Objective White matter hyperintensities (WMH) detectable by magnetic resonance imaging (MRI)are part of the spectrum of vascular injury associated with aging of the brain and are thought to reflect ischemic damage to the small deep cerebral vessels. WMH are associated with an increased risk of cognitive and motor dysfunction, dementia, depression, and stroke. Despite a significant heritability, few genetic loci influencing WMH burden have been identified. Methods We performed a meta-analysis of genome-wide association studies (GWAS) for WMH burden in 9,361 stroke-free individuals of European descent from 7 community-based cohorts. Significant findings were tested for replication in 3,024 individuals from 2 additional cohorts. Results We identified 6 novel risk-associated single nucleotide polymorphisms (SNPs)in one locus on chromosome 17q25 encompassing 6 known genes including WBP2, TRIM65, TRIM47, MRPL38, FBF1, and ACOX1. The most significant association was for rs3744028 (Pdiscovery= 4.0×10−9; Preplication =1.3×10−7; Pcombined =4.0×10−15). Other SNPs in this region also reaching genome-wide significance are rs9894383 (P=5.3×10−9), rs11869977 (P=5.7×10−9), rs936393 (P=6.8×10−9), rs3744017 (P=7.3×10−9), and rs1055129 (P=4.1×10−8). Variant alleles at these loci conferred a small increase in WMH burden (4–8% of the overall mean WMH burden in the sample). Interpretation This large GWAS of WMH burden in community-based cohorts of individuals of European descent identifies a novel locus on chromosome 17. Further characterization of this locus may provide novel insights into the pathogenesis of cerebral WMH.

Background and Purpose— It is unknown whether white matter lesions (WML) develop abruptly in previously normal brain areas, or whether tissue changes are already present before WML become apparent on MRI. We therefore investigated whether development of WML is preceded by quantifiable changes in normal-appearing white matter (NAWM). Methods— In 689 participants from the general population (mean age 67 years), we performed 2 MRI scans (including diffusion tensor imaging and Fluid Attenuation Inversion Recovery [FLAIR] sequences) 3.5 years apart using the same 1.5-T scanner. Using automated tissue segmentation, we identified NAWM at baseline. We assessed which NAWM regions converted into WML during follow-up and differentiated new WML into regions of WML growth and de novo WML. Fractional anisotropy, mean diffusivity, and FLAIR intensity of regions converting to WML and regions of persistent NAWM were compared using 3 approaches: a whole-brain analysis, a regionally matched approach, and a voxel-wise approach. Results— All 3 approaches showed that low fractional anisotropy, high mean diffusivity, and relatively high FLAIR intensity at baseline were associated with WML development during follow-up. Compared with persistent NAWM regions, NAWM regions converting to WML had significantly lower fractional anisotropy (0.337 vs 0.387; P <0.001), higher mean diffusivity (0.910×10 –3 mm 2 /s vs 0.729×10 –3 mm 2 /s; P <0.001), and relatively higher normalized FLAIR intensity (1.233 vs –0.340; P <0.001). This applied to both NAWM developing into growing and de novo WML. Conclusions— White matter changes in NAWM are present and can be quantified on diffusion tensor imaging and FLAIR before WML develop. This suggests that WML develop gradually, and that visually appreciable WML are only the tip of the iceberg of white matter pathology.

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C erebral white matter lesions (WMLs) are highly prevalent in the elderly population and increase the risk of dementia and stroke.1 Although believed to be vascular in origin, the exact etiology of WMLs is still unknown. On the basis of pathological and epidemiological studies, blood pressure is considered to be one of the most important factors by damaging the cerebral small vessels.2,3 Because blood pressure is modifiable, blood pressure control seems an important candidate for the prevention of WML progression.The earliest studies demonstrating an association between high blood pressure and WMLs were cross-sectional by design, which limits causal inferences. [4][5][6][7][8][9][10][11][12][13] More recently, studies have used longitudinal designs and found similar results.14-23 Yet, because WML progression is strongly influenced by the WML load at baseline, 15 it is unknown to what extent associations of blood pressure with WML progression are affected by the baseline WML load. Moreover, to provide stronger evidence for a temporal relationship, blood pressure should preferably be measured before the window in which WML progression is determined, instead of during this window. In addition, the use of different MRI scanners or scanning protocols when measuring WML progression can possibly lead to systematic biases. Previous studies have addressed 1 or 2 of these issues, but none of them addressed all.It is also unknown whether the associations between blood pressure and WML progression are present for systolic, diastolic, and pulse pressure. Moreover, the influence of medication use and control of hypertension on WML progression remains unclear. We hypothesized that blood pressure would relate to WML progression even when taking baseline WML load into account and that medication use and adequate control of hypertension would reduce this progression.We tested this hypothesis in a population-based longitudinal MRI study in which we measured systolic, diastolic, and pulse pressure before MRI scanning; evaluated the influence of the WML load at baseline; and used exactly the same scanners and scanning protocol at baseline and follow-up.Abstract-High blood pressure is considered an important risk factor for cerebral white matter lesions (WMLs) in the aging population. In a longitudinal population-based study of 665 nondemented persons, we investigated the longitudinal relationship of systolic blood pressure, diastolic blood pressure, and pulse pressure with annual progression of WMLs.Means of blood pressure were calculated over a 5-year period before longitudinal MRI scanning. WML progression was subsequently measured on 2 scans 3.5 years apart. We performed analyses with linear regression models and evaluated adjustments for age, sex, cardiovascular risk factors, and baseline WML volume. In addition, we evaluated whether treatment of hypertension is related to less WML progression. Continuing medical education (CME) credit is available for this article. Go to http://cme.ahajournals.org to take the quiz.

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Background: Uric acid has been associated with focal vascular brain disease. However, it is unknown whether uric acid also relates to global brain changes such as brain atrophy. We therefore studied the relation of uric acid to brain atrophy and whether this is accompanied by worse cognitive function. Methods: In 814 persons of the population-based Rotterdam Study (mean age 62.0 years), we studied the relation of uric acid levels to brain tissue atrophy and cognition using linear regression models adjusted for age, sex and putative confounders. Brain atrophy was assessed using automated processing of magnetic resonance imaging. Cognition was assessed using a validated neuropsychological test battery and we computed compound scores of cognitive domains. Results: Higher uric acid levels were associated with white matter atrophy [difference in Z-score of white matter volume per standard deviation increase in uric acid: -0.07 (95% CI: -0.12; -0.01)], but not with gray matter atrophy. This was particularly marked when comparing hyperuricemic to normouricemic persons [Z-score difference: -0.27 (-0.43; -0.11)]. Worse cognition was primarily found in persons with hyperuricemia [-0.28 (-0.48; -0.08)]. Conclusions: Hyperuricemia is related to white matter atrophy and worse cognition.

The maintenance of normal body weight is disrupted in patients with anorexia nervosa (AN) for prolonged periods of time. Prior to the onset of AN, premorbid body mass index (BMI) spans the entire range from underweight to obese. After recovery, patients have reduced rates of overweight and obesity. As such, loci involved in body weight regulation may also be relevant for AN and vice versa. Our primary analysis comprised a cross-trait analysis of the 1000 single nucleotide polymorphisms (SNPs) with the lowest p-values in a genome-wide association meta-analysis (GWAMA) of AN (GCAN) for evidence of association in the largest published GWAMA for BMI (GIANT). Subsequently we performed sex-stratified analyses for these 1000 SNPs. Functional ex vivo studies on four genes ensued. Lastly, a look-up of GWAMA-derived BMI related loci was performed in the AN GWAMA. We detected significant associations (p-values < 5×10−5, Bonferroni corrected p < 0.05) for 9 SNP alleles at 3 independent loci. Interestingly, all AN susceptibility alleles were consistently associated with increased BMI. None of the genes (chr. 10: CTBP2, chr. 19: CCNE1, chr. 2: CARF and NBEAL1; the latter is a region with high linkage disequilibrium) nearest to these SNPs has previously been associated with AN or obesity. Sex-stratified analyses revealed that the strongest BMI signal originated predominantly from females (chr. 10 rs1561589; poverall: 2.47 × 10−06/pfemales: 3.45 × 10−07/pmales: 0.043). Functional ex vivo studies in mice revealed reduced hypothalamic expression of Ctbp2 and Nbeal1 after fasting. Hypothalamic expression of Ctbp2 was increased in diet induced obese (DIO) mice as compared to age-matched lean controls. We observed no evidence for associations for the look-up of BMI related loci in the AN GWAMA. A cross-trait analysis of AN and BMI loci revealed variants at three chromosomal loci with potential joint impact. The chromosome 10 locus is particularly promising given that the association with obesity was primarily driven by females. In addition, the detected altered hypothalamic expression patterns of Ctbp2 and Nbeal1 as a result of fasting and DIO implicate these genes in weight regulation.

With a compressed sensing factor of 1.3, 3D-FLAIR is 27% faster and preserves diagnostic performance for the detection of MS plaques at 3T.