Online Appendix figure 1.

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Online Appendix figure 1. Spline graphs of the relationship between markers of adiposity and systolic blood pressure level. The p-values for deviation from linearity are based on ANOVA-tests, comparing each regression model with and without addition of a quadratic term for the adiposity measure.

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Weight (kg)

Systolic Blood Pressure (mmHg)

P-value for nonlinearity, 1 df, 0.27

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Body-Mass Index (kg/m2)

70 80 90 100 110 120

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Waist (cm)

0.7 0.8 0.9 1.0 1.1

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Waist-To-Hip Ratio

1 2 3 4 5

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Subcutaneous Adipose Tissue (cm)

4 6 8 10 12 14

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Visceral Adipose Tissue (cm)

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Online Appendix figure 2. Spline graphs of the relationship between markers of adiposity and diastolic blood pressure level. The p-values for non-linearity are based on ANOVA-tests, comparing each regression model with and without addition of a quadratic term for the adiposity measure.

50 60 70 80 90 100 110 120

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Weight (kg)

Diastolic Blood Pressure (mmHg)

20 25 30 35

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Body-Mass Index (kg/m2)

70 80 90 100 110 120

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Waist (cm)

0.7 0.8 0.9 1.0 1.1

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Waist-To-Hip Ratio

1 2 3 4 5

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Subcutaneous Adipose Tissue (cm)

4 6 8 10 12 14

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Visceral Adipose Tissue (cm)

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Online Appendix figure 3. Sensitivity analyses: standardized differences of adiposity markers in relation to adjusted blood pressure level, assuming the average effect of treatment is approximately half the size estimated by Law et al[21].

Estimated blood pressure differences related to 1 SD (standard deviation) difference of each adiposity marker (second column) are based on linear regression models, adjusted for age, gender, estimated glomerular filtration rate (eGFR based on the MDRD score), current smoking status, and history of coronary artery disease, cerebrovascular disease, peripheral artery disease, and abdominal aortic aneurysm. Observed blood pressure was adjusted by adding 5/2.5 mmHg and 2.5/1.25 mmHg for the first and every subsequent class of blood pressure lowering medication used. * indicates significant difference (p<0.05) between men and women.

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Online Appendix figure 4. Sensitivity analyses: standardized differences of adiposity markers in relation to adjusted blood pressure level, disregarding beta- blocker use.

Estimated blood pressure differences related to 1 SD (standard deviation) difference of each adiposity marker (second column) are based on linear regression models, adjusted for age, gender, estimated glomerular filtration rate (eGFR based on the MDRD score), current smoking status, and history of coronary artery disease, cerebrovascular disease, peripheral artery disease, and abdominal aortic aneurysm. Observed blood pressure was adjusted by adding 10/5 mmHg and 5/2.5 mmHg for the first and every subsequent class of blood pressure lowering medication used.

Beta-blocker use was disregarded because of the questionable impact of beta-blockers on blood pressure level[22]. * indicates significant difference (p<0.05) between men and women.

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Online appendix table 1. Baseline characteristics of the 185 patients followed-up after 3.7 years.

Total study population Subgroup followed-up P for difference

Characteristic n = 4,556 n = 185

Male gender 3,391 (74.4%) 150 (81.1%) 0.05

Age (years) 59.8 ±10 56.9 ±9 <0.01

History of vascular disease

Coronary Artery Disease 2,957 (64.9%) 120 (64.9%) 0.95

Cerebrovascular Disease 1,260 (27.7%) 43 (23.2%) 0.22

Peripheral Artery Disease 688 (15.1%) 39 (21.1%) 0.04

Abdominal Aortic Aneurysm 341 (7.5%) 10 (5.4%) 0.36

Type 2 diabetes mellitus 773 (17.0%) 25 (13.5%) 0.26

eGFR (mL/min/1.73m²)¹ 76.4 ±18 78.7 ±16 0.06

Current smoking 2,208 (48.5%) 102 (55.1%) 0.04

Systolic blood pressure (mmHg)² 140 ±21 140 ±20 0.59

Diastolic blood pressure (mmHg)² 82 ±11 81 ±10 0.29

Blood pressure-lowering medication 3,515 (77.2%) 146 (78.9%) 0.64

Weight (kg) 82 ±14 84 ±13 0.08

BMI (kg/m²) 27.0 ±4.0 27.2 ±4.0 0.60

Waist circumference (cm) 96 ±12 94 ±10 0.13

Waist-to-hip ratio 0.92 ±0.10 0.91 ±0.10 0.02

Subcutaneous adipose tissue (cm) 2.4 ±1.0 2.2 ±1.0 0.02

Visceral adipose tissue (cm) 9.2 ±3.0 9.2 ±2.0 0.95

Data are presented as No. (%) or mean ±SD. 1. eGFR = Glomerular Filtration Rate, estimated by the Modification of Diet in Renal Disease (MDRD) equation. 2. Blood pressure values are without adjustment for blood pressure lowering medication use.

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Online appendix table 2. Markers of adiposity, blood pressure, and blood pressure lowering medication use at baseline and after 3.7 years [IQR 3.5-4.0 years] follow-up.

Baseline After 3.7 years

follow-up P for difference

Characteristic n = 185 n = 185

Age (years) 56.9 ±9 60.5 ±9 <0.01

Blood pressure (mmHg)¹

Systolic blood pressure 139.5 ±20 140.8 ±18 0.53

Diastolic blood pressure 81.2 ±10 79.1 ±10 0.06

Pulse pressure 58.3 ±14 61.6 ±16 0.04

Markers of adiposity

Weight (kg) 84.1 ±13 83.4 ±14 0.08

BMI (kg/m²) 27.2 ±4 27.1 ±4 0.60

Waist circumference (cm) 94.4 ±10 96.6 ±12 0.13

Waist-to-hip ratio 0.91 ±0.07 0.93 ±0.08 0.02

Subcutaneous adipose tissue (cm) 2.2 ±1.0 2.1 ±1.1 0.02

Visceral adipose tissue (cm) 9.2 ±2.3 9.2 ±2.3 0.95

Blood pressure-lowering medication

Any 146 (78.9%) 153 (82.7%) 0.43

Beta-blockers 113 (61.1%) 97 (52.4%) 0.12

Diuretics 24 (13.0%) 52 (28.1%) <0.01

ACE-inhibitors 43 (23.2%) 58 (31.4%) 0.10

Calcium antagonists 47 (25.4%) 44 (23.8%) 0.81

Angiotensin-2-receptor antagonists 21 (11.4%) 42 (22.7%) 0.01

Alpha-1-receptor antagonists 2 (1.1%) 3 (1.6%) -

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Data are presented as No. (%) or mean ±SD. P-values are based on a Pearson Chi-square or T-tests. 1.

Blood pressure values are without adjustment for blood pressure lowering medication use.