The Adam Levine and Klemera-Doubal (KDM) method is what we use to calculate biological age. This method combines various biomarkers—including those related to metabolic, cardiac, lung, kidney, liver functions, immune function, inflammation markers, and blood cell counts—to produce a composite score that estimates biological age. The approach involves a complex algorithm that minimizes the distance between multiple regression lines and biomarker points within a multidimensional space. Since the calculations are run through a complex code, it's not something that can be easily demonstrated as a simple calculation.
Here’s an example that explains the origin and workings of these calculations.
Additionally, here’s a foundational study that examines the effectiveness of biological age calculation. Research has shown that biological age, when calculated using this method, is a more reliable predictor of mortality. In a study using data from the National Health and Nutrition Examination Survey (NHANES III), this method outperformed others like multiple linear regression (MLR) and principal component analysis (PCA) in predicting mortality outcomes over an 18-year follow-up period. It was particularly effective because it provided robust estimates that were highly correlated with mortality.
Even so, limitations remain with biological clock calculations. The aging process involves complex interactions among numerous biological pathways, and no single biomarker can capture the entire process. Therefore, while the composite nature of biological age estimates is necessary, it also complicates the measurement. Additionally, the algorithms used to calculate biological age are often derived from specific population datasets, which means the results may not be universally applicable, especially to populations with different genetic, environmental, or lifestyle factors.