Design and methods of the NiCK study: neurocognitive assessment and magnetic resonance imaging analysis of children and young adults with chronic kidney disease

Abstract Background Chronic kidney disease is strongly linked to neurocognitive deficits in adults and children, but the pathophysiologic processes leading to these deficits remain poorly understood. The NiCK study (Neurocognitive Assessment and Magnetic Resonance Imaging Analysis of Children and Young Adults with Chronic Kidney Disease) seeks to address critical gaps in our understanding of the biological basis for neurologic abnormalities in chronic kidney disease. In this report, we describe the objectives, design, and methods of the NiCK study. Design/methods The NiCK Study is a cross-sectional cohort study in which neurocognitive and neuroimaging phenotyping is performed in children and young adults, aged 8 to 25 years, with chronic kidney disease compared to healthy controls. Assessments include (1) comprehensive neurocognitive testing (using traditional and computerized methods); (2) detailed clinical phenotyping; and (3) multimodal magnetic resonance imaging (MRI) to assess brain structure (using T1-weighted MRI, T2-weighted MRI, and diffusion tensor imaging), functional connectivity (using functional MRI), and blood flow (using arterial spin labeled MRI). Primary analyses will examine group differences in neurocognitive testing and neuroimaging between subjects with chronic kidney disease and healthy controls. Mechanisms responsible for neurocognitive dysfunction resulting from kidney disease will be explored by examining associations between neurocognitive testing and regional changes in brain structure, functional connectivity, or blood flow. In addition, the neurologic impact of kidney disease comorbidities such as anemia and hypertension will be explored. We highlight aspects of our analytical approach that illustrate the challenges and opportunities posed by data of this scope. Discussion The NiCK study provides a unique opportunity to address key questions about the biological basis of neurocognitive deficits in chronic kidney disease. Understanding these mechanisms could have great public health impact by guiding screening strategies, delivery of health information, and targeted treatment strategies for chronic kidney disease and its related comorbidities

Online Supplementary Materials

Dryad Online Supplementary Materials with supplementary tables: Table.e-1, Table.e-2, Table.e-3, Table.e-4, Table.e-5, Table.e-6, Table.e-7, Table.e-8, and supplementary figures Figure.e-1, Figure.e-2, Figure.e-3, Figure.e-4 , Figure.e-5, Figure.e-6, Figure.e-7 and supplementary sections Section.e-1, Section.e-2, Section.e-3, Section.e-4, Section.e-5, Section.e-6, Section.e-7, Section.e-8, Section.e-9, Section.e-1

Online Supplementary Materials

Dryad Online Supplementary Materials with supplementary tables: Table.e-1, Table.e-2, Table.e-3, Table.e-4, Table.e-5, Table.e-6, Table.e-7, Table.e-8, and supplementary figures Figure.e-1, Figure.e-2, Figure.e-3, Figure.e-4 , Figure.e-5, Figure.e-6, Figure.e-7 and supplementary sections Section.e-1, Section.e-2, Section.e-3, Section.e-4, Section.e-5, Section.e-6, Section.e-7, Section.e-8, Section.e-9, Section.e-1

Data from: White matter lesions: spatial heterogeneity, links to risk factors, cognition, genetics, atrophy

Objectives: To investigate spatial heterogeneity of white matter lesions or hyperintensities (WMH). Methods: MRI scans of 1836 participants (median age 52.2±13.16) encompassing a wide age range (22–84 years) from the cross-sectional Study of Health in Pomerania (SHIP, Germany) were included as discovery set identifying spatially distinct components of WMH using a structural covariance approach. Scans of 307 participants (median age 73.8±10.2, with 747 observations) from the Baltimore Longitudinal Study of Aging (BLSA, USA) were included to examine differences in longitudinal progression of these components. The associations of these components with vascular risk factors, cortical atrophy, Alzheimer’s disease (AD) genetics and cognition were then investigated using linear regression. Results: WMH were found to occur non-uniformly, with higher frequency within spatially heterogeneous patterns encoded by four components, which were consistent with common categorizations of deep and periventricular WMH, while further dividing the latter into posterior, frontal and dorsal components. Temporal trends of the components differed both cross-sectionally and longitudinally. Frontal periventricular WMH were most distinctive as they appeared in the 5th decade of life, whereas the other components appeared later in life during the 6th decade. Furthermore, frontal WMH were associated with systolic blood pressure and with pronounced atrophy including AD-related regions. AD polygenic risk score was associated with the dorsal periventricular component in elderly. Cognitive decline was associated with the dorsal component. Conclusions: These results support the hypothesis that the appearance of WMH follows age and disease-dependent regional distribution patterns, potentially influenced by differential underlying pathophysiological mechanisms, and possibly with a differential link to vascular and neurodegenerative changes

Data from: White matter lesions: spatial heterogeneity, links to risk factors, cognition, genetics, atrophy

Objectives: To investigate spatial heterogeneity of white matter lesions or hyperintensities (WMH). Methods: MRI scans of 1836 participants (median age 52.2±13.16) encompassing a wide age range (22–84 years) from the cross-sectional Study of Health in Pomerania (SHIP, Germany) were included as discovery set identifying spatially distinct components of WMH using a structural covariance approach. Scans of 307 participants (median age 73.8±10.2, with 747 observations) from the Baltimore Longitudinal Study of Aging (BLSA, USA) were included to examine differences in longitudinal progression of these components. The associations of these components with vascular risk factors, cortical atrophy, Alzheimer’s disease (AD) genetics and cognition were then investigated using linear regression. Results: WMH were found to occur non-uniformly, with higher frequency within spatially heterogeneous patterns encoded by four components, which were consistent with common categorizations of deep and periventricular WMH, while further dividing the latter into posterior, frontal and dorsal components. Temporal trends of the components differed both cross-sectionally and longitudinally. Frontal periventricular WMH were most distinctive as they appeared in the 5th decade of life, whereas the other components appeared later in life during the 6th decade. Furthermore, frontal WMH were associated with systolic blood pressure and with pronounced atrophy including AD-related regions. AD polygenic risk score was associated with the dorsal periventricular component in elderly. Cognitive decline was associated with the dorsal component. Conclusions: These results support the hypothesis that the appearance of WMH follows age and disease-dependent regional distribution patterns, potentially influenced by differential underlying pathophysiological mechanisms, and possibly with a differential link to vascular and neurodegenerative changes

White matter hyperintensities and imaging patterns of brain ageing in the general population

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia