Ph.D

Mark Twain said "It isn’t what you don’t know; it’s what you know that ain’t so that gets you into trouble." But this may be incorrect, because the quote is also attributed to Will Rogers and Yogi Berra, among others.

Regardless of who said it, that paradigm kept appearing this past month. Rather than reading about new advances in medicine, I came across a cluster of articles that suggested prior knowledge was aberrant. Now don’t get me wrong. I know (there is that word again) that medical knowledge changes. Ben Franklin said, "In this world nothing can be said to be certain, except death and taxes." Ben Franklin is less well known for his medical research, which concluded that wet clothing and cold, damp air did not cause the common cold, but breathing putrefied air from other people in close quarters did spread disease (J. R. Soc. Med. 2005;98:534-8). Unfortunately, Ben’s arguments, which preceded the discoveries of germs by Pasteur, Lister, and Koch, still haven’t convinced Dr. Mom.

I warn medical students and residents that half of what I was taught in medical school has since been proven obsolete or frankly wrong. I have no reason to believe that my teaching is any better.

My favorite example of this has been the treatment of ulcers. My medical school curriculum emphasized quantitative physiology, so we had three lectures on the nature of the gastric mucosa, acid production, protective barriers, and the potential of new medications to heal ulcers that previously would perforate and require surgery. The technique of gastric freezing, used in the 1960s, had been discredited and supplanted with the use of antacids and a bland milk diet. Unfortunately, the intake of extra calcium actually stimulated a rebound in stomach acid production. The newly discovered H₂ receptor antagonists worked better. My professors also expounded on the latest research, which showed that a new class of medications could directly inhibit the proton pump. Finally, it seemed then, modern medicine would be able to control the acid that caused ulcers, thereby permitting healing, although relapses were common. These medications quickly became the best sellers for the next 20 years. That financial success didn’t stop someone from later claiming that ulcers were actually caused by an infection, not by stress, lifestyle, and excess acid. After 2 decades of ridiculing that suggestion, the medical establishment awarded Dr. Barry J. Marshall and Dr. J. Robin Warren a Nobel Prize in 2005 for discovering Helicobacter pylori.

So it isn’t unusual for me to read articles that tell me what I know ain’t so. My first example is entitled "Oseltamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments," and challenges the effectiveness of such influenza treatments as Tamiflu (BMJ 2014;348:g2545). Local ED doctors this past winter have not promoted use of the medication in otherwise healthy children. They suggest fluids, rest, and antipyretics seem to be almost as effective with fewer side effects.

My second example is an article that asserts that circumcision may be the best thing since sliced bread (Mayo Clinic Proceedings 2014;89:677-86). If not that good, at least it is medically justified and should be paid for by Medicaid, according to those authors.

The third article contradicts data published by the Centers for Disease Control and Prevention in February 2014 and suggests that the prevalence of childhood obesity has not peaked (JAMA Pediatr. 2014 [doi:10.1001/jamapediatrics.2014.21]).

I don’t have enough space here to debate those articles. Read them and decide for yourself. I am worried about the overall state of medical research, as outlined by Dr. Richard Smith, the former editor of BMJ in his blog entitled "Medical research – still a scandal." The typical pediatrician will not wield much influence over the forces to which Dr. Smith refers. But medical students, residents, and the average physician can – and must – develop better skills at critiquing what they read.

The history of the treatment of ulcers is an excellent example of how scientific progress is made. The examples in these three articles have a different nuance. They suggest that medical research is confounding, not advancing, knowledge. And that could definitely land us in trouble.

Dr. Powell practices as a hospitalist at SSM Cardinal Glennon Children’s Medical Center in St. Louis. He is associate professor of pediatrics at Saint Louis University. He is also listserv moderator for the American Academy of Pediatrics Section on Hospital Medicine and is a member of the Law and Bioethics Affinity Group of the American Society for Bioethics and Humanities. Dr. Powell said he had no relevant financial disclosures. E-mail him at pdnews@frontlinemedcom.com.

Mark Twain said "It isn’t what you don’t know; it’s what you know that ain’t so that gets you into trouble." But this may be incorrect, because the quote is also attributed to Will Rogers and Yogi Berra, among others.

Regardless of who said it, that paradigm kept appearing this past month. Rather than reading about new advances in medicine, I came across a cluster of articles that suggested prior knowledge was aberrant. Now don’t get me wrong. I know (there is that word again) that medical knowledge changes. Ben Franklin said, "In this world nothing can be said to be certain, except death and taxes." Ben Franklin is less well known for his medical research, which concluded that wet clothing and cold, damp air did not cause the common cold, but breathing putrefied air from other people in close quarters did spread disease (J. R. Soc. Med. 2005;98:534-8). Unfortunately, Ben’s arguments, which preceded the discoveries of germs by Pasteur, Lister, and Koch, still haven’t convinced Dr. Mom.

I warn medical students and residents that half of what I was taught in medical school has since been proven obsolete or frankly wrong. I have no reason to believe that my teaching is any better.

My favorite example of this has been the treatment of ulcers. My medical school curriculum emphasized quantitative physiology, so we had three lectures on the nature of the gastric mucosa, acid production, protective barriers, and the potential of new medications to heal ulcers that previously would perforate and require surgery. The technique of gastric freezing, used in the 1960s, had been discredited and supplanted with the use of antacids and a bland milk diet. Unfortunately, the intake of extra calcium actually stimulated a rebound in stomach acid production. The newly discovered H₂ receptor antagonists worked better. My professors also expounded on the latest research, which showed that a new class of medications could directly inhibit the proton pump. Finally, it seemed then, modern medicine would be able to control the acid that caused ulcers, thereby permitting healing, although relapses were common. These medications quickly became the best sellers for the next 20 years. That financial success didn’t stop someone from later claiming that ulcers were actually caused by an infection, not by stress, lifestyle, and excess acid. After 2 decades of ridiculing that suggestion, the medical establishment awarded Dr. Barry J. Marshall and Dr. J. Robin Warren a Nobel Prize in 2005 for discovering Helicobacter pylori.

So it isn’t unusual for me to read articles that tell me what I know ain’t so. My first example is entitled "Oseltamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments," and challenges the effectiveness of such influenza treatments as Tamiflu (BMJ 2014;348:g2545). Local ED doctors this past winter have not promoted use of the medication in otherwise healthy children. They suggest fluids, rest, and antipyretics seem to be almost as effective with fewer side effects.

My second example is an article that asserts that circumcision may be the best thing since sliced bread (Mayo Clinic Proceedings 2014;89:677-86). If not that good, at least it is medically justified and should be paid for by Medicaid, according to those authors.

The third article contradicts data published by the Centers for Disease Control and Prevention in February 2014 and suggests that the prevalence of childhood obesity has not peaked (JAMA Pediatr. 2014 [doi:10.1001/jamapediatrics.2014.21]).

I don’t have enough space here to debate those articles. Read them and decide for yourself. I am worried about the overall state of medical research, as outlined by Dr. Richard Smith, the former editor of BMJ in his blog entitled "Medical research – still a scandal." The typical pediatrician will not wield much influence over the forces to which Dr. Smith refers. But medical students, residents, and the average physician can – and must – develop better skills at critiquing what they read.

The history of the treatment of ulcers is an excellent example of how scientific progress is made. The examples in these three articles have a different nuance. They suggest that medical research is confounding, not advancing, knowledge. And that could definitely land us in trouble.

Dr. Powell practices as a hospitalist at SSM Cardinal Glennon Children’s Medical Center in St. Louis. He is associate professor of pediatrics at Saint Louis University. He is also listserv moderator for the American Academy of Pediatrics Section on Hospital Medicine and is a member of the Law and Bioethics Affinity Group of the American Society for Bioethics and Humanities. Dr. Powell said he had no relevant financial disclosures. E-mail him at pdnews@frontlinemedcom.com.

Mark Twain said "It isn’t what you don’t know; it’s what you know that ain’t so that gets you into trouble." But this may be incorrect, because the quote is also attributed to Will Rogers and Yogi Berra, among others.

Regardless of who said it, that paradigm kept appearing this past month. Rather than reading about new advances in medicine, I came across a cluster of articles that suggested prior knowledge was aberrant. Now don’t get me wrong. I know (there is that word again) that medical knowledge changes. Ben Franklin said, "In this world nothing can be said to be certain, except death and taxes." Ben Franklin is less well known for his medical research, which concluded that wet clothing and cold, damp air did not cause the common cold, but breathing putrefied air from other people in close quarters did spread disease (J. R. Soc. Med. 2005;98:534-8). Unfortunately, Ben’s arguments, which preceded the discoveries of germs by Pasteur, Lister, and Koch, still haven’t convinced Dr. Mom.

I warn medical students and residents that half of what I was taught in medical school has since been proven obsolete or frankly wrong. I have no reason to believe that my teaching is any better.

My favorite example of this has been the treatment of ulcers. My medical school curriculum emphasized quantitative physiology, so we had three lectures on the nature of the gastric mucosa, acid production, protective barriers, and the potential of new medications to heal ulcers that previously would perforate and require surgery. The technique of gastric freezing, used in the 1960s, had been discredited and supplanted with the use of antacids and a bland milk diet. Unfortunately, the intake of extra calcium actually stimulated a rebound in stomach acid production. The newly discovered H₂ receptor antagonists worked better. My professors also expounded on the latest research, which showed that a new class of medications could directly inhibit the proton pump. Finally, it seemed then, modern medicine would be able to control the acid that caused ulcers, thereby permitting healing, although relapses were common. These medications quickly became the best sellers for the next 20 years. That financial success didn’t stop someone from later claiming that ulcers were actually caused by an infection, not by stress, lifestyle, and excess acid. After 2 decades of ridiculing that suggestion, the medical establishment awarded Dr. Barry J. Marshall and Dr. J. Robin Warren a Nobel Prize in 2005 for discovering Helicobacter pylori.

So it isn’t unusual for me to read articles that tell me what I know ain’t so. My first example is entitled "Oseltamivir for influenza in adults and children: systematic review of clinical study reports and summary of regulatory comments," and challenges the effectiveness of such influenza treatments as Tamiflu (BMJ 2014;348:g2545). Local ED doctors this past winter have not promoted use of the medication in otherwise healthy children. They suggest fluids, rest, and antipyretics seem to be almost as effective with fewer side effects.

My second example is an article that asserts that circumcision may be the best thing since sliced bread (Mayo Clinic Proceedings 2014;89:677-86). If not that good, at least it is medically justified and should be paid for by Medicaid, according to those authors.

The third article contradicts data published by the Centers for Disease Control and Prevention in February 2014 and suggests that the prevalence of childhood obesity has not peaked (JAMA Pediatr. 2014 [doi:10.1001/jamapediatrics.2014.21]).

I don’t have enough space here to debate those articles. Read them and decide for yourself. I am worried about the overall state of medical research, as outlined by Dr. Richard Smith, the former editor of BMJ in his blog entitled "Medical research – still a scandal." The typical pediatrician will not wield much influence over the forces to which Dr. Smith refers. But medical students, residents, and the average physician can – and must – develop better skills at critiquing what they read.

The history of the treatment of ulcers is an excellent example of how scientific progress is made. The examples in these three articles have a different nuance. They suggest that medical research is confounding, not advancing, knowledge. And that could definitely land us in trouble.

Dr. Powell practices as a hospitalist at SSM Cardinal Glennon Children’s Medical Center in St. Louis. He is associate professor of pediatrics at Saint Louis University. He is also listserv moderator for the American Academy of Pediatrics Section on Hospital Medicine and is a member of the Law and Bioethics Affinity Group of the American Society for Bioethics and Humanities. Dr. Powell said he had no relevant financial disclosures. E-mail him at pdnews@frontlinemedcom.com.

In the recent column, ("CAD: One Step Forward, One Step Back," Internal Medicine News digital network Oct. 28, 2010), Dr. Matthew Taylor discussed the "step backward" that occurred in genetic risk prediction of coronary artery disease (CAD) with the discovery that variations in the KIF6 gene might not be clinically associated with CAD after all. More research and vigorous discussion about the role of this gene in this disease – and about early clinical adoption of newly discovered risk factors in general – are sure to follow.

Of course, KIF6 is only one of many genetic markers that have been linked to CAD risk. Many are already in the public domain and available clinically through health care providers and via direct-to-consumer marketing. What are we to do with these?

One approach to answering such questions is to turn to an evidence review. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative seeks to evaluate genetic tests and innovations with respect to implementation in clinical practice and public health. Much like the U.S. Preventive Services Task Force, EGAPP commissions evidence reviews and then issues recommendations based upon the evidence. More information, as well as evidence reviews and recommendations reports, is available at www.egappreviews.org.

In December, EGAPP published a report on genomic profiling to assess cardiovascular risk (Genet. Med. 2010;12:839-43). The overarching question was whether genomic profiling to identify undiagnosed individuals who are at increased risk for cardiovascular disease (CVD) leads to improved cardiovascular outcomes. Overall, the assessment was that there is insufficient evidence to support such testing in the general population. The net health benefit was deemed to be low, and clinical use was discouraged unless improved clinical outcomes are demonstrated in future research.

It is worth exploring the report further to understand the current state of knowledge and anticipate potential new developments. The evidence review looked at all eight genetic testing panels marketed for CVD risk prediction that were commercially available in February 2008. Collectively, 58 genetic variants were identified among 29 genes. Of these, 38 were reported to have some association with risk for coronary heart disease (CHD), consisting of coronary artery disease, ischemic heart disease, or myocardial infarction. Data for association with stroke were weaker.

Only two test manufacturers (deCODE Genetics and Interleukin Genetics) indicated that they are Clinical Laboratory Improvement Amendments–certified and provided detailed test method and validation information. For the other tests, there was frequently insufficient information to identify even the specific genetic variants being tested, much less the analytic validity of the approach. Nonetheless, the existing scientific technology was considered adequate to allow satisfactory accuracy and reliability of the tests for detecting genetic variation.

Regarding clinical utility, there are no published data on the long-term outcomes associated with genetic testing for CHD risk prediction. The anticipated benefit is improved identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease.

Among all the markers, 24 were deemed to have some degree of credibility and/or statistical significance. However, combining them in a statistical model did not provide a clinically useful stand-alone predictive test. Even the 9p21 genetic marker, when combined with traditional cardiac risk factors, provided only 0%-5% improvement in risk assessment for CHD.

Regarding clinical utility, there are no published data on the long-term outcomes associated with such testing. In theory, such tests would improve identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease. Potential harms must be considered, too. Among false positives – those identified incorrectly as being at increased risk for CHD – there may be unnecessarily increased anxiety and treatment-associated adverse events, without any reduction in morbidity or mortality. There also are financial costs associated with false positives. An additional risk is false reassurance for those who are at increased risk, but who are not identified by genetic testing. There is room for optimism, as early data suggest at least short-term cardiac risk reduction without clinical harms, but additional and longer-term studies are still needed.

There are notable limitations to this report. Most of the data so far were obtained in whites of European ancestry. Gene-disease associations and effect sizes may be quite different in other populations. In addition, many of the studies are relatively small and underpowered. Larger and newer studies are still identifying additional candidate genes and markers. Even more challenging is that we still do not know how to combine multiple genetic factors to develop a composite risk assessment, nor do we know how to combine a genetic risk assessment with traditional cardiac risk predications. Most models assume complete independence and simply multiply the odds ratio of each identified variant. However, this approach has not been validated and could yield falsely elevated or diminished risk scores.

The true power of genetic association studies to identify risk factors for common diseases may in fact not lie in better identification of those at increased risk. Rather, elucidation of the biological basis of such associations holds the promise of improving our understanding and eventual treatment of the underlying disease process.

So how should a physician deal with the availability of cardiac risk assessment tests? Clearly, these are best suited for people who consider themselves early adopters. The 9p21 marker appears to be the most clinically relevant marker at this point. Indeed, the risk associated with this genetic variant appears highest in younger individuals, so perhaps it might be appropriate to consider obtaining such information in a person under age 55 years who needs further encouragement to modify his or her cardiac risks.

Ideally, patients and their providers will engage in a discussion of the potential risks and benefits prior to any testing. Then, in the truest and oldest model of personalized medicine, they can decide together if pursuing such testing is appropriate on a case-by-case basis.

This column, "Genetics in Your Practice," regularly appears in Internal Medicine News, an Elsevier publication. Dr. Levy is an assistant professor in the division of general internal medicine and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore. Dr. Taylor is associate professor in the department of internal medicine and director of adult clinical genetics at the University of Colorado at Denver.

To respond to this column, e-mail Dr. Taylor at imnews@elsevier.com.

In the recent column, ("CAD: One Step Forward, One Step Back," Internal Medicine News digital network Oct. 28, 2010), Dr. Matthew Taylor discussed the "step backward" that occurred in genetic risk prediction of coronary artery disease (CAD) with the discovery that variations in the KIF6 gene might not be clinically associated with CAD after all. More research and vigorous discussion about the role of this gene in this disease – and about early clinical adoption of newly discovered risk factors in general – are sure to follow.

Of course, KIF6 is only one of many genetic markers that have been linked to CAD risk. Many are already in the public domain and available clinically through health care providers and via direct-to-consumer marketing. What are we to do with these?

One approach to answering such questions is to turn to an evidence review. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative seeks to evaluate genetic tests and innovations with respect to implementation in clinical practice and public health. Much like the U.S. Preventive Services Task Force, EGAPP commissions evidence reviews and then issues recommendations based upon the evidence. More information, as well as evidence reviews and recommendations reports, is available at www.egappreviews.org.

In December, EGAPP published a report on genomic profiling to assess cardiovascular risk (Genet. Med. 2010;12:839-43). The overarching question was whether genomic profiling to identify undiagnosed individuals who are at increased risk for cardiovascular disease (CVD) leads to improved cardiovascular outcomes. Overall, the assessment was that there is insufficient evidence to support such testing in the general population. The net health benefit was deemed to be low, and clinical use was discouraged unless improved clinical outcomes are demonstrated in future research.

It is worth exploring the report further to understand the current state of knowledge and anticipate potential new developments. The evidence review looked at all eight genetic testing panels marketed for CVD risk prediction that were commercially available in February 2008. Collectively, 58 genetic variants were identified among 29 genes. Of these, 38 were reported to have some association with risk for coronary heart disease (CHD), consisting of coronary artery disease, ischemic heart disease, or myocardial infarction. Data for association with stroke were weaker.

Only two test manufacturers (deCODE Genetics and Interleukin Genetics) indicated that they are Clinical Laboratory Improvement Amendments–certified and provided detailed test method and validation information. For the other tests, there was frequently insufficient information to identify even the specific genetic variants being tested, much less the analytic validity of the approach. Nonetheless, the existing scientific technology was considered adequate to allow satisfactory accuracy and reliability of the tests for detecting genetic variation.

Regarding clinical utility, there are no published data on the long-term outcomes associated with genetic testing for CHD risk prediction. The anticipated benefit is improved identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease.

Among all the markers, 24 were deemed to have some degree of credibility and/or statistical significance. However, combining them in a statistical model did not provide a clinically useful stand-alone predictive test. Even the 9p21 genetic marker, when combined with traditional cardiac risk factors, provided only 0%-5% improvement in risk assessment for CHD.

Regarding clinical utility, there are no published data on the long-term outcomes associated with such testing. In theory, such tests would improve identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease. Potential harms must be considered, too. Among false positives – those identified incorrectly as being at increased risk for CHD – there may be unnecessarily increased anxiety and treatment-associated adverse events, without any reduction in morbidity or mortality. There also are financial costs associated with false positives. An additional risk is false reassurance for those who are at increased risk, but who are not identified by genetic testing. There is room for optimism, as early data suggest at least short-term cardiac risk reduction without clinical harms, but additional and longer-term studies are still needed.

There are notable limitations to this report. Most of the data so far were obtained in whites of European ancestry. Gene-disease associations and effect sizes may be quite different in other populations. In addition, many of the studies are relatively small and underpowered. Larger and newer studies are still identifying additional candidate genes and markers. Even more challenging is that we still do not know how to combine multiple genetic factors to develop a composite risk assessment, nor do we know how to combine a genetic risk assessment with traditional cardiac risk predications. Most models assume complete independence and simply multiply the odds ratio of each identified variant. However, this approach has not been validated and could yield falsely elevated or diminished risk scores.

The true power of genetic association studies to identify risk factors for common diseases may in fact not lie in better identification of those at increased risk. Rather, elucidation of the biological basis of such associations holds the promise of improving our understanding and eventual treatment of the underlying disease process.

So how should a physician deal with the availability of cardiac risk assessment tests? Clearly, these are best suited for people who consider themselves early adopters. The 9p21 marker appears to be the most clinically relevant marker at this point. Indeed, the risk associated with this genetic variant appears highest in younger individuals, so perhaps it might be appropriate to consider obtaining such information in a person under age 55 years who needs further encouragement to modify his or her cardiac risks.

Ideally, patients and their providers will engage in a discussion of the potential risks and benefits prior to any testing. Then, in the truest and oldest model of personalized medicine, they can decide together if pursuing such testing is appropriate on a case-by-case basis.

This column, "Genetics in Your Practice," regularly appears in Internal Medicine News, an Elsevier publication. Dr. Levy is an assistant professor in the division of general internal medicine and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore. Dr. Taylor is associate professor in the department of internal medicine and director of adult clinical genetics at the University of Colorado at Denver.

To respond to this column, e-mail Dr. Taylor at imnews@elsevier.com.

In the recent column, ("CAD: One Step Forward, One Step Back," Internal Medicine News digital network Oct. 28, 2010), Dr. Matthew Taylor discussed the "step backward" that occurred in genetic risk prediction of coronary artery disease (CAD) with the discovery that variations in the KIF6 gene might not be clinically associated with CAD after all. More research and vigorous discussion about the role of this gene in this disease – and about early clinical adoption of newly discovered risk factors in general – are sure to follow.

Of course, KIF6 is only one of many genetic markers that have been linked to CAD risk. Many are already in the public domain and available clinically through health care providers and via direct-to-consumer marketing. What are we to do with these?

One approach to answering such questions is to turn to an evidence review. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative seeks to evaluate genetic tests and innovations with respect to implementation in clinical practice and public health. Much like the U.S. Preventive Services Task Force, EGAPP commissions evidence reviews and then issues recommendations based upon the evidence. More information, as well as evidence reviews and recommendations reports, is available at www.egappreviews.org.

In December, EGAPP published a report on genomic profiling to assess cardiovascular risk (Genet. Med. 2010;12:839-43). The overarching question was whether genomic profiling to identify undiagnosed individuals who are at increased risk for cardiovascular disease (CVD) leads to improved cardiovascular outcomes. Overall, the assessment was that there is insufficient evidence to support such testing in the general population. The net health benefit was deemed to be low, and clinical use was discouraged unless improved clinical outcomes are demonstrated in future research.

It is worth exploring the report further to understand the current state of knowledge and anticipate potential new developments. The evidence review looked at all eight genetic testing panels marketed for CVD risk prediction that were commercially available in February 2008. Collectively, 58 genetic variants were identified among 29 genes. Of these, 38 were reported to have some association with risk for coronary heart disease (CHD), consisting of coronary artery disease, ischemic heart disease, or myocardial infarction. Data for association with stroke were weaker.

Only two test manufacturers (deCODE Genetics and Interleukin Genetics) indicated that they are Clinical Laboratory Improvement Amendments–certified and provided detailed test method and validation information. For the other tests, there was frequently insufficient information to identify even the specific genetic variants being tested, much less the analytic validity of the approach. Nonetheless, the existing scientific technology was considered adequate to allow satisfactory accuracy and reliability of the tests for detecting genetic variation.

Regarding clinical utility, there are no published data on the long-term outcomes associated with genetic testing for CHD risk prediction. The anticipated benefit is improved identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease.

Among all the markers, 24 were deemed to have some degree of credibility and/or statistical significance. However, combining them in a statistical model did not provide a clinically useful stand-alone predictive test. Even the 9p21 genetic marker, when combined with traditional cardiac risk factors, provided only 0%-5% improvement in risk assessment for CHD.

Regarding clinical utility, there are no published data on the long-term outcomes associated with such testing. In theory, such tests would improve identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease. Potential harms must be considered, too. Among false positives – those identified incorrectly as being at increased risk for CHD – there may be unnecessarily increased anxiety and treatment-associated adverse events, without any reduction in morbidity or mortality. There also are financial costs associated with false positives. An additional risk is false reassurance for those who are at increased risk, but who are not identified by genetic testing. There is room for optimism, as early data suggest at least short-term cardiac risk reduction without clinical harms, but additional and longer-term studies are still needed.

There are notable limitations to this report. Most of the data so far were obtained in whites of European ancestry. Gene-disease associations and effect sizes may be quite different in other populations. In addition, many of the studies are relatively small and underpowered. Larger and newer studies are still identifying additional candidate genes and markers. Even more challenging is that we still do not know how to combine multiple genetic factors to develop a composite risk assessment, nor do we know how to combine a genetic risk assessment with traditional cardiac risk predications. Most models assume complete independence and simply multiply the odds ratio of each identified variant. However, this approach has not been validated and could yield falsely elevated or diminished risk scores.

The true power of genetic association studies to identify risk factors for common diseases may in fact not lie in better identification of those at increased risk. Rather, elucidation of the biological basis of such associations holds the promise of improving our understanding and eventual treatment of the underlying disease process.

So how should a physician deal with the availability of cardiac risk assessment tests? Clearly, these are best suited for people who consider themselves early adopters. The 9p21 marker appears to be the most clinically relevant marker at this point. Indeed, the risk associated with this genetic variant appears highest in younger individuals, so perhaps it might be appropriate to consider obtaining such information in a person under age 55 years who needs further encouragement to modify his or her cardiac risks.

Ideally, patients and their providers will engage in a discussion of the potential risks and benefits prior to any testing. Then, in the truest and oldest model of personalized medicine, they can decide together if pursuing such testing is appropriate on a case-by-case basis.

This column, "Genetics in Your Practice," regularly appears in Internal Medicine News, an Elsevier publication. Dr. Levy is an assistant professor in the division of general internal medicine and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore. Dr. Taylor is associate professor in the department of internal medicine and director of adult clinical genetics at the University of Colorado at Denver.

To respond to this column, e-mail Dr. Taylor at imnews@elsevier.com.

In the recent column, ("CAD: One Step Forward, One Step Back," Internal Medicine News digital network Oct. 28, 2010), Dr. Matthew Taylor discussed the "step backward" that occurred in genetic risk prediction of coronary artery disease (CAD) with the discovery that variations in the KIF6 gene might not be clinically associated with CAD after all. More research and vigorous discussion about the role of this gene in this disease – and about early clinical adoption of newly discovered risk factors in general – are sure to follow.

Of course, KIF6 is only one of many genetic markers that have been linked to CAD risk. Many are already in the public domain and available clinically through health care providers and via direct-to-consumer marketing. What are we to do with these?

One approach to answering such questions is to turn to an evidence review. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative seeks to evaluate genetic tests and innovations with respect to implementation in clinical practice and public health. Much like the U.S. Preventive Services Task Force, EGAPP commissions evidence reviews and then issues recommendations based upon the evidence. More information, as well as evidence reviews and recommendations reports, is available at www.egappreviews.org.

In December, EGAPP published a report on genomic profiling to assess cardiovascular risk (Genet. Med. 2010;12:839-43). The overarching question was whether genomic profiling to identify undiagnosed individuals who are at increased risk for cardiovascular disease (CVD) leads to improved cardiovascular outcomes. Overall, the assessment was that there is insufficient evidence to support such testing in the general population. The net health benefit was deemed to be low, and clinical use was discouraged unless improved clinical outcomes are demonstrated in future research.

It is worth exploring the report further to understand the current state of knowledge and anticipate potential new developments. The evidence review looked at all eight genetic testing panels marketed for CVD risk prediction that were commercially available in February 2008. Collectively, 58 genetic variants were identified among 29 genes. Of these, 38 were reported to have some association with risk for coronary heart disease (CHD), consisting of coronary artery disease, ischemic heart disease, or myocardial infarction. Data for association with stroke were weaker.

Only two test manufacturers (deCODE Genetics and Interleukin Genetics) indicated that they are Clinical Laboratory Improvement Amendments–certified and provided detailed test method and validation information. For the other tests, there was frequently insufficient information to identify even the specific genetic variants being tested, much less the analytic validity of the approach. Nonetheless, the existing scientific technology was considered adequate to allow satisfactory accuracy and reliability of the tests for detecting genetic variation.

Regarding clinical utility, there are no published data on the long-term outcomes associated with genetic testing for CHD risk prediction. The anticipated benefit is improved identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease.

Among all the markers, 24 were deemed to have some degree of credibility and/or statistical significance. However, combining them in a statistical model did not provide a clinically useful stand-alone predictive test. Even the 9p21 genetic marker, when combined with traditional cardiac risk factors, provided only 0%-5% improvement in risk assessment for CHD.

Regarding clinical utility, there are no published data on the long-term outcomes associated with such testing. In theory, such tests would improve identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease. Potential harms must be considered, too. Among false positives – those identified incorrectly as being at increased risk for CHD – there may be unnecessarily increased anxiety and treatment-associated adverse events, without any reduction in morbidity or mortality. There also are financial costs associated with false positives. An additional risk is false reassurance for those who are at increased risk, but who are not identified by genetic testing. There is room for optimism, as early data suggest at least short-term cardiac risk reduction without clinical harms, but additional and longer-term studies are still needed.

There are notable limitations to this report. Most of the data so far were obtained in whites of European ancestry. Gene-disease associations and effect sizes may be quite different in other populations. In addition, many of the studies are relatively small and underpowered. Larger and newer studies are still identifying additional candidate genes and markers. Even more challenging is that we still do not know how to combine multiple genetic factors to develop a composite risk assessment, nor do we know how to combine a genetic risk assessment with traditional cardiac risk predications. Most models assume complete independence and simply multiply the odds ratio of each identified variant. However, this approach has not been validated and could yield falsely elevated or diminished risk scores.

The true power of genetic association studies to identify risk factors for common diseases may in fact not lie in better identification of those at increased risk. Rather, elucidation of the biological basis of such associations holds the promise of improving our understanding and eventual treatment of the underlying disease process.

So how should a physician deal with the availability of cardiac risk assessment tests? Clearly, these are best suited for people who consider themselves early adopters. The 9p21 marker appears to be the most clinically relevant marker at this point. Indeed, the risk associated with this genetic variant appears highest in younger individuals, so perhaps it might be appropriate to consider obtaining such information in a person under age 55 years who needs further encouragement to modify his or her cardiac risks.

Ideally, patients and their providers will engage in a discussion of the potential risks and benefits prior to any testing. Then, in the truest and oldest model of personalized medicine, they can decide together if pursuing such testing is appropriate on a case-by-case basis.

This column, "Genetics in Your Practice," regularly appears in Internal Medicine News, an Elsevier publication. Dr. Levy is an assistant professor in the division of general internal medicine and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore. Dr. Taylor is associate professor in the department of internal medicine and director of adult clinical genetics at the University of Colorado at Denver.

To respond to this column, e-mail Dr. Taylor at imnews@elsevier.com.

In the recent column, ("CAD: One Step Forward, One Step Back," Internal Medicine News digital network Oct. 28, 2010), Dr. Matthew Taylor discussed the "step backward" that occurred in genetic risk prediction of coronary artery disease (CAD) with the discovery that variations in the KIF6 gene might not be clinically associated with CAD after all. More research and vigorous discussion about the role of this gene in this disease – and about early clinical adoption of newly discovered risk factors in general – are sure to follow.

Of course, KIF6 is only one of many genetic markers that have been linked to CAD risk. Many are already in the public domain and available clinically through health care providers and via direct-to-consumer marketing. What are we to do with these?

One approach to answering such questions is to turn to an evidence review. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative seeks to evaluate genetic tests and innovations with respect to implementation in clinical practice and public health. Much like the U.S. Preventive Services Task Force, EGAPP commissions evidence reviews and then issues recommendations based upon the evidence. More information, as well as evidence reviews and recommendations reports, is available at www.egappreviews.org.

In December, EGAPP published a report on genomic profiling to assess cardiovascular risk (Genet. Med. 2010;12:839-43). The overarching question was whether genomic profiling to identify undiagnosed individuals who are at increased risk for cardiovascular disease (CVD) leads to improved cardiovascular outcomes. Overall, the assessment was that there is insufficient evidence to support such testing in the general population. The net health benefit was deemed to be low, and clinical use was discouraged unless improved clinical outcomes are demonstrated in future research.

It is worth exploring the report further to understand the current state of knowledge and anticipate potential new developments. The evidence review looked at all eight genetic testing panels marketed for CVD risk prediction that were commercially available in February 2008. Collectively, 58 genetic variants were identified among 29 genes. Of these, 38 were reported to have some association with risk for coronary heart disease (CHD), consisting of coronary artery disease, ischemic heart disease, or myocardial infarction. Data for association with stroke were weaker.

Only two test manufacturers (deCODE Genetics and Interleukin Genetics) indicated that they are Clinical Laboratory Improvement Amendments–certified and provided detailed test method and validation information. For the other tests, there was frequently insufficient information to identify even the specific genetic variants being tested, much less the analytic validity of the approach. Nonetheless, the existing scientific technology was considered adequate to allow satisfactory accuracy and reliability of the tests for detecting genetic variation.

Regarding clinical utility, there are no published data on the long-term outcomes associated with genetic testing for CHD risk prediction. The anticipated benefit is improved identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease.

Among all the markers, 24 were deemed to have some degree of credibility and/or statistical significance. However, combining them in a statistical model did not provide a clinically useful stand-alone predictive test. Even the 9p21 genetic marker, when combined with traditional cardiac risk factors, provided only 0%-5% improvement in risk assessment for CHD.

Regarding clinical utility, there are no published data on the long-term outcomes associated with such testing. In theory, such tests would improve identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease. Potential harms must be considered, too. Among false positives – those identified incorrectly as being at increased risk for CHD – there may be unnecessarily increased anxiety and treatment-associated adverse events, without any reduction in morbidity or mortality. There also are financial costs associated with false positives. An additional risk is false reassurance for those who are at increased risk, but who are not identified by genetic testing. There is room for optimism, as early data suggest at least short-term cardiac risk reduction without clinical harms, but additional and longer-term studies are still needed.

There are notable limitations to this report. Most of the data so far were obtained in whites of European ancestry. Gene-disease associations and effect sizes may be quite different in other populations. In addition, many of the studies are relatively small and underpowered. Larger and newer studies are still identifying additional candidate genes and markers. Even more challenging is that we still do not know how to combine multiple genetic factors to develop a composite risk assessment, nor do we know how to combine a genetic risk assessment with traditional cardiac risk predications. Most models assume complete independence and simply multiply the odds ratio of each identified variant. However, this approach has not been validated and could yield falsely elevated or diminished risk scores.

The true power of genetic association studies to identify risk factors for common diseases may in fact not lie in better identification of those at increased risk. Rather, elucidation of the biological basis of such associations holds the promise of improving our understanding and eventual treatment of the underlying disease process.

So how should a physician deal with the availability of cardiac risk assessment tests? Clearly, these are best suited for people who consider themselves early adopters. The 9p21 marker appears to be the most clinically relevant marker at this point. Indeed, the risk associated with this genetic variant appears highest in younger individuals, so perhaps it might be appropriate to consider obtaining such information in a person under age 55 years who needs further encouragement to modify his or her cardiac risks.

Ideally, patients and their providers will engage in a discussion of the potential risks and benefits prior to any testing. Then, in the truest and oldest model of personalized medicine, they can decide together if pursuing such testing is appropriate on a case-by-case basis.

This column, "Genetics in Your Practice," regularly appears in Internal Medicine News, an Elsevier publication. Dr. Levy is an assistant professor in the division of general internal medicine and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore. Dr. Taylor is associate professor in the department of internal medicine and director of adult clinical genetics at the University of Colorado at Denver.

To respond to this column, e-mail Dr. Taylor at imnews@elsevier.com.

In the recent column, ("CAD: One Step Forward, One Step Back," Internal Medicine News digital network Oct. 28, 2010), Dr. Matthew Taylor discussed the "step backward" that occurred in genetic risk prediction of coronary artery disease (CAD) with the discovery that variations in the KIF6 gene might not be clinically associated with CAD after all. More research and vigorous discussion about the role of this gene in this disease – and about early clinical adoption of newly discovered risk factors in general – are sure to follow.

Of course, KIF6 is only one of many genetic markers that have been linked to CAD risk. Many are already in the public domain and available clinically through health care providers and via direct-to-consumer marketing. What are we to do with these?

One approach to answering such questions is to turn to an evidence review. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) initiative seeks to evaluate genetic tests and innovations with respect to implementation in clinical practice and public health. Much like the U.S. Preventive Services Task Force, EGAPP commissions evidence reviews and then issues recommendations based upon the evidence. More information, as well as evidence reviews and recommendations reports, is available at www.egappreviews.org.

In December, EGAPP published a report on genomic profiling to assess cardiovascular risk (Genet. Med. 2010;12:839-43). The overarching question was whether genomic profiling to identify undiagnosed individuals who are at increased risk for cardiovascular disease (CVD) leads to improved cardiovascular outcomes. Overall, the assessment was that there is insufficient evidence to support such testing in the general population. The net health benefit was deemed to be low, and clinical use was discouraged unless improved clinical outcomes are demonstrated in future research.

It is worth exploring the report further to understand the current state of knowledge and anticipate potential new developments. The evidence review looked at all eight genetic testing panels marketed for CVD risk prediction that were commercially available in February 2008. Collectively, 58 genetic variants were identified among 29 genes. Of these, 38 were reported to have some association with risk for coronary heart disease (CHD), consisting of coronary artery disease, ischemic heart disease, or myocardial infarction. Data for association with stroke were weaker.

Only two test manufacturers (deCODE Genetics and Interleukin Genetics) indicated that they are Clinical Laboratory Improvement Amendments–certified and provided detailed test method and validation information. For the other tests, there was frequently insufficient information to identify even the specific genetic variants being tested, much less the analytic validity of the approach. Nonetheless, the existing scientific technology was considered adequate to allow satisfactory accuracy and reliability of the tests for detecting genetic variation.

Regarding clinical utility, there are no published data on the long-term outcomes associated with genetic testing for CHD risk prediction. The anticipated benefit is improved identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease.

Among all the markers, 24 were deemed to have some degree of credibility and/or statistical significance. However, combining them in a statistical model did not provide a clinically useful stand-alone predictive test. Even the 9p21 genetic marker, when combined with traditional cardiac risk factors, provided only 0%-5% improvement in risk assessment for CHD.

Regarding clinical utility, there are no published data on the long-term outcomes associated with such testing. In theory, such tests would improve identification of those at higher risk of disease, which could lead to improved clinical outcomes by virtue of increased efforts at risk reduction (through behavior change and pharmacologic treatment) and more aggressive screening for and management of manifest disease. Potential harms must be considered, too. Among false positives – those identified incorrectly as being at increased risk for CHD – there may be unnecessarily increased anxiety and treatment-associated adverse events, without any reduction in morbidity or mortality. There also are financial costs associated with false positives. An additional risk is false reassurance for those who are at increased risk, but who are not identified by genetic testing. There is room for optimism, as early data suggest at least short-term cardiac risk reduction without clinical harms, but additional and longer-term studies are still needed.

There are notable limitations to this report. Most of the data so far were obtained in whites of European ancestry. Gene-disease associations and effect sizes may be quite different in other populations. In addition, many of the studies are relatively small and underpowered. Larger and newer studies are still identifying additional candidate genes and markers. Even more challenging is that we still do not know how to combine multiple genetic factors to develop a composite risk assessment, nor do we know how to combine a genetic risk assessment with traditional cardiac risk predications. Most models assume complete independence and simply multiply the odds ratio of each identified variant. However, this approach has not been validated and could yield falsely elevated or diminished risk scores.

The true power of genetic association studies to identify risk factors for common diseases may in fact not lie in better identification of those at increased risk. Rather, elucidation of the biological basis of such associations holds the promise of improving our understanding and eventual treatment of the underlying disease process.

So how should a physician deal with the availability of cardiac risk assessment tests? Clearly, these are best suited for people who consider themselves early adopters. The 9p21 marker appears to be the most clinically relevant marker at this point. Indeed, the risk associated with this genetic variant appears highest in younger individuals, so perhaps it might be appropriate to consider obtaining such information in a person under age 55 years who needs further encouragement to modify his or her cardiac risks.

Ideally, patients and their providers will engage in a discussion of the potential risks and benefits prior to any testing. Then, in the truest and oldest model of personalized medicine, they can decide together if pursuing such testing is appropriate on a case-by-case basis.

This column, "Genetics in Your Practice," regularly appears in Internal Medicine News, an Elsevier publication. Dr. Levy is an assistant professor in the division of general internal medicine and McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore. Dr. Taylor is associate professor in the department of internal medicine and director of adult clinical genetics at the University of Colorado at Denver.

To respond to this column, e-mail Dr. Taylor at imnews@elsevier.com.