention from addressing the root causes of those social differences, which compromises the rigor and potential positive effect of the research. Recommendation 1. Researchers should not use race as a proxy for human genetic variation. In particular, researchers should not assign genetic ancestry group labels to individuals or sets of individuals based on their race, whether self-identified or not [11].

Hung et al were not clear on what they meant by ‘race’ but expressed confidence in ‘race’ and in their use of mathematics and big assumptions about the relationships between ‘race’ and personal ancestry [64,68]. They described selecting a subset from the Million Veteran Program databank “…with genetic information available, of African ancestry” and used the method of “genetically inferred ancestry” described by Fang et al, in 2019 [105].

NASEM subsequently noted:

There is a pervasive misconception and belief that humans can be grouped into discrete innate categories…. The illusion of discontinuity between racialized groups has supported a history of typological and hierarchical thinking…. These modes of thinking often spill over to other descent-associated population descriptors such as ethnicity and ancestry (Byeon et al., 2021; Fang et al., 2019). The structure of human genetic variation, though, is the result of human population movement and mixing and so is more related to geography than to any racial or ethnic classification [11].

3.2.4 ‘Race’ in research design

NASEM noted,

Conclusion 4-8. In the absence of measured environmental factors, researchers often wrongly attribute unexplained phenotypic variance between populations to unmeasured genetic differences. Recommendation 4. Researchers conducting human genetics studies should directly evaluate the environmental factors or exposures that are of potential relevance to their studies, rather than rely on population descriptors as proxies [11].

The data from Hung et al are valuable but needed to be explored [64,68]. The ‘single-race’ study design presupposed an association between genetic biology and ‘race’, made analytic assumptions, then unconsciously overlooked more direct causes of AKI in the data. Selective reporting on APOL1 genotypes appeared to justify the myth of ‘race’ as biology.

The directed acyclic graph (DAG) [106] in Fig 14 is a causal framework the study might have imagined, in modeling their choices, but with our modifications to show potential “back door paths” that could explain the outcomes better than a direct effect of APOL1. The grey oval represents their focus on biology with no attempt to consider possible differential effects of racism between APOL1 HR and LR genotypes. Their meta-analysis models included some demographic variables, but none related to education, employment, colorism, socioeconomic status, or any other SDOH, ignoring all the context our re-analysis of veterans suggests is important. Nor did they justify choosing any variables for the analysis, which is important because adding more to a model can introduce selection bias.

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Fig 14.

Causal directed acyclic graph (DAG) that the study might have imagined (with our modifications). One back door path from APOL1 to kidney disease is via West African ancestry (i.e., West African ancestry increases odds a person has APOL1 high-risk copies but also alters physical appearance, and physical appearance feeds discrimination). The study assumed the 10 ancestry principal components (plus other variables in other models) analytically controlled for this to justify focusing on relationships in the shaded area. First, we disagree that ancestry principal components controlled for the back door path via skin tone. Second, even if principal components addressed the confound of skin tone, that does not account for the history of discrimination that is its own confound, including generational wealth as a significant factor. Third, given their regional dependence and low frequencies, the inclusion of both N264K and APOL1 in one analysis is problematic. This approach leads to redundancy and collinearity, resulting in uninterpretable coefficients.

We found several potential back-door paths. First, darker skin tone and colorism: regional ancestry often has aspects of physical appearance, and our re-analysis suggests back door paths attributable to physical appearance and SDOH that undermine analysis focused solely on biology and genetics. Second, generational wealth: even comparing patients who can all ‘pass’ as White, Black Americans denied generational wealth had limited opportunities, which the research design did not address (i.e., the problem is more than colorism alone, which also was not addressed).

Third, West African ancestry and the N264K gene: regional distribution of N264K variant, with quite low frequencies in African-American veterans, also likely correlates with physical traits, and because few subjects showed this combination of traits, we do not know whether all combinations truly exist in nature.

3.2.5 Equity and APOL1

Roberts noted,

The issue is not whether genes affect health—of course they do—but whether genetic difference explains racial disparities in health [107].

The APOL1 mechanism that explains ‘racial’ differences in KF is unknown [108]. Higher RVs of the APOL1 gene were identified in all haplotype backgrounds, but the magnitude of their effect was dependent on expression levels. Equity is a confounding factor in all these findings.

‘Race’ affects COVID outcomes through misdiagnosed lung disease [109,110,111], mismeasured blood oxygen levels [112,113], and inequitable care [114]. The different ‘racial’ systems in Brazil and the US converge in colorism. However, because links between ‘race’, skin color, and ancestry varied under different ‘racial’ systems, a broader range of physical appearances and SDOH might weaken APOL1 associations with ‘race’ in the Brazilian population (i.e., more noise in the data) [115]. Given the wide confidence intervals (recalculated from p value and z-statistic) in the Brazilian data [65] and a general caution with accepting the null hypothesis of no difference, we cannot say the ORs are significantly different when comparing the same risk factors between the two samples. However, the remarkably similar ORs between US veterans and Brazilians for a variety of non-genetic covariates support other aspects of personal circumstance as more important than the APOL1 gene.

Chen et al adjusted for age, sex, and baseline eGFR in a general population study and eliminated correlation between KF and APOL1 [116]. Grams et al found that ‘racial’ disparities (e.g., in income and education) explained AKI disparities better than APOL1 risk genotypes [117]. Grams et al also noted, “the majority of blacks with the high-risk genotype experience eGFR decline similar to blacks without the high-risk genotype” [118].

3.2.6 Focal segmental glomerulosclerosis

Our re-analyses did not conclude that APOL1 HR genotype has no effect on AKI and CKD. Hung et al showed that APOL1 HR genotype modestly increased risk of focal segmental glomerulosclerosis (FSGS) in veterans [68], which is consistent with other recent reports [119,120,121,122], including low penetrance [123], which may depend on cumulative effects of multiple cofactors:

…the development of kidney disease is rare, suggesting the need for a second hit [124].

Veteran status appears to partially mitigate the FSGS risk, leading to relatively few FSGS and KF events, small absolute percentage rates, and ORs within the block of modest ORs, Figs 8 and 10.

The risk of stereotyping dark skin as a sign of APOL1 HR genotype follows trans-Atlantic enslavement of West African people (i.e., descendants of formerly enslaved people—both still living in the US and recently immigrated from Caribbean countries). However, mass migration, globalization, and more recent US immigration fueled by military and economic conflicts in Central and East African countries continue to diversify African ancestral histories. Even if the likelihood were increased by visual examination, who and when to test for APOL1 HR genotype may hinge on understanding reduced disparity in veterans. Nonspecific Population Health measures may mitigate accumulated ‘second hit(s)’ from SDOH—even before any component cofactors are fully characterized.

3.3 Potential implications

Commercial interests drive innovation, but there is also profit potential in misuse of ‘race’ [125]. Research into low-cost preventive measures and SDOH usually requires government funding, so we highlight three such areas for further study: 1. illiteracy and deprivation cofactors (implied by outcome differences between veterans and the general population), 2. role of ‘race’ when searching for genetic causes without an hypothesis, and 3. international collaboration on ethics that could discourage misuse of ‘race’.

3.3.1 Proxy for illiteracy and deprivation

Illiteracy varies by district, Fig 12, and does not cause but is associated with CKD [126,127], implicating broad consequences of reading failure (e.g., difficulty obtaining jobs with US health benefits that are not universal). Up to 90% of high school graduates may be illiterate [128]. A 2001 summary of decades of reading research funded by the National Institutes of Health (NIH) concluded:

Failure to develop basic reading skills by age nine predicts a lifetime of illiteracy…. On the other hand, … provision of comprehensive early reading interventions can reduce the percentage of children reading below the basic level in the fourth grade… to six percent or less [129].

In contrast, literacy diminished the association between long-term illness and Black ‘race’ and removed the predictive power of “education” and being African American [130]. Adult literacy programs did not improve CKD [131], but early intervention can succeed [132,133,134,135]. Further research might prompt literacy testing in medical research [136,137] to prevent ‘race’ from being a proxy for illiteracy.

Deprivation is associated with CKD [138,139]. The origin of US housing segregation “…has now largely been forgotten” [140,141]. However, patients from disadvantaged neighborhoods, Fig 13 [142], suffer health effects of childhood poisonings [143,144,145] and prenatal exposures [146,147] that increase CKD risk [148,149,150]. Healthcare access and tracking of serial creatinine may allow early intervention for pre-chronic kidney disease (preCKD) [1]. Further research (e.g., in zip codes of the few White and Black neighborhoods with similar demographics) might show whether early exposures contribute to purported ‘racial’ disparities in GFR and to “senile nephrosclerosis” found in one-third of young but absent in 10% of elderly kidney donors [151].

3.3.2 Racialized genome-wide associations

Genome-wide association studies (GWASs) search outcomes data without an hypothesis and often without a known mechanism of action. However, structural racism based on pigment (a gene product) may associate socially constructed environmental insults with single-nucleotide polymorphisms (SNPs) in linkage disequilibrium with pigmentary and other racialized genes [152,153]. Colorism may enhance the illusion of ‘race’ as biology by varying polygenic scores with pigment, both directly (e.g., through prenatal and childhood health, education, employment, access) and indirectly (e.g., through circumstances of parents and grandparents), especially confounding GWASs [154]. Associations alone cannot tell whether historical violence against groups of people based on appearance induced heritable changes in gene function through interactions between social environment and gene expression.

Instead of combining Black veteran and Black general populations [155], GWASs comparing their results might reveal SNPs that appear under racialized socioeconomic and gene-environment stresses (e.g., illiteracy, deprivation).

3.3.3 International bioethics

Because ‘race’ is socially constructed, our research merely confirms well known facts: conclusions of biological ‘race’ will always be false, and re-analyses will always find social causes. Research providing no explanation for ‘racial’ differences or no justification for including ‘race’ beyond “predictive accuracy” is especially problematic. NASEM noted:

…studies that are poorly designed to answer research questions are scientifically invalid and unethical…. What is necessary is an understanding of the underlying issues during study design and long before data analyses: the moment of publication is far too late [11].

Although studies perpetuate the ‘race’ myth in ways that are “largely unintentional” [156], Hunt and Megyesi found, in 2008, that most genetics researchers 1. understood that ‘race’ is invalid, 2. used ‘race’ as a proxy for biological cofactors without bothering to explore further (simply assumed cofactors would be difficult to identify and measure), 3. could list serious potential negative consequences of misusing ‘race’, but 4. did so anyway [157]. In 2024, genetics journal editors encouraged the allowed form of social commentary: genetic scientists rationalizing misuse of socially constructed ‘race’ [158] to continue publishing BS under “…the tranquilizing drug of gradualism” [159]. Their predecessors did the same, 75 years ago:

For the social scientists gathered by UNESCO to write its 1950 ‘Statement against race and racial prejudice’, race had to be disproved on scientific grounds, a task which they saw as easily achievable. The Statement claimed that:

The division of the human species into ‘races’ is partly conventional and partly arbitrary and does not imply any hierarchy whatsoever. Many anthropologists stress the importance of human variation, but believe that ‘racial’ divisions have limited scientific interest and may even carry the risk of inviting abusive generalization. (UNESCO, 1968: 270)

Directly after the publication of the Statement, it was followed by an alternative Statement written mainly by geneticists also involved in the UNESCO project…. that race continued to be of scientific usefulness for describing ‘groups of mankind possessing well-developed and primarily heritable physical differences from other groups’ (Comas, 1961: 304) and should not be confused with politics [160].

The American Medical Association (AMA) recently described the determination of its founder

…to explicitly exclude women and Black physicians…. [His] role was highly active, not passive, and his choice for a racist direction was pursued with energy and force [161].

In 1880, as founding editor of The Journal of the American Medical Association (JAMA), this racist man established the traditions of medical publishing that keep medical research inside the grey oval, Fig 14, blocking study of the role of ‘race’ in research findings. Like another journal that rejected our claim of relevance (to discuss the role of illiteracy and sociology of deprivation in our research), JAMA is known for excluding discussion of the racism behind socially constructed ‘race’ and for shunting consideration of social cofactors to “Commentary” [57]. Because advocating change takes more words than “stay the course”, modern word, figure, and reference limits also favor the status quo. Persistent misuse of ‘race’ suggests the philosophy of human medical science needs reexamination [162,163,164,165,166].

American bioethics favors autonomy almost to the exclusion of its other three founding principles, but as Fins noted, we should “…move beyond narrow questions of patient choice, particularly when the disenfranchised are not in a position to exercise that choice” [167]. Fins added, “…that while rights are necessary to avoid exploitation, they are not sufficient and that instead there should be a focus on capabilities that promote human flourishing” (personal comm). In this case, it is about identifying nascent kidney disease with metrics that are accurate and accountable to scientific and ethical norms [1,11]. Further research might show whether an appeal to international bioethics could end misuse of ‘race’ in medical research.

CONCLUSION

Re-analysis of ‘race’ reveals cross-cultural conflicts in its social origins such that research associating socially constructed ‘race’ with any explanation other than structural racism will always be wrong.

Selection and training of servicemembers create comparable starting conditions for veterans. Those in Veterans Administration Healthcare showed comparable social determinants of health and comparable kidney outcomes, which should be the expected norm for all under more equal social conditions—regardless of socially constructed ‘race’, ethnicity, or nationality. Further research to identify essential veteran cofactors may improve general population health, reduce ‘racial’ disparities, and redirect some resources to prevention.

Apolipoprotein L1 high-risk variants increase focal segmental glomerulosclerosis, but the increased risk appears too small to justify treatment at the population level. Broad application of nonspecific Population Health measures may further mitigate risk.

Despite scholarly consensus and 100 years of academic articles that biological ‘race’ is scientifically invalid, misuse persists in medical research, suggesting an hypothesis for further study: we may need to 1. work together, 2. discourage standing in the doorway, and 3. appeal to principles of international bioethics to end misuse of ‘race’.

METHODS

5.1 Ethics statement

‘Race’ in research

NASEM advised, “Researchers should be as transparent as possible about the specific procedures used to name groups within their data sets” [11]. We did not present ‘race’ as an input to clinical decisions, a means of stratifying care, or an hypothesis for genetic difference without data. One primary source used ‘race’ designated in Medicare Denominator File or VA data sources [63]. We explored the approach to ‘race’ and ancestry in the other primary sources (see Discussion).

Human subjects

The primary sources used information collected for non-research activities in VA Healthcare or obtained informed consent from all participants after receiving IRB approval. Secondary re-analysis of publicly available data is not considered human subjects research and is exempt from IRB review [168].

Privacy

The study included no individually identifiable patient health information.

5.2 Re-analyses

Our main hypothesis is that because ‘race’ is socially constructed, social context and social cofactors will remove or explain ‘racial’ differences in CKD and KF. A corollary is that re-analyzing studies that endorse biological ‘race’ will always be fruitful. During recent questioning of eGFR ‘race corrections’, our preliminary re-analysis of a study of KF in Black and White veterans elicited responses referencing APOL1 (e.g., “the kidney failure gene”) as “validating” ‘racial’ difference in kidney disease. We broadened our literature search.

Relevant to our hypothesis, we searched PubMed (on February 24, 2024) for titles or abstracts that included “veteran” and “APOL1”. Of three studies directly addressing kidney disease, we added re-analysis of two (Sections 2.2 and 2.3) and noted the third in Potential Implication for GWASs (Section 3.3.2).

Our secondary re-analyses involved extracting primary data of veterans from previously published tables and figures (i.e., data of all subjects in two of the studies and 80% of subjects in the third), using knowledge of socially constructed ‘race’ and obvious social cofactors to reorganize the data into simple, accessible graphs and charts to better compare RRs and ORs of CKD rates and KF outcomes.

5.2.1 Re: Removing ‘race correction’

The large study of KF in veterans by Choi et al [63] presented enough data to reverse the eGFR ‘race correction’ (Section 2.1). We repurposed data from its first and second tables, removed ‘race correction’ by dividing MDRD eGFRs by 1.21, and examined eGFR prevalence and rates of KF against eGFR results with and without ‘race correction’. Mathematically, transforming a variable by a constant has a one-to-one impact on the magnitude of the regression coefficient [169], and the estimated association will change proportional to that number, which may be useful if an actual difference could be overlooked. However, if no true ‘racial’ difference exists, the association between eGFR prevalence rates and KF rates for White and Black patients should be similar, and ‘race correction’ would inflate the association’s magnitude, implying more KF than found.

We estimated the association among White patients, Black patients with unadjusted eGFR rates, and the same Black patients with eGFR ‘race correction’ removed, hypothesizing that the ratio of associations for ‘race-corrected’ data and ‘not-race-corrected’ data would equal 1.21, the exact magnitude of ‘race correction’. Additionally, we hypothesized that there would not be a significant difference in associations between White and Black patients after removing ‘race correction’.

5.2.2 Veteran and APOL1

Re: APOL1, AKI, and COVID

We re-analyzed a study by Hung et al [64], calculating RR and OR from primary data in their first and second tables. Although we dismissed this article in an early version of this manuscript (due to uneven distribution of vasopressors and mechanical ventilation between those with and without AKI), repeated encounters with references to it as “proof” that genetic susceptibility causes ‘racial’ disparity prompted our full re-analysis and PubMed search.

We adjusted the APOL1 RR to remove the confounding of high-impact cofactors. For comparison, we included a similar study in the Brazilian general population [65], where ‘race’ is defined differently. Because some CIs were unusually asymmetric, we recalculated them from the given p values and z-statistics.

Re: APOL1 and N264K

We similarly re-analyzed another study by Hung et al [68] that examined associations between APOL1 LR and HR genotypes, N264K variants, and the US taxonomy of socially constructed ‘race’. We focused on the data from US veterans (the bulk of their data) and created simple graphs to better show the relationships within the data. Because N264K+ is an APOL1 variant more common in Europe (occurring in less than 5% of US Black veterans), we further narrowed to the 95% of Black veterans without N264K, calculating and ordering results by ORs between the LR and HR (N264K–) subgroups.

Data Availability

All data re-analyzed in the present work are contained in the manuscript or are publicly available in the original source articles.

Competing Interests

The authors have declared that no competing interests exist.

Funding

The authors received no specific funding for this work.

ACKNOWLEDGMENTS

In memory of Rear Admiral W. Norman Johnson and many others who endured kidney failure after being prescribed nephrotoxic drugs that might have been avoided with early warning and caution, and of the Rev. Dr. Canon Cyril C. Burke, Sr., who taught ethics and whose final medical care was complicated by ‘race’.

The authors gratefully acknowledge Edward Feller, MD, FACP, FACG, Ruth Levy Guyer, PhD, James M. Gilchrist, Professor Emeritus of Neurology at Southern Illinois University School of Medicine, and anonymous colleagues for critical review of a draft of this article, and Joseph J. Fins, MD, MACP, FRCP, and John C. Kotelly, PhD, US Air Force mathematician (ret.), for their insights.

Footnotes

This is a final revision. Revised Visual Abstract. Minor revision Fig 9. Corrected reference formatting errors. Updated Authors. Clarifications throughout the text.

ABBREVIATIONS

AKI
acute kidney injury

APOL1
apolipoprotein L1

BS
bad science

CKD
chronic kidney disease

COVID
coronavirus disease

eGFR
estimated GFR

FSGS
focal segmental glomerulosclerosis

GFR
glomerular filtration rate

GWAS
genome-wide association study

HR
high risk

LR
low risk

IRB
institutional review board

KF
kidney failure

MDRD
Modification of Diet in Renal Disease

NASEM
National Academies of Sciences Engineering and Medicine

OR
odds ratio

RR
relative risk

RV
risk variant

SDOH
social determinants of health

VA
Veterans Administration

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