Classification of obesity targeted personalized dietary weight loss management based on carbohydrate tolerance

Astrup, Arne; Hjorth, Mads F.

doi:10.1038/s41430-018-0227-6

Cited by 14 publications

(4 citation statements)

References 21 publications

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“…Certain features of the standard 75g OGTT response curves are widely accepted to be representative of glycemic control, most notably the fasting and 2 hour post-load glucose values are used in the diagnosis of prediabetes and type 2 diabetes mellitus (T2DM) [2]. The area under the plasma glucose/insulin response curve (AUC) is an extensively employed measure to compare responses [3] and has been successfully used in targeted and even personalized nutrition approaches [4][5][6]. However, the AUC is a somewhat crude measure that may often lead to ambiguous classifications [7].…”

Section: Introductionmentioning

confidence: 99%

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

et al. 2021

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Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals’ challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals’ responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals’ metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

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Section: Introductionmentioning

confidence: 99%

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

et al. 2021

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“…The effect on appetite has been suggested to be that the absorbed carbohydrate elevating the blood glucose needs to enter the brain (and possibly other tissues) before it can lead to satiety and reduce hunger. The findings suggest that postprandial glucostatic control may be important for control of appetite, and that reduced carbohydrate intake may have a beneficial effect on body weight management in individuals with compromised glucose control [ 17 , 80 ]. We do not know all details about the populations in the studies included in this meta-analysis, so the effect on body weight management may be masked by the fact that certain individuals perhaps responded strongly to the interventions while others did not.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, it remains unclear whether dietary protein is beneficial for body weight management in the context of overweight. Furthermore, recent findings suggest that successful body weight management with specific diets varying in macronutrient content may be highly dependent on specific phenotypes including degree of glucose tolerance, appetite and eating behavior characteristics as well as different genotypes [ 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Are Dietary Proteins the Key to Successful Body Weight Management? A Systematic Review and Meta-Analysis of Studies Assessing Body Weight Outcomes after Interventions with Increased Dietary Protein

2021

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The primary aim was to systematically review the current evidence investigating if dietary interventions rich in protein lead to improved body weight management in adults with excessive body weight. The secondary aim was to investigate potential modifying effects of phenotyping. A systematic literature search in PubMed, Web of Science, and Cochrane Library identified 375 randomized controlled trials with 43 unique trials meeting the inclusion criteria. The Cochrane collaboration tool was used for a thorough risk of bias assessment. Based on 37 studies evaluating effects of dietary protein on body weight, the participants with increased protein intake (ranging from 18–59 energy percentage [E%]) were found to reduce body weight by 1.6 (1.2; 2.0) kg (mean [95% confidence interval]) compared to controls (isocaloric interventions with energy reduction introduced in certain studies). Individuals with prediabetes were found to benefit more from a diet high in protein compared to individuals with normoglycemia, as did individuals without the obesity risk allele (AA genotype) compared to individuals with the obesity risk alleles (AG and GG genotypes). Thus, diets rich in protein would seem to have a moderate beneficial effect on body weight management.

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“…Results from post hoc, explorative studies using data and samples from randomized controlled trials (RCTs) on the Healthy Nordic Diet [32,68,95] or specific food components [30,96] thereof, have proven useful to elucidate the factors of importance to explaining/predicting the response of a Healthy Nordic Diet or important food components thereof. However, so far, no controlled intervention study has evaluated the effects of a personalized approach taking such factors into account vs. a general Healthy Nordic Diet and it therefore remains to be elucidated to what extent personalized nutrition strategies vs. general advice based on a Healthy Nordic Diet has advantages and whether it is feasible at a large scale [97][98][99].…”

Section: Discussionmentioning

confidence: 99%

Biomarkers of a Healthy Nordic Diet—From Dietary Exposure Biomarkers to Microbiota Signatures in the Metabolome

Landberg

Hanhineva

2019

Nutrients

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Whole diets and dietary patterns are increasingly highlighted in modern nutrition and health research instead of single food items or nutrients alone. The Healthy Nordic Diet is a dietary pattern typically associated with beneficial health outcomes in observational studies, but results from randomized controlled trials are mixed. Dietary assessment is one of the greatest challenges in observational studies and compliance is a major challenge in dietary interventions. During the last decade, research has shown the great importance of the gut microbiota in health and disease. Studies have have both shown that the Nordic diet affects the gut microbiota and that the gut microbiota predicts the effects of such a diet. Rapid technique developments in the area of high-throughput mass spectrometry have enabled the large-scale use of metabolomics both as an objective measurement of dietary intake as well as in providing the final readout of the endogenous metabolic processes and the impact of the gut microbiota. In this review, we give an update on the current status on biomarkers that reflect a Healthy Nordic Diet or individual components thereof (food intake biomarkers), biomarkers that show the effects of a Healthy Nordic Diet and biomarkers reflecting the role of a Healthy Nordic Diet on the gut microbiota as well as how the gut microbiota or derived molecules may be used to predict the effects of a Healthy Nordic Diet on different outcomes.

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Classification of obesity targeted personalized dietary weight loss management based on carbohydrate tolerance

Cited by 14 publications

References 21 publications

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

Are Dietary Proteins the Key to Successful Body Weight Management? A Systematic Review and Meta-Analysis of Studies Assessing Body Weight Outcomes after Interventions with Increased Dietary Protein

Biomarkers of a Healthy Nordic Diet—From Dietary Exposure Biomarkers to Microbiota Signatures in the Metabolome

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