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Precision Medicine & Health Equity on a Global Scale

Cardiometabolic diseases include type 2 diabetes, cardiovascular disease, metabolic liver disease, and numerous other complications. These diseases are on the rise globally, with the most rapid increases seen in low- and middle- income countries (LMICs). There are many modifiable environmental exposures that trigger underlying genetic predispositions to disease. Obesity is often, yet not always, a core driver of cardiometabolic diseases. Collectively, cardiometabolic diseases are the predominant avoidable causes of global mortality.

The aetiology, clinical manifestations, and pathogenesis of cardiometabolic diseases exhibit significant variation within and between populations globally. There is no one-size-fits all solution for the prevention and management of these disease, yet this is the assumption underlying many public health strategies.  

Precision medicine takes a different approach, where variation in the effects of risk factors and interventions is leveraged to help optimize healthcare. Acknowledging that fully personalised approaches are probably unrealistic for most complex diseases, precision medicine instead focuses on tailoring healthcare to defined population subgroups. This may involve optimizing algorithms (for diagnosis, prevention, treatment, and prognosis) using a range of person-level features such as ancestry, biology, behaviour, environment, and clinical characteristics, and subjective factors like the person’s capabilities, needs, and preferences. Consideration of the capacity and accessibility of local resources and infrastructures is also paramount.

Precision medicine is often inaccessible to people living in LMICs, as well as to underserved communities in high-income countries. There is a moral imperative to develop and implement precision medicine solutions within and for these populations, as a failure to do so will widen global health disparities.

Evidenced from migration studies and trans-ethnic comparisons shows that performance of prediction scores and the effects of risk factors and treatments vary from one sociodemographic group to the next. Indeed, even within the same broad ethnic group, extrapolation of precision medicine algorithms can fail dismally. Thus, the notion that research findings derived in affluent, European ancestry populations (currently the source of most relevant data) can be effectively extrapolated to other groups is problematic.

Some LMICs already possess the requisite conditions to develop and implementation of precision medicine, owing to advancements made in combating infectious diseases and mother and infant mortality. Accordingly, in LMICs, there is often less inertia in healthcare infrastructures than in high-income countries, owing to traditions of task-shifting and task-sharing and more advanced home-based diagnostic testing and healthcare delivery. It is also possible that the improvements in health and wellbeing that precision medicine can facilitate will be asymptotic, with the most marked advancements (per unit investment) in countries with less developed healthcare systems, enabling ‘leapfrog’ development – a phenomenon seen in other areas of technology such as finance and telecommunications.

It is also true that the combined analysis of data across diverse populations often yields insights that would not be made within any one of these groups independently. This has been shown extensively in population genetics, where transethnic analyses have revealed disease-associated loci that are not visible in ethnicity-specific analyses, some of which may represent novel drug targets that may be of value across ethnicities. Thus, there is an argument that undertaking research germane to precision medicine across a diversity of populations and settings will yield collective benefits.

The solution to this challenge is complex but can be broken down into three key components: i) better data in regions where precision health solutions are needed; ii) capacity development in data science and translational implementation within target regions; iii) precision health algorithms optimised to target populations and local care implementation pathways.


Research highligths (of >550 total papers | H-index: >125 | citations: >75,000):

1.    Cefalu WT, Franks PW*, Rosenblum ND, Zaghloul N, Florez JC, Giorgino F, Ji L, Ma RCW, Mathieu C, Misra S, Ramirez AH, Roden M, Scherer PE, Sheu W H-H, Stehouwer C, Woo M, Pragnell M, Anand SS, Carnethon M, Chambers JC, Dennis JM, Gloyn AL, Herder C, Holt RIG, Manuel DG, Redondo MJ, Tandon N, Tsang JS, Udler MS, Rich SS. A global initiative to deliver precision health in diabetes. Nature Medicine. (in press).   *Initiative co-chair

2.    Lim SS, Semnani-Azad Z, Morieri ML, Ng AH, Ahmad A, Fitipaldi H, Boyle J, Collin C, Dennis JM, Langenberg C, Loos RJF, Morrison M, Ramsay M, Sanyal AJ, Sattar N, Hivert MF, Gomez MF, Merino J, Tobias DK, Trenell MI, Rich SS, Sargent JL, Franks PW*. Reporting guidelines for precision medicine research of clinical relevance: the BePRECISE Checklist. Nature Medicine. (in press).   *Guidelines Chair/corresponding author

3.    Tobias D., Merino J., et al. [197 coauthors] … Franks PW*. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nature Medicine. 2023.    *Senior/corresponding author; Consensus Report Chair

4.    Franks PW*., Cefalu WT., Dennis J., Florez JC., Mathieu C., Morton RW., Ridderstråle M., Sillesen HH., Stehouwer CDA. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diab Endo. 2023.    *Senior/corresponding author

5.    Misra S., Aguilar-Salinas CA., Chikowore T., Konradsen F., Ma RCW., Mbau L., Mohan V., Morton RW., Nyirenda MJ., Tapela N., Franks PW*. The case for precision medicine in the prevention, diagnosis and treatment of cardiometabolic diseases in low- and middle-income countries. Lancet Diab Endo. 2023.    *Senior/corresponding author

6.    Coral DE (PhD Student), et al. [10 coauthors]… Franks PW*. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes. Nature Metabolism. 5(2):237-247. 2023.    *Senior/corresponding author

7.    Bar N, et al. [12 coauthors] … Franks PW, Pedersen O, Segal E. A reference map of potential determinants for the human serum metabolome. Nature. 588(7836):135-140. 2020

8.    Berry SE, et al. [15 coauthors] … Franks PW*, Spector TD*. Human postprandial responses to food and potential for precision nutrition. Nature Medicine. 26(6):964-973. 2020.   *joint senior authors

9.    Atabaki-Pasdar N (PhD student), et al. [40 coauthors] … Franks PW*. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts. PLoS Medicine. 19;17(6):e1003149. 2020.  *Senior/corresponding author

10. Franks PW & McCarthy MI. Exposing the exposures responsible for type 2 diabetes and obesity. Science. 7;354(6308):69-73. 2016


Expertis relaterad till FN:s globala mål

2015 godkände FN:s medlemsstater 17 Globala mål för en hållbar utveckling, för att utrota fattigdomen, skydda planeten och garantera välstånd för alla. Den här personens arbete relaterar till följande Globala mål:

  • SDG 3 – God hälsa och välbefinnande
  • SDG 11 – Hållbara städer och samhällen


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