Updated July 13 to correct apolipoprotein nomenclature.
By the time late-onset Alzheimer’s disease emerges from the shadows, the tangled web of biological intrigue that caused the disease is nearly impossible to tease apart. Now, in a study incorporating a hefty amount of imaging and biomarker data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), researchers present AD as a pathological chain of events tripped off by malfunctions in the brain’s vasculature. Based on a data-driven model of disease progression spanning 30 years, they claim that cerebral blood flow wanes first, then amyloid builds up, brain metabolism declines, and neurons eventually die. The researchers, led by Alan Evans of the Montreal Neurological Institute in Quebec, also contend that fluid biomarkers of vascular, immune, and metabolic dysfunction appeared more abnormal than fluid markers classically associated with AD pathology, such as CSF Aβ42 and tau. Many scientists questioned the authors’ novel methodology, wondering if it distorted the results. Others were impressed by the scope of the analysis and think the findings, published June 25 in Nature Communications, bolster the hypothesis that vascular dysfunction occurs in the earliest stages of AD.
Berislav Zlokovic of the University of Southern California in Los Angeles said the study confirms the prominence of vascular dysregulation in AD. “Given that the findings are from one of the largest and most comprehensive biomarker analyses ever conducted, it is probably a good time for us to begin thinking about how to update our model of the Alzheimer’s pathophysiological cascade by including vascular dysfunction as a driver of disease pathogenesis.”
Vicious Vessels. Vascular abnormalities appeared first and remained more prominent than any other biological factor throughout AD progression. [Image courtesy of Iturria-Medina et al., Nature Communications 2016.]
Efforts to detect and treat AD as early as possible necessitate understanding how and when the disease starts. This is a tall order, because the disease process may begin two decades or more prior to the first clinical symptoms. During that time multiple factors, including genetic variation, cardiovascular disease, and metabolic disorders such as diabetes, may influence the onset of amyloid and tau pathology, and researchers debate which most likely trigger the disease. Risk factors may also differ significantly within populations, and multiple factors might be at work in the same person.
While many studies have modeled the progression of AD, none have yet included as many potential disease correlates at once. The most commonly cited staging diagram—devised by Clifford Jack of the Mayo Clinic in Rochester, Minnesota—overlays individual progression curves for amyloid-PET, CSF Aβ42 and tau, cognitive impairment, and brain glucose metabolism, showing each biological factor as worsening over time in a consecutive manner (see image below). It does not track progression of vascular dysregulation or functional impairment by fMRI, which many consider a more direct measure of neuronal function than FDG-PET measures of glucose utilization (see Feb 2013 conference coverage). Rather, it considers vascular dysregulation as one of several co-morbidities that may hasten the onset of clinical symptoms in people who already harbor latent disease, according to the revised model presented in the 2013 paper (see Jack et al., 2013).
Furthermore, the Jack and other progression models are hypothetical, based on data from multiple studies, as opposed to a model that tracks progression of multiple factors in the same cohort. Evans’ group previously published a data-driven model of regional amyloid pathology in the brain using ADNI data, which suggested that Aβ spread quickly throughout connected regions (see Nov 2014 news). However, Evans pointed out that amyloid is but a single factor in AD progression, and ADNI data on multiple factors are ripe for the picking.
First author Yasser Iturria-Medina and colleagues drew from ADNI’s massive resource of imaging, fluid biomarker, and clinical data to chart changes in multiple biological factors across LOAD progression in 40- to 70-year-olds. First, the researchers used 7,700 brain images of 1,171 people to track changes in five biological factors. In healthy controls as well as people with diagnoses ranging from early mild cognitive impairment (EMCI) to late cognitive impairment (LMCI) to LOAD, researchers used arterial spin labeling to measure cerebral blood flow, florbetapir-PET to analyze amyloid deposition, FDG-PET as a measure of brain glucose metabolism, functional magnetic resonance imaging (fMRI) to look at neuronal function, and structural MRI to watch for brain atrophy. The researchers took stock of each of these measurements in 78 regions spanning the entirety of the brain’s gray matter. The measurements drew from a combination of cross-sectional and longitudinal data, as the number of follow-up measurements varied between people and imaging modalities in the heterogeneous ADNI data set.
For each imaging modality, signal values were fitted on a scale between -1 and 1, around a mean of zero. Then, for each age, disease stage, and brain region, the researchers developed an “abnormality value” based on how much the signal differed from that in healthy controls. They could then trace and compare these abnormality values, which lie between 0 and 1, to one another. Cerebral blood flow crossed into the abnormal zone first, and it remained the most abnormal factor throughout the course of the disease. This vascular component was 80 percent more abnormal across all brain regions and disease stages than any other biological factor. Some researchers cautioned that presenting the data as relative values this way may inflate small effects.
Amyloid deposition came next in terms of how abnormal it was, followed by metabolic dysfunction, functional impairment, and gray matter atrophy. Interestingly, when the researchers added cognitive data and classical AD biomarker data to their analysis, they found that memory problems occurred early in the disease process when vascular dysregulation was obvious and amyloid deposition, metabolic and functional impairments were beginning to crop up, but prior to the detection of abnormalities in CSF Aβ42, tau, or p-tau. They concluded that cognitive decline in LOAD is not the culmination of many brain changes, but rather it manifests even when early abnormalities, such as vascular dysregulation, are the only ones in play. To Evans and colleagues, the findings also indicate that the classical CSF biomarkers associated with AD are not the earliest red flags for disease, a conclusion that contradicts previous studies and also the Jack progression curves.
Evans told Alzforum he was surprised by this finding, but that it was rigorously scrutinized and not simply a product of the authors’ abnormality index technique. Rather, he said the large number of subjects included in the study allowed the researchers to detect small effect sizes in memory and other biological factors that smaller studies would not have picked up.
“Their approach is a tour de force in the field of progression tracking,” commented Ashish Raj of Weill Cornell Medical College in New York. “Although others have used similar techniques for aligning biomarkers along a common age axis, this study stands out in the amount of data sets and biomarkers used.”
Other researchers viewed the results with skepticism, however. David Holtzman of Washington University in St. Louis pointed out that other longitudinal and cross-sectional studies have reported that CSF Aβ42 begins to drop prior to the detection of amyloid on PiB scans, which are more sensitive than the florbetapir scans used in this study. He also noted that the ADNI dataset is skewed toward older, more impaired people. “I am very concerned about the conclusion drawn from this data, namely that vascular dysregulation is the earliest event leading to LOAD,” Holtzman wrote (see comment below). Anne Fagan, also of Wash U, commented that while subtle changes in episodic memory are known to occur in the preclinical phase of disease, the finding that such changes occur before all other pathological measures does not agree with other studies, and may reflect the authors’ use of abnormality indices.
In a separate abnormality analysis, the researchers examined changes in the concentrations of 87 CSF and 146 plasma protein biomarkers. In the CSF, they found that heart type fatty acid binding protein (hFABP) stood out as the most abnormal marker. This protein has been linked to LOAD progression and neurodegeneration before, and also serves as a marker for cardiovascular disorders (see Alzbiomarker; Chiasserini et al., 2010; Guo et al., 2013; and Ghani et al., 2000). Cortisol and apolipoprotein (a)—two proteins also associated with cardiovascular malfunctions—were next in line (see Toledo et al., 2012; Erqou et al., 2010). The classical AD biomarkers tau, p-tau, and Aβ42 ranked fourth, sixth, and 13th, respectively. The researchers suggested that hFABP, Apo(a), and cortisol could serve as biomarkers to include in any study seeking to detect AD at its earliest stages.
In plasma, interferon-γ-induced protein (IP-10) ranked most abnormal. This protein is a marker for peripheral inflammation and also regulates angiogenesis (see Oxenkrug 2011; Bodnar et al., 2006). Pregnancy-associated plasma protein A (PAPP-A)—a predictor of cardiovascular distress including heart attack—ranked next (see Li et al., 2013). Proinsulin, the precursor to insulin, ranked third in abnormality, hinting at a connection with metabolic dysfunction.
Henrik Zetterberg of the University of Gothenburg in Sweden wondered whether the new biomarker findings were a product of the researchers’ use of abnormality indices, as well. He questioned whether the technique could have artificially expanded differences between controls and diseased individuals. Several researchers, including Zetterberg, Fagan, and Holtzman, said the statistical methods used in the paper were not sufficiently understandable, and that it was unclear based on the methods section alone exactly how abnormality was determined.
One other potential caveat is that reportedly, a quarter of ADNI participants with MCI are amyloid-negative (see Aug 2013 conference coverage). This could mean they were not developing AD, and instead could harbor any number of other cognition-damaging pathologies, including vascular dementia, hippocampal sclerosis, Lewy body disease, or frontotemporal lobar degeneration. While Evans and colleagues did remove outliers based on spurious results on cognitive tests, they did not specifically remove people who had no signs of amyloid deposition, Iturria-Medina told Alzforum. Holtzman and others cautioned that this could skew the results away from amyloid prominence in AD. However, Iturria-Medina contends that the binary separation of subjects based on amyloid positivity is a flawed concept that prevents a deeper understanding of the causal mechanisms underlying disease.
The authors took care to point out that their progression model does not prove that vascular dysfunction is the root cause of AD; however, the fact that it precedes amyloid deposition does underscore its importance in the disease process, Evans insists. The findings support the idea that Aβ accumulates in the brain due to insufficient clearance, rather than Aβ overproduction, the researchers wrote.
Cacophony of Curves. A comparison of the popular progression model proposed by Jack et al. (top) and a new model using ADNI data. [Image courtesy of Iturria-Medina et al., Nature Communications 2016.]
Alex Roher of Banner Sun Health Research Institute, Sun City, Arizona, praised the study for its thorough and unbiased approach. “They put all the cards on the table, and then to the surprise of many people in the field, they found that the most important marker of AD progression is cardiovascular dysfunction,” Roher said. While the progression model cannot prove this definitively, Roher said the results were highly suggestive that vascular problems precede, and likely cause, the deposition of amyloid. “A magic wand doesn’t cause amyloid to build up,” Roher said. “If you have a protein that accumulates, it does so for a reason.”
The next step will be to determine which vascular problems—hypertension, atherosclerosis, or others—cause the disease, Roher said. He added that a thorough cardiovascular work-up should become a part of any clinical exam regarding AD, and that more research should focus on systemic causes of AD, rather than a singular focus on the accumulation of amyloid. Hypertension is an established risk factor for LOAD.
Costantino Iadecola of Weill Cornell Medical College in New York said the study confirms myriad observational and animal studies that have pointed to the prominence of vascular dysfunction in AD. “This study put it all together in the same patients,” Iadecola said. “The power of that is tremendous.” However, he cautioned against drawing a causal relationship between vascular problems and amyloid deposition based on these results alone. He added that while vascular abnormalities preceded amyloid-PET scan abnormalities, it is impossible to know whether toxic amyloid oligomers, which are not detected on the scans, may have been present prior to the detection of plaques. “Now we know that plaques are more of a tombstone rather than where the action is,” he said. “The cognitive problems the researchers detected early in the disease may therefore be due to amyloid oligomers, vascular problems, or both.”
Evans acknowledged this could be the case, and said that his lab is in the process of developing a directional, causal model that could sort out these relationships. However, he added that the fact that memory problems and vascular dysregulation were apparent prior to abnormalities in CSF Aβ42 further suggests that vascular issues precede amyloid accumulation.
Roy Weller of the University of Southampton in England called the study a significant step forward in understanding AD progression. He added that while Aβ may accumulate due to insufficient clearance by a faltering vascular system, more attention should be given to other routes of clearance. “In assessing the role of arteries and capillaries in the brain, the perivascular route for elimination of fluid and solutes including Aβ should not be ignored,” he wrote. Experimental studies have shown that perivascular elimination of fluid and solutes is impaired with increasing age and this is reflected in the accumulation of Aβ in the walls of capillaries and arteries as cerebral amyloid angiopathy in Alzheimer’s disease (see Weller et al., 2015; Bakker et al., 2016).
Evans also mentioned the importance of the recently discovered glymphatic system, which drains solutes from the brain (see Aug 2012 news; Illif et al., 2013). However, he added that the efficiency of Aβ drainage is intimately linked to the pulsation of arteries in the brain. Therefore, vascular problems such as atherosclerosis would compromise the clearance of Aβ and other solutes from the blood vessels as well as glymphatics, he said.
All in all, Evans said the take-home message from this giant data analysis should be that AD is a multifactorial disease. While the vascular system may play a starring role, the exact causes of cognitive decline may differ between people depending on a slew of environmental, genetic, and lifestyle factors.—Jessica Shugart
- HAI—Sharper Curves: Revamping a Biomarker Staging Model
- The Epidemic in Your Head? New Model Casts Amyloid as Intra-Brain Contagion
- Suspected Non-Amyloid Pathology (SNAP)—Not an Open or Shut Case
- Brain Drain—“Glymphatic” Pathway Clears Aβ, Requires Water Channel
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