Mois : février 2025

A new study suggests that people with hypertension who receive care in their preferred language are less likely to have a major cardiovascular event or die from any cause [1].

Let’s find a common language

Communication between the doctor and the patient is important, and it is becoming clear how important. A new study by Canadian scientists, published in JAMA Network Open, suggests that the impact of communication quality on health outcomes can be drastic.

Few things hinder communication as much as a language barrier. In immigrant-rich countries like the US and Canada, where people speak dozens of languages and often struggle to express themselves in the dominant one, this presents a major challenge.

In a large cohort of more than 100,000 patients with hypertension, the researchers identified 5,229 who primarily spoke an allophone language, which in Canada is a language other than English, French, or an Indigenous language. The researchers asked how communicating with a regular primary care provider in the patient’s preferred language affects the incidence of major adverse cardiovascular events (MACE), which, in this study, included hospitalization with acute coronary syndrome, heart failure, or stroke along with death by a cardiovascular cause.

Populational studies cannot establish causation and have to deal with multiple variables, but the researchers did a thorough job of accounting for possible confounding factors. Those included age, sex, marital status, educational level, household income, geographic region, urban or rural residence, Indigenous identity, immigrant status, knowledge of English, smoking, diabetes, obesity, history of heart disease, and history of stroke. All in all, the respondents reported speaking nearly 100 different languages.

More understanding means less risk

The study showed that participants whose preferred language was not English or French were 36% less likely to have a major adverse cardiovascular event if they received care from their regular primary care physician in their preferred language (including via translation services). A secondary analysis showed a similar correlation for all-cause hospitalization and mortality. They were 27% and 28% lower, respectively, for people who received primary care in their preferred language.

This is not the first study to explore the connection between language-concordant care (in which the physician speaks the patient’s native or preferred language) and health outcomes. The paper notes that studies conducted in the US have shown better glycemic control, blood pressure, and low-density lipoprotein cholesterol (LDL) levels in non–English-speaking patients who received primary care from physicians in their preferred language compared to those who communicated with their doctor strictly in English [2].

Talking about solutions

Michael Reaume, a resident in the Faculty of Medicine’s Department of Nephrology at the University of Ottawa and the study’s lead author, said, “If there was a new medication that decreased the risk of major adverse cardiovascular event by 36% or all-cause mortality by 28%, this medication would immediately be offered to our patients. We need to start thinking about language barriers in our health care systems in a similar way.”

“This starts by collecting preferred language for all patients systematically,” he noted. “This information is critical as it allows us to match patients to health care providers who have proficiency in their preferred language, while also identifying patients who would benefit from professional interpretation services.”

However, there might be a simple solution: AI. Several studies have recently shown that large language models (LLMs) are superior to human primary care providers in communicating with patients. The chatbots outperform humans on nearly all parameters, including thoroughness and empathy [3]. What’s more, these models can naturally converse in multiple languages.

Literature

[1] Reaume, M., Labossière, M. N., Batista, R., Van Haute, S., Tangri, N., Rigatto, C., … & Lix, L. M. (2025). Patient-Physician Language Concordance and Cardiovascular Outcomes Among Patients With Hypertension. JAMA Network Open, 8(2), e2460551-e2460551.

[2] Fernandez, A., Schillinger, D., Warton, E. M., Adler, N., Moffet, H. H., Schenker, Y., … & Karter, A. J. (2011). Language barriers, physician-patient language concordance, and glycemic control among insured Latinos with diabetes: the Diabetes Study of Northern California (DISTANCE). Journal of general internal medicine, 26, 170-176.

[3] Goh, E., Gallo, R., Hom, J., Strong, E., Weng, Y., Kerman, H., … & Chen, J. H. (2024). Large language model influence on diagnostic reasoning: a randomized clinical trial. JAMA Network Open, 7(10), e2440969-e2440969.

The Arc Institute, a nonprofit research organization, has published a manuscript on its creation of Evo 2, an AI foundation model that is capable of both understanding and building full genomes of organisms.

A new step in understanding biology

The authors of this paper, a group of professionals largely from the Arc Institute and well-known universities in California, begin by discussing Evo 2’s unprecedented size. Unlike the original Evo, which was only trained on organisms that lack nuclei (prokaryotes), this model was trained on organisms with nuclei (eukaryotes) as well, a classification that includes everything from amoebae to human beings, and a total of 9.3 trillion base pairs were included in its training set.

The researchers created two variants, one with 7 billion parameters (7B) and another with 40 billion parameters (40B), and both models use a context window of a million single base pairs. This model is open source, including both the training and inference code along with its parameters and the training data originating from OpenGenome2.

This paper goes into detail describing how the model was trained. Like the commonly known large language models (LLMs), this model was fundamentally trained to predict the next “token”; instead of predicting the next word in the English language, however, Evo 2 was built to predict the next DNA base pair. This model was built on StripedHyena2, a convolutional, multi-hybrid system that directs it to think in different, layered ways (stripes) about the training information it’s receiving.

Predicting the effects of mutations

The researchers found that Evo 2 was able to predict whether or not a genetic mutation would impact essential function, which had never been accomplished before in eukaryotes. Evo 2 had learned to predict the likelihood of mutations as they related to start and stop codons; this, the researchers claimed, meant that it had an understanding of such “fundamental genetic features” despite solely being trained on base pairs and not taught what they meant.

Furthermore, by testing its predictions against known effects in RNA sequences, the researchers determined that the model was able to accurately ascertain whether any given mutation would affect the essential function of the sequence, and it was even able to grasp that effects in noncoding regions would have significant consequences. The 40B model was found to be substantially better than the 7B model at this.

This held true even for sequences derived from human beings. Mutations in the BRCA1 gene often lead to breast cancer, and 40B Evo2 was able to predict whether or not any given mutation in this gene would be dangerous or not, especially when it was specifically supervised to do so, even beating out specialized models made for the purpose. This, the researchers note, is in spite of the model being trained on only one reference human genome within its expansive dataset; its predictions are fundamentally derived from how organisms work, not humans in particular.

Grasping genetics from the ground up

The researchers took a close look at Evo2’s thought process. They realized that it was accurately able to identify features associated with CRISPR-related phage sequences within E.coli bacteria. Rather than memorizing the bacterial phages themselves, the model identified the CRISPR spacers instead. Similarly, the model was able to identify frameshift mutations and premature stop codons. It was able to identify exons and introns that it learned from the human genome and notice them in the woolly mammoth genome, which it had never been trained on.

As this is a generative AI, the researchers set it to the task of generating genomes. The genomes it created were found to have many natural features, including reasonable chromatin accessibility, although the authors judged its performance based on other algorithms and did not actually create any physical structures based on Evo2’s outputs. They posit that their model can, with further training related to sequences and their associated functions, be used to generate effective genetic structures.

To prevent this open-source model from being used for bioterrorism, the researchers intentionally excluded infectious diseases from its training set, and they red-teamed their model to ensure that it was no better than random chance in generating or understanding the effects of infectious diseases. However, they did note that they cannot prevent malefactors from training the model with such diseases.

This model may have significant benefits for diagnosing and treating both mitochondrial dysfunction and genomic instability, such as by identifying and better understanding the age-related mutations that give some cells or mitochondria a reproductive advantage over others at the expense of the overall organism. It may even be possible for future research to use this model to test individual people for mutated cells or even to create individually targeted gene therapies. It is still a foundational model, however, and nothing based on Evo2 has been put to such tasks.

This manuscript was published on the Arc Institute’s website and not in a peer-reviewed publication. However, the depth and detail of this paper’s explanations, along with its authorship of researchers from reputable institutions, lend weight to its claims being correct. As this is an open-source tool for the research community, it will swiftly become clear whether or not it can be used to advance oncology, develop treatments for genetic diseases, or directly impact aging at the genetic level.

No other human endeavor today holds more promise than understanding and targeting aging. The molecular mechanisms that drive aging impact overall vigor, environmental stress resistance, reproductive health, and broad disease risk, and they fundamentally change what life means by radically changing our relationship to death. Intriguingly, aging mechanisms are highly evolutionarily conserved, so much so that the same subcellular changes that drive aging in single-celled organisms and small animals also drive aging in larger mammals, including companion pets and humans. Truly, from applications to beneficiaries, the potential of targeting aging to transform life is vast.

Seeking to realize the promise of longevity technologies (defined here as any intervention that extends healthy organismal lifespan), longevity biotechnology (LongBio) has continued to mature over the last 10 years.

LongBio’s focus has largely centered on its biopharmaceutical applications. Indeed, many people have even ventured to assert that biopharmaceutical applications define longevity biotechnology itself [1]. Biopharma LongBio has understandably led the charge, as the need for new disease treatments is great, and geroscience theory makes a compelling case that aging targets overlap with myriad disease states.

However, longevity biotech applications are not limited to treating disease; they hold the key to a radical transformation in industries far beyond healthcare.

The foundational science behind longevity is simple: maintaining and extending the healthy function of biological systems. Yet, outside of applications into current medical practice, the potential of longevity biotech remains vastly underexplored. From conservation biology to space travel, the ability to preserve, optimize, and extend the vitality of living organisms is an untapped goldmine.

Expanding the scope of longevity biotechnology

Dietary supplements and consumer products

Products marketed as “anti-aging” existed well before LongBio. The major challenge with consumer-ready LongBio products is the same with any other product: does it work as claimed? With no regulatory body that assesses non-clinical product claims and the morass around treating longevity/aging as an FDA-approved indication, quality assurance across consumer LongBio products is left to the manufacturer. Interestingly, the dominance of biopharma LongBio and the lack of clarity around aging as an indication have set the stage for consumer LongBio groups becoming best positioned to truly move our understanding of longevity technologies forward.

To find something, the most obvious strategy is to directly look for it. This is true for longevity technologies. However, the target-based reductionism prevalent in biopharma drug development has created a situation where, instead of directly measuring extended lifespan (the gold standard analysis for a longevity technology), longevity technologies are called such based on whether they modify a known “Hallmark of Aging” [2].

At its most extreme, biopharma LongBio is fundamentally misaligned for assessing whether an intervention extends healthy lifespan. For biopharma LongBio, disease indications are the focus, not longevity. Consumer LongBio products, on the other hand, are focused on directly extending healthy lifespan. This creates a major opportunity for these groups to validate longevity technologies by directly measuring healthy lifespan.

A major unmet need in this space is an unbiased, third-party system to evaluate longevity claims made by consumer LongBio products. Elevating standards of quality and supporting companies’ scientific efforts by choosing their products will create alignment and lead to better longevity technologies being developed faster.

Biomarkers and precision longevity

Along with effective longevity technologies, biomarkers are predictive of successful extended healthy lifespan is the biggest unmet need in LongBio. Today’s biomarkers, most prominently the numerous “clocks” in the consumer marketplace, have questionable use beyond serving as entertainment products.

Advancements in longevity biomarkers, biological indicators of aging and health, can refine personalized medicine by predicting disease risk, optimizing interventions, and even guiding lifestyle choices based on real-time biological data. In a commercial sense, longevity-focused biomarker technology could create a new wave of diagnostics, health optimization services, and AI-driven longevity coaching.

Reproductive health

The connection between reproductive health and longevity is becoming clearer, opening doors for new fertility-enhancing treatments that also promote long-term vitality. Longevity biotech could support extended reproductive windows, healthier pregnancies, and delayed reproductive aging, creating opportunities in both clinical and consumer health markets.

Veterinary and pet longevity

Veterinary medicine is already seeing interest in longevity applications for pets, but why stop there? A broader application of these technologies could enhance the health and lifespan of pets outside of veterinary care. The pet care industry alone, valued at over $200 billion globally, is primed for disruption through longevity-driven innovations.

Longevity in unexplored commercial frontiers

Conservation biology & remediation

Longevity technologies could revolutionize conservation efforts by extending the lifespans and/or reproductive viability of endangered and threatened species. A longer-lived, healthier wildlife population could improve biodiversity conservation, making longevity biotech a potential tool in ecosystem restoration.

Completely overlooked as of now is how improved healthy lifespan and stress resistance could improve the function of organisms that perform bioremediation and carbon fixing. Longevity technologies may also provide benefit in the context of environmental mitigation, which seeks to offset environmental and ecological damage produced during real estate and other development. Combined, remediation, environmental mitigation, and carbon markets have a combined market size of over $1 trillion.

Agriculture & food security

Healthier, longer-living crops and livestock could transform agriculture, reducing losses due to disease and environmental stress while increasing yield stability. Longevity technologies could enhance plant resilience, while longevity-focused veterinary applications could improve livestock productivity, addressing both economic and ethical concerns in food production.

Military performance & readiness

Servicemember readiness, retention, and performance are all priorities for leaders across the Department of Defense. Longevity biotech offers unprecedented potential for improving soldier performance, recovery, and retention. Enhancing cellular resilience, injury recovery, and cognitive longevity could create a new era of soldier optimization: one where extended healthspans mean fewer medical discharges and greater operational readiness. The military has always been a driver of cutting-edge biotech, and LongBio should be no exception.

Space medicine & interstellar longevity

Human space travel is limited by biological aging, radiation exposure, and long-term health deterioration. Longevity biotechnologies could mitigate these risks by promoting cellular repair, bolstering immune resilience, and extending astronaut viability for deep-space missions. As commercial space travel expands, so too will the demand for longevity-focused space medicine solutions.

Industrial & environmental applications

Any system reliant on biological organisms—whether it be wastewater treatment using microbial life or bio-based manufacturing—stands to benefit from longevity advancements. Healthier, longer-lived microbes, plants, and engineered biological systems could drive new efficiencies in bioindustrial processes.

The next commercial revolution

The potential of longevity biotechnology extends far beyond personal health and wellness; it is a foundational tool for any industry that relies on the health of living systems. The next wave of biotech-driven commercialization will not just be about treating disease but about optimizing the very biology of life itself. As we expand our understanding of aging and cellular health, we will uncover new markets, drive unprecedented innovation, and reshape industries that have yet to recognize the longevity revolution knocking at their door.

The question isn’t whether longevity biotechnology will expand into new industries—it’s how quickly we’ll seize the opportunity.

Literature

[1] Boekstein, N. et al. Defining a longevity biotechnology company. Nature Biotechnology 41, 1053-1055 (2023).

[2] López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: An expanding universe. Cell 186, 243-278 (2023).