Pre-obese children’s dysbiotic gut microbiome and unhealthy diets may predict the development of obesity

It is widely accepted that the intestinal microbiome is connected to obesity, as key mediator of the diet impact on the host metabolic and immunological status. To investigate whether the individual gut microbiome has a potential in predicting the onset and progression of diseases, here we characterized the faecal microbiota of 70 children in a two-time point prospective study, within a four-year window. All children had normal weight at the beginning of this study, but 36 of them gained excessive weight at the subsequent check-up. Microbiome data were analysed together with the hosts’ diet information, physical activity, and inflammatory parameters. We find that the gut microbiota structures were stratified into a discrete number of groups, characterized by different biodiversity that correlates with inflammatory markers and dietary habits, regardless of age, gender, and body weight. Collectively, our data underscore the importance of the microbiome–host–diet configuration as a possible predictor of obesity.

O besity and associated metabolic diseases are linked to diet and gut microbiome in an intimate way 1 . Prevalence rates of obesity have increased dramatically in the past decades. In 2014, 1.9 billion adults worldwide were overweight and 600 million of them were obese (World Health Organization, 2016, http://www.who.int/mediacentre/factsheets/fs311/en/). Obesity, deriving from a positive energy balance that results from a surplus in ingested with respect to the expended energy, is considered a major risk factor for health, with important consequences on quality of life, life expectancy, and healthcare costs 2 . The intestinal microbiome is a pivotal emerging factor that can affect human metabolic homeostasis and promote the risk of metabolic complications connected to obesity. Even if there is a lack of consensus on the obese-type microbiome configuration, taxonomic and functional alterations have been suggested to contribute to the pathogenesis of obesity in both humans and animal models [3][4][5] . The altered microbial profile occurring in obese people is considered as an extreme deviation from the microbiota-host mutualism, resulting from the response to a high-fat high-sugar diet 5 . The obesity-related gut microbiota is generally characterized by a low degree of biodiversity and enrichment in pathobiont bacteria, such as members of the family Enterobacteriaceae, as well as Erysipelotrichaceae and the sulphate reducer species Bilophila wadsworthia 4,6 . This dysbiotic microbial structure is probably involved in the manifestation of obesity in a multifactorial way 7 . Coherently with the energy harvest hypothesis 3 , the gut microbiome of obese individuals possesses higher efficiency in energy extraction from the diet, providing an extra supply of calories to the host [8][9][10] . Furthermore, the concomitant overload of the intestinal microbial ecosystem with proinflammatory Enterobacteriaceae and sulphate-reducing bacteria may consolidate the obesity-associated inflammation and insulin resistance 11 .
The prevalence of obesity is increasing worldwide, particularly, in children 12 , and this has been closely associated with cardiovascular risk factors, such as hypertension, insulin resistance, and dyslipidaemia, during adulthood 13 . However, links among diet, microbiome structure, and child health are still unclear. To test the hypothesis that the composition and/or the diversity of the microbiome had an impact on the onset of obesity, we explored the faecal microbiota structure in 70 children in a prospective study, at a baseline survey and a follow-up after 4 years. All children were normal weight at baseline, but 36 developed an excessive weight gain until follow-up. We also collected comprehensive data on lifestyle, such as dietary intake and physical activity, as well as medical history, anthropometry, measures of physiological, immunological, psychological parameters, and socioeconomic status. According to our findings, clusters of dietary patterns are associated with specific differences in the gut microbiota and health datasets: the normal weight children ate differently and had a distinctive microbiome configuration compared to obese children. Measures of increased inflammation in obese children suggest that dietary adjustments might promote healthier adolescence and adulthood by modulating the intestinal microbiota.

Results
Microbiota structure, health correlations, and healthy growth. To investigate links between the gut microbiota and obesity, health, diet, and other lifestyle factors, we analysed the faecal microbial composition in 70 children at two time points (T1, T3) within a 4-year window. Children were stratified by timing and weight status: at T1, all children were normal weight of which 34 are referred as T1_N who remained normal weight (mean ± sd, age (years): 7 ± 2; BMI (kg m −2 ): 16 ± 1), and 36 as T1_O (age: 8 ± 2; BMI: 16 ± 2), who gained excessive weight; accordingly, at T3, 34 subjects had maintained their normal weight (T3_N; age: 11 ± 2; BMI: 17 ± 2), while 36 had gained excessive weight (T3_O; age: 12 ± 2; BMI: 20 ± 3). We generated 7.9 million sequence reads from 16s rRNA gene V3-V4 amplicons, with an average of 56,485 (±22,321, sd) paired-end reads per sample, for 20,360 OTUs (Operational Taxonomic Units) grouped at 97% of sequence identity. When examining OTU abundance, we identified four subject clusters, one of which (C3) included the majority of obese subjects, before and after they had developed obesity ( Supplementary Fig. 1). For 18 out of the 70 children, the most relevant variable that drove the separation was the sample origin, with samples T1 and T3 from the same individual clustering together in the dendrogram (p = 0.0001, Fisher's test).
To identify trends in the gut microbiota, we established coabundance associations of genera ( Supplementary Fig. 2), and then clustered correlated bacterial taxa into four co-abundance groups (CAGs), describing the microbiota structures found across the whole dataset. The dominant (i.e. the most abundant) genera in these CAGs were Bacteroides (green), Prevotella (yellow), Dorea (violet), and Bifidobacterium (blue). The CAG relationships are termed Wiggum plots, where genus abundance is represented as a disc proportional to normalized over-abundance (Fig. 1). The four subject divisions, as identified by OTU clustering ( Supplementary Fig. 1), were superimposed on the unweighted (Fig. 1) and weighted ( Supplementary Fig. 3) UniFrac Principal Coordinates Analysis (PCoA) plots, allowing defining four clusters, C1-C4. Within a spectrum of microbiota structures, these clusters represent groups of individuals who have a significantly different microbiota layout from each other, as defined by the permutation multivariate analysis of variance (MANOVA) test on UniFrac data (p < 0.001). We then constructed Wiggum plots for the gut microbiota for each of the 4 groups (Fig. 1). The microbiota variation from the groups dominated by normal weight children (C1/C2) to the groups dominated by obese children (C3/C4) was accompanied by distinctive CAG dominance, most relevantly by abundances of Prevotella CAG (C1) and Dorea and Bacteroides CAGs (C4). Significant associations between several health/inflammation measurements and the major axes from unweighted UniFrac PCoA analysis are shown in Table 1. In particular, when considering the whole cohort, a shift of the microbiota structure towards positive values of the PCo1 axis was associated with inflammation, i.e. serum levels of C-reactive protein (CRP) and IL-6. Interestingly, other inflammatory markers, such as interleukin-15 (IL-15), tumour necrosis factor alpha (TNF-α), interferon gamma-induced protein 10 (IP-10), interleukin 6 (IL-6), and interleukin 8 (IL-8), correlated only with the microbiota profiles from children developing obesity. As expected, there was minimal variability amongst normal weight subjects. It should be noted that the education level score and physical activity score (time spent in moderate to vigorous physical activity, MVPA) were also associated with the microbiota structure, but in an independent way with respect to inflammatory parameters and the lean/obese phenotype ( Supplementary Fig. 4). Furthermore, the microbial biodiversity was associated with the inflammatory status. Indeed, we observed a gradual change of the level of biodiversity along PCo1, from the highest level in the samples belonging to the C2 cluster to the lowest values in the C4 microbiomes (p < 0.000001, Kruskal-Wallis test; Supplementary  Fig. 5). On the other hand, when comparing the biodiversity of the child microbiota among the original groupings (T1_N, T3_N,  T1_O, T3_O), we only detected a significant difference using the   Table 1 Shannon index. In particular, we found higher biodiversity in the T1_N microbiome than T1_O samples (p < 0.01, Wilcoxon test), but this evidence was not confirmed with the other metrics. It should be noted that both the unweighted and weighted UniFrac PCoA space was not correlated with either the child's age (p = 0.7 for unweighted UniFrac; p = 0.6 for weighted UniFrac, permutational correlation test), gender (p = 0.2 for unweighted UniFrac; p = 0.4 for weighted UniFrac; permutational MANOVA) or maturation stage according to Tanner classification 14 (p = 0.2 for unweighted UniFrac; p = 0.6 for weighted UniFrac), meaning that the associations described above were irrespective of these variables.
Taken together, these results indicate that the gut microbiota may exist under a number of configurations, which are associated with host metabolic and immunological factors and, in the context of other individual lifestyle and genetic variables, may be involved (or not) in the development of the multifactorial obese phenotype.
Impact of diet on the gut microbiota. To identify the food types with the most significant contribution (p < 0.05, permutational correlation test) to the microbiota ordination, we superimposed the food data from Food Frequency Questionnaires (FFQs) on the unweighted UniFrac PCoA plot of Fig. 1 (Fig. 2a). Remarkably, a higher consumption of milk, fish, seeds, and whole meal bread was associated with the configurations C1 and C2 of the gut microbiota. On the other hand, the microbiota configurations C3 and C4 were associated with a higher consumption of dairy products, pizza, sausages, and sweetened drinks. Differently, as mentioned above, the microbiota diversity was inversely correlated to the first axis. In line with the available literature on the diet as the major driver of the microbiota structure 15 , differences in food consumption may thus contribute to differences in microbiota diversity between groups. Mean values of food consumption per day for each of the four microbiota groups are reported in Supplementary Table 1, along with more information on food categories. Furthermore, by focusing on macronutrients, we found that the most discriminant category was carbohydrate, whose consumption increased in a gradual manner along the first axis (Fig. 2b). In light of the fact that diet, in terms of excess energy intake, is a major cause of obesity 1 , it is important to note  that the microbiota configurations (C1-C4) were independent of the total daily caloric intake, and that an increase of caloric intake was observed at T3, compared to the first time point, in accordance with the growth of children ( Supplementary Fig. 6).
Microbiota, diet, and physical activity relation to obesity. FFQ data were further explored in a Correspondence Analysis, where the first axis, describing over 9% of the dataset variance, contained most of the discriminating food types identified in the a b *** *** By matching the stratifications of subjects in dietary and microbiota groups, we sought for redundant combinations associated with the obese phenotype. In particular, in the light of the obtained results, the combination D2 diet and C3/C4 microbiota was exclusively associated with a disease-promoting and inflammation status. Seven obese children out of 36 were D2-C3/C4, whereas none of T1_N/T3_N children possessed this configuration (p = 0.0006, Fisher's exact test). It is important to note that the only T1_N child who fell in the D2 group (C2 microbiota group) showed a high MVPA score (higher than 75% of T1 subjects), suggesting a protective role of physical activity in children consuming a diet associated with a low HFD index. This was also confirmed for the combination D5 diet and C3/C4 microbiota, in which fell seven obese children and only one T1_N child with high MVPA score (p = 0.008). However, it should be pointed out that, even if the combinations D2/D5 diet and C3/C4 microbiota were associated with the obese phenotype, there were obese children harbouring different profiles. This stresses that obesity is a complex mosaic, in which several endogenous and exogenous factors, including host genetics, contribute to health decline. Interestingly, when looking at T1 samples in a prospective manner, we consolidated our hypothesis, by detecting D2/D5-C3/C4 configurations exclusively in normal weight children who showed excessive weight gain at T3. This finding suggests a sort of predictive potential of the microbiome-host-diet configuration, even if the model clearly needs to be implemented with more subjects, sampling points and  Microbiota signatures of obesity. UniFrac PCoA analysis showed weak but significant separation between subjects with (T3_O) or without obesity (T1_N, T3_N, T1_O), according to both unweighted (p = 0.02, permutation test with pseudo-F ratios; Fig. 4a) and weighted (p = 0.05; Fig. 4b) distance metrics. Family-level microbiota assignment highlighted a readjustment within the phylum Bacteroidetes, with a higher proportion of Bacteroidaceae and a lower proportion of Prevotellaceae in obese children when compared to the normal weight counterparts (Fig. 4c). In addition, obese children showed a higher contribution of the genus Lachnospira compared to normal weight children at the same time point (T3_N). When looking at T1_O/T3_O children in a longitudinal way, we denoted an increase in the relative abundance of Proteobacteria and a decrease in the proportions of the families Clostridiaceae and Ruminococcaceae after the onset of obesity. On the other hand, when focusing on normal weight children, we observed only a sensible reduction in Proteobacteria at the second time point (T3_N) compared to the baseline. A complete summary of the significant differences in the gut microbiota between groups is reported in Table 2. It is important to note that differences between obese (T3_O) and non-obese (T1_O and T3_N) children involved major microbiota components, whereas differences between non-obese children (T1_N, T3_N, and T1_O) involved only minor components, proving that obesity is associated with certain gut microbiota profiles, although alone cannot be used as unique predictive tool.

Discussion
In this study, we explored the structure of the gut microbiota of 70 children in a two-time point prospective study, with samplings being separated by a period of 4 years. During this time window, half of the children maintained their normal weight, while the rest gained excessive weight. The data were integrated with dietary intake information, measures of physical activity and inflammatory parameters. By analysing the degree of similarity among the 140 gut microbial profiles, we identified four significantly different compositional clusters, each representing a gut microbiota steady state. For each of these steady states, we obtained a peculiar compositional profile, as highlighted by the respective dominance of the four CAGs. In particular, the steady state C1 was characterized by Prevotella and Bacteroides CAGs, while the steady state C2 was dominated by the Prevotella CAG. Conversely, steady states C3 and C4 were more heterogeneous, with the first showing the concomitant presence of all the four CAGs (Prevotella, Bacteroides, Bifidobacterium, and Dorea), and the second lacking only the Bifidobacterium CAG. Steady states were characterized by a different degree of bacterial diversity, with C2 showing the highest level of microbial diversity followed by C1, C3, and C4. When seeking for the prevalence of sample types within the four steady states, we found that samples from preobese (T1_O) and obese (T3_O) children were largely more prevalent in the low-diversity clusters, C3 and C4. In addition, when we explored connections between the gut microbiome steady states and inflammation, we found positive associations with C3 and C4. Taken together, our data indicate that the lowdiversity gut microbiota configurations C3 and C4 can represent obesogenic gut microbiome layouts, predisposing children to metabolic inflammation and obesity. In light of the importance of diet as a gut microbiome modulator 15,17-23 , we explored connections between dietary habits and microbiome steady states. To this aim, the individual dietary profiles at T1 and T3 were clustered into five dietary groups, from D1 to D5, each being characterized by a different abundance of macronutrients: protein, fat, and carbohydrate. When we calculated the HFD index 16 , the dietary clusters D1 and D3 showed the highest potential to promote health, while D2 the lowest. Interestingly, when we sought for redundant combinations between diet and microbiome clusters, potentially predisposing to obesity, we found that the combinations D2/D5 and C3/C4 were more prevalent in pre-obese (T1_O) and obese children (T3_O) compared to the other subjects. In particular, the combination D2 diet and C3/C4 microbiome steady state was exclusively observed in obese children, while seven of the eight children that showed the combination D5 and C3/C4 were obese. Finally, the combinations D2/D5 and C3/C4 at T1 were distinctive of normal weight children who showed excessive weight gain at T3, supporting the relevance of the combination of diet and microbiome structure as a possible predictor of obesity. It is also tempting to speculate that the differences in food intake contributed to the observed microbiota differences. Consistent with the gut microbiome arrangements reported in other studies, we observed higher levels of Bilophila in children that consumed more milk 24 , and higher contribution of Prevotella in children with higher intake of whole meal bread 25 . On the other hand, we found more Bacteroides and Oscillospira in children who ate more ham and sausages, as already described in adults following an animal-based, low-fibre diet 15 . As demonstrated by Zhernakova and colleagues 26 , we also found evidence of an inverse correlation between microbiota diversity and consumption of sugarsweetened drinks. It is worth noting that a high diversity in the gut microbial ecosystem, together with high levels of short-chain fatty acid production were reported in rural children of Burkina Faso, whose diet is rich in complex carbohydrates and fibre 27 . Similarly, a high-diversity gut microbiome, with enrichment of genes involved in the metabolism of complex polysaccharides, was found in the Hadza, a hunter-gatherer population following a heavily plant-based diet 28 . In line with these findings, our results showed that the microbiota diversity was higher in children who ate more foods containing oligosaccharides, such as honey and whole milk 29 , with the latter being also a source of fat-soluble Vitamin D, whose deficiency is associated with obesity in children and adolescents 30 . The microbiota diversity was also higher in children with high consumption of complex polysaccharides, such as whole meal bread, nuts, and seeds. The link between diet and microbiota also clearly involves human physiology. Indeed, it has been demonstrated that the dietary fat increases the amount of endotoxins in the blood 31 , and that circulating endotoxin levels are associated with elevated TNF-α, IL-8, and IL-6 concentrations 32,33 . In agreement with these data, we found higher plasmatic levels of IL-6, IL-8, and TNF-α associated with an overabundance of gram-negative bacteria, such as Veillonella, Akkermansia, Bacteroides, and Parabacteroides, in the C3/C4 configurations. On the other hand, we found that the consumption of fish was directly connected to a microbiota configuration with low inflammatory grade, as it has been reported for lardconsuming mice transplanted with the microbiota of fish oilconsuming mice 34 . Importantly, the health-microbiota associations were statistically significant even when the model was adjusted for age, and robust to gender and maturation stage according to Tanner classification 14 .
Finally, we also detected robust microbiome signatures of obesity as previously reported 1,4,6 , such as the reduction of the ecosystem diversity. Furthermore, a higher proportion of Bacteroidaceae and a lower abundance of Prevotellaceae characterized the obese children with respect to normal weight children, consistently with the long-term dietary effects reported by Wu and colleagues 17 . Looking longitudinally, the onset of obesity in children was connected to an increase of Proteobacteria and a decrease of Clostridiaceae and Ruminococcaceae, suggesting that the obesity development also involves changes in the abundance of key gut microbiome components.
In conclusion, our data highlight the importance of the individual microbiome configuration as a mediator of the dietary impact on the individual metabolic and immunological homeostasis. According to our findings, the individual gut microbiome configuration-in terms of steady state-together with the longterm dietary habit can be considered as a predictive tool for the development of obesity in children (Fig. 5). Hence, our data pave the way for a new perspective, where dietary recommendations to reduce the obesity risk in children are specifically tailored based on the individual microbiome structure, with the precise purpose of avoiding combinations of diet and microbiome configuration that are likely to favour the onset of obesity. Our data also stress the multifactorial nature of obesity, where gut microbiome dysbioses and interacting factors (e.g. diet) are only a part of the complex mosaic of determinants of this phenotypic trait (Fig. 5). Future studies on larger cohorts, based on shotgun metagenomics and possibly providing for more extensive sampling, are needed  Fig. 5 The mosaic aetiology of obesity. The gut microbiota diversity is likely altered at multiple stages by the diet. Unhealthy diets may promote an inflammatory state that, in turn, is strictly interconnected with the gut microbial configuration. The combination of these three factors (unhealthy diets, inflammation and a dysbiotic, low-diverse and pro-inflammatory microbial layout) may favour the onset of obesity. High physical activity may protect the human host from obesity, even when diet and microbiota are in a low-diversity and pro-inflammatory configuration. However, human genetics can lead the host to develop obesity, regardless of the microbiome configuration to better unravel the contribution of the gut microbiota, as well as of specific species and/or strains, to this complex mosaic.

Methods
The Collection of clinical, behavioural, and nutritional data. Examinations of children included anthropometry, blood pressure, accelerometry, genetic data from saliva, and physiological markers in blood and urine. Supplementary Table 2 gives an overview on the assessment methods applied in the study and a description of the collected variables.
Dietary intake and behaviour were measured in detail using a validated semiquantitative FFQ 37,38 and a self-administered computer-assisted 24-h dietary recall, which is linked to a tailor-made European database of food composition tables 39,40 as described below.
DNA extraction and sequencing. Total microbial DNA was extracted from faecal samples by the repeated bead-beating plus column method 41 with some additional steps as reported by Turroni et al. 42 . The V3-V4 hypervariable region of the 16s rRNA gene was amplified using the 341F and 805R primers with added Illumina adapter overhang sequences as previously reported 43 . Indexed libraries were prepared by limited-cycle PCR using Nextera technology and the final library was diluted to 6 pM with 20% PhiX control. Sequencing was performed on Illumina MiSeq platform using a 2 × 300 bp paired-end protocol, according to the manufacturer's instructions.
Bioinformatics and biostatistics. Paired-end reads were processed combining PANDAseq 44 and QIIME 45 . High-quality sequences were clustered into OTUs at 97% sequence similarity by UCLUST 46 . Taxonomy was assigned with the RDP classifier against the Greengenes database (May 2013 release). Chimeric OTUs were identified using ChimeraSlayer 47 and then filtered out. All singleton OTUs were discarded.
Steady states were identified through hierarchical Ward linkage clustering based on the Spearman correlation coefficients of the proportion of OTUs, filtered for OTU subject prevalence of at least 20%. We then verified that each cluster showed significant correlations between samples within the group (multiple testing using the Benjamini-Hochberg method) and that the clusters were statistically significantly different from each other (permutational MANOVA using the Spearman distance matrix as input, function Adonis of the vegan package in R).
The R packages Stats and Vegan were used for statistical analysis. In particular, to compare the gut microbiota structure among different groups for α-diversity and macronutrient intake, we used the Wilcoxon test. The significance of data separation in the PCoA was assessed by a permutation test with pseudo-F ratios (function Adonis in Vegan). Cluster separation in hierarchical clustering analyses was tested using Fisher's exact test. Significant differences in bacterial relative abundance at different phylogenetic levels among groups were assessed by Mann-Whitney U tests or Kruskal-Wallis test. p values were corrected for multiple comparisons using the Benjamini-Hochberg method when appropriate. False discovery rate (FDR) ≤ 0.05 was considered as statistically significant.
Correlation analysis of clinical data and gut microbiota. Correlations between microbiota composition and host metadata, including inflammatory markers and other health parameters were analysed using quantile (median) regression tests, adjusted for age. Median regression is less influenced by outliers than the classical linear regression because it gives less relevance to extreme values. The potential impact of gender and maturation stage according to Tanner classification 14 (whose information was available only at T3 for children who were 8 years old or older, i.e. 68 out of 70) on the microbiota structure was also evaluated. We carried out the analysis by using the R package quantreg, as already performed by Claesson et al. 49 .
Analysis of nutritional data. Dietary data was collected through a semiquantitative FFQ, weighted by 7-day consumption frequencies. Forty-six items were in common between FFQs at T1 and T3. Additional four items were obtained from questions about the type of milk and yoghurt consumed (skimmed or fullfat). For all FFQs the lowest frequency option was 'never or less than once a week', for foods with the highest frequency, the option was '4 or more times per day' 37,38 . At the same time, subjects were asked to compile 24-h dietary recalls with their parents, for retrieving more detailed information about the composition of their diet 39,40 . After considering several methodological approaches to quantify food frequency, we elected to convert the frequency of consumption assessed with the FFQ to a continuous scale of daily consumption (e.g. if the food was eaten 2 times per day, then the daily consumption was 2). When the frequency was reported as a range (e.g. eaten 1-3 times per week), the mean of the range (e.g. 2) was used to calculate the daily consumption. The superimposition of the food frequencies on the microbiota PCoA space was conducted using the envfit function of the Vegan package of R. A corrected p value ≤ 0.01 was considered as statistically significant. Macronutrient data were taken from dietary recalls, in particular in I.Family (T3), dietary intake of the previous 24 h was assessed using the validated web-based SACANA (Self-Administered Children, Adolescents and Adult Nutrition Assessment) 24-h dietary recall tool, which is based on the validated SACINA (Self-Administered Children, Infants and Adult Nutrition Assessment) offline version 39 used in the IDEFICS study (T1). Children reported their diet and entered the type and amount (g) of all drinks and foods consumed during the previous day. While in SACINA all information was reported by the parents, in SACANA, children reported for themselves with the help of their parents 50 or from a dietician or trained study nurse during the survey examinations.
Co-abundance analysis. CAGs were identified as previously described 49 . In brief, associations among bacterial genera, present in at least two samples with relative abundance >0.1%, were evaluated by the Kendall correlation test, displayed using hierarchical Ward clustering with the Spearman correlation-based distance metrics and utilized to determine co-abundant groups of bacterial genera. Significant associations were controlled for multiple testing using the q-value method (FDR ≤ 0.05) 51 . Permutational MANOVA was used to determine whether the CAGs were significantly different from each other. Wiggum plot networks were created using the Cytoscape software (http://www.cytoscape.org/), as previously reported 49 . The circle size represents the bacterial abundance and connections between nodes represent positive and significant Kendall correlations among genera (FDR ≤ 0.05).

Data availability
Sequencing reads were deposited as raw data, as a whole and separately for each sample, along with available metadata, in the MG-RAST database with the accession codes mgm4780879.3 to mgm4781018.3 (https://www.mg-rast.org/ mgmain.html?mgpage=project&project=mgp84098). All relevant data are available from the authors.