Precision medicine in the ageing world: The role of biospecimen sciences
Show all authors
Silvia Riondino, Patrizia Ferroni, Mario Roselli, ...
First Published March 11, 2019 Editorial
https://doi.org/10.1177/1724600818816995
Article information
Article has an altmetric score of 1 Open Access Creative Commons Attribution, Non Commercial 4.0 License
Keywords Elderly, precision medicine, risk assessment model, biomarker discovery, methods
The trends in population growth and aging worldwide see the population of the so-called “oldest olds” (≥85 years) growing faster than the populations of other age groups (2017 Revision of World Population Prospects, last accessed 12 December 2018).1 Accordingly, the median age is rising, as is the prevalence of chronic diseases, including cancers and cardiovascular diseases, all associated co-morbidities, and late-life disabilities. The lengthening of life expectancy benefits from the improvement of public health services (resulting in better preventive strategies) and from a higher level of education, leading to the adoption of healthy lifestyles, with a favorable net gain in terms of quality of life. Accordingly, the health care projections for the elderly foresee an increased longevity represented on the one hand by healthier aging subjects, and on the other by an increased demand for appropriate standards of care and, therefore, health care resources and a rise in medical costs.2 In fact, older people are typically more vulnerable not only to chronic diseases, but also to their treatment, the response to which can greatly vary in patients in whom aging significantly impacts with an increase in side effects. Moreover, potentially avoidable risk factors are a cause of further disabilities and premature death in older people.3
In this light, the clinical approach to the elderly patient needs to take into account all these factors, which, together, give rise to new clinical entities, where every patient is a world apart, and in which the axiom of “one size fits all” lacks sense. It must be kept in mind that among all the fields of scientific progress (genomics, proteomics, metabolomics, etc.), in this cluster of patients, the majority is represented by personomics (the influence of the unique circumstances of the person).4 In recent years, we have witnessed a growing demand of personalized medicine that allows us not only to identify the most suitable treatment, in terms of both safety and effectiveness for every given patient, but also to provide indications to those patients for whom the treatment could be useless or even harmful.5 Indeed, elderly patients frequently receive multiple medications and are susceptible to adverse drug events (ADE), often due to drug–drug, rather than to drug–disease, interactions, and to the patient’s reduced ability to metabolize drugs. This might have a significant impact on clinical decision making and prognostic evaluation.6
In order to achieve the ambitious goal to tailor treatment to individual needs, research is focusing on promoting the implementation of biomarker discovery studies, which might offer precious tools for preventive strategies and possible clues on the evolution of the disease, the lack of response to drugs, or the occurrence of side effects. All this is of utmost importance in the aging patient, in whom biomarkers could help in predicting morbidity, mortality, disability, and multiple adverse health outcomes, thus allowing tailored health care, based on his/her individual characteristics.7 Certainly, the possibility to identify specific biomarkers for a given pathology and for different age groups might allow research studies to be designed by combining more biomarkers and applying them to patients with multiple coexisting health conditions, which are so prevalent in the elderly. In fact, due to the presence of multiple co-morbidities and to the heterogeneity of their health status, elderly patients are often excluded from randomized controlled trials of treatment. This inevitably results in a double-edged sword, in which (a) the concentrations of drugs employed in trials can rarely be achieved in elderly patients; and (b) the number of ADEs in the aging “real world” is underestimated.8 If this is true and applies to common classes of drugs, its implications are even more dangerous when considering anticancer treatments. In this light, the use of risk assessment tools to identify the ADE risk of each patient for a proper stratification may significantly contribute to the optimization of therapeutic strategies. Lessons from cancer chemotherapy tell us that one option is to identify specific profiles in patients that might benefit from a tailored, targeted treatment, resulting from biomarker testing.5
In the ageing world, it has thus become imperative to overcome the lack of information concerning elderly people by increasing the number of clinical trials specifically designed to include them. The outcome measures, especially in oncological trials, are being reviewed accordingly, preferring to consider disease-free survival (DFS) rather than overall survival (OS), due to the risk of this frail population of dying for non-oncological causes.9 In this light, functional status and patient-reported outcomes (PROs) should be included as secondary outcome measures when analyzing the prolongation of an “active” life expectancy, rather than life expectancy tout court.10 The heterogeneity of the aging population, far from preventing its inclusion in clinical trials, conversely could represent a trigger for designing stratified adaptive phase II studies that require a smaller sample size and a faster accrual to evaluate the feasibility of a therapeutic strategy.11
Given its enormous potential of applications, the translation from biomarker discovery to clinical utility requires that the identified biomarker should be confirmed and validated on a large number of specimens in order to assess its reproducibility, specificity, and sensitivity.12 From this context stems the great interest of the biomedical research in Biological Banks. These facilities, defined in the Oviedo Convention as “operational units that provide a service for the storage and management of biological material and associated clinical data, in accordance with a good laboratory practice, privacy law and ethics guidelines,” are well suited to the growing demand for homogeneous biological samples in terms of pathology, clinical features, and collection/storage procedures, to be included in research protocols, as recommended by the guidelines of the National Institutes of Health Biorepository and Biospecimen Research Branch.13 Nonetheless, there is still a considerable lack of scientific data assessing the effects of specimen handling variables on sample quality, to the point that the scientific community has witnessed the development of a new research area, “biospecimen science,” which is intended to define the precise relationships between biospecimen handling and the quality and reproducibility of research data for a given disease.14 In this respect, the integration of research and clinical data of samples stored in biological bank facilities cannot rule out Information and Communication Technology (ICT) tools to gain knowledge on the specimen features and to trace the procedures it has undergone.15 So far, this has resulted in optimization of the standard operating procedures (SOPs),16 harmonization of the available information, and development of data integration processes, such as system warehouse infrastructures.17 Besides, the integration of computerized data derived from studies of biomarker discovery will help to define algorithms for risk stratification.
The need to adopt the best practices originates also from the expansion of the type of sectors concerned by the science of biobanks, from the scientific ones to those concerning ethical and legal matters, in the field of privacy protection.18,19 Indeed, biosample information is archived through computer support, which allows a complete recovery of data in accordance with the protection of existing legislation on the privacy of the donor subject. In this context, privacy protection should take into account an adequate preparation of technological and organizational measures aimed at preventing accidental loss of data or the external non-authorized access, while preserving the digital memory in time of the stored information, in accordance with the so-called principle of “privacy by design,”18 as advocated in the General Data Protection Regulation (GDPR) 2016/679 of the European Parliament.20
Another important issue that needs to be taken into account is the informed consent that any subject relating to a biorepository must provide. This subject is becoming increasingly relevant considering that Biobanks and biomarker discovery science are responsible for “big data” generation, which implies a higher level of control of all lifecycle processing of personal data.19 Thus, the issue of protection of personal data is a field of increasing importance and, in elderly patients, is not of secondary value, given the amount of personal information shared on the internet, through medical device connection within the growing applications of telemedicine and home care. In fact, integrated care systems acquire vital clinical signs and habits, data that are then monitored for a long period and analyzed in order to identify useful parameters and to develop algorithms capable of defining alert rules supporting independent living for elderly people with chronic disease.21 This is even more evident nowadays, when cancer patients can be effectively treated at home with several oral anticancer drugs without the need for hospital admission.
In conclusion, the ageing population is associated with an increase in multimorbidity, chronic diseases, and disability, which render it frail and vulnerable, and results in increased health care costs. Identifying ways for optimizing therapeutic interventions, thus reducing prescriptions and minimizing harm in this population, is becoming a priority in order to develop decision-making processes that classify patients according to their risk.22 Indeed, health risk assessment models are recommended preventive care interventions, which are capable of implementing disease prevention and health promotion in older individuals.3
In this light, the possibility of creating algorithms that include the patient’s age, co-morbidities, and polypharmacy may enhance the sensitivity of existing available biomarkers and allow the discovery of new, more specific, ones, and enable the development of appropriate testing for this particular cluster of patients.
Declaration of conflicting interest
The author(s) declare that there is no conflict of interest.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the European Social Fund, under the Italian Ministries of Education, University and Research (PNR 2015-2020 ARS01_01163 PerMedNet – CUP B66G18000220005) and Economic Development (“HORIZON 2020” PON I&C 2014-2020 – F/050383/01-03/X32).
References
1. 2017 Revision of World Population Prospects. https://population.un.org/wpp/ (2017, accessed 12 December 2018).
Google Scholar
2. Roselli, M, Guadagni, F, Ferroni, P. Aging: an important risk factor in chemotherapy-associated vascular diseases. Expert Rev Anticancer Ther 2013; 13: 899–901.
Google Scholar | Medline
3. Stuck, AE, Moser, A, Morf, U. Effect of health risk assessment and counselling on health behaviour and survival in older people: a pragmatic randomised trial. PLoS Med 2015; 12: e1001889.
Google Scholar | Medline
4. Ziegelstein, RC. Personomics. JAMA Int Med 2015; 175: 888–889.
Google Scholar | Medline
5. Riondino, S, Ferroni, P, Spila, A. Ensuring sample quality for biomarker discovery studies –use of ICT tools to trace biosample life-cycle. Cancer Genomics Proteomics 2015; 12: 291–299.
Google Scholar | Medline
6. Scott, IA, Gray, LC, Martin, JH. Minimizing inappropriate medications in older populations: a 10-step conceptual framework. Am J Med 2012; 125: 529–537.
Google Scholar | Medline | ISI
7. Wu, IC, Lin, CC, Hsiung, CA. Emerging roles of frailty and inflammaging in risk assessment of age-related chronic diseases in older adults: the intersection between aging biology and personalized medicine. Biomedicine (Taipei) 2015; 5: 1.
Google Scholar | Medline
8. Davies, EA, O’Mahony, MS. Adverse drug reactions in special populations – the elderly. Br J Clin Pharmacol 2015; 80: 796–807.
Google Scholar | Medline | ISI
9. Wildiers, H, Mauer, M, Pallis, A. End points and trial design in geriatric oncology research: a joint European organisation for research and treatment of cancer–Alliance for Clinical Trials in Oncology–International Society of Geriatric Oncology position article. J Clin Oncol 2013; 31: 3711–3718.
Google Scholar | Medline
10. Le Saux, O, Falandry, C, Gan, HK. Changes in the use of end points in clinical trials for elderly cancer patients over time. Ann Oncol 2017; 28: 2606–2611.
Google Scholar | Medline
11. Cabarrou, B, Sfumato, P, Mourey, L. Addressing heterogeneity in the design of phase II clinical trials in geriatric oncology. Eur J Cancer 2018; 103: 120–126.
Google Scholar | Medline
12. Issaq, HJ, Waybright, TJ, Veenstra, TD. Cancer biomarker discovery: Opportunities and pitfalls in analytical methods. Electrophoresis 2011; 32: 967–975.
Google Scholar | Medline
13. NCI First-generation guidelines for NCI-supported Biorepositories . Federal Registry 2006; 71: 25184–25203.
Google Scholar
14. Moore, HM, Compton, CC, Alper, J. International approaches to advancing biospecimen science. Canc Epidemiol Biomarkers Prev 2011; 20: 729–732.
Google Scholar | Medline | ISI
15. Nanni, U, Betsou, F, Riondino, S. SPRECware: software tools for Standard PREanalytical Code (SPREC) labeling – effective exchange and search of stored biospecimens. Int J Biol Markers 2012; 27: e272–279.
Google Scholar | SAGE Journals | ISI
16. Engel, KB, Vaught, J, Moore, HM. National Cancer Institute biospecimen evidence-based practices: a novel approach to pre-analytical standardization. Biopreserv Biobank. 2014; 12: 148–150.
Google Scholar | Medline
17. Nanni, U, Spila, A, Riondino, S. RFID as a new ICT tool to monitor specimen life cycle and quality control in a biobank. Int J Biol Markers 2011; 26: 129–135.
Google Scholar | SAGE Journals | ISI
18. Guadagni, F, Scarpato, N, Ferroni, P. Personal and sensitive data in the e-health-IoT universe. In The Second International Conference on Cognitive Internet of Things Technologies (COIOTE2015) Revised Selected Papers, Part II (eds Mandler, B, Barja, J, Mitre Campista, ME), Rome, Italy, 27–29 October 2015, pp.504–514.
Google Scholar
19. Beskow, LM, Dombeck, CB, Thompson, CP. Informed consent for biobanking: consensus-based guidelines for adequate comprehension. Genet Med 2015; 17: 226–233.
Google Scholar | Medline
20. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679 (accessed 12 December 2018).
Google Scholar
21. Gokalp, H, de Folter, J, Verma, V. Integrated telehealth and telecare for monitoring frail elderly with chronic disease. Telemed J E Health. Epub ahead of print 21 August 2018. DOI: 10.1089/tmj.2017.0322.
Google Scholar
22. Jameson, JL, Longo, DL. Precision medicine – personalized, problematic, and promising. N Engl J Med 2015; 372: 2229–2234.
Google Scholar | Medline | ISI
View Abstract
Show all authors
Silvia Riondino, Patrizia Ferroni, Mario Roselli, ...
First Published March 11, 2019 Editorial
https://doi.org/10.1177/1724600818816995
Article information
Article has an altmetric score of 1 Open Access Creative Commons Attribution, Non Commercial 4.0 License
Keywords Elderly, precision medicine, risk assessment model, biomarker discovery, methods
The trends in population growth and aging worldwide see the population of the so-called “oldest olds” (≥85 years) growing faster than the populations of other age groups (2017 Revision of World Population Prospects, last accessed 12 December 2018).1 Accordingly, the median age is rising, as is the prevalence of chronic diseases, including cancers and cardiovascular diseases, all associated co-morbidities, and late-life disabilities. The lengthening of life expectancy benefits from the improvement of public health services (resulting in better preventive strategies) and from a higher level of education, leading to the adoption of healthy lifestyles, with a favorable net gain in terms of quality of life. Accordingly, the health care projections for the elderly foresee an increased longevity represented on the one hand by healthier aging subjects, and on the other by an increased demand for appropriate standards of care and, therefore, health care resources and a rise in medical costs.2 In fact, older people are typically more vulnerable not only to chronic diseases, but also to their treatment, the response to which can greatly vary in patients in whom aging significantly impacts with an increase in side effects. Moreover, potentially avoidable risk factors are a cause of further disabilities and premature death in older people.3
In this light, the clinical approach to the elderly patient needs to take into account all these factors, which, together, give rise to new clinical entities, where every patient is a world apart, and in which the axiom of “one size fits all” lacks sense. It must be kept in mind that among all the fields of scientific progress (genomics, proteomics, metabolomics, etc.), in this cluster of patients, the majority is represented by personomics (the influence of the unique circumstances of the person).4 In recent years, we have witnessed a growing demand of personalized medicine that allows us not only to identify the most suitable treatment, in terms of both safety and effectiveness for every given patient, but also to provide indications to those patients for whom the treatment could be useless or even harmful.5 Indeed, elderly patients frequently receive multiple medications and are susceptible to adverse drug events (ADE), often due to drug–drug, rather than to drug–disease, interactions, and to the patient’s reduced ability to metabolize drugs. This might have a significant impact on clinical decision making and prognostic evaluation.6
In order to achieve the ambitious goal to tailor treatment to individual needs, research is focusing on promoting the implementation of biomarker discovery studies, which might offer precious tools for preventive strategies and possible clues on the evolution of the disease, the lack of response to drugs, or the occurrence of side effects. All this is of utmost importance in the aging patient, in whom biomarkers could help in predicting morbidity, mortality, disability, and multiple adverse health outcomes, thus allowing tailored health care, based on his/her individual characteristics.7 Certainly, the possibility to identify specific biomarkers for a given pathology and for different age groups might allow research studies to be designed by combining more biomarkers and applying them to patients with multiple coexisting health conditions, which are so prevalent in the elderly. In fact, due to the presence of multiple co-morbidities and to the heterogeneity of their health status, elderly patients are often excluded from randomized controlled trials of treatment. This inevitably results in a double-edged sword, in which (a) the concentrations of drugs employed in trials can rarely be achieved in elderly patients; and (b) the number of ADEs in the aging “real world” is underestimated.8 If this is true and applies to common classes of drugs, its implications are even more dangerous when considering anticancer treatments. In this light, the use of risk assessment tools to identify the ADE risk of each patient for a proper stratification may significantly contribute to the optimization of therapeutic strategies. Lessons from cancer chemotherapy tell us that one option is to identify specific profiles in patients that might benefit from a tailored, targeted treatment, resulting from biomarker testing.5
In the ageing world, it has thus become imperative to overcome the lack of information concerning elderly people by increasing the number of clinical trials specifically designed to include them. The outcome measures, especially in oncological trials, are being reviewed accordingly, preferring to consider disease-free survival (DFS) rather than overall survival (OS), due to the risk of this frail population of dying for non-oncological causes.9 In this light, functional status and patient-reported outcomes (PROs) should be included as secondary outcome measures when analyzing the prolongation of an “active” life expectancy, rather than life expectancy tout court.10 The heterogeneity of the aging population, far from preventing its inclusion in clinical trials, conversely could represent a trigger for designing stratified adaptive phase II studies that require a smaller sample size and a faster accrual to evaluate the feasibility of a therapeutic strategy.11
Given its enormous potential of applications, the translation from biomarker discovery to clinical utility requires that the identified biomarker should be confirmed and validated on a large number of specimens in order to assess its reproducibility, specificity, and sensitivity.12 From this context stems the great interest of the biomedical research in Biological Banks. These facilities, defined in the Oviedo Convention as “operational units that provide a service for the storage and management of biological material and associated clinical data, in accordance with a good laboratory practice, privacy law and ethics guidelines,” are well suited to the growing demand for homogeneous biological samples in terms of pathology, clinical features, and collection/storage procedures, to be included in research protocols, as recommended by the guidelines of the National Institutes of Health Biorepository and Biospecimen Research Branch.13 Nonetheless, there is still a considerable lack of scientific data assessing the effects of specimen handling variables on sample quality, to the point that the scientific community has witnessed the development of a new research area, “biospecimen science,” which is intended to define the precise relationships between biospecimen handling and the quality and reproducibility of research data for a given disease.14 In this respect, the integration of research and clinical data of samples stored in biological bank facilities cannot rule out Information and Communication Technology (ICT) tools to gain knowledge on the specimen features and to trace the procedures it has undergone.15 So far, this has resulted in optimization of the standard operating procedures (SOPs),16 harmonization of the available information, and development of data integration processes, such as system warehouse infrastructures.17 Besides, the integration of computerized data derived from studies of biomarker discovery will help to define algorithms for risk stratification.
The need to adopt the best practices originates also from the expansion of the type of sectors concerned by the science of biobanks, from the scientific ones to those concerning ethical and legal matters, in the field of privacy protection.18,19 Indeed, biosample information is archived through computer support, which allows a complete recovery of data in accordance with the protection of existing legislation on the privacy of the donor subject. In this context, privacy protection should take into account an adequate preparation of technological and organizational measures aimed at preventing accidental loss of data or the external non-authorized access, while preserving the digital memory in time of the stored information, in accordance with the so-called principle of “privacy by design,”18 as advocated in the General Data Protection Regulation (GDPR) 2016/679 of the European Parliament.20
Another important issue that needs to be taken into account is the informed consent that any subject relating to a biorepository must provide. This subject is becoming increasingly relevant considering that Biobanks and biomarker discovery science are responsible for “big data” generation, which implies a higher level of control of all lifecycle processing of personal data.19 Thus, the issue of protection of personal data is a field of increasing importance and, in elderly patients, is not of secondary value, given the amount of personal information shared on the internet, through medical device connection within the growing applications of telemedicine and home care. In fact, integrated care systems acquire vital clinical signs and habits, data that are then monitored for a long period and analyzed in order to identify useful parameters and to develop algorithms capable of defining alert rules supporting independent living for elderly people with chronic disease.21 This is even more evident nowadays, when cancer patients can be effectively treated at home with several oral anticancer drugs without the need for hospital admission.
In conclusion, the ageing population is associated with an increase in multimorbidity, chronic diseases, and disability, which render it frail and vulnerable, and results in increased health care costs. Identifying ways for optimizing therapeutic interventions, thus reducing prescriptions and minimizing harm in this population, is becoming a priority in order to develop decision-making processes that classify patients according to their risk.22 Indeed, health risk assessment models are recommended preventive care interventions, which are capable of implementing disease prevention and health promotion in older individuals.3
In this light, the possibility of creating algorithms that include the patient’s age, co-morbidities, and polypharmacy may enhance the sensitivity of existing available biomarkers and allow the discovery of new, more specific, ones, and enable the development of appropriate testing for this particular cluster of patients.
Declaration of conflicting interest
The author(s) declare that there is no conflict of interest.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the European Social Fund, under the Italian Ministries of Education, University and Research (PNR 2015-2020 ARS01_01163 PerMedNet – CUP B66G18000220005) and Economic Development (“HORIZON 2020” PON I&C 2014-2020 – F/050383/01-03/X32).
References
1. 2017 Revision of World Population Prospects. https://population.un.org/wpp/ (2017, accessed 12 December 2018).
Google Scholar
2. Roselli, M, Guadagni, F, Ferroni, P. Aging: an important risk factor in chemotherapy-associated vascular diseases. Expert Rev Anticancer Ther 2013; 13: 899–901.
Google Scholar | Medline
3. Stuck, AE, Moser, A, Morf, U. Effect of health risk assessment and counselling on health behaviour and survival in older people: a pragmatic randomised trial. PLoS Med 2015; 12: e1001889.
Google Scholar | Medline
4. Ziegelstein, RC. Personomics. JAMA Int Med 2015; 175: 888–889.
Google Scholar | Medline
5. Riondino, S, Ferroni, P, Spila, A. Ensuring sample quality for biomarker discovery studies –use of ICT tools to trace biosample life-cycle. Cancer Genomics Proteomics 2015; 12: 291–299.
Google Scholar | Medline
6. Scott, IA, Gray, LC, Martin, JH. Minimizing inappropriate medications in older populations: a 10-step conceptual framework. Am J Med 2012; 125: 529–537.
Google Scholar | Medline | ISI
7. Wu, IC, Lin, CC, Hsiung, CA. Emerging roles of frailty and inflammaging in risk assessment of age-related chronic diseases in older adults: the intersection between aging biology and personalized medicine. Biomedicine (Taipei) 2015; 5: 1.
Google Scholar | Medline
8. Davies, EA, O’Mahony, MS. Adverse drug reactions in special populations – the elderly. Br J Clin Pharmacol 2015; 80: 796–807.
Google Scholar | Medline | ISI
9. Wildiers, H, Mauer, M, Pallis, A. End points and trial design in geriatric oncology research: a joint European organisation for research and treatment of cancer–Alliance for Clinical Trials in Oncology–International Society of Geriatric Oncology position article. J Clin Oncol 2013; 31: 3711–3718.
Google Scholar | Medline
10. Le Saux, O, Falandry, C, Gan, HK. Changes in the use of end points in clinical trials for elderly cancer patients over time. Ann Oncol 2017; 28: 2606–2611.
Google Scholar | Medline
11. Cabarrou, B, Sfumato, P, Mourey, L. Addressing heterogeneity in the design of phase II clinical trials in geriatric oncology. Eur J Cancer 2018; 103: 120–126.
Google Scholar | Medline
12. Issaq, HJ, Waybright, TJ, Veenstra, TD. Cancer biomarker discovery: Opportunities and pitfalls in analytical methods. Electrophoresis 2011; 32: 967–975.
Google Scholar | Medline
13. NCI First-generation guidelines for NCI-supported Biorepositories . Federal Registry 2006; 71: 25184–25203.
Google Scholar
14. Moore, HM, Compton, CC, Alper, J. International approaches to advancing biospecimen science. Canc Epidemiol Biomarkers Prev 2011; 20: 729–732.
Google Scholar | Medline | ISI
15. Nanni, U, Betsou, F, Riondino, S. SPRECware: software tools for Standard PREanalytical Code (SPREC) labeling – effective exchange and search of stored biospecimens. Int J Biol Markers 2012; 27: e272–279.
Google Scholar | SAGE Journals | ISI
16. Engel, KB, Vaught, J, Moore, HM. National Cancer Institute biospecimen evidence-based practices: a novel approach to pre-analytical standardization. Biopreserv Biobank. 2014; 12: 148–150.
Google Scholar | Medline
17. Nanni, U, Spila, A, Riondino, S. RFID as a new ICT tool to monitor specimen life cycle and quality control in a biobank. Int J Biol Markers 2011; 26: 129–135.
Google Scholar | SAGE Journals | ISI
18. Guadagni, F, Scarpato, N, Ferroni, P. Personal and sensitive data in the e-health-IoT universe. In The Second International Conference on Cognitive Internet of Things Technologies (COIOTE2015) Revised Selected Papers, Part II (eds Mandler, B, Barja, J, Mitre Campista, ME), Rome, Italy, 27–29 October 2015, pp.504–514.
Google Scholar
19. Beskow, LM, Dombeck, CB, Thompson, CP. Informed consent for biobanking: consensus-based guidelines for adequate comprehension. Genet Med 2015; 17: 226–233.
Google Scholar | Medline
20. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32016R0679 (accessed 12 December 2018).
Google Scholar
21. Gokalp, H, de Folter, J, Verma, V. Integrated telehealth and telecare for monitoring frail elderly with chronic disease. Telemed J E Health. Epub ahead of print 21 August 2018. DOI: 10.1089/tmj.2017.0322.
Google Scholar
22. Jameson, JL, Longo, DL. Precision medicine – personalized, problematic, and promising. N Engl J Med 2015; 372: 2229–2234.
Google Scholar | Medline | ISI
View Abstract
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