Immune cells battle an infection.
Measuring immune resilience
The researchers measured immune resilience in two ways. The first is based on the relative quantities of two types of immune cells, CD4+ T cells and CD8+ T cells. CD4+ T cells coordinate the immune system’s response to pathogens and are often used to measure immune health (with higher levels typically suggesting a stronger immune system). However, in 2021, the researchers found that a low level of CD8+ T cells (which are responsible for killing damaged or infected cells) is also an important indicator of immune health. In fact, patients with high levels of CD4+ T cells and low levels of CD8+ T cells during SARS-CoV-2 and HIV infection were the least likely to develop severe COVID and AIDS.
Individuals with optimal levels of immune resilience were more likely to live longer.
In the same 2021 study, the researchers identified a second measure of immune resilience that involves two gene expression signatures correlated with an infected person’s risk of death. One of the signatures was linked to a higher risk of death; it includes genes related to inflammation — an essential process for jumpstarting the immune system but one that can cause considerable damage if left unbridled. The other signature was linked to a greater chance of survival; it includes genes related to keeping inflammation in check. These genes help the immune system mount a balanced immune response during infection and taper down the response after the threat is gone. The researchers found that participants who expressed the optimal combination of genes lived longer.
Immune resilience and longevity
The researchers assessed levels of immune resilience in nearly 50,000 participants of different ages and with various types of challenges to their immune systems, including acute infections, chronic diseases, and cancers. Their evaluation demonstrated that individuals with optimal levels of immune resilience were more likely to live longer, resist HIV and influenza infections, resist recurrence of skin cancer after kidney transplant, survive COVID infection, and survive sepsis.
However, a person’s immune resilience fluctuates all the time. Study participants who had optimal immune resilience before common symptomatic viral infections like a cold or the flu experienced a shift in their gene expression to poor immune resilience within 48 hours of symptom onset. As these people recovered from their infection, many gradually returned to the more favorable gene expression levels they had before. However, nearly 30% who once had optimal immune resilience did not fully regain that survival-associated profile by the end of the cold and flu season, even though they had recovered from their illness.
Intriguingly, some people who are 90+ years old still have optimal immune resilience, suggesting that these individuals’ immune systems have an exceptional capacity to control inflammation and rapidly restore proper immune balance.
This could suggest that the recovery phase varies among people and diseases. For example, young female sex workers who had many clients and did not use condoms — and thus were repeatedly exposed to sexually transmitted pathogens — had very low immune resilience. However, most of the sex workers who began reducing their exposure to sexually transmitted pathogens by using condoms and decreasing their number of sex partners experienced an improvement in immune resilience over the next 10 years.
Immune resilience and aging
The researchers found that the proportion of people with optimal immune resilience tended to be highest among the young and lowest among the elderly. The researchers suggest that, as people age, they are exposed to increasingly more health conditions (acute infections, chronic diseases, cancers, etc.) which challenge their immune systems to undergo a “respond-and-recover” cycle. During the response phase, CD8+ T cells and inflammatory gene expression increase, and during the recovery phase, they go back down.
However, over a lifetime of repeated challenges, the immune system is slower to recover, altering a person’s immune resilience. Intriguingly, some people who are 90+ years old still have optimal immune resilience, suggesting that these individuals’ immune systems have an exceptional capacity to control inflammation and rapidly restore proper immune balance despite the many respond-and-recover cycles that their immune systems have faced.
Public health ramifications could be significant. Immune cell and gene expression profile assessments are relatively simple to conduct, and being able to determine a person’s immune resilience can help identify whether someone is at greater risk for developing diseases, how they will respond to treatment, and whether, as well as to what extent, they will recover.
A recent study in The Lancet Oncology showed that AI found 20 percent more cancers on mammogram screens than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.