Biological age acceleration independently predicts long-term all-cause and cause-specific mortality among U.S. adults with Cardio-Kidney-Metabolic syndrome stages 0-3, with Phenotypic Age Acceleration showing superior prognostic value over KDM-based Biological Age Acceleration.
Key Findings
Results
KDM-BAA positivity was associated with significantly increased risk of all-cause, CVD, and non-CVD mortality in adults with CKM syndrome stages 0-3.
KDM-BAA positivity increased all-cause mortality risk by 41% after full adjustment for confounders.
KDM-BAA positivity increased CVD mortality risk by 74%.
KDM-BAA positivity increased non-CVD mortality risk by 31%.
Cox proportional hazards models were used with full confounder adjustment.
Analysis was based on 16,837 NHANES participants from 1999-2010 and 2015-2018 linked to mortality data through 2019.
Results
PAA positivity was associated with larger mortality risk increases than KDM-BAA across all-cause, CVD, and non-CVD mortality.
PAA positivity increased all-cause mortality risk by 103% after full adjustment.
PAA positivity increased CVD mortality risk by 79%.
PAA positivity increased non-CVD mortality risk by 112%.
PAA showed superior prognostic value compared to KDM-BAA across all mortality outcomes.
Both metrics showed consistent positive associations across multiple subgroups and sensitivity analyses.
Results
Over a median follow-up of 11.33 years, 10.8% of the weighted population, representing approximately 9.75 million U.S. adults, died.
The study population comprised 16,837 participants with CKM syndrome stages 0-3.
Data were drawn from NHANES 1999-2010 and 2015-2018 cycles.
Mortality was ascertained through linkage to mortality records through 2019.
The weighted population death count was estimated at 9.75 million adults.
Results
LASSO-Cox-based nomograms demonstrated favorable discrimination and good calibration for mortality prediction in CKM syndrome patients.
Training set AUCs were 0.776 and 0.775 at 10-year and 20-year prediction horizons, respectively.
Validation set AUCs were 0.765 and 0.757 at 10-year and 20-year prediction horizons, respectively.
The nomograms were described as showing 'good calibration' and 'potential clinical applicability'.
LASSO-Cox regression was used to develop the risk prediction models.
Methods
Biological age was estimated using two distinct algorithmic approaches: the Klemera-Doubal Method Biological Age and the Phenotypic Age algorithm, with acceleration defined as residuals from these estimates.
KDM-BA uses the Klemera-Doubal Method to calculate biological age from clinical biomarkers.
Phenotypic Age is derived from a separate algorithm based on clinical laboratory measures.
Biological Age Acceleration (BAA) was defined as the residual after regressing biological age on chronological age.
Positivity for BAA indicated that an individual's biological age exceeded their chronological age.
Results
Both KDM-BAA and PAA showed consistent positive associations with mortality across multiple subgroup analyses and sensitivity analyses.
Subgroup analyses were conducted but specific subgroup breakdowns are not detailed in the abstract.
Multiple sensitivity analyses were performed to test robustness of findings.
The associations were described as 'consistent' across all subgroup and sensitivity analyses.
CKM syndrome stages examined ranged from stage 0 through stage 3.
What This Means
This research suggests that measuring how fast a person is biologically aging — beyond what their calendar age would predict — can help forecast their risk of dying from heart disease and other causes. The study examined nearly 17,000 American adults who had varying degrees of Cardio-Kidney-Metabolic (CKM) syndrome, a condition involving overlapping problems with the heart, kidneys, and metabolism such as obesity, diabetes, and high blood pressure. Two different methods of calculating 'biological age acceleration' were tested: the Klemera-Doubal Method (KDM-BAA) and Phenotypic Age Acceleration (PAA). Both methods flag people whose bodies appear older than their actual age based on blood and clinical test results.
The findings show that people whose biological age was accelerated — meaning their body appeared older than it actually was — had substantially higher risks of dying over about 11 years of follow-up. Those with accelerated phenotypic age had roughly double the risk of dying from any cause and from non-cardiovascular causes, and about 79% higher risk of dying from cardiovascular disease. The Phenotypic Age method appeared to be a stronger predictor than the KDM method. Using these biological age measures along with other clinical information, the researchers built prediction tools (nomograms) that could estimate an individual's 10- and 20-year mortality risk with reasonably good accuracy.
This research suggests that tracking biological aging in people with CKM syndrome — even at early stages — could help doctors identify who is at highest risk for premature death and potentially guide earlier or more targeted interventions. Since biological age can be estimated from routine clinical lab tests, this approach may be practical to implement in clinical settings. The study adds to a growing body of evidence that biological age is a meaningful health metric beyond chronological age, particularly in people with cardiometabolic and kidney conditions.
Chen X, Huang Y, Huang Y, Li Y, Chen X, Wang Y, et al.. (2026). Biological age acceleration and mortality risk among U.S. adults with cardio-kidney-metabolic syndrome, stages 0-3: An NHANES cohort study.. Medicine. https://doi.org/10.1097/MD.0000000000050450