HLA Class I and Genetic Susceptibility to Type 1 Diabetes: Results From the Type 1 Diabetes Genetics Consortium

OBJECTIVE-We report here genotyping data and type 1 diabetes association analyses for HLA class I loci (A, B, and C) on 1,753 multiplex pedigrees from the Type 1 Diabetes Genetics Consortium (T1DGC), a large international collaborative study. RESEARCH DESIGN AND METHODS-Complete eight-locus HLA genotyping data were generated. Expected patient class I (HLA-A, -B, and -C) allele frequencies were calculated, based on linkage disequilibrium (LD) patterns with observed HLA class II DRB1-DQA1-DQB1 haplotype frequencies. Expected frequencies were compared to observed allele frequencies in patients. RESULTS-Significant type 1 diabetes associations were observed at all class I HLA loci. After accounting for LD with HLA class II, the most significantly type 1 diabetes-associated alleles were B*5701 (odds ratio 0.19; P = 4 x 10(-11)) and B*3906 (10.31; P = 4 X 10(-10)). Other significantly type 1 diabetes-associated alleles included A*2402, A*0201, B*1801, and C*0501 (predisposing) and A*1101, A*3201, A*6601, B*0702, B*4403, B*3502, C*1601, and C*0401 (protective). Some alleles, notably B*3906, appear to modulate the risk of all DRB1-DQA1-DQB1 haplotypes on which they reside, suggesting a class I effect that is independent of class H. Other class I type 1 diabetes associations appear to be specific to individual class H haplotypes. Some apparent associations (e.g., C*1601) could be attributed to strong LD to another class I susceptibility locus (B*4403). CONCLUSIONS-These data indicate that HLA class I alleles, in addition to and independently from HLA class H alleles, are associated with type 1 diabetes. Diabetes 59:2972-2979, 201

HLA DPA1, DPB1 Alleles and Haplotypes Contribute to the Risk Associated With Type 1 Diabetes: Analysis of the Type 1 Diabetes Genetics Consortium Families

OBJECTIVE-To determine the relative risk associated with DPA1 and DPB1 alleles and haplotypes in type I diabetes. RESEARCH DESIGN AND METHODS-The frequency of DPA1 and DPB1 alleles and haplotypes in type I diabetic patients was compared to the family based control frequency in 1,771 families directly and conditional on FILA (B)-DRB1-DQA1-DQB1 linkage disequilibrium. A relative predispositional analysis (RPA) was performed in the presence or absence of the primary HLA DR-DQ associations and the contribution of DP haplotype to individual DR-DQ haplotype risks examined. RESULTS-Eight DPAI and thirty-eight DPB1 alleles forming seventy-four DPA1-DPB1 haplotypes were observed, nineteen DPB1 alleles were associated with multiple DPA1 alleles Following both analyses, type I diabetes susceptibility was significantly associated with DPB1*0301 (DPA1*0103-DPB1*0301) and protection with DPB1*0402 (DPA1*0103-DPB1*0402) and DPA1*0103-DPB1*0101 but not DPA1*0201-DPB1*0101. In addition, DPB1*0202 (DPA1*0103-DPB1*0202) and DPB1*0201 (DPA1*0103-DPB1*0201) were significantly associated with susceptibility in the presence of the high risk and protective DR-DQ haplotypes Three associations (DPB1*0301, *0402, and *0202) remained statistically significant when only the extended HLA-A1-B8-DR3 haplotype was considered, suggesting that DPB1 alone may delineate the risk associated with this otherwise conserved haplotype CONCLUSIONS-HLA DP allelic and haplotypic diversity contributes significantly to the risk for type I diabetes; DPB1*0301 (DPA1*0103-DPB1*0301) is associated with susceptibility and DPB1*0402 (DPA1*0103-DPB1*0402) and DPA1*0103-DPB1*0101 with protection Additional evidence is presented for the susceptibility association of DPB1*0202 (DPA1*0103-DPB1*0202) and for a contributory role of individual amino acids and DPA1 or a gene in linkage disequilibrium in DR3-DPB1*0101 positive haplotypes Diabetes 59:2055-2062, 201

HLA genotyping in the international Type 1 Diabetes Genetics Consortium

Background Although human leukocyte antigen (HLA) DQ and DR loci appear to confer the strongest genetic risk for type 1 diabetes, more detailed information is required for other loci within the HLA region to understand causality and stratify additional risk factors. The Type 1 Diabetes Genetics Consortium (T1DGC) study design included high-resolution genotyping of HLA-A, B, C, DRB1, DQ, and DP loci in all affected sibling pair and trio families, and cases and controls, recruited from four networks worldwide, for analysis with clinical phenotypes and immunological markers

Protocol of the baseline assessment for the Environments for Healthy Living (EHL) Wales cohort study

Background Health is a result of influences operating at multiple levels. For example, inadequate housing, poor educational attainment, and reduced access to health care are clustered together, and are all associated with reduced health. Policies which try to change individual people's behaviour have limited effect when people have little control over their environment. However, structural environmental change and an understanding of the way that influences interact with each other, has the potential to facilitate healthy choices irrespective of personal resources. The aim of Environments for Healthy Living (EHL) is to investigate the impact of gestational and postnatal environments on health, and to examine where structural change can be brought about to optimise health outcomes. The baseline assessment will focus on birth outcomes and maternal and infant health. Methods/Design EHL is a longitudinal birth cohort study. We aim to recruit 1000 pregnant women in the period April 2010 to March 2013. We will examine the impact of the gestational environment (maternal health) and the postnatal environment (housing and neighbourhood conditions) on subsequent health outcomes for the infants born to these women. Data collection will commence during the participants' pregnancy, from approximately 20 weeks gestation. Participants will complete a questionnaire, undergo anthropometric measurements, wear an accelerometer, compile a food diary, and have environmental measures taken within their home. They will also be asked to consent to having a sample of umbilical cord blood taken following delivery of their baby. These data will be complemented by routinely collected electronic data such as health records from GP surgeries, hospital admissions, and child health and development records. Thereafter, participants will be visited annually for follow-up of subsequent exposures and child health outcomes. Discussion The baseline assessment of EHL will provide information concerning the impact of gestational and postnatal environments on birth outcomes and maternal and infant health. The findings can be used to inform the development of complex interventions targeted at structural, environmental factors, intended to reduce ill-health. Long-term follow-up of the cohort will focus on relationships between environmental exposures and the later development of adverse health outcomes, including obesity and diabetes

Fifteen years of the Australian imaging, biomarkers and lifestyle (AIBL) study: Progress and observations from 2,359 older adults spanning the spectrum from cognitive normality to Alzheimer\u27s disease

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\u27s disease dementia (AD)) as an \u27Inception cohort\u27 who underwent detailed ssessments every 18 months. Over the past decade, an additional 1247 subjects have been added as an \u27Enrichment cohort\u27 (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

Comprehensive analysis of epigenetic clocks reveals associations between disproportionate biological ageing and hippocampal volume

The concept of age acceleration, the difference between biological age and chronological age, is of growing interest, particularly with respect to age-related disorders, such as Alzheimer’s Disease (AD). Whilst studies have reported associations with AD risk and related phenotypes, there remains a lack of consensus on these associations. Here we aimed to comprehensively investigate the relationship between five recognised measures of age acceleration, based on DNA methylation patterns (DNAm age), and cross-sectional and longitudinal cognition and AD-related neuroimaging phenotypes (volumetric MRI and Amyloid-β PET) in the Australian Imaging, Biomarkers and Lifestyle (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Significant associations were observed between age acceleration using the Hannum epigenetic clock and cross-sectional hippocampal volume in AIBL and replicated in ADNI. In AIBL, several other findings were observed cross-sectionally, including a significant association between hippocampal volume and the Hannum and Phenoage epigenetic clocks. Further, significant associations were also observed between hippocampal volume and the Zhang and Phenoage epigenetic clocks within Amyloid-β positive individuals. However, these were not validated within the ADNI cohort. No associations between age acceleration and other Alzheimer’s disease-related phenotypes, including measures of cognition or brain Amyloid-β burden, were observed, and there was no association with longitudinal change in any phenotype. This study presents a link between age acceleration, as determined using DNA methylation, and hippocampal volume that was statistically significant across two highly characterised cohorts. The results presented in this study contribute to a growing literature that supports the role of epigenetic modifications in ageing and AD-related phenotypes

Epigenetics and developmental programming of welfare and production traits in farm animals

The concept that postnatal health and development can be influenced by events that occur in utero originated from epidemiological studies in humans supported by numerous mechanistic (including epigenetic) studies in a variety of model species. Referred to as the ‘developmental origins of health and disease’ or ‘DOHaD’ hypothesis, the primary focus of large-animal studies until quite recently had been biomedical. Attention has since turned towards traits of commercial importance in farm animals. Herein we review the evidence that prenatal risk factors, including suboptimal parental nutrition, gestational stress, exposure to environmental chemicals and advanced breeding technologies, can determine traits such as postnatal growth, feed efficiency, milk yield, carcass composition, animal welfare and reproductive potential. We consider the role of epigenetic and cytoplasmic mechanisms of inheritance, and discuss implications for livestock production and future research endeavours. We conclude that although the concept is proven for several traits, issues relating to effect size, and hence commercial importance, remain. Studies have also invariably been conducted under controlled experimental conditions, frequently assessing single risk factors, thereby limiting their translational value for livestock production. We propose concerted international research efforts that consider multiple, concurrent stressors to better represent effects of contemporary animal production systems

Formulation Pre-screening of Inhalation Powders Using Computational Atom–Atom Systematic Search Method

The synthonic modeling approach provides a molecule-centered understanding of the surface properties of crystals. It has been applied extensively to understand crystallization processes. This study aimed to investigate the functional relevance of synthonic modeling to the formulation of inhalation powders by assessing cohesivity of three active pharmaceutical ingredients (APIs, fluticasone propionate (FP), budesonide (Bud), and salbutamol base (SB)) and the commonly used excipient, α-lactose monohydrate (LMH). It is found that FP (−11.5 kcal/mol) has a higher cohesive strength than Bud (−9.9 kcal/mol) or SB (−7.8 kcal/mol). The prediction correlated directly to cohesive strength measurements using laser diffraction, where the airflow pressure required for complete dispersion (CPP) was 3.5, 2.0, and 1.0 bar for FP, Bud, and SB, respectively. The highest cohesive strength was predicted for LMH (−15.9 kcal/mol), which did not correlate with the CPP value of 2.0 bar (i.e., ranking lower than FP). High FP–LMH adhesive forces (−11.7 kcal/mol) were predicted. However, aerosolization studies revealed that the FP–LMH blends consisted of agglomerated FP particles with a large median diameter (∼4–5 μm) that were not disrupted by LMH. Modeling of the crystal and surface chemistry of LMH identified high electrostatic and H-bond components of its cohesive energy due to the presence of water and hydroxyl groups in lactose, unlike the APIs. A direct comparison of the predicted and measured cohesive balance of LMH with APIs will require a more in-depth understanding of highly hydrogen-bonded systems with respect to the synthonic engineering modeling tool, as well as the influence of agglomerate structure on surface–surface contact geometry. Overall, this research has demonstrated the possible application and relevance of synthonic engineering tools for rapid pre-screening in drug formulation and design

A blood-based biomarker panel indicates IL-10 and IL-12/23p40 are jointly associated as predictors of β-amyloid load in an AD cohort

Alzheimer\u27s Disease (AD) is the most common form of dementia, characterised by extracellular amyloid deposition as plaques and intracellular neurofibrillary tangles of tau protein. As no current clinical test can diagnose individuals at risk of developing AD, the aim of this project is to evaluate a blood-based biomarker panel to identify individuals who carry this risk. We analysed the levels of 22 biomarkers in clinically classified healthy controls (HC), mild cognitive impairment (MCI) and Alzheimer\u27s participants from the well characterised Australian Imaging, Biomarker and Lifestyle (AIBL) study of aging. High levels of IL-10 and IL-12/23p40 were significantly associated with amyloid deposition in HC, suggesting that these two biomarkers might be used to detect at risk individuals. Additionally, other biomarkers (Eotaxin-3, Leptin, PYY) exhibited altered levels in AD participants possessing the APOE ϵ4 allele. This suggests that the physiology of some potential biomarkers may be altered in AD due to the APOE ϵ4 allele, a major risk factor for AD. Taken together, these data highlight several potential biomarkers that can be used in a blood-based panel to allow earlier identification of individuals at risk of developing AD and/or early stage AD for which current therapies may be more beneficial