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Metabolic Score for Insulin Resistance

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The Metabolic Score for Insulin Resistance (METS-IR) is a metabolic index designed to quantify peripheral insulin sensitivity in humans. It was first described by Bello-Chavolla et al. in 2018[1][2] and developed by the Metabolic Research Disease Unit at the Instituto Nacional de Ciencias Médicas Salvador Zubirán.[3] METS-IR was validated in the Mexican population against the euglycemic hyperinsulinemic clamp and the frequently-sampled intravenous glucose tolerance test.[1] It offers a non-insulin-based alternative to traditional methods such as SPINA Carb, HOMA-IR, and QUICKI. METS-IR is currently validated for assessing cardiometabolic risk in Latino population.[1]

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Derivation and validation

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METS-IR was generated using linear regression against the M value adjusted by lean body mass obtained from the glucose clamp technique in Mexican subjects with and without type 2 diabetes mellitus. It is estimated using fasting laboratory values including glucose (in mg/dL), triglycerides (mg/dL) and high-density lipoprotein cholesterol (HDL-C, in mg/dL) along with body-mass index (BMI). The index can be estimated using the following formula:[4]

The index holds a significant correlation with the M-value adjusted by lean mass (ρ = −0.622) obtained from the euglycemic hyperinsulinaemic clamp study adjusted for age and gender as well as minimal model estimates of glucose sensitivity.[5] In an open population cohort study in Mexican population, METS-IR was shown to predict incident type 2 diabetes mellitus and a value of METS-IR >50.0 suggested up to three-fold higher risk of developing type 2 diabetes after an average of three years.[1] In a nation-wide population-based study of Chinese subjects, METS-IR was also shown to identify subjects with metabolic syndrome independent of adiposity.[6] METS-IR also predicts visceral fat content, subcutaneous adipose tissue, fasting insulin levels and ectopic fat accumulation in liver and pancreas.[1]

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Comparison to other indexes

METS-IR was compared with other non-insulin-based methods for estimating insulin sensitivity, including the Triglyceride-Glucose index (TyG),[7] the triglyceride to HDL-C ratio,[8] and the TyG-BMI index,[9] showing a higher correlation and area under the ROC curve.[1] However, in a study of Chinese subjects, Yu et al. found that TyG and TG/HDL-C performed better, likely due to ethnic differences in body composition.[10] Given the role of ethnicity in modifying the performance of insulin sensitivity fasting-based indexes, further evaluations in different populations are required to establish performance of non-insulin-based methods.[11]

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See also

References

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