Model Of Biological Aging Research Articles

Gene Age Gap Estimate (GAGE) for major depressive disorder: a penalized biological age model using gene expression.

Recent associations between Major Depressive Disorder (MDD) and measures of premature aging suggest accelerated biological aging as a potential biomarker for MDD susceptibility or MDD as a risk factor for age-related diseases. Statistical and machine learning regression models of biological age have been trained on various sources of high dimensional data to predict chronological age. Residuals or "gaps" between the predicted biological age and chronological age have been used for statistical inference, such as testing whether an increased age gap is associated with a given disease state. Recently, a gene expression-based model of biological age showed a higher age gap for individuals with MDD compared to healthy controls (HC). In the current study, we propose a machine learning approach that simplifies gene selection by using a least absolute shrinkage and selection operator (LASSO) penalty to construct an expression-based Gene Age Gap Estimate (GAGE) model. We construct the LASSO-GAGE (L-GAGE) model in an RNA-Seq study of 78 unmedicated individuals with MDD and 79 HC and then test for accelerated biological aging in MDD. When testing L-GAGE association with MDD, we account for factors such as sex and chronological age to mitigate regression to the mean effects. The L-GAGE shows higher biological aging in MDD subjects than HC, but the elevation is not statistically significant. However, when we dichotomize chronological age, the interaction between MDD status and age is significant in L-GAGE model. This effect remains statistically significant even after adjusting for chronological age and sex. We find cytomegalovirus (CMV) serostatus is associated with elevated L-GAGE. We also investigate feature selection methods Random Forest and nearest neighbor projected distance regression (NPDR) to characterize age related genes, and we find functional enrichment of infectious disease and SARS-COV pathways.

What are blue zones? An argument in favor of its definition based on the most successful model of biological aging

What are blue zones? An argument in favor of its definition based on the most successful model of biological aging

A biological age model based on physical examination data to predict mortality in a Chinese population

Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.

Association of biological age with health outcomes and its modifiable factors.

Identifying the clinical implications and modifiable and unmodifiable factors of aging requires the measurement of biological age (BA) and age gap. Leveraging the biomedical traits involved with physical measures, biochemical assays, genomic data, and cognitive functions from the healthy participants in the UK Biobank, we establish an integrative BA model consisting of multi-dimensional indicators. Accelerated aging (age gap >3.2 years) at baseline is associated incident circulatory diseases, related chronic disorders, all-cause, and cause-specific mortality. We identify 35 modifiable factors for age gap (p < 4.81 × 10-4 ), where pulmonary functions, body mass, hand grip strength, basal metabolic rate, estimated glomerular filtration rate, and C-reactive protein show the most significant associations. Genetic analyses replicate the possible associations between age gap and health-related outcomes and further identify CST3 as an essential gene for biological aging, which is highly expressed in the brain and is associated with immune and metabolic traits. Our study profiles the landscape of biological aging and provides insights into the preventive strategies and therapeutic targets for aging.

The biological age model for evaluating the degree of aging in centenarians

BackgroundBiological age (BA) has been used to assess individuals' aging conditions. However, few studies have evaluated BA models' applicability in centenarians. MethodsImportant organ function examinations were performed in 1798 cases of the longevity population (80∼115 years old) in Hainan, China. Eighty indicators were selected that responded to nutritional status, cardiovascular function, liver and kidney function, bone metabolic function, endocrine system, hematological system, and immune system. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal method (KDM), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (lightGBM) methods. A tenfold crossover validated the efficacy of models. ResultsA total of 1398 participants were enrolled, of whom centenarians accounted for 49.21%. Seven aging markers were obtained, including estimated glomerular filtration rate, albumin, pulse pressure, calf circumference, body surface area, fructosamine, and complement 4. Eight BA models were successfully constructed, namely MLR, PCA, KDM1, KDM2, RF, SVM, XGBoost and lightGBM, which had the worst R2 of 0.45 and the best R2 of 0.92. The best R2 for cross-validation was KDM2 (0.89), followed by PCA (0.62). ConclusionIn this study, we successfully applied eight methods, including traditional methods and machine learning, to construct models of biological age, and the performance varied among the models.

A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures.

Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and in how to interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the "Mouse Aging Cell Atlas" (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for over 20 murine tissues and created a multi-tissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation 'clocks' in data of aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.

Model on Impingement and consequences of Alzheimer’s disease in community‐ introductory study.

AbstractBackgroundAlzheimer disease demands salutation and envisioning the cause for the disease. Despite the evolving scientific discoveries, the inhabitation among the global population reminds hiking alarmingly. The track of ways the spread of disease should leave a hint for the further research on the origin of the disease.MethodThis is a speculation much closure to current environment, to analyze effect and the pace of spread over a community. Penna model of biological aging [1] and model of colonization (lattice model) [2] are primitive base. For the model both inherited and non‐ inherited Alzheimer disease is taken into account and is obtruded into the lattice simulated on external factors such as age, genome, genetic mutation, mating partner, offspring and visualizing the change in population, symptoms and progress of the disease.ResultOutcome helps in providing an introductory knowledge on the impact Alzheimer creates in the society. It shows the growth on the level of symptoms in the population, the influence of the inheritance on the population and their progress during the course of time. The symptom to analyze the level of disease at respective age of the individual is monitored. The executions for variations in the input arguments are studied to determine the effect of disease from different perspectives.ConclusionThe Preliminary results show reasonable changes on the level of symptoms for the inherited Alzheimer’s disease. The results shell out some introductory ideas about growth of the level of symptoms of AD in the population during the course of the disease increasing number of offspring and number of mating will give a greater view of the consequences in the model and is predictable for the current scenario.1. Stauffer, D. (2007). The Penna model of biological aging. Bioinformatics and Biology Insights, 1, 117793220700100005.2. Mroz, I., &amp;Pękalski, A. (1999). Model of populations colonizing a new habitat. The European Physical Journal B‐Condensed Matter and Complex Systems, 10(1), 181‐186.

Progress in biological age research.

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Biological age models based on a healthy Han Chinese population

BackgroundBiological age (BA) may reflect the actual aging state in humans better than chronological age (CA). The study aimed to construct BA models suitable for the Chinese Han population by selecting appropriate aging markers and evaluation methods. MethodsA total of 1207 individuals (21∼91 years) from the Han Chinese population in Beijing were examined for essential organ functions, and 156 cardiovascular, pulmonary function, and atherosclerotic indices and clinical and genetic factors were used as candidate markers of aging. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), and the Klemera and Doubal method (KDM). Models were internally and externally validated using cross-validation and disease populations. ResultsNine aging markers were selected. Two MLR, three PCA, and three KDM models were successfully constructed. External validation showed that the difference between CA and BA was most significant in the PCA3 and KDM2 models, while there was no significant difference in the MLR1 and MLR2 models; the fitted lines for BA in the disease population were higher than those in the healthy population in the MLR1, MLR2, KDM1, and KDM2 models, while the other models showed the opposite. ConclusionsBased on a healthy population in Beijing, nine markers representing multiple organ/system functions were screened from the candidate markers, eight methods were successfully used to construct BA models, and the KDM2 model was found to potentially be more appropriate for assessing BA in the Chinese Han population.

Predicting the incidence of age-related diseases based on biological age: The 11-year national health examination data follow-up

PurposeAs the population ages rapidly, the incidence of age-related diseases (ARDs) is also increasing fast. Predicting the incidence of ARDs is a challenge since the rates of individual aging vary, and objective assessments of the stages of aging based on chronological age (CA) may be inaccurate. Thus, in this study, we developed a biological age (BA) model based on the National Health Examination (NHE) data and analyzed the model prediction results for the incidence of 16 ARDs. MethodsThis study was based on the 2002–2019 National Health Information Databases of the National Health Insurance Service (NHIS-NHID). The data from a total of 10,002,494 subjects were selected between 2009 and 2010, and the principal component analysis (PCA) was performed to develop the BA model. The Cox-proportional hazard model was used to perform predictive analysis of the ARD incidence. ResultsFor the unit increase in the difference between corrected biological age (cBA) and chronological age (CA), the hazard ratios (HRs) of ARDs increased significantly for both sexes (p < 0.001). In descending order, the corresponding ARDs’ HRs were obesity (1.655), chronic renal failure (1.362), hypertension (1.301), hyperlipidemia (1.264), diabetes mellitus (1.261), fracture (1.119), dementia (1.163), cataract (1.116), myocardial infarction (1.097), stroke (1.169), macular degeneration (1.075), osteoarthritis (1.059), osteoporosis (1.124), Parkinson's disease (1.048), and chronic obstructive pulmonary disease (1.026). ConclusionsIn this study, the incidence of 16 ARDs were analyzed based on BA. Therefore, conducting the NHIS health examination can facilitate the prevention of ARDs by estimating HRs for at least 16 diseases.

Biological age for chronic kidney disease patients using index model.

The estimation of biological age (BA) is an important asymptomatic measure that can be used to understand the physical changes and the aging process of a living being. Factors that contribute towards profiling the human biological age can be diverse. Therefore, this study focuses on developing a BA model for patients with Chronic Kidney Disease (CKD). The procedure commences with the selection of significant biomarkers using a correlation test. Appropriate weighting is then assigned to each selected biomarker using the indexing method to produce a BA index. The BA index is matched to the age variation within the sample to acquire additional terms for the chronological age leading ultimately to the estimated BA. From a sample of 190 patients (133 trained data and 57 testing data) obtained from the University of Malaya Medical Centre (UMMC), Malaysia, the intensity of the BA is found to be between three to nine years from the chronological age. Visual observations further validate the high similarities between the training and testing data sets.

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study.

BackgroundIndividual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion.ObjectiveThis study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.MethodsCarefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age.ResultsThe first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes.ConclusionsEstimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory.Trial RegistrationClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768International Registered Report Identifier (IRRID)RR2-10.2196/19209

Assessment of cognitive function of the elderly by serum metabolites of brain-gut axis

Objective: To investigate the feasibility of assessing cognitive function of the elderly by serum metabolites of brain-gut axis. Methods: Convenience sampling was used to select 100 and 60 participants from the healthy population cohort and microecological balance cohort of the longevity population in Guangxi, to constitute subset of healthy population and longevity population, respectively. A questionnaire was used to investigate the demographic characteristics of the subjects, 2-5 ml of fasting venous blood was collected from the subjects, and the serum untargeted metabolomics was determined by liquid chromatography tandem mass spectrometry. The biomarkers related to the brain-gut axis were collected through literature retrieval, and the results were intersected with the untargeted metabolites and annotated. Spearman correlation analysis was used to screen serum metabolites of brain-gut axis associated with aging, and multiple linear regression method was used to construct biological age model. The mini mental status examination was used to evaluate the cognitive function of longevity population subsets. The differences of biological age and chronological age of longevity population subsets with different cognitive function were compared. Results: The M (Q1, Q3) of subset of healthy population and longevity population were 64 (38, 72) and 97 (95, 99) years old, respectively, and there were 50 (50.0%) and 44 (73.3%) females, respectively. Nine serum metabolites of brain-gut axis were obtained by initial screening, which were propionic acid, glutamic acid, γ-aminobutyric acid (GABA), lactic acid, 5-hydroxytryptamine (5-HT), tryptophan, trimethylamine oxide, dopamine and canine urea. Spearman correlation analysis showed that glutamic acid and dopamine were positively correlated with aging (r values were 0.208 and 0.524, respectively, all P values<0.05), and tryptophan, 5-HT and GABA were negatively correlated with aging (r values were -0.308, -0.533 and -0.213, respectively, all P values<0.05). The biological age model was constructed as: y=49.81-1.18×10-5× GABA-1.82×10-4×5-HT+1.99×10-3×dopamine+1.65×10-6×glutamic acid -2.04×10-6×tryptophan+2.36×gender, where y was the biological age (years), the items on the right were the intercept item, the relative concentration of each metabolite, and gender (male=1, female=2). The coefficient of determination of model was 0.50 (P<0.001). The M (Q1, Q3) of the chronological age of the subset of longevity population with poor, moderate and good cognitive function were 97 (94, 100), 97 (93, 101) and 96 (94, 101) years old, respectively, and there was no statistical significance in pairwise comparison (all P values>0.05). The M (Q1, Q3) of the biological age of the subjects with better cognitive function was 51 (38, 54) years old, which was lower than that of the subjects with poor cognitive function [57 (47, 61)] (P=0.040). Conclusion: The biological age model can be constructed based on serum metabolites of brain-gut axis and used to evaluate the cognitive function of the elderly.

BIOLOGICAL AGE: METHODOLOGY FOR DETERMINING

The analytical review is devoted to the methodology for quantifying age-related changes, namely, the determination of biological age (BA). In contrast to the chronological age (CA), which is the same for all people born at the same time, BA can vary significantly and characterizes the rate of aging. Diagnostics of accelerated aging is of great clinical importance, as it allows predicting the development of age-related pathology. In addition, the BA calculation is used to assess various effects on the body, including geroprotectors. To calculate BA, a set of biomarkers of aging (BMA) is used. The most accurate method for determining BA is by measuring the methylation of certain genes (Horvath clock). However, this method is not available for clinical practice yet. Therefore, the development of BA models based on clinical BMS remains relevant. In addition, there is a need to assess the degree of age-related changes at the level of individual functional systems of the body (functional age). The list of BMA available for use in clinical practice is given in this review. The requirements are given that allow using the indicator of age-related changes as BMA. The main methods for calculating BV are multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal method (KDM), and neural network analysis. The criteria for assessing the quality of the BA model are described in this review. The MLR method, due to its simplicity, is most often used in clinical practice, however, it has a significant drawback, namely, the systematic error in calculating age, which is most pronounced in the extreme age groups. The article provides a method for correcting this error developed by the authors. The methodology of using MLR to obtain a formula for determining BA from a dataset BMA is described in detail. The theoretical foundations of the use of neural network algorithms for assessing the rate of aging are presented. The use of neural networks for assessing BA has shown their high efficiency by all criteria, in particular, the absence of a systematic error inherent in MLR-based methods. Keywords: biological age, biomarkers of aging, calculation methods.

A simple method for correction of the systematic error in calculating biological age by the multiple regression equation

There was proposed аsimple method of the correction of systematic error of the equation of multiple regression while calculating biological age. The principle of this method is in the vertical transfer of points on the graph "predicted age -chronologicalage" on a diagonalline. At the same time, the distances from the points to the regression line are maintained. This correction eliminates the systematic error of the multiple regression equation and increases the accuracyof the biological age model.

A simple method for correction of the systematic error in calculating biological age by the multiple regression equation

There was proposed аsimple method of the correction of systematic error of the equation of multiple regression while calculating biological age. The principle of this method is in the vertical transfer of points on the graph "predicted age -chronologicalage" on a diagonalline. At the same time, the distances from the points to the regression line are maintained. This correction eliminates the systematic error of the multiple regression equation and increases the accuracyof the biological age model.

Deviation from the biological age model as an early indication of chilling injury on kiwifruit

Deviation from the biological age model as an early indication of chilling injury on kiwifruit

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development.

BackgroundActions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span.ObjectiveThis interdisciplinary study intends to develop a biological age model and explore its usefulness.MethodsThe model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course.ResultsWe completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021.ConclusionsWe expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale.Trial RegistrationClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study).International Registered Report Identifier (IRRID)DERR1-10.2196/19209

Mortality curves using a bit-string aging model

In this work, we studied the shape of mortality curves for a population under different conditions using a bit-string model for biological aging. More specifically, we studied how the model parameters affect the shape of the mortality curves. Although these curves are in reasonable agreement with the Gompertz law for the increase of mortality, for a extended range of ages, other parameters play an important role to determine the overall behavior of the mortality’s curves.

Modelling biological age based on plasma peptides in Han Chinese adults.

Age-related disease burdens increased over time, and whether plasma peptides can be used to accurately predict age in order to explain the variation in biological indicators remains inadequately understood. Here we first developed a biological age model based on plasma peptides in 1890 Chinese Han adults. Based on mass spectrometry, 84 peptides were detected with masses in the range of 0.6-10.0 kDa, and 13 of these peptides were identified as known amino acid sequences. Five of these thirteen plasma peptides, including fragments of apolipoprotein A-I (m/z 2883.99), fibrinogen alpha chain (m/z 3060.13), complement C3 (m/z 2190.59), complement C4-A (m/z 1898.21), and breast cancer type 2 susceptibility protein (m/z 1607.84) were finally included in the final model by performing a multivariate linear regression with stepwise selection. This biological age model accounted for 72.3% of the variation in chronological age. Furthermore, the linear correlation between the actual age and biological age was 0.851 (95% confidence interval: 0.836-0.864) and 0.842 (95% confidence interval: 0.810-0.869) in the training and validation sets, respectively. The biological age based on plasma peptides has potential positive effects on primary prevention, and its biological meaning warrants further investigation.