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This study aimed to determine the energy requirements, physiological consequences, and recovery rate from the Australian Special Forces Selection Course. Ninety-three male soldiers (mean ± SD, 28.1 ± 3.6 years, 1.81 ± 0.1 m, 85.1 ± 8.1 kg) volunteered for this study. Body composition via dual-energy X-ray absorptiometry, hormones and resting metabolic rate were assessed before, immediately after, and at 1, 3, 5, and 8 weeks post-course. Energy expenditure, assessed via doubly-labelled water during the first 10 days of the course significantly exceeded energy intake (expenditure: 7680 ± 1095 kcal.day–1, intake: 3859 ± 704 kcal.day–1). Body mass (Δ –6.8 ± 1.9 kg, p < 0.01), fat mass (Δ –4.2 ± 1.0 kg, p < 0.0001) and lean mass (Δ –3.0 ± 1.7 kg, p < 0.0001) were significantly reduced in response to the course and returned to baseline 1–3 weeks post-course. Total testosterone, free testosterone, free triiodothyronine, free thyroxine and insulin like growth factor-1 significantly (p < 0.001) declined following the course, while cortisol and sex hormone binding globulin increased (p < 0.001). All hormones, except insulin like growth factor-1, returned to baseline concentrations within 1–3 weeks post-course. Resting metabolic rate decreased (p < 0.01) in response to the course, and subsequently rebounded above baseline levels at 1 week post-course. The Special Forces Selection Course involved high energy output and a substantial caloric deficit, resulting in body mass loss and significant hormonal disruption that took weeks to recover. These results highlight the energy requirements, physiological consequences, and recovery processes from the Australian Special Forces Selection Course. [ABSTRACT FROM AUTHOR]

Background: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD., Method: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants., Results: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98])., Conclusion: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Vacher, Canovas, Laws and Doecke.)

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., Competing Interests: The following authors have no CoI to declare: CF, SRRS, SB, JB, PB, BMB, SCB, CC, JD, VD, KAE, LE, AF, JF, SLG, SG, RG, EH, RH, LJ, AK, FL, NTL, QXL, LL, YYL, AL, SLM, LMa, SM, LMi, MP, KP, TP, MRa, AR, JR, MRo, RR, OS, GS, BS, MS, HRS, KT, TT, CT, BT, RT, MV, MWe, MWo, YX. AIB is a shareholder in Alterity Therapeutics Ltd, Cogstate Ltd, Mesoblast Ltd, and is a paid consultant for, and has a profit share interest in, Collaborative Medicinal Development Pty Ltd. SC has received payments from Biogen for advice in relation to CSF biomarkers of Alzheimer’s disease. SML has previously been a paid consultant to Alzhyme. RNM is founder of, and owns stock in, Alzhyme, and is a co-founder of the KaRa Institute of Neurological Diseases. PM is a full-time employee of Cogstate Ltd. CLM is an advisor to Prana Biotechnology Ltd and a consultant to Eli Lilly. CCR has served on scientific advisory boards for Bayer Pharma, Elan Corporation, GE Healthcare and AstraZeneca, has received speaker honoraria from Bayer Pharma and GE Healthcare, and has received research support from Bayer Pharma, GE Healthcare, Piramal Lifesciences and Avid Radiopharmaceuticals. VLV has served as a consultant for Bayer Pharma and received research support from a NEDO grant from Japan. DA has served on scientific advisory boards for Novartis, Eli Lilly, Janssen, and Pfizer Inc., (© 2021 – The authors. Published by IOS Press.)