Medical Bulletin 18/ January/ 2025
Here are the top medical news for the day:
New AI Model Detects Early Signs of Cognitive Decline in Menopausal Women: Study Finds
A new study suggests that machine learning models can more quickly and affordably identify women with severe subjective cognitive decline during the menopause transition, effectively opening the door to better management of cognitive health. Results of the study are published online in Menopause.
Subjective cognitive decline refers to a person’s perceived decline in memory or other cognitive functions.
Existing testing for cognitive performance is largely based on models typically incorporating various laboratory indicators such as blood glucose, blood lipids, and brain imaging. The complexity and high cost of these models often make them impractical to implement in a clinical setting. In comparison, questionnaire-based models offer a simpler and more cost-effective alternative. These models rely on a number of independent variables, including sociodemographic, work-related, menstrual-related, lifestyle-related, and mental health-related factors.
In this latest study involving more than 1,200 women undergoing the menopause transition, researchers were able to develop and validate a machine learning model for identifying women experiencing severe subjective cognitive decline, along with associated factors.
These findings provide a novel guidance for interventions designed to preserve cognitive health in women undergoing the menopause transition. Additional research is needed to validate these results and identify additional potential influencing factors.
“This study highlights how the use of machine learning can be employed to identify women experiencing severe subjective cognitive decline during the menopause transition and potential associated factors. Early identification of high-risk persons may allow for targeted interventions to protect cognitive health. Future studies involving objective measures of cognition and longitudinal follow-up are crucial to better understanding these associations,” says Dr. Stephanie Faubion, medical director for The Menopause Society.
Reference: DOI: 10.1097/GME.0000000000002500
Can Memory Influence What and How Much We Eat?
In a new study researchers have identified the brain’s food-specific memory system and its direct role in overeating and diet-induced obesity. The findings are present in nature metabolism.
The study describes a specific population of neurons in the mouse brain that encode memories for sugar and fat, profoundly impacting food intake and body weight.
These neurons encode memories of the spatial location of nutrient-rich foods, acting as a “memory trace,” particularly for sugar and fat. Silencing these neurons impairs an animal's ability to recall sugar-related memories, reduces sugar consumption, and prevents weight gain, even when animals are exposed to diets that contribute to excessive weight gain. Conversely, reactivating these neurons enhances memory for food, increasing consumption and demonstrating how food memories influence dietary behavior.
These findings introduce two new concepts: first, evidence that specific neurons in the brain store food-related memories, and second, that these memories directly impact food intake. The study’s findings open new possibilities for addressing overeating and obesity.
“While it’s no surprise that we remember pleasurable food experiences, it was long assumed that these memories had little to no impact on eating behavior,” said Monell Associate Member Guillaume de Lartigue, PhD. “What’s most surprising is that inhibition of these neurons prevents weight gain, even in response to diets rich in fat and sugar.”
“These neurons are critical for linking sensory cues to food intake,” said Dr. de Lartigue. “Their ability to influence both memory and metabolism makes them promising targets for treating obesity in today’s food-rich world.”
Reference: Yang, M., Singh, A., de Araujo, A. et al. Separate orexigenic hippocampal ensembles shape dietary choice by enhancing contextual memory and motivation. Nat Metab (2025). https://doi.org/10.1038/s42255-024-01194-6
New Study Reveals How Saliva Activates Coagulation in Hemophilia A Patients
A recent study provides insights into the mechanisms of coagulation in persons with haemophilia A. The research team was able to show that saliva contains special vesicles that trigger rapid coagulation of the blood of haemophilic patients. The results were published in the scientific journal Blood.
The researchers studied the importance of the body's own fluids for blood coagulation, which had been forgotten for decades. The researchers discovered that the saliva of haemophilia A patients contains extrinsic tenase complexes, which are located on vesicles.
Extrinsic tenase complexes are protein complexes that consist of two coagulation factors (tissue factor TF and factor VIIa) and initiate the activation of the coagulation cascade when they come into contact with blood.
Analyses by the study authors confirm that mucosal bleeding in the mouth of these patients is indeed rare and stops quickly.
The scientists were able to prove that the coagulation-promoting properties of maternal milk, amniotic fluid, urine -- and now also saliva -- are due to the presence of extracellular vesicles with extrinsic tenase complexes.
The results provide important insights into the mechanisms of coagulation and contribute to a better understanding of haemophilia A.
Reference: https://www.meduniwien.ac.at/web/en/ueber-uns/news/2025/news-in-january-2025/saliva-activates-coagulation-in-persons-with-haemophilia-a/
Updated Obesity Guidelines for Indians, Moving Beyond BMI
A team of Indian doctors, including from All India Institute of Medical Sciences (AIIMS), Delhi, has in a new study redefined obesity for the Indian population. Findings are published in The Lancet Diabetes and Endocrinology.
Traditionally Body Mass Index (BMI) was used to define obesity, but the new approach focuses on abdominal obesity, comorbid diseases.
This new classification, which comes 15 years after the last definition, marks a significant step forward in addressing the unique health challenges posed by obesity in Asian Indians. The need for updated obesity guidelines stemmed from several critical factors such as the outdated BMI criteria, which relied exclusively on Body Mass Index (BMI, a ratio of weight in kg/height in meter square) for diagnosis; as well as emerging Health Data that showed a correlation between abdominal obesity in Asian Indians and the early onset of comorbid diseases.
According to the study, abdominal fat — closely linked to insulin resistance and prevalent in Asian Indians — is now a key factor in the diagnosis. The new definition also integrates the presence of comorbidities — such as diabetes and cardiovascular disease — into the diagnostic process, ensuring that obesity-related health risks are better accounted for, and taken care of in management. It also includes mechanical problems associated with obesity such as knee and hip osteoarthritis etc, or shortness of breath during daily activities, which produce a poor quality of life.
“A distinct definition of obesity for Indians is crucial for the early detection of related diseases and the development of targeted management strategies. This study fills critical gaps in our understanding and offers a clear, rational approach to tackling obesity in the Indian population,” said Dr. Naval Vikram, Professor of Medicine, at AIIMS, New Delhi.
The revised guidelines introduce a two-stage classification system, addressing both generalised and abdominal obesity. Stage 1 includes increased adiposity (BMI more than 23 kg/m²) without apparent effects on organ functions or routine daily activities. While this stage may not cause any pathological problems, it can progress to Stage 2, leading to other comorbidities.
Stage 2 is an advanced state of obesity with an increased BMI of more than 23 kg/2, and abdominal adiposity with excess waist circumference (WC) or waist-to-height ratio (W-HtR). It includes impact on physical and organ functions — knee arthritis due to excess weight, or presence of type 2 diabetes.
The study called for tailored weight reduction strategies, based on the above classification, to tackle obesity.
Reference: Definition and diagnostic criteria of clinical obesity, Rubino, Francesco et al. The Lancet Diabetes & Endocrinology, Volume 0, Issue 0
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