The study presents a method for estimating phenotypic age based on clinical markers and DNA methylation (DNAm) data. Using data from the National Health and Nutrition Examination Survey (NHANES), a Cox penalized regression model was applied to identify biomarkers associated with aging-related mortality. A lambda value of 0.0192 was selected for the model, resulting in ten variables, including chronological age, for the phenotypic age score. These variables were then used in a parametric proportional hazards model based on the Gompertz distribution to estimate 10-year mortality risk. The mortality score was converted into years using a specific equation, resulting in the phenotypic age estimate. The study also validated the DNAm PhenoAge against various aging-related outcomes, including morbidity, mortality, and specific diseases such as Alzheimer's, Down syndrome, HIV, Parkinson's, and dementia. DNAm PhenoAge was found to be significantly associated with these conditions, with higher DNAm PhenoAge scores correlating with increased risk. The method was tested across multiple cohorts, including the WHI, FHS, NAS, JHS, and others, and showed consistent results across different populations and age groups. The DNAm PhenoAge was also compared to other epigenetic age estimators, such as the Horvath DNAm Age, and was found to have strong predictive power for mortality and other aging-related outcomes. The study also explored the relationship between DNAm PhenoAge and various social, behavioral, and demographic factors, finding significant differences between racial/ethnic groups and associations with lifestyle factors such as exercise, diet, and smoking. The study further examined the relationship between DNAm PhenoAge and immune system aging, finding that DNAm PhenoAge was negatively correlated with certain blood cell types and positively correlated with others, suggesting it may capture aspects of immunosenescence. Additionally, the study evaluated the relationship between DNAm PhenoAge and other biological markers, such as telomere length and body mass index, finding significant associations. Overall, the study demonstrates that DNAm PhenoAge is a robust and accurate measure of biological age, with strong associations to various aging-related outcomes and health indicators. The method has potential applications in predicting healthspan and lifespan, and in identifying individuals at higher risk for age-related diseases.The study presents a method for estimating phenotypic age based on clinical markers and DNA methylation (DNAm) data. Using data from the National Health and Nutrition Examination Survey (NHANES), a Cox penalized regression model was applied to identify biomarkers associated with aging-related mortality. A lambda value of 0.0192 was selected for the model, resulting in ten variables, including chronological age, for the phenotypic age score. These variables were then used in a parametric proportional hazards model based on the Gompertz distribution to estimate 10-year mortality risk. The mortality score was converted into years using a specific equation, resulting in the phenotypic age estimate. The study also validated the DNAm PhenoAge against various aging-related outcomes, including morbidity, mortality, and specific diseases such as Alzheimer's, Down syndrome, HIV, Parkinson's, and dementia. DNAm PhenoAge was found to be significantly associated with these conditions, with higher DNAm PhenoAge scores correlating with increased risk. The method was tested across multiple cohorts, including the WHI, FHS, NAS, JHS, and others, and showed consistent results across different populations and age groups. The DNAm PhenoAge was also compared to other epigenetic age estimators, such as the Horvath DNAm Age, and was found to have strong predictive power for mortality and other aging-related outcomes. The study also explored the relationship between DNAm PhenoAge and various social, behavioral, and demographic factors, finding significant differences between racial/ethnic groups and associations with lifestyle factors such as exercise, diet, and smoking. The study further examined the relationship between DNAm PhenoAge and immune system aging, finding that DNAm PhenoAge was negatively correlated with certain blood cell types and positively correlated with others, suggesting it may capture aspects of immunosenescence. Additionally, the study evaluated the relationship between DNAm PhenoAge and other biological markers, such as telomere length and body mass index, finding significant associations. Overall, the study demonstrates that DNAm PhenoAge is a robust and accurate measure of biological age, with strong associations to various aging-related outcomes and health indicators. The method has potential applications in predicting healthspan and lifespan, and in identifying individuals at higher risk for age-related diseases.