Metabolic risk, obesity and diabetes mellitus stand at the crossroads that lead both to oncological and cardiovascular disease. These diseases are highly prevalent and constitute risk factors that will assume epidemic proportions in the near future contributing to the increased prevalence of both oncological and cardiovascular disease. Many forms of cancer, including gastric cancer, carry a grim prognosis. Likewise, heart failure, the ultimate stage of any cardiac insult, remains of dismal prognosis, being comparable to the most aggressive forms of cancer. Together, they pose the major threat to healthy aging and constitute an unbearable burden to communal health care systems.
So far, the pathogenesis for these diseases could not be tracked to a single gene or pathway. Such complex diseases can only be described by comprehensive models that account for a variety of influences, including life-style choices, systemic insults, epigenetics and individual genetic traits.
DOCnet constitutes a joint network endeavour which seeks to unravel the mutual and individualized pathways of disease in patients at metabolic risk, taking a crucial step towards precision medicine with the ultimate aim of better health care and healthy aging.
DOCnet is built upon large biobanks, clinical databases and well characterized population-based cohorts established by strategic R&D&I institutions from the North of Portugal (IPATIMUP, FMUP(UnIC) and ISPUP (EPIUnit)).Their complementary know-how and high research standards join the willingness of an emblematic and long term partner of all these institutions Centro Hospitalar São João (CHSJ), the largest tertiary care centre in the North of Portugal.
Briefly, based on retrospectively and prospectively collected well characterized clinical data and biological samples from gastric cancer and thyroid cancer, stable chronic heart failure, cardiac surgery patient and population-based cohorts, a centralized information technology infrastructure will be developed, including omics-based analysis and public health research. This approach will enable the selection of patient subgroups at metabolic risk, controlling for their ongoing therapies, and to select appropriate normal population controls.
A gold standard omics approach to diseased tissue and plasma will unravel the intertwined pathophysiological routes of the diseases, their modulation by concurrent therapy, and will pinpoint – by way of state–of–the–art network analysis and systems biology – what are the main targets most likely responsible for disease onset.
These data will be used for validation by proof-of-concept experimental models and complementary studies. The availability of both tissue and plasma samples in some cohorts will strengthen the biological relevance of plasma biomarkers. Ex vivo and in vitro techniques will provide further functional correlation.
Selected markers will then be cross-checked in the background healthy population, as risk estimators, and in the patient cohorts, as prognosis predictors.
DOCnet envisions the development of new prediction tools translated in clinical care as clinical decision support systems, leading to more precise and accurate preventive and treatment options. Indeed, follow-up data and repeat evaluation of the population and patient cohorts will highlight the role of the biomarkers.
Under the premise that biological changes deriving from adverse environmental and social circumstances are potentially reversible and preventable, we will also focus on the quantification of the distribution and impact of the levels of multiple biomarkers, by consolidating and updating data on previously established population-based cohorts of northern Portuguese residents of different ages, through the collection of primary data from participants and integration with their electronic health records (EHR).
This approach will allow the assessment of the time sequence of events, under a life course approach, essential for the identification of critical periods where there is potential for prevention. Also, it will contribute to develop tools for prediction towards personalized preventive care, integrating social determinants of health and biomarkers for more accurate and precise stratification. With this approach we will:
1) widen the scope leftwards in the natural history of noncommunicable diseases, such as cancer and cardiovascular disease, aiming for higher efficacy, safety and cost-effectiveness of preventive interventions;
2) accommodating psychosocial factors along the lifecourse, together with objective biological markers, into the complex exposome that determines health states, under a systems approach to population health.