EMIF Metabolic

EMIF-Metabolic Project background

Accumulating evidence indicates that obesity is closely associated with an increased risk of metabolic complications such as type 2 diabetes, coronary heart disease, non-alcoholic fatty liver disease and cancers.  However, the association between obesity and any of these complications is complex, with high inter-individual variability in susceptibility to specific metabolic complications of obesity. This association is expected to be additionally influenced by different constitutional, environmental and obesity-specific factors, thus further complicating the development of adequate treatments.

EMIF-Metabolic Project Objectives

Prof Ulf Smith, University of Gothenburg EMIF-Metabolic will focus on two distinct pathways through which individuals who are obese may vary in their risk of the complications of obesity. One is to identify genetic causes of obesity and their relation to the metabolic complications of obesity. The other pathway is to characterize individuals and identify markers associated with metabolic risk irrespective of degree of obesity based on the knowledge that many obese individuals do not become dysmetabolic and insulin resistant. In both instances the complementary approaches of studying extreme phenotypes and population-based cohorts will be followed.

The discovery of predictors of the metabolic complications of adult and paediatric obesity has lead to innovative diagnostic tests, paved the way to novel therapeutics targeted to high-risk individuals, and provided the infrastructure to select individuals for such targeted pharmacological interventions (genetic, epigenetic and ‘omics platforms).

Identified novel mechanisms and potential targets for therapy were characterized in appropriate cell- and animal-based models to facilitate transition to human studies. To this end, EMIF-Metabolic has brought together top European experts in genetics, epidemiology, genomics and other ‘omic’ technologies, human and murine physiology and systems approaches and state-of-the-art technologies.

Work Packages Overview

EMIF-MET

Identify predictors of metabolic complications in obesity

EMIF-MET
EMIF-MET Work Packages (WP) Evaluate epidemiology of obesity-related clinical conditions in large real-world databases WP8 Validate sensitivity/specificity of novel biomarkers vs. currently available biomarkers WP7 Characterise heterogeneity in metabolic consequences of obesity in small, medium and large clinical populations with outcome data WP6 Identify novel biomarkers and mechanisms for obesity-associated complications using extreme phenotypes and unbiased “omics” technologies, as well as targeted approaches WP5
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EMIF-Metabolic Project Achievements

Cohort Development

Molecules related to insulin secretion capacity, insulin resistance and NAFLD identified in -omics biomarker discovery programme, currently undergoing validation in a sample set of 6000 individuals

Identification of adipokines related to insulin resistance
Mannose is related to T2D and incidence outcome

Discovery

Potential new therapeutic target for NAFLD/NASH under investigation in Platform databases and largest BMI cohort identified in primary care patients

EMIF-Metabolic NAFLD/NASH Cohort
EMIF-Metabolic BMI Cohort

WP 5 Use carefully characterized extreme phenotypes to identify biomarkers of metabolic risk

Peter Arner (KI) – Markku Laakso (UEF) – Dawn Waterworth (GSK)

KI (CO-LEAD), UEF (CO-LEAD), GSK (CO-LEAD), UGOT, UCAM, UH, INSERM, ULEI, UGLA, VKJK, Janssen, PFIZER, NOVO, Amgen, BI

GOALS & OBJECTIVES

The main objective is:

To characterize in detail using “omics” technologies extreme phenotypes related to the complications of obesity (including insulin resistance and liver fat in first degree relatives of diabetics). Response to weight loss will also be characterized. The most interesting biomarkers will subsequently be further evaluated in tissue culture and animal models and taken forward for further validation in medium sized cohorts in WP6. The most interesting markers will be those most correlated with the endpoints of interest and with properties that would lend themselves to being clinically useful; such as robustness, variability, frequency, tissue specificity and ease of detection.

The sub-objectives are:

  1. Detailed omics analysis in well characterized samples with extreme phenotypes or dietary interventions
  2. Comprehensive integrated analysis of detailed omics data using data reduction/mining/statistical techniques to inform marker selection.
  3. Experimental assessment of selected biomarkers in cell-based and animal models to assess validity as drug targets and diagnostic tools.

WP 6 The investigation of heterogeneity in the metabolic consequences of obesity in medium-sized well phenotyped cohort studies

Nick Wareham (UCAM) – Dawn Waterworth (GSK) – Julia Brosnan (PFIZER)

UCAM (CO-LEAD), GSK (CO-LEAD), PFIZER (CO-LEAD), UGOT, UH, UEF, INIPI, VKJK, ULEI, Janssen, Amgen, BI

GOALS & OBJECTIVES

The main objective is:

To use candidate and discovery approaches to identify biomarkers which are associated with the metabolic consequences of obesity and to investigate their causal significance in medium-sized well phenotyped cohorts and large-scale nested case-cohort studies.

The sub-objectives are:

  1. To select quantitative metabolic cohort studies to test biomarkers for the prediction of the metabolic consequences of obesity.
  2. To use candidate and discovery approaches to identify potential biomarkers.
  3. To study the association and likelihood of causality of biomarkers for the prediction of the metabolic consequences of obesity.

WP 7 Validate novel risk factors in general population in adults and children

Naveed Sattar (UGLA) – Katrina Loomis (PFIZER)

UGLA (CO-LEAD), PFIZER (CO-LEAD), UGOT, UCPH, Janssen, GSK, ROCHE, Amgen, BI

GOALS & OBJECTIVES

The main objective is:

The overall objective of this WP is to measure then test the ability of new metabolic biomarkers to predict obesity-related outcomes using retrospective and prospective studies.

The sub-objectives are:

  1. Identify cohorts from EHR which have available baseline serum / plasma samples, acceptable phenotyping (i.e. conventional vascular risk factors, family history data and level of BMI or waist circumference) and with access to outcomes for vascular disease (i.e. CHD events), incident diabetes, and other obesity-related comorbidities such as fatty liver disease or sleep apnoea.
  2. To measure new biomarkers in established biobanks with available serum or plasma from cohorts with at least conventional vascular risk factors measured at baseline (plus BMI) and with documented outcomes covering at least CHD events but potentially also including incident cancers, diabetes and fatty liver disease.
  3. Compare the ability of biomarkers to predict obesity-related outcomes in comparison to conventionally measured risk factors and simple demographic information.
  4. Test the predictive capacity of the most promising biomarkers by utilising samples taken from trials held by Industry participants or from EHR/biobank studies.

Very limited access to samples for biomarker analysis

  • Confirming new metabolic biomarkers identified in WP5 and WP6 will require EMIF to bring in additional biobanks or cohort resources with available biospecimens.

WP 8 Identify and select individuals for pharmacological or non-pharmacological interventions

Ele Ferrannini (UNIPI) – Stuart Kendrick (GSK) – Janet Addison (Amgen)

UNIPI (CO-LEAD), GSK (CO-LEAD), Amgen (CO-LEAD), UCPH, Janssen, PFIZER, BI

GOALS & OBJECTIVES

The main objective is:

The pharmaceutical intervention work package is designed to deliver real world observational data and data from randomised controlled studies, validating the extreme phenotype approach for classification of obesity, diseases related to obesity, and biomarkers identified by other work packages. It is also designed to test the feasibility of electronic healthcare record driven recruitment to intervention trials.

The sub-objectives are:

  1. Establish whether or not EHR’s can be used to conduct feasibility for our clinical trials?
  2. Establish whether or not EHR’s can be used to contact the patients for recruitment into our clinical trials
  3. Better understanding relationship (adverse effect & therapeutic effect) between extreme phenotypes and response to prescribed drugs
  4. Validate biomarkers to predict therapeutic response in interventional trials
  5. Utilize clinical trial data generated by EFPIA partners to investigate the liver complications of obesity.