<p>Insulin resistance is a chronic, low-grade inflammatory condition and a central pathological feature of obesity and related conditions, including metabolic syndrome (MetS). Oxidative stress, inflammatory pathways, and dysregulation of adipokine secretion may contribute to its development. This study aimed to clarify these biological relationships using principal component analysis (PCA) in individuals with MetS. The study involved 190 adults diagnosed with MetS. Serum concentrations of metabolic and inflammatory biomarkers (high-sensitivity C-reactive protein [hs-CRP], interleukin-6 [IL-6], and tumor necrosis factor-alpha [TNF-α]), oxidative stress indicators (glutathione peroxidase [GPx], superoxide dismutase [SOD], total antioxidant capacity [TAC], and total oxidant status [TOS]), and adipokines (omentin-1, adiponectin, visfatin, and leptin) were measured. Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated. PCA was conducted to investigate the associations between clusters of these biomarkers and HOMA-IR. Compared to individuals with HOMA-IR ≤ 2.5, those with HOMA-IR &gt; 2.5 had significantly elevated serum triglycerides and TNF-α concentrations, as well as significantly lower levels of adiponectin, omentin-1, and TAC. PCA extracted 4 components explaining 59.45% of the total variance: lipid profile-related factor, oxidative stress-related factor, inflammatory-related factor, and adipokine profile factor. Of all 4 principal components, the inflammatory-related factor (i.e., hs-CRP and TNF-α) and the adipokine profile factor (i.e., leptin, adiponectin, and omentin-1) were independently associated with HOMA-IR &gt; 2.5. Adipokine dysregulation, along with increased inflammation and oxidative stress, was associated with insulin resistance in MetS. PCA-derived biomarker clusters provide a mechanistic and integrative comprehension of metabolic risk stratification and may enable risk assessment and therapeutic strategies in the future.</p>

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Principal component analysis of adipokine, metabolic, oxidative, and inflammatory markers associated with insulin resistance in metabolic syndrome

  • Soudabeh Hamedi-Shahraki,
  • Mandana Moradi,
  • Filiz Mercantepe,
  • Farshad Amirkhizi,
  • Aleksandra Klisic

摘要

Insulin resistance is a chronic, low-grade inflammatory condition and a central pathological feature of obesity and related conditions, including metabolic syndrome (MetS). Oxidative stress, inflammatory pathways, and dysregulation of adipokine secretion may contribute to its development. This study aimed to clarify these biological relationships using principal component analysis (PCA) in individuals with MetS. The study involved 190 adults diagnosed with MetS. Serum concentrations of metabolic and inflammatory biomarkers (high-sensitivity C-reactive protein [hs-CRP], interleukin-6 [IL-6], and tumor necrosis factor-alpha [TNF-α]), oxidative stress indicators (glutathione peroxidase [GPx], superoxide dismutase [SOD], total antioxidant capacity [TAC], and total oxidant status [TOS]), and adipokines (omentin-1, adiponectin, visfatin, and leptin) were measured. Homeostatic model assessment for insulin resistance (HOMA-IR) was calculated. PCA was conducted to investigate the associations between clusters of these biomarkers and HOMA-IR. Compared to individuals with HOMA-IR ≤ 2.5, those with HOMA-IR > 2.5 had significantly elevated serum triglycerides and TNF-α concentrations, as well as significantly lower levels of adiponectin, omentin-1, and TAC. PCA extracted 4 components explaining 59.45% of the total variance: lipid profile-related factor, oxidative stress-related factor, inflammatory-related factor, and adipokine profile factor. Of all 4 principal components, the inflammatory-related factor (i.e., hs-CRP and TNF-α) and the adipokine profile factor (i.e., leptin, adiponectin, and omentin-1) were independently associated with HOMA-IR > 2.5. Adipokine dysregulation, along with increased inflammation and oxidative stress, was associated with insulin resistance in MetS. PCA-derived biomarker clusters provide a mechanistic and integrative comprehension of metabolic risk stratification and may enable risk assessment and therapeutic strategies in the future.