diabetes

Precision Medicine in Diabetes: A Consensus Report From the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) Diabetes Care 2020;43:1617–1635 | https://doi.org/10.2337/dci20-0022

The convergence of advances in medical science, human biology, data science, and technologyhasenabledthegenerationofnewinsightsintothephenotypeknownas “diabetes.” Increased knowledge of this condition has emerged from populations aroundtheworld, illuminating thedifferences inhow diabetespresents, itsvariable prevalence, and how best practice in treatment varies between populations. In parallel, focus has been placed on the development of tools for the application of precision medicine to numerous conditions. This Consensus Report presents the American Diabetes Association (ADA) Precision Medicine in Diabetes Initiative in partnership with the European Association for the Study of Diabetes (EASD), including its mission, the current state of the field, and prospects for the future. Expert opinions are presented on areas of precision diagnostics and precision therapeutics (including prevention and treatment), and key barriers to and oppor- tunities for implementation of precision diabetes medicine, with better care and outcomes around the globe, are highlighted. Cases where precision diagnosis is already feasible and effective (i.e., monogenic forms of diabetes) are presented, while the major hurdles to the global implementation of precision diagnosis of complex forms of diabetes are discussed. The situation is similar for precision therapeutics, in which the appropriate therapy will often change over time owing to the manner in which diabetes evolves within individual patients. This Consensus Report describes a foundation for precision diabetes medicine, while highlighting what remains to be doneto realize its potential. This, combined witha subsequent, detailedevidence- based review (due 2022), will provide a roadmap for precision medicine in diabetes that helps improve the quality of life for all those with diabetes.

RATIONALE FOR PRECISION MEDICINE IN DIABETES

The practice of medicine centers on the individual. From the beginning, the physician has examined the patient suffering from illness, ascertained his/her signs and symptoms, related them to the medical knowledge available at the time, recognized patterns that fit a certain category and, based on the practical wisdom accumulated via empirical trial and error, applied a given remedy that is best suited to the situation at hand. Thus, the concept of precision medicine, often defined as

1Department of Pediatrics, Columbia University Irving Medical Center, New York, NY 2Department of Medicine, Columbia University Irving Medical Center, New York, NY 3American Diabetes Association, Arlington, VA 4Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 5Diabetes Unit, Massachusetts General Hospital, Boston, MA 6Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, MA 7Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 8Department of Medicine, Harvard Medical School, Boston, MA 9Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, U.K. 10Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 11National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 12Wellcome Centre for Human Genetics, Univer- sity of Oxford, Oxford, U.K. 13Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K. 14School of Medicine, Trinity College, Dublin, Ireland 15Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 16Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, Uni- versity of Dundee, Dundee, Scotland, U.K. 17DepartmentofMedicine,UniversityofChicago, Chicago, IL 18Department of Pediatrics, University of Chi- cago, Chicago, IL 19Duke University School of Medicine, Durham, NC 20CenterforPublicHealthGenomics,Universityof Virginia, Charlottesville, VA

Wendy K. Chung,1,2 Karel Erion,3

Jose C. Florez,4,5,6,7,8 Andrew T. Hattersley,9

Marie-France Hivert,5,10 Christine G. Lee,11

Mark I. McCarthy,12,13 John J. Nolan,14

Jill M. Norris,15 Ewan R. Pearson,16

Louis Philipson,17,18 Allison T. McElvaine,19

William T. Cefalu,11 Stephen S. Rich,20,21

and Paul W. Franks22,23

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providing the right therapy for the right patient at the right time, is not novel. What has changed radically is our ability to characterize and understand human biological variation through 1) assess- ment of the genetic and metabolic state, 2) leveraging data to inform disease cat- egories, and 3) science-guided preventive and treatment decisions tailored to spe- cific pathological conditions. Coupling these with detailed information about lifestyleandenvironment,availablethrough digital devices and technologies that collect those measures, as well as data abstracted from electronic medical re- cords, present unparalleled opportuni- ties to optimize diabetes medicine. Diabetes mellitus is diagnosed by the

presence of hyperglycemia that is higher than a threshold blood glucose concen- tration which predisposes to microvas- cularend-organcomplications.However, hyperglycemia is the end product of nu- merouspathophysiologicalprocessesthat often emerge over many years and con- verge on the inability of the pancreatic b-cells to secrete enough insulin to meet the demands of target tissues. In clinical practice, absolute insulin deficiency can be detected from the autoimmune de- struction of b-cells in type 1 diabetes (T1D), which represents ;10% of all diabetes cases. Making the diagnosis of T1D is critical for survival, given the therapeutic requirement of exogenous administration of insulin. However, less commonly, hyperglycemia might derive from an inherited or de novo loss of function in a single gene (e.g., mono- genic diabetes, comprising 2–3% of all diabetes diagnosed in children or young adults). Diabetes can also appear after pancreatitis or organ transplantation, during pregnancy, or as a result of cystic fibrosis. Most individuals with diabetes, however, are likely to be diagnosed with type 2 diabetes (T2D), which includes defects in one or (more often) multiple physiological pathways (e.g., b-cell

insufficiency,fataccumulationormiscom- partmentalization, inflammation, incretin resistance, dysfunctional insulin signaling).

Our modern capacity to comprehen- sively interrogate diverse axes of biology has facilitated the approach of studying anindividualtoinfergeneralprinciples,from which a discrete treatment plan is se- lected. These axes include developmental/ metabolic context, genomic variation, chromatin signals that mark genes as active or repressed in tissues, expressed transcripts, biomarkers of disease, and in- creasedknowledgeoflifestyle/environmental risk factors. Parallel advances in compu- tational power and analytical methods required to appropriately interrogate “big data” are driving insights that may radically transform the practice of med- icine. Yet, at this time, the individual physicianoftenlacksthetimeandtraining needed to incorporate these insights into medical decision making. Thus, the trans- lation of the rapidly accumulating new knowledge into practice requires careful evaluation and translational strategies involving specialist training, education, and policy considerations.

The failure to adequately understand thediversemolecularandenvironmental processes that underlie diabetes and our inability to identify the pathophysiolog- ical mechanisms that trigger diabetes in individual patients limit our ability to pre- vent and treat the disease. Public health strategies have struggled to slow the ep- idemic, even in countries with the greatest financial and scientific resources. Pharma- cologicaltherapies,comprising12different drugclassescurrentlyapprovedbytheU.S. Food and Drug Administration (FDA), may, at best, control blood glucose and modify diseasecoursebutdonotprovideacureor result in the remission of disease. More- over, these agents are sometimes pre- scribed based on nonmedical considerations (cost, side effects, patient preference, or comorbidities), which may overlook the biological mechanism. Thus, more people

are developing diabetes worldwide and have disease progressing to complica- tions, incurring a significant health care burden and cost.

There are, however, several reasons for hope. First, diabetes caused by single gene defects can be characterized and targeted therapies are particularly effec- tive (1,2). Second, islet autoantibody bio- markers and genomic risk have clarified autoimmune diabetes from other forms of the disease (3,4), thereby facilitating immune intervention trials and preonset monitoring to reduce risk of severe com- plications and aiding in detection of en- vironmental triggers (5). Third, multiple biomarkers and genetic variants have been shown to alter risk of T2D, re- vealing previously unsuspected biolog- ical pathways and providing new targets. Fourth, T2D has been shown to be a complex combination of multiple con- ditionsandprocesses,definedbyprocess- specific subgroups in which individuals with extreme burdens ofrisk inparticular pathways reside and for whom a specific therapeutic approach may be optimal (6). Finally, the tools, resources, and data now exist to determine the bio- logical and lifestyle/environmental pre- dictors of drug response, as measured by a variety of clinical outcomes (7).

THE PRECISION MEDICINE IN DIABETES INITIATIVE

Theideaofprecisiondiabetesmedicineis gainingmomentum,basedupontheprom- ise of reducing the enormous and grow- ing burden of diabetes worldwide. To address this, the Precision Medicine in Diabetes Initiative (PMDI) was launched in 2018 by the American Diabetes Asso- ciation (ADA), in partnership with the European Association for the Study of Diabetes (EASD). The PMDI has part- nered subsequently with other organ- izations (the U.S. National Institute of Diabetes and Digestive and Kidney Dis- eases [NIDDK] and JDRF).

21Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Lund University, Malmo, Sweden 23Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA

Corresponding author: Paul W. Franks, paul [email protected]

S.S.R. and P.W.F contributed equally to this Consensus Report and are co-chairs of the Pre- cision Medicine in Diabetes Initiative.

M.I.M is currently affiliated with Genentech, South San Francisco, CA

This article is being simultaneously published in Diabetologia (DOI: 10.1007/s00125-020-05181-w) and Diabetes Care (DOI: 10.2337/dci20-0022)

by the European Association for the Study of Diabetes and the American Diabetes Association.

© 2020 by the American Diabetes Association and the European Association for the Study of Diabetes. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www .diabetesjournals.org/content/license.

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The mandate of the PMDI is to establish consensus on the viability and potential implementation of precision medicine for the diagnosis, prognosis, prevention, and treatment of diabetes, through expert con- sultation, stakeholder engagement, and systematic evaluation of available evidence. This mandate is pursued in order to realize a future of longer, healthier lives for people with diabetes. The PMDI is focused on assessing

evidence, promoting research, providing education, and developing guidelines for the application of precision medicine in diabetes. The 2019 ADA Scientific Ses- sions (held in June 2019) sponsored a research symposium focused on preci- sion medicine, followed by a PMDI stake- holder meeting (held in October 2019) that was attended by experts in areas germane to precision diabetes medicine from around the world. Future PMDI symposia will extend the themes of pre- cision diabetes medicine during the 2020 ADA Scientific Sessions and EASD Annual Meeting. In the coming years, educational approaches to translate the science into practice will be the target of a series of postgraduate education sym- posia. A global clinical research network focused on precision diabetes medicine is also being planned, along with other education and information dissemination activities (see Fig. 1 for an overview of key objectives). The purpose of the work underlying

the ADA/EASD PMDI consensus reports,

of which this is the first, is to define relevant terminology (Text Box 1) and review the current status of diagnostics and therapeutics (prevention and treat- ment) in diabetes, including key areas of opportunity and where further inquiry is needed(Text Boxes2–4).Particular focus is placed on elucidating the etiological heterogeneity of diabetes, which involves a combination of approaches including contemporaneous measures of risk fac- tors, biomarkers, and genomics, as well as lifestyle and pharmacological interven- tions. Monogenic diabetes is one of few areas where precision diabetes medi- cine has been proven feasible and is practiced (as discussed at a recent Di- abetes Care Editors’ Expert Forum; M.C. Riddle, personal communication). This first Consensus Report does not seek to address extensively the role of pre- cision medicine in the complications of diabetes, which is a topic for future evaluation. In addition, we do not discuss diabetes digital device technology, as this is addressed in a joint ADA/EASD consensus report (8,9). A second PMDI consensus report will be published docu- menting the findings of a systematic evidence review, focusing on precision diagnostics and precision therapeutics (prevention and treatment).

An Executive Oversight Committee, comprising representatives from the founding organizations, ADA (L.P.) and EASD (J.J.N.), and the two co-chairs of the initiative (P.W.F. and S.S.R.), provide

PMDI governance. The Executive Over- sight Committee is responsible for en- suring that the PMDI activities are executed. Leadership and direction of the PMDI are provided by members of the PMDI Steer- ing Committee, currently composed of academic leaders in precision diabetes medicine from the U.S. (W.K.C., J.C.F., J.M.N.)andEurope(A.T.H.,M.I.M.,E.R.P.), a representative from NIDDK (C.G.L.), and theExecutiveOversightCommitteemem- bers (L.P., J.J.N., P.W.F., S.S.R.). The Steer- ingCommitteeisresponsibleforproviding guidance for PMDI activities and engages in developingprecisiondiabetesmedicine education, drafting consensus statements, and building interest/working groups to achieve its mission. The Executive Over- sight Committee and the Steering Com- mittee work closely together under the banner of the PMDITask Force. Member- ship of the Steering Committee will ex- pand to include experts from around the world and across multiple areas of ex- pertise germane to the topic of precision diabetes medicine.

Work for this Consensus Report began attheOctober2019stakeholder meeting in Madrid. The meeting included presen- tations and roundtable discussions. At the conclusion of the meeting, a writing group meeting attended by the PMDI Task Force and stakeholders was held to determine what should be addressed in the Consensus Report. Following the meet- ing,consensuswasreachedbythePMDITask Force through bimonthly callsandelectronic

Figure 1—PMDI activities. PM, precision medicine; RFA, research funding announcement.

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communication. Relevant experts out- side of the Task Force were asked to contribute sections as needed. The Con- sensus Report was then peer reviewed by experts in the field and by the clinical committees of the founding organiza- tions. The report was then submitted to Diabetes Care and Diabetologia for si- multaneous publication.

PRECISION DIABETES MEDICINE: WHAT IT IS AND WHAT IT IS NOT

Precision diabetes medicine refers to an approach to optimize the diagnosis, pre- diction, prevention, or treatment of di- abetes by integrating multidimensional data,accountingforindividualdifferences (Text Box 1). The major distinction from standard medical approaches is the use of complex data to characterize the individ- ual’s health status, predisposition, prog- nosis, and likely treatment response. Precision medicine also focuses on iden- tifying patients who, despite a diagnosis, do not require treatment (or require less than might conventionally be prescribed).

These data may stem from traditional sources such as clinical records, as well as from emergent sources of “big data” such as individual medical records from very large cohorts of patients; geomo- bility patterns obtained from devices; behavioralmonitors(e.g., actigraphy for exercise and sleep assessments); ingest- ible, subcutaneous, or wearable sensors (e.g., for blood glucose monitoring); and genomicandother ’omicsdata.Integration of patient preferences, patient-centered outcomes, cost-effectiveness, and shared decision making will guide how pre- cision diabetes medicine is formulated and applied.

There are several terms sometimes usedinterchangeablywith precision med- icine, including “personalized medicine,” “individualized medicine,” and “stratified medicine.” The 2020 ADA Standards of Medical Care in Diabetes (ADA SOC) places considerable emphasis on the personalization of diabetes medicine, highlighting that “clinicians care for patients and not populations” (10) (p.

S2). This reflects the appreciation of individual differences with respect to symptomatology, presentation, behaviors, preferences,socialcircumstances,response to treatment, comorbidities, or clinical course. For precision diabetes medicine to be effective, it must be tailored to the individual. Thus, the ADA SOC instructs the clinician to adapt guidelines to each patient’s characteristics, circumstances, and preferences, including the patient’s food security, housing, and financial sta- bility.InthecontextofthePMDI,thisisnot considered to be precision medicine; rather, this final step in the process of translating knowledge into practice is personalized (or individualized) medi- cine.Incontrast,precision(orstratified) medicineemphasizestailoringdiagnostics ortherapeutics (prevention or treatment) to subgroups of populations sharing sim- ilar characteristics, thereby minimizing error and risk while maximizing efficacy. Included within precision diabetes med- icine is the monitoring of disease pro- gression usingadvanced technologiesor

Text Box 1—Definitions

c Precision diagnosis involves refining the characterization of the diabetes diagnosis for therapeutic optimization or to improve prognostic clarity using information about a person’s unique biology, environment, and/or context. ○ Precision diagnostics may involve subclassifying the diagnosis into subtypes, such as is the case in MODY, or utilizing probabilistic algorithms that help refine a diagnosis without categorization.

○ Carefuldiagnosisisoftennecessaryforsuccessfulprecisiontherapy,whetherforpreventionortreatment.Thisistruewheresubgroup(s)ofthe population must be defined, within which targeted interventions will be applied, and also where one seeks to determine whether progression toward disease has been abated.

○ Precisiondiagnosiscanbeconceptualizedasa pathwaythatmovesthroughstages,ratherthanasa singlestep.Thediagnosticstagesinclude1) an evaluation of prevalence based on epidemiology, including age, or age at diagnosis of diabetes, sex, and ancestry; 2) probability based on clinical features; and 3) diagnostic tests that are interpreted in the light of 1) and 2). A diagnosis in precision medicine is a probability-based decision, typically made at a specific point in the natural history of a disease, and neither an absolute truth nor a permanent state.

c Precision therapeutics involves tailoring medical approaches using information about a person’s unique biology, environment, and/or context for the purposes of preventing or treating disease (see Precision prevention and Precision treatment, below).

c Precision prevention includes using information about a person’s unique biology, environment, and/or context to determine their likely responses to health interventions and risk factors and/or to monitor progression toward disease. ○ Precision prevention should optimize the prescription of health enhancing interventions and/or minimize exposure to specific risk factors for that individual. Precision prevention may also involve monitoring of health markers or behaviors in people at high risk of disease, to facilitate targeted prophylactic interventions.

c Precision treatment involves using information about a person’s unique biology, environment, and/or context to guide the choice of an efficacious therapy to achieve the desired therapeutic goal or outcome, while reducing unnecessary side effects. ○ Today, the objective of precision therapy is to maximize the probability that the best treatment of all those available is selected for a given patient. It is possible that in the future, precision diabetes medicines will be designed according to the biological features of specific patient subgroups, rather than for the patient population as a whole.

c Precision prognostics focuses on improving the precision and accuracy with which a patient’s disease-related outcomes are predicted using information about their unique biology, environment, and/or context. ○ Thefocusofprecisionprognosticsincludespredictingthe riskandseverityof diabetescomplications,patient-centered outcomes,and/orearly mortality.

c Precision monitoring may include the detailed assessment of biological markers (e.g., continuous glucose monitoring), behaviors (e.g., physical activity), diet, sleep, and psychophysiological stress. ○ Precision monitoring can be achieved using digital apps, cutaneous or subcutaneous sensors, ingestible sensors, blood assays etc. ○ The intelligentprocessing, integration, andinterpretationof the data obtained throughprecisionmonitoring are key determinants of success. ○ Precision monitoring may be valuable for precision prevention (e.g., in T1D), precision diagnostics (e.g., where diagnoses are based on time- varying characteristics), and precision prognostics (e.g., where disease trajectories are informative of the development of key outcomes).

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considering how patient features affect the reliability of assays. The application of precision diabetes medicine may sub- stantially reduce errors in diagnostic (Fig. 2), therapeutic (Fig. 3), and prognostic (Fig. 4) processes. For example, the in- terrogation of large sets of longitudinal clinical data could identify disease sub- types and match the patient to others with a similar disease profile; through knowledge of treatment efficacy and outcomes, more precise prognosis and optimization of therapies for thispatient byconcordancetosimilarsubgroupswould emerge (Text Box 1 and Figs. 3 and 4).

PRECISION DIAGNOSTICS

What are the Requirements for Precision Diagnosis? Precision diagnostics (Text Box 2) em- ploys methods to subclassify patients to enable the successful application of pre- cision medicine approaches (Fig. 2). This will facilitate matching precise preven- tion strategies and treatments to indi- viduals either at risk for or diagnosed with diabetes. Ideally, a precision diag- nostic test should be 1) robust (high test-retest reliability within and between laboratories); 2) able to define a discrete subgroup giving insights into disease

etiology, prognosis, and treatment response; 3) widely available; 4) easily performed with accepted norms for interpretation; 5) in- expensive (or at least cost-effective); and 6) approved by regulatory authorities.

Precision diagnosis can be conceptu- alized as a pathway that moves through stages, rather than as a single step. The diagnostic stages include assessing the:

c expected prevalence based on epide- miology, including age, or age at di- agnosis of diabetes, sex, and ancestry,

c probable clinical diagnosis using clinical features and other data, and

Text Box 2—Precision diagnostics: background, barriers to implementation, and research gaps

c Type1diabetes.Bestdiagnosticresultsdependonintegratingalldiagnosticmodalities,notbyrelyingonpriorprevalence,clinicalfeatures,ortest resultsinisolation.Theageatwhichtheinitial isletautoantibody appearsandthetypeofautoantibody (e.g.,whichofthefourprimaryantibodies among ICA512, insulin, GAD, and ZnT8) may be important in defining etiological subtypes of T1D. The majority of the genetic risk of T1D is now known,andthesensitivityandspecificityofaT1Dgeneticriskscore(T1D-GRS)bothexceed80%.Despitethis,ahighT1D-GRSwillhavelowpositive predictivevalueinpatientpopulationswheretheoverallprevalenceofT1Dislow,suchasthoseaged.50yearswhendiabetesisdiagnosed.Itwill likely prove most useful when the T1D-GRS is combined with clinical features and islet autoantibodies. At present, there is no immune-based test sufficiently reproducible and robust that it can be used diagnostically.

c Type2diabetes.Categoriesbasedonclusteranalysisatdiagnosiscanprovideinsightsintolikelyprogression,riskofcomplications,andtreatment response, which offer an exciting approach to subclassification of T2D. At this time, the available genetic data for T2D do not have sufficient predictiveaccuracy toreplaceexistingdelineativeapproaches. Althoughthe subcategorization of T2Dusing genetic datais informativeregarding theetiologicalprocessesthatunderliethedisease,themethodsdescribedsofar(6,101)arenotintendedtobeusedtosubclassifyaT2Ddiagnosis nor are the existing genetic data sufficient for this purpose for the majority of individuals with T2D. Treatment response and progression can be predicted from clinical features (137). An advantage of using clinical features for diagnosis of T2D is that they are widely available and easily obtained (e.g., sex, BMI, HbA1c); however, a potential limitation is that they may vary over time.

c Barriers to implementation. One of several important translational barriers facing the proposed clustering approach for T1D and T2D is that a fasting C-peptide measurement is required at the time of diagnosis, which is not routinely performed in clinical practice, and the reliability of C-peptide assays varies considerably between laboratories (41). Another limitation is that the biomarkers used to define these clusters change overtimedependingonthediseasecourseoritstreatment,suchthatthisapproachcanonlybeappliedtonewlydiagnosedindividuals,butnotto individuals years before disease onset or the many millions of people with long-standing diabetes worldwide. Moreover, the current approaches for clustering in T2D require continuously distributed data to be categorized, which typically results in loss of power. Thus, these methods do not yield good predictive accuracy, a major expectation in precision medicine, but this may change as the approach is refined.

c Research gaps. Based on limited ideal tests and uncertainty in etiology, more research is needed in T1D and T2D in order to define subtypes and decide the best interventional and therapeutic approaches.

Text Box 3—Precision prevention: background, barriers to implementation, and research gaps

c Type 1 diabetes. In T1D, precision prevention mainly involves the optimization of monitoring methods, thereby facilitating early detection and treatment. The reasons most prevention trials in T1D have not been effective may include failure to consider the individual’s unique T1D risk profile (e.g., genetic susceptibility) and their unique response to the preventive agent (immune therapy or dietary intervention). Without considering the unique genetic profiles of children, interventions aimed at preventing type 1 diabetes (e.g., dietary intervention or immunotherapy) may be unlikely to succeed. Thus, precision prevention in T1D is likely to involve stratification of at-risk populations and innovative monitoring technologies.

c Type 2 diabetes. T2D has many avenues for prevention; thus, the possibilities for precision approaches, possibly through tailoring of diet, are broad. To date, prevention of T2D has focused on people with prediabetes. To be cost-effective, it will likely be necessary to stratify the population with prediabetes such that only those with other relevant risk factors are the focus of preventative interventions. Relevant risk factors may include lifestyle, socioeconomic status, family history, ethnicity, and/or certain biomarker profiles, including genetics.

c Barriers to implementation. The effective implementation of precision prevention will require that appropriate technologies are available, the general public has the willingness to embrace the approach and that those in greatest need can access precision prevention programs. A communication plan used by the interventionalist and the patient’s perception of risk should be a focus of precision prevention strategies.

c Research gaps. There are critical areas of research required for implementation of precision prevention in diabetes, including determining for whomonline careis moreeffective than in-personcare, the typesof staff deliveringthe lifestylemodification programs, the impactof groupand/ orindividualinteraction,andthefrequencyofsuchsessions.Thereisalsouncertaintyabouthowbesttoprovideandsustainlifestylemodification. In addition, emphasis should be placed on identifying profiles that indicate the likely response to specific lifestyle interventions (focusing on specific diets, exercise programs, and other behavioral factors) and sensitivity to risk factors (such as sleep disturbance, stress, depression, poor diet, sedentary behaviors, smoking, certain drugs, and obesity).

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c modification by diagnostic tests that are interpreted in the light of preva- lence and diagnosis.

A diagnosis in precision medicine is a probability-baseddecision,typicallymade at a point in the natural history of a disease, reflecting neither an absolute truth nor a permanent state. Presenting the degree of uncertainty in a manner that is intuitive to the patient and prac- titioner is critical if the precision diag- nosis is to be effective.

Precision Diagnosis in Clinical Practice

Interpreting HbA1c in Diagnosis and

Monitoring

Data and outcomes from the widespread useofglycatedhemoglobin(HbA1c),rather than blood glucose levels, for diagnosis has led to a precision approach for the diagnosis of diabetes. The level of HbA1c

will depend on factors that impact hemoglobin and red cell stability as well as average glucose values (10). Genetic testing can reveal unsuspected variants that alter HbA1c. Thus, knowl- edge of the patient’s ancestry and specific genetic information can guide interpretation of assay results for di- agnosis and the monitoring of blood glucose.

Diagnosing T1D Versus T2D

Currently,themostcommonsteptoward precisiondiagnosisthatismadeinclinical diabetes medicine is the classification of T1D versus T2D, the two most prevalent subcategories with different etiologies and different treatment requirements. Part of the diagnostic dilemma is that neither T1D nor T2D are monolithic entities and robust “gold standards” are not universally agreed. Diagnostic issues arise when expected clinical

features are discordant from established norms (e.g., people diagnosed with di- abetes who are young and have obesity, or old and slim, or who are a rare subtype in that clinical setting) (11). Islet auto- antibody positivity varies by clinical set- ting (e.g., in people without diabetes, individuals diagnosed with probable T1D as children, individuals with clinical fea- tures of T2D), resulting in an altered prior probability of T1D that reflects the dif- ferent prevalence in these diverse set- tings. The best diagnosis depends on integrating all diagnostic modalities, as demonstrated in predicting long term C-peptide negativity in individuals diag- nosed with diabetes between 20 and 40 years of age, where an integrated model outperformed diagnosis based on clinical features, circulating antibodies, or genetics used in isolation (3). The frequency of misdiagnosis of T1D and

Text Box 4—Precision medicine approaches to treat diabetes: background, barriers to implementation, and research gaps

c Type 1 diabetes. The only existing therapy is insulin for T1D. Developments in long-acting and glucose-sensitive insulins are improving the health and well-being of people with T1D, as are technological advances in continuous glucose monitoring devices, insulin pumps, closed-loop systems, and the artificial pancreas.

c Type 2 diabetes. It has long been recognized that T2D is heterogeneous in its etiology, clinical presentation, and pathogenesis. Yet, traditionally, trials of therapeutic intervention do not recognize this variation.

c Monogenicformsofdiabetesare already amenabletoprecisiontreatment, if correctlydiagnosed.For example,HNF1A-MODY(MODY3),HNF4A- MODY (MODY1), and ABCC8-MODY (MODY12) are acutely sensitive to the glucose-lowering effects of sulfonylureas. Alternatively, individuals with GCK-MODY (MODY2) can have unnecessary treatments stopped.

c With increasing efforts to map patients with T2D in etiological space using clinical and molecular phenotype, physiology, and genetics, it is likely that this increasingly granular view of T2D will lead to increasing precision therapeutic paradigms requiring evaluation and potential implementation.Geneticvariationnotonlycancaptureetiologicalvariation(i.e.,geneticvariantsassociatedwithdiabetesrisk) butalsovariation in drug pharmacokinetics (absorption, distribution, metabolism, and excretion [ADME]) and in drug action (pharmacodynamics).

c In contrast, “true” T2D is a common complex disease characterized by thousands of etiological variants, each contributing to a small extent to diabetes risk. Thus, itremains uncertainthat genetic variantswill be identified that are highly predictiveof drugoutcomes in T2D,even if process- specific polygenic risk scores are derived (where all variants on an etiological pathway are combined to increase power).

c Barriers to implementation. The current and growing burden of diabetes is not from western white populations but from other ethnic groups, in particular South and East Asians. Yet, these populations are underrepresented in clinical trials and, in particular, in attempts to understand variation in drug outcomes. ○ Because the diabetes phenotype can vary markedly by ethnic group, it is likely that complications and drug outcomes will differ between populations.

○ Many of the approaches gaining traction in precision medicine generate massive data sets that are burdensome to store and require powerful computational servers for analysis.

○ Undertaking appropriatelydesignedclinicaltrials for precisiontreatmentsthat meetthe current expectations of regulatory authorities may be challenging, given the many subgroups within which treatments will need to be evaluated. Innovative clinical trials will likely be needed and real-world evidence will likely need to be part of the evaluation process.

○ Translatingcomplexinformationtopatientsaboutgenetic(andother ’omics)testsinaclear,concise,andclinicallyrelevantmannerwillrequire health care providers to be appropriately trained.

c Research gaps. For drug outcomes, there is a pressing need to move beyond early glycemic response and examine variation in response in terms of cardiovascular outcomes and mortality rates, especially of the newer agents such as SGLT2i and GLP-1RA, with focus on specific patient subgroups. Identifying predictive markers (especially genetic markers) of serious adverse events in patients treated with these drugs presents an additional area urgently in need of greater attention. ○ Need for functional studies to determine the mechanism(s) of action underlying specific gene variants ○ Need for better understanding of the pathophysiology of diabetes to inform on new therapeutic targets ○ Need to study broader populations/ethnic groups ○ Need for understanding outcomes of highest relevance to patients ○ Need for decision-support tools to implement precision diabetes medicine in clinical practice ○ Need to demonstrate that approaches are cost-effective

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T2D in middle-aged and elderly adults (11,12) suggests that precise diagnostic approachesareneeded,especiallyasfailure to recognize insulin-deficient states can be fatal.

Monogenic Diabetes

A Diabetes Care Editors’ Expert Forum (M.C. Riddle, personal communication) has concluded recently that a monogenic diabetes diagnosis is closest to meeting all criteria for a perfect diagnostic test as it defines a discrete subgroup giving insights into etiology, prognosis, and treatment response (1,2). Most cases of monogenic diabetes remain misdiag- nosed. Perhaps the best example of pre- cision diabetes medicine is the excellent and long-lasting glycemic response to oral sulfonylureas in insulin-dependent infantsdiagnosedwithneonataldiabetes caused by abnormalities in the b-cell potassium channel (13–17). In GCK- MODY (MODY2), it is established that patients do not require (18), or respond to, oral medication (19). Other MODY diagnoses (HNF1A [MODY3], HNF4A [MODY1] and ABCC8 [MODY12]) are acutely sensitive to the glucose-lowering effects of sulfonylureas (20–22); how- ever, unlessthediagnosisisprecise,these therapeutic benefits are lost. With the clear benefits of precision diagnosis of

monogenic diabetes, it is important to reduce barriers to its implementation. For example, the cost of performing molecular genetic testing is high and universal testing is not cost-effective. It isthus necessary to limit testing to those most likely to have a monogenic diag- nosis. Moreover, identification proto- cols require prescreening based on clinical features (e.g., family history, age at onset, phenotype including syn- dromic features) and nongenetic testing (islet autoantibodies and C-peptide).

One approach for implementing pre- cision medicine in the case of monogenic diabetes would be to:

c test all infants diagnosed with diabe- tes in the first 6 months of age, be- cause .80% have a monogenic cause of neonatal diabetes;

c use a MODY calculator to identify those whose clinical features suggest a high likelihoodofMODY(www.diabetesgenes .org/mody-probability-calculator/) (23);

c test individuals with pediatric diabetes when at least three islet autoantibod- ies are antibody negative (24).

The effective use of these pregenetic selection criteria should greatly improve the likelihood of correctly diagnosing

monogenic diabetes without the burden of costly genetic screens. Although di- agnostic molecular genetic testing uti- lizes robust analysis of germline DNA, which is virtually unchanged throughout life, there are still issues with its imple- mentation. One issue is the incorrect interpretation of the genetic informa- tion, leading to inaccurate identification of causal mutations in both clinical prac- tice and in the published research lit- erature (25). Curation of pathogenic variantsformonogenicdiabetesiscritical and is currently being addressed by in- ternational consortia. As a result of tech- nological advances, multiple causes of monogenicdiabetescanbetestedforina single next-generation sequencing test. This approach is generally advantageous as it does mean that syndromic mono- genic diabetes is diagnosed genetically when the patient presents with isolated diabetes. This will allow other features to be examined and treated appropriately before clinical presentation. Examples of this are neonatal diabetes (2), HNF1B- MODY (MODY5) (26), WFS1 (Wolfram syndrome) (27), and mitochondrial di- abetes (28). For these patients, the ge- netic diagnosis of diabetes will have implications far beyond the prognosis and care of diabetes, as the patient

Figure 2—Precision diagnostics

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withcertaintypesofmonogenicdiabetes will also be at high risk of developmental delay, neurological disease, developmen- tal kidney disease, liver failure, deafness, and cardiomyopathy.

Diagnosing Latent Autoimmune Diabetes

in Adults

Latent autoimmune diabetes in adults (LADA) is not currently recognized by the ADA as a formal subtype of diabetes. Nevertheless, LADA reveals some of the difficulties in diabetes subtyping. It was shown that the presence of GAD auto- antibodies in patients with T2D was associated with progression to early in- sulin therapy (29); yet, controversy re- mains as to whether LADA is a discrete subtype, a milder form of T1D, or a mixture of some patients with T1D and others with T2D. The uncertainty is increased by variation in the diagnostic criteria, with initial treatment based upon physician preference as well as the patient’s presentation (30). In addi- tion, among those with GAD autoanti- bodies, the phenotype varies with different autoantibody levels (31).

Subcategories of Common Forms of

Diabetes

The subcategorization of T1D or T2D may not always be the optimal approach for precision diabetes diagnosis or therapy.

Nevertheless,theabilitytodelineateT1D or T2D using nontraditional data and approaches may lead to improvements in prevention or treatment of the dis- ease, including diabetes subclassifica- tions beyond T1D or T2D. Subcategories in T1D. The age at which the initial islet autoantibody appears and the type of autoantibody (e.g., which of the four primary antibodies among islet cell autoantigen 512/islet antigen 2 [ICA512/ IA-2], insulin, GAD, zinc transporter 8 [ZnT8]) may be important in defining etiological subtypes of T1D (32). Data supporting this potential subcategory are based upon those diagnosed in the first 10 years of life and in pre- dominantly white European popula- tions. The relevance to other ethnic groups and those diagnosed later in life is uncertain.

Thegeneticvariantsaccountingforthe majority of risk of T1D are now known, and the sensitivity and specificity of T1D genetic risk scores (T1D-GRS) both ex- ceed 80% (5,33–35); however, a high T1D-GRSwillhave lowpositivepredictive value in populations with a typically low prevalence. A T1D-GRS may prove most useful when integrated with clinical fea- tures and islet autoantibodies (3,4). There is variation in the genetic suscep- tibility with age at diagnosis but, at

present, genetics is not suggested as an approach for defining subtypes of T1D.

There is strong evidence for enrich- ment of immune cell types that are associated with genetic risk of T1D, par- ticularly T cells (CD41 and CD81) and B cells(CD191).However,atpresent,there is no immune-based test sufficiently re- producible and robust that it can be used diagnostically for T1D.

Persistent endogenous b-cell function in T1D is associated with greater poten- tial for improved glycemic control and reduced complications (36).A stimulated C-peptide measurement represents a candidate for defining subcategories of T1D with different treatment aims. C-peptide levels exponentially fall in the “honeymoon period” after T1D di- agnosis (37) but have been shown to be stable 7 years after diagnosis (38). Per- sistent C-peptide is associated with a later age of diagnosis, although there are few data to predict those likely to maintain high levels of C-peptide. Subcategories in T2D. Family history of T2D, as a surrogate for precise genetic evaluation, fails to meet many of the criteriaofarobusttestasanyassessment changes over time and depends on the relatives selected for reporting the “fam- ily.” The value of a family history may be

Figure 3—Precision therapeutics

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greatest in monogenic diabetes, in which a pedigree will often demonstrate a pat- ternofinheritanceconsistentwithasingle gene disorder and a consistent phenotype. T2D treatment response and disease

progression can be predicted from con- tinuous clinical features with specific models.These models appear to perform better than dividing into cluster-based subgroups (7). An advantage of using clinical features is that they are widely available and easily obtained (e.g., sex, BMI, HbA1c). However, they are limited by the fact that clinical features may vary over time and with the natural history of thedisease.Incorporationoflongitudinal change with treatment response could be a strength as the model’s prediction would change in concert with changes in the phenotype of the patient. Recent research has attempted to de-

fine subcategories of T2D (and T1D) based on cluster analysis at diagnosis to provide insights into likely progres- sion,riskofcomplications,andtreatment response (39,40). Barriers facing this and other approaches include collection of data that are not routinely obtained (e.g., a fasting C-peptide at the time of diagnosis, with considerable variation in results between laboratories [41]) and the change in biomarkers over time that are dependent on disease course or its

treatment. Genetic data have been used todefineT2D subcategories by clustering genetic variantsthat associate with phys- iological traits and which are correlated with clinical outcomes (6). At this time, the available genetic data for T2D and the clustering does not have sufficient predictive accuracy to replace existing delineative approaches. None of the methods described above are estab- lished for subclassification of T2D in clinical practice; nevertheless, it is true that in a minority of patients, their specific type of diabetes may be ade- quately characterized using genetic clus- tering (42,43).

PRECISION THERAPEUTICS

Accurate diagnosis is necessary for suc- cessful precision therapy, whether for prevention or treatment (Fig. 3). This is truewheresubgroup(s)ofthepopulation must be defined to determine which targeted interventions will be applied, as well as for determination of treatment outcome. In monogenic diabetes, there are no currently known options for pre- vention. In T1D, precision prevention currently involves mainly the optimiza- tion of monitoring methods (Text Box 3), thereby facilitating timely early detec- tion, preventing early complications

and allowing appropriate treatment. In contrast, T2D has many avenues for prevention; thus, the possibilities for precision approaches, possibly through tailoring of lifestyle (e.g., diet), are broad in T2D.

Precision Prevention in Diabetes (Text Box 3)

Type 1 Diabetes

T1D is characterized by damage, impair- ment, and eventual destruction of the insulin-producing pancreatic b-cells, thought to be the result of an autoim- mune process. T1D progression has been grouped into discrete “stages” (44). Stage 1 is defined by the presence of $2 islet autoantibodies, with normal blood glucose; stage 2 is defined by the presence of $2 islet autoantibodies with elevation of blood glucose, signaling the functional impairment of the b-cells; and stage 3 is characterized by symptoms of dysglycemia, such as polyuria or diabetic ketoacidosis, although not all symptoms need be present. A clinical diagnosis of T1D typically is not given until stage 3. T1D is nearly inevitable once $2 islet autoantibodies appear, particularly in those of younger age, with a lifetime diabetes risk approaching 100% (45,46). Approximately half of the risk of T1D is due to genetic factors, with over 30% of

Figure 4—Precision prognostics

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the genetic risk attributable to genes of the human leukocyte antigen (HLA) com- plexbut also including more than 50 non- HLA loci (35). Unknown environmental factors are thought to trigger the auto- immune process that results in initial b-cell damage and progression toward symptomatic T1D (47). Primary prevention trials ingenetically

susceptible individuals who have not yet developed autoantibodies (i.e., pre- stage1)andsecondarypreventiontrialsin children with stages 1 and 2 have been conducted (48) using dietary interven- tions and immune-targeting approaches. Dietary manipulation studies have been largely unsuccessful in reducing islet au- toimmunity (49–51) or T1D (52). Previous intervention studies among individuals at stage 1 or stage 2 have been unable to slow, halt, or reverse the destruction of insulin-producing b-cells. Of nine completed secondary prevention trials (53–60), only one (using an anti-CD3 antibody) has shown a slight delay in progression to T1D (61). Most prevention trials in T1D have not

been effective, partially because the unique T1D genetic risk profile of the individual and their unique response to thepreventiveagent(immunetherapyor dietary intervention) have not been con- sidered. For example, the inflammatory response to infection with enteroviruses implicated in the onset of T1D has been shown to be genetically mediated (62) and diet has had different effects on development of autoimmunity and pro- gression to T1D (63) dependent on

genetic risk. Several studies have sug- gested that susceptibility to islet auto- immunity and progression to T1D may be related to the ability to adequately use vitamin D, as higher cord blood 25- hydroxyvitamin D was associated with a decreased risk of T1D, but only in children who were homozygous for a vitamin D receptor gene (VDR) variant (64). Risk of islet autoimmunity was observed with reduced dietary intake of the n-3 fatty acid a-linolenic acid, but only in those with a specific genotype in the fatty acid desaturase gene (FADS) cluster (65). Thus, without considering the unique genetic profiles of children, dietary supplementation may not be successful, arguing for an appropriately validated precision approach.

Type 2 Diabetes

The emergence of T2D as a global public health crisis during recent decades has motivated numerous large randomized controlled trials assessing the efficacy of pharmacological or lifestyle interven- tions for prevention. An emphasis has been placed on intervening in people with “prediabetes,” defined as a person with levels of fasting blood glucose, 2-h blood glucose, or HbA1c that are chron- ically elevated but below the diagnostic thresholds for diabetes. Although pre- diabetes is a major risk factor for T2D and other diseases (66), intervening in everyone with prediabetes may not be cost-effective (67). Aggressive precision prevention in those with relevant risk factors is discussed in the current ADA

SOC (68). Youth with prediabetes should be the focus of preventive interventions, es- pecially those with overweight or obesity and who have one or more additional risk factors (e.g., maternal history or exposure to gestational diabetes mellitus [GDM], a positive family history of diabetes in first- or second-degree relatives, signs of insulin resistance, or specific high-risk ancestry).

Multiple interventions in adults with T2D have been evaluated for risk reduc- tion and prevention, both in the short and the long term. A recent systematic review (69) reported that after active interventions lasting from 6 months to.6years,relativeriskreductionachieved from lifestyle interventions (39%) was simi- lartothatattainedfromuseofdrugs(36%); however, only lifestyle interventions had a sustained reduction in risk once the in- tervention period had ended. Analysis of the postintervention follow-up period (;7 years) revealed a risk reduction of 28% with lifestyle modification compared with a nonsignificant risk reduction of 5% from drug interventions.

Most lifestyle intervention programs usestandardizedapproachesdesignedto change diet and exercise habits for re- ducing body weight. The Diabetes Pre- vention Program (DPP) evaluated the efficacy of lifestyle intervention and met- formin therapy, compared with standard of care and placebo (control), for delay or prevention of diabetes in those with impaired glucose regulation at baseline. Although the reductions in diabetes risk from lifestyle (58% reduction) and metformin (31% reduction) compared

Text Box 5—Precision medicine approaches to lessen treatment burden and improve quality of life

c Diagnosis and disease management. A more specific diagnosis has the potential to reduce uncertainty and manage future expectations about diseasecourse.Thisisclearlythecaseforsomemonogenicformsofdiabetes,wherediagnosisisnearlycertain,givenitsstronggeneticindication, and the specific treatment is coupled to the subcategory (genetic subtype) of disease. Emerging knowledge regarding subtypes of T2D indicates that there is potential to classify individuals with diabetes at risk for progression to complications.

c Misdiagnosis. Inaccurate classificationof thetype ofdiabetes,either fromlack ofprecisionorinadequateclinicalattentiontodetail at thetimeof presentation, can have long-lasting, adverse effects on mental health and quality of life. In the pediatric and younger adult population, the risk of misclassification isincreasingasboth “true”T1Dand“true” T2Dclassifications are confusedthroughthe growing obesityepidemic inyouth(T2D) and older ages at onset (T1D). In addition, monogenic variants of diabetes can be misdiagnosed as either T1D or T2D. A precision approach to diagnosis with appropriate standardized laboratory support and increased research to obtain novel biomarkers of disease has the potential to solve this problem.

c Complications. Worry about complications is an issue for all people with diabetes. Currently, people with diabetes (either T1D or T2D) are given a label of being unequivocally at risk for reduced life span, amputation, kidney failure, and blindness. A more precise diagnosis, prognosis, and strategy to predict and prevent complications has the potential to greatly reduce disease burden and distress and improve quality of life. Nevertheless, there is also a risk that more precise prognostification may cause distress if the options for successful intervention are limited or incompatible with the patient’s needs or desires.

c Stigmatization. A major burden for people with diabetes is that the disease is often consideredthe fault of the patient. This is particularly true for T2D, as it is often labeled as “just” a lifestyle disease. Clinical care of those with diabetes often results in a singular approach to treatment, regardless of their specific needs, life situation, and other conditions. A clinical process that makes diagnosis more precise and includes a patient- oriented evaluation and response to needs has the potential to lessen stigma and reduce associated distress.

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with the control intervention were im- pressive (70), there was considerable variation across the study population (71), with many participants developing T2D during the active intervention period (the first 2.8 years of the trial). Thus, the DPP lifestyle intervention did not truly “prevent” diabetes. Indeed, in the de- cade after randomization, during which participants were offered lifestyle rein- forcement semiannually, the average duration before disease onset was ;3 years (72). Those participants in the DPP who progressed most rapidly were those who lost the least weight in the early stages of the intervention (73), with genetic variants representing significant predictorsofpeakweightlossandweight loss maintenance (74). Results from the DPP and other large prevention trials suggest that a “one-size-fits-all” lifestyle intervention strategy will not be effica- cious for everyone, particularly if it can- not be sustained, strengthening the case for precision lifestyle interventions in T2D prevention. Although precision diabetes medicine

is much more than genetics, the majority of relevant research has focused on

evaluating the role of genetic variants in precision prevention. Large epidemi- ological studies (75) and intervention trials (76,77) strongly suggest that stan- dard approaches for lifestyle modifica- tion are equally efficacious in preventing diabetes regardless of the underlying genetic risk. This contrasts with the extensive epidemiological evidence sug- gesting that the relationship of lifestyle with obesity is dependent on genetic risk (78–81); however, with few exceptions (e.g., [74]), analyses in large randomized controlled trials have failed to show that these same genetic variants modify weight loss in response to lifestyle in- tervention (82). It is also important to recognize that knowledge of increased genetic risk for diabetes may not moti- vateimprovementsinlifestylebehaviors. Indeed, knowledge of increased genetic risk for diabetes may decrease motiva- tion to modify behavior in genetic fatal- ists (83).

Diet recommendations optimized to the individual have been shown to re- duce postprandial glycemic excursions to a greater extent than standard approaches in healthy individuals (84).

Meal compositions that induce the most favorable glycemic profiles have been guided by models derived from an indi- vidual’s biological data (e.g., microbiome, genome, and metabolome), information on lifestyle factors (e.g., sleep and exer- cise), and postprandial glycemia following the consumption of a series of standard- ized meals. Although these studies indi- catethatpersonalized dietplansmay help minimize postprandial glycemic excur- sions, no studies have reported the long-term impact of adhering to person- alized diets on glycemic control.

Of the 12 approved classes of diabetes drugs, many having been assessed for efficacyinprevention. Overall,drugs that enhance insulinaction haveproven more effective in diabetes prevention than those that increase insulin secretion. Some of the variability in the diabetes- reducing effect of metformin in the DPP has been associated with variation in the SLC47A1 gene that encodes the multi- drug and toxin extrusion 1 (MATE1) transporter protein (85). In the DPP Out- comes Study, the effects of lifestyle, metformin, and placebo interventions on weight reduction during the 6–15

Figure 5—The path to precision diabetes medicine. HEA, health economic assessment. Adapted from Fitipaldi et al. (136).

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years that followed the end of the randomized intervention phase were assessed (86). As a percentage of base- line weight, those assigned to metformin maintained an average weight loss of 6.2% compared with the lifestyle inter- vention group, which maintained a weight loss of 3.7%, and the placebo group, which maintained a weight loss of 2.8%. In the subgroup of DPP partic- ipants who lost ,5% baseline weight at 1 year post-randomization (poor res- ponders), body weight during the follow- ing 14 years remained essentially unchanged, whether receiving metformin or placebo interventions. In contrast, those partic- ipants in the lifestyle intervention group who lost ,5% baseline weight gained and sustained ;2 kg excess body weight in the years that followed. These findings reveal a subgroup of DPP participants in whom lifestyle intervention led to weight gain, which presents a potential avenue for stratified intervention, where individuals who are unlikely to respond well to lifestyle modification might be better served by other therapeutic approaches.

Precision Treatment (Text Box 4) Once diabetes develops, a variety of therapeutic steps may be clinically in- dicated to improve disease manage- ment. These steps include:

c glucose monitoring c patient education and lifestyle inter- vention (87)

c surgery c drug treatments to lower HbA1c c drug treatments to lower cardiovascu- lar risk (e.g., statins, antihypertensives)

c drug treatments targeting specific complications (e.g., ACE inhibitors/angio- tensin II receptor blockers [ARBs] and sodium–glucose cotransporter 2 [SGLT2] inhibitors for proteinuric kidney disease, fibrates for retinopathy, atypical analge- sics for painful neuropathy, and statins and antihypertensives for cardiovascular disease)

For each of these treatments, there will be patients who respond well and those who respond less well, in addition to those who have adverse outcomes fromthetherapy.Thus,precisiontreatment can be considered as using patient char- acteristics to guide the choice of an efficacious therapy to achieve the

desired therapeutic goal or outcome while reducing unnecessary side ef- fects (Fig. 3). Given the broad scope of precision treatment, pharmacolog- ical therapy in T2D has the best evi- dence base for precision therapeutics at present.

Subcategories and Drug Outcomes

Traditionally, trials of therapeutic inter- ventions do not recognize variation in etiologic processes that lead to develop- ment of T2D. The MASTERMIND consor- tium recently reanalyzed data from the A Diabetes Outcome Progression Trial (ADOPT) and Rosiglitazone Evaluated for Cardiac Outcomes and Regulation of Glycaemia in Diabetes (RECORD) studies in order to highlight how clin- ical phenotype can be used to help guide treatment intervention. In ADOPT, on average, men without obesity showed a greater HbA1c reduction over 5 years with sulfonylureas than they did with thiazolidinediones; however, women with obesity treated with thiazolidinediones had sustained HbA1c lowering over the 5 years compared with sulfonylureas (88). When considering the clinical and physiological variables used to subgroup individuals with diabetes (39), the in- sulin-resistant cluster defined in ADOPT and RECORD responded better to thia- zolidinediones while the older patient cluster responded better to sulfonyl- ureas (7).

Similar studies have been undertaken to investigate how simple clinical varia- bles can be used to predict glycemic response to dipeptidyl peptidase 4 inhib- itors (DPP4i). In studies undertaken using prospective (Predicting Response to In- cretin Based Agents in Type 2 Diabetes study [PRIBA]) and primary care data in the U.K. (Clinical Practice Research Data- link [CPRD]), an insulin-resistant pheno- type of obesity and high triacylglycerols was associated with reduced initial re- sponsetoDPP4iand more rapidfailure of therapy (89).

As outlined under PRECISION DIAGNOSTICS and elsewhere (the upcoming Expert Forum), the most current examples of how genetics impacts precision treat- ment can be seen in monogenic diabe- tes, for which single gene mutations are causal for the development of diabetes and for which targeted treatments can, in effect, bypass the etiological defect (e.g., sulfonylurea sensitivity in HNF1A-

MODY [MODY3] [20] and insulin inde- pendence with high-dose sulfonylureas in neonatal diabetes due to KATP channel defects [14]). In some instances, pre- cision treatment may result in cessa- tion of unnecessary medication, as is the case in people with GCK-MODY (MODY2), where blood glucose remains somewhat elevated, but stable, over time.

Unlike monogenic forms of diabetes, T2D is a common complex disease char- acterized by thousands of etiological gene variants. It is uncertain whether individual genetic variants will be highly predictive of drug outcomes. Similar to the underlying genetic architecture of T2D, it is possible that drug response in T2D will be influenced by many genetic variants of small to modest effect. Ge- netic studies of drug response in T2D have largely been based on candidate genes of known etiological processes or drug pathways. These studies have been limited in their success. For example, some studies have shown that the KCNJ11/ABCC8 E23K/S119A risk variant increases glycemic response to sulfony- lureas (90–92); in contrast, the TCF7L2 diabetes risk variant reduces glycemic response to sulfonylureas (93–95). The PPARG Pro12Ala diabetes risk variant has been associated with reduced gly- cemic response to thiazolidinediones (96–98).

Genome-wide association studies (GWAS) have the potential to provide novel insights as they make no assump- tions about drug mechanism or disease process, in contrast to candidate gene/ pathway studies. Only GWAS of metfor- minhave been reported to date (99,100), identifying that variants at the ATM/ NPAT and SLC2A2 loci are associated with an altered glycemic response. In SLC2A2, the noncoding rs8192675 vari- ant C allele is associated with greater response to metformin and is associated with reduced expression of the SLC2A2 transporter in liver, intestines, and kid- neys. In individuals with obesity, those with two copies of the C allele had an absolute HbA1c reduction of ;1.55% (compared with a reduction of ;1.1% in those without the C allele). While this may appear to be a small difference, the SLC2A2 genotype effect is the equivalent of a difference in metformin dose of 550 mg, or about half the average effect of starting a DPP4i.

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When considering etiological varia- tion, recent work partitioning diabe- tes-associated genetic variants by their presumed etiological process (parti- tioned polygenic scores) (6,42,101) may define genetically driven dominant processes. These processes, such as b-celldysfunction,lipodystrophy,orobe- sity, could respond differently to drugs that act on these pathways, such as sulfonylureas,glucagon-likepeptide1re- ceptor agonist (GLP-1RA), DPP4i, and thiazolidinediones. Genetic variation can not only capture

etiological variation but also variation in drug pharmacokinetics (absorption, dis- tribution, metabolism, excretion [ADME]) and in drug action (pharmacodynamics). Studies of ADME genes have revealed some variants with a moderate to large effect. For example, the 8% of the white population who carry two loss-of-function variants in CYP2C9 are 3.4 times more likely to achieve HbA1c target than those with normal function cytochrome P450 family 2 subfamily C member 9 (CYP2C9) due to reduced metabolism of sulfonyl- ureas and increased serum concentrations (102). SLCO1B1 and CYP2C8 genotypes that alter liver uptake and metabolism of rosiglitazone can alter glycemic response (HbA1c) by as much as 0.7% (103). While these studies have promoted pharmaco- genetic approaches in precision diabetes therapeutics, some studies have been surprisingly negative. For example, loss- of-function variants in the SLC22A1 gene, encoding the organic cation transporter 1 (OCT1), which transports metformin intotheliver(104,105),donotreducethe glucose-lowering efficacy of metformin in patients with T2D (106,107). Thus, there is genetic evidence that metformin does not work to lower glucose solely via hepatic mechanisms. The diabetes phenotype is markedly

different across ethnic groups; thus, it is likely that drug outcomes will differ between populations. The current and growing burden of diabetes is growing rapidly in all populations, particularly in South and East Asians, yet these pop- ulations are underrepresented in clinical and drug outcomes trials. A lack of systematic reviews and meta-analyses from these high-prevalence regions still points to differences in drug response. For example, the DPP4i response is greater in Asian than white people (108), a result supported by a subgroup

analysis of the Trial Evaluating Cardio- vascular Outcomes with Sitagliptin (TE- COS) showing a greater HbA1c reduction to sitagliptin in East Asians compared with white individuals (109). Glycemic response to metformin has also been reported to differ by ethnic group, with African American individuals having a greater response than European Amer- icans (110).

At this time, it is evident that we have the potential to use simple clinical (e.g., BMI, sex, ethnicity), physiological, and genetic variables to predict who is more or less likely to benefit from a treatment. The reducing costs of genotyping panels mean that genotype information could potentially be available at the point of prescribing, when the modest effect sizes described may start to have clinical utility. There is a need to develop implementa- tion and evaluation strategies to assess the effectiveness and cost-effectiveness of such approaches compared with con- ventional treatment approaches.

PRECISION APPROACHES TO DIABETES IN PREGNANCY

In women, being affected by GDM is a major risk factor for T2D. The risk of developing T2D in women with prior GDM approaches 70% after the index pregnancy (111), climbing to an 84% risk of developing T2D in women of East Indian ancestry (112). Currently, genetic studies of GDM have identified those variants known to increase risk of T2D (113);however, othervariantshave been shown to influence glycemic traits spe- cificallyinpregnancy(114).Furthermore, like T2D, GDM is a heterogeneous con- dition linked to primary defects in either insulin secretion or sensitivity (115,116). GDM can also result from monogenic forms of diabetes, as numerous studies have shown. Models that attempt to predict pregnancy complications (117) or subsequent T2D (118) in GDM using clinical characteristics, biomarkers, and/ or genetic variants have yet to be adopted, even though both lifestyle interventions and metformin use have demonstrated benefits in reducing the risk of T2D in women with prior GDM (119).

The target for all patients with T1D or T2D in pregnancy is to achieve as near normal glucose as possible, particularly aroundthetime ofconception (to reduce developmental anomalies) and in the

third trimester (to reduce the risk of macrosomia) (120). In pregnancy, the only clear exception so far is for mothers with GCK-MODY (MODY2) as fetal growth is determined predominantly by fetal genotype (121). In mothers whose fetus inherits the mother’s GCK-MODY muta- tion, fetal growth is normal despite the maternal hyperglycemia; thus, treatment of the maternal hyperglycemia is not recommended (121,122). Establishing whether the fetus is likely to be affected isusuallydeterminedbyultrasoundscan. In the future, the use of noninvasive cell- free DNA methods in maternal blood will likely establish fetal risk (123). In GDM, whether maternal hyperglycemia is closely monitored and treated in the third trimester is based on the degree of hyperglycemia determined by an oral glucose tolerance test at 24–28 weeks’ gestation (10). In the future, this decision could be modified by nonglycemic factors that impact fetal growth.

PATIENT-CENTERED MENTAL HEALTH AND QUALITY-OF-LIFE OUTCOMES

Precision diabetes medicine holds the promise of reducing uncertainty by pro- viding therapies that are more effective, less burdensome, and with fewer adverse outcomes, which ultimately improve qual- ity of life and reduce premature death (see Text Box 5). Highly relevant in this context is mental health (e.g., risk of distress and depression), yet little has been done to investigate how precision medicine might play a useful role in improving mental health outcomes.

Depression and anxiety are twice as common in people with diabetes than in the general population, occurring in up to 20% of adult patients (124). Distress occurs in ;30% of people with diabetes (125) reflecting the emotional and psy- chological burden that comes with di- abetes and its complications, the life adjustments it requires, and anxiety about hypoglycemia or the impact on the fetus for GDM. Distress has been reported as being more common in patients in secondary rather than pri- mary care and in populations with non- European ancestry. Depression is more common in lower- and middle-income countries, where ;75% of people with T2D reside (125). Both depression and distress in diabetes are more common in

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those who progress from oral agents to insulin therapy (126). The onset of com- plications with the initiation of a more complexpatternoftreatmentisassociated with increased rates of depression (126). There are key points in the life course

of a person with diabetes when both rational and irrational fears are often elevated, typically coinciding with “events,” including:

c increased medication dose c transition to insulin or other injectables or devices

c emergence of complications or wors- ening of complications

c following a severe hypoglycemic event c change in diabetes care provider.

In many cases, patient self-evaluations may be distorted at these times because the patient attributes blame for the disease to themself, the future feels uncertain and distress peaks. In the setting of precision diabetes medicine, providers should assess symptoms of diabetes distress, depression, anxiety, disordered eating, and cognitive capaci- ties using appropriate standardized and validated tools at the initial visit, at periodic intervals, and when there is a change in disease, treatment, or life circumstance (127), information that, when combined with other data, are likely to improve the precision of clinical decision making. Psychological counseling can help pa-

tients understand and manage their emotional reactions to major events by developing a more optimistic outlook and more realistic, modulated, and adap- tive emotional reactions (128). Precision medicine may be used in the future to help predict the frequency and extent of emotional crises. As a result, precision diabetes medicine may lessen the patient burden, help patients to objectivize their disease, and provide targets for behav- ioral and point-of-care interventions at critical moments in theclinical care cycle. Effective and tailored education and pro- fessional counseling will be necessary to mitigate the risk that a clearer prognosis may raise anxiety about the future for some patients.

EQUITY IN PRECISION DIABETES MEDICINE

Theexperience with monogenic diabetes has shown that there is a large degree of

regional, national, and international var- iation in how, and how often, these cases are diagnosed (1,129,130). This variation is, in part, due to differences in access to general medical care and treatments, access to relevant health care professio- nals with the necessary education, tra- ining, and experience, and access to laboratories with the necessary experi- ence, assays, and standards (131). A precision approach to diabetes care will require that the relevant laboratory methods and assays are carefully stan- dardized and comparable. Assessments that need to be standardized include:

c T1D-associated autoantibodies c C-peptide c clinical genetic/genomic risk scores c decision-support interpretation.

A challenge is that the frequency of various diabetes phenotypes and risk genotypes may vary by regions of the world and between ethnicities within a region. Forexample, T2D oftenmanifests very differently in Native Americans than in people of European ancestry, with Native Americans tending to develop diabetes at a much younger age and experience loss of b-cell function earlier in the life course of the disease (132). Recent insights following the ADA Pre- cision Diabetes Medicine meeting in Madrid (held in October 2019) confirm that case-based interactive learning is an excellent way to support this type of postgraduate education for clinicians at all levels of training.

THE ROAD TO IMPLEMENTATION

Advances in science allow for generation of large-scale biological and physiological data that can be harnessed for precision diagnostic (Fig. 2), therapeutic (Fig. 3), and prognostic (Fig. 4) purposes. Pro- grams are needed to train, foster, and retain individuals with biological and data science expertise who will contrib- ute to precision diabetes medicine ef- forts. Furthermore, clinicians, scientists, and regulators must collaborate to de- velop standards and safeguards for pro- tecting the accumulated “precise” data, which in some instances may lead to unintended and sensitive revelations, on individuals in a secure manner across populations and across countries. World- wide differences in prevalence of the

forms of diabetes necessitates inclusion of currently understudied populations for the development of precision diag- nostics and therapeutics. As a result, the precise subtype of diabetes a particular individual is diagnosed with may vary in different populations based on subtype frequency or genetic or dietary or life- style differences.

The communication strategy used by the interventionalist and the patient’s perception of risk may be important factors contributing to the successful implementation of precision diabetes medicine. Both personal and societal barriers may exist to the implementation of precision prevention across geo- graphic regions and countries. Discus- sions with global and regional regulatory agencies will be needed todetermine the level of evidence needed for approval and adoption of precision diagnostics and therapeutics. The development of tools and strategies to synthesize patient data and facilitate shared decision mak- ing will be needed to translate evidence for precision diabetes medicine into in- dividualized diabetes care, accounting for patient preferences and behaviors, health literacy, and socioeconomic con- siderations.Pragmaticstudiesofdecision- support systems utilizing rich information in these health care systems, particularly those with biobank-linked electronic health care records, are needed to guide implementation of precision diabetes medicine into clinical practice and to generate the much needed cost-efficacy data for broader adoption.

BUILDING PARTNERSHIPS

Partnerships must be established be- tween the scientific community, pa- tients, health care systems, providers, payors, industry, and regulatory bodies involved in the development, evaluation, approval, adoption, and implementation of precision diagnostics, monitoring, and therapeuticsthataredeemedacceptable for safe, efficacious, and cost-effective use in precision diabetes care. Making the most of the opportunities offered by precision diabetes medicine will require many different stakeholders to form highly effective partnerships. Without networks of partnerships that span aca- demic institutions, corporations, payors, regulators, and medical and public interest groups with shared understanding and

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vision (Fig. 5), precision diabetes medi- cineis destined to fail.Partners inmaking precision diabetes medicine a reality include: People with diabetes. People with di-

abetes are the most important sta- keholders. In Western countries, between 1 in 10 and 1 in 20 people have diabetes, while in other parts of the world, diabetes is more prevalent (1 in 3 in some Middle Eastern popula- tions (133), and 1 in 2 in some Native American tribes [132]). The precision approach to diabetes will require effec- tive patient-facing, bidirectional commu- nication strategies that explain what precision medicine is and how it works. People with diabetes should be invited to contribute to research through advisory and advocacy positions, to contribute to postgraduate educational programs for clinicians and to play a central role in discussions with politicians, regulators, and payors. Regulatory agencies. The transition

from current diabetes clinical practice to a precision medicine approach will have important implications for the de- velopment, prescription, and regulation of diagnostics and therapeutics. Involve- ment of regulators at the earliest stages of the precision diabetes medicine work- flow will be critical to the successful implementation of the precision ap- proach. Recognizing these challenges, the FDA and the European Medicines Agency have initiated discussions relat- ing to standards for evidence and the design of future clinical trials for pre- cision diabetes medicine (134). Payors. Payment for medical care re-

lated to diabetes varies greatly, includ- ing between regions within countries, with costs for diabetes often hidden in other areas of medical care. Fragmenta- tion of sites of delivery for diabetes care and its costs directly impact payment policies. There is evidence in the case of monogenic diabetes that a precision medicine approach is cost-effective (135). The delay, or prevention, of com- plications (the major contributor to di- abetes costs) through precision diabetes medicine may be the strongest driver for adoption. Product manufacturers. Diabetes

technology, including the development of wearable devices for glucose mo- nitoring and for regulating insulin infu- sions (i.e., the artificial pancreas), has

developed rapidly and is an example of widespread personalized diabetes medicine. Technology and pharmaceu- tical implementation is currently at a pre-precision level, and treatment guidelines are quite generic. The Euro- pean Federation of Pharmaceutical Industries and Associations (EFPIA) Di- abetes Platform, in which six leading pharmaceutical companies are develop- ing shared policy goals focused on im- proving diabetes clinical outcomes, has initiated multiple projects with strong precision diabetes medicine agendas, with other public-private partnerships focused on precision diabetes medicine underway (136).

Private and public supporters of re- search. Support for diabetes research funding has struggled as its priority has fallen among the general public and some political decision makers, where cancer and cardiovascular disease rankconsistently higherthandiabeteson the public agenda. For precision diabetes medicine to meaningfully improve the lives of patients, it will be necessary to build highly effective networks of key stakeholders, such that common agen- das are agreed to and funding for re- search and implementation is made available. This, in turn, requires that the evidence justifying a precision di- abetes medicine approach is clearly ar- ticulated to all major decision makers, including funders.

Clinicians and professional organiza- tions. Medical care for the person with diabetes involves a wide spectrum of health care providers, including tertiary andsecondaryspecialists,generalintern- ists, primary care doctors, nurses, die- titians, podiatrists, pharmacists, and other paramedical professionals. Several organizations are engaged in the PMDI (ADA, EASD,NIDDK) and representatives of professional bodies in Asia, Africa, and elsewhere are being engaged by the PMDI to ensure global impact. Tailoring educational modules and content to different professional and cultural set- tings is ideally suited to these partner organizations.

General public. The enormous burden that diabetes places on many health care systems is usually shouldered by the general public, owing to the high costs of treating the disease and loss of public revenue through decreased pro- ductivity. The effective implementation

of precision prevention will require that the general public embraces the approach and that those in greatest need can access precision prevention programs. Diabetes messaging for the general public can be modeled on precision oncology, for which public advocacy and engage- ment have been successful, effectively utilizing social media as well as traditional media to communicate not only its strengths and weaknesses but also its benefits and risks.

SUMMARY AND FUTURE PERSPECTIVES

Precision diabetes medicine has found a firm foothold in the diagnosis and treat- ment of monogenic diabetes, while the application of precision medicine to other types of diabetes is at this time aspirational, rather than standard of care. The ability to integrate the diag- nosis of monogenic diabetes into routine clinical care is one example where diag- nostics are essential and meet many of the characteristics of the ideal test. De- spite an excellent diagnostic paradigm, there are no known avenues for pre- vention in monogenic diabetes, although careful monitoring in presymptomatic variant carriers may lead to early de- tection of diabetes and rapid treatment.

Future precision diabetes medicine approaches are likely to include diagnos- tic algorithms for defining diabetes subtypes in order to decide the best interventional and therapeutic ap- proaches. The scope and potential for precision treatment in diabetes is vast, yet deep understanding is lacking. It will be imperative to determine when and how the application of therapeutics in precision diabetes medicine improves outcomes in a cost-effective fashion.

There are many important stakehold- ers whose engagement will be necessary for the implementation of precision di- abetes medicine to succeed (Fig. 5). Progress in translating advances in bio- logy and technology will be governed by the identification, accurate measure- ment, and scalable deployment of agents for diagnosis and therapy, so broad stakeholder engagement is essential. It is crucial that precision approaches are available to the full diversity of human populations and societal contexts, such that precision diabetes medicine does not widen health disparity but achieves

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the greatest benefits to all individuals and society as a whole. Highly functional partnerships with patient representa- tives and public organizations will be required to reap the benefits of precision diabetes medicine.

Acknowledgments. The authors thank P. Sim- ing (Lund University) for editorial assistance, H. Fitipalidi(LundUniversity)forassistancewiththe design of the figures and Prof. H. Mulder (Lund University) for technical critique. The authors acknowledge the invited peer reviewers who provided comments on an earlier draft of this report: Helen Colhoun (University of Edinburgh), Boris Draznin (University of Colorado School of Medicine), Torben Hansen (University of Copen- hagen), Pål Njølstad (University of Bergen), and Matthew C. Riddle (Oregon Health and Science University). Funding. Funding for the PMDI is from the American Diabetes Association. In-kind support has been provided by the academic institutions of each Task Force member. The ideas and opinions expressed in this report were derived inpartfromworkundertakenbythecoauthors,for which they report the following support: W.K.C. (NIH: R01DK52431, P30 DK26687, U54 TR001873, and U54DK118612); A.T.H. (Wellcome Trust Se- nior Investigator: 098395; National Institute for Health Research [NIHR] Senior Investigator and support of Exeter NIHR Clinical Research Facility; MedicalResearchCouncil[MRC]:MR-K005707-1); M.-F.H. (ADA 1-15-ACE-26, NIH 5R01HD94150-02); M.I.M. (Wellcome Trust Senior Investigator and NIHR Senior Investigator: 203141, 212259, 098381; NIDDK: U01-DK105535); J.M.N. (NIH: R01 DK104351, R21 AI142483); E.R.P. (Wellcome Trust New In- vestigator award: 102820/Z/13/Z); L.P. (NIH: R01DK104942, P30 DK02059, U54DK118612); S.S.R. (NIH: DP3 DK111906, R01 DK122586; Uni- versity of Virginia StrategicInvestmentFundSIF88); P.W.F. (European Research Council: CoG-2015_ 681742_NASCENT; Swedish Research Council; Novo Nordisk Foundation; European Diabetes Research Foundation; Swedish Heart Lung Foundation; In- novative Medicines Initiative of the European Union: no. 115317 – DIRECT and no. 115881 – RHAPSODY; no. 875534 – SOPHIA). Duality of Interest. W.K.C. is on the scientific advisory board of the Regeneron Genetics Cen- ter. J.C.F. has received a speaking honorarium from Novo Nordisk and consulting fees from Janssen Pharmaceuticals. M.I.M. has in the past 3 years served on advisory panels for Pfizer, Novo Nordisk A/S, and Zoe Global Ltd.; has received honoraria from Merck, Pfizer, Novo Nordisk, and Eli Lilly; and has received research funding from Abbvie, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk A/S, Pfizer, Roche, Sanofi, Servier, and Takeda. As of June 2019, M.I.M. is an employee of Genentech and a holder of Roche stock. E.R.P. has received re- search funding from Boehringer Ingelheim, Eli Lilly, Janssen, Novo Nordisk A/S, Sanofi, and Servier and honoraria from Eli Lilly. L.P. has received research funding from Janssen and Provention Bio. P.W.F. has received research funding from Boehringer Ingelheim, Eli Lilly, Janssen, Novo Nordisk A/S, Sanofi, and Servier;

received consulting fees from Eli Lilly, Novo Nordisk, and Zoe Global Ltd.; and has stock options in Zoe Global Ltd. No other potential conflicts of interest relevant to this article were reported.

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