卢旺达年轻成年人的膳食模式、全球膳食质量得分及其相关社会人口因素的探索性研究:使用经食物清单验证的半定量食物频率问卷进行的横断面研究。

IF 3.9 2区 医学 Q2 NUTRITION & DIETETICS Nutrition & Metabolism Pub Date : 2024-11-14 DOI:10.1186/s12986-024-00859-z
Phenias Nsabimana, Befikadu Tariku Gutema, Kate Langley, Hilda Vasanthakaalam, Stefaan De Henauw, Jérome W Somé, Souheila Abbeddou
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引用次数: 0

摘要

背景:卢旺达的经济增长与食品体系、医疗和其他服务的获取、生活方式和营养转型等方面的重大变化息息相关。然而,我们对卢旺达饮食模式的了解仍然有限。本研究旨在确定卢旺达年轻成年人的饮食习惯,并评估相关因素:方法:在一项横断面研究中,使用一份经过开发和验证的半定量食物频率调查问卷,收集了 1218 名参与者(18-35 岁)从 2023 年 1 月底到 4 月的食物摄入量数据,调查时间跨度为一年。饮食习惯采用两个指标进行评估:全球饮食质量评分(GDQS)和饮食模式。最新的指标是通过探索性因素分析得出的:卢旺达成年人的平均 GDQS 为 24.1;64.4% 的受访者具有较高的 GDQS,尤其是城市受访者和受过教育的受访者。南部省的比例最高,为 77.4%。确定了三种饮食模式:"现代"(加工食品和饮料含量高)、"传统"(富含谷物、根茎和植物蛋白)和 "品种少"(多样化食品含量低,但糖和盐含量高)。膳食模式因居住地、省份、性别、年龄、社会类别、资产和教育水平的不同而存在明显差异:这项研究发现了卢旺达成年人不同的膳食模式,表明了与城市化相关的营养转型。研究结果凸显了进一步研究卢旺达人口饮食、肥胖和代谢综合征之间关系的必要性。
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Exploratory dietary patterns, the global diet quality score, and their associated socio-demographic factors among young adults in Rwanda: a cross-sectional study using a food list-validated, semi-quantitative food frequency questionnaire.

Background: Economic growth in Rwanda is associated with significant changes in food systems, access to health and other services, lifestyle, and nutritional transitions. Nevertheless, our knowledge of dietary patterns in Rwanda remains limited. The present study aimed to identify the dietary habits of young adult population in Rwanda and to assess associated factors.

Methods: A developed and validated semi-quantitative food frequency questionnaire covering a one-year period was used to collect data on food intake of 1,218 participants (18-35 years old) from end of January to April 2023 in a cross-sectional study. Dietary habits were assessed using two indicators: the Global Diet Quality Score (GDQS) and dietary patterns. The latest was developed using exploratory factor analysis.

Results: Rwandan adults had a mean GDQS of 24.1; 64.4% had high GDQS, especially urban, and educated respondents. The Southern province led at 77.4%. Three dietary patterns were identified: "Modern" (high in processed foods and drinks), "Traditional" (rich in cereals, roots, and plant-based proteins), and "low variety" (low in diverse foods but high in sugar and salt). Dietary patterns significantly varied by residency, province, sex, age, social category, asset, and education level.

Conclusion: This study identified distinct dietary patterns among adult population of Rwanda, suggesting a nutritional transition associated with urbanization. The findings highlight the need for further research into the relationships between diet, obesity, and metabolic syndrome in Rwandan population.

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来源期刊
Nutrition & Metabolism
Nutrition & Metabolism 医学-营养学
CiteScore
8.40
自引率
0.00%
发文量
78
审稿时长
4-8 weeks
期刊介绍: Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.
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