We obtained and used data from the ADNI database (http://adni.loni.usc.edu) for the preparation of this article. The ADNI was launched in 2003 as a public–private partnership led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI is to test whether serial magnetic resonance imaging (MRI), PET, other biological markers, and clinical and neuropsychological assessments can be combined to measure the progression of MCI and early AD. For up-to-date information, see http://www.adni-info.org. This study was approved by the Research Ethics Committee of National Center for Geriatrics and Gerontology. We confirm that all methods were performed in accordance with the relevant guidelines and regulations.
Participants
In this study, we included patients diagnosed with MCI or AD as well as CN participants. The inclusion criteria were as follows: for CN, Mini-Mental State Examination (MMSE) scores between 24 and 30 and non-depressed, non-MCI, and non-demented status; for participants with MCI, MMSE scores between 24 and 30, objective memory loss evidenced by education-adjusted scores on the Wechsler Memory Scale Logical Memory II, a Clinical Dementia Rating (CDR) of 0.5, lack of significant levels of impairment in other cognitive domains, essentially preserved activities of daily living, and an absence of dementia; and for participants with AD, MMSE scores between 20 and 26 and meeting the National Institute of Neurological and Communicative Disorders and Stroke & the Alzheimer's Disease and Related Disorders Association criteria for probable AD 30.
ADNI-3 data were obtained from participants aged 55 through 95 years who underwent both AV-45 and AV-1451 scans. The participants also completed a neuropsychological assessment. Data from 387 participants were eventually used in this study, including 234 CN participants and 153 patients diagnosed with MCI (n = 115) or AD dementia (n = 38). We extracted general participant information (age, sex, years of education, MMSE score, and Alzheimer’s Disease Assessment Scale-cognitive subscale-11 [ADAS] score) from the ADNI databases.
Aβ and tau PET analysis
We analyzed 18F-AV45 and 18F-AV-1451 imaging data from ADNI-3 as of January 15, 2021. The protocol for acquisition and image preprocessing of these data is publicly available on the ADNI website (http://adni.loni.usc.edu/).
In the dataset, mean AV-45 uptake was shown in the cortical gray matter regions of interest (ROIs) for all participants. The ROIs included the bilateral frontal, lateral temporal, and lateral parietal and anterior/posterior cingulate cortices, as defined by the ADNI group. ROI-based AV-45 standardized uptake value ratios (SUVRs) were calculated with reference to the mean AV-45 uptake of the whole cerebellum. The details of the data-processing method are shown in “UC Berkeley- AV45 Analysis Methods (PDF)” (https://ida.loni.usc.edu/pages/access/studyData.jsp).
For the AV-1451 dataset, tracer retention was quantified in ROIs that anatomically approximated the pathological stages of tangle deposition delineated by Braak and Braak 31. Weighted mean SUVR was calculated from three composite ROIs that corresponded to the anatomical definitions of Braak stages 1 & 2 (transentorhinal), 3 & 4 (medial temporal and limbic), and 5 & 6 (neocortical) with reference to the mean AV-1451 uptake of the inferior cerebellum. The details of the data-processing method are shown in “UC Berkeley-Flortaucipir (AV-1451) processing methods (PDF)” (https://ida.loni.usc.edu/pages/access/studyData.jsp).
Statistics
Differences in demographic characteristics between the spring-to-summer (from March to August) and fall-to-winter (from September to February) birth groups were examined using t-tests for continuous variables and χ2 tests for dichotomous variables.
The mean SUVR values of AV-45 from composite ROIs of cortical gray matter regions as an index of regional Aβ were compared between groups of spring-to-summer and fall-to-winter birth groups with analysis of covariance (ANCOVA) using age, sex, years of education, and ADAS score as covariates.
The mean SUVR values of AV-1451 from composite ROIs as an index of tau accumulation in regions corresponding to Braak stages 1 & 2, 3 & 4, and 5 & 6 were compared between the spring-to-summer and fall-to-winter birth groups using multiple analysis of covariance (MANCOVA) with age, sex, years of education, AV-45 SUVR, and ADAS score as covariates. Follow-up ANCOVA was performed to examine the group differences in AV-1451 SUVRs in each region with age, sex, years of education, mean SUVR value of AV-45, and ADAS score as covariates.
In addition, a multiple linear regression analysis was performed to determine whether the season of birth was a predictor of AV-45 and/or AV-1451 SUVR, for which a difference was shown in the above-mentioned analysis. The dependent variables were the AV-45 and/or AV-1451 SUVRs, and the independent variables were the season of birth (spring-to-summer vs. fall-to-winter birth), age, sex, years of education, AV45 SUVR (only in the analysis of AV-1451 as the dependent variable), and ADAS score. To examine the effect of seasonal birth on the regional AV-45 and/or AV-1451 SUVR, hierarchical regression equations with steps of predictor variables were fitted. Scores for the measures of age, sex, years of education, AV-45 SUVR (only in the analysis of AV-1451 as the dependent variable), and ADAS score were added in the first step to control for other predictor variables. The birth season was included in the final step.
SPSS for Windows 26.0 (IBM Japan, Tokyo, Japan) was used for statistical analysis. Statistical tests were two-tailed, and significance was defined as a p-value less than 0.05/n using the Bonferroni correction (where n refers to the number of multiple comparisons).