健康促進のためのターゲットファイバーダイエットを提案(Scientists call for targeted fibre diets to boost health)

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2025-03-20 ロイヤルメルボルン工科大学(RMIT)

RMIT大学の研究者らは、食物繊維の種類に応じて腸内細菌が異なる代謝物を生成し、健康への影響が変わることを明らかにしました。特に、発酵性の高い食物繊維は短鎖脂肪酸を生成し、抗炎症作用を持つ一方、発酵性の低い繊維はアミノ酸誘導体を生成し、これが炎症性腸疾患や大腸癌のリスク増加と関連する可能性があります。この研究は、個々の健康状態や腸内細菌の組成に基づいた食物繊維の選択が、より効果的な健康維持に役立つことを示唆しています。

<関連情報>

水溶性と不溶性を超えて:食物繊維の健康効果を分類する包括的な枠組み Beyond soluble and insoluble: A comprehensive framework for classifying dietary fibre’s health effects

Christo Opperman, Mahsa Majzoobi, Asgar Farahnaky, Rohan Shah, Thi Thu Hao Van, Vishal Ratanpaul, Ewan W. Blanch, Charles Brennan, Rajaraman Eri
Food Research International  Available online: 31 January 2025
DOI:https://doi.org/10.1016/j.foodres.2025.115843

Graphical abstract

Created in BioRender. Opperman, C. (2024)
https://BioRender.com/s71l653.

健康促進のためのターゲットファイバーダイエットを提案(Scientists call for targeted fibre diets to boost health)

Highlights

  • Current classification doesn’t accurately predict physiological effects.
  • Dietary fibre requires a novel way to understand its structure–function relationship.
  • We are proposing utilising 5 properties to categorise all fibres.
  • The proposed system can improve health outcomes.

Abstract

Despite evolving definitions, dietary fibre classifications remain simplistic, often reduced to soluble and insoluble types. This binary system overlooks the complexity of fibre structures and their diverse health effects. Indeed, soluble fibre is not just soluble but has important qualities such as fermentability, attenuating insulin secretion, and lowering serum cholesterol. However, this limited classification fails to account for dietary fibre diversity and predict their full range of physiological effects. This article proposes a holistic classification framework that accounts for different fibre types and can be used to accurately infer their physiological outcomes. This proposed classification framework comprises of five constituents: backbone structure, water-holding-capacity, structural charge, fibre matrix and fermentation rate. This model more accurately captures the structural and functional diversity of dietary fibres, offering a refined approach to predicting their health benefits.

医療・健康
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