Gut microbiome and short-chain fatty acids associated with the efficacy of growth hormone treatment in children with short stature

Abstract

Objective: To investigate associations between fecal microbiota, short-chain fatty acids (SCFAs), and the efficacy of recombinant human growth hormone (rhGH) treatment in children with growth hormone deficiency (GHD) or idiopathic short stature (ISS).

Methods: A 2-phase cohort study was conducted. Phase I included 102 participants (GHD: n = 33, ISS: n = 28, controls: n = 41) for cross-sectional analysis using 16S rRNA sequencing and targeted metabolomics to compare microbial diversity, predicted metabolic pathways, and SCFA levels. Phase II longitudinally monitored 61 rhGH-treated children (GHD = 33, ISS = 28) over 2 years, assessing growth velocity, IGF-1 levels, and fecal microbiota/SCFA dynamics. Statistical analyses included alpha/beta diversity metrics, LEfSe, PERMANOVA, and redundancy analysis (RDA) to link microbial/SCFA profiles with clinical outcomes.

Results: (1). Microbiota Dysbiosis: Untreated GHD/ISS children exhibited reduced beneficial taxa (e.g., Faecalibacterium, Akkermansia) and increased pathobionts (e.g., Streptococcus, Collinsella) compared to controls (PERMANOVA: R ² = 0.114, P = 0.001). (2). Metabolic Pathways: GHD/ISS groups showed enrichment in xenobiotic degradation (e.g., atrazine) and deficits in nutrient-associated pathways (e.g., carotenoid biosynthesis). (3). rhGH Effects: Treatment increased beneficial taxa (e.g., Bifidobacterium, Faecalibacterium) and modulated amino acid/lipid metabolism pathways (e.g., glycine-serine-threonine metabolism, P = 0.035). (4). SCFAs and Growth Velocity: Higher growth velocity percentiles correlated with elevated acetic acid (GHD-treated: 1952 ± 962.4 vs. untreated: 1290 ± 886.0 μg/g, P = 0.037) and butyric acid levels.

Conclusion: GHD, ISS, and healthy children have different fecal microbiota compositions and SCFA metabolisms. rhGH therapy partially restores microbial balance and alters metabolic pathways, with SCFA levels associated with treatment efficacy. These findings highlight the gut microbiome as a potential modulator of rhGH response and provide insight into microbiota-targeted therapies to improve growth outcomes (e.g., "probiotic interventions").

Keywords: IGF-1 (insulin-like growth factor 1); SCFAs (short-chain fatty acids); growth hormone treatment; microbiota; short stature.

Figure 1

Participant flow and group assignments. Phase 1: 69 children with short stature and 41 healthy volunteers were recruited. After screening, 65 children were randomized into treatment (n = 32) and non-treatment (n = 33) groups. After 26 weeks, 3 treatment and 1 non-treatment participants dropped out. The treatment group included 17 GHD and 12 ISS patients; the non-treatment group had 16 GHD and 16 ISS patients. The control group comprised 41 healthy volunteers. Phase 2: All short stature patients received rhGH treatment, with 33 GHD and 28 ISS patients completing ≥2 years. Follow-ups every 12 weeks tracked growth and other parameters. GHD, growth hormone deficiency; ISS, idiopathic short stature; IGF-1, insulin growth factor 1; rhGH, recombined human growth hormone; HTSDS, height standard deviation score; GV, height velocity; BA, bone age; CA, chronological age.

Figure 2

Microbiome diversity analysis. (A) Rarefaction curves showing observed features as a function of sequencing depth for control, GHD_treated, GHD_untreated, ISS_treated, and ISS_untreated groups. (B) Alpha diversity measured by the Shannon index, with significant differences identified by Kruskal–Wallis test and Benjamini, Krieger, and Yekutieli correction. (C) Pielou evenness index, analyzed similarly. (D) Principal Coordinate Analysis (PCoA) based on Bray-Curtis dissimilarity, illustrating community structure variation among groups (PERMANOVA: R² = 0.067, F = 3.524, P = 0.001). (E) Non-metric Multidimensional Scaling (NMDS) plot showing community composition differences, with similar PERMANOVA results (F = 3.524, P = 0.001).

Figure 3

Microbiome composition and differential abundance analysis. (A) Bar chart showing the relative abundance of bacterial genera in control (n = 41), GHD_untreated (n = 16), and ISS_untreated (n = 16) groups. (B) LEfSe LDA score plot highlighting differentially abundant taxa between the groups, with LDA scores > 2.5 indicating significant differences. (C) LEfSe cladogram visualizing taxonomic distribution and abundance differences among the groups, where nodes represent taxa and colors indicate group-specific enrichment. Only taxa with LDA scores > 2.5 are displayed.

Figure 4

Comparison of predicted Gut microbiome metabolic pathways. (A) Comparison between control (n = 41) and GHD_untreated (n = 16) groups. (B) Comparison between control (n = 41) and ISS_untreated (n = 16) groups. For both panels, the left side shows the mean proportion of each metabolic pathway, and the right side displays the difference in mean proportions along with 95% confidence intervals. Statistical significance was assessed using Welch's t-test (CI method: Welch's inverse, 0.95).

Figure 5

Microbiome composition and differential abundance analysis. (A) Bar chart showing the relative abundance of bacterial genera in GHD-treated (n = 17), GHD-untreated (n = 16), ISS-treated (n = 12), and ISS-untreated (n = 16) samples. Each bar represents the percentage of sequences assigned to different genera. (B) LEfSe (Linear Discriminant Analysis Effect Size) LDA score plot highlighting differentially abundant taxa between the groups. Taxa with an LDA score greater than 2.5 are considered statistically significant, and the length of the bar indicates the magnitude of the difference. (C) LEfSe cladogram visualizing the taxonomic distribution of differentially abundant taxa with an LDA score greater than 2.5. Nodes represent taxa, with the size of the node indicating abundance and the color representing the group with higher abundance. Significant taxa are highlighted with colored circles and lines.

Figure 6

Comparison of predicted gut microbiome metabolic pathways. (A) Comparison between GHD-treated (n = 17) and GHD-untreated (n = 16) groups. (B) Comparison between ISS-treated (n = 12) and ISS-untreated (n = 16) groups. The mean proportions of each metabolic pathway are represented by bars, with the differences in mean proportions between groups shown alongside 95% confidence intervals. Statistical significance was assessed using Welch's t-test, and confidence intervals were calculated using Welch's inversion method. P-values for each pathway are indicated.

Figure 7

Comparison of short-chain fatty acids (SCFAs) in GHD and ISS subjects. (A) GHD Treated vs. GHD Untreated: Heatmap showing the relative abundance of six SCFAs (acetic, propionic, butyric, isobutyric, isovaleric, and valeric acids) in GHD-treated (n = 17) and GHD-untreated (n = 16) groups. The color gradient from light blue to red indicates low to high abundance. (B) ISS Treated vs. ISS Untreated: Heatmap comparing the SCFA profiles in ISS-treated (n = 12) and ISS-untreated (n = 16) groups, using the same color gradient. (C) Acetic Acid Concentration Comparison: Violin plot showing the distribution of acetic acid concentrations between GHD-untreated and GHD-treated groups, with a significant difference indicated by an unpaired t-test (P = 0.037), denoted by an asterisk (*).

Figure 8

Comparison of microbial composition and metabolic profiles across different growth velocity (GV) percentiles. (A) Bar chart showing the percentage distribution of bacterial taxa across three GV percentile groups: GV ≥ P97 (red), P90 ≤ GV < P97 (blue), and P75 ≤ GV < P90 (cyan). Each bar represents the relative abundance of various bacterial taxa within each GV percentile group. (B) LEfSe LDA score plot highlighting significant differences in bacterial taxa between GV < P97 and GV ≥ P97 groups. The LDA scores (log10) are plotted on the x-axis, with higher scores indicating greater differences in abundance. Significant taxa are highlighted in red for GV ≥ P97 and cyan for GV < P97. (C) LEfSe cladogram illustrating taxonomic distribution and significant differences in microbial abundance between GV < P97 and GV ≥ P97 groups. The outer ring represents the taxonomic hierarchy, while the inner rings show the abundance of taxa in each group. Significant taxa are highlighted in red for GV ≥ P97 and cyan for GV < P97, with the size of the segments indicating relative abundance. (D) Heatmap depicting metabolic profiles associated with different GV percentiles. The heatmap compares the concentration of various metabolites (e.g., butyric acid, acetic acid, propionic acid, hexanoic acid, valeric acid, isobutyric acid) across three GV percentile groups: GV ≥ P97 (red), P90 ≤ GV < P97 (blue), and P75 ≤ GV < P90 (cyan). Color intensity indicates metabolite concentration, with darker shades representing higher concentrations.

Figure 9

Redundancy analysis (RDA) of clinical efficacy indicators and fecal microbiota/SCFAs. (A) RDA1 (24.71% variance) and RDA2 (21.53% variance) showing associations between clinical factors (e.g., growth velocity, birth weight, mid-parental height, BMI, IGF1) and microbial genera. Vectors indicate explanatory variables; positions of genera show relationships with these factors. (B) RDA1 (87.56% variance) and RDA2 (9.26% variance) highlighting connections between clinical factors and SCFAs. Vectors represent explanatory variables; positions of SCFAs indicate relationships with these factors. Positive correlations are shown by vectors pointing in the same direction as the genera or SCFAs, while negative correlations are indicated by vectors pointing in opposite directions.