A method of using neural network model to predict the effect of low-carb diet weight loss intervention

A diet, random forest model technology, applied in the field of biomedicine, can solve problems such as lack of weight loss effectiveness

Active Publication Date: 2022-04-15
GUANGDONG INST OF MICROBIOLOGY GUANGDONG DETECTION CENT OF MICROBIOLOGY +1
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Problems solved by technology

[0004] Although weight loss associated with low-carbohydrate dietary interventions has been reported in different clinical trials, the exact benefits and sustainability remain many challenges
Lack of sufficient evidence and effective methods to assess the effectiveness of low-carb diet interventions for weight loss

Method used

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  • A method of using neural network model to predict the effect of low-carb diet weight loss intervention
  • A method of using neural network model to predict the effect of low-carb diet weight loss intervention
  • A method of using neural network model to predict the effect of low-carb diet weight loss intervention

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Embodiment 1

[0026] 1. Experimental method

[0027] 1 research object

[0028] The subjects of this study were from the prospective clinical study led by the Department of Endocrinology and Metabolism, Zhujiang Hospital, Southern Medical University: "Strategy research on weight control of overweight / obese patients with low-carbohydrate diet and calorie-restricted diet intervention - DISCOVER study" (hereinafter referred to as Normal diet control group and unlimited calorie low carbohydrate diet group in "DISCOVER Study". This study complied with the ethical guidelines of the Declaration of Helsinki, was approved by the Ethics Committee of Zhujiang Hospital of Southern Medical University, and registered in China Clinical Trial Registration Center in advance (clinical trial approval number: ChiCTR1800015156). The subjects voluntarily participated in this study and signed a written informed consent form. The trial period is from April 2018 to October 2018.

[0029] 2 diagnostic criteria

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Abstract

The invention discloses a method for predicting the weight loss intervention effect of a low-carbon diet by using a neural network model. Obtain the relative abundance and weight loss index of the intestinal flora at the genus level before weight loss of the subject to be evaluated, use the relative abundance data and weight loss index as the parameters of the neural network model, and use the neural network model to predict the weight loss of the low-carb diet intervention effect. Low-carb diet interventions lead to significant weight loss and elevated certain gut flora. Using advanced computational algorithms, such as random forests and artificial neural networks (ANNs), we discovered butyrate-producing bacteria following a low-carb diet intervention. We demonstrate that the relative abundance of Bacteroidetes bacteria is a positive outcome predictor of weight loss in individuals following a low-carbohydrate dietary intervention. Furthermore, using these unique gut microbial structures at baseline, we established a neural network algorithm-based predictive model to assess the weight loss potential of each clinical trial, with the aim of developing effective weight loss strategies.

Description

technical field [0001] The invention belongs to the field of biomedicine, and in particular relates to a method for predicting the weight loss intervention effect of a low-carbon diet by using a neural network model. Background technique [0002] Obesity is a chronic metabolic disease caused by several factors, including but not limited to consumption of cheap and calorie-dense foods, decreased physical activity, insulin resistance, depression and social anxiety. Obesity or obesity-related chronic diseases affect more than 2 billion people worldwide. According to the "2020 Report on Chronic Diseases and Nutrition of Chinese Residents", overweight or obesity plagues more than half of Chinese adults. Despite advances on multiple fronts, obesity remains a high risk factor for a range of chronic diseases, such as cardiovascular disease (CVD), diabetes and cancer, and has adverse health effects. In the obese population, BMI-related CVD was responsible for 41% of deaths and 34% ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): C12Q1/6883C12Q1/689C12Q1/04G16H50/30G16H50/50G16H10/20G06K9/62G06N3/04
CPCC12Q1/6883C12Q1/689G16H50/30G16H50/50G16H10/20G06N3/04G06F18/213G06F18/24323
Inventor 谢黎炜张素素刘秉东刘志红黄柳菁
Owner GUANGDONG INST OF MICROBIOLOGY GUANGDONG DETECTION CENT OF MICROBIOLOGY
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