We found widespread lower FA in the white matter of healthy young adults who carry a recently identified risk gene for late-onset Alzheimer’s disease. Effects occurred in multiple regions, including several known to degenerate in AD. Such regions included the corpus callosum, fornix, cingulum, SLF and ILF (Liu et al., 2009
; Stricker et al., 2009
). This suggests that the CLU
-C related variability found here might create a local vulnerability important for disease onset. These effects are remarkable as they already exist early in life and are associated with a risk gene that is very prevalent (~36% of Caucasians carry two copies of the risk-conferring genetic variant, CLU
Higher FA does not always imply better neuronal function, and there are neurogenetic syndromes where FA is abnormally high in some brain regions, but function is abnormal (Hoeft et al., 2007
). Accepting these as counterexamples, lower FA is generally a sign of poorer fiber coherence, myelination, and poorer function, as noted in a recent review of DTI studies across many domains of neuropsychiatry (Thomason and Thompson, in press
Lower FA may indicate reduced myelin integrity or axonal damage. We found a significant increase in Drad
widely throughout the white matter without associated significant decreases in Dax
. These results suggest that reduced myelin integrity, rather than axonal degeneration, may be responsible for the lower FA we found in many of these regions (Di Paola et al., 2010
). Increased regional Drad
has previously been demonstrated in AD patients versus controls (Choi et al., 2005
; Salat et al., 2008
; Stricker et al., 2009
; Zhang et al., 2009
; Di Paola et al., 2010
) and in healthy APOE
4+ subjects versus those who do not carry the APOE
4 allele (Nierenberg et al., 2005
). In AD patients, increased Drad
has been attributed to degeneration of the myelin sheath (Di Paola et al., 2010
), but our demonstration of increased Drad
in healthy young CLU
-C carriers raises the question of whether the increased Drad
seen in AD patients in past studies may also stem in part from inadequate myelination that occurs developmentally as a result of genes that increase AD risk. This is not to say that reduced myelin integrity does not play a role in AD, but rather that both developmental differences and age- or disease-related degeneration may contribute to that reduced integrity.
Genetic risk for reduced FA may increase the risk for later cognitive impairment through developmental insufficiency. A lesser degree of myelination in CLU-C carriers may arise during development, which may not translate into poorer cognition in youth as the brain can compensate via redundant functionality. However, when exacerbated by other factors, such as age-related neuronal atrophy and plaque and tangle burden in AD, reduced myelin integrity could facilitate cognitive impairment. As our study examined how a common AD risk gene affects young adults who have no observable cognitive deficits, it is unlikely that we are seeing the earliest possible signs of AD-associated brain changes. More likely, the reduced fiber integrity represents an early developmental vulnerability that may reduce brain resilience to later AD pathology; in other words, its mechanism of action may not be part of the classical AD pathways that lead to abnormal amyloid plaque and neurofibrillary tangle accumulation in the brain.
Lower FA in late-onset Alzheimer’s disease may be promoted by suboptimal amyloid processing in the brain. Amyloid plaques and neurofibrillary tangles -the primary pathological hallmarks of AD- accumulate in the brain decades before symptoms appear. Neurofibrillary tangles are detectable in ~20% of subjects aged 26–35 (Braak and Braak, 1997
), and greater amyloid deposition in healthy elderly subjects is correlated with greater neuropsychological decline over the preceding decade (Resnick et al., 2010
). Clusterin is found in amyloid plaques (Calero et al., 1999
), and transports soluble β-amyloid (Aβ) across the blood-brain barrier into brain parenchyma (Zlokovic et al., 1996
). Aβ then may damage the oligodendrocytes, which generate myelin, as reported in vitro
(Roth et al., 2005
). However, in our young healthy sample, lower FA may reflect variability in lipid processing as the lipid-rich myelin sheath develops; it is unlikely to be evidence of a disease mechanism or a biomarker of AD. Myelin abnormalities and axonal swelling may contribute to synaptic loss and precede amyloid deposition in AD (Bartzokis, 2009
). If so, the CLU
risk variant could increase AD risk in two ways, an early-acquired vulnerability paired with suboptimal amyloid processing in later life.
Although the additive model we used assesses evidence for an aggregate risk of carrying increasing numbers of alleles, the associated cellular processes that result in lower FA are not necessarily the primary pathways by which the SNP confers AD risk. Rather, our findings suggest one way in which vulnerability to AD may be increased.
Thus far, only one neuroimaging study has examined brain differences in CLU
-C carriers (Biffi et al., 2010
). That study found that the CLU
genotypes were not associated with MRI measures. However, in that study structural MRI was used, which is less sensitive than DTI to altered fiber microstructure and myelination. The authors of that study noted that the effects on brain structure of different gene variants that increase AD risk may be specific to particular and disparate aspects of brain structure. This segregation of gene effect on neuroimaging traits can offer important insights into the mechanisms through which the polymorphisms impact AD risk (Biffi et al., 2010
While 398 subjects would be a small sample on which to identify new genetic risk factors for AD using genome-wide association scanning (GWAS), it is in fact the largest DTI study to date to examine the effects of Alzheimer’s disease genetic risk factors on DTI FA. Prior studies found effects of APOE
4 on FA in sample sizes that ranged from 29 to 69 (Nierenberg et al., 2005
; Persson et al., 2006
; Smith et al., 2008
; Honea et al., 2009
). The effects of familial AD genes were detected in only 20 adults (Ringman et al., 2007
). Admittedly, the odds ratios for those risk factors are greater than for CLU
. However, our statistical power was boosted not only by our much larger sample size of 398 subjects, but also by our scanning at a stronger field strength (4 Tesla as opposed to 1.5 T or 3 T in the previous studies) and with more diffusion-weighted gradients (94 in our study versus 6 or 12 in previous studies). It is therefore not surprising that we had the power to detect the existing effect.
Although the CLU
risk variant was a candidate gene, whose effects we set out here to assess, one may also consider the value of making a correction, across studies, for examining multiple AD risk genes. Because of the very strict voxelwise corrections for multiple comparisons required in imaging genetics when using FDR, the rates of false positives (even when examining several hundred SNPs that are not expected to have a significant relationship with the data) remains well below 0.05 (0.2–4.1% for 720 SNPs) as determined empirically (Meyer-Lindenberg et al., 2008
). The fact that the CLU
rs11136000 is a candidate variant chosen a priori
based on its relationship to lipid transport and Alzheimer’s disease makes it unlikely that our strong results were due to false positives.
We do not yet have available to us a comparably large data set in which to independently replicate our results. This remains a limitation of our study as genetic studies typically employ very large samples and, where possible, they replicate effects to avoid the risk of false discoveries. Continued data collection and collaborative efforts that allow for larger sample sizes will remedy this in the future. However, our results remain valuable as a focus for ongoing efforts by our group and others.
Quantitative mapping of structural brain differences in those at genetic risk for AD is crucial for evaluating treatment and prevention strategies. Once identified, brain differences can be monitored to determine how lifestyle choices influence brain health and disease risk. Many lifestyle factors that heighten the risk for dementia- such as exercise and body mass index - have effects on brain structure and the level of brain atrophy (Ho et al., 2010a
; Ho et al., 2010b
; Raji et al., 2010
). Additionally, regular exercise and a healthful diet may reduce the risk of cognitive decline, particularly in those genetically at risk for AD (Rovio et al., 2005
; Scarmeas et al., 2009
), or those carrying common risk alleles generally associated with brain structure deficits in healthy adults (Ho et al., 2010c
). Targeting adults at greatest risk for cognitive deterioration can also improve the power of clinical trials (Kohannim et al., 2010
). Future DTI studies of CLU
-C in those imaged with amyloid- or tangle-sensitive PET probes will also help to relate lower white matter integrity to AD pathology as it emerges.