Variation in metabolic responses to meal challenges differing in glycemic index in healthy women: Is it meaningful?
© Krishnan et al; licensee BioMed Central Ltd. 2012
Received: 26 July 2011
Accepted: 29 March 2012
Published: 29 March 2012
Established clinical tests are commonly used in disease diagnosis, but tools that enhance identification of metabolic dysfunctions are needed. This study was conducted to identify typical and atypical metabolite temporal patterns in response to paired meal challenge tests.
Metabolic responses to high and low glycemic index (GI) meals were tested in 24 healthy pre-menopausal women, aged 20-50 y, with BMI of 25-30 kg/m2 using a cross-over design. On test days, blood glucose, insulin, leptin and non-esterified fatty acids were measured after an overnight fasting, and for 8 h following test meal consumption. The data were range scaled, and multivariate statistics were used to assess the presence of distinct response groups to the meal challenge tests.
As expected, participants showed higher circulating glucose and insulin in response to the high GI compared to the low GI meal challenge. However, using range-scaling and Principal Component Analysis, three distinct groups were identified based on differential responses to the paired challenges. Members of the most populated group (n = 18) displayed little deviation from the expected response to the two meal challenges. Two minor groups (n = 3/group) with distinct responses were observed, one suggestive of sub-clinical insulin resistance, and the other suggestive of hyperleptinemia.
The differential responses of glucose, insulin and leptin to low and high glycemic test meals revealed three response groups. Dietary intervention studies traditionally evaluate group responses, and aim to identify the overall effect in the population studied. In contrast, our study analyzed the variance in the meal challenge responses, using an integrated physiological approach, rather than a reductionist approach. This phenotyping approach may be useful for detecting subclinical metabolic dysfunctions, and it could contribute to improved personalized nutrition management. This study is registered in ClinicalTrials.gov, record #200210295
KeywordsGlycemic index Phenotyping Meal challenge tests Range scaling Principal component analysis
Body mass index
Low glycemic index
High glycemic index
Non-esterified fatty acid
Principal component analysis.
Meal challenge tests are common tools used to identify metabolite response patterns in human studies. Populations can also be segregated into specific metabolic phenotypes or metabotypes based on their response to a fixed dietary exposure . Recent evidence suggests that the postprandial lipid profile of individuals can be a means to achieve metabolic phenotyping, using postprandial time course data, and statistical tools .
The postprandial glycemic response to ingesting carbohydrate-containing foods can be highly variable between individuals, especially in those with impaired glucose tolerance . The postprandial glycemic surge is tempered by an individual's ability to secrete adequate insulin, and clearance of glucose depends on insulin sensitivity of tissues. Downstream, the homeostatic regulation of fuel utilization and storage is also subject to dynamic control through the interaction of different hormonal mediators. For instance, leptin, an endocrine hormone primarily secreted from the adipose tissue, plays an integral part in the hypothalamic regulation of energy homeostasis , and circulates in direct proportion to body fat mass . However, variations in leptin concentration in individuals with comparable adiposity suggest that other factors regulate leptin production and release .
Numerous studies have indicated that insulin can act as a regulator of both adipose leptin secretion and circulating leptin concentrations [7–10]. Insulin-dependent activation of the insulin receptor in adipocytes increases leptin mRNA expression within a few hours . Also, leptin has been shown to inhibit the pancreatic beta cell secretion of insulin , thereby creating a bidirectional circuit of hormone interplay termed the adipo-insular axis. Leptin suppression of insulin is speculated to be involved with long-term regulation of basal insulin secretion .
Recent evidence indicates a role for leptin resistance and hyperleptinemia in the metabolic dysfunction that leads to diabetes . Leptin resistance in the hypothalamic center can disrupt body weight regulation . Leptin resistance in pancreatic beta cells can disrupt leptin suppression of insulin via the adipo-insular axis and promote hyperinsulinemia .
Here we have used low and high glycemic index meal challenges in an endeavor to identify response patterns that can provide insight into early metabolic disruption. By investigating glucose, insulin and leptin responses to these meals, we observed the expected differential glycemic responses . Further, we applied novel data treatments and achieved a clear stratification of individuals based on their response patterns to low and high GI meals.
Subjects and methods
Twenty-four women between the ages of 20 to 50 years of age participated after giving informed consent and passing initial health screening consisting of medical history, clinical blood chemistries, and blood pressure measurements. All volunteers were overweight with body mass index (BMI) in the range of 25.0 to 29.9 kg/m2. Volunteers were excluded if they had indications of cardiovascular or metabolic disorders, were pregnant or had been pregnant within 18 months prior to the study, were taking medications or herbal supplements to induce weight loss or changes in appetite, or were smokers. Volunteers agreed to remain weight stable during the period of the study and refrain from restrictive dieting or changes in their habitual physical activity level.
Composition of Run-In Diets1 and Test Meals2
High GI Diet
Low GI Diet
Test day protocols
Following each 3-d period of consuming the run-in diets, participants reported to the human studies laboratory at 0700 after an overnight, 12-h fast. Their height and weight were measured using a stadiometer and a weight scale. Body composition for fat and fat free mass was measured using a DEXA scan. Then an intravenous catheter was inserted to sample blood at 10 specific time points: 0 min (fasted state); time of blood draw was 0800, and 30, 60, 90, 120, 150, 210, 270, 360, and 480 min following the test meal that was consumed between 0900 and 0915. Indwelling catheters were flushed periodically with normal saline.
Blood collection and analyses
All blood samples were collected in vacutainers containing sodium fluoride/potassium oxalate for glucose determinations, K3 EDTA for leptin determinations, or no additives for insulin and non-esterified fatty acid (NEFA) determinations. To obtain serum, blood was allowed to stand at room temperature for 10 min before centrifuging, whereas blood samples for plasma were immediately chilled on ice. All samples were centrifuged in a refrigerated Centra CL3R centrifuge (International Equipment Co. Chattanooga, TN) for 10 min at 1300 g. Plasma and sera were stored at -70°C prior to analyses. Glucose and NEFA were determined via enzymatic assays (Roche Diagnostics Corp., Indianapolis, IN) using a Hitachi 902 Automatic Analyzer (Boehringer Manheim Corp., Indianapolis, IN). Serum insulin was determined via a solid-phase, two-site, chemiluminescent enzyme-labeled immunometric assay using an Immulite analyzer (Diagnostic Products Corp., Los Angeles, CA). Plasma leptin was determined using a radioimmunoassay (Millipore/Linco, Billerica, MA).
Data transformation and statistical analyses
This "adjusted range-scaling" presumes that the daily minimum reports on an individuals' basal status, and that the HGI meal represents a maximum challenge response, and together these represent the best measure of an individuals' ability to respond to a glycemic challenge.
A PCA was performed on the resulting data sets of leptin, insulin and glucose to assess variance in metabolic response. A PCA transforms a data matrix (e.g. metabolite array by subject) into a set of uncorrelated variables, or principal components, that are ranked by their ability to describe the maximum variance in the data, and being uncorrelated (i.e. orthogonal or perpendicular) to the previous principal component, with each subject receiving a score based on their number of metabolite input data in each principal component . The degree of influence each variable has within a given principal component is reported by the loadings for that variable. Thus, PCA analyzes variance in the dataset, and scores each participant based on this variance. In this case, PCA plots depict the spread of data that has been scaled to enhance the differential response to HGI and LGI in insulin, leptin and glucose. PCA was performed using Bristol Chemometric PCA Add-in http://www.chm.bris.ac.uk/org/chemometrics/ for Microsoft Excel. ANOVAs with Tukeys and Bonferroni's multiple comparison tests were used on raw, range-scaled and area under the curve values (computed using the trapezoid rule) for insulin, leptin and glucose to identify differences.
Characteristics of study population (n = 24) measured on test days1
76.4 ± 6.0
27.2 ± 1.3
30.2 ± 8.4
46.8 ± 4.7
29.7 ± 2.6
76.1 ± 5.8
27.1 ± 1.1
30.4 ± 8.4
46.8 ± 4.4
29.2 ± 2.9
66.0 to 89.7
24.3 to 29.0
19.8 to 46.4
38.1 to 58.1
22.6 to 34.6
Compared to RG1, subjects clustering in the upper left PCA quadrant, identified as Response Group 2 (RG2) had lower postprandial leptin, higher insulin and higher glucose relative responses to the two meal challenges (Figure 3). On the other hand, subjects with significantly elevated principal component 1 scores, identified as Response Group 3 (RG3) had high leptin and glucose, with similar insulin responses to RG1.
Metabolic characteristics of RG1, RG2 and RG3
Mean ± SEM
Mean ± SEM
Mean ± SEM
77.3 ± 1.4
66.0 - 89.7
71.6 ± 0.6
69.3 - 72.9
74.6 ± 2.9
69.2 - 83.7
27.1 ± 0.3
24.3 - 29.5
26.8 ± 0.5
25.7 - 28.1
27.8 ± 0.5
26.8 - 29.7
29.9 ± 2.0
19.8 - 46.6
27.7 ± 3.2
21.2 - 36.8
35.3 ± 4.7
22.1 - 44.8
30.0 ± 0.6a
25.7 - 34.6
25.5 ± 1.1b
22.6 - 29.0
30.0 ± 1.4a, b
27.7 - 34.4
0.60 ± 0.05
0.14 - 0.97
0.51 ± 0.08
0.27 - 0.77
0.35 ± 0.08
0.12 - 0.57
Summary of RG2 and RG31 compared to RG1
Variation in human data can be generated from multiple sources. Identifying deviations between individual responses to dietary challenges and grouping individuals with similar phenotypic responses is one means of "metabotyping." Inter-individual variability in human studies can interfere with the identification of human metabotypes, but appropriate data transformations can facilitate data interpretation. These tools can reduce the volume and complexity of data sets and can quiet variance from both biological and analytical sources. Hence, pre-treatment of data has been widely used to organize data, enabling efficient analysis with improvement in statistical power . In the current study, we transformed data to an individualized response scale to reduce inter-individual variability, which enhanced the ability to identify distinct groups within the measured response profiles. The response profile of RG1 appears consistent with the expected differential response to a low and high glycemic meal challenge. RG2 and RG3 appear to indicate alternate physiological responses within this clinically healthy population. We hypothesize that these distinct "response profiles" are putative metabolic phenotypes that can be identified based on their postprandial response to a single meal challenge.
The range of a dataset is affected by both biologically significant inter-individual variability, and measurement or analytical errors. Range-scaling first centers the data matrix, followed by scaling each data point to the range of the data array. This transforms the dataset into one containing values bound by ± 50% of the mean. Centering reduces offsets of the data set, decreasing the variability introduced by large differences in magnitude, while shifting the scale of the resulting data set by the magnitude of the mean. Once variance is reduced, this cleaner dataset is more proficient at revealing trends that would likely have gone unnoticed in untreated data . When metabolites are analyzed together using a multivariate technique that identifies trends in data based on variance, often the variables that exhibit large changes under similar treatment or challenge conditions tend to dominate the results. In this study-insulin which is highly responsive to the glycemic index of meals could be considered such a metabolite, as opposed to leptin, which might not display such large variations in magnitude. Range-scaling controls for this, by placing each metabolite on a 0-100% scale of its own experimental response range . The data was nadir-corrected for each test day, and scaled to the range of the high glycemic index response in order to better highlight the individual's relative responses to the HGI and LGI challenges. Raw data and mean-centered data failed to show this discrimination. One drawback of range scaling is its sensitivity to outliers in the dataset , as is PCA, albeit a conservative multivariate analysis technique. When applying PCA, one must be cautious of outliers in the dataset that define the variance of the distribution instead of the inherent biological variation that is the target. It is also important to note that the PCA was performed on "auto-scaled" data (i.e. data transformed to unit variance) prior to the analysis, which allows each variable to have equal weight in the analysis . Performing the analysis on the adjusted-range scaled data without further transformation did reveal RG3 as distinct from RG1 and RG2 (p = 0.11), while RG2 subjects could not be excluded from RG1 using a test for statistical outliers. Mean centering the data improved the RG3 difference (p = 0.001), but increased the variance in the subjects making up the RG2 group. In this dataset, nadir-adjusted range scaling enabled the PCA to identify clustering of individuals with similar postprandial responses better than the raw dataset or the conventional range scaled dataset.
Circulating leptin exhibits a diurnal rhythm and increases about 4-h following a mixed meal . In humans, acute leptin responses to meals-high fat, high carbohydrate, and high GI-have been studied, yielding variable results. High carbohydrate as opposed to high fat meals have been shown to increase leptin secretion . Compared to a low GI meal, a high GI meal has been shown to result in lower postprandial leptin concentrations, but only over a 2-h period , a time frame too short to observe insulin-induced leptin secretion.
In vitro and animal model studies have suggested that the adipoinsular axis is a long term regulatory circuit that likely plays a role in the metabolic response to meals. The postprandial insulin response is modulated by circulating concentrations of leptin. The theory behind the adipo-insular axis posits that chronic leptin secretion, and its downstream insulin suppression is maintained in normal healthy individuals at an optimal level. Analyzing the insulin, glucose and leptin response to the meal challenges can give insight into the functioning of this axis. The adipo-insular axis can play an important role in establishing fasting and postprandial glycemia, and glucose clearance from circulation.
In this study, the response group designated RG1 (n = 18) is most likely representative of the general population, i.e. normal, healthy individuals with typical GI meal induced responses. On the other hand, RG2 displays the highest insulin AUC profile of the 3 groups associated with a lower leptin response. This relatively high insulin response is irrespective of the meal composition (seen both in HGI and LGI) since the 3 × 3 ANOVA indicated that RG2 had higher HGI insulin response than RG1 and RG3 challenged with either diet and at early- mid- and late-postprandial phases. The relatively elevated insulin response in RG2 could indicate reduced insulin sensitivity as compared to RG1, despite RG2 having a lower fat mass than RG1. Also, while RG1 and RG3 display different responses to the two meal challenges, RG2 displays similar insulin and glucose responses to both the meal challenges. This further is indicative of RG2's errant metabolic fine-tuning to meal composition.
In contrast, relative to RG1, RG3 displays a higher early postprandial leptin AUC, in association with higher postprandial glucose, and a similar insulin AUC response. Since RG3 insulin response is similar to or slightly lower than RG1, but ineffective at maintaining glycemia similar to that of RG1, could indicate either a reduced sensitivity or a higher postprandial suppression of insulin by leptin. It can be speculated that in RG3, the meal challenges elicited a higher leptin response, with stronger suppression of insulin secretion, resulting in lower circulating insulin, and higher glucose concentrations. In addition, this group has a significantly higher fat mass than RG2, although not different from RG1. Postprandial leptin secretion is likely a function of the insulin sensitivity of adipose tissue. Ideally, in response to a HGI meal, the higher insulin secretion will be regulated by leptin, but optimally so as to not disrupt glucose clearance. Thus, impairment in this axis can result in poor glycemic control as exhibited by RG3.
The number of subjects in each of the response groups (n = 3) is small. While individual variation or noise might explain this observation, the three clusters observed in the PCA scores plot of the transformed data could also indicate clustering of similar metabolic response profiles. Further mechanistic studies are necessary to evaluate if the identified groups are distinct phenotypes. Another limitation of the current study is that it is not possible to distinguish the influence of the run in diets on the postprandial meal challenge response that we have characterized. It is likely that the response groups that we have identified are a function of an acclimation to the 3 days of the run in diets, in addition to the meal challenge. Also, the subjects were challenged just once with each of the two meal challenges, and this is a limitation of this study.
Together, these findings suggest that the use of meal challenges and statistical exploratory tools could lead to metabolic phenotyping. This may enable better population sorting to identify health risks associated with specific postprandial phenotypic responses. Scaling, transformations, and non-parametric multivariate analyses have identified subsets from the study population of differing metabolic response profiles. Depending on how similar or dissimilar the individuals' response is to previous literature suggested postprandial metabolic events (which are likely also the majority of the normal, healthy population's responses) a metabolic fingerprint of this variation can be used to classify metabolic status at a finer gradient. Even the current clinically normal responses can be better characterized by using meal challenge responses and statistical tools to identify subgroups of metabolic characteristics that can form the basis of putative phenotypes. Such efforts may ultimately allow greater diagnostic finesse.
The authors would like to thank the Human Studies Support Staff of the Western Human Nutrition Research Center for assisting with the conduct of the study.
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