- Email: [email protected]
Identification of relevant biomarkers for type 2 diabetes
Correspondence
and prevention of opening of the mitochondrial permeability transition pore on reperfusion.2 Moreover, we provided experimental evidence that metformin increases formation of the purine nucleoside adenosine, and that adenosine receptor stimulation is crucial for the infarct size-limiting effect of metformin.3 In addition to its acute cardioprotective effect, metformin beneficially modulates cardiac remodelling after myocardial infarction.4 Adenosine receptor stimulation not only increases tolerance against ischemia-reperfusion, but can also retard the process of atherosclerosis.5 Therefore, in the CAMERA trial,1 the use of the adenosine receptor antagonist caffeine might have attenuated any anti-atherosclerotic effects of metformin. Based on the strong preclinical evidence of a direct cardioprotective effect of metformin, we feel that the study by Preiss and colleagues1 should not temper the hope that metformin improves cardiovascular prognosis in patients at risk of cardiovascular events. In addition to exploring the effects of metformin on atherosclerosis in larger trials, it is important to investigate whether metformin limits myocardial ischemia-reperfusion injury and postinfarction remodelling in patients in a trial that controls for known potential confounders, such as caffeine intake, as much as possible. With interest, we await the results of two ongoing randomised controlled trials in The Netherlands aimed at studying these cardioprotective effects of metformin (NCT01438723, NCT01217307). All authors are investigators of the Metformin in Coronary Artery Bypass grafting (MetCAB) study (NCT01438723) on the effect of metformin on cardiac injury in the setting of cardiac bypass grafting. The authors have received unrestricted research grants from AstraZeneca (NPR and GAR) and Boehringer-Ingelheim (GAR) and have served on Scientific Advisory Boards for AstraZeneca (NPR) and Novartis (GAR).
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*Niels P Riksen, Saloua el Messaoudi, Gerard A Rongen [email protected] Department of Pharmacology-Toxicology (SeM, GAR) and Department of Internal Medicine (NPR), Radboud University Medical Centre, Geert Grooteplein 10, Nijmegen, 6525GA, Netherlands 1
2
3
4
5
Preiss D, Lloyd SM, Ford I, et al. Metformin for non-diabetic patients with coronary heart disease (the CAMERA study): a randomised controlled trial. Lancet Diabetes Endocrinol 2013; published online Nov 7. DOI:10.1016/ S2213-8587(13)70152-9. El Messaoudi S, Rongen GA, de Boer RA, Riksen NP. The cardioprotective effects of metformin. Curr Opin Lipidol 2011; 22: 445–53. Paiva M, Riksen NP, Davidson SM, et al. Metformin prevents myocardial reperfusion injury by activating the adenosine receptor. J Cardiovasc Pharmacol 2009; 53: 373–78. Gundewar S, Calvert JW, Jha S, et al. Activation of AMP-activated protein kinase by metformin improves left ventricular function and survival in heart failure. Circ Res 2009; 104: 403–11. Riksen NP, Rongen GA. Targeting adenosine receptors in the development of cardiovascular therapeutics. Expert Rev Clin Pharmacol 2012; 5: 199–218.
Identification of relevant biomarkers for type 2 diabetes In clinical practice, all measurable biological parameters that can objectively be evaluated, are called biomarkers, irrespective of whether the biological marker is obtained from physical examination of the body (ie, blood tests) or anywhere else, such as from health records.1 A biomarker could be used for screening or diagnosis of a disease (eg, glucose and glycated haemoglobin tests for type 2 diabetes) or for monitoring treatment.1 In The Lancet Diabetes & Endocrinology, Lee Roberts and colleagues2 provide a review on potential biomarkers for insulin resistance and type 2 diabetes, focusing on aminoacid and lipid metabolomics. These metabolites might contribute to early identification of type 2 diabetes risk in addition to uncovering novel therapeutic targets for this disease.2 The authors summarise the evidence for a potential role of branched-chain aminoacids in contributing to insulin resistance
and predicting risk of type 2 diabetes. In particular, a main component composed of all branched-chain aminoacids and their metabolites was strongly correlated with insulin resistance, rather than concentrations of branched-chain aminoacids per se above a certain threshold.2 This might reflect the complex mechanisms that regulate levels of aminoacids and their effects on type 2 diabetes.2 Also, it is not known whether relative or absolute differences in biomarker level are most important to predict risk of future type 2 diabetes. Likewise, little is known about the relevance of changes in biomarkers with time (ie, biomarker trajectories) in individual patients, and whether these changes are associated with long-term outcome.3,4 In this context, some relevant questions are: 1) When is the optimum time for biomarker measurements in the course of disease? 2) To what extent do biomarker concentrations change over time (eg, long-term stability)? 3) Do biomarker trajectories have effects on long-term risk? Statistical models assume a constant risk over time, and the so-called estimated risk is usually stronger when the biomarker is measured shortly before the onset of disease (eg, glucose as predictor of type 2 diabetes).5 4) Would slightly elevated biomarker concentrations reflect a risk in the far future, whereas a very high level would indicate an increased risk over a shorter time period? For example, the relation between inflammatory biomarkers with acute or chronic cardiovascular disease can be different.6 Another aspect is that environmental factors contribute to biomarker concentrations and thus modify the relation between biomarkers and type 2 diabetes.2 Roberts and colleagues2 provide an important example in that the association between increased palmitoleate (a monounsaturated fatty acid) concentrations and insulin
www.thelancet.com/diabetes-endocrinology Vol 2 February 2014
Correspondence
resistance might indicate that dietary intake of carbohydrates increases de-novo lipogenesis. Palmitate, a precursor for palmitoleate, is the direct product of de-novo lipogenesis.2 Such a biomarker might reflect changes in behaviour, such as decreased physical activity or increased carbohydrate consumption. If behaviour is changed appropriately and concomitant changes in biomarker concentrations occur, one might infer that a behavioural modification works. This warrants consideration in implementation of personalised screening and prevention strategies.2 In the future, use of highthroughput data analysis will become available in parallel with novel technological platforms. Before this, an integrative approach to analysing gene–proteome–metabolite data can be used to find relevant biomarkers for assessment of risk of future type 2 diabetes, especially
in specific population groups (eg, obese individuals or individuals with a family history of type 2 diabetes).1 Furthermore, statistical models should be developed to take into account both the changes in risk and biomarker trajectories. This work was supported by the Netherlands Heart Foundation, Dutch Diabetes Research Foundation, and Dutch Kidney Foundation; within the framework of (the Center for Translational Molecular Medicine) project PREDICCt (grant 01C-104-07). AA is supported by a Rubicon grant from the Netherlands Organization for Scientific Research (NWO). We declare that we have no conflicts of interest. None of the study sponsors had a role in interpretation, in writing the report, or in the decision to submit for publication.
*Ali Abbasi, Ronald P Stolk, Stephan JL Bakker [email protected]; [email protected] MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK (AA); Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
www.thelancet.com/diabetes-endocrinology Vol 2 February 2014
(AA, RPS); and Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, Netherlands (SJLB) 1
2
3
4
5
6
Wang TJ. Assessing the role of circulating, genetic, and imaging biomarkers in cardiovascular risk prediction. Circulation 2011; 123: 551–65. Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol 2013; 2: 65–75. Færch K, Witte DR, Tabák AG, et al. Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 2013; 1: 43–51. Wills AK, Lawlor DA, Matthews FE, et al. Life course trajectories of systolic blood pressure using longitudinal data from eight UK cohorts. PLoS Med 2011; 8: e1000440. Tabak AG, Herder C, Rathmann W, Brunner EJ, Kivimaki M. Prediabetes: a high-risk state for diabetes development. Lancet 2012; 379: 2279–90. Pearson TA, Mensah GA, Alexander RW, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 2003; 107: 499–511.
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and prevention of opening of the mitochondrial permeability transition pore on reperfusion.2 Moreover, we provided experimental evidence that metformin increases formation of the purine nucleoside adenosine, and that adenosine receptor stimulation is crucial for the infarct size-limiting effect of metformin.3 In addition to its acute cardioprotective effect, metformin beneficially modulates cardiac remodelling after myocardial infarction.4 Adenosine receptor stimulation not only increases tolerance against ischemia-reperfusion, but can also retard the process of atherosclerosis.5 Therefore, in the CAMERA trial,1 the use of the adenosine receptor antagonist caffeine might have attenuated any anti-atherosclerotic effects of metformin. Based on the strong preclinical evidence of a direct cardioprotective effect of metformin, we feel that the study by Preiss and colleagues1 should not temper the hope that metformin improves cardiovascular prognosis in patients at risk of cardiovascular events. In addition to exploring the effects of metformin on atherosclerosis in larger trials, it is important to investigate whether metformin limits myocardial ischemia-reperfusion injury and postinfarction remodelling in patients in a trial that controls for known potential confounders, such as caffeine intake, as much as possible. With interest, we await the results of two ongoing randomised controlled trials in The Netherlands aimed at studying these cardioprotective effects of metformin (NCT01438723, NCT01217307). All authors are investigators of the Metformin in Coronary Artery Bypass grafting (MetCAB) study (NCT01438723) on the effect of metformin on cardiac injury in the setting of cardiac bypass grafting. The authors have received unrestricted research grants from AstraZeneca (NPR and GAR) and Boehringer-Ingelheim (GAR) and have served on Scientific Advisory Boards for AstraZeneca (NPR) and Novartis (GAR).
106
*Niels P Riksen, Saloua el Messaoudi, Gerard A Rongen [email protected] Department of Pharmacology-Toxicology (SeM, GAR) and Department of Internal Medicine (NPR), Radboud University Medical Centre, Geert Grooteplein 10, Nijmegen, 6525GA, Netherlands 1
2
3
4
5
Preiss D, Lloyd SM, Ford I, et al. Metformin for non-diabetic patients with coronary heart disease (the CAMERA study): a randomised controlled trial. Lancet Diabetes Endocrinol 2013; published online Nov 7. DOI:10.1016/ S2213-8587(13)70152-9. El Messaoudi S, Rongen GA, de Boer RA, Riksen NP. The cardioprotective effects of metformin. Curr Opin Lipidol 2011; 22: 445–53. Paiva M, Riksen NP, Davidson SM, et al. Metformin prevents myocardial reperfusion injury by activating the adenosine receptor. J Cardiovasc Pharmacol 2009; 53: 373–78. Gundewar S, Calvert JW, Jha S, et al. Activation of AMP-activated protein kinase by metformin improves left ventricular function and survival in heart failure. Circ Res 2009; 104: 403–11. Riksen NP, Rongen GA. Targeting adenosine receptors in the development of cardiovascular therapeutics. Expert Rev Clin Pharmacol 2012; 5: 199–218.
Identification of relevant biomarkers for type 2 diabetes In clinical practice, all measurable biological parameters that can objectively be evaluated, are called biomarkers, irrespective of whether the biological marker is obtained from physical examination of the body (ie, blood tests) or anywhere else, such as from health records.1 A biomarker could be used for screening or diagnosis of a disease (eg, glucose and glycated haemoglobin tests for type 2 diabetes) or for monitoring treatment.1 In The Lancet Diabetes & Endocrinology, Lee Roberts and colleagues2 provide a review on potential biomarkers for insulin resistance and type 2 diabetes, focusing on aminoacid and lipid metabolomics. These metabolites might contribute to early identification of type 2 diabetes risk in addition to uncovering novel therapeutic targets for this disease.2 The authors summarise the evidence for a potential role of branched-chain aminoacids in contributing to insulin resistance
and predicting risk of type 2 diabetes. In particular, a main component composed of all branched-chain aminoacids and their metabolites was strongly correlated with insulin resistance, rather than concentrations of branched-chain aminoacids per se above a certain threshold.2 This might reflect the complex mechanisms that regulate levels of aminoacids and their effects on type 2 diabetes.2 Also, it is not known whether relative or absolute differences in biomarker level are most important to predict risk of future type 2 diabetes. Likewise, little is known about the relevance of changes in biomarkers with time (ie, biomarker trajectories) in individual patients, and whether these changes are associated with long-term outcome.3,4 In this context, some relevant questions are: 1) When is the optimum time for biomarker measurements in the course of disease? 2) To what extent do biomarker concentrations change over time (eg, long-term stability)? 3) Do biomarker trajectories have effects on long-term risk? Statistical models assume a constant risk over time, and the so-called estimated risk is usually stronger when the biomarker is measured shortly before the onset of disease (eg, glucose as predictor of type 2 diabetes).5 4) Would slightly elevated biomarker concentrations reflect a risk in the far future, whereas a very high level would indicate an increased risk over a shorter time period? For example, the relation between inflammatory biomarkers with acute or chronic cardiovascular disease can be different.6 Another aspect is that environmental factors contribute to biomarker concentrations and thus modify the relation between biomarkers and type 2 diabetes.2 Roberts and colleagues2 provide an important example in that the association between increased palmitoleate (a monounsaturated fatty acid) concentrations and insulin
www.thelancet.com/diabetes-endocrinology Vol 2 February 2014
Correspondence
resistance might indicate that dietary intake of carbohydrates increases de-novo lipogenesis. Palmitate, a precursor for palmitoleate, is the direct product of de-novo lipogenesis.2 Such a biomarker might reflect changes in behaviour, such as decreased physical activity or increased carbohydrate consumption. If behaviour is changed appropriately and concomitant changes in biomarker concentrations occur, one might infer that a behavioural modification works. This warrants consideration in implementation of personalised screening and prevention strategies.2 In the future, use of highthroughput data analysis will become available in parallel with novel technological platforms. Before this, an integrative approach to analysing gene–proteome–metabolite data can be used to find relevant biomarkers for assessment of risk of future type 2 diabetes, especially
in specific population groups (eg, obese individuals or individuals with a family history of type 2 diabetes).1 Furthermore, statistical models should be developed to take into account both the changes in risk and biomarker trajectories. This work was supported by the Netherlands Heart Foundation, Dutch Diabetes Research Foundation, and Dutch Kidney Foundation; within the framework of (the Center for Translational Molecular Medicine) project PREDICCt (grant 01C-104-07). AA is supported by a Rubicon grant from the Netherlands Organization for Scientific Research (NWO). We declare that we have no conflicts of interest. None of the study sponsors had a role in interpretation, in writing the report, or in the decision to submit for publication.
*Ali Abbasi, Ronald P Stolk, Stephan JL Bakker [email protected]; [email protected] MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK (AA); Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, Netherlands
www.thelancet.com/diabetes-endocrinology Vol 2 February 2014
(AA, RPS); and Department of Internal Medicine, University of Groningen, University Medical Center Groningen, Groningen, Netherlands (SJLB) 1
2
3
4
5
6
Wang TJ. Assessing the role of circulating, genetic, and imaging biomarkers in cardiovascular risk prediction. Circulation 2011; 123: 551–65. Roberts LD, Koulman A, Griffin JL. Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome. Lancet Diabetes Endocrinol 2013; 2: 65–75. Færch K, Witte DR, Tabák AG, et al. Trajectories of cardiometabolic risk factors before diagnosis of three subtypes of type 2 diabetes: a post-hoc analysis of the longitudinal Whitehall II cohort study. Lancet Diabetes Endocrinol 2013; 1: 43–51. Wills AK, Lawlor DA, Matthews FE, et al. Life course trajectories of systolic blood pressure using longitudinal data from eight UK cohorts. PLoS Med 2011; 8: e1000440. Tabak AG, Herder C, Rathmann W, Brunner EJ, Kivimaki M. Prediabetes: a high-risk state for diabetes development. Lancet 2012; 379: 2279–90. Pearson TA, Mensah GA, Alexander RW, et al. Markers of inflammation and cardiovascular disease: application to clinical and public health practice: a statement for healthcare professionals from the Centers for Disease Control and Prevention and the American Heart Association. Circulation 2003; 107: 499–511.
107