Insulin and Insulin Receptor Variants: Structural and Functional Insights into Insulin Resistance

Insulin resistance (IR), a defining pathological feature of type 2 diabetes mellitus, results from impaired signaling through the insulin receptor (INSR). This review summarizes the findings from 31 next-generation sequencing studies published between 2010 and 2025 to systematically characterize various amino acid-altering variants in the insulin (INS) and INSR genes. All reported variants were aligned with the reference protein sequences, and functional interpretations were made using previously published AI-assisted structural prediction tools, including AlphaFold, EVE, and DeepVariant. Across the literature, 140 unique variants were identified, the majority of which were missense substitutions clustered within conserved ligand-binding regions or tyrosine kinase activation motifs. Recurrent INSR hotspots such as Gly366, Cys682, and Tyr1185 were consistently described as evolutionarily conserved residues associated with reduced structural stability and diminished receptor activation in computational analyses. Published physicochemical data further suggested that several INS and INSR variants display lower predicted isoelectric points and higher instability indices compared to wild-type proteins, indicating decreased folding stability. Collectively, these findings highlight three convergent molecular themes: structural destabilization, reduced ligand-receptor affinity, and impaired downstream signaling. This integrated synthesis provides mechanistic insight into how INS and INSR variants may contribute to IR and underscores their potential relevance for emerging precision-diagnostic and therapeutic strategies in metabolic disease.

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Publication Details

Journal
Korean Journal of Clinical Laboratory Science
Published
2025-12-26
DOI
https://doi.org/10.15324/kjcls.2025.57.4.450
Primary Topic
Metabolism, Diabetes, and Cancer
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article

Insulin and Insulin Receptor Variants: Structural and Functional Insights into Insulin Resistance

K. Lee, Hyun Ho Sung, Ho-Keun CHOI
Korean Journal of Clinical Laboratory Science
Metabolism, Diabetes, and Cancer
article

Insulin and Insulin Receptor Variants: Structural and Functional Insights into Insulin Resistance

K. Lee, Hyun Ho Sung, Ho-Keun CHOI
article en

Abstract

Insulin resistance (IR), a defining pathological feature of type 2 diabetes mellitus, results from impaired signaling through the insulin receptor (INSR). This review summarizes the findings from 31 next-generation sequencing studies published between 2010 and 2025 to systematically characterize various amino acid-altering variants in the insulin (INS) and INSR genes. All reported variants were aligned with the reference protein sequences, and functional interpretations were made using previously published AI-assisted structural prediction tools, including AlphaFold, EVE, and DeepVariant. Across the literature, 140 unique variants were identified, the majority of which were missense substitutions clustered within conserved ligand-binding regions or tyrosine kinase activation motifs. Recurrent INSR hotspots such as Gly366, Cys682, and Tyr1185 were consistently described as evolutionarily conserved residues associated with reduced structural stability and diminished receptor activation in computational analyses. Published physicochemical data further suggested that several INS and INSR variants display lower predicted isoelectric points and higher instability indices compared to wild-type proteins, indicating decreased folding stability. Collectively, these findings highlight three convergent molecular themes: structural destabilization, reduced ligand-receptor affinity, and impaired downstream signaling. This integrated synthesis provides mechanistic insight into how INS and INSR variants may contribute to IR and underscores their potential relevance for emerging precision-diagnostic and therapeutic strategies in metabolic disease.

Korean Journal of Clinical Laboratory ScienceVol. 57(4)
Dongnam Health University (KR), Seoyeong University (KR), Sangji University (KR)
Openalex Percentile: Top 36%
Metabolism, Diabetes, and Cancer
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