A person’s daily step count, when analyzed alongside genetic susceptibility, can provide a more accurate assessment of type 2 diabetes (T2D) risk than either measure alone, according to new research. The findings suggest that individuals with a high genetic predisposition to T2D may need to achieve higher levels of daily physical activity to obtain the same protective benefits seen in those with lower genetic risk.

Understanding Type 2 Diabetes Risk

Type 2 diabetes is a chronic metabolic disease characterized by insulin resistance and impaired glucose regulation. Its development is influenced by a complex interaction between genetic factors and lifestyle behaviors, particularly physical activity, diet, and body weight. While regular exercise is widely known to reduce diabetes risk, this study highlights that the amount of activity required for meaningful risk reduction may differ from person to person based on genetic makeup.

Step Count Thresholds Vary by Genetic Susceptibility

Researchers found that step count thresholds associated with reduced T2D risk were not uniform across the population. Instead, they varied significantly according to an individual’s polygenic risk score (PRS), a measure that aggregates the effects of many genetic variants associated with diabetes susceptibility.

Individuals with low genetic risk experienced substantial reductions in T2D risk at relatively moderate daily step counts. In contrast, those with high polygenic risk needed to accumulate significantly more daily steps to achieve a comparable reduction in risk. This finding underscores that genetic susceptibility can modify how the body responds to lifestyle interventions such as physical activity.

The Role of Wearable Devices in Risk Assessment

The study leveraged wearable devices to objectively measure daily step counts, providing a more accurate representation of habitual physical activity than self-reported data. Wearable-derived metrics offer continuous, real-world insights into movement patterns and can capture subtle differences in activity levels that may influence metabolic health.

When researchers incorporated wearable-based step data into prediction models alongside polygenic risk scores, the models demonstrated improved accuracy in identifying individuals at elevated risk for type 2 diabetes. This combined approach outperformed models that relied solely on traditional clinical risk factors.

Why Genetics Matters

Polygenic risk scores reflect inherited susceptibility to metabolic dysfunction, including insulin resistance and impaired beta-cell function. For individuals with a high PRS, these underlying biological vulnerabilities may blunt the protective effects of lower levels of physical activity, making higher step counts necessary to counterbalance genetic risk.

Importantly, the findings do not suggest that physical activity is ineffective for those at high genetic risk. Rather, they emphasize that more intensive or sustained activity may be required to achieve meaningful protection.

Implications for Personalized Prevention

The results support a shift toward more personalized approaches to diabetes prevention. Instead of a single, universal step-count recommendation, future guidelines may benefit from incorporating genetic risk profiles to tailor physical activity targets.

For example:

• Individuals at low genetic risk may achieve protection with moderate increases in daily steps.

• Those at high genetic risk may benefit from higher step goals combined with additional lifestyle interventions such as dietary changes and weight management.

Such precision prevention strategies could improve adherence, motivation, and long-term outcomes by aligning recommendations more closely with individual risk profiles.

Public Health and Clinical Significance

Type 2 diabetes continues to rise globally, placing enormous strain on healthcare systems. Early identification of high-risk individuals is critical for effective prevention. The integration of wearable technology and genetic data offers a scalable and data-driven approach to risk stratification.

Clinicians may eventually use this combined information to guide personalized counseling, monitor progress over time, and intervene earlier in individuals most likely to benefit from targeted lifestyle modifications.

Conclusion

Combining daily step counts from wearable devices with polygenic risk scores significantly enhances the prediction of type 2 diabetes risk. The study demonstrates that genetic susceptibility influences the amount of physical activity required to reduce diabetes risk, with individuals at higher genetic risk needing higher daily step counts to achieve comparable benefits. These findings mark an important step toward personalized, precision-based strategies for preventing type 2 diabetes and improving metabolic health across diverse populations.