AI helps unravel a cause of Alzheimer’s and identify a therapeutic candidate

Alzheimer’s heterogeneity & amyloid hypothesis debate

• Several commenters note “Alzheimer’s” likely covers multiple distinct diseases, with this work focusing on late‑onset AD.
• The amyloid hypothesis is heavily debated:
  • Some claim it is “absolutely not correct,” citing: drugs that clear amyloid without clinical benefit; other drugs that help without affecting amyloid; and autopsy cases with high amyloid but no dementia.
  • Others counter that multiple amyloid-targeting drugs have recently shown modest slowing of decline, so the hypothesis can’t be dismissed outright.
  • There is criticism of simplistic narratives that one fraudulent researcher derailed the entire field; monocausal explanations are seen as implausible.
  • Several stress the logical point that bad arguments or fraud do not by themselves prove the underlying hypothesis false.

Biochemistry, APOE, choline, sleep & hormones

• Discussion connects APOE‑ε4, increased choline demand, PHGDH activity, and serine synthesis as a plausible mechanistic chain.
• Choline intake is linked in cited work to lower dementia odds and better cognition; commenters trade practical advice on dietary vs supplement sources and safety concerns (e.g., TMAO risk, alpha‑GPC drawbacks).
• Slow‑wave sleep enhancement is mentioned as a promising avenue in dementia; choline and sleep are linked.
• Estrogen’s role in endogenous choline production (via PEMT) is highlighted, tying menopause, HRT, and dementia risk.

How AI/AlphaFold was actually used

• Many note the underlying research is mostly conventional biochemistry and cell biology; AI’s role is confined to protein structure prediction (AlphaFold 3) and perhaps ChatGPT for grammar.
• Some argue the university press release overhypes AI: AlphaFold contributes a small part (predicting a DNA‑binding–like substructure), while most key results come from wet‑lab experiments.
• Others respond that even a small but new computational capability that enables or accelerates a critical structural insight fairly counts as “AI helps.”

Protein folding, structure vs sequence

• Extended discussion explains how proteins with very different gene sequences can share nearly identical 3D structures and functions.
• AlphaFold is described as mainly learning from existing sequence–structure relationships, not pure first‑principles physics, but still able to detect homologies that older tools miss.
• Crystallography could, in principle, yield similar insights, but is slow, expensive, and often infeasible; AI folding is seen as a powerful shortcut.

AI hype, anthropomorphism & tool framing

• Commenters distinguish between:
  • Overhyped generative chatbots (often unreliable for coding, legal, or medical advice), and
  • Domain‑specific ML used as “statistical pattern finders” for biology, where enthusiasm is higher.
• Many object to headlines that sound like “AI discovered the cure,” arguing this misleads the public and fuels quasi‑religious views of LLMs.
• Others argue it’s normal English to say tools “help” (like seatbelts or telescopes), though some worry AI personification is uniquely harmful.

Health data, AI, and healthcare systems

• One thread argues that centralized, interoperable medical records (possibly via universal healthcare) would supercharge ML discovery of early disease signals.
• Others note universal healthcare and centralized records are orthogonal; many systems have one without the other.
• Practical obstacles are raised: privacy laws, fragmented EHRs, lack of structured data, and public mistrust after tech platforms misused personal data.
• Some see a role for LLMs in turning free‑text clinical notes into structured datasets for downstream analysis.

Emotional context & expectations

• Multiple commenters share personal experiences with relatives who have Alzheimer’s and express both hope and caution.
• Some worry that focusing on single pathways in a fundamentally age‑related, complex process (senescence) may miss the forest; others push back that AD is a specific, devastating condition and targeted work like this is essential.
• Overall sentiment: optimism about rigorous, AI‑assisted biology, coupled with strong skepticism toward AI‑centric marketing and oversimplified scientific stories.