The present study analyzed data and blood samples collected from men recruited as a part of the Geelong Osteoporosis Study (GOS), an ongoing prospective population-based study. In brief, age-stratified samples of men and women were selected at random from electoral rolls for the Barwon Statistical Division in south-eastern Australia12. A total of 1,540 men were recruited at the baseline from 2001 to 2006 (67% participation), followed by 5-, 6- and 15-year re-assessment phases. This study includes a cross-sectional analysis of data and blood samples collected from 449 men during the 15-year follow-up phase (2016–2020). Participants were mostly Caucasian (~98%). They provided information on their lifestyle and demographic characteristics in addition to undergoing mental and physical health assessments. Inclusion criteria were a listing on the electoral rolls for the Barwon Statistical Division and residence in the area for a minimum of 6 months. All participants provided written informed consent to participate in the study, which was approved by the Human Research Ethics Committee at Barwon Health. All procedures performed were in accordance with the ethical standards of the institutional and national research committees and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Assessment procedures and sample collection
Cognitive function was evaluated using a computer-based neuropsychology battery, the CogState Brief Battery (CBB), which has been described previously13,14,15. The CBB requires participants to respond to stimuli cards as a part of a detection (DET), identification (IDN), one-card learning (OCL) and one-back (OBK) task that assessed cognitive performance across four domains, namely psychomotor function , visual identification/attention, recognition memory/learning and working memory, respectively. Both a practice trial and a real test were included for each task. The tasks were completed by participants in a quiet room accompanied by a researcher. For the tasks DET, IDN and OBK, scores were calculated by measuring the time (milliseconds) taken to answer correctly, which was then normalized using a log10 transformation. For the OCL task, scores were calculated based on the accuracy of participant response and normalized using an arcsine square-root transformation. Further, scores for the overall cognitive function (OCF) were determined by combining the primary measures in the four domains. Thus, for the tasks DET, IDN and OBK, lower scores suggested better cognitive performance and for OCL and OCF, higher scores indicated better performance. The individual scores on the four tasks and composite scores were used in the present analysis. In addition, participants underwent the Mini-Mental State Examination (MMSE), which assessed their overall cognitive function16.
Details on sociodemographic variables such as education, smoking and marital status were acquired from self-reports. Education was defined as a nominal factor based on secondary education completion. Similarly, marital status (living with a partner) was defined as living with a partner (coded “1”) or not (coded “0”). Participants who reported smoking at least one cigarette per day were defined as current smokers. The Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Non-Patient Edition (SCID-I/NP) was used to determine a lifetime history of mood disorders, as described previously17.
DNA extraction and genotyping
Blood collected from participants after overnight fasting was separated into different aliquots of serum, plasma and buffy coats, and stored at −80°C until use. Total genomic DNA was isolated from buffy coats using QIAamp® DNA Mini Kit (Qiagen, Hilden, Germany) as per the manufacturer’s instructions. The DNA samples were genotyped for the SNPs rs429358 (APOE ε4), rs7412 (APOE ε2) and rs744373 (BIN1) at the Australian Genome Research Facility, Brisbane using the Agena Bioscience MassARRAY® platform. The carrier status was defined by the presence of at least one copy of the risk allele. Hence, in the present study GG/GA and AA genotypes were referred as BIN1 G+ and BIN1 G−, respectively. Similarly, APOE ε4 + referred to the presence of at least one ε4 allele. The allelic distribution for both BIN1 and APOE did not start from the Hardy–Weinberg equilibrium.
Characteristics were compared across BIN1 G+ and BIN1 G−, and APOE ε4+ and APOE ε4- participants using Student’s t-tests for continuous variables and chi-squared tests for categorical variables. Simple linear regression analyzes were conducted to investigate the association between BIN1 career status and cognitive function. The outcome, cognitive function included scores on each of the four tasks and OCF. Further, multivariable linear regression models adjusted for age and APOE career status were developed for each outcome. Similarly, unadjusted and age-adjusted linear regression analyzes were conducted with APOE status as the exposure variable and cognitive function as the outcome. Following this, interactions between BIN1 and APOE risk alleles were explored using unadjusted and age-adjusted regression models for all five outcomes. Finally, the association between BIN1 carrier status and cognitive function was compared among APOE ε4 carriers and non-carriers to investigate whether the effect of BIN1 differs between the two groups. Benjamini–Hochberg correction was applied to adjust for false discovery rate due to multiple testing18. All statistical analyzes were performed using Stata/SE 17.0 and Python 3.8.5.