Impact statement
There are 1.8 billion young people in the world today – 40% of the global population is under 24. We should aim to support children and young people with maintaining a long and healthy life and develop a system by which we personalise their health and healthcare for a better future. New advances in the fields of genomics, proteomics and cytogenetics have revolutionised our ability to understand the mechanisms underpinning the evolution and manifestation of existing diseases, discover new diseases, develop targeted therapies for specific populations of children to improve outcomes and predict children’s response to therapy, their risk of relapse and their prognosis. Importantly, our ability to diagnose disease early in life, and in particular using genomics in newborn screening supports early intervention and prevention and provides a greater understanding of the evolution and manifestation of diseases over longer periods. Critical to this process is the study of disease in the context of exposome, a new paradigm that encompasses the totality of human environmental exposures from conception onwards. A personalised approach to children’s health requires knowledge of the patient as an individual and their surrounding ecosystem. New technologies that can provide accurate tracking and recording of environmental data and technologies that can generate predictive models relating to future outcomes will add to the factors that facilitate a precision-based approach to managing health and disease over the life course. Furthermore, the ability to capture health data across large populations of children allows us to understand population characteristics in specific diseases that in turn drive individual interventions and changes. Precision medicine, precision diagnostics and the technological advances underpinning these rapidly advancing fields will change the way in which we understand disease, sustain health and improve quality of life. Focussing our efforts early in life is an investment for future health and healthcare.
Introduction
Healthcare is evolving from a traditional ‘one-size-fits-all’ approach to a model that accounts for the prediction of individual patient disease risks, a tailored approach to the investigation and the development of targeted interventions. A better understanding of the underlying mechanisms and causes of rare and chronic diseases combined with technological advancements are transforming medicine from a reactive to a pro-active and preventive discipline that has been encapsulated in the ‘4P’ approach to medicine (also referred to as P4 Medicine) to maintain health and well-being and to prevent and predict disease and thus respond accordingly to individual needs – ‘4P – Preventive, Predictive, Participatory and Personalised’ (Hood et al., Reference Hood, Heath, Phelps and Lin2004; Flores et al., Reference Flores, Glusman, Brogaard, Price and Hood2013). A personalised approach to medicine requires knowledge of the patient as an individual and their surrounding ecosystem, by understanding their molecular and genetic components, their cells and tissues, the whole person and the population and environment in which they exist. This breadth and depth of knowledge of an individual and their environmental interaction underpins the science of systems biology (National Research Council (US), 2009). Systems biology is founded on the principle that the interaction between individual parameters at the genetic, molecular and cellular level with social and environmental parameters as the component parts of complex biological systems supports a more ‘predictive’ approach by which computational models can be used to predict outcomes, and individuals can be stratified more comprehensively according to their disease risks. Systems medicine focuses on the approach of systems biology to human disease by combining knowledge that ranges from individual genomic sequencing to understanding global data sets that track patient populations and their interaction with the environment (Hood et al., Reference Hood, Balling and Auffray2012).
Advances in technology and computational analysis over the last 20 years have radically enhanced our ability to collect, store and analyse data to create biological networks derived from computational models that demonstrate how factors within biological systems can maintain health or lead to disease. Importantly, factors within these computational models can be modified to predict an individual’s risk of future ill-health. Ultimately, the aim of systems medicine is to develop an ‘individual data profile’ founded upon multi-dimensional longitudinal health data, ranging from the individuals genomic/proteomic profile, hospital data from historic interactions and lifestyle and environmental data. These data have two fundamental origins – data that are derived when an individual interacts with a system such as a hospital or for health research, or alternatively, when the individual offers or inputs the data as part of an interaction with a digital platform encompassed in the participatory component of the 4P model. The latter of these has been facilitated by the advance in technologies that allow individual consumers to input and track their own health data and interact with others to monitor their health and disease risk. Portable devices such as mobile phones, tablets and laptops, novel sensors and digital platforms help to facilitate the participatory component of the 4P model extending health data collection and analysis into homes, workplaces and schools. Data collection from new devices can be passive, such that the individual allows the device to track activity without direct input, or active by which the individual inputs the data. In principle, if information can be collected early in life, this has the potential to provide richer longitudinal data about health, disease, lifestyle and environmental interaction, which could help modify factors that prevent disease, support earlier diagnoses or maintain health. Returning to the systems medicine approach, data could be derived from a number of sources including primary and secondary care health data, genomics data derived from genomic newborn screening and whole genome sequencing (WGS), educational data from schools and universities, and additional data on lifestyle and environmental exposure, sometimes referred to as the exposome, encompassing environmental exposure from the prenatal period onwards. For children, the exposome encompasses general exposure to the external environment such as climate, familial and social factors and education, and specific exposure such as infections, radiation, home factors such as diet, smoking and alcohol consumption in parents, and physical activity. The challenge as we develop tools to individualise healthcare and to prevent and predict disease in children and young people is our ability to acquire data from multiple sources including those which are controlled by the consumer. Data captured from an early age will rely on empowering children, young people and their families to permit the use of their data, particularly data that may be from non-health related and personalsources, trusting that their data will be utilised appropriately to support their health and well-being and will be stored securely (National Research Council (US), 2011). The value of using data from multiple sources must be easily demonstrable to families in a way that prevention, disease modification and individualised care in the future are supported by meaningful and actionable information that is in a way that children, young people and parents can understand, and in a way that parents can utilise to support their children in preventing ill-health or seeking earlier healthcare support.
Precision diagnostics is a branch of precision medicine using the 4P approach by which individual diseases are diagnosed based upon genomic variation, lifestyle and the environment. Understanding subpopulations with similar genomic information and the environmental and lifestyle factors that predict disease onset allows healthcare professionals and individuals to prevent disease onset or intervene early to prevent disease progression. This approach has the potential to develop a radically more cost-effective approach to healthcare, particularly in the paediatric population, where rare diseases are more prevalent. Moreover, a better understanding of modifiable lifestyle factors in childhood may help to predict or prevent future disease, thus underpinning the value of prospective data collection. Tailored therapies by stratifying individuals into subgroups according to their disease profile, response to therapy and prognosis and preventing disease through risk factor modification will result in a reduction in healthcare expenditure (Wang et al., Reference Wang, Cho, Wong and Liu2017). Furthermore, understanding the molecular and cellular origins of disease in children and young people has resulted in a paradigm shift in thinking about ‘causes’ rather than ‘symptoms’ of disease leading to a proposal for a new taxonomy for disease classification that could change the approach to clinical decision-making, diagnosis, therapy and prognosis (Blower et al., Reference Blower, Swallow, Maturana, Stones, Phillips, Dimitri, Marshman, Knapp, Dean, Higgins, Kellar, Curtis, Mills and Martin-Kerry2020).
Disease reclassification and predicting future disease in children
Precision diagnostics is founded on the ability to predict disease and the ability to adopt a personalised approach. The understanding of a disease at a molecular level will determine the onset of the disease or symptoms, or when a perturbation within a biological system will result in the manifestation of the disease. This will vary between individuals, and thus, the understanding of the biological environment at a molecular level is fundamental in determining the individual’s disease susceptibility, the severity of the disease, response to specific therapies and prognosis. At a genetic level, mutations range from loss or gain of entire chromosomes, loss of smaller regions of DNA, for example, copy number variants (CNVs), to changes in the structure of the genome in the form of translocations, inversions and insertions, or changes in the sequence of the nucleotides (Lalonde et al., Reference Lalonde, Rentas, Lin, Dulik, Skraban and Spinner2020). Advances in genomics, and in particular the development of next-generation sequencing (Lalonde et al., Reference Lalonde, Rentas, Lin, Dulik, Skraban and Spinner2020), have had a transformative effect on diagnostics in children, resulting in targeted panels for specific diagnoses, and examination of the entire exome or genome based upon clinical manifestations. Whole exome sequencing and Whole Genome Sequencing (WGS) have the potential to increase the diagnostic yield by up to 50% and as such there has been a call to adopt WGS as a first-tier test, particularly conditions in children with genetic heterogeneity (Stavropoulos et al., Reference Stavropoulos, Merico, Jobling, Bowdin, Monfared, Thiruvahindrapuram, Nalpathamkalam, Pellecchia, Yuen, Szego, Hayeems, Shaul, Brudno, Girdea, Frey, Alipanahi, Ahmed, Babul-Hirji, Porras, Carter, Chad, Chaudhry, Chitayat, Doust, Cytrynbaum, Dupuis, Ejaz, Fishman, Guerin, Hashemi, Helal, Hewson, Inbar-Feigenberg, Kannu, Karp, Kim, Kronick, Liston, MacDonald, Mercimek-Mahmutoglu, Mendoza-Londono, Nasr, Nimmo, Parkinson, Quercia, Raiman, Roifman, Schulze, Shugar, Shuman, Sinajon, Siriwardena, Weksberg, Yoon, Carew, Erickson, Leach, Klein, Ray, Meyn, Scherer, Cohn and Marshall2016; Meng et al., Reference Meng, Pammi, Saronwala, Magoulas, Ghazi, Vetrini, Zhang, He, Dharmadhikari, Qu, Ward, Braxton, Narayanan, Ge, Tokita, Santiago-Sim, Dai, Chiang, Smith, Azamian, Robak, Bostwick, Schaaf, Potocki, Scaglia, Bacino, Hanchard, Wangler, Scott, Brown, Hu, Belmont, Burrage, Graham, Sutton, Craigen, Plon, Lupski, Beaudet, Gibbs, Muzny, Miller, Wang, Leduc, Xiao, Liu, Shaw, Walkiewicz, Bi, Xia, Lee, Eng, Yang and Lalani2017; Posey et al., Reference Posey, Harel, Liu, Rosenfeld, James, Coban Akdemir, Walkiewicz, Bi, Xiao, Ding, Xia, Beaudet, Muzny, Gibbs, Boerwinkle, Eng, Sutton, Shaw, Plon, Yang and Lupski2017; Lionel et al., Reference Lionel, Costain, Monfared, Walker, Reuter, Hosseini, Thiruvahindrapuram, Merico, Jobling, Nalpathamkalam, Pellecchia, Sung, Wang, Bikangaga, Boelman, Carter, Cordeiro, Cytrynbaum, Dell, Dhir, Dowling, Heon, Hewson, Hiraki, Inbar-Feigenberg, Klatt, Kronick, Laxer, Licht, MacDonald, Mercimek-Andrews, Mendoza-Londono, Piscione, Schneider, Schulze, Silverman, Siriwardena, Snead, Sondheimer, Sutherland, Vincent, Wasserman, Weksberg, Shuman, Carew, Szego, Hayeems, Basran, Stavropoulos, Ray, Bowdin, Meyn, Cohn, Scherer and Marshall2018). Many of these conditions are congenital, manifesting at birth, infancy or early childhood. A genetic diagnosis can then inform prognosis, anticipatory care and surveillance, targeted management and future family planning. Even within conditions harbouring the same ‘macro’ genetic mutation, there can be a high degree of variability based upon the location of the specific mutation and the interaction with other genes within the same molecular pathway (Costain et al., Reference Costain, Cohn and Malkin2020). In turn, this allows individualised planning, targeted therapies and risk profiling.
This approach has led to significant clarification in disorders with heterogeneous manifestations which present in childhood, and the reclassification of disease based upon genotype rather than clinical presentation. For example, the heterogeneous clinical manifestations of Noonan syndrome, a condition that presents with a distinct facial phenotype, short stature, cardiac anomalies, developmental delay and other features (Dahlgren and Noordam, Reference Dahlgren and Noordam2022), have been reclassified and incorporated into a group of genetic mutations that are collectively known as the RAS-opathies (Gripp et al., Reference Gripp, Schill, Schoyer, Stronach, Bennett, Blaser, Brown, Burdine, Burkitt-Wright, Castel, Darilek, Dias, Dyer, Ellis, Erickson, Gelb, Green, Gross, Ho, Holder, Inoue, Jelin, Kennedy, Klein, Kontaridis, Magoulas, McConnell, McCormick, Neel, Prada, Rauen, Roberts, Rodriguez-Viciana, Rosen, Rumbaugh, Sablina, Solman, Tartaglia, Thomas, Timmer, Venkatachalam, Walsh, Wolters, Yi, Zenker and Ratner2020). RAS-opathies define germline mutations in genes that encode components or regulators of the Ras/mitogen-activated protein kinase pathway (Rauen, Reference Rauen2013) and share many of the phenotypic manifestations of Noonan syndrome. RAS-opathies also include neurofibromatosis type 1, Noonan syndrome with multiple lentigines, Noonan syndrome-like disorder with loose anagen hair, Noonan syndrome-like disorder with or without juvenile myelomonocytic leukaemia, capillary malformation-arteriovenous malformation syndrome, Costello syndrome, cardio-facio-cutaneous syndrome, SYNGAP1-related intellectual disability and Legius syndrome. Mutations associated with Noonan syndrome alone include CBL, BRAF, KRAS, LZTR1, MAP2K1 (MEK1), NRAS, PTPN11, RAF1, RIT1, SOS1 and SOS2 (Riller and Rieux-Laucat, Reference Riller and Rieux-Laucat2021; Leoni et al., Reference Leoni, Blandino, Delogu, De Rosa, Onesimo, Verusio, Marino, Lanza, Rigante, Tartaglia and Zampino2022). Thus, this provides an example of one of many genetic disorders in children that may have been mistakenly classified according to facial and systemic phenotype, or previously described as ‘non-classical’ or ‘atypical’ which can now be accurately genotyped to define the appropriate disorder, the predicted consequences, therapy and prognosis.
Similarly, osteogenesis imperfecta (OI, brittle bone disease), a rare disease in childhood, was originally classified by Sillence in 1979 into four subtypes (OI I–IV) based upon mode of inheritance, clinical manifestation and severity (Sillence et al., Reference Sillence, Senn and Danks1979). However, 20 different subtypes of this condition have emerged based upon a more refined understanding of the wide range of genes that encode relevant proteins. These proteins are involved in processes such as the synthesis of type I collagen and the differentiation, regulation and activity of bone-forming cells (osteoblasts) and bone-absorbing cells (osteoclasts). Importantly, this has led to an exponential rise in targeted therapies for bone fragility in OI based upon the genotype-derived causative mechanisms, thus supporting the value of genomic reclassification and precision diagnostics to develop novel targeted therapies for rare diseases in children (Zaripova and Khusainova, Reference Zaripova and Khusainova2020). The reduction in the cost of genomic sequencing is facilitating significant advances in the classification of diseases but additionally in predicting the onset of disease. In the UK, Genomics England has embarked on a discovery science programme aiming to sequence and analyse the whole genome in up to 200,000 babies for a set of actionable genetic conditions which may affect their health in early years. This aims to ensure timely diagnosis, access to treatment pathways and enable better outcomes and quality of life for babies and their families (Genomics England, 2021). Rapid genomic testing in critically ill-children is also advancing the field of precision diagnostics and therapy in paediatrics. The first national healthcare system-funded implementation of rapid genomic sequencing for acutely unwell children commenced in England on 1st October 2019 led by the NHS Genomic Medicine Service. The diagnostic yield was 38% with the molecular diagnosis directing management in 94% of patients (Stark and Ellard, Reference Stark and Ellard2022).
Next-generation cytogenetics such as optical genome mapping (OGM) has also advanced the speed and ability of detecting genetic abnormalities (Lam et al., Reference Lam, Hastie, Lin, Ehrlich, Das, Austin, Deshpande, Cao, Nagarajan, Xiao and Kwok2012). Cytogenetics is the genetic discipline that examines chromosomes for abnormalities. Karyotyping, chromosomal microarray analysis (CMA), multiple ligation-dependent probe amplification and fluorescent in situ hybridisation (FISH) are examples of currently utilised cytogenetic techniques but have limited resolution or limited region coverage. For example, CMA, despite having higher resolution for the detection of repeat regions of the genome known as CNVs, is limited in detecting balanced translocations and inversions (Levy and Wapner, Reference Levy and Wapner2018). Next-generation sequencing methods can detect sequence variants, but they are unable to accurately resolve CNVs. Moreover, complete genetic profiling in specific diseases either for diagnosis or for risk stratification of prognosis often relies on a combination of techniques which is time consuming and costly. OGM is a novel technology that analyses ultra-high molecular weight DNA molecules that provide a high-resolution genome-wide image. Each patient’s unique map is aligned to a reference genome map to detect CNVs and structural anomalies. OGM has strong concordance with other cytogenetic techniques such as FISH and CMA but has also advanced the detection of additional chromosomal abnormalities that are clinically relevant and that are inaccessible to standard techniques, allowing for further disease stratification in diseases such as acute lymphoblastic leukaemia (ALL) (Lestringant et al., Reference Lestringant, Duployez, Penther, Luquet, Derrieux, Lutun, Preudhomme, West, Ouled-Haddou, Devoldere, Marolleau, Garçon, Jedraszak and Ferret2021; Rack et al., Reference Rack, De Bie, Ameye, Gielen, Demeyer, Cools, De Keersmaecker, Vermeesch, Maertens, Segers, Michaux and Dewaele2022). OGM is now recognised as key genomic technology capable of detecting all classes of structural variants in many disorders, which will undoubtedly improve the speed and accuracy of diagnosis of rare disorders in children (Levy and Wapner, Reference Levy and Wapner2018; Mantere et al., Reference Mantere, Neveling, Pebrel-Richard, Benoist, van der Zande, Kater-Baats, Baatout, van Beek, Yammine and Oorsprong2020) but also improve risk stratification in common haematological disorders in children such as ALL. Stratification in ALL has previously relied on a combination of genetic tests which is time consuming and expensive. In recent studies categorising subtypes of ALL, OGM increased the detection rate and cytogenetic resolution and abrogated the need for cascade testing using multiple cytogenetic techniques, resulting in reduced turnaround times and cost saving (Neveling et al., Reference Neveling, Mantere, Vermeulen, Oorsprong, van Beek, Kater-Baats, Pauper, van der Zande, Smeets, Weghuis, Stevens-Kroef and Hoischen2021; Rack et al., Reference Rack, De Bie, Ameye, Gielen, Demeyer, Cools, De Keersmaecker, Vermeesch, Maertens, Segers, Michaux and Dewaele2022). OGM has also been applied to other areas of paediatrics to improve the diagnostic yield including neurodevelopmental paediatrics (Shieh et al., Reference Shieh, Penon-Portmann, Wong, Levy-Sakin, Verghese, Slavotinek, Gallagher, Mendelsohn, Tenney, Beleford, Perry, Chow, Sharo, Brenner, Qi, Yu, Klein, Martin, Kwok and Boffelli2020), myotonic dystrophy (Otero et al., Reference Otero, Poukalov, Hildebrandt, Thornton, Jinnai, Fujimura, Kimura, Hagerman, Sampson, Day and Wang2021), disorders of sex development (Barseghyan et al., Reference Barseghyan, Délot and Vilain2018) and with the potential for widespread application for prenatal diagnostic testing (Sahajpal et al., Reference Sahajpal, Barseghyan, Kolhe, Hastie and Chaubey2021). Given the extended capabilities of OGM, many undiagnosed conditions in paediatrics will be elucidated using this new technique and other future developments as we enter an era of next-generation cytogenetics (Mantere et al., Reference Mantere, Neveling, Pebrel-Richard, Benoist, van der Zande, Kater-Baats, Baatout, van Beek, Yammine and Oorsprong2020).
Developing biomarkers and targeted therapies to treat childhood diseases
Proteomics
While advances in genomics and cytogenetics are revolutionising our understanding of diseases in childhood, these advances should not be considered in isolation. A systems biology or systems medicine approach is used to consider genetic mutations in the context of or interaction with other biological systems. In vitro and in vivo functional studies examine the translational effect of genetic mutations at a molecular level. Translational studies may include the large-scale impact on protein synthesis and function (proteomics) (Sahajpal et al., Reference Sahajpal, Barseghyan, Kolhe, Hastie and Chaubey2021) and metabolic function within the biological system (metabolomics). It is through the study of proteins within the sphere of proteomics that protein modifications can be characterised, and the targets of drugs identified. The value of proteomics within the context of precision diagnostics in children and adults is the understanding of the post-translational modifications that proteins undergo in response to a variety of intracellular and extracellular signals. Protein phosphorylation is important in protein signalling and disruption of protein phosphorylation due to alteration in protein kinases or phosphatases can lead to oncogenesis (Graves and Haystead, Reference Graves and Haystead2002). The process of cell growth, programmed cell death and the decision to proceed through the cell cycle are all regulated by signal transduction through protein complexes (Hunter, Reference Hunter1995). Dysregulation of these processes results in potential cancer development. Disruption of protein localisation can also have a profound effect on cellular function and can result in diseases in childhood such as cystic fibrosis (Graves and Haystead, Reference Graves and Haystead2002). Detailed knowledge about the structure, function, post-translational modifications, localisation, compartmentalisation and protein–protein interactions within cells supports the development of targeted drug therapies and the use of biomarkers to determine disease development and for monitoring treatment effect (Wilkins et al., Reference Wilkins, Sanchez, Gooley, Appel, Humphery-Smith, Hochstrasser and Williams1996; García-Foncillas et al., Reference García-Foncillas, Argente, Bujanda, Cardona, Casanova, Fernández-Montes, Horcajadas, Iñiguez, Ortiz, Pablos and Pérez Gómez2021). However, proteomics provides an added layer of complexity as protein expression is altered by chronicity and environmental conditions (Al-Amrani et al., Reference Al-Amrani, Al-Jabri, Al-Zaabi, Alshekaili and Al-Khabori2021). To enable drug development and the development of novel biomarkers, public resources of curated signal transduction pathways have been developed (Chatr-aryamontri et al., Reference Chatr-aryamontri, Ceol, Palazzi, Nardelli, Schneider, Castagnoli and Cesareni2007; Mi et al., Reference Mi, Guo, Kejariwal and Thomas2007; Schaefer et al., Reference Schaefer, Anthony, Krupa, Buchoff, Day, Hannay and Buetow2009; Kandasamy et al., Reference Kandasamy, Mohan, Raju, Keerthikumar, Kumar, Venugopal, Telikicherla, Navarro, Mathivanan, Pecquet, Gollapudi, Tattikota, Mohan, Padhukasahasram, Subbannayya, Goel, Jacob, Zhong, Sekhar, Nanjappa, Balakrishnan, Subbaiah, Ramachandra, Rahiman, Prasad, Lin, Houtman, Desiderio, Renauld, Constantinescu, Ohara, Hirano, Kubo, Singh, Khatri, Draghici, Bader, Sander, Leonard and Pandey2010; Croft et al., Reference Croft, O’Kelly, Wu, Haw, Gillespie, Matthews, Caudy, Garapati, Gopinath, Jassal, Jupe, Kalatskaya, Mahajan, May, Ndegwa, Schmidt, Shamovsky, Yung, Birney, Hermjakob, D’Eustachio and Stein2011; Kanehisa et al., Reference Kanehisa, Goto, Sato, Furumichi and Tanabe2012; Kerrien et al., Reference Kerrien, Aranda, Breuza, Bridge, Broackes-Carter, Chen, Duesbury, Dumousseau, Feuermann, Hinz, Jandrasits, Jimenez, Khadake, Mahadevan, Masson, Pedruzzi, Pfeiffenberger, Porras, Raghunath, Roechert, Orchard and Hermjakob2012; Franceschini et al., Reference Franceschini, Szklarczyk, Frankild, Kuhn, Simonovic, Roth, Lin, Minguez, Bork, von Mering and Jensen2013; Holman et al., Reference Holman, Dasari and Tabb2013; Schmidt et al., Reference Schmidt, Forne and Imhof2014). Proteomic-derived biomarker development is classified as diagnostic, predictive and prognostic, based on their uses.
Diagnostic biomarkers indicate if a patient has a specific disease, predictive biomarkers can predict the response to therapy, and prognostic biomarkers support the prediction of the clinical outcomes. Paediatric sepsis provides an example underpinning the value of using a precision diagnostic approach that sub-categorises patients to allow directed therapy to improve outcome and predict prognosis. Sepsis is a leading cause of mortality in children worldwide (Glaab et al., Reference Glaab, Baudot, Krasnogor, Schneider and Valencia2012). Given the heterogeneity of presentation in paediatric septic shock caused by viral or bacterial pathogens, this acts as an excellent model to demonstrate the value of proteomics in relation to precision diagnostic, predictive and prognostic biomarkers in children. Over a decade ago, whole blood RNA was first used to identify genes that were up- and down-regulated in a cohort of paediatric patients with septic shock, demonstrating that these patients had a unique gene expression signature (Wong et al., Reference Wong, Shanley and Sakthivel2007; Liu et al., Reference Liu, Johnson, Cousens, Perin, Scott, Lawn, Rudan, Campbell, Cibulskis, Li, Mathers and Black2012). Early work identified three subclasses of children with septic shock who were defined by age, illness severity and complications classified by 100 genes; younger patients were more likely to have a higher level of illness severity, a higher degree of organ failure and a higher mortality rate (Cvijanovich et al., Reference Cvijanovich, Shanley, Lin, Allen, Thomas, Checchia, Anas, Freishtat, Monaco, Odoms, Sakthivel and Wong2008; Wong et al., Reference Wong, Cvijanovich and Lin2009). Further work in this field has led to the categorisation of children with septic shock into two endotypes which through iterations are now classified by only four genes (Wong et al., Reference Wong, Atkinson, Cvijanovich, Anas, Allen, Thomas, Bigham, Weiss, Fitzgerald, Checchia, Meyer, Quasney, Hall, Gedeit, Freishtat, Nowak, Raj, Gertz and Lindsell2011, Reference Wong, Cvijanovich, Anas, Allen, Thomas, Bigham, Weiss, Fitzgerald, Checchia, Meyer, Shanley, Quasney, Hall, Gedeit, Freishtat, Nowak, Shekhar, Gertz, Dawson, Howard, Harmon, Beckman, Frank and Lindsell2015). Of note, endotype A was noted to have repression of genes corresponding to glucocorticoid receptor signalling and thus adaptive immunity, and use of adjunctive corticosteroids in this group was associated with a 4-fold increase in mortality, thus helping to determine which children with septic shock would benefit from adjunctive corticosteroids and which would not (Wong et al., Reference Wong, Atkinson, Cvijanovich, Anas, Allen, Thomas, Bigham, Weiss, Fitzgerald, Checchia, Meyer, Quasney, Hall, Gedeit, Freishtat, Nowak, Raj, Gertz and Lindsell2011, Reference Wong, Sweeney and Lindsell2017). In addition, multiple serum protein biomarkers with known biological mechanisms in combination with mRNA biomarkers have been used in mortality risk stratification of children with septic shock (Wong et al., Reference Wong, Atkinson and Cvijanovich2016). Combining metabolomic and inflammatory protein mediator profiling early after presentation of paediatric sepsis may differentiate children with sepsis requiring intensive care from those with or without sepsis who can be safely cared for without intensive intervention, thus enabling diagnostic and triage decisions in children with sepsis (Wong et al., Reference Wong, Cvijanovich, Anas, Allen, Thomas, Bigham, Weiss, Fitzgerald, Checchia, Meyer, Quasney, Hall, Gedeit, Freishtat, Nowak, Raj, Gertz, Grunwell and Lindsell2017). Recent work has also helped to define the presence of bacterial or viral pathogens based upon the presence of biomarkers including procalcitonin, neutrophil gelatinase-associated lipocalin-2 and resistin (Mickiewicz et al., Reference Mickiewicz, Thompson, Blackwood, Jenne, Winston, Vogel and Joffe2015) with further advances using host RNA signatures to further discriminate between bacterial and viral infection (Nijman et al., Reference Nijman, Oostenbrink, Moll, Casals-Pascual, von Both, Cunnington, De, Eleftheriou, Emonts, Fink, van der Flier, de Groot, Kaforou, Kohlmaier, Kuijpers, Lim, Maconochie, Paulus, Martinon-Torres, Pokorn, Romaine, Calle, Schlapbach, Smit, Tsolia, Usuf, Wright, Yeung, Zavadska, Zenz, Levin, Herberg and Carrol2021). Other developments in this field include the use of proteomic profiling to distinguish late onset sepsis and necrotising enterocolitis in neonates (Pennisi et al., Reference Pennisi, Moniri, Miscourides, Miglietta, Moser, Habgood-Coote, Herberg, Levin, Kaforou, Rodriguez-Manzano and Georgiou2022), potentially predictive, early diagnostic and prognostic metabolomic and proteomic biomarkers in neonatal necrotising enterocolitis (Chatziioannou et al., Reference Chatziioannou, Wolters, Sarafidis, Thomaidou, Agakidis, Govorukhina, Kuivenhoven, Bischoff and Theodoridis2018), and the combination of metabolomic, transcriptomic, genomic and proteomic profiles as an ‘integrated-omics’ systems medicine approach to the stratification, diagnosis, treatment and prognostics in paediatric sepsis (Agakidou et al., Reference Agakidou, Agakidis, Gika and Sarafidis2020). Outside the field of neonatal and paediatric sepsis, there are other examples where proteomics has the potential to further our understanding of conditions in children. In the field of paediatric allergy, novel proteomics methods have been used to identify the presence of 36 digested cow’s milk proteins in breast milk which would be missed by immunochemical methods, and thus may help to improve our understanding of cow’s milk protein allergy in exclusively breast-fed infants and to support new approaches to allergy prevention (Zhu et al., Reference Zhu, Garrigues, Van den Toorn, Stahl and Heck2019). Recently, proteomics profiling as part of a multi-omics approach has been used to predict neurodegeneration in children with Down’s syndrome pointing to a disruption of IGF1 signalling as a potential contributor to or biomarker to the neurodegenerative process, again potentially providing novel pathways for targeted treatments (Araya et al., Reference Araya, Kinning, Coughlan, Smith, Granrath, Enriquez-Estrada, Worek, Sullivan, Rachubinski, Wolter-Warmerdam, Hickey, Galbraith, Potter and Espinosa2022). In paediatric oncology, proteogenomic studies are furthering our understanding of relapse and treatment resistance in children with acute myeloid leukaemia, providing new approaches to predict relapse during disease progression, novel biomarkers and new targets for novel drug therapies (Stratmann et al., Reference Stratmann, Vesterlund, Umer, Eshtad, Skaftason, Herlin, Sundström, Eriksson, Höglund, Palle, Abrahamsson, Jahnukainen, Munthe-Kaas, Zeller, Tamm, Lindskog, Cavelier, Lehtiö and Holmfeldt2022). Other areas where proteomics provides value to prediction, diagnostics and personalised interventions include chronic kidney disease and peritoneal dialysis in children (Cummins et al., Reference Cummins, Korte, Bhayana, Merchant, Barati, Smoyer and Klein2022; Trincianti et al., Reference Trincianti, Meleca, La Porta, Bruschi, Candiano, Garbarino, Kajana, Preda, Lugani, Ghiggeri, Angeletti, Esposito and Verrina2022), drug metabolism and personalised dose optimisation in children (Streekstra et al., Reference Streekstra, Russel, van de Steeg and de Wildt2021; van Groen et al., Reference van Groen, Allegaert, Tibboel and de Wildt2022) and the use of multi-omics derived biomarkers to develop prediction models in children with asthma to guide therapy (Golebski et al., Reference Golebski, Kabesch, Melén, Potočnik, van Drunen, Reinarts, Maitland-van der Zee and Vijverberg2020; Kang et al., Reference Kang, Ahn, Lee, Yeom, Kim and Hong2022). Proteomics either in isolation or part of a multi-omics approach has revolutionised our ability to develop individual disease profiling across multiple areas of paediatrics with new diagnostic, predictive and prognostic methodologies, and new targets for novel drug development.
Precision diagnostics for targeted drug therapies
An understanding of genetic causes of specific diseases has resulted in the recent increase in the development of gene therapies in children regulated under the guidelines for advanced therapy medicinal products. Broadly, gene therapies were previously categorised into ex vivo and in vivo (Langley and Wong, Reference Langley and Wong2017). Ex vivo is the route taken predominantly for the gene modification of bone marrow-derived cells and epidermal sheets. Ex vivo gene therapies are based upon the process of extracting immature bone marrow cells from the patient, employing a viral vector to integrate a functional copy of the gene into the genome of the target cells. The genetically modified cells are then re-administered to the patient in the form of an autologous gene-modified cell transplant. Examples of ex vivo gene therapies include the treatment of primary immune deficiencies and metabolic disorders (Qasim et al., Reference Qasim, Gaspar and Thrasher2007; Buckland and Bobby Gaspar, Reference Buckland and Bobby Gaspar2014) with more recent approvals granted for rare diseases such as metachromatic leukodystrophy (treated with atidarsagene autotemcel) (Rivat et al., Reference Rivat, Santilli, Gaspar and Thrasher2012) and cerebral adrenoleukodystrophy (treated with elivaldogene autotemcel) (Fumagalli et al., Reference Fumagalli, Calbi, Natali Sora, Sessa, Baldoli, Rancoita, Ciotti, Sarzana, Fraschini, Zambon, Acquati, Redaelli, Attanasio, Miglietta, De Mattia, Barzaghi, Ferrua, Migliavacca, Tucci, Gallo, Del Carro, Canale, Spiga, Lorioli, Recupero, Fratini, Morena, Silvani, Calvi, Facchini, Locatelli, Corti, Zancan, Antonioli, Farinelli, Gabaldo, Garcia-Segovia, Schwab, Downey, Filippi, Cicalese, Martino, Di Serio, Ciceri, Bernardo, Naldini, Biffi and Aiuti2022). The first UK patient to receive gene therapy in the UK celebrated his 21st birthday last year after having been treated for severe combined immunodeficiency (Keam, Reference Keam2021). In vivo gene therapy primarily uses a viral vector (commonly the adeno-associated virus) to deliver the therapy directly to the patient rather than the transplant of gene-corrected cells. Glybera (alipogene tiparvovec) was the first gene therapy treatment to receive an European Commission marketing authorisation and the first gene therapy to be approved anywhere in the world, was used to treat lipoprotein lipase deficiency, an inherited condition with an incidence of 1/500,000. This therapy has since been withdrawn due to the rarity of the disease lack of cost-effectiveness (Great Ormond Street Hospital for Children (GOSH), 2021; Kastelein et al., Reference Kastelein, Ross and Hayden2013). There are currently five gene therapy treatments approved in Europe – Luxturna (for individuals with an inherited retinal disease caused by mutations in both copies of the RPE65 gene), Zolgensma (to treat spinal muscular atrophy), two chimeric antigen receptor T cell therapies (Yescarta – to treat non-Hodgkin lymphoma and Kymriah for the treatment of adult patients with relapsed or refractory follicular lymphoma) and Strimvelis (the gamma-retrovirus for adenosine deaminase-severe combined immunodeficiency). Multiple other clinical trials are ongoing to identify candidate genes amenable to in vivo gene therapies (Ylä-Herttuala, Reference Ylä-Herttuala2012; Mendell et al., Reference Mendell, Al-Zaidy and Rodino-Klapac2021). The recent advent of genome editing uses a different approach to correct genetic differences by introducing molecular tools to change existing DNA. Approaches include the use of zinc finger nucleases (Lee et al., Reference Lee, Cho, Huang, Xu, Quach, Hu and Wong2021), transcription activator-like effector nucleases (Carroll, Reference Carroll2011) and clustered regularly interspaced short palindromic repeats (Liu et al., Reference Liu, Gaj, Yang, Wang, Shui, Kim, Kanchiswamy, Kim and Barbas2015), which has catalysed the implementation of new clinical trials of gene editing to cure rare and common diseases in children that otherwise result in death in childhood or early adulthood (Lee et al., Reference Lee, Jeong, Lee, Jung, Shin, Kim, Lee, Jung, Kim, Kim and Kim2018, Reference Lee, Gillam, Visvanathan, Hansford and McCarthy2021).
Biomarker-driven directed therapies in the treatment of cancer in children are still early in development. A recent systematic review reporting on the clinical utility of precision medicine in the treatment of paediatric cancer has reported on the use of molecular techniques such as array comparative hybridisation, immunohistochemistry, next-generation sequencing, RNA sequencing, single nucleotide polymorphism array, targeted panel-based sequencing, whole-exome sequencing and WGS to identify genomic targets, which have guided the allocation of targeted drugs (Salsman and Dellaire, Reference Salsman and Dellaire2017). Genes which are transcribed in any one condition are known as the transcriptome; the process of determining the genetic codes contained in the transcriptome and their relative proportions is known as transcriptome sequencing or transcriptomics. Of note, the value of understanding the transcriptome has been highlighted through the increased benefit of identifying additional therapeutic targets that were not identified by genomic analysis alone (Marks et al., Reference Marks, Oberg, Pendrick, Sireci, Glasser, Coval, Zylber, Chung, Pang, Turk, Hsiao, Mansukhani, Glade Bender, Kung and Sulis2017; Uddin et al., Reference Uddin, Rudin and Sen2020). Targetable mutations were found in 48.0% of patients, with 41.7% who received targeted drugs demonstrating an objective response. However, accessibility and thus inequity of access to targeted oncological therapies were cited as problematic, with only 27% of patients receiving targeted treatments, highlighting the gulf between access to precision diagnostics and subsequent therapies (Salsman and Dellaire, Reference Salsman and Dellaire2017). Challenges cited included the need to access drugs off-licence due to lack of paediatric dosing schedules, lack of access to clinical trials at their treating centre or ineligibility owing to advanced disease or trial restriction to adult patients or the dependency on compassionate access facilitated by pharmaceutical companies (Salsman and Dellaire, Reference Salsman and Dellaire2017). Moreover, heterogeneity in outcome reporting across clinical trials may lead to bias in the interpretation of treatment effect. This underpins the need for consistency in clinical trial outcomes to clearly define the clinical and economic value of a precision diagnostics approach to targeted therapy. Large molecular databases and global collaborative clinical trials have been assembled to help demonstrate the value of targeted therapies which will in turn enhance our understanding in the future and improve access to novel therapies (Pincez et al., Reference Pincez, Clément, Lapouble, Pierron, Kamal, Bieche, Bernard, Fréneaux, Michon, Orbach, Aerts, Pacquement, Bourdeaut, Jiménez, Thébaud, Oudot, Vérité, Taque, Owens, Doz, Le Tourneau, Delattre and Schleiermacher2000; Brien et al., Reference Brien, Valerio and Armstrong2016; Harttrampf et al., Reference Harttrampf, Lacroix, Deloger, Deschamps, Puget, Auger, Vielh, Varlet, Balogh, Abbou, Allorant, Valteau-Couanet, Sarnacki, Gamiche-Rolland, Meurice, Minard-Colin, Grill, Brugieres, Dufour, Gaspar, Michiels, Vassal, Soria and Geoerger2017; Linzey et al., Reference Linzey, Marini, Pasternak, Smith, Miklja, Zhao, Kumar-Sinha, Paul, Harris, Robertson, Hoffman, Chinnaiyan, Mody and Koschmann2018; George et al., Reference George, Izquierdo, Campbell, Koutroumanidou, Proszek, Jamal, Hughes, Yuan, Marshall, Carceller, Chisholm, Vaidya, Mandeville, Angelini, Wasti, Bexelius, Thway, Gatz, Clarke, Al-Lazikani, Barone, Anderson, Tweddle, Gonzalez, Walker, Barton, Depani, Eze, Ahmed, Moreno, Pearson, Shipley, Jones, Hargrave, Jacques, Hubank and Chesler2019; Hansford, Reference Hansford2019; Gojo et al., Reference Gojo, Pavelka, Zapletalova, Schmook, Mayr, Madlener, Kyr, Vejmelkova, Smrcka, Czech, Dorfer, Skotakova, Azizi, Chocholous, Reisinger, Lastovicka, Valik, Haberler, Peyrl, Noskova, Pál, Jezova, Veselska, Kozakova, Slaby, Slavc and Sterba2020).
A digital approach to precision diagnostics and medicine in paediatrics
The value of population data
The focus on precision diagnostics in paediatrics has largely focused on the value of data derived from ‘omics’ profiling as a means of supporting diagnoses, risk stratification, directing therapy (pharmacogenomics and pharmacometabolomics) and determining prognostic indicators. While this approach is revolutionising the way patients are managed, a systems medicine approach should also consider data relating to patient populations and external factors that may impact on patient diagnosis, monitoring and care. While precision in the initial diagnosis enhances a personalised approach to therapy, longitudinal patient monitoring facilitates a dynamic precision approach to ‘diagnosing’ new or recurrent issues. As medical device development in paediatrics and child health gathers momentum (Vo et al., Reference Vo, Parsons and Seibel2020), the value of personalised and population data to direct patient management is attracting attention. For example, large data sets relating to growth in children combined with automated systems for monitoring linear growth are facilitating the early identification of growth disorders of children at a greater frequency while reducing the number of referrals of children with normal growth parameters (Dimitri, Reference Dimitri2019). For patients diagnosed with disorders of growth requiring growth hormone therapy, large-scale data acquisition using a growth hormone delivery device with a connected monitoring platform (easypod™), which automatically transmits adherence data via an online portal (easypod™ connect), can be used to predict an individual’s response to therapy relative to a population undergoing the same treatment, by integrating methodologies such as machine learning. This facilitates clinical decision support to modify therapy in individual patients (Sankilampi et al., Reference Sankilampi, Saari, Laine, Miettinen and Dunkel2013). This approach of using patient population data to ‘diagnose’ poor adherence to therapy has been a catalyst to develop a digital ecosystem to support service users and service providers in the management of growth disorders requiring growth hormone therapy. This includes the development of online educational materials to support digital literacy and patient management (Dimitri et al., Reference Dimitri, Fernandez-Luque, Koledova, Bagha and Shabbir2000; Su et al., Reference Su, Malik, Jheeta, Lin, Su, Koledova and Graham2022), digital monitoring of patient therapy, adherence and preferences (Boman et al., Reference Boman, Fernandez-Luque, Koledova, Kause and Lapatto2021; Spataru et al., Reference Spataru, Quarteroni, Arnaud, van Dommelen, Koledova and Le Masne2021; Spataru et al., Reference Spataru, van Dommelen, Arnaud, Le Masne, Quarteroni and Koledova2022; van Dommelen et al., Reference van Dommelen, Koledova and Wit2018; Koledova et al., Reference Koledova, Tornincasa and van Dommelen2020) and the development of a framework to guide future digital developments including automated referral pathways, enhanced digital communication, digital medical and psychological support, gamification to support adherence to therapy, access to digital resources, digital reporting of patient reported outcomes and safety and assessment reporting (Dimitri et al., Reference Dimitri, Fernandez-Luque, Banerjee, Bergadá, Calliari, Dahlgren, de Arriba, Lapatto, Reinehr, Senniappan, Thomas-Teinturier, Tsai, Anuar Zaini, Bagha and Koledova2021).
The application of machine learning in paediatric diagnosis
The advent of machine learning has facilitated the use of large patient data sets or ‘training sets’ to generate and refine predictive models. Large data sets already exist in multiple areas of medicine, and these are now being employed to predict disease in children. Machine learning has superior capabilities to traditional data analytics methods based upon the ability to rapidly process large volumes of complex data, to explore and extrapolate data relationships through pattern recognition that are not recognisable through other methods and improving efficiency and accuracy through data acquisition. Deep learning lets the data train the computer leading to predictive models that become stronger as more data are added. Imaging data sets that are being used in paediatrics to predict abnormalities have been frequently reported in the literature in the last few years with examples including (but not limited to) the detection of abnormalities in chest radiographs (Chen et al., Reference Chen, Yu, Chen, Lin, Lee, Chen, Jiang, Su, Tsai, Tsai, Tsai and Lu2020; Padash et al., Reference Padash, Mohebbian, Adams, Henderson and Babyn2022), the diagnosis of effusions in elbow joints (Huhtanen et al., Reference Huhtanen, Nyman, Doncenco, Hamedian, Kawalya, Salminen, Sequeiros, Koskinen, Pudas, Kajander, Niemi, Hirvonen, Aronen and Jafaritadi2022), the detection of intracranial pathology on CT imaging, defining abnormalities as critical or non-critical (Titano et al., Reference Titano, Badgeley, Schefflein, Pain, Su, Cai, Swinburne, Zech, Kim, Bederson, Mocco, Drayer, Lehar, Cho, Costa and Oermann2018), the assessment of left ventricular function from birth to 18 years and the diagnosis of coronary artery lesions in Kawasaki disease using echocardiography (Lee et al., Reference Lee, Eun, Hwang and Eun2022; Zuercher et al., Reference Zuercher, Ufkes, Erdman, Slorach, Mertens and Taylor2022) and the assessment of paediatric brain tumours (Grist et al., Reference Grist, Withey, Bennett, Rose, MacPherson, Oates, Powell, Novak, Abernethy, Pizer, Bailey, Clifford, Mitra, Arvanitis, Auer, Avula, Grundy and Peet2021; Huang et al., Reference Huang, Shlobin, Lam and DeCuypere2022). However, challenges remain in achieving an acceptable diagnostic accuracy and the acquisition of adequate training data sets in cohorts, particularly in relation to rare diseases due to the size of the patient populations. Machine learning has been applied to continuous conventional electroencephalography to determine the diagnostic accuracy of detecting and monitoring seizures in neonates. The artificial intelligence (AI) platform did not improve the number of neonates diagnosed with seizures, although the quantification of seizure burden was greater, thus providing a more accurate means of monitoring seizure frequency and duration (Pavel et al., Reference Pavel, Rennie, de Vries, Blennow, Foran, Shah, Pressler, Kapellou, Dempsey, Mathieson, Pavlidis, van Huffelen, Livingstone, Toet, Weeke, Finder, Mitra, Murray, Marnane and Boylan2020). The DeepGestalt platform utilising computer vision and deep-learning algorithms for facial image analysis has shown great potential in the phenotypic evaluation of syndromes including the initial syndromic diagnosis and the ability to subclassify different genetic subtypes within the same syndromic diagnosis (Gurovich et al., Reference Gurovich, Hanani, Bar, Nadav, Fleischer, Gelbman, Basel-Salmon, Krawitz, Kamphausen, Zenker, Bird and Gripp2019). Patient population data have also been used to detect the earlier presentation of more common presentations in paediatrics such as neonatal sepsis (Masino et al., Reference Masino, Harris, Forsyth, Ostapenko, Srinivasan, Bonafide, Balamuth, Schmatz and Grundmeier2019), the risk stratification of infants with bronchiolitis to promptly identify infants at risk of deterioration (Raita et al., Reference Raita, Camargo, Macias, Mansbach, Piedra, Porter, Teach and Hasegawa2020), improving the prediction of clinical outcomes in children presenting to the emergency department with the benefit of better identifying critically ill children while reducing the over triaging of children who are less ill (Raita et al., Reference Raita, Camargo, Macias, Mansbach, Piedra, Porter, Teach and Hasegawa2020) and predicting the need for hospitalisation of children with asthma (Patel et al., Reference Patel, Chamberlain and Chamberlain2018). Notably, this last example also demonstrates the value of using environmental data including weather data, population influenza patterns and socioeconomic status to improve predictability. The value of data acquisition from social media platforms on smartphones and tables to support the diagnosis of mental health issues in young people and adults such as depression (Reece and Danforth, Reference Reece and Danforth2017), post-traumatic stress disorder (Reece et al., Reference Reece, Reagan, Lix, Dodds, Danforth and Langer2017) and schizophrenia (Hänsel et al., Reference Hänsel, Lin, Sobolev, Muscat, Yum-Chan, De Choudhury, Kane and Birnbaum2021) highlights the value of ‘non-health’ data to support diagnoses. Given that children and young people are high users of technology and social media, it is important to consider the value of personalised data on commercially available platforms in the future to inform on health, well-being and disease. For example, Instagram posts have been used as a predictor of physical activity in adults aged 18–30 years (Liu et al., Reference Liu, Perdew, Lithopoulos and Rhodes2021). A comprehensive review of machine learning in paediatrics is covered elsewhere (Clarke et al., Reference Clarke, Parmesar, Saleem and Ramanan2022), supporting the value of AI in the field of precision diagnostics in paediatrics. As electronic patient records become the global norm, it is likely that the application AI will become fundamental to improve diagnosis and clinical outcomes. However, large and accurate data sets will be required to ensure accurate outputs from machine learning models, and legal and governance structures will need to be in place to regulate the use of these data. Given the relative paucity of large paediatric data sets, interoperability across systems and continental or global data sharing of high-quality data may be required to optimise outputs but will also need to be viewed in the context of generalisability across different healthcare systems.
Future perspectives
Advances in science, technology and data acquisition and analytics have already revolutionised paediatric healthcare. Adopting the ‘4P’ systems medicine approach (Hood et al., Reference Hood, Heath, Phelps and Lin2004; Flores et al., Reference Flores, Glusman, Brogaard, Price and Hood2013) to ensure healthcare is preventive, predictive, participatory and personalised will rely on the combination of multiple data sources and methodologies to maintain health and prevent and predict disease. The value of combining ‘omics’ data has advanced the science of molecular precision medicine to understand the molecular basis of disease, the risk of developing it and its evolution and pathogenesis in children; the speed of technological advances in genomics, proteomics, transcriptomics and AI will further improve this (Williams et al., Reference Williams, Liu, Regner, Jotterand, Liu and Liang2018; García-Foncillas et al., Reference García-Foncillas, Argente, Bujanda, Cardona, Casanova, Fernández-Montes, Horcajadas, Iñiguez, Ortiz, Pablos and Pérez Gómez2021).
The future of precision diagnostics in paediatrics will in part rely on data acquisition that has not been commonly used in medicine before from commercial and social media platforms, and from environmental sources, knowing that the lifestyle, demographic and environmental factors play a key role in the presentation and course of disease. Personalised platforms on mobile phone and tablets, digital monitoring systems within homes and remote sensing technologies can gather data on personal habits, lifestyle, physical activity and diet, and environmental data such as climate data can be derived from institutional sources. Collectively, these factors are encapsulated in the concept of the ‘exposome’. The ‘exposome’ has been proposed as a new paradigm to encompass the totality of human environmental (non-genetic) exposures from conception onwards (Wild, Reference Wild2012; Vrijheid, Reference Vrijheid2014). Thus, the exposome will play a fundamental part in precision diagnostics through the life course. In this context, there is a value in collecting data from early life to truly reflect response to changes in the exposome which in turn support disease prevention, predicting the future risk of developing a disease and supporting the management of disease in an environmental context once established. To facilitate this, advances in sensing devices and other technologies will facilitate collection and remote access to these data (Martin Sanchez et al., Reference Martin Sanchez, Gray, Bellazzi and Lopez-Campos2014). In addition to the challenges of collecting and extrapolating data, data analytics and combining data sets, the context in which these data are derived must be considered. Despite the inherent affinity children and young people have with technology, there is a reluctance in this population to simply ‘hand over’ data, and data acquisition must be contextualised to social context. Children and young people are reluctant to use technology to collect health data if this does not fit in with social norms, thus risking discrimination. Concerns have been raised surrounding data sharing, use and confidentiality of personal information, with children and young people needing reassurance that their data are being used by trusted organisations, that it is stored safely and securely, with a desire to control their data and privacy, and to minimise the risk of misinterpretation of their health data, particularly in the context of them having a health-related problem (Blower et al., Reference Blower, Swallow, Maturana, Stones, Phillips, Dimitri, Marshman, Knapp, Dean, Higgins, Kellar, Curtis, Mills and Martin-Kerry2020). Participation is framed as a central tenet of personalised medicine. As we move in the direction of whole-scale personalised medicine that involves the collection and storage of data in children, particularly in relation to the future predictability or certainty of disease, the ethical and legal considerations around informed consent, data protection, autonomy and privacy must be considered carefully, particularly for those who are not old enough to express their views. There are potential legal and ethical ramifications from extensive testing and use of data that may have unintended consequences, for example, the identification of a secondary disease that does not manifest until adulthood, that in turn may limit testing in children. Thus, guidelines on how to pursue children’s participation in personalised medicine would be of benefit in the future (Ó Cathaoir, Reference Ó Cathaoir2021).
Conclusion
The way data is changing health and healthcare in the 21st century could be likened to the impact that the introduction of antibiotics had on bacterial disease in the early 20th century – it will save and improve many lives. We have already seen significant advances in diagnosis, drug discovery and interventions in healthcare as a result of advances in the ‘omics’ and cytogenetics. The future acquisition and combination of data from multiple sources including data from individuals, their lifestyle behaviours and their immediate and wider environments will add to our understanding of the manifestation of health and disease over time. A life course approach starting from birth or even in the prenatal period is required to understand how human development and changes in the environment lead to future health and ill-health, and how environmental and physical factors impact on congenital and acquired diseases of childhood. As we advance in the field of personalised medicine, we need to consider a paradigm shift in precision diagnostics to incorporate the need to ‘sustain health’ in addition to accurately diagnosing disease. In this respect, this shift in diagnostics should encompass the need to ‘diagnose’ good health. The principle of ‘start early and change more’ underpins the value of applying personalised medicine early in life for a better future.
Open peer review
To view the open peer review materials for this article, please visit http://doi.org/10.1017/pcm.2023.4.
Competing interest
The author declares no conflict of interest.
Open access
Comments
Dear Editor
Thank you for inviting me to submit my manuscript on precision diagnostics in paediatrics. I trust that you will find my manuscript an accurate reflection on the current advances in the sciences
Yours Sincerely
Paul Dimitri