The association of Aβ amyloid and composite cognitive measures in healthy older adults and MCI

Identification of Leukocyte Surface P2X7 as a Biomarker Associated with Alzheimer's Disease

Alzheimer's disease (AD) has shown altered immune responses in the periphery. We studied P2X7 (a proinflammatory receptor and a scavenger receptor) and two integrins, CD11b and CD11c, on the surface of circulating leukocytes and analysed their associations with Aβ-PET, brain atrophy, neuropsychological assessments, and cerebrospinal fluid (CSF) biomarkers. Total 287 age-matched, sex-balanced participants were recruited in a discovery cohort and two validation cohorts through the AIBL study and studied using tri-colour flow cytometry. Our results demonstrated reduced expressions of P2X7, CD11b, and CD11c on leukocytes, particularly monocytes, in Aβ +ve cases compared with Aβ -ve controls. P2X7 and integrin downregulation was observed at pre-clinical stage of AD and stayed low throughout disease course. We further constructed a polygenic risk score (PRS) model based on 12 P2RX7 risk alleles to assess the genetic impact on P2X7 function in AIBL and ADNI cohorts. No significant association was identified between the P2RX7 gene and AD, indicating that P2X7 downregulation in AD is likely caused by environmental changes rather than genetic factors. In conclusion, the downregulation of P2X7 and integrins at pre-clinical stage of AD indicates altered pro-inflammatory responses, phagocytic functions, and migrating capabilities of circulating monocytes in early AD pathogenesis. Our study not only improves our understanding of peripheral immune involvement in early stage of AD but also provides more insights into novel biomarker development, diagnosis, and prognosis of AD.

Differential Patterns of Domain-Specific Cognitive Complaints and Awareness Across the Alzheimer's Disease Spectrum

Background: Characterizing self- and informant-reported cognitive complaints, as well as awareness of cognitive decline (ACD), is useful for an early diagnosis of Alzheimer's disease (AD). However, complaints and ACD related to cognitive functions other than memory are poorly studied. Furthermore, it remains unclear which source of information is the most useful to distinguish various groups on the AD spectrum. Methods: Self- and informant-reported complaints were measured with the Everyday Cognition questionnaire (ECog-Subject and ECog-StudyPartner) in four domains (memory, language, visuospatial, and executive). ACD was measured as the subject-informant discrepancy in the four ECog scores. We compared the ECog and ACD scores across cognitive domains between four groups: 71 amyloid-positive individuals with amnestic AD, 191 amnestic mild cognitive impairment (MCI), or 118 cognitively normal (CN), and 211 amyloid-negative CN controls, selected from the ADNI database. Receiver operating characteristic curves analysis was performed to evaluate the accuracy of the ECog and ACD scores in discriminating clinical groups. Results: Self- and informant-reported complaints were generally distributed as follows: memory, language, executive, and visuospatial (from the most severe to the least severe). Both groups of CN participants presented on average more memory and language complaints than their informant. MCI participants showed good agreement with their informants. AD participants presented anosognosia in all domains, but especially for the executive domain. The four ECog-StudyPartner sub-scores allowed excellent discrimination between groups in almost all classifications and performed significantly better than the other two classifiers considered. The ACD was excellent in distinguishing the participants with AD from the two groups of CN participants. The ECog-Subject was the least accurate in discriminating groups in four of the six classifications performed. Conclusion: In research, the study of complaint and anosognosia should not be reduced solely to the memory domain. In clinical practice, non-amnestic complaints could also be linked to Alzheimer's disease. The presence of an informant also seems necessary given its accuracy as a source of information.

Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI

To improve understanding of Alzheimer’s disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.

Fifteen Years of the Australian Imaging, Biomarkers and Lifestyle (AIBL) Study: Progress and Observations from 2,359 Older Adults Spanning the Spectrum from Cognitive Normality to Alzheimer's Disease

Background:

The Australian Imaging, Biomarkers and Lifestyle (AIBL) Study commenced in 2006 as a prospective study of 1,112 individuals (768 cognitively normal (CN), 133 with mild cognitive impairment (MCI), and 211 with Alzheimer’s disease dementia (AD)) as an ‘Inception cohort’ who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an ‘Enrichment cohort’ (as of 10 April 2019).

Objective:

Here we provide an overview of these Inception and Enrichment cohorts of more than 8,500 person-years of investigation.

Methods:

Participants underwent reassessment every 18 months including comprehensive cognitive testing, neuroimaging (magnetic resonance imaging, MRI; positron emission tomography, PET), biofluid biomarkers and lifestyle evaluations.

Results:

AIBL has made major contributions to the understanding of the natural history of AD, with cognitive and biological definitions of its three major stages: preclinical, prodromal and clinical. Early deployment of Aβ-amyloid and tau molecular PET imaging and the development of more sensitive and specific blood tests have facilitated the assessment of genetic and environmental factors which affect age at onset and rates of progression.

Conclusion:

This fifteen-year study provides a large database of highly characterized individuals with longitudinal cognitive, imaging and lifestyle data and biofluid collections, to aid in the development of interventions to delay onset, prevent or treat AD. Harmonization with similar large longitudinal cohort studies is underway to further these aims.

Early Identification of Alzheimer's Disease in Mouse Models: Application of Deep Neural Network Algorithm to Cognitive Behavioral Parameters

Alzheimer's disease (AD) is a worldwide burden. Diagnosis is complicated by the fact that AD is asymptomatic at an early stage. Studies using AD-modeled animals offer important and useful insights. Here, we classified mice with a high risk of AD at a preclinical stage by using only their behaviors. Wild-type and knock-in AD-modeled (App NL-G-F/NL-G-F ) mice were raised, and their cognitive behaviors were assessed in an automated monitoring system. The classification utilized a machine learning method, i.e., a deep neural network, together with optimized stepwise feature selection and cross-validation. The AD risk could be identified on the basis of compulsive and learning behaviors (89.3% ± 9.8% accuracy) shown by AD-modeled mice in the early age (i.e., 8-12 months old) when the AD symptomatic cognitions were relatively underdeveloped. This finding reveals the advantage of machine learning in unveiling the importance of compulsive and learning behaviors for early AD diagnosis in mice.

Identification of Pre-Clinical Alzheimer's Disease in a Population of Elderly Cognitively Normal Participants

Alzheimer's disease (AD) has a long pathological process, with an approximate lead-time of 20 years. During the early stages of the disease process, little evidence of the building pathology is identifiable without cerebrospinal fluid and/or imaging analyses. Clinical manifestations of AD do not present until irreversible pathological changes have occurred. Given an opportunity to provide treatment prior to irreversible pathological change, this study aims to identify a subgroup of cognitively normal (CN) participants from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), where subtle changes in cognition are indicative of early AD-related pathology. Using a Bayesian method for unsupervised clustering via mixture models, we define an aggregate measure of posterior probabilities (AMPP score) establishing the likelihood of pre-clinical AD. From Baseline through to 54 months, visuo-spatial function had the greatest contribution to the AMPP score, followed by attention and processing speed and visual memory. Participants with the highest AMPP scores had both increasing neo-cortical amyloid burden and decreasing hippocampus volume over 54 months, compared to those in the lowest category with stable amyloid burden and hippocampus volume. The identification of a possible pre-clinical stage in CN participants via this method, without the aid of disease specific biomarkers, represents an important step in utilizing the strength of cognitive composite scores for the early detection of AD pathology.

Characteristics of Cognitive Deficit in Amnestic Mild Cognitive Impairment With Subthreshold Depression

It is widely recognized that depression may precipitate the incidence of dementia in the elderly individuals and individuals with amnestic mild cognitive impairment (aMCI) in particular. However, the association between subthreshold depression (SD) and cognitive deficits in patients with aMCI remains unclear. To address this, we collected demographic information and conducted a battery of neuropsychological cognitive assessments in 33 aMCI participants with SD (aMCI/SD+), 33 nondepressed aMCI participants (aMCI/SD-), and 53 normal controls (NC). Both aMCI groups showed significantly poorer performance in most cognitive domains relative to the NC group (ie, memory, language, processing speed, and executive function). Notably, the aMCI/SD+ group showed significantly poorer attention/working memory compared with the aMCI/SD- group. Multiple linear regression analyses revealed a significant negative association between the severity of depressive symptoms and attention/working memory capacity (β = - .024, P = .024), accounting for 8.28% of the variations in this cognitive domain. All statistical analyses were adjusted by age, sex, and years of education. A logistic regression model had an accuracy of 72.4% in discriminating between the aMCI/SD+ and aMCI/SD- groups based on individual cognitive profiles over 6 domains. Our findings indicate that patients with aMCI with and without SD have distinct patterns of cognitive impairment. This finding may facilitate the diagnosis and treatment of SD in patients with aMCI.

Associating Cognition With Amyloid Status Using Partially Ordered Set Analysis

Background: The presence of brain amyloid-beta positivity is associated with cognitive impairment and dementia, but whether there are specific aspects of cognition that are most linked to amyloid-beta is unclear. Analysis of neuropsychological test data presents challenges since a single test often requires drawing upon multiple cognitive functions to perform well. It can thus be imprecise to link performance on a given test to a specific cognitive function. Our objective was to provide insight into how cognitive functions are associated with brain amyloid-beta positivity among samples consisting of cognitively normal and mild cognitively impaired (MCI) subjects, by using partially ordered set models (POSETs). Methods: We used POSET classification models of neuropsychological test data to classify samples to detailed cognitive profiles using ADNI2 and AIBL data. We considered 3 gradations of episodic memory, cognitive flexibility, verbal fluency, attention and perceptual motor speed, and performed group comparisons of cognitive functioning stratified by amyloid positivity (yes/no) and age (<70, 70-80, 81-90 years). We also employed random forest methods stratified by age to assess the effectiveness of cognitive testing in predicting amyloid positivity, in addition to demographic variables, and APOE4 allele count. Results: In ADNI2, differences in episodic memory and attention by amyloid were found for <70, and 70-80 years groups. In AIBL, episodic memory differences were found in the 70-80 years age group. In both studies, no cognitive differences were found in the 81-90 years group. The random forest analysis indicates that variable importance in classification depends on age. Cognitive testing that targets an intermediate level of episodic memory and delayed recall, in addition to APOE4 allele count, are the most important variables in both studies. Conclusions: In the ADNI2 and AIBL samples, the associations between specific cognitive abilities and brain amyloid-beta positivity depended on age, but in general episodic memory was most consistently predictive of brain amyloid-beta positivity. Random forest methods and OOB error rates establish the feasibility of predicting the presence of brain beta-amyloid using cognitive testing, APOE4 genotyping and demographic variables.

Increased Carbohydrate Intake is Associated with Poorer Performance in Verbal Memory and Attention in an APOE Genotype-Dependent Manner

Evidence suggests that a diet low in carbohydrates can impact on cognitive performance among those with Alzheimer's disease (AD). However, there is a lack of data assessing this relationship among cognitively normal (CN) older adults at increased future risk of developing AD due to carriage of the apolipoprotein E (APOE) ɛ4 allele. We assessed the cross-sectional association between carbohydrate intake, cognitive performance, and cerebral amyloid-β (Aβ) load in CN older adults, genotyped for APOEɛ4 allele carrier status. Greater carbohydrate intake was associated with poorer performance in verbal memory in APOEɛ4 allele non-carriers, and poorer performance in attention in APOEɛ4 allele carriers. There were no associations between carbohydrate intake and cerebral Aβ load. These results provide support to the idea that decreasing carbohydrate intake may offer neurocognitive benefits, with specific cognitive domains affected in an APOE genotype-dependent manner. These findings warrant further investigation utilizing a longitudinal study design.

Cortical β-amyloid burden, gray matter, and memory in adults at varying APOE ε4 risk for Alzheimer's disease

Models of preclinical Alzheimer's disease (AD) propose that cerebral amyloidosis leads to neurodegeneration and subsequent cognitive decline. This study investigated whether APOE genotype is related to β-amyloid (Aβ) burden in brain regions preferentially affected by AD and whether Aβ burden is associated with gray-matter (GM) fraction (as a marker of neurodegeneration) and episodic memory performance in cognitively normal middle-aged individuals at varying genetic risk for AD. Three groups of cognitively normal participants aged 50-65 years with a first-degree family history of AD (APOE genotype ε4ε4 [n = 15], ε3ε4 [n = 15], and ε3ε3 [n = 15]) underwent [¹¹C]PiB positron emission tomography scans to quantify cortical Aβ, brain magnetic resonance imaging, and neuropsychological testing. APOE ε4ε4 participants demonstrated significantly higher cortical Aβ burden than APOE ε3ε3 (p < 0.001). Furthermore, cortical Aβ burden was inversely associated with cortical GM fraction (p = 0.017) but not episodic memory performance. In cognitively normal, middle-aged individuals, Aβ burden is significantly associated with GM fraction but not episodic memory performance. These findings are consistent with models of preclinical AD in which neurodegeneration occurs before manifest cognitive decline.

Amyloid β-associated cognitive decline in the absence of clinical disease progression and systemic illness

Introduction

High levels of amyloid β (Aβ) are associated with cognitive decline in cognitively normal (CN) older adults. This study investigated the nature of cognitive decline in healthy individuals who did not progress to mild cognitive impairment or dementia.

Method

Cognition was measured over 72 months and compared between low (Aβ−) and high (Aβ+) CN older adults (n = 335) who did not progress to mild cognitive impairment or dementia and who remained free of severe or uncontrolled systemic illness.

Results

Compared to the Aβ− group, the Aβ+ group showed no cognitive impairment at baseline but showed substantial decline in verbal learning, episodic memory, and attention over 72 months.

Discussion

Moderate cognitive decline, particularly for learning and memory, was associated with Aβ+ in CN older adults in the absence of clinical disease progression and uncontrolled or serious comorbid illness.

Plasma Cortisol, Brain Amyloid-β, and Cognitive Decline in Preclinical Alzheimer's Disease: A 6-Year Prospective Cohort Study

BACKGROUND

Hypothalamic-pituitary-adrenal axis dysregulation, which is typically assessed by measuring cortisol levels, is associated with cognitive dysfunction, hippocampal atrophy, and increased risk for mild cognitive impairment and Alzheimer's disease (AD). However, little is known about the role of hypothalamic-pituitary-adrenal axis dysregulation in moderating the effect of high levels of amyloid-β (Aβ+) on cognitive decline in the preclinical phase of AD, which is often protracted, and thus offers opportunities for prevention and early intervention.

METHODS

Using data from a 6-year multicenter prospective cohort study, we evaluated the relation between Aβ level, plasma cortisol level, and cognitive decline in 416 cognitively normal older adults.

RESULTS

Results revealed that Aβ+ older adults experienced faster decline than Aβ- older adults in all cognitive domains (Cohen's d at 6-year assessment = 0.37-0.65). They further indicated a significant interaction between Aβ and cortisol levels for global cognition (d = 0.32), episodic memory (d = 0.50), and executive function (d = 0.59) scores, with Aβ+ older adults with high cortisol levels having significantly faster decline in these domains compared with Aβ+ older adults with low cortisol levels. These effects were independent of age, sex, APOE genotype, anxiety symptoms, and radiotracer type.

CONCLUSIONS

In cognitively healthy older adults, Aβ+ is associated with greater cognitive decline and high plasma cortisol levels may accelerate the effect of Aβ+ on decline in global cognition, episodic memory, and executive function. These results suggest that therapies targeted toward lowering plasma cortisol and Aβ levels may be helpful in mitigating cognitive decline in the preclinical phase of AD.

Predicting Alzheimer disease from a blood-based biomarker profile: A 54-month follow-up

OBJECTIVE

We assessed a blood-based signature, which previously demonstrated high accuracy at stratifying individuals with high or low neocortical β-amyloid burden (NAB), to determine whether it could also identify individuals at risk of progression to Alzheimer disease (AD) within 54 months.

METHODS

We generated the blood-based signature for 585 healthy controls (HCs) and 74 participants with mild cognitive impairment (MCI) from the Australian Imaging, Biomarkers and Lifestyle Study who underwent clinical reclassification (blinded to biomarker findings) at 54-month follow-up. The individuals were split into estimated high and low NAB groups based on a cutoff of 1.5 standardized uptake value ratio. We assessed the predictive accuracy of the high and low NAB groupings based on progression to mild cognitive impairment or AD according to clinical reclassification at 54-month follow-up.

RESULTS

Twelve percent of HCs with estimated high NAB progressed in comparison to 5% of HCs with estimated low NAB (odds ratio = 2.4). Forty percent of the participants with MCI who had estimated high NAB progressed in comparison to 5% of the participants with MCI who had estimated low NAB (odds ratio = 12.3). These ratios are in line with those reported for Pittsburgh compound B-PET results. Individuals with estimated high NAB had faster rates of memory decline than those with estimated low NAB.

CONCLUSION

These findings suggest that a simple blood-based signature not only provides estimates of NAB but also predicts cognitive decline and disease progression, identifying individuals at risk of progressing toward AD at the prodromal and preclinical stages.

Subjective memory decline predicts greater rates of clinical progression in preclinical Alzheimer's disease

INTRODUCTION

The objective of this study was to determine the utility of subjective memory decline (SMD) to predict episodic memory change and rates of clinical progression in cognitively normal older adults with evidence of high β-amyloid burden (CN Aβ+).

METHODS

Fifty-eight CN Aβ+ participants from the Australian Imaging, Biomarkers, and Lifestyle study responded to an SMD questionnaire and underwent comprehensive neuropsychological assessments. Participant data for three follow-up assessments were analyzed.

RESULTS

In CN Aβ+, subjects with high SMD did not exhibit significantly greater episodic memory decline than those with low SMD. High SMD was related to greater rates of progression to mild cognitive impairment or Alzheimer's disease (AD) dementia (hazard ratio = 5.1; 95% confidence interval, 1.4-20.0, P = .02) compared with low SMD. High SMD was associated with greater depressive symptomatology and smaller left hippocampal volume.

DISCUSSION

High SMD is a harbinger of greater rates of clinical progression in preclinical AD. Although SMD reflects broader diagnostic implications for CN Aβ+, more sensitive measures may be required to detect early subtle cognitive change.

APOE and BDNF polymorphisms moderate amyloid β-related cognitive decline in preclinical Alzheimer's disease

Accumulation of β-amyloid (Aβ) in the brain is associated with memory decline in healthy individuals as a prelude to Alzheimer's disease (AD). Genetic factors may moderate this decline. We examined the role of apolipoprotein E (ɛ4 carrier[ɛ4(+)], ɛ4 non-carrier[ɛ4(-)]) and brain-derived neurotrophic factor (BDNF(Val/Val), BDNF(Met)) in the extent to which they moderate Aβ-related memory decline. Healthy adults (n=333, Mage=70 years) enrolled in the Australian Imaging, Biomarkers and Lifestyle study underwent Aβ neuroimaging. Neuropsychological assessments were conducted at baseline, 18-, 36- and 54-month follow-ups. Aβ positron emission tomography neuroimaging was used to classify participants as Aβ(-) or Aβ(+). Relative to Aβ(-)ɛ4(-), Aβ(+)ɛ4(+) individuals showed significantly faster rates of cognitive decline over 54 months across all domains (d=0.40-1.22), while Aβ(+)ɛ4(-) individuals showed significantly faster decline only on verbal episodic memory (EM). There were no differences in rates of cognitive change between Aβ(-)ɛ4(-) and Aβ(-)ɛ4(+) groups. Among Aβ(+) individuals, ɛ4(+)/BDNF(Met) participants showed a significantly faster rate of decline on verbal and visual EM, and language over 54 months compared with ɛ4(-)/BDNF(Val/Val) participants (d=0.90-1.02). At least two genetic loci affect the rate of Aβ-related cognitive decline. Aβ(+)ɛ4(+)/BDNF(Met) individuals can expect to show clinically significant memory impairment after 3 years, whereas Aβ(+)ɛ4(+)/BDNF(Val/Val) individuals can expect a similar degree of impairment after 10 years. Little decline over 54 months was observed in the Aβ(-) and Aβ(+) ɛ4(-) groups, irrespective of BDNF status. These data raise important prognostic issues in managing preclinical AD, and should be considered in designing secondary preventative clinical trials.

Anxiety symptoms, cerebral amyloid burden and memory decline in healthy older adults without dementia: 3-year prospective cohort study

Although beta-amyloid, anxiety and depression have linked cross-sectionally to reduced memory function in healthy older adults without dementia, prospective data evaluating these associations are lacking. Using data an observational cohort study of 178 healthy older adults without dementia followed for 3 years, we found that anxiety symptoms significantly moderated the relationship between beta-amyloid level and decline in verbal (Cohen's d = 0.65) and episodic (Cohen's d = 0.38) memory. Anxiety symptoms were additionally linked to greater decline in executive function, irrespective of beta-amyloid and other risk factors. These findings suggest that interventions to mitigate anxiety symptoms may help delay memory decline in otherwise healthy older adults with elevated beta-amyloid.

Dietary patterns and cognitive decline in an Australian study of ageing

The aim of this paper was to investigate the association of three well-recognised dietary patterns with cognitive change over a 3-year period. Five hundred and twenty-seven healthy participants from the Australian Imaging, Biomarkers and Lifestyle study of ageing completed the Cancer Council of Victoria food frequency questionnaire at baseline and underwent a comprehensive neuropsychological assessment at baseline, 18 and 36 months follow-up. Individual neuropsychological test scores were used to construct composite scores for six cognitive domains and a global cognitive score. Based on self-reported consumption, scores for three dietary patterns, (1) Australian-style Mediterranean diet (AusMeDi), (2) western diet and (3) prudent diet were generated for each individual. Linear mixed model analyses were conducted to examine the relationship between diet scores and cognitive change in each cognitive domain and for the global score. Higher baseline adherence to the AusMeDi was associated with better performance in the executive function cognitive domain after 36 months in apolipoprotein E (APOE) ɛ4 allele carriers (P<0.01). Higher baseline western diet adherence was associated with greater cognitive decline after 36 months in the visuospatial cognitive domain in APOE ɛ4 allele non-carriers (P<0.01). All other results were not significant. Our findings in this well-characterised Australian cohort indicate that adherence to a healthy diet is important to reduce risk for cognitive decline, with the converse being true for the western diet. Executive function and visuospatial functioning appear to be particularly susceptible to the influence of diet.

Amyloid-beta and depression in healthy older adults: a systematic review

OBJECTIVE

Depression has been shown to be a risk factor for Alzheimer's disease (AD), and in older adults may provide a marker for the beginning of the prodromal phase of AD. The purpose of this systematic review is to examine the relationship between amyloid-β (Aβ), a key biomarker of AD, and depression in older adults.

METHOD

The literature search was limited to studies conducted from 2006 to 2014 that were published in English in peer-reviewed journals. Studies were selected if they included a group of older adults who either met established criteria for Major Depressive Disorder or Dysthymia; or were assessed for depressive symptoms on a standardised measure. Studies were also required to include an outcome variable that was a direct measure of Aβ levels in either blood or cerebrospinal fluid (CSF) samples, or via neuroimaging techniques such as positron emission tomography (PET).

RESULTS

Nineteen studies were identified, 15 of which found significant differences in Aβ levels between depressed and non-depressed older adults. However, studies were limited by their cross-sectional design, reliance on blood-based measures of Aβ, and potential sample bias.

CONCLUSIONS

Future investigations should consider prospective longitudinal design using neuroimaging and CSF measures of Aβ.

Genetic algorithm with logistic regression for prediction of progression to Alzheimer's disease

Background

Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development.

Results

Multiple sets of neuropsychological tests were identified by GA to best predict conversions between clinical categories, with a cross validated AUC (area under the ROC curve) of 0.90 for prediction of HC conversion to MCI/AD and 0.86 for MCI conversion to AD within 36 months.

Conclusions

This study showed the potential of GA application in the neural science area. It demonstrated that the combination of a small set of variables is superior in performance than the use of all the single significant variables in the model for prediction of progression of disease. Variables more frequently selected by GA might be more important as part of the algorithm for prediction of disease development.