Alzheimer's Disease: Experimental Tests

Number: 0349

(Replaces CPB 436)


Aetna considers the following tests/measurements experimental and investigational for the diagnosis and assessment of persons with Alzheimer disease and related dementias because their clinical value remains unproven for this indication:

  • Apolipoprotein E (apoE)
  • ATP-binding cassette transporter (ABCA7)
  • Bcl-2 rs956572 polymorphism testing
  • Beta amyloid 42 (BA-24, Aβ42) protein (cerebrospinal fluid and plasma)
  • Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1)
  • Cerebrospinal fluid (CSF) chitinase enzyme activity
  • Circadian rhythm analysis
  • CSF microRNAs (e.g., hsa-miR-27a-3p)
  • CSF neurogranin
  • CSF phosphorylated tau at threonine 181 (ptau181), tau/Aβ42, and ptau181/Aβ42
  • CSF prion protein concentration
  • CSF soluble amyloid precursor proteins (sAPP) level/CSF β-secretase activity
  • CSF stathmin protein level
  • CSF total tau (t-tau)
  • CSF visinin-like protein-1 (VILIP-1) level
  • Cognitive event-related potentials (cognitive evoked potentials)
  • DNA methylation profiling (brain tissue or peripheral blood)
  • Electronystagmography (in the absence of signs of vertigo or balance disorder)
  • Genetic testing (e.g., presenilin-1 gene [PSEN1], presenilin-2 gene [PSEN2], apolipoprotein E epsilon 4 allele, amyloid precursor gene, etc.)
  • Genetic variation of mitochondrial DNA
  • Homocysteine (serum level)
  • INNO-BIA AlzBio3 immunoassay kit (a multiplex immunoassay that allows simultaneous quantification of amyloid-beta, p-tau, and t-tau)
  • Insulin degrading enzyme polymorphisms
  • Long-term measurement of cortisol
  • Macular thickness
  • Microtubule-associated protein tau (MAPT)
  • N-terminal pro-brain natriuretic peptide (NT-proBNP)
  • Olfactory screening tests
  • Plasma clusterin level
  • Plasma prion protein concentration
  • Plasma tau
  • Pituitary adenylate cyclase-activating polypeptide (PACAP)
  • Red blood cell omega-3 fatty acid level
  • Resting state eye-closed cortical electroencephalography
  • Serum ceramides
  • Serum insulin-like growth factor-1 (IGF-1; also known as somatomedin C)
  • Serum microRNAs
  • Serum neurofilament light concentration
  • Tau protein
  • Transforming growth factor-beta1 (TGF-β1)
  • TREM2 (triggering receptor expressed on myeloid cells 2)
  • Tympanometry (in the absence of hearing loss)
  • Urinary AD7c-NTP (neuronal thread protein/neural thread protein)
  • Videopupillography and tropicamide drop test


Alzheimer disease (AD) is the most common form of dementia. It is a neurologic condition characterized by loss of mental ability severe enough to interfere with normal activities of daily living, lasting at least six months and not present from birth. AD usually occurs in adulthood and is marked by a decline in cognitive functions such as remembering, reasoning and planning. The diagnosis of AD is a clinical diagnosis, focusing on the exclusion of other causes of senile dementia.

Both the tau and BA-42 molecules are components of the neurofibrillary tangles associated with AD.  Levels of these molecules found in the cerebro-spinal fluid (CSF) have been investigated as a diagnostic test for AD.  Additionally, there is an association between the apolipoprotein E (apoE) epsilon 4 genotype and AD.  The apoE genotype consists of one of several different combinations of the 3 different alleles, which are labeled 2, 3 or 4.  It has been shown that the presence of one allele (apoE4) is over-represented in patients with late onset AD.

There is inadequate data regarding the positive and negative predictive values of CSF levels of tau and BA-42 in the diagnosis of AD in patients with clinical symptoms consistent with possible AD.  While the presence of an apoE4 allele may be associated with an increased risk of AD, the associated positive and negative predictive values are inadequate to validate apoE genotyping as a diagnostic test for AD.  Additionally, there is no information regarding how such testing would influence the management of the patient.  A high positive predictive value may not be clinically useful since the diagnosis can be made clinically.

The Agency for Health Care Policy and Research Clinical Practice Guideline addressed the diagnosis and assessment of AD and related dementias.  The guideline stated that “it is not yet possible to depend on apoE genotyping for definitive guidance about diagnosis or treatment of Alzheimer's disease” (Costa et al, 1996).  Furthermore, the algorithm presented in the Guideline for the recognition and initial assessment of AD did not incorporate measurement of CSF levels of the peptides tau and BA-42.  The Alzheimer's Disease Guidelines Panel concluded that the role of these markers in the diagnosis and management of patients with AD are questions for further research. 

Genetic tests are laboratory studies of human deoxyribonucleic acid (DNA), chromosomes, genes or gene products to diagnose the presence of a variation associated with a high risk of having or transmitting a specific genetic disorder. Genetic screening for persons who may be at high risk for AD, such as those with the apolipoprotein E 4 gene locus on chromosome 19, is a highly debated issue, particularly because currently no demonstrated intervention can prevent or delay the dementing process, and no evidence exists about predicting age of onset for person with this genotype.

Identifiable genetic mutations are rare causes of AD.  Persons with early onset of AD (before age 65) may show an autosomal dominant pattern of inheritance.  Nearly all of the autosomal dominant familial forms of AD are related to mutations in one of three different genes: mutations of the amyloid precursor protein (APP) gene on chromosome 21 and genes encoding presenilin 1 (PS1) on chromosome 14 and presenilin 2 (PS2) on chromosome 1.  However, only 2 to 10 % of all persons with AD have early onset disease, and genetic mutations have been identified in 30 to 50 % of these patients.  In addition, although detection of genetic mutations may have prognostic significance, there is no evidence that genetic testing for AD would alter the management of patients such that clinical outcomes are improved.

The PS-1 DNA Sequencing Test, replacing the Symptomatic PS-1 Analysis and Interpretation Test (Athena Diagnostics), is used for evaluating patients with progressive demential with onset before age 65 with a positive family history of early-onset AD.  It detects sequence variations in the presenilin 1 (PS-1) gene by means of polymerase chain reaction and DNA sequencing.  However, there is insufficient evidence to support its clinical value at this time.

Efforts to develop biologic markers for the presence of AD, such as tests that could be performed on samples of blood or CSF, are important research topics for confirming a suspected diagnosis of AD.

According to the Canadian Consensus Conference on Dementia (1999) and the American Academy of Neurology's practice parameter on Diagnosis of Dementia (2001), the usefulness of tests for tau, BA-42, and apoE for the diagnosis of AD has not been established.

The AD-associated neuronal thread protein (AD7c-NTP) gene encodes an approximately 41 kD membrane-spanning phosphoprotein that causes apoptosis and neuritic sprouting in transfected neuronal cells.  The AD7c-NTP gene is over-expressed in AD beginning early in the course of disease.  The levels of neuronal thread protein in post-mortem brain tissue correlate with the levels measured in paired ventricular fluid samples, suggesting that the protein is secreted or released by dying cells into CSF.  Recent studies have suggested that urine test for AD7c-NTP could be used to assess the risk of developing AD.  de la Monte and Wands (2002) reported that elevated levels of AD7c-NTP can be detected in both CSF and urine of patients with early or moderately severe AD, and the CSF and urinary levels of AD7c-NTP correlate with the severity of dementia.  The authors reported that the newest configuration of the AD7c-NTP assay, termed “7c Gold”, has greater than 90 % sensitivity and specificity for detecting early AD.  Munzar et al (2002) reported that the competitive enzyme-linked immuno-sorbent assay (ELISA)-format AD7C-NTP test in urine is an accurate method for determining AD7c-NTP levels in AD and could be used as a biochemical marker for AD.  Additional studies are needed to validate these preliminary results and to demonstrate the impact of AD7c-NTP screening on clinical outcomes.

In a 2001 American Academy of Neurology's practice parameter for the diagnosis of dementia (Knopman et al, 2001) stated that “no laboratory tests have yet emerged that are appropriate for routine use in the clinical evaluation of patients with suspected AD”.  The practice parameter concluded that further research is needed to improve clinical definitions of dementia and its subtypes, as well as to determine the utility of various instruments of neuroimaging, biomarkers, and genetic testing in increasing diagnostic accuracy.

The tropicamide drop test has been proposed as a rapid, non-invasive method for the early diagnosis of AD based on the observation that patients with AD exhibit greater pupillary dilation following administration of a diluted solution (0.01 %) of the cholinergic antagonist, tropicamide.  However, subsequent studies reported that pupillary response to tropicamide does not differentiate between AD patients and healthy subjects.  The tropicamide drop test is associated with high individual variability in the pupillary response to topically applied drugs.  Furthermore, the reliability (test-retest) of the tropicamide drop test is questionable.

Mild cognitive impairment is a transition period between physiological aging and dementia.  Each year more than 12 % of individuals with mild cognitive impairment develop AD.  In a controlled study, Eibenstein and colleagues (2005) assessed the presence of an olfactory deficit in patients with amnesic mild cognitive impairment (aMCI).  A total of 29 subjects diagnosed with aMCI and a homogeneous control group of 29 subjects were enrolled in the study.  Olfactory function was assessed by the Sniffin' Sticks Screening Test (SSST) and the Mini Mental State Examination, the Clinical Dementia Rating, the Geriatric Depression Scale and the Mental Deterioration Battery were used to evaluate the neurocognitive status.  Individuals with aMCI showed a significant impairment of their olfactory identification compared to controls (SSST score: 8.3 +/- 2.1 versus 10.8 +/- 0.9; p < 0.001).  These results suggested that olfactory tests should be part of the diagnostic armamentarium of pre-clinical dementia.  They noted that a long-term follow-up might confirm the olfactory identification function as an early and reliable marker in the diagnosis of pre-clinical dementia.

Hampel and Shen (2009) noted that AD is characterized by the progressive formation of insoluble amyloid plaques and vascular deposits consisting of the amyloid beta-peptide (Abeta) in the brain.  Pathological mechanisms are already active early in the pre-symptomatic stage of AD.  Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1), also known as beta-secretase, is one of the 2 key enzymes in APP processing; the other being gamma-secretase.  The Abeta peptide results from cleavage of APP initially by BACE1 to produce the C99 fragment and releases soluble APPbeta (sAPPbeta); C99 is then further cleaved by gamma-secretase leading to the Abeta peptide.  Increased BACE1 activity and elevated levels of insoluble Abeta peptide have been shown in brain tissue of patients with sporadic AD.  Since the CSF is in direct contact with the extra-cellular space of the central nervous system, biochemical changes in the brain can potentially be reflected in CSF.  Thus, CSF-based detection of BACE1 levels and activity might be valuable in aiding early detection and prediction, particularly in pre-clinical or even pre-symptomatic subjects who are at risk of AD.  Recently, these researchers were among the first groups to quantitatively analyze the enzymatic activities and protein levels of BACE1 in the CSF.  Preliminary research using recently developed BACE1 ELISAs, BACE1 enzymatic activity, sAPPbeta and total Abeta1-x ELISAs were used by examining these hypothesis driven functional candidate markers in subjects with clinically diagnosed AD and amnestic mild cognitive impairment (MCI).  Two sandwich ELISAs were used and BACE1 enzymatic activities were seen by synthetic fluorescence substrate and total Abeta levels by sandwich-ELISA.  Moreover, elevated CSF levels of BACE1 protein were associated with an increased risk ratio in MCI.  Interestingly, amnestic MCI subjects showed increased levels of BACE1 activity compared to healthy controls (HC) and AD patients.  For total Abeta and tau, increased CSF levels were associated with a higher risk of MCI compared to HC as well.  BACE1 activity was significantly correlated with BACE1 protein concentration and total Abeta levels, with Abeta being itself correlated with the BACE1 protein level.  The authors concluded that currently, independent studies are ongoing to validate BACE1 and functionally associated proteins as candidate biomarkers for early detection, prediction, progression as well as for biological activity in AD.

Schmand and colleagues (2010) noted that abnormal levels of biomarkers in CSF and atrophy of medial temporal lobe (MTL) structures on magnetic resonance imaging (MRI) are being used increasingly to diagnose early AD.  These investigators evaluated the claim that these biomarkers can detect pre-clinical AD before behavioral (i.e., memory) symptoms arise.  They included all relevant longitudinal studies of CSF and MRI biomarkers published between January 2003 and November 2008.  Subjects were not demented at baseline but some declined to MCI or to AD during follow-up.  Measures of tau and beta-amyloid in CSF, MTL atrophy on MRI, and performance on delayed memory tasks were extracted from the papers or obtained from the investigators.  A total of 21 MRI studies and 14 CSF studies were retrieved.  The effect sizes of total tau, phosphorylated tau and amyloid beta 42 ranged from 0.91 to 1.11.  The effect size of MTL atrophy was 0.75.  Memory performance had an effect size of 1.06.  Atrophy of MTL and memory impairment tended to increase when assessed closer to the moment of diagnosis, whereas effect sizes of CSF biomarkers tended to increase when assessed longer before the diagnosis.  The authors concluded that memory impairment is a more accurate predictor of early AD than atrophy of MTL on MRI, whereas CSF abnormalities and memory impairment are about equally predictive.  Consequently, the CSF and MRI biomarkers are not very sensitive to pre-clinical AD.  Cerebro-spinal fluid markers remain promising, but studies with long follow-up periods in elderly subjects who are normal at baseline are needed to evaluate this promise.

In a cross-sectional study, Carlesimo et al (2010) examined the relationship between age-related memory decline and MRI hippocampal anatomical changes in a cohort of healthy individuals.  A total of 76 healthy individuals (44 males and 32 females), ranging in age from 20 to 80 years, were recruited.  These individuals were submitted to a 3-T MRI protocol with a whole-brain T1-weighted and diffusion-weighted scanning and a neuropsychological assessment.  For each subject, these researchers calculated the volumes of the total brain (gray + white matter) and hippocampi.  The segmented hippocampi defined the binary masks where mean values of mean diffusivity (MD) and fractional anisotropy (FA) were calculated.  Neuropsychological evaluation included tests of verbal memory (15-min delayed recall of a 15-word list) and visuospatial memory (20-min delayed reproduction of Rey complex figure).  Hippocampal MD, but not hippocampal FA, hippocampal volume, or total brain volume, predicted performance of individuals beyond their 50s on tests of verbal as well as visuo-spatial memory.  The author concluded that high mean diffusivity values in the hippocampal formation of healthy elderly individuals predict memory decline, as reflected by performance on tests of declarative verbal and visual-spatial memory.

In the present study, none of the subjects fulfilled the clinical or neuropsychological criteria for MCI.  Nevertheless, in a number of subjects beyond their 50s, high MD values in the hippocampal formation were accompanied by performance scores that were not truly pathological, but fell in the lower portion of the normal range on tests of declarative memory.  The relevant question is if these individuals represent a very early stage in the progression of AD, anterior to the development of an aMCI, or whether, instead, they represent the lower portion of the normal distribution formed by aged individuals who will not develop dementia.  The authors stated that longitudinal studies that follow the outcome of these individuals are needed to discriminate between these 2 alternative hypotheses.

In an editorial that accompanied the afore-mentioned article, Schuff (2010) noted that these findings by Carlesimo et al raise several important issues such as 
  1. they imply that memory deficits in healthy individuals have a biologic underpinning without apparent tissue loss, though the processes underlying the variations in diffusivity are largely unclear, and
  2. alterations in the brain's microstructure may potentially provide new clues for a separation of normal aging from pathology. 

However, unless prospective studies are conducted, the issue remains open whether mean diffusivity is better as early predictor of AD than volume.  It also would be premature to dismiss the value of anatomical MRI, because new developments in MRI have led to improvements in mapping the anatomy of the hippocampus, including differentiation of the subfields.

Hooshmand et al (2010) examined the relation between serum levels of homocysteine (tHcy) and holotranscobalamin (holoTC), the active fraction of vitamin B12, and risk of incident AD in a sample of Finnish community-dwelling elderly.  A dementia-free sample of 271 subjects aged 65 to 79 years derived from the Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) study was followed-up for 7 years to detect incident AD.  The association between serum tHcy and holoTC with AD was analyzed with multiple logistic regression after adjusting for several potential confounders, including common vascular risk factors.  The odds ratios (ORs) (95 % confidence interval [CI]) for AD were 1.16 (1.04 to 1.31) per increase of 1 μmol/L of tHcy at baseline and 0.980 (0.965 to 0.995) for each increase of 1 pmol/L baseline holoTC.  Adjustment for several potential confounders including age, sex, education, APOE ε4 allele, body mass index, Mini-Mental State Examination, smoking, stroke, and blood pressure did not alter the associations: ORs (95 % CI) for AD became 1.19 (1.01 to 1.39) for tHcy and 0.977 (0.958 to 0.997) for holoTC.  Adjusting for holoTC attenuated the tHcy-AD link (OR changed from 1.16 to 1.10, 95 % CI: 0.96 to 1.25).  The holoTC-AD relationship was less influenced by controlling for tHcy (OR changed from 0.980 to 0.984, 95 % CI: 0.968 to 1.000).  Addition of folate did not change any of the results.  The authors concluded that these findings suggested that both tHcy and holoTC may be involved in the development of AD.  The tHcy-AD link may be partly explained by serum holoTC.  The authors stated that the role of holoTC in AD should be further investigated; further studies on the role of sensitive markers of B12 status in identifying individuals who are at increased risk of AD are needed.

In an editorial that accompanied the afore-mentioned study, Seshadri (2010) stated that observational studies and larger clinical trials are indicated, targeting older persons with MCI, simultaneously assessing holoTC (and B12) and plasma tHcy (and folate, methylmalonic acid).  Careful examination of the evidence is needed to ascertain who is the perpetrator in the complex pathology of AD and other dementias.

Perneczky et al (2011) examined if soluble amyloid precursor proteins (sAPP) in CSF improve the identification of patients with incipient AD in a group of patients with MCI.  A cohort study with follow-up assessments of 58 patients with MCI with baseline CSF sampling was conducted: 21 patients had progressed to probable AD (MCI-AD), 27 still had MCI, 8 had reverted to normal (MCI-NAD), and 2 patients with fronto-temporal dementia (FTD) were excluded.  Sixteen additional patients with FTD were included to explore the specificity of the CSF markers.  Cerebro-spinal fluid concentrations of sAPPα, sAPPβ, tau, and Aβ(1-42) were measured with sensitive and specific ELISAs.  Associations between diagnostic status, CSF protein concentrations, and other patient characteristics were explored using multiple logistic regression analyses with stepwise variable selection.  The optimal sensitivity and specificity of the best models were derived from receiver operating characteristic curves.  The MCI-AD group had significantly higher sAPPβ concentrations than the MCI-NAD and the FTD groups.  A combination of sAPPβ, tau, and age differentiated the MCI-AD and the MCI-NAD groups with a sensitivity of 80.0 % and a specificity of 81.0 %.  The best model for the differentiation of the MCI-AD and the FTD groups included sAPPβ and tau, and showed a sensitivity of 95.2 % and a specificity of 81.2 %.  Aβ(1-42) and sAPPα did not significantly contribute to the models.  The authors concluded that these findings suggested that sAPPβ may be clinically useful, and superior to Aβ(1-42), in the early and differential diagnosis of incipient AD.  Limitations of this study included patient recruitment at a specialized memory clinic, which may restrict the generalization of the results to the general population with incipient AD and the lack of pathologic confirmation of AD and FTD.  Also, the recruitment of a modest number of patients and the relatively short follow-up period may have under-estimated the predictive value of sAPPβ.  The authors stated that replications of these findings in larger multi-center studies are needed.  Further studies are needed to examine the clinical value of sAPPβ for the differentiation of AD from healthy aging and from other neurodegenerative disorders, and to investigate its use as a marker for anti-amyloid treatment response.

In a case-cohort study, Schrijvers et al (2011) evaluated the potential of plasma clusterin as a biomarker of the presence, severity, and risk of AD.  Plasma levels of clusterin were measured at baseline (1997 to 1999) in 60 individuals with prevalent AD, a random subcohort of 926 participants, and an additional 156 participants diagnosed with AD during follow-up until January 1, 2007 (mean [SD], 7.2 [2.3] years).  Main outcome measures included prevalent AD, severity of AD measured by the Mini-Mental State Examination (MMSE) score, and the risk of developing AD during follow-up.  The likelihood of prevalent AD increased with increasing plasma levels of clusterin (OR per SD increase of plasma clusterin level, 1.63; 95 % CI: 1.21 to 2.20; adjusted for age, sex, education level, apolipoprotein E status, diabetes, smoking, coronary heart disease, and hypertension).  Among patients with AD, higher clusterin levels were associated with more severe disease (adjusted difference in MMSE score per SD increase in clusterin levels, -1.36; 95 % CI: -2.70 to -0.02; p = 0.047).  Plasma clusterin levels were not related to the risk of incident AD during total follow-up (adjusted hazard ratio [HR], 1.00; 95 % CI: 0.85 to 1.17; p for trend = 0.77) or within 3 years of baseline (adjusted HR, 1.09; 95 % CI: 0.84 to1.42; p for trend = 0.65).  The authors concluded that plasma clusterin levels were significantly associated with baseline prevalence and severity of AD, but not with incidence of AD.  They stated that increased clusterin levels do not precede development of AD and thus are not a potential early marker of subclinical disease.

Tan and colleagues (2012) stated that higher dietary intake and circulating levels of docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA) have been related to a reduced risk for dementia, but the pathways underlying this association remain unclear.  These investigators examined the cross-sectional relation of red blood cell (RBC) fatty acid levels to subclinical imaging and cognitive markers of dementia risk in a middle-aged to elderly community-based cohort.  They related RBC DHA and EPA levels in dementia-free Framingham Study participants (n = 1,575; 854 women, age 67 +/- 9 years) to performance on cognitive tests and to volumetric brain MRI, with serial adjustments for age, sex, and education (model A, primary model), additionally for APOE ε4 and plasma homocysteine (model B), and also for physical activity and body mass index (model C), or for traditional vascular risk factors (model D).  Participants with RBC DHA levels in the lowest quartile (Q1) when compared to others (Q2-4) had lower total brain and greater white matter hyper-intensity volumes (for model A: β +/- SE = -0.49 +/- 0.19; p = 0.009, and 0.12 +/- 0.06; p = 0.049, respectively) with persistence of the association with total brain volume in multi-variable analyses.  Participants with lower DHA and ω-3 index (RBC DHA+EPA) levels (Q1 versus Q2-4) also had lower scores on tests of visual memory (β +/- SE = -0.47 +/- 0.18; p = 0.008), executive function (β +/- SE = -0.07 +/- 0.03; p = 0.004), and abstract thinking (β +/- SE = -0.52 +/- 0.18; p = 0.004) in model A, the results remaining significant in all models.  The authors concluded that lower RBC DHA levels are associated with smaller brain volumes and a "vascular" pattern of cognitive impairment even in persons free of clinical dementia.  Moreover, they stated that "the association between lower RBC omega-3 fatty acid levels and markers of accelerated cognitive and structural brain aging observed here should be confirmed in other populations and extended in the future to include dementia outcomes".

Tarawneh et al (2012) stated that measures of neuronal damage/dysfunction are likely good surrogates for disease progression in AD.  Cerebro-spinal fluid markers of neuronal injury may offer utility in predicting disease progression and guiding prognostic and outcome assessments in therapeutic trials.  Visinin-like protein-1 (VILIP-1) has demonstrated potential utility as a marker of neuronal injury.  These researchers investigated the utility of VILIP-1 and VILIP-1/Aβ42 in predicting rates of cognitive decline in early AD.  Individuals with a clinical diagnosis of very mild or mild AD (n = 60) and baseline CSF measures of VILIP-1, tau, p-tau181, and Aβ42 were followed longitudinally for an average of 2.6 years.  Annual assessments included the Clinical Dementia Rating (CDR), CDR-sum of boxes (CDR-SB), and global composite scores.  Mixed linear models assessed the ability of CSF biomarker measures to predict rates of cognitive decline over time.  Baseline CSF VILIP-1 and VILIP-1/Aβ42 levels predicted rates of future decline in CDR-SB and global composite scores over the follow-up period.  Individuals with CSF VILIP-1 greater than or equal to 560 pg/ml (corresponding to the upper tercile) progressed much more rapidly in CDR-SB (1.61 boxes/year; p = 0.0077) and global scores (-0.53 points/year; p = 0.0002) than individuals with lower values (0.85 boxes/year and -0.15 points/year, respectively) over the follow-up period.  Cerebro-spinal fluid tau, p-tau181, tau/Aβ42, and p-tau181/Aβ42 also predicted more rapid cognitive decline in CDR-SB and global scores over time.  The authors concluded that these findings suggested that CSF VILIP-1 and VILIP-1/Aβ42 predict rates of global cognitive decline similarly to tau and tau/Aβ42, and may be useful CSF surrogates for neurodegeneration in early AD.  Drawbacks of this study included relatively small sample size and short follow-up period.  These findings need to be validated in well-designed studies with standardized assay, larger sample size and longer durations of follow-up.

Watabe-Rudolph et al (2012) analyzed the level of novel biomarkers of DNA damage and telomere dysfunction (chitinase activity, N-acetyl-glucosaminidase activity, stathmin, and EF-1α) in CSF of 94 patients with AD, 41 patients with non-AD dementia, and 40 control patients without dementia.  Enzymatic activity of chitinase (chitotriosidase activity) and stathmin protein level were significantly increased in CSF of patients with AD and non-AD dementia compared with that of no dementia control patients.  As a single marker, chitinase activity was most powerful for distinguishing patients with AD from no dementia patients with an accuracy of 85.8 % using a single threshold.  Discrimination was even superior to clinically standard CSF markers that showed an accuracy of 78.4 % (β-amyloid) and 77.6 % (tau).  Combined analysis of chitinase with other markers increased the accuracy to a maximum of 91 %.  The biomarkers of DNA damage were also increased in CSF of patients with non-AD dementia compared with no dementia patients, and the new biomarkers improved the diagnosis of non-AD dementia as well as the discrimination of AD from non-AD dementia.  The authors concluded that taken together, the findings in this study provided experimental evidence that DNA damage markers are significantly increased in AD and non-AD dementia.  Moreover, they stated that prospective clinical trials are needed to evaluate whether determination of chitinase enzyme activity and stathmin protein level in CSF could improve the diagnosis and prediction of disease progression of AD.

Hudson et al (2012) noted that although several studies have described an association between AD and genetic variation of mitochondrial DNA (mtDNA), each has implicated different mtDNA variants, so the role of mtDNA in the etiology of AD remains uncertain.  These researchers tested 138 mtDNA variants for association with AD in a powerful sample of 4,133 AD case patients and 1,602 matched controls from 3 Caucasian populations.  Of the total population, 3,250 case patients and 1,221 elderly controls met the quality control criteria and were included in the analysis.  In the largest study to-date, these investigators failed to replicate the published findings.  Meta-analysis of the available data showed no evidence of an association with AD.  The authors concluded that the current evidence linking common mtDNA variations with AD is not compelling.

Mielke et al (2012) examined if serum ceramides and sphingomyelins (SM) were associated with an increased risk of all-cause dementia and AD.  Participants included 99 women without dementia aged 70 to 79, with baseline serum SM and ceramides, enrolled in a longitudinal population-based study and followed for up to 6 visits over 9 years.  Baseline lipids, in tertiles, were examined in relation to all-cause dementia and AD using discrete time Cox proportional survival analysis.  Lipids were analyzed using electrospray ionization tandem mass spectrometry.  A total of 27 (27.3 %) of the 99 women developed incident dementia.  Of these, 18 (66.7 %) were diagnosed with probable AD.  Higher baseline serum ceramides, but not SM, were associated with an increased risk of AD; these relationships were stronger than with all-cause dementia.  Compared to the lowest tertile, the middle and highest tertiles of ceramide d18:1-C16:0 were associated with a 10-fold (95 % CI: 1.2 to 85.1) and 7.6-fold increased risk of AD (95 % CI: 0.9 to 62.1), respectively.  The highest tertiles of ceramide d18:1-C24:0 (HR = 5.1, 95 % CI: 1.1 to 23.6) and lactosylceramide (HR = 9.8, 95 % CI: 1.2 to 80.1) were also associated with risk of AD.  Total and high-density lipoprotein cholesterol and triglycerides were not associated with dementia or AD.  The authors concluded that results from this preliminary study suggested that particular species of serum ceramides are associated with incident AD and warrant continued examination in larger studies.  Drawbacks of this study include a small sample size of women only, as well as a single baseline measurement of the biomarker. 

Genetic testing has been proposed as an aid in the diagnosis of a type of Alzheimer disease (AD) known as early-onset familial AD (EOFAD). EOFAD is a rare form of AD in which onset occurs at age 30 to 60 years. Most cases are caused by mutations in one of three known genes: PSEN1, PSEN2 and APP. Testing of the APOE gene has also been proposed to predict susceptibility to early- and late-onset AD in asymptomatic individuals. 

The familial form of AD is due to mutations in 3 major genes (APP gene, presenilin 1 [PSEN1] gene and presenilin 2 [PSEN2] gene).  Chen et al (2012) stated that association studies of presenilin-2 (PSEN2) polymorphisms and sporadic AD have yielded inconsistent results, possibly because single studies often lack sufficient statistical power.  These investigators performed a meta-analysis to evaluate the association of the 2 most extensively studied PSEN2 polymorphisms, rs8383 and 5'indel, with the risk of sporadic AD.  They systematically reviewed relevant studies retrieved by Medline, Pubmed, Embase, AlzGene, and CNKI.  Data were analyzed using the Stata (v11.0) software package.  The fixed effects model or random-effects model were applied depending on between-study heterogeneity.  Publication bias was evaluated using Egger's test and Begg's funnel plots.  Overall, the meta-analysis included 6 case-control studies for each polymorphism with 2,186 confirmed AD cases and 2,507 healthy controls in total.  Analysis suggested a significant association between  single-nucleotide polymorphism (SNP) rs8383 polymorphism and AD risk with no evidence of between-study heterogeneity or publication bias.  In contrast, these researchers found no evidence for an association between the 5'indel polymorphism and AD risk.  Further stratified analyses by apolipoprotein ε4 status or ethnicity also failed to reveal a statistically significant association between the 5'indel polymorphism of PSEN2 and AD risk.  The authors concluded that this analysis supported the hypothesis that the PSEN2 rs8383 polymorphism is associated with an enlarged risk of sporadic AD.  However, they stated that larger scale association studies are needed to further validate the association of PSEN2 polymorphisms with sporadic AD risk and to define potential gene-gene interactions.

Gerrish et al (2012) noted that rare mutations in AβPP, PSEN1, and PSEN2 cause uncommon early onset forms of AD, and common variants in microtubule-associated protein tau (MAPT) are associated with risk of other neurodegenerative disorders.  These researchers sought to establish whether common genetic variation in these genes confer risk to the common form of AD that occurs later in life (greater than 65 years).  These investigators therefore tested single-nucleotide polymorphisms at these loci for association with late-onset AD (LOAD) in a large case-control sample consisting of 3,940 cases and 13,373 controls.  Single-marker analysis did not identify any variants that reached genome-wide significance, a result that is supported by other recent genome-wide association studies.  However, these investigators did observe a significant association at the MAPT locus using a gene-wide approach (p = 0.009).  They also observed suggestive association between AD and the marker rs9468, which defines the H1 haplotype, an extended haplotype that spans the MAPT gene and has previously been implicated in other neurodegenerative disorders including Parkinson's disease, progressive supranuclear palsy, and corticobasal degeneration.  The authors concluded that common variants at AβPP, PSEN1, and PSEN2 and MAPT are unlikely to make strong contributions to susceptibility for LOAD.  However, the gene-wide effect observed at MAPT indicates a possible contribution to disease risk which requires further study.

Jonsson et al (2013) stated that sequence variants, including the ε4 allele of apolipoprotein E, have been associated with the risk of the common late-onset form of AD.  Few rare variants affecting the risk of late-onset AD have been found.  These investigators obtained the genome sequences of 2,261 Icelanders and identified sequence variants that were likely to affect protein function.  They imputed these variants into the genomes of patients with AD and control participants and then tested for an association with AD.  These researchers performed replication tests using case-control series from the United States, Norway, the Netherlands, and Germany.  They also tested for a genetic association with cognitive function in a population of unaffected elderly persons.  A rare missense mutation (rs75932628-T) in the gene encoding the triggering receptor expressed on myeloid cells 2 (TREM2), which was predicted to result in an R47H substitution, was found to confer a significant risk of AD in Iceland (OR, 2.92; 95 % CI: 2.09 to 4.09; p = 3.42×10(-10)).  The mutation had a frequency of 0.46 % in controls 85 years of age or older.  These investigators observed the association in additional sample sets (OR, 2.90; 95 % CI: 2.16 to 3.91; p = 2.1×10(-12) in combined discovery and replication samples).  They also found that carriers of rs75932628-T between the ages of 80 and 100 years without AD had poorer cognitive function than non-carriers (p = 0.003).  The authors concluded that the findings of this study strongly implicate variant TREM2 in the pathogenesis of AD.

Guerreiro et al (2013) used genome, exome, and Sanger sequencing to analyze the genetic variability in TREM2 in a series of 1,092 patients with AD and 1,107 controls (the discovery set).  These researchers then performed a meta-analysis on imputed data for the TREM2 variant rs75932628 (predicted to cause a R47H substitution) from 3 genome-wide association studies of AD and tested for the association of the variant with disease.  These investigators genotyped the R47H variant in an additional 1,887 cases and 4,061 controls.  They then assayed the expression of TREM2 across different regions of the human brain and identified genes that are differentially expressed in a mouse model of AD and in control mice.  These researchers found significantly more variants in exon 2 of TREM2 in patients with AD than in controls in the discovery set (p = 0.02).  There were 22 variant alleles in 1,092 patients with AD and 5 variant alleles in 1,107 controls (p < 0.001).  The most commonly associated variant, rs75932628 (encoding R47H), showed highly significant association with AD (p < 0.001).  Meta-analysis of rs75932628 genotypes imputed from genome-wide association studies confirmed this association (p = 0.002), as did direct genotyping of an additional series of 1,887 patients with AD and 4,061 controls (p < 0.001).  Trem2 expression differed between control mice and a mouse model of AD.  The authors concluded that heterozygous rare variants in TREM2 are associated with a significant increase in the risk of AD.  Moreover, they stated that “We and others have predicted that heterozygous loss-of-function variants may represent a substantial component of risk for common late-onset diseases.  Our findings support this hypothesis, and we believe additional loss-of-function variants will be identified as risk factors for Alzheimer's disease and other late-onset complex disorders”.

In an editorial that accompanied the afore-mentioned studies by Jonsson et al (2013) as well as Guerreiro et al (2013), Neumann and Daly (2013) stated that “Pursuing the hypothesis that low-prevalence variants cause Alzheimer's disease with a moderate-to-high effect size, two groups of researchers convincingly show in the Journal that rare variants in TREM2, encoding triggering receptor expressed on myeloid cells 2 protein, cause susceptibility to late-onset Alzheimer's disease, with an odds ratio similar to that of the apolipoprotein E ε4 allele.  Although the most compelling TREM2 variant (encoding a substitution of arginine by histidine at residue 47 [R47H] of the TREM2 protein) is rare, with an allelic prevalence of 0.63 % in Iceland, these findings implicate a gene and naturally arising perturbation that may generate new insights into the pathogenesis of late-onset Alzheimer's disease …. We therefore suggest that the degeneration of neurons in these diseases and in TREM2-associated Alzheimer's disease is driven by a chronic inflammatory process with dysfunction in the microglial phagocytosis or inflammatory pathway.  These studies provide a new path for experimental inquiry into the biologic roots of Alzheimer's disease”.

Zhang et al (2013) stated that the association between insulin degrading enzyme (IDE) gene polymorphisms and AD risk has been widely reported, but results were somewhat controversial.  To assess the association between IDE polymorphisms and AD risk, a meta-analysis was performed.  These investigators systematically reviewed relevant studies retrieved by PubMed, Embase, AlzGene, CNKI and Web of Science.  Finally, 8 articles were identified for rs3758505 polymorphism and 5 for rs1832196 polymorphism.  The pooled ORs were performed for all the 4 genetic models.  Subgroup analysis was also performed by ethnicity.  Results suggested that rs3758505 polymorphism was unlikely to be associated with genetic susceptibility of AD based on the current published studies.  However, for the rs1832196 polymorphism, significant association with AD was found by the dominant model in overall and subgroup analysis.  However, larger scale association studies are necessary to further validate the association of IDE polymorphisms with sporadic AD risk and to define potential gene-gene interactions.

Caraci et al (2012) stated that an impairment of the transforming growth factor-beta1 (TGF-β1) signaling pathway has been demonstrated to be specific to the AD brain and, particularly, to the early phase of the disease.  Transforming growth factor-β1 is a neurotrophic factor responsible for the initiation and maintenance of neuronal differentiation and synaptic plasticity.  The deficiency of TGF-β1 signaling is associated with Aβ pathology and neurofibrillary tangle formation in AD animal models.  Reduced TGF-β1 signaling seems to contribute both to microglial activation and to ectopic cell-cycle re-activation in neurons, 2 events that contribute to neurodegeneration in the AD brain.  The neuroprotective features of TGF-β1 indicate the advantage of rescuing TGF-β1 signaling as a means to slow down the neurodegenerative process in AD.

Chang et al (2013) noted that the association between TGF-β1 gene polymorphisms and AD risk has been widely reported, but results were somewhat controversial and under-powered.  To derive a more precise estimation of the relationship between TGF-β1 polymorphisms and AD risk, these researchers conducted a meta-analysis of all available case-control studies relating the T869C and/or C-509T polymorphisms of the TGF-β1 gene to the risk of developing AD.  Eligible articles were identified by search of databases including Pub Med, Web of Science, the Chinese Biomedical Database (CBM), Chinese National Knowledge Infrastructure (CNKI) and the Wan Fang (Chinese) for the period up to March 2012.  A total of 14 articles were identified, 10 with 1,657 cases and 6,971 controls for T869C polymorphism and 8 with 2,618 cases and 7,473 controls for C-509T polymorphism.  The pooled ORs were performed for the allele contrasts, additive genetic model, dominant genetic model and recessive genetic model, respectively.  Subgroup analysis was also performed by ethnicity.  With respect to T869C and C-509T polymorphism, the combined results showed that there were no significant differences in genotype distribution between AD and control based on all studies.  When stratifying for the race, there were also no statistically significant differences in genotype distribution between AD and controls.  The authors concluded that the findings of this meta-analysis did not provide evidence of confirming association between the T869C and/or C-509T polymorphisms of the TGF-β1 gene and AD.

Reitz and colleagues (2013) noted that genetic variants associated with susceptibility to LOAD are known for individuals of European ancestry, but whether the same or different variants account for the genetic risk of AD in African American individuals is unknown.  Identification of disease-associated variants helps identify targets for genetic testing, prevention, and treatment.  These researchers identified genetic loci associated with LO AD in African Americans. The Alzheimer Disease Genetics Consortium (ADGC) assembled multiple data sets representing a total of 5,896 African Americans (1,968 case participants, 3,928 control participants) 60 years or older that were collected between 1989 and 2011 at multiple sites.  The association of AD with genotyped and imputed SNPs was assessed in case-control and in family-based data sets.  Results from individual data sets were combined to perform an inverse variance-weighted meta-analysis, first with genome-wide analyses and subsequently with gene-based tests for previously reported loci.  Main outcome measure was presence of AD according to standardized criteria.  Genome-wide significance in fully adjusted models (sex, age, APOE genotype, population stratification) was observed for a SNP in ATP-binding cassette transporter (ABCA7) (rs115550680, allele = G; frequency, 0.09 cases and 0.06 controls; OR, 1.79 [95 % CI: 1.47 to 2.12]; p = 2.2 × 10(-9)), which is in linkage disequilibrium with SNPs previously associated with AD in Europeans (0.8 < D' < 0.9).  The effect size for the SNP in ABCA7 was comparable with that of the APOE ϵ4-determining SNP rs429358 (allele = C; frequency, 0.30 cases and 0.18 controls; OR, 2.31 [95 % CI: 2.19 to 2.42]; p = 5.5 × 10(-47)).  Several loci previously associated with AD but not reaching significance in genome-wide analyses were replicated in gene-based analyses accounting for linkage disequilibrium between markers and correcting for number of tests performed per gene (CR1, BIN1, EPHA1, CD33; 0.0005 < empirical p < 0.001).  The authors concluded that in this meta-analysis of data from African American participants, AD was significantly associated with variants in ABCA7 and with other genes that have been associated with AD in individuals of European ancestry.  Moreover, they stated that replication and functional validation of this finding is needed before this information is used in clinical settings.

Sala Frigerio et al (2013) evaluated microRNAs (miRNAs) as potential biomarkers for AD by analyzing the expression level of miRNAs in CSF of patients with AD dementia and non-affected control subjects.  Using quantitative PCR, these researchers profiled the expression level of 728 miRNAs in CSF of non-affected control subjects and patients with clinically ascertained AD dementia, and they further compared the expression level of candidate miRNAs in 37 control subjects and 35 patients with AD dementia.  The level of hsa-miR-27a-3p in CSF is reduced in patients with dementia due to AD in 2 different cohorts of subjects (cohort 1: p = 0.008; cohort 2: p = 0.015; 2-tailed unpaired Welch t-test).  Moreover, low levels of hsa-miR-27a-3p were accompanied by high CSF tau levels and low CSF β-amyloid levels.  The authors concluded that the findings of this pilot study highlighted hsa-miR-27a-3p as a candidate biomarker for AD and provided the groundwork for further confirmation studies in larger cohorts and in other hospitals.

Roe and colleagues (2013) examined CSF AD biomarkers (β-amyloid 42 [Aβ42], tau, phosphorylated tau at threonine 181 [ptau181], tau/Aβ42, and ptau181/Aβ42) predict future decline in non-cognitive outcomes among individuals cognitively normal at baseline.  Longitudinal data from participants (n = 430) who donated CSF within 1 year of a clinical assessment indicating normal cognition and were aged 50 years or older were analyzed.  Mixed linear models were used to test whether baseline biomarker values predicted future decline in function (instrumental activities of daily living), weight, behavior, and mood.  Clinical Dementia Rating Sum of Boxes and Mini-Mental State Examination scores were also examined.  Abnormal levels of each biomarker were related to greater impairment with time in behavior (p < 0.035) and mood (p < 0.012) symptoms, and more difficulties with independent activities of daily living (p < 0.012).  However, biomarker levels were unrelated to weight change with time (p > 0.115).  As expected, abnormal biomarker values also predicted more rapidly changing MMSE (p < 0.041) and CDR-SB (p < 0.001) scores compared with normal values.  The authors concluded that CSF biomarkers among cognitively normal individuals are associated with future decline in some, but not all, non-cognitive AD symptoms studied.  Moreover, they stated that additional work is needed to determine the extent to which these findings generalize to other samples.  These investigators also noted that future research should test whether the ratio of tau/Aβ42 and ptau181/Aβ42 are better predictors of decline in non-cognitive outcomes compared with individual molecular marker alone.

Schmidt et al (2013) stated that prion protein concentration [PrP (c)] has been suggested to play a role in AD pathophysiology.  Cerebrospinal fluid concentrations of PrP (c) have been shown to be reduced in AD compared with healthy controls.  Furthermore, serum levels of PrP (c) have recently been reported to be associated with the cognitive status of healthy elderly subjects.  These investigators hypothesized that CSF levels of PrP (c) could be associated with cognitive function of AD patients at the time of diagnosis.  Alzheimer’s disease patients (n = 114) included into an observational study underwent CERAD testing and lumbar puncture at time of diagnosis/study inclusion; CSF PrP (c) was determined.  Generalized linear models were fitted to assess the associations of PrP (c) plus a variety of possible confounding factors and CERAD subscale measures.  No association of CSF PrP (c) and cognitive status could be established, while other factors (i.e., use of anti-psychotic drugs, use of anti-dementia drugs, female sex, pre-progression time) were related to worse cognitive function in some domains.  The authors concluded that CSF PrP (c) appears not to be a useful biochemical surrogate of cognitive status in AD at the time of diagnosis.  

Schmidt et al (2014) noted that recently, PrPc has been linked to AD pathogenesis; and a relation of PrPc plasma levels with cognitive status and decline of healthy elderly subjects has been reported.  These researchers hypothesized baseline plasma levels of PrPc to be associated with AD progression in cognitive and functional domains.  Alzheimer’s disease patients (n = 84) were included into an observational study at time of diagnosis.  Baseline plasma PrPc levels were determined.  Decline was assessed annually (mean follow-up time 3 years) with the aid of different standardized tests (MMSE, iADL, bADL, GDS, UPDRSIII).  Multiple regression analyses were used to uncover potential associations between decline and PrPc levels.  No association of PrPc and decline could be established.  Presence of diabetes mellitus was linked to slower deterioration.  Intake of neuroleptic drugs or memantine was associated with faster progression.  The authors concluded that plasma PrPc at baseline could not be shown to be related to AD progression in this study.

Garcia-Martin et al (2014) examined the use of macular thickness as a potential biomarker of mild AD (20 patients with mild AD and 28 age-matched control subjects).  Based on their findings, these researchers proposed that the first affected area of the retina in mild AD may be the macular area.  As the neurodegeneration process progresses, a significant decline in the peri-papillary retinal nerve fiber layer (RNFL) thickness becomes apparent.  However, whether or not the macula is really the first area involved in early AD, or simply the first place with enough retinal ganglion cells to discern an effect deserves further investigation.

Westwood and colleagues (2014) related serum insulin-like growth factor-1 (IGF-1) to risk of AD dementia and to brain volumes in a dementia-free community sample spanning middle and older ages.  Dementia-free Framingham participants from generation 1 (n = 789, age of 79 ± 4 years, 64 % women) and generation 2 (n = 2,793, age of 61 ± 9 years, 55 % women; total = 3,582, age of 65 ± 11 years, 57 % women) had serum IGF-1 measured in 1990 to 1994 and 1998 to 2001, respectively, and were followed prospectively for incident dementia and AD dementia.  Brain MRI was obtained in stroke- and dementia-free survivors of both generations 1 (n = 186) and 2 (n = 1,867) during 1999 to 2005.  Baseline IGF-1 was related to risk of incident dementia using Cox models and to total brain and hippocampal volumes using linear regression in multi-variable models adjusted for age, sex, APOE ε4, plasma homocysteine, waist-hip ratio, and physical activity.  Mean IGF-1 levels were 144 ± 60 μg/L and 114 ± 37 μg/L in in generation 1 and generation 2, respectively.  These investigators observed 279 cases of incident dementia (230 AD dementia) over a mean follow-up of 7.4 ± 3.1 years.  Persons with IGF-1 in the lowest quartile had a 51 % greater risk of AD dementia (HR = 1.51, 95 % CI: 1.14 to 2.00; p = 0.004).  Among persons without dementia, higher IGF-1 levels were associated with greater total brain volumes (β/SD increment in IGF-1 was 0.55 ± 0.24, p = 0.025; and 0.26 ± 0.06, p < 0.001, for generations 1 and 2, respectively).  The authors concluded that lower serum levels of IGF-1 were associated with an increased risk of developing AD dementia and higher levels with greater brain volumes even among middle-aged community-dwelling participants free of stroke and dementia.  They stated that higher levels of IGF-1 may protect against subclinical and clinical neurodegeneration.  Moreover, the authors stated that this study was limited to persons of European ancestry, and therefore confirmation in independent samples and in persons of other racial backgrounds is needed.

Danborg et al (2014) stated that microRNAs (miRNA) are biological molecules transcribed from non-protein coding regions of the genome, participating in regulating cellular processes.  MiRNAs in biofluids may possess neurodegenerative disease biomarker potential for screening tests, differential diagnosis and disease progression monitoring.  This systematic review clarified biomarker potential of miRNAs detected in biofluids of neurodegenerative disease patients.  A total of 33 and 10 miRNAs displayed significant expression between patients with multiple sclerosis and AD, respectively, compared to healthy controls in a minimum of 2 studies

Tan et al (2014) noted that recent findings that human serum contains stably expressed miRNAs have revealed a great potential of serum miRNA signature as disease fingerprints to diagnosis.  These researchers used genome-wide serum miRNA expression analysis to investigate the value of serum miRNAs as biomarkers for the diagnosis of AD.  Illumina HiSeq 2000 sequencing followed by individual quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) assays was used to test the difference in levels of serum miRNAs between 50 AD patients and 50 controls in the screening stages.  The detected serum miRNAs then were validated by qRT-PCR in 158 patients and 155 controls.  MiR-98-5p, miR-885-5p, miR-483-3p, miR-342-3p, miR-191-5p, and miR-let-7d-5p displayed significantly different expression levels in AD patients compared with controls.  Among the 6 miRNAs, miR-342-3p has the best sensitivity (81.5 %) and specificity (70.1 %) and was correlated to MMSE score.  This study identified 6 serum miRNAs that distinguish AD patients from healthy controls with high sensitivity and specificity.  The authors concluded that serum miRNA panel (or miR-342-3p alone) may serve as a novel, non-invasive biomarker for AD.  These preliminary findings need to be validated by well-designed studies.

Han et al (2014) stated that there is growing evidence that pituitary adenylate cyclase-activating polypeptide (PACAP) is associated with AD pathology in animal models, but human studies are needed.  These researchers studied the brains of patients with pathologically confirmed late-onset AD and age-matched cognitively normal (CN) subjects to investigate the expression of PACAP messenger RNA (34 AD and 14 CN) and protein (12 AD and 11 CN) in a case-control study.  They reported that PACAP levels are reduced in multiple brain regions, including the entorhinal cortex, the middle temporal gyrus, the superior frontal gyrus, and the primary visual cortex.  This reduction was correlated with higher amyloid burden (CERAD plaque density) in the entorhinal cortex and superior frontal gyrus but not in the primary visual cortex, a region spared in most cases of AD.  PACAP expression is lower in advanced Braak stages (V and VI) than in moderate stages (III and IV).  Increased PACAP levels were associated with decreased scores on the Dementia Rating Scale, a global cognitive measure.  Finally, CSF levels paralleled brain levels in AD but not in Parkinson dementia or fronto-temporal dementia brains.  The authors concluded that the close relationship between PACAP reduction and the severity of AD pathology suggested that down-regulation of PACAP may contribute to AD pathogenesis.  Moreover, they stated that “Translation of PACAP research to human subjects may provide us with not only new early biomarkers but also exciting therapeutic options for AD.  Additional studies are needed to clarify the nature of the relationship between PACAP reductions and the neuropathologic features of AD and the extent to which it might provide a target for therapeutic interventions”.

In a pilot study, Marksteiner et al (2014) searched for changes in plasma levels of 27 vascular-related proteins of healthy controls, patients with MCI and AD.  In a sample of 80 participants, these researchers showed that out of these 27 proteins, 6 proteins were slightly changed (up to 1.5×) in AD (alpha2-macroglobulin, apolipoprotein-A1, plasminogen activator inhibitor, RAGE, Tissue Inhibitors of Metalloproteinases-1 and Trombospondin-2) and 1 marker (serum amyloid A) was enhanced up to 6× but with a very high variance.  However, N-terminal pro-brain natriuretic peptide (NT-proBNP) was significantly enhanced both in MCI and AD patients (1.9×).  In a second analysis of a sample of 110 subjects including younger healthy controls, these investigators confirmed that NT-proBNP has the potential to be a stable candidate protein for both diagnosis and AD disease progression.

Schaffer et al (2015) reviewed the current diagnostic standards for AD with an emphasis on early diagnosis using the CSF biomarkers amyloid-beta, total tau (t-tau), and phosphorylated tau (p-tau) and fluorodeoxyglucose positron emission tomography (PET) imaging.  Abnormal levels of these CSF biomarkers and decreased cerebral uptake of glucose have recently been used in the early diagnosis of AD in experimental studies.  These promising biomarkers can be measured using immunoassays performed in singleplex or multiplex formats.  Although presently, there are no Food and Drug Administration (FDA)-approved in-vitro diagnostics (IVDs) for early detection of AD, a multiplex immunoassay measuring a panel of promising AD biomarkers in CSF may be a likely IVD candidate for the clinical AD diagnostic market.  Specifically, the INNO-BIA AlzBio3 immunoassay kit, performed using bead arrays on the xMAP Luminex analyzer, allows simultaneous quantification of amyloid-beta, t-tau, and p-tau biomarkers.

Rivero-Santana and colleagues (2017) stated that differential diagnosis in dementia is at present one of the main challenges both in clinical practice and research; CSF biomarkers are included in the current diagnostic criteria of AD but their clinical utility is still unclear.  These investigators performed a systematic review of studies analyzing the diagnostic performance of CSF Aβ42, t-tau, and p-tau in the discrimination between AD and fronto-temporal lobar degeneration (FTLD) dementias.  The following electronic databases were consulted until May 2016: Medline and PreMedline, Embase, PsycInfo, CINAHL, Cochrane Library, and CRD.  For the first-time in the field, a Hierarchical Summary Receiver Operating Characteristic (HRSOC) model was applied, which avoids methodological problems of meta-analyses based on summary points of sensitivity and specificity values.  They also investigated relevant confounders of CSF biomarkers' diagnostic performance such as age, disease duration, and global cognitive impairment.  The p-tau/Aβ42 ratio showed the best diagnostic performance.  No statistically significant effects of the confounders were observed.  Nonetheless, the p-tau/Aβ42 ratio may be especially indicated for younger patients; p-tau may be preferable for less cognitively impaired patients (high MMSE scores) and the t-tau/Aβ42 ratio for more cognitively impaired patients (low MMSE scores).  The authors concluded that p-tau/Aβ42 ratio has potential for being implemented in the clinical routine for the differential diagnosis between AD and FTLD.  They stated that it is of utmost importance that future studies report information on confounders such as age, disease duration, and cognitive impairment, which should also stimulate understanding of the role of these factors in disease mechanisms and pathophysiology.

Cerebrospinal Fluid β-Secretase Activity

In a cross-sectional, multi-center study, Alcolea  et al (2015) examined CSF markers involved in amyloid precursor protein processing, neuronal damage, and neuro-inflammation in the pre-clinical stages of AD and participants with suspected non-Alzheimer pathology (SNAP).  These researchers collected CSF from 266 cognitively normal volunteers participating in the SIGNAL study to investigate markers involved in amyloid precursor protein processing (Aβ42, sAPPβ, β-secretase activity), neuronal damage (total-tau [t-tau], phospho-tau [p-tau]), and neuro-inflammation (YKL-40).  They analyzed the relationship among biomarkers, clinical variables, and the APOE genotype, and compared biomarker levels across the pre-clinical stages of the National Institute on Aging-Alzheimer's Association classification: stage 0, 1, 2, 3, and SNAP.  The median age in the whole cohort was 58.8 years (range of 39.8 to 81.6).  Participants in stages 2-3 and SNAP had higher levels of YKL-40 than those in stages 0 and 1.  Participants with SNAP had higher levels of sAPPβ than participants in stage 0 and 1.  No differences were found between stages 0, 1, and 2-3 in sAPPβ and β-secretase activity in CSF.  Age correlated with t-tau, p-tau, and YKL-40.  It also correlated with Aβ42, but only in APOE ε4 carriers.  Aβ42 correlated positively with t-tau, sAPPβ, and YKL-40 in participants with normal Aβ42.  The authors concluded that the findings of this study suggested that inflammation in the CNS increases in normal aging and is intimately related to markers of neurodegeneration in the preclinical stages of AD and SNAP; sAPPβ and β-secretase activity are not useful diagnostic or staging markers in pre-clinical AD.

Furthermore, an UpToDate review on “Clinical features and diagnosis of Alzheimer disease” (Wolk and Dickerson, 2016) states that “In general, the topographic biomarkers are less specific than the molecular biomarkers but correlate better with the emergence of clinical symptoms.  None of these tests is valid as a stand-alone diagnostic test, but research criteria have incorporated both molecular and topographic biomarker data into the research definitions of both symptomatic and pre-symptomatic forms of AD, anticipating that once biomarkers become more standardized they will be incorporated into clinical diagnostic algorithms for AD.  At present, the use of biomarkers is limited primarily to investigational studies and clinical trials, and testing is not universally available or reimbursed by most insurers”.

Cerebrospinal Fluid Neurogranin

Kvartsberg and co-workers (2015) developed 3 monoclonal anti-neurogranin (NGRN) antibodies.  Mass spectrometry and a novel ELISA were used to analyze CSF NGRN in 3 independent clinical cohorts including patients with AD dementia (n = 100 in total), MCI patients (n = 40) and controls (n = 80 in total).  These researchers showed in 3 independent clinical cohorts a marked increase in CSF NGRN levels in AD dementia (p < 0.001 in all studies).  In addition, high CSF NGRN levels at the MCI stage predicted progression to dementia due to AD with a HR of 12.8 (95 % CI: 1.6 to 103.0, p = 0.02).  In amyloid-positive MCI patients, high CSF NGRN correlated with a more rapid change in cognition during clinical follow-up (p = 0.03).  The authors concluded that the findings of this study suggested that CSF NGRN is a novel AD biomarker that may be used to monitor synaptic degeneration, and correlates with the rate of cognitive decline in prodromal AD.

Kester and colleagues (2015) stated that NGRN appeared to be a promising novel CSF biomarker for synaptic loss; however, clinical, and especially longitudinal, data are sparse.  These researchers examined the utility of NGRN, with repeated CSF sampling, for diagnosis, prognosis, and monitoring of AD.  They performed a longitudinal study of consecutive patients who underwent 2 lumbar punctures between the beginning of 1995 and the end of 2010 within the memory clinic-based Amsterdam Dementia Cohort.  The study included 163 patients: 37 cognitively normal participants (mean [SE] age of 64 [2] years; 38 % female; and mean [SE] MMSE score of 28 [0.3]), 61 patients with MCI (mean [SE] age of 68 [1] years; 38 % female; and mean [SE] MMSE score of 27 [0.3]), and 65 patients with AD (mean [SE] age of 65 [1] years; 45 % female; and mean [SE] MMSE score of 22 [0.7]).  The mean (SE) interval between lumbar punctures was 2.0 (0.1) years, and the mean (SE) duration of cognitive follow-up was 3.8 (0.2) years.  Measurements of CSF NGRN levels were obtained in January and February 2014.  Main outcome measure was levels of NGRN in CSF samples.  Baseline CSF levels of NGRN in patients with AD (median level of 2,381 pg/ml [interquartile range (IQR), 1,651 to 3,416 pg/ml]) were higher than in cognitively normal participants (median level of 1,712 pg/ml [IQR, 1,206 to 2,724 pg/ml]) (p = 0.04).  Baseline NGRN levels were highly correlated with total tau and tau phosphorylated at threonine 181 in all patient groups (all p < 0.001), but not with Aβ42.  Baseline CSF levels of NGRN were also higher in patients with MCI who progressed to AD (median level of 2,842 pg/ml IQR, 1,882 to 3,950 pg/ml]) compared with those with stable MCI (median level of 1,752 pg/ml [IQR, 1,024 to 2,438 pg/ml]) (p = .004), and they were predictive of progression from MCI to AD (HR, 1.8 [95 % CI: 1.1 to 2.9]; stratified by tertiles).  Linear mixed-model analyses demonstrated that within-person levels of NGRN increased over time in cognitively normal participants (mean [SE] level of 90 [45] pg/ml per year; p < 0.05) but not in patients with MCI or AD.  The authors concluded that neurogranin is a promising biomarker for AD because levels were elevated in patients with AD compared with cognitively normal participants and predicted progression from MCI to AD.  Within-person levels of NGRN increased in cognitively normal participants but not in patients with later stage MCI or AD, which suggested that NGRN may reflect pre-symptomatic synaptic dysfunction or loss.

The authors stated that levels of NGRN in CSF were much higher in this study than in the study by Kvartsberg et al (2015), which also used an ELISA.  Differences in absolute values may be a result of the different antibodies used in the 2 assays.  Further studies comparing both ELISAs are needed to clarify this matter.  However, the direction and order of magnitude of the variance between patient groups were the same.  It is possible that differences in the calibration and/or antibodies used contributed to the absolute differences.  Among the drawbacks, the cognitively normal group was a mixed group of subjects that also included patients with psychiatric disorders and temporal lobe epilepsy, which may hamper generalizability.  Furthermore, the cognitively normal group was biased toward subjects who showed decline (6 progressed to MCI, and 4 to dementia), which could have even diluted the baseline effect.  This bias could be due to the fact that subjects who progressed were more likely to return to the clinic for a second lumbar puncture.  However, because of this follow-up, these investigators were able to evaluate change over time in NGRN levels for all stages of the AD continuum.  Another drawback was that the size of the group of cognitively normal subjects was too small to reliably analyze for risk of progression with Cox proportional hazards models.

Wellington and associates (2016) evaluated the specificity of the dendritic protein NGRN in CSF from patients with a broad range of neurodegenerative diseases including a variety of dementias, tauopathies, and synucleinopathies.  An optimized immunoassay was used to analyze CSF NGRN in a retrospective cohort of 331 participants with different neurodegenerative diseases, including healthy controls (n = 19), biomarker-proven AD (n = 100), genetic AD (n = 2), behavioral variant fronto-temporal dementia (n = 20), speech variant fronto-temporal dementia (n = 21), Lewy body dementia (n = 13), Parkinson disease (n = 31), progressive supranuclear palsy (n = 46), multiple system atrophy (n = 29), as well as a heterogeneous group with non-neurodegenerative cognitive impairment (n = 50).  CSF NGRN concentrations and correlations of CSF NGRN with total tau, phosphorylated tau, and β-amyloid 42 concentrations, MMSE score, and disease duration in the different groups were investigated.  Median CSF NGRN concentration was higher in patients with AD compared to both controls (p < 0.001) and all other disease groups (all p < 0.001) except speech variant fronto-temporal dementia.  There were no significant differences in CSF NGRN concentrations between any other neurodegenerative groups and controls.  In addition, these researchers found strong correlations between NGRN and total tau (p < 0.001) and phosphorylated tau (p < 0.001).  The authors concluded that these findings confirmed an increase in CSF NGRN concentration in patients with AD as previously reported and showed that this is specific to AD and not seen in a range of other neurodegenerative diseases.

This study had several drawbacks:
  1. the small sample size in some groups, most notably in those with genetic AD.  With this caveat, it is of interest to note that one of the highest NGRN values was observed in a PSEN1 mutation carrier (A426P, NGRN 1,162 pg/ml) -- whether this reflects familial AD in general or specific mutations/mechanisms needs to be assessed in larger cohorts,
  2. the relatively high coefficients of variation of the NGRN assay (20 % to 30 %).  This made it harder to interpret individual CSF NGRN concentrations in relation to fixed cut-off points, as well as to detect treatment-induced changes over time in CSF NGRN concentrations in clinical trials.  Developing more precise assays for CSF NGRN is an important area for further study,
  3. while the patients with AD had their clinical diagnosis supported by biomarkers, the other diagnoses were made on clinical grounds only, as diagnostically useful biomarkers for these disorders are presently lacking.

Furthermore, all controls were AD biomarker-negative.  This enrichment approach allowed for a very pure control group to be defined, with the advantage that these results may reflect “real” AD versus “true” control differences; in less well characterized individuals and in particular given that a proportion of apparently healthy controls may have pre-symptomatic AD changes, results may be less discriminatory.  However, the similar CSF NGRN concentrations in the control group compared with the other non-AD groups suggested that this may not be a major issue in practice.

The authors proposed that high CSF NGRN concentrations reflect synapse degeneration in AD-affected brain regions and that CSF NGRN has potential as a diagnostic marker for AD in combination with existing CSF biomarkers.  They stated that further studies are needed to test the hypothesis that CSF NGRN may have utility as a very early and potentially pre-symptomatic biomarker for AD, as a prognostic marker in the clinic, and as an outcome measure in clinical trials

Plasma Tau

Mattsson and colleagues (2016) examined if plasma tau is altered in AD and whether it is related to changes in cognition, CSF biomarkers of AD pathology (including β-amyloid [Aβ] and tau), brain atrophy, and brain metabolism.  This was a study of plasma tau in prospectively followed patients with AD (n = 179), patients with mild cognitive impairment (n = 195), and cognitive healthy controls (n = 189) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and cross-sectionally studied patients with AD (n = 61), MCI (n = 212), and subjective cognitive decline (n = 174) and controls (n = 274) from the Biomarkers for Identifying Neurodegenerative Disorders Early and Reliably (BioFINDER) study at Lund University, Sweden.  A total of 1,284 participants were studied.  Associations were tested between plasma tau and diagnosis, CSF biomarkers, MRI measures, 18fluorodeoxyglucose-PET, and cognition.  Higher plasma tau was associated with AD dementia, higher CSF tau, and lower CSF Aβ42, but the correlations were weak and differed between ADNI and BioFINDER.  Longitudinal analysis in ADNI showed significant associations between plasma tau and worse cognition, more atrophy, and more hypo-metabolism during follow-up.  The authors concluded that plasma tau partly reflected AD pathology, but the overlap between normal aging and AD was large, especially in patients without dementia.  They stated that despite group-level differences, these results did not support plasma tau as an AD biomarker in individual people; future studies may test longitudinal plasma tau measurements in AD.

The authors noted that while this study was the largest published on plasma tau, but there were some drawbacks:
  1. ADNI and BioFINDER samples were handled with different protocols and analyzed at different laboratories with different kit lots, which may have contributed to the varying results.  Some ADNI participants had plasma tau below the lower limit of quantification of the assay (14 CNs, 11 patients with MCI, 10 patients with AD).  These measurements were uncertain but they were included because excluding them would have biased the data toward higher plasma tau (excluding them did not change the main results, data not shown);
  2. these researchers used only 1 plasma tau assay.  It is possible that other assays capture tau fragments that are less sensitive to peripheral degradation and more likely to reflect AD pathology, and
  3. these investigators did not co-vary for co-pathologies that may affect plasma tau levels.

Bcl-2 rs956572 Polymorphism Testing:

Chang and colleagues (2018) noted that AD is a complex neurodegenerative disease, and genetic differences may mediate neuronal degeneration.  In humans, a single-nucleotide polymorphism in the B-cell chronic lymphocytic leukemia/lymphoma-2 (Bcl-2) gene, rs956572, has been found to significantly modulate Bcl-2 protein expression in the brain.  The Bcl-2 AA genotype has been associated with reduced Bcl-2 levels and lower gray matter volume in healthy populations.  These investigators hypothesized that different Bcl-2 genotype groups may modulate large-scale brain networks that determine neurobehavioral test scores.  Gray matter structural covariance networks (SCNs) were constructed in 104 patients with AD using T1-weighted MRI with seed-based correlation analysis.  Patients were stratified into 2 genotype groups on the basis of Bcl-2 expression (G carriers, n = 76; A homozygotes, n = 28).  Four SCNs characteristic of AD were constructed from seeds in the default mode network, salience network, and executive control network, and cognitive test scores served as the major outcome factor.  For the G carriers, influences of the SCNs were observed mostly in the default mode network, of which the peak clusters anchored by the posterior cingulate cortex seed determined the cognitive test scores.  In contrast, genetic influences in the A homozygotes were found mainly in the executive control network, and both the dorsolateral prefrontal cortex seed and the inter-connected peak clusters were correlated with the clinical scores.  Despite a small number of cases, the A homozygotes showed greater co-variance strength than the G carriers among all 4 SCNs.  The authors concluded that these findings suggested that the Bcl-2 rs956572 polymorphism is associated with different strengths of structural covariance in AD that determine clinical outcomes.  The greater co-variance strength in the 4 SCNs shown in the A homozygotes suggested that different Bcl-2 polymorphisms play different modulatory roles.  Moreover, these researchers stated that they will investigate the biological meaning of co-variance strength in future studies with longitudinal follow-up.

The authors stated that this study had several drawbacks.  Direct comparisons between the G carriers and A homozygotes showed between-group differences in NPI total scores, aggression, and sleep sub-scores.  However, network analysis did not find any relationships linking the SCNs to any of the significant clinical results.  Because the study design used a seed-based approach, it was possible that the changes were mediated by different functional networks.  The use of independent component analysis may help to overcome this limitation.  Another important drawback of this study was that these investigators did not include a control group, and the inclusion of a control group may have helped to elucidate whether the Bcl-2 functional polymorphisms exerted similar GM modulation patterns in healthy elderly subjects.  However, associations between rs956572 functional polymorphisms and regional GM volume and functional states in healthy subjects had been reported.  Given the differences in methods, sample sizes, and populations analyzed, it was difficult to compare the findings in this study directly with these reports.  However, the authors found regional similarities in this study.  Another potential drawback was that the seed-based analysis emphasized the SCNs that showed positive correlations with the seeds.  Because the objective of this trial was to test the hypothesis of the risk of different genotype groups, anti-correlation patterns suggestive of a compensatory process in each genotype group were not explored.  Because clinical significance was established in restricted nodes showing co-variance interactions, whether the seed or peak clusters imparted an equal amount of information within the whole network remained an important issue that needs to be investigated in future studies.  Last, structural co-variance data could not be directly referred to as a connectivity or degenerative biomarker, although the patterns of SCN had been shown to mirror those of intrinsic connectivity patterns in healthy control subjects.  The authors stated that further studies with longitudinal cohorts are needed to validate the interpretation regarding greater co-variance strength in the AA genotype and faster degeneration.  In addition, to elucidate the functional effect of the A allele observed in this study, a larger sample cohort is needed in future studies so that direct comparisons of the AA and GG genotypes with equal and adequate case numbers are possible.

Circadian Rhythm Analysis:

Musiek and colleagues (2018) stated that circadian rhythm disturbances occur in symptomatic AD and have been hypothesized to contribute to disease pathogenesis.  However, it is unknown whether circadian changes occur during the pre-symptomatic phase of the disease.  In a cross-sectional study, these researchers examined the associations between circadian function, aging, and pre-clinical AD pathology in cognitively normal adults.  This trial was conducted using community volunteers from the Knight Alzheimer's Disease Research Center at Washington University in St Louis.  Cognitively normal participants (n = 205) underwent 7 to 14 days of actigraphy in their home environment between 2010 and 2012, in addition to clinical assessment, amyloid imaging with Pittsburgh Compound B (PiB), and CSF biomarker collection.  Data collected from 3 years before to 6 months after actigraphy were included; 16 participants were excluded owing to incomplete data collection.  Circadian rhythm analysis was performed on actigraphy data using 3 methods: cosinor, non-parametric, and empirical mode decomposition.  Pre-clinical AD was assessed by longitudinal clinical assessment, amyloid imaging with PiB, and CSF biomarker collection.  Data from 189 participants were included in the analyses.  The mean (SD) age was 66.6 (8.3) years, and 121 participants (64 %) were women.  Older age (β = 0.247; p = 0.003) and male sex (β = 0.170; p = 0.04), in the absence of amyloid pathology, were associated with a significant increase in intra-daily variability, a non-parametric measure of rest-activity rhythm fragmentation, as well as decreased amplitude by several measures.  After correction for age and sex, the presence of pre-clinical amyloid plaque pathology, assessed by positive PiB imaging (mean [SD], 0.804 [0.187] for PiB negative versus 0.875 [0.178] for PiB positive; p = 0.05) or increasing CSF phosphorylated-tau to amyloid β 42 ratio (β = 0.231; p = 0.008), was associated with increased intra-daily variability, indicating rest-activity rhythm fragmentation.  The authors concluded that pre-clinical AD was associated with rest-activity rhythm fragmentation, independent of age or sex.  Aging was also associated with circadian dysfunction independently of pre-clinical AD pathology, particularly in men.  They stated that the presence of circadian rhythm abnormalities in the pre-clinical phase of AD suggested that circadian dysfunction could contribute to early disease pathogenesis or serve as a biomarker of pre-clinical disease.

This study had several drawbacks: The authors could not exclude the possibility that the age- or sex-related circadian changes observed in the amyloid-negative group were caused by non-amyloid pathologies, which are commonly seen in the aged brain.  They did not have information on medications and co-morbidities, specifically sleep disorders.  Sleep apnea in particular was not assessed, although it is common and may influence amyloid burden. Furthermore, non-circadian factors (e.g., voluntary exercise) can influence rest-activity measurements.  Replication using other circadian parameters (such as core body temperature) could be considered to confirm these findings.

DNA Methylation Profiling (Brain Tissue or Peripheral Blood):

Li and colleagues (2016) ) stated that AD is a common aging-related neurodegenerative illness.  Recently, many studies have tried to identify AD- or aging-related DNA methylation (DNAm) biomarkers from peripheral whole blood (PWB).  However, the origin of PWB biomarkers is still controversial.  In this study, by analyzing 2,565 DNAm profiles for PWB and brain tissue, these researchers showed that aging-related DNAm CpGs (Age-CpGs) and AD-related DNAm CpGs (AD-CpGs) observable in PWB both mainly reflected DNAm alterations intrinsic in leukocyte subtypes rather than methylation differences introduced by the increased ratio of myeloid to lymphoid cells during aging or AD progression.  The PWB Age-CpGs and AD-CpGs significantly overlapped 107 sites (p = 2.61×10-12) and 97 had significantly concordant methylation alterations in AD and aging (p < 2.2×10-16), which were significantly enriched in nervous system development, neuron differentiation and neurogenesis.  More than 60.8 % of these 97 concordant sites were found to be significantly correlated with age in normal peripheral CD4+ T cells and CD14+ monocytes as well as in 4 brain regions, and 44 sites were also significantly differentially methylated in different regions of AD brain tissue.  The authors concluded that the PWB DNAm alterations related to both aging and AD could be exploited for identification of AD biomarkers.

Watson and associates (2016) performed a genome-wide screen of DNA methylation using the Illumina Infinium HumanMethylation450 platform on bulk tissue samples from the superior temporal gyrus of patients with AD and non-demented controls.  They paired a sliding window approach with multi-variate linear regression to characterize AD-associated differentially methylated regions (DMRs).  These researchers identified 479 DMRs exhibiting a strong bias for hyper-methylated changes, a subset of which were independently associated with aging.  DMR intervals overlapped 475 RefSeq genes enriched for gene ontology categories with relevant roles in neuron function and development, as well as cellular metabolism, and included genes reported in AD genome-wide and epigenome-wide association studies.  DMRs were enriched for brain-specific histone signatures and for binding motifs of transcription factors with roles in the brain and AD pathology.  Notably, hyper-methylated DMRs preferentially overlapped poised promoter regions, marked by H3K27me3 and H3K4me3, previously shown to co-localize with aging-associated hyper-methylation.  Finally, the integration of DMR-associated single nucleotide polymorphisms with AD genome-wide association study risk loci and brain expression quantitative trait loci highlighted multiple potential DMRs of interest for further functional analysis.  The authors characterized changes in DNA methylation in the superior temporal gyrus of patients with AD, highlighting novel loci that facilitate better characterization of pathways and mechanisms underlying AD pathogenesis, and improve the understanding of epigenetic signatures that may contribute to the development of disease; and suggested more targeted research in this area may be needed.  They stated that future challenges in the field include the development of effective strategies for integrating epigenetic and transcriptomic profiles with genetic datasets, as a means to better understand the roles of different forms of variation in AD.

Gao and co-workers (2018) noted that abnormal DNA methylation patterns have been demonstrated to be associated with the pathogenesis of AD.  These researchers identified differential methylation in the superior temporal gyrus (STG) of patients with late-onset AD based on epigenome-wide DNA methylation data by bioinformatics analysis.  The genome-wide DNA methylation data in the STG region of 34 patients with late-onset AD and 34 controls without dementia were recruited from the Gene Expression Omnibus database.  Through systemic quality control, differentially methylated CpG sites were determined by the Student's t-test and mean methylation value differences between the 2 conditions.  Hierarchical clustering analysis was applied to assess the classification performance of differentially methylated CpGs.  Functional analysis was performed to examine the biological functions of the genes associated with differentially methylated CpGs.  A total of 17,895 differentially methylated CpG sites were initially identified, including 11,822 hyper-methylated CpGs and 6,073 hypo-methylated CpGs.  Further analysis examined 2,211 differentially methylated CpGs (covering 1,991 genes).  Subjects with AD demonstrated distinctive DNA methylation patterns when compared with the controls, with a classification accuracy value of 1.  Hyper-methylation was mainly detected for genes regulating the cell cycle progression, whereas hypo-methylation was observed in genes involved in transcription factor binding.  The authors concluded that the findings of this study demonstrated widespread and distinctive DNA methylation alterations in late-onset AD.  They stated that identification of AD-associated epigenetic biomarkers may allow for the development of novel diagnostic and therapeutic targets.

Long-Term Measurement of Cortisol:

In a prospective, longitudinal study, Ennis and associates (2017) examined if cortisol dysregulation was related to AD risk.  Participants were from the Baltimore Longitudinal Study of Aging (BLSA) and submitted multiple 24-hour urine samples over an average interval of 10.56 years.  Urinary free cortisol (UFC) and creatinine (Cr) were measured, and a UFC/Cr ratio was calculated to standardize UFC.  To measure cortisol regulation, these researchers used within-person UFC/Cr level (i.e., within-person mean), change in UFC/Cr over time (i.e., within-person slope), and UFC/Cr variability (i.e., within-person coefficient of variation).  Cox regression was used to assess whether UFC/Cr measures predicted AD risk.  UFC/Cr level and UFC/Cr variability, but not UFC/Cr slope, were significant predictors of AD risk an average of 2.9 years before AD onset.  Elevated UFC/Cr level and elevated UFC/Cr variability were related to a 1.31- and 1.38-times increase in AD risk, respectively.  In a sensitivity analysis, increased UFC/Cr level and increased UFC/Cr variability predicted increased AD risk an average of 6 years before AD onset.  The authors concluded that cortisol dysregulation as manifested by high UFC/Cr level and high UFC/Cr variability may modulate the down-stream clinical expression of AD pathology or be a pre-clinical marker of AD. 

The authors noted that this study had several drawbacks: Use of 24-hour UFC provided a good measure of total daily cortisol production and excretion but obscured the cortisol circadian rhythm, which may be an important biomarker.  CSF free cortisol, although difficult to collect in large longitudinal studies, would have provided an alternative measure of centrally active glucocorticoids.  Few studies had correlated UFC to CSF cortisol, with one reporting a strong correlation.  These researchers found significant relationships between UFC/Cr measures and AD risk using data for which the last cortisol collection occurred on average from 2.9 to 6 years before AD onset.  Because AD has a prolonged prodromal period, it is difficult to distinguish causative factors from prodromal changes.  The use of AD onset rather than diagnosis in this study excluded data collected during the immediate prodromal period but included data collected during the pre-clinical phase of AD, when pathology may already be evident.

Resting State Eye-Closed Cortical Electroencephalography:

Babiloni and co-workers (2018) tested the hypothesis that cortical sources of resting state eyes-closed electroencephalographic (rsEEG) rhythms reveal different abnormalities in cortical neural synchronization in groups of patients with mild cognitive impairment due to AD (ADMCI) and dementia with Lewy bodies (DLBMCI) as compared to cognitively normal elderly (Nold) subjects.  Clinical and rsEEG data in 30 ADMCI, 23 DLBMCI, and 30 Nold subjects were available in an international archive.  Age, gender, and education were carefully matched in the 3 groups.  The MMSE score was matched between the ADMCI and DLBMCI groups.  Individual alpha frequency peak (IAF) was used to determine the delta, theta, alpha1, alpha2, and alpha3 frequency band ranges.  Fixed beta1, beta2, and gamma bands were also considered.  eLORETA estimated the rsEEG cortical sources.  Receiver operating characteristic curve (ROCC) classified these sources across individuals.  Compared to Nold, IAF showed marked slowing in DLBMCI and moderate in ADMCI.  Furthermore, the posterior alpha 2 and alpha 3 source activities were more abnormal in the ADMCI than the DLBMCI group, while widespread delta source activities were more abnormal in the DLBMCI than the ADMCI group.  The posterior delta and alpha sources correlated with the MMSE score and correctly classified the Nold and MCI individuals (area under the ROCC > 0.85).  The authors concluded that the ADMCI and DLBMCI patients showed different features of cortical neural synchronization at delta and alpha frequencies underpinning brain arousal and vigilance in the quiet wakefulness.  They stated that future prospective cross-validation studies are needed to test the clinical validity of these rsEEG markers.

Serum Neurofilament Light Concentration:

In a cross-sectional study, Weston and colleagues (2017) examined if serum neurofilament light (NfL) concentration is increased in familial AD (FAD), both pre- and post-symptom onset, and whether it is associated with markers of disease stage and severity.  These investigators recruited 48 individuals from families with PSEN1 or APP mutations to this trial -- 18 had symptomatic AD and 30 were asymptomatic but at 50 % risk of carrying a mutation.  Serum NfL was measured using an ultrasensitive immunoassay on the single molecule array (Simoa) platform.  Cognitive testing and MRI were performed; 33 participants had serial MRI, allowing calculation of atrophy rates.  Genetic testing established mutation status.  A generalized least squares regression model was used to compare serum NfL among symptomatic mutation carriers, pre-symptomatic carriers, and non-carriers, adjusting for age and sex.  Spearman coefficients assessed associations between serum NfL and the following: estimated years to/from symptom onset (EYO), cognitive measures, and MRI measures of atrophy.  A total of 19 of the asymptomatic participants were mutation carriers (mean EYO -9.6); 11 were non-carriers.  Compared with non-carriers, serum NfL concentration was higher in both symptomatic (p < 0.0001) and pre-symptomatic mutation carriers (p = 0.007).  Across all mutation carriers, serum NfL correlated with EYO (ρ = 0.81, p < 0.0001) and multiple cognitive and imaging measures, including MMSE (ρ = -0.62, p = 0.0001), Clinical Dementia Rating Scale sum of boxes (ρ = 0.79, p < 0.0001), baseline brain volume (ρ = -0.62, p = 0.0002), and whole-brain atrophy rate (ρ = 0.53, p = 0.01).  The authors concluded that serum NfL concentration was increased in FAD prior to symptom onset and correlated with measures of disease stage and severity.  They stated that serum NfL may thus be a feasible biomarker of early AD-related neurodegeneration; these findings supported the further investigation of serum NfL as an easily accessible biomarker of early AD-related neurodegeneration.

These investigators stated that while these findings were encouraging, there were a number of issues regarding the utility of NfL as a biomarker of early AD.  While as a group the pre-symptomatic carriers had higher mean NfL than non-carriers, there was a degree of overlap in observed values.  The utility of serum NfL to diagnose pre-symptomatic AD at the individual level therefore remains uncertain and needs re-assessment in independent cohorts.  The changes in serum NfL through the course of the disease were analyzed in cross-sectional data only, so it was also not known whether serum NfL tracks progression at an individual level.  Furthermore, while these results supported the use of serum NfL as a marker of neurodegeneration in AD, NfL is not a specific marker to AD and has been shown to increase in a number of other conditions, including HIV-associated dementia, progressive supranuclear palsy, frontotemporal dementia, and amyotrophic lateral sclerosis.  It may therefore be that serum NfL will be most useful for identifying and tracking AD-related neurodegeneration when combined with a test to confirm underlying AD molecular pathology, e.g., CSF tau/Aβ1-42 or amyloid PET.  The authors also noted that this study had several drawbacks -- the sample size was not large (n = 48), owing primarily to the relative rarity of FAD mutations.  However, this remains one of the largest single-center FAD cohorts yet reported.  For a number of participants, not all cognitive and imaging assessments were completed.  However, minimal changes were seen when re-running the analyses to include only those participants who had completed all assessments.  These researchers estimated the age when each mutation carrier would be expected to develop symptoms based on parental age at onset, which was closely associated with actual age at onset; however, this remained a proxy measure, and it is only with longitudinal follow-up that age at onset can be confirmed.

In an editorial that accompanied the afore-mentioned study, Mielke and Otto (2017) stated that “The results by Weston et al on serum NfL as a blood-based marker of AD severity and disease progression are promising.  The next question is how to translate this research for use at the clinical level.  In addition to the extensive approval process for a clinically approved laboratory panel, other steps are needed.  First, studies suggest that other plasma measures (e.g., total tau10) may also be non-specific markers of neurodegeneration.  A comparison of these potential markers is needed to determine which one, or a combination, is most clinically useful.  Second, there is a need to understand what serum NfL and other potential markers look like in the population -- their range; associations with age, race, sex, and co-morbidities; and intra-individual variation.  Understanding these aspects will help fast track serum NfL and other potential blood-based markers into useful clinical practice in both rural and urban settings”.

Table: CPT Codes / HCPCS Codes / ICD-10 Codes
Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

CPT codes not covered for indications listed in the CPB:

Bcl-2 rs956572 polymorphism testing, Resting state eye-closed cortical electroencephalography, serum neurofilament light concentration - no specific code :

81401 Molecular pathology procedure, Level 2 (eg, 2-10 SNPs, 1 methylated variant, or 1 somatic variant [typically using nonsequencing target variant analysis], or detection of a dynamic mutation disorder/triplet repeat)
81406 Molecular pathology procedure, Level 7 (eg, analysis of 11-25 exons by DNA sequence analysis, mutation scanning or duplication/deletion variants of 26-50 exons, cytogenomic array analysis for neoplasia)
82172 Apolipoprotein, each
82530 Cortisol; free [long-term measurement of cortisol]
82533 Cortisol; total [long-term measurement of cortisol]
83090 Homocysteine
83520 Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified [ATP-binding cassette transporter (ABCA7) test, cerebrospinal fluid microRNAs, cerebrospinal fluid prion protein concentration, phosphorylated tau at threonine 181, tau/Aβ42, ptau181/Aβ42, plasma prion protein concentration or transforming growth factor-beta1,CSF total tau (t-tau), simultaneous quantification of amyloid-beta, p-tau, t-tau, CSF neurogranin, and plasma tau]
83880 Natriuretic peptide
84305 Somatomedin
92540 Basic vestibular evaluation, includes spontaneous nystagmus test with eccentric gaze fixation nystagmus, with recording, positional nystagmus test, minimum of 4 positions, with recording, optokinetic nystagmust test, bidirectional foveal and peripheral stimulation, with recording, and oscillating tracking test, with recording
92541 - 92548 Vestibular function tests, with recording (e.g., ENG, PENG), and medical diagnostic evaluation [in the absence of signs of vertigo or balance disorder]
92550 Tympanometry and reflex threshold measurements
92567 Tympanometry (impedance testing) [in the absence of hearing loss]
92568 - 92569 Acoustic reflex testing [in the absence of hearing loss]
92570 Acoustic immittance testing, includes tympanometry (impedance testing), acoustic reflex threshold testing, and acoustic reflex decay testing

Other CPT codes related to the CPB:

88271 - 88275 Molecular cytogenetics

HCPCS codes not covered for indications listed in the CPB:

S3852 DNA analysis for APOE epsilon 4 allele for susceptibility to Alzheimer's disease
S3855 Genetic testing for detection of mutations in the presenilin, 1 gene

ICD-10 codes not covered for indications listed in the CPB (not all-inclusive):

F01.50 - F03.91 Dementia
F07.0 - F07.9 Personality and behavioral disorders due to known physiological condition
G30.0 - G30.9 Alzheimer's disease
G31.84 Mild cognitive impairment, so stated
R41.1 - R41.3 Amnesia
R43.0 - R43.9 Disturbances of smell and taste
Z13.858 Encounter for screening for other nervous system disorders [screening for dementia]
Z82.0 Family history of epilepsy and other diseases of the nervous system [family history of Alzheimer's disease]

The above policy is based on the following references:

  1. McKhann G, Brachman D, Folstein M, et al. Clinical diagnosis of Alzheimer's disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34(7):939-944.
  2. Arai H, Terajima M, Miura M, et al. Tau in cerebrospinal fluid: A potential diagnostic marker in Alzheimer's disease. Ann Neurol. 1995;38:649-652.
  3. Motter R, Vigo-Pelfrey C, Kholodenko D, et al. Reduction of B-amyloid peptide 42 in the cerebrospinal fluid of patients with Alzheimer's disease. Ann Neurol. 1995;38:643-648.
  4. Plassman BL, Breitner JCS. Apolipoprotein E and cognitive decline in Alzheimer's disease. Neurology. 1996;47:317-320.
  5. Peterson RC, Smith GE, Ivnik RJ, et al. Apolipoprotein E status as a predictor of the development of Alzheimer's disease in memory-impaired individuals. JAMA. 1995;273:1274-1278.
  6. Costa PT, Williams TF, Somerfield M, et al. Recognition and initial assessment of Alzheimer's disease and related dementias. Clinical Practice Guideline No. 19. AHCPR Publication No. 97-0702. Rockville, MD: Agency for Health Care Policy and Research (AHCPR); November 1996.
  7. Roses AD, Strittmatter WJ, Pericak-Vance MA, et al. Clinical application of apolipoprotein E genotyping to Alzheimer's disease [letter]. Lancet. 1994;343:1564-1565.
  8. Slooter AJ, Houwing-Duistermaat JJ, van Harskamp F, et al. Apolipoprotein E genotype and progression of Alzheimer's disease: The Rotterdam Study. J Neurol. 1999;246(4):304-308.
  9. Foster GR, Scott DA, Payne S. The use of CT scanning in dementia: A systematic review. Int J Technol Assess Health Care. 1999;15(2):406-423.
  10. Felician O, Sandson TA. The neurobiology and pharmacotherapy of Alzheimer's disease. J Neuropsychiatry Clin Neurosci. 1999;11(1):19-31.
  11. Knopman D. Cerebrospinal fluid beta-amyloid and tau proteins for the diagnosis of Alzheimer disease. Arch Neurol. 2001;58(3):349-350.
  12. Ahmed MB. Alzheimer's disease: Recent advances in etiology, diagnosis, and management. Tex Med. 2001;97(12):50-58.
  13. Ripova D, Strunecka A. An ideal biological marker of Alzheimer's disease: Dream or reality? Physiol Res. 2001;50(2):119-129.
  14. Mega Ms. Differential diagnosis of dementia: Clinical examination and laboratory assessment. Clin Cornerstone. 2001;3(4):1-14.
  15. Patterson CJ. Gauthier S, Bergman H, et al. The recognition, assessment and management of dementing disorders: Conclusions from the Canadian Consensus Conference on Dementia. CMAJ. 1999;160(12 Suppl): S1-S15.
  16. Knopman DS, DeKosky ST, Cummings JL, et al. Practice Parameter: Diagnosis of Dementia: (An evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001;56(9):1143-1153.
  17. de la Monte SM, Wands JR. The AD7c-ntp neuronal thread protein biomarker for detecting Alzheimer's disease. Front Biosci. 2002;7:d989-d996.
  18. Munzar M, Levy S, Rush R, Averback P. Clinical study of a urinary competitive ELISA for neural thread protein in Alzheimer disease. Neurol Clin Neurophysiol. 2002;2002(1):2-8.
  19. Alberta Heritage Foundation for Medical Research (AHFMR). AD7C urine test for NTP protein. Technotes. Edmonton, AB: AHFMR; 1999.
  20. U.S. Preventive Services Task Force. Screening for dementia: Recommendation and rationale. Ann Intern Med. 2003;138(11):925-926.
  21. Olichney JM, Hillert DG. Clinical applications of cognitive event-related potentials in Alzheimer's disease. Phys Med Rehabil Clin N Am. 2004;15(1):205-233.
  22. Hampel H, Mitchell A, Blennow K, et al. Core biological marker candidates of Alzheimer's disease - perspectives for diagnosis, prediction of outcome and reflection of biological activity. J Neural Transm. 2004;111(3):247-272.
  23. Flirski M, Sobow T. Biochemical markers and risk factors of Alzheimer's disease. Curr Alzheimer Res. 2005;2(1):47-64.
  24. Sunderland T, Gur RE, Arnold SE. The use of biomarkers in the elderly: Current and future challenges. Biol Psychiatry. 2005;58(4):272-276.
  25. Eibenstein A, Fioretti AB, Simaskou MN, et al. Olfactory screening test in mild cognitive impairment. Neurol Sci. 2005;26(3):156-160.
  26. Boustani M, Peterson B, Harris R, et al. Screening for dementia. Preventive Services Task Force Systematic Evidence Review No. 20. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ); 2003.
  27. National Horizon Scanning Centre (NHSC). New tests for Alzheimer's disease -- horizon scanning revew. New and Emerging Technology Briefing. Birmingham, UK: National Horizon Scanning Centre (NHSC); 2002.
  28. Ferrante D. SPECT for the diagnosis and assessment of dementia and Alzheimer's disease [summary]. Report ITB No. 14. Buenos Aires, Argentina: Institute for Clinical Effectiveness and Health Policy (IECS); 2004.
  29. Matchar DB, Kulasingam SL, Huntington A, et al. Positron emission tomography, single photon emission computed tomography, computed tomography, functional magnetic resonance imaging, and magnetic resonance spectroscopy and for the diagnosis and management of Alzheimer's dementia. Technology Assessment. Prepared by the Duke Center for Clinical Health Policy Research and Evidence Practice Center for the Agency for Healthcare Research and Quality (AHRQ) under Contract No 290-02-0025. Rockville, MD: AHRQ; April 30, 2004.
  30. Augustovski F, Pichon Riviere A, Alcaraz A, et al. Functional magnetic resonance imaging for brain pathologies [summary]. Report IRR No. 50. Buenas Aires, Argentina: Institute for Clinical Effectiveness and Health Policy (IECS); 2005.
  31. Lleo A. Berezovska O, Growdon JH, Hyman BT. Clinical, pathological, and biochemical spectrum of Alzheimer disease associated with PS-1 mutations. Am J Geriatr Psychiatry. 2004;12(2):146-156.
  32. Mann DM, Pickering-Brown SM, Takeuchi A, Iwatsubo T. Amyloid angiopathy and variability in amyloid beta deposition is determined by mutation position in presenilin-1-linked Alzheimer's disease. Am J Pathol. 2001;158(6):2165-2175.
  33. Selkoe DJ. Alzheimer's disease: Genes, proteins, and therapy. Physiol Rev. 2001;81(2):741-766.
  34. Crystal HA, Davies P. Toward a plasma marker for Alzheimer disease: Some progress, but still a long way to go. Neurology. 2008;70(8):586-587.
  35. Carlson NE, Moore MM, Dame A, et al. Trajectories of brain loss in aging and the development of cognitive impairment. Neurology. 2008;70(11):828-833.
  36. Weiner MW. Expanding ventricles may detect preclinical Alzheimer disease. Neurology. 2008;70(11):824-825.
  37. Hampel H, Shen Y. Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) as a biological candidate marker of Alzheimer's disease. Scand J Clin Lab Invest. 2009;69(1):8-12.
  38. Schmand B, Huizenga HM, van Gool WA. Meta-analysis of CSF and MRI biomarkers for detecting preclinical Alzheimer's disease. Psychol Med. 2010;40(1):135-145.
  39. Carlesimo GA, Cherubini A, Caltagirone C, Spalletta G. Hippocampal mean diffusivity and memory in healthy elderly individuals: A cross-sectional study. Neurology. 2010;74(3):194-200.
  40. Schuff N. A new sensitive MRI marker for memory deficits in normal aging. Neurology. 2010;74(3):188-189.
  41. Hooshmand B, Solomon A, Kåreholt I, et al. Homocysteine and holotranscobalamin and the risk of Alzheimer disease: A longitudinal study. Neurology. 2010;75(16):1408-1414.
  42. Seshadri S. Beauty and the beast: B12, homocysteine, and the brain: A bemusing saga! Neurology. 2010;75(16):1402-1403.
  43. Williams JW, Plassman BL, Burke J, et al. Preventing Alzheimer’s disease and cognitive decline. Evidence Report/Technology Assessment No. 193. (Prepared by the Duke Evidence-based Practice Center under Contract No. HHSA 290-2007-10066-I.) AHRQ Publication No. 10-E005. Rockville, MD: Agency for Healthcare Research and Quality. April 2010. Available at: Accessed March 31, 2011.
  44. Ford AH, Almeida OP. Effect of homocysteine lowering treatment on cognitive function: A systematic review and meta-analysis of randomized controlled trials. J Alzheimers Dis. 2012;29(1):133-149.
  45. Quiroz YT, Ally BA, Celone K, et al. Event-related potential markers of brain changes in preclinical familial Alzheimer disease. Neurology. 2011;77(5):469-475.
  46. Gupta VB, Laws SM, Villemagne VL, et al; AIBL Research Group. Plasma apolipoprotein E and Alzheimer disease risk: The AIBL study of aging. Neurology. 2011;76(12):1091-1098.
  47. Perneczky R, Tsolakidou A, Arnold A, et al. CSF soluble amyloid precursor proteins in the diagnosis of incipient Alzheimer disease. Neurology. 2011;77(1):35-38.
  48. Schrijvers EM, Koudstaal PJ, Hofman A, Breteler MM. Plasma clusterin and the risk of Alzheimer disease. JAMA. 2011;305(13):1322-1326.
  49. Tan ZS, Harris WS, Beiser AS, et al. Red blood cell omega-3 fatty acid levels and markers of accelerated brain aging. Neurology. 2012;78(9):658-664.
  50. Tarawneh R, Lee JM, Ladenson JH, et al. CSF VILIP-1 predicts rates of cognitive decline in early Alzheimer disease. Neurology. 2012;78(10):709-719.
  51. Watabe-Rudolph M, Song Z, Lausser L, et al. Chitinase enzyme activity in CSF is a powerful biomarker of Alzheimer disease. Neurology. 2012;78(8):569-577.
  52. Hudson G, Sims R, Harold D, et al; GERAD1 Consortium. No consistent evidence for association between mtDNA variants and Alzheimer disease. Neurology. 2012;78(14):1038-1042.
  53. Mielke MM, Bandaru VV, Haughey NJ, et al. Serum ceramides increase the risk of Alzheimer disease: The Women's Health and Aging Study II. Neurology. 2012;79(7):633-641.
  54. Chen C, Zhou Z, Li M, et al. Presenilin-2 polymorphisms and risk of sporadic AD: Evidence from a meta-analysis. Gene. 2012;503(2):194-199.
  55. Gerrish A, Russo G, Richards A, et al. The role of variation at AβPP, PSEN1, PSEN2, and MAPT in late onset Alzheimer's disease. J Alzheimers Dis. 2012;28(2):377-387.
  56. Jonsson T, Stefansson H, Steinberg S, et al. Variant of TREM2 associated with the risk of Alzheimer's disease. N Engl J Med. 2013;368(2):107-116.
  57. Guerreiro R, Wojtas A, Bras J, et al; Alzheimer Genetic Analysis Group. TREM2 variants in Alzheimer's disease. N Engl J Med. 2013;368(2):117-127.
  58. Neumann H, Daly MJ. Variant TREM2 as risk factor for Alzheimer's disease. N Engl J Med. 2013;368(2):182-184.
  59. Zhang Y, Wang B, Wan H, et al. Meta-analysis of the insulin degrading enzyme polymorphisms and susceptibility to Alzheimer's disease. Neurosci Lett. 2013;541:132-137.
  60. Caraci F, Spampinato S, Sortino MA, et al. Dysfunction of TGF-β1 signaling in Alzheimer's disease: Perspectives for neuroprotection. Cell Tissue Res. 2012;347(1):291-301.
  61. Chang WW, Zhang L, Jin YL, Yao YS. Meta-analysis of the transforming growth factor-β1 polymorphisms and susceptibility to Alzheimer's disease. J Neural Transm. 2013;120(2):353-360.
  62. Reitz C, Jun G, Naj A, et al; Alzheimer Disease Genetics Consortium. Variants in the ATP-binding cassette transporter (ABCA7), apolipoprotein E ϵ4,and the risk of late-onset Alzheimer disease in African Americans. JAMA. 2013;309(14):1483-1492.
  63. Sala Frigerio C, Lau P, Salta E, et al. Reduced expression of hsa-miR-27a-3p in CSF of patients with Alzheimer disease. Neurology. 2013;81(24):2103-2106.
  64. Roe CM, Fagan AM, Grant EA, et al. CSF biomarkers of Alzheimer disease: "Noncognitive" outcomes. Neurology. 2013;81(23):2028-2031.
  65. Schmidt C, Artjomova S, Hoeschel M, Zerr I. CSF prion protein concentration and cognition in patients with Alzheimer disease. Prion. 2013;7(3):229-234.
  66. Schmidt C, Becker H, Peter C, et al. Plasma prion protein concentration and progression of Alzheimer disease. Prion. 2014;8(2). pii: 27964.
  67. Garcia-Martin ES, Rojas B, Ramirez AI, et al.  Macular thickness as a potential biomarker of mild Alzheimer's disease. Ophthalmology. 2014;121(5):1149-1151.
  68. Westwood AJ, Beiser A, Decarli C, et al. Insulin-like growth factor-1 and risk of Alzheimer dementia and brain atrophy. Neurology. 2014;82(18):1613-1619.
  69. Danborg PB, Simonsen AH, Waldemar G, Heegaard NH. The potential of microRNAs as biofluid markers of neurodegenerative diseases -- a systematic review. Biomarkers. 2014;19(4):259-268.
  70. Tan L, Yu JT, Tan MS, et al. Genome-wide serum microRNA expression profiling identifies serum biomarkers for Alzheimer's disease. J Alzheimers Dis. 2014;40(4):1017-1027.
  71. Han P, Liang W, Baxter LC, et al. Pituitary adenylate cyclase-activating polypeptide is reduced in Alzheimer disease. Neurology. 2014;82(19):1724-1728.
  72. Marksteiner J, Imarhiagbe D, Defrancesco M, et al. Analysis of 27 vascular-related proteins reveals that NT-proBNP is a potential biomarker for Alzheimer's disease and mild cognitive impairment: A pilot-study. Exp Gerontol. 2014;50:114-121.
  73. Schaffer C, Sarad N, DeCrumpe A, et al.  Biomarkers in the diagnosis and prognosis of Alzheimer's disease. J Lab Autom. 2015;20(5):589-600.
  74. Growdon ME, Schultz AP, Dagley AS, et al. Odor identification and Alzheimer disease biomarkers in clinically normal elderly. Neurology. 2015;84(21):2153-2160.
  75. Alcolea D, Martínez-Lage P, Sánchez-Juan P, et al. Amyloid precursor protein metabolism and inflammation markers in preclinical Alzheimer disease. Neurology. 2015;85(7):626-633.
  76. Wolk DA, Dickerson BC. Clinical features and diagnosis of Alzheimer disease. UpToDate Inc., Waltham, MA. Last reviewed January 2016.
  77. Kvartsberg H, Duits FH, Ingelsson M, et al. Cerebrospinal fluid levels of the synaptic protein neurogranin correlates with cognitive decline in prodromal Alzheimer's disease. Alzheimers Dement. 2015;11(10):1180-1190.
  78. Kester MI, Teunissen CE, Crimmins DL, et al. Neurogranin as a cerebrospinal fluid biomarker for synaptic loss in symptomatic Alzheimer disease. JAMA Neurol. 2015;72(11):1275-1280.
  79. Wellington H, Paterson RW, Portelius E, et al. Increased CSF neurogranin concentration is specific to Alzheimer disease. Neurology. 2016;86(9):829-835.
  80. Mattsson N, Zetterberg H, Janelidze S, et al.; ADNI Investigators. Plasma tau in Alzheimer disease. Neurology. 2016;87(17):1827-1835.
  81. Rivero-Santana A, Ferreira D, Perestelo-Perez L, et al. Cerebrospinal fluid biomarkers for the differential diagnosis between Alzheimer's disease and frontotemporal lobar degeneration: Systematic review, HSROC analysis, and confounding factors. J Alzheimers Dis. 2017;55(2):625-644.
  82. Li H, Guo Z, Guo Y, et al. Common DNA methylation alterations of Alzheimer's disease and aging in peripheral whole blood. Oncotarget. 2016;7(15):19089-19098.
  83. Watson CT, Roussos P, Garg P, et al. Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer's disease. Genome Med. 2016;8(1):5.
  84. Lafaille-Magnan ME, Poirier J, Etienne P, et al; PREVENT-AD Research Group. Odor identification as a biomarker of preclinical AD in older adults at risk. Neurology. 2017;89(4):327-335.
  85. Ennis GE, An Y, Resnick SM, et al. Long-term cortisol measures predict Alzheimer disease risk. Neurology. 2017;88(4):371-378.
  86. Weston PSJ, Poole T, Ryan NS, et al. Serum neurofilament light in familial Alzheimer disease: A marker of early neurodegeneration. Neurology. 2017;89(21):2167-2175.
  87. Mielke MM, Otto M. Neurofilaments in blood. (Almost) facing clinical application. Neurology. 2017;89(21):2126-2127.
  88. Chang CC, Chang YT, Huang CW, et al. Associations of Bcl-2 rs956572 genotype groups in the structural covariance network in early-stage Alzheimer's disease. Alzheimers Res Ther. 2018;10(1):17.
  89. Gao Z, Fu HJ, Zhao LB, et al. Aberrant DNA methylation associated with Alzheimer's disease in the superior temporal gyrus. Exp Ther Med. 2018;15(1):103-108.
  90. Babiloni C, Del Percio C, Lizio R, et al. Abnormalities of resting state cortical EEG rhythms in subjects with mild cognitive impairment due to Alzheimer's and Lewy body diseases. J Alzheimers Dis. 2018;62(1):247-268.
  91. Musiek ES, Bhimasani M, Zangrilli MA, et al. Circadian rest-activity pattern changes in aging and preclinical Alzheimer disease. JAMA Neurol. 2018 Jan 29 [Epub ahead of print].

Videopupillography/Tropicamide Drop Test

  1. Scinto LF, Daffner KR, Dressler D, et al. A potential noninvasive neurobiological test for Alzheimer's disease. Science. 1994;266(5187):1051-1054.
  2. Gomez-Tortosa E, del Barrio A, Jimenez-Alfaro I. Pupil response to tropicamide in Alzheimer's disease and other neurodegenerative disorders. Acta Neurol Scand. 1996;94(2):104-109.
  3. Graff-Radford NR, Lin SC, Brazis PW, et al. Tropicamide eyedrops cannot be used for reliable diagnosis of Alzheimer's disease. Mayo Clin Proc. 1997;72(6):495-504.
  4. Growdon JH, Graefe K, Tennis M, et al. Pupil dilation to tropicamide is not specific for Alzheimer's disease. Arch Neurol. 1997;54(7):841-844.
  5. Kalman J, Kanka A, Magloczky E, et al. Increased mydriatic response to tropicamide is a sign of cholinergic hypersensitivity but not specific to late-onset sporadic type of Alzheimer's dementia. Biol Psychiatry. 1997;41(8):909-911.
  6. Kardon RH. Drop the Alzheimer's drop test. Neurology. 1998;50:588-591.
  7. Ferrario E, Molaschi M, Villa L, et al. Is videopupillography useful in the diagnosis of Alzheimer's disease? Neurology. 1998;50(3):642-644.
  8. Verghese J. Is videopupillography useful in diagnosing Alzheimer's disease? Neurology. 1999;52(3):674-675.