Discussion
overweight, weight loss
OBESE, INSULIN RESISTANCE, INSULIN SENSITIVITY
In the NutriTech study, whole-body MRI and plasma biomarker analyses were used to assess the effects of energy restriction on insulin sensitivity and metabolic health of overweight subjects. The use of dietary challenges aimed to assess whether changes in phenotypic flexibility could be more effectively detected by measuring postprandial markers rather than fasting samples. The outcome of this study indicates that despite an average BW loss of 6.4% in the WLG, only very few classical metabolic effects were observed. Although an estimated 60% of the cohort adhered to 75% of the dietary prescription, there were significant interindividual differences in visceral fat reduction induced by the weight loss intervention, which matches differences in response to the 2 dietary challenge tests. Another important finding was the variation in parameters that report insulin sensitivity. Although fasting insulin significantly decreased and HOMA-IR significantly increased after the intervention in the WLG during the MMTT, this was not observed during the OGTT. Moreover, the change in the postprandial estimation of insulin sensitivity by the MI following weight loss was also different between the OGTT and MMTT. This variation in response has been previously commented on by others [The dietary intervention resulted in an average weight loss of over 5 kg, accompanied by significant reductions in adipose mass, as well as waist and hip circumference, and ectopic fat mass. Consistent with previous studies on weight loss [The effects of weight loss on muscle lipid content are still debated [The reduction in fasting plasma glucose was modest (−3,5% The dietary challenges (OGTT and MMTT) performed before and after 12 wk of energy restriction revealed subtle changes in phenotypic flexibility when postprandial changes in plasma levels of glucose, insulin, and amino acids markers of insulin resistance (Ile, Leu, Val, Trp, Glu, and Ala) were considered [The weight loss intervention had its most striking effect on an individual level in the WLG, as demonstrated by the correlations between changes in various parameters, in particular, changes where fat distribution showed strong associations with changes in multiple metabolic parameters (Although the number of subjects was limited, an analysis aimed at identifying a metabolic signature associated with individual susceptibility to improved metabolism following a weight loss intervention successfully identified plasma metabolites during the OGTT and MMTT that served this purpose, as demonstrated by a PLS-DA (The analysis of dietary diaries suggests that the group with improved insulin sensitivity following weight loss was also one with a more positive change in diet quality during the intervention. This group demonstrated a larger increase in fiber intake and a pronounced reduction in the intake of saturated fat, which was not observed in the group with any improvement in insulin sensitivity (As with all studies investigating the relationship between dietary change and metabolism in overweight and obese cohorts, self-reported intake should be cautiously interpreted. Although we estimated compliance with the energy restrictions in the WLG to be good, the urine metabolomics analyses suggest a careful interpretation of the diet quality. We observed a small but significant improvement in dietary quality in the WLG, which was not seen in the WMG. This may explain why the difference in metabolic outcomes was less clear than expected.In conclusion, we demonstrate that moderate weight loss affected some metabolic parameters with a large interindividual variation. We provide novel evidence that a dietary challenge may be a sensitive tool to detect subtle changes in the metabolism induced by weight loss, offering an advantage to conventional plasma analysis after overnight fasting. OGTT was more sensitive than MMTT in detecting changes in markers of insulin sensitivity. We also observed that some participants exhibited improved insulin sensitivity whereas others did not, despite similar decreases in BW. A metabolic signature for such predisposition was proposed, suggesting that extensive phenotyping and dietary challenges are useful tools for the development of personalized nutrition. Full interpretation of complex nutritional data at an individual level, such as that presented in this study, needs new methodologies of interpreting and visualizing such as those offered by machine learning, AI, and modeling.