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诺瓦食物分类系统:巴西流行病学的一项贡献。

Nova food classification system: a contribution from Brazilian epidemiology.

作者信息

Louzada Maria Laura da Costa, Gabe Kamila Tiemann

机构信息

Universidade de São Paulo, Center for Epidemiological Research in Nutrition and Health - São Paulo (SP), Brazil.

出版信息

Rev Bras Epidemiol. 2025 Jun 2;28:e250027. doi: 10.1590/1980-549720250027. eCollection 2025.

DOI:10.1590/1980-549720250027
PMID:40465876
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12129239/
Abstract

This essay presents the Nova food classification, a conceptual innovation of Brazilian epidemiology, describing its genesis, the scientific evidence derived from its application, and its implications for public health. Created in 2010, Nova classifies foods into four groups based on the degree of processing: unprocessed or minimally processed foods, processed culinary ingredients, processed foods, and ultra-processed foods. Since its development, several epidemiological studies have demonstrated the negative impacts of high consumption of ultra-processed foods on health, such as associations with various non-communicable diseases, including obesity, diabetes, cardiovascular diseases and mental health outcomes. In Brazil, the consumption of these foods has increased significantly in recent decades, with the caloric share of ultra-processed foods rising from 12.6 to 18.4% between 2002-2003 and 2017-2018, with this increase being more pronounced among more vulnerable socioeconomic groups. Nova underpins the recommendations of the Brazilian Dietary Guidelines and has played a crucial role in informing public policies, such as the update of the National basic food basket and the guidelines of the National School Feeding Program, which aim to limit the access to ultra-processed foods. Finally, the essay addresses the political and scientific challenges, including the need for more experimental studies to strengthen the evidence and the potential of fiscal and marketing regulation strategies that take into account the impact of food processing on health.

摘要

本文介绍了诺瓦食品分类法,这是巴西流行病学的一项概念创新,描述了其起源、应用所产生的科学证据及其对公共卫生的影响。诺瓦食品分类法于2010年创建,根据加工程度将食品分为四类:未加工或最低限度加工的食品、加工过的烹饪配料、加工食品和超加工食品。自其发展以来,多项流行病学研究表明,高消费超加工食品对健康有负面影响,比如与各种非传染性疾病有关联,包括肥胖症、糖尿病、心血管疾病和心理健康问题。在巴西,近几十年来这些食品的消费量显著增加,2002 - 2003年至2017 - 2018年间,超加工食品的热量占比从12.6%升至18.4%,在社会经济较脆弱群体中这种增长更为明显。诺瓦食品分类法为巴西饮食指南的建议提供了依据,并在为公共政策提供信息方面发挥了关键作用,比如更新国家基本食品篮以及国家学校供餐计划的指南,这些政策旨在限制超加工食品的获取。最后,本文探讨了政治和科学挑战,包括需要更多实验研究来加强证据,以及财政和营销监管策略的潜力,这些策略考虑到了食品加工对健康的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4b4/12129239/d450c3b68b2d/1980-5497-rbepid-28-e250027-gf02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4b4/12129239/a5d368d14e87/1980-5497-rbepid-28-e250027-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4b4/12129239/d450c3b68b2d/1980-5497-rbepid-28-e250027-gf02.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4b4/12129239/a5d368d14e87/1980-5497-rbepid-28-e250027-gf01.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d4b4/12129239/d450c3b68b2d/1980-5497-rbepid-28-e250027-gf02.jpg

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QuestNova: innovation in food consumption assessment according to industrial processing.QuestNova:基于工业加工的食品消费评估创新。
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Ultra-processed foods cause weight gain and increased energy intake associated with reduced chewing frequency: A randomized, open-label, crossover study.超加工食品导致体重增加和能量摄入增加,与咀嚼频率降低有关:一项随机、开放标签、交叉研究。
Diabetes Obes Metab. 2024 Nov;26(11):5431-5443. doi: 10.1111/dom.15922. Epub 2024 Sep 12.
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Food price trends during the COVID-19 pandemic in Brazil.
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PLoS One. 2024 May 23;19(5):e0303777. doi: 10.1371/journal.pone.0303777. eCollection 2024.
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Adherence to the ultra-processed dietary pattern and risk of depressive outcomes: Findings from the NutriNet Brasil cohort study and an updated systematic review and meta-analysis.坚持超加工饮食模式与抑郁结局风险:巴西营养网队列研究及最新系统评价与荟萃分析的结果
Clin Nutr. 2024 May;43(5):1190-1199. doi: 10.1016/j.clnu.2024.03.028. Epub 2024 Apr 1.
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Ultra-processed food exposure and adverse health outcomes: umbrella review of epidemiological meta-analyses.超加工食品暴露与健康不良结局:流行病学荟萃分析的伞式综述。
BMJ. 2024 Feb 28;384:e077310. doi: 10.1136/bmj-2023-077310.
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Nova diet quality scores and risk of weight gain in the NutriNet-Brasil cohort study.巴西 NutriNet 队列研究中新的饮食质量评分与体重增加风险。
Public Health Nutr. 2023 Nov;26(11):2366-2373. doi: 10.1017/S1368980023001532. Epub 2023 Jul 31.
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