Fifty-three medical schools just confessed they've been graduating doctors who received, on average, 1.2 hours of nutrition education per year. Per year.

 

Health and Human Services (HHS) and the Department of Education convened those schools — spanning 31 states — this week to announce a commitment to a minimum of 40 hours of nutrition training starting fall 2026. For context: that's roughly the amount of time a first-year med student spends learning to tie surgical knots. The pressure came from the top — Kennedy and McMahon hosted school leaders at HHS alongside the presidents of the American Medical Association and Association of American Medical Colleges to make the moment feel like a reckoning, not a memo.

 

Here's the sneaky part: three-quarters of US medical schools currently have no required clinical nutrition courses, and only 14% of residency programs include a nutrition curriculum. Meaning the people telling patients what to eat have had almost no formal training on the subject.

 

The mechanism question here isn't biological — it's structural. Nutrition never had a powerful specialty lobby. Cardiology, oncology, surgery — they all fought for curriculum real estate. Nutrition didn't.

 

HHS is also putting $5 million behind an NIH nutrition education challenge to back the transition with actual coursework development.

 

Source: HHS Press Release

 

 

Nearly half of people in Germany, Switzerland, and Austria say they experience from "Frühjahrsmüdigkeit" — spring fatigue — that bone-tired, low-energy drag that hits every March. Turns out, the data says they're imagining it.

Researchers tracked 418 adults with repeated assessments every 6 weeks across a full year, measuring fatigue, daytime sleepiness, insomnia symptoms, and sleep quality. The result? Essentially nothing moved. Bayesian models found extreme-to-strong evidence against any meaningful variation across seasons, months, or photoperiod length — and this held across every single outcome measure.

 

Here's the sneaky part: 47% of participants self-reported being "affected" by spring fatigue. Yet the longitudinal data showed no corresponding signal whatsoever. The subjective prevalence and the objective data are living in completely different realities.

 

The proposed explanation is genuinely interesting: the German language has a word for this feeling, and that label appears to drive attribution bias, confirmation bias, and selective recall — basically, tiredness gets noticed in spring because there's a name for it. Wine tastes better when it's labeled expensive. Same principle.

 

One exception did emerge: day-to-day fatigue decreased slightly with longer photoperiods — but that's the opposite of spring fatigue lore.

 

Takeaway: When patients complain of spring fatigue, this is probably not a distinct syndrome requiring workup. It's a cultural narrative — and a remarkably convincing one.

 

Source: Blume & Vorster, bioRxiv preprint

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Adults with prediabetes who ate one avocado and one cup of mango daily for 8 weeks showed measurably better vascular function — while the control group quietly got worse.

 

The primary endpoint was flow-mediated vasodilation (FMD), a noninvasive marker of endothelial function and an early surrogate for cardiovascular risk. On the fruit-forward diet, FMD ticked up about 1%; on the control diet, it dropped about 1.2% — a statistically significant 2-point swing from simply swapping avocado and mango for refined carbs. Fruit intake tripled from 0.9 to 3 cups daily, fiber doubled, and vitamin C more than doubled.

 

Here's the sneaky part: glucose, insulin, inflammatory markers, and body weight didn't move. The vascular benefit arrived without any of the metabolic signals one might expect to explain it.

The leading mechanistic hypothesis centers on nitric oxide bioavailability — prediabetes disrupts cellular redox balance, and bioactive compounds in avocado and mango (mangiferin, gallotannins, avocatin B) may help restore it. Fiber, potassium, and vitamin C are also implicated, given all three rose significantly on the intervention diet.

 

For patients with prediabetes asking what one concrete dietary change looks like, this trial offers a surprisingly specific answer — and the vascular data to back it up.

 

Source: Journal of the American Heart Association

 

So get this: an AI trained on nothing more than photos of the back of a patient’s hand can detect acromegaly better than endocrinologists.

 

In a multicenter Japanese study of 716 patients and 11,480 hand images, researchers trained a deep-learning model to analyze 2 views: the dorsal hand and a clenched “fist sign.” On a held-out test set, the model achieved 89% sensitivity and 91% specificity, with an AUC of 0.96—and its overall diagnostic score outperformed 10 experienced endocrinologists, whose F1 scores ranged from 0.43 to 0.63. The algorithm averaged predictions from four hand photos per patient and consistently focused on areas like finger joints, fingernails in the clenched fist, and the base of the thumb. Notably, many patients in the dataset had already undergone treatment or were in biochemical remission, meaning the physical changes weren’t necessarily dramatic.

 

Here’s the twist: the model deliberately avoided faces and fingerprints. It relied only on non-identifying hand images, sidestepping the privacy issues that have slowed adoption of facial-recognition approaches for acromegaly screening.

 

Why this works isn’t fully clear. Acromegaly causes soft-tissue hypertrophy, joint-space widening, and subtle structural changes in the hands—features that may be difficult for clinicians to quantify visually but that neural networks can detect at scale. Automatic acromegaly detection …

 

Clinical takeaway: This is not a diagnostic replacement for IGF-1 testing. But for a disease that often goes a decade before diagnosis, a simple hand-photo screening tool—especially in primary care, telehealth, or routine checkups—could flag patients earlier and prompt endocrine referral.

 

Source: The Journal of Clinical Endocrinology & Metabolism