AIMC Topic: Weight Gain

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Associations between weight gain, integrase inhibitors antiretroviral agents, and gut microbiome in people living with HIV: a cross-sectional study.

Scientific reports
Dolutegravir and bictegravir are second-generation HIV integrase strand transfer inhibitors (INSTIs) that were previously associated with abnormal weight gain. This monocentric cross-sectional study investigates associations between weight gain durin...

Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.

JMIR public health and surveillance
BACKGROUND: Rapid weight gain (RWG) during infancy, defined as an upward crossing of one centile line on a weight growth chart, is highly predictive of subsequent obesity risk. Identification of infant RWG could facilitate obesity risk assessment fro...

Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.

Diabetes, obesity & metabolism
AIMS: Numerous risk factors for the development of obesity have been identified, yet the aetiology is not well understood. Traditional statistical methods for analysing observational data are limited by the volume and characteristics of large dataset...

Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.

The Journal of surgical research
INTRODUCTION: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weig...

Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol.

Nutrients
BACKGROUND/OBJECTIVES: Child acute malnutrition is a global public health problem, affecting 45 million children under 5 years of age. The World Health Organization recommends monitoring weight gain weekly as an indicator of the correct treatment. Ho...

Prediction of growth and feed efficiency in mink using machine learning algorithms.

Animal : an international journal of animal bioscience
The feed efficiency (FE) expresses as the amount of feed required per unit of BW gain. Since feed cost is the major input cost in the mink industry, evaluating of FE is a crucial step for competitiveness of the mink industry. However, the FE measures...

Weight gained during treatment predicts 6-month body mass index in a large sample of patients with anorexia nervosa using ensemble machine learning.

The International journal of eating disorders
OBJECTIVE: This study used machine learning methods to analyze data on treatment outcomes from individuals with anorexia nervosa admitted to a specialized eating disorders treatment program.

Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.

The Journal of surgical research
INTRODUCTION: Weight gain among young adults continues to increase. Identifying adults at high risk for weight gain and intervening before they gain weight could have a major public health impact. Our objective was to develop and test electronic heal...

Liver Damage Is Related to the Degree of Being Underweight in Anorexia Nervosa and Improves Rapidly with Weight Gain.

Nutrients
Background: The present study investigates the relationship between hypertransaminasemia and malnutrition on the basis of a very large number of patients. We assume that the level of transaminases not only reflects the extent of underlying liver cell...

Artificial intelligence as an analytic approximation to evaluate associations between parental feeding behaviours and excess weight in Colombian preschoolers.

The British journal of nutrition
Parental practices can affect children's weight and BMI and may even be related to a high prevalence of obesity. Therefore, the aim of this study was to evaluate the relationship between parents' practices related to feeding their children and excess...