Ehe European Association for the Study of Diabetes

Biomarkers and large datasets – insights into diabetes and CVD

Naveed Sattar, University of Glasgow, Glasgow, UK

MedWire News – European Association for the Study of Diabetes (Lisbon, Portugal), September 13th, 2011: Speaking on the first morning of the 47th European Association for the Study of Diabetes (EASD) conference in Lisbon, Portugal, Sattar discussed the contribution of biomarkers, and large datasets to diabetes and cardiovascular disease (CVD) risk prediction and research in the 46th Minkowski Lecture.

He discussed how biomarkers have given insight into the pathogenesis of diabetes and how they are able to help predict diabetes and its complications. In addition, he discussed how recent large studies have been able to challenge conventional wisdom about diabetes, for example, the commonly held belief that diabetes is a coronary heart disease (CHD) equivalent.

Diabetes pathogenesis and prediction using biomarkers

High levels of serum alanine aminotransferase (ALT) were shown to predict diabetes in the West of Scotland Coronary Prevention Study, explained Sattar [1,2]. This discovery was backed up by findings from the British Women’s Heart and Health Study, which showed that high ALT and γ-glutamyltransferase (GGT) both predicted diabetes, in addition to the presence of non-alcoholic fatty liver disease (NAFLD) [3].

The West of Scotland Coronary Prevention Study also suggested that hepatic fat accumulation increases the risk for incident diabetes [2,4].

Sattar emphasized that 50–70% of people with Type 2 diabetes have NAFLD and that it has been demonstrated that reducing liver fat can reduce the risk for diabetes [5].

Regarding other biomarkers that may give insights into diabetes pathogenesis, Sattar explained that whilst high adiponectin has predicted a low risk for diabetes in some studies, in other studies it has predicted an increased risk for CVD death, and therefore further investigation is needed before it can be reliably used as a predictive biomarker [6].

Similarly, he suggested that the “jury is still out” on the value of inflammatory biomarkers such as c-reactive protein for improving the understanding of diabetes pathogenesis; “More trials are needed,” said Sattar.

He made the point that diabetes prediction perfection is less critical than CVD prediction, explaining that if 2–3% of people are missed on first testing, they can be picked up the second-time round.

He explained that research suggests that the vast majority (approximately 76%) of Type 2 diabetes cases can be successfully predicted using a simple diabetes risk assessment, such as one that can be carried out on a clinic computer in 30 seconds or less [7,8].

Clinicians “might want to use their clinical judgment” about whether a patient who is having their cholesterol tested should also have their glycated hemoglobin measured, he added, saying that this is likely to be necessary only in patients within the top 40% for diabetes risk.

A potential future role of biomarkers is for the prediction of diabetes-related complications, added Sattar. He said that there is great potential for biomarker use to improve CVD and microvascular complication prediction, as well as to forecast when patients are likely to require insulin therapy owing to declining β-cell function.

Finally, he suggested that the combination of biomarkers and genetics might help predict causal pathways and a patient’s response to treatment.

Challenging conventional wisdom using large datasets

In the second half of his talk, Sattar challenged several conventions in the field of diabetic medicine.

The first of these was the assumption that diabetes is a CHD risk equivalent. If this was the case, he said, then we would expect people with diabetes to have a three to five-fold increased risk for CVD compared with nondiabetic individuals.

However, although patients with diabetes do have an overall risk for CVD that is greater than nondiabetic patients, the average risk increase is more in the range of a two-fold increase.

Sattar explained that the duration of diabetes has a major effect on CVD risk, with newly diagnosed patients having a fairly low CVD risk and patients with a disease duration of over 10 years having CHD risk equivalent [9].

“Diabetes is not a CHD risk equivalent on diagnosis, but lifetime risk is high,” he said.

Contrary to popular belief, mortality in patients with diabetes has decreased in recent years compared with mortality in nondiabetic individuals. Sattar emphasized that much greater numbers of participants are needed to obtain meaningful results regarding mortality from randomized controlled trials.

Of note, the results of a recent systematic review published by Sattar and colleagues suggest that proteinuria is a significant predictor of CVD death in patients with diabetes [10]. “Proteinuria is a very strong signal for harm,” stressed Sattar.

Another widely held belief is that high blood glucose levels corresponds to high CVD event risk. Yet, study results do not support this; they show that lowering blood pressure and low-density lipoprotein cholesterol have a significantly greater effect on CVD event rates than lowering blood glucose levels [11].

Sattar challenged the assumed link between high triglycerides and CVD, saying that high triglycerides are much more likely to predict increased diabetes risk than increased CVD risk. He added that there may be at least one explanation for the failure of fibrates to significantly reduce CVD event rates.

“Once you factor in cholesterol and high-density lipoprotein, triglycerides are no longer a risk factor for CVD,” he said.

Obesity and being overweight are known risk factors for diabetes, but Sattar described new data suggesting that men have an increased risk for diabetes at lower body mass indices (BMIs) compared with women, and that their risk assessments should be adjusted accordingly.

“Diabetes is more in middle-aged men in many parts of the world than in middle-aged women,” said Sattar.

“Women have to travel further up the BMI scale to develop diabetes.” A final assumption that has recently been challenged is the theory that statin therapy reduces the risk for diabetes. In reality, a recent study demonstrated that statin treatment actually increases the risk for incident diabetes by a significant 9% compared with no statin treatment, concluded Sattar [12].

References

1. Sattar N, Scherbakova O, Ford I, et al. Elevated alanine aminotransferase predicts new-onset Type 2 diabetes independently of classical risk factors, metabolic syndrome, and C-reactive protein in the west of Scotland coronary prevention study. Diabetes 2004;53:2855–2860.

2. Sattar N, McConnachie A, Ford I, et al. Serial metabolic measurements and conversion to Type 2 diabetes in the west of Scotland coronary prevention study: specific elevations in alanine aminotransferase and triglycerides suggest hepatic fat accumulation as a potential contributing factor. Diabetes 2007;56:984–991.

3. Fraser A, Harris R, Sattar N, et al. Alanine aminotransferase, gamma-glutamyltransferase, and incident diabetes: the British Women’s Heart and Health Study and meta-analysis. Diabetes Care 2009;32:741–750.

4. Preiss D, Sattar N. Non-alcoholic fatty liver disease: an overview of prevalence, diagnosis, pathogenesis and treatment considerations. Clin Sci (Lond) 2008;115:141–150.

5. Lawlor DA, Sattar N, Smith GD, Ebrahim S. The associations of physical activity and adiposity with alanine aminotransferase and gamma-glutamyltransferase. Am J Epidemiol 2005;161:1081–1088.

6. Sattar N. Adiponectin and raised mortality in Type 1 diabetes: any credible explanatory mechanisms? J Intern Med 2011; Advance online publication.

7. Wannamethee SG, Papacosta O, Whincup PH, et al. The potential for a two-stage diabetes risk algorithm combining non-laboratory-based scores with subsequent routine non-fasting blood tests: results from prospective studies in older men and women. Diabet Med 2011;28:23–30.

8. Preiss D, Khunti K, Sattar N. Combined cardiovascular and diabetes risk assessment in primary care. Diabet Med 2011;28:19–22.

9. Wannamethee SG, Shaper AG, Whincup PH, et al. Impact of diabetes on cardiovascular disease risk and all-cause mortality in older men: influence of age at onset, diabetes duration, and established and novel risk factors. Arch Intern Med 2011;171:404–410.

10. Preiss D, Sattar N, McMurray JJ. A systematic review of event rates in clinical trials in diabetes mellitus: the importance of quantifying baseline cardiovascular disease history and proteinuria and implications for clinical trial design. Am Heart J 2011;161:210–219.e1.

11. Ray KK, Seshasai SR, Wijesuriya S, et al. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomized controlled trials. Lancet 2009;373:1765–1772.

12. Preiss D, Seshasai SR, Welsh P, et al. Risk of incident diabetes with intensive-dose compared with moderate-dose statin therapy: a meta-analysis. JAMA 2011;305:2556-2564.

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Publicerad: |2011-09-21|