Figure 1
Results of linear regression analysis for grey matter volumes that were most significantly associated with kidney function markers. A total of 4 functional regions were significantly associated with kidney function in univariable analysis after accounting for multiple comparisons (see Table 3). Grey matter volumes were standardized for total intracranial volume. The grey matter volumes are presented for the left and right hemispheres and for both hemispheres combined. Standardized coefficients (βeta-estimates) and 95% confidence intervals (CIs) are presented per increase of z-score transformed volumes and each kidney marker, in univariable analysis and multivariable analysis adjusted for age and demographics (i.e. sex, education, depression), and in separate multivariable analysis adjusted for clinical vascular factors and biomarkers (i.e. smoking, obesity, systolic blood pressure, diastolic blood pressure, number of CVD risk factors, diabetes mellitus, total cholesterol level, HDL-C, CRP, pulse wave velocity, inflammation biomarker score, and cardiovascular biomarker score). Of note, based on the inspection of the changes in the cystatin-C/eGFRcys regression coefficients, age and cardiovascular BS were the most potent confounders of the association between kidney function and structural abnormalities in the respective regression models.

Results of linear regression analysis for grey matter volumes that were most significantly associated with kidney function markers. A total of 4 functional regions were significantly associated with kidney function in univariable analysis after accounting for multiple comparisons (see Table 3). Grey matter volumes were standardized for total intracranial volume. The grey matter volumes are presented for the left and right hemispheres and for both hemispheres combined. Standardized coefficients (βeta-estimates) and 95% confidence intervals (CIs) are presented per increase of z-score transformed volumes and each kidney marker, in univariable analysis and multivariable analysis adjusted for age and demographics (i.e. sex, education, depression), and in separate multivariable analysis adjusted for clinical vascular factors and biomarkers (i.e. smoking, obesity, systolic blood pressure, diastolic blood pressure, number of CVD risk factors, diabetes mellitus, total cholesterol level, HDL-C, CRP, pulse wave velocity, inflammation biomarker score, and cardiovascular biomarker score). Of note, based on the inspection of the changes in the cystatin-C/eGFRcys regression coefficients, age and cardiovascular BS were the most potent confounders of the association between kidney function and structural abnormalities in the respective regression models.

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