A large genetic study suggests that Alzheimer’s disease and Parkinson’s disease are linked to the body’s metabolism in strikingly different ways. That may help explain why metabolic treatments, including GLP-1-based drugs, do not necessarily have the same effects across neurodegenerative diseases.
Alzheimer’s and Parkinson’s are often grouped together as neurodegenerative disorders. But beneath that shared label, they may follow strikingly different metabolic patterns.
In a new study, researchers analysed genetic links between 249 circulating metabolites, proglucagon and the risk of Alzheimer’s and Parkinson’s. The result was striking: Alzheimer’s aligned metabolically with obesity, type 2 diabetes, cardiovascular disease and stroke, whereas Parkinson’s often pointed in the opposite direction.
“That was quite remarkable,” says Sara E. Stinson, Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Norway. “You have two neurodegenerative disorders, but Alzheimer’s was acting much more similarly to cardiometabolic diseases, while Parkinson’s showed the opposite pattern.”
The study points to lipids, glutamine and proglucagon-related pathways as disease-specific metabolic clues. Although Alzheimer’s and Parkinson’s sometimes produced similar blood-metabolite patterns, the underlying genetic signals pointed in very different directions.
“If you look at the measured metabolites alone, some of the patterns can look similar,” explains Sara E. Stinson. “But at the genetic level, they diverge.”
The distinction matters because incretin-based therapeutics are increasingly being investigated for the treatment of neurodegenerative diseases. Some studies have suggested GLP1-RA reduces cognitive decline, as well as dementia risk in patients with type 2 diabetes, whereas trials slowing Alzheimer’s disease progression have produced negative results.
“That contrast was very interesting to us,” says Sara E. Stinson. “It raises the possibility that metabolic dysfunction may contribute to dementia risk long before neurodegeneration is already established.”
Metabolic treatments may therefore need to be tailored far more specifically to individual neurodegenerative diseases.
“I would not expect the same metabolic strategy to work equally well across all neurodegenerative diseases,” argues Sara E. Stinson. “It depends on the disease, the pathways involved and probably also at which point in the disease process you intervene.”
Metabolism may shape dementia risk long before symptoms appear
The link between metabolism and brain disease has attracted growing attention in recent years. Large epidemiological studies have repeatedly linked obesity, type 2 diabetes and cardiovascular disease to increased risk of cognitive decline and dementia. A recent Lancet Commission even identified diabetes as one of the potentially modifiable risk factors associated with later dementia risk.
Age and inherited genetic risk cannot be changed. Metabolic health can be modified, at least in part. This makes the connection especially important if researchers want to understand not only who is at risk but also when prevention may still be possible.
“There is clearly some sort of connection between metabolic health earlier in life and later risk of dementia,” says Sara E. Stinson. “But genetically, the picture has been much less clear.”
Questions about how biology earlier in life shapes disease decades later have interested her since she began her research career.
“When I started in research, I was very curious about life-course dynamics,” says Sara E. Stinson. “How things happening much earlier in life can influence health much later on.”
Traditional risk factors such as body-mass index (BMI) or diabetes diagnoses are biologically crude measures, she explains.
“Two people can have the same BMI but very different metabolic profiles,” explains Sara E. Stinson. “Metabolites give a much more detailed window into what is actually happening biologically.”
Blood molecules provide a sharper picture than BMI alone
That became the starting-point for new studies. Rather than focusing on a handful of clinical risk factors, researchers at the Centre for Precision Psychiatry, Oslo University Hospital aimed to examine hundreds of circulating metabolites – small molecules in the blood that provide a biochemical snapshot of how the body handles fat, sugar, amino acids and energy.
“We were interested in whether there might be shared metabolic patterns between cardiometabolic disease and neurodegeneration,” says Sara E. Stinson. “People had looked at individual metabolites before, but no one had really compared the broader metabolic-genetic architecture across these diseases.”
The researchers also wanted to challenge a longstanding idea: that Alzheimer’s is primarily a disorder of the brain alone. Historically, most Alzheimer’s research has focused on the accumulation of amyloid plaques, phosphorylated tau, and other processes inside the nervous system itself.
“Historically, Alzheimer’s research has focused very heavily on what happens inside the nervous system,” notes Sara E. Stinson. “But we wanted to challenge the idea that Alzheimer’s should only be viewed as a brain disease.”
Looking beyond the brain for early disease signals
They suspected that metabolic processes throughout the body might influence disease risk long before classical neurodegenerative changes become visible in the brain.
“We were interested in whether there could be metabolic drivers that happen before the classical brain pathology becomes obvious,” says Sara E. Stinson. “That is why we approached this from more of a whole-body perspective.”
This perspective is becoming increasingly relevant since GLP-1 receptor agonists are now being investigated for how they might affect neurodegenerative disease. Some studies of people with diabetes have suggested reduced cognitive decline or lower dementia risk among GLP1-RA users, whereas trials in established Alzheimer’s have produced more mixed results.
The study became one of the largest genetic investigations to date of how metabolism intersects with neurodegenerative disease. At its centre was a simple question: do Alzheimer’s and Parkinson’s share the same metabolic-genetic architecture – or do they follow separate biological paths?
“We wanted to understand whether the metabolic patterns behind these diseases were truly shared or whether they were pointing to different biology,” explains Sara E. Stinson. “That meant looking beneath the blood measurements themselves and into the genetics behind them.”
Genetic data from more than 300,000 people
To investigate how metabolism and neurodegenerative disease might be genetically connected, the researchers combined several large-scale genomic and molecular datasets into a single analytical framework.
The foundation of the study was a large genome-wide association study of blood metabolites using data from the UK Biobank and the Estonian Biobank. In total, the datasets included genetic and metabolic information from more than 300,000 individuals.
“Researchers, including our group, had already identified genetic variants associated with hundreds of metabolites in the blood,” says Sara E. Stinson. “That became the starting-point for asking how those same genetic patterns relate to Alzheimer’s and Parkinson’s.”
The researchers analysed 249 circulating metabolites, including lipids, lipoproteins, fatty acids and amino acids. They also included proglucagon – the precursor hormone that gives rise to molecules such as glucagon and GLP-1.
“That part was especially interesting because GLP-1 signalling sits right at the intersection between glucose metabolism, lipid metabolism and neuroinflammation,” explains Sara E. Stinson.
The researchers looked beneath the blood measurements
The team then compared the metabolite genetics against large genetic datasets for Alzheimer’s, Parkinson’s, type 2 diabetes, coronary artery disease, stroke and BMI.
“We were much more interested in the overall architecture than in one isolated marker,” recalls Sara E. Stinson. “The question was whether these diseases share coordinated metabolic-genetic patterns.”
They then moved through the data in stages: first asking whether the diseases shared broad genetic patterns, then which metabolites shared the most overlap between diseases, and finally whether specific metabolites might drive disease risk.
First, they calculated genetic correlations – statistical measures of whether genetic variants associated with one trait also tend to influence another.
“You can think of it almost like genetic fingerprints,” says Sara E. Stinson. “If two diseases are driven by related biology, you often see similarities in their correlation patterns.”
They then estimated polygenic overlap – essentially asking whether the diseases shared parts of the same genetic foundation, even when the effects pointed in similar or opposite directions.
Alzheimer’s and Parkinson’s could sometimes look metabolically similar in patients, even when their underlying genetic relationships pointed in very different directions.
“When you measure the metabolites of people who already have neurodegeneration, you may partly be seeing consequences of the disease itself,” adds Sara E. Stinson. “Genetics allows us to look one layer deeper – to ask what may be contributing to disease risk before that stage.”
Testing whether metabolites may help drive disease risk
Finally, to move from shared patterns toward possible causality, the team performed Mendelian randomization analysis. This uses naturally occurring genetic variation as a kind of natural experiment to ask whether a metabolite may actively contribute to disease risk rather than simply appear alongside the disease.
“That does not prove causality in an absolute sense,” says Sara E. Stinson. “But it gives a stronger indication of directionality.”
Using these analyses, the researchers identified several metabolites that showed evidence of potentially causal relationships with Alzheimer’s, including HDL-related lipid measures and glutamine.
They also searched for shared genetic hotspots between metabolites and disease risk. One particularly important region involved the apolipoprotein E gene (APOE) – the strongest known genetic risk region linked to Alzheimer’s disease.
“We found that proglucagon-related signals overlapped with APOE-associated regions,” explains Sara E. Stinson. “That was exciting because it potentially links incretin signalling, lipid biology and Alzheimer’s risk within the same broader framework.”
Finally, they examined where in the body the shared genes were most active by analysing which tissues these genes were expressed across both the brain and the rest of the body.
For Alzheimer’s, the shared metabolic genes showed enrichment not only in brain tissue but also in the liver, pancreas and heart. Parkinson’s showed a narrower pattern more strongly linked to brain regions, cellular energy metabolism and skeletal muscle.
“That whole-body pattern was much more pronounced for Alzheimer’s,” says Sara E. Stinson. “Parkinson’s looked more connected to neuronal and motor-related biology.”
Similar blood signals hid very different genetics
When those genetic signals were followed, the apparent similarity between Alzheimer’s and Parkinson’s began to break apart.
“One of the most surprising things was how consistently the differences appeared across the analyses,” reflects Sara E. Stinson. “The patterns kept pointing in the same direction no matter which method we used.”
At first glance, the diseases did not look entirely different. When the researchers examined measured metabolite profiles in patients, Alzheimer’s and Parkinson’s showed moderately similar phenotypic patterns. But when they analysed the underlying genetic architecture, the picture changed dramatically.
“At the phenotypic level, some of the patterns looked relatively similar,” says Sara E. Stinson. “But genetically, Alzheimer’s aligned much more closely with cardiometabolic disease, whereas Parkinson’s often showed the opposite direction.”
That became one of the study’s central messages.
“The same blood signature does not necessarily mean the same disease process,” she emphasises.
Alzheimer’s and Parkinson’s diverged most clearly in lipid biology
Across multiple analyses, Alzheimer’s showed strong genetic overlap with metabolic traits linked to obesity, type 2 diabetes, coronary artery disease and stroke, whereas Parkinson’s displayed largely inverse relationships.
The deeper the analysis went, the less the two diseases seemed to belong to the same metabolic story.
Genetic risk for Alzheimer’s disease also appeared to influence a broad range of circulating metabolites. In total, genetic risk for Alzheimer’s was associated with changes across more than 170 metabolic markers, particularly lipids and lipoproteins.
The relationship also appeared to run in the other direction: some metabolic signals may contribute to the risk of Alzheimer’s, while Alzheimer’s-related genetic liability may itself reshape metabolism.
“It was not just one isolated lipid signal,” says Sara E. Stinson. “It looked more like a broad reorganisation of lipid metabolism, as we would expect.”
Alzheimer’s reshaped metabolism far more broadly
By contrast, Parkinson’s showed far weaker metabolic-genetic overlap overall.
Several HDL-related metabolites – linked to how the body transports and processes cholesterol – stood out in the causal analysis. Higher levels of XL-HDL cholesterol esters and lower levels of triglycerides within large HDL particles were genetically associated with increased Alzheimer’s risk.
The signal did not point simply to “fat” in a general sense but to systems involved in cholesterol transport and lipid handling.
“What became interesting was the mechanistic aspect,” adds Sara E. Stinson. “This involves how cholesterol is packaged, transported and processed through lipoprotein systems such as HDL and APOE-related pathways.”
The amino acid glutamine also emerged as a potentially important signal. Genetically lower glutamine levels were associated with increased Alzheimer’s risk, whereas Parkinson’s showed the opposite trend.
“Glutamine sits at a really interesting intersection between brain signalling and systemic metabolism,” explains Sara E. Stinson. “That makes it a plausible link between metabolic dysfunction and neurodegeneration.”
Compared with Alzheimer’s, Parkinson’s showed far fewer robust metabolic relationships and much weaker overlap with cardiometabolic disease.
The two diseases pointed to different biological worlds
Pathway analyses pushed the contrast even further.
For Alzheimer’s, the shared genetic signals were enriched in pathways related to lipid metabolism, cholesterol transport and lipoprotein remodelling – processes already strongly implicated in APOE biology and amyloid regulation.
Parkinson’s, in contrast, showed enrichment for pathways linked to mitochondrial function, vesicle trafficking and cellular stress responses.
“It almost looked like two different metabolic worlds,” says Sara E. Stinson. “Alzheimer’s appeared deeply connected to systemic metabolic dysfunction, whereas Parkinson’s pointed much more toward neuronal stress and cellular energy biology.”
GLP-1-related signals converged on Alzheimer’s risk genes
Shared genetic loci also appeared between proglucagon signalling and Alzheimer’s, including the APOE ε2 variant known to protect against Alzheimer’s.
“That overlap was particularly interesting,” notes Sara E. Stinson. “It potentially connects incretin signalling, lipid metabolism and Alzheimer’s risk within the same biological framework.”
Overall, very few of the metabolic-genetic signals were shared between Alzheimer’s and Parkinson’s.
“That was important,” says Sara E. Stinson. “The difference was not just about direction. There was also surprisingly limited shared genetic-metabolic overlap between the diseases.”
By the end of the analysis, the idea of one shared metabolic blueprint for neurodegeneration had largely fallen apart.
“Our results suggest that these diseases should probably be thought about much more individually,” adds Sara E. Stinson. “Even though they share some clinical features, the underlying biology may be fundamentally distinct.”
The findings may help explain why GLP-1 results have been mixed
If Alzheimer’s and Parkinson’s follow different metabolic paths, prevention and treatment may also have to follow different routes.
“There is a tendency to assume that if a metabolic treatment works in one neurodegenerative disease, it should probably work in others as well,” says Sara E. Stinson. “But our results suggest it may be much more disease-specific than that.”
Metabolism may not offer one universal route into neurodegenerative disease after all – and treatments targeting metabolism may need to be tailored far more specifically to individual diseases.
This question has become especially urgent because of the rapid rise of GLP-1-based drugs. Originally developed for type 2 diabetes and obesity, these therapies are now being investigated for possible effects on cognitive ageing and neurodegenerative disease.
So far, the results have been mixed. Some studies of people with diabetes have suggested reduced cognitive decline among GLP-1 users, whereas trials involving patients with established Alzheimer’s have shown far more modest effects.
For Sara E. Stinson, this mismatch points to one of the most important questions in the field: timing.
“My expectation would be that interventions become much harder once neurodegeneration is already far progressed,” she explains. “By that stage, the disease biology may already be too established to reverse easily.”
The timing of metabolic dysfunction may matter enormously
Metabolic health may therefore matter most years before symptoms appear – when the brain may still be more biologically responsive to intervention.
“If metabolic stress and inflammation accumulate over many years, that could potentially influence brain ageing long before clinical disease develops,” says Sara E. Stinson.
Another striking observation was that the relationship between metabolism and Alzheimer’s risk appeared to shift across the lifespan.
Epidemiological studies show that higher BMI earlier in adulthood is associated with increased Alzheimer’s risk, whereas lower BMI later in life is often linked to worse disease progression.
“We think there may be a kind of metabolic switch during ageing,” says Sara E. Stinson. “Early metabolic dysfunction may increase risk, but once neurodegeneration develops, the disease itself can begin changing metabolism, eating behaviour and body weight.”
That could help explain why metabolic signals measured late in disease sometimes point in different directions than genetic risk patterns.
Metabolism should therefore not be viewed only as a downstream consequence of neurodegeneration but potentially as part of the disease process itself – at least in Alzheimer’s.
“Historically, Alzheimer’s has been treated mainly as a brain disease,” notes Sara E. Stinson. “But our findings suggest that whole-body metabolism may also play an important role.”
Alzheimer’s may involve much more than the brain alone
The tissue analyses reinforced that idea. Genes shared between Alzheimer’s and metabolic traits showed activity not only in the brain, but also in tissues such as the liver, pancreas and heart.
“That broader tissue pattern was one of the things that stood out most clearly for Alzheimer’s,” says Sara E. Stinson. “Parkinson’s looked much more centred around neuronal and motor-related biology.”
Sara E. Stinson emphasises, however, that the study is a starting-point rather than a clinical answer. Most of the genetic data came from people of European ancestry, meaning that the findings still need to be tested in other populations and validated in long-term patient cohorts. The proglucagon signal also has to be interpreted carefully because current proteomic methods cannot fully distinguish between all individual proglucagon-derived hormones.
“Genetics can help us distinguish between metabolic changes that may contribute to disease and those that appear later as consequences of disease progression,” explains Sara E. Stinson. “But understanding how these signals translate into actual disease mechanisms and treatment responses is the next challenge.”
The findings could eventually help improve how dementia risk is predicted and prevented. Research groups, including Sara E. Stinson’s, are already developing tools that estimate dementia risk and age of onset using genetics, brain imaging and cognitive testing. Sara E. Stinson thinks that metabolic markers could eventually become part of that picture as well.
“I can imagine adding some of these metabolic markers on top of existing prediction tools,” she suggests. “Potentially that could improve how early we identify people at increased risk.”
Long-term cohorts may reveal when the signals begin
Follow-up studies are now being prepared using cohort data from Norway, with blood samples collected as early as the 1990s and linked to decades of health registry follow-up.
Access to large longitudinal health datasets is one reason Sara E. Stinson originally moved from Canada to Scandinavia for her PhD research.
“The ability to combine genetics, metabolomics and long-term health registries at this scale is really unique,” she says.
The goal is to see whether the metabolic-genetic signals identified in the new study are already visible decades before disease develops – and whether they can help predict how brain ageing unfolds.
“That is one of the really exciting next steps,” says Sara E. Stinson. “We will be able to study these metabolic patterns much earlier in life and follow people for more than 35 years.”
More broadly, the findings support a shift in how brain ageing is understood: not only as something happening inside the brain but as a process shaped by whole-body metabolism.
“We are all getting older, and both cardiometabolic disease and neurodegenerative disease are becoming more common,” says Sara E. Stinson. “Understanding how long-term metabolic health shapes brain ageing may become increasingly important for healthy ageing overall.”
