Natural history of nonketotic hyperglycinemia in 65 patients
Bibliographic record
Abstract
Abstract Alzheimer’s disease is a multifactorial disorder with a heterogeneous patient population. Comorbidities such as hypertension, hypercholesterolemia and diabetes are known contributors to the disease progression. Indeed, therapies targeting these disorders have been shown efficient in dementia prevention. However, their mechanistic contribution to Alzheimer’s pathology and neurodegeneration has not been fully clarified. In the current study, we used CSF samples from a memory clinic cohort of 90 patients without diagnosed hypertension, hypercholesterolemia, or diabetes nor other neurodegenerative disorder, to investigate 13 molecular markers representing key mechanisms underlying Alzheimer’s pathogenesis. Levels were compared between clinical groups of subjective cognitive decline, mild cognitive impairment, and Alzheimer’s disease. Associations between markers and groups of markers were analyzed by linear regression. Two-step cluster analysis was used to determine patient clusters. Two key markers were further analyzed by immunofluorescence staining in hippocampus from control and AD individuals without hypertension, hypercholesterolemia nor diabetes. CSF angiotensinogen, thioredoxin-1, and interleukin-15 were the biomarkers with the most prominent associations with Alzheimer’s pathology, synaptic and axonal damage. Synaptosomal-associated protein 25 kDa and neurofilament light chain were increased in mild cognitive impairment and Alzheimer cases. When we grouped biomarkers by biological function, we found that inflammatory and survival components were associated with Alzheimer’s pathology, synaptic dysfunction and axonal damage. Moreover, a vascular/metabolic component was associated with synaptic dysfunction. In data-driven analysis, two patient clusters were identified; Older participants with increased CSF markers of oxidative stress, vascular pathology and neuroinflammation were assigned to cluster 1, that was also smaller and characterized by increased synaptic and axonal damage, compared to individuals in cluster 2. Clinical groups were evenly distributed between the clusters. Analysis of post-mortem hippocampal tissue, showed that, compared to controls, angiotensinogen staining was higher in Alzheimer’s disease and was also found to co-localize with phosphorylated-tau. In a population free of common comorbidities, we could still find associations between Alzheimer’s disease biomarkers and markers of pathways associated with increased risk for Alzheimer’s disease (i.e., neuroinflammation, vascular function, oxidative stress and cholesterol homeostasis), suggesting that these pathways are contributing to Alzheimer’s disease mechanisms even in absence of clinically diagnosed comorbidities. The identification of distinct biomarker-driven endophenotypes of cognitive disorder patients, further highlights the biological heterogeneity of Alzheimer’s disease and the importance of developing tailored prevention and treatment strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".