Clinical Endocrinology News

In seconds, Ravi Parikh, MD, an oncologist at the Emory University School of Medicine in Atlanta, had a summary of his patient’s entire medical history. Normally, Parikh skimmed the cumbersome files before seeing a patient. However, the artificial intelligence (AI) tool his institution was testing could list the highlights he needed in a fraction of the time.

“On the whole, I like it ... it saves me time,” Parikh said of the tool. “But I’d be lying if I told you it was perfect all the time. It’s interpreting the [patient] history in some ways that may be inaccurate,” he said.

Within the first week of testing the tool, Parikh started to notice that the large language model (LLM) made a particular mistake in his patients with prostate cancer. If their prostate-specific antigen test results came back slightly elevated — which is part of normal variation — the LLM recorded it as disease progression. Because Parikh reviews all his notes — with or without using an AI tool — after a visit, he easily caught the mistake before it was added to the chart. “The problem, I think, is if these mistakes go under the hood,” he said.

In the data science world, these mistakes are called hallucinations. And a growing body of research suggests they’re happening more frequently than is safe for healthcare. The industry promised LLMs would alleviate administrative burden and reduce physician burnout. But so far, studies show these AI-tool mistakes often create more work for doctors, not less. To truly help physicians and be safe for patients, some experts say healthcare needs to build its own LLMs from the ground up. And all agree that the field desperately needs a way to vet these algorithms more thoroughly.
 

Prone to Error

Right now, “I think the industry is focused on taking existing LLMs and forcing them into usage for healthcare,” said Nigam H. Shah, MBBS, PhD, chief data scientist for Stanford Health. However, the value of deploying general LLMs in the healthcare space is questionable. “People are starting to wonder if we’re using these tools wrong,” he told this news organization.

In 2023, Shah and his colleagues evaluated seven LLMs on their ability to answer electronic health record–based questions. For realistic tasks, the error rate in the best cases was about 35%, he said. “To me, that rate seems a bit high ... to adopt for routine use.”

A study earlier this year by the UC San Diego School of Medicine showed that using LLMs to respond to patient messages increased the time doctors spent on messages. And this summer, a study by the clinical AI firm Mendel found that when GPT-4o or Llama-3 were used to summarize patient medical records, almost every summary contained at least one type of hallucination.

“We’ve seen cases where a patient does have drug allergies, but the system says ‘no known drug allergies’ ” in the medical history summary, said Wael Salloum, PhD, cofounder and chief science officer at Mendel. “That’s a serious hallucination.” And if physicians have to constantly verify what the system is telling them, that “defeats the purpose [of summarization],” he said.
 

A Higher Quality Diet

Part of the trouble with LLMs is that there’s just not enough high-quality information to feed them. The algorithms are insatiable, requiring vast swaths of data for training. GPT-3.5, for instance, was trained on 570 GB of data from the internet, more than 300 billion words. And to train GPT-4o, OpenAI reportedly transcribed more than 1 million hours of YouTube content.

However, the strategies that built these general LLMs don’t always translate well to healthcare. The internet is full of low-quality or misleading health information from wellness sites and supplement advertisements. And even data that are trustworthy, like the millions of clinical studies and the US Food and Drug Administration (FDA) statements, can be outdated, Salloum said. And “an LLM in training can’t distinguish good from bad,” he added.

The good news is that clinicians don’t rely on controversial information in the real world. Medical knowledge is standardized. “Healthcare is a domain rich with explicit knowledge,” Salloum said. So there’s potential to build a more reliable LLM that is guided by robust medical standards and guidelines.

It’s possible that healthcare could use small language models, which are LLM’s pocket-sized cousins, and perform tasks needing only bite-sized datasets requiring fewer resources and easier fine-tuning, according to Microsoft’s website. Shah said training these smaller models on real medical data might be an option, like an LLM meant to respond to patient messages that could be trained with real messages sent by physicians.

Several groups are already working on databases of standardized human medical knowledge or real physician responses. “Perhaps that will work better than using LLMs trained on the general internet. Those studies need to be done,” Shah said.

Jon Tamir, assistant professor of electrical and computer engineering and co-lead of the AI Health Lab at The University of Texas at Austin, said, “The community has recognized that we are entering a new era of AI where the dataset itself is the most important aspect. We need training sets that are highly curated and highly specialized.

“If the dataset is highly specialized, it will definitely help reduce hallucinations,” he said.
 

Cutting Overconfidence

A major problem with LLM mistakes is that they are often hard to detect. Hallucinations can be highly convincing even if they’re highly inaccurate, according to Tamir.

When Shah, for instance, was recently testing an LLM on de-identified patient data, he asked the LLM which blood test the patient last had. The model responded with “complete blood count [CBC].” But when he asked for the results, the model gave him white blood count and other values. “Turns out that record did not have a CBC done at all! The result was entirely made up,” he said.

Making healthcare LLMs safer and more reliable will mean training AI to acknowledge potential mistakes and uncertainty. Existing LLMs are trained to project confidence and produce a lot of answers, even when there isn’t one, Salloum said. They rarely respond with “I don’t know” even when their prediction has low confidence, he added.

Healthcare stands to benefit from a system that highlights uncertainty and potential errors. For instance, if a patient’s history shows they have smoked, stopped smoking, vaped, and started smoking again. The LLM might call them a smoker but flag the comment as uncertain because the chronology is complicated, Salloum said.

Tamir added that this strategy could improve LLM and doctor collaboration by honing in on where human expertise is needed most.
 

Too Little Evaluation

For any improvement strategy to work, LLMs — and all AI-assisted healthcare tools — first need a better evaluation framework. So far, LLMs have “been used in really exciting ways but not really well-vetted ways,” Tamir said.

While some AI-assisted tools, particularly in medical imaging, have undergone rigorous FDA evaluations and earned approval, most haven’t. And because the FDA only regulates algorithms that are considered medical devices, Parikh said that most LLMs used for administrative tasks and efficiency don’t fall under the regulatory agency’s purview.

But these algorithms still have access to patient information and can directly influence patient and doctor decisions. Third-party regulatory agencies are expected to emerge, but it’s still unclear who those will be. Before developers can build a safer and more efficient LLM for healthcare, they’ll need better guidelines and guardrails. “Unless we figure out evaluation, how would we know whether the healthcare-appropriate large language models are better or worse?” Shah asked.
 

A version of this article appeared on Medscape.com.

In seconds, Ravi Parikh, MD, an oncologist at the Emory University School of Medicine in Atlanta, had a summary of his patient’s entire medical history. Normally, Parikh skimmed the cumbersome files before seeing a patient. However, the artificial intelligence (AI) tool his institution was testing could list the highlights he needed in a fraction of the time.

“On the whole, I like it ... it saves me time,” Parikh said of the tool. “But I’d be lying if I told you it was perfect all the time. It’s interpreting the [patient] history in some ways that may be inaccurate,” he said.

Within the first week of testing the tool, Parikh started to notice that the large language model (LLM) made a particular mistake in his patients with prostate cancer. If their prostate-specific antigen test results came back slightly elevated — which is part of normal variation — the LLM recorded it as disease progression. Because Parikh reviews all his notes — with or without using an AI tool — after a visit, he easily caught the mistake before it was added to the chart. “The problem, I think, is if these mistakes go under the hood,” he said.

In the data science world, these mistakes are called hallucinations. And a growing body of research suggests they’re happening more frequently than is safe for healthcare. The industry promised LLMs would alleviate administrative burden and reduce physician burnout. But so far, studies show these AI-tool mistakes often create more work for doctors, not less. To truly help physicians and be safe for patients, some experts say healthcare needs to build its own LLMs from the ground up. And all agree that the field desperately needs a way to vet these algorithms more thoroughly.
 

Prone to Error

Right now, “I think the industry is focused on taking existing LLMs and forcing them into usage for healthcare,” said Nigam H. Shah, MBBS, PhD, chief data scientist for Stanford Health. However, the value of deploying general LLMs in the healthcare space is questionable. “People are starting to wonder if we’re using these tools wrong,” he told this news organization.

In 2023, Shah and his colleagues evaluated seven LLMs on their ability to answer electronic health record–based questions. For realistic tasks, the error rate in the best cases was about 35%, he said. “To me, that rate seems a bit high ... to adopt for routine use.”

A study earlier this year by the UC San Diego School of Medicine showed that using LLMs to respond to patient messages increased the time doctors spent on messages. And this summer, a study by the clinical AI firm Mendel found that when GPT-4o or Llama-3 were used to summarize patient medical records, almost every summary contained at least one type of hallucination.

“We’ve seen cases where a patient does have drug allergies, but the system says ‘no known drug allergies’ ” in the medical history summary, said Wael Salloum, PhD, cofounder and chief science officer at Mendel. “That’s a serious hallucination.” And if physicians have to constantly verify what the system is telling them, that “defeats the purpose [of summarization],” he said.
 

A Higher Quality Diet

Part of the trouble with LLMs is that there’s just not enough high-quality information to feed them. The algorithms are insatiable, requiring vast swaths of data for training. GPT-3.5, for instance, was trained on 570 GB of data from the internet, more than 300 billion words. And to train GPT-4o, OpenAI reportedly transcribed more than 1 million hours of YouTube content.

However, the strategies that built these general LLMs don’t always translate well to healthcare. The internet is full of low-quality or misleading health information from wellness sites and supplement advertisements. And even data that are trustworthy, like the millions of clinical studies and the US Food and Drug Administration (FDA) statements, can be outdated, Salloum said. And “an LLM in training can’t distinguish good from bad,” he added.

The good news is that clinicians don’t rely on controversial information in the real world. Medical knowledge is standardized. “Healthcare is a domain rich with explicit knowledge,” Salloum said. So there’s potential to build a more reliable LLM that is guided by robust medical standards and guidelines.

It’s possible that healthcare could use small language models, which are LLM’s pocket-sized cousins, and perform tasks needing only bite-sized datasets requiring fewer resources and easier fine-tuning, according to Microsoft’s website. Shah said training these smaller models on real medical data might be an option, like an LLM meant to respond to patient messages that could be trained with real messages sent by physicians.

Several groups are already working on databases of standardized human medical knowledge or real physician responses. “Perhaps that will work better than using LLMs trained on the general internet. Those studies need to be done,” Shah said.

Jon Tamir, assistant professor of electrical and computer engineering and co-lead of the AI Health Lab at The University of Texas at Austin, said, “The community has recognized that we are entering a new era of AI where the dataset itself is the most important aspect. We need training sets that are highly curated and highly specialized.

“If the dataset is highly specialized, it will definitely help reduce hallucinations,” he said.
 

Cutting Overconfidence

A major problem with LLM mistakes is that they are often hard to detect. Hallucinations can be highly convincing even if they’re highly inaccurate, according to Tamir.

When Shah, for instance, was recently testing an LLM on de-identified patient data, he asked the LLM which blood test the patient last had. The model responded with “complete blood count [CBC].” But when he asked for the results, the model gave him white blood count and other values. “Turns out that record did not have a CBC done at all! The result was entirely made up,” he said.

Making healthcare LLMs safer and more reliable will mean training AI to acknowledge potential mistakes and uncertainty. Existing LLMs are trained to project confidence and produce a lot of answers, even when there isn’t one, Salloum said. They rarely respond with “I don’t know” even when their prediction has low confidence, he added.

Healthcare stands to benefit from a system that highlights uncertainty and potential errors. For instance, if a patient’s history shows they have smoked, stopped smoking, vaped, and started smoking again. The LLM might call them a smoker but flag the comment as uncertain because the chronology is complicated, Salloum said.

Tamir added that this strategy could improve LLM and doctor collaboration by honing in on where human expertise is needed most.
 

Too Little Evaluation

For any improvement strategy to work, LLMs — and all AI-assisted healthcare tools — first need a better evaluation framework. So far, LLMs have “been used in really exciting ways but not really well-vetted ways,” Tamir said.

While some AI-assisted tools, particularly in medical imaging, have undergone rigorous FDA evaluations and earned approval, most haven’t. And because the FDA only regulates algorithms that are considered medical devices, Parikh said that most LLMs used for administrative tasks and efficiency don’t fall under the regulatory agency’s purview.

But these algorithms still have access to patient information and can directly influence patient and doctor decisions. Third-party regulatory agencies are expected to emerge, but it’s still unclear who those will be. Before developers can build a safer and more efficient LLM for healthcare, they’ll need better guidelines and guardrails. “Unless we figure out evaluation, how would we know whether the healthcare-appropriate large language models are better or worse?” Shah asked.
 

A version of this article appeared on Medscape.com.

In seconds, Ravi Parikh, MD, an oncologist at the Emory University School of Medicine in Atlanta, had a summary of his patient’s entire medical history. Normally, Parikh skimmed the cumbersome files before seeing a patient. However, the artificial intelligence (AI) tool his institution was testing could list the highlights he needed in a fraction of the time.

“On the whole, I like it ... it saves me time,” Parikh said of the tool. “But I’d be lying if I told you it was perfect all the time. It’s interpreting the [patient] history in some ways that may be inaccurate,” he said.

Within the first week of testing the tool, Parikh started to notice that the large language model (LLM) made a particular mistake in his patients with prostate cancer. If their prostate-specific antigen test results came back slightly elevated — which is part of normal variation — the LLM recorded it as disease progression. Because Parikh reviews all his notes — with or without using an AI tool — after a visit, he easily caught the mistake before it was added to the chart. “The problem, I think, is if these mistakes go under the hood,” he said.

In the data science world, these mistakes are called hallucinations. And a growing body of research suggests they’re happening more frequently than is safe for healthcare. The industry promised LLMs would alleviate administrative burden and reduce physician burnout. But so far, studies show these AI-tool mistakes often create more work for doctors, not less. To truly help physicians and be safe for patients, some experts say healthcare needs to build its own LLMs from the ground up. And all agree that the field desperately needs a way to vet these algorithms more thoroughly.
 

Prone to Error

Right now, “I think the industry is focused on taking existing LLMs and forcing them into usage for healthcare,” said Nigam H. Shah, MBBS, PhD, chief data scientist for Stanford Health. However, the value of deploying general LLMs in the healthcare space is questionable. “People are starting to wonder if we’re using these tools wrong,” he told this news organization.

In 2023, Shah and his colleagues evaluated seven LLMs on their ability to answer electronic health record–based questions. For realistic tasks, the error rate in the best cases was about 35%, he said. “To me, that rate seems a bit high ... to adopt for routine use.”

A study earlier this year by the UC San Diego School of Medicine showed that using LLMs to respond to patient messages increased the time doctors spent on messages. And this summer, a study by the clinical AI firm Mendel found that when GPT-4o or Llama-3 were used to summarize patient medical records, almost every summary contained at least one type of hallucination.

“We’ve seen cases where a patient does have drug allergies, but the system says ‘no known drug allergies’ ” in the medical history summary, said Wael Salloum, PhD, cofounder and chief science officer at Mendel. “That’s a serious hallucination.” And if physicians have to constantly verify what the system is telling them, that “defeats the purpose [of summarization],” he said.
 

A Higher Quality Diet

Part of the trouble with LLMs is that there’s just not enough high-quality information to feed them. The algorithms are insatiable, requiring vast swaths of data for training. GPT-3.5, for instance, was trained on 570 GB of data from the internet, more than 300 billion words. And to train GPT-4o, OpenAI reportedly transcribed more than 1 million hours of YouTube content.

However, the strategies that built these general LLMs don’t always translate well to healthcare. The internet is full of low-quality or misleading health information from wellness sites and supplement advertisements. And even data that are trustworthy, like the millions of clinical studies and the US Food and Drug Administration (FDA) statements, can be outdated, Salloum said. And “an LLM in training can’t distinguish good from bad,” he added.

The good news is that clinicians don’t rely on controversial information in the real world. Medical knowledge is standardized. “Healthcare is a domain rich with explicit knowledge,” Salloum said. So there’s potential to build a more reliable LLM that is guided by robust medical standards and guidelines.

It’s possible that healthcare could use small language models, which are LLM’s pocket-sized cousins, and perform tasks needing only bite-sized datasets requiring fewer resources and easier fine-tuning, according to Microsoft’s website. Shah said training these smaller models on real medical data might be an option, like an LLM meant to respond to patient messages that could be trained with real messages sent by physicians.

Several groups are already working on databases of standardized human medical knowledge or real physician responses. “Perhaps that will work better than using LLMs trained on the general internet. Those studies need to be done,” Shah said.

Jon Tamir, assistant professor of electrical and computer engineering and co-lead of the AI Health Lab at The University of Texas at Austin, said, “The community has recognized that we are entering a new era of AI where the dataset itself is the most important aspect. We need training sets that are highly curated and highly specialized.

“If the dataset is highly specialized, it will definitely help reduce hallucinations,” he said.
 

Cutting Overconfidence

A major problem with LLM mistakes is that they are often hard to detect. Hallucinations can be highly convincing even if they’re highly inaccurate, according to Tamir.

When Shah, for instance, was recently testing an LLM on de-identified patient data, he asked the LLM which blood test the patient last had. The model responded with “complete blood count [CBC].” But when he asked for the results, the model gave him white blood count and other values. “Turns out that record did not have a CBC done at all! The result was entirely made up,” he said.

Making healthcare LLMs safer and more reliable will mean training AI to acknowledge potential mistakes and uncertainty. Existing LLMs are trained to project confidence and produce a lot of answers, even when there isn’t one, Salloum said. They rarely respond with “I don’t know” even when their prediction has low confidence, he added.

Healthcare stands to benefit from a system that highlights uncertainty and potential errors. For instance, if a patient’s history shows they have smoked, stopped smoking, vaped, and started smoking again. The LLM might call them a smoker but flag the comment as uncertain because the chronology is complicated, Salloum said.

Tamir added that this strategy could improve LLM and doctor collaboration by honing in on where human expertise is needed most.
 

Too Little Evaluation

For any improvement strategy to work, LLMs — and all AI-assisted healthcare tools — first need a better evaluation framework. So far, LLMs have “been used in really exciting ways but not really well-vetted ways,” Tamir said.

While some AI-assisted tools, particularly in medical imaging, have undergone rigorous FDA evaluations and earned approval, most haven’t. And because the FDA only regulates algorithms that are considered medical devices, Parikh said that most LLMs used for administrative tasks and efficiency don’t fall under the regulatory agency’s purview.

But these algorithms still have access to patient information and can directly influence patient and doctor decisions. Third-party regulatory agencies are expected to emerge, but it’s still unclear who those will be. Before developers can build a safer and more efficient LLM for healthcare, they’ll need better guidelines and guardrails. “Unless we figure out evaluation, how would we know whether the healthcare-appropriate large language models are better or worse?” Shah asked.
 

A version of this article appeared on Medscape.com.

From the largest healthcare companies to solo practices, just every organization in medicine faces a risk for costly cyberattacks. In recent years, hackers have threatened to release the personal information of patients and employees — or paralyze online systems — unless they’re paid a ransom.

Should companies pay? It’s not an easy answer, a pair of experts told colleagues in an American Medical Association (AMA) cybersecurity webinar on October 18. It turns out that each choice — pay or don’t pay — can end up being costly.

This is just one of the new challenges facing the American medical system on the cybersecurity front, the speakers said. Others include the possibility that hackers will manipulate patient data — turning a medical test negative, for example, when it’s actually positive — and take advantage of the powers of artificial intelligence (AI).

The AMA held the webinar to educate physicians about cybersecurity risks and defenses, an especially hot topic in the wake of February’s Change Healthcare hack, which cost UnitedHealth Group an estimated $2.5 billion — so far — and deeply disrupted the American healthcare system.

Cautionary tales abound. Greg Garcia, executive director for cybersecurity of the Health Sector Coordinating Council, a coalition of medical industry organizations, pointed to a Pennsylvania clinic that refused to pay a ransom to prevent the release of hundreds of images of patients with breast cancer undressed from the waist up. Garcia told webinar participants that the ransom was $5 million.
 

Risky Choices

While the Federal Bureau of Investigation recommends against paying a ransom, this can be a risky choice, Garcia said. Hackers released the images, and the center has reportedly agreed to settle a class-action lawsuit for $65 million. “They traded $5 million for $60 million,” Garcia added, slightly misstating the settlement amount.

Health systems have been cagey about whether they’ve paid ransoms to prevent private data from being made public in cyberattacks. If a ransom is demanded, “it’s every organization for itself,” Garcia said.

He highlighted the case of a chain of psychiatry practices in Finland that suffered a ransomware attack in 2020. The hackers “contacted the patients and said: ‘Hey, call your clinic and tell them to pay the ransom. Otherwise, we’re going to release all your psychiatric notes to the public.’ ”

Cyberattacks continue. In October, Boston Children’s Health Physicians announced that it had suffered a “ recent security incident” involving data — possibly including Social Security numbers and treatment information — regarding patients and employees. A hacker group reportedly claimed responsibility and wants the system, which boasts more than 300 clinicians, to pay a ransom or else it will release the stolen information.
 

Should Paying Ransom Be a Crime?

Christian Dameff, MD, MS, an emergency medicine physician and director of the Center for Healthcare Cybersecurity at the University of California (UC), San Diego, noted that there are efforts to turn paying ransom into a crime. “If people aren’t paying ransoms, then ransomware operators will move to something else that makes them money.”

Dameff urged colleagues to understand we no longer live in a world where clinicians only bother to think of technology when they call the IT department to help them reset their password.

New challenges face clinicians, he said. “How do we develop better strategies, downtime procedures, and safe clinical care in an era where our vital technology may be gone, not just for an hour or 2, but as is the case with these ransomware attacks, sometimes weeks to months.”

Garcia said “cybersecurity is everybody’s responsibility, including frontline clinicians. Because you’re touching data, you’re touching technology, you’re touching patients, and all of those things combine to present some vulnerabilities in the digital world.”
 

Next Frontier: Hackers May Manipulate Patient Data

Dameff said future hackers may use AI to manipulate individual patient data in ways that threaten patient health. AI makes this easier to accomplish.

“What if I delete your allergies in your electronic health record, or I manipulate your chest x-ray, or I change your lab values so it looks like you’re in diabetic ketoacidosis when you’re not so a clinician gives you insulin when you don’t need it?”

Garcia highlighted another new threat: Phishing efforts that are harder to ignore thanks to AI.

“One of the most successful way that hackers get in, disrupt systems, and steal data is through email phishing, and it’s only going to get better because of artificial intelligence,” he said. “No longer are you going to have typos in that email written by a hacking group in Nigeria or in China. It’s going to be perfect looking.”

What can practices and healthcare systems do? Garcia highlighted federal health agency efforts to encourage organizations to adopt best practices in cybersecurity.

“If you’ve got a data breach, and you can show to the US Department of Health & Human Services [HHS] you have implemented generally recognized cybersecurity controls over the past year, that you have done your best, you did the right thing, and you still got hit, HHS is directed to essentially take it easy on you,” he said. “That’s a positive incentive.”
 

Ransomware Guide in the Works

Dameff said UC San Diego’s Center for Healthcare Cybersecurity plans to publish a free cybersecurity guide in 2025 that will include specific information about ransomware attacks for medical specialties such as cardiology, trauma surgery, and pediatrics.

“Then, should you ever be ransomed, you can pull out this guide. You’ll know what’s going to kind of happen, and you can better prepare for those effects.”

Will the future president prioritize healthcare cybersecurity? That remains to be seen, but crises do have the capacity to concentrate the mind, experts said.

The nation’s capital “has a very short memory, a short attention span. The policymakers tend to be reactive,” Dameff said. “All it takes is yet another Change Healthcare–like attack that disrupts 30% or more of the nation’s healthcare system for the policymakers to sit up, take notice, and try to come up with solutions.”

In addition, he said, an estimated two data breaches/ransomware attacks are occurring per day. “The fact is that we’re all patients, up to the President of the United States and every member of the Congress is a patient.”

There’s a “very existential, very palpable understanding that cyber safety is patient safety and cyber insecurity is patient insecurity,” Dameff said.

A version of this article appeared on Medscape.com.

From the largest healthcare companies to solo practices, just every organization in medicine faces a risk for costly cyberattacks. In recent years, hackers have threatened to release the personal information of patients and employees — or paralyze online systems — unless they’re paid a ransom.

Should companies pay? It’s not an easy answer, a pair of experts told colleagues in an American Medical Association (AMA) cybersecurity webinar on October 18. It turns out that each choice — pay or don’t pay — can end up being costly.

This is just one of the new challenges facing the American medical system on the cybersecurity front, the speakers said. Others include the possibility that hackers will manipulate patient data — turning a medical test negative, for example, when it’s actually positive — and take advantage of the powers of artificial intelligence (AI).

The AMA held the webinar to educate physicians about cybersecurity risks and defenses, an especially hot topic in the wake of February’s Change Healthcare hack, which cost UnitedHealth Group an estimated $2.5 billion — so far — and deeply disrupted the American healthcare system.

Cautionary tales abound. Greg Garcia, executive director for cybersecurity of the Health Sector Coordinating Council, a coalition of medical industry organizations, pointed to a Pennsylvania clinic that refused to pay a ransom to prevent the release of hundreds of images of patients with breast cancer undressed from the waist up. Garcia told webinar participants that the ransom was $5 million.
 

Risky Choices

While the Federal Bureau of Investigation recommends against paying a ransom, this can be a risky choice, Garcia said. Hackers released the images, and the center has reportedly agreed to settle a class-action lawsuit for $65 million. “They traded $5 million for $60 million,” Garcia added, slightly misstating the settlement amount.

Health systems have been cagey about whether they’ve paid ransoms to prevent private data from being made public in cyberattacks. If a ransom is demanded, “it’s every organization for itself,” Garcia said.

He highlighted the case of a chain of psychiatry practices in Finland that suffered a ransomware attack in 2020. The hackers “contacted the patients and said: ‘Hey, call your clinic and tell them to pay the ransom. Otherwise, we’re going to release all your psychiatric notes to the public.’ ”

Cyberattacks continue. In October, Boston Children’s Health Physicians announced that it had suffered a “ recent security incident” involving data — possibly including Social Security numbers and treatment information — regarding patients and employees. A hacker group reportedly claimed responsibility and wants the system, which boasts more than 300 clinicians, to pay a ransom or else it will release the stolen information.
 

Should Paying Ransom Be a Crime?

Christian Dameff, MD, MS, an emergency medicine physician and director of the Center for Healthcare Cybersecurity at the University of California (UC), San Diego, noted that there are efforts to turn paying ransom into a crime. “If people aren’t paying ransoms, then ransomware operators will move to something else that makes them money.”

Dameff urged colleagues to understand we no longer live in a world where clinicians only bother to think of technology when they call the IT department to help them reset their password.

New challenges face clinicians, he said. “How do we develop better strategies, downtime procedures, and safe clinical care in an era where our vital technology may be gone, not just for an hour or 2, but as is the case with these ransomware attacks, sometimes weeks to months.”

Garcia said “cybersecurity is everybody’s responsibility, including frontline clinicians. Because you’re touching data, you’re touching technology, you’re touching patients, and all of those things combine to present some vulnerabilities in the digital world.”
 

Next Frontier: Hackers May Manipulate Patient Data

Dameff said future hackers may use AI to manipulate individual patient data in ways that threaten patient health. AI makes this easier to accomplish.

“What if I delete your allergies in your electronic health record, or I manipulate your chest x-ray, or I change your lab values so it looks like you’re in diabetic ketoacidosis when you’re not so a clinician gives you insulin when you don’t need it?”

Garcia highlighted another new threat: Phishing efforts that are harder to ignore thanks to AI.

“One of the most successful way that hackers get in, disrupt systems, and steal data is through email phishing, and it’s only going to get better because of artificial intelligence,” he said. “No longer are you going to have typos in that email written by a hacking group in Nigeria or in China. It’s going to be perfect looking.”

What can practices and healthcare systems do? Garcia highlighted federal health agency efforts to encourage organizations to adopt best practices in cybersecurity.

“If you’ve got a data breach, and you can show to the US Department of Health & Human Services [HHS] you have implemented generally recognized cybersecurity controls over the past year, that you have done your best, you did the right thing, and you still got hit, HHS is directed to essentially take it easy on you,” he said. “That’s a positive incentive.”
 

Ransomware Guide in the Works

Dameff said UC San Diego’s Center for Healthcare Cybersecurity plans to publish a free cybersecurity guide in 2025 that will include specific information about ransomware attacks for medical specialties such as cardiology, trauma surgery, and pediatrics.

“Then, should you ever be ransomed, you can pull out this guide. You’ll know what’s going to kind of happen, and you can better prepare for those effects.”

Will the future president prioritize healthcare cybersecurity? That remains to be seen, but crises do have the capacity to concentrate the mind, experts said.

The nation’s capital “has a very short memory, a short attention span. The policymakers tend to be reactive,” Dameff said. “All it takes is yet another Change Healthcare–like attack that disrupts 30% or more of the nation’s healthcare system for the policymakers to sit up, take notice, and try to come up with solutions.”

In addition, he said, an estimated two data breaches/ransomware attacks are occurring per day. “The fact is that we’re all patients, up to the President of the United States and every member of the Congress is a patient.”

There’s a “very existential, very palpable understanding that cyber safety is patient safety and cyber insecurity is patient insecurity,” Dameff said.

A version of this article appeared on Medscape.com.

From the largest healthcare companies to solo practices, just every organization in medicine faces a risk for costly cyberattacks. In recent years, hackers have threatened to release the personal information of patients and employees — or paralyze online systems — unless they’re paid a ransom.

Should companies pay? It’s not an easy answer, a pair of experts told colleagues in an American Medical Association (AMA) cybersecurity webinar on October 18. It turns out that each choice — pay or don’t pay — can end up being costly.

This is just one of the new challenges facing the American medical system on the cybersecurity front, the speakers said. Others include the possibility that hackers will manipulate patient data — turning a medical test negative, for example, when it’s actually positive — and take advantage of the powers of artificial intelligence (AI).

The AMA held the webinar to educate physicians about cybersecurity risks and defenses, an especially hot topic in the wake of February’s Change Healthcare hack, which cost UnitedHealth Group an estimated $2.5 billion — so far — and deeply disrupted the American healthcare system.

Cautionary tales abound. Greg Garcia, executive director for cybersecurity of the Health Sector Coordinating Council, a coalition of medical industry organizations, pointed to a Pennsylvania clinic that refused to pay a ransom to prevent the release of hundreds of images of patients with breast cancer undressed from the waist up. Garcia told webinar participants that the ransom was $5 million.
 

Risky Choices

While the Federal Bureau of Investigation recommends against paying a ransom, this can be a risky choice, Garcia said. Hackers released the images, and the center has reportedly agreed to settle a class-action lawsuit for $65 million. “They traded $5 million for $60 million,” Garcia added, slightly misstating the settlement amount.

Health systems have been cagey about whether they’ve paid ransoms to prevent private data from being made public in cyberattacks. If a ransom is demanded, “it’s every organization for itself,” Garcia said.

He highlighted the case of a chain of psychiatry practices in Finland that suffered a ransomware attack in 2020. The hackers “contacted the patients and said: ‘Hey, call your clinic and tell them to pay the ransom. Otherwise, we’re going to release all your psychiatric notes to the public.’ ”

Cyberattacks continue. In October, Boston Children’s Health Physicians announced that it had suffered a “ recent security incident” involving data — possibly including Social Security numbers and treatment information — regarding patients and employees. A hacker group reportedly claimed responsibility and wants the system, which boasts more than 300 clinicians, to pay a ransom or else it will release the stolen information.
 

Should Paying Ransom Be a Crime?

Christian Dameff, MD, MS, an emergency medicine physician and director of the Center for Healthcare Cybersecurity at the University of California (UC), San Diego, noted that there are efforts to turn paying ransom into a crime. “If people aren’t paying ransoms, then ransomware operators will move to something else that makes them money.”

Dameff urged colleagues to understand we no longer live in a world where clinicians only bother to think of technology when they call the IT department to help them reset their password.

New challenges face clinicians, he said. “How do we develop better strategies, downtime procedures, and safe clinical care in an era where our vital technology may be gone, not just for an hour or 2, but as is the case with these ransomware attacks, sometimes weeks to months.”

Garcia said “cybersecurity is everybody’s responsibility, including frontline clinicians. Because you’re touching data, you’re touching technology, you’re touching patients, and all of those things combine to present some vulnerabilities in the digital world.”
 

Next Frontier: Hackers May Manipulate Patient Data

Dameff said future hackers may use AI to manipulate individual patient data in ways that threaten patient health. AI makes this easier to accomplish.

“What if I delete your allergies in your electronic health record, or I manipulate your chest x-ray, or I change your lab values so it looks like you’re in diabetic ketoacidosis when you’re not so a clinician gives you insulin when you don’t need it?”

Garcia highlighted another new threat: Phishing efforts that are harder to ignore thanks to AI.

“One of the most successful way that hackers get in, disrupt systems, and steal data is through email phishing, and it’s only going to get better because of artificial intelligence,” he said. “No longer are you going to have typos in that email written by a hacking group in Nigeria or in China. It’s going to be perfect looking.”

What can practices and healthcare systems do? Garcia highlighted federal health agency efforts to encourage organizations to adopt best practices in cybersecurity.

“If you’ve got a data breach, and you can show to the US Department of Health & Human Services [HHS] you have implemented generally recognized cybersecurity controls over the past year, that you have done your best, you did the right thing, and you still got hit, HHS is directed to essentially take it easy on you,” he said. “That’s a positive incentive.”
 

Ransomware Guide in the Works

Dameff said UC San Diego’s Center for Healthcare Cybersecurity plans to publish a free cybersecurity guide in 2025 that will include specific information about ransomware attacks for medical specialties such as cardiology, trauma surgery, and pediatrics.

“Then, should you ever be ransomed, you can pull out this guide. You’ll know what’s going to kind of happen, and you can better prepare for those effects.”

Will the future president prioritize healthcare cybersecurity? That remains to be seen, but crises do have the capacity to concentrate the mind, experts said.

The nation’s capital “has a very short memory, a short attention span. The policymakers tend to be reactive,” Dameff said. “All it takes is yet another Change Healthcare–like attack that disrupts 30% or more of the nation’s healthcare system for the policymakers to sit up, take notice, and try to come up with solutions.”

In addition, he said, an estimated two data breaches/ransomware attacks are occurring per day. “The fact is that we’re all patients, up to the President of the United States and every member of the Congress is a patient.”

There’s a “very existential, very palpable understanding that cyber safety is patient safety and cyber insecurity is patient insecurity,” Dameff said.

A version of this article appeared on Medscape.com.

This transcript has been edited for clarity .

Robert A. Harrington, MD: I’m here in London at the European Society of Cardiology meetings, at theheart.org | Medscape Cardiology booth, using the meetings as an opportunity to meet with colleagues to talk about recent things that they’ve been writing about.

Today I’m joined by a good friend and colleague, Dr. Dhruv Kazi from Beth Israel Deaconess in Boston. Thanks for joining us.

Dhruv S. Kazi, MD, MS: Thank you for having me.

Harrington: Dr. Kazi is an associate professor of medicine at Harvard Medical School. He’s also the associate director of the Smith Center, which is an outcomes research center at the Beth Israel Deaconess. Thanks for joining us.

Kazi: Excited to be here.

Harrington: The topic I think you know that I want to discuss is a really important paper. There are two papers. They’re part of the American Heart Association’s 100th anniversary celebration, if you will. Many of the papers looked back at where science taken us.

With your coauthor, Karen Joynt Maddox, your papers are looking forward. They’re about the burden of cardiovascular disease in 2050. One paper really focused on what I would call the clinical and public health issues. Yours is focused on the economics. Is that a good description?

Kazi: Perfect.

Harrington: Tell us what you, Karen, and the other writers set out to do. What were you asked to do?

Kazi: As you know, the American Heart Association is entering its second century. Part of this was an exercise to say, where will the country be in 2050, which is a long enough time horizon for us to start planning for the future. What are the conditions that affect the magnitude of the disease, and the kinds of people who will be affected, that we should be aware of?

We looked back and said, if prior trends remain the same, where will we be in 2050, accounting for changes in demographics, changes in the composition of the population, and knowing that some of the cardiovascular risk factors are getting worse?

Harrington: For me, what was really striking is that, when I first saw the title and read “2050,” I thought, Oh, that’s a long way away. Then as I started reading it, I realized that this is not so far away.

Kazi: Absolutely.

Harrington: If we’re going to make a difference, it might take us 25 years.

Kazi: Especially if we set ourselves ambitious goals, we›re going to have to dig deep. Business-as-usual is not going to get us there.

Harrington: No. What I think has happened is we›ve spent so much time taking care of acute illness. Case fatality rates are fantastic. I was actually making the comment yesterday to a colleague that when I was an intern, the 30-day death rate from acute myocardial infarction was about 20%.

Kazi: Oh, wow.

Harrington: Now it’s 5%. That’s a big difference in a career.
 

Trends in the Wrong Direction

Kazi: There are fundamental trends. The decline in case fatalities is a really positive development, and I would hope that, going forward, that would continue. Those are risk-adjusted death rates and what is happening is that risk is going up. This is a function of the fact that the US population is aging; 2030 will be the first year that all the baby boomers will be over the age of 65.

By the mid-2030s, we’ll have more adults over the age of 65 than kids. That aging of the population is going to increase risk. The second is — and this is a positive development — we are a more diverse population, but the populations that are minoritized have higher cardiovascular risk, for a variety of reasons.

As the population of Asian Americans increases and doubles, in fact, as the population of Hispanic Americans doubles, we’re going to see an increase in risk related to cardiovascular disease. The third is that, over the past decade, there are some risk factors that are going in the wrong direction.

Harrington: Let’s talk about that because that’s humbling. I’m involved, as you know, with the American Heart Association, as are you. Despite all the work on Life’s Simple 7 and now Life’s Essential 8, we still have some issues.

Kazi: The big ones that come to mind are hypertension, diabetes, and obesity, all of which are trending in the wrong direction. Hypertension, we were gaining traction; and then over the past decade, we’ve slipped again. As you know, national blood pressure control rates have declined in many populations.

Harrington: Rather substantially.

Kazi: Substantially so, which has implications, in particular, for stroke rates in the future and stroke rates in young adults in the future. Obesity is a problem that we have very little control over. We’re already at 40% on average, which means that some populations are already in the 60% range.

Harrington: We also have obesity in kids — the burden, I’ll call it, of obesity. It’s not that you become obese in your thirties or your forties; you›re becoming obese as a teenager or even younger.

Kazi: Exactly. Since the 1990s, obesity in US adults has doubled, but obesity in US children has quadrupled. It’s starting from a lower base, but it’s very much an escalating problem.

Harrington: Diabetes is tightly linked to it but not totally explained.

Kazi: Exactly. The increase in diabetes is largely driven by obesity, but it›s probably also driven by changes in diet and lifestyle that don›t go through obesity.

Harrington: Yeah, it’s interesting. I think I have this figure correctly. It used to be rare that you saw a child with type 2 diabetes or what we call type 2 diabetes.

Kazi: Yeah.

Harrington: Now, the vast majority of kids with diabetes have type 2 diabetes.

Kazi: In the adolescents/young adults age group, most of it is type 2.

Harrington: Diabetes going up, obesity up, hypertension not well controlled, smoking combustible cigarettes way down.

Kazi: Yeah.

Harrington: Cholesterol levels. I was surprised. Cholesterol looked better. You said — because I was at a meeting where somebody asked you — that’s not explained by treatment.

Kazi: No, it’s not, at least going back to the ‘70s, but likely even sooner. I think that can only be attributed to substantial dietary changes. We are consuming less fat and less trans-fat. It’s possible that those collectively are improving our cholesterol levels, possibly at the expense of our glucose levels, because we basically substituted fats in our diet with more carbs at a population level.
 

Cigarettes and Vaping

Harrington: Some things certainly trend in the right direction but others in a really difficult direction. It’s going to lead to pretty large changes in risk for coronary disease, atrial fibrillation, and heart failure.

Kazi: I want to go back to the tobacco point. There are definitely marked declines in tobacco, still tightly related to income in the country. You see much higher prevalence of tobacco use in lower-income populations, but it’s unclear to me where it’s going in kids. We know that combustible tobacco use is going down but e-cigarettes went up. What that leads to over the next 30 years is unclear to me.

Harrington: That is a really important comment that’s worth sidebarring. The vaping use has been a terrible epidemic among our high schoolers. What is that going to lead to? Is it going to lead to the use of combustible cigarettes and we’re going to see that go back up? It remains to be seen.

Kazi: Yes, it remains to be seen. Going back to your point about this change in risk factors and this change in demographics, both aging and becoming a more diverse population means that we have large increases in some healthcare conditions.

Coronary heart disease goes up some, there›s a big jump in stroke — nearly a doubling in stroke — which is related to hypertension, obesity, an aging population, and a more diverse population. There are changes in stroke in the young, and atrial fibrillation related to, again, hypertension. We’re seeing these projections, and with them come these pretty large projections in changes in healthcare spending.
 

Healthcare Spending Not Sustainable

Harrington: Big. I mean, it’s not sustainable. Give the audience the number — it’s pretty frightening.

Kazi: We’re talking about a quadrupling of healthcare costs related to cardiovascular disease over 25 years. We’ve gotten used to the narrative that healthcare in the US is expensive and drugs are expensive, but this is an enormous problem — an unsustainable problem, like you called it.

It’s a doubling as a proportion of the economy. I was looking this up this morning. If the US healthcare economy were its own economy, it would be the fourth largest economy in the world.

Harrington: Healthcare as it is today, is it 21% of our economy?

Kazi: It’s 17% now. If it were its own economy, it would be the fourth largest in the world. We are spending more on healthcare than all but two other countries’ total economies. It’s kind of crazy.

Harrington: We’re talking about a quadrupling.

Kazi: Within that, the cardiovascular piece is a big piece, and we›re talking about a quadrupling.

Harrington: That’s both direct and indirect costs.

Kazi: The quadrupling of costs is just the direct costs. Indirect costs, for the listeners, refer to costs unrelated to healthcare but changes in productivity, either because people are disabled and unable to participate fully in the workforce or they die early.

The productivity costs are also increased substantially as a result. If you look at both healthcare and productivity, that goes up threefold. These are very large changes.

Harrington: Let’s now get to what we can do about it. I made the comment to you when I first read the papers that I was very depressed. Then, after I went through my Kübler-Ross stages of depression, death, and dying, I came to acceptance.

What are we going to do about it? This is a focus on policy, but also a focus on how we deliver healthcare, how we think about healthcare, and how we develop drugs and devices.

The drug question is going to be the one the audience is thinking about. They say, well, what about GLP-1 agonists? Aren’t those going to save the day?

Kazi: Yes and no. I’ll say that, early in my career, I used to be very attracted to simple solutions to complex problems. I’ve come to realize that simple solutions are elegant, attractive, and wrong. We›re dealing with a very complex issue and I think we’re going to need a multipronged approach.

The way I think about it is that there was a group of people who are at very high risk today. How do we help those individuals? Then how do we help the future generation so that they’re not dealing with the projections that we’re talking about.

My colleague, Karen Joynt Maddox, who led one of the papers, as you mentioned, has an elegant line in the paper where she says projections are not destiny. These are things we can change.

Harrington: If nothing changes, this is what it’s going to look like.

Kazi: This is where we’re headed.

Harrington: We can change. We’ve got some time to change, but we don’t have forever.

Kazi: Yes, exactly. We picked the 25-year timeline instead of a “let’s plan for the next century” timeline because we want something concrete and actionable. It’s close enough to be meaningful but far enough to give us the runway we need to act.

Harrington: Give me two things from the policy perspective, because it’s mostly policy.

Kazi: There are policy and clinical interventions. From the policy perspective, if I had to list two things, one is expansion of access to care. As we talk about this big increase in the burden of disease and risk factors, if you have a large proportion of your population that has hypertension or diabetes, you’re going to have to expand access to care to ensure that people get treated so they can get access to this care before they develop the complications that we worry about, like stroke and heart disease, that are very expensive to treat downstream.

The second, more broadly related to access to care, is the access to medications that are effective. You bring up GLP-1s. I think we need a real strategy for how we can give people access to GLP-1s at a price that is affordable to individuals but also affordable to the health system, and to help them stay on the drugs.

GLP-1s are transformative in what they do for weight loss and for diabetes, but more than 50% of people who start one are off it at 12 months. There’s something fundamentally wrong about how we’re delivering GLP-1s today. It’s not just about the cost of the drugs but the support system people need to stay on.

Harrington: I’ve made the comment, in many forms now, that we know the drugs work. We have to figure out how to use them.

Kazi: Exactly, yes.

Harrington: Using them includes chronicity. This is a chronic condition. Some people can come off the drugs, but many can’t. We’re going to have to figure this out, and maybe the newer generations of drugs will help us address what people call the off-ramping. How are we going to do that? I think you’re spot-on. Those are critically important questions.

Kazi: As we looked at this modeling, I’ll tell you — I had a come-to-Jesus moment where I was like, there is no way to fix cardiovascular disease in the US without going through obesity and diabetes. We have to address obesity in the US. We can’t just treat our way out of it. Obesity is fundamentally a food problem and we’ve got to engage again with food policy in a meaningful way.

Harrington: As you know, with the American Heart Association, we›re doing a large amount of work now on food as medicine and food is medicine. We are trying to figure out what the levers are that we can pull to actually help people eat healthier diets.

Kazi: Yes. Rather than framing it as an individual choice that people are eating poorly, it’s, how do we make healthy diets the default in the environment?

Harrington: This is where you get to the children as well.

Kazi: Exactly.

Harrington: I could talk about this all day. I’ve had the benefit of reading the papers now a few times and talking to you on several occasions. Thank you for joining us.

Kazi: Thank you.
 

Dr. Harrington, Stephen and Suzanne Weiss Dean, Weill Cornell Medicine; Provost for Medical Affairs, Cornell University, New York, NY, disclosed ties with Baim Institute (DSMB); CSL (RCT Executive Committee); Janssen (RCT Char), NHLBI (RCT Executive Committee, DSMB Chair); PCORI (RCT Co-Chair); DCRI, Atropos Health; Bitterroot Bio; Bristol Myers Squibb; BridgeBio; Element Science; Edwards Lifesciences; Foresite Labs; Medscape/WebMD Board of Directors for: American Heart Association; College of the Holy Cross; and Cytokinetics. Dr. Kazi, Associate Director, Smith Center for Outcomes Research, Associate Professor, Department of Medicine (Cardiology), Harvard Medical School, Director, Department of Cardiac Critical Care Unit, Beth Israel Deaconess Medical Center, Boston, Massachusetts, has disclosed receiving a research grant from Boston Scientific (grant to examine the economics of stroke prevention).

A version of this article appeared on Medscape.com.

This transcript has been edited for clarity .

Robert A. Harrington, MD: I’m here in London at the European Society of Cardiology meetings, at theheart.org | Medscape Cardiology booth, using the meetings as an opportunity to meet with colleagues to talk about recent things that they’ve been writing about.

Today I’m joined by a good friend and colleague, Dr. Dhruv Kazi from Beth Israel Deaconess in Boston. Thanks for joining us.

Dhruv S. Kazi, MD, MS: Thank you for having me.

Harrington: Dr. Kazi is an associate professor of medicine at Harvard Medical School. He’s also the associate director of the Smith Center, which is an outcomes research center at the Beth Israel Deaconess. Thanks for joining us.

Kazi: Excited to be here.

Harrington: The topic I think you know that I want to discuss is a really important paper. There are two papers. They’re part of the American Heart Association’s 100th anniversary celebration, if you will. Many of the papers looked back at where science taken us.

With your coauthor, Karen Joynt Maddox, your papers are looking forward. They’re about the burden of cardiovascular disease in 2050. One paper really focused on what I would call the clinical and public health issues. Yours is focused on the economics. Is that a good description?

Kazi: Perfect.

Harrington: Tell us what you, Karen, and the other writers set out to do. What were you asked to do?

Kazi: As you know, the American Heart Association is entering its second century. Part of this was an exercise to say, where will the country be in 2050, which is a long enough time horizon for us to start planning for the future. What are the conditions that affect the magnitude of the disease, and the kinds of people who will be affected, that we should be aware of?

We looked back and said, if prior trends remain the same, where will we be in 2050, accounting for changes in demographics, changes in the composition of the population, and knowing that some of the cardiovascular risk factors are getting worse?

Harrington: For me, what was really striking is that, when I first saw the title and read “2050,” I thought, Oh, that’s a long way away. Then as I started reading it, I realized that this is not so far away.

Kazi: Absolutely.

Harrington: If we’re going to make a difference, it might take us 25 years.

Kazi: Especially if we set ourselves ambitious goals, we›re going to have to dig deep. Business-as-usual is not going to get us there.

Harrington: No. What I think has happened is we›ve spent so much time taking care of acute illness. Case fatality rates are fantastic. I was actually making the comment yesterday to a colleague that when I was an intern, the 30-day death rate from acute myocardial infarction was about 20%.

Kazi: Oh, wow.

Harrington: Now it’s 5%. That’s a big difference in a career.
 

Trends in the Wrong Direction

Kazi: There are fundamental trends. The decline in case fatalities is a really positive development, and I would hope that, going forward, that would continue. Those are risk-adjusted death rates and what is happening is that risk is going up. This is a function of the fact that the US population is aging; 2030 will be the first year that all the baby boomers will be over the age of 65.

By the mid-2030s, we’ll have more adults over the age of 65 than kids. That aging of the population is going to increase risk. The second is — and this is a positive development — we are a more diverse population, but the populations that are minoritized have higher cardiovascular risk, for a variety of reasons.

As the population of Asian Americans increases and doubles, in fact, as the population of Hispanic Americans doubles, we’re going to see an increase in risk related to cardiovascular disease. The third is that, over the past decade, there are some risk factors that are going in the wrong direction.

Harrington: Let’s talk about that because that’s humbling. I’m involved, as you know, with the American Heart Association, as are you. Despite all the work on Life’s Simple 7 and now Life’s Essential 8, we still have some issues.

Kazi: The big ones that come to mind are hypertension, diabetes, and obesity, all of which are trending in the wrong direction. Hypertension, we were gaining traction; and then over the past decade, we’ve slipped again. As you know, national blood pressure control rates have declined in many populations.

Harrington: Rather substantially.

Kazi: Substantially so, which has implications, in particular, for stroke rates in the future and stroke rates in young adults in the future. Obesity is a problem that we have very little control over. We’re already at 40% on average, which means that some populations are already in the 60% range.

Harrington: We also have obesity in kids — the burden, I’ll call it, of obesity. It’s not that you become obese in your thirties or your forties; you›re becoming obese as a teenager or even younger.

Kazi: Exactly. Since the 1990s, obesity in US adults has doubled, but obesity in US children has quadrupled. It’s starting from a lower base, but it’s very much an escalating problem.

Harrington: Diabetes is tightly linked to it but not totally explained.

Kazi: Exactly. The increase in diabetes is largely driven by obesity, but it›s probably also driven by changes in diet and lifestyle that don›t go through obesity.

Harrington: Yeah, it’s interesting. I think I have this figure correctly. It used to be rare that you saw a child with type 2 diabetes or what we call type 2 diabetes.

Kazi: Yeah.

Harrington: Now, the vast majority of kids with diabetes have type 2 diabetes.

Kazi: In the adolescents/young adults age group, most of it is type 2.

Harrington: Diabetes going up, obesity up, hypertension not well controlled, smoking combustible cigarettes way down.

Kazi: Yeah.

Harrington: Cholesterol levels. I was surprised. Cholesterol looked better. You said — because I was at a meeting where somebody asked you — that’s not explained by treatment.

Kazi: No, it’s not, at least going back to the ‘70s, but likely even sooner. I think that can only be attributed to substantial dietary changes. We are consuming less fat and less trans-fat. It’s possible that those collectively are improving our cholesterol levels, possibly at the expense of our glucose levels, because we basically substituted fats in our diet with more carbs at a population level.
 

Cigarettes and Vaping

Harrington: Some things certainly trend in the right direction but others in a really difficult direction. It’s going to lead to pretty large changes in risk for coronary disease, atrial fibrillation, and heart failure.

Kazi: I want to go back to the tobacco point. There are definitely marked declines in tobacco, still tightly related to income in the country. You see much higher prevalence of tobacco use in lower-income populations, but it’s unclear to me where it’s going in kids. We know that combustible tobacco use is going down but e-cigarettes went up. What that leads to over the next 30 years is unclear to me.

Harrington: That is a really important comment that’s worth sidebarring. The vaping use has been a terrible epidemic among our high schoolers. What is that going to lead to? Is it going to lead to the use of combustible cigarettes and we’re going to see that go back up? It remains to be seen.

Kazi: Yes, it remains to be seen. Going back to your point about this change in risk factors and this change in demographics, both aging and becoming a more diverse population means that we have large increases in some healthcare conditions.

Coronary heart disease goes up some, there›s a big jump in stroke — nearly a doubling in stroke — which is related to hypertension, obesity, an aging population, and a more diverse population. There are changes in stroke in the young, and atrial fibrillation related to, again, hypertension. We’re seeing these projections, and with them come these pretty large projections in changes in healthcare spending.
 

Healthcare Spending Not Sustainable

Harrington: Big. I mean, it’s not sustainable. Give the audience the number — it’s pretty frightening.

Kazi: We’re talking about a quadrupling of healthcare costs related to cardiovascular disease over 25 years. We’ve gotten used to the narrative that healthcare in the US is expensive and drugs are expensive, but this is an enormous problem — an unsustainable problem, like you called it.

It’s a doubling as a proportion of the economy. I was looking this up this morning. If the US healthcare economy were its own economy, it would be the fourth largest economy in the world.

Harrington: Healthcare as it is today, is it 21% of our economy?

Kazi: It’s 17% now. If it were its own economy, it would be the fourth largest in the world. We are spending more on healthcare than all but two other countries’ total economies. It’s kind of crazy.

Harrington: We’re talking about a quadrupling.

Kazi: Within that, the cardiovascular piece is a big piece, and we›re talking about a quadrupling.

Harrington: That’s both direct and indirect costs.

Kazi: The quadrupling of costs is just the direct costs. Indirect costs, for the listeners, refer to costs unrelated to healthcare but changes in productivity, either because people are disabled and unable to participate fully in the workforce or they die early.

The productivity costs are also increased substantially as a result. If you look at both healthcare and productivity, that goes up threefold. These are very large changes.

Harrington: Let’s now get to what we can do about it. I made the comment to you when I first read the papers that I was very depressed. Then, after I went through my Kübler-Ross stages of depression, death, and dying, I came to acceptance.

What are we going to do about it? This is a focus on policy, but also a focus on how we deliver healthcare, how we think about healthcare, and how we develop drugs and devices.

The drug question is going to be the one the audience is thinking about. They say, well, what about GLP-1 agonists? Aren’t those going to save the day?

Kazi: Yes and no. I’ll say that, early in my career, I used to be very attracted to simple solutions to complex problems. I’ve come to realize that simple solutions are elegant, attractive, and wrong. We›re dealing with a very complex issue and I think we’re going to need a multipronged approach.

The way I think about it is that there was a group of people who are at very high risk today. How do we help those individuals? Then how do we help the future generation so that they’re not dealing with the projections that we’re talking about.

My colleague, Karen Joynt Maddox, who led one of the papers, as you mentioned, has an elegant line in the paper where she says projections are not destiny. These are things we can change.

Harrington: If nothing changes, this is what it’s going to look like.

Kazi: This is where we’re headed.

Harrington: We can change. We’ve got some time to change, but we don’t have forever.

Kazi: Yes, exactly. We picked the 25-year timeline instead of a “let’s plan for the next century” timeline because we want something concrete and actionable. It’s close enough to be meaningful but far enough to give us the runway we need to act.

Harrington: Give me two things from the policy perspective, because it’s mostly policy.

Kazi: There are policy and clinical interventions. From the policy perspective, if I had to list two things, one is expansion of access to care. As we talk about this big increase in the burden of disease and risk factors, if you have a large proportion of your population that has hypertension or diabetes, you’re going to have to expand access to care to ensure that people get treated so they can get access to this care before they develop the complications that we worry about, like stroke and heart disease, that are very expensive to treat downstream.

The second, more broadly related to access to care, is the access to medications that are effective. You bring up GLP-1s. I think we need a real strategy for how we can give people access to GLP-1s at a price that is affordable to individuals but also affordable to the health system, and to help them stay on the drugs.

GLP-1s are transformative in what they do for weight loss and for diabetes, but more than 50% of people who start one are off it at 12 months. There’s something fundamentally wrong about how we’re delivering GLP-1s today. It’s not just about the cost of the drugs but the support system people need to stay on.

Harrington: I’ve made the comment, in many forms now, that we know the drugs work. We have to figure out how to use them.

Kazi: Exactly, yes.

Harrington: Using them includes chronicity. This is a chronic condition. Some people can come off the drugs, but many can’t. We’re going to have to figure this out, and maybe the newer generations of drugs will help us address what people call the off-ramping. How are we going to do that? I think you’re spot-on. Those are critically important questions.

Kazi: As we looked at this modeling, I’ll tell you — I had a come-to-Jesus moment where I was like, there is no way to fix cardiovascular disease in the US without going through obesity and diabetes. We have to address obesity in the US. We can’t just treat our way out of it. Obesity is fundamentally a food problem and we’ve got to engage again with food policy in a meaningful way.

Harrington: As you know, with the American Heart Association, we›re doing a large amount of work now on food as medicine and food is medicine. We are trying to figure out what the levers are that we can pull to actually help people eat healthier diets.

Kazi: Yes. Rather than framing it as an individual choice that people are eating poorly, it’s, how do we make healthy diets the default in the environment?

Harrington: This is where you get to the children as well.

Kazi: Exactly.

Harrington: I could talk about this all day. I’ve had the benefit of reading the papers now a few times and talking to you on several occasions. Thank you for joining us.

Kazi: Thank you.
 

Dr. Harrington, Stephen and Suzanne Weiss Dean, Weill Cornell Medicine; Provost for Medical Affairs, Cornell University, New York, NY, disclosed ties with Baim Institute (DSMB); CSL (RCT Executive Committee); Janssen (RCT Char), NHLBI (RCT Executive Committee, DSMB Chair); PCORI (RCT Co-Chair); DCRI, Atropos Health; Bitterroot Bio; Bristol Myers Squibb; BridgeBio; Element Science; Edwards Lifesciences; Foresite Labs; Medscape/WebMD Board of Directors for: American Heart Association; College of the Holy Cross; and Cytokinetics. Dr. Kazi, Associate Director, Smith Center for Outcomes Research, Associate Professor, Department of Medicine (Cardiology), Harvard Medical School, Director, Department of Cardiac Critical Care Unit, Beth Israel Deaconess Medical Center, Boston, Massachusetts, has disclosed receiving a research grant from Boston Scientific (grant to examine the economics of stroke prevention).

A version of this article appeared on Medscape.com.

This transcript has been edited for clarity .

Robert A. Harrington, MD: I’m here in London at the European Society of Cardiology meetings, at theheart.org | Medscape Cardiology booth, using the meetings as an opportunity to meet with colleagues to talk about recent things that they’ve been writing about.

Today I’m joined by a good friend and colleague, Dr. Dhruv Kazi from Beth Israel Deaconess in Boston. Thanks for joining us.

Dhruv S. Kazi, MD, MS: Thank you for having me.

Harrington: Dr. Kazi is an associate professor of medicine at Harvard Medical School. He’s also the associate director of the Smith Center, which is an outcomes research center at the Beth Israel Deaconess. Thanks for joining us.

Kazi: Excited to be here.

Harrington: The topic I think you know that I want to discuss is a really important paper. There are two papers. They’re part of the American Heart Association’s 100th anniversary celebration, if you will. Many of the papers looked back at where science taken us.

With your coauthor, Karen Joynt Maddox, your papers are looking forward. They’re about the burden of cardiovascular disease in 2050. One paper really focused on what I would call the clinical and public health issues. Yours is focused on the economics. Is that a good description?

Kazi: Perfect.

Harrington: Tell us what you, Karen, and the other writers set out to do. What were you asked to do?

Kazi: As you know, the American Heart Association is entering its second century. Part of this was an exercise to say, where will the country be in 2050, which is a long enough time horizon for us to start planning for the future. What are the conditions that affect the magnitude of the disease, and the kinds of people who will be affected, that we should be aware of?

We looked back and said, if prior trends remain the same, where will we be in 2050, accounting for changes in demographics, changes in the composition of the population, and knowing that some of the cardiovascular risk factors are getting worse?

Harrington: For me, what was really striking is that, when I first saw the title and read “2050,” I thought, Oh, that’s a long way away. Then as I started reading it, I realized that this is not so far away.

Kazi: Absolutely.

Harrington: If we’re going to make a difference, it might take us 25 years.

Kazi: Especially if we set ourselves ambitious goals, we›re going to have to dig deep. Business-as-usual is not going to get us there.

Harrington: No. What I think has happened is we›ve spent so much time taking care of acute illness. Case fatality rates are fantastic. I was actually making the comment yesterday to a colleague that when I was an intern, the 30-day death rate from acute myocardial infarction was about 20%.

Kazi: Oh, wow.

Harrington: Now it’s 5%. That’s a big difference in a career.
 

Trends in the Wrong Direction

Kazi: There are fundamental trends. The decline in case fatalities is a really positive development, and I would hope that, going forward, that would continue. Those are risk-adjusted death rates and what is happening is that risk is going up. This is a function of the fact that the US population is aging; 2030 will be the first year that all the baby boomers will be over the age of 65.

By the mid-2030s, we’ll have more adults over the age of 65 than kids. That aging of the population is going to increase risk. The second is — and this is a positive development — we are a more diverse population, but the populations that are minoritized have higher cardiovascular risk, for a variety of reasons.

As the population of Asian Americans increases and doubles, in fact, as the population of Hispanic Americans doubles, we’re going to see an increase in risk related to cardiovascular disease. The third is that, over the past decade, there are some risk factors that are going in the wrong direction.

Harrington: Let’s talk about that because that’s humbling. I’m involved, as you know, with the American Heart Association, as are you. Despite all the work on Life’s Simple 7 and now Life’s Essential 8, we still have some issues.

Kazi: The big ones that come to mind are hypertension, diabetes, and obesity, all of which are trending in the wrong direction. Hypertension, we were gaining traction; and then over the past decade, we’ve slipped again. As you know, national blood pressure control rates have declined in many populations.

Harrington: Rather substantially.

Kazi: Substantially so, which has implications, in particular, for stroke rates in the future and stroke rates in young adults in the future. Obesity is a problem that we have very little control over. We’re already at 40% on average, which means that some populations are already in the 60% range.

Harrington: We also have obesity in kids — the burden, I’ll call it, of obesity. It’s not that you become obese in your thirties or your forties; you›re becoming obese as a teenager or even younger.

Kazi: Exactly. Since the 1990s, obesity in US adults has doubled, but obesity in US children has quadrupled. It’s starting from a lower base, but it’s very much an escalating problem.

Harrington: Diabetes is tightly linked to it but not totally explained.

Kazi: Exactly. The increase in diabetes is largely driven by obesity, but it›s probably also driven by changes in diet and lifestyle that don›t go through obesity.

Harrington: Yeah, it’s interesting. I think I have this figure correctly. It used to be rare that you saw a child with type 2 diabetes or what we call type 2 diabetes.

Kazi: Yeah.

Harrington: Now, the vast majority of kids with diabetes have type 2 diabetes.

Kazi: In the adolescents/young adults age group, most of it is type 2.

Harrington: Diabetes going up, obesity up, hypertension not well controlled, smoking combustible cigarettes way down.

Kazi: Yeah.

Harrington: Cholesterol levels. I was surprised. Cholesterol looked better. You said — because I was at a meeting where somebody asked you — that’s not explained by treatment.

Kazi: No, it’s not, at least going back to the ‘70s, but likely even sooner. I think that can only be attributed to substantial dietary changes. We are consuming less fat and less trans-fat. It’s possible that those collectively are improving our cholesterol levels, possibly at the expense of our glucose levels, because we basically substituted fats in our diet with more carbs at a population level.
 

Cigarettes and Vaping

Harrington: Some things certainly trend in the right direction but others in a really difficult direction. It’s going to lead to pretty large changes in risk for coronary disease, atrial fibrillation, and heart failure.

Kazi: I want to go back to the tobacco point. There are definitely marked declines in tobacco, still tightly related to income in the country. You see much higher prevalence of tobacco use in lower-income populations, but it’s unclear to me where it’s going in kids. We know that combustible tobacco use is going down but e-cigarettes went up. What that leads to over the next 30 years is unclear to me.

Harrington: That is a really important comment that’s worth sidebarring. The vaping use has been a terrible epidemic among our high schoolers. What is that going to lead to? Is it going to lead to the use of combustible cigarettes and we’re going to see that go back up? It remains to be seen.

Kazi: Yes, it remains to be seen. Going back to your point about this change in risk factors and this change in demographics, both aging and becoming a more diverse population means that we have large increases in some healthcare conditions.

Coronary heart disease goes up some, there›s a big jump in stroke — nearly a doubling in stroke — which is related to hypertension, obesity, an aging population, and a more diverse population. There are changes in stroke in the young, and atrial fibrillation related to, again, hypertension. We’re seeing these projections, and with them come these pretty large projections in changes in healthcare spending.
 

Healthcare Spending Not Sustainable

Harrington: Big. I mean, it’s not sustainable. Give the audience the number — it’s pretty frightening.

Kazi: We’re talking about a quadrupling of healthcare costs related to cardiovascular disease over 25 years. We’ve gotten used to the narrative that healthcare in the US is expensive and drugs are expensive, but this is an enormous problem — an unsustainable problem, like you called it.

It’s a doubling as a proportion of the economy. I was looking this up this morning. If the US healthcare economy were its own economy, it would be the fourth largest economy in the world.

Harrington: Healthcare as it is today, is it 21% of our economy?

Kazi: It’s 17% now. If it were its own economy, it would be the fourth largest in the world. We are spending more on healthcare than all but two other countries’ total economies. It’s kind of crazy.

Harrington: We’re talking about a quadrupling.

Kazi: Within that, the cardiovascular piece is a big piece, and we›re talking about a quadrupling.

Harrington: That’s both direct and indirect costs.

Kazi: The quadrupling of costs is just the direct costs. Indirect costs, for the listeners, refer to costs unrelated to healthcare but changes in productivity, either because people are disabled and unable to participate fully in the workforce or they die early.

The productivity costs are also increased substantially as a result. If you look at both healthcare and productivity, that goes up threefold. These are very large changes.

Harrington: Let’s now get to what we can do about it. I made the comment to you when I first read the papers that I was very depressed. Then, after I went through my Kübler-Ross stages of depression, death, and dying, I came to acceptance.

What are we going to do about it? This is a focus on policy, but also a focus on how we deliver healthcare, how we think about healthcare, and how we develop drugs and devices.

The drug question is going to be the one the audience is thinking about. They say, well, what about GLP-1 agonists? Aren’t those going to save the day?

Kazi: Yes and no. I’ll say that, early in my career, I used to be very attracted to simple solutions to complex problems. I’ve come to realize that simple solutions are elegant, attractive, and wrong. We›re dealing with a very complex issue and I think we’re going to need a multipronged approach.

The way I think about it is that there was a group of people who are at very high risk today. How do we help those individuals? Then how do we help the future generation so that they’re not dealing with the projections that we’re talking about.

My colleague, Karen Joynt Maddox, who led one of the papers, as you mentioned, has an elegant line in the paper where she says projections are not destiny. These are things we can change.

Harrington: If nothing changes, this is what it’s going to look like.

Kazi: This is where we’re headed.

Harrington: We can change. We’ve got some time to change, but we don’t have forever.

Kazi: Yes, exactly. We picked the 25-year timeline instead of a “let’s plan for the next century” timeline because we want something concrete and actionable. It’s close enough to be meaningful but far enough to give us the runway we need to act.

Harrington: Give me two things from the policy perspective, because it’s mostly policy.

Kazi: There are policy and clinical interventions. From the policy perspective, if I had to list two things, one is expansion of access to care. As we talk about this big increase in the burden of disease and risk factors, if you have a large proportion of your population that has hypertension or diabetes, you’re going to have to expand access to care to ensure that people get treated so they can get access to this care before they develop the complications that we worry about, like stroke and heart disease, that are very expensive to treat downstream.

The second, more broadly related to access to care, is the access to medications that are effective. You bring up GLP-1s. I think we need a real strategy for how we can give people access to GLP-1s at a price that is affordable to individuals but also affordable to the health system, and to help them stay on the drugs.

GLP-1s are transformative in what they do for weight loss and for diabetes, but more than 50% of people who start one are off it at 12 months. There’s something fundamentally wrong about how we’re delivering GLP-1s today. It’s not just about the cost of the drugs but the support system people need to stay on.

Harrington: I’ve made the comment, in many forms now, that we know the drugs work. We have to figure out how to use them.

Kazi: Exactly, yes.

Harrington: Using them includes chronicity. This is a chronic condition. Some people can come off the drugs, but many can’t. We’re going to have to figure this out, and maybe the newer generations of drugs will help us address what people call the off-ramping. How are we going to do that? I think you’re spot-on. Those are critically important questions.

Kazi: As we looked at this modeling, I’ll tell you — I had a come-to-Jesus moment where I was like, there is no way to fix cardiovascular disease in the US without going through obesity and diabetes. We have to address obesity in the US. We can’t just treat our way out of it. Obesity is fundamentally a food problem and we’ve got to engage again with food policy in a meaningful way.

Harrington: As you know, with the American Heart Association, we›re doing a large amount of work now on food as medicine and food is medicine. We are trying to figure out what the levers are that we can pull to actually help people eat healthier diets.

Kazi: Yes. Rather than framing it as an individual choice that people are eating poorly, it’s, how do we make healthy diets the default in the environment?

Harrington: This is where you get to the children as well.

Kazi: Exactly.

Harrington: I could talk about this all day. I’ve had the benefit of reading the papers now a few times and talking to you on several occasions. Thank you for joining us.

Kazi: Thank you.
 

Dr. Harrington, Stephen and Suzanne Weiss Dean, Weill Cornell Medicine; Provost for Medical Affairs, Cornell University, New York, NY, disclosed ties with Baim Institute (DSMB); CSL (RCT Executive Committee); Janssen (RCT Char), NHLBI (RCT Executive Committee, DSMB Chair); PCORI (RCT Co-Chair); DCRI, Atropos Health; Bitterroot Bio; Bristol Myers Squibb; BridgeBio; Element Science; Edwards Lifesciences; Foresite Labs; Medscape/WebMD Board of Directors for: American Heart Association; College of the Holy Cross; and Cytokinetics. Dr. Kazi, Associate Director, Smith Center for Outcomes Research, Associate Professor, Department of Medicine (Cardiology), Harvard Medical School, Director, Department of Cardiac Critical Care Unit, Beth Israel Deaconess Medical Center, Boston, Massachusetts, has disclosed receiving a research grant from Boston Scientific (grant to examine the economics of stroke prevention).

A version of this article appeared on Medscape.com.

On October 1, 2024, International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for central centrifugal cicatricial alopecia (CCCA) and frontal fibrosing alopecia (FFA) took effect for the first time, a development characterized as a critical advancement in the field of hair loss disorders.

“CCCA and FFA are conditions that require early diagnosis and intervention to prevent irreversible hair loss,” Maria Hordinsky, MD, professor of dermatology at the University of Minnesota, Minneapolis, and a member of the Board of Directors, Scarring Alopecia Foundation (SAF), said in an interview.

Ms. Jefferson

For her part, Jefferson said that she will continue to advocate for patients with scarring alopecia as a medical student and when she becomes a physician. “I hope in the near future we will see an externally led FDA Patient-Focused Drug Development meeting for both CCCA and FFA, further advancing care and research for these conditions,” she said in an interview.

McMichael, Hordinsky, and Jefferson had no relevant disclosures to report.

A version of this article appeared on Medscape.com.

On October 1, 2024, International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for central centrifugal cicatricial alopecia (CCCA) and frontal fibrosing alopecia (FFA) took effect for the first time, a development characterized as a critical advancement in the field of hair loss disorders.

“CCCA and FFA are conditions that require early diagnosis and intervention to prevent irreversible hair loss,” Maria Hordinsky, MD, professor of dermatology at the University of Minnesota, Minneapolis, and a member of the Board of Directors, Scarring Alopecia Foundation (SAF), said in an interview.

Ms. Jefferson

For her part, Jefferson said that she will continue to advocate for patients with scarring alopecia as a medical student and when she becomes a physician. “I hope in the near future we will see an externally led FDA Patient-Focused Drug Development meeting for both CCCA and FFA, further advancing care and research for these conditions,” she said in an interview.

McMichael, Hordinsky, and Jefferson had no relevant disclosures to report.

A version of this article appeared on Medscape.com.

On October 1, 2024, International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes for central centrifugal cicatricial alopecia (CCCA) and frontal fibrosing alopecia (FFA) took effect for the first time, a development characterized as a critical advancement in the field of hair loss disorders.

“CCCA and FFA are conditions that require early diagnosis and intervention to prevent irreversible hair loss,” Maria Hordinsky, MD, professor of dermatology at the University of Minnesota, Minneapolis, and a member of the Board of Directors, Scarring Alopecia Foundation (SAF), said in an interview.

Ms. Jefferson

For her part, Jefferson said that she will continue to advocate for patients with scarring alopecia as a medical student and when she becomes a physician. “I hope in the near future we will see an externally led FDA Patient-Focused Drug Development meeting for both CCCA and FFA, further advancing care and research for these conditions,” she said in an interview.

McMichael, Hordinsky, and Jefferson had no relevant disclosures to report.

A version of this article appeared on Medscape.com.

Trying to reduce your carbohydrate intake means going against nearly a million years of evolution.

Humans are among a few species with multiple copies of certain genes that help us break down starch — carbs like potatoes, beans, corn, and grains — so that we can turn it into energy our bodies can use.

However, it’s been difficult for researchers to pinpoint when in human history we acquired multiple copies of these genes because they’re in a region of the genome that’s hard to sequence.

A recent study published in Science suggests that humans may have developed multiple copies of the gene for amylase — an enzyme that’s the first step in starch digestion — over 800,000 years ago, long before the agricultural revolution. This genetic change could have helped us adapt to eating starchy foods.

The study shows how “what your ancestors ate thousands of years ago could be affecting our genetics today,” said Kelsey Jorgensen, PhD, a biological anthropologist at The University of Kansas, Lawrence, who was not involved in the study.

The double-edged sword has sharpened over all those centuries. On one hand, the human body needs and craves carbs to function. On the other hand, our modern-day consumption of carbs, especially calorie-dense/nutritionally-barren processed carbs, has long since passed “healthy.”
 

How Researchers Found Our Carb-Lover Gene

The enzyme amylase turns complex carbs into maltose, a sweet-tasting sugar that is made of two glucose molecules linked together. We make two kinds of amylases: Salivary amylase that breaks down carbs in our mouths and pancreatic amylase that is secreted into our small intestines.

Modern humans have multiple copies of both amylases. Past research showed that human populations with diets high in starch can have up to nine copies of the gene for salivary amylase, called AMY1.

To pinpoint when in human history we acquired multiple copies of AMY1, the new study utilized novel techniques, called optical genome mapping and long-read sequencing, to sequence and analyze the genes. They sequenced 98 modern-day samples and 68 ancient DNA samples, including one from a Siberian person who lived 45,000 years ago.

The ancient DNA data in the study allowed the researchers to track how the number of amylase genes changed over time, said George Perry, PhD, an anthropological geneticist at The Pennsylvania State University-University Park (he was not involved in the study).

Based on the sequencing, the team analyzed changes in the genes in their samples to gauge evolutionary timelines. Perry noted that this was a “very clever approach to estimating the amylase copy number mutation rate, which in turn can really help in testing evolutionary hypotheses.”

The researchers found that even before farming, hunter-gatherers had between four and eight AMY1 genes in their cells. This suggests that people across Eurasia already had a number of these genes long before they started growing crops. (Recent research indicates that Neanderthals also ate starchy foods.)

“Even archaic hominins had these [genetic] variations and that indicates that they were consuming starch,” said Feyza Yilmaz, PhD, an associate computational scientist at The Jackson Laboratory in Bar Harbor, Maine, and a lead author of the study.

However, 4000 years ago, after the agricultural revolution, the research indicates that there were even more AMY1 copies acquired. Yilmaz noted, “with the advance of agriculture, we see an increase in high amylase copy number haplotypes. So genetic variation goes hand in hand with adaptation to the environment.” 

A previous study showed that species that share an environment with humans, such as dogs and pigs, also have copy number variation of amylase genes, said Yilmaz, indicating a link between genome changes and an increase in starch consumption.
 

Potential Health Impacts on Modern Humans

The duplications in the AMY1 gene could have allowed humans to better digest starches. And it’s conceivable that having more copies of the gene means being able to break down starches even more efficiently, and those with more copies “may be more prone to having high blood sugar, prediabetes, that sort of thing,” Jorgensen said.

Whether those with more AMY1 genes have more health risks is an active area of research. “Researchers tested whether there’s a correlation between AMY1 gene copies and diabetes or BMI [body mass index]. And so far, some studies show that there is indeed correlation, but other studies show that there is no correlation at all,” said Yilmaz.

Yilmaz pointed out that only 5 or 10% of carb digestion happens in our mouths, the rest occurs in our small intestine, plus there are many other factors involved in eating and metabolism.

“I am really looking forward to seeing studies which truly figure out the connection between AMY1 copy number and metabolic health and also what type of factors play a role in metabolic health,” said Yilmaz.

It’s also possible that having more AMY1 copies could lead to more carb cravings as the enzyme creates a type of sugar in our mouths. “Previous studies show that there’s a correlation between AMY1 copy number and also the amylase enzyme levels, so the faster we process the starch, the taste [of starches] will be sweeter,” said Yilmaz.

However, the link between cravings and copy numbers isn’t clear. And we don’t exactly know what came first — did the starch in humans’ diet lead to more copies of amylase genes, or did the copies of the amylase genes drive cravings that lead us to cultivate more carbs? We’ll need more research to find out.
 

How Will Today’s Processed Carbs Affect Our Genes Tomorrow?

As our diet changes to increasingly include processed carbs, what will happen to our AMY1 genes is fuzzy. “I don’t know what this could do to our genomes in the next 1000 years or more than 1000 years,” Yilmaz noted, but she said from the evidence it seems as though we may have peaked in AMY1 copies.

Jorgensen noted that this research is focused on a European population. She wonders whether the pattern of AMY1 duplication will be repeated in other populations “because the rise of starch happened first in the Middle East and then Europe and then later in the Americas,” she said.

“There’s individual variation and then there’s population-wide variation,” Jorgensen pointed out. She speculates that the historical diet of different cultures could explain population-based variations in AMY1 genes — it’s something future research could investigate. Other populations may also experience genetic changes as much of the world shifts to a more carb-heavy Western diet.

Overall, this research adds to the growing evidence that humans have a long history of loving carbs — for better and, at least over our most recent history and immediate future, for worse.
 

A version of this article appeared on Medscape.com.

Trying to reduce your carbohydrate intake means going against nearly a million years of evolution.

Humans are among a few species with multiple copies of certain genes that help us break down starch — carbs like potatoes, beans, corn, and grains — so that we can turn it into energy our bodies can use.

However, it’s been difficult for researchers to pinpoint when in human history we acquired multiple copies of these genes because they’re in a region of the genome that’s hard to sequence.

A recent study published in Science suggests that humans may have developed multiple copies of the gene for amylase — an enzyme that’s the first step in starch digestion — over 800,000 years ago, long before the agricultural revolution. This genetic change could have helped us adapt to eating starchy foods.

The study shows how “what your ancestors ate thousands of years ago could be affecting our genetics today,” said Kelsey Jorgensen, PhD, a biological anthropologist at The University of Kansas, Lawrence, who was not involved in the study.

The double-edged sword has sharpened over all those centuries. On one hand, the human body needs and craves carbs to function. On the other hand, our modern-day consumption of carbs, especially calorie-dense/nutritionally-barren processed carbs, has long since passed “healthy.”
 

How Researchers Found Our Carb-Lover Gene

The enzyme amylase turns complex carbs into maltose, a sweet-tasting sugar that is made of two glucose molecules linked together. We make two kinds of amylases: Salivary amylase that breaks down carbs in our mouths and pancreatic amylase that is secreted into our small intestines.

Modern humans have multiple copies of both amylases. Past research showed that human populations with diets high in starch can have up to nine copies of the gene for salivary amylase, called AMY1.

To pinpoint when in human history we acquired multiple copies of AMY1, the new study utilized novel techniques, called optical genome mapping and long-read sequencing, to sequence and analyze the genes. They sequenced 98 modern-day samples and 68 ancient DNA samples, including one from a Siberian person who lived 45,000 years ago.

The ancient DNA data in the study allowed the researchers to track how the number of amylase genes changed over time, said George Perry, PhD, an anthropological geneticist at The Pennsylvania State University-University Park (he was not involved in the study).

Based on the sequencing, the team analyzed changes in the genes in their samples to gauge evolutionary timelines. Perry noted that this was a “very clever approach to estimating the amylase copy number mutation rate, which in turn can really help in testing evolutionary hypotheses.”

The researchers found that even before farming, hunter-gatherers had between four and eight AMY1 genes in their cells. This suggests that people across Eurasia already had a number of these genes long before they started growing crops. (Recent research indicates that Neanderthals also ate starchy foods.)

“Even archaic hominins had these [genetic] variations and that indicates that they were consuming starch,” said Feyza Yilmaz, PhD, an associate computational scientist at The Jackson Laboratory in Bar Harbor, Maine, and a lead author of the study.

However, 4000 years ago, after the agricultural revolution, the research indicates that there were even more AMY1 copies acquired. Yilmaz noted, “with the advance of agriculture, we see an increase in high amylase copy number haplotypes. So genetic variation goes hand in hand with adaptation to the environment.” 

A previous study showed that species that share an environment with humans, such as dogs and pigs, also have copy number variation of amylase genes, said Yilmaz, indicating a link between genome changes and an increase in starch consumption.
 

Potential Health Impacts on Modern Humans

The duplications in the AMY1 gene could have allowed humans to better digest starches. And it’s conceivable that having more copies of the gene means being able to break down starches even more efficiently, and those with more copies “may be more prone to having high blood sugar, prediabetes, that sort of thing,” Jorgensen said.

Whether those with more AMY1 genes have more health risks is an active area of research. “Researchers tested whether there’s a correlation between AMY1 gene copies and diabetes or BMI [body mass index]. And so far, some studies show that there is indeed correlation, but other studies show that there is no correlation at all,” said Yilmaz.

Yilmaz pointed out that only 5 or 10% of carb digestion happens in our mouths, the rest occurs in our small intestine, plus there are many other factors involved in eating and metabolism.

“I am really looking forward to seeing studies which truly figure out the connection between AMY1 copy number and metabolic health and also what type of factors play a role in metabolic health,” said Yilmaz.

It’s also possible that having more AMY1 copies could lead to more carb cravings as the enzyme creates a type of sugar in our mouths. “Previous studies show that there’s a correlation between AMY1 copy number and also the amylase enzyme levels, so the faster we process the starch, the taste [of starches] will be sweeter,” said Yilmaz.

However, the link between cravings and copy numbers isn’t clear. And we don’t exactly know what came first — did the starch in humans’ diet lead to more copies of amylase genes, or did the copies of the amylase genes drive cravings that lead us to cultivate more carbs? We’ll need more research to find out.
 

How Will Today’s Processed Carbs Affect Our Genes Tomorrow?

As our diet changes to increasingly include processed carbs, what will happen to our AMY1 genes is fuzzy. “I don’t know what this could do to our genomes in the next 1000 years or more than 1000 years,” Yilmaz noted, but she said from the evidence it seems as though we may have peaked in AMY1 copies.

Jorgensen noted that this research is focused on a European population. She wonders whether the pattern of AMY1 duplication will be repeated in other populations “because the rise of starch happened first in the Middle East and then Europe and then later in the Americas,” she said.

“There’s individual variation and then there’s population-wide variation,” Jorgensen pointed out. She speculates that the historical diet of different cultures could explain population-based variations in AMY1 genes — it’s something future research could investigate. Other populations may also experience genetic changes as much of the world shifts to a more carb-heavy Western diet.

Overall, this research adds to the growing evidence that humans have a long history of loving carbs — for better and, at least over our most recent history and immediate future, for worse.
 

A version of this article appeared on Medscape.com.

Trying to reduce your carbohydrate intake means going against nearly a million years of evolution.

Humans are among a few species with multiple copies of certain genes that help us break down starch — carbs like potatoes, beans, corn, and grains — so that we can turn it into energy our bodies can use.

However, it’s been difficult for researchers to pinpoint when in human history we acquired multiple copies of these genes because they’re in a region of the genome that’s hard to sequence.

A recent study published in Science suggests that humans may have developed multiple copies of the gene for amylase — an enzyme that’s the first step in starch digestion — over 800,000 years ago, long before the agricultural revolution. This genetic change could have helped us adapt to eating starchy foods.

The study shows how “what your ancestors ate thousands of years ago could be affecting our genetics today,” said Kelsey Jorgensen, PhD, a biological anthropologist at The University of Kansas, Lawrence, who was not involved in the study.

The double-edged sword has sharpened over all those centuries. On one hand, the human body needs and craves carbs to function. On the other hand, our modern-day consumption of carbs, especially calorie-dense/nutritionally-barren processed carbs, has long since passed “healthy.”
 

How Researchers Found Our Carb-Lover Gene

The enzyme amylase turns complex carbs into maltose, a sweet-tasting sugar that is made of two glucose molecules linked together. We make two kinds of amylases: Salivary amylase that breaks down carbs in our mouths and pancreatic amylase that is secreted into our small intestines.

Modern humans have multiple copies of both amylases. Past research showed that human populations with diets high in starch can have up to nine copies of the gene for salivary amylase, called AMY1.

To pinpoint when in human history we acquired multiple copies of AMY1, the new study utilized novel techniques, called optical genome mapping and long-read sequencing, to sequence and analyze the genes. They sequenced 98 modern-day samples and 68 ancient DNA samples, including one from a Siberian person who lived 45,000 years ago.

The ancient DNA data in the study allowed the researchers to track how the number of amylase genes changed over time, said George Perry, PhD, an anthropological geneticist at The Pennsylvania State University-University Park (he was not involved in the study).

Based on the sequencing, the team analyzed changes in the genes in their samples to gauge evolutionary timelines. Perry noted that this was a “very clever approach to estimating the amylase copy number mutation rate, which in turn can really help in testing evolutionary hypotheses.”

The researchers found that even before farming, hunter-gatherers had between four and eight AMY1 genes in their cells. This suggests that people across Eurasia already had a number of these genes long before they started growing crops. (Recent research indicates that Neanderthals also ate starchy foods.)

“Even archaic hominins had these [genetic] variations and that indicates that they were consuming starch,” said Feyza Yilmaz, PhD, an associate computational scientist at The Jackson Laboratory in Bar Harbor, Maine, and a lead author of the study.

However, 4000 years ago, after the agricultural revolution, the research indicates that there were even more AMY1 copies acquired. Yilmaz noted, “with the advance of agriculture, we see an increase in high amylase copy number haplotypes. So genetic variation goes hand in hand with adaptation to the environment.” 

A previous study showed that species that share an environment with humans, such as dogs and pigs, also have copy number variation of amylase genes, said Yilmaz, indicating a link between genome changes and an increase in starch consumption.
 

Potential Health Impacts on Modern Humans

The duplications in the AMY1 gene could have allowed humans to better digest starches. And it’s conceivable that having more copies of the gene means being able to break down starches even more efficiently, and those with more copies “may be more prone to having high blood sugar, prediabetes, that sort of thing,” Jorgensen said.

Whether those with more AMY1 genes have more health risks is an active area of research. “Researchers tested whether there’s a correlation between AMY1 gene copies and diabetes or BMI [body mass index]. And so far, some studies show that there is indeed correlation, but other studies show that there is no correlation at all,” said Yilmaz.

Yilmaz pointed out that only 5 or 10% of carb digestion happens in our mouths, the rest occurs in our small intestine, plus there are many other factors involved in eating and metabolism.

“I am really looking forward to seeing studies which truly figure out the connection between AMY1 copy number and metabolic health and also what type of factors play a role in metabolic health,” said Yilmaz.

It’s also possible that having more AMY1 copies could lead to more carb cravings as the enzyme creates a type of sugar in our mouths. “Previous studies show that there’s a correlation between AMY1 copy number and also the amylase enzyme levels, so the faster we process the starch, the taste [of starches] will be sweeter,” said Yilmaz.

However, the link between cravings and copy numbers isn’t clear. And we don’t exactly know what came first — did the starch in humans’ diet lead to more copies of amylase genes, or did the copies of the amylase genes drive cravings that lead us to cultivate more carbs? We’ll need more research to find out.
 

How Will Today’s Processed Carbs Affect Our Genes Tomorrow?

As our diet changes to increasingly include processed carbs, what will happen to our AMY1 genes is fuzzy. “I don’t know what this could do to our genomes in the next 1000 years or more than 1000 years,” Yilmaz noted, but she said from the evidence it seems as though we may have peaked in AMY1 copies.

Jorgensen noted that this research is focused on a European population. She wonders whether the pattern of AMY1 duplication will be repeated in other populations “because the rise of starch happened first in the Middle East and then Europe and then later in the Americas,” she said.

“There’s individual variation and then there’s population-wide variation,” Jorgensen pointed out. She speculates that the historical diet of different cultures could explain population-based variations in AMY1 genes — it’s something future research could investigate. Other populations may also experience genetic changes as much of the world shifts to a more carb-heavy Western diet.

Overall, this research adds to the growing evidence that humans have a long history of loving carbs — for better and, at least over our most recent history and immediate future, for worse.
 

A version of this article appeared on Medscape.com.

As a physician, you might be contacted by the media to provide your professional opinion and advice. Or you might be looking for media interview opportunities to market your practice or side project. And if you do research, media interviews can be an effective way to spread the word. It’s important to prepare for a media interview so that you achieve the outcome you are looking for. Here are six tips I learned from writing health articles, interviewing experts, and being interviewed myself. 

Keep your message simple. When you are a subject expert, you might think that the basics are obvious or even boring, and that the nuances are more important. However, most of the audience is looking for big-picture information that they can apply to their lives. Consider a few key takeaways, keeping in mind that your interview is likely to be edited to short sound bites or a few quotes. It may help to jot down notes so that you cover the fundamentals clearly. You could even write and rehearse a script beforehand. If there is something complicated or subtle that you want to convey, you can preface it by saying, “This is confusing but very important …” to let the audience know to give extra consideration to what you are about to say.

Avoid extremes and hyperbole. Sometimes, exaggerated statements make their way into medical discussions. Statements such as “it doesn’t matter how many calories you consume — it’s all about the quality” are common oversimplifications. But you might be upset to see your name next to a comment like this because it is not actually correct. Check the phrasing of your key takeaways to avoid being stuck defending or explaining an inaccurate statement when your patients ask you about it later. 

Ask the interviewers what they are looking for. Many medical topics have some controversial element, so it is good to know what you’re getting into. Find out the purpose of the article or interview before you decide whether it is right for you. It could be about another doctor in town who is being sued; if you don’t want to be associated with that story, it might be best to decline the interview. 

Explain your goals. You might accept or pursue an interview to raise awareness about an underrecognized condition. You might want the public to identify and get help for early symptoms, or you might want to create empathy for people coping with a disease you treat. Consider why you are participating in an interview, and communicate that to the interviewer to ensure that your objective can be part of the final product. 

Know whom you’re dealing with. It is good to learn about the publication/media channel before you agree to participate. It may have a political bias, or perhaps the interview is intended to promote a specific product. If you agree with and support their purposes, then you may be happy to lend your opinion. But learning about the “voice” of the publication in advance allows you to make an informed decision about whether you want to be identified with a particular political ideology or product endorsement.

Ask to see your quotes before publication. It’s good to have the opportunity to make corrections in case you are accidentally misquoted or misunderstood. It is best to ask to see quotes before you agree to the interview. Some reporters may agree to (or even prefer) a written question-and-answer format so that they can directly quote your responses without rephrasing your words. You could suggest this, especially if you are too busy for a call or live meeting.

As a physician, your insights and advice can be highly beneficial to others. You can also use media interviews to propel your career forward. Doing your homework can ensure that you will be pleased with the final product and how your words were used. 
 

Dr. Moawad, Clinical Assistant Professor, Department of Medical Education, Case Western Reserve University School of Medicine, Cleveland, Ohio, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

As a physician, you might be contacted by the media to provide your professional opinion and advice. Or you might be looking for media interview opportunities to market your practice or side project. And if you do research, media interviews can be an effective way to spread the word. It’s important to prepare for a media interview so that you achieve the outcome you are looking for. Here are six tips I learned from writing health articles, interviewing experts, and being interviewed myself. 

Keep your message simple. When you are a subject expert, you might think that the basics are obvious or even boring, and that the nuances are more important. However, most of the audience is looking for big-picture information that they can apply to their lives. Consider a few key takeaways, keeping in mind that your interview is likely to be edited to short sound bites or a few quotes. It may help to jot down notes so that you cover the fundamentals clearly. You could even write and rehearse a script beforehand. If there is something complicated or subtle that you want to convey, you can preface it by saying, “This is confusing but very important …” to let the audience know to give extra consideration to what you are about to say.

Avoid extremes and hyperbole. Sometimes, exaggerated statements make their way into medical discussions. Statements such as “it doesn’t matter how many calories you consume — it’s all about the quality” are common oversimplifications. But you might be upset to see your name next to a comment like this because it is not actually correct. Check the phrasing of your key takeaways to avoid being stuck defending or explaining an inaccurate statement when your patients ask you about it later. 

Ask the interviewers what they are looking for. Many medical topics have some controversial element, so it is good to know what you’re getting into. Find out the purpose of the article or interview before you decide whether it is right for you. It could be about another doctor in town who is being sued; if you don’t want to be associated with that story, it might be best to decline the interview. 

Explain your goals. You might accept or pursue an interview to raise awareness about an underrecognized condition. You might want the public to identify and get help for early symptoms, or you might want to create empathy for people coping with a disease you treat. Consider why you are participating in an interview, and communicate that to the interviewer to ensure that your objective can be part of the final product. 

Know whom you’re dealing with. It is good to learn about the publication/media channel before you agree to participate. It may have a political bias, or perhaps the interview is intended to promote a specific product. If you agree with and support their purposes, then you may be happy to lend your opinion. But learning about the “voice” of the publication in advance allows you to make an informed decision about whether you want to be identified with a particular political ideology or product endorsement.

Ask to see your quotes before publication. It’s good to have the opportunity to make corrections in case you are accidentally misquoted or misunderstood. It is best to ask to see quotes before you agree to the interview. Some reporters may agree to (or even prefer) a written question-and-answer format so that they can directly quote your responses without rephrasing your words. You could suggest this, especially if you are too busy for a call or live meeting.

As a physician, your insights and advice can be highly beneficial to others. You can also use media interviews to propel your career forward. Doing your homework can ensure that you will be pleased with the final product and how your words were used. 
 

Dr. Moawad, Clinical Assistant Professor, Department of Medical Education, Case Western Reserve University School of Medicine, Cleveland, Ohio, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

As a physician, you might be contacted by the media to provide your professional opinion and advice. Or you might be looking for media interview opportunities to market your practice or side project. And if you do research, media interviews can be an effective way to spread the word. It’s important to prepare for a media interview so that you achieve the outcome you are looking for. Here are six tips I learned from writing health articles, interviewing experts, and being interviewed myself. 

Keep your message simple. When you are a subject expert, you might think that the basics are obvious or even boring, and that the nuances are more important. However, most of the audience is looking for big-picture information that they can apply to their lives. Consider a few key takeaways, keeping in mind that your interview is likely to be edited to short sound bites or a few quotes. It may help to jot down notes so that you cover the fundamentals clearly. You could even write and rehearse a script beforehand. If there is something complicated or subtle that you want to convey, you can preface it by saying, “This is confusing but very important …” to let the audience know to give extra consideration to what you are about to say.

Avoid extremes and hyperbole. Sometimes, exaggerated statements make their way into medical discussions. Statements such as “it doesn’t matter how many calories you consume — it’s all about the quality” are common oversimplifications. But you might be upset to see your name next to a comment like this because it is not actually correct. Check the phrasing of your key takeaways to avoid being stuck defending or explaining an inaccurate statement when your patients ask you about it later. 

Ask the interviewers what they are looking for. Many medical topics have some controversial element, so it is good to know what you’re getting into. Find out the purpose of the article or interview before you decide whether it is right for you. It could be about another doctor in town who is being sued; if you don’t want to be associated with that story, it might be best to decline the interview. 

Explain your goals. You might accept or pursue an interview to raise awareness about an underrecognized condition. You might want the public to identify and get help for early symptoms, or you might want to create empathy for people coping with a disease you treat. Consider why you are participating in an interview, and communicate that to the interviewer to ensure that your objective can be part of the final product. 

Know whom you’re dealing with. It is good to learn about the publication/media channel before you agree to participate. It may have a political bias, or perhaps the interview is intended to promote a specific product. If you agree with and support their purposes, then you may be happy to lend your opinion. But learning about the “voice” of the publication in advance allows you to make an informed decision about whether you want to be identified with a particular political ideology or product endorsement.

Ask to see your quotes before publication. It’s good to have the opportunity to make corrections in case you are accidentally misquoted or misunderstood. It is best to ask to see quotes before you agree to the interview. Some reporters may agree to (or even prefer) a written question-and-answer format so that they can directly quote your responses without rephrasing your words. You could suggest this, especially if you are too busy for a call or live meeting.

As a physician, your insights and advice can be highly beneficial to others. You can also use media interviews to propel your career forward. Doing your homework can ensure that you will be pleased with the final product and how your words were used. 
 

Dr. Moawad, Clinical Assistant Professor, Department of Medical Education, Case Western Reserve University School of Medicine, Cleveland, Ohio, has disclosed no relevant financial relationships.

A version of this article appeared on Medscape.com.

The US Food and Drug Administration (FDA) has announced a voluntary manufacturer-initiated recall of more than 7000 bottles of duloxetine delayed-release capsules due to unacceptable levels of a potential carcinogen.

Duloxetine (Cymbalta) is a serotonin-norepinephrine reuptake inhibitor used to treat major depressive disorder, generalized anxiety disorder, fibromyalgia, chronic musculoskeletal pain, and neuropathic pain associated with diabetic peripheral neuropathy.

The recall is due to the detection of the nitrosamine impurity, N-nitroso duloxetine, above the proposed interim limit.

Nitrosamines are common in water and foods, and exposure to some levels of the chemical is common. Exposure to nitrosamine impurities above acceptable levels and over long periods may increase cancer risk, the FDA reported.

“If drugs contain levels of nitrosamines above the acceptable daily intake limits, FDA recommends these drugs be recalled by the manufacturer as appropriate,” the agency noted on its website.

The recall was initiated by Breckenridge Pharmaceutical and covers 7107 bottles of 500-count, 20 mg duloxetine delayed-release capsules. The drug is manufactured by Towa Pharmaceutical Europe and distributed nationwide by BPI.

The affected bottles are from lot number 220128 with an expiration date of 12/2024 and NDC of 51991-746-05.

The recall was initiated on October 10 and is ongoing.

“Healthcare professionals can educate patients about alternative treatment options to medications with potential nitrosamine impurities if available and clinically appropriate,” the FDA advises. “If a medication has been recalled, pharmacists may be able to dispense the same medication from a manufacturing lot that has not been recalled. Prescribers may also determine whether there is an alternative treatment option for patients.”

The FDA has labeled this a “class II” recall, which the agency defines as “a situation in which use of or exposure to a violative product may cause temporary or medically reversible adverse health consequences or where the probability of serious adverse health consequences is remote.”

Nitrosamine impurities have prompted a number of drug recalls in recent years, including oral anticoagulants, metformin, and skeletal muscle relaxants.

The impurities may be found in drugs for a number of reasons, the agency reported. The source may be from a drug’s manufacturing process, chemical structure, or the conditions under which it is stored or packaged.
 

A version of this article appeared on Medscape.com.

The US Food and Drug Administration (FDA) has announced a voluntary manufacturer-initiated recall of more than 7000 bottles of duloxetine delayed-release capsules due to unacceptable levels of a potential carcinogen.

Duloxetine (Cymbalta) is a serotonin-norepinephrine reuptake inhibitor used to treat major depressive disorder, generalized anxiety disorder, fibromyalgia, chronic musculoskeletal pain, and neuropathic pain associated with diabetic peripheral neuropathy.

The recall is due to the detection of the nitrosamine impurity, N-nitroso duloxetine, above the proposed interim limit.

Nitrosamines are common in water and foods, and exposure to some levels of the chemical is common. Exposure to nitrosamine impurities above acceptable levels and over long periods may increase cancer risk, the FDA reported.

“If drugs contain levels of nitrosamines above the acceptable daily intake limits, FDA recommends these drugs be recalled by the manufacturer as appropriate,” the agency noted on its website.

The recall was initiated by Breckenridge Pharmaceutical and covers 7107 bottles of 500-count, 20 mg duloxetine delayed-release capsules. The drug is manufactured by Towa Pharmaceutical Europe and distributed nationwide by BPI.

The affected bottles are from lot number 220128 with an expiration date of 12/2024 and NDC of 51991-746-05.

The recall was initiated on October 10 and is ongoing.

“Healthcare professionals can educate patients about alternative treatment options to medications with potential nitrosamine impurities if available and clinically appropriate,” the FDA advises. “If a medication has been recalled, pharmacists may be able to dispense the same medication from a manufacturing lot that has not been recalled. Prescribers may also determine whether there is an alternative treatment option for patients.”

The FDA has labeled this a “class II” recall, which the agency defines as “a situation in which use of or exposure to a violative product may cause temporary or medically reversible adverse health consequences or where the probability of serious adverse health consequences is remote.”

Nitrosamine impurities have prompted a number of drug recalls in recent years, including oral anticoagulants, metformin, and skeletal muscle relaxants.

The impurities may be found in drugs for a number of reasons, the agency reported. The source may be from a drug’s manufacturing process, chemical structure, or the conditions under which it is stored or packaged.
 

A version of this article appeared on Medscape.com.

The US Food and Drug Administration (FDA) has announced a voluntary manufacturer-initiated recall of more than 7000 bottles of duloxetine delayed-release capsules due to unacceptable levels of a potential carcinogen.

Duloxetine (Cymbalta) is a serotonin-norepinephrine reuptake inhibitor used to treat major depressive disorder, generalized anxiety disorder, fibromyalgia, chronic musculoskeletal pain, and neuropathic pain associated with diabetic peripheral neuropathy.

The recall is due to the detection of the nitrosamine impurity, N-nitroso duloxetine, above the proposed interim limit.

Nitrosamines are common in water and foods, and exposure to some levels of the chemical is common. Exposure to nitrosamine impurities above acceptable levels and over long periods may increase cancer risk, the FDA reported.

“If drugs contain levels of nitrosamines above the acceptable daily intake limits, FDA recommends these drugs be recalled by the manufacturer as appropriate,” the agency noted on its website.

The recall was initiated by Breckenridge Pharmaceutical and covers 7107 bottles of 500-count, 20 mg duloxetine delayed-release capsules. The drug is manufactured by Towa Pharmaceutical Europe and distributed nationwide by BPI.

The affected bottles are from lot number 220128 with an expiration date of 12/2024 and NDC of 51991-746-05.

The recall was initiated on October 10 and is ongoing.

“Healthcare professionals can educate patients about alternative treatment options to medications with potential nitrosamine impurities if available and clinically appropriate,” the FDA advises. “If a medication has been recalled, pharmacists may be able to dispense the same medication from a manufacturing lot that has not been recalled. Prescribers may also determine whether there is an alternative treatment option for patients.”

The FDA has labeled this a “class II” recall, which the agency defines as “a situation in which use of or exposure to a violative product may cause temporary or medically reversible adverse health consequences or where the probability of serious adverse health consequences is remote.”

Nitrosamine impurities have prompted a number of drug recalls in recent years, including oral anticoagulants, metformin, and skeletal muscle relaxants.

The impurities may be found in drugs for a number of reasons, the agency reported. The source may be from a drug’s manufacturing process, chemical structure, or the conditions under which it is stored or packaged.
 

A version of this article appeared on Medscape.com.

SAN FRANCISCO — While the physical toll of cancer is well documented, the financial toll can also be severe and lasting.

Overall, patients with cancer tend to face higher rates of debt collection, medical collections, and bankruptcies, as well as lower credit scores, according to two new studies presented at the American College of Surgeons Clinical Congress 2024.

“These are the first studies to provide numerical evidence of financial toxicity among cancer survivors,” Benjamin C. James, MD, with Beth Israel Deaconess Medical Center and Harvard Medical School, both in Boston, Massachusetts, who worked on both studies, said in a statement. “Previous data on this topic largely relies on subjective survey reviews.”

In one study, researchers used the Massachusetts Cancer Registry to identify 99,175 patients diagnosed with cancer between 2010 and 2019 and matched them with 188,875 control individuals without cancer. Researchers then assessed financial toxicity using Experian credit bureau data for participants.

Overall, patients with cancer faced a range of financial challenges that often lasted years following their diagnosis.

Patients were nearly five times more likely to experience bankruptcy and had average credit scores nearly 80 points lower than control individuals without cancer. The drop in credit scores was more pronounced for survivors of bladder, liver, lung, and colorectal cancer (CRC) and persisted for up to 9.5 years.

For certain cancer types, in particular, “we are looking years after a diagnosis, and we see that the credit score goes down and it never comes back up,” James said.

The other study, which used a sample of 7227 patients with CRC from Massachusetts, identified several factors that correlated with lower credit scores.

Compared with patients who only had surgery, peers who underwent radiation only experienced a 62-point drop in their credit score after their diagnosis, while those who had chemotherapy alone had just over a 14-point drop in their credit score. Among patients who had combination treatments, those who underwent both surgery and radiation experienced a nearly 16-point drop in their credit score and those who had surgery and chemoradiation actually experienced a 2.59 bump, compared with those who had surgery alone.

Financial toxicity was worse for patients younger than 62 years, those identifying as Black or Hispanic individuals, unmarried individuals, those with an annual income below $52,000, and those living in deprived areas.

The studies add to findings from the 2015 North American Thyroid Cancer Survivorship Study, which reported that 50% of thyroid cancer survivors encountered financial toxicity because of their diagnosis.

James said the persistent financial strain of cancer care, even in a state like Massachusetts, which mandates universal healthcare, underscores the need for “broader policy changes and reforms, including reconsidering debt collection practices.”

“Financial security should be a priority in cancer care,” he added.

The studies had no specific funding. The authors have disclosed no relevant conflict of interest.

A version of this article first appeared on Medscape.com.

SAN FRANCISCO — While the physical toll of cancer is well documented, the financial toll can also be severe and lasting.

Overall, patients with cancer tend to face higher rates of debt collection, medical collections, and bankruptcies, as well as lower credit scores, according to two new studies presented at the American College of Surgeons Clinical Congress 2024.

“These are the first studies to provide numerical evidence of financial toxicity among cancer survivors,” Benjamin C. James, MD, with Beth Israel Deaconess Medical Center and Harvard Medical School, both in Boston, Massachusetts, who worked on both studies, said in a statement. “Previous data on this topic largely relies on subjective survey reviews.”

In one study, researchers used the Massachusetts Cancer Registry to identify 99,175 patients diagnosed with cancer between 2010 and 2019 and matched them with 188,875 control individuals without cancer. Researchers then assessed financial toxicity using Experian credit bureau data for participants.

Overall, patients with cancer faced a range of financial challenges that often lasted years following their diagnosis.

Patients were nearly five times more likely to experience bankruptcy and had average credit scores nearly 80 points lower than control individuals without cancer. The drop in credit scores was more pronounced for survivors of bladder, liver, lung, and colorectal cancer (CRC) and persisted for up to 9.5 years.

For certain cancer types, in particular, “we are looking years after a diagnosis, and we see that the credit score goes down and it never comes back up,” James said.

The other study, which used a sample of 7227 patients with CRC from Massachusetts, identified several factors that correlated with lower credit scores.

Compared with patients who only had surgery, peers who underwent radiation only experienced a 62-point drop in their credit score after their diagnosis, while those who had chemotherapy alone had just over a 14-point drop in their credit score. Among patients who had combination treatments, those who underwent both surgery and radiation experienced a nearly 16-point drop in their credit score and those who had surgery and chemoradiation actually experienced a 2.59 bump, compared with those who had surgery alone.

Financial toxicity was worse for patients younger than 62 years, those identifying as Black or Hispanic individuals, unmarried individuals, those with an annual income below $52,000, and those living in deprived areas.

The studies add to findings from the 2015 North American Thyroid Cancer Survivorship Study, which reported that 50% of thyroid cancer survivors encountered financial toxicity because of their diagnosis.

James said the persistent financial strain of cancer care, even in a state like Massachusetts, which mandates universal healthcare, underscores the need for “broader policy changes and reforms, including reconsidering debt collection practices.”

“Financial security should be a priority in cancer care,” he added.

The studies had no specific funding. The authors have disclosed no relevant conflict of interest.

A version of this article first appeared on Medscape.com.

SAN FRANCISCO — While the physical toll of cancer is well documented, the financial toll can also be severe and lasting.

Overall, patients with cancer tend to face higher rates of debt collection, medical collections, and bankruptcies, as well as lower credit scores, according to two new studies presented at the American College of Surgeons Clinical Congress 2024.

“These are the first studies to provide numerical evidence of financial toxicity among cancer survivors,” Benjamin C. James, MD, with Beth Israel Deaconess Medical Center and Harvard Medical School, both in Boston, Massachusetts, who worked on both studies, said in a statement. “Previous data on this topic largely relies on subjective survey reviews.”

In one study, researchers used the Massachusetts Cancer Registry to identify 99,175 patients diagnosed with cancer between 2010 and 2019 and matched them with 188,875 control individuals without cancer. Researchers then assessed financial toxicity using Experian credit bureau data for participants.

Overall, patients with cancer faced a range of financial challenges that often lasted years following their diagnosis.

Patients were nearly five times more likely to experience bankruptcy and had average credit scores nearly 80 points lower than control individuals without cancer. The drop in credit scores was more pronounced for survivors of bladder, liver, lung, and colorectal cancer (CRC) and persisted for up to 9.5 years.

For certain cancer types, in particular, “we are looking years after a diagnosis, and we see that the credit score goes down and it never comes back up,” James said.

The other study, which used a sample of 7227 patients with CRC from Massachusetts, identified several factors that correlated with lower credit scores.

Compared with patients who only had surgery, peers who underwent radiation only experienced a 62-point drop in their credit score after their diagnosis, while those who had chemotherapy alone had just over a 14-point drop in their credit score. Among patients who had combination treatments, those who underwent both surgery and radiation experienced a nearly 16-point drop in their credit score and those who had surgery and chemoradiation actually experienced a 2.59 bump, compared with those who had surgery alone.

Financial toxicity was worse for patients younger than 62 years, those identifying as Black or Hispanic individuals, unmarried individuals, those with an annual income below $52,000, and those living in deprived areas.

The studies add to findings from the 2015 North American Thyroid Cancer Survivorship Study, which reported that 50% of thyroid cancer survivors encountered financial toxicity because of their diagnosis.

James said the persistent financial strain of cancer care, even in a state like Massachusetts, which mandates universal healthcare, underscores the need for “broader policy changes and reforms, including reconsidering debt collection practices.”

“Financial security should be a priority in cancer care,” he added.

The studies had no specific funding. The authors have disclosed no relevant conflict of interest.

A version of this article first appeared on Medscape.com.

TOPLINE: 

High-intensity exercise suppresses ghrelin levels more than moderate-intensity exercise, leading to a greater reduction in hunger. This effect may be more pronounced in women than in men.

METHODOLOGY:

• Ghrelin circulates in acylated and deacylated forms and is associated with hunger perceptions. Previous studies have indicated that acute exercise can modulate ghrelin levels, but data on the effect of exercise intensity on ghrelin levels and appetite remain limited.
• To close this gap, researchers examined 14 adults, including eight men (mean age, 43.1 years; body mass index [BMI], 22.2) and six women (mean age, 32.2 years; BMI, 22.7) who fasted overnight and then completed exercises of varying intensity.
• Participants completed a maximal graded cycle ergometer lactate threshold (LT) and peak oxygen consumption (VO2_peak) test to determine the exercise intensity.
• Three calorically matched cycle exercise bouts were conducted: Control (no exercise), moderate-intensity (power output at LT), and high-intensity (power output associated with 75% of the difference between LT and VO2_peak).
• Total ghrelin, acylated ghrelin, deacylated ghrelin, and lactate levels were measured at baseline and at multiple intervals post-exercise; appetite ratings were assessed using a visual analog scale at baseline and every 30 minutes thereafter.

TAKEAWAY:

• Total ghrelin levels were significantly lower during high-intensity exercise than during moderate-intensity and no exercise (P < .0001 for both).
• Both men and women had significantly lower deacylated ghrelin levels during high-intensity exercise than during moderate-intensity (P < .0001) and no exercise (P = .002), whereas only women had significantly lower acylated ghrelin levels during high-intensity exercise (P < .0001).
• Hunger scores were higher in the moderate-intensity exercise group than in the no exercise group (P < .01), with no differences found between high-intensity exercise and moderate-intensity or no exercise.
• Lactate levels were significantly higher during high-intensity exercise than during moderate-intensity and no exercise (P < .0001 for both).

IN PRACTICE:

“Exercise should be thought of as a ‘drug,’ where the ‘dose’ should be customized based on an individual’s personal goals,” the lead author said in a news release. “Our research suggests that high-intensity exercise may be important for appetite suppression, which can be particularly useful as part of a weight loss program.”

SOURCE:

This study was led by Kara C. Anderson, PhD, Department of Kinesiology, University of Virginia, Charlottesville, Virginia, and was published online on October 24, 2024, in the Journal of the Endocrine Society.

LIMITATIONS: 

The real-world application of the study was limited as participants were tested under fasting conditions, which may not have reflected typical exercise scenarios. The differences in fitness levels and exercise caloric expenditure between men and women may have affected the findings. The study only included lean individuals, limiting the applicability of the findings to individuals with overweight or obesity.

DISCLOSURES:

The study was supported by funds from the School of Education and Human Development, University of Virginia, and the National Institute of Diabetes and Digestive and Kidney Diseases. One author reported serving as an editor for the Journal of the Endocrine Society, which played no role in the evaluation of the manuscript.
 

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.

TOPLINE: 

High-intensity exercise suppresses ghrelin levels more than moderate-intensity exercise, leading to a greater reduction in hunger. This effect may be more pronounced in women than in men.

METHODOLOGY:

• Ghrelin circulates in acylated and deacylated forms and is associated with hunger perceptions. Previous studies have indicated that acute exercise can modulate ghrelin levels, but data on the effect of exercise intensity on ghrelin levels and appetite remain limited.
• To close this gap, researchers examined 14 adults, including eight men (mean age, 43.1 years; body mass index [BMI], 22.2) and six women (mean age, 32.2 years; BMI, 22.7) who fasted overnight and then completed exercises of varying intensity.
• Participants completed a maximal graded cycle ergometer lactate threshold (LT) and peak oxygen consumption (VO2_peak) test to determine the exercise intensity.
• Three calorically matched cycle exercise bouts were conducted: Control (no exercise), moderate-intensity (power output at LT), and high-intensity (power output associated with 75% of the difference between LT and VO2_peak).
• Total ghrelin, acylated ghrelin, deacylated ghrelin, and lactate levels were measured at baseline and at multiple intervals post-exercise; appetite ratings were assessed using a visual analog scale at baseline and every 30 minutes thereafter.

TAKEAWAY:

• Total ghrelin levels were significantly lower during high-intensity exercise than during moderate-intensity and no exercise (P < .0001 for both).
• Both men and women had significantly lower deacylated ghrelin levels during high-intensity exercise than during moderate-intensity (P < .0001) and no exercise (P = .002), whereas only women had significantly lower acylated ghrelin levels during high-intensity exercise (P < .0001).
• Hunger scores were higher in the moderate-intensity exercise group than in the no exercise group (P < .01), with no differences found between high-intensity exercise and moderate-intensity or no exercise.
• Lactate levels were significantly higher during high-intensity exercise than during moderate-intensity and no exercise (P < .0001 for both).

IN PRACTICE:

“Exercise should be thought of as a ‘drug,’ where the ‘dose’ should be customized based on an individual’s personal goals,” the lead author said in a news release. “Our research suggests that high-intensity exercise may be important for appetite suppression, which can be particularly useful as part of a weight loss program.”

SOURCE:

This study was led by Kara C. Anderson, PhD, Department of Kinesiology, University of Virginia, Charlottesville, Virginia, and was published online on October 24, 2024, in the Journal of the Endocrine Society.

LIMITATIONS: 

The real-world application of the study was limited as participants were tested under fasting conditions, which may not have reflected typical exercise scenarios. The differences in fitness levels and exercise caloric expenditure between men and women may have affected the findings. The study only included lean individuals, limiting the applicability of the findings to individuals with overweight or obesity.

DISCLOSURES:

The study was supported by funds from the School of Education and Human Development, University of Virginia, and the National Institute of Diabetes and Digestive and Kidney Diseases. One author reported serving as an editor for the Journal of the Endocrine Society, which played no role in the evaluation of the manuscript.
 

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.

TOPLINE: 

High-intensity exercise suppresses ghrelin levels more than moderate-intensity exercise, leading to a greater reduction in hunger. This effect may be more pronounced in women than in men.

METHODOLOGY:

• Ghrelin circulates in acylated and deacylated forms and is associated with hunger perceptions. Previous studies have indicated that acute exercise can modulate ghrelin levels, but data on the effect of exercise intensity on ghrelin levels and appetite remain limited.
• To close this gap, researchers examined 14 adults, including eight men (mean age, 43.1 years; body mass index [BMI], 22.2) and six women (mean age, 32.2 years; BMI, 22.7) who fasted overnight and then completed exercises of varying intensity.
• Participants completed a maximal graded cycle ergometer lactate threshold (LT) and peak oxygen consumption (VO2_peak) test to determine the exercise intensity.
• Three calorically matched cycle exercise bouts were conducted: Control (no exercise), moderate-intensity (power output at LT), and high-intensity (power output associated with 75% of the difference between LT and VO2_peak).
• Total ghrelin, acylated ghrelin, deacylated ghrelin, and lactate levels were measured at baseline and at multiple intervals post-exercise; appetite ratings were assessed using a visual analog scale at baseline and every 30 minutes thereafter.

TAKEAWAY:

• Total ghrelin levels were significantly lower during high-intensity exercise than during moderate-intensity and no exercise (P < .0001 for both).
• Both men and women had significantly lower deacylated ghrelin levels during high-intensity exercise than during moderate-intensity (P < .0001) and no exercise (P = .002), whereas only women had significantly lower acylated ghrelin levels during high-intensity exercise (P < .0001).
• Hunger scores were higher in the moderate-intensity exercise group than in the no exercise group (P < .01), with no differences found between high-intensity exercise and moderate-intensity or no exercise.
• Lactate levels were significantly higher during high-intensity exercise than during moderate-intensity and no exercise (P < .0001 for both).

IN PRACTICE:

“Exercise should be thought of as a ‘drug,’ where the ‘dose’ should be customized based on an individual’s personal goals,” the lead author said in a news release. “Our research suggests that high-intensity exercise may be important for appetite suppression, which can be particularly useful as part of a weight loss program.”

SOURCE:

This study was led by Kara C. Anderson, PhD, Department of Kinesiology, University of Virginia, Charlottesville, Virginia, and was published online on October 24, 2024, in the Journal of the Endocrine Society.

LIMITATIONS: 

The real-world application of the study was limited as participants were tested under fasting conditions, which may not have reflected typical exercise scenarios. The differences in fitness levels and exercise caloric expenditure between men and women may have affected the findings. The study only included lean individuals, limiting the applicability of the findings to individuals with overweight or obesity.

DISCLOSURES:

The study was supported by funds from the School of Education and Human Development, University of Virginia, and the National Institute of Diabetes and Digestive and Kidney Diseases. One author reported serving as an editor for the Journal of the Endocrine Society, which played no role in the evaluation of the manuscript.
 

This article was created using several editorial tools, including AI, as part of the process. Human editors reviewed this content before publication. A version of this article appeared on Medscape.com.

Primary adrenal insufficiency (Addison’s disease) results from a dysfunction of the adrenal glands, which may be secondary to autoimmune diseases, genetic conditions, infections, and vasculopathies,or may be drug-induced (e.g. checkpoint inhibitors), among others . In contrast, secondary adrenal insufficiency results from pituitary dysfunction of low adrenocorticotropic hormone (ACTH). The most common cause of primary adrenal insufficiency in developed countries is autoimmune adrenalitis, which accounts for upwards of 90% of cases. Typically, 21-hydroxylase autoantibodies are identified and account for destruction of the adrenal cortex through cell-mediated and humoral immune responses.

Dr. Sophia M. Akhiyat

Dr. Sophia M. Akhiyat

Dr. Bilu Martin is a board-certified dermatologist in private practice at Premier Dermatology, MD, in Aventura, Florida. More diagnostic cases are available at mdedge.com/dermatology. To submit a case for possible publication, send an email to dermnews@mdedge.com.

References

J Am Acad Dermatol. 2014 May;70(5):Supplement 1AB118. doi: 10.1016/j.jaad.2014.01.491.

Michels A, Michels N. Am Fam Physician. 2014 Apr 1;89(7):563-568.

Kauzman A et al. J Can Dent Assoc. 2004 Nov;70(10):682-683.

Primary adrenal insufficiency (Addison’s disease) results from a dysfunction of the adrenal glands, which may be secondary to autoimmune diseases, genetic conditions, infections, and vasculopathies,or may be drug-induced (e.g. checkpoint inhibitors), among others . In contrast, secondary adrenal insufficiency results from pituitary dysfunction of low adrenocorticotropic hormone (ACTH). The most common cause of primary adrenal insufficiency in developed countries is autoimmune adrenalitis, which accounts for upwards of 90% of cases. Typically, 21-hydroxylase autoantibodies are identified and account for destruction of the adrenal cortex through cell-mediated and humoral immune responses.

Dr. Sophia M. Akhiyat

Dr. Sophia M. Akhiyat

Dr. Bilu Martin is a board-certified dermatologist in private practice at Premier Dermatology, MD, in Aventura, Florida. More diagnostic cases are available at mdedge.com/dermatology. To submit a case for possible publication, send an email to dermnews@mdedge.com.

References

J Am Acad Dermatol. 2014 May;70(5):Supplement 1AB118. doi: 10.1016/j.jaad.2014.01.491.

Michels A, Michels N. Am Fam Physician. 2014 Apr 1;89(7):563-568.

Kauzman A et al. J Can Dent Assoc. 2004 Nov;70(10):682-683.

Primary adrenal insufficiency (Addison’s disease) results from a dysfunction of the adrenal glands, which may be secondary to autoimmune diseases, genetic conditions, infections, and vasculopathies,or may be drug-induced (e.g. checkpoint inhibitors), among others . In contrast, secondary adrenal insufficiency results from pituitary dysfunction of low adrenocorticotropic hormone (ACTH). The most common cause of primary adrenal insufficiency in developed countries is autoimmune adrenalitis, which accounts for upwards of 90% of cases. Typically, 21-hydroxylase autoantibodies are identified and account for destruction of the adrenal cortex through cell-mediated and humoral immune responses.

Dr. Sophia M. Akhiyat

Dr. Sophia M. Akhiyat

Dr. Bilu Martin is a board-certified dermatologist in private practice at Premier Dermatology, MD, in Aventura, Florida. More diagnostic cases are available at mdedge.com/dermatology. To submit a case for possible publication, send an email to dermnews@mdedge.com.

References

J Am Acad Dermatol. 2014 May;70(5):Supplement 1AB118. doi: 10.1016/j.jaad.2014.01.491.

Michels A, Michels N. Am Fam Physician. 2014 Apr 1;89(7):563-568.

Kauzman A et al. J Can Dent Assoc. 2004 Nov;70(10):682-683.