| 研究課題/領域番号 |
19K20199
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分59040:栄養学および健康科学関連
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| 研究機関 | 愛知医科大学 |
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研究代表者 |
王 超辰 愛知医科大学, 医学部, 講師 (00758063)
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| 研究期間 (年度) |
2019-04-01 – 2022-03-31
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| 研究課題ステータス |
中途終了 (2021年度)
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| 配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2020年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
2019年度: 2,080千円 (直接経費: 1,600千円、間接経費: 480千円)
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| キーワード | chrononutrition / NDNS / nutritional epidemiology / latent class analysis / correspondence analysis / NDNS RP / diabetes / Chrono-nutrition / Latent class analysis / Correspondence analysis / Circadian eating pattern / MLCA |
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| 研究開始時の研究の概要 |
The importance of circadian rhythms has been recognized for long, while its impact on nutrition is still largely unknown. The existence of grazers, early, and late eaters according to the timing of energy intake has been revealed recently.
We plan to find time and quantity circadian eating patterns in the general population using the existing databases through objective, data-driven statistical approaches. Furthermore, associations between these patterns and health outcomes will be explored and provide evidence for strategies in lifestyle intervention and in prevention of chronic diseases.
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| 研究実績の概要 |
Our studies used the UK's nutrition survey data to understand the real-world food choices at different time in the day and the association with type 2 diabetes (T2D).
We identified low, moderate, high carbohydrate (CH) eaters in the adult population and estimated the associations between these eaters and T2D by survey-designed multivariable regression models. On average, low-CH eaters consumed the highest amount of total energy and had higher percentages of energy contributed by fat and alcohol, especially after 8 pm. Moderate CH-eaters consumed the lowest amount of total energy while their meals were eaten later in the day. High CH-eaters consumed most of their CH and energy earlier in the day and within the time slots of traditional mealtimes. Low-CH eaters had greater odds than high-CH eaters of having T2D in self-reported but not in previously undiagnosed diabetics.
Moreover, we used unsupervised learning techniques to understand how foods were chosen in the day of time. We identified relationships between foods and time and described how these associations may vary by T2D. We analyzed the contingency table which cross-classifying food groups with eating time slots by correspondence analysis (CA). CA biplots were generated to explore the associations between food groups and time of eating across diabetes strata. We found that foods consumed in the evening/night time tend to be highly processed, easily accessible, and rich in added sugar or saturated fat. Individuals with undiagnosed diabetes are more likely to consume unhealthy foods at night.
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