Moderation: A. Simm, Halle (Saale); M. Gogol, Coppenbrügge
Among the problems for the development of therapies to fight chronic diseases of aging, the need to integrate scientific, population-level, and clinical information is becoming more and more important. The solution of this problem is connected to big data — the collection, storage and analysis of vast amounts of health and diseases related information gathered from disparate sources. Based on such data bases, precision medicine (focusing on the treatment of ill patients) as well as precision health (to prevent or forestall diseases before they occur) can be developed. Different aspects of this topic will be presented in this session.
Many candidate biomarkers of human ageing have been proposed but in all cases their variability in cross-sectional studies is considerable, and therefore no single measurement has proven to serve a useful marker to determine, on its own, biological age. A plausible reason for this is the intrinsic multi-causal and multi-system nature of the ageing process. The recently completed MARK-AGE study was a large-scale integrated project supported by the European Commission. The major aim of this project was to conduct a population study comprising about 3200 subjects in order to identify a set of biomarkers of ageing which, as a combination of parameters with appropriate weighting, would measure biological age better than any marker in isolation. The strategy and use of hypothesis-free, data-intensive approaches to explore cellular proteins, miRNA, mRNA and plasma proteins as healthy ageing biomarkers, using ageing models and directly within samples from adults of different ages are described.
The existing data protection laws hinder the efficient use and combination of “big” (anonymized, clinic-owned) and “small” (non-anonymized, individually owned) health data. We suggest a framework to make the combination of big and small health data in an individualized health data bank possible and acceptable to patients, health care providers, politicians, and the public at large. Such an infrastructure is needed for the inclusion of real life health outcome measures for determining the effects of treatments.
Reimbursement of health-related costs in any health care system must be based on empirical evidence concerning safety, efficacy and cost-effectiveness of medical interventions. A limiting factor in evidence-based health care decision is that empirical evidence should apply to the individual patient. Large amounts of “Small health data” combined with Big Health Data offers new opportunities to optimize health care decisions by centering them around the individual patients’ needs. The combination of “big” with “small” data can provide systematic empirical evidence for patients and health care providers to inform treatment decisions. This requires a publicly controlled individualized health data bank (iHealthDB) that allows for the combination of big and small health data. This data bank should allow to upload individualized health data in different data formats (“small data”) and to combine these with anonymized personal data (“big data”).
Beyond the envisaged establishment of an individualized health data bank, the framework may also be applied for the setting up of a framework for electronic patient files. In addition, the goals of the project are in line with the WHO (2015) call for individualized evidence and care provision as well as the Swiss Federal Office of Public Health’s strategy “Health 2020” that emphasizes the importance of organizing health care around the needs of individuals and giving patients and health care providers equal rights in health care decisions.
Lifespan is a complex trait, and longitudinal data for humans are naturally scarce. We report the results of Cox regression and Pearson correlation analyses using data of the Study of Health in Pomerania (SHIP), with mortality information of 1518 participants (113 of which died), over a time span of more than 10 years. While some of the Cox regression biomarkers were firmly established in many studies before, others align with an ”integrated albunemia” model of aging proposed recently.
Medicine and healthcare are dominated by visions which point into different directions and which, on the surface, seem to be in deep conflict with each other. Preventing Overdiagnosis, Choosing Wisely, Right Care or Less-Is-More aim to achieve a better selection of medical interventions. On the other hand, Personalized or Individualized, Targeted or Omics-based Medicine convey promises that in the near future patients will benefit from diagnosis which are free of errors, leading to therapies which are effective because they are tailored to each individual patient.
Driven by systematically generating hype around these promises appears to be equivalent to less critical or completely missing assessment of these procedures and interventions which are expected to deliver better care for the patients with better outcomes. A closer look easily shows an increasing lack of validation and systematic evaluation, in an inverse relationship to the noise made around Big Data, Digitisation, Translational Medicine and Innovation. Innovation is overstretched on one side while on the other side methodology seems to have been wound back to where we were decades ago. Anecdotes are used as “proof”, markers are used as surrogates while the whole range of assessment tools from systematic methodological research in the last decades is declared unnecessary. Extreme statements explain that the era of causality is over, we have entered the era of correlation, without limitation to new findings as long as there is access to all data.
There is no doubt that many of the suggested new procedures will be very beneficial for patients. These procedures have to be identified with maximum speed, while those without added value for patients have to be eliminated. To achieve this, the classical instruments for the assessment of interventions have to be applied to the new world in a rigid way, for the maximum benefit for patients.
Antes, G. Big Data und Personalisierte Medizin: Goldene Zukunft oder leere Versprechungen? Dtsch Arztebl 2016; 113(15)