Ketones and improved cognition in MCI: Links to ApoE and white matter energetics
Bibliographic record
Abstract
Abstract Background The capacity of ketones to improve cognition in mild cognitive impairment (MCI) may depend on ApoE4 status. In the BENEFIC RCT (NCT02551419), improvement in three cognitive domains was directly related to ketones from medium chain triglyceride (kMCT) improving global brain energy status. Post‐kMCT, ketone uptake increased 2.9‐fold in total white matter (WM) and was significantly positively correlated to improved processing speed composite score in 8/9 WM fascicles, especially the fornix (r = 0.47‐0.61; p = 0.014‐0.072; n=16‐17/group). Here we report from the same trial the relationship to both ApoE4(‐) and to WM structural properties post‐intervention. Methods In MCI, cognitive outcomes were assessed before and after a 6‐month intervention involving an active arm (15 g kMCT twice/day; n=39) and placebo arm (non‐ketogenic vegetable oil; n=43), both emulsified into a lactose‐free skim milk drink. Brain imaging (18F‐fluorodeoxyglucose [FDG] and 11C‐acetoacetate PET, diffusion MRI at 3 Tesla) was done on a subset of the main cohort. Results Participant demographics and imaging methods were previously reported (Fortier et al. Alz Dementia 2019). In ApoE4(‐) participants, two measures of executive function (verbal fluency [p≤0.016] and Stroop total errors [0.008]) improved more post‐kMCT (n=28) compared to placebo (n=32). Faster completion of the Trail‐making test (motor speed and visual scanning conditions that measure attention and processing speed), was also directly correlated to higher ketone uptake in most of these nine fascicles. Six months on kMCT did not significantly affect WM fascicle structural properties measured by diffusion imaging (apparent fiber density, free water). Diffusion‐tensor imaging metrics and FDG uptake were also unaffected in total WM and in most individual fascicles. Conclusions In MCI, the beneficial effect of kMCT on executive function was greater in ApoE4(‐). Better processing speed and attention was directly linked to improved WM energetics, specifically higher ketone uptake in fascicles affected in AD, but did not alter WM structural properties measurable by diffusion imaging. Ketones improved outcomes in all five main cognitive domains in MCI so a ketone‐based prevention‐type trial to delay the onset of AD is warranted. Acknowledgements: Supported by the Alzheimer Association, Nestlé Health Science, MITACS, FRQS and USherbrooke.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".