Table 3.
Cases/controls | Model 1c | Model 2—adjusted for WC | Model 3—adjusted for weight | Model 4—adjusted for HC | |
---|---|---|---|---|---|
OR (95% CI) | OR (95% CI) | OR (95% CI) | OR (95% CI) | ||
C2 | |||||
Per SD increment | 1624/1624 | 1.15 (1.06–1.24) | 1.14 (1.06–1.23) | 1.15 (1.06–1.24) | 1.14 (1.06–1.24) |
C2 (quintiles)a | |||||
1 | 287/322 | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) |
2 | 291/326 | 1.02 (0.81–1.28) | 1.00 (0.79–1.26) | 1.00 (0.80–1.27) | 1.00 (0.80–1.26) |
3 | 322/324 | 1.15 (0.91–1.44) | 1.12 (0.89–1.41) | 1.13 (0.90–1.42) | 1.13 (0.89–1.42) |
4 | 311/326 | 1.12 (0.89–1.41) | 1.09 (0.87–1.37) | 1.09 (0.87–1.37) | 1.10 (0.87–1.38) |
5 | 413/326 | 1.54 (1.21–1.95) | 1.51 (1.19–1.91) | 1.53 (1.20–1.93) | 1.52 (1.20–1.93) |
P trendb | 0.0002 | 0.0005 | 0.0004 | 0.0004 | |
PC ae C36:3 | |||||
Per SD increment | 1624/1624 | 0.88 (0.82–0.95) | 0.90 (0.83–0.97) | 0.90 (0.83–0.96) | 0.89 (0.83–0.96) |
PC ae C36:3 (quintiles)a | |||||
1 | 367/325 | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) | 1.00 (ref.) |
2 | 363/323 | 0.99 (0.80–1.23) | 1.01 (0.82–1.25) | 1.02 (0.82–1.26) | 1.02 (0.82–1.26) |
3 | 357/326 | 0.96 (0.77–1.19) | 0.98 (0.79–1.22) | 0.98 (0.79–1.22) | 0.97 (0.78–1.21) |
4 | 264/326 | 0.70 (0.56–0.88) | 0.73 (0.58–0.91) | 0.73 (0.58–0.91) | 0.72 (0.58–0.91) |
5 | 273/324 | 0.73 (0.58–0.91) | 0.77 (0.61–0.96) | 0.76 (0.61–0.96) | 0.75 (0.60–0.95) |
P trendb | 0.0003 | 0.0020 | 0.0016 | 0.0010 |
CI confidence interval, HC hip circumference, OR odds ratio, SD standard deviation, WC waist circumference
aQuintile cut-points were determined on control participants
For log-transformed C2, cut-points were as follows, in log(μmol/L): < 1.18/1.18–1.37/1.37–1.55/1.55–1.77/≥ 1.77. For log-transformed PC ae C36:3, cut-points were as follows, in log(μmol/L): < 1.81/1.81–1.94/1.94–2.04/2.04–2.16/≥ 2.16
bFor test of linear trends across quintiles, participants were assigned the median value in each category and the corresponding variable was modeled as a continuous term
cConditional logistic regression conditioned on matching factors