Here’s a breakdown of the provided text, focusing on the key findings and conclusions regarding Polygenic Risk Scores (PGS) for BMI and obesity:
Key Findings:
PGS Performance Varies by Ancestry:
PGS performance was generally intermediate between ancestry-matched and multi-ancestry PGSs, except for East Asian-like and European-like ancestry.
The PGS showed the highest prediction accuracy (explained variance of 17.6%) in participants of European (EUR) ancestry from the UK Biobank (UKBB).
Performance was significantly lower in populations with African-like ancestry (6.3% explained variance), African American populations (5.1%), and the GPC-UGR population from rural southwestern Uganda (2.2%).
Demographic Factors within EUR Ancestry (UKBB):
Explained variance was slightly higher in males than females.
Explained variance was higher in younger participants compared to older age groups.
The PGS was better at differentiating between participants with and without obesity within the EUR population.
PGS and Obesity Severity/Progression:
The Area Under the Receiver Operating Characteristic Curve (AUC) for the PGS increased with the severity of obesity. In children,a higher PGS (≥10th percentile) was associated with faster BMI increase compared to those with a lower PGS.
The added value of PGS for predicting BMI was greatest at a vrey young age (up to age five), before measured BMI becomes a strong predictor. In older children,measured BMI is more informative.
Children’s higher mean PGS is a predictor of future obesity risk.
PGS in the first few years after birth was a more reliable predictor of BMI in early adulthood.
PGS was more predictive of BMI than other body composition traits like body fat percentage or waist-to-hip ratio.
PGS and Weight Management Interventions:
Individuals with a higher PGS showed greater weight loss in the first year of an intervention (ILI) but were also more likely to regain weight after the first year, highlighting the need for ongoing support.
Crucial Caveats and Future Potential:
A higher genetic risk (PGS) does not mean obesity is inevitable.
Individuals with higher PGS may be more responsive to environmental and lifestyle changes. Preventative strategies can be effective.
Crucially, implementation of PGS-based tools must account for population differences to avoid worsening health inequities, especially for underrepresented groups like those of African ancestry.
Future potential exists for PGS to guide lifestyle interventions and new weight loss drug therapies.
Conclusions:
BMI PGSs have potential as a tool for predicting adult obesity throughout life, especially in early life.
This tool can identify individuals at high risk of obesity, allowing for timely and effective preventive strategies.
Though,the use of PGS in clinical or public health practice requires careful attention to population differences and ethical considerations of genetic risk prediction.
