diagnostics
The Chemistry Clicked: Two NIH-Supported Researchers Win 2022 Nobel Prize in Chemistry
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Through the years, NIH has supported a total of 169 researchers who have received or shared 101 Nobel Prizes. That’s quite a testament to the world-leading science that NIH pursues and its continued impact on improving human health and well-being.
Those numbers include the news late last week that the 2022 Nobel Prize in Chemistry was shared by two long-time grantees for their work on a transformative scientific approach known as “click chemistry.” This form of chemistry has made it possible for researchers to snap together, like LEGO pieces, molecular building blocks to form hybrid biomolecules, often with easy-to-track imaging agents attached. Not only has click chemistry expanded our ability to explore the molecular underpinnings of a wide range of biological processes, but it has provided us with new tools for developing drugs, diagnostics, and a wide array of “smart” materials.
For K. Barry Sharpless, Scripps Research, La Jolla, CA, October 5, 2022 marked the second time that he’s received an early-morning congratulatory call from The Royal Swedish Academy of Sciences. The first such call came in 2001, when Sharpless got the news that he was a co-winner of the Nobel Prize in Chemistry for his discovery of asymmetric catalytic reactions.
This time around, Sharpless was recognized for his groundbreaking studies in the mid-1990s with click chemistry, a term that he coined himself. His initial work established click chemistry as a fast-and-reliable way to attach molecules of interest in the lab [1]. He and co-recipient Morten Meldal, University of Copenhagen, Denmark, who is not funded by NIH, then independently introduced a copper-catalyzed click that further refined the chemistry and helped popularize it across biology and the material sciences [2,3].
For Carolyn R. Bertozzi of Stanford University, Palo Alto, CA, it is her first Nobel. Bertozzi was recognized for expanding the use of click chemistry with so-called bioorthogonal chemistry, which is a copper-free version of the approach that can be used inside living cells without the risk of metal-associated toxicities [4,5].
Bertozzi’s work has been especially interesting to me because of her focus on glycans, which I’ve studied throughout my career. Glycans are the carbohydrate molecules that coat the surfaces of our cells and most secreted proteins. They are essential to life, and, in higher organisms, play fundamental roles in basic processes such as metabolism, immunity, and cellular communication.
Glycans also remain poorly understood, largely because, until recently, they have been so difficult for basic scientists to study with traditional techniques. That has changed with development of new tools to study glycans and the enzymes that assemble them. My long-time collaborator, Kelly Ten Hagen, a senior investigator at NIH’s National Institute of Dental and Craniofacial Research, and I collaborated with Carolyn on identifying small molecules that inhibit the enzyme responsible for the first step in mucin-type O-glycosylation [6]
In the early 2000s, Bertozzi and her team introduced bioorthogonal chemistry, which enabled researchers to label glycans and visualize them in a range of cells and living organisms. Her team’s pioneering approach quickly became an essential tool in basic science labs around the world that study glycans, leading to a number of stunning discoveries that would have otherwise been difficult or impossible.
For clinical researchers, click chemistry has emerged as a workhorse in drug discovery and the improved targeting of cancer chemotherapies and other small-molecule drugs. The approach also is being used to improve delivery of antibody-based therapies and to create new biomaterials. Meanwhile, in the material sciences, click chemistry has been used to solve a number of problems in working with polymers and to expand their industrial uses.
Click chemistry is an excellent example of how advances in basic science can build the foundation for a wide range of practical applications, including those aimed at improving human health. It also highlights the value of strong, sustained public funding for fundamental research, and NIH is proud to have supported Sharpless continuously since 1975 and Bertozzi since 1999. I send my sincere congratulations to both of these most-deserving scientists.
References:
[1] Click Chemistry: Diverse chemical function from a few good reactions. Kolb, HC, Finn, MG, Sharpless, KB. Angew. Chem. Int. Ed. 2001, 40 (11), 2004–2021
[2] A stepwise huisgen cycloaddition process: Copper(I)-catalyzed regioselective “Llgation” of azides and terminal alkynes. Rostovtsev VV, Green LG, Fokin VV, Sharpless KB. Angew. Chem. Int. Ed. 2002, 41 (14), 2596–2599.
[3] Peptidotriazoles on solid phase: [1,2,3]-Triazoles by regiospecific copper(I)-catalyzed 1,3-dipolar cycloadditions of terminal alkynes to azides. Tornøe CW, Sengeløv H, Meldal M. J. Org. Chem. 2002, 67 (9), 3057–3064.
[4] A strain-promoted [3 + 2] azide−alkyne cycloaddition for covalent modification of biomolecules in living systems. Agard NJ, Prescher JA, Bertozzi CR. J. Am. Chem. Soc. 2004, 126 (46), 15046–15047
[5] In vivo imaging of membrane associated glycans in developing zebrafish. Laughlin ST, Baskin JM, Amacher SL, Bertozzi CR. Science 2008, 320 (5876), 664–667.
[6] Small molecule inhibitors of mucin-type O-glycosylation from a uridine-based library. Hang, HC, Yu, C, Ten Hagen, KG, Tian, E, Winans, KA, Tabak, LA, Bertozzi, Chem Biol. 2004 Jul;11(7):1009-1016.
Links:
The Nobel Prize in Chemistry 2022 (The Royal Swedish Academy of Sciences, Stockholm)
Video: Announcement of the 2022 Nobel Prize in Chemistry (YouTube)
Click Chemistry and Bioorthogonal Chemistry (The Royal Swedish Academy of Sciences)
Sharpless Lab (Scripps Research, La Jolla, CA)
Bertozzi Group (Stanford University, Palo Alto, CA)
NIH Support:
K. Barry Sharpless: National Institute of General Medical Sciences
Carolyn R. Bertozzi: National Cancer Institute; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences
RADx Initiative: Bioengineering for COVID-19 at Unprecedented Speed and Scale
Posted on by Bruce J. Tromberg, Ph.D., National Institute of Biomedical Imaging and Bioengineering
As COVID-19 rapidly expanded throughout the world in April 2020, many in the biomedical technology community voiced significant concerns about the lack of available diagnostic tests. At that time, testing for SARS-CoV-2, the coronavirus that causes COVID-19, was conducted exclusively in clinical laboratories by order of a health-care provider. “Over the counter” (OTC) tests did not exist, and low complexity point of care (POC) platforms were rare. Fewer than 8 million tests were performed in the U.S. that month, and it was clear that we needed a radical transformation to make tests faster and more accessible.
By February 2022, driven by the Omicron variant surge, U.S. capacity had increased to a new record of more than 1.2 billion tests in a single month. Remarkably, the overwhelming majority of these—more than 85 percent—were “rapid tests” conducted in home and POC settings.
The story behind this practice-changing, “test-at-home” transformation is deeply rooted in technologic and manufacturing innovation. The NIH’s National Institute of Biomedical Imaging and Bioengineering (NIBIB), working collaboratively with multiple partners across NIH, government, academia, and the private sector, has been privileged to play a leading role in this effort via the Rapid Acceleration of Diagnostics (RADx®) initiative. On this two-year anniversary of RADx, we take a brief look back at its formation, impact, and potential for future growth.
On April 24, 2020, Congress recognized that testing was an urgent national need and appropriated $1.5 billion to NIH via an emergency supplement [1]. The goal was to substantially increase the number, type, and availability of diagnostic tests in only five to six months. Since the “normal” commercialization cycle for this type of diagnostic technology is typically more than five years, we needed an entirely new approach . . . fast.
The RADx initiative was launched just five days after that challenging Congressional directive [2]. Four NIH RADx programs were eventually created to support technology development and delivery, with the goal of matching test performance with community needs [3].The first two programs, RADx Tech and RADx Advanced Technology Platforms (ATP), were developed by NIBIB and focused on innovation for rapidly creating, scaling up, and deploying new technologies.
RADx Tech is built around NIBIB’s Point of Care Technologies Research Network (POCTRN) and includes core activities for technology review, test validation, clinical studies, regulatory authorization, and test deployment. Overall, the RADx Tech network includes approximately 900 participants from government, academia, and the private sector with unique capabilities and resources designed to decrease inherent risk and guide technologies from design and development to fully disseminated commercial products.
At the core of RADx Tech operations is the “innovation funnel” rapid review process, popularized as a shark tank [4]. A total of 824 complete applications were submitted during two open calls in a four-month period, beginning April 2020 and during a one-month period in June 2021. Forty-seven projects received phase 1 funding to validate and lower the inherent risk of developing these technologies. Meanwhile, 50 companies received phase 2 contracts to support FDA authorization studies and manufacturing expansion [5]
Beyond test development, RADx Tech has evolved to become a key contributor to the U.S. COVID-19 response. The RADx Independent Test Assessment Program (ITAP) was launched in October 2021 to accelerate regulatory authorization of new tests as a joint effort with the Food and Drug Administration (FDA) [6]. The ITAP acquires analytical and clinical performance data and works closely with FDA and manufacturers to shave weeks to months off the time it normally takes to receive Emergency Use Authorization (EUA).
The RADx Tech program also created a Variant Task Force to monitor the performance of tests against each new coronavirus “variant of concern” that emerges. This helps to ensure that marketed tests continue to remain effective. Other innovative RADx Tech projects include Say Yes! Covid Test, the first online free OTC test distribution program, and Project Rosa, which conducts real-time variant tracking across the country [7].
RADx Tech, by any measure, has exceeded even the most-optimistic expectations. In two years, RADx Tech-supported companies have received 44 EUAs and added approximately 2 billion tests and test products to the U.S. capacity. These remarkable numbers have steadily increased from more than16 million tests in September 2020, just five months after the program was established [8].
RADx Tech has also made significant contributions to the distribution of 1 billion free OTC tests via the government site, COVID.gov/tests. It has also provided critical guidance on serial testing and variants that have improved test performance and changed regulatory practice [9,10]. In addition, the RADx Mobile Application Reporting System (RADx MARS) reduces barriers to test reporting and test-to-treat strategies’ The latter offers immediate treatment options via telehealth or a POC location whenever a positive test result is reported. Finally, the When to Test website provides critical guidance on when and how to test for individuals, groups, and communities.
As we look to the future, RADx Tech has enormous potential to impact the U.S. response to other pathogens, diseases, and future pandemics. Major challenges going forward include improving home tests to work as well as lab platforms and building digital health networks for capturing and reporting test results to public health officials [11].
A recent editorial published in the journal Nature Biotechnology noted, “RADx has spawned a phalanx of diagnostic products to market in just 12 months. Its long-term impact on point of care, at-home, and population testing may be even more profound [12].” We are now poised to advance a new wave of precision medicine that’s led by innovative diagnostic technologies. It represents a unique opportunity to emerge stronger from the pandemic and achieve long-term impact.
References:
[1] Public Law 116 -139—Paycheck Protection Program and Health Care Enhancement Act.
[2] NIH mobilizes national innovation initiative for COVID-19 diagnostics, NIH news release, April 29, 2020.
[3] Rapid scaling up of Covid-19 diagnostic testing in the United States—The NIH RADx Initiative. Tromberg BJ, Schwetz TA, Pérez-Stable EJ, Hodes RJ, Woychik RP, Bright RA, Fleurence RL, Collins FS. N Engl J Med. 2020 Sep 10;383(11):1071-1077.
[4] We need more covid-19 tests. We propose a ‘shark tank’ to get us there. Alexander L. and Blunt R., Washington Post, April 20, 2020.
[5] RADx® Tech/ATP dashboard, National Institute of Biomedical Imaging and Bioengineering, NIH.
[6] New HHS actions add to Biden Administration efforts to increase access to easy-to-use over-the-counter COVID-19 tests. U.S. Department of Health and Human Services Press Office, October 25, 2021.
[7] A method for variant agnostic detection of SARS-CoV-2, rapid monitoring of circulating variants, detection of mutations of biological significance, and early detection of emergent variants such as Omicron. Lai E, et al. medRxiV preprint, January 9, 2022.
[9] Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection. Smith RL, et al. J Infect Dis. 2021 Sep 17;224(6):976-982.
[10] Comparison of rapid antigen tests’ performance between Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) variants of SARS-CoV-2: Secondary analysis from a serial home self-testing study. Soni A, et al. MedRxiv preprint, March 2, 2022.
[11] Reporting COVID-19 self-test results: The next frontier. Health Affairs, Juluru K., et al. Health Affairs, February 11, 2022.
[12] Radical solutions. Nat Biotechnol. 2021 Apr;39(4):391.
Links:
Get Free At-Home COVID Tests (COVID.gov)
When to Test (Consortia for Improving Medicine with Innovation & Technology, Boston)
RADx Programs (NIH)
RADx® Tech and ATP Programs (National Institute of Biomedical Imaging and Biomedical Engineering/NIH)
Independent Test Assessment Program (NIBIB)
Mobile Application Reporting through Standards (NIBIB)
Point-of-Care Technologies Research Network (POCTRN) (NIBIB)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the eighth in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.]
A More Precise Way to Knock Out Skin Rashes
Posted on by Lawrence Tabak, D.D.S., Ph.D.
The NIH is committed to building a new era in medicine in which the delivery of health care is tailored specifically to the individual person, not the hypothetical average patient as is now often the case. This new era of “precision medicine” will transform care for many life-threatening diseases, including cancer and chronic kidney disease. But what about non-life-threatening conditions, like the aggravating rash on your skin that just won’t go away?
Recently, researchers published a proof-of-principle paper in the journal Science Immunology demonstrating just how precision medicine for inflammatory skin rashes might work [1]. While more research is needed to build out and further refine the approach, the researchers show it’s now technologically possible to extract immune cells from a patient’s rash, read each cell’s exact inflammatory features, and relatively quickly match them online to the right anti-inflammatory treatment to stop the rash.
The work comes from a NIH-funded team led by Jeffrey Cheng and Raymond Cho, University of California, San Francisco. The researchers focused their attention on two inflammatory skin conditions: atopic dermatitis, the most common type of eczema, which flares up periodically to make skin red and itchy, and psoriasis vulgaris. Psoriasis causes skin cells to build up and form a scaly rash and dry, itchy patches. Together, atopic dermatitis and psoriasis vulgaris affect about 10 percent of U.S. adults.
While the rashes caused by the two conditions can sometimes look similar, they are driven by different sets of immune cells and underlying inflammatory responses. For that reason, distinct biologic therapies, based on antibodies and proteins made from living cells, are now available to target and modify the specific immune pathways underlying each condition.
While biologic therapies represent a major treatment advance for these and other inflammatory conditions, they can miss their targets. Indeed, up to half of patients don’t improve substantially on biologics. Part of the reason for that lack of improvement is because doctors don’t have the tools they need to make firm diagnoses based on what precisely is going on in the skin at the molecular and cellular levels.
To learn more in the new study, the researchers isolated immune cells, focusing primarily on T cells, from the skin of 31 volunteers. They then sequenced the RNA of each cell to provide a telltale portrait of its genomic features. This “single-cell analysis” allowed them to capture high-resolution portraits of 41 different immune cell types found in individual skin samples. That’s important because it offers a much more detailed understanding of changes in the behavior of various immune cells that might have been missed in studies focused on larger groupings of skin cells, representing mixtures of various cell types.
Of the 31 volunteers, seven had atopic dermatitis and eight had psoriasis vulgaris. Three others were diagnosed with other skin conditions, while six had an indeterminate rash with features of both atopic dermatitis and psoriasis vulgaris. Seven others were healthy controls.
The team produced molecular signatures of the immune cells. The researchers then compared the signatures from the hard-to-diagnose rashes to those of confirmed cases of atopic dermatitis and psoriasis. They wanted to see if the signatures could help to reach clearer diagnoses.
The signatures revealed common immunological features as well as underlying differences. Importantly, the researchers found that the signatures allowed them to move forward and classify the indeterminate rashes. The rashes also responded to biologic therapies corresponding to the individuals’ new diagnoses.
Already, the work has identified molecules that help to define major classes of human inflammatory skin diseases. The team has also developed computer tools to help classify rashes in many other cases where the diagnosis is otherwise uncertain.
In fact, the researchers have launched a pioneering website called RashX. It is enabling practicing dermatologists and researchers around the world to submit their single-cell RNA data from their difficult cases. Such analyses are now being done at a small, but growing, number of academic medical centers.
While precision medicine for skin rashes has a long way to go yet before reaching most clinics, the UCSF team is working diligently to ensure its arrival as soon as scientifically possible. Indeed, their new data represent the beginnings of an openly available inflammatory skin disease resource. They ultimately hope to generate a standardized framework to link molecular features to disease prognosis and drug response based on data collected from clinical centers worldwide. It’s a major effort, but one that promises to improve the diagnosis and treatment of many more unusual and long-lasting rashes, both now and into the future.
Reference:
[1] Classification of human chronic inflammatory skin disease based on single-cell immune profiling. Liu Y, Wang H, Taylor M, Cook C, Martínez-Berdeja A, North JP, Harirchian P, Hailer AA, Zhao Z, Ghadially R, Ricardo-Gonzalez RR, Grekin RC, Mauro TM, Kim E, Choi J, Purdom E, Cho RJ, Cheng JB. Sci Immunol. 2022 Apr 15;7(70):eabl9165. {Epub ahead of publication]
Links:
The Promise of Precision Medicine (NIH)
Atopic Dermatitis (National Institute of Arthritis and Musculoskeletal and Skin Diseases /NIH)
Psoriasis (NIAMS/NIH)
RashX (University of California, San Francisco)
Raymond Cho (UCSF)
Jeffrey Cheng (UCSF)
NIH Support: National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Center for Advancing Translational Sciences
Artificial Intelligence Getting Smarter! Innovations from the Vision Field
Posted on by Michael F. Chiang, M.D., National Eye Institute
One of many health risks premature infants face is retinopathy of prematurity (ROP), a leading cause of childhood blindness worldwide. ROP causes abnormal blood vessel growth in the light-sensing eye tissue called the retina. Left untreated, ROP can lead to lead to scarring, retinal detachment, and blindness. It’s the disease that caused singer and songwriter Stevie Wonder to lose his vision.
Now, effective treatments are available—if the disease is diagnosed early and accurately. Advancements in neonatal care have led to the survival of extremely premature infants, who are at highest risk for severe ROP. Despite major advancements in diagnosis and treatment, tragically, about 600 infants in the U.S. still go blind each year from ROP. This disease is difficult to diagnose and manage, even for the most experienced ophthalmologists. And the challenges are much worse in remote corners of the world that have limited access to ophthalmic and neonatal care.
Artificial intelligence (AI) is helping bridge these gaps. Prior to my tenure as National Eye Institute (NEI) director, I helped develop a system called i-ROP Deep Learning (i-ROP DL), which automates the identification of ROP. In essence, we trained a computer to identify subtle abnormalities in retinal blood vessels from thousands of images of premature infant retinas. Strikingly, the i-ROP DL artificial intelligence system outperformed even international ROP experts [1]. This has enormous potential to improve the quality and delivery of eye care to premature infants worldwide.
Of course, the promise of medical artificial intelligence extends far beyond ROP. In 2018, the FDA approved the first autonomous AI-based diagnostic tool in any field of medicine [2]. Called IDx-DR, the system streamlines screening for diabetic retinopathy (DR), and its results require no interpretation by a doctor. DR occurs when blood vessels in the retina grow irregularly, bleed, and potentially cause blindness. About 34 million people in the U.S. have diabetes, and each is at risk for DR.
As with ROP, early diagnosis and intervention is crucial to preventing vision loss to DR. The American Diabetes Association recommends people with diabetes see an eye care provider annually to have their retinas examined for signs of DR. Yet fewer than 50 percent of Americans with diabetes receive these annual eye exams.
The IDx-DR system was conceived by Michael Abramoff, an ophthalmologist and AI expert at the University of Iowa, Iowa City. With NEI funding, Abramoff used deep learning to design a system for use in a primary-care medical setting. A technician with minimal ophthalmology training can use the IDx-DR system to scan a patient’s retinas and get results indicating whether a patient should be sent to an eye specialist for follow-up evaluation or to return for another scan in 12 months.
Many other methodological innovations in AI have occurred in ophthalmology. That’s because imaging is so crucial to disease diagnosis and clinical outcome data are so readily available. As a result, AI-based diagnostic systems are in development for many other eye diseases, including cataract, age-related macular degeneration (AMD), and glaucoma.
Rapid advances in AI are occurring in other medical fields, such as radiology, cardiology, and dermatology. But disease diagnosis is just one of many applications for AI. Neurobiologists are using AI to answer questions about retinal and brain circuitry, disease modeling, microsurgical devices, and drug discovery.
If it sounds too good to be true, it may be. There’s a lot of work that remains to be done. Significant challenges to AI utilization in science and medicine persist. For example, researchers from the University of Washington, Seattle, last year tested seven AI-based screening algorithms that were designed to detect DR. They found under real-world conditions that only one outperformed human screeners [3]. A key problem is these AI algorithms need to be trained with more diverse images and data, including a wider range of races, ethnicities, and populations—as well as different types of cameras.
How do we address these gaps in knowledge? We’ll need larger datasets, a collaborative culture of sharing data and software libraries, broader validation studies, and algorithms to address health inequities and to avoid bias. The NIH Common Fund’s Bridge to Artificial Intelligence (Bridge2AI) project and NIH’s Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Program project will be major steps toward addressing those gaps.
So, yes—AI is getting smarter. But harnessing its full power will rely on scientists and clinicians getting smarter, too.
References:
[1] Automated diagnosis of plus disease in retinopathy of prematurity using deep convolutional neural networks. Brown JM, Campbell JP, Beers A, Chang K, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Chiang MF; Imaging and Informatics in Retinopathy of Prematurity (i-ROP) Research Consortium. JAMA Ophthalmol. 2018 Jul 1;136(7):803-810.
[2] FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Food and Drug Administration. April 11, 2018.
[3] Multicenter, head-to-head, real-world validation study of seven automated artificial intelligence diabetic retinopathy screening systems. Lee AY, Yanagihara RT, Lee CS, Blazes M, Jung HC, Chee YE, Gencarella MD, Gee H, Maa AY, Cockerham GC, Lynch M, Boyko EJ. Diabetes Care. 2021 May;44(5):1168-1175.
Links:
Retinopathy of Prematurity (National Eye Institute/NIH)
Diabetic Eye Disease (NEI)
Michael Abramoff (University of Iowa, Iowa City)
Bridge to Artificial Intelligence (Common Fund/NIH)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s institutes and centers to contribute occasional guest posts to the blog as a way to highlight some of the cool science that they support and conduct. This is the second in the series of NIH institute and center guest posts that will run until a new permanent NIH director is in place.]
A Race-Free Approach to Diagnosing Chronic Kidney Disease
Posted on by Dr. Francis Collins
Race has a long and tortured history in America. Though great strides have been made through the work of leaders like Dr. Martin Luther King, Jr. to build an equal and just society for all, we still have more work to do, as race continues to factor into American life where it shouldn’t. A medical case in point is a common diagnostic tool for chronic kidney disease (CKD), a condition that affects one in seven American adults and causes a gradual weakening of the kidneys that, for some, will lead to renal failure.
The diagnostic tool is a medical algorithm called estimated glomerular filtration rate (eGFR). It involves getting a blood test that measures how well the kidneys filter out a common waste product from the blood and adding in other personal factors to score how well a person’s kidneys are working. Among those factors is whether a person is Black. However, race is a complicated construct that incorporates components that go well beyond biological and genetic factors to social and cultural issues. The concern is that by lumping together Black people, the algorithm lacks diagnostic precision for individuals and could contribute to racial disparities in healthcare delivery—or even runs the risk of reifying race in a way that suggests more biological significance than it deserves.
That’s why I was pleased recently to see the results of two NIH-supported studies published in The New England Journal of Medicine that suggest a way to take race out of the kidney disease equation [1, 2]. The approach involves a new equation that swaps out one blood test for another and doesn’t ask about race.
For a variety of reasons, including socioeconomic issues and access to healthcare, CKD disproportionately affects the Black community. In fact, Blacks with the condition are also almost four times more likely than whites to develop kidney failure. That’s why Blacks with CKD must visit their doctors regularly to monitor their kidney function, and often that visit involves eGFR.
The blood test used in eGFR measures creatinine, a waste product produced from muscle. For about the past 20 years, a few points have been automatically added to the score of African Americans, based on data showing that adults who identify as Black, on average, have a higher baseline level of circulating creatinine. But adjusting the score upward toward normal function runs the risk of making the kidneys seem a bit healthier than they really are and delaying life-preserving dialysis or getting on a transplant list.
A team led by Chi-yuan Hsu, University of California, San Francisco, took a closer look at the current eGFR calculations. The researchers used long-term data from the Chronic Renal Insufficiency Cohort (CRIC) Study, an NIH-supported prospective, observational study of nearly 4,000 racially and ethnically diverse patients with CKD in the U.S. The study design specified that about 40 percent of its participants should identify as Black.
To look for race-free ways to measure kidney function, the researchers randomly selected more than 1,400 of the study’s participants to undergo a procedure that allows kidney function to be measured directly instead of being estimated based on blood tests. The goal was to develop an accurate approach to estimating GFR, the rate of fluid flow through the kidneys, from blood test results that didn’t rely on race.
Their studies showed that simply omitting race from the equation would underestimate GFR in Black study participants. The best solution, they found, was to calculate eGFR based on cystatin C, a small protein that the kidneys filter from the blood, in place of the standard creatinine. Estimation of GFR using cystatin C generated similarly accurate results but without the need to factor in race.
The second NIH-supported study led by Lesley Inker, Tufts Medical Center, Boston, MA, came to similar conclusions. They set out to develop new equations without race using data from several prior studies. They then compared the accuracy of their new eGFR equations to measured GFR in a validation set of 12 other studies, including about 4,000 participants.
Their findings show that currently used equations that include race, sex, and age overestimated measured GFR in Black Americans. However, taking race out of the equation without other adjustments underestimated measured GFR in Black people. Equations including both creatinine and cystatin C, but omitting race, were more accurate. The new equations also led to smaller estimated differences between Black and non-Black study participants.
The hope is that these findings will build momentum toward widespread adoption of cystatin C for estimating GFR. Already, a national task force has recommended immediate implementation of a new diagnostic equation that eliminates race and called for national efforts to increase the routine and timely measurement of cystatin C [3]. This will require a sea change in the standard measurements of blood chemistries in clinical and hospital labs—where creatinine is routinely measured, but cystatin C is not. As these findings are implemented into routine clinical care, let’s hope they’ll reduce health disparities by leading to more accurate and timely diagnosis, supporting the goals of precision health and encouraging treatment of CKD for all people, regardless of their race.
References:
[1] Race, genetic ancestry, and estimating kidney function in CKD. Hsu CY, Yang W, Parikh RV, Anderson AH, Chen TK, Cohen DL, He J, Mohanty MJ, Lash JP, Mills KT, Muiru AN, Parsa A, Saunders MR, Shafi T, Townsend RR, Waikar SS, Wang J, Wolf M, Tan TC, Feldman HI, Go AS; CRIC Study Investigators. N Engl J Med. 2021 Sep 23.
[2] New creatinine- and cystatin C-based equations to estimate GFR without race. Inker LA, Eneanya ND, Coresh J, Tighiouart H, Wang D, Sang Y, Crews DC, Doria A, Estrella MM, Froissart M, Grams ME, Greene T, Grubb A, Gudnason V, Gutiérrez OM, Kalil R, Karger AB, Mauer M, Navis G, Nelson RG, Poggio ED, Rodby R, Rossing P, Rule AD, Selvin E, Seegmiller JC, Shlipak MG, Torres VE, Yang W, Ballew SH,Couture SJ, Powe NR, Levey AS; Chronic Kidney Disease Epidemiology Collaboration. N Engl J Med. 2021 Sep 23.
[3] A unifying approach for GFR estimation: recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. Delgado C, Baweja M, Crews DC, Eneanya ND, Gadegbeku CA, Inker LA, Mendu ML, Miller WG, Moxey-Mims MM, Roberts GV, St Peter WL, Warfield C, Powe NR. Am J Kidney Dis. 2021 Sep 22:S0272-6386(21)00828-3.
Links:
Chronic Kidney Disease (National Institute of Diabetes and Digestive and Kidney Diseases/NIH)
Explaining Your Kidney Test Results: A Tool for Clinical Use (NIDDK)
Chronic Renal Insufficiency Cohort Study
Chi-yuan Hsu (University of California, San Francisco)
Lesley Inker (Tufts Medical Center, Boston)
NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases
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