Design
A clustered random sampling, neural network modeling study.
Time and setting
The investigation was conducted in several communities in Nanchang, Ji’an and Yichun, Jiangxi province, China from May 2008 to September 2010. The blood sample testing and model establishment were performed at Nanchang University, Nanchang, Jiangxi province, China from October 2010 to December 2010.
Subjects
Using a cluster sampling method, 4 350 people older than 60 years from six communities were investigated. Subjects who were suspected to have AD were initially screened using routine methods. Among the subjects, 2 073 were males, accounting for 47.66% of the sample, and 2 277 were female, accounting for 52.34% of the sample.
A self-designed questionnaire was used to collect epidemiological information for all checked objects, and the ADL scale[27] was used to evaluate activities of daily living. For AD (cases): (1) screening by Mini-Mental State Examination[28]; Mini-Mental State Examination scores ≤17 points were considered to indicate illiteracy, ≤20 points as primary level, ≤22 points as secondary level, ≤23 points as university; (2) on this basis, the clinical history and signs and other related information, with reference to the International Classification of Diseases, 10th edition (ICD-10)[29], the Chinese Classification of Mental Disorders diagnostic criteria for suspected case confirmation[30], and the Clinical Dementia Rating were used to evaluate the severity of AD[31]; (3) using the Hamminsk’s ischemia rating scale to exclude vascular dementia and mixed dementia[32]; (4) reliable informants provided health-related background information for each subject. For non-AD (controls): all participants were free of cardiovascular disease, neurological disease, and congenital dementia; living in the same urban community; they and their families were willing to cooperate. Of 214 persons diagnosed with AD, 60 AD patients were finally included for BP-ANN modeling along with 60 non-AD (control) individuals.
Methods
Sample collection and laboratory testing
In addition to measuring demographic characteristics and the personal medical history questionnaire, 10 mL of ulnar venous blood was collected from each of the 120 participants. The blood samples were impregnated with anticoagulant and refrigerated. Of the 10 mL blood sample, 2 mL was used for the detection of nine elements: aluminium, copper, zincum, ferrum, calcium, manganese, cadmium, creatinine, selenium; and 8 mL was used for the detection of 5-hydroxytryptamine, dopamine, and acetylcholine receptor antibody. The reference substances for element detection were supplied by the National Center, including HNO3 (chemically pure), Mg(NO3)2, Ni(NO3)2 (chemically pure) and deionized water. The macro and trace elements were analyzed using a TAS-990 atomic absorption spectrophotometer (Shimadzu, Kyoto, Japan), a Shimadzu AA-680 atomic absorption spectrophotometer (Shimadzu) and a GFA-4B graphite atomic device (Shimadzu). Related neurotransmitters were detected by radioimmunoassay. The assay kit of acetylcholine receptor antibody was obtained from RSR Ltd., Cardiff, UK, while the assay kit of 5-hydroxytryptamine and dopamine were from Biosource Europe SA, Nivelles, Belgium.
BP-ANN establishment
SPSS 13.0 software (SPSS, Chicago, IL, USA) was used to establish the database, and the BP-ANN was built with Clementine 12.0 software (SPSS). The data flow is shown in Figure 2. Before modeling, the input variables were concentrated and screened. The variables included gender, age, educational level, occupation, marital status, history of hypertension, history of cerebral vascular accident, history of head injury, history of mental illness, family history of dementia, history of major adverse life, character, scores of ADL, aluminium, copper, zincum, ferrum, calcium, manganese, cadmium, creatinine, selenium, 5-hydroxytryptamine, dopamine, acetylcholine receptor antibody, six variables were selected, including scores of ADL, creatinine, 5-hydroxytryptamine, dopamine, age, and aluminium, which had a strong correlation with the output variable (y = AD illness), P < 0.001.
The 120 modeling subjects were assigned to two groups, the training set (n = 85, 71%), used to train the network; and the test set (n = 35, 29%), for the detection of network convergence. Thus, the BP-ANN model was built with the above six selected variables as the input layer, and AD illness as the output layer. Training parameters: impulse items = 0.9, the initial learning rate = 0.02, the maximum weight to adjust the rate of change was not greater than 0.005; activation function was a Sigmoid function, and expression was . The architecture of the BP-ANN we constructed was a three-tier network with one input layer, six nodes; one hidden layer, three nodes; and one output layer, one node. The network is shown in Figure 3.
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Statistical analysis
Continuous variables were expressed as mean ± SD and compared using a two-tailed unpaired student’s t-test; categorical variables were compared using chi-square analysis. MedCalc V.11.5.0.0 Software (MedCalc Software, Mariakerke, Belgium) was used to analyze the results of the BP-ANN output (propensity scores) as a receiver- operating characteristic curve.
