Table 1. Results of associations of genetic instruments with facial aging outcome.
| Analysis | Sample | N | Odds ratio | Interaction P value 3 |
|---|---|---|---|---|
| Associations of rs16969968 on facial aging 1 | ||||
| Main analysis | Ever smokers | 137,869 | 1.062 [1.043, 1.081] | 7.72x10-6 |
| Never smokers | 167,781 | 1.004 [0.988, 1.021] | ||
| Sensitivity analysis | Ever smokers | 137,869 | 1.062 [1.043, 1.081] | 7.46x10-6 |
| Never smokers | 167,781 | 1.004 [0.988, 1.021] | ||
| Estimates of causal effect of lifetime smoking on facial aging 2 | ||||
| Main analysis | Ever and never smokers | 305,662 | 1.293 [1.089, 1.534] | NA |
| Sensitivity analysis | 1.294 [1.090, 1.536] | NA | ||
Main analysis: Adjusted for age, sex and first 10 genetic principal components. Sensitivity Analysis: Adjusted for age, sex and first 40 genetic principal components.
1 Direct test of association between smoking heaviness SNP and facial aging, in ever and never smokers separately. Estimates are the change of odds of reporting looking ‘older than you are’ versus looking ‘younger than you are’ or ‘about the same’, or looking ‘older than you are’ or ‘about the same’ versus ‘younger than you are’, for each additional smoking-increasing allele of rs16969968.
2 Two stage IV probit regression. Estimates are the change of odds of reporting facial aging category ‘older than you are’ for a 1 SD increase in lifetime smoking score. Calculated by taking the exponent of 1.6 times the probit estimate [24].
3 Interaction P value generated using meta regression (metan command in Stata).