AIMC Topic: Postmenopause

Clear Filters Showing 11 to 20 of 25 articles

Artificial neural networks to predict future bone mineral density and bone loss rate in Japanese postmenopausal women.

BMC research notes
OBJECTIVE: Predictions of the future bone mineral density and bone loss rate are important to tailor medicine for women with osteoporosis, because of the possible presence of personal risk factors affecting the severity of osteoporosis in the future....

The effect of genistein on glucose control and insulin sensitivity in postmenopausal women: A meta-analysis.

Maturitas
Preclinical studies have revealed the beneficial effects of genistein in pancreatic β-cell functions. The results of randomized controlled trials (RCTs) in assessing the effects of genistein on glucose metabolism are inconsistent, however. The aim of...

Relationship between vitamin D and body fat distribution evaluated by DXA in postmenopausal women.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVE: The aim of this study was to explore the relationship between 25-hydroxyvitamin D (25[OH]D) serum concentrations and body fat distribution in a sample of postmenopausal women.

Improving prediction of fragility fractures in postmenopausal women using random forest.

Computers in biology and medicine
Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a...

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

Breast (Edinburgh, Scotland)
PURPOSE: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused ...

Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Fracture prediction is essential in managing patients with osteoporosis and is an integral component of many fracture prevention guidelines. We aimed to identify the most relevant clinical fracture risk factors in contemporary populations by training...

Enhanced osteoporotic fracture prediction in postmenopausal women using Bayesian optimization of machine learning models with genetic risk score.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
This study aimed to enhance the fracture risk prediction accuracy in major osteoporotic fractures (MOFs) and hip fractures (HFs) by integrating genetic profiles, machine learning (ML) techniques, and Bayesian optimization. The genetic risk score (GRS...