Substrate utilization and metabolic profile in response to overfeeding with a high-fat diet in South Asian and white men: a sedentary lifestyle study

A Systematic Review of Literature on the Representation of Racial and Ethnic Minority Groups in Clinical Nutrition Interventions

The racial and ethnic disparities in diet-related chronic diseases are major concerns. This systematic review examines the extent to which diet-induced changes in health outcomes, such as cardiometabolic, inflammation, cancer, bone health, and kidney function outcomes, etc., have been reported and discussed by race or ethnicity in randomized trials with 2 or more diet arms that recruited both minority and non-Hispanic White groups. Databases (i.e., PubMed, Cochrane Library, and Web of Science) were searched up to August 2021. Thirty-four studies that discussed effects of defined dietary interventions on health outcomes by racial or ethnic minority group compared with non-Hispanic Whites were included in the systematic review (PROSPERO registration number: CRD42021229256). Acute trials and those with 1 diet arm that accounted for race or ethnicity in their analyses and studies that focused on a single racial or ethnic group were discussed separately. Most studies were conducted in Black compared with White adults testing effects of energy restriction, macronutrient modification, sodium reduction, or variations of the Dietary Approaches to Stop Hypertension (DASH) diet on cardiometabolic outcomes. There was limited focus on other minority groups. Evidence suggests greater blood pressure reduction for Black adults compared with Whites particularly with DASH (or similar) diets. Overall, there was limited consideration for group-specific eating patterns and diet acceptability. Overall risk of bias was low. With emerging precision nutrition initiatives that aim to optimize metabolic responses in population subgroups through tailored approaches, it is imperative to ensure adequate representation of racial and ethnic subgroups for addressing health disparities. Factors that help explain variability in responses such as socioecological context should be included and adequately powered. Given the racial and ethnic disparities in chronic diseases, studying the adoption, maintenance, and effectiveness of dietary interventions on health outcomes among different groups is critical for developing approaches that can mitigate diet-related health disparities.

Energy Metabolism in Relation to Diet and Physical Activity: A South Asian Perspective

The prevalence of overweight and obesity is on the rise around the world, not only in the West, but also in Asian countries. South Asian countries in particular are experiencing a rapid increase in overweight and obesity, that coexists with the rapid increase in non-communicable diseases linked to obesity such as diabetes and cardiovascular disease than any other country in Asia. The phenomena observed in South Asian countries are due to the size of the population, the ageing of the population, the high degree of urbanization and the lifestyle changes in favor of increased energy consumption and reduced physical activity. The imbalance between energy consumption and energy expenditure results in the development of a positive energy balance that, over time, accumulates in higher body fat. South Asians were reported to have a more unfavorable body composition with a higher percentage of body fat than Caucasians with an equivalent BMI. Body composition is a major determinant of resting energy expenditure. It has been reported that South Asians have a lower resting energy expenditure than Caucasians with the same BMI. Resting energy expenditure accounts for the majority of total daily energy expenditure and, therefore, plays a crucial role in achieving the balance between energy intake and expenditure.

Determinants of the postprandial triglyceride response to a high-fat meal in healthy overweight and obese adults

Background

Dyslipidemia is a feature of impaired metabolic health in conjunction with impaired glucose metabolism and central obesity. However, the contribution of factors to postprandial lipemia in healthy but metabolically at-risk adults is not well understood. We investigated the collective contribution of several physiologic and lifestyle factors to postprandial triglyceride (TG) response to a high-fat meal in healthy, overweight and obese adults.

Methods

Overweight and obese adults (n = 35) underwent a high-fat meal challenge with blood sampled at fasting and hourly in the 4-hour postprandial period after a breakfast containing 50 g fat. Incremental area under the curve (iAUC) and postprandial magnitude for TG were calculated and data analyzed using a linear model with physiologic and lifestyle characteristics as explanatory variables. Model reduction was used to assess which explanatory variables contributed most to the postprandial TG response.

Results

TG responses to a high-fat meal were variable between individuals, with approximately 57 % of participants exceeded the nonfasting threshold for hypertriglyceridemia. Visceral adiposity was the strongest predictor of TG iAUC (β = 0.53, p = 0.01), followed by aerobic exercise frequency (β = 0.31, p = 0.05), insulin resistance based on HOMA-IR (β = 0.30, p = 0.04), and relative exercise intensity at which substrate utilization crossover occurred (β = 0.05, p = 0.04). For postprandial TG magnitude, visceral adiposity was a strong predictor (β = 0.43, p < 0.001) followed by aerobic exercise frequency (β = 0.23, p = 0.01), and exercise intensity for substrate utilization crossover (β = 0.53, p = 0.01).

Conclusions

Postprandial TG responses to a high-fat meal was partially explained by several physiologic and lifestyle characteristics, including visceral adiposity, insulin resistance, aerobic exercise frequency, and relative substrate utilization crossover during exercise.

Trial Registration

ClinicalTrials.gov, NCT04128839, Registered 16 October 2019 – Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-021-01543-4.

The Impact of Macronutrient Intake on Non-alcoholic Fatty Liver Disease (NAFLD): Too Much Fat, Too Much Carbohydrate, or Just Too Many Calories?

Non-alcoholic fatty liver disease (NAFLD) is a growing epidemic, in parallel with the obesity crisis, rapidly becoming one of the commonest causes of chronic liver disease worldwide. Diet and physical activity are important determinants of liver fat accumulation related to insulin resistance, dysfunctional adipose tissue, and secondary impaired lipid storage and/or increased lipolysis. While it is evident that a hypercaloric diet (an overconsumption of calories) promotes liver fat accumulation, it is also clear that the macronutrient composition can modulate this risk. A number of other baseline factors modify the overfeeding response, which may be genetic or environmental. Although it is difficult to disentangle the effects of excess calories vs. specifically the individual effects of excessive carbohydrates and/or fats, isocaloric, and hypercaloric dietary intervention studies have been implemented to provide insight into the effects of different macronutrients, sub-types and their relative balance, on the regulation of liver fat. What has emerged is that different types of fat and carbohydrates differentially influence liver fat accumulation, even when diets are isocaloric. Furthermore, distinct molecular and metabolic pathways mediate the effects of carbohydrates and fat intake on hepatic steatosis. Fat accumulation appears to act through impairments in lipid storage and/or increased lipolysis, whereas carbohydrate consumption has been shown to promote liver fat accumulation through de novo lipogenesis. Effects differ dependent upon carbohydrate and fat type. Saturated fat and fructose induce the greatest increase in intrahepatic triglycerides (IHTG), insulin resistance, and harmful ceramides compared with unsaturated fats, which have been found to be protective. Decreased intake of saturated fats and avoidance of added sugars are therefore the two most important dietary interventions that can lead to a reduction in IHTG and potentially the associated risk of developing type 2 diabetes. A healthy and balanced diet and regular physical activity must remain the cornerstones of effective lifestyle intervention to prevent the development and progression of NAFLD. Considering the sub-type of each macronutrient, in addition to the quantity, are critical determinants of liver health.

Effect of Over- and Underfeeding on Body Composition and Related Metabolic Functions in Humans

PURPOSE OF REVIEW

Methodological limitations of body composition methods limit the validity of changes in body composition that are used to interpret metabolic outcome parameters of weight loss and weight gain.

RECENT FINDINGS

Direct assessment of energy balance is necessary for the assessment of early weight changes (i.e., within the 1st week of weight change), whereas body composition analysis with a high accuracy and a low minimal detectable change is recommended to assess ongoing changes. The sequence of underfeeding and overfeeding impacts the method inherent assumptions, and the considerable day-to-day and inter-individual variance in body composition changes is a challenge to the precision of methods. Weight loss-associated changes in body composition do not resemble their changes with subsequent hypercaloric re-feeding. Individual body components are related to specific metabolic functions where the structure-function relationships change with changes in energy balance. Analysis of structure-function relationships in response to weight changes needs to address (a) the validity, precision, and different outcome parameters of body composition methods and (b) the variance of results taking into account study protocols and the dynamics of weight changes. As for future studies, repeated measurements of body weight, body composition, and metabolic functions are needed before, during, and after weight changes focusing on the intra- and interindividual variances of weight change rather than on mean data only.