The triglyceride-glucose index as an adiposity marker and a predictor of fat loss induced by a low-calorie diet
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Background: This study aimed to investigate the putative role of the Triglyceride-glucose index (TyG index) computed as ln[TG (mg/dL) × glucose (mg/dL)/2] and derived proxies as predictors of adiposity and weight loss changes after a low calorie diet (LCD) intervention.
Methods: A total of 744 adult participants from the multicenter DIOGenes intervention study were prescribed a LCD (800 kcal/day) during 8 weeks. Body composition and fat content at baseline and after 8 weeks were estimated by DEXA/BIA. A multivariate analysis approach was used to estimate the difference in ∆Weight1-2 (kg), ∆BMI1-2 (kg/m2) or ∆Fat1-2 (%) between the basal value (point 1) and after 8 weeks following a LCD (point 2), respectively. The TyG index at baseline (TyG1), after following the LCD for 8 weeks (TyG2), or the TyG index differences between both time points (∆TyG1-2) were analyzed as predictors of weight and fat changes.
Results: TyG1 was associated with ∆Weight1-2 (kg) and ∆BMI1-2 (kg/m2), with β=0.812 (p = 0.017) and β=0.265 (p = 0.018), respectively. Also, TyG2 values were inversely related to ∆Fat1-2 (%), β=-1.473 (p = 0.015). Moreover, ∆TyG1-2 was associated with ∆Weight1-2 (kg) and ∆Fat1-2 (%), β= 0.689 (p = 0.045) and β=1.764 (p = 0.002), respectively. Furthermore, an association between TyG2 and resistance to fat loss was found (p = 0.015).
Conclusion: TyG1 index is a good predictor of weight loss induced by LCD. Moreover, TyG2 was closely related to resistance to fat loss, while ∆TyG1-2 values were positively associated with body fat changes. Therefore, TyG index and derived estimations could be used as markers of individualized responses to energy restriction and a surrogate of body composition outcomes in clinical/epidemiological settings in obesity conditions.
|Journal||European Journal of Clinical Investigation|
|Number of pages||12|
|Publication status||Published - 2022|
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- Faculty of Science - TyG index, Statistical predictors, Weight loss, Fat loss, Precision medicine