P Zimmet

Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia

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Publications (617)3251.71 Total impact

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    ABSTRACT: AimsTo investigate if consumption of pulses was associated with a reduced risk of developing abnormal glucose metabolism, increases in body weight and increases in waist circumference in a multi-ethnic cohort in Mauritius.Methods Population-based surveys were performed in Mauritius in 1992 and in 1998. Pulse consumption was estimated from a food frequency questionnaire in 1992 and outcomes were measured in 1998. At both time points, anthropometry was undertaken and an oral glucose tolerance test was performed.ResultsMauritian women with the highest consumption of pulses (highest tertile) had a reduced risk of developing abnormal glucose metabolism [odds ratio 0.52; 95% CI 0.27, 0.99) compared with those with the lowest consumption, and also after multivariable adjustments. In women, a high consumption of pulses was associated with a smaller increase in BMI.Conclusions High consumption of pulses was associated with a reduced risk of abnormal glucose metabolism and a smaller increase in BMI in Mauritian women. Promotion of pulse consumption could be an important dietary intervention for the prevention of Type 2 diabetes and obesity in Mauritius and should be examined in other populations and in clinical trials.This article is protected by copyright. All rights reserved.
    Diabetic Medicine 10/2014; · 3.24 Impact Factor
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    Adrian J Cameron, Paul Z Zimmet
    The Medical journal of Australia 07/2014; 201(1):25-26. · 2.85 Impact Factor
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    ABSTRACT: It is believed that diabetes risk scores need to be ethnic specific. However, this prerequisite has not been tested. We examined the performance of several risk models, developed in various populations, in a Europid and a South Asian population. The performance of 14 published risk prediction models were tested in two prospective studies: the Australian Diabetes, Obesity and Lifestyle (AusDiab) study and the Mauritius non-communicable diseases survey. Eight models were developed in Europid populations; the remainder in various non-Europid populations. Model performance was assessed using area under the receiver operating characteristic curves (discrimination), Hosmer-Lemeshow tests (goodness-of-fit) and Brier scores (accuracy). In both AusDiab and Mauritius, discrimination was highest for a model developed in a mixed population (non-Hispanic white and African American) and lowest for a model developed in a Europid population. Discrimination for all scores was higher in AusDiab than in Mauritius. For almost all models, goodness-of-fit was poor irrespective of the ethnicity of the development cohort, and accuracy was higher in AusDiab compared to Mauritius. Our results suggest that similarity of ethnicity or similarity of diabetes risk may not be the best way of identifying models that will perform well in another population. Differences in study methodology likely account for much of the difference in the performance. Thus, identifying models which use measurements that are clearly described and easily reproducible for both research and clinical settings may be more important.
    Acta diabetologica. 07/2014;
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    ABSTRACT: Context: Adipokines actuate chronic, low-grade inflammation through a complex network of immune markers but the current understanding of these networks is incomplete. The soluble isoform of the interleukin-1 receptor accessory protein (sIL1RAP) occupies an important position in the inflammatory pathways involved in obesity. The pathogenetic and clinical influences of sIL1RAP are unknown. Objective: To elucidate whether plasma levels of sIL1RAP are reduced in obesity, using affluent clinical, biochemical and genetic data from two diverse cohorts. Design, Setting and Participants: The study was conducted in two cohorts - the San Antonio Family Heart Study (n = 1,397 individuals from 42 families) and South Asians living in Mauritius n = 230). Main outcome measures: Plasma sIL1RAP levels were measured using an enzyme-linked immunosorbent assay. The genetic basis of sIL1RAP levels were investigated using both a large scale gene expression profiling study and a genome-wide association study. Results: A significant decrease in plasma sIL1RAP levels were observed in obese subjects even after adjustment for age and sex. sIL1RAP levels demonstrated a strong inverse association with obesity measures in both populations. All associations were more significant in females. Plasma sIL1RAP levels were significantly heritable, correlated with IL1RAP transcript levels (NM 134470), showed evidence for shared genetic influences with obesity measures and were significantly associated with the rs2885373 SNP (p=6.7 x 10(-23)) within the IL1RAP gene. Conclusions: Plasma sIL1RAP levels are reduced in obesity and can potentially act as biomarkers of obesity. Mechanistic studies are required to understand the exact contribution of sIL1RAP to pathogenesis of obesity.
    The Journal of clinical endocrinology and metabolism. 06/2014;
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    ABSTRACT: To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.
    Nature Genetics 03/2014; 46(3):234-244. · 35.21 Impact Factor
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    ABSTRACT: Cardiovascular disease poses a major challenge for the 21st century, exacerbated by the pandemics of obesity, metabolic syndrome and type 2 diabetes. While best standards of care, including high-dose statins, can ameliorate the risk of vascular complications, patients remain at high risk of cardiovascular events. The Residual Risk Reduction Initiative (R3i) has previously highlighted atherogenic dyslipidaemia, defined as the imbalance between proatherogenic triglyceride-rich apolipoprotein B-containing-lipoproteins and antiatherogenic apolipoprotein A-I-lipoproteins (as in high-density lipoprotein, HDL), as an important modifiable contributor to lipid-related residual cardiovascular risk, especially in insulin-resistant conditions. As part of its mission to improve awareness and clinical management of atherogenic dyslipidaemia, the R3i has identified three key priorities for action: i) to improve recognition of atherogenic dyslipidaemia in patients at high cardiometabolic risk with or without diabetes; ii) to improve implementation and adherence to guideline-based therapies; and iii) to improve therapeutic strategies for managing atherogenic dyslipidaemia. The R3i believes that monitoring of non-HDL cholesterol provides a simple, practical tool for treatment decisions regarding the management of lipid-related residual cardiovascular risk. Addition of a fibrate, niacin (North and South America), omega-3 fatty acids or ezetimibe are all options for combination with a statin to further reduce non-HDL cholesterol, although lacking in hard evidence for cardiovascular outcome benefits. Several emerging treatments may offer promise. These include the next generation peroxisome proliferator-activated receptoralpha agonists, cholesteryl ester transfer protein inhibitors and monoclonal antibody therapy targeting proprotein convertase subtilisin/kexin type 9. However, long-term outcomes and safety data are clearly needed. In conclusion, the R3i believes that ongoing trials with these novel treatments may help to define the optimal management of atherogenic dyslipidaemia to reduce the clinical and socioeconomic burden of residual cardiovascular risk.
    Cardiovascular Diabetology 01/2014; 13(1):26. · 4.21 Impact Factor
  • K George M M Alberti, Paul Z Zimmet
    The lancet. Diabetes & endocrinology. 01/2014; 2(1):e1-2.
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    ABSTRACT: Background: Bioelectrical impedance (BIA) represents a simple, inexpensive and non-invasive method that is often used to assess fat-mass (FM) and fat-free mass (FFM) in large population-based cohorts. Objective: The aim of this study was to describe the reference ranges and examine the influence of age and gender on FM, FFM and skeletal muscle mass (SMM) as well as height-adjusted estimates of FM [fat mass index (FMI)], FFM [fat-free mass index (FFMI)] and SMM [SMM index (SMI)] in a national, population-based cohort of Australian adults. Design and Participants: The analytical sample included a total of 8,582 adults aged 25-91 years of Europid origin with complete data involved in the cross-sectional 1999-2000 Australian, Diabetes, Obesity and Lifestyle (AusDiab) Study. Measurements: Bioelectrical impedance analysis was used to examine components of body composition. Demographic information was derived from a household interview. Results: For both genders, FFM, SMM and SMI decreased linearly from the age of 25 years, with the exception that in men SMI was not related to age and FFM peaked at age 38 years before declining thereafter. The relative loss from peak values to ≥75 years in FFM (6-8%) and SMM (11-15%) was similar between men and women. For FM and FMI, there was a curvilinear relationship with age in both genders, but peak values were detected 6-7 years later in women with a similar relative loss thereafter. For FFMI there was no change with age in men and a modest increase in women. Conclusion: In Australian adults there is heterogeneity in the age of onset, pattern and magnitude of changes in the different measures of muscle and fat mass derived from BIA, but overall the age-related losses were similar between men and women.
    The Journal of Nutrition Health and Aging 01/2014; 18(5):540-546. · 2.39 Impact Factor
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    ABSTRACT: To assess in a single cohort whether annual weight and waist circumference (WC) change has varied over time. Longitudinal cohort study with three surveys (1) 1999/2000; (2) 2004/2005 and (3) 2011/2012. Generalised linear mixed models with random effects were used to compare annualised weight and WC change between surveys 1 and 2 (period 1) with that between surveys 2 and 3 (period 2). Models were adjusted for age to analyse changes with time rather than age. Models were additionally adjusted for sex, education status, area-level socioeconomic disadvantage, ethnicity, body mass index, diabetes status and smoking status. The Australian Diabetes, Obesity and Lifestyle study (AusDiab)-a population-based, stratified-cluster survey of 11247 adults aged ≥25 years. 3351 Australian adults who attended each of three surveys and had complete measures of weight, WC and covariates. Weight and WC were measured at each survey. Change in weight and WC was annualised for comparison between the two periods. Mean weight and WC increased in both periods (0.34 kg/year, 0.43 cm/year period 1; 0.13 kg/year, 0.46 cm/year period 2). Annualised weight gain in period 2 was 0.11 kg/year (95% CI 0.06 to 0.15) less than period 1. Lesser annual weight gain between the two periods was not seen for those with greatest area-level socioeconomic disadvantage, or in men over the age of 55. In contrast, the annualised WC increase in period 2 was greater than period 1 (0.07 cm/year, 95% CI 0.01 to 0.12). The increase was greatest in men aged 55+ years and those with a greater area-level socioeconomic disadvantage. Between 2004/2005 and 2011/2012, Australian adults in a national study continued to gain weight, but more slowly than 1999/2000-2004/2005. While weight gain may be slowing, this was not observed for older men or those in more disadvantaged groups, and the same cannot be said for WC.
    BMJ Open 01/2014; 4(1):e003667. · 2.06 Impact Factor
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    ABSTRACT: The number of people with diabetes worldwide has more than doubled during the past 20 years. One of the most worrying features of this rapid increase is the emergence of type 2 diabetes in children, adolescents, and young adults. Although the role of traditional risk factors for type 2 diabetes (eg, genetic, lifestyle, and behavioural risk factors) has been given attention, recent research has focused on identifying the contributions of epigenetic mechanisms and the effect of the intrauterine environment. Epidemiological data predict an inexorable and unsustainable increase in global health expenditure attributable to diabetes, so disease prevention should be given high priority. An integrated approach is needed to prevent type 2 diabetes, taking into account its many origins and heterogeneity. Thus, research needs to be directed at improved understanding of the potential role of determinants such as the maternal environment and other early life factors, as well as changing trends in global demography, to help shape disease prevention programmes.
    The lancet. Diabetes & endocrinology. 01/2014; 2(1):56-64.
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    ABSTRACT: Diabetes in pregnancy carries an increased risk of adverse pregnancy outcomes for both the mother and foetus, but it also provides an excellent early opportunity for intervention in the life course for both mother and baby. In the context of the escalating epidemic of chronic diseases among Indigenous Australians, it is vital that this risk is reduced as early as possible in the life course of the individual. The aims of the PANDORA Study are to: (i) accurately assess rates of diabetes in pregnancy in the Northern Territory (NT) of Australia, where 38% of babies are born to Indigenous mothers; (ii) assess demographic, clinical, biochemical, anthropometric, socioeconomic and early life development factors that may contribute to key maternal and neonatal birth outcomes associated with diabetes in pregnancy; and (iii) monitor relevant post-partum clinical outcomes for both the mothers and their babies.Methods/design: Eligible participants are all NT women with diabetes in pregnancy aged 16 years and over. Information collected includes: standard antenatal clinical information, diagnosis and management of diabetes in pregnancy, socio-economic status, standard clinical birth information (delivery, gestational age, birth weight, adverse antenatal and birth outcomes). Cord blood is collected at the time of delivery and detailed neonatal anthropometric measurements performed within 72 hours of birth. Information will also be collected regarding maternal post-partum glucose tolerance and cardio-metabolic risk factor status, breastfeeding and growth of the baby up to 2 years post-partum in the first instance. This study will accurately document rates and outcomes of diabetes in pregnancy in the NT of Australia, including the high-risk Indigenous Australian population. The results of this study should contribute to policy and clinical guidelines with the goal of reducing the future risk of obesity and diabetes in both mothers and their offspring.
    BMC Pregnancy and Childbirth 12/2013; 13(1):221. · 2.52 Impact Factor
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    ABSTRACT: India currently has more than 60 million people with Type 2 Diabetes Mellitus (T2DM) and this is predicted to increase by nearly two-thirds by 2030. While management of those with T2DM is important, preventing or delaying the onset of the disease, especially in those individuals at 'high risk' of developing T2DM, is urgently needed, particularly in resource-constrained settings. This paper describes the protocol for a cluster randomised controlled trial of a peer-led lifestyle intervention program to prevent diabetes in Kerala, India.Methods/designA total of 60 polling booths are randomised to the intervention arm or control arm in rural Kerala, India. Data collection is conducted in two steps. Step 1 (Home screening): Participants aged 30--60 years are administered a screening questionnaire. Those having no history of T2DM and other chronic illnesses with an Indian Diabetes Risk Score value of >=60 are invited to attend a mobile clinic (Step 2). At the mobile clinic, participants complete questionnaires, undergo physical measurements, and provide blood samples for biochemical analysis. Participants identified with T2DM at Step 2 are excluded from further study participation. Participants in the control arm are provided with a health education booklet containing information on symptoms, complications, and risk factors of T2DM with the recommended levels for primary prevention. Participants in the intervention arm receive: (1) eleven peer led small group sessions to motivate, guide and support in planning, initiation and maintenance of lifestyle changes; (2) two diabetes prevention education sessions led by experts to raise awareness on T2DM risk factors, prevention and management; (3) a participant handbook containing information primarily on peer support and its role in assisting with lifestyle modification; (4) a participant workbook to guide self-monitoring of lifestyle behaviours, goal setting and goal review; (5) the health education booklet that is given to the control arm. Follow-up assessments are conducted at 12 and 24 months. The primary outcome is incidence of T2DM. Secondary outcomes include behavioural, psychosocial, clinical, and biochemical measures. An economic evaluation is planned. Results from this trial will contribute to improved policy and practice regarding lifestyle intervention programs to prevent diabetes in India and other resource-constrained settings.Trial registrationAustralia and New Zealand Clinical Trials Registry: ACTRN12611000262909.
    BMC Public Health 11/2013; 13(1):1035. · 2.08 Impact Factor
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    ABSTRACT: The relationships between smoking and glycaemic variables have not been well explored. We compared HbA1c, fasting plasma glucose (FPG) and 2 h plasma glucose (2H-PG) in current, ex- and never-smokers. This meta-analysis used individual data from 16,886 men and 18,539 women without known diabetes in 12 DETECT-2 consortium studies and in the French Data from an Epidemiological Study on the Insulin Resistance Syndrome (DESIR) and Telecom studies. Means of three glycaemic variables in current, ex- and never-smokers were modelled by linear regression, with study as a random factor. The I (2) statistic was used to evaluate heterogeneity among studies. HbA1c was 0.10% (95% CI 0.08, 0.12) (1.1 mmol/mol [0.9, 1.3]) higher in current smokers and 0.03% (0.01, 0.05) (0.3 mmol/mol [0.1, 0.5]) higher in ex-smokers, compared with never-smokers. For FPG, there was no significant difference between current and never-smokers (-0.004 mmol/l [-0.03, 0.02]) but FPG was higher in ex-smokers (0.12 mmol/l [0.09, 0.14]). In comparison with never-smokers, 2H-PG was lower (-0.44 mmol/l [-0.52, -0.37]) in current smokers, with no difference for ex-smokers (0.02 mmol/l [-0.06, 0.09]). There was a large and unexplained heterogeneity among studies, with I (2) always above 50%; I (2) was little changed after stratification by sex and adjustment for age and BMI. In this study population, current smokers had a prevalence of diabetes that was 1.30% higher as screened by HbA1c and 0.52% lower as screened by 2H-PG, in comparison with never-smokers. Across this heterogeneous group of studies, current smokers had a higher HbA1c and lower 2H-PG than never-smokers. This will affect the chances of smokers being diagnosed with diabetes.
    Diabetologia 09/2013; · 6.49 Impact Factor
  • Paul Z Zimmet
    The Medical journal of Australia 08/2013; 199(4):225-6. · 2.85 Impact Factor
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    ABSTRACT: To examine the independent and joint associations of diet quality and television viewing time with abnormal glucose metabolism (AGM) in men and women. Cross-sectional data from 5346 women and 4344 men from the 1999-2000 Australian Diabetes, Obesity and Lifestyle Study were examined. Diet quality scores were derived from a food frequency questionnaire and categorised into tertiles (high; moderate; low). Television viewing time was dichotomised into low (≤14 hours/week) and high (>14 hours/week). AGM was defined as impaired fasting glucose, impaired glucose tolerance, known or newly diagnosed diabetes based on an oral glucose tolerance test. Regression analyses adjusted for confounding variables. Diet quality and television viewing time were significantly associated with AGM in women, independent of waist circumference. Compared to women with high diet quality/low television viewing time, women with low diet quality/low television viewing time and women with low diet quality/high television viewing time were significantly more likely to have AGM. Associations were not observed in men. Both poor diet quality and prolonged television viewing should be addressed to reduce risk of AGM in women. Further understanding of modifiable risk factors in men is warranted.
    Preventive Medicine 07/2013; · 3.50 Impact Factor
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    ABSTRACT: AimTo assess factors influencing glycaemic control following gastric bypass surgery in patients with Type 2 diabetes and BMI 7%). Analysis was conducted using binary logistic regression, and cut-points obtained from receiver operator characteristics. ResultsExcellent glycaemic control was achieved in 31 (30%) at 1 year. Diabetes duration of 27 kg/m2 provided independent predictors and useful cut-points. Likelihood of excellent glycaemic control for an individual could be estimated using loge (Odds) = –6.7 + (0.26 × BMI) + (–1.2 × diabetes duration). Baseline BMI of 16%) were associated with excellent glycaemic control. Higher BMI was associated with greater percentage weight loss. Conclusion In patients with Type 2 diabetes and BMI
    Diabetic Medicine 04/2013; 30(4). · 3.24 Impact Factor
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    K George M M Alberti, Paul Zimmet
    Nature Reviews Endocrinology 03/2013; · 11.03 Impact Factor
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    ABSTRACT: BACKGROUND: As the global prevalence of diabetes increases, so will the numbers of people with diabetic retinopathy. Our review aimed to provide a comprehensive picture of available studies of diabetic retinopathy and how prevalence varies around the developed and developing world. METHODS: A detailed literature search using PubMed was undertaken. The following search term was used: 'diabetic retinopathy AND prevalence'. The titles and abstracts of all publications identified by the search were reviewed and 492 studies were retrieved. Inclusion and exclusion criteria were applied. RESULTS: A total of 72 articles from 33 countries were included. There were only 26 population-based studies using fundus photography (12 in developing countries), of which only 16 (eight in developing countries) were published since 2000. Prevalence estimates varied from as low as 10% to as high as 61% in persons with known diabetes and from 1.5 to 31% in newly diagnosed diabetes. Across all the studies, the median (interquartile range) prevalence of any diabetic retinopathy in known diabetes was 27.9% (22-37%) and 10.5% (6-16%) in newly diagnosed diabetes. Prevalence of diabetic retinopathy was higher in developing countries. CONCLUSION: Significant gaps exist in that reliable population-based data from developing nations and indigenous populations in particular are lacking. Major differences in study characteristics and methodologies make comparisons very difficult. More research is required and study methodologies must be better standardized. This will provide important information for prevention and treatment strategies. © 2013 Baker IDI Heart and Diabetes Institute. Diabetic Medicine © 2013 Diabetes UK.
    Diabetic Medicine 01/2013; · 3.24 Impact Factor
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    ABSTRACT: AIMS: To assess and compare costs associated with diabetes and lesser degrees of glucose intolerance in Australia. METHODS: The Australian Diabetes, Obesity and Lifestyle study collected data on the use of health services and health related expenditure in 2004-2005. Complications data were collected through physical examination and biochemical tests or questionnaire. Data were available on 6101 participants. Age- and sex-adjusted direct healthcare costs, direct non-healthcare costs and government subsidies were estimated according to glucose tolerance status. RESULTS: Annual direct per person costs were A$1898 for those with normal glucose tolerance to A$4390 for those with known diabetes. Costs were substantially higher in people with diabetes and both micro- and macrovascular complications. The total annual cost of diabetes in 2005 for Australians aged ≥30 years was A$10.6 billion (A$4.4 billion in direct costs; A$6.2 billion in government subsidies) which equates to A$14.6 billion in 2010 dollars. Total annual excess cost associated with diabetes in 2005 was A$4.5 billion (A$2.2 billion in direct costs; A$2.3 billion in government subsidies). CONCLUSION: The excess cost of diabetes to individuals and government is substantial and is greater in those with complications. Costs could potentially be reduced by preventing the development of diabetes or its complications.
    Diabetes research and clinical practice 01/2013; · 2.74 Impact Factor
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    ABSTRACT: Two recent studies demonstrated that bariatric surgery induced remission of type 2 diabetes very soon after surgery and far too early to be attributed to weight loss. In this study, we sought to explore the mechanism/s of this phenomenon by testing the effects of proteins from the duodenum-jejunum conditioned-medium (CM) of db/db or Swiss mice on glucose uptake in vivo in Swiss mice and in vitro in both Swiss mice soleus and L6 cells. We studied the effect of sera and CM proteins from insulin resistant (IR) and insulin-sensitive subjects on insulin signaling in human myoblasts. db/db proteins induced massive IR either in vivo or in vitro, while Swiss proteins did not. In L6 cells, only db/db proteins produced a noticeable increase in basal (473)Ser-Akt phosphorylation, lack of GSK3β inhibition and a reduced basal (389)Thr-p70-S6K1 phosphorylation. Human IR serum markedly increased basal (473)Ser-Akt phosphorylation in a dose-dependent manner. Human CM IR proteins increased by about twofold both basal and insulin-stimulated (473)Ser-Akt. Basal (9)Ser-GSK3β phosphorylation was increased by IR subjects serum with a smaller potentiating effect of insulin. These findings show that jejunal proteins either from db/db mice or from insulin resistant subjects impair muscle insulin signaling, thus inducing insulin resistance.
    PLoS ONE 01/2013; 8(2):e56258. · 3.53 Impact Factor

Publication Stats

38k Citations
3,251.71 Total Impact Points

Institutions

  • 2009–2014
    • Baker IDI Heart and Diabetes Institute
      • Clinical Diabetes and Epidemiology Research Group
      Melbourne, Victoria, Australia
    • National University of Singapore
      • Singapore Eye Research Institute
      Singapore, Singapore
    • Singapore Eye Research Institute
      Tumasik, Singapore
  • 2013
    • The Kings College
      Denmark, South Carolina, United States
  • 2008–2013
    • University of Sydney
      • School of Public Health
      Sydney, New South Wales, Australia
    • MS Australia
      Melbourne, Victoria, Australia
    • Royal Prince Alfred Hospital
      Camperdown, New South Wales, Australia
    • University of Wisconsin, Madison
      • Department of Ophthalmology and Visual Sciences
      Mississippi, United States
    • Université du Droit et de la Santé Lille 2
      Lille, Nord-Pas-de-Calais, France
    • Imperial College London
      • Division of Diabetes, Endocrinology and Metabolism
      London, ENG, United Kingdom
    • Institut Pasteur de Lille
      Lille, Nord-Pas-de-Calais, France
  • 2007–2013
    • University of Queensland 
      • • Cancer Prevention Research Centre
      • • School of Population Health
      Brisbane, Queensland, Australia
    • Baker College, Australia
      Hornsby, New South Wales, Australia
    • Imperial College Healthcare NHS Trust
      Londinium, England, United Kingdom
  • 2012
    • University of Michigan
      Ann Arbor, Michigan, United States
    • McKinsey
      New York City, New York, United States
    • Laval University
      Québec, Quebec, Canada
  • 1987–2012
    • Monash University (Australia)
      • • School of Public Health and Preventive Medicine
      • • Department of Epidemiology and Preventive Medicine
      • • Monash Medical Centre
      • • Department of Biochemistry and Molecular Biology
      • • Department of Physiology
      Melbourne, Victoria, Australia
  • 2011
    • The Royal Children's Hospital
      Melbourne, Victoria, Australia
  • 1995–2011
    • Deakin University
      • School of Exercise and Nutrition Sciences
      Geelong, Victoria, Australia
    • Shizuoka University
      Sizuoka, Shizuoka, Japan
    • Victoria University Melbourne
      Melbourne, Victoria, Australia
  • 1994–2011
    • University of Melbourne
      • • Department of Medicine
      • • Department of Ophthalmology
      • • Department of Obstetrics and Gynaecology
      Melbourne, Victoria, Australia
  • 2010
    • Mayo Clinic - Rochester
      Rochester, Minnesota, United States
    • University of Colorado
      Denver, Colorado, United States
  • 2008–2009
    • University of Helsinki
      • Department of Dental Public Health
      Helsinki, Province of Southern Finland, Finland
  • 1982–2009
    • Diabetes Australia, Victoria
      Melbourne, Victoria, Australia
  • 2006–2008
    • Charles Darwin University
      • • Institute of Advanced Studies
      • • Menzies School of Health Research
      Palmerston, Northern Territory, Australia
    • Lund University
      • Department of Clinical Sciences
      Lund, Skane, Sweden
    • Waikato Hospital
      Hamilton City, Waikato, New Zealand
  • 2005–2008
    • Umeå University
      • Department of Public Health and Clinical Medicine
      Umeå, Västerbotten, Sweden
    • Southwest Foundation For Biomedical Research
      San Antonio, Texas, United States
    • Cornell University
      • Department of Nutritional Sciences
      Ithaca, NY, United States
    • University of Auckland
      • Waikato Clinical School
      Auckland, Auckland, New Zealand
    • Mount Sinai Hospital
      New York City, New York, United States
    • St. Marys Medical Center
      West Palm Beach, Florida, United States
  • 2000–2008
    • University of Western Australia
      • School of Medicine and Pharmacology
      Perth City, Western Australia, Australia
  • 2006–2007
    • Newcastle University Medicine Malaysia
      Bharu, Johor, Malaysia
  • 1996–2007
    • The Chinese University of Hong Kong
      • • Department of Medicine and Therapeutics
      • • Prince of Wales Hospital
      Hong Kong, Hong Kong
    • Hospital Clínico San Carlos
      Madrid, Madrid, Spain
  • 2004
    • VU University Medical Center
      Amsterdamo, North Holland, Netherlands
    • Kangbuk Samsung Hospital
      Sŏul, Seoul, South Korea
  • 2003
    • Melbourne Pathology
      Melbourne, Victoria, Australia
    • National Institutes of Health
      Maryland, United States
  • 1998–2003
    • Newcastle University
      Newcastle-on-Tyne, England, United Kingdom
    • Western Australia Health
      Perth City, Western Australia, Australia
    • University of Minnesota Duluth
      Duluth, Minnesota, United States
  • 1988–2003
    • National Public Health Institute
      Helsinki, Southern Finland Province, Finland
  • 2002
    • Sahlgrenska University Hospital
      Goeteborg, Västra Götaland, Sweden
    • Sir Charles Gairdner Hospital
      Perth City, Western Australia, Australia
  • 1997–1998
    • Madras Diabetes Research Foundation
      • Department of Cell and Molecular Biology
      Chennai, Tamil Nādu, India
  • 1996–1997
    • Melbourne Institute of Technology
      Melbourne, Victoria, Australia
  • 1988–1997
    • University of Tasmania
      Hobart Town, Tasmania, Australia
  • 1987–1993
    • Australian National University
      Canberra, Australian Capital Territory, Australia
  • 1989
    • Papua New Guinea Institute of Medical Research
      New Garoka, Eastern Highlands, Papua New Guinea
  • 1979–1989
    • Royal Melbourne Hospital
      Melbourne, Victoria, Australia
  • 1984
    • St. Vincent's Hospital Melbourne
      Melbourne, Victoria, Australia
    • University of Kuopio
      Kuopio, Eastern Finland Province, Finland
  • 1978
    • Alfred Hospital
      Melbourne, Victoria, Australia