Medical Bulletin 11/December/2025

Sleep has long been known to affect health, influencing everything from the immune system to the heart. But this study takes that understanding one step further by showing that sleep insufficiency is closely tied to shorter life expectancy across nearly every U.S. county. Researchers analyzed years of nationwide data collected by the Centers for Disease Control and Prevention (CDC) from 2019 to 2025, comparing county-level sleep habits with average lifespan. For this analysis, “sufficient sleep” was defined as getting at least seven hours per night, as recommended by the American Academy of Sleep Medicine.

The OHSU team, led by Dr. Andrew McHill, and composed largely of graduate students from the university’s Sleep, Chronobiology and Health Laboratory, used advanced statistical modeling to explore behavioral influences on longevity. Among all lifestyle variables—diet, exercise, social isolation, and smoking—sleep emerged as the second most important factor influencing life expectancy, surpassed only by smoking.

The correlation was remarkably consistent. In almost every dataset, counties where residents averaged less than seven hours of sleep per night had significantly lower life expectancy than areas where people slept seven to nine hours. The study didn’t pinpoint exact biological mechanisms, but the researchers note that poor sleep disrupts cardiovascular health, immune regulation, and brain function—all major contributors to aging and chronic disease.

Dr. McHill says the takeaway is simple: “We often treat sleep as optional, something to catch up on later. But this research shows that consistent, adequate sleep is as vital as eating well or staying active—perhaps even more so.”

So, a full night’s rest isn’t just about waking up refreshed—it could be one of the most powerful longevity tools we already have.

REFERENCE: McAuliffe, K. E., et al. (2025). Sleep insufficiency and life expectancy at the state-county level in the united states, 2019-2025. SLEEP Advances. doi: 10.1093/sleepadvances/zpaf090. https://academic.oup.com/sleepadvances/advance-article/doi/10.1093/sleepadvances/zpaf090/8373869

Study Finds Plant-Based Diet May Greatly Lower Risk of Stroke

A single plate of food can shape your brain’s future. With stroke rates climbing sharply over the last 25 years—especially in developing countries—scientists are sounding the alarm on how lifestyle choices, particularly diet, can make or break your risk.

A study published in the U.S. National Library of Medicine found that plant-based diets significantly lower major stroke triggers, including high blood pressure, diabetes, and high cholesterol, while strengthening overall heart and brain health.

Stroke is now one of the world’s leading causes of death and disability. While age and genetics play a small role, research shows that more than 90% of stroke cases stem from modifiable factors such as smoking, poor diet, inactivity, obesity, and excessive alcohol use. This means millions of strokes each year could be prevented through smarter eating habits—and that’s exactly where plant-based nutrition stands out.

Scientists have identified several evidence-based diets that dramatically cut stroke risk, including the DASH, Mediterranean, and Nordic diets. Each recommends fresh fruits, vegetables, nuts, whole grains, and healthy fats, while limiting meat, sugar, and processed foods. The DASH diet alone can lower systolic blood pressure by over 5 mmHg, reducing stroke risk by nearly one-third. Similarly, the Mediterranean diet—rich in olive oil, legumes, and seafood—has shown major drops in both stroke and heart disease rates.

Beyond blood pressure, diet plays a powerful role in controlling blood sugar and cholesterol. Plant-heavy diets improve insulin sensitivity and can reduce diabetes risk by more than 50%. They’re also rich in soluble fiber, which lowers harmful LDL cholesterol by up to 20%, keeping arteries flexible and free from clots. The Portfolio diet, featuring soy protein, almonds, and plant sterols, has achieved comparable results in clinical studies.

Researchers are even uncovering links between diet and gut health. People eating mostly plant-based meals produce less TMAO, a compound formed from red meat that’s associated with artery damage and stroke. Combined with fiber, antioxidants, and vitamins like B6, B9, and B12, plant foods create a protective shield for your cardiovascular system.

In short, every fruit, grain, or veggie on your plate nudges your body toward better circulation and a lower stroke risk. Simple, consistent eating choices today can safeguard your brain for decades to come.

REFERENCE: Lin, Chin-Lona,b,*. Stroke and diets – A review. Tzu Chi Medical Journal 33(3):p 238-242, Jul–Sep 2021. | DOI: 10.4103/tcmj.tcmj_168_20

AI Tool Shows Strong Potential for Accurate Brain Tumor Diagnosis Without Surgery

What if artificial intelligence could diagnose brain tumors more accurately than ever before—before a surgeon even touches a scalpel? Researchers at Thomas Jefferson University have developed an automated machine learning (AutoML) model that can do just that, distinguishing between two look-alike brain tumors with over 97% accuracy. Their findings, published in Otolaryngology–Head and Neck Surgery, could transform how doctors plan surgeries and improve outcomes for patients with skull base tumors.

The study focused on two benign but often-confused tumors that grow near the base of the brain: pituitary macroadenomas and parasellar meningiomas. Although both appear similar on standard MRI scans, they require very different surgical approaches, and pre-surgery biopsies are risky or rarely performed. Misidentifying one can lead to suboptimal operations or longer recovery times.

Led by Dr. Gurston G. Nyquist, a professor of Otolaryngology and Neurological Surgery, the team used a dataset of 1,628 MRI images from 116 patients to train their AutoML model. Unlike traditional AI systems that demand expert programming, AutoML automatically selects, trains, and optimizes algorithms, making it easier to integrate into hospitals. The model’s performance was then validated across 959 additional MRI images, proving its reliability.

Results were striking: Overall accuracy: 97.55%; Pituitary macroadenomas: 97% sensitivity, 98.96% specificity; Parasellar meningiomas: 98.41% sensitivity, 95.53% specificity

The researchers designed the system to adjust its confidence thresholds depending on clinical needs. For example, a high-sensitivity mode (99.39%) could help community hospitals catch more potential tumors, while a high-specificity mode (99.31%) could reduce false alarms in specialist centers.

Beyond diagnostics, the model could assist surgeons in planning procedures, expedite referrals, and even serve as an educational tool for trainees. Looking ahead, the Jefferson team plans to enhance the model by combining MRI data with clinical information and hormonal profiles, potentially expanding its use to other head and neck conditions like thyroid nodules or vocal cord lesions.

This breakthrough brings AI one step closer to the operating room—simplifying tumor diagnosis, saving time, and paving the way for more precise, customized care.

REFERENCE: Elliott M. Sina et al, Automated Machine Learning Differentiation of Pituitary Macroadenomas and Parasellar Meningiomas Using Preoperative Magnetic Resonance Imaging, Otolaryngology–Head and Neck Surgery (2025). DOI: 10.1002/ohn.70034