Precision medicine in pediatric growth disorders: Integrating clinical phenotype, genetics, IGF-1 biology and artificial intelligence: A systematic scoping review of PubMed-indexed literature (2000-2026).
Integrating genetic, biomarker, and AI tools can enhance individualized rhGH therapy in pediatric growth disorders, but further validation is needed.
Where it sits
this study against the rest of the hgh (somatropin) corpusSummary and findings
This systematic scoping review synthesizes evidence on genetic determinants, GH-IGF-1 axis biomarkers, and AI tools for individualizing rhGH therapy in pediatric growth disorders. It evaluates monogenic and polygenic genetic markers, IGF-1/IGFBP-3 ratios, and AI-based diagnostic tools. The review integrates these findings into a Precision-Medicine Cascade framework for clinical decision-making.
Abstract
ecombinant human growth hormone (rhGH) has been used for four decades under a largely population-based dosing paradigm, yet growth response varies substantially among children with apparently similar auxological phenotypes. Advances in genomics, GH-insulin-like growth factor-1 (IGF-1) axis biomarkers, mathematical and machine-learning (ML) prediction models, and digital health technologies now allow individualized characterization of growth disorders, forming the basis of an emerging precision-endocrinology paradigm. (1) to synthesize evidence on monogenic and polygenic genetic determinants of pediatric growth faltering and/or short stature and their diagnostic yield; (2) to evaluate GH-IGF-1 axis biomarkers and pharmacogenetic markers, including the IGF-1/IGFBP-3 molar ratio as a likely better indicator of bioactive IGF-1 than IGF-1 alone, together with mathematical/ML prediction models, for individualizing rhGH therapy; and (3) to appraise artificial intelligence (AI) and digital-health tools, bone-age algorithms, facial-recognition phenotyping, adherence-prediction models, and smartphone growth-monitoring, as instruments for operationalizing precision endocrinology in children and adolescents with growth disorders. This is a systematic review employing narrative synthesis (a systematic scoping review). Reporting explicitly followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) checklist rather than the PRISMA 2020 statement for systematic reviews and meta-analyses, because the heterogeneous outcome metrics across genetic, diagnostic-accuracy, prediction-model, and AI/digital-health studies preclude meta-analytic pooling of a single quantitative effect size; PRISMA-ScR is the methodologically appropriate reporting framework for a review mapping evidence across such conceptually distinct domains. PubMed/MEDLINE was searched for English-language, pediatric (0-18 years) studies published between 2000 and 2026. Two-stage screening (title/abstract, then full text) was performed. Quality was appraised using design-appropriate tools: an adapted Newcastle-Ottawa Scale for genetic-association studies, QUADAS-2 for diagnostic-accuracy biomarker studies, PROBAST/TRIPOD-informed criteria for prediction-model and ML studies, and reference-standard/external-validation criteria for AI-imaging studies. Sixty-two studies were retained for qualitative synthesis. Monogenic defects (SHOX, ACAN, NPR2) and exome-sequencing panels explain a meaningful minority (approximately one-quarter) of previously "idiopathic" short stature, while genome-wide association studies and polygenic scores capture a substantial share of the remaining heritable variance, with polygenic risk scores achieving areas under the receiver-operating-characteristic curve up to 0.84 for predicting adult short stature. The IGF-1/IGFBP-3 M ratio outperforms IGF-1 alone for diagnosing GH deficiency (sensitivity 87.5%, specificity 83.0%), reflecting the greater bioavailability of free, unbound IGF-1 relative to that carried in the ternary IGF-1/IGFBP-3/acid-labile-subunit complex. GH-receptor exon-3 (d3) pharmacogenetic variants and machine-learning models (random forest, transcriptomic classifiers) improve prediction of individual rhGH response beyond classical mathematical models. AI-based bone-age algorithms achieve near-radiologist accuracy with reduced inter-observer variability, computer-aided facial-phenotyping tools show comparable diagnostic accuracy for syndromic short-stature disorders such as Noonan and Turner syndrome, and connected-device/ML adherence-monitoring and smartphone growth-tracking tools objectively detect suboptimal adherence and growth faltering earlier than conventional clinic-based surveillance; network meta-analyses of once-weekly long-acting rhGH formulations further suggest that reduced injection burden can translate into modestly improved height outcomes relative to daily rhGH. These findings are synthesized into a Precision-Medicine Cascade, a practice-oriented framework showing how genotype, biomarker, and digital data streams can be layered onto routine auxological assessment to guide same-visit clinical decisions on diagnostic work-up, dosing, and monitoring frequency. Converging genetic, biomarker, computational, and digital-health evidence supports a feasible, evidence-grounded trajectory toward individualized therapy, including rhGH and emerging growth-plate-targeted agents, in pediatric growth disorders. The Precision-Medicine Cascade proposed here offers pediatric endocrinologists an immediately applicable framework for integrating these tools into everyday practice. However, current tools remain adjunctive rather than replacement for clinical judgment, and prospective, ethnically diverse validation of integrated precision-endocrinology pathways is required before routine adoption.
Background
The study addresses the variability in growth response among children receiving recombinant human growth hormone (rhGH) therapy, despite similar phenotypes. Traditional population-based dosing paradigms have not accounted for individual genetic and biomarker differences. Advances in genomics, biomarkers, and AI offer potential for precision medicine approaches in pediatric endocrinology, making this review timely and relevant.
Methods
This is a systematic scoping review of PubMed-indexed literature from 2000 to 2026, focusing on pediatric growth disorders. It employs narrative synthesis and follows the PRISMA-ScR guidelines. The review includes 62 studies, covering genetic determinants, biomarkers, and AI tools. Quality appraisal used design-appropriate tools for different study types.
Results
Monogenic defects such as SHOX, ACAN, and NPR2 explain about one-quarter of idiopathic short stature cases. Polygenic risk scores show an AUC of up to 0.84 for predicting adult short stature. The IGF-1/IGFBP-3 molar ratio outperforms IGF-1 alone in diagnosing GH deficiency, with sensitivity of 87.5% and specificity of 83.0%. AI-based tools, including bone-age algorithms and facial-phenotyping, demonstrate high diagnostic accuracy.
Interpretation
The review highlights the potential of integrating genetic, biomarker, and AI tools into clinical practice for individualized rhGH therapy. While statistically significant, the clinical significance of these findings requires further validation. The proposed Precision-Medicine Cascade framework is promising but remains adjunctive to clinical judgment. Prospective validation in diverse populations is necessary.
Key findings
- Monogenic defects explain approximately one-quarter of idiopathic short stature.
- Polygenic risk scores achieve AUC up to 0.84 for predicting adult short stature.
- IGF-1/IGFBP-3 M ratio sensitivity 87.5%, specificity 83.0% for GH deficiency.
- AI-based bone-age algorithms achieve near-radiologist accuracy.
- Network meta-analyses suggest modestly improved height outcomes with weekly rhGH.
Limitations
- Heterogeneous outcome metrics preclude meta-analytic pooling.
- Current tools are adjunctive, not replacements for clinical judgment.
- Prospective validation in diverse populations is required.
- Relies on narrative synthesis rather than quantitative analysis.