Session 1. Residual risk in the multi-morbid patient

Nephrologist perspective - The Kidney: the risk amplifier

Per-Henrik Groop
Professor of Internal Medicine at the University of Helsinki

Professor Per-Henrik Groop, MD, DMSc, FRCPE graduated from the University of Helsinki in 1982. It was here where he defended his thesis on ‘The relationship between GIP and beta-cell function in man’ in 1989. Following post-doctoral studies at Guy’s Hospital, University of London, under Professor Giancarlo Viberti, Professor Groop returned to Helsinki as Consultant of Nephrology. He served as Professor of Nephrology (Chair) 2010-2015 and is currently Professor of Internal Medicine (Chair) at the University of Helsinki. He is also Chief Physician at the Department of Nephrology, University of Helsinki and Helsinki University Hospital and Principal Investigator of the Finnish Diabetic Nephropathy (FinnDiane) Study at the Folkhälsan Research Center in Helsinki, Finland. He is Adjunct Professor at the Department of Diabetes, Monash University, Melbourne, Australia.

His research is focused on the dissection of the pathogenesis of diabetic complications withspecial emphasis on diabetic nephropathy. In order to provide a unique set of clinical resources with high power to identify genes and genetic variants associated with diabetic complications, Professor Groop initiated the large, nationwide FinnDiane Study in 1997. To date, this landmark study comprises 9000 patients with Type 1 Diabetes and their family members recruited via a comprehensive network of 92 hospitals and healthcare centres throughout Finland. His FinnDiane Research Group represents an inter-disciplinary team of 45 scientists, post-graduate students and personnel.

CKD in individuals with type 2 diabetes is common affecting up to more than every second individual. However, screening around the globe is suboptimal averaging less than half of the individuals with type 2 diabetes. This means that a large proportion of those with type 2 diabetes does not even know that they have CKD. CKD is asymptomatic and the only way to find out whether an individual has CKD is to screen for albuminuria and the kidney function (eGFR 60 ml/min is considered CKD). These two measures are cheap and easy to perform, but they are not utilized for the benefit of our patients as recommended. Primary care physicians have a great responsibility to find affected individuals and to initiate prognosis-improving treatment. The presence of CKD comes with grim consequences, increased risk of premature mortality, increased risk of cardiovascular disease such as myocardial infarction and/or stroke, increased risk of ending up in hospital because of heart failure, and increased risk of developing kidney failure with need for dialysis treatment. However, many patients with type 2 diabetes succumb to cardiovascular disease before they develop kidney failure. On average, an individual with type 2 diabetes has 6 years shorter life expectancy because of having diabetes, 12 shorter life expectancy if having suffered a myocardial infarction or a stroke, but 16 years shorter life expectancy, if early signs of CKD are found. Thus, CKD comes with grim consequences, and doctors need to take action.


1. The understand the importance of screening for CKD

2. To be aware of the consequences of CKD in type 2 diabetes

3. To familiarizer with effective prognosis-improving treatment for CKD in type 2 diabetes

Learning outcomes:

To understand that CKD in type 2 diabetes is a common asymptomatic complication with grim consequences that needs to be detected and effectively treated.


Primary care perpective  - Novel approaches to predicting CV risk & managing uncertainties: exploiting Artificial Intelligence





Carlos Brotons
Family Physician and Researcher

Carlos Brotons is a family physician and a researcher, and he is the head of the Research Unit of the Sardenya Primary Health Care Center, affiliated to the Biomedical Research Institute Sant Pau, in Barcelona, Spain). Dr. Brotons was the first president and now a member of the European Network for Prevention and Health Promotion in Primary care/General Practice (EUROPREV-Wonca Europe network),  and he is also a member of the Board of the European Primary Care Cardiovascular Society (EPCCS-Wonca Europe special interest group).  He has represented the European Society of Family Medicine/General Practice at the European Task Force for cardiovascular disease prevention in clinical practice, responsible for the development of the European guidelines. He was the first president of the Spanish Interdisciplinary Vascular Prevention Committee, integrated by 13 Spanish scientific societies, supported by the Spanish Ministry of Health.

His special interest is in cardiovascular risk factors and prevention of cardiovascular diseases, and he has been principal investigator in many research projects and clinical trials. He has published many articles about this subject in peer-reviewed academic journals.

The current European guidelines introduce a novel, dedicated, type 2 diabetes mellitus (T2DM)-specific, 10-year CVD risk score (SCORE2-Diabetes) for patients with T2DM without atherosclerotic cardiovascular diseases  or severe target-organ damage and serve as a guide for clinical decision-making in patients with T2DM at low, moderate, high, or very high risk of fatal and non-fatal CVD events (myocardial infarction, stroke).

SCORE2-Diabetes integrates information on conventional CVD risk factors (i.e. age, smoking status, systolic blood pressure, and total and high-density lipoprotein cholesterol) with diabetes-specific information (e.g. age at diabetes diagnosis, HbA1c, and eGFR). Additional risk scores that attempt to estimate lifetime risk in individuals with diabetes (such as the DIAL2 [DIAbetes Lifetime] model, which is calibrated to different European countries) can also be used to aid treatment decisions.

The future of risk prediction lies in shifting from population-based risk scores towards a more personalized risk prediction model where a large number of patient-related variables over time potentially can be integrated. The ability of artificial intelligence to rapidly process large amounts of data has led to the development of machine learning-based risk prediction with increased accuracy.

Learning outcomes

  • Understand the impact of novel approaches to predict CV risk in diabetic patients.
  • Consider lifetime CV risk in diabetic patients.
  • Shift from population-based risk scores towards personalized risk prediction.

Learning objectives

  • To explore characteristics of new approaches to predict CV risk in diabetic patients.
  • To assess advantages and limitations of lifetime CV risk in diabetic patients.
  • To explore the role of artificial intelligence in the future of risk prediction.