Gut microbiota profile of Indonesian stunted children and children with normal nutritional status

The gut microbiota has been shown to play a role in energy metabolism of the host. Dysbiosis of the gut microbiota may predispose to obesity on the one hand, and stunting on the other. The aim of the study was to study the difference in gut microbiota composition of stunted Indonesian children and children of normal nutritional status between 3 and 5 years. Fecal samples and anthropometric measurements, in addition to economic and hygiene status were collected from 78 stunted children and 53 children with normal nutritional status in two regions in Banten and West Java provinces: Pandeglang and Sumedang, respectively. The gut microbiota composition was determined by sequencing amplicons of the V3-V4 region of the 16S rRNA gene. The composition was correlated to nutritional status and anthropometric parameters. Macronutrient intake was on average lower in stunted children, while energy-loss in the form of short-chain fatty acids (SCFA) and branched-chain fatty acids (BCFA) appeared to be higher in stunted children. In stunted children, at the phylum level the relative abundance of Bacteroidetes (44.4%) was significantly lower than in normal children (51.3%; p-value 2.55*10−4), while Firmicutes was significantly higher (45.7% vs. 39.8%; p-value 5.89*10−4). At the genus level, overall Prevotella 9 was the most abundant genus (average of 27%), and it was significantly lower in stunted children than in normal children (23.5% vs. 30.5%, respectively; q-value 0.059). Thirteen other genera were significantly different between stunted and normal children (q-value < 0.1), some of which were at low relative abundance and present in only a few children. Prevotella 9 positively correlated with height (in line with its higher relative abundance in normal children) and weight. In conclusion, Prevotella 9, which was the most abundant genus in the children, was significantly lower in stunted children. The abundance of Prevotella has been correlated with dietary fibre intake, which was lower in these stunted children. Since fibres are fermented by the gut microbiota into SCFA, and these SCFA are a source of energy for the host, increasing the proportion of Prevotella in stunted children may be of benefit. Whether this would prevent the occurrence of stunting or even has the potential to revert it, remains to be seen in follow up research.


Introduction
Background 2 Scientific background and explanation of rationale Theories used in designing behavioral interventions n.a.

Participants 3
Eligibility criteria for participants, including criteria at different levels in recruitment/sampling plan (e.g., cities, clinics, subjects)  6 Method of recruitment (e.g., referral, self-selection), including the sampling method if a systematic sampling plan was implemented Methods used to collect data and any methods used to enhance the quality of measurements  5 Information on validated instruments such as psychometric and biometric properties -n.a.

Sample Size 7
How sample size was determined and, when applicable, explanation of any interim analyses and stopping rules -n.a., not interve ntion Assignment Method 8 Unit of assignment (the unit being assigned to study condition, e.g., individual, group, community)  6 + Figure  1 Method used to assign units to study conditions, including details of any restriction (e.g., blocking, stratification, minimization) -n.a. Blinding (masking) 9 Whether or not participants, those administering the interventions, and those assessing the outcomes were blinded to study condition assignment; if so, statement regarding how the blinding was accomplished and how it was assessed. n.a.

Unit of Analysis 10
Description of the smallest unit that is being analyzed to assess intervention effects (e.g., individual, group, or community) If the unit of analysis differs from the unit of assignment, the analytical method used to account for this (e.g., adjusting the standard error estimates by the design effect or using multilevel analysis) -n.a.

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Statistical methods used to compare study groups for primary methods outcome(s), including complex methods of correlated data  7 Statistical methods used for additional analyses, such as a subgroup analyses and adjusted analysis -n.a.
Methods for imputing missing data, if used n.a.
Statistical software or programs used  7

Results
Participant flow 12 Flow of participants through each stage of the study: enrollment, assignment, allocation, and intervention exposure, follow-up, analysis (a diagram is strongly recommended)  Figure  1 o Enrollment: the numbers of participants screened for eligibility, found to be eligible or not eligible, declined to be enrolled, and enrolled in the study  Figure  1 o Assignment: the numbers of participants assigned to a study condition  Table 1 Baseline characteristics for each study condition relevant to specific disease prevention research -n.a.
Baseline comparisons of those lost to follow-up and those retained, overall and by study condition -n.a.

Comparison between study population at baseline and target population of interest
 Table 1 Inclusion of aspects employed to help minimize potential bias induced due to non-randomization (e.g., matching) -n.a.
TREND Statement Checklist -PONE-D-20-26845; non-randomized, cross-sectional study -Baseline equivalence 15 Data on study group equivalence at baseline and statistical methods used to control for baseline differences -n.a.

Numbers analyzed 16
Number of participants (denominator) included in each analysis for each study condition, particularly when the denominators change for different outcomes; statement of the results in absolute numbers when feasible -n.a.
Indication of whether the analysis strategy was "intention to treat" or, if not, description of how non-compliers were treated in the analyses -n.a., not interven tion Outcomes and estimation 17 For each primary and secondary outcome, a summary of results for each estimation study condition, and the estimated effect size and a confidence interval to indicate the precision -n.a.
Inclusion of null and negative findings -n.a.
Inclusion of results from testing pre-specified causal pathways through which the intervention was intended to operate, if any -n.a.

Ancillary analyses
18 Summary of other analyses performed, including subgroup or restricted analyses, indicating which are pre-specified or exploratory -n.a.

Adverse events 19
Summary of all important adverse events or unintended effects in each study condition (including summary measures, effect size estimates, and confidence intervals) -n.a.

Interpretation 20
Interpretation of the results, taking into account study hypotheses, sources of potential bias, imprecision of measures, multiplicative analyses, and other limitations or weaknesses of the study 

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Discussion of results taking into account the mechanism by which the intervention was intended to work (causal pathways) or alternative mechanisms or explanations -n.a., not interven tion Discussion of the success of and barriers to implementing the intervention, fidelity of implementation -n.a., not interven tion Discussion of research, programmatic, or policy implications -Generalizability 21 Generalizability (external validity) of the trial findings, taking into account the study population, the characteristics of the intervention, length of follow-up, incentives, compliance rates, specific sites/settings involved in the study, and other contextual issues  14 Overall Evidence

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General interpretation of the results in the context of current evidence and current theory 