Why You Can’t Blame Your Behavior On Your Gut Microbiome
See a hot pizza sitting on a table. Count the missing pieces: three. They tasted delicious and yes, you've eaten enough—but you're still eyeing a fourth piece. Do you reach out and take it, or not?
"The difficulty comes in translating the animal data into the human situation."
Your behavior in that next moment is anything but simple: as far as scientists can tell, it comes down to a complex confluence of circumstances, genes, and personality characteristics. And the latest proposed addition to this list is the gut microbiome—the community of microorganisms, including bacteria, archaea, fungi, and viruses—that are full-time residents of your digestive tract.
It is entirely plausible that your gut microbiome might influence your behavior, scientists say: a well-known communication channel, called the gut-brain axis, runs both ways between your brain and your digestive tract. Gut bugs, which are close to the action, could amplify or dampen the messages, thereby shaping how you act. Messages about food-related behaviors could be particularly susceptible to interception by these microorganisms.
Perhaps it's convenient to imagine your resident microbes sitting greedily in your gut, crying for more pizza and tricking your brain into getting them what they want. The problem is, there's a distinct lack of scientific support for this actually happening in humans.
John Bienenstock, professor of pathology and molecular medicine at McMaster University (Canada), has worked on the gut microbiome-behavior connection for several decades. "There's a lot of evidence now in animals—particularly in mice," he says.
Indeed, his group and others have shown that, by eliminating or altering gut bugs, they can make mice exhibit different social behaviors or respond more coolly to stress; they can even make a shy mouse turn brave. But Bienenstock cautions: "The difficulty comes in translating the animal data into the human situation."
Animal behaviors are worlds apart from what we do on a daily basis—from brushing our teeth to navigating complex social situations.
Not that it's an easy task to figure out which aspects of animal research are relevant to people in everyday life. Animal behaviors are worlds apart from what we do on a daily basis—from brushing our teeth to navigating complex social situations.
Elaine Hsiao, assistant professor of integrative biology and physiology at UCLA, has also looked closely at the microbiome-gut-brain axis in mice and pondered how to translate the results into humans. She says, "Both the microbiome and behavior vary substantially [from person to person] and can be strongly influenced by environmental factors—which makes it difficult to run a well-controlled study on effects of the microbiome on human behavior."
She adds, "Human behaviors are very complex and the metrics used to quantify behavior are often not precise enough to derive clear interpretations." So the challenge is not only to figure out what people actually do, but also to give those actions numerical codes that allow them to be compared against other actions.
Hsiao and colleagues are nevertheless attempting to make connections: building on some animal research, their recent study found a three-way association in humans between molecules produced by their gut bacteria (that is, indole metabolites), the connectedness of different brain regions as measured through functional magnetic resonance imaging, and measures of behavior: questionnaires assessing food addiction and anxiety.
Meanwhile, other studies have found it may be possible to change a person's behavior through either probiotics or gut-localized antibiotics. Several probiotics even show promise for altering behavior in clinical conditions like depression. Yet how these phenomena occur is still unknown and, overall, scientists lack solid evidence on how bugs control behavior.
Bienenstock, however, is one of many continuing to investigate. He says, "Some of these observations are very striking. They're so striking that clearly something's up."
He says that after identifying a behavior-changing bug, or set of bugs, in mice: "The obvious next thing is: How [is it] occurring? Why is it occurring? What are the molecules involved?" Bienenstock favors the approach of nailing down a mechanism in animal models before starting to investigate its relevance to humans.
He explains, "[This preclinical work] should allow us to identify either target molecules or target pathways, which then can be translated."
Bienenstock also acknowledges the 'hype' that appears to surround this particular field of study. Despite the decidedly slow emergence of data linking the microbiome to human behavior, scientific reviews have appeared in brain-related scientific journals—for instance, Trends in Cognitive Sciences; CNS Drugs—with remarkable frequency. Not only this, but popular books and media articles have given the idea wings.
It might be compelling to blame our microbiomes for behaviors we don't prefer or can't explain—like reaching for another slice of pizza. But until the scientific observations yield stronger results, we still lack proof that we're doing what we do—or eating what we eat—exclusively at the behest of our resident microorganisms.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans 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.