White Matter Connectivity Abnormalities in Prediabetes and Type 2 Diabetes: The Maastricht Study

Vergoossen, Laura; Schram, Miranda T.; Jong, Joost J. de; Stehouwer, Coen D. A.; Schaper, Nicolaas C.; Henry, Ronald M.A.; Kallen, Carla; Dagnelie, Pieter C.; Boxtel, Martin P. van; Eussen, Simone J. P. M.; Backes, Walter H.; Jansen, Jacobus F.A.

doi:10.2337/dc19-0762

Cited by 37 publications

(52 citation statements)

References 35 publications

(35 reference statements)

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“…In the Maastricht Study, Laura W. Vergoossen et al ( 37 ) used whole-brain white matter tractography to find that prediabetes, T2DM, and continuous measures of hyperglycemia are associated with fewer white matter connections and weaker organization of white matter networks.…”

Section: Resultsmentioning

confidence: 99%

Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

Wang

Wei

et al. 2020

Front. Endocrinol.

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White matter (WM) disease is recognized as an important cause of cognitive decline and dementia. White matter lesions (WMLs) appear as white matter hyperintensities (WMH) on T2-weighted magnetic resonance imaging (MRI) scans of the brain. Previous studies have shown that type 2 diabetes (T2DM) is associated with WMH. In this review, we reviewed the literature on the relationship between T2DM and WMH in PubMed and Cochrane over the past five years and explored the possible links among the presence of T2DM, the course or complications of diabetes, and WMH. We found that: (1) Both from a macro- and micro-scopic point of view, most studies support the relationship of a larger WMH and a decrease in the integrity of WMH in T2DM; (2) From the relationship between brain structural changes and cognition in T2DM, the poor performance in memory, attention, and executive function tests associated with abnormal brain structure is consistent; (3) Diabetic microangiopathy or peripheral neuropathy may be associated with WMH, suggesting that the brain may be a target organ for T2DM microangiopathy; (4) Laboratory markers such as insulin resistance and fasting insulin levels were significantly associated with WMH. High HbA1c and high glucose variability were associated with WMH but not glycemic control.

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Section: Resultsmentioning

confidence: 99%

Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

Wang

Wei

et al. 2020

Front. Endocrinol.

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“…Specifically, the authors examined the number of WM connections (node degree) between several brain regions as well as topology (graph measures, including clustering coefficient—number of connections between nearest neighbors of a region as a proportion of total possible connections, labeled as “clusters”; local efficiency—average efficiency of local clusters; global efficiency—the inverse of the average shortest path length; and communicability—a measure of all possible paths of communication between regions) (Vergoossen et al, 2020). Results showed that individuals with prediabetes had significantly lower node degrees (i.e., fewer WM connections) compared to those with normoglycemia (Vergoossen et al, 2020). Prediabetics also had smaller tract volumes of several intrahemispheric connections associated with aging, compared to those with normoglycemia (Vergoossen et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Results showed that individuals with prediabetes had significantly lower node degrees (i.e., fewer WM connections) compared to those with normoglycemia (Vergoossen et al, 2020). Prediabetics also had smaller tract volumes of several intrahemispheric connections associated with aging, compared to those with normoglycemia (Vergoossen et al, 2020). In addition, the local efficiency and clustering coefficient were lower (i.e., weaker local connectivity) in prediabetics compared to those with normoglycemia, although these effects diminished in fully adjusted models (Vergoossen et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…Prediabetics also had smaller tract volumes of several intrahemispheric connections associated with aging, compared to those with normoglycemia (Vergoossen et al, 2020). In addition, the local efficiency and clustering coefficient were lower (i.e., weaker local connectivity) in prediabetics compared to those with normoglycemia, although these effects diminished in fully adjusted models (Vergoossen et al, 2020). In general, continuous measures of hyperglycemia were associated with lower node degree, as well as higher communicability (likely the result of increased WML load) (Vergoossen et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

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Brain deficits in prediabetic adults: A systematic review

Furlano

Horst

Nagamatsu

2021

J of Neuroscience Research

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Previous findings on the relationship between prediabetes (the precursor stage of type 2 diabetes) and brain health in humans are inconsistent. Thus, this systematic review of cross‐sectional and longitudinal studies aimed to summarize what is currently known about brain deficits in prediabetic adults. Following the PRISMA reporting standards for systematic reviews, we conducted a comprehensive review of peer‐reviewed journal articles published from 2009 to present, focusing on studies that assessed brain volume, structural connectivity, and cerebrovascular health in prediabetic adults and older adults (i.e., 18 years or older). We systematically searched PsychINFO, Scopus, Web of Science, Ovid MEDLINE, CINAHL, and EMbase databases. Quality assessment was based on the NIH Quality Assessment Tool for Observational and Cross‐sectional Studies. In total, 19 studies were included in our review. Results from these studies show that prediabetes may be associated with deficits in brain structure and pathology, however, several studies also refute these findings. Moreover, we identified clear inconsistencies in study methodologies, including diabetes measures and classification, across studies that may account for these conflicting findings.

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“…The next step was to perform connectivity analysis to obtain white matter tracts from and to all the AAL2 brain regions. A previous study of our group confirmed the robustness of tract volume as a measure for the edge weighting [28]; therefore, for each connection, the tract volume was calculated as the number of voxels visited by at least one tract between the areas concerned multiplied by the voxel volume (in mm 3 ) (as previously described [32]). The obtained connectivity matrix with tract volumes was normalized to intracranial volume to reduce inter-subject variation [37].…”

Section: Image Preprocessingmentioning

confidence: 99%

Association of physical activity and sedentary time with structural brain networks—The Maastricht Study

et al. 2020

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We assessed whether objectively measured low- and high-intensity physical activity (LPA and HPA) and sedentary time (ST) were associated with white matter connectivity, both throughout the whole brain and in brain regions involved in motor function. In the large population-based Maastricht Study (n = 1715, age 59.6 ± 8.1 (mean ± standard deviation) years, and 48% women), the amounts of LPA, HPA, and ST were objectively measured during 7 days by an activPAL accelerometer. In addition, using 3T structural and diffusion MRI, we calculated whole brain node degree and node degree of the basal ganglia and primary motor cortex. Multivariable linear regression analysis was performed, and we report standardized regression coefficients (stβ) adjusted for age, sex, education level, wake time, diabetes status, BMI, office systolic blood pressure, antihypertensive medication, total-cholesterol-to-HDL-cholesterol ratio, lipid-modifying medication, alcohol use, smoking status, and history of cardiovascular disease. Lower HPA was associated with lower whole brain node degree after full adjustment (stβ [95%CI] = − 0.062 [− 0.101, − 0.013]; p = 0.014), whereas lower LPA (stβ [95%CI] = − 0.013 [− 0.061, 0.034]; p = 0.580) and higher ST (stβ [95%CI] = − 0.030 [− 0.081, 0.021]; p = 0.250) was not. In addition, lower HPA was associated with lower node degree of the basal ganglia after full adjustment (stβ [95%CI] = − 0.070 [− 0.121, − 0.018]; p = 0.009). Objectively measured lower HPA, but not lower LPA and higher ST, was associated with lower whole brain node degree and node degree in specific brain regions highly specialized in motor function. Further research is needed to establish whether more HPA may preserve structural brain connectivity.

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White Matter Connectivity Abnormalities in Prediabetes and Type 2 Diabetes: The Maastricht Study

Cited by 37 publications

References 35 publications

Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

Relationship Between Type 2 Diabetes and White Matter Hyperintensity: A Systematic Review

Brain deficits in prediabetic adults: A systematic review

Association of physical activity and sedentary time with structural brain networks—The Maastricht Study

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