Chemically-interesterified (CIE) fats are trans-fat free and are increasingly being used as an alternative to hydrogenated oils for food manufacturing industries to optimize their products’ characteristics and nutrient compositions. The metabolic effects of CIE fats on insulin activity, lipids, and adiposity in humans are not well established. We investigated the effects of CIE fats rich in palmitic (C16:0, IEPalm) and stearic (C18:0, IEStear) acids on insulin resistance, serum lipids, apolipoprotein concentrations, and adiposity, using C16:0-rich natural palm olein (NatPO) as the control. We designed a parallel, double-blind clinical trial. Three test fats were used to prepare daily snacks for consumption with a standard background diet over a period of 8 weeks by three groups of a total of 85 healthy, overweight adult volunteers. We measured the outcome variables at weeks 0, 6, and at the endpoint of 8. After 8 weeks, there was no significant difference in surrogate biomarkers of insulin resistance in any of the IE fat diets (IEPalm and IEStear) compared to the NatPO diet. The change in serum triacylglycerol concentrations was significantly lower with the IEStear diet, and the changes in serum leptin and body fat percentages were significantly lower in the NatPO-diet compared to the IEPalm diet. We conclude that diets containing C16:0 and C18:0-rich CIE fats do not affect markers of insulin resistance compared to a natural C16:0-rich fat (NatPO) diet. Higher amounts of saturated fatty acids (SFAs) and longer chain SFAs situated at the sn-1,3 position of the triacylglycerol (TAG) backbones resulted in less weight gain and lower changes in body fat percentage and leptin concentration to those observed in NatPO and IEStear.

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Quantifying the distributions of disease risk in space and time jointly is a key element for understanding spatio-temporal phenomena while also having the potential to enhance our understanding of epidemiologic trajectories. However, most studies to date have neglected time dimension and focus instead on the “average” spatial pattern of disease risk, thereby masking time trajectories of disease risk. In this study we propose a new idea titled “spatio-temporal kernel density estimation (stKDE)” that employs hybrid kernel (i.e., weight) functions to evaluate the spatio-temporal disease risks. This approach not only can make full use of sample data but also “borrows” information in a particular manner from neighboring points both in space and time via appropriate choice of kernel functions. Monte Carlo simulations show that the proposed method performs substantially better than the traditional (i.e., frequency-based) kernel density estimation (trKDE) which has been used in applied settings while two illustrative examples demonstrate that the proposed approach can yield superior results compared to the popular trKDE approach. In addition, there exist various possibilities for improving and extending this method.

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