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A new diagnostic tool can effectively discriminate different rheumatologic conditions and could potentially aid in the diagnosis of early inflammatory arthritis.
The algorithm — called Genetic Probability tool (G-PROB) — uses genetic information to calculate the probability of certain diseases.
“At such an early stage of disease, it’s not always easy to determine what the final outcome will be with respect to final diagnosis,” said John Bowes, PhD, a senior lecturer in the division of musculoskeletal & dermatological sciences at the University of Manchester in the United Kingdom. He was a senior author of the newest study of G-PROB. “What we are hoping for here is that genetics can help [clinicians] with the decision-making process and hopefully accelerate the correct diagnosis and get individuals onto the correct treatment as early as possible.”
Creating the Algorithm
G-PROB was first developed by an international group of scientists with the goal of using genetic risk scores to predict the probabilities of common diagnoses for patients with early signs of arthritis, such as synovitis and joint swelling. According to the study authors, about 80% of these types of patients are eventually diagnosed with the following conditions: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and gout.
The algorithm combines existing knowledge about single-nucleotide polymorphisms from prior genomic studies to create genetic risk scores — also called polygenic risk score (PRS) — for multiple diseases. Using these scores, the program then calculates the probabilities of certain diagnoses for a patient, based on the assumption that at least one disease was present.
In this first study, researchers trained the tool on simulated data and then tested it in three patient cohorts totaling about 1700 individuals from the Electronic Medical Records and Genomics database and Mass General Brigham Biobank. In the initial study, G-PROB identified a likely diagnosis in 45% of patients, with a positive predictive value (PPV) of 64%. Adding these genetic scores to clinical data improved diagnostic accuracy from 39% to 51%.
Validating G-PROB
But data from these biobanks may not necessarily be representative of early arthritis in patients appearing in outpatient clinics, noted Dr. Bowes. In this new study, researchers sought to independently validate the original study’s findings using data from the Norfolk Arthritis Register, a community-based, long-term observational study on inflammatory polyarthritis. The team applied G-PROB in this cohort and then compared the tool’s probabilities for common rheumatic conditions to the final clinician diagnosis.
The study ultimately included 1047 individuals with early inflammatory arthritis with genotype data. In the cohort, more than 70% (756 individuals) were diagnosed with RA. Of the remaining patients, 104 had PsA, 18 had SLE, 16 had AS, and 12 had gout. The research team also added an “other diseases” category to the algorithm. A total of 141 patients fell into this category and were diagnosed with diseases including chronic pain syndrome (52 individuals), polymyalgia rheumatica (29 individuals), and Sjögren’s syndrome (9 individuals).
G-PROB was best at excluding diagnoses: Probabilities under 5% for a single disease corresponded to a negative predictive value (NPV) of 96%. If probabilities for two diseases were both < 5%, the NPV was 94%.
For patients with a single probability above 50%, the tool had a PPV of 70.3%. In 55.7% of all patients, the disease with the highest probability ended up being the final diagnosis.
Generally, PRSs, as well as tests using biomarkers, were better at excluding diagnoses than affirming them, noted Matthew Brown, MBBS, MD, a professor of medicine at King’s College London, who was not involved with the research. If disease prevalence is low, then a test aimed at diagnosis of that disease would be better at excluding a diagnosis than affirming it, he explained.
However, he noted that G-PROB’s PPV may have performed better if researchers had started by using established PRS scores to form the algorithm, rather than developing these genetic scores independently using internal datasets.
Can G-PROB Improve Diagnosis?
The new study’s key contribution was that it independently validated findings from a previous study, noted Katherine Liao, MD, a rheumatologist at Brigham and Women’s Hospital in Boston, Massachusetts. She coauthored an accompanying editorial to the newest study and coauthored the original G-PROB paper.
This new study also brought up an important question about G-PROB that has yet to be tested: Will this tool help clinicians make more efficient and accurate diagnoses in practice?
A prospective trial would be necessary to begin answering this question, both Dr. Bowes and Dr. Liao agreed. For example, one clinician group would have access to G-PROB data, while another would not, and “see if that helps [the first group] make the diagnosis faster or more accurately,” Dr. Liao said.
Dr. Bowes was also interested in exploring if combining G-PROB with other clinical data would improve diagnostic performance.
“Genetics isn’t the full story,” he said. Dr. Bowes saw genetics as one additional, complementary tool in a clinician’s toolbox.
Future studies were needed to understand the clinical utility of genetic information in conjunction with current diagnostic practices, such as imaging, physical exams, and lab results, Dr. Liao and her editorial coauthors argued.
“For example, in cardiovascular disease, the clinical utility of polygenic risk scores has been defined by their ability to improve risk stratification beyond what is already achieved with more common risk factors and measures such as cholesterol levels, smoking status, and coronary calcium scores,” Dr. Liao and her coauthors wrote. “Similarly, a polygenic risk score for breast cancer would not be clinically implemented alone for risk prediction but rather as one risk factor among others, such as hormonal and reproductive factors and prior mammographic data.”
Future of Genetics in Rheumatology
An additional hurdle for using tools like G-PROB was that a patient must have undergone DNA sequencing, and these data must be available to clinicians. Even a decade ago, this type of testing may have seemed unrealistic to incorporate in daily practice, Dr. Liao noted, but technological advancements continue to make genetic sequencing more accessible to the public.
There are already efforts in the United Kingdom to incorporate genetics into healthcare, including trials for PRSs and heart disease, noted Dr. Bowes, as well as large-scale studies such as Our Future Health.
“As these population-based studies expand more, a high proportion of individuals should hopefully have access to this kind of data,” he said.
Brown added that genetic testing is already used to make rheumatology diagnoses.
“[HLA] B-27 testing, for example, is an extremely commonly used test to assist in the diagnosis of ankylosing spondylitis. Is it that different to change to a PRS as opposed to a straight HLA testing? I don’t think it is,” he said.
While there would need to be systematic training for clinicians to understand how to calculate and use PRSs in daily practice, Dr. Brown did not think this adjustment would be too difficult.
“There is a lot of exceptionalism about genetics, which is actually inappropriate,” he said. “This is actually just a quantitative score that should be easy for people to interpret.”
Dr. Bowes and Dr. Brown reported no relevant financial relationships. Dr. Liao worked as a consultant for UCB.
A version of this article appeared on Medscape.com.
A new diagnostic tool can effectively discriminate different rheumatologic conditions and could potentially aid in the diagnosis of early inflammatory arthritis.
The algorithm — called Genetic Probability tool (G-PROB) — uses genetic information to calculate the probability of certain diseases.
“At such an early stage of disease, it’s not always easy to determine what the final outcome will be with respect to final diagnosis,” said John Bowes, PhD, a senior lecturer in the division of musculoskeletal & dermatological sciences at the University of Manchester in the United Kingdom. He was a senior author of the newest study of G-PROB. “What we are hoping for here is that genetics can help [clinicians] with the decision-making process and hopefully accelerate the correct diagnosis and get individuals onto the correct treatment as early as possible.”
Creating the Algorithm
G-PROB was first developed by an international group of scientists with the goal of using genetic risk scores to predict the probabilities of common diagnoses for patients with early signs of arthritis, such as synovitis and joint swelling. According to the study authors, about 80% of these types of patients are eventually diagnosed with the following conditions: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and gout.
The algorithm combines existing knowledge about single-nucleotide polymorphisms from prior genomic studies to create genetic risk scores — also called polygenic risk score (PRS) — for multiple diseases. Using these scores, the program then calculates the probabilities of certain diagnoses for a patient, based on the assumption that at least one disease was present.
In this first study, researchers trained the tool on simulated data and then tested it in three patient cohorts totaling about 1700 individuals from the Electronic Medical Records and Genomics database and Mass General Brigham Biobank. In the initial study, G-PROB identified a likely diagnosis in 45% of patients, with a positive predictive value (PPV) of 64%. Adding these genetic scores to clinical data improved diagnostic accuracy from 39% to 51%.
Validating G-PROB
But data from these biobanks may not necessarily be representative of early arthritis in patients appearing in outpatient clinics, noted Dr. Bowes. In this new study, researchers sought to independently validate the original study’s findings using data from the Norfolk Arthritis Register, a community-based, long-term observational study on inflammatory polyarthritis. The team applied G-PROB in this cohort and then compared the tool’s probabilities for common rheumatic conditions to the final clinician diagnosis.
The study ultimately included 1047 individuals with early inflammatory arthritis with genotype data. In the cohort, more than 70% (756 individuals) were diagnosed with RA. Of the remaining patients, 104 had PsA, 18 had SLE, 16 had AS, and 12 had gout. The research team also added an “other diseases” category to the algorithm. A total of 141 patients fell into this category and were diagnosed with diseases including chronic pain syndrome (52 individuals), polymyalgia rheumatica (29 individuals), and Sjögren’s syndrome (9 individuals).
G-PROB was best at excluding diagnoses: Probabilities under 5% for a single disease corresponded to a negative predictive value (NPV) of 96%. If probabilities for two diseases were both < 5%, the NPV was 94%.
For patients with a single probability above 50%, the tool had a PPV of 70.3%. In 55.7% of all patients, the disease with the highest probability ended up being the final diagnosis.
Generally, PRSs, as well as tests using biomarkers, were better at excluding diagnoses than affirming them, noted Matthew Brown, MBBS, MD, a professor of medicine at King’s College London, who was not involved with the research. If disease prevalence is low, then a test aimed at diagnosis of that disease would be better at excluding a diagnosis than affirming it, he explained.
However, he noted that G-PROB’s PPV may have performed better if researchers had started by using established PRS scores to form the algorithm, rather than developing these genetic scores independently using internal datasets.
Can G-PROB Improve Diagnosis?
The new study’s key contribution was that it independently validated findings from a previous study, noted Katherine Liao, MD, a rheumatologist at Brigham and Women’s Hospital in Boston, Massachusetts. She coauthored an accompanying editorial to the newest study and coauthored the original G-PROB paper.
This new study also brought up an important question about G-PROB that has yet to be tested: Will this tool help clinicians make more efficient and accurate diagnoses in practice?
A prospective trial would be necessary to begin answering this question, both Dr. Bowes and Dr. Liao agreed. For example, one clinician group would have access to G-PROB data, while another would not, and “see if that helps [the first group] make the diagnosis faster or more accurately,” Dr. Liao said.
Dr. Bowes was also interested in exploring if combining G-PROB with other clinical data would improve diagnostic performance.
“Genetics isn’t the full story,” he said. Dr. Bowes saw genetics as one additional, complementary tool in a clinician’s toolbox.
Future studies were needed to understand the clinical utility of genetic information in conjunction with current diagnostic practices, such as imaging, physical exams, and lab results, Dr. Liao and her editorial coauthors argued.
“For example, in cardiovascular disease, the clinical utility of polygenic risk scores has been defined by their ability to improve risk stratification beyond what is already achieved with more common risk factors and measures such as cholesterol levels, smoking status, and coronary calcium scores,” Dr. Liao and her coauthors wrote. “Similarly, a polygenic risk score for breast cancer would not be clinically implemented alone for risk prediction but rather as one risk factor among others, such as hormonal and reproductive factors and prior mammographic data.”
Future of Genetics in Rheumatology
An additional hurdle for using tools like G-PROB was that a patient must have undergone DNA sequencing, and these data must be available to clinicians. Even a decade ago, this type of testing may have seemed unrealistic to incorporate in daily practice, Dr. Liao noted, but technological advancements continue to make genetic sequencing more accessible to the public.
There are already efforts in the United Kingdom to incorporate genetics into healthcare, including trials for PRSs and heart disease, noted Dr. Bowes, as well as large-scale studies such as Our Future Health.
“As these population-based studies expand more, a high proportion of individuals should hopefully have access to this kind of data,” he said.
Brown added that genetic testing is already used to make rheumatology diagnoses.
“[HLA] B-27 testing, for example, is an extremely commonly used test to assist in the diagnosis of ankylosing spondylitis. Is it that different to change to a PRS as opposed to a straight HLA testing? I don’t think it is,” he said.
While there would need to be systematic training for clinicians to understand how to calculate and use PRSs in daily practice, Dr. Brown did not think this adjustment would be too difficult.
“There is a lot of exceptionalism about genetics, which is actually inappropriate,” he said. “This is actually just a quantitative score that should be easy for people to interpret.”
Dr. Bowes and Dr. Brown reported no relevant financial relationships. Dr. Liao worked as a consultant for UCB.
A version of this article appeared on Medscape.com.
A new diagnostic tool can effectively discriminate different rheumatologic conditions and could potentially aid in the diagnosis of early inflammatory arthritis.
The algorithm — called Genetic Probability tool (G-PROB) — uses genetic information to calculate the probability of certain diseases.
“At such an early stage of disease, it’s not always easy to determine what the final outcome will be with respect to final diagnosis,” said John Bowes, PhD, a senior lecturer in the division of musculoskeletal & dermatological sciences at the University of Manchester in the United Kingdom. He was a senior author of the newest study of G-PROB. “What we are hoping for here is that genetics can help [clinicians] with the decision-making process and hopefully accelerate the correct diagnosis and get individuals onto the correct treatment as early as possible.”
Creating the Algorithm
G-PROB was first developed by an international group of scientists with the goal of using genetic risk scores to predict the probabilities of common diagnoses for patients with early signs of arthritis, such as synovitis and joint swelling. According to the study authors, about 80% of these types of patients are eventually diagnosed with the following conditions: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and gout.
The algorithm combines existing knowledge about single-nucleotide polymorphisms from prior genomic studies to create genetic risk scores — also called polygenic risk score (PRS) — for multiple diseases. Using these scores, the program then calculates the probabilities of certain diagnoses for a patient, based on the assumption that at least one disease was present.
In this first study, researchers trained the tool on simulated data and then tested it in three patient cohorts totaling about 1700 individuals from the Electronic Medical Records and Genomics database and Mass General Brigham Biobank. In the initial study, G-PROB identified a likely diagnosis in 45% of patients, with a positive predictive value (PPV) of 64%. Adding these genetic scores to clinical data improved diagnostic accuracy from 39% to 51%.
Validating G-PROB
But data from these biobanks may not necessarily be representative of early arthritis in patients appearing in outpatient clinics, noted Dr. Bowes. In this new study, researchers sought to independently validate the original study’s findings using data from the Norfolk Arthritis Register, a community-based, long-term observational study on inflammatory polyarthritis. The team applied G-PROB in this cohort and then compared the tool’s probabilities for common rheumatic conditions to the final clinician diagnosis.
The study ultimately included 1047 individuals with early inflammatory arthritis with genotype data. In the cohort, more than 70% (756 individuals) were diagnosed with RA. Of the remaining patients, 104 had PsA, 18 had SLE, 16 had AS, and 12 had gout. The research team also added an “other diseases” category to the algorithm. A total of 141 patients fell into this category and were diagnosed with diseases including chronic pain syndrome (52 individuals), polymyalgia rheumatica (29 individuals), and Sjögren’s syndrome (9 individuals).
G-PROB was best at excluding diagnoses: Probabilities under 5% for a single disease corresponded to a negative predictive value (NPV) of 96%. If probabilities for two diseases were both < 5%, the NPV was 94%.
For patients with a single probability above 50%, the tool had a PPV of 70.3%. In 55.7% of all patients, the disease with the highest probability ended up being the final diagnosis.
Generally, PRSs, as well as tests using biomarkers, were better at excluding diagnoses than affirming them, noted Matthew Brown, MBBS, MD, a professor of medicine at King’s College London, who was not involved with the research. If disease prevalence is low, then a test aimed at diagnosis of that disease would be better at excluding a diagnosis than affirming it, he explained.
However, he noted that G-PROB’s PPV may have performed better if researchers had started by using established PRS scores to form the algorithm, rather than developing these genetic scores independently using internal datasets.
Can G-PROB Improve Diagnosis?
The new study’s key contribution was that it independently validated findings from a previous study, noted Katherine Liao, MD, a rheumatologist at Brigham and Women’s Hospital in Boston, Massachusetts. She coauthored an accompanying editorial to the newest study and coauthored the original G-PROB paper.
This new study also brought up an important question about G-PROB that has yet to be tested: Will this tool help clinicians make more efficient and accurate diagnoses in practice?
A prospective trial would be necessary to begin answering this question, both Dr. Bowes and Dr. Liao agreed. For example, one clinician group would have access to G-PROB data, while another would not, and “see if that helps [the first group] make the diagnosis faster or more accurately,” Dr. Liao said.
Dr. Bowes was also interested in exploring if combining G-PROB with other clinical data would improve diagnostic performance.
“Genetics isn’t the full story,” he said. Dr. Bowes saw genetics as one additional, complementary tool in a clinician’s toolbox.
Future studies were needed to understand the clinical utility of genetic information in conjunction with current diagnostic practices, such as imaging, physical exams, and lab results, Dr. Liao and her editorial coauthors argued.
“For example, in cardiovascular disease, the clinical utility of polygenic risk scores has been defined by their ability to improve risk stratification beyond what is already achieved with more common risk factors and measures such as cholesterol levels, smoking status, and coronary calcium scores,” Dr. Liao and her coauthors wrote. “Similarly, a polygenic risk score for breast cancer would not be clinically implemented alone for risk prediction but rather as one risk factor among others, such as hormonal and reproductive factors and prior mammographic data.”
Future of Genetics in Rheumatology
An additional hurdle for using tools like G-PROB was that a patient must have undergone DNA sequencing, and these data must be available to clinicians. Even a decade ago, this type of testing may have seemed unrealistic to incorporate in daily practice, Dr. Liao noted, but technological advancements continue to make genetic sequencing more accessible to the public.
There are already efforts in the United Kingdom to incorporate genetics into healthcare, including trials for PRSs and heart disease, noted Dr. Bowes, as well as large-scale studies such as Our Future Health.
“As these population-based studies expand more, a high proportion of individuals should hopefully have access to this kind of data,” he said.
Brown added that genetic testing is already used to make rheumatology diagnoses.
“[HLA] B-27 testing, for example, is an extremely commonly used test to assist in the diagnosis of ankylosing spondylitis. Is it that different to change to a PRS as opposed to a straight HLA testing? I don’t think it is,” he said.
While there would need to be systematic training for clinicians to understand how to calculate and use PRSs in daily practice, Dr. Brown did not think this adjustment would be too difficult.
“There is a lot of exceptionalism about genetics, which is actually inappropriate,” he said. “This is actually just a quantitative score that should be easy for people to interpret.”
Dr. Bowes and Dr. Brown reported no relevant financial relationships. Dr. Liao worked as a consultant for UCB.
A version of this article appeared on Medscape.com.
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All rights reserved. This material may not be published, broadcast, copied, or otherwise reproduced or distributed without the prior written permission of Frontline Medical Communications Inc.</copyrightNotice> </rightsInfo> </provider> <abstract/> <metaDescription>A new diagnostic tool can effectively discriminate different rheumatologic conditions and could potentially aid in the diagnosis of early inflammatory arthritis</metaDescription> <articlePDF/> <teaserImage>299971</teaserImage> <teaser>The tool may aid in accelerating accurate diagnosis and getting patients on the right treatments earlier.</teaser> <title>Tool Uses Genetics to Assist With Diagnosis of Early Inflammatory Arthritis</title> <deck/> <disclaimer/> <AuthorList/> <articleURL/> <doi/> <pubMedID/> <publishXMLStatus/> <publishXMLVersion>1</publishXMLVersion> <useEISSN>0</useEISSN> <urgency/> <pubPubdateYear/> <pubPubdateMonth/> <pubPubdateDay/> <pubVolume/> <pubNumber/> <wireChannels/> <primaryCMSID/> <CMSIDs/> <keywords/> <seeAlsos/> <publications_g> <publicationData> <publicationCode>rn</publicationCode> <pubIssueName/> <pubArticleType/> <pubTopics/> <pubCategories/> <pubSections/> </publicationData> <publicationData> <publicationCode>im</publicationCode> <pubIssueName/> <pubArticleType/> <pubTopics/> <pubCategories/> <pubSections/> </publicationData> <publicationData> <publicationCode>fp</publicationCode> <pubIssueName/> <pubArticleType/> <pubTopics/> <pubCategories/> <pubSections/> </publicationData> </publications_g> <publications> <term canonical="true">26</term> <term>21</term> <term>15</term> </publications> <sections> <term canonical="true">27970</term> <term>39313</term> </sections> <topics> <term canonical="true">289</term> <term>282</term> <term>241</term> <term>299</term> <term>216</term> <term>183</term> <term>290</term> </topics> <links> <link> <itemClass qcode="ninat:picture"/> <altRep contenttype="image/jpeg">images/240125ab.jpg</altRep> <description role="drol:caption">Dr. John Bowes</description> <description role="drol:credit">University of Manchester</description> </link> <link> <itemClass qcode="ninat:picture"/> <altRep contenttype="image/jpeg">images/240125ac.jpg</altRep> <description role="drol:caption">Dr. Matthew Brown</description> <description role="drol:credit">Queensland University of Technology</description> </link> <link> <itemClass qcode="ninat:picture"/> <altRep contenttype="image/jpeg">images/2401038e.jpg</altRep> <description role="drol:caption">Dr. Katherine Liao</description> <description role="drol:credit">Dr. Liao</description> </link> </links> </header> <itemSet> <newsItem> <itemMeta> <itemRole>Main</itemRole> <itemClass>text</itemClass> <title>Tool Uses Genetics to Assist With Diagnosis of Early Inflammatory Arthritis</title> <deck/> </itemMeta> <itemContent> <p>A new diagnostic tool can effectively discriminate different rheumatologic conditions and could potentially aid in the diagnosis of early inflammatory arthritis.</p> <p>The algorithm — called Genetic Probability tool (G-PROB) — uses genetic information to calculate the probability of certain diseases.<br/><br/>[[{"fid":"299971","view_mode":"medstat_image_flush_left","fields":{"format":"medstat_image_flush_left","field_file_image_alt_text[und][0][value]":"Dr. John Bowes, senior lecturer in the division of musculoskeletal & dermatological sciences at the University of Manchester in the United Kingdom","field_file_image_credit[und][0][value]":"University of Manchester","field_file_image_caption[und][0][value]":"Dr. John Bowes"},"type":"media","attributes":{"class":"media-element file-medstat_image_flush_left"}}]]“At such an early stage of disease, it’s not always easy to determine what the final outcome will be with respect to final diagnosis,” said <span class="Hyperlink"><a href="https://research.manchester.ac.uk/en/persons/j.bowes">John Bowes, PhD</a></span>, a senior lecturer in the division of musculoskeletal & dermatological sciences at the University of Manchester in the United Kingdom. He was a senior author of the <span class="Hyperlink"><a href="https://acrjournals.onlinelibrary.wiley.com/doi/10.1002/art.42760">newest study of G-PROB</a></span>. “What we are hoping for here is that genetics can help [clinicians] with the decision-making process and hopefully accelerate the correct diagnosis and get individuals onto the correct treatment as early as possible.”<br/><br/></p> <h2>Creating the Algorithm</h2> <p>G-PROB was <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7341896/">first developed</a> by an international group of scientists with the goal of using genetic risk scores to predict the probabilities of common diagnoses for patients with early signs of arthritis, such as synovitis and joint swelling. According to the study authors, about 80% of these types of patients are eventually diagnosed with the following conditions: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), psoriatic arthritis (PsA), ankylosing spondylitis (AS), and gout.</p> <p>The algorithm combines existing knowledge about single-nucleotide polymorphisms from prior genomic studies to create genetic risk scores — also called polygenic risk score (PRS) — for multiple diseases. Using these scores, the program then calculates the probabilities of certain diagnoses for a patient, based on the assumption that at least one disease was present.<br/><br/>In this first study, researchers trained the tool on simulated data and then tested it in three patient cohorts totaling about 1700 individuals from the Electronic Medical Records and Genomics database and Mass General Brigham Biobank. In the initial study, G-PROB identified a likely diagnosis in 45% of patients, with a positive predictive value (PPV) of 64%. Adding these genetic scores to clinical data improved diagnostic accuracy from 39% to 51%.<br/><br/></p> <h2>Validating G-PROB</h2> <p>But data from these biobanks may not necessarily be representative of early arthritis in patients appearing in outpatient clinics, noted Dr. Bowes. In <span class="Hyperlink">this new study</span>, researchers sought to independently validate the original study’s findings using data from the <a href="https://www.nnuh.nhs.uk/departments/rheumatology/norfolk-arthritis-register/">Norfolk Arthritis Register</a>, a community-based, long-term observational study on inflammatory polyarthritis. The team applied G-PROB in this cohort and then compared the tool’s probabilities for common rheumatic conditions to the final clinician diagnosis.</p> <p>The study ultimately included 1047 individuals with early inflammatory arthritis with genotype data. In the cohort, more than 70% (756 individuals) were diagnosed with RA. Of the remaining patients, 104 had PsA, 18 had SLE, 16 had AS, and 12 had gout. The research team also added an “other diseases” category to the algorithm. A total of 141 patients fell into this category and were diagnosed with diseases including chronic pain syndrome (52 individuals), polymyalgia rheumatica (29 individuals), and Sjögren’s syndrome (9 individuals).<br/><br/>G-PROB was best at excluding diagnoses: Probabilities under 5% for a single disease corresponded to a negative predictive value (NPV) of 96%. If probabilities for two diseases were both < 5%, the NPV was 94%.<br/><br/>For patients with a single probability above 50%, the tool had a PPV of 70.3%. In 55.7% of all patients, the disease with the highest probability ended up being the final diagnosis.<br/><br/>Generally, PRSs, as well as tests using biomarkers, were better at excluding diagnoses than affirming them, noted <a href="https://www.kcl.ac.uk/people/matthew-brown">Matthew Brown, MBBS, MD</a>, a professor of medicine at King’s College London, who was not involved with the research. If disease prevalence is low, then a test aimed at diagnosis of that disease would be better at excluding a diagnosis than affirming it, he explained.[[{"fid":"299972","view_mode":"medstat_image_flush_left","fields":{"format":"medstat_image_flush_left","field_file_image_alt_text[und][0][value]":"Dr. Matthew Brown, professor of medicine at King’s College London","field_file_image_credit[und][0][value]":"Queensland University of Technology","field_file_image_caption[und][0][value]":"Dr. Matthew Brown"},"type":"media","attributes":{"class":"media-element file-medstat_image_flush_left"}}]]<br/><br/>However, he noted that G-PROB’s PPV may have performed better if researchers had started by using established PRS scores to form the algorithm, rather than developing these genetic scores independently using internal datasets.<br/><br/></p> <h2>Can G-PROB Improve Diagnosis?</h2> <p>The new study’s key contribution was that it independently validated findings from a previous study, noted <a href="https://dbmi.hms.harvard.edu/people/katherine-liao">Katherine Liao, MD</a>, a rheumatologist at Brigham and Women’s Hospital in Boston, Massachusetts. She coauthored an <a href="https://acrjournals.onlinelibrary.wiley.com/doi/10.1002/art.42769">accompanying editorial</a> to the newest study and coauthored the original G-PROB paper.[[{"fid":"281607","view_mode":"medstat_image_flush_left","fields":{"format":"medstat_image_flush_left","field_file_image_alt_text[und][0][value]":"Dr. Katherine Liao, a rheumatologist at Brigham and Women's Hospital in Boston","field_file_image_credit[und][0][value]":"Dr. Liao","field_file_image_caption[und][0][value]":"Dr. Katherine Liao"},"type":"media","attributes":{"class":"media-element file-medstat_image_flush_left"}}]]</p> <p>This new study also brought up an important question about G-PROB that has yet to be tested: Will this tool help clinicians make more efficient and accurate diagnoses in practice?<br/><br/>A prospective trial would be necessary to begin answering this question, both Dr. Bowes and Dr. Liao agreed. For example, one clinician group would have access to G-PROB data, while another would not, and “see if that helps [the first group] make the diagnosis faster or more accurately,” Dr. Liao said.<br/><br/>Dr. Bowes was also interested in exploring if combining G-PROB with other clinical data would improve diagnostic performance.<br/><br/>“Genetics isn’t the full story,” he said. Dr. Bowes saw genetics as one additional, complementary tool in a clinician’s toolbox.<br/><br/>Future studies were needed to understand the clinical utility of genetic information in conjunction with current diagnostic practices, such as imaging, physical exams, and lab results, Dr. Liao and her editorial coauthors argued. <br/><br/>“For example, in cardiovascular disease, the clinical utility of polygenic risk scores has been defined by their ability to improve risk stratification beyond what is already achieved with more common risk factors and measures such as cholesterol levels, smoking status, and coronary calcium scores,” Dr. Liao and her coauthors wrote. “Similarly, a polygenic risk score for breast cancer would not be clinically implemented alone for risk prediction but rather as one risk factor among others, such as hormonal and reproductive factors and prior mammographic data.”<br/><br/></p> <h2>Future of Genetics in Rheumatology</h2> <p>An additional hurdle for using tools like G-PROB was that a patient must have undergone DNA sequencing, and these data must be available to clinicians. Even a decade ago, this type of testing may have seemed unrealistic to incorporate in daily practice, Dr. Liao noted, but technological advancements continue to make genetic sequencing more accessible to the public.</p> <p>There are already efforts in the United Kingdom to incorporate genetics into healthcare, including trials for PRSs and heart disease, noted Dr. Bowes, as well as large-scale studies such as <a href="https://ourfuturehealth.org.uk/">Our Future Health</a>.<br/><br/>“As these population-based studies expand more, a high proportion of individuals should hopefully have access to this kind of data,” he said.<br/><br/>Brown added that genetic testing is already used to make rheumatology diagnoses.<br/><br/>“[HLA] B-27 testing, for example, is an extremely commonly used test to assist in the diagnosis of ankylosing spondylitis. Is it that different to change to a PRS as opposed to a straight HLA testing? I don’t think it is,” he said.<br/><br/>While there would need to be systematic training for clinicians to understand how to calculate and use PRSs in daily practice, Dr. Brown did not think this adjustment would be too difficult.<br/><br/>“There is a lot of exceptionalism about genetics, which is actually inappropriate,” he said. “This is actually just a quantitative score that should be easy for people to interpret.”<br/><br/>Dr. Bowes and Dr. Brown reported no relevant financial relationships. Dr. Liao worked as a consultant for UCB.</p> <p> <em> <em>A version of this article appeared on </em> <span class="Hyperlink"><a href="https://www.medscape.com/viewarticle/tool-uses-genetics-diagnose-early-inflammatory-arthritis-2024a10000sz">Medscape.com</a>.</span> </em> </p> </itemContent> </newsItem> <newsItem> <itemMeta> <itemRole>teaser</itemRole> <itemClass>text</itemClass> <title/> <deck/> </itemMeta> <itemContent> </itemContent> </newsItem> </itemSet></root>
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