BackgroundThe phenotypic and genetic heterogeneity of autism spectrum disorder (ASD) presents considerable challenges in understanding etiological pathways, selecting effective therapies, providing genetic counselling, and predicting clinical outcomes. With advances in genetic and biological research alongside rapid-pace technological innovations, there is an increasing imperative to access large, representative, and diverse cohorts to advance knowledge of ASD. To date, there has not been any single collective effort towards a similar resource in Australia, which has its own unique ethnic and cultural diversity. The Australian Autism Biobank was initiated by the Cooperative Research Centre for Living with Autism (Autism CRC) to establish a large-scale repository of biological samples and detailed clinical information about children diagnosed with ASD to facilitate future discovery research.MethodsThe primary group of participants were children with a confirmed diagnosis of ASD, aged between 2 and 17 years, recruited through four sites in Australia. No exclusion criteria regarding language level, cognitive ability, or comorbid conditions were applied to ensure a representative cohort was recruited. Both biological parents and siblings were invited to participate, along with children without a diagnosis of ASD, and children who had been queried for an ASD diagnosis but did not meet diagnostic criteria. All children completed cognitive assessments, with probands and parents completing additional assessments measuring ASD symptomatology. Parents completed questionnaires about their child’s medical history and early development. Physical measurements and biological samples (blood, stool, urine, and hair) were collected from children, and physical measurements and blood samples were collected from parents. Samples were sent to a central processing site and placed into long-term storage.DiscussionThe establishment of this biobank is a valuable international resource incorporating detailed clinical and biological information that will help accelerate the pace of ASD discovery research. Recruitment into this study has also supported the feasibility of large-scale biological sample collection in children diagnosed with ASD with comprehensive phenotyping across a wide range of ages, intellectual abilities, and levels of adaptive functioning. This biological and clinical resource will be open to data access requests from national and international researchers to support future discovery research that will benefit the autistic community.

Background Frailty is a strong predictor of adverse outcomes. However, longitudinal drivers of frailty are not well understood. This study aimed at investigating the longitudinal trajectories of a frailty index (FI) from adulthood to late life and identifying the factors associated with the level and rate of change in FI. Methods An age-based latent growth curve analysis was performed in the Swedish Adoption/Twin Study of Aging (N = 1,842; aged 29–102 years) using data from up to 15 measurement waves across 27 years. A 42-item FI was used to measure frailty at each wave. Results A bilinear, two-slope model with a turning point at age 65 best described the age-related change in FI, showing that the increase in frailty was more than twice as fast after age 65. Underweight, obesity, female sex, overweight, being separated from one’s co-twin during childhood, smoking, poor social support, and low physical activity were associated with a higher FI at age 65, with underweight having the largest effect size. When tested as time-varying covariates, underweight and higher social support were associated with a steeper increase in FI before age 65, whereas overweight and obesity were associated with less steep increase in FI after age 65. Conclusions Factors associated with the level and rate of change in frailty are largely actionable and could provide targets for intervention. As deviations from normal weight showed the strongest associations with frailty, future public health programs could benefit from monitoring of individuals with abnormal BMI, especially those who are underweight.

Background: Digital multiplex gene expression profiling is overcoming the limitations of many tissue-processing and RNA extraction techniques for the reproducible and quantitative molecular classification of disease. We assessed the effect of different skin biopsy collection/storage conditions on mRNA quality and quantity and the NanoString nCounter™ System's ability to reproducibly quantify the expression of 730 immune genes from skin biopsies. Methods: Healthy human skin punch biopsies (n = 6) obtained from skin sections from four patients undergoing routine abdominoplasty were subject to one of several collection/storage protocols, including: i) snap freezing in liquid nitrogen and transportation on dry ice; ii) RNAlater (ThermoFisher) for 24 h at room temperature then stored at − 80°C; iii) formalin fixation with further processing for FFPE blocks; iv) DNA/RNA shield (Zymo) stored and shipped at room temperature; v) placed in TRIzol then stored at − 80°C; vi) saline without RNAse for 24 h at room temperature then stored at − 80°C. RNA yield and integrity was assessed following extraction via NanoDrop, QuantiFluor with RNA specific dye and a Bioanalyser (LabChip24, PerkinElmer). Immune gene expression was analysed using the NanoString Pancancer Immune Profiling Panel containing 730 genes. Results: Except for saline, all protocols yielded total RNA in quantities/qualities that could be analysed by NanoString nCounter technology, although the quality of the extracted RNA varied widely. Mean RNA integrity was highest from samples that were placed in RNALater (RQS 8.2 ± 1.15), with integrity lowest from the saline stored sample (RQS < 2). There was a high degree of reproducibility in the expression of immune genes between all samples with the exception of saline, with the number of detected genes at counts < 100, between 100 and 1000 and > 10,000 similar across extraction protocols.

Background. Frailty is a strong predictor of adverse aging outcomes. However, the longitudinal drivers of frailty are not well understood. This study aimed at investigating the longitudinal trajectories of a frailty index (FI) from adulthood to late life and identifying the predictors of the level and rate of change in FI. Methods. An age-based latent growth curve analysis was performed in the Swedish Adoption/Twin Study of Aging (N=1,842; aged 29-102 years) using data from up to 15 measurement waves across 27 years. A 42-item FI was used to measure frailty at each wave. Results. A bilinear, two-slope model with a turning point at age 65 best described the age-related change in FI, showing that the rate of increase in frailty was more than twice as fast after age 65. Underweight, obesity, female sex, overweight, being separated from one's co-twin during childhood, smoking, poor social support and low physical activity were associated with a higher level of FI at age 65, with underweight having the largest effect size. When tested as time-varying predictors, underweight and higher social support were associated with a steeper increase in FI before age 65, whereas overweight and obesity were associated with less steep increase in FI after age 65. Conclusions. Predictors for the level and rate of change in frailty are largely actionable and could provide targets for intervention. Underweight increased the risk of higher FI trajectory until age 65, whereas being overweight or obese were associated with slower progression of frailty towards the oldest ages.