Metabolic phenotypes of obesity influence triglyceride and inflammation homoeostasis

We examined the degree of postprandial triglyceride (TG) response over the day, representing a highly dynamic state, with continuous metabolic adaptations, among normal-weight, overweight and obese patients, according to their metabolically healthy or abnormal status.

A total of 1002 patients from the CORDIOPREV clinical trial (NCT00924937) were submitted to an oral fat load test meal with 0·7 g fat/kg body weight (12% saturated fatty acids (SFA), 10% polyunsaturated fatty acids (PUFA), 43% monounsaturated fatty acids (MUFA), 10% protein and 25% carbohydrates). Serial blood test analysing lipid fractions and inflammation markers (high-sensitivity C-reactive protein (hs-CRP)) were drawn at 0, 1, 2, 3 and 4 h during postprandial state. We explored the dynamic response according to six body size phenotypes: (i) normal weight, metabolically healthy; (ii) normal weight, metabolically abnormal; (iii) overweight, metabolically healthy; (iv) overweight, metabolically abnormal; (v) obese, metabolically healthy; and (vi) obese, metabolically abnormal.

Metabolically healthy patients displayed lower postprandial response of plasma TG and large triacylglycerol-rich lipoproteins (TRLs)-TG, compared with those metabolically abnormal, independently whether or not they were obese (P < 0·001 and P < 0·001, respectively). Moreover, the area under the curve (AUC) of TG and AUC of large TRLs-TG were greater in the group of metabolically abnormal compared with the group of metabolically healthy (P < 0·001 and P < 0·001, respectively). Interestingly, metabolically abnormal subjects displayed higher postprandial response of plasma hs-CRP than did the subgroup of normal, overweight and obese, metabolically healthy patients (P < 0·001).

Our findings showed that certain types of the metabolic phenotypes of obesity are more favourable modulating phenotypic flexibility after a dynamic fat load test, through TG metabolism and inflammation homoeostasis. To identify, these phenotypes may be the best strategy for personalized treatment of obesity.