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Prospective care: a personalized, preventative approach to medicine

Ralph Snyderman1† & Ziggy Yoediono2

†Author for correspondence1Duke University, Duke University Medical Center, Box 3059, Durham, NC 27710, USAE-mail: snyde001@mc.duke.edu2Duke University, Center for Research onProspective Health Care, Durham, NC 27710, USA

10.2217/14622416.7.1.5 © 20

“We now have emerging capabilities to understand each

individual's risk of developing disease, for tracking this risk over

time, and for recommending appropriate therapeutics”

Need for transformation to a prospective healthcare systemThe concept of prospective healthcare beganevolving over a decade ago, when it becameincreasingly apparent that the US healthcare sys-tem’s focus on treating late-stage disease ratherthan preventing it was a major impediment to itseffectiveness. As a consequence, almost 75% ofthe money spent on healthcare in the USA is onlate-stage chronic disease. It was also becomingobvious that amongst the most important conse-quences of the genomic revolution was theincreasing capability to predict health risks, totrack the evolution of disease from a state ofhealth and to intervene early. Despite the pro-found impact that these new capabilities couldhave on the practice of medicine, neither leadersof medical education nor healthcare providerswere positioning themselves to utilize them toimprove care. Simplified, neither the healthcaresystem at Duke University (NC, USA), norother institutions throughout the USA, werepositioned to take advantage of the emergingcapabilities that, for the first time in history, werepermitting personalized, preventative approachesto care.

As a leader of one of the nation’s major aca-demic health systems, I felt obligated to deliverthe message and facilitate change. Indeed, thetransformation of medicine by science a centuryago was led by the development of the modernday academic medical center structure, whichhas been the main vector through whichprogress, as well as the current disease-orientedpractice of medicine, has come about. It is natu-rally the responsibility of academic medicine tosupport and develop the changes whereby medi-cal education and care can now be based on theemerging capabilities to practice prospectively,

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based on determining each individual’s healthrisks and developing plans to mitigate them –‘prospective care’ [1].

Our opportunities for productive change arestrikingly reminiscent of medicine roughly a cen-tury ago, when scientific discoveries were firstdeveloping powerful applications to the practiceof medicine; for example, microbiology, roent-genology and biochemistry. However, initially theprofession did not welcome these advances, andindeed resisted them. It was not until many dec-ades after fundamental discoveries, showing thepower of science to understand disease, that theywere incorporated into the training of physiciansand the practice of medicine. Similarly, today wehave emerging capabilities to understand eachindividual’s risk of developing disease, for trackingthis risk over time, and for recommending appro-priate therapeutics. Nonetheless, the healthcaresystem is not yet poised or positioning itself tochange its orientation from a disease-based to aprospective personalized approach.

Disease concept evolution from reductionism to emergenceCurrent approaches to healthcare are largelyreductionist, based on the concept that a disease isdue to a pathologic event initiated by a specificinitiating factor, and that if the defect is found, itcan be reversed and fixed. This concept of ‘find itand fix it’ is a natural consequence of the applica-tion of reductionist science into the practice ofmedicine approximately a century ago. The majortransformation of medicine from one of mythol-ogy and anecdotal observation to scientific experi-mentation was driven by discoveries by greatssuch as Koch, Lister and Pasteur, who founded theconcept of ‘germ theory,’ which explained manyof the major diseases of that era. This indicatedthat single initiating agents could lead to majorand, at times, catastrophic host responses. Thereductionist approach has been a powerful tool infacilitating the broad range of scientific disciplinessupporting medicine. This approach has enabledmedicine to reverse some diseases, prolong lifeand, at times, effect wondrous cures. However, thereductionist approach to medicine is limited inthat the evolution of virtually all diseases is more

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complicated (Figure 1). Disease development,including infectious disease, is based on an indi-vidual’s inborn baseline susceptibilities, followedby environmental exposures that convert thesepotential susceptibilities into the development ofdisease under certain conditions. By focusing pri-marily on the reductionist model of disease devel-opment, the US healthcare system has,unfortunately, not effectively dealt with complexchronic diseases. At present, individuals with fiveor more chronic conditions account for roughlytwo-thirds of all healthcare expenses. A morerational approach to healthcare would be person-alized prevention and, when necessary, interven-tion at the earliest possible stage before irreversiblepathology sets in (Figure 2).

“A key feature of prospective care is strategic personalized

health planning.”

Incorporating genomic medicine into personalized health planningA key feature of prospective care is strategic per-sonalized health planning. This requires a healthrisk assessment, including determining an indi-vidual’s baseline susceptibility to disease, theircurrent health status and current risks for major,chronic or uniquely inherited diseases. To effectrational care, the individual and their providershould develop a strategic health plan to mitigaterisk and track health status in order to determineif any particular diseases are developing [2–4].

Many of the technologies that enable prospec-tive care – risk assessment, disease tracking andpersonalized therapy – are now evolving, andwere not even available ten years ago. The capa-bilities for this evolution will stem, in part, fromgenomics, proteomics, metabolomics, advancedmedical technologies, and the ability to store andanalyze large amounts of data to be used topredict susceptibility and risk (Figure 3).

Identifying predictive biomarkers requires pre-dictive models, which are statistical representa-tions of measured outcomes of multiple pastevents. These models, which utilize mathemati-cal equations, distributions or rules, have enabledthe forecasting of many nonhealthcare events,such as stock market trends and satellite orbitpaths. For biomarker identification, the predic-tive models consist of risk predictor variablesmined from particular clinical cohorts, to whichmyriad procedures for establishing models can beapplied. Such technology, along with the incor-poration of emerging biomarker analysis, willenable medicine to utilize causative predictivedata in a prospective manner.

Research is already identifying how biologiccomponents, including genes, act as networks toregulate various metabolic processes, includingthe development of those that are pathogenic.The measurement of components of the networkin health and disease will provide a rich source ofpredictive biomarkers.

Genomic medicine will thus be a catalyst ena-bling strategic personalized health planning. Dueto the success of the Human Genome Projectand the advancement of high-throughput ‘omics’

Figure 1. Concepts of disease.

Reductionism: single factor

Emergence: multiple factors

Causative factor Disease

Baseline risk

Environmentalfactors

Preclinicalprogression

Diseaseinitiation

Diseaseprogression

Irreversible damage

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Prospective care: a personalized, preventative approach to medicine – EDITORIAL

technology, the elucidation of disease patho-genesis at the molecular level has made enor-mous strides. Such understanding facilitates adomino effect of improvement: identification ofnew biomarkers, which improves the sorting ofmyriad biological data, which then allows theproduction of a best fit model for quantifyingdisease evolution and event prediction. Theoverall effect is the increasingly more sophisti-cated and accurate incorporation of genomicdata as a risk predictive and tracking tool.

“Genomic medicine will provide a rich source of predictive

biomarkers and will thus be a catalyst enabling strategic

personalized health planning.”

Currently, clinical data, family history, clinicallaboratories, and genetic pedigree are the maintypes of data utilized for baseline risk assessment.

Unfortunately, the accuracy of such traditionalmethods is generally limited to ‘you are at anincreased risk for disease/event X,’ with no quan-titative time or probability parameters provided.

The prospective strategic personalized healthplan would increase the predictive accuracy andvalidity of baseline risk by incorporating morecausal biomarkers, including inherited genomicsand dynamic factors, such as gene expression,proteomics, and so on. Single nucleotide poly-morphisms (SNPs), haplotype mapping, andgene sequencing for abnormal alleles are alreadyproviding good baseline risk data in selectiveareas. As an example, SNPs are the main type ofclinically usable biomarker data, for example, forthe diagnosis of Mendelian diseases such as longQT syndrome and cystic fibrosis [5].

The translation of genomic medicine into aprospective strategic personalized health planrequires decision-support tools. Quantitativeassessment by such tools would likely encom-pass three disease progression phases and anintervention phase: baseline risk, preclinical

Figure 2. The development of chronic disease and opportunities for intervention.

Disease burden generally increases over time and is correlated with less reversibility and greater cost of intervention.

Co

st

1/re

vers

ibili

ty

Time

Dis

ease

bu

rden

Baseline risk Initiatingevents

Earliest moleculardetection

Earliest clinicaldetection

Typical currentintervention

Figure 3. Risk assessment process.

SNP: Single nucleotide polymorphism.

Query largeclinical databases:clinical, genotype/SNPs, geneexpression, proteomic,metabolomic, imaging

Identify mostimportant risk predictivebiomarkers

Analyzebiomarkersfor correlationto disease/event onset

Identify bestrisk predictivemodel

Integratepatient data

Analyze risk

Predict outcomes

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Figure 4. Tools requ

SNP: Single nucleotide po

Decisionsupport tools:

Dis

ease

bu

rden

Sources of newbiomarkers:

StabSNPsHaplGene

Ba

progression, disease initiation/progression, andtherapeutic decision support (Figure 4).

“The translation of genomic medicine into a prospective

strategic personalized health plan requires decision-support tools.”

Baseline risk can lead to preclinical progressionas a consequence of initiating events taking effect.Traditional clinical medicine has only recentlybegun to formally assess this stage in selective areas,predominantly for cardiovascular and selectivecancer risk. A prospective strategic personalizedhealth plan will broaden such capabilities and for-malize the concept by incorporating dynamicgenomics: gene expression, proteomics, metabo-lomics, and molecular imaging. Obviously, thesefields are still in their infancy with regard to predic-tive value, but it can be assumed that progress willbe rapid and facilitated by technologic advances.Subsets of genomic medicine are actually in cur-rent use, especially in cardiology for applicationssuch as the development of atherosclerosis [6].

Although SNPs are mainly utilized, gene expres-sion patterns are often used in oncology to distin-guish between tumors that appear histologically

similar. Oncogenomics has witnessed the emer-gence of numerous technologies. For example,diagnostic tools have been developed to quantita-tively predict disease recurrence among breast can-cer patients and assess the likelihood of beneficialresponse from certain chemotherapies. Geneexpression has also been adapted to predict thelikelihood of heart transplant rejection [7].

Two technologies have facilitated the utiliza-tion of proteomics. The first is 2-D gels, whichidentify proteins correlated with particular dis-eases. Second is mass spectroscopy, which todayenables the sifting of myriad peptide and pro-tein structures from biologic fluids and diseasetissues. As a result, proteomics has alreadyfound a clinical utilization in the risk stratifica-tion of individuals with acute coronarysyndromes [8–10].

Nuclear magnetic resonance and mass spec-troscopy have enabled the quantitative analysisof various types of metabolites and even metabo-lite patterns, which are required for disease riskprediction/tracking. Metabolomics has beenutilized to risk stratify those with coronary arterydisease [11,12].

From preclinical progression, the disease bur-den may advance to actual disease initia-tion/progression. Typical medical intervention

ired for prediction and personalized care.

lymorphism.

Assess risk Refine assessment Predict/diagnose Monitor progressionPredict eventsInform therapeutics

Time

Co

st

1/re

vers

ibili

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le genomics:

otype mapping sequencing

Dynamic genomics:Gene expressionProteomicsMetabolomicsMolecular imaging

seline risk Preclinicalprogression

Disease initiation and progression

Therapeutic decision support

Baseline risk Initiatingevents

Earliestmoleculardetection

Earliestclinicaldetection

Typicalcurrentintervention

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Prospective care: a personalized, preventative approach to medicine – EDITORIAL

usually occurs during the latter stages of diseaseprogression, when disease burden is welladvanced and less amenable to intervention.Clinical features, histopathology and diagnosticimaging are currently the main tools utilized atthis stage, and would continue to be an integralpart of the prospective strategic personalizedhealth plan. However, they will be markedlyimproved by algorithmic disease tracking toolsmeasuring causative biomarkers.

Pharmacogenomics is an integral area con-cerned with therapeutic decision support, andsuch genomic data should be utilized in severalways. The first way is to utilize genetic markersto predict individuals who will or will notrespond beneficially to the drug. For example,recent data have shown that genetic variationsinfluence factors such as cholesterol production,lipoprotein catabolism, and intestinal choles-terol absorption, which in turn influence statinresponsiveness and drug metabolism. The sec-ond way is to use such data to decrease drugtoxicity by identifying groups of patients whoare most vulnerable to complications, therebyprecluding a scenario of minimal benefit butsignificant adverse reactions [13,14]. The thirdway is to analyze gene expression in diseased or

surrogate tissues to inform the aggressiveness ofthe disease (for example, cancer), as well as themost appropriate therapeutic regimen.

Individual responsibilityProspective care requires not only a major changein the healthcare delivery system (as well as reim-bursement and many legal protections), but alsoprovides a major opportunity and responsibilityon the part of the individual. A major responsibil-ity for mitigating risk will rest on the individualwho will clearly need the support of an appropri-ate healthcare system. Much of this will be in edu-cation, in addition to mentoring when necessary.Full implementation of this approach, includingan improved understanding of inherited suscepti-bilities, measures of disease progression and thera-peutic decision support, will take many years to beimplemented. Nonetheless, the emerging capabil-ities resulting from genomic research are provid-ing some useful tools now, and will progressivelymove healthcare from a reactive disease-orientedmodel to a more strategic, prospective approach.

DisclosuresRalph Snyderman, MD, is the Chairman and Founder ofProventys, Inc, and sits on the Board of Directors of XDx, Inc.

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