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RISS 인기검색어
- 검색키워드
- 저자 : Hirohito Sone (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Hirohito Sone 대한골대사학회 2024 대한골대사학회지 Vol.31 No.1
Background: No consensus exists regarding which anthropometric measurements are related to bone mineral density (BMD), and this relationship may vary according to sex and age. A large Japanese cohort was analyzed to provide an understanding of the relationship between BMD and anthropometry while adjusting for known confounding factors. Methods: Our cohort included 10,827 participants who underwent multiple medical checkups including distal forearm BMD scans. Participants were stratified into four groups according to age (≥50 years or <50 years) and sex. The BMD values were adjusted for confounding factors, after which single and partial correlation analyses were performed. The prevalence of osteopenia was plotted for each weight index (weight or body mass index [BMI]) class. Results: Cross-sectional studies revealed that weight was more favorably correlated than BMI in the older group (R=0.278 and 0.212 in men and R=0.304 and 0.220 in women, respectively), whereas weight and BMI were weakly correlated in the younger age groups. The prevalence of osteopenia exhibited a negative linear relationship with weight among older women ≥50 years of age, and an accelerated increase was observed with decreasing weight in older men weighing <50 kg and younger women weighing <60 kg. When weight was replaced with BMI, the prevalence was low in most subgroups classified by weight. Conclusions: Weight, rather than BMI, was the most important indicator of osteopenia but it might not be predictive of future bone loss.
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Machine Learning Approach to Drug Treatment Strategy for Diabetes Care
Kazuya Fujihara,Hirohito Sone 대한당뇨병학회 2023 Diabetes and Metabolism Journal Vol.47 No.3
Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in diabetes care. Many classes of antihyperglycemic drugs are currently approved to treat hyperglycemia in patients with type 2 diabetes mellitus, with several new drugs having been developed during the last decade. Diabetes-related complications have been reduced substantially worldwide. Prioritization of therapeutic agents varies according to national guidelines. However, since the characteristics of participants in clinical trials differ from patients in actual clinical practice, it is difficult to apply the results of such trials to clinical practice. Machine learning approaches became highly topical issues in medicine along with rapid technological innovations in the fields of information and communication in the 1990s. However, adopting these technologies to support decision-making regarding drug treatment strategies for diabetes care has been slow. This review summarizes data from recent studies on the choice of drugs for type 2 diabetes mellitus focusing on machine learning approaches.
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