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www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8 1
Review
Precision nutrition for prevention and management of 
type 2 diabetes 
Dong D Wang, Frank B Hu
Precision nutrition aims to prevent and manage chronic diseases by tailoring dietary interventions or recommendations 
to one or a combination of an individual’s genetic background, metabolic profile, and environmental exposures. 
Recent advances in genomics, metabolomics, and gut microbiome technologies have offered opportunities as well as 
challenges in the use of precision nutrition to prevent and manage type 2 diabetes. Nutrigenomics studies have 
identified genetic variants that influence intake and metabolism of specific nutrients and predict individuals’ 
variability in response to dietary interventions. Metabolomics has revealed metabolomic fingerprints of food and 
nutrient consumption and uncovered new metabolic pathways that are potentially modified by diet. Dietary 
interventions have been successful in altering abundance, composition, and activity of gut microbiota that are relevant 
for food metabolism and glycaemic control. In addition, mobile apps and wearable devices facilitate real-time 
assessment of dietary intake and provide feedback which can improve glycaemic control and diabetes management. 
By integrating these technologies with big data analytics, precision nutrition has the potential to provide personalised 
nutrition guidance for more effective prevention and management of type 2 diabetes. Despite these technological 
advances, much research is needed before precision nutrition can be widely used in clinical and public health settings. 
Currently, the field of precision nutrition faces challenges including a lack of robust and reproducible results, the 
high cost of omics technologies, and methodological issues in study design as well as high-dimensional data analyses 
and interpretation. Evidence is needed to support the efficacy, cost-effectiveness, and additional benefits of precision 
nutrition beyond traditional nutrition intervention approaches. Therefore, we should manage unrealistically high 
expectations and balance the emerging field of precision nutrition with public health nutrition strategies to improve 
diet quality and prevent type 2 diabetes and its complications.
Introduction
In 2017, 425 million adults worldwide were living with 
diabetes, a vast majority of whom had type 2 diabetes.1 
The pronounced increase in prevalence during the past 
few decades is a consequence of the global pandemic of 
obesity driven by urbanisation and its related lifestyle 
changes.2 Substantial evidence indicates that type 2 
diabetes can be largely prevented through adherence to a 
healthy lifestyle, which includes consumption of a 
high-quality diet, regular exercise, and maintenance of a 
healthy bodyweight.3 Type 2 diabetes is clinically 
managed by healthy diets and lifestyles combined with 
glucose-lowering agents that aim to prevent or delay both 
acute symptoms of hyperglycaemia and complications of 
the disease.4 As recommended by the Dietary Guidelines 
for Americans5 and the American Diabetes Association,6 
a healthy dietary pattern that protects against 
type 2 diabetes is rich in fruits, vegetables (except 
potatoes), whole grains, nuts, and legumes, and low in 
refined grains, red or processed meats, and sugar-
sweetened beverages.7 Along with this dietary pattern, an 
improvement in the quality of fats and carbohydrates 
consumed is important.7 In practice, this can be achieved 
by replacing saturated fat and high-glycaemic index 
carbohydrates with unsaturated fats and carbohydrates 
with a lower glycaemic index and higher fibre content.
Current dietary recommendations are based on 
population averages and often do not take into account 
individual variability in response to nutritional 
components. Although successful in reducing the 
population-level chronic disease burden,8 dietary 
guidelines based on population averages may not be 
best suited for a given individual. In addition, 
type 2 diabetes is a heterogeneous disease from a 
genetic, pathophysiological, and clinical viewpoint.9 
Current understanding of the pathophysiological 
mechanisms of type 2 diabetes remains insufficient to 
explain the large variability between individuals in both 
the development and the clinical manifestations of the 
disease.10,11 In addition, individual responses to dietary, 
lifestyle, and pharmaceutical interventions varies 
considerably. Recently, the concept of precision nutrition 
(also known as personalised nutrition) has gained a 
great deal of interest in the scientific community and 
the general public.12 In this Review, we examine 
precision nutrition and relevant evidence from 
population-based studies, and we discuss promises and 
challenges in the context of dietary prevention and 
management of type 2 diabetes.
Goals of precision nutrition
The Precision Medicine Initiative, launched in 2015, aims 
to provide safer and more effective ways to prevent and 
treat disease.13 A key mission of this initiative is to “tailor 
treatment and prevention strategies to people’s unique 
characteristics, including their genome sequence, 
microbiome composition, health history, lifestyle, and 
diet”.14 Analogous to precision medicine, precision 
nutrition adapts nutrition interventions and recom-
mendations on the basis of individual characteristics to 
prevent and manage chronic diseases such as 
type 2 diabetes. In a broader sense, precision nutrition 
Lancet Diabetes Endocrinol 2018 
Published Online 
February 9, 2018 
http://dx.doi.org/10.1016/ 
S2213-8587(18)30037-8
Department of Nutrition 
(D D Wang ScD, Prof F B Hu MD), 
and Department of 
Epidemiology (Prof F B Hu), 
Harvard T H Chan School of 
Public Health, and Channing 
Division of Network Medicine 
(Prof F B Hu), Department of 
Medicine, Brigham and 
Women’s Hospital and Harvard 
Medical School, Boston, MA, 
USA
Correspondence to: 
Prof Frank B Hu, Department of 
Nutrition, Harvard T H Chan 
School of Public Health, Boston, 
MA 02115, USA 
nhbfh@channing.harvard.edu
http://crossmark.crossref.org/dialog/?doi=10.1016/S2213-8587(18)30037-8&domain=pdf
2 www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8
Review
can also enable more precise dietary assessment and 
disease risk stratification, both of which are fundamental 
for more effective targeting of dietary approaches for 
prevention and management of type 2 diabetes.
Recent advances in omics technologies and wearable 
devices have improved the prospect of applying precision 
nutrition to prevention and management of type 2 diabetes 
(figure 1). The huge increase in genome-wide association 
studies (GWAS) in the past decade has generated extensive 
new knowledge about the genetic architecture of the 
disease.15 High-throughput next-generation sequencing 
technology has made whole-exome and whole-genome 
sequencing studies feasible and has underpinned studies 
of biological axes beyond the genome, such as the 
transcriptome, epigenome, and microbiome. Mass 
spectrometry and nuclear magnetic resonance have been 
applied to analyse small-molecule metabolites in 
biospecimens, allowing for a comprehensive assessment 
of an individual’s metabolic status. Meanwhile, mobile 
technologies and wearable devices can facilitate real-time 
data collection of diet, lifestyle, and biochemical variables 
and encourage individuals to actively participate in their 
own behaviour change and disease management.
Precision nutrition can integrate data from emerging 
technologies and traditional nutritional assessments into 
epidemiological or dietary intervention studies of 
type 2 diabetes to achieve several goals: to better under-
stand the mechanisms underlying different res ponses by 
individuals to a dietary exposure or inter vention, in terms 
of the riskof type 2 diabetes and glycaemic outcomes; to 
achieve better assessment of dietary intakes and 
nutritional status in free-living populations; to identify 
novel biomarkers that are more effective than traditional 
biomarkers at predicting risk of disease and its 
complications; to identify new targets for lifestyle and 
pharmacological interventions; and to provide person-
alised dietary and lifestyle guidance for more effective 
prevention and management.
Figure 1: Conceptual framework for precision nutrition in prevention and management of type 2 diabetes
(1) General recommendations for healthy diets. (2) Dietary intake interacts with level of physical activity. (3) Dietary approaches interact with antidiabetic 
medications in management of type 2 diabetes. (4) Various omics technologies, such as genomics, metabolomics, metagenomic and metatrascriptomic analysis of 
the gut microbiota, and epigenomics, provide powerful tools for deep phenotyping individual characteristics and understanding mechanisms underlying diet and 
type 2 diabetes. (5) Validated questionnaires such as food frequency questionnaires are the most important and feasible tools for measuring long-term usual diet in 
nutritional epidemiological research. (6) Wearable devices and mobile apps provide objective and real-time diet and physical activity measurements. (7) Application 
of omics technologies to improve dietary assessment in free-living populations. (8) Wearable devices provide continuous measures of blood glucose and other 
physiological variables. Data input from (9) self-reported questionnaire-based dietary assessment tools in epidemiological studies, (10) omics studies, (11) wearable 
devices, and (12) conventional clinical measures, such as fasting glucose and lipids. (13) Results derived by use of big-data analytics inform development and 
application of precision nutrition. (14) Wearable devices and mobile apps provide useful tools for monitoring and implementing precision nutrition. (15) Precision 
nutrition aims to provide personalised nutrition guidance to achieve more effective dietary prevention and management of type 2 diabetes.
Diet Questionnaires
Diet and type 2 diabetes
Type 2 diabetes
Measurement and mechanism Data integration and analysis Translation and intervention
Omics
Big data analytics 
Physical activity
Wearable devices
Medication
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Precision nutrition
17·022·0
www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8 3
Review
Overview of recent advances in type 2 diabetes 
omics studies
GWAS, and more recently exome sequencing studies, 
have identified more than 100 loci reproducibly associated 
with risk of type 2 diabetes and glycaemic traits.16 Most 
identified loci have a small effect size and are com-
mon across populations, with a few low-frequency 
exceptions.15,17,18 Causal variants have only been identified 
for a few loci. These GWAS loci collectively explain less 
than 10% of the heritability of the disease.19 Overall, 
GWAS have not yet led to meaningful clinical advances 
in prevention or management of type 2 diabetes. 
Researchers have also discovered genetic variants directly 
related to dietary intake and nutrient metabolism, such 
as polyunsaturated fat intake,20,21 macronutrient intake,22 
alcohol drinking,23 coffee consumption,24 and zinc 
transport.25 Interestingly, many BMI loci identified 
through GWAS are linked to hypothalamic function and 
energy homoeostasis, and therefore closely related to 
food intake.26 These findings suggest that taking into 
account individual genotypes when formulating dietary 
recommendations may be more effective in improving 
nutritional status.
Studies on diet and changes in transcription and 
methylation patterns have elucidated potential causal 
variants of type 2 diabetes and the biological basis 
underlying the effect of dietary exposure on predisposition 
to the disorder. Most transcriptomic studies using 
unbiased microarray and RNASeq approaches have 
focused on changes in the transcriptome induced by 
dietary interventions during a brief time period, such as 
several days. A few studies have evaluated transcriptomic 
response to long-term dietary interventions, such as the 
Mediterranean diet,27 or they have examined differentially 
expressed transcripts due to different habitual dietary 
exposures.28 Genome-wide DNA methylation studies 
comparing patients who have type 2 diabetes with healthy 
controls found varying levels of DNA methylation in 
pancreatic islets for thousands of CpG sites, corresponding 
to a large number of genes, including many known 
type 2 diabetes loci such as TCF7L2, FTO, and PPARG.29–31 
Some genes implicated in these identified CpG sites also 
displayed altered mRNA expression. These findings shed 
light on the mechanisms underlying epigenetic regulation 
of transcriptional activity contributing to β-cell function, 
insulin secretion, and the development of type 2 diabetes.32
In recent years, high-throughput metabolomics has 
been widely applied in population-based research on 
type 2 diabetes. In a meta-analysis33 of eight prospective 
studies including 8000 individuals (1940 cases of 
type 2 diabetes), our group found statistically significant 
positive associations of plasma concentrations of 
branched-chain aminoacids (BCAAs), including leucine 
and valine, and aromatic aminoacids (AAAs), including 
tyrosine and phenylalanine, but inverse associations 
with glycine and glutamine, with type 2 diabetes. These 
metabolites are influenced by both dietary intake and 
metabolism and therefore could be nutritional inter-
vention targets for prevention.
The application of next-generation sequencing 
technologies (eg, shotgun metagenomic and meta-
transcriptomic sequencing) for comprehensive mapping 
of microbes has offered new insights into the role of gut 
microbiota in glycaemic control and pathobiology of 
type 2 diabetes.34 Accumulating evidence suggests an 
association between the overall composition and diversity 
of the gut microbiota and type 2 diabetes,35 although 
causality remains uncertain. In addition, the relative 
abundance of specific gut microbes is linked to risk of 
the disease.36 For example, increased abundance of 
Akkermansia muciniphila has been associated with 
improvement in glucose control and lower risk of type 2 
diabetes.37,38
In summary, recent genomics studies have identified a 
large number of type 2 diabetes loci that can be used to 
characterise an individual’s genetic predisposition to 
the disease. Metabolomics studies have identified 
metabolites associated with type 2 diabetes that can be 
potentially modified by diet. There is also increasing 
evidence that the gut microbiome plays a role in 
glycaemic control and pathophysiology of the disease. 
These findings provide a scientific basis for the use of 
personalised risk characterisation and stratification in 
dietary interventions.
Dietary assessment in free-living populations 
and causal inference
Dietary assessment in free-living populations is 
challenging because of random and systematic errors in 
commonly used self-report tools. Omics technologies 
offer the potential for more accurate assessment of 
dietary intake and nutritional status. Improvements in 
the quantification of dietary intake, characterisation of 
dietary patterns, and assessment of adherence to dietary 
interventions are fundamental in building an evidence 
base to support precision nutrition.
Metabolomics analysis has the potential to capture the 
complexity of habitual diet better than do traditional 
single biomarkers. New metabolomics technologies are 
able to profile a large number of metabolites derived from 
ingestion and absorption of foods, measure endogenous 
biotransformation of nutrients by both the host and gut 
microbiota, and uncover diet-induced metabolic response.Several studies have identified new biomarkers for acute 
food exposure, short-term food or nutrient intake, and 
long-term dietary intake.39,40 Urinary proline betaine and 
4-hydroxy-proline betaine have been identified as 
biomarkers for citrus foods. Plasma ether-linked 
phospholipids and plasmalogens, dihexosylceramide, 
and GM3 ganglioside are biomarkers that can distinguish 
between major dietary fat sources. Plasma and urinary 
anserine is a marker for chicken whereas plasma and 
urinary trimethylamine-N-oxide and carnitines are 
markers for red meat and fish.41,42 More recently, 
4 www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8
Review
Garcia-Perez and colleagues43 identified a combination of 
multiple metabolites, including those showing higher 
intake of fruits (eg, hippurate, tartrate, and glycolate), 
vegetables (eg, N-acetyl-S-methyl-cysteine sulphoxide and 
S-methylcysteine sulphoxide), fish (dimethylamine), and 
lean white meat (1-methylhistidine and 3-methylhistidine). 
These metabolite combinations were indicative of overall 
dietary pattern in a controlled crossover feeding study 
and were validated in a subpopulation of cohort studies.43 
Andersen and colleagues44 identified combinations of 
urinary metabolites that quantified compliance to either a 
new Nordic diet or an average Danish diet.
Several studies have used metabolomics to uncover 
novel dietary patterns and understand the role of diet in 
prevention of type 2 diabetes.45–49 Floegel and co-
workers46 made use of serum metabolites previously 
linked to risk of type 2 diabetes to derive dietary patterns 
in the EPIC-Potsdam study.46 A dietary pattern with high 
intake of red meat and low intake of whole-grain bread 
and tea was associated with a metabolic profile high in 
BCAAs and AAAs. A second dietary pattern with high 
intake of coffee, cake, cookies, and canned fruit and fish 
was linked to a metabolic profile (including many 
phosphatidylcholines with a high degree of unsatu-
ration) associated with low risk of type 2 diabetes. 
Metabolomics analysis has also been applied to assess 
long-term adherence to the dietary intervention in the 
PREDIMED trial.50 In this trial, Vazquez-Fresno and 
colleagues found multiple urinary metabolites, 
including BCAAs, creatine, creatinine, oleic acid, and 
metabolites related to carbohydrate and gut microbiota 
metabolism, that could reflect adherence to the 
Mediterranean diet during the 3 year intervention.
Overall, current metabolomics platforms show only 
modest ability to distinguish different dietary patterns 
and have not yielded biomarkers with high sensitivity and 
specificity for food or nutrient intake. This failure is partly 
because metabolites reflect not only dietary intakes, but 
also metabolic activity, the microbiome, and genetic 
background. Therefore, these technologies are unlikely to 
replace traditional assessment tools such as validated 
dietary questionnaires and well established nutritional 
biomarkers. However, they can serve as complementary 
tools for measuring dietary intakes in observational 
studies and assessing compliance to dietary interventions.
Recently, the Mendelian randomisation method has 
become popular in nutritional epidemiological studies to 
determine the causal effect of dietary intake as predicted 
by genotypes on health outcomes. Mendelian random-
isation mimics a natural randomised controlled trial 
because genotypes are assigned to individuals randomly at 
birth. Therefore, this method can largely eliminate 
unmeasured confounding and reverse causation, which 
are two major biases in epidemiological studies. A recent 
study used a single nucleotide polymorphism (rs6754311) 
as a proxy for milk intake; each allele increment was 
associated with a 66 g increase in daily milk consumption.51 
Genetically predicted milk intake was not significantly 
associated with risk of type 2 diabetes or multiple glycaemic 
traits. Genetic markers may also be indicators of variation 
in nutrient metabolism. Several research groups have used 
genetic variants in the fatty acid desaturases gene family as 
surrogates of the activity of Δ5 desaturase (FADS1) and Δ6 
desaturase (FADS2), key enzymes in the metabolism of 
polyunsaturated fatty acids. Genotype-predicted 
Δ5 desaturase activity was inversely associated with risk of 
type 2 diabetes whereas genotype-predicted Δ6 desaturase 
activity was positively associated,52 suggesting a causal 
effect of the activity of these enzymes on development of 
the disease and a mediating role in the effect of 
polyunsaturated fatty acid intake on risk.
It is noteworthy that only a few genetic variants have 
been found to strongly predict and thus serve as good 
proxies for dietary intake and nutritional status. In 
addition, Mendelian randomisation analyses can be 
subject to confounding by population stratification, 
pleiotropy of genetic effects, and inadequate statistical 
power.53
Identification of susceptible or responsive 
individuals through gene–diet interactions
Type 2 diabetes is a complex phenotype that is influenced 
by both genetic and environmental factors as well as 
their interactions.54 Studies that apply gene–diet inter-
action findings to precision nutrition could provide a 
scientific basis for genotype-based precision nutrition. 
Some classic examples of translating genetic information 
into personalised nutrition are dietary modifications for 
individuals with mutations in the phenylalanine 
hydroxylase gene that lead to phenylketonuria55 and 
mutations in the lactase gene affecting lactase 
persistence.56 However, the genetic basis of these dietary 
modifications usually involves single-gene mutations 
with large effect sizes, whereas such clear examples do 
not exist for type 2 diabetes.
Earlier studies of gene–diet interactions for type 2 
diabetes focused on candidate genetic variants.57 This 
approach did not generate robust evidence,58 as shown by 
Li and colleagues,59 who were unable to replicate eight 
published statistically significant inter actions between 
single-gene variants and macro nutrient intake in relation 
to type 2 diabetes in a large cohort from the EPIC-InterAct 
study. Since 2008, investigators have been using polygenic 
scores, which are combinations of multiple susceptibility 
loci identified by GWAS. That these scores reflect the 
polygenic nature of type 2 diabetes is crucial when 
exploring gene–diet interactions for this disease. Some 
evidence supports an interaction between a polygenic 
score of ten type 2 diabetes risk alleles and a western 
dietary pattern, characterised by high intakes of red and 
processed meat, butter, high-fat dairy products, and refined 
grains.60 In a recent study, Langenberg and co-workers61 
tested the interaction between a polygenic score of 
49 type 2 diabetes loci and a Mediterranean dietary pattern 
www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8 5
Review
in EPIC-InterAct but found no statistically significant 
interaction effect. However, healthy dietary patterns have 
been found to mitigate the effects of genetic variants on 
obesity62 and coronary heart disease.63 On the other hand, 
unhealthy diet habits, such as regular consumption of 
sugar-sweetened beverages, fried food, and saturated fat 
intake, were found to exacerbate the effects of genetic 
variants on obesity.64–67 Evidence on gene–diet interactions 
for obesity is crucial for prevention of type 2 diabetes given 
that a small reduction in bodyweight will lead to clinically 
meaningful improvement in glycaemic control.68 In 
addition, robust evidence indicates that the FTO variant 
can modify the association between physical activity, 
another major lifestyle risk factor of type 2 diabetes, and 
body adiposity.69 These findings imply that individuals with 
greater genetic predispositionsmight benefit more from 
interventions to improve diet quality or physical activity, 
although this hypothesis has not been explicitly tested in 
randomised trials.
In the Diabetes Prevention Program (DPP), Florez and 
colleagues70 found that lifestyle intervention and 
metformin treatment attenuated the effect of the TCF7L2 
genotype on the progression from prediabetes to 
type 2 diabetes, although the interaction between the 
genotype and interventions was not statistically significant 
(table). The POUNDS LOST trial investigated interactions 
between intervention diets varying in macronutrient 
composition and type 2 diabetes genetic risk scores on 
markers of glucose homoeostasis.71,72 Among white 
participants, those with a lower genetic risk score showed 
more favourable responses to low-protein diets, including 
greater decreases in fasting insulin, HbA1c, and 
homoeostatic model assessment (HOMA) for insulin 
resistance, and a lesser increase in HOMA for β-cell 
function, than did those with a higher risk score 
within 2 years of follow-up.71 In response to low-fat diets, 
participants with a lower genetic risk score showed more 
positive responses in fasting glucose, insulin resistance, 
and insulin sensitivity during 6 months of follow-up.72
Only a few observational studies and randomised 
controlled trials have tested gene–diet interactions in 
relation to type 2 diabetes and glycaemic traits. Although 
there is some indication that individuals with varying 
genotypes respond differently to dietary interventions in 
terms of glycaemic markers, few independent 
replications exist. Overall, current evidence is not 
sufficient to make personalised dietary recommendations 
for diabetes prevention or manage ment based on genetic 
information. Several limitations of current studies are 
worth noting. First, some studies were cross-sectional 
and therefore were susceptible to reverse causation and 
had insufficient control of confounding factors. Second, 
measurement errors in self-reported dietary intake, 
pathological heterogeneity of type 2 diabetes, and modest 
effect sizes of genetic variants collectively lead to limited 
statistical power that may obscure true interaction 
effects, leading to false negative findings. Third, 
gene–diet interactions have been difficult to replicate, 
probably because of heterogeneity in diet and lifestyle 
across populations, insufficient statistical power, and 
lack of standardisation in dietary assessment and 
analytical approaches.
Identification of nutritional intervention 
targets for prevention of type 2 diabetes
In addition to assessing individual metabolic state, 
metabolomics can capture metabolic changes in response 
to extrinsic exposures such as dietary intake and can 
uncover metabolic pathways through which dietary 
exposures influence risk of type 2 diabetes. Floegel and 
colleagues78 identified several metabolic networks that 
correlated with both dietary intake and metabolic disease 
outcomes. For example, metabolic networks consisting of 
particular metabolites within the subclasses of phos-
pholipid, sphingomyelin, lyso-phosphatidylcholines, and 
acyl alkyl-phosphatidylcholines were positively associated 
with coffee intake, but inversely associated with risk of 
obesity and type 2 diabetes,78,79 delineating potential 
pathways through which coffee may be protective.
Recent studies within randomised controlled trials have 
used metabolomics to provide novel evidence to explain 
heterogeneity in individual responses to a specific dietary 
intervention. In a study based on two weight-loss trials—
the POUND LOST trial and the DIRECT trial—weight-
loss diets decreased metabolites that were associated with 
risk of type 2 diabetes, including BCAAs and AAAs.74 
More importantly, the observed decreases in the 
circulating aminoacid metabolites were predictive of 
improvement in insulin resistance inde pendent of weight 
loss. In the DPP, Walford and colleagues75 found that a 
lifestyle intervention that incorporated dietary 
modification was effective in in creasing betaine 
concentration from baseline to 2 year follow-up, which 
predicted lower risk of type 2 diabetes. In a subpopulation 
from the PREDIMED trial, a Mediter ranean diet rich in 
extra-virgin olive oil decreased plasma concentrations of 
BCAA (Ruiz-Canela M, University of Navarra, personal 
communication). Moreover, a decrease in BCAA 
concentrations from baseline to year 1 predicted lower risk 
of disease in the remainder of the follow-up period, 
whereas baseline BCAA concentrations modified the 
effect of the Mediterranean diet intervention on risk of 
type 2 diabetes.
Metabolomics techniques are powerful tools for 
uncovering novel biomarkers linking dietary components 
and risk of type 2 diabetes, which can be useful for 
distinguishing responders from non-responders to a 
dietary intervention. However, current studies have 
generally focused on several candidate metabolites or a 
limited number of targeted metabolites, and there are 
few independent replications of findings. Whether the 
iden tified metabolites represent causal biological 
pathways linking dietary intake to risk of the disease is 
largely unknown. Metabolomics studies that cover all 
6 www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8
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detectable, untargeted metabolites are needed to provide 
a less biased understanding of potential metabolic 
pathways. Addition ally, replication of statistically signifi-
cant findings in independent populations, and functional 
studies of iden tified metabolites are needed to provide 
causal evidence supporting the role of meta bolites as 
intervention targets.
Recent studies have demonstrated that both short-term 
dietary changes and long-term habitual diet could 
influence the composition of gut microbiota.76,80–83 In a 
5 day feeding study, David and coworkers80 showed that 
high intakes of animal fat and protein increased 
bile-resistant bacteria including genera of Alistipes, 
Bilophila, and Bacteroides, and decreased types of bacteria 
Topic Study population Intervention Duration Key findings
Florez et al70 Interaction between 
polymorphisms of TCF7L2 
and lifestyle intervention
3548 overweight adults with elevated 
fasting glucose and impaired glucose 
tolerance but without diabetes from the 
Diabetes Prevention Program
Lifestyle (including intensive 
training in diet) vs placebo pills; no 
intensive motivational counselling 
on lifestyle
3 years 
(average)
Effect of risk-conferring TT genotype at 
rs7903146 was stronger in the placebo group 
than in the lifestyle-intervention group, but 
no significant genotype-intervention 
interaction in relation to incident type 2 
diabetes
Huang et al71 Interaction between genetic 
risk score of 31 type 2 
diabetes GWAS SNPs and 
dietary intervention varying 
in protein content
596 overweight or obese non-diabetic 
white adults from the POUNDS LOST trial
Low-protein vs high-protein diets 2 years Significant interactions between 
intervention diet varying in protein content 
and a diabetes genetic risk score on fasting 
insulin, HbA1c, HOMA-B, and HOMA-IR at 
2 years (pinteraction= 0·02, 0·04, 0·01, and 0·05, 
respectively).
Wang et al72 Interaction between a genetic 
risk score of 14 fasting 
glucose-associated SNPs and 
dietary intervention varying 
in fat content
733 overweight or obese nondiabetic 
adults from the POUNDS LOST trial
Low-fat vs high-fat diets 2 years Significant interaction between genetic risk 
score and dietary fat on 6 month changes in 
fasting glucose, HOMA-IR, and insulin 
resistance after multivariable adjustment 
(pinteraction=0·007, 0·045, and 0·028, 
respectively)
The Look 
AHEAD Research 
Group73
Interaction between 
glutamate-ammonia ligase 
gene polymorphism and 
lifestyle intervention
3845 overweight or obese participants 
with type 2 diabetes from the Look AHEAD 
trial
Intensive lifestyleintervention 
(including dietary modification) vs 
diabetes support and education 
programme
Median 
9·6 years
No significant interaction between lifestyle 
intervention and the gene for the incidence 
of cardiovascular disease among individuals 
with type 2 diabetes
Zheng et al74 Effect of dietary intervention 
varying in protein content on 
circulating metabolites and 
subsequent effect on glucose 
homoeostasis
774 adults from the POUNDS LOST trial 
and 318 adults from the DIRECT trial; all 
participants were overweight or obese 
without diabetes
Average-protein diets vs high-
protein diets
2 years Average-protein diets showed stronger effects 
than did high-protein diets on reducing 
concentrations of BCAAs and AAAs at 
6 months independent of weight change. 
Decreases in circulating aminoacid metabolites 
were predictive of improvement in insulin 
resistance independent of weight loss
Walford et al75 Effect of lifestyle intervention 
on circulating metabolites 
and subsequent effect on risk 
of type 2 diabetes
757 overweight adults with elevated 
fasting glucose and impaired glucose 
tolerance but without diabetes from 
Diabetes Prevention Program
Lifestyle (including intensive 
training in diet) vs placebo pills; no 
intensive motivational counselling 
on lifestyle
3 years 
(average)
Lifestyle intervention that incorporated 
dietary modification was effective in 
increasing betaine level from baseline to 
2 year follow-up, which predicted lower 
incidence of type 2 diabetes
Ruiz-Canela 
(University of 
Navarra, 
personal 
communication)
Effect of Mediterranean diet 
intervention on circulating 
metabolites and subsequent 
effect on risk of type 2 
diabetes
892 participants with baseline high 
cardiometabolic risk but nondiabetic from 
the PREDIMED trial
Mediterranean diet rich in extra-
virgin olive oil vs low-fat diet
4·8 years 
(average)
Mediterranean diet rich in extra-virgin olive oil 
led to a decrease in plasma BCAA and 
attenuated the positive association between 
baseline BCAA and incidence of 
type 2 diabetes. Changes in BCAA from 
baseline to year 1 were positively associated 
with risk of type 2 diabetes
Kovatcheva-
Datchary76
Identification of gut microbial 
signatures in response to 
barley kernel-based bread 
intervention
39 healthy individuals Barley kernel-based bread vs white 
wheat flour bread (crossover 
design)
3 days Increased abundance of Prevotella copri after 
consumption of barley kernel-based bread
Zeevi et al77 Comparison of effect of a 
personalised diet with 
traditional dietary advice
800 participants (initial cohort); 
100 participants (validation cohort); 
26 participants (randomised controlled trial)
A personalised diet designed with a 
machine-learning algorithm that 
integrated blood variables, dietary 
habits, anthropometric data, 
physical activity, and gut 
microbiota, compared with 
traditional dietary advice
1 week Personalised diets showed better 
performance in lowering postprandial blood 
glucose, fluctuations in blood glucose, and 
maximum postprandial blood glucose, 
compared with traditional dietary advice
GWAS=genome-wide association study. SNPs=single nucleotide polymorphisms. HOMA-B=homoeostasis model assessment of β-cell function. HOMA-IR=homoeostasis model assessment of insulin resistance. 
BCCAs=branched-chain aminoacids. AAAs=aromatic aminoacids. 
Table: Summary of key randomised controlled trials on precision nutrition and prevention and management of type 2 diabetes
www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8 7
Review
with an enhanced capacity to ferment polysaccharides, 
including Roseburia spp, Eubacterium rectale, and 
Ruminococcus bromii. Two recent studies found that 
long-term intakes of various dietary factors were 
associated with the diversity and composition of gut 
microbiota.81,83 Hallmarks of a western-style dietary 
pattern, including sugar-sweetened beverages, higher 
energy intake, snacking, and whole milk were associated 
with lower microbiota diversity, whereas consumption of 
coffee, tea, and red wine was associated with high 
microbiota diversity.
Recent evidence suggests that dietary interventions 
may affect glycaemic control through modification of the 
composition of gut microbiota. After a 6 week calorie-
restricted diet enriched with fibre and protein,38 the 
abundance of A muciniphila, a mucin-degrading 
bacterium, was inversely associated with measures of 
fasting glucose and insulin sensitivity in 49 participants 
in France. Baseline abundance of this organism appeared 
to modify the effect of diet on insulin sensitivity markers. 
In a study of 39 healthy participants in Sweden, 
Kovatcheva-Datchary and colleagues76 found that the ratio 
of Prevotella spp to Bacteroides spp was statistically 
significantly higher in participants who showed improved 
glucose metabolism after the 3 day intervention of barley 
kernel-based bread than in those who did not show 
improved glucose metabolism.
By combining metabolomics and the gut microbiome, 
Wu and colleagues84 provided some early mechanistic 
evidence to explain heterogeneous responses to dietary 
pattern in a cross-sectional study of 15 vegans and 
six omnivores. The investigators found that a plant-based 
diet was necessary but not sufficient for the bacterial 
production of short-chain fatty acids, a group of metabolites 
implicated in host metabolic regulation and risk of 
type 2 diabetes.85 A high yield of short-chain fatty acids 
required high enrichment of genera such as Prevotella, but 
low abundance of Ruminococcus bromii.
Thus, there is increasing evidence that diet has both 
short-term and long-term influences on composition of 
the gut microbiota. Certain dietary interventions, 
particularly plant-based high-fibre diets, may be able 
to modify specific types of gut microbes implicated 
in regulation of glucose metabolism. Integrating meta-
bolomics and gut microbiome technologies can reveal 
the functional activity of gut microbiota and the role of 
gut microbes in modifying individual responses to a 
dietary exposure. However, research on gut microbiota is 
still at a nascent stage and is not ready for translation 
into clinical practice. Existing studies are limited by 
insufficient statistical power, cross-sectional study 
design, and lack of information on functional activity and 
strain-level variation of the gut microbiota. Future large 
studies within prospective cohorts and long-term 
randomised controlled trials are needed to examine the 
modifiable capacity of the gut microbiome and its role in 
the prevention and management of type 2 diabetes.
Personalised nutrition for the management of 
type 2 diabetes
The use of omics tools to elucidate differing individual 
responses to dietary exposures, in terms of the progression 
of type 2 diabetes or the onset of associated complications, 
seems highly relevant for personalised nutrition for the 
management of the disease. However, such evidence is 
very sparse. The Look AHEAD randomised trial compared 
the effect of an intensive lifestyle intervention with 
diabetes support and an education programme on 
cardiovascular disease in patients with type 2 diabetes. A 
recent study investigated whether the effect of the 
intensive lifestyle intervention varied across different 
genotypes of a genetic variant (s10911021) related to 
coronary heart disease, but failed to identify statistically 
significant interaction effects.73 Numerous antidiabetic 
medications with different mechanisms of action add 
another layer of complexity to studies on personalised 
nutrition in patients with type 2 diabetes.86 For example, 
metabolomics studies have found that metformin and 
sulphonylurea affect the circulating metabolome pro-
foundly.75,87,88 Some of the identified metabolites, such as 
sphingomyelins, overlap with metabolomic signatures of 
dietary intakes, suggesting that antidiabetic medicationsmay potentially interfere with the effects of dietary intakes 
on multiple metabolic pathways. Additionally, recent 
studies suggest that a large proportion of the previously 
identified gut microbial signatures associated with 
type 2 diabetes can be explained by metformin use89 and 
that the effects of dietary interventions on gut microbiota 
are masked by metformin.90 Beyond these glucose-lowering 
agents, some specific characteristics of type 2 diabetes, 
such as duration, severity, comorbidities (eg, renal or 
hepatic impairment), and pathophysiology of the disease 
(eg, the relative contributions of the defect in insulin 
secretion and insulin resistance), are also important 
considerations for studying and designing personalised 
nutrition approaches for the management of type 2 
diabetes. However, to our knowledge, no studies have 
comprehensively explored how these factors modify the 
responses of the human body to dietary interventions to 
influence the progression of the disease.
Integrated omics technologies and big-data 
analytics
Precision nutrition requires integration of multiple kinds 
of omics data as well as information from both traditional 
sources, such as questionnaire interviews and standard 
clinical tests, and modern sources such as electronic 
medical records, mobile apps, and wearable devices.91,92 
New bioinformatics tools for data analysis and 
visualisation, such as big-data analytics, are imperative 
given the recent increase in the volume and complexity of 
available data. However, integration of data from multiple 
disparate sources and the application of big-data analytics 
have not yet provided valuable physiological insights that 
can be used in clinical practice. Researchers using these 
8 www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8
Review
new methods have faced challenges including issues with 
incomplete and unreliable input data, as well as 
misleading interpretations of findings owing to a lack of 
expert knowledge. In addition, big data usually require 
many statistical tests which create an increased chance of 
false-positive results. Therefore, independent replication 
of findings has become even more important.93
Several recent studies combined multiple data sources 
and applied big-data analytics to inform personalised 
nutrition interventions. In one example, Price and 
colleagues94 showed the benefits of combining infor mation 
collected by genomics, metabolomics, proteomics, 
microbiome analysis, and wearable devices in an integrated 
framework to develop personalised dietary interventions. 
In another study, Zeevi and co-workers77 examined the 
utility of personalised nutrition in reducing postprandial 
blood glucose (figure 2). They developed a machine-
learning algorithm for predicting postprandial glycaemic 
responses based on integrated data on dietary intake, 
biomarkers, physical activity, sleep, anthropometric 
variables, and gut microbiota that were collected through 
instruments including questionnaires, smartphones, a 
continuous glucose monitor, physical examinations, blood 
tests, and 16S rRNA metagenomics profiling. The 
investigators reported that nutrition interventions based 
on this algorithm were more effective in reducing 
postprandial blood glucose than was traditional dietary 
advice. Although this proof-of-concept study is an 
important first step, several methodological issues 
complicated the interpretation and translation of the 
findings. One of the key limitations was that the authors 
were unable to demonstrate whether the observed high 
variability in postprandial glycaemic responses was due to 
intra-individual or interindividual variability.95 The clinical 
applications of this approach are challenging because of 
the huge amounts of data that need to be collected, 
analysed, and interpreted. In addition, although wearable 
devices showed the potential for scalable use in 
continuously monitoring health behaviours and motivating 
behavioural changes of participants in the short term, little 
is known about their sustainability and effectiveness in the 
long term.96 Long-term studies of this type that have a 
more refined study design as well as simple and 
cost-efficient data collection tools are clearly needed.
Figure 2: Personalised nutrition in reducing postprandial blood glucose
(A) Illustration of the experimental design of Zeevi and colleagues.77 (B) Left, mean postprandial glycaemic responses to personalised dietary intervention (good diet) 
vs control diet (bad diet), and right, traditional dietary advice (good diet) vs control diet (bad diet) at each timepoint of intervention period (by weeks). (C) Left, mean 
postprandial glycaemic responses to personalised dietary intervention vs control diet, and right, traditional dietary advice vs control diet. PPGR=postprandial glucose 
response. Good diet=meals predicted to have low postprandial glycaemic responses. Bad diet=meals predicted to have high postprandial glycaemic responses. 
*p<0·001, †p<0·01, ‡p<0·05, §p<0·1, ns=not significant (Mann-Whitney U test). iAUC=incremental area under the curve. Adapted from Zeevi and colleagues,77 by 
permission of Elsevier.
B C
A
Per person profiling
Diary (food, sleep, physical activity)
Using smartphone-adjusted website
5435 days, 46 898 meals, 9·8 million calories, 2532 exercises
Gut microbiome
 16S rRNA
 Metagenomics
Blood tests
Questionnaires
 Food frequency
 Lifestyle
 Medical
Anthropometrics
Computational analysis
Continuous glucose monitoring
Using a subcutaneous sensor (iPro2)
130 000 hours, 1·56 million glucose measurements
Standardised meals (50 g available carbohydrates)
Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7
Bread Bread Bread
and
butter
Bread
and
butter
Glucose Glucose Fructose
G G F
Main cohort
800 participants
PPGR prediction
Validation cohort Dietary intervention
100 participants 26 participants
100
P6 P10 P3 P8 P2 P5 P9 P4 P1 P11 P7 P12
0
PP
GR
 (i
AU
C,
 m
g/
dL
.h
)
Predictor-based arm
20
40
60
80
100
120
140
Pre
dic
tor
Ex
pe
rt
0
PP
GR
 (i
AU
C,
 m
g/
dL
.h
)
20
40
60
80
100
E7E8 E9 E4 E14 E11 E10 E12 E5 E3 E2 E1 E6 E13
Expert-based arm
* † ‡ ‡ ‡ * ‡ * † ‡ ns ns * ‡ ‡ ns* * § § §† † ‡ ‡ ‡ns ns
Bad diet week
Good diet week
www.thelancet.com/diabetes-endocrinology Published online February 9, 2018 http://dx.doi.org/10.1016/S2213-8587(18)30037-8 9
Review
Conclusions and future directions
Rapidly evolving omics technologies have offered 
unparalleled opportunities to assess individual charac-
teristics including the genome, epigenome, metabolome, 
and microbiome, which can be integrated into nutritional 
epidemiological studies and dietary intervention trials. In 
addition, mobile apps and wearable devices have the 
potential to improve real-time assessment of dietary intake 
and provide feedback, thus improving glycaemic control 
and management of type 2 diabetes (figure 1). Despite 
these advances, precision nutrition is still in its infancy 
and much research is needed before it can be widely used 
in clinical and public health settings. Major challenges 
exist in applying precision nutrition to the prevention and 
management of type 2 diabetes, including a lack of robust 
and reproducible results, the high cost of omics 
technologies, and methodological issues in study design 
and high-dimensional data analyses. The cutting-edge 
omics technologies have not yet delivered reliable and 
clinically scalable biomarkers for predicting both disease 
outcomes and interindividual variability to a specific 
dietary exposure. For example, when biomarkers recently 
identified by GWAS and metabolomics studies were added 
to a risk prediction model of traditional risk factors, the 
model showed only a modest improvement in predicting 
risk of type 2 diabetes.97 Likewise, metabolomics and 
microbiome technologies are not yet sensitive and specific 
enoughfor use in clinical management of type 2 diabetes, 
although they are rapidly evolving and might be 
incorporated into personalised nutrition in the future, as 
demonstrated by the proof-of-concept study done by Zeevi 
and colleagues.77 Various commercial companies have 
started to market personalised nutrition assessment and 
treatment based on genotypes, but the benefits of such 
approaches on improving diet quality and health outcomes 
have not been demonstrated. Appropriately designed 
intervention studies are needed to perform head-to-head 
comparisons between personalised nutrition interventions 
and traditional approaches such as nutrition advice 
targeting behavioural changes. Furthermore, the value of 
personalised nutrition depends on whether the additional 
cost and complexity incurred by new laboratory tests and 
modification of interventions can be offset by its benefits.98 
However, no comprehensive attempts have been made to 
rigorously evaluate efficacy, cost-effectiveness, and sus-
tainability of personalised nutrition in the prevention and 
management of type 2 diabetes.
To use precision nutrition to prevent and manage 
type 2 diabetes, it is important to address the current 
challenges by establishing a solid evidence base. This can 
be accomplished through the emphasis of more rigorous 
study design, integration of high-dimensional big data 
from various sources, development of computational 
approaches for handling big data, and reduction in the cost 
of the omics analyses. Although this Review is focused on 
biomarkers identified by omics technologies, individual 
dietary choices are likely to depend on a range of broader 
influences including health-related lifestyles (eg, exercise, 
television watching, and sleep) and socioeconomic 
characteristics (eg, income, education, social networks, 
and neighbourhood food environment).99 These factors 
may play a more important role than biomarkers in 
modifying an individual’s response to a dietary exposure 
and should receive equal or even higher priority in the 
process of developing evidence-based precision nutrition. 
Thus, it is essential that population-wide, public health 
approaches, such as nutrition education, food and health 
policy, and government regulation and legislation, remain 
fundamental strategies for improving overall diet quality 
and nutritional status. It is important to ensure that 
investment in precision nutrition is balanced against the 
limited resources available for public health nutrition.
Contributors
DDW performed the literature search. DDW and FBH designed the 
study and wrote the manuscript.
Declaration of interests
FBH reports personal fees from Metagenics and grants from California 
Walnut Commission, outside the submitted work. DDW declares no 
competing interests.
Acknowledgments
DDW’s research is supported by a postdoctoral fellowship from the 
American Heart Association (16POST31100031). FBH’s research is 
supported by National Institutes of Health grants HL60712, HL118264, 
and DK102896.
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	Precision nutrition for prevention and management of type 2 diabetes
	Introduction
	Goals of precision nutrition
	Overview of recent advances in type 2 diabetes omics studies
	Dietary assessment in free-living populations and causal inference
	Identification of susceptible or responsive individuals through gene–diet interactions
	Identification of nutritional intervention targets for prevention of type 2 diabetes
	Personalised nutrition for the management of type 2 diabetes
	Integrated omics technologies and big-data analytics
	Conclusions and future directions
	Acknowledgments
	References

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