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#### The Glycemic Response The glycemic response is the body's r...

### Background on Diabetes Diabetes is an umbrella term referrin...

### What is PPG? Postprandial blood glucose (PPG) levels are a m...

#### Measuring Glycemic Response Today, glycemic response is pre...

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#### (Some) Microbes are your Friends! Humans have complex mutua...

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Article

Personalized Nutrition by Prediction of Glycemic

Responses

Graphical Abstract

Highlights

d High interpersonal variability in post-meal glucose observed

in an 800-person cohort

d Using personal and microbiome features enables accurate

glucose response prediction

d Prediction is accurate and superior to common practice in an

independent cohort

d Short-term personalized dietary interventions successfully

lower post-meal glucose

Authors

David Zeevi, Tal Korem, Niv Zmora, ...,

Zamir Halpern, Eran Elinav, Eran Segal

Correspondence

eran.elinav@weizman n.ac.il (E.E.),

eran.segal@weizmann.ac.il (E.S.)

In Brief

People eating identical meals present

high variability in post-meal blood

glucose response. Personalized diets

created with the help of an accurate

predictor of blood glucose response that

integrates parameters such as dietary

habits, physical activity, and gut

microbiota may successfully lower post-

meal blood glucose and its long-term

metabolic consequences.

Zeevi et al., 2015, Cell 163, 1079–1094

November 19, 2015 ª2015 Elsevier Inc.

http://dx.doi.org/10.1016/j.cell.2015.11.001

Article

Personalized Nutrition by Prediction

of Glycemic Responses

David Zeevi,

1,2,8

Tal Korem,

1,2,8

Niv Zmora,

3,4,5,8

David Israeli,

6,8

Daphna Rothschild,

1,2

Adina Weinberger,

1,2

Orly Ben-Yacov,

1,2

Dar Lador,

1,2

Tali Avnit-Sagi,

1,2

Maya Lotan-Pompan,

1,2

Jotham Suez,

Jemal Ali Mahdi,

Elad Matot,

1,2

Gal Malka,

1,2

Noa Kosower,

1,2

Michal Rein,

1,2

Gili Zilberman-Schapira,

Lenka Dohnalova

Meirav Pevsner-Fischer,

Rony Bikovsky,

1,2

Zamir Halpern,

5,7

Eran Elinav,

3,9,

and Eran Segal

1,2,9,

Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel

Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 7610001, Israel

Immunology Department, Weizmann Institute of Science, Rehovot 7610001, Israel

Internal Medicine Department, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel

Research Center for Digestive Tract and Liver Diseases, Tel Aviv Sourasky Medical Center, Sackler Faculty of Medicine, Tel Aviv University,

Tel Aviv 6423906, Israel

Day Care Unit and the Laboratory of Imaging and Brain Stimulation, Kfar Shaul Hospital, Jerusalem Center for Mental Health,

Jerusalem 9106000, Israel

Digestive Center, Tel Aviv Sourasky Medical Center, Tel Aviv 6423906, Israel

Co-ﬁrst author

Co-senior author

*Correspondence: eran.elinav@weizmann.ac.il (E.E.), eran.segal@weizmann.ac.il (E.S.)

http://dx.doi.org/10.1016/j.cell.2015.11.001

SUMMARY

Elevated postprandial blood glucose levels consti-

tute a global epidemic and a major risk factor for pre-

diabetes and type II diabetes, but existing dietary

methods for controlling them have limited efﬁcacy.

Here, we continuously monitored week-long glucose

levels in an 800-person cohort, measured responses

to 46,898 meals, and found high variability in the

response to identical meals, suggesting that univer-

sal dietary recommendations may have limited

utility. We devised a machine-learning algorithm

that integrates blood parameters, dietary habits, an-

thropometrics, physical activity, and gut microbiota

measured in this cohort and showed that it accu-

rately predicts personalized postprandial glycemic

response to real-life meals. We validated these

predictions in an independent 100-person cohort.

Finally, a blinded randomized controlled dietary

intervention based on this algorithm resulted in

signiﬁcantly lower postprandial responses and

consistent alterations to gut microbiota conﬁgura-

tion. Together, our results suggest that personalized

diets may successfully modify elevated postprandial

blood glucose and its metabolic consequences.

INTRODUCTION

Blood glucose levels are rapidly increasing in the population, as

evident by the sharp incline in the prevalence of prediabetes and

impaired glucose tolerance estimated to affect, in the U.S. alone,

37% of the adult population (Bansal, 2015). Prediabetes, charac-

terized by chronically impaired blood glucose responses, is a sig-

niﬁcant risk factor for type II diabetes mellitus (TIIDM), with up to

70% of prediabetics eventually developing the disease (Nathan

et al., 2007). It is also linked to other manifestations, collectively

termed the metabolic syndrome, including obesity, hypertension,

non-alcoholic fatty liver disease, hypertriglyceridemia, and cardio-

vascular disease (Grundy, 2012). Thus, maintaining normal blood

glucose levels is considered critical for preventing and controlling

the metabolic syndrome (Riccardi and Rivellese, 2000).

Dietary intake is a central determinant of blood glucose levels,

and thus, in order to achieve normal glucose levels it is impera-

tive to make food choices that induce normal postprandial (post-

meal) glycemic responses (PPGR; Gallwitz, 2009). Postprandial

hyperglycemia is an independent risk factor for the development

of TIIDM (American Diabetes Association., 2015a), cardiovascu-

lar disease (Gallwitz, 2009), and liver cirrhosis (Nishida et al.,

2006) and is associated with obesity (Blaak et al., 2012), and

enhanced all-cause mortality in both TIIDM (Cavalot et al.,

2011) and cancer (Lamkin et al., 2009).

Despite their importance, no method exists for predicting

PPGRs to food. The current practice is to use the meal carbohy-

drate content (American Diabetes Association., 2015b; Bao

et al., 2011), even though it is a poor predictor of the PPGR

(Conn and Newburgh, 1936). Other methods aimed at estimating

PPGRs are the glycemic index, which quantiﬁes PPGR to con-

sumption of a single tested food type, and the derived glycemic

load (Jenkins et al., 1981). It thus has limited applicability in as-

sessing the PPGR to real-life meals consisting of arbitrary food

combinations and varying quantities (Dodd et al., 2011),

consumed at different times of the day and at different proximity

to physical activity and other meals. Indeed, studies examining

the effect of diets with a low glycemic index on TIIDM risk, weight

loss, and cardiovascular risk factors yielded mixed results

(Greenwood et al., 2013; Kristo et al., 2013; Schwingshackl

and Hoffmann, 2013).

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1079

Nuts (456,000)

Beef (444,000)

Legumes (420,000)

Fruit (400,000)

Poultry (386,000)

Rice (331,000)

Other (4,010,000)

Baked goods (542,000)

Vegetables (548,000)

Sweets (639,000)

Dairy (730,000)

Bread (919,000)

Overall energy documented: 9,807,000 Calories

Glucose (mg/dl)

Time

Anthropometrics

Blood tests

Gut microbiome

16S rRNA

Metagenomics

Questionnaires

Food frequency

Lifestyle

Medical

Diary (food, sleep, physical activity)

Continuous glucose monitoring

Day 1 Day 2 Day 3 Day 4

Day 5 Day 6 Day 7

Standardized meals (50g available carbohydrates)

GGF

Bread Bread Bread &

butter

Bread &

butter

Glucose Glucose Fructose

Per person profiling

Computational analysis

Main

cohort

800 Participants

Validation

cohort

100 Participants

PPGR

prediction

26 Participants

Dietary

intervention

Glucose (mg/dl)

Day

BMI

1234567

Standardized meal

Lunch

Snack

Dinner

Postprandial glycemic response

(PPGR; 2-hour iAUC)

5,435 days, 46,898 meals, 9.8M Calories, 2,532 exercises

130K hours, 1.56M glucose measurements

Frequency

HbA1c%

45% 33% 22% 76% 21% 3%

% Protein

% Carbohydrate

% Fat

1000

2000

020406080100

Frequency

% of meal

Carbohydrate

Fat

Protein

Sleep

PCo1 (10.5%)

PCo2 (5.2%)

Study participants MetaHIT - stool

HMP - stool HMP - oral

PCo1 (27.9%)

PCo2 (2.2%)

Using smartphone-adjusted website

Using a subcutaneous sensor (iPro2)

Participant 141

HMP - urogenital

Figure 1. Proﬁling of Postprandial Glycemic Responses, Clinical Data, and Gut Microbiome

(A) Illustration of our experimental design.

(B and C) Distribution of BMI and glycated hemoglobin (HbA1c%) in our cohort. Thresholds for overweight (BMI R 25 kg/m

), obese (BMI R 30 kg/m

prediabetes (HbA1c% R 5.7%) and TIIDM (R6.5%) are shown.

(legend continued on next page)

1080 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

More broadly, ascribing a single PPGR to each food assumes

that the response is solely an intrinsic property of the consumed

food. However, the few small-scale (n = 23–40) studies that

examined interpersonal differences in PPGRs found high vari-

ability in the response of different people to the same food

(Vega-Lo

pez et al., 2007; Vrolix and Mensink, 2010), but the fac-

tors underlying this variability have not been systematically

studied.

Factors that may affect interpersonal differences in PPGRs

include genetics (Carpenter et al., 2015), lifestyle (Dunstan

et al., 2012), insulin sensitivity (Himsworth, 1934), and exocrine

pancreatic and glucose transporters activity levels (Gibbs

et al., 1995). Another factor that may be involved is the gut micro-

biota. Pioneering work by Jeffrey Gordon and colleagues previ-

ously showed that it associates with the propensity for obesity

and its complications, and later works also demonstrated asso-

ciations with glucose intolerance, TIIDM, hyperlipidemia, and in-

sulin resistance (Le Chatelier et al., 2013; Karlsson et al., 2013;

Qin et al., 2012; Suez et al., 2014; Turnbaugh et al., 2006; Zhang

et al., 2013). However, little is known about the association of gut

microbiota with PPGRs.

Here, we set out to quantitatively measure individualized

PPGRs, characterize their variability across people, and identify

factors associated with this variability. To this end, we continu-

ously monitored glucose levels during an entire week in a cohort

of 800 healthy and prediabetic individuals and also measured

blood parameters, anthropometrics, physical activity, and self-

reported lifestyle behaviors, as well as gut microbiota composi-

tion and function. Our results demonstrate high interpersonalvari-

ability in PPGRs to the same food. We devised a machine learning

algorithm that integrates these multi-dimensional data and accu-

rately predicts personalized PPGRs, which we further validated in

an independently collected 100-person cohort. Moreover, we

show that personally tailored dietary interventions based on these

predictions result in signiﬁcantly improved PPGRs accompanied

by consistent alterations to the gut microbiota.

RESULTS

Measurements of Postprandial Responses, Clinical

Data, and Gut Microbiome

To comprehensively characterize PPGRs, we recruited 800

individuals aged 18–70 not previously diagnosed with TIIDM

(Figure 1A, Table 1). The cohort is representative of the adult

non-diabetic Israeli population (Israeli Center for Disease Con-

trol, 2014), with 54% overweight (BMI R 25 kg/m

) and 22%

obese (BMI R 30 kg/m

, Figures 1B, 1C, and S1). These proper-

ties are also characteristic of the Western adult non-diabetic

population (World Health Organization, 2008).

Each participant was connected to a continuous glucose

monitor (CGM), which measures interstitial ﬂuid glucose every

5 min for 7 full days (the ‘‘connection week’’), using subcutane-

ous sensors (Figure 1D). CGMs estimate blood glucose levels

with high accuracy (Bailey et al., 2014) and previous studies

found no signiﬁcant differences between PPGRs extracted

from CGMs and those obtained from either venous or capillary

blood (Vrolix and Mensink, 2010). We used blinded CGMs and

thus participants were unaware of their CGM levels during the

connection week. Together, we recorded over 1.5 million

glucose measurements from 5,435 days.

While connected to the CGM, participants were instructed to

log their activities in real-time, including food intake, exercise

and sleep, using a smartphone-adjusted website (www.

personalnutrition.org) that we developed (Figure S2A). Each

food item within every meal was logged along with its weight

by selecting it from a database of 6,401 foods with full nutritional

values based on the Israeli Ministry of Health database that we

further improved and expanded with additional items from certi-

ﬁed sources. To increase compliance, participants were

informed that accurate logging is crucial for them to receive an

accurate analysis of their PPGRs to food (ultimately provided

to each of them). During the connection week, participants

were asked to follow their normal daily routine and dietary habits,

except for the ﬁrst meal of every day, which we provided as one

of four different types of standardized meals, each consisting of

50 g of available carbohydrates. This resulted in a total of 46,898

real-life meals with close-to or full nutritional values (median of 54

(D) Example of continuous glucose monitoring (CGM) for one participant during an entire week. Colored area within zoom-in shows the incremental area under the

glucose curve (iAUC) which we use to quantify the meal’s PPGR.

(E) Major food components consumed by energy intake.

(F) Distribution of meals (dots) by macronutrient content. Inset shows histogram of meals per macronutrient.

(G) Bray-Curtis based PCoA of metagenome-based bacterial abundances of stool samples in our cohort and in the U.S. HMP and European MetaHIT cohorts.

Inset shows PCoA when samples from other HMP body sites are added. See also Figure S2.

Table 1. Cohorts Description

Main Cohort

Validation

Cohort

p Value

Number of participants (n) 800 100

Sex (% female) 60% 60% 1

Age (y) Mean ± SD 43.3 ± 13.1 42.4 ± 12.6 0.972

BMI (kg/m^2) Mean ± SD 26.4 ± 5.1 26.5 ± 4.8 0.867

BMI R 25 428 (54%) 50 (50%)

BMI R 30 173 (22%) 18 (18%)

HbA1c% Mean ± SD 5.43 ± 0.45 5.50 ± 0.55 0.492

HbA1c% R 5.7 189 (24%) 31 (31%)

HbA1c% R 6.5 23 (3%) 3 (3%)

Total cholesterol

(non-fasting, mg/dl)

Mean ± SD

186.8 ± 37.5 182.7 ± 35.7 0.231

HDL cholesterol

(non-fasting, mg/dl)

Mean ± SD

59.0 ± 17.8 55.0 ± 16.1 0.371

Waist-to-hip

circumference

ratio Mean ± SD

0.83 ± 0.12 0.84 ± 0.07 0.818

KS - Kolmogorov-Smirnov test. See also Figure S1.

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1081

Figure 2. High Interpersonal Variability in the Postprandial Glycemic Response to the Same Meal

(A) PPGRs associate with risk factors. Shown are PPGRs, BMI, HbA1c%, age, and wakeup glucose of all participants, sorted by median standardized meal PPGR

(top, red dots). Correlation of factors with the median PPGRs to standardized meals is shown along with a moving average line.

(legend continued on next page)

1082 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

meals per participant) and 5,107 standardized meals. The PPGR

of each meal was calculated by combining reported meal time

with CGM data and computing the incremental area under the

glucose curve in the 2 hr after the meal (iAUC; Wolever and Jen-

kins, 1986; Figure 1D).

Prior to CGM connection, a comprehensive proﬁle was

collected from each participant, including: food frequency, life-

style, and medical background questionnaires; anthropometric

measures (e.g., height, hip circumference); a panel of blood

tests; and a single stool sample, used for microbiota proﬁling

by both 16S rRNA and metagenomic sequencing.

With a total of 10,000,000 Calories logged, our data provide a

global view into the cohort’s dietary habits, showing the fraction

that each food source contributes to the cohort’s overall energy

intake (e.g., dairy, 7%; sweets, 6%; Figure 1E), and macronutrient

intake (Figures S2B–S2D). Analysis of the caloric breakdown of

every meal by macronutrients revealed that protein intake varies

relatively little across meals (80% of meals have 5%–35% pro-

tein), while fat and carbohydrates have a wide and bimodal distri-

bution, whereone of the modes corresponds to fat-free meals and

constitutes 18% of all meals (Figure 1F).

Principal coordinates analysis (PCoA) on the Bray-Curtis

dissimilarity between metagenome-based relative abundances

(RA) revealed a similar degree of variability in the microbiomes

of our cohort and stool samples of the US HMP (Human Micro-

biome Project Consortium, 2012) and European MetaHIT (Niel-

sen et al., 2014) cohorts (Figure 1G). The ﬁrst two principal coor-

dinates show some distinction between our cohort and the other

cohorts, but when HMP samples from other body sites are

added to the PCoA, stool samples from all three cohorts cluster

together and separate from the rest, indicative of overall similar-

ity in the gut microbiota composition of individuals from these

three distinct geographical regions (Figure 1G).

Postprandial Glycemic Responses Associate with

Multiple Risk Factors

Our data replicate known associations of PPGRs with risk fac-

tors, as the median standardized meal PPGR was signiﬁcantly

correlated with several known risk factors including BMI (R =

0.24, p < 10

10

), glycated hemoglobin (HbA1c%, R = 0.49, p <

10

), wakeup glucose (R = 0.47, p < 10

10

), and age (R =

0.42, p < 10

10

, Figure 2A). These associations are not conﬁned

to extreme values but persist along the entire range of PPGR

values, suggesting that the reduction in levels of risk factors is

continuous across all postprandial values, with lower values

associated with lower levels of risk factors even within the normal

value ranges (Figure 2A).

Utilizing the continuous nature of the CGMs, we also examined

the association between risk factors and the glucose level of

each participant at different percentiles (0–100) with respect

to all glucose measurements from the connection week. These

levels are affected by the PPGRs while also reﬂecting the general

glycemic control state of the participant. All percentiles signiﬁ-

cantly associated with risk factors (wakeup glucose, BMI,

HbA1c%, and age; Figures S3A–S3D). The percentile at which

the glucose level correlation was highest varied across risk fac-

tors. For example, BMI had the highest correlation with the 40

glucose value percentile, whereas for HbA1c% percentile 95 had

the highest correlation (Figures S3A and S3C). These results

suggest that the entire range of glucose levels of an individual

may have clinical relevance, with different percentiles being

more relevant for particular risk factors.

High Interpersonal Variability in the Postprandial

Response to Identical Meals

Next, we examined intra- and interpersonal variability in the

PPGR to the same food. First, we assessed the extent to which

PPGRs to three types of standardized meals that were given

twice to every participant (Figure 1A), are reproducible within

the same person. Indeed, the two replicates showed high agree-

ment (R = 0.77 for glucose, R = 0.77 for bread with butter, R =

0.71 for bread, p < 10

10

in all cases), demonstrating that the

PPGR to identical meals is reproducible within the same person

and that our experimental system reliably measures this repro-

ducibility. However, when comparing the PPGRs of different

people to the same meal, we found high interpersonal variability,

with the PPGRs of every meal type (except fructose) spanning

the entire range of PPGRs measured in our cohort (Figures 2B,

2C, and S3E–S3H). For example, the average PPGR to bread

across 795 people was 44 ± 31 mg/dl*h (mean ± SD), with the

bottom 10% of participants exhibiting an average PPGR below

15 mg/dl*h and the top 10% of participants exhibiting an average

PPGR above 79 mg/dl*h. The large interpersonal differences in

PPGRs are also evident in that the type of meal that induced

the highest PPGR differs across participants and that different

participants might have opposite PPGRs to pairs of different

standardized meals (Figures 2D and 2E).

Interpersonal variability was not merely a result of participants

having high PPGRs to all meals, since high variability was also

observed when the PPGR of each participant was normalized to

his/her own PPGR to glucose (Figures S3I–S3K). For white bread

and fructose, for which such normalized PPGRs were previously

measured, the mode of the PPGR distribution in our cohort had

excellent agreement with published values (Foster-Powell et al.,

(B) Kernel density estimation (KDE) smoothed histogram of the PPGR to four types of standardized meals provided to participants (each with 50 g of available

carbohydrates). Dashed lines represent histogram modes (See also Figure S3).

(C) Example of high interpersonal variability and low intra-personal variability in the PPGR to bread across four participants (two replicates per participant

consumed on two different mornings).

(D) Heatmap of PPGR (average of two replicates) of participants (rows) to three types of standardized meals (columns) consumed in replicates. Clustering is by

each participant’s relative rankings of the three meal types.

(E) Example of two replicates of the PPGR to two standardized meals for two participants exhibiting reproducible yet opposite PPGRs.

(F) Box plot (box, IQR; whiskers, 10–90 percentiles) of the PPGR to different real-life meals along with amount of carbohydrates consumed (green; mean ± std).

(G) Same as (E), for a pair of real-life meals, each containing 20 g of carbohydrates.

(H) Heatmap (subset) of statistically signiﬁcant associations (p < 0.05, FDR corrected) between participants’ standardized meals PPGRs and participants’ clinical

and microbiome data (See also Figure S4 for the full heatmap).

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1083

2002), further validating the accuracy of our data (bread: 65 versus

71; fructose: 15 versus 19, Figures S3I and S3K).

Next, we examined variability in the PPGRs to the multiple

real-life meals reported by our participants. Since real-life meals

vary in amounts and may each contain several different food

components, we only examined meals that contained 20–40 g

of carbohydrates and had a single dominant food component

whose carbohydrate content exceeded 50% of the meal’s car-

bohydrate content. We then ranked the resulting dominant foods

that had at least 20 meal instances by their population-average

PPGR (Figure 2F). For foods with a published glycemic index,

our population-average PPGRs agreed with published values

(R = 0.69, p < 0.0005), further supporting our data (Table S1).

For example, the average PPGR to rice and potatoes was

relatively high, whereas that for ice cream, beer, and dark

chocolate was relatively low, in agreement with published data

(Atkinson et al., 2008; Foster-Powell et al., 2002). Similar to stan-

dardized meals, PPGRs to self-reported meals highly varied

across individuals, with both low and high responders noted

for each type of meal (Figures 2F and 2G).

Postprandial Variability Is Associated with Clinical and

Microbiome Proﬁles

We found multiple signiﬁcant associations between the stan-

dardized meal PPGRs of participants and both their clinical and

gut microbiome data (Figures 2H and S4). Notably, the TIIDM

and metabolic syndrome risk factors HbA1c%, BMI, systolic

blood pressure, and alanine aminotransferase (ALT) activity are

all positively associated with PPGRs to all types of standardized

meals, reinforcing the medical relevance of PPGRs. In most stan-

dardized meals, PPGRs also exhibit a positive correlation with

CRP, whose levels rise in response to inﬂammation ( Figure 2H).

With respect to microbiome features, the phylogenetically

related Proteobacteria and Enterobacteriaceae both exhibit pos-

itive associations with a few of the standardized meals PPGR

(Figure 2H). These taxa have reported associations with poor

glycemic control, and with components of the metabolic syn-

drome including obesity, insulin resistance, and impaired lipid

proﬁle (Xiao et al., 2014). RAs of Actinobacteria are positively

associated with the PPGR to both glucose and bread, which is

intriguing since high levels of this phylum were reported to asso-

ciate with a high-fat, low-ﬁber diet (Wu et al., 2011).

At the functional level, the KEGG pathways of bacterial

chemotaxis and of ﬂagellar assembly, reported to increase

in mice fed high-fat diets and decrease upon prebiotics adminis-

tration ( Everard et al., 2014), exhibit positive associations

with several standardized meal PPGRs (Figure 2H). The KEGG

pathway of ABC transporters, reported to be positively associ-

ated with TIIDM (Karlsson et al., 2013) and with a Western

high-fat/high-sugar diet (Turnbaugh et al., 2009), also exhibits

positive association with several standardized meal PPGRs (Fig-

ure 2H). Several bacterial secretion systems, including both type

II and type III secretion systems that are instrumental in bacterial

infection and quorum sensing (Sandkvist, 2001) are positively

associated with most standardized meal PPGRs (Figure 2H).

Finally, KEGG modules for transport of the positively charged

amino acids lysine and arginine are associated with high PPGR

to standardized foods, while transport of the negatively charged

amino acid glutamate is associated with low PPGRs to these

foods.

Taken together, these results show that PPGRs vary greatly

across different people and associate with multiple person-spe-

ciﬁc clinical and microbiome factors.

Prediction of Personalized Postprandial Glycemic

Responses

We next asked whether clinical and microbiome factors could be

integrated into an algorithm that predicts individualized PPGRs.

To this end, we employed a two-phase approach. In the ﬁrst,

discovery phase, the algorithm was developed on the main

cohort of 800 participants, and performance was evaluated us-

ing a standard leave-one-out cross validation scheme, whereby

PPGRs of each participant were predicted using a model trained

on the data of all other participants. In the second, validation

phase, an independent cohort of 100 participants was recruited

and proﬁled, and their PPGRs were predicted using the model

trained only on the main cohort (Figure 3A).

Given non-linear relationships between PPGRs and the

different factors, we devised a model based on gradient boost-

ing regression (

Friedman, 2001).

This model predicts PPGRs us-

ing the sum of thousands of different decision trees. Trees are

inferred sequentially, with each tree trained on the residual of

all previous trees and making a small contribution to the overall

prediction (Figure 3A). The features within each tree are selected

by an inference procedure from a pool of 137 features represent-

ing meal content (e.g., energy, macronutrients, micronutrients);

daily activity (e.g., meals, exercises, sleep times); blood param-

eters (e.g., HbA1c%, HDL cholesterol); CGM-derived features;

questionnaires; and microbiome features (16S rRNA and meta-

genomic RAs, KEGG pathway and module RAs and bacterial

growth dynamics - PTRs; Korem et al., 2015).

As a baseline reference, we used the ‘‘carbohydrate counting’’

model, as it is the current gold standard for predicting PPGRs

(American Diabetes Association., 2015b; Bao et al., 2011). On

our data, this model that consists of a single explanatory variable

representing the meal’s carbohydrate amount achieves a

modest yet statistically signiﬁcant correlation with PPGRs (R =

0.38, p < 10

10

, Figure 3B). A model using only meal Caloric

content performs worse (R = 0.33, p < 10

10

, Figure 3C). Our pre-

dictor that integrates the above person-speciﬁc factors predicts

the held-out PPGRs of individuals with a signiﬁcantly higher cor-

relation (R = 0.68, p < 10

10

, Figure 3D). This correlation ap-

proaches the presumed upper bound limit set by the 0.71–0.77

correlation that we observed between the PPGR of the same

person to two replicates of the same standardized meal.

Validation of Personalized Postprandial Glycemic

Response Predictions on an Independent Cohort

We further validated our model on an independent cohort of 100

individuals that we recruited separately. Data from this additional

cohort were not available to us while developing the algorithm.

Participants in this cohort underwent the same proﬁling as in

the main 800-person cohort. No signiﬁcant differences were

found between the main and validation cohorts in key parame-

ters, including age, BMI, non-fasting total and HDL cholesterol,

and HbA1c% (Table 1, Figure S1).

1084 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

Notably, our algorithm, derived solely using the main 800 par-

ticipants cohort, achieved similar performance on the 100 partic-

ipants of the validation cohort (R = 0.68 and R = 0.70 on the main

and validation cohorts, respectively, Figures 3D and 3E). The

reference carbohydrate counting model achieved the same per-

formance as in the main cohort (R = 0.38). This result further sup-

ports the ability of our algorithm to provide personalized PPGR

predictions.

Factors Underlying Personalized Predictions

To gain insight into the contribution of the different features in

the algorithm’s predictions, we examined partial dependence

plots (PDP), commonly used to study functional relations be-

tween features used in predictors such as our gradient boosting

regressor and an outcome (PPGRs in our case; Hastie et al.,

2008). PDPs graphically visualize the marginal effect of a given

feature on prediction outcome after accounting for the average

effect of all other features. While this effect may be indicative of

feature importance, it may also be misleading due to higher-or-

der interactions (Hastie et al., 2008). Nonetheless, PDPs are

commonly used for knowledge discovery in large datasets

such as ours.

Predicted PPGR

(iAUC, mg/dl

R=0.70

Validation cohort

prediction

Personal features

Meal features

Main

cohort

800 participants

Validation

cohort

100 participants

Time, nutrients,

prev. exercise

Meal response predictor

Meal

responses

Train predictor

Cross-validation

Leave-one-person-out

020 255 30

x4000

Use predictor to predict meal responses

Boosted decision trees

Meal response prediction

Predicted Measured

16S MG

Participant

Measured PPGR

(iAUC, mg/dl

Meal Carbohydrates (g)

R=0.38

Carbohydrate-only

prediction

Predicted PPGR

(iAUC, mg/dl

R=0.68

Main cohort prediction

(cross-validation)

Measured PPGR

(iAUC, mg/dl

Calories-only

prediction

R=0.33

Meal Calories (g)

BQA

Figure 3. Accurate Predictions of Personal-

ized Postprandial Glycemic Responses

(A) Illustration of our machine-learning scheme for

predicting PPGRs.

(B–E) PPGR predictions. Dots represent predicted

(x axis) and CGM-measured PPGR (y axis) for

meals, for a model based: only on the meal’s car-

bohydrate content (B); only on the meal’s Caloric

content (C); our predictor evaluated in leave-one-

person-out cross validation on the main 800-per-

son cohort (D); and our predictor evaluated on the

independent 100-person validation cohort (E).

Pearson correlation of predicted and measured

PPGRs is indicated.

As expected, the PDP of carbohydrates

(Figure 4A) shows that as the meal carbo-

hydrate content increases, our algorithm

predicts, on average, a higher PPGR. We

term this relation, of higher predicted

PPGR with increasing feature value, as

non-beneﬁcial (with respect to prediction),

and the opposite relation, of lower pre-

dicted PPGR with increasing feature

value, as beneﬁcial (also with respect to

prediction; see PDP legend in Figure 4).

However, since PDPs display the overall

contribution of each feature across the

entire cohort, we asked whether the rela-

tionship between carbohydrate amount

and PPGRs varies across people. To this

end, for each participant we computed

the slope of the linear regression between

the PPGR and carbohydrate amount of all

his/her meals. As expected, this slope was

positive for nearly all (95.1%) participants, reﬂective of higher

PPGRs in meals richer in carbohydrates. However, the magni-

tude of this slope varies greatly across the cohort, with the

PPGR of some people correlating well with the carbohydrate

content (i.e., carbohydrates ‘‘sensitive’’) and that of others exhib-

iting equally high PPGRs but little relationship to the amount

of carbohydrates (carbohydrate ‘‘insensitive’’; Figure 4B). This

result suggests that carbohydrate sensitivity is also person

speciﬁc.

The PDP of fat exhibits a beneﬁcial effect for fat since our al-

gorithm predicts, on average, lower PPGR as the meal’s ratio

of fat to carbohydrates (Figure 4C) or total fat content (Fig-

ure S5A) increases, consistent with studies showing that adding

fat to meals may reduce the PPGR (Cunningham and Read,

1989). However, here too, we found that the effect of fat varies

across people. We compared the explanatory power of a linear

regression between each participant’s PPGR and meal carbohy-

drates, with that of regression using both fat and carbohydrates.

We then used the difference in Pearson R between the two

models as a quantitative measure of the added contribution of

fat (Figure 4D). For some participants we observed a reduction

in PPGR with the addition of fat, while for others meal fat content

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1085

Eubacterium rectale

PTR (59)

430

n.d. 1.1 1.2

PTR

223

0.75 0.8 0.85

Coprococcus catus

PTR (53)

158

455

103

n.d. 1.1

1.2

PTR

Time from

last sleep (12)

4971

10947

Time (min)

0 400 800 1200

Partial dependence

(a.u.)

0.6

0.3

-0.3

M00514

TtrS-TtrR TCS (27)

M00496

NblS-NblR TCS (28)

M00256

Cell div. trans. sys. (30)

Bacteroides

dorei (45)

Alistipes

putredinis (48)

Relative abundance

Alanine aminotransferase (ALT)

Age

BMI

Systolic blood pressure

Non-fasting total cholesterol

Glucose fluctuations (noise, σ/μ)

HbA1c%

Waist-to-hip ratio

Partial dependence

(a.u.)

0.3

0.1

-0.1

Partial dependence

(a.u.)

Partial dependence

(a.u.)

-3

-6

Meal

carbohydrates (2)

Weight (g)

PPGR (iAUC, mg/dl

Meal carbohydrates (g)

Participant 49

Participant 145

-1

Meal sodium (5)

Meal

dietary fiber (14)

390

401

105

523

632

164

334

448

424

323

8400

7518

8623

7290

Meal water (21)

Amount (ml)

0 300 600

5588

103306968

8697

Weight (mg) Weight (g)

Meal carbohydrates (g)

Meal fat (g)

Participant 267 Participant 465

Color Scale

P<0.005 P<0.01 P<0.05 n.s. P<0.005P<0.01P<0.05

Positive associationNegative association

Frequency

Carbohydrate-PPGR slope

R-difference

Frequency

PPGR (iAUC, mg/dl

0 40 80 120

0 1000 2000 0 3 6 9 12

24-hour

dietary fiber (25)

7575

8342

Weight (g)

0 20 40

n.d. 10

-6

-5

n.d. 10

-6

-5

n.d. 10

-3

n.d. 10

-4

-3

-2

n.d. 10

-4

-3

-2

Relative abundance Relative abundance Relative abundance Relative abundance

Parabacteroides

distasonis (63)

101

363

332

Relative abundance

n.d. 10

-4

-3

-2

Phylum

Bacteroidetes (95)

144

436

217

n.d. 10

-0.6

-0.2

Relative abundance

Ratio mapped to

gene-set (93)

320

448

Ratio mapped

PDP Legend

0.6

0.3

-0.3

-0.6

Feature name

(Feature rank)

Feature value

85 - # undetected

372- # with

above-zero

contribution

497 - #with below-

zero contribution

n.d 10

-6

-5

Feature

distribution

Negative trend:

Feature is beneficial

Partial dependence

(a.u.)

Meal

fat / carbohydrates (4)

log

(fat/carbs)

-2

-4

7611

8303

-2 -1 0 1 2

Above-zero contribution segment

Below-zero contribution segment

Slope > 0

95.1%

R difference:

0.21

R difference:

0.11

M00514 TtrS-TtrR TCS (27)

M00513 LuxQN/CqsS-LuxU-LuxO TCS (38)

M00472 NarQ-NarP TCS (41)

Alistipes putredinis (48)

M00664 Nodulation (49)

M00453 QseC-QseB TCS (50)

Sp. in genus Subdoligranulumun (54)

M00035 Methionine degradation (55)

Eubacterium rectale PTR (59)

M00112 Tocopherol biosynthesis (60)

Streptococcus salivarius PTR (65)

M00412 ESCRT-III complex (70)

Eubacterium eligens PTR (79)

M00066 Lactosylceramide biosynth. (80)

Akkermansia muciniphila PTR (82)

Alistipes finegoldii (83)

Bacteroides xylanisolvens (85)

Eubacterium rectale (87)

Akkermansia muciniphila (96)

M00156 Cytochrome c oxidase (98)

Phylum Euryarchaeota (99)

Phylum Cyanobacteria (107)

Figure 4. Factors Underlying the Prediction

of Postprandial Glycemic Responses

(A) Partial dependence plot (PDP) showing the

marginal contribution of the meal’s carbohydrate

content to the predicted PPGR (y axis, arbitrary

units) at each amount of meal carbohydrates

(x axis). Red and green indicate above and below

zero contributions, respectively (number indicate

meals). Boxplots (bottom) indicate the carbohy-

drates content at which different percentiles (10,

25, 50, 75, and 90) of the distribution of all meals

across the cohort are located. See PDP legend.

(B) Histogram of the slope (computed per partici-

pant) of a linear regression between the carbohy-

drate content and the PPGR of all meals. Also

shown is an example of one participant with a low

slope and another with a high slope.

(D) Histogram of the difference (computed per

participant) between the Pearson R correlation of

two linear regression models, one between the

PPGR and the meal carbohydrate content and

another when adding fat and carbohydrate*fat

content. Also shown is an example of the carbo-

hydrate and fat content of all meals of one partici-

pant with a relatively low R difference (carb alone

correlates well with PPGR) and another with a

relatively high difference (meals with high fat

content have lower PPGRs). Dot color and size

correspond to the meal’s PPGR.

(E) Additional PDPs.

(F) Microbiome PDPs. The number of participants

in which the microbiome feature was not detected

is indicated (left, n.d.). Boxplots (box, IQR; whiskers

10–90 percentiles) based only on detected values.

(G) Heatmap of statistically signiﬁcant correlations

(Pearson) between microbiome features termed

beneﬁcial (green) or non-beneﬁcial (red) and

several risk factors and glucose parameters .

See also Figure S5.

1086 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

did not add much to the explanatory power of the regressor

based only on the meal’s carbohydrates content (Figure 4D).

Interestingly, while dietary ﬁbers in the meal increase the pre-

dicted PPGR, their long-term effect is beneﬁcial as higher

amount of ﬁbers consumed in the 24 hr prior to the meal reduces

the predicted PPGR (Figure 4E). The meal’s sodium content, the

time that passed since last sleeping, and a person’s cholesterol

levels or age all exhibit non-beneﬁcial PDPs, while the PDPs of

the meal’s alcohol and water content display beneﬁcial effects

(Figures 4E and S5A). As expected, the PDP of HbA1c% shows

a non-beneﬁcial effect with increased PPGR at higher HbA1c%

values; intriguingly, higher PPGRs are predicted, on average, for

individuals with HbA1c% above 5.5%, which is very close to

the prediabetes threshold of 5.7% (Figure S5A).

The 72 PDPs of the microbiome-based features used in our

predictor were either beneﬁcial (21 factors), non-beneﬁcial (28),

or non-decisive (23) in that they mostly decreased, increased,

or neither, as a function of the microbiome feature. The resulting

PDPs had several intriguing trends. For example, growth of Eu-

bacterium rectale was mostly beneﬁcial, as in 430 participants

with high inferred growth for E. rectale it associates with a lower

PPGR (Figure 4F). Notably, E. rectale can ferment dietary carbo-

hydrates and ﬁbers to produce metabolites useful to the host

(Duncan et al., 2007), and was associated with improved post-

prandial glycemic and insulinemic responses (Martı

´nez

et al.,

2013), as well as negatively associated with TIIDM (Qin et al.,

2012). RAs of Parabacteroides distasonis were found non-bene-

ﬁcial by our predictor (Figure 4F) and this species was also sug-

gested to have a positive association with obesity (Ridaura et al.,

2013). As another example, the KEGG module of cell-division

transport system (M00256) was non-beneﬁcial, and in the 164

participants with the highest levels for it, it associates with a

higher PPGR (Figure 4F). Bacteroides thetaiotaomicron was

non-beneﬁcial (Figure S5B), and it was associated with obesity

and was suggested to have increased capacity for energy har-

vest (Turnbaugh et al., 2006). In the case of Alistipes putredinis

and the Bacteroidetes phylum, the non-beneﬁcial classiﬁcation

that our predictor assigns to both of them is inconsistent with

previous studies that found them to be negatively associated

with obesity (Ridaura et al., 2013; Turnbaugh et al., 2006). This

may reﬂect limitations of the PDP analysis or result from a

more complex relationship between these features, obesity,

and PPGRs.

To assess the clinical relevance of the microbiome-based

PDPs, we computed the correlation between several risk factors

and overall glucose parameters, and the factors with beneﬁcial

and non-beneﬁcial PDPs across the entire 800-person cohort.

We found 20 statistically signiﬁcant correlations (p < 0.05, FDR

corrected) where microbiome factors termed non-beneﬁcial

correlated with risk factors, and those termed beneﬁcial ex-

hibited an anti-correlation (Figure 4G). For example, higher levels

of the beneﬁcial methionine degradation KEGG module

(M00035) resulted in lower PPGRs in our algorithm, and across

the cohort, this module anti-correlates with systolic blood pres-

sure and with BMI (Figure 4G). Similarly, ﬂuctuations in glucose

levels across the connection week correlates with nitrate respira-

tion two-component regulatory system (M00472) and with lacto-

sylceramide biosynthesis (M00066), which were both termed

non-beneﬁcial. Glucose ﬂuctuations also anti-correlate with

levels of the tetrathionate respiration two-component regulatory

system (M00514) and with RAs of Alistipes ﬁnegoldii, both

termed beneﬁcial (Figure 4G). In 14 other cases, factors with

beneﬁcial or non-beneﬁcial PDPs were correlated and anti-

correlated

with risk factors, respectively.

These results suggest that PPGRs are associated with

multiple and diverse factors, including factors unrelated to

meal content.

Personally Tailored Dietary Interventions Improve

Postprandial Responses

Next, we asked whether personally tailored dietary interventions

based on our algorithm could improve PPGRs. We designed a

two-arm blinded randomized controlled trial and recruited 26

new participants. A clinical dietitian met each participant and

compiled 4–6 distinct isocaloric options for each type of meal

(breakfast, lunch, dinner, and up to two intermediate meals), ac-

commodating the participant’s regular diet, eating preferences,

and dietary constraints. Participants then underwent the same

1-week proﬁling of our main 800-person cohort (except that

they consumed the meals compiled by the dietitian), thus

providing the inputs (microbiome, blood parameters, CGM,

etc.) that our algorithm needs for predicting their PPGRs.

Participants were then blindly assigned to one of two arms

(Figure 5A). In the ﬁrst, ‘‘prediction arm,’’ we applied our algo-

rithm in a leave-one-out scheme to rank every meal of each

participant in the proﬁling week (i.e., the PPGR to each predicted

meal was hidden from the predictor). We then used these rank-

ings to design two 1-week diets: (1) a diet composed of the

meals predicted by the algorithm to have low PPGRs (the

‘‘good’’ diet); and (2) a diet composed of the meals with high pre-

dicted PPGRs (the ‘‘bad’’ diet). Every participant then followed

each of the two diets for a full week, during which they were con-

nected to a CGM and a daily stool sample was collected

(if available). The order of the 2 diet weeks was randomized

for each participant and the identity of the intervention weeks

(i.e., whether they are ‘‘good’’ or ‘‘bad’’) was kept blinded from

CRAs, dietitians and participants.

The second, ‘‘expert arm,’’ was used as a gold standard for

comparison. Participants in this arm underwent the same

process as the prediction arm except that instead of using our

predictor for selecting their ‘‘good’’ and ‘‘bad’’ diets a clinical

dietitian and a researcher experienced in analyzing CGM data

(collectively termed ‘‘expert’’) selected them based on their

measured PPGRs to all meals during the proﬁling week. Specif-

ically, meals that according to the expert’s analysis of their CGM

had low and high PPGRs in the proﬁling week were selected

for the ‘‘good’’ and ‘‘bad’’ diets, respectively. Thus, to the

extent that PPGRs are reproducible within the same person,

this expert-based arm should result in the largest differences

between the ‘‘good’’ and ‘‘bad’’ diets because the selection of

meals in the intervention weeks is based on their CGM data.

Notably, for 10 of the 12 participants of the predictor-based

arm, PPGRs in the ‘‘bad’’ diet were signiﬁcantly higher than in

the ‘‘good’’ diet (p < 0.05, Figure 5C). Differences between the

two diets are also evident in fewer glucose spikes and fewer

ﬂuctuations in the raw week-long CGM data (Figure 5B). The

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1087

success of the predictor was comparable to that of the expert-

based arm, in which signiﬁcantly lower PPGRs in the ‘‘good’’

versus the ‘‘bad’’ diet were observed for 8 of its 14 participants

(p < 0.05, 11 of 14 participants with p < 0.1, Figure 5C).

When combining the data across all participants, the ‘‘good’’

diet exhibited signiﬁcantly lower PPGRs than the ‘‘bad’’ diet

(p < 0.05, Figure 5D) as well as improvement in other measures

of blood glucose metabolism in both study arms, speciﬁcally,

Breakfast

Lunch

Snack

Dinner

123456

1 B2 B3 B4 B6B5

L1 L2 L3 L4

L6L5

S1 S2 S3 S4 S6S5

D1 D2 D3 D4 D6D5

Day

Dietitian prescribed meals

One week profiling

(26 participants)

16S MG

Personal features

Carbs > 10g?

HbA1c>5.7%?

BMI>25?

Firmicutes>5%?

020255 30

x4000

Predictor-based

Expert-based

‘Good’ diet

‘Bad’ diet

Choose meals for dietary intervention weeks

‘Good’ diet

‘Bad’ diet

Measure and analyze intervention weeks

Glucose (mg/dl) Glucose (mg/dl)

14 participants

(E1, E2, ..., E14)

165432

Day

Text meal identifier

Color-coded response

‘Bad’ diet week

‘Good’ diet week

‘Bad’ diet week

‘Good’ diet week

(blue - low; yellow - high)

Find best

and worst meals

for each row

Predictor-based arm Expert-based arm

Predictor

Expert

****** ****** * *** * ** ***

n.s. n.s.

***** ******

n.s.

†††

n.s.

***

n.s.

*** *** ** ** ****

‘Bad’ diet week

‘Good’ diet week

PPGR (iAUC, mg/dl

Glucose fluctuations (noise, σ/μ)

Max PPGR (iAUC, mg/dl

Profiling week measured

PPGR (iAUC, mg/dl

Intervention predicted

PPGR (iAUC, mg/dl

R=0.70 R=0.80

Participant P3

Participant E7

Intervention measured

PPGR (iAUC, mg/dl

Intervention measured

PPGR (iAUC, mg/dl

Pizza

Hummus

Potatoes

Chicken liver

Participants

Food consumed

during ‘good’

diet week

Food consumed

during ‘bad’

diet week

BQA

Schnitzel

P10

P11

P12

E14

E11

E10

E12

E13

E14

E13

P10

P11

P12

12 participants

(P1, P2, ..., P12)

Figure 5. Personally Tailored Dietary Interventions Improve Postprandial Glycemic Responses

(A) Illustration of the experimental design of our two-arm blinded randomized controlled trial.

(B) Continuous glucose measurements of one participant from the expert arm (top) and another from the predictor arm (bottom) across their ‘‘good’’ (green) and

‘‘bad’’ (red) diet weeks.

(C) Boxplot of meal PPGRs during the ‘‘bad’’ (red) and ‘‘good’’ (green) diet weeks for participants in both the predictor (left) and expert (right) arms. Statistical

signiﬁcance is marked (Mann-Whitney U-test, ***p < 0.001, **p < 0.01, *p < 0.05, y p < 0.1, n.s. not signiﬁcant).

(D) As in (C), but for a grouping of all meals of all participants in each study arm (p, Wilcoxon signed-rank test).

(E) Boxplot of the blood glucose ﬂuctuations (noise) of participants in both the ‘‘bad’’ (red) and ‘‘good’’ (green) diet weeks for both study arms. Blood glucose

ﬂuctuations per participant are deﬁned as the ratio between the standard deviation and mean of his/her weeklong blood glucose levels (p, Wilcoxon signed-

rank test).

(F) As in (E), but for the maximum PPGR of each participant.

(G) Subset of dominant food components prescribed in the ‘‘good’’ (green) diet of some participants and in the ‘‘bad’’ (red) diet of other participants. See also

Figure S6 for the full matrix.

(H) Dot plot between the CGM-measured PPGR of meals during the proﬁling week (x axis) and the average CGM-measured PPGR of the same meals during the

dietary intervention weeks (y axis). Meals of all participants in both study arms are shown.

(I) As in (H), but when PPGRs in the dietary intervention weeks are predicted by our predictor using only the ﬁrst proﬁling week data of each participant.

Boxplots - box, IQR; whiskers 1.5*IQR.

1088 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

lower ﬂuctuations in glucose levels across the CGM connection

week (p < 0.05, Figure 5E), and a lower maximal PPGR (p < 0.05,

Figure 5F) in the ‘‘good’’ diet.

Both study arms constitute personalized nutritional interven-

tions and thus demonstrate the efﬁcacy of this approach in

lowering PPGRs. However, the predictor-based approach has

broader applicability since it can predict PPGRs to arbitrary un-

seen meals, whereas the ‘‘expert’’-based approach will always

require CGM measurements of the meals it prescribes.

Post hoc examination of the prescribed diets revealed the

personalized aspect of the diets in both arms in that multiple

dominant food components (as in Figure 2F) prescribed in the

‘‘good’’ diet of some participants were prescribed in the ‘‘bad’’

diet of others ( Figures 5G and S6). This occurs when compo-

nents induced opposite CGM-measured PPGRs across partici-

pants (expert arm) or were predicted to have opposite PPGRs

(predictor arm).

The correlation between the measured PPGR of meals during

the proﬁling week and the average CGM-measured PPGR of the

same meals during the dietary intervention was 0.70 (Figure 5H),

which is similar to the reproducibility observed for standardized

meals (R = 0.71–0.77). Thus, as in the case of standardized meals,

a meal’s PPGR during the proﬁling week was not identical to its

PPGR in the dietary intervention week. Notably, using only the ﬁrst

proﬁling week data of each participant, our algorithm predicted

the average PPGRs of meals in the dietary intervention weeks

with an even higher correlation (R = 0.80, Figure 5I). Since our pre-

dictor also incorporates context-speciﬁc factors (e.g., previous

meal content, time since sleep), this result also suggests that

such factors may be important determinants of PPGRs.

Taken together, these results show the utility of personally

tailored dietary interventions for improving PPGRs in a short-

term intervention period, and the ability of our algorithm to devise

such interventions.

Alterations in Gut Microbiota Following Personally

Tailored Dietary Interventions

Finally, we used the daily microbiome samples collected during

the intervention weeks to ask whether the interventions induced

signiﬁcant changes in the gut microbiota. Previous studies

showed that even short-term dietary interventions of several

days may signiﬁcantly alter the gut microbiota (David et al.,

2014; Korem et al., 2015).

We detected changes following the dietary interventions that

were signiﬁcant relative to a null hypothesis of no change derived

from the ﬁrst week, in which there was no intervention, across all

participants (Figures 6A and 6B). While many of these signiﬁcant

changes were person-speciﬁc, several taxa changed consis-

tently in most participants (p < 0.05, FDR corrected, Figure 6C

and S7). Moreover, in most cases in which the consistently

changing taxa had reported associations in the literature, the

direction of change in RA following the ‘‘good’’ diet was in agree-

ment with reported beneﬁcial associations. For example, Biﬁdo-

bacterium adolescentis, for which low levels were reported to be

associated with greater weight loss (Santacruz et al., 2009),

generally decrease in RA following the ‘‘good’’ diet and increase

following the ‘‘bad’’ diet (Figure 6C,D). Similarly, TIIDM has been

associated with low levels of Roseburia inulinivorans (Qin et al.,

2012; Figure 6E), Eubacterium eligens (Karlsson et al., 2013),

and Bacteroides vulgatus (Ridaura et al., 2013), and all these

bacteria increase following the ‘‘good’’ diet and decrease

following the ‘‘bad’’ diet (Figure 6C). The Bacteroidetes phylum,

for which low levels associate with obesity and high fasting

glucose (Turnbaugh et al., 2009), increases following the

‘‘good’’ diet and decreases following the ‘‘bad’’ diet (Figure 6

C).

Low

levels of Anaerostipes associate with improved glucose

tolerance and reduced plasma triglyceride levels in mice (Ever-

ard et al., 2011) and indeed these bacteria decrease following

the ‘‘good’’ diet and increase following the ‘‘bad’’ diet (Figure 6C).

Finally, low levels of Alistipes putredinis associate with obesity

(Ridaura et al., 2013) and this bacteria increased following the

‘‘good’’ diet (Figure 6C).

These ﬁndings demonstrate that while both baseline micro-

biota composition and personalized dietary intervention vary be-

tween individuals, several consistent microbial changes may be

induced by dietary intervention with a consistent effect on PPGR.

DISCUSSION

In this work we measured 46,898 PPGRs to meals in a popula-

tion-based cohort of 800 participants. We demonstrate that

PPGRs are highly variable across individuals even when they

consume the same standardized meals. We further show that

an algorithm that integrates clinical and microbiome features

can accurately predict personalized PPGRs to complex, real-

life meals even in a second independently collected validation

cohort of 100 participants. Finally, personalized dietary interven-

tions based on this algorithm induced lower PPGRs and were

accompanied by consistent gut microbiota alterations.

Our study focused on PPGRs, as they were shown to be

important in achieving proper glycemic control, and when

disturbed are considered an independent disease risk factor

(American Diabetes Association., 2015a; Gallwitz, 2009). PPGRs

in our study also associated with several risk factors, including

BMI, HbA1c%, and wakeup glucose. In addition to its centrality

in glucose homeostasis, PPGRs serves as a convenient and ac-

curate endpoint, enabling continuous ‘‘point-of-care’’ collection

of dozens of quantitative measurements per person during a

relatively short follow up period. Such continuous assessment

of PPGRs is complementary to other equally important clinical

parameters such as BMI and HbA1c%, for which changes typi-

cally occur over longer timescales and are thus difﬁcult to corre-

late to nutritional responses in real time.

In line with few small-scale studies that previously examined

individual PPGRs (Vega-Lo

pez et al., 2007; Vrolix and Mensink,

2010), we demonstrate on 800 individuals that the PPGR of

different people to the same food can greatly vary. The most

compelling evidence for this observation is the controlled setting

of standardized meals, provided to all participants in replicates.

This high interpersonal variability suggests that at least with re-

gard to PPGRs, approaches that grade dietary ingredients as

universally ‘‘good’’ or ‘‘bad’’ based on their average PPGR in

the population may have limited utility for an individual.

We report several associations between microbiome features

and variability in PPGRs across people. In some cases, such as

for Actinobacteria, Proteobacteria, and Enterobacteriaceae, the

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1089

123456 123456

‘Bad’ diet week (day) ‘Good’ diet week (day)

Glucose (mg/dl)

Fold change

(w.r.t days 0-3)

Participant E3

123456 123456

‘Good’ diet week (day) ‘Bad’ diet week (day)

Glucose (mg/dl)

Participant P8

Bifidobacterium (G)

Bifidobacterium pseudocatenulatum (S)

Actinobacteria (C)

Alistipes putredinis (S)

Akkermansia muciniphila (S)

Parabacteroides merdae (S)

Streptococcus thermophilus (S)

Corpobacter fastidiosus (S)

Lactobacillus ruminis (S)

Bifidobacterium (G)

Bifidobacterium pseudocatenulatum (S)

Actinobacteria (Phylum)

Actinobacteria (Class)

Bifidobacteriales (Order)

Coriobacteriales (Order)

Bifidobacteriaceae (Family)

Coriobacteriaceae (Family)

Bifidobacterium (Genus)

Collinsella (Genus)

Anaerostipes (Genus)

Dorea (Genus)

Bifidobacterium adolescentis (Species)

Collinsella aerofaciens (Species)

Anaerostipes hadrus (Species)

Eubacterium hallii (Species)

Dorea longicatena (Species)

Bacteroidetes (Phylum)

Viruses (Phylum)

Proteobacteria (Phylum)

Bacteroidia (Class)

Gammaproteobacteria (Class)

Deltaproteobacteria (Class)

Betaproteobacteria (Class)

Bacteroidales (Order)

Enterobacteriales (Order)

Burkholderiales (Order)

Viruses, noname (Order)

Desulfovibrionales (Order)

Prevotellaceae (Family)

Bacteroidaceae (Family)

Sutterellaceae (Family)

Prevotella (Genus)

Bacteroides (Genus)

Barnesiella (Genus)

Ruminococcus lactaris (Species)

Eubacterium eligens (Species)

Roseburia inulinivorans (Species)

Bacteroides vulgatus (Species)

Bacteroides stercoris (Species)

Alistipes putredinis (Species)

Bacteria decreasing in ‘good’ diet week Bacteria increasing in ‘good’ diet week

Paricipants - ‘good’ diet weekParicipants - ‘bad’ diet week

E14

E12

P10

E11

E14

E12

P10

E11

Bifidobacterium adolescentis

Day

Fold change (with respect to days 0-3)Fold change (with respect to days 0-3)

Day

Roseburia inulinivorans

‘Good’ diet week

‘Bad’ diet week

‘Good’ diet week

‘Bad’ diet week

Fold change (days 4-7 vs. days 0-3)

-0.5 -0.25 0 0.25 0.5

Statistically significant

decrease (P<0.05)

Statistically significant

inecrease (P<0.05)

Fold change

(w.r.t days 0-3)

Figure 6. Dietary Interventions Induce Consistent Alterations to the Gut Microbiota Composition

(A) Top: Continuous glucose measurements of a participant from the expert arm for both the ‘‘bad’’ diet (left) and ‘‘good’’ diet (right) week. Bottom: Fold change

between the relative abundance (RA) of taxa in each day of the ‘‘bad’’ (left) or ‘‘good’’ (right) weeks and days 0–3 of the same week. Shown are only taxa that exhibit

statistically signiﬁcant changes with respect to a null hypothesis of no change derived from changes in the ﬁrst proﬁling week (no intervention) of all participants.

(B) As in (A) for a participant from the predictor arm. See also Figure S7 for changes in all participants.

(C) Heatmap of taxa with opposite trends of change in RA between ‘‘good’’ and ‘‘bad’’ intervention weeks that was consistent across participant and statistically

signiﬁcant (Mann-Whitney U-test between changes in the ‘‘good’’ and ‘‘bad’’ weeks, p < 0.05, FDR corrected). Left and right column blocks shows bacteria

increasing and decreasing in their RA following the ‘‘good’’ diet, respectively, and conversely for the ‘‘bad’’ diet. Colored entries represent the (log) fold change

between the RA of a taxon (x axis) between days 4–7 and 0–3 within each participant (y axis). Asterisks indicate a statistically signiﬁ cant fold change.

See also Figure S7 for all changes.

(legend continued on next page)

1090 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

direction of our associations are consistent with previous asso-

ciations reported between these taxa and higher-level pheno-

types such as dietary habits, obesity and overall glycemic

control (Wu et al., 2011; Xiao et al., 2014), raising testable hy-

potheses about how these taxa may mediate these host meta-

bolic effects. However, in most other cases we identify yet un-

known associations with particular biosynthesis pathways or

transport and secretion systems, which may be contributed by

different taxa in different individuals. These correlations thus pro-

vide concrete new pointers for further mechanistic research,

aimed at establishing causal roles for these bacterial taxa and

functional pathways in determining PPGRs.

Our study further attempts to analyze real-life meals that are

consumed in complex food combinations, at different times of

the day, and in varying proximity to previous meals, physical ac-

tivity, and sleep. While clearly of higher translational relevance,

the use of ‘‘real-life’’ nutritional input also introduces noise into

the meal composition data. Despite that, our results show that

predictions for such meals can be made informative by inte-

grating data from a large cohort into a carefully structured pre-

dictor. Even better predictions can likely be achieved with further

research.

Our algorithm takes as input a comprehensive clinical and mi-

crobiome proﬁle and employs a data-driven unbiased approach

to infer the major factors that are predictive of PPGRs. Intro-

specting the resulting algorithm shows that its predictions inte-

grate multiple diverse features that are unrelated to the content

of the meal itself. These include contents of previous meals,

time since sleep, proximity to exercise, and several micro-

biome-based factors. With respect to microbiome factors, our

algorithm identiﬁes multiple functional pathways and bacterial

taxa as either beneﬁcial or non-beneﬁcial, such that in partici-

pants with increasing levels for these factors the algorithm pre-

dicts a lower or higher PPGR, respectively. In many such cases,

microbiome factors found to be beneﬁcial with respect to PPGRs

are also negatively associated with risk factors such as HbA1c%

and cholesterol levels.

Dietary interventions based on our predictor showed signiﬁ-

cant improvements in multiple aspects of glucose metabolism,

including lower PPGRs and lower ﬂuctuations in blood glucose

levels within a short 1-week intervention period. It will be inter-

esting to evaluate the utility of such personalized intervention

over prolonged periods of several months and even years. If suc-

cessful, prolonged individualized dietary control of the PPGR

may be useful in controlling, ameliorating, or preventing a set

of disorders associated with chronically impaired glucose con-

trol, including obesity, prediabetes, TIIDM, and non-alcoholic

fatty liver disease (Grundy, 2012). These intriguing possibilities,

and the microbiome changes that accompany them, merit

further studies. Of equal interest and importance, our individual-

ized nutritional study protocols may be applicable to address

other clinically relevant issues involving nutritional modiﬁcations,

such as TIIDM and TIDM patient-speciﬁc determination of medi-

cation (e.g., insulin and oral hypoglycemics) dosing and timing.

Employing similar individualized prediction of nutritional ef-

fects on disease development and progression may also be

valuable in rationally designing nutritional interventions in a vari-

ety of inﬂammatory, metabolic, and neoplastic multi-factorial

disorders. More broadly, accurate personalized predictions of

nutritional effects in these scenarios may be of great practical

value, as they will integrate nutritional modiﬁcations more exten-

sively into the clinical decision-making scheme.

EXPERIMENTAL PROCEDURES

Human Cohorts

Approved by Tel Aviv Sourasky Medical Center Institutional Review Board

(IRB), approval numbers TLV-0658-12, TLV-0050-13 and TLV-0522-10; Kfar

Shaul Hospital IRB, approval number 0-73; and Weizmann Institute of Science

Bioethics and Embryonic Stem Cell Research oversight committee. Reported

to http://clinicaltrials.gov/, NCT: NCT01892956.

Study Design

Study participants were healthy individuals aged 18–70 able to provide

informed consent and operate a glucometer. Prior to the study, participants

ﬁlled medical, lifestyle, and nutritional questionnaires. At connection week

start, anthropometric, blood pressure and heart-rate measurements were

taken by a CRA or a certiﬁed nurse, as well as a blood test. Glucose was

measured for 7 days using the iPro2 CGM with Enlite sensors (Medtronic,

MN, USA), independently calibrated with the Contour BGM (Bayer AG, Lever-

kusen, Germany) as required. During that week participants were instructed to

record all daily activities, including standardized and real-life meals, in real-

time using their smartphones; meals were recorded with exact components

and weights. Full inclusion and exclusion criteria are detailed in Supplemental

Experimental Procedures. Questionnaires used can be found in Data S1.

Standardized Meals

Participants were given standardized meals (glucose, bread, bread and butter,

bread and chocolate, and fructose), calculated to have 50 g of available carbo-

hydrates. Participants were instructed to consume these meals immediately

after their night fast, not to modify the meal, and to refrain from eating or per-

forming strenuous physical activity before, and for 2 hr following consumption.

Stool Sample Collection

Participants sampled their stool following detailed printed instructions. Sam-

pling was done using a swab (n = 776) or both a swab and an OMNIgene-

GUT (OMR-200; DNA Genotek) stool collection kit (n = 413, relative abun-

dances (RA) for the same person are highly correlated (R = 0.99 p < 10

10

) be-

tween swabs and OMNIIgene-GUT collection methods). Collected samples

were immediately stored in a home freezer (20



C), and transferred in a pro-

vided cooler to our facilities where it was stored at 80



C(20



C for OMNII-

gene-GUT kits) until DNA extraction. All samples were taken within 3 days of

connection week start.

Genomic DNA Extraction and Filtering

Genomic DNA was puriﬁed using PowerMag Soil DNA isolation kit (MoBio)

optimized for Tecan automated platform. For shotgun sequencing, 100 ng of

puriﬁed DNA was sheared with a Covaris E220X sonicator. Illumina compatible

libraries were prepared as described (Suez et al., 2014). For 16S rRNA

sequencing, PCR ampliﬁcation of the V3/4 region using the 515F/806R 16S

(D) For Biﬁdobacterium adolescentis, which decreased signiﬁcantly following the ‘‘good’’ diet interven tions (see panel C), sho wn is the average and standard

deviation of the (log) fold change of all participants in each day of the ‘‘good’’ (top) diet week relative to days 0–3 of the ‘‘good’’ week. Same for the ‘‘bad’’ diet week

(bottom) in which B. adolescentis increases signiﬁcantly (see panel C). Grey lines show fold changes (log) in individual participants.

(E) As in (D), for Roseburia inulinivorans.

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1091

rRNA gene primers was performed followed by 500 bp paired-end sequencing

(Illumina MiSeq).

Microbial Analysis

We used USearch8.0 (Edgar, 2013) to obtain RA from 16S rRNA reads. We

ﬁltered metagenomic reads containing Illumina adapters, ﬁltered low quality

reads and trimmed low quality read edges. We detected host DNA by mapping

with GEM (Marco-Sola et al., 2012) to the Human genome with inclusive pa-

rameters, and removed those reads. We obtained RA from metagenomic

sequencing via MetaPhlAn 2 (Truong et al., 2015) with default parameters.

We assigned length-normalized RA of genes, obtained by similar mapping

with GEM to the reference catalog of (Li et al., 2014), to KEGG Orthology

(KO) entries (Kanehisa and Goto, 2000), and these were then normalized to a

sum of 1. We calculated RA of KEGG modules and pathways by summation.

We considered only samples with >10K reads of 16S rRNA, and >10 M meta-

genomic reads (>1.5 M for daily samples in diet intervention cohort).

Associating PPGRs with Risk Factors and Microbiome Proﬁle

We calculated the median PPGR to standardized meals for each participant

who consumed at least four of the standardized meals and correlated it with

clinical parameters (Pearson). We also calculated the mean PPGR of replicates

of each standardized meal (if performed) and correlated (Pearson) these values

with (a) blood tests; (b) anthropometric measurements; (c) 16S rRNA RA at the

species to phylum levels; (d) MetaPhlAn tag-level RA; and (e) RA of KEGG

genes. We capped RA at a minimum of 1e-4 (16S rRNA), 1e-5 (MetaPhlAn)

and 2e-7 (KEGG gene). For 16S rRNA analysis we removed taxa present in

less than 20% of participants. Correlations on RAs were performed in logspace.

Enrichment analysis of higher phylogenetic levels (d) and KEGG pathways

and modules (e) was performed by Mann-Whitney U-test between log(p val-

ue)*sign(R) of above correlations (d, e) of tags or genes contai ned in the higher

order groups and log(p value)*sign(R) of the correlations of the rest of the tags

or gene s.

FDR Correction

FDR was employed at the rate of 0.15, per tested variable (e.g., glucose stan-

dardized PPGR) per association test (e.g., with blood tests) for analyses in Fig-

ure 2G and Figure S4; per phylogenetic level in Figure 6 and Figure S7; and on

the entire association matrix in Fig ure 4G.

Meal Preprocessing

We merged meals logged less than 30 min apart and removed meals logged

within 90 min of other meals. We also removed very small (<15 g and <70 Cal-

ories) meals and meals with very large (>1 kg) components, meals with incom-

plete logging and meals consumed at the ﬁrst and last 12 hr of the connection

week.

PPGR Predictor

Microbiome derived features were selected according to number of estimators

using them in an additional predictor run on training data. For detailed feature

list see Supplemental Experimental Procedures. We predicted PPGRs using

stochastic gradient boosting regression, such that 80% of the samples and

40% of the features were randomly sampled for each estimator. The depth

of the tree at each estimator was not limited, but leaves were restricted to

have at least 60 instances (meals). We used 4000 estimators with a learning

rate of 0.002.

Microbiome Changes during Dietary Intervention

We determined the signiﬁcantly changing taxa of each participant by a Z test of

fold-change in RA between the beginning and end of each intervention week

against a null hypothesis of no change and standard deviation calculated

from at least 25-fold changes across the ﬁrst proﬁling week (no intervention)

of corresponding taxa from all participants with similar initial RA. We checked

whether a change was consistent across the cohort for each taxa by perform-

ing Mann-Whitney U-test between the Z statistics of the ‘‘good’’ intervention

weeks and those of the ‘‘bad’’ intervention weeks across all participants.

A detailed description of methods used in this paper can be found in the

Supplemental Experimental Procedures.

ACCESSION NUMBERS

The accession number for the data reported in this paper is ENA:

PRJEB11532.

SUPPLEMENTAL INFORMATION

Supplemental Information includes Supplemental Experimental Procedures,

seven ﬁgures, one table and one dataset and can be found with this article

online at http://dx.doi.org/10.1016/j.cell.2015.11.001.

AUTHOR CONTRIBUTIONS

T.K. and D.Z. conceived the project, designed and conducted all analyses, in-

terpreted the results, wrote the manuscript and are listed in random order. D.R.

conceived and directed the dietary intervention (DI) study and designed and

conducted analyses. T.K., D.Z., and A.W designed protocols and supervised

data collection. N.Z., D.I., Z.H., and E.E. coordinated and supervised clinical

aspects of data collection. T.K., D.Z., N.Z. and D.I. equally contributed to

this work. A.W. conceived the project, developed protocols, directed and per-

formed sample sequencing. M.R. and O.B.-Y. supervised the DI study. O.B.-Y.

conducted analyses and wrote the manuscript. D.L. conducted analyses, in-

terpreted results and advised nutritional decisions. T.A.-S. and M.L.-P. devel-

oped protocols and together with E.M. performed metagenomic extraction

and sequencing. N.Z., J.S., J.A.M., G.Z.-S., L.D., and M.P.-F. developed pro-

tocols and performed 16S sequencing. G.M., N.K. and R.B. coordinated and

designed data collection . Z.H. conceived the project and provided infrastruc-

ture. E.E. and E.S. conceived and directed the project and analyses, designed

data collection protocols, designed and conducted the analyses, interpreted

the results, and wrote the manuscript.

ACKNOWLEDGMENTS

We thank the Segal and Elinav group members for fruitful discussions; Keren

Segal, Yuval Dor, Tali Raveh-Sadka, Michal Levo, and Leeat Keren for fruitful

discussions and critical insights to the manuscript; Guy Raz and Ran Chen

for website development; Shira Zelber-Sagi for discussions; and Noya Horo-

witz for writing and submitting documents for review by IRBs. This research

was supported by the Weizmann Institute of Science. T.K., D.Z., and D.R. are

supported by the Ministry of Science, Technology, and Space, Israel. T.K. is

supported by the Foulkes Foundation. E.E. is supported by Yael and Rami Un-

gar, Israel; Leona M. and Harry B. Helmsley Charitable Trust; the Gurwin Family

Fund for Scientiﬁc Research; Crown Endowment Fund for Immunological

Research; estate of Jack Gitlitz; estate of Lydia Hershkovich; the Benoziyo

Endowment Fund for the Advancement of Science; John L. and Vera Schwartz,

Paciﬁc Palisades; Alan Markovitz, Canada; Cynthia Adelson, Canada; estate of

Samuel and Alwyn J. Weber; Mr. and Mrs. Donald L. Schwarz, Sherman Oaks;

grants funded by the European Research Council (ERC); the Israel Science

Foundation (ISF); E.E. is the incumbent of the Rina Gudinski Career Develop-

ment Chair. E.S. is supported by a research grant from Jack N. Halpern, and

Mr. and Mrs. Donald L. Schwarz and by grants from the ERC and the ISF.

Received: October 5, 2015

Revised: October 29, 2015

Accepted: October 30, 2015

Published: November 19, 2015

REFERENCES

American Diabetes Association (2015a). (5) Prevention or delay of type 2 dia-

betes. Diabetes Care 38 (Suppl ), S31–S32.

American Diabetes Association (2015b). (4) Foundations of care: education,

nutrition, physical activity, smoking cessation, psychosocial care, and immu-

nization. Diabetes Care 38

(Suppl ),

S20–S30.

1092 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

Atkinson, F.S., Foster-Powell, K., and Brand-Miller, J.C. (2008). International

tables of glycemic index and glycemic load values: 2008. Diabetes Care 31,

2281–2283.

Bailey, T.S., Ahmann, A., Brazg, R., Christiansen, M., Garg, S., Watkins, E.,

Welsh, J.B., and Lee, S.W. (2014). Accuracy and acceptability of the 6-day

Enlite continuous subcutaneous glucose sensor. Diabetes Technol. Ther. 16,

277–283.

Bansal, N. (2015). Prediabetes diagnosis and treatment: A review. World J.

Diabetes 6, 296–303.

Bao, J., Gilbertson, H.R., Gray, R., Munns, D., Howard, G., Petocz, P., Cola-

giuri, S., and Brand-Miller, J.C. (2011). Improving the estimation of mealtime

insulin dose in adults with type 1 diabetes: the Normal Insulin Demand for

Dose Adjustment (NIDDA) study. Diabetes Care 34, 2146–2151.

Blaak, E.E., Antoine, J.-M., Benton, D., Bjo

rck, I., Bozzetto, L., Brouns, F., Dia-

mant, M., Dye, L., Hulshof, T., Holst, J.J., et al. (2012). Impact of postprandial

glycaemia on health and prevention of disease. Obes. Rev. 13, 923–984.

Carpenter, D., Dhar, S., Mitchell, L.M., Fu, B., Tyson, J., Shwan, N.A.A., Yang,

F., Thomas, M.G., and Armour, J.A.L. (2015). Obesity, starch digestion and

amylase: association between copy number variants at human salivary

(AMY1) and pancreatic (AMY2) amylase genes. Hum. Mol. Genet. 24, 3472–

3480.

Cavalot, F., Pagliarino, A., Valle, M., Di Martino, L., Bonomo, K., Massucco, P.,

Anfossi, G., and Trovati, M. (2011). Postprandial blood glucose predicts car-

diovascular events and all-cause mortality in type 2 diabetes in a 14-year

follow-up: lessons from the San Luigi Gonzaga Diabetes Study. Diabetes

Care 34, 2237–2243.

Conn, J.W., and Newburgh, L.H. (1936). The glycemic response to isogluco-

genic quantities of protein and carbohydrate. J. Clin. Invest. 15, 665–671.

Cunningham, K.M., and Read, N.W. (1989). The effect of incorporating fat into

different components of a meal on gastric emptying and postprandial blood

glucose and insulin responses. Br. J. Nutr. 61 , 285–290.

David, L.A., Maurice, C.F., Carmody, R.N., Gootenberg, D.B., Button, J.E.,

Wolfe, B.E., Ling, A.V., Devlin, A.S., Varma, Y., Fischbach, M.A., et al.

(2014). Diet rapidly and reproducibly alters the human gut microbiome. Nature

505, 559–563.

Dodd, H., Williams, S., Brown, R., and Venn, B. (2011). Calculating meal glyce-

mic index by using measured and published food values compared with

directly measured meal glycemic index. Am. J. Clin. Nutr. 94, 992–996.

Duncan, S.H., Belenguer, A., Holtrop, G., Johnstone, A.M., Flint, H.J., and Lob-

ley, G.E. (2007). Reduced dietary intake of carbohydrates by obese subjects

results in decreased concentrations of butyrate and butyrate-producing bac-

teria in feces. Appl. Envir on. Microbiol. 73, 1073–1078.

Dunstan, D.W., Kingwell, B.A., Larsen, R., Healy, G.N., Cerin, E., Hamilton,

M.T., Shaw, J.E., Bertovic, D.A., Zimmet, P.Z., Salmon, J., and Owen, N.

(2012). Breaking up prolonge d sitting reduces postprandial glucose and insulin

responses. Diabetes Care 35, 976–983.

Edgar, R.C. (2013). UPARSE: highly accurate OTU sequences from microbial

amplicon reads. Nat. Methods 10, 996–998.

Everard, A., Lazarevic, V., Derrien, M., Girard, M., Muccioli, G.G., Neyrinck,

A.M., Possemiers, S., Van Holle, A., Franc¸ ois, P., de Vos, W.M., et al. (2011).

Responses of gut microbiota and glucose and lipid metabolism to prebiotics

in genetic obese and diet-induced leptin-resistant mice. Diabetes 60, 2775–

2786.

Everard, A., Lazarevic, V., Gaı

a, N., Johansson, M., Sta

hlman, M., Backhed, F.,

Delzenne, N.M., Schrenzel, J., Franc¸ ois, P., and Cani, P.D. (2014). Microbiome

of prebiotic-treated mice reveals novel targets involved in host response

during obesity. ISME J. 8, 2116–2130.

Foster-Powell, K., Holt, S.H.A., and Brand-Miller, J.C. (2002). International

table of glycemic index and glycemic load values: 2002. Am. J. Clin. Nutr.

76, 5–56.

Friedman, J.H. (2001). Greedy Function Approximation: A Gradient Boosting

Machine. Ann. Stat. 29,

1189–1232.

Gallwitz, B. (2009). Implications of postprandial glucose and weight control in

people with type 2 diabetes: understanding and implementing the International

Diabetes Federation guidelines. Diabetes Care 32 (Suppl 2 ), S322–S325.

Gibbs, E.M., Stock, J.L., McCoid, S.C., Stukenbrok, H.A., Pessin, J.E., Steven-

son, R.W., Milici, A.J., and McNeish, J.D. (1995). Glycemic improvement in dia-

betic db/db mice by overexpression of the human insulin-regulatable glucose

transporter (GLUT4). J. Clin. Invest. 95, 1512–1518.

Greenwood, D.C., Threapleton, D.E., Evans, C.E.L., Cleghorn, C.L., Nykjaer,

C., Woodhead, C., and Burley, V.J. (2013). Glycemic index, glycemic load, car-

bohydrates, and type 2 diabetes: systematic review and dose-response meta-

analysis of prospective studies. Diabetes Care 36, 4166–4171.

Grundy, S.M. (2012). Pre-diabetes, metabolic syndrome, and cardiovascular

risk. J. Am. Coll. Cardi ol. 59, 635–643.

Hastie, T., Tibshirani, R., and Friedman, J. (2008). The Elements of Statistical

Learning: Data Mining, Inference and Prediction (Stanford: Springer).

Himsworth, H.P. (1934). Dietetic factors inﬂuencing the glucose tolerance and

the activity of insulin. J. Physiol. 81, 29–48.

Human Microbiome Project Consortium (2012). Structure, function and diver-

sity of the healthy human microbiome. Nature 486, 207–214.

Israeli Center for Disease Control (2014). Health 2013 (Jerusalem: Israeli Min-

istry of Health).

Jenkins, D.J., Wolever, T.M., Taylor, R.H., Barker, H., Fielden, H., Baldwin,

J.M., Bowling, A.C., Newman, H.C., Jenkins, A.L., and Goff, D.V. (1981). Gly-

cemic index of foods: a physiological basis for carbohydrate exchange. Am. J.

Clin. Nutr. 34, 362–366.

Kanehisa, M., and Goto, S. (2000). KEGG: kyoto encyclopedia of genes and

genomes. Nucleic Acids Res. 28, 27–30.

Karlsson, F.H., Tremaroli, V., Nookaew, I., Bergstro

m, G., Behre, C.J., Fager-

berg, B., Nielsen, J., and Ba

ckhed, F. (2013). Gut metagenome in European

women with normal, impaired and diabetic glucose control. Nature 498,

99–103.

Korem, T., Zeevi, D., Suez, J., Weinberger, A., Avnit-Sagi, T., Pompan-Lotan,

M., Matot, E., Jona, G., Harmelin, A., Cohen, N., et al. (2 015). Growth dynamics

of gut microbiota in health and disease inferred from single metagenomic sam-

ples. Science 349, 1101–1106.

Kristo, A.S., Matthan, N.R., and Lichtenstein, A.H. (2013). Effect of diets

differing in glycemic index and glycemic load on cardiovascular risk factors:

review of randomized controlled-feeding trials. Nutrients 5, 1071–1080.

Lamkin, D.M., Spitz, D.R., Shahzad, M.M.K., Zimmerman, B., Lenihan, D.J.,

Degeest, K., Lubaroff, D.M., Shinn, E.H., Sood, A.K., and Lutgendorf, S.K.

(2009). Glucose as a prognostic factor in ovarian carcinoma. Cancer 115,

1021–1027.

Le Chatelier, E., Nielsen, T., Qin, J., Prifti, E., Hildebrand, F., Falony, G.,

Almeida, M., Arumugam, M., Batto, J.-M., Kennedy, S., et al.; MetaHIT con-

sortium (2013). Richness of human gut microbiome correlates with metabolic

markers. Nature 500, 541–546.

Li, J., Jia, H., Cai, X., Zhong, H., Feng, Q., Sunagawa, S., Arumugam, M.,

Kultima, J.R. , Prifti, E., Nielsen, T., et al.; MetaHIT Consortium; MetaHIT

Consortium (2014). An integrated catalog of reference genes in the human

gut microbiome. Nat. Biotechnol. 32, 834–841.

Marco-Sola, S., Sammeth, M., Guigo

, R., and Ribeca, P. (2012). The GEM

mapper: fast, accurate and versatile alignment by ﬁltration. Nat. Methods 9,

1185–1188.

Martı

nez, I., Lattimer, J.M., Hubach, K.L., Case, J.A., Yang, J., Weber, C.G.,

Louk, J.A., Rose, D.J., Kyureghian, G., Peterson, D.A., et al. (2013). Gut micro-

biome composition is linked to whole grain-induced immunological improve-

ments. ISME J. 7, 269–280.

Nathan

, D.M., Davidson, M.B., DeFronzo, R.A., Heine, R.J., Henry, R.R.,

Pratley, R., and Zinman, B.; American Diabetes Association (2007). Impaired

fasting glucose and impaired glucose tolerance: implications for care. Dia-

betes Care 30, 753–759.

Nielsen, H.B., Almeida, M., Juncker, A.S., Rasmussen, S., Li, J., Sunagawa, S.,

Plichta, D.R., Gautier, L., Pedersen, A.G., Le Chatelier, E., et al.; MetaHIT

Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc. 1093

Consortium; MetaHIT Consortium (2014). Identiﬁcation and assembly of ge-

nomes and genetic elements in complex metagenomic samples without using

reference genomes. Nat. Biotechnol. 32, 822–828.

Nishida, T., Tsuji, S., Tsujii, M., Arimitsu, S., Haruna, Y., Imano, E., Suzuki, M.,

Kanda, T., Kawano, S., Hiramatsu, N., et al. (2006). Oral glucose tolerance test

predicts prognosis of patients with liver cirrhosis. Am. J. Gastroenterol. 101,

70–75.

Qin, J., Li, Y., Cai, Z., Li, S., Zhu, J., Zhang, F., Liang, S., Zhang, W., Guan, Y.,

Shen, D., et al. (2012). A metagenome-wide association study of gut micro-

biota in type 2 diabetes. Nature 490, 55–60.

Riccardi, G., and Rivellese, A.A. (2000). Dietary treatment of the metabolic syn-

drome–the optimal diet. Br. J. Nutr. 83 (Suppl 1 ), S143–S148.

Ridaura, V.K., Faith, J.J., Rey, F.E., Cheng, J., Duncan, A.E., Kau, A.L., Grifﬁn,

N.W., Lombard, V., Henrissat, B., Bain, J.R., et al. (2013). Gut microbiota from

twins discordant for obesity modulate metabolism in mice. Science 341,

1241214.

Sandkvist, M. (2001). Type II secretion and pathogenesis. Infect. Immun. 69,

3523–3535.

Santacruz, A., Marcos, A., Wa

rnberg, J., Martı

´,

A., Martin-Matillas, M., Cam-

poy, C., Moreno, L.A., Veiga, O., Redondo-Figuero, C., Garagorri, J.M.,

et al.; EVASYON Study Group (2009). Interplay between weight loss and gut

microbiota composition in overweight adolescents. Obesity (Silver Spring)

17, 1906–1915.

Schwingshackl, L., and Hoffmann, G. (2013). Long-term effects of low glyce-

mic index/load vs. high glycemic index/load diets on parameters of obesity

and obesity-associated risks: a systematic review and meta-analysis. Nutr.

Metab. Cardiovasc. Dis. 23, 699–706.

Suez, J., Korem, T., Zeevi, D., Zilberman-Schapira, G., Thaiss, C.A., Maza, O.,

Israeli, D., Zmora, N., Gilad, S., Weinberger, A., et al. (2014). Artiﬁcial sweet-

eners induce glucose intolerance by altering the gut microbiota. Nature 514,

181–186.

Truong, D.T., Franzosa, E.A., Tickle, T.L., Scholz, M., Weingart, G., Pasolli, E.,

Tett, A., Huttenhower, C., and Segata, N. (2015). MetaPhlAn2 for enhanced

metagenomic taxonomic proﬁling. Nat. Methods 12, 902–903.

Turnbaugh, P.J., Ley, R.E., Mahowald, M.A., Magrini, V., Mardis, E.R., and

Gordon, J.I. (2006). An obesity-associated gut microbiome with increased

capacity for energy harvest. Nature 444, 1027–1031.

Turnbaugh, P.J., Hamady, M., Yatsunenko, T., Cantarel, B.L., Duncan, A., Ley,

R.E., Sogin, M.L., Jones, W.J., Roe, B.A., Affourtit, J.P., et al. (2009). A core gut

microbiome in obese and lean twins. Nature 457, 480–484.

Vega-Lo

pez, S., Ausman, L.M., Grifﬁth, J.L., and Lichtenstein, A.H. (2007).

Interindividual variability and intra-individual reproducibility of glycemic index

values for commercial white bread. Diabetes Care 30, 1412–1417.

Vrolix, R., and Mensink, R.P. (2010). Variability of the glycemic response to

single food products in healthy subjects. Contemp. Clin. Trials 31, 5–11.

Wolever, T.M., and Jenkins, D.J. (1986). The use of the glycemic index in

predicting the blood glucose response to mixed meals. Am. J. Clin. Nutr. 43,

167–172.

World Health Organization (2008). Global Health Observatory Data Repository

(Disease and Injury Country Estimates).

Wu, G.D., Chen, J., Hoffmann, C., Bittinger, K., Chen, Y.-Y., Keilbaugh, S.A.,

Bewtra, M., Knights, D., Walters, W.A., Knight, R., et al. (2011). Linking long-

term dietary patterns with gut microbial enterotypes. Science 334, 105–108.

Xiao, S., Fei, N., Pang, X., Shen, J., Wang, L., Zhang, B., Zhang, M., Zhang, X.,

Zhang, C., Li, M., et al. (2014). A gut microbiota-targeted dietary inter vention

for amelioration of chronic inﬂammation underlying metabolic syndrome.

FEMS Microbiol. Ecol. 87, 357–367.

Zhang, X., Shen, D., Fang, Z., Jie, Z., Qiu, X., Zhang, C., Chen, Y., and Ji, L.

(2013). Human gut microbiota changes reveal the progression of glucose intol-

erance.

PLoS ONE 8, e71108.

1094 Cell 163, 1079–1094, November 19, 2015 ª2015 Elsevier Inc.

Discussion

### What is PPG? Postprandial blood glucose (PPG) levels are a measurement of the body's glucose levels after a meal. In healthy individuals, glucose levels begin to rise about 10 minutes after eating. It will take about an hour to reach a peak glucose level (<140 mg/dl), and another 2-3 hours to return to baseline (70-110 mg/dl) in response to insulin secretion. Once blood glucose levels are low, insulin is no longer needed. Pre-diabetic and diabetic individuals have higher fasting glucose levels and higher baseline insulin levels. To return to baseline, insulin must be produced in higher quantities for a longer period of time. This results in a peak that is more gradual than in their healthy counterparts. Cells gradually become less responsive to insulin and blood glucose levels remain high. #### Continuous Glucose Monitors (CGMs) CGMs are commonly used by patients with Type I Diabetes. In recent years, there has been a push to develop an [artificial pancreas device](https://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/HomeHealthandConsumer/ConsumerProducts/ArtificialPancreas/ucm259548.htm) that monitors blood glucose to administer insulin and/or glucagon as necessary. Researchers are currently investigating ways to use CGMs to manage Type II Diabetes. #### Measuring Glycemic Response Today, glycemic response is predicted through one of the following nutritional metrics: * __Glycemic Index (GI)__: Ranks foods from 0-100 based on how they affect blood glucose levels. * __Glycemic Load (GL)__: Combines the quantity and quality of the carbohydrates to predict the effect. Calculated as [GI * Carbohydrates (g)]/100. Though widely accepted, GI and GL are not very effective metrics in predicting glycemic response. There are a number of other biological factors that play into metabolism, so different people likely have different responses to the same food. ### Measurements of Blood Glucose Blood glucose is most commonly measured using these metrics: #### HbA1c Level Glucose binds to the hemoglobin found in your red blood cells to create glycated hemoglobin. Because red blood cells have a life span of 2-3 months, measuring the amount of glycated hemoglobin in your blood is an indicator of a person's blood glucose over the past few months. #### Wakeup Glucose Level Also known as fasting plasma glucose (FPG). This is your blood glucose level after 8+ hours of fasting, so it's used as your baseline measurement. #### Oral Glucose Tolerance Test (OOGT) This test monitors an individual's blood glucose for three hours after they have a sugary drink. This measures a person's sensitivity to glucose and their glycemic response. #### NIH Reference Ranges |Diagnosis | HbA1c (%) | FPG (mg/dL) | OGTT (mg/dL)| | |-------------|:----------:|:----------------:|:---------------:|---| |Normal| < 5.7 | < 100| < 139 | | | Prediabetes | 5.7 - 6.4| 100 - 125| 140 - 199 | | | Diabetes | > 6.5| > 126 | > 200 | | #### The Glycemic Response The glycemic response is the body's reaction to consumption of carbohydrates. Monitoring one's glycemic response is an important factor in managing diabetes mellitus. Today, nutritional metrics use carbohydrate content and composition to predict glycemic response. However, an individual's response is dependent on a slew of biological factors, so an individualized approach may be more appropriate. This paper proposes the application of machine learning to personalized nutrition, suggesting that their algorithm served as a more accurate indicator of glycemic response than the current nutritional paradigm. ### Background on Diabetes Diabetes is an umbrella term referring to a group of conditions that are characterized by excessive thirst and urination. The less common *diabetes insipidus* is related to insufficient fluid regulation, while *diabetes mellitus* consists of high blood sugar. These are aptly named after the Latin *insipidus* and *mel*, translating to "tasteless" and "honey," respectively. Diabetes mellitus is further defined as Type I or Type II based on the cause of high glucose: * __Type I__ (TIDM): An autoimmune condition in which one's body attacks the insulin-producing beta cells of the pancreas. Patients with TIDM cannot produce insulin, so they use insulin injections after meals to facilitate glucose uptake. TIDM is often diagnosed at a young age. * __Type II__ (TIIDM): The pancreas outputs high volumes of insulin in response to high glucose levels, and after many years this can lead to *insulin resistance*. Fat, liver, and muscle cells no longer respond to insulin and uptake glucose as they should. TIIDM is treated with lifestyle changes and in some cases, medication. As insulin resistance develops over long periods of time, TIIDM is also termed "adult onset" diabetes. If you're thinking "insu-whaaaat?" here's a graphic of how glucose regulation works in your body: ![glucose regulation](https://i.pinimg.com/originals/e4/28/e9/e428e91483ab1168b6f67e8bd4cb6a09.jpg "Glucose Regulation") #### The Kyoto Encyclopedia of Genes and Genomes (KEGG) Minoru Kanehisa of Kyoto University started KEGG as an online resource for researchers to share cell and molecular findings from large genetic studies. These findings are useful in making higher level discoveries. KEGG includes 18 databases for a variety of systems and organisms. The KEGG Pathway database are a collection of hand-drawn pathway maps of molecular behavior in a variety of areas. You can learn more about KEGG [here](http://www.kegg.jp/kegg/kegg1a.html). #### (Some) Microbes are your Friends! Humans have complex mutualistic relationship with the estimated 100 trillion microbes that reside in their gut. Also known as the *gut microbiota*, these fellows are involved in a variety of vital physiological processes. Although the first "settlers" are transferred from mother to child during birth, the gut microbiota are quite dynamic. Diet, exercise, medication, and other factors can affect the diversity of one's gut. Imbalances can disrupt biological processes and, in extreme cases, cause disease. For a more information about the gut microbiota, check out [this article](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5433529/).

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