96 results about "Biological age" patented technology

The invention belongs to the technical field of health assessment, and particularly relates to an aging clock construction method based on a causal machine joint model. The method comprises the steps of 1, obtaining a multi-modal data set of a target crowd based on multi-modal data fusion and standardization preprocessing; 2, performing two-stage screening on the multi-modal data set to obtain core features; 3, analyzing interaction among multiple groups of schools based on the extended proportion advantage logistic regression hybrid model, and then carrying out P value calibration based on a fused saddle point approximation method; 4, constructing an aging clock based on a mixed causal effect graph model, evaluating a multi-dimensional causal relationship, and calculating the biological age of the individual; according to the method, clinical phenotype data and multi-omics data are fused, a machine learning and causal inference combined method is adopted, the individual aging degree and health risk are accurately analyzed, a high-performance aging clock model is provided for primary medical institutions, and scientific basis and decision support are provided for health management and early disease intervention.

The invention discloses a biological age assessment method, which comprises the following steps: acquiring a key protein index of a to-be-assessed person, and inputting an improved biological age assessment model I to obtain the biological age of the to-be-assessed person; the improved biological age assessment model I is constructed and obtained based on an elastic network regression algorithm by taking proteome data and body composition age of an individual as training data. The invention also discloses a construction method of the model, namely a two-step method. According to the method, the user is allowed to calculate the biological age according to the proteome data of the user, personalized monitoring of the muscle aging state is achieved, and the method has great guiding significance in the aspects of identifying individuals with premature aging of muscle functions and formulating personalized precise anti-aging schemes.

The ageing of the population is a key issue in the modern societies. The inventors have therefore searched how to determine parameters linked with the biological age of the subject. This has led them to find that characterizing mitochondria in an area of the subject enables to deduce such parameters. For this, the inventors has developed a tool providing with characterizing parameters of a mitochondrion, and notably a morphology parameter of the mitochondrion and an ultrastructure parameter of the mitochondrion. This opens the way of exploiting the characterizing parameters of mitochondria for multiples applications, such as therapy, medicine screening or clinical follow-up for aging related disorders or mitochondrial disorders.

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 present application relates to the technical field of bioinformatics, and particularly relates to a method for constructing a biological age prediction model based on DNA methylation. The method comprises the following steps: obtaining whole genome methylation sequencing data of a human peripheral blood sample; classifying cell subpopulations of the human peripheral blood sample, and performing single-cell RNA sequencing processing on each cell subpopulation to obtain single-cell sequencing data including lymphocytes, neutrophils and monocytes; performing tissue-specific analysis on CpG sites within a range of 2000 base pairs upstream and downstream of each cell subpopulation-specific transcription factor binding site according to the single-cell sequencing data and the whole genome methylation sequencing data to obtain candidate marker site data. The present application can provide strong support for early detection of accelerated aging, prediction of related disease risks and guidance of precision medicine.

An apparatus (1) for the collection and transfer of a sample of biological material, comprising at least an elongate body (2) having a gripping portion (2i) to be gripped by a user and at least an end portion (2e), a collection element (3) having a unique end portion and configured to collect and / or aspirate a defined quantity of a sample of biological material present within a cavity of a patient's body (20), the collection element (3) being configured to be introduced into the cavity of the patient's body, the collection element (3), comprising, the elongate body (2) comprises a first graduated scale (4) having at least a first measurement marking (4i) located at a first distance from the end portion (2e) of the body or the end portion of the collection element, the first distance being equal to a first distance value representing a first plurality of measurement data of a distance (D) existing between a first part and a second part of the body of each of a first plurality of subjects having a first biological age or belonging to a first predetermined biological age range, or related in a predetermined manner, optionally, the first part and the second part being a first part and a second part of a cavity of the body of the subject, the first measurement marking (4i) being configured and intended to provide at least a visual and / or tactile indication of a predetermined insertion depth (100) of the elongate body (2) and the collection element (3) within the cavity of the patient's body depending on the age of the patient to the patient or the operator.

The present invention provides a device and a method capable of constructing a model that has high prediction accuracy and is suitable for a specific group from a small number of DNA methylation data items obtained from the specific group while reducing the number of samples required for model construction. A biological age measurement device 1 according to the present invention measures the biological age on the basis of the DNA methylation level. By using, as an explanatory variable matrix (n×p matrix (p≤q)), first explanatory variable information based on DNA methylation information in CpG sites at q points included in DNA of each sample of a first population composed of n samples, and using, as an objective variable vector (n-dimensional), first objective variable information based on biological age information of the n samples, the biological age measurement device 1 performs transfer learning on the basis of a biological age measurement model estimated by a second population different from the first population, and outputs the learning result as a trained biological age measurement model.

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 present disclosure relates generally to compositions and methods for reducing the biological age and / or reducing the rate of biological age increase in an adult animal. More specifically, the present disclosure relates to administering formulations comprising an effective amount of glycine and / or functional derivatives, alone or in combination with a dietary regimen such as caloric limiting, elderly diet or low protein diet. The preparation can reduce the biological age of tissues, organs or physiological systems in adult animals in need at the same time; or treating, preventing or delaying the onset of organ disease, or the decline of organ function, or a condition associated therewith, in the adult animal in need thereof.

The present disclosure generally relates to compositions and methods for reducing the biological age and / or the rate of biological age increase in adult animals. More specifically, the present disclosure relates to administering a formulation comprising an effective amount of glycine and / or a functional derivative thereof, alone or in combination with a dietary regimen, such as a calorie-restricted diet, an aging diet, or a low-protein diet. The formulation can simultaneously reduce the biological age of a tissue, organ, or physiological system in an adult animal in need thereof; or treat, prevent, or delay the onset of organ disease, or organ function decline, or conditions associated therewith, in an adult animal in need thereof.

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.

Disclosed herein are compositions and methods useful for reducing biological age measured by DNA methylation and the treatment or amelioration of various diseases, disorders, or conditions promoted by increased biological age. Some aspects pertain to a pharmaceutical composition comprising 17-ethynyl-10R, 13S-dimethyl 2, 3, 4, 7, 8R, 9S, 10, 11, 12, 13, 14S, 15, 16, 17-hexadecahydro-1H-cyclopenta[a]phenanthrene-3R, 7R, 17S-triol, including solid states thereof. Also presented herein is the surprising discovery that exposing a subject to the compositions disclosed herein can treat, reduce, or ameliorate a condition related to biological clocks.

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.

Aspects of the subject technology provide for training a machine learning model based on data from a healthy cohort of study participants. The machine learning model can be used to predict an age of a user based on physiological sensor data and determine a biological age of a user. An age gap can be determined between the user's chronological age and biological age and a notification or recommendation made to the user based on the age gap. An age gap rate of change can be made across multiple age gap determinations.