Department of Nutrition, Food Sciences and Physiology, University of Navarra, 31008 Pamplona, Spain; Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain.
Department of Nutrition, Food Sciences and Physiology, University of Navarra, 31008 Pamplona, Spain; Center for Nutrition Research, University of Navarra, 31008 Pamplona, Spain; Centro de Investigación Biomédica en Red de la Fisiopatología de la Obesidad y Nutrición (CIBERobn), Institute of Health Carlos III, 28029 Madrid, Spain; Navarra Institute for Health Research (IdiSNA), 31008 Pamplona, Spain.
Clin Nutr. 2022 Aug;41(8):1712-1723. doi: 10.1016/j.clnu.2022.06.008. Epub 2022 Jun 16.
BACKGROUND & AIMS: The response to weight loss depends on the interindividual variability of determinants such as gut microbiota and genetics. The aim of this investigation was to develop an integrative model using microbiota and genetic information to prescribe the most suitable diet for a successful weight loss in individuals with excess of body weight.
A total of 190 Spanish overweight and obese participants were randomly assigned to two hypocaloric diets for 4 months: 61 women and 29 men followed a moderately high protein (MHP) diet, and 72 women and 28 men followed a low fat (LF) diet. Baseline fecal DNA was sequenced and used for the construction of four microbiota subscores associated with the percentage of BMI loss for each diet (MHP and LF) and for each sex. Bootstrapping techniques and multiple linear regression models were used for the selection of families, genera and species included in the subscores. Finally, two total microbiota scores were generated for each sex. Two genetic subscores previously reported to weight loss were used to generate a total genetic score. In an attempt to personalize the weight loss prescription, several linear mixed models that included interaction with diet between microbiota scores and genetic scores for both, men and women, were studied.
The microbiota subscore for the women who followed the MHP-diet included Coprococcus, Dorea, Flavonifractor, Ruminococcus albus and Clostridium bolteaea. For LF-diet women, Cytophagaceae, Catabacteriaceae, Flammeovirgaceae, Rhodobacteriaceae, Clostridium-x1vb, Bacteriodes nordiiay, Alistipes senegalensis, Blautia wexlerae and Psedoflavonifractor phocaeensis. For MHP-diet men, Cytophagaceae, Acidaminococcaceae, Marinilabiliaceae, Bacteroidaceae, Fusicatenibacter, Odoribacter and Ruminococcus faecis; and for LF-men, Porphyromanadaceae, Intestinimonas, Bacteroides finegoldii and Clostridium bartlettii. The mixed models with microbiota scores facilitated the selection of diet in 72% of women and in 84% of men. The model including genetic information allows to select the type of diet in 84% and 73%, respectively.
Decision algorithm models can help to select the most adequate type of weight loss diet according to microbiota and genetic information.
This trial was registered at www.
gov as NCT02737267 (https://clinicaltrials.gov/ct2/show/NCT02737267?term=NCT02737267&cond=obekit&draw=2&rank=1).
体重减轻的反应取决于肠道微生物群和遗传等决定因素的个体间可变性。本研究的目的是开发一种整合模型，利用微生物组和遗传信息为超重和肥胖个体开出最合适的饮食处方，以实现成功的减肥。
共有 190 名西班牙超重和肥胖参与者被随机分配到两种低热量饮食中 4 个月：61 名女性和 29 名男性遵循中高蛋白（MHP）饮食，72 名女性和 28 名男性遵循低脂（LF）饮食。基线粪便 DNA 进行测序，并用于构建与每种饮食（MHP 和 LF）和每种性别相关的 4 个微生物组子分数，以预测 BMI 减轻的百分比。使用引导技术和多元线性回归模型选择子分数中包含的科、属和种。最后，为每个性别生成两个总微生物组分数。使用先前报道的与体重减轻相关的两个遗传子分数来生成总遗传分数。为了尝试个性化减肥处方，研究了几种线性混合模型，这些模型包括男女两性微生物组分数与遗传分数之间的饮食相互作用。
女性 MHP 饮食组的微生物组子分数包括 Coprococcus、Dorea、Flavonifractor、Ruminococcus albus 和 Clostridium bolteaea。对于 LF 饮食的女性，Cytophagaceae、Catabacteriaceae、Flammeovirgaceae、Rhodobacteriaceae、Clostridium-x1vb、Bacteriodes nordiiay、Alistipes senegalensis、Blautia wexlerae 和 Psedoflavonifractor phocaeensis。对于 MHP 饮食的男性，Cytophagaceae、Acidaminococcaceae、Marinilabiliaceae、Bacteroidaceae、Fusicatenibacter、Odoribacter 和 Ruminococcus faecis；对于 LF 饮食的男性，Porphyromanadaceae、Intestinimonas、Bacteroides finegoldii 和 Clostridium bartlettii。包括微生物组分数的混合模型有助于在 72%的女性和 84%的男性中选择饮食。纳入遗传信息的模型分别允许在 84%和 73%的情况下选择饮食类型。
决策算法模型可帮助根据微生物组和遗传信息选择最合适的减肥饮食类型。
本试验在 www.clinicaltrials.gov 上注册为 NCT02737267（https://clinicaltrials.gov/ct2/show/NCT02737267?term=NCT02737267&cond=obekit&draw=2&rank=1）。
