2 25 Cardiovascular risk factors are related to distinct white matter hyperintensity MRI phenotypes 2.4 RESULTS A total of 178 participants met the inclusion criteria and were included, of which 23 participants had to be excluded from the current study for the following reasons: WMH segmentation errors (n = 2), over- segmentation of WMH (n = 8), anatomic abnormalities (n = 2), missing scans (n = 3), a large cyst (n = 1), MRI motion artefacts (n = 4) or other artefacts (n = 3). Baseline characteristics of the remaining 155 participants are shown in Table 2.1. A total of 47 % of the participants had hypertension, 34 % had hyperlipidemia, 16 % had diabetes and the mean BMI was 27 ± 4 kg/m2. Presence of hypertension was associated with a more irregular shape of periventricular/confluent WMH (a lower convexity: B (95 % CI): −0.12 (−0.22–−0.03); p = 0.01; a higher concavity index: 0.06 (0.02–0.10); p = 0.01) (see Table 2.2), but not with total WMH volume (0.22 (−0.15–0.59); p = 0.24). Presence of diabetes was associated with a higher deep WMH volume (B (95 % CI): 0.97 (0.25–1.70); p = 0.02) (see Table 2.3). Trends were found for an association between the presence of diabetes and a higher total WMH volume (0.45 (−0.05–0.95); p = 0.07) and a higher perivascular/confluent WMH volume (0.43 (−0.07–0.92); p = 0.09). No associations were found between presence of diabetes and WMH shape markers (see Table 2.2). Neither BMI nor hyperlipidemia were associated with WMH volume (B (95 % CI): 0.97 (0.25–1.70); p = 0.02) (see Table 2.3). Trends were found for an association between the presence of diabetes and a higher total WMH volume (0.45 (−0.05–0.95); p = 0.07) and a higher perivascular/confluent WMH volume (0.43 (−0.07–0.92); p = 0.09). No associations were found between presence of diabetes and WMH shape markers (see Table 2.2). Neither BMI nor hyperlipidemia were associated with WMH volume or shape markers. The mean WMH shape markers stratified for cardiovascular risk factor are shown in Table 2.4 and the mean WMH volumes values stratified for cardiovascular risk factor can be found in Table 2.5. In secondary analyses, we performed a stepwise linear regression to investigate which of the significantly associated cardiovascular risk factors accounts for most of the variation of the WMH marker. The results of these analyses were in line with our primary linear regression analyses (see the Supplementary results). In other secondary analyses, age was associated with periventricular/confluent WMH shape (convexity, concavity index and fractal dimension) and WMH volumes (see Supplementary Tables S.2.8.2 and S.2.8.3). Furthermore, sex was associated with periventricular/confluent WMH shape (solidity).
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