School of Mathematics and Statistics - Research Publications
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ItemHLA Alleles Associated With Risk of Ankylosing Spondylitis and Rheumatoid Arthritis Influence the Gut Microbiome(WILEY, 2019-10)OBJECTIVE: HLA alleles affect susceptibility to more than 100 diseases, but the mechanisms that account for these genotype-disease associations are largely unknown. HLA alleles strongly influence predisposition to ankylosing spondylitis (AS) and rheumatoid arthritis (RA). Both AS and RA patients have discrete intestinal and fecal microbiome signatures. Whether these changes are the cause or consequence of the diseases themselves is unclear. To distinguish these possibilities, we examined the effect of HLA-B27 and HLA-DRB1 RA risk alleles on the composition of the intestinal microbiome in healthy individuals. METHODS: Five hundred sixty-eight stool and biopsy samples from 6 intestinal sites were collected from 107 healthy unrelated subjects, and stool samples were collected from 696 twin pairs from the TwinsUK cohort. Microbiome profiling was performed using sequencing of the 16S ribosomal RNA bacterial marker gene. All subjects were genotyped using the Illumina CoreExome SNP microarray, and HLA genotypes were imputed from these data. RESULTS: Associations were observed between the overall microbial composition and both the HLA-B27 genotype and the HLA-DRB1 RA risk allele (P = 0.0002 and P = 0.00001, respectively). These associations were replicated using the stool samples from the TwinsUK cohort (P = 0.023 and P = 0.033, respectively). CONCLUSION: This study shows that the changes in intestinal microbiome composition seen in AS and RA are at least partially due to effects of HLA-B27 and HLA-DRB1 on the gut microbiome. These findings support the hypothesis that HLA alleles operate to cause or increase the risk of these diseases through interaction with the intestinal microbiome and suggest that therapies targeting the microbiome may be effective in preventing or treating these diseases.
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ItemAn improved clinical model to predict stimulated C-peptide in children with recent-onset type 1 diabetes(WILEY, 2019-03)BACKGROUND: Stimulated C-peptide measurement after a mixed meal tolerance test (MMTT) is the accepted gold standard for assessing residual beta-cell function in type 1 diabetes (T1D); however, this approach is impractical outside of clinical trials. OBJECTIVE: To develop an improved estimate of residual beta-cell function in children with T1D using commonly measured clinical variables. SUBJECTS/METHODS: A clinical model to predict 90-minute MMTT stimulated C-peptide in children with recent-onset T1D was developed from the combined AbATE, START, and TIDAL placebo subjects (n = 46) 6 months post-recruitment using multiple linear regression. This model was then validated in a clinical cohort (Hvidoere study group, n = 262). RESULTS: A model of estimated C-peptide at 6 months post-diagnosis, which included age, gender, body mass index (BMI), hemoglobin A1c (HbA1c), and insulin dose predicted 90-minute stimulated C-peptide measurements (adjusted R2 = 0.63, P < 0.0001). The predictive value of insulin dose and HbA1c alone (IDAA1c) for 90-minute stimulated C-peptide was significantly lower (R2 = 0.37, P < 0.0001). The slopes of linear regression lines of the estimated and stimulated 90-minute C-peptide levels obtained at 6 and 12 months post diagnosis in the Hvidoere clinical cohort were R2 = 0.36, P < 0.0001 at 6 months and R2 = 0.37, P < 0.0001 at 12 months. CONCLUSIONS: A clinical model including age, gender, BMI, HbA1c, and insulin dose predicts stimulated C-peptide levels in children with recent-onset T1D. Estimated C-peptide is an improved surrogate to monitor residual beta-cell function outside clinical trial settings.
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ItemExploring the association between BMI and mortality in Australian women and men with and without diabetes: the AusDiab study(SPRINGER, 2019-05)AIMS/HYPOTHESIS: There is conflicting evidence about the obesity paradox-the counterintuitive survival advantage of obesity among certain subpopulations of individuals with chronic conditions. It is believed that results supporting the obesity paradox are due to methodological flaws, such as collider bias. The aim of this study was to examine the association between obesity and mortality in Australian men and women. In addition, we explored whether obesity would appear to be protective if the analysis was restricted to a subpopulation with disease, and to discuss the potential role of collider bias in producing such a result. METHODS: The examined cohort included 10,575 Australian adults (4844 men and 5731 women) aged 25-91 years who were recruited for the AusDiab baseline survey in 1999 and followed-up through 2014. The main predictor variable was BMI categorised as normal weight (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2) and obese (≥30 kg/m2), and the outcome of interest was all-cause mortality. Hazard ratios were estimated from Cox proportional hazards regression models in the entire cohort and then in subpopulations with and without diabetes. RESULTS: A total of 1477 deaths occurred during 145,384 person-years (median 14.6 years) of follow-up. Mortality was higher in obese than in normal-weight individuals for the full population (HR 1.18; 95% CI 1.05, 1.32). When an interaction between diabetes status and BMI category was added to the model, there was no evidence of an interaction between BMI and diabetes status (p = 0.92). When participants with and without diabetes were analysed separately, there was no evidence of an association between obesity and mortality in those with diabetes (HR 0.91; 95% CI 0.62, 1.33). CONCLUSIONS/INTERPRETATION: In the entire AusDiab cohort, we found a significantly higher mortality among obese participants as compared with their normal-weight counterparts. We found no difference in the obesity-mortality association between individuals with and without diabetes.
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ItemDynamic molecular changes during the first week of human life follow a robust developmental trajectory.(Nature Research (part of Springer Nature), 2019-03-12)Systems biology can unravel complex biology but has not been extensively applied to human newborns, a group highly vulnerable to a wide range of diseases. We optimized methods to extract transcriptomic, proteomic, metabolomic, cytokine/chemokine, and single cell immune phenotyping data from <1 ml of blood, a volume readily obtained from newborns. Indexing to baseline and applying innovative integrative computational methods reveals dramatic changes along a remarkably stable developmental trajectory over the first week of life. This is most evident in changes of interferon and complement pathways, as well as neutrophil-associated signaling. Validated across two independent cohorts of newborns from West Africa and Australasia, a robust and common trajectory emerges, suggesting a purposeful rather than random developmental path. Systems biology and innovative data integration can provide fresh insights into the molecular ontogeny of the first week of life, a dynamic developmental phase that is key for health and disease.
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ItemDietary intake influences gut microbiota development of healthy Australian children from the age of one to two years(NATURE PORTFOLIO, 2019-08-28)Early life nutrition is a vital determinant of an individual's life-long health and also directly influences the ecological and functional development of the gut microbiota. However, there are limited longitudinal studies examining the effect of diet on the gut microbiota development in early childhood. Here, up to seven stool samples were collected from each of 48 healthy children during their second year of life, and microbiota dynamics were assessed using 16S rRNA gene amplicon sequencing. Children's dietary information was also collected during the same period using a validated food frequency questionnaire designed for this age group, over five time points. We observed significant changes in gut microbiota community, concordant with changes in the children's dietary pattern over the 12-month period. In particular, we found differential effects on specific Firmicutes-affiliated lineages in response to frequent intake of either processed or unprocessed foods. Additionally, the consumption of fortified milk supplemented with a Bifidobacterium probiotic and prebiotics (synbiotics) further increased the presence of Bifidobacterium spp., highlighting the potential use of synbiotics to prolong and sustain changes in these lineages and shaping the gut microbiota community in young children.
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ItemTemporal development of the oral microbiome and prediction of early childhood caries(NATURE PORTFOLIO, 2019-12-24)Human microbiomes are predicted to assemble in a reproducible and ordered manner yet there is limited knowledge on the development of the complex bacterial communities that constitute the oral microbiome. The oral microbiome plays major roles in many oral diseases including early childhood caries (ECC), which afflicts up to 70% of children in some countries. Saliva contains oral bacteria that are indicative of the whole oral microbiome and may have the ability to reflect the dysbiosis in supragingival plaque communities that initiates the clinical manifestations of ECC. The aim of this study was to determine the assembly of the oral microbiome during the first four years of life and compare it with the clinical development of ECC. The oral microbiomes of 134 children enrolled in a birth cohort study were determined at six ages between two months and four years-of-age and their mother's oral microbiome was determined at a single time point. We identified and quantified 356 operational taxonomic units (OTUs) of bacteria in saliva by sequencing the V4 region of the bacterial 16S RNA genes. Bacterial alpha diversity increased from a mean of 31 OTUs in the saliva of infants at 1.9 months-of-age to 84 OTUs at 39 months-of-age. The oral microbiome showed a distinct shift in composition as the children matured. The microbiome data were compared with the clinical development of ECC in the cohort at 39, 48, and 60 months-of-age as determined by ICDAS-II assessment. Streptococcus mutans was the most discriminatory oral bacterial species between health and current disease, with an increased abundance in disease. Overall our study demonstrates an ordered temporal development of the oral microbiome, describes a limited core oral microbiome and indicates that saliva testing of infants may help predict ECC risk.
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ItemA Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types(FRONTIERS MEDIA SA, 2019-11-07)Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.