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Indian pediatrics

July 23, 2026 · via Europe PMC

Genotypic and Phenotypic Profile of Hereditary Spastic Paraplegia in Children: A Single-Centre Study from Northern India.

Das N, Shambhavi A, Sait H, Moirangthem A, Saxena D, Phadke SR

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Slowly progressive lower-limb spasticity in a child, even without family history, should prompt consideration of hereditary spastic paraplegia, which can mimic cerebral palsy.

What was studied

A prospective case series of 21 children from 16 families with genetically confirmed HSP (exome sequencing) at a tertiary center in Northern India, 2018-2023.

Key findings

  • Median symptom onset was 5 years and median age at diagnosis was 8 years, typically with toe walking and progressive spasticity.
  • 18 variants in eight genes were found, including five novel variants.
  • Autosomal recessive inheritance predominated (13 of 16 families); SPG35 was the commonest subtype (six families), then SPG11 and SPG52.
  • Recurrent variants suggested possible founder effects.

Clinical relevance

A diagnostic delay of several years is evident, and genetic diagnosis allows accurate counseling and avoids mislabeling as cerebral palsy.

Who should care

Neonatology

Limitations

The cohort is small, from a single referral center and likely selected toward genetically confirmed cases, limiting generalizability and frequency estimates.

Offer genetic testing to children with progressive spasticity or a cerebral palsy-like picture that does not fit the history.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

OBJECTIVE

To characterize the genotypic and phenotypic spectrum of children with genetically confirmed hereditary spastic paraplegia (HSP) at a tertiary care center in Northern India.

METHODS

This prospective case series included patients with genetically confirmed (whole exome sequencing or clinical exome sequencing) HSP attending the Medical Genetics Clinic of a tertiary care center in Northern India between 2018 and 2023. Neurological and radiological assessments were also conducted.

RESULTS

A total of 21 patients from 16 families were included. The median (q1, q3) age of onset of symptoms was 5 (1.5, 8.5) years, and the median (q1, q3) age of diagnosis was 8 (5, 13.5) years. The most common features at diagnosis were toe walking with progressive spasticity of lower limbs. Genetic testing identified 18 variants across eight different genes, including six pathogenic variants, 10 likely pathogenic variants, and two variants of uncertain significance (VUS). Thirteen families had autosomal recessive (AR) HSP, two had autosomal dominant (AD) HSP, and one had X-linked HSP. The most frequently identified subtype was SPG35 (spastic paraplegia type 35), observed in six families, followed by SPG11 in three families and SPG52 in two families. There was one family each with SPG18B, SPG15, CSPSD (cataracts, spastic paraplegia, and speech delay), SPG4, and SPG2.

CONCLUSIONS

Hereditary spastic paraplegia exhibits genotypic and phenotypic heterogeneity with a predominance of AR inheritance. Five novel variants and some recurrent variants, suggesting potential founder effects, were noted. HSP should be suspected in cases with slowly progressive spasticity in lower limbs, even without a family history, which may mimic cerebral palsy.

DOI

10.1007/s13312-026-00403-7

PMID

42490029

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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