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International Journal of Epidemiology, 2018, 1–11
doi: 10.1093/ije/dyy274
Opinion
Downloaded from https://academic.oup.com/ije/advance-article-abstract/doi/10.1093/ije/dyy274/5240939 by Universite and EPFL Lausanne user on 21 December 2018
Opinion
Precision nutrition: hype or hope for public
health interventions to reduce obesity?
1 1 2
AngelineChatelan, *MurielleBochud andKatherineLFrohlich
1
Institute of Social and Preventive Medicine, Lausanne University Hospital, University of Lausanne,
Lausanne, Switzerland and 2De´partement de me´decine sociale et pre´ventive, Ecole de Sante´ Publique
& Institut de recherche en sante´ publique de l’Universite´ de Montre´al, Universite´ de Montre´al,
Montreal, QC, Canada
*Corresponding author. Institut universitaire de me´decine sociale et pre´ventive (IUMSP), Biopoˆle 2, Route de la Corniche
10, CH-1010 Lausanne, Switzerland. E-mail: angeline.chatelan@chuv.ch
Editorial decision 5 November 2018; Accepted 17 November 2018
Abstract
High-income countries are experiencing an obesity epidemic that follows a socioeco-
nomic gradient, affecting groups of lower socioeconomic status disproportionately.
Recent clinical findings have suggested new perspectives for the prevention and
treatment of obesity, using personalized dietary approaches. Precision nutrition (PN),
also called personalized nutrition, has been developed to deliver more preventive and
practical dietary advice than ‘one-size-fits-all’ guidelines. With interventions becoming
increasingly plausible at a large scale thanks to artificial intelligence and smartphone
applications, some have begun to view PN as a novel way to deliver the right dietary
intervention to the right population. We argue that large-scale PN, if taken alone, might
be of limited interest from a public health perspective. Building on Geoffrey Rose’s
theory regarding the differences in individual and population causes of disease, we show
that large-scale PN can only address some individual causes of obesity (causes of cases).
This individual-centred approach is likely to have a small impact on the distribution of
obesity at a population level because it ignores the population causes of obesity (causes
of incidence). The latter are embedded in the populations’ social, cultural, economic and
political contexts that make environments obesogenic. Additionally, the most socially
privileged groups in the population are the most likely to respond to large-scale PN
interventions. This could have the undesirable effect of widening social inequalities in
obesity. We caution public health actors that interventions based only on large-scale PN
are unlikely, despite current expectations, to improve dietary intake or reduce obesity at
apopulationlevel.
Key words: Precision nutrition, personalized nutrition, obesity, population interventions, social inequalities in
health, obesogenic environments
C
VTheAuthor(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association. 1
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/),
which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact
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2 International Journal of Epidemiology, 2018, Vol. 0, No. 0
Key Messages
• Some public health actors have begun to view large-scale precision nutrition as a novel opportunity to provide the
right dietary intervention to the right population at the right time. Downloaded from https://academic.oup.com/ije/advance-article-abstract/doi/10.1093/ije/dyy274/5240939 by Universite and EPFL Lausanne user on 21 December 2018
• Large-scale precision nutrition is an individual-centred approach focusing on behavioural modification in large num-
bers, and not a true population approach as defined by Geoffrey Rose.
• Large-scale precision nutrition is likely to have a limited impact on obesity at a population level as it neglects popula-
tion causes of obesity that are rooted in obesogenic environments.
• Early adoption and achievement of improved dietary habits based on precision nutrition are more likely among more
socially privileged members of the population, which would exacerbate socioeconomic inequalities in diet and obesity.
• If taken alone, interventions based on large-scale precision nutrition are unlikely to improve dietary intake or reduce
obesity at a population level.
optimal diet is not the same for everyone. In brief, PN aims
Introduction
Most high-income countries are experiencing an obesity at delivering tailored nutritional recommendations based on
1 combined information from individuals’ gut microbiota, ge-
epidemic, since 1975. For example, in the USA, more than
37–42
one in three adults and one in six children were estimated netic, physiological, and behavioural backgrounds.
2 Following these promising results in clinical re-
to be obese in 2015. Obesity has been linked to
search,27–36 some large public research funders, such as the
numerous non-communicable diseases such as diabetes,
43
cardiovascular disease, osteoarthritis and certain forms of EU Horizon 2020 programme, have encouraged
3–5 researchers to test solutions providing tailored nutritional
cancer. According to the 2016 Global Burden of
6 advice to large numbers of people, including healthy indi-
Disease, an unbalanced diet, obesity, and high fasting
plasma glucose were among the top six leading risk factors viduals. An international trial, Food4Me, was recently
for disability-adjusted life-years in high-income countries. launched with 1600 volunteers to test the opportunities
44
In these countries, the incidence and prevalence of obesity and challenges of PN in the general population. Within
this context, some39,42,45,46 have begun to consider PN as
follows a socioeconomic gradient, whereby individuals
with lower education, occupation and income are dispro- an emerging tool for public health to reduce obesity and
portionally affected.7–9 In Spain, Italy and France for in- obesity-related diseases, notably because precision
stance, the least educated women are over four times as approaches have a marked preventive component.
likely to be obese as the most educated ones.10 In parallel, advances in ‘omics’ technologies and wear-
Diet is a major modifiable determinant of obesity. able devices facilitate less costly collection and analysis of
Multiple public health interventions to improve population massive data. This makes scaleable delivery of tailored nu-
38,39,42
dietary intake have been implemented to date. Some tritional advice increasingly plausible. Thanks to
individual-centred interventions have aimed at providing in- these technical developments and the clinical context
formation about healthy eating. They used, for example, explainedabove,PNcouldbeviewedasanovelopportunity
mass campaigns to disseminate dietary guidelines (e.g. ‘5 a to provide the right dietary intervention to the right popula-
11–13 47–49
day’) and food guides (e.g. MyPlate in the USA). More tion at the right time, and on a large scale. In this paper,
recent interventions have focused on shaping the food envi- we explore the promises and potential limitations of inter-
ronment through structural measures. Classical examples ventions based on large-scale PN. We question their rele-
14,15 vance in balancing individuals’ diet and addressing obesity
are compulsory nutritional standards for school meals
16–18 at a population level. We build our argument on Geoffrey
or taxes on sugar-sweetened beverages. So far out-
50,51
comes have been disappointing. People largely fail to follow Rose’s theory regarding the differences in individual
19–22 and population causes of disease. We finally argue that
the dietary guidelines. As for obesity, the prevalence
2,23 24 large-scale PN could possibly have the unintended effect of
has not declined, and social inequalities in diet and
10,25,26 exacerbating social inequalities in obesity.
obesity havepersisted or even increased.
27–35
Recent research findings, particularly by Zeevi
36
et al. have suggested new perspectives for the prevention
andtreatment of obesity-related diseases, using personalized Whatislarge-scaleprecisionnutrition?
dietary approaches. Precision nutrition (PN), also called per- 36
Modelled after PN in clinical settings, large-scale PN
sonalized nutrition, is based on the postulate that the relies on the collection and analysis of several types of data
International Journal of Epidemiology, 2018, Vol. 0, No. 0 3
from eating behaviour, physical activity, deep phenotyp- cholesterol, carotenes and omega 3 index; (iii) diet and
ing, nutrigenomics, microbiomics/metagenomics and phenotype plus genotype (i.e. specific variants on five diet-
37–42 44
metabolomics (Table 1). These data serve to define responsive genes).
the appropriate diet for each individual, or more realisti- Once the desired level of precision/information is de- Downloaded from https://academic.oup.com/ije/advance-article-abstract/doi/10.1093/ije/dyy274/5240939 by Universite and EPFL Lausanne user on 21 December 2018
49,52
cally, each population sub-stratum. Different amounts fined, data can be collected on a large scale using personal
of data can be collected and analysed depending on the in- smartphones and other relatively inexpensive and reliable
frastructure availability and financial resources. For exam- wearable devices, such as an electronic food diary and
ple, in the Food4Me trial, the intervention involved the wristband for accelerometry.38,39 In parallel, new tools
delivery of personalized nutrition advice based on data (Table 1), such as dried blood spot testing42 already rou-
53
from: (i) current diet; or (ii) diet plus phenotypic traits tinely used for the Guthrie test in newborns, and simple
such as waist circumference, serum glucose, total 36,54
stool kits, enable biosample collection from home or a
Table1.Potential sources of data for tailored nutritional advice in large-scale precision nutrition interventions
Data Aimsofdatacollection Methodstoproducedata Infrastructures and tools to col-
lect, analyse and store data
Eating behaviour Toevaluate: Dietary assessment on several days • Driedbloodspottesting
• Dietary intake (e.g. food using: • Saliva swabs
consumption, use of nutrient • Onlinefooddiary • Stool kits
supplements) • Smartphoneapplications (self- • Shipmentmaterial
• Eating behaviour description and quantification of • Localpharmacynetworks
consumedfoods) • Accelerometers
• Digital photography (semi-auto- • Smartphoneandotherdigital
maticidentification and quantifi- technologies
cation of consumed foods) • Biobanks
Physical activity Tomeasurephysicalactivity Accelerometry techniques using: • Linkagewithelectronic
level • Wearable/portable devices (e.g. health records
Toestimateenergyexpenditure wristband) • Biomedical laboratories
• Onlinequestionnaire • Artificial intelligence etc.
Deep Toassess: Anthropometric measurements
phenotyping • Bodycomposition (e.g. weight, waist circumfer-
• Nutritional status ence, bone densitometry)
• Otherrisk factors for Clinical chemistry from various
diet-related diseases bio-samples (e.g. plasma, urine,
saliva) to assess visceral fat dis-
tribution, insulin resistance,
low-density lipoprotein choles-
terol, nutrient deficiencies, etc.
Nutrigenomics Tolookforgeneticvariants DNAextractionandgenotypingof
associated with diet-related selected loci from whole-blood
diseases and/or responsive to samples
dietary changes
Microbiomics/ Tounderstandtheinterplay Faeces collection to sequence the
metagenomics betweendietandgut microorganismspresent in the
microbiota gut for microbial profiling and
detection of dysbiosis
Metabolomics Tounderstandhowthebody Complexchemicalanalysesfrom
metabolizes/uses nutrients biosamples (e.g. serum, plasma,
urine) using:
• Nuclearmagneticresonance
spectroscopy
• Massspectrometry-based
techniques
4 International Journal of Epidemiology, 2018, Vol. 0, No. 0
local pharmacy. The Food4Me intervention was entirely challenges are currently being addressed by some countries
internet-delivered, for instance. Participants themselves that have launched large-scale precision medicine projects,
collected both biosamples, using the saliva swabs for geno- such as the Precision Medicine Initiative in one million US
72 Downloaded from https://academic.oup.com/ije/advance-article-abstract/doi/10.1093/ije/dyy274/5240939 by Universite and EPFL Lausanne user on 21 December 2018
typing and dried blood spots for phenotyping. They fol- residents, and the human biomonitoring project
lowed online demonstrations, and sent their biological (HBM4EU) in 28 European countries.73 On the other
material by conventional mail.44 The advances of labora- hand, the effectiveness associated with both identifying the
tory analytical techniques (e.g. DNA sequencing, mass individual risk and delivering personal messages for pre-
39,42
spectrometry), bioinformatics, and artificial intelli- vention and treatment of obesity-related disease is dis-
4,38,40,74–77
gence (e.g. machine-learning algorithms, deep learn- puted. The 2018 Lancet review by Wang and
36,38,55,56 38
ing) render the analysis and interpretation of Hu concluded that evidence is currently lacking to sup-
large datasets less and less expensive and time-consuming. port the additional benefits of PN over ‘one-size-fits-all’
Lastly, smartphone applications allow large-scale dis- nutrition intervention in the prevention and treatment of
semination of personalized advice directly to individuals. type 2 diabetes. Evidence regarding effectiveness and cost-
For instance, the applications delivered by the companies effectiveness of large-scale PN in the general population is
57 58
DayTwo and Viome can provide a personal score for even scarcer. To date, the Food4Me trial has determined
foods or recipes regarding their potential positive or nega- that participants receiving personalized advice had a
tive impact on blood glucose level. The enterprise habit59 healthier diet compared with controls receiving standard
even offers detailed menu plans to comply with personal- guidelines after the 6-month intervention (completion rate:
78
ized recommended intake in terms of protein, carbohy- 79%). However, no significant changes in weight or
drate and fat. waist circumference were observed, even when phenotypic
or genotypic data were considered to personalized diet.
The question of effectiveness on population health will
Large-scale precision nutrition: promises probably remain open for some years.
andchallenges
Thecentral promise of large-scale PN is personalized inter-
ventions based on more: (i) preventive (predictive and ac- Obeseindividualsandobesepopulations
curate); (ii) practical (understandable and implementable); In public health, two main traditional strategies have
and (iii) dynamic nutritional advice than ‘one-size-fits-all’ existed for preventive interventions: high-risk and popula-
39 50,51
guidelines. First, PN advocates presume that nutritional tion approaches. The traditional population approach
advice is likely to be more predictive because the personal seeks an improvement of overall population health by
risk of developing specific diseases (e.g. based on polygenic shifting the distribution of exposure risk in a favourable di-
risk scores) and biomedical context can be consid- rection in the entire population (Figure 1A). With the as-
40,49,60
ered. Advice could also be more accurate due to sumption that ‘a large number of people at a small risk
more precise dietary intake and nutritional status assess- maygive rise to more cases of disease than the small num-
61–65
ment and better anticipation of interpersonal variabil- ber who are at a high risk’, the population approach con-
ity in food metabolic response.36,66,67 Second, personalized trasts with the high-risk approach.50 The high-risk
nutritional advice may be easily understood, as messages approach proposes targeted interventions addressed only
could be delivered in a simpler way using modern commu- to individuals screened for their higher probability of de-
nication techniques.68,69 Advice may also be more imple- veloping the disease.50
mentable as adapted to actual food consumption, personal Large-scale PN targets the whole population in the
68–70
food preferences and lifestyle. Third, nutritional ad- spirit of a traditional population approach. Both preven-
vice would evolve following the personal dietary and bio- tive strategies can be used for primary and secondary pre-
medical evolutions of each individual as automatically vention. However, large-scale PN interventions
processed and refined over time through new data.39 In substantially differ from the traditional population inter-
sum,large-scale PN promises better individual risk identifi- ventions, in the way of achieving the distribution shift. The
cation through comprehensive screening and behavioural former targets individual risk with precision behavioural
modification in line with these identified risks. measures in large numbers, whereas the latter targets over-
At present, large-scale PN faces two main challenges, all population risk with structural/environmental meas-
however. On the one hand, its application on a large scale ures, as shown below.
raises organizational, legal and ethical questions, notably In the 1985 seminal article ‘Sick individuals and sick
50
regarding biobank management, data protection and in- populations’, still considered relevant for modern public
42,52,71 79
formed consent. However, these technical health, Geoffrey Rose suggested a distinction be made
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