Alzheimer’s prevention may be less about new drugs, more about income, zip code and education
That your risk of Alzheimer’s disease depends on your salary, what you ate as a child, or the block where you live may seem implausible. But researchers are discovering that social determinants of health (SDOH) play an outsized role in Alzheimer’s disease and related dementias, possibly more than age, and new strategies are emerging for how to address these factors.
At the 2022 Alzheimer’s Association International Conference, a series of presentations offered evidence that a string of socioeconomic factors—such as employment status, social support networks, education and home ownership—significantly affected dementia risk, even when adjusting data for genetic risk. What’s more, memory declined more rapidly in people who earned lower wages and slower in people who had parents of higher socioeconomic status.
In 2020, a first-of-its kind study in JAMA linked Alzheimer’s incidence to “neighborhood disadvantage,” which is based on SDOH indicators. Through autopsies, researchers analyzed brain tissue markers related to Alzheimer’s and found an association with these indicators. In 2022, Ryan Powell, the lead author of that study, published further findings that neighborhood disadvantage was connected with having more neurofibrillary tangles and amyloid plaques, the main pathological features of Alzheimer's disease.
As of yet, little is known about the biological processes behind this, says Powell, director of data science at the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health. “We know the association but not the direct causal pathway.”
The corroborative findings keep coming. In a Nature study published a few months after Powell’s study, every social determinant investigated affected Alzheimer’s risk except for marital status. The links were highest for income, education, and occupational status.
Clinical trials on new Alzheimer’s medications get all the headlines but preventing dementia through policy and public health interventions should not be underestimated.
The potential for prevention is significant. One in three older adults dies with Alzheimer's or another dementia—more than breast and prostate cancers combined. Further, a 2020 report from the Lancet Commission determined that about 40 percent of dementia cases could theoretically be prevented or delayed by managing the risk factors that people can modify.
Take inactivity. Older adults who took 9,800 steps daily were half as likely to develop dementia over the next 7 years, in a 2022 JAMA study. Hearing loss, another risk factor that can be managed, accounts for about 9 percent of dementia cases.
Clinical trials on new Alzheimer’s medications get all the headlines but preventing dementia through policy and public health interventions should not be underestimated. Simply slowing the course of Alzheimer’s or delaying its onset by five years would cut the incidence in half, according to the Global Council on Brain Health.
Minorities Hit the Hardest
The World Health Organization defines SDOH as “conditions in which people are born, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life.”
Anyone who exists on processed food, smokes cigarettes, or skimps on sleep has heightened risks for dementia. But minority groups get hit harder. Older Black Americans are twice as likely to have Alzheimer’s or another form of dementia as white Americans; older Hispanics are about one and a half times more likely.
This is due in part to higher rates of diabetes, obesity, and high blood pressure within these communities. These diseases are linked to Alzheimer’s, and SDOH factors multiply the risks. Blacks and Hispanics earn less income on average than white people. This means they are more likely to live in neighborhoods with limited access to healthy food, medical care, and good schools, and suffer greater exposure to noise (which impairs hearing) and air pollution—additional risk factors for dementia.
Related Reading: The Toxic Effects of Noise and What We're Not Doing About it
Plus, when Black people are diagnosed with dementia, their cognitive impairment and neuropsychiatric symptom are more advanced than in white patients. Why? Some African-Americans delay seeing a doctor because of perceived discrimination and a sense they will not be heard, says Carl V. Hill, chief diversity, equity, and inclusion officer at the Alzheimer’s Association.
Misinformation about dementia is another issue in Black communities. The thinking is that Alzheimer’s is genetic or age-related, not realizing that diet and physical activity can improve brain health, Hill says.
African Americans are severely underrepresented in clinical trials for Alzheimer’s, too. So, researchers miss the opportunity to learn more about health disparities. “It’s a bioethical issue,” Hill says. “The people most likely to have Alzheimer’s aren’t included in the trials.”
The Cure: Systemic Change
People think of lifestyle as a choice but there are limitations, says Muniza Anum Majoka, a geriatric psychiatrist and assistant professor of psychiatry at Yale University, who published an overview of SDOH factors that impact dementia. “For a lot of people, those choices [to improve brain health] are not available,” she says. If you don’t live in a safe neighborhood, for example, walking for exercise is not an option.
Hill wants to see the focus of prevention shift from individual behavior change to ensuring everyone has access to the same resources. Advice about healthy eating only goes so far if someone lives in a food desert. Systemic change also means increasing the number of minority physicians and recruiting minorities in clinical drug trials so studies will be relevant to these communities, Hill says.
Based on SDOH impact research, raising education levels has the most potential to prevent dementia. One theory is that highly educated people have a greater brain reserve that enables them to tolerate pathological changes in the brain, thus delaying dementia, says Majoka. Being curious, learning new things and problem-solving also contribute to brain health, she adds. Plus, having more education may be associated with higher socioeconomic status, more access to accurate information and healthier lifestyle choices.
The chasm between what researchers know about brain health and how the knowledge is being applied is huge. “There’s an explosion of interest in this area. We’re just in the first steps,” says Powell. One day, he predicts that physicians will manage Alzheimer’s through precision medicine customized to the patient’s specific risk factors and needs.
Raina Croff, assistant professor of neurology at Oregon Health & Science University School of Medicine, created the SHARP (Sharing History through Active Reminiscence and Photo-imagery) walking program to forestall memory loss in African Americans with mild cognitive impairment or early dementia.
Participants and their caregivers walk in historically black neighborhoods three times a week over six months. A smart tablet provides information about “Memory Markers” they pass, such as the route of a civil rights march. People celebrate their community and culture while “brain health is running in the background,” Croff says.
Photos and memory prompts engage participants in the SHARP program.
The project began in 2015 as a pilot study in Croff’s hometown of Portland, Ore., expanded to Seattle, and will soon start in Oakland, Calif. “Walking is good for slowing [brain] decline,” she says. A post-study assessment of 40 participants in 2017 showed that half had higher cognitive scores after the program; 78 percent had lower blood pressure; and 44 percent lost weight. Those with mild cognitive impairment showed the most gains. The walkers also reported improved mood and energy along with increased involvement in other activities.
It’s never too late to reap the benefits of working your brain and being socially engaged, Majoka says.
In Milwaukee, the Wisconsin Alzheimer’s Institute launched the The Amazing Grace Chorus® to stave off cognitive decline in seniors. People in early stages of Alzheimer’s practice and perform six concerts each year. The activity provides opportunities for social engagement, mental stimulation, and a support network. Among the benefits, 55 percent reported better communication at home and nearly half of participants said they got involved with more activities after participating in the chorus.
Private companies are offering intervention services to healthcare providers and insurers to manage SDOH, too. One such service, MyHello, makes calls to at-risk people to assess their needs—be it food, transportation or simply a friendly voice. Having a social support network is critical for seniors, says Majoka, noting there was a steep decline in cognitive function among isolated elders during Covid lockdowns.
About 1 in 9 Americans age 65 or older live with Alzheimer’s today. With a surge in people with the disease predicted, public health professionals have to think more broadly about resource targets and effective intervention points, Powell says.
Beyond breakthrough pills, that is. Like Dorothy in Kansas discovering happiness was always in her own backyard, we are beginning to learn that preventing Alzheimer’s is in our reach if only we recognized it.
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the bookThe Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
When David M. Kurtz was doing his clinical fellowship at Stanford University Medical Center in 2009, specializing in lymphoma treatments, he found himself grappling with a question no one could answer. A typical regimen for these blood cancers prescribed six cycles of chemotherapy, but no one knew why. "The number seemed to be drawn out of a hat," Kurtz says. Some patients felt much better after just two doses, but had to endure the toxic effects of the entire course. For some elderly patients, the side effects of chemo are so harsh, they alone can kill. Others appeared to be cancer-free on the CT scans after the requisite six but then succumbed to it months later.
"Anecdotally, one patient decided to stop therapy after one dose because he felt it was so toxic that he opted for hospice instead," says Kurtz, now an oncologist at the center. "Five years down the road, he was alive and well. For him, just one dose was enough." Others would return for their one-year check up and find that their tumors grew back. Kurtz felt that while CT scans and MRIs were powerful tools, they weren't perfect ones. They couldn't tell him if there were any cancer cells left, stealthily waiting to germinate again. The scans only showed the tumor once it was back.
Blood cancers claim about 68,000 people a year, with a new diagnosis made about every three minutes, according to the Leukemia Research Foundation. For patients with B-cell lymphoma, which Kurtz focuses on, the survival chances are better than for some others. About 60 percent are cured, but the remaining 40 percent will relapse—possibly because they will have a negative CT scan, but still harbor malignant cells. "You can't see this on imaging," says Michael Green, who also treats blood cancers at University of Texas MD Anderson Medical Center.
The new blood test is sensitive enough to spot one cancerous perpetrator amongst one million other DNA molecules.
Kurtz wanted a better diagnostic tool, so he started working on a blood test that could capture the circulating tumor DNA or ctDNA. For that, he needed to identify the specific mutations typical for B-cell lymphomas. Working together with another fellow PhD student Jake Chabon, Kurtz finally zeroed-in on the tumor's genetic "appearance" in 2017—a pair of specific mutations sitting in close proximity to each other—a rare and telling sign. The human genome contains about 3 billion base pairs of nucleotides—molecules that compose genes—and in case of the B-cell lymphoma cells these two mutations were only a few base pairs apart. "That was the moment when the light bulb went on," Kurtz says.
The duo formed a company named Foresight Diagnostics, focusing on taking the blood test to the clinic. But knowing the tumor's mutational signature was only half the process. The other was fishing the tumor's DNA out of patients' bloodstream that contains millions of other DNA molecules, explains Chabon, now Foresight's CEO. It would be like looking for an escaped criminal in a large crowd. Kurtz and Chabon solved the problem by taking the tumor's "mug shot" first. Doctors would take the biopsy pre-treatment and sequence the tumor, as if taking the criminal's photo. After treatments, they would match the "mug shot" to all DNA molecules derived from the patient's blood sample to see if any molecular criminals managed to escape the chemo.
Foresight isn't the only company working on blood-based tumor detection tests, which are dubbed liquid biopsies—other companies such as Natera or ArcherDx developed their own. But in a recent study, the Foresight team showed that their method is significantly more sensitive in "fishing out" the cancer molecules than existing tests. Chabon says that this test can detect circulating tumor DNA in concentrations that are nearly 100 times lower than other methods. Put another way, it's sensitive enough to spot one cancerous perpetrator amongst one million other DNA molecules.
They also aim to extend their test to detect other malignancies such as lung, breast or colorectal cancers.
"It increases the sensitivity of detection and really catches most patients who are going to progress," says Green, the University of Texas oncologist who wasn't involved in the study, but is familiar with the method. It would also allow monitoring patients during treatment and making better-informed decisions about which therapy regimens would be most effective. "It's a minimally invasive test," Green says, and "it gives you a very high confidence about what's going on."
Having shown that the test works well, Kurtz and Chabon are planning a new trial in which oncologists would rely on their method to decide when to stop or continue chemo. They also aim to extend their test to detect other malignancies such as lung, breast or colorectal cancers. The latest genome sequencing technologies have sequenced and catalogued over 2,500 different tumor specimens and the Foresight team is analyzing this data, says Chabon, which gives the team the opportunity to create more molecular "mug shots."
The team hopes that that their blood cancer test will become available to patients within about five years, making doctors' job easier, and not only at the biological level. "When I tell patients, "good news, your cancer is in remission', they ask me, 'does it mean I'm cured?'" Kurtz says. "Right now I can't answer this question because I don't know—but I would like to." His company's test, he hopes, will enable him to reply with certainty. He'd very much like to have the power of that foresight.
This article is republished from our archives to coincide with Blood Cancer Awareness Month, which highlights progress in cancer diagnostics and treatment.
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.