50 results about "Obesity risk" patented technology

A nutritional composition for infants at risk of developing obesity later in life comprises a protein source, a lipid source and a carbohydrate source and has a protein content of less than 1.8 g / 100 kcal and an energy density of less than 650 kcal / litre.

The use of probiotic bacteria capable of promoting the development of an early bifidogenic intestinal microbiota in the manufacture of a medicament or therapeutic nutritional composition for reducing the risk of development of overweight or obesity of an infant later in life.

The invention proposes a nutritional composition comprising selected oligosaccharides that reduces the abundance of Streptococcus bacteria in the gut of infants or young mammals. The infants are preferably infants in needs presenting a relatively high count of Streptococcus. Ultimately the reduction of streptococcus and the related microbiota balance affects and lowers the risk of adiposity or obesity later in life.

The present invention provides compositions and methods for identifying a subject as having a genetic predisposition to obesity or at risk of developing obesity. The present invention also provides compositions and methods for expressing a CREBRF polypeptide of the invention in a cell or precursor thereof and cells expressing a nucleic acid molecule encoding a CREBRF polypeptide of the invention.

The invention discloses an intestinal flora and obesity personalized diagnosis method based on a graph attention network, and relates to the technical field of obesity diagnosis, and the method comprises the following steps: collecting gene data, building a graph structure-based model for the data to predict obesity risk and provide personalized health suggestions, each piece of microbiome data is expressed as a weighted undirected graph, a microbiome graph is constructed, the association strength between microbiome groups is calculated, and the microbiome data is expressed as a graph structure. According to the method, the graph structure model considering the relevance between the microbiome groups is constructed, the self-adaptive graph convolutional network and the heterogeneous graph attention network are utilized, multi-modal information interaction between the microbiome and clinical data is deeply mined, the processing capacity of high-dimensional and complex microbiome data is improved, and the multi-modal information interaction between the microbiome and clinical data is improved. The accurate prediction of obesity risks and the provision of personalized health suggestions are realized, the prediction accuracy and generalization ability of the model are significantly improved, and a more effective personalized obesity diagnosis effect is achieved.

Provided is a method of measuring the expression level of FSTL3 gene in a biological sample and correlating the measured expression level with the detection of a risk of developing diabetes. Also provided is a method of measuring the expression level of FSTL3 gene in an individual with a BMI value less than 25 not clinically determined as obesity and correlating the measured expression level with the detection of a risk of developing obesity. Further provided is a method of measuring the expression level of FSTL3 gene in an individual with a BMI value less than 25 and correlating the measured expression level with the detection of a risk of developing diabetes. Further provided is a method of measuring an inhibin βB gene expression level and correlating the ratio of expression level of FSTL3 gene to inhibin βB gene with the detection of a risk of developing obesity or diabetes.

The invention discloses a personal obesity risk prediction system and method based on AI of big data, and belongs to the field of medical health, and the system comprises a data collection module, a multi-dimensional feature construction module, a risk label dynamic generation module, a model training and risk prediction module, a credibility evaluation and calibration module and the like. The system collects multi-source heterogeneous data through wearable equipment, a biochemical interface and a health platform API (Application Program Interface), uniformly encodes the multi-source heterogeneous data into a standard time sequence and then constructs behavior-metabolism-environment coupling characteristics. And performing joint modeling on the dynamic features and the labels by adopting a graph neural network in combination with causal factorization, and outputting an individual obesity risk prediction result. The result credibility is improved through Monte Carlo Dropout and a confidence interval calibration mechanism, and calibration information is fed back to a feature construction link to optimize a modeling strategy. Finally, the key risk factors are presented in the form of a visual thermodynamic diagram and a causal path diagram, and an individualized intervention suggestion vector is generated. The method has the beneficial effects of improving prediction accuracy and enhancing individual intervention pertinence.

The invention discloses a slowly digestible biscuit containing okra seed procyanidine and a preparation method of the slowly digestible biscuit. The preparation method is characterized by comprising the following steps: (1) fully screening 500g of flour, (2) mixing 40g of xylitol and 40g of stevioside, adding a proper amount of water, heating and melting into syrup, filtering for use, and sufficiently whipping 40g of butter, (3) preparing a dough, and adding 0.5 g of water-redissolved okra seed procyanidine freeze-dried powder, two eggs and whipped butter in the flour mixing process, (4) rolling the dough until the thickness of the dough is about 2-3mm, (5) molding the dough by using a mold, baking the molded dough in an oven, and controlling the surface temperature of the biscuits to be 125 DEG C and the baking time to be 20 minutes, and (6) cooling the biscuits to 38-40 DEG C. The defects that the biscuits are high in fat and high in sugar, various diseases are caused and obesity risks exist are ingeniously overcome, the natural plant extract, namely the okra seed extract, with procyanidine as the main component is used for improving the characters of the biscuits, and the defectthat an artificially synthesized additive is prone to generating various side effects on the human body is effectively overcome.

The invention discloses a children obesity early warning method and system based on obesity risk factor analysis, and the method comprises the steps: carrying out the feature screening of collected children multi-dimensional health related data containing parental information, birth history and lifestyle, and recognizing the important risk factors and weights of children obesity; important risk factors are supplemented through literature review; the method comprises the following steps: formulating scale questions including parental information, birth history and lifestyle based on important risk factors and weights in combination with clinical expert opinions, assigning scores to the scale questions, dividing according to a scale score result to form children obesity risk prediction levels, and providing personalized health management schemes for subjects with different risk levels. The method is low in cost, easy to implement, high in compliance and based on non-invasive characteristics, and is particularly suitable for early obesity screening and early warning of 3-6-year-old children.

The invention provides a bacterial cellulose fermented milk beverage and a preparation method thereof, and belongs to the technical fields of fermentation process and beverage processing. According tothe preparation method of the bacterial cellulose fermented milk beverage provided by the invention, the bacterial cellulose fermented milk beverage is prepared by performing fermentation on bacterial cellulose, as well as carrying out homogenization with xylitol, a stabilizer and so on; and thus, the bacterial cellulose fermented milk beverage is abundant in viable bacteria, and contains a plurality of functional substances which are necessary for maintaining survival of the viable bacteria. The bacterial cellulose fermented milk beverage is low in calorie and free of fat, as well as capableof producing long-lasting satiety so as to reduce obesity risk, adsorbing harmful substances, increasing excretion volume and promoting expelling of toxin from the intestinal tract. Moreover, the preparation method of the bacterial cellulose fermented milk beverage is simple and mild in conditions; and the raw materials are also simple and easy to obtain, so that mass production is facilitated.

The invention discloses an individual obesity prediction model, comprising the following contents: (1) collecting the objective information of a tested individual; (2) converting the obtained information into specific data; and (3) substituting the specific data into a formula to build the individual obesity prediction model. According to the invention, on the basis of international chronic disease sickening risk estimation model and frames, relevant research data and literatures about Chinese are collected so as to research a method for estimating chronic disease sickening risks. An individual obesity risk estimation method, model and technology which are suitable for the morbidity characteristics of patients in China are built by only collecting indexes (such as food, physical activities and the like) relevant to the life style, and are applied to the industries, such as grass-root health service institutions, hospitals, health management companies, insurance and the like. The individual obesity prediction model can be widely used for screening high-risk individuals with obesity and making a personalized intervening measure, thereby providing a scientific evidence for people suffering from obesity in China and having important social and economic benefits.

The invention relates to the technical field of medical data privacy protection and information security, in particular to an obesity risk prediction report generation system and method based on a knowledge base, and the system comprises a data collection module, a risk prediction model module, a knowledge base module and a report generation module. According to the method, multi-dimensional data of intestinal microorganisms, gene detection, dietary habits, lifestyles and the like of the user are analyzed, a knowledge base and machine learning model combination technology is utilized, a personalized obesity risk assessment report can be generated, and therefore multi-dimensional and accurate obesity risk assessment is achieved.

A method of reducing the accumulation of fat mass in a neonatal human infant at risk thereof which method comprises administering to the infant during at least a part of the neonatal period a therapeutic amount of a nutritional composition comprising proteins in an amount such that the composition contains more than 2.4 g of protein per 100 kcal. As weight gain during the first week of life has been associated with overweight in adulthood, this may offer a method of reducing the risk of developing obesity in later life. Also claimed is the administration of DHA to the mother during gestation period.

Disclosed is a blood plasma specific metabolite profile, in particular a biomarker composition obtained via obese population blood plasma specific metabolite profile screening. Also disclosed are a use of the biomarker composition for obesity risk assessment, diagnosis, early diagnosis and pathological staging, and an obesity risk assessment, diagnosis, early diagnosis and pathological staging method. The biomarker composition of the present invention may be used for the early diagnosis of obesity.

The invention belongs to the technical field of application of edible fat, and particularly discloses a preparation method of transesterification butterfat. The preparation method comprises the following steps of step (1), heating 3 tons of natural anhydrous butter to 33-37 DEG C, and performing stirring to enable the natural anhydrous butter to melt; step (2) maintaining the temperature stable, adding lipases of certain concentration as a catalyst, and performing a reaction for 18-24 hours; and step (3), after the reaction is completed, performing heating to 85 DEG C for inactivating enzymes.The preparation method and application of the transesterification butterfat have the beneficial effects that the lipases is used for transesterification, so that free cholesterol contained in the butterfat can react to obtain esterified cholesterol, the absorption rate is reduced, and the obesity risk can be reduced. In the process, the content of myristic acid is also reduced (detection proves that C14:0 is reduced to about 2% from 10%), the content of stearic acid is increased (detection proves that C18:0 is increased to about 14% from 12%, C18:1 is increased to about 40% from 26%, and C18:2 is increased to about 14% from 3%).

The invention provides a method of in-vitro measuring obesity-causing risk, which comprises following steps: (1) preparing a sample containing nucleic acid; (2) detecting the genotypes of a peroxisome proliferator-activated receptor (PPARG) allele and syndecan3 (SDC3) allele; and (3) identifying the genotypes of the PPARG allele and the SDC3 allele to obtain an identifying result, wherein if the PPARG allele and the SDC3 allele are mutated at the same time, the sample is sourced from an individual having increasing obesity-causing risk. The method is used for determining the obesity-causing risk with the genotypes of the PPARG gene and the SDC3 gene. Compared with a method in which the genotype of single gene is employed, the method is increased in the accuracy of prediction, so that possibility of mistake generated by determining the obesity-causing risk just by the same specific single gene in different individuals is reduced. The method also can be used as a basis for providing corresponding health-caring strategy.

A nutritional composition comprising a significant amount of arachidonic acid (ARA) is described. Such nutritional composition is particularly suitable for infants below the age of 3 years, preferably between birth and 12 months of life. The composition can be used to reduce the risk of developing overweight / obesity and / or insulin resistance later in life.

The invention discloses a teenager obesity risk early warning system, which relates to the technical field of teenager human body health weight risk prediction and evaluation, and comprises an electronic scale, a data acquisition module, a dynamic growth and development correction module, a behavior-physiology linkage analysis module, an edge calculation early warning module and a family-school linkage feedback module. Through dynamic evaluation, multi-source data integration, real-time early warning and collaborative intervention, the limitation that a traditional method is inaccurate in evaluation, lagged in early warning and single in intervention is overcome, the accuracy, timeliness and prevention and control effect of adolescent obesity risk monitoring are remarkably improved, and the method is easy and convenient to operate and suitable for long-term use.

The invention belongs to the application field of microbial flora analysis, and particularly relates to a kit for detecting the obesity risk and application thereof. The OUT sequence amplified by thekit is as shown in the SEQ ID NO:1-SEQ ID NO:15. The invention further provides non-medical purpose application of the kit in obesity risk detection, the obesity risk is obtained by calculating floraabundance with a calculation formula as shown in the specification, wherein Ij is a risk index, Aij is the relative abundance of culture I in a sample j, N is a subset of an enriched flora selected from a health human flora, M is a subset of enriched flora related to obesity, and absolute values of N and M are respectively the number of cultures of the first and second subsets. According to the kit, the obesity risk is analyzed through the OUT sequence and flora abundance by selecting a specific microbial flora, along with high accuracy, small amount of flora to be analyzed, convenience and rapidness.

A decision support method and system is provided for monitoring and treating pediatric obesity. Embodiments include generating obesity risk curves corresponding to obesity risk levels, for example, severe and morbid obesity risk levels. Generating obesity risk curves depends on predicting at least one health proxy such as, for example, spend data and chronic conditions. Generating severe obesity curves depends on an age-dependent multiplier. An obesity risk level is assigned to a target pediatric patient using the obesity risk curves dependent on the age-dependent multiplier. In some aspects, an intervening response is initiated based on the assigned obesity risk level.

The invention discloses a nucleic acid sequence for detecting an obesity risk site, a kit and a detection method thereof. A nucleic acid sequence combination disclosed by the invention is proposed specific to five polymorphic sites (rs9939609, rs1558902, rs571312, rs17782313 and rs9356744), and comprises a wild upstream primer, a mutant upstream primer and a universal downstream primer of each site. The kit for rapidly detecting the obesity risk site and prepared from the nucleic acid sequence combination has the characteristics of convenience in use, easiness and convenience in operation, high automation degree, lower pollution in an operation process, good detection effect, high sensitivity, high specificity, high accuracy and high precision. The detection method disclosed by the invention is convenient and rapid to use through adoption of a complete closed-tube operation; a detection result is obtained by directly exploring a fluorescent signal value in a PCR (Polymerase Chain Reaction) process, so that PCR posttreatment or electrophoresis detection is not required, the technical problems of great probability of pollution and false positive value generation in the conventional PCR technology are solved, and non-specific amplification can be effectively avoided; the detection method is suitable for large-scale sample detection.

The invention relates to an obesity risk prediction system based on multi-modal iconography, and relates to the field of electrical digital data processing, and the system comprises a layer-by-layer construction device which is used for obtaining an obesity risk prediction model; and the risk prediction device is used for intelligently predicting the fatty liver grade and the easily fat food name of the to-be-analyzed person according to the liver QCT picture information of the to-be-analyzed person and the fMRI imaging data of the obesity-related brain region by adopting an obesity risk prediction model. The invention also relates to an obesity risk prediction method based on multi-modal iconography. According to the invention, the technical problems of insufficient obesity risk parameter analysis precision and single parameter type of the to-be-analyzed person are solved; synchronous intelligent prediction of two obesity risk parameters, namely the fatty liver grade and the easy-to-fat food name, of a person to be analyzed can be realized based on multiple items of basic data screened in a targeted manner by adopting an obesity risk prediction model based on multi-modal iconography of a customized structure, so that the technical problem is solved.

The present invention relates to methods and compositions for predicting the risk of obesity. In particular, the present invention provides methods and compositions for determining a subject's risk of obesity based on the presence of polymorphisms in the growth hormone secretagogue receptor (GHSR).

The invention discloses an obesity risk prediction device based on sequencing and data analysis and a prediction method thereof, comprising the following steps: processing original SNP sample data; creating a preliminary model, including classifying obesity and performing regression analysis on height and weight ; create a site scoring model, the site scoring model includes Logistic Regression model, SVM model, Decision Tree model and Random Forest model; use the data in the database to optimize the model; analyze the optimized model; introduce the obesity prediction of integrated learning Model. The invention combines machine learning and gene analysis, utilizes a huge database of genotypes and physiological information of corresponding individuals, and realizes the prediction of individual obesity risk and the assessment of abnormal risk of physiological information.

The invention discloses an obesity risk assessment method and system based on multi-source human body features and machine learning. According to the method, basic human body parameters such as height, weight and waistline are obtained through standardized measurement, and novel obesity evaluation indexes are constructed based on mathematical modeling. On the basis, a CatBoost machine learning algorithm is adopted to establish an obesity risk prediction model, a nested cross validation strategy is utilized to optimize the performance of the model, and a three-level risk layering system is established. In addition, the system develops a visual interface, and automatic calculation of indexes and visual output of risk levels are realized. According to the method, accurate evaluation can be completed only through three conventional body measurement data, and an effective tool is provided for public health screening.

A method of reducing the accumulation of fat mass in a neonatal human infant at risk thereof which method comprises administering to the infant during at least a part of the neonatal period a therapeutic amount of a nutritional composition comprising proteins in an amount such that the composition contains more than 2.4 g of protein per 100 kcal. As weight gain during the first week of life has been associated with overweight in adulthood, this may offer a method of reducing the risk of developing obesity in later life. Also claimed is the administration of DHA to the mother during gestation period.

The invention discloses a children obesity risk prediction system based on deep learning, and belongs to the technical field of data processing, and the system comprises a children sequence data integration module, an intelligent children obesity prediction module, a learnable parameter optimization module and a prediction report generation module. According to the method, a graph structure between children is constructed, local time features are extracted through convolution operation, position coding and a multi-head self-attention mechanism are introduced, global time features are extracted, the local time features and the global time features are fused, a learnable adjacent matrix and a dynamic weighting matrix are combined, and spatio-temporal features are obtained for prediction. According to the method, individual positions are divided by setting a division threshold, mutation positions are generated based on the selected individual positions, the fitness change rate and the motion control coefficient, updating is completed according to fitness comparison and individual position detection, optimal parameters are found, the obesity risk of children can be evaluated more accurately, and a more reliable basis is provided for early intervention.

The invention relates to the technical field of bioinformatics, in particular to a child obesity risk assessment model construction method, device and system, and the method comprises the steps: S1, obtaining a child obesity data set, collecting a lipid metabolism related gene set and a glucose metabolism related gene set, and taking an intersection; s2, performing difference analysis on the children obesity data set by using R packet lima; setting a threshold value of the differential expression gene, and identifying the differential expression gene; s3, data obtained in S2 and data obtained in S1 are intersected, and a differential expression gene set related to lipid metabolism and glycometabolism is obtained; s4, screening key genes through a machine learning algorithm, and determining weight coefficients of the key genes; constructing a risk scoring model; wherein the key genes comprise an APOE gene, an ADM gene and an MAPK14 gene. By screening related genes and fusing a plurality of data, scientific processing of core related gene expression data is realized, doctors are helped to extract useful information from a large amount of data, and clinical work development is assisted.

Compositions and methods for treating or preventing obesity are provided. The compositions use compounds which act as vitamin D receptor agonists / activators (VDRA), preferably Vit D or calcitriol analogs or derivatives. The disclosed compositions can administered to a subject in need thereof, to treat on or more symptoms of obesity. The compounds can be used to promote a healthy body composition and to avoid the risks associated with overweight and / or obesity, such as cardiovascular disease cancers, urinary incontinence, diabetes and the like. The disclosed compositions can also be used prophylactically to treat animals that are overweight with a risk of becoming obese or animals that are at risk of becoming overweight and are on a high fat diet.

A decision support method and system is provided for monitoring and treating pediatric obesity. Embodiments include generating obesity risk curves corresponding to obesity risk levels, for example, severe and morbid obesity risk levels. Generating obesity risk curves depends on predicting at least one health proxy such as, for example, spend data and chronic conditions. Generating severe obesity curves depends on an age-dependent multiplier. An obesity risk level is assigned to a target pediatric patient using the obesity risk curves dependent on the age-dependent multiplier. In some aspects, an intervening response is initiated based on the assigned obesity risk level.