Age-related diseases touch all of us directly or indirectly through family and friends and they account for most of the chronic disease burden in the world. As population age and people grow older thanks to advanced medical care, the prevalence of diabetes, atherosclerosis and dementia will rise. In particular, the parallel obesity epidemic further increases the burden from chronic morbidity. From an evolutionary point of view, this is what we are supposed to do; eat as much as possible with the least effort possible. For those people who have genetic predisposition to gain excessive weight or to get type 2 diabetes, the environmental exposure enables and escalates the metabolic dysfunction and serious complications follow. While life style interventions can be effective (but difficult to implement) for metabolic dysfunction, dementia remains one of the most common chronic conditions without an effective treatment. Many of the big chronic diseases such as diabetes and heart disease are intertwined and the molecular risk factors are difficult to identify using traditional methods; we postulate that network biology and systems thinking are the most effective paradigms for the discovery of new therapies and effective translation into interventions for the mosaic of phenotypic subgroups we see in human populations.
We are a newly established interdisciplinary group with expertise in epidemiology, biostatistics, computational analyses, genetics, biochemistry and molecular biology techniques. We are situated on Level 7 at SAHMRI, and affiliated with EMBL Australia and University of Adelaide School of Biological Sciences.
Integrative genomics of circulating metabolites
Genetic studies of diabetes, heart and kidney disease have produced a large set of weak statistical associations between genetic variants and clinical diseases status. However, uncovering the mechanisms behind these associations is difficult. We hypothesise that the genetic risk for obesity, diabetes and related metabolic abnormalities is transmitted via gradual degradation of the gene regulatory networks that maintain a healthy metabolic balance. To investigate these networks, we are currently analysing results from two large human genetics studies from Finland and Estonia (18,000 individuals, 15M genetic loci and 150 metabolic traits) to detect pathways and co-regulated gene modules that mediate the effects of genetic polymorphisms. In the second phase, we will overlay the genetic finding on 144 tissue-specific gene networks from the GIANT database (and other resources) to identify tissue-specific key driver genes for the pathogenetic cellular processes. Lastly, we will test the effect of these genes in vitro and in vivo using cell cultures and mouse models. By integrating multiple sources of genetic and molecular information, we expect to reveal the most important genes and clues about the cellular mechanisms that could be targeted for therapeutic effect.
Prevention of metabolic disease by life-long multi-drug intervention
We already know how to prevent obesity and diabetes: eat better, exercise more and relax. However, implementing a successful public health intervention in the current obesogenic environment has proven difficult. Food supplements such as vitamins and minerals are highly effective in preventing diseases from malnutrition, and there is growing interest in adding widely-used cholesterol, blood pressure and diabetes drugs as population-wide preventative measures of metabolic dysfunction. We have launched a project to find out if a multi-drug life-long intervention is a biologically viable therapeutic option, and how it compares to a healthy life style. Our aim is to create a multi-strain genetically susceptible mouse population model of diabetes, heart disease and kidney disease. We will then test the effects of high-fat diet with and without a combination of drugs that on their own lower cholesterol, blood glucose or blood pressure. We expect to identify the gene regulatory networks and metabolic pathways that underpin the development of chronic disease in an obesogenic environment, and to provide evidence on the viability of preventative pharmacotherapy against metabolic diseases.
A Multivariate Statistical Framework for Big Data in Health and Disease
Biostatistics is rapidly evolving towards a “Big Data” future, where high-resolution health-related information from mobile and wearable devices is integrated with population and medical records, mental health metrics and patient histories. Understanding and interpreting these complex information patterns into usable knowledge poses a continuing challenge to scientists, clinical practitioners, policy makers and the general public. For this reason, we are developing new computational pipelines to extract knowledge from large time-dependent datasets. A self-organising map (SOM) is an unsupervised neural network algorithm that can reveal multi-variate patterns in a high-dimensional dataset. In the past, we have integrated a sophisticated statistical engine into the SOM to verify whether the observed patterns deviate from the random expectation, and we have applied the framework in multiple cross-sectional and longitudinal human datasets. Our next task is to develop an R-package of the time-dependent SOM that greatly increases our capacity to handle multi-variate event-based data (e.g. from mobile devices). This will enable us to better identify vulnerable subgroups and to provide the basic knowledge to develop tailored intervention strategies.
Personalized online engagement strategies to enable novel diet and lifestyle interventions in young Australians with psychological stress
Principal investigator: A/Prof Niranjan Bidargaddi, Flinders University SA
Effective diet and lifestyle management strategies are lacking for young adults, particularly when they are experiencing psychological distress, despite the widely known interplay between obesity and mental health. Innovative lifestyle modification approaches are needed that are personalised, easy to disseminate and encourage long-term adoption and maintenance of healthy behaviors. Smartphone ownership is highest amongst young adults, who spend in a typical month around 30 hours using over 28 apps. There are now over 40,000 personal health apps (PHAs), accessed by over 500 million smart phone users worldwide, most of which focus on weight management, nutrition or physical activity and thus highly relevant for addressing obesity in young Australians. However, this very choice makes it difficult to identify PHAs that engage, suit personal needs and are thus effective. The objective of this study is to investigate the interplay between individual characteristics, engagement with PHAs, and health outcomes. We will recruit a cohort of young adults visiting ReachOut, the most popular online mental health service for young adults from across Australia. We will direct this cohort, which can benefit the most from lifestyle interventions, to personalised online wellbeing interventions, identified through our current research with the Young and Well Cooperative Research Centre on wellbeing apps and the EU Horizon 2020 project, NoHoW on weight loss maintenance. We will collect data on personal profiles, engagement with apps, and longitudinal health outcomes including objective data on physical activity, sleep and diet, and subsequently, analyze the data using innovative clustering methods to determine which apps engage and are effective for whom. This analysis will generate critical knowledge for the development of personalised strategies for diet and weight management at a public health level, focusing on both the adoption and maintenance of healthy behaviours.
Metabolic phenotypes and longitudinal profiles of complications and health care costs in type 1 diabetes
Collaborators: Dr Raija Lithovius & Prof Per-Henrik Groop, University of Helsinki
In 2008, we published one of the first studies that utilised metabolic subtypes of disease to characterise a nation-wide cohort of individuals and their risk of premature death (the FinnDiane Study). Seven years later, we will revisit the dataset and investigate what has happened to the metabolic subgroups of patients in terms of diabetic complications, prescribed medications and their ability to reduce risk factors and cumulative costs from hospitalization.
The original study comprised the FinnDiane cohort of 4,197 patients with type 1 diabetes with baseline data collected between 1998 and 2005 and mortality data from a 2008 snapshot of the national population registry in Finland. For the new study, we already have access to drug prescription registry from the late nineties to 2009, and are in the process of obtaining access to more recent records from 2009-2014. In addition, significant new data from NMR metabolomics and genome-wide genotyping have been obtained from the baseline samples, and a large proportion of the patients have also made a follow-up clinical visit with blood and urine samples.
We intend to leverage this unique resource in an unbiased manner to the models we defined in the previous study; this will provide us with reliable criteria to assess complications risk in patients with type 1 diabetes, and whether the current medical guidelines are able to capture important phenotypic features and if the pattern of drug prescriptions during follow-up reflect the risk profile at baseline.
Dynamic metabolic responses to successive high-fat meals in patients with type 1 diabetes and non-diabetic controls
Collaborators: Dr Markku Lehto, University of Helsinki
We spend most of our waking hours in a postprandial state. Therefore, diet and its dynamic effects on metabolism play a key role in our health and, importantly, the diversity of systemic responses to food intake may partly explain the complex patterns of metabolic dysfunction in human populations. In this study, we investigate the postprandial effects of three consecutive high fat meals on circulating metabolites in healthy individuals and in people with type 1 diabetes (T1D).
Non-diabetic controls (20 men and 21 women) and patients with T1D (25 men and 27 women) were given three high-caloric fat containing meals (2600kcal) for breakfast at 8am (965kcal, 58% of total energy (E%) from fats), lunch at 12pm (870kcal, 44E% fats) and dinner at 4pm (779kcal, 46E% fats). The mean age was 37.0±11.2 in the control group and 44.3±10.2 in the T1D group (average diabetes duration 28.7±13.4 years). Blood samples were drawn at fasting (8am) and every two hours thereafter until 6pm, and the following morning. Biochemical measures including lipoprotein subclass lipids, fatty acid species and amino acids were quantified by NMR metabolomics, along with standard clinical biomarkers.
Our preliminary data suggests that there is a substantial diversity in how humans respond to high-fat dietary stimuli. While part of this diversity may be explained by baseline obesity and diabetes, we are planning future studies using dynamic metabolomics in human cohorts to provide important additional information on the interactions between diet and health.
Nutritional profiles and patterns of morbidity in the Jiangsu Longitudinal Nutrition Study
Collaborator: Dr Zumin Shi, University of Adelaide
The Jiangsu Longitudinal Nutrition Study is an ongoing cohort study investigating the relationship between nutrition and non-communicable chronic diseases. It uses the sub-sample from Jiangsu Province of the Chinese National Nutrition and Health Survey in 2002 as baseline. The six counties and two prefectures that participate in the study represent a geographically and economically diverse population. At baseline, complete dietary information was available for 2849 individuals. Of those, 1682 were identified for follow-up in 2007 and 1282 participated to the follow-up interview (76%). In addition, 1020 had complete information for every chronic disease status both at baseline and follow-up (80%) and will be included in prospective analyses.
This project aims to analyze the population-based diversity of nutritional profiles using the high-quality data from the Jiangsu cohort. The dietary records were collected by professional nutritionists who monitored household food consumption and analyzed the nutritional content of the meals, which ensures higher accuracy compared to self-monitored food diaries or food frequency questionnaires. We will apply the self-organizing map to identify typical nutritional profiles and their circulating biochemical indicators, and then test how these subtypes are associated with the morbidity burden from common chronic diseases.
The SAHMRI community acknowledges and respects the traditional owners, the family clans who are the Kaurna Nation from the Adelaide Plains region of South Australia. We acknowledge the clans of the Kaurna Nation and the sacred knowledge they hold for their country. We pay our respects to the Kaurna Nation, their ancestors and the descendants of these living family clans today.