60 results about "Biological age" patented technology

The invention belongs to the technical field of life science, and particularly relates to a biological age construction method and system based on clinical physiological and biochemical indexes and an accelerated aging condition evaluation method. Initial clinical indexes and actual ages of the participants are collected, and outcome variables of the participants are recorded in a follow-up visit mode and comprise death or survival conditions, cardiovascular disease morbidity conditions and corresponding event occurrence time; screening key clinical indexes according to absolute Spearman correlation coefficients of the initial clinical indexes and actual ages; the key clinical indexes and the actual age are used as independent variables, death is used as a dependent variable, and a relational expression between the biological age and the key clinical indexes and the actual age is obtained through a Gompertz proportional risk model. According to the method, the biological age is calculated based on conventional clinical physiological and biochemical indexes, and compared with the biological age calculated based on omics and image data, the method can be more conveniently and efficiently applied to the majority of people, and aging evaluation is incorporated into conventional medical care items.

The application discloses a kind of based on DNA methylation's construction method of biological age prediction model, the application is downloaded from GEO data website, derived from Chinese population, contains calendar age data, whole blood sample 450k methylation chip data, by the method of elastic network combined bootstrap, 31 methylation sites of modeling candidate are screened, multiple linear regression, support vector machine, random forest and gradient boosting regression tree are used to carry out preliminary construction and evaluation of model, then further filter methylation sites using full subset regression, obtain methylation age prediction model based on 18 methylation sites.Finally, using any provincial team natural population data, methylation age prediction model is optimized, and finally the biological age prediction model based on 18 methylation sites is obtained.The model is suitable for Chinese population, the number of methylation sites contained is less, is not affected by blood cell components, and the prediction accuracy is good.

The invention provides a marker for evaluating biological age, a probe composition and application thereof. The marker is an SCGN gene and / or a TRIM59 gene. According to the present invention, with the application of the marker, the methylation state of the genes can be sensitively and specifically detected, and the marker is used for constructing the age prediction model based on DNA methylation so as to detect the biological age.

The application relates to a biological age prediction method and device, an electronic device and a storage medium, wherein the biological age prediction method comprises the following steps: acquiring target multi-omics data of a to-be-detected individual; inputting each omics data in the target multi-omics data into a corresponding trained aging clock model for prediction to obtain each predicted biological age corresponding to each omics data; and combining the predicted biological ages to obtain a target biological age of the to-be-detected individual. According to the application, the accuracy of biological age evaluation is improved.

The invention provides a female age prediction system and method fusing facial images and biochemical indexes, and belongs to the field of artificial intelligence and health management, and the system comprises a data collection and preprocessing module, a dual-channel feature extraction module, a multi-modal feature topology fusion module, an age prediction and health evaluation module, and a personalized health intervention module. According to the method, a biochemical feature topological space and a facial feature topological space are innovatively constructed, feature fusion is realized through a cross-modal attention interaction network and a topological consistency keeping mechanism, the system predicts the biological age and generates a three-dimensional health risk score based on the fused features, and dynamic calibration is carried out in combination with female physiological cycle data, so that the health risk of the female is evaluated. According to the method, the technical contradictions that the traditional detection method is high in cost due to high precision and obvious in error due to low cost are solved, and accurate and reliable female physiological age evaluation is provided.

This disclosure relates generally to a method and system for analyzing an effect of age-related variations on the electrocardiogram (ECG). State-of-the-art methods have delved into supervised deep-learning approaches based on regression that does not reflect the relation between a chronological age (CA) and a biological age (BA) of the subject. The disclosed method involves an unsupervised learning approach with a three-step training strategy combining model training and deep ECG features clustering with a controlled initialization. A deep learning model is trained to obtain an age-informed convolutional autoencoder network by identifying one or more errors while processing the reconstructed ECG. Combining the CA and ECG, the trained deep learning model reveals the BA of the heart as a contributing biomarker for estimating the overall BA of the body.

The invention relates to the technical field of machine learning medical prediction, in particular to a diabetes-related pancreatic cancer risk prediction method based on a machine learning model and biological age, and the method comprises the steps: constructing a health reference population queue and a type 2 diabetes application verification queue; determining a core index panel through an automatic machine learning process, and training by adopting a regularization survival analysis model to obtain biological age and age acceleration; based on multi-modal features such as age acceleration, predicting a future pancreatic cancer absolute risk probability by using a machine learning competitive risk model, performing risk grade division, calculating an equal-risk age and supporting risk trajectory simulation according to the future pancreatic cancer absolute risk probability; and packaging the model into a risk prediction toolkit with an adaptive calibration function, and outputting a comprehensive risk assessment report. According to the method, the biological age is trained by adopting the pure health queue, so that the interference of the disease state on aging measurement is avoided, and the prediction precision of the pancreatic cancer risk of the type 2 diabetes mellitus population is improved.

The invention discloses a multi-organ system biological age assessment method, and relates to the technical field of health assessment, and the method comprises the steps: S1, obtaining data; step S2, preprocessing the data; s3, screening biological age construction indexes of the multi-organ system; and S4, estimating the biological age of the multi-organ system. According to the method, the defects of an existing biological age model in the aspects of crowd applicability and prediction accuracy are overcome, accurate evaluation of the biological age of a multi-organ system is achieved, early recognition and accurate prevention and control of senescence-related diseases can be achieved, systematicness and heterogeneity of the senescence process are revealed, and the method is suitable for popularization and application. And more evidences are provided for pertinently intervening the senescence process.

The application discloses a method for predicting biological age based on enteroscopy pictures, and belongs to the field of digestive endoscopy image analysis. The method first screens enteroscopy pictures without abnormalities, and completes quality control and edge cutting; then constructs an image package with multiple pictures in a single report, extracts features and weighted aggregation by using a multi-instance learning model based on attention; and converts age prediction into a classification task and introduces a Gaussian soft label to realize high-precision biological age prediction. The application can mine aging-related features from enteroscopy images without obvious abnormalities, does not need additional equipment and invasive operation, has strong generalization, and can be used for risk stratification of enteroscopy-negative people and precise prevention and treatment of colorectal cancer.

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

The application discloses a biological age evaluation method and system based on multi-modal clinical data, wherein the evaluation method comprises the following steps: step 1, data acquisition and multi-modal construction; step 2, data preprocessing and feature expression; step 3, reference population construction and feature screening; step 4, biological age prediction model training and verification; step 5, biological age acceleration index calculation; and step 6, standardization and risk assessment output. By fusing heart ultrasound examination data and blood biomarker data, the application realizes comprehensive characterization of the aging state of an individual, further calculates a biological age acceleration index, and uses the biological age acceleration index for cardiovascular disease risk assessment, so as to solve the problems that a single-modal biological age model in the prior art cannot comprehensively depict the aging process and cannot simultaneously reflect changes in organ function and changes in systemic physiological state.

The present invention comprises a method for determining the biological age of an animal, wherein the method comprises determining the level of at least one biomarker in said animal, and wherein the at least 1 biomarker is selected from the biomarkers as listed in Table 1.

The present invention comprises a method for determining the biological age of an animal, wherein the method comprises determining the level of at least one biomarker in said animal, and wherein the at least 1 biomarker is selected from the biomarkers as listed in Table 1.

The invention discloses a biological age assessment method and system based on full life cycle DNA methylation and application, and belongs to the technical field of bioinformatics. The method comprises the following steps: firstly, carrying out quality control and standardization treatment on obtained original DNA methylation data; then, screening differential methylation sites significantly related to calendar age as features; then, by taking calendar age as a target variable, constructing a regression model by adopting a LightGBM gradient lifting framework to obtain a methylated clock model; then, predicting the DNA methylation age of the individual by using the model, and calculating an epigenetic age acceleration value based on the deviation between the DNA methylation age and the calendar age; and finally, carrying out correlation analysis on the age acceleration value and health or physiological indexes of different life stages, so as to evaluate the biological aging state of the individual and predict related health risks. The method covers the whole life cycle, is suitable for Chinese population, and can provide an effective tool for clinical disease risk prediction, health management and anti-aging intervention effect evaluation.

Provided herein are methods and compositions for an epigenetic clock comprising differentiation-independent methylation sites to asses biological age of a human subject.

The compositions and methods reduce the biological age of an adult animal and / or reduce the rate of increase in the biological age of an adult animal. More specifically, the present disclosure relates to administering formulations comprising an effective amount of spermidine, alone or in combination with a dietary regimen such as a standard diet, caloric restriction, elderly diet, or high protein diet. The preparation can simultaneously reduce the biological age of tissues, organs or physiological systems in adult animals in need; or treating, preventing or delaying the onset of organ disease, or the decline of organ function, or a condition associated therewith, in an adult animal in need thereof.

The application discloses a biological age evaluation method, comprising the following steps: obtaining a key protein index of a person to be evaluated, inputting an improved biological age evaluation model I, and obtaining the biological age of the person to be evaluated; the improved biological age evaluation model I is obtained based on an elastic network regression algorithm by taking proteomic data and body composition age of an individual as training data. The application also discloses a construction method of the model, i.e., a two-step method. The application allows a user to calculate the biological age according to the proteomic data, realizes personalized monitoring of muscle aging state, and has great guiding significance in identifying individuals with early muscle function decline and formulating personalized and accurate anti-aging schemes.

This invention relates to a multi-organ biological age and disease risk assessment system based on chest CT radiomics, belonging to the field of medical image computer-aided analysis technology. The invention aims to address the problem that a single global image age cannot characterize organ-specific aging and lacks a closed-loop clinical risk quantification mechanism. The technical solution includes: receiving chest CT radiomics features and demographic parameters of the subject through an input layer; performing spatial mapping using a feature layer; outputting a 10-dimensional biological age through a three-stage cascaded machine learning model in the age prediction layer; calculating the age acceleration rate through a bias correction layer; quantifying the disease risk multiple by coupling a Cox proportional hazards model through a risk assessment layer; and finally, generating clinical interpretation through a large language model driven by an intelligent reporting layer. This invention achieves low-cost and non-invasive organ-level aging assessment, accurately capturing allometric aging, and improving the efficiency of clinical risk stratification and the standardization of interpretation.

The invention provides a biological age prediction correction method and system based on individual differences, relates to the field of artificial intelligence, and solves the technical problems of systematic prediction deviation, missing of individual difference modeling and insufficient clinical interpretability caused by the fact that a biological age prediction model only depends on a time age label and a statistical loss function in the prior art. The method comprises the following steps: acquiring multi-modal physiological data and corresponding time and age of a plurality of healthy individual samples; predicting an initial biological age prediction value of the individual sample based on the physiological data; and based on the time age of the individual sample and the corresponding initial biological age prediction value, fitting to obtain an average biological age prediction function, calculating an aging difference value of the individual sample relative to the same-age group, further constructing a composite loss function, and training the biological age prediction model by minimizing the composite loss function to obtain a biological age prediction result. And obtaining a biological age prediction correction model. The method is used in the biological age prediction correction process.

The invention provides a composition for detecting the biological age and application thereof, the composition comprises nucleic acid for detecting the methylation state of a target gene, and the target gene is one or more than two of an HIF1AN gene, a KLF14 gene and an SCGN gene. The invention also provides a kit comprising the composition, and application of the composition in preparation of the kit for detecting the biological age in vitro.

The subject-matter of the present disclosure relates to a method of recommending an action for a user of an application to take to make a health improvement. The computer-implemented method comprises: receiving a plurality of user inputs responding to queries about a health of the user; generating a biological age based on the user inputs; displaying at least one recommendation to the user for managing the biological age; and receiving at least one further user input to make an action in response to the at least one recommendation.

This disclosure provides a method and storage medium for calculating aging rate, relating to the field of biological data analysis technology. The method includes: modeling the complex nonlinear relationship between the actual age, biomarkers, and mortality risk of a biological object using a mortality risk prediction model constructed based on a neural network; obtaining a mortality risk function for the target biological object through the determined mortality risk prediction model, and extracting an aging rate index with clear biological significance from the mortality risk function; and mapping the target aging rate to biological age through an age-aging relationship function, thereby accurately reflecting the degree of aging of the target biological object.

Chronological age is an important predictor of morbidity and mortality, however it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a “biological age” using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, a metabolomic signature of biological aging was developed that can predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a large group of healthy adults and was then applied to a cohort of healthy older adults who were tracked longitudinally over a 5-10 year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.

Described herein are methods for determining – inter alia – the age of a subject comprising: calculating a probability distribution of three or more nucleic acid target sequences in cells or biological fluids of the subject; calculating the level of DNA methylation and its probabilistic distribution at each of the nucleic acid target sequences; and determining the age of the subject by comparing the probability distribution of allele chaos within the nucleic acid target sequences relative to a control probability distribution to obtain an average Jensen-Shannon distance (JSD) and an average percent methylation for each nucleic acid target sequence.

The present invention relates to a method for identifying the aging degree of a subject or a tissue or organ thereof, and for evaluating the effect of anti-aging interventions. Specifically, provided is a method for identifying the aging degree of a subject or a cell, tissue or organ thereof, or for evaluating the biological age of the subject or diagnosing premature aging disorders, which method predicts the aging status of a tissue or organ of a subject on the basis of the levels of DNA methylation, gene transcripts, proteins or metabolites. The present invention further relates to the use of metformin in the preparation of a drug for treating, preventing or delaying aging-related diseases or conditions in primates, preferably humans.

Provided herein are methods for assessing ageing process, lifespan, life expectancy, biological age and the rate of aging by determining pangenome gene alterations. Also provided are methods for increasing lifespan and / or inhibiting aging.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging, and can be used to stratify populations to predict certain age related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retina or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. The methods disclosed herein teach a multi-modal Transformer-based architecture with cross-attention which was able to combine facial, tongue and retina images to estimate BA. The model was trained using facial, tongue and retina images from 11, 223 healthy subjects, and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. The approach was validated on a test population of 2,840 individuals with six chronic diseases, and obtained significant difference between chronological age (CA) and BA (AgeDiff) than that of healthy subjects. AgeDiff has the potential to be utilized as a standalonfe biomarker, or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. The results therefore highlight the feasibility of using multi-modal images to estimate and interrogate the aging process.

The invention relates to a biological age prediction model training method, an age prediction method, an age prediction device and electronic equipment, and the method comprises the steps: calling a data reading interface, and loading biological sample data; the biological sample data comprises demographic data and protein data; the protein data comprises expression quantity data of various proteins; preprocessing the biological sample data to obtain a preprocessed data set; constructing a multi-layer perceptron model, and performing initial training on the multi-layer perceptron model based on the preprocessed data set to obtain a preliminary training model; based on weight parameters of the preliminary training model, screening high-contribution characteristic proteins related to biological age to generate a characteristic protein set; and performing feature optimization training on the preliminary training model based on the feature protein set, and outputting the model after the feature optimization training as a biological age prediction model. According to the method, the training accuracy and the model generalization ability of the biological age prediction model can be improved, and the technical application cost is reduced.

The application discloses a biological age evaluation method and system based on whole life cycle DNA methylation and application, and belongs to the technical field of bioinformatics. First, the obtained original DNA methylation data is subjected to quality control and standardization processing; then, differential methylation sites significantly related to calendar age are screened as features; next, taking calendar age as a target variable, a regression model is constructed by adopting a LightGBM gradient boosting framework to obtain a methylation clock model; subsequently, the model is used to predict the DNA methylation age of an individual, and an epigenetic age acceleration value is calculated based on the deviation of the DNA methylation age from the calendar age; finally, the age acceleration value is subjected to correlation analysis with health or physiological indexes in different life stages, so as to evaluate the biological aging state of the individual and predict related health risks. The application covers the whole life cycle, is suitable for Chinese population, and can provide an effective tool for clinical disease risk prediction, health management and anti-aging intervention effect evaluation.

This utility model relates to medical diagnostic equipment and in vitro laboratory diagnostics, including microfluidic impedance cytometers for rapid assessment of the white blood cell (WBC) count in whole blood. It can be used in automated systems for calculating biological age based on telomere indices (T / S) and / or associated biomarkers. The technical result is the elimination of biological age (BA) bias arising from interindividual differences in the white blood cell count (proportion of granulocytes and lymphocytes), without the use of a separate blood analyzer, thanks to integrated multi-frequency impedance classification of WBC and automatic transfer of the calculated proportions to the telomere computing circuit (TEL-BA / BA-Dock) for T / S → BA regression correction.The technical result is achieved in that the impedance microcytometer "WBC-Adjust" is made in the form of an embedded (inline) micromodule installed downstream of the qPCR cartridge, and contains a microfluidic channel with a hydrodynamic focusing system, an array of measuring electrodes connected to a multi-frequency impedance meter, an electronic unit for classifying cells according to a set of "size-multi-frequency dielectric profile" features, a communication interface (wired / wireless) for transmitting calculated WBC shares to the TEL-BA / BA-Dock, as well as a controller implementing self-testing protocols, temperature monitoring and synchronization with the main measuring cycle of the telomeric cartridge.