Cardiovascular Risk Prediction in Patients with Diabetes – When One Biomarker is Not Enough

Nov 03, 2016   |  Raphael Wurm, MD;

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Diabetes mellitus has long been regarded as a classical risk factor for cardiovascular disease.1 This risk, however, escapes the predictive capability of classical risk prediction tools, such as the DECODE scoring system or the Framingham Risk Score.2 This might be in part because these trials did not include sufficient numbers of patients with diabetes or because pathophysiological processes leading to cardiovascular disease are inherently different in patients with diabetes.

As diabetes is increasing in prevalence worldwide,3 there is also an increasing need for personalized medicine. This comes after the realization that the traditional one size fits all approach is unsustainable given the current prediction models and could lead to overtreatment.4,5

NT-proBNP is a functional marker of cardiac performance and was initially evaluated and established as a diagnostic and prognostic marker in the field of heart failure.6 However, recent research made it evident that this marker is useful in the general population and also in patients with diabetes.7,8 In this setting, it was found to be superior to proteinuria in predicting cardiovascular events.9 Ultimately, this research cumulated in two trials successfully using NT-proBNP as a triage tool to guide therapy in patients with diabetes free from apparent cardiovascular disease.10,11 While these two studies were different in design and hypotheses, they were united in that they provided very compelling evidence for the possibility of targeted primary cardiovascular prevention in patients with diabetes who were pre-selected by elevated NT-proBNP levels. Interestingly, the treatment ("intensified") group in PONTIAC (Nt-proBNP Guided Primary Prevention of CV Events in Diabetic Patients) showed similar event-rates when compared to a control group of low-risk patients (as defined by NT-proBNP <125 pg/ml) which was not randomized.10

Two further trials, EXAMINE (Cardiovascular Outcomes Study of Alogliptin in Patients With Type 2 Diabetes and Acute Coronary Syndrome)12 and SAVOR-TIMI 53 (Does Saxagliptin Reduce the Risk of Cardiovascular Events When Used Alone or Added to Other Diabetes Medications),13 evaluated the treatment effect of gliptins on cardiovascular outcome in patients with diabetes. They found that the drugs had an effect, either positive or negative, on outcome predominately in groups with elevated NT-proBNP levels.

In a recent overview, Felker and Ahmad wondered if NT-proBNP was only the tip of the iceberg of cardiovascular risk prediction in diabetes and concluded that there is an immanent need for additional research to reach the bottom of the sea.14 Indeed, from a pathophysiologic point of view, NT-proBNP covers only the hemodynamic and fluid homoeostatic pathway during development of a cardiovascular disease. Inflammatory pathways and morphologic alterations are not captured by this hormone.

Troponin T, a cardiac contractility protein that is released into the circulation upon myocardial cell damage. The high-sensitive form (hsTnT) is predictive of cardiovascular outcomes and mortality in the general population and in patients with diabetes.15 Slight, subclinical alterations were shown to be associated with structural alterations, especially left ventricular hypertrophy.16 In patients with diabetes, hsTnT is independent of NT-proBNP and provides additional predictive information.17

More recently, growth differentiation factor 15 (GDF-15) has received attention as a marker of inflammation and a sign of atherosclerosis.18,19 It predicts cardiovascular events in a general elderly population as well as in patients with heart failure.20,21 Importantly, it was shown to provide additive value to NT-proBNP in the setting of heart failure.22 With regards to diabetes, GDF-15 levels correlate with insulin resistance and are generally increased when compared to a healthy matched population.23,24 GDF-15 is also positively correlated with age, body mass index and c-reactive protein levels,25 hinting at a role of GDF-15 as a general marker for an unfavourable cardio-metabolic risk profile. Importantly, GDF-15 seems to be independent of natriuretic peptides in so far as it is not altered by exogenous administration of b-type natriuretic peptides.26 This provides evidence for a different pathophysiological way of activation, making a combinatory approach that harnesses a maximum of prognostic information attractive.

Such an approach was used by our group to predict cardiovascular events in patients with diabetes.27 We measured NT-proBNP, hsTnT, and GDF-15 in a prospective study of 746 patients that were followed for five years. Each marker was predictive for the endpoint of cardiovascular disease or death after adjustment for traditional risk factors. In a second step, we added the less established markers hsTnT and GDF-15 to a model that already included NT-proBNP and traditional clinical and laboratory risk markers. This led to a significant improvement in predictive ability, and this effect was clearly owed to the complementarity of the markers. In actual numbers, the addition of the two markers led to a net reclassification improvement of 33% (22% for patients without events and 11% for patients experiencing an event). This way of combining biomarkers that represent distinctive pathways within the disease leads to a good predictive value, but is not really feasible for everyday application.

In a third step we investigated a clinically more suitable approach: rather than using a Cox model, we used simple cut-offs for NT-proBNP, hsTnT, and GDF-15. Patients were then grouped according to the number of elevated markers (0–3). This led to the identification of a high-risk group (3 elevated markers) that would potentially benefit from focused workup and targeted therapy, a concept that was tested successfully in PONTIAC, using only a single NT-proBNP measurement. However, this study also identified a large subgroup of 46% of patients with no elevated marker that yielded an annual risk of 1.7%. This risk is for all intents and purposes not significantly different from an individual who does not suffer from diabetes, all else being equal. Importantly, it is unlikely that any additional treatment would be able to noticeably reduce this event rate.

It seems that this finding has implications not only for treatment but for study design as well. The regulating bodies in the US (FDA) and Europe (EMA) are calling for novel anti-diabetic medication to be tested for cardiovascular safety. Clearly, for any effect, be it harmful or beneficial, to be statistically significant, the event rate has to be considered. It therefore appears prudent to turn to biomarkers for pre-selection of patients with diabetes that are at an increased risk for these kinds of trials.

The exact mechanisms by which diabetes causes cardiovascular disease remain largely unknown. In any case, it is most likely a multifactorial process that involves different and independent pathophysiological axis. It is therefore almost absurd to believe that any single marker would be sufficient for accurate risk prediction in this setting, but rather that a combination of markers will ultimately be necessary to achieve this goal. Especially when organ systems are so closely intertwined, as is the case in the cardio-renal axis (see Braam et al.28 for a recent review), a disease that potentially affects both directly and simultaneously and indirectly through each other, will need even more markers than the ones currently researched. It is therefore worth looking towards other areas of cardiovascular research for inspiration:

A strategy of combining more than one biomarker to predict cardiovascular events was successfully used by Sabatine et al., who used a panel of four biomarkers (MR-proANP, MR-proADM, CT-proET-1, and Copeptin) to predict events in a population of patients with ischemic heart disease enrolled in a trial to test a novel therapy.29 Not only were the markers studied predictive of outcome in the whole population, careful combination also allowed for selection of a subgroup of patients in the treatment arm that benefitted significantly more from the drug.

In conclusion, we are currently only at the tip of the iceberg when it comes to using biomarkers for cardiovascular risk assessment in diabetes. However, with no end of the diabetic pandemic in sight and healthcare systems reaching their limits worldwide, we believe that research in this field is indispensable. It will get us closer to true personalized medicine – an approach that should not only identify patients at risk, but also those at almost-normal risk levels, so that scarce resources can be distributed optimally.

References

  1. Diabetes mellitus: a major risk factor for cardiovascular disease: a joint editorial statement by the American Diabetes Association; the National Heart, Lung, and Blood Institute; the Juvenile Diabetes Foundation International; the National Institute of Diabetes and Digestive and Kidney Diseases; and the American Heart Association. Circulation 1999;100:1132-3.
  2. Coleman RL, Stevens RJ, Retnakaran R, Holman RR. Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes. Diabetes Care 2007;30:1292-3.
  3. Shaw JE, Sicree RA, Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res Clin Pract 2010;87:4-14.
  4. Huang ES, Basu A, O'Grady M, Capretta JC. Projecting the future diabetes population size and related costs for the U.S. Diabetes Care 2009;32:2225-9.
  5. McCoy RG, Van Houten HK, Ross JS, Montori VM, Shah ND. HbA1c overtesting and overtreatment among US adults with controlled type 2 diabetes, 2001-13: observational population based study. BMJ 2015;351:h6138.
  6. Maisel A, Mueller C, Adams K, et al. State of the art: using natriuretic peptide levels in clinical practice. Eur J Heart Fail 2008;10:824-39.
  7. Huelsmann M, Neuhold S, Strunk G, et al. NT-proBNP has a high negative predictive value to rule-out short-term cardiovascular events in patients with diabetes mellitus. Eur Heart J 2008;29:2259-64.
  8. Bruno G, Landi A, Barutta F, et al. N-terminal probrain natriuretic peptide is a stronger predictor of cardiovascular mortality than C-reactive protein and albumin excretion rate in elderly patients with type 2 diabetes: the Casale Monferrato population-based study. Diabetes Care 2013;36:2677-82.
  9. Clodi M, Resl M, Neuhold S, et al. A comparison of NT-proBNP and albuminuria for predicting cardiac events in patients with diabetes mellitus. Eur J Prev Cardiol 2012;19:944-51.
  10. Huelsmann M, Neuhold S, Resl M, et al. PONTIAC (NT-proBNP selected prevention of cardiac events in a population of diabetic patients without a history of cardiac disease): a prospective randomized controlled trial. J Am Coll Cardiol 2013;62:1365-72.
  11. Ledwidge M, Gallagher J, Conlon C, et al. Natriuretic peptide-based screening and collaborative care for heart failure: the STOP-HF randomized trial. JAMA 2013;310:66-74.
  12. Zannad F, Cannon CP, Cushman WC, et al. Heart failure and mortality outcomes in patients with type 2 diabetes taking alogliptin versus placebo in EXAMINE: a multicentre, randomised, double-blind trial. Lancet 2015;385:2067-76.
  13. Scirica BM, Braunwald E, Raz I, et al. Heart failure, saxagliptin, and diabetes mellitus: observations from the SAVOR-TIMI 53 randomized trial. Circulation 2014;130:1579-88.
  14. Felker GM, Ahmad T. Natriuretic peptides and primary prevention: the new world? J Am Coll Cardiol 2013;62:1373-5.
  15. Lepojarvi ES, Piira OP, Kiviniemi AM, et al. Usefulness of highly sensitive troponin as a predictor of short-term outcome in patients with diabetes mellitus and stable coronary artery disease (from the ARTEMIS Study). Am J Cardiol 2016;117:515-21.
  16. Masson S, Latini R, Mureddu GF, et al. High-sensitivity cardiac troponin T for detection of subtle abnormalities of cardiac phenotype in a general population of elderly individuals. J Intern Med 2013;273:603-17.
  17. Hillis GS, Welsh P, Chalmers J, et al. The relative and combined ability of high-sensitivity cardiac troponin T and N-terminal pro-B-type natriuretic peptide to predict cardiovascular events and death in patients with type 2 diabetes. Diabetes Care 2014;37:295-303.
  18. Rohatgi A, Patel P, Das SR, et al. Association of growth differentiation factor-15 with coronary atherosclerosis and mortality in a young, multiethnic population: observations from the Dallas Heart Study. Clin Chem 2012;58:172-82.
  19. Zimmers TA, Jin X, Hsiao EC, Mcgrath SA, Esquela AF, Koniaris LG. Growth differentiation factor-15/macrophage inhibitory cytokine-1 induction after kidney and lung injury. Shock 2005;23:543-8.
  20. Eggers KM, Kempf T, Wallentin L, Wollert KC, Lind L. Change in growth differentiation factor 15 concentrations over time independently predicts mortality in community-dwelling elderly individuals. Clin Chem 2013;59:1091-8.
  21. Anand IS, Kempf T, Rector TS, et al. Serial measurement of growth-differentiation factor-15 in heart failure: relation to disease severity and prognosis in the Valsartan Heart Failure Trial. Circulation 2010;122:1387-95.
  22. Chan MM, Santhanakrishnan R, Chong JP, et al. Growth differentiation factor 15 in heart failure with preserved vs. reduced ejection fraction. Eur J Heart Fail 2016;18:81-8.
  23. Vila G, Riedl M, Anderwald C, et al. The relationship between insulin resistance and the cardiovascular biomarker growth differentiation factor-15 in obese patients. Clin Chem 2011;57:309-16.
  24. Schindler K, Vila G, Hoppichler F, et al. The impact of type 2 diabetes on circulating adipokines in patients with metabolic syndrome. Obes Facts 2012;5:270-6.
  25. Dostalova I, Roubicek T, Bartlova M, et al. Increased serum concentrations of macrophage inhibitory cytokine-1 in patients with obesity and type 2 diabetes mellitus: the influence of very low calorie diet. Eur J Endocrinol 2009;161:397-404.
  26. Resl M, Vila G, Grimm G, et al. Effects of B-type natriuretic peptide on cardiovascular biomarkers in healthy volunteers. J Appl Physiol 2015;118:395-9.
  27. Resl M, Clodi M, Vila G, et al. Targeted multiple biomarker approach in predicting cardiovascular events in patients with diabetes. Heart 2016. [Epub ahead of print]
  28. Braam B, Joles JA, Danishwar AH, Gaillard CA. Cardiorenal syndrome--current understanding and future perspectives. Nat Rev Nephrol 2014;10:48-55.
  29. Sabatine MS, Morrow DA, de Lemos JA, et al. Evaluation of multiple biomarkers of cardiovascular stress for risk prediction and guiding medical therapy in patients with stable coronary disease. Circulation 2012;125:233-40.

Keywords: Acute Coronary Syndrome, Adamantane, Atherosclerosis, Biomarkers, Body Mass Index, C-Reactive Protein, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Dipeptides, Dipeptidyl-Peptidase IV Inhibitors, Endothelin-1, Heart Failure, Hemodynamics, Hypertrophy, Left Ventricular, Natriuretic Peptide, Brain, Peptide Fragments, Piperidines, Primary Prevention, Proteinuria, Risk Factors, Troponin T, Uracil, Diabetes Mellitus, Metabolic Syndrome


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