The promise of a single "biological age" score is compelling, but a critical review in *npj Aging* argues we should be cautious PMID:41419512. The paper highlights that many aging clocks lack rigorous clinical validation and may offer less practical value than established medical risk assessments, urging a shift from novelty to proven clinical utility. ### Key Findings A critical examination of aging clocks reveals several fundamental challenges to their current use in a clinical or health optimization context. - **Lack of Clinical Validation:** Many aging clocks are validated against chronological age but not against hard clinical outcomes like mortality or specific disease incidence, limiting their practical use. - **False Precision:** Clocks often provide a single biological age number without communicating the statistical uncertainty or confidence interval, creating a misleading sense of precision. - **No Proven Advantage:** The authors argue that researchers often fail to demonstrate that these novel clocks outperform established, simpler clinical risk scores that are already used in medical practice. - **Actionability is Missing:** The central critique is the lack of clear, actionable insights. A change in a biological age score often does not translate to a specific, evidence-based medical recommendation. ### The Longevity Context The skepticism surrounding aging clocks reflects a larger challenge in modern medicine: the gap between algorithmic prediction and practical clinical utility PMID:41419512. Many complex machine learning models function as "black boxes," making it difficult for experts to understand, validate, and trust their outputs for clinical decision-making PMID:38804963. A prediction, no matter how accurate, is only valuable if it is medically actionable—meaning it directly informs a decision that improves a health outcome. This distinction between a prediction and a useful intervention is critical but often overlooked in the rush to develop new algorithms PMID:35363504. In fact, research on other predictive models has demonstrated that even algorithms with strong predictive performance can ultimately lack real-world clinical actionability, failing to provide doctors with clear next steps PMID:39331682. Aging clocks currently fall into this category: they provide a number but often fail to offer a clear, validated advantage over existing tools for guiding health interventions. ### Actionable Protocol Adopt a critical, evidence-based framework for evaluating all health metrics, including biological age clocks. Prioritize validated data and clear actions over novel but unproven scores. 1. **Prioritize Clinically Validated Markers:** Focus on tracking established biomarkers that have clear links to disease risk and well-defined interventions. Examples include ApoB for cardiovascular disease, HbA1c for metabolic health, and hs-CRP for systemic inflammation. 2. **Demand a Clear Action Plan:** Before adopting any new health metric, ask: "What specific, evidence-based action would I take if this number changes for the worse?" If a clear protocol does not exist, the metric's utility is questionable. 3. **Verify Against Hard Outcomes:** When evaluating an aging clock, look for human studies that validate it against hard clinical endpoints (e.g., all-cause mortality, cardiovascular events, cancer incidence), not just its correlation with chronological age. 4. **Invest in Proven Interventions, Not Score Chasing:** Allocate resources (time, attention, and money) toward lifestyle, dietary, and exercise interventions known to improve validated health markers and reduce disease risk, rather than focusing on lowering a single, often abstract, biological age number.