Medical Bulletin 21/May/2025

The study was conducted by Brazilian researchers who analyzed the brains of 1,781 deceased individuals through autopsies. Drinking habits were reported by relatives, and participants were divided into four groups: never drinkers, moderate drinkers (up to seven standard drinks weekly), heavy drinkers (eight or more drinks weekly), and former drinkers (those who hadn’t consumed alcohol in at least three months but previously drank heavily). A standard drink was defined as 14 grams of alcohol—equivalent to 12 ounces of beer or 5 ounces of wine.

While the study has limitations due to reliance on second-hand accounts of alcohol consumption, health experts say the findings clearly show alcohol’s harmful impact on brain health. “The findings of this study confirm what we have thought for a long time, in that chronic alcohol use can be associated with dementia and Alzheimer’s disease,” said Dr. Mike Sevilla, a family medicine physician in Ohio.

The study found that heavy drinkers had a 133% higher risk of hyaline arteriolosclerosis, a condition involving hardening of the arteries that can impair memory function. Former heavy drinkers had an 89% increased risk, while moderate drinkers had a 60% higher risk.

Reference: Justo, A. F. O., Paradela, R., Gomes Goncalves, N., Ribeiro Paes, V., Leite, R. E. P., Nitrini, R., ... & Suemoto, C. K. (2025). Association Between Alcohol Consumption, Cognitive Abilities, and Neuropathologic Changes: A Population-Based Autopsy Study. Neurology, 104(9), e213555.

Can Eating Dessert Help You Lose Weight and Reduce Cravings?

New Delhi: A new study published in the journal Physiology and Behavior suggests that including small portions of craved foods, such as desserts, in a structured meal plan may help individuals lose weight and manage cravings more effectively. Researchers from the University of Illinois Urbana-Champaign found that participants who incorporated desired foods into their diets not only lost more weight over 12 months but also maintained lower levels of cravings during a year-long maintenance phase.

The clinical trial was part of a broader effort to digitize the in-person weight-loss program known as the Individualized Dietary Improvement Program into an online version called EMPOWER. The participants, 30 adults with obesity and health conditions such as hypertension or diabetes, engaged in 22 online nutrition sessions. These sessions included lessons on nutrient-rich food selection and strategies to manage cravings.

Cravings were tracked through questionnaires assessing specific foods—such as sweets, fast food, and carbohydrates—and the overall frequency and intensity of cravings. Participants also weighed themselves daily, transmitting the data via Wi-Fi scales to researchers.

At the end of the first year, 24 participants remained and had lost an average of 7.9% of their starting weight. By the end of the maintenance phase, 20 participants completed the program with an average weight loss of 6.7%. Those who lost more than 5% experienced consistent reductions in cravings, especially for sweets and high-fat foods.

“If you are eating and snacking randomly, it’s very hard to control,” nutrition professor Manabu T. Nakamura said. “Some dietary programs exclude certain foods. Our plan used an ‘inclusion strategy,’ in which people incorporated small portions of craved foods within a well-balanced meal.”

Consistency is another key to managing cravings and weight, Nakamura said. “The popular myth is you have to have a very strong will to fend off temptation, but that is not the case. Fluctuations in eating patterns, meal times and amounts trigger cravings, too. You have to be consistent.”

Reference: Alfouzan, N. W., & Nakamura, M. T. (2025). Reduced food cravings correlated with a 24-month period of weight loss and weight maintenance. Physiology & behavior, 291, 114813.

AI Tool Predicts Postpartum Depression Risk at Birth with High Accuracy: Study 

New Delhi: A new machine learning model developed by researchers at Mass General Brigham shows promising potential in predicting postpartum depression (PPD) risk using data available at the time of delivery. Published in the American Journal of Psychiatry, the study demonstrates how clinical and demographic information from electronic health records (EHR) can be used to identify individuals at higher risk for PPD, potentially allowing for earlier intervention and support.

Traditionally, PPD is screened for during postpartum visits, typically 6 to 8 weeks after childbirth, leaving many individuals without mental health support during the critical early weeks. The new model addresses this gap by analyzing factors such as medical history, demographics, and visit records—data already available in EHRs at the time of delivery.

To build and test the model, the team used data from 29,168 patients who gave birth between 2017 and 2022 across two academic medical centers and six community hospitals. Among them, 9 percent met the study’s criteria for PPD within six months post-delivery. The model effectively ruled out PPD in 90 percent of cases and identified nearly 30 percent of high-risk individuals who later developed the condition.

Importantly, the model performed consistently across age, race, and ethnicity, and even among those without prior psychiatric diagnoses. Incorporating prenatal scores from the Edinburgh Postnatal Depression Scale further improved accuracy.

"This is exciting progress toward developing a predictive tool that, paired with clinicians' expertise, could help improve maternal mental health," said lead author Mark Clapp, MD, MPH, of the Department of Obstetrics and Gynecology at Massachusetts General Hospital, a founding member of the Mass General Brigham healthcare system. "With further validation, and in collaboration with clinicians and patients, we hope to achieve earlier identification and ultimately improved mental health outcomes for postpartum patients."

Reference: https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/machine-learning-model-helps-identify-patients-at-risk-of-postpartum-depression