An adaptive genetic algorithm for selection of blood-based biomarkers for prediction of Alzheimer's disease progression.

BACKGROUND: Alzheimer's disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD. RESULTS: The model has been explored in terms of parameter optimization to predict conversion from healthy stage to AD with high accuracy. Several feature sets were selected - the resulting prediction moddels showed higher area under the ROC values (0.83-0.89). The results has shown consistency with some of the medical research reported in literature. CONCLUSION: The AGA has proven useful in selecting the best combination of biomarkers for prediction of AD progression. The algorithm presented here is generic and can be extended to other data sets generated in projects that seek to identify combination of biomarkers or other features that are predictive of disease onset or progression.

Self and informant memory concerns align in healthy memory complainers and in early stages of mild cognitive impairment but separate with increasing cognitive impairment.

BACKGROUND: Information provided by an informant about a patient with cognitive change is an essential component of clinical history taking. How an informant's report relates to the patient's phenomenological experience of memory loss is yet to be understood. The aim was to examine patterns of relationships between self and informant reports from a phenomenological perspective. METHODS: Forty-three healthy non-memory complainers (HC-NMC), 37 healthy subjective memory complainers (HC-SMC) and 43 individuals with mild cognitive impairment (MCI) were administered a semi-structured interview, which measured their concerns of frequency of memory lapses and impact on mood. Informants responded to questionnaires. RESULTS: Self-reported concerns of increasing frequency and impacted mood related to informant concerns in HC-SMCs. MCI with lower informant concern showed a similar pattern to HC-SMCs on complaints of increasing frequency. In those with higher informant concern, self-reports markedly separated from informant concern. The MCI group with greater informant concern performed comparatively poor on verbal and non-verbal memory measures. CONCLUSIONS: Our results suggest that the association between self-reported and informant memory concerns is moderated by MCI severity. Self and informant reports of increasing memory lapse frequency aligned in HC-SMC and MCIs with low informant concern, suggesting a similar dyadic experience of memory change. In MCIs with greater informant concern, the pattern changed exposing a changing insight with advancing memory impairment. These individuals are potentially reflecting a 'forgetting that they forget' phenomenon in elements of their concern.

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.

Changes in plasma amyloid beta in a longitudinal study of aging and Alzheimer's disease.

BACKGROUND: A practical biomarker is required to facilitate the preclinical diagnosis of Alzheimer's disease (AD). METHODS: Plasma amyloid beta (Aß)1-40, Aß1-42, Aßn-40, and Aßn-42 peptides were measured at baseline and after 18 months in 771 participants from the Australian Imaging Biomarkers and Lifestyle (AIBL) study of aging. Aß peptide levels were compared with clinical pathology, neuroimaging and neuropsychological measurements. RESULTS: Although inflammatory and renal function covariates influenced plasma Aß levels significantly, a decrease in Aß1-42/Aß1-40 was observed in patients with AD, and was also inversely correlated with neocortical amyloid burden. During the 18 months, plasma Aß1-42 decreased in subjects with mild cognitive impairment (MCI) and in those transitioning from healthy to MCI. CONCLUSION: Our findings are consistent with a number of published plasma Aß studies and, although the prognostic value of individual measures in any given subject is limited, the diagnostic contribution of plasma Aß may demonstrate utility when combined with a panel of peripheral biomarkers.

Evaluation of GammaH2AX in Buccal Cells as a Molecular Biomarker of DNA Damage in Alzheimer's Disease in the AIBL Study of Ageing.

In response to double-stranded breaks (DSBs) in chromosomal DNA, H2AX (a member of histone H2A family) becomes phosphorylated to form γH2AX. Although increased levels of γH2AX have been reported in the neuronal nuclei of Alzheimer's disease (AD) patients, the understanding of γH2AX responses in buccal nuclei of individuals with mild cognitive impairment (MCI) and AD remain unexplored. In the current study, endogenous γH2AX was measured in buccal cell nuclei from MCI (n = 18) or AD (n = 16) patients and in healthy controls (n = 17) using laser scanning cytometry (LSC). The γH2AX level was significantly elevated in nuclei of the AD group compared to the MCI and control group, and there was a concomitant increase in P-trend for γH2AX from the control group through MCI to the AD group. Receiver-operating characteristic curves were carried out for different γH2AX parameters; γH2AX in nuclei resulted in the greatest area under the curve value of 0.7794 (p = 0.0062) with 75% sensitivity and 70% specificity for the identification of AD patients from control. In addition, nuclear circularity (a measure of irregular nuclear shape) was significantly higher in the buccal cell nuclei from the AD group compared with the MCI and control groups. Additionally, there was a positive correlation between the nuclear circularity and γH2AX signals. The results indicated that increased DNA damage is associated with AD.

Adipose triglyceride lipase regulation of skeletal muscle lipid metabolism and insulin responsiveness.

Adipose triglyceride lipase (ATGL) is important for triglyceride (TG) metabolism in adipose tissue, and ATGL-null mice show increased adiposity. Given the apparent importance of ATGL in TG metabolism and the association of lipid deposition with insulin resistance, we examined the role of ATGL in regulating skeletal muscle lipid metabolism and insulin-stimulated glucose disposal. ATGL expression in myotubes was reduced by small interfering RNA and increased with a retrovirus encoding GFP-HA-ATGL. ATGL was also overexpressed in rats by in vivo electrotransfer. ATGL was down-regulated in skeletal muscle of obese, insulin-resistant mice and negatively correlated with intramyocellular TG levels. ATGL small interfering RNA in myotubes reduced TG hydrolase activity and increased TG content, whereas ATGL overexpression induced the reciprocal response, indicating that ATGL is an essential TG lipase in skeletal muscle. ATGL overexpression in myotubes increased the oxidation of fatty acid liberated from TG and diglyceride and ceramide contents. These responses in cells were largely recapitulated in rats overexpressing ATGL. When ATGL protein expression and TG hydrolase activity in obese, insulin-resistant rats were restored to levels observed in lean rats, TG content was reduced; however, the insulin resistance induced by the high-fat diet persisted. In conclusion, ATGL TG hydrolysis in skeletal muscle is a critical determinant of lipid metabolism and storage. Although ATGL content and TG hydrolase activity are decreased in obese, insulin-resistant phenotypes, overexpression does not rescue the condition, indicating reduced ATGL is unlikely to be a primary cause of obesity-associated insulin resistance.

Increased insulin-stimulated Akt pSer473 and cytosolic SHP2 protein abundance in human skeletal muscle following acute exercise and short-term training.

The purpose of the present study was to determine in human skeletal muscle whether a single exercise bout and 7 days of consecutive endurance (cycling) training 1) increased insulin-stimulated Akt pSer(473) and 2) altered the abundance of the protein tyrosine phosphatases (PTPases), PTP1B and SHP2. In healthy, untrained men (n = 8; 24 +/- 1 yr), glucose infusion rate during a hyperinsulinemic euglycemic clamp, when compared with untrained values, was not improved 24 h following a single 60-min bout of endurance cycling but was significantly increased ( approximately 30%; P < 0.05) 24 h following completion of 7 days of exercise training. Insulin-stimulated Akt pSer(473) was approximately 50% higher (P < 0.05) 24 h following the acute bout of exercise, with this effect remaining after 7 days of training (P < 0.05). Insulin-stimulated insulin receptor and insulin receptor substrate-1 tyrosine phosphorylation were not altered 24 h after acute exercise and short-term training. Insulin did not acutely regulate the localization of the PTPases, PTP1B or SHP2, although cytosolic protein abundance of SHP2 was increased (P < 0.05; main effect) 24 h following acute exercise and short-term training. In conclusion, insulin-sensitive Akt pSer(473) and cytosolic SHP2 protein abundance are higher after acute exercise and short-term training, and this effect appears largely due to the residual effects of the last bout of prior exercise. The significance of exercise-induced alterations in cytosolic SHP2 and insulin-stimulated Akt pSer(473) on the improvement in insulin sensitivity requires further elucidation.