TITLE: Modulation of gut microbiota dysbioses in type 2 diabetic patients by
macrobiotic Ma-Pi 2 diet

  ABSTRACT:
The gut microbiota exerts a role in type 2 diabetes (T2D), and deviations from a
mutualistic ecosystem layout are considered a key environmental factor contributing to the
disease. Thus, the possibility of improving metabolic control in T2D by correcting gut
microbiome dysbioses through diet has been evaluated. Here, we explore the potential of
two different energy-restricted dietary approaches – the fibre-rich macrobiotic Ma-Pi 2
diet or a control diet recommended by Italian professional societies for T2D treatment –
to correct gut microbiota dysbioses in T2D patients. In a previous 21-d open-label MADIAB
trial, fifty-six overweight T2D patients were randomised to the Ma-Pi 2 or the control
diet. For the present study, stools were collected before and after intervention from a
subset of forty MADIAB participants, allowing us to characterise the gut microbiota by 16S
rRNA sequencing and imputed metagenomics. To highlight microbiota dysbioses in T2D, the
gut microbiota of thirteen normal-weight healthy controls were characterised. According to
our findings, both diets were effective in modulating gut microbiome dysbioses in T2D,
resulting in an increase of the ecosystem diversity and supporting the recovery of a
balanced community of health-promoting SCFA producers, such as
Faecalibacterium, Roseburia,
Lachnospira, Bacteroides and
Akkermansia. The Ma-Pi 2 diet, but not the control diet, was also
effective in counteracting the increase of possible pro-inflammatory groups, such as
Collinsella and Streptococcus, in the gut ecosystem,
showing the potential to reverse pro-inflammatory dysbioses in T2D, and possibly
explaining the greater efficacy in improving the metabolic control.

BODY:
Type 2 diabetes (T2D) is markedly increasing its prevalence in Westernised countries(

1

), and it represents a challenging problem for national healthcare systems(

2

). Recent insights provided evidence of an altered gut microbiota (GM) in T2D
subjects, suggesting a possible role for gut micro-organisms in the disease onset(

3

–

7

).  Intestinal micro-organisms, and their metabolic products, have been shown to exert relevant
functions in regulating host metabolic pathways(

8

). Indeed, GM components can modulate different factors contributing to the host
metabolic phenotype, such as intestinal gluconeogenesis, insulin sensitivity, lipid
accumulation and glucose control(

9

,

10

). Although a mutualistic GM composition is crucial to support the host energy
homeostasis, certain GM dysbioses can result in profound deregulations of the host metabolism,
supporting the onset and consolidation of metabolic diseases, such as T2D(

11

,

12

). Moreover, a pro-inflammatory layout of the gut microbial ecosystem has been
suggested to be the basis of chronic inflammatory processes observed in T2D, and the new
concept of metabolic infection has been proposed(

13

,

14

). As a result of an increased gut permeability, endotoxins from pro-inflammatory
GM components can penetrate the epithelial barrier and aggravate metabolic inflammation and
insulin resistance in T2D(

15

). Thus, the GM has the potential to exert a multifactorial role in T2D, and
deviations from a health-promoting GM composition could represent a key determinant
contributing to the disease onset(

11

,

16

–

18

). As diet has been recognised as a potent modulator of the composition and
metabolism of the human GM(

19

), the possibility to improve metabolic control in T2D by developing selective
diets that are able to correct the GM dysbioses has been considered(

11

,

16

,

20

).  Very recently, the Ma-Pi 2 macrobiotic diet has been reported to be more effective than a
control (CTR) diet, which is based on the dietary guidelines recommended by professional
societies in Italy, for the improvement of metabolic control in T2D patients(

21

). Conceived by Mario Pianesi, the Ma-Pi 2 diet is a fibre-rich macrobiotic diet
with a prebiotic potential, which is in line with the dietary recommendations of the Academy
of Nutrition and Dietetics(

22

). Specifically, the Ma-Pi 2 diet is enriched in complex carbohydrates, legumes,
fermented products, sea salt and green tea, and it excludes fat and protein from animal source
and added sugars. In a 21-d controlled open-label trial (MADIAB trial), fifty-six overweight
T2D patients were randomised (1:1 ratio) to the Ma-Pi 2 macrobiotic diet or the CTR diet. At
the end of this short-term nutritional intervention, the Ma-Pi 2 macrobiotic diet proved to be
more effective in reducing fasting and postprandial blood glucose, glycated Hb (HbA1c), serum
cholesterol, homeostasis model assessment of insulin resistance (HOMA-IR), BMI and waist and
hip circumferences compared with the CTR diet(

21

). According to the authors, the greater effect of the high-fibre Ma-Pi 2 diet on
several metabolic parameters of T2D patients was probably because of, at least in part, an
inherent capability of favouring the recovery of a mutualistic GM layout. To verify this
hypothesis, in this study, we specifically compared the efficacy of the Ma-Pi 2 and the CTR
diet in modulating GM dysbioses in a subset of forty overweight T2D patients participating in
the MADIAB trial, for whom stools were successfully collected before and after intervention.
To this aim, stools were analysed for the microbiota composition by next-generation sequencing
(NGS) of the 16S rRNA gene and imputed metagenomics. To provide a picture of the baseline GM
dysbioses in the enrolled T2D patients, their microbiota profiles were compared with those of
thirteen normal-weight healthy controls. Our findings suggest that the Ma-Pi 2 diet has the
potential to reverse compositional and functional GM dysbioses in T2D, favouring the recovery
of a mutualistic configuration capable of supporting the host energy homeostasis.

BODY.METHODS.STUDY DESIGN:
The design of the MADIAB trial is described in Soare et al.(

21

). Briefly, it was designed as a 21-d controlled open-label trial, in which the
participants were assigned (1:1) to the Ma-Pi 2 macrobiotic diet or a CTR diet based on
the dietary guidelines for T2D recommended by professional societies in Italy. The trial
was conducted in accordance with the Declaration of Helsinki and the Good Clinical
Practice guidelines, and the study was approved by the Institutional Review Board of
University Campus Bio-Medico (trial registration number ISRCTN10467793; http://www.isrctn.com/ISRCTN10467793). Written informed consent was obtained from
all subjects/patients. The Department of Endocrinology and Diabetes of the University
Campus Bio-Medico in Rome (Italy) recruited overweight or obese (BMI 27–45
kg/m2) subjects, aged 40–77 years and affected by T2D (Table 1). Associated metabolic syndrome was evaluated according to the
National Cholesterol Education Program Adult Treatment Panel III criteria, although it was
not an inclusion criterion. Inclusion criteria were as follows: T2D diagnosed at least 1
year before the start of the trial, treated exclusively with dietary intervention, oral
hypoglycaemic drugs or both for 6 months before study entry. Exclusion criteria were as
follows: the use of insulin either at present or at any time in the 2 year before the
study, current use of corticosteroid therapy or any other drug that can interfere with
carbohydrate metabolism, alcohol abuse and pregnancy. Subjects who already followed a
macrobiotic diet were excluded from the study. Participants' eating habits concerning the
period antecedent the study start were assessed using qualitative and quantitative
questionnaires (online Supplementary Table S1). Eligible T2D subjects were divided into
two groups according to the diet randomly assigned (twenty-eight participants were
randomised to the Ma-Pi 2 diet and twenty-eight to the CTR diet), and they were
accommodated in two different hotels, which were located close to each other.
Randomisation was stratified by BMI at baseline and by sex.Table 1Anthropometric characteristics of type 2 diabetes (T2D) patients, measured at T0
and T1 (after 21-d Ma-Pi 2 or control diet (CTR) intervention), and normal-weight
healthy controls (Mean values and standard deviations)No.ArmAge (years)SexWeight T0 (kg)Mean weight T0 (kg)
sd
Weight T1 (kg)Mean weight T1 (kg)
sd
BMI T0 (kg/m2)Mean BMI T0 (kg/m2)
sd
BMI T1 (kg/m2)Mean BMI T1 (kg/m2)
sd
1Ma-Pi 250F99·188·417·593·582·816·541·434·36·539·132·26·12Ma-Pi 272M108·6103·234·132·43Ma-Pi 277F72·868·735334Ma-Pi 267M8882·53028·25Ma-Pi 272F10496·450·7476Ma-Pi 262M72·468·624·3237Ma-Pi 276M130121·745·542·68Ma-Pi 263F108·4100·543·240·19Ma-Pi 259M95·789·135·43310Ma-Pi 275F73·97033·231·511Ma-Pi 275F60·155·228·926·512Ma-Pi 273M88·982·930·728·713Ma-Pi 270F75·871·531·329·514Ma-Pi 271F81·177·230·929·515Ma-Pi 262M74·769·229·627·416Ma-Pi 257F116·9109·336·834·417Ma-Pi 260M8175·227·725·618Ma-Pi 276M77·67229·927·719Ma-Pi 261F75·669·931·32920Ma-Pi 272F77·773·331·129·321Ma-Pi 266F93·488·840381CTR60F122·288·215·5 11985·514·950·732·16·349·331·16·12CTR56M103·198·636·534·93CTR51M88·385·933·732·84CTR57F8885·933·132·35CTR72F55·955·322226CTR73F 7775·230·830·17CTR74M89·386·827·326·58CTR68M80·778·824·924·49CTR74M72·869·628·827·510CTR72M78·575·128·22711CTR53M105·4100·232·130·612CTR69M85·983·328·127·213CTR74F80·777·833·632·314CTR67M104·5100·636·234·915CTR62M80·378·329·528·816CTR69M104·2101·234·833·817CTR63F85·783·531·931·118CTR64F72·469·327·626·419CTR65F101·899·840·739·91IT138M78·265·610·7N/AN/AN/A22·922·01·8N/AN/AN/A2IT234F62·4N/A20N/A3IT329F60·9N/A22·4N/A4IT427M87·2N/A22·9N/A5IT530M72·3N/A23·2N/A6IT632F58·4N/A22·7N/A7IT939F54·3N/A20·3N/A8IT1040F53·1N/A18·3N/A9IT1140F67·6N/A24·8N/A10IT1230F57·5N/A22N/A11IT1421F59·3N/A21·3N/A12IT1529M80·4N/A24·6N/A13IT1632F61·3N/A21·2N/AF, female; M, male; IT, Italian healthy controls.
  Stratified random sampling was used to ensure that the groups contained similar numbers
of patients with BMI≥35·0 kg/m2 (the median in the cohort of eligible patients)
and similar number of male patients. The menus were designed as a 2-d diet repeated
cyclically along the whole duration of the study. Both diets were energy intake restricted
to 7949 kJ (1900 kcal) for men and 7113 kJ (1700 kcal) for women (online Supplementary
Table S2). In particular, the CTR diet derived energy from 40–60 % carbohydrate, 10–20 %
protein, 30 % fat and ≥20 g/4184 kJ (1000 kcal) fibre. It was adapted to the Mediterranean
culinary style. Vegetables, fruit, cereal, fish and white meat typical of the
Mediterranean style were used; alcohol and sucrose consumption was forbidden. Diet meal
plans and recipes are described in the Additional File 1 of the study by Soare et
al.(

21

). Briefly, daily diet administration was organised in three meals (breakfast,
lunch and dinner) and two snacks, 2·5 h after breakfast and lunch. Every participant was
informed that a leftover ≥5 % of the total food intake meant the dismissal from the trial.
Participants were asked to keep their exercise habits unvaried during the intervention
period, and their physical activity was registered using a pedometer (Tri-axial activity
monitor, XL-18/XL-18 CN-AND; A&D Medical). Primary outcomes were the change in
fasting blood glucose (FBG) and postprandial blood glucose (PPBG) levels from the baseline
(T0) to the 21st day of treatment (T1). Secondary outcomes included changes from baseline
in plasma concentration of HbA1c, total cholesterol, LDL-cholesterol, HDL-cholesterol and
the LDL:HDL ratio, C-reactive protein (CRP), TNF-α and IL-6, as well as
insulin resistance, body weight, BMI, waist and hip circumference and GM composition. For
GM analysis, participants were asked to provide a faecal sample before (T0) and after the
21-d nutritional intervention (T1). Stool samples were successfully collected from a
subset of forty T2D patients participating in the MADIAB trial aged 50–77 years (mean age
66 years): twenty-one assigned to the Ma-Pi 2 diet and nineteen to the CTR diet. The gut
microbiome study was conducted in this subset of forty MADIAB participants.  In addition, thirteen healthy controls, aged 21–40 years (mean age 32 years) and with
18·3–24·6 kg/m2 BMI, were enrolled for the study (Table 1). They were asked to provide a faecal sample, and their eating
habits were assessed using qualitative and quantitative questionnaires as well (online
Supplementary Table S1). All samples were immediately frozen at −20°C, and then
transferred within 1 week to −80°C and stored there until processing.

BODY.METHODS.16S :
Total microbial DNA was extracted from faeces using the DNeasy Blood & Tissue Kit
(Qiagen) by introducing three FastPrep (MP Biomedicals) bead-beating 1-min steps at 5·5
movements/s, with 5-min incubation in ice between treatments(

23

). DNA recovery was evaluated using the NanoDrop ND-1000 spectrophotometer
(NanoDrop Technologies) and 2200 TapeStation instrument (Agilent Technologies). For each
sample, the V3–V4 region of the 16S rRNA gene was PCR-amplified in 25-μl volumes
containing 12·5 ng of microbial DNA, 2× KAPA HiFi HotStart ReadyMix (Kapa Biosystems) and
200 nmol/l of S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21 primers(

24

) carrying Illumina overhang adapter sequences (Bio-Fab Research). Thermal
cycle consisted of an initial denaturation at 95°C for 3 min, twenty-five cycles of
denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 30 s and
a final extension step at 72°C for 5 min. Amplicons of about 460 bp were purified with a
magnetic bead-based clean-up system (Agencourt AMPure XP; Beckman Coulter) and sequenced
on Illumina MiSeq platform using a 2×300 bp paired end protocol, according to the
manufacturer's instructions (Illumina). Briefly, indexed libraries were prepared by
limited-cycle PCR using Nextera technology and further cleaned up with AMPure XP magnetic
beads (Beckman Coulter). Libraries were pooled at equimolar concentrations, denatured and
diluted to 6 pmol/l before loading onto the MiSeq flow cell. Amplicon sequences were
deposited in the MG-RAST database
(http://metagenomics.anl.gov/linkin.cgi?project=17675).

BODY.METHODS.BIOINFORMATICS AND STATISTICS:
Raw sequences were processed using a pipeline combining PANDAseq (paired-end assembler
for Illumina sequences)(

25

) and QIIME (Quantitative Insights Into Microbial Ecology)(

26

). High-quality reads were binned into operational taxonomic units (OTU) at a
0·97 similarity threshold using UCLUST(

27

). Taxonomy was assigned using the RDP (Ribosomal Database Project) classifier
against Greengenes database (May 2013 release). Chimera filtering was performed by
discarding all singleton OTU. α Rarefaction was analysed by using the
Faith's phylogenetic diversity, Chao1, observed species and Shannon index metrics.
β Diversity was estimated by computing weighted and unweighted UniFrac
distances. Weighted UniFrac distances were used for principal coordinates analysis (PCoA)
and plotted by the rgl and vegan packages of R. Data separation in the PCoA was tested
using a permutation test with pseudo F ratios (function adonis in the
vegan package). Heat-map analysis was performed using the R ggplot2 package.  Metagenome imputation of Greengenes-picked OTU was performed using PICRUSt (Phylogenetic
Investigation of Communities by Reconstruction of Unobserved States)(

28

) with default settings. The KEGG (Kyoto Encyclopedia of Genes and Genomes)
Orthology (KO) database(

29

) was used for functional annotation. Procrustes superimposition was conducted
on the normalised KO gene data set and phylogenetic compositional data using vegan and
rgl.  The correlation between age and GM diversity was computed by Kendall τ
correlation test. To identify which feature in each diet contributed to the modulation of
single bacterial groups, a graphical representation of phylogenetic data and dietary
metadata was obtained using GraPhlAn(

30

).  All statistical analyses were performed in R, version 3.1.3. Significant differences were
assessed by Wilcoxon's signed rank-sum test. When appropriate, a paired test was used.
Where necessary, P values were corrected for multiple comparisons using
the Benjamini–Hochberg method. P<0·05 was considered statistically
significant.  Statistical analysis of the primary and other secondary outcomes from the subset of forty
MADIAB participants who provided stool samples was carried out as reported by Soare
et al.(

21

). Briefly, quantitative variables were summarised using percentiles, and the
comparison between time points and dietary groups was performed using the Wilcoxon's
signed rank-sum test.

BODY.RESULTS.COMPARISON OF THE GUT MICROBIOTA COMPOSITIONAL STRUCTURE BETWEEN OVERWEIGHT TYPE 2
DIABETES PATIENTS AND HEALTHY CONTROLS:
The phylogenetic structure of the GM ecosystem of forty overweight T2D patients before
(T0) and after (T1) nutritional intervention, and thirteen normal-weight healthy adults,
was characterised by NGS of the V3–V4 region of the 16S ribosomal DNA (rDNA) (online
Supplementary Fig. S1). A total of 3 198 509 high-quality reads were obtained, with a mean
of 30 277 (sd 4850) reads per sample. Reads were clustered in 30 874 OTU at 97 %
identity. Rarefaction curves obtained with Shannon, Chao1, PD (phylogenetic diversity)
whole tree and observed species phylogenetic metrics approximated the saturation level
after 3300 reads (online Supplementary Fig. S1).  To characterise GM dysbioses in T2D at the enrolment, the compositional structure of the
GM at T0 was compared with that of healthy controls. In the online Supplementary Table S1,
we provide the dietary behaviours during the 6 months before the trial for all T2D
patients, as well as the dietary habits of the healthy controls. T2D patients were
characterised by a significant reduction of the GM Shannon diversity index
(P<0·05, Wilcoxon's signed rank-sum test). Even if it cannot be
excluded that the age differences between T2D patients (mean age 66 years) and healthy
controls (mean age 32 years) contribute, at least in part, to the observed differences in
GM diversity, we failed to detect any significant correlation between age and microbiome
diversity in our data set (online Supplementary Fig. S2). The PCoA of the weighted UniFrac
distances resulted in a significant segregation between the two groups (Fig. 1(a)) (P<0·001,
permutation test with pseudo F ratios), confirming the presence of
compositional differences in the GM structure of T2D patients and healthy controls. To
identify the microbial genera responsible for this separation, the biplot of the average
bacterial coordinates weighted by the corresponding bacterial abundance per sample was
superimposed on the PCoA plot (Fig. 1(b)), as
previously carried out by Lozupone et al.(

31

). The GM genera that clustered with T2D patients and healthy controls and the
corresponding relative abundance values are reported in Table 2. According to our biplot analysis, T2D patients showed a higher load of
several potentially pro-inflammatory GM components, such as Enterobacteriaceae,
Collinsella
(

32

) and Streptococcus. In particular, Enterobacteriaceae,
Collinsella and Streptococcus were, respectively, 8·5,
3·5 and 4·1 times more in T2D patients than in healthy controls. T2D patients also showed
a significantly higher abundance of Lactobacillus, with an increase of
about 150 times, and Lachnospiraceae:Ruminococcus, which was 1·1 times
more than in healthy controls. Conversely, T2D patients were slightly depleted in several
health-promoting SCFA producers, such as Bacteroides (0·7 times less),
Prevotella (0·8 times less), Lachnospira (0·7 times
less), Roseburia (0·2 times less) and Faecalibacterium
(0·4 times less). Taken together, our data demonstrate the presence of dysbioses in the GM
of T2D patients, characterised by an overall reduction of mutualistic GM components in
favour of a corresponding increase in potential pro-inflammatory groups.Fig. 1Comparison of the gut microbiota compositional structure between overweight type 2
diabetes (T2D) patients at baseline and healthy controls. (a) Principal coordinates
analysis (PCoA) based on weighted UniFrac distances shows separation between forty
overweight T2D patients at T0 and thirteen normal-weight healthy controls.
, Healthy controls; ,
T2D patients. P<0·001; permutation test with pseudo
F ratios. (b) Superimposition of microbial genera on the PCoA
plot in order to identify the genera involved in this separation. Sphere width is
proportional to the mean relative abundance of the genus across all samples. The two
components explain 37·8 and 23·9 % of the variance, respectively. ,
, Centroids for each group with indication
of standard errors on each coordinate axis; uncl, unclassified.
Table 2Relative genus abundance in forty overweight type 2 diabetes (T2D) patients and
thirteen normal-weight healthy controls (Percentages and standard deviations)*
GeneraRel. Ab. (T2D, %)
sd
Rel. Ab. (healthy, %)
sd

P

Bifidobacterium
3·320·057·230·050·007
Collinsella
2·670·020·600·010·01
Bacteroides
3·050·0410·80·090·002
Streptococcus
3·620·070·730·010·09
Dorea
2·060·011·560·010·1
Lachnospira
0·470·011·550·010·0006
Roseburia
5·380·076·980·050·07Lachnospiraceae:Ruminococcus
4·980·052·330·020·01
Faecalibacterium
4·950·047·850·050·02
Oscillospira
1·180·011·710·010·04Unclassified Enterobacteriaceae1·790·030·190·010·2
Prevotella
0·420·011·970·040·1
Eubacterium
1·180·020·260·010·08
Dialister
1·080·020·950·020·8
Lactobacillus
2·980·080·020·010·0002Rel.Ab., relative abundance.*Relative P values calculated by applying the Wilcoxon's signed
rank-sum test are also reported


BODY.RESULTS.STRUCTURAL MODULATION OF THE GUT MICROBIOTA OF TYPE 2 DIABETES PATIENTS AFTER
NUTRITIONAL INTERVENTION:
T2D patients participating in the MADIAB trial were randomised (1:1 ratio) to follow the
Ma-Pi 2 or the CTR diet. Primary and secondary outcomes for the fifty-one patients who
completed the trial were provided by Soare et al.(

21

). As stool samples for gut microbiome characterisation were successfully
collected before and after intervention from twenty-one patients assigned to the Ma-Pi 2
diet and nineteen patients assigned to the CTR diet, primary and secondary outcomes were
re-analysed in this subset of forty MADIAB participants. The obtained data confirmed a
significant reduction of FBG and PPBG in both diet groups, which was significantly higher
for patients following the Ma-Pi 2 diet compared with those following the CTR diet. With
regard to secondary outcomes, the Ma-Pi 2 diet group showed a significantly higher
reduction in HOMA-IR, total cholesterol, LDL-cholesterol and LDL:HDL ratio compared with
the CTR group (Table 3). Furthermore, although
both diets were effective in reducing the plasma TNF-α levels, only the
Ma-Pi 2 dietary intervention resulted in a significant reduction in plasma levels of CRP
and IL-6 (Table 4).Table 3Primary and secondary outcome comparison between Ma-Pi 2 and control (CTR) diet at
T0 and T1 (Medians and 1st–3rd quartile ranges)Ma-Pi 2 diet (n 21)CTR diet (n 19)T0T1T0T1Median1st–3rd quartileMedian1st–3rd quartile
P
*
Median1st–3rd quartileMedian1st–3rd quartile
P
†

P
‡

P
§
Fasting blood glucose (mg/l)12601110; 1530950850; 10000·00713801125; 164510801025; 11500·0040·70·0002Postprandial blood glucose (mg/l)12701090; 17901000940; 10600·00914701360; 213012751117; 15370·0090·030·0002HbA1c (%)6·56·10; 7·76·15·8; 7·00·26·96·4; 7·56·86·2; 7·10·40·70·3HOMA-IR3·31·4; 4·21·00·6; 2·10·00043·20·9; 4·61·41·1; 2·20·080·80·1Total cholesterol (mg/l)17801550; 229012301030; 13900·000218301520; 214015601375; 18950·10·90·01LDL-cholesterol (mg/l)1020870; 1340630430; 8700·00031050880; 1270850670; 10450·070·90·03HDL-cholesterol (mg/l)490390; 520440380; 5100·5500440; 570470405; 5700·70·40·3LDL:HDL ratio2·42·0; 3·01·50·9; 1·90·0042·01·9; 2·81·81·4; 2·30·20·20·2Weight (kg)81·175·6; 99·177·270·0; 93·50·285·979·4; 102·583·375·2; 92·70·20·80·7BMI (kg/m2)31·330·0; 36·829·528·2; 34·40·230·827·8; 34·230·126·9; 33·30·40·10·4Waist circumference (cm)108·1100·3; 114·7103·495·8; 11·70·2105·5101·8; 112·8102·9100·1; 108·00·40·60·9Hip circumference (cm)109·5103·3; 123·8105·3101·8; 120·00·4109·1100·6; 117·5107·599·5; 115·10·60·50·8HbA1c, glycated Hb; HOMA-IR, homeostasis model assessment of insulin
resistance.*
P values indicated for T0 and T1 for Ma-Pi 2 diet.†
P values indicated for T0 and T1 for CTR diet.‡
P values indicated for Ma-Pi 2 and CTR diet at T0.§
P values indicated for Ma-Pi 2 and CTR diet at T1.
Table 4Inflammatory marker comparison between Ma-Pi 2 and control (CTR) diet at T0 and T1
(Medians and 1st–3rd quartile ranges)Ma-Pi 2 diet (n 21)CTR diet (n 19)T0T1T0T1Median1st–3rd quartileMedian1st–3rd quartile
P
*
Median1st–3rd quartileMedian1st–3rd quartile
P value†

P value‡

P value§
CRP (mg/l)3·21·2; 11·81·00·3; 2·10·0042·71·4; 5·91·61·1; 4·90·40·80·03TNF-α (pg/ml)23·30·0; 35·70·00·0; 2·10·00126·211·2; 38·94·00·5; 8·40·0020·40·01IL-6 (pg/ml)4·30·8; 6·42·92·4; 5·10·0063·30·1; 5·53·52·1; 5·80·40·20·6CRP, C-reactive protein.*
P values indicated for T0 and T1 for Ma-Pi 2 diet.†
P values indicated for T0 and T1 for CTR diet.‡
P values indicated for Ma-Pi 2 and CTR diet at T0.§
P values indicated for Ma-Pi 2 and CTR diet at T1.
  We then explored the impact of dietary interventions on the GM ecosystem of T2D patients.
To visualise the total variation in the data set, a PCoA analysis of the weighted UniFrac
distances between the Ma-Pi 2 and CTR groups at T0 and T1 and the healthy controls was
performed (online Supplementary Fig. S3). The PC2 axis – which accounts for 24 % of the
total variability – was positively associated with T2D (P<0·05,
Wilcoxon's signed rank-sum test). Thus, the efficacy of dietary interventions in
redressing T2D-associated GM-dysbioses was assessed by comparing the variation of the
average (±sem) PC2 coordinates for each group of samples (Fig. 2). Although at T0 both Ma-Pi 2 and CTR diet groups showed
significantly higher PC2 coordinates compared with healthy controls
(P=0·01 and P=0·03, respectively), at T1 no significant
difference between both diet groups and healthy controls was observed. These data support
the potential of both dietary interventions to redress T2D-associated GM dysbioses,
favouring the recovery of a mutualistic ecosystem layout.Fig. 2Variation of the weighted UniFrac PC2 coordinates between the study groups.
, Ma-Pi 2 diet group at T0;
, control (CTR) diet group at T0;
, healthy controls; ,
Ma-Pi 2 diet group at T1; , CTR diet group at T1. For each group,
average (±sem, error bar) PC2 coordinates are shown. The significance of
the differences between the PC2 coordinates of the groups is indicated as follows: *
P<0·001 (Ma-Pi 2 diet group at T0 v. T1),
** P=0·01 (CTR diet group at T0 v. T1), ***
P=0·01 (Ma-Pi 2 diet group at T0 v. healthy
controls), **** P=0·03 (CTR diet group at T0 v.
healthy controls); Wilcoxon's signed rank-sum test.
  The specificity of each dietary intervention in correcting the GM dysbioses in T2D
patients was evaluated by comparing the compositional structure of the GM ecosystem before
(T0) and after (T1) the nutritional intervention, as reported in the two PCoA plots of
weighted UniFrac distances, which also included the responding bacterial genera and
biochemical parameters (Fig. 3). A significant
segregation between the GM profiles at T0 and T1 was obtained for both interventions
(P=0·01 and P=0·04 for Ma-Pi 2 and CTR diet,
respectively; permutation test with pseudo F ratios), indicating that
both diets were effective in modulating the GM of T2D patients. Although both diets tended
to increase the GM Shannon diversity (P=0·09 and P=0·06
for Ma-Pi 2 and CTR diet, respectively; Wilcoxon's signed rank-sum test), no significant
difference in within-group diversity between time points was observed (Fig. 3). Interestingly, in the case of the Ma-Pi 2
diet, we detected significant associations between changes in GM components and changes in
biochemical parameters (Fig. 3(a)). In particular,
Faecalibacterium, which increased at T1, showed an inverse association
with FBG. Analogously, Bacteroides and Akkermansia, both
positively responding to the Ma-Pi 2 diet, showed an inverse trend with respect to total
and LDL-cholesterol. Conversely, Lachnospiraceae:Ruminococcus, decreasing
after the Ma-Pi 2 diet, was positively associated with FBG.Fig. 3Comparison of the gut microbiota compositional structure of type 2 diabetes (T2D)
patients before and after the nutritional interventions. (a) Principal coordinates
analysis (PCoA) based on weighted UniFrac distances for T2D subjects following the
Ma-Pi 2 diet (n 21) shows separation between T0 ()
and T1 (). The two components explain 35·0 and
24·8 % of the variance, respectively. P=0·01; permutation test with
pseudo F ratios. (b) PCoA based on weighted UniFrac distances for
T2D subjects following the control (CTR) diet (n 19).
, T0; , T1. The two
components explain 32·0 and 20·2 % of the variance, respectively.
P=0·04; permutation test with pseudo F ratios.
Lines connect T0 and T1 samples from the same patient. ,
 responding bacterial genera and
biochemical parameters, respectively; , direction of
significant correlations; , , ,
, centroids for each time point.
  To dissect the specific GM compositional changes in response to the dietary regimen, we
focused our analysis on the core GM community, defined as the sum of the microbial genera
present at a relative abundance >1 % in at least 30 % of the subjects. Selected
genera accounted together for >80 % of the total ecosystem. To estimate the degree
of dysbiosis of the core GM in T2D, the ratio of the median genus abundance in T2D
patients and healthy controls was calculated. The heat map of the log-ratios at T0 and T1
for the Ma-Pi 2 and the CTR diet is shown in Fig.
4. According to our findings, both diets were effective in counteracting the
decrease of Bacteroides, Dorea and
Faecalibacterium in T2D patients, as demonstrated by the recovery of
median abundance values similar to those of healthy controls. Moreover, both Ma-Pi 2 and
CTR diet resulted in an increase of Akkermansia above the abundance
values characteristic of healthy controls. On the other hand, both diets supported the
reduction of Lachnospiraceae:Ruminococcus down to the median abundance
values shown by healthy controls. Diet-specific effects on the GM composition of T2D
patients regarded only a few genera under-represented in T2D patients compared with
healthy controls: Lachnospira and Roseburia, for which
only the CTR diet favoured the recovery of health-like median abundance values, and
Oscillospira, for which the recovery of health-like values occurred
only in the Ma-Pi 2 diet group. Conversely, the genera Collinsella and
Streptococcus, and unclassified Lachnospiraceae, which were enriched in
T2D patients compared with healthy controls, decreased down to health-like abundances only
in patients who underwent the Ma-Pi 2 diet intervention.Fig. 4Impact of dietary interventions on the taxonomic structure of the core microbiota
in type 2 diabetes (T2D) patients. Heat maps were calculated for both Ma-Pi 2 and
control (CTR) diet, based on the log-ratio of the median genus abundance in T2D
patients (T0 and T1 samples) and healthy controls. The colour code and segment line
reveal the deviation in terms of fold change from the median profile of healthy
subjects (). T2D patients following the Ma-Pi 2
diet (n 21); T2D patients following the CTR diet
(n 19); healthy controls (n 13).
  To highlight specific dietary features affecting the GM composition, a comparison between
the microbiota profiles at T1 of the two groups was performed, taking into account also
the dietary features of the two different interventions (Fig. 5). T1 samples from the Ma-Pi 2 diet group showed significantly higher
abundance of Peptostreptococcaceae (P=0·02; Wilcoxon's signed rank-sum
test) and Leuconostocaceae (P=0·0002), which were also positively
correlated with the ingested amount of dietary components that were represented in a
higher percentage in Ma-Pi 2 compared with the CTR diet (whole grain 28·1
v. 8·5 %, vegetables, sauces and herbs 64·5 v. 32·0 %),
or only present in the Ma-Pi 2 diet (seeds, seaweeds and fermented products) (see also
online Supplementary Table S2). On the contrary, bacteria from the family
Erysipelotrichaceae showed significantly higher abundance in T1 samples from the CTR diet
group (P=0·008), and were positively correlated with the ingested amount
of dietary components present only in CTR diet (bread, cereal, pasta and grains, meats,
eggs and meat broths, dairy products, fruit and fruit juice and extra-virgin olive oil).
The same positive correlations were obtained for Coriobacteriaceae, even if the abundance
of this bacterial family was not significantly higher in the CTR diet group T1 samples.
This bacterial group also showed a positive correlation with the food category 'other
starch' (containing legumes and potatoes), which was more represented in the CTR diet than
in the Ma-Pi 2 diet (5·5 v. 3·0 %). This food category was also
correlated to the Ruminococcaceae family.Fig. 5Impact of dietary components on the gut microbiota taxonomic structure. Cladogram
obtained with the GraPhlAn tool, showing the family-level gut microbiota profile of
T1 samples from both intervention groups with a phylum-based colour code
(, Firmicutes; ,
Bacteroidetes; , Proteobacteria; ,
Actinobacteria; , Verrucomicrobia; ,
Synergistetes). Families with relative abundance of at least 0·5 % in at least two
samples are plotted. Larger circles identify bacterial families having a positive
correlation with at least one dietary component; the names of these families are
reported. Filled circles identify bacterial families that showed significantly
higher abundance in T1 samples of the Ma-Pi 2 diet group ()
or the CTR diet group (). Bacterial family-food component
correlations are indicated by filled boxes in the external rings of the plot,
referring to the list of dietary components. T2D patients following the Ma-Pi 2 diet
(n 21); T2D patients following the CTR diet (n
19).


BODY.RESULTS.IMPACT OF NUTRITIONAL INTERVENTION ON THE FUNCTIONAL CONFIGURATION OF THE GUT
MICROBIOME OF TYPE 2 DIABETES PATIENTS:
To gain insight into the GM functions, gut metagenomes structures were inferred from the
respective phylogenetic profiles using PICRUSt(

28

), as previously performed by David et al.(

19

) and Morgan et al.(

33

). A total of 329 KEGG pathways were generated, of these 249 showed a
significantly different abundance between healthy controls and T2D patients, and
thirty-four showed a significant variation in response to the Ma-Pi 2 diet, whereas no
KEGG pathway responded significantly to the CTR diet. The Procrustes analysis of the 16S
rDNA sequences and imputed KO gene data set co-illustrates the data, supporting the
significant association between taxonomic and inferred functional profiles of the gut
microbiome across our study cohort (P=0·001, Protest; online
Supplementary Fig. S4). The PCoA of the imputed functional GM profiles showing segregation
among the Ma-Pi 2 and CTR groups at T0 and T1 and healthy controls is provided as the
online Supplementary Fig. S5.  The Euclidean principal component analysis (PCA) of the KO level gene abundances showed
separate clustering between the gut microbiome structure of T2D patients at T0 and healthy
controls (P=0·03, permutation test with pseudo F
ratios). To identify functions responsible for this separation, the average coordinates of
the metabolic pathways in all samples, weighted by gene count per sample, were obtained
and the resulting biplot of pathway distribution was superimposed on the PCA plot (Fig. 6). The pathways clustering with T2D patients and
healthy controls are listed in the online Supplementary Table S3. In the context of this
functional separation, T2D patients showed a general perturbation in microbiome pathways
involved in the metabolism of amino acids, lipids and secondary metabolites. In
particular, T2D patients were depleted in genes involved in the metabolism of
d-arginine and d-ornithine, as well as of d-glutamine and
d-glutamate, while being enriched in pathways involved in tyrosine metabolism and
alanine, aspartate and glutamate metabolism. This functional layout may lead to a reduced
production of arginine – as a result of both the reduction of pathways directly involved
in arginine biosynthesis and the lower amount of glutamate available for arginine
biosynthesis – and to a corresponding increase in alanine levels. Although arginine has
been reported as insulinogenic(

34

), an increased alanine production has been connected to augmented energy
intake, cholesterol level and BMI(

35

). Furthermore, an increased tyrosine metabolism in T2D patients could result
in higher levels of the 4-cresol metabolite, which has been associated with diverse
inflammatory conditions in the gut(

36

). Finally, compared with healthy controls, the GM of T2D patients showed
increased abundance of functions involved in the arachidonic acid metabolism.
Interestingly, the bacterial metabolism of this key inflammatory intermediate is a
well-recognised immune-escaping strategy for proteobacteria(

37

). A higher level of arachidonic acid metabolism in the GM of T2D patients,
together with the increase in the biosynthesis of polyketide sugars – bacterial secondary
metabolites with a range of biological functions including immunosuppression(

38

) – might be the result of the hypothesised bloom of pro-inflammatory
pathobionts in the gut.Fig. 6Functional dysbioses of the gut microbiome in type 2 diabetes (T2D) patients.
Metabolic pathways were superimposed on the principal component analysis plot based
on Euclidean distances, and the pathways responsible for the separation are shown.
, Healthy controls (n
13); , T2D patients (n 40).
Sphere width is proportional to the mean relative abundance of the function across
all samples. The two components explain 14·0 and 9·2 % of the variance,
respectively. P=0·03; permutation test with pseudo
F ratios. , , Centroids for each
group with indication of standard errors on each coordinate axis.
  With the aim to explore the impact of nutritional interventions on the functional
configuration of the gut microbiome in T2D patients, the Euclidean PCA of KO level gene
abundances at T0 and T1 for both dietary groups was carried out. Differently from the CTR
diet, which resulted in no significant functional change in the predicted gut metagenome
(P=0·4, permutation test with pseudo F ratios), the
Ma-Pi 2 diet was effective in modulating the GM metagenome in T2D patients, as
demonstrated by significant sample clustering according to the time point
(P=0·007). As previously described, the corresponding biplot of metabolic
pathway distribution was superimposed on the PCA plot (Fig. 7). The pathways clustering with the Ma-Pi 2 diet time points are listed in
online Supplementary Table S3. According to our findings, the Ma-Pi 2 diet effectively
counteracted the functional dysbioses in the GM of T2D patients, resulting in the decrease
of the abundance of several T2D-associated GM functional markers, such as alanine
metabolism, arachidonic acid metabolism and polyketide sugar biosynthesis, as well as in
the reduction of GM functions related to oxidative phosphorylation and glycosphingolipid
biosynthesis. Moreover, the Ma-Pi 2 diet favoured an increase in GM functions involved in
d-glutamine and d-glutamate metabolism, previously reported as reduced
in T2D patients, and in the biosynthesis of unsaturated fatty acids, which have been shown
to improve lipoprotein profile, glycaemic control and antioxidant status in T2D(

39

,

40

). Finally, the Ma-Pi 2 diet resulted in the increase of GM pathways involved
in the metabolism of taurine, cysteine, methionine, valine, leucine and isoleucine,
potentially providing the host with an additional source of important nutrients and
essential amino acids.Fig. 7Impact of Ma-Pi 2 dietary intervention on the functional configuration of the gut
microbiome in type 2 diabetes (T2D) patients. Metabolic pathways were superimposed
on the principal component analysis plot based on Euclidean distances in T2D
patients before (T0, ) and after (T1, )
the Ma-Pi 2 diet (n 21). The two components explain 22·3 and 14·9 %
of the variance, respectively. P=0·007; permutation test with
pseudo F ratios. , ,
Centroids for each group with indication of standard errors on each coordinate
axis.


BODY.DISCUSSION:
In the present study, the GM phylogenetic and functional dysbioses in forty overweight T2D
patients participating in the MADIAB trial(

21

) were explored. In agreement with previous findings(

11

), T2D involved a significant reduction of the GM diversity, which is a common
feature shared by several disease-associated dysbiotic microbiome configurations, such as
those associated with inflammatory bowel disease and age(

41

,

42

). According to our results, the reduction of the GM compositional diversity in
T2D corresponded to phylogenetic changes. T2D patients were indeed enriched in
Lactobacillus, Lachnospiraceae:Ruminococcus and in
several potential pro-inflammatory GM components, such as Enterobacteriaceae,
Collinsella and Streptococcus
(

32

,

39

,

43

–

46

), whereas they were depleted in important health-promoting SCFA producers, such
as members of Lachnospiraceae, Faecalibacterium,
Bacteroides and Prevotella. Strengthening these findings,
analogous T2D-associated GM structural dysbioses have already been identified in previous
studies(

3

,

6

,

7

,

47

). Subsequently, we explored the changes in GM functions matching these
compositional perturbations by inferred metagenomics. Our data suggest deregulation in
pathways involved in the metabolism of amino acids, lipids and secondary metabolites in the
GM of T2D patients, including a reduced abundance of functions for the metabolism of
d-arginine and d-ornithine, as well as of d-glutamine and
d-glutamate, a corresponding increase in the metabolism of tyrosine, alanine,
aspartate and glutamate, and a higher load of functions involved in arachidonic acid
metabolism and polyketide sugar biosynthesis.  The observed T2D-related dysbiotic microbial community could exert a multifactorial role in
the disease onset, contributing to metabolic and immune deregulation. Indeed, the T2D GM is
slightly depleted in fibrolytic health-promoting mutualists, fundamental for providing
butyrate and propionate from the degradation of indigestible plant polysaccharides and
starch, such as the butyrate-producing Dorea, Lachnospira,
Roseburia and Faecalibacterium, and the
propionate-producing Bacteroides and Prevotella
(

48

). Even if the biological relevance of this depletion of SCFA producers remains
to be determined, it could result in the reduction of bioavailability of these crucial GM
metabolites in the gut, with consequences on the host metabolic and immunological
homeostasis. For instance, butyrate and propionate are important for host glucose
control(

11

), insulin sensitivity regulation, insulin signalling and intestinal
gluconeogenesis(

9

,

49

); also, they represent potent immune modulators, being involved in peripheral
regulatory T-cell generation(

50

) and in the regulation of pro-inflammatory cytokine production(

51

). In parallel, the observed increase of potential pro-inflammatory
micro-organisms in the gut of T2D patients, such as Enterobacteriaceae,
Collinsella and Streptococcus
(

32

,

39

,

43

–

46

), could further contribute to raise the host inflammatory level, supporting the
evolution of insulin resistance(

52

). Finally, the functional GM layout of T2D patients we obtained by imputed
metagenomics suggests additional mechanisms involved in the GM contribution to the disease,
including the reduced production of the insulinogenic arginine(

34

), and the increased production of alanine, a recognised marker of higher energy
intake and cholesterol level(

35

), which could contribute to the loss of metabolic control in T2D. Moreover, the
higher potential for the production of tyrosine metabolites could further boost
pro-inflammatory stimuli in the gut(

36

). Interestingly, the survey of GM in T2D patients allowed us to find traces of
functions related to immune escaping, such as pathways involved in the metabolism of
arachidonic acid and polyketide sugar biosynthesis, further highlighting the ongoing bloom
of pro-inflammatory GM components in T2D(

39

). However, the functional conclusions derived from imputed metagenomics must be
taken with caution until experimentally confirmed by shotgun metagenomics.  In the subset of forty MADIAB participants – twenty-one assigned to the Ma-Pi 2 diet and
nineteen to the CTR diet – we then explored the efficacy of the nutritional interventions in
supporting the recovery of a mutualistic GM configuration in T2D patients. Primary and
secondary outcomes were re-analysed for this patient subset included in the gut microbiome
study, confirming that the Ma-Pi 2 diet was associated with a greater reduction in FBG and
PPBG, total serum cholesterol, CRP and IL-6 in T2D patients. According to gut microbiome
data, both Ma-Pi 2 and CTR diet were able to modulate the GM dysbioses in T2D patients,
supporting the recovery of a healthy-like compositional structure and resulting in an
increased ecosystem diversity, which represents a strategic feature for a healthy GM
ecosystem(

53

). In addition, both diets supported the recovery of a balanced health-promoting
community of fibrolytic SCFA producers in the gut of T2D patients, by re-increasing the
abundance of propionate and butyrate producers (i.e. Bacteroides,
Dorea, Faecalibacterium) and consolidating a
healthy-like abundance of Roseburia, Lachnospira,
Lachnospiraceae:Ruminococcus and Oscillospira. Moreover,
both dietary interventions resulted in the increase of Akkermansia, a human
mucus coloniser recently correlated to an improved metabolic profile(

54

), providing the rationale for its use in the prevention or treatment of obesity
and associated metabolic disorders. Finally, it is important to point out that only the
Ma-Pi 2 diet showed the potential to counteract the rise of putative pro-inflammatory
components (i.e. Collinsella and Streptococcus) in the GM
ecosystem of T2D patients. When we sought associations between GM components and biochemical
parameters, significant trends were obtained only in the case of the Ma-Pi 2 diet. Indeed,
in Ma-Pi 2-treated subjects, the abundance of Faecalibacterium was
negatively associated with FBG and, analogously, the abundance of
Bacteroides and Akkermansia showed an inverse association
with total and LDL-cholesterol. Conversely, Lachnospiraceae:Ruminococcus
was positively associated with FBG. These findings suggest that these micro-organisms, the
first increasing and the latter decreasing in response to the Ma-Pi 2 diet, could represent
Ma-Pi 2-responding GM components associated with primary and secondary study outcomes.
Supporting our findings, the negative association between Faecalibacterium
and FBG concentration had already been detected in a study exploring the structural
modulation of the GM in T2D subjects treated with a Chinese herbal formula(

55

). Moreover, in a study carried out in mice, Akkermansia
muciniphila treatment has been demonstrated to reverse high-fat diet-induced
metabolic disorders, including fat-mass gain, metabolic endotoxaemia, adipose tissue
inflammation and insulin resistance(

54

). On the other hand, Lachnospiraceae:Ruminococcus has recently
been positively correlated with insulin resistance in obese women(

56

). According to imputed metagenomics, only the Ma-Pi 2 diet resulted in a
significant modulation of the functional microbiome layout in T2D patients. In particular,
the decrease of several markers of functional GM dysbioses in T2D patients, such as
imbalances in alanine metabolism, arachidonic acid metabolism and polyketide sugar
biosynthesis, was observed. Moreover, the Ma-Pi 2 diet favoured the reduction of GM
functions related to oxidative phosphorylation and glycosphingolipids biosynthesis.
Anaerobic respiration provides an ecological advantage for Enterobacteriaceae in an inflamed
gut(

39

), whereas glycosphingolipids are powerful bacterial modulators of the host
inflammatory response(

40

). Thus, the reduction in abundance of these pathways further suggests the
potential of Ma-Pi 2 diet to counteract the ongoing bloom of pro-inflammatory pathobionts in
T2D. Finally, the Ma-Pi 2 diet favoured the increase of GM functions involved in the
biosynthesis of unsaturated fatty acids and essential amino acids, showing an added
potential to promote the recovery of metabolic control and a balanced nutritional status in
T2D patients.  In conclusion, both Ma-Pi 2 and CTR diets showed the potential to support the recovery of
GM-host mutualism in T2D patients, favouring the restoration of fibrolytic SCFA-producing GM
components, thus promoting metabolic control of T2D patients. Differently from the CTR diet,
the Ma-Pi 2 diet was effective in counteracting the rise of possible pro-inflammatory
micro-organisms in T2D patients. This suggests that the Ma-Pi 2 diet may have the potential
to reduce GM-dependent pro-inflammatory stimuli in the gut that, increasing chronic
inflammation, can lead to insulin resistance in T2D(

57

). Even if direct causation still needs to be proved, and this conclusion must be
taken with adequate caution, this peculiar property shown by the Ma-Pi 2 diet could partly
explain the greater improvements in metabolic control following Ma-Pi 2 dietary intervention
compared with the CTR diet(

21

).

Question: Compared to healthy controls what was the result of T2D (type 2 diabetes) patients on load of several potentially pro-inflammatory GM components?
Evidence: Ruminococcus, which was 1·1 times
more than in healthy controls
Answer: significantly increased