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Exploring socio-economic, biochemical, and genetic factors influencing thyroid status in Indian school-going adolescents

Journal of Human Genetics (2025)Cite this article

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Abstract

Thyroid hormones are central to regulating metabolism, growth, and development, yet their complex interactions with socioeconomic, metabolic, and genetic factors remain understudied in diverse populations. We compared thyroid profiles - free triiodothyronine (FT3), free thyroxine (FT4), and thyroid-stimulating hormone (TSH) in Indian adolescents with anthropometric traits, metabolic markers, and socioeconomic status (SES). We observed that adolescents from higher SES backgrounds exhibited greater metabolic dysregulation, altered thyroid profiles, and abnormalities in lipid and adipokine levels. Subclinical (16.1%) and clinical hypothyroidism (1.1%) were found to be prevalent in this population but were not associated with obesity. Instead, they showed links with dyslipidemia and altered adipokine profiles. To investigate the genetic basis of thyroid traits, we conducted an exome-wide association study (ExWAS, Nā€‰=ā€‰4324), and a two-staged genome-wide association study (GWAS, Nā€‰=ā€‰4854). The ExWAS revealed two novel loci for TSH (GYS2 and CEP162) and fifteen novel loci for FT4, including ZNF467, P3H3, CRLF3, SPATA2L, MEFV, THNSL2, COL27A1, COL28A1, IGSF3, ZNF732, MOG, GABBR1, HPF1, LOC440563, and SPEG. The GWAS identified novel associations at near-genome-wide significance for TSH (ACTL7B) and FT4 (LINC00648, YTHDC1, and C2CD4B). We also replicated established associations in FOXE1 and IGFBP5. Our findings suggest that SES, metabolic health, and genetics jointly influence thyroid function in Indian adolescents. The identification of population-specific loci emphasizes the importance of ancestry-informed genetic studies and supports the development of precision interventions to enhance pediatric thyroid health.

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Fig. 1: Distribution of thyroid hormones TSH and FT4 in different school types.
Fig. 2: Novel associations for thyroid traits (TSH, FT4, and FT3).

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Acknowledgements

The authors thank all the students and their families for their kind co-operation and participation in the study. Special acknowledgment to Praveen Gupta, MD, Premas Life Sciences Pvt. Ltd., for always providing help whenever required. J.M.N thanks the Council of Scientific and Industrial Research, Government of India for the Senior Research Fellowship. SC thanks the Department of Science and Technology, Govt. of India for INSPIRE faculty fellowship.

Funding

Major funding for this study was sponsored by the Department of Biotechnology, Government of India under two projects: ā€˜Genetics and systems biology of childhood obesity in India and Denmarkā€™ (BIOCHILD) [GAP 0089] and ā€˜Childhood Obesity: inflammatory markers, gene variation and epigeneticsā€™ (GLUE) [N 1292]. Partial funding for this study was also granted by the Department of Science & Technology, Government of India (PURSE II CDST/SR/PURSE PHASE II/11).

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Authors and Affiliations

  1. Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India

    Janaki M. Nair

  2. CSIR-Institute of Genomics and Integrative Biology, Delhi, India

    Khushdeep BandeshĀ &Ā Anil K. Giri

  3. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India

    Khushdeep BandeshĀ &Ā Anil K. Giri

  4. International Life Sciences Institute (ILSI), New Delhi, India

    Raman K. Marwaha

  5. National Institute of Biomedical Genomics, Kalyani, West Bengal, India

    Analabha Basu

  6. Department of Endocrinology and Metabolism, All India Institute of Medical Sciences, New Delhi, India

    Nikhil Tandon

  7. Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, New Delhi, India

    Shraddha Chakraborty

  8. School of Health Sciences and Translational Research, Sister Nivedita University, Kolkata, India

    Dwaipayan Bharadwaj

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  1. Janaki M. Nair
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Contributions

JMN: Literature search, visualization, data analysis, data interpretation, writing; KB: Data collection, intellectual inputs; AKG: Data collection, data curation, data analysis, intellectual inputs; RKM: Sample collection; AB: Study design, conceptualization, methodology, statistical analysis; NT: Sample collection, and phenotyping; SC: Data collection, data curation, data analysis, writing, interpretation, intellectual inputs; DB: Study design, conceptualization, methodology, funding acquisition, investigation, and supervised the entire study. DB is the guarantor of this work and, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Shraddha Chakraborty or Dwaipayan Bharadwaj.

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Nair, J.M., Bandesh, K., K. Giri, A. et al. Exploring socio-economic, biochemical, and genetic factors influencing thyroid status in Indian school-going adolescents. J Hum Genet (2025). https://doi.org/10.1038/s10038-025-01432-z

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