Table 2.
CPP2 size (per 100 nm) | T50 (per 100 min) | |||||
---|---|---|---|---|---|---|
CAC score | ||||||
n | Coefficient (95% CI) | p | n | Coefficient (95% CI) | p | |
Unadjusted | 291 | 0.02 (−0.36, 0.39) | 0.92 | 283 | 0.07 (−0.32, 0.46) | 0.73 |
Model 1 | 291 | −0.09 (−0.37, 0.19) | 0.51 | 283 | −0.03 (−0.36, 0.29) | 0.83 |
Model 2 | 286 | −0.23 (−0.55, 0.08) | 0.15 | 278 | 0.06 (−0.28, 0.41) | 0.71 |
Model 3 | 286 | −0.19 (−0.53, 0.16) | 0.29 | 278 | 0.10 (−0.28, 0.47) | 0.62 |
Model 4 | 272 | −0.15 (−0.44, 0.14) | 0.30 | 264 | −0.02 (−0.38, 0.33) | 0.90 |
Model 5 | 289 | −0.14 (−0.43, 0.15) | 0.35 | 281 | −0.02 (−0.36, 0.32) | 0.93 |
TAC score | ||||||
n | Coefficient (95% CI) | p | n | Coefficient (95% CI) | p | |
Unadjusted | 203 | 0.25 (−0.35, 0.84) | 0.42 | 197 | 0.06 (−0.71, 0.83) | 0.88 |
Model 1 | 203 | 0.04 (−0.43, 0.51) | 0.86 | 197 | −0.12 (−0.74, 0.50) | 0.70 |
Model 2 | 201 | −0.13 (−0.59, 0.32) | 0.56 | 195 | 0.06 (−0.59, 0.71) | 0.85 |
Model 3 | 201 | −0.03 (−0.63, 0.58) | 0.93 | 195 | 0.11 (−0.65, 0.86) | 0.78 |
Model 4 | 188 | −0.11 (−0.56. 0.34) | 0.62 | 182 | 0.003 (−0.65, 0.65) | >0.99 |
Model 5 | 202 | 0.03 (−0.44, 0.49) | 0.91 | 196 | −0.10 (−0.74, 0.53) | 0.76 |
PWV (m/s) | ||||||
n | % Change (95% CI) | p | n | % Change (95% CI) | p | |
Unadjusted | 311 | 1.64 (−0.93, 4.28) | 0.21 | 298 | 1.58 (−1.15, 4.38) | 0.26 |
Model 1 | 306 | −0.02 (−2.19, 2.20) | 0.99 | 293 | 1.04 (−1.29. 3.44) | 0.38 |
Model 2 | 302 | −0.32 (−2.60, 2.01) | 0.78 | 289 | 1.42 (−1.01, 3.92) | 0.25 |
Model 3 | 302 | −0.61 (−3.19, 2.04) | 0.65 | 289 | 1.83 (−0.87, 4.60) | 0.19 |
Model 4 | 289 | 0.33 (−2.59, 1.98) | 0.78 | 276 | 1.95 (−0.53, 4.49) | 0.12 |
Model 5 | 305 | −0.17 (−2.38, 2.08) | 0.88 | 292 | 1.18 (−1.19, 3.60) | 0.33 |
For CAC and TAC scores, we transformed calcification score [Ln(calcification score+1)], then used Tobit regression with left censoring at 0 and bootstrap techniques with 999 repetitions. For PWV, we performed multiple linear regression after logarithmic transformation of PWV.
Abbreviations: CAC, coronary arterial calcification; TAC, thoracic aortic calcification; PWV, pulse wave velocity.
Model 1: adjusted for demographics (age, sex, race), comorbidities (diabetes, coronary artery disease)
Model 2: adjusted for demographics, comorbidities, serum markers of mineral metabolism (calcium, phosphorous, FGF-23)
Model 3: adjusted for demographics, comorbidities, serum albumin, fetuin-A
Model 4: adjusted for demographics, comorbidities, dp-MGP, osteoprotegerin
Model 5: adjusted for demographics, comorbidities, C-reactive protein