Abstract
As the incidence of obesity and type 2 diabetes (T2D) is occurring at a
younger age, studying adolescent nutrient metabolism can provide
insights on the development of T2D. Metabolic challenges, including an
oral glucose tolerance test (OGTT) can assess the effects of
perturbations in nutrient metabolism. Here, we present alterations in
the global metabolome in response to an OGTT, classifying the influence
of obesity and insulin resistance (IR) in adolescents that arrived at
the clinic fasted and in a random-fed state. Participants were
recruited as lean (n = 55, aged 8–17 years, BMI percentile 5–85%) and
overweight and obese (OVOB, n = 228, aged 8–17 years, BMI percentile ≥
85%). Untargeted metabolomics profiled 246 annotated metabolites in
plasma at t0 and t60 min during the OGTT. Our results suggest that
obesity and IR influence the switch from fatty acid (FA) to glucose
oxidation in response to the OGTT. Obesity was associated with a
blunted decline of acylcarnitines and fatty acid oxidation
intermediates. In females, metabolites from the Fasted and Random-Fed
OGTT were associated with HOMA-IR, including diacylglycerols,
leucine/isoleucine, acylcarnitines, and phosphocholines. Our results
indicate that at an early age, obesity and IR may influence the
metabolome dynamics in response to a glucose challenge.
Keywords: oral glucose tolerance test, metabolomics, fatty acids,
adolescents, acylcarnitines, obesity, insulin resistance, glucose
challenge
1. Introduction
As the prevalence of prediabetes and type 2 diabetes (T2D) in
adolescents is increasing [[42]1], it is vital to identify metabolic
dysfunction prior to disease onset to classify individual risk and
implement preventative strategies. Classically, an oral glucose
tolerance test (OGTT) diagnoses impaired glucose tolerance
(IGT)/prediabetes and T2D, by measuring the acute trajectory of glucose
in response to ingestion a 75-g glucose solution. Profiling the
metabolome in response to an OGTT can provide a deeper phenotyping of
T2D risk, expanding upon measuring traditional glucose levels to a
profile of metabolic response across multiple pathways. Several studies
have demonstrated the widespread response of the metabolome to an OGTT
in adults, observing alterations in proteolysis, lipolysis,
ketogenesis, and glycolysis in healthy individuals in response to the
challenge [[43]2,[44]3]. These studies suggest an acute increase of
glycolytic intermediates and rapid inhibition of lipolysis [[45]4] and
proteolysis [[46]5], as evidenced by decreases in amino acids, free
fatty acids (FFA), and acylcarnitine (AC) intermediates of
beta-oxidation.
Adolescent obesity is a strong risk factor for the development of T2D,
as a nationwide study observed that severe obesity increases the
incidence of T2D in early adulthood in both males and females [[47]6].
In fasted plasma, obesity is associated with alterations in the
metabolome, including elevations in lipids, branched chain amino acids
(BCAA), and aromatic amino acids [[48]7,[49]8,[50]9]. In adults, the
metabolome response to an OGTT was profiled in the context of obesity,
observing a delayed reduction of FFA and higher levels of amino acids,
including isoleucine and leucine, at 30 min post-OGTT in adults with
obesity [[51]10]. In adolescents, Müllner et al. [[52]11] identified
metabolites in response to an OGTT associated with obesity, including
elevations of AC 2:0, glutamate, alanine, and pyruvate, suggesting a
mismatch between beta-oxidation and TCA-cycle activity.
Pediatricians are more likely to order non-fasting tests compared with
a gold standard fasting OGTT because of the inconvenience of fasting
tests [[53]12]. As a result, a random plasma glucose sample and a 1-h
non-fasting glucose challenge have been assessed for the prediction of
T2D, offering strong discrimination for identifying prediabetes in
adolescents [[54]13]. Furthermore, evidence has suggested a tight link
between post-meal glucose levels and T2D complications [[55]14],
supporting the utility of a random-fed glucose challenge. Although the
fasted and non-fasted metabolome have established differences [[56]15],
it is uncertain how the metabolome response to an OGTT differs in
individuals arriving to the clinic fasted and in a random-fed state. In
addition, the influence of insulin resistance (IR) on the fasted vs.
random-fed metabolome response to an OGTT is unknown. At fasting,
multiple metabolic pathways are associated with IR and T2D including
BCAA metabolism [[57]16], beta oxidation [[58]17], and bile acids
[[59]18]. In adults arriving to the clinic fasted, Nowak et al.
observed correlations between AC response to the OGTT with a degree of
IR, as individuals with worsening IR had a blunted decline of
medium-chain ACs, products of beta-oxidation [[60]19].
Our main objectives were to assess the metabolome response to an OGTT
by (1) comparing the response to a fasted OGTT in participants with
overweight and obesity with that in controls and (2) determining
differential metabolite responses to a fasted vs. random-fed OGTT among
participants who were overweight and had obesity. Furthermore, we
sought to identify metabolite responses to an OGTT associated with
insulin resistance (as measured by HOMA-IR) in participants who were
overweight and had obesity at the Fasted and Random-Fed Visit,
considering the influence of sex. Results from these analyses
identified a panel of metabolites that can be profiled by fasted or
random glucose challenges with the potential to predict longitudinal
T2D risk.
2. Materials and Methods
2.1. Research Design
The cohort consisted of adolescents who were either overweight or obese
(OVOB) (BMI percentile ≥85th for sex/age [[61]20]) and lean adolescents
(BMI percentile <85th for sex/age [[62]20]), aged 8–17 years at time of
enrollment, recruited from primary care and pediatric specialty clinics
in southeast Michigan (2015–2018). Individuals were excluded if they
had known diabetes, use of medications known to affect glucose
metabolism (oral steroids, metformin, insulin, or sulfonylureas),
verbal report of pregnancy, or acute or chronic infections. Written
informed consent was obtained from the parent/guardian for all
participants and participants ≥10 years provided written assent. The
study was approved by the University of Michigan Institutional Review
Board.
Participants attended study visits at the Michigan Clinical Research
Unit, where a medical history, vital signs, anthropometrics, and
laboratory evaluation were performed. During the Fasted OGTT Challenge,
OVOB (n = 228) and lean participants (n = 55) arrived after an
overnight fast for a formal OGTT ([63]Figure 1), with fasting times
ranging from 9 h and 35 min to 19 h and 21 min ([64]Figure S1a). The
OGTT dosage consisted of 1.75 g glucose/kg body weight, with the
maximum dosage of 75 g glucose (Glucola, Fisherbrand) ([65]Figure S1b).
Blood samples were drawn at baseline (t0) and every 30 min (t30, t60,
t90, and t120 min) following the challenge. Glucose and insulin levels
were profiled from blood samples collected at t0, t30, t60, t90, and
t120 min following the OGTT. The untargeted metabolome was profiled
from blood samples collected at t0 and t60 min following the fasted
OGTT. Approximately a week later, OVOB individuals (n = 228) returned
for a glucose challenge in a random fed state, where participants were
not given instructions on the timing of their last meal (50-g,
Random-Fed OGTT challenge) ([66]Figure 1), with fasting times varying
from 5 min to 14 h and 16 min (n = 166 reported last mealtimes)
([67]Figure S1c). Our objective was to mimic a random OGTT that is
already performed in the clinic as a screening test for gestational
diabetes. The 50-g random OGTT has been previously shown have
reasonable discrimination for identifying children with prediabetes
[[68]13]. Glucose and insulin levels were profiled from blood samples
collected at t0 and t60 min following the OGTT. The untargeted
metabolome was profiled from blood samples collected at t0 and t60 min
following the Random-Fed OGTT.
Figure 1.
[69]Figure 1
[70]Open in a new tab
Study Design. Overweight and obese (OVOB) and lean participants were
recruited prior to the first visit (Fasted Visit), where an oral
glucose tolerance test (OGTT) (75 g) was administered in the fasted
state. The OVOB participants returned approximately a week later for an
OGTT (50 g) in a random fed state (Random-Fed Visit). Blood samples
were collected before and during the OGTT and used for glucose,
insulin, and untargeted metabolomics assays. Mean age and range of ages
reported (years).
2.2. Laboratory Measurements
The Michigan Diabetes Research Center (MDRC, Ann Arbor, USA) laboratory
performed glucose homeostasis assays. Glucose was measured using the
glucose hexokinase method and run on a Randox rX Daytona chemistry
analyzer (Randox Laboratories Limited, Crumlin, UK). Insulin was
profiled using a double-antibody radioimmunoassay [[71]21]. The
homeostatic model assessment for insulin resistance (HOMA-IR) utilized
glucose and insulin measurement to estimate insulin resistance and beta
cell function [[72]22]. Glucose area under the curve (AUC) was
estimated between t0 and t120 min by integrating, using the trapezoid
method and stopping when glucose values dropped below baseline
(GraphPad PrismVersion 8.4.3). Insulin AUC was estimated between t0 and
t120 by integrating using the trapezoid method (GraphPad PrismVersion
8.4.3). Hemoglobin A1c (HbA1c) was determined using a Tosoh G7 HPLC
Analyzer (Tosoh Biosciences Inc., San Francisco, CA, USA). Biologically
implausible outliers were removed (n = 1 removed from t0 insulin
value). Impaired fasting glucose (IFG) was defined as fasting plasma
glucose ≥ 100 mg/dL; impaired glucose tolerance (IGT) was defined as
the 2-h glucose ≥ 140 mg/dL; and prediabetes was defined as IFG, IGT,
or HbA1c between 5.7–6.4% [[73]23].
2.3. Untargeted Metabolomics
Untargeted metabolomics analyses were performed by the Michigan
Regional Comprehensive Metabolomics Resource Core (MRC^2) (Ann Arbor,
MI, USA). Metabolites were extracted from plasma samples using a
solvent of methanol, acetonitrile, and acetone (1:1:1) including
internal standards (100 mL extraction solvent and 4 mL internal
standards). Samples were reconstituted with a solvent containing
methanol and H[2]0 (2:98). Untargeted metabolomics was performed on an
Agilent system consisting of an Infinity Lab II UPLC coupled with a
6545 qTOF mass spectrometer (Agilent Technologies, Santa Clara, CA)
using a JetStream electrospray ionization source. The eluent was
analyzed in both positive and negative ion mode electrospray
ionization. Chromatographic peaks, representative of metabolite
features, were detected using a modified version of existing commercial
software (Agilent MassHunter Qualitative Analysis). Data normalization
accounted for drift removal within and between batches by utilizing
pooled reference samples that were analyzed within each batch using the
Systematic Error Removal using Random Forest (SERRF) method.
Metabolites were identified via comparing their MS/MS spectra to
authentic standards, purchased internal or external standards ran on
the same instrument. For this analysis, annotated metabolites (n = 246)
were selected. Missing peak intensities were imputed by K-nearest
neighbor (K = 5) in metabolites with ≥70% detection across samples. R
package “impute” was used for imputation. Metabolites with less than
70% detection across samples were removed. Biologically implausible
metabolite peak intensity values were removed.
2.4. Statistical Analyses
Descriptive statistics were computed for categorical variables
(Pearson’s chi-square test) and continuous variables (unpaired
Students’ t-test), stratified by OVOB and lean. Sex-stratified analyses
were evaluated. Main analysis objectives are outlined in [74]Figure S2.
Peak intensities (PI) of metabolites were utilized for statistical
analyses.
Metabolite differences were identified between OVOB and lean at the
Fasted Visit ([75]Figure S2a). At t0, linear regression models were run
assessing the effect of group (ß[group], OVOB or lean) on metabolite
levels (log2 and standardized), adjusting for sex and age at the Fasted
Visit.
[MATH: Metabolite=ßo+ßgroupX+ßageX+ßsexX+ϵi :MATH]
(1)
Differential metabolites were identified using an adjusted p-value
(false discovery rate [FDR] < 0.1) [[76]24]. Positive ß[group] values
represent elevations in OVOB and negative ß[group] values represent
lower in OVOB. Differential metabolites were selected for metabolite
set enrichment analysis (MSEA) [[77]25] to identify biologically
meaningful pathways associated with BMI in the metabolomics data. Human
Metabolome Database (HMDB) IDs were mapped to 58 of the 66 differential
metabolites at t0. Pathway enrichment analysis used the Small Molecular
Pathway Database (SMPDB), which includes 99 metabolite sets based on
normal human metabolic pathways. Over Representation Analyses (ORA)
with the hypergeometric test was used to determine if metabolite
pathways are represented more than expected by chance, denoting
significance using a one-tailed p-value (unadjusted and FDR reported).
Enrichment analyses were run through Metaboanalyst 4.0 [[78]26]. At t0,
sex differences in the metabolome were considered using unpaired
Student’s t-test ([79]Figure S2a). Differential metabolites were
identified using an adjusted p-value (FDR < 0.05).
Paired t-tests distinguished metabolites that significantly differed
between t0 and t60 in each group and state (lean-Fasted, OVOB-Fasted,
and OVOB-Random-Fed) using untransformed PI (FDR < 0.05) ([80]Figure
S2b). Fold changes were calculated to represent metabolite response
using log2(t60 PI/t0 PI). To assess if the metabolite response to the
OGTT was associated with BMI group, unpaired t-tests distinguished
variations in metabolite fold changes between OVOB and lean at the
Fasted Visit (FDR < 0.1) ([81]Figure S2c).
OVOB participants returned to the clinic for a Random-Fasted OGTT
Challenge. Differences in glucose and insulin levels between the Fasted
and Random-Fed OGTT Challenge were computed (paired t-test). Linear
regression models were run separately at t0 and t60 assessing the
influence of state (ß[state], Fasted or Random-Fed) on metabolite
levels (log2 and standardized across state at each time), adjusting for
sex and age at the Fasted Visit ([82]Figure S2d),
[MATH: Metabolite=ßo+ßstateX+ßageX+ßsexX+ϵi :MATH]
(2)
Differential metabolites were identified using an adjusted p-value (FDR
< 0.1). Positive ß[state] values represent elevated in OVOB-Fasted and
negative ß[state] values represent elevated in OVOB-Random-Fed.
In OVOB individuals, metabolites were identified that are associated
with IR, measured by HOMA-IR ([83]Figure S2e). Sex stratified models
were run considering differences in glucose homeostasis measures by
sex. Linear regression models were run separately at the Fasted Visit
(t0, t60, fold change) and the Random-Fed Visit (t0, t60, fold change)
on metabolite levels (log2 and standardized across each time and
state), adjusting for age at the Fasted Visit.
[MATH: HOMA−IR=ßo+ßmetaboliteX+ßageX+ϵi :MATH]
(3)
Differential metabolites were identified using an adjusted p-value (FDR
< 0.1). All statistical analyses were performed in R version 4.0.
3. Results
3.1. Participant Characteristics
Descriptive characteristics of the study population from the Fasted
OGTT Challenge are reported in [84]Table 1. Most of the cohort was
Caucasian and non-Hispanic and included more females (n = 160) than
males (n = 123), with similar distributions of sex, race, and ethnicity
between OVOB and lean groups. No group trend was observed between IGT,
IFG, and prediabetes status, with 15% of OVOB and 12% of lean having
prediabetes. Three of the six lean participants who classified as
prediabetic had a BMI percentile of 84%, potentially explaining why
group trends were not observed. No group differences were observed in
glucose levels during the OGTT ([85]Figure 2) or glucose response
measured by AUC glucose. Group differences in the insulin response to
the OGTT were observed, with a larger insulin response, measured by AUC
insulin, and higher insulin levels beginning at t0 and continuing
through the completion of blood draws in OVOB ([86]Figure 2). Sex
differences were observed with higher levels of glucose t120 (p =
0.024), insulin t90 (p = 0.002), insulin t120 (p < 0.001), and BMI
percentile (p = 0.007) in females and higher levels of glucose t30 (p =
0.003) in males ([87]Table S1). Females had a larger insulin response
to the OGTT than males, measured by insulin AUC (p = 0.016) ([88]Figure
S3), suggesting a small decrease in insulin sensitivity.
Table 1.
Characteristics of study participants at the Fasted Visit, stratified
by weight group. Significance denoted with unadjusted p-value < 0.05
(bolded).
Categorical Variables OVOB Lean p -Value ^1
n (%) n (%)
sex
Male 97 (43%) 26 (47%) 0.5254
Female 131 (57%) 29 (53%)
race
Asian/Pacific Islander 4 (2%) 4 (7%) 0.1745
African American/Black 59 (26%) 12 (22%)
White 135 (59%) 32 (58%)
more than one race 19 (8%) 6 (11%)
did not wish to report 11 (5%) 1 (2%)
ethnicity
Hispanic 18 (8%) 5 (9%) 0.7707
non-Hispanic 210 (92%) 50 (91%)
abnormal 2-hr plasma glucose (≥140 mg/dL)
Yes 16 (7%) 3 (5%) 0.6776
No 212 (93%) 52 (95%)
abnormal fasting plasma glucose (≥100 mg/dL)
Yes 8 (4%) 1 (2%) 0.5213
No 220 (96%) 54 (98%)
ADA prediabetes (FPG≥ 100 mg/dL or 2-hr PG ≥ 140 mg/dL or HbA1c ≥ 5.7%)
Yes 35 (15%) 6 (12%) 0.4009
No 193 (85%) 49 (88%)
Continuous Variables OVOB Lean p -Value ^2
Mean (SD) Mean (SD)
age (years) 12.9 (2.5) 13.0 (2.6) 0.7301
BMI percentile 95 (4) 59 (27) 7.52 ^−14
HOMA-IR 5.13 (2.99) 2.80 (1.21) ^3 4.53 ^−8
HbA1c 5.2 (0.3) 5.1 (0.3) ^3 0.2082
fast time (hours) 14.0 (1.3) 14.1 (1.4) 0.7606
fasting OGTT response
glucose (t0) (mg/dL) 84 (8) 85 (8) 0.7475
glucose (t30) (mg/dL) 126 (22) 132 (25) 0.1119
glucose (t60) (mg/dL) 112 (29) 117 (26) 0.2182
glucose (t90) (mg/dL) 107 (26) 106 (21) 0.6263
glucose (t120) (mg/dL) 102 (24) 98 (22) 0.2562
insulin (t0) (µU/mL) 24 (14) 13 (5) ^3 1.55 × 10^−8
insulin (t30) (µU/mL) 194 (132) 112 (72) 4.09 × 10^−9
insulin (t60) (µU/mL) 156 (117) 87 (53) 7.04 × 10^−10
insulin (t90) (µU/mL) 145 (126) 80 (56) 2.69 × 10^−8
insulin (t120) (µU/mL) 133 (117) 65 (57) 2.42 × 10^−9
AUC glucose 3121 (2112) 3294 (1711) 0.5228
AUC insulin 17,223 (11,206) 9648 (5468) 1.33 × 10^−11
[89]Open in a new tab
^1 Represents Pearson’s chi-square test for categorical variables. ^2
Represents unpaired t-test for continuous variables. ^3 n = 54.
Figure 2.
[90]Figure 2
[91]Open in a new tab
Kinetics of blood glucose and insulin response to the glucose test at
the Fasted Visit. OVOB and lean participants arrived fasted prior to
the consumption of the glucose challenge. Blood glucose and insulin
were profiled before and during the OGTT. Mean values reported for lean
(dark pink dash) and OVOB (dark slate gray dash). OVOB, overweight and
obese.
3.2. Influence of Obesity and Sex on the Fasting Metabolome
At the Fasted Visit, 66 metabolites were significantly associated with
BMI group (OVOB vs. lean) at t0, adjusting for sex and age ([92]Table
S2). Select differential metabolites by BMI group are shown in
[93]Figure 3. Short-chain ACs were elevated in OVOB compared to lean,
including AC 3:0, 5:0, and 5:0-OH, representing alterations in BCAA
metabolism. No differences were observed in BCAAs, potentially because
our analysis did not account for muscle mass differences between OVOB
and lean [[94]7]. The aromatic amino acid tryptophan and its metabolite
kynurenine was significantly elevated in OVOB, in contrast to the
literature that consistently shows phenylalanine and tyrosine being
elevated with obesity [[95]27]. The biomarker 3-indolepropionate, a
tryptophan metabolite that has been associated with a reduced
likelihood of developing T2D [[96]28], was significantly elevated in
lean participants. Very long-chain FAs were elevated in lean at t0,
with no BMI group differences observed in long-chain FAs. In lean
participants, there were higher levels of beta-oxidation AC
intermediates (AC 10:0, AC12:0, AC 14:1, AC 14:2, AC 16:0, and AC 18:2)
and omega oxidation dicarboxylic FAs (FA 10:0-COOH, FA 11:0-COOH, and
FA 16:0-COOH), potentially suggesting increased flux through FA
oxidation pathways in lean individuals at fasting. Several
lysophospholipids were elevated in lean compared to OVOB at t0,
including lysophosphocholine (LPC) 16:0, LPC 17:0, LPC 18:1, LPC 18:2,
LPC 20:0, and LPE 18:2, which parallels studies in pediatrics [[97]8]
and adults [[98]29]. Multiple lipid species, including diglycerides
(DG), phosphocholine (PC), and sphingomyelin (SM), were elevated in
OVOB individuals, due to elevation in fat mass and consistent with
previous observations [[99]7]. Chenodeoxycholate (CDCA), a primary bile
acid synthesized in the liver, was elevated in OVOB at t0. Multiple
conjugated bile acids were differential between BMI groups at t0,
including glycocholate, taurocholate, and tauro-alpha/beta-muricholate
were elevated in lean and hyodeoxycholate was elevated in OVOB.
Figure 3.
[100]Figure 3
[101]Open in a new tab
Differential metabolites between OVOB and lean participants during the
Fasted OGTT Challenge. Linear regression analyses identified
metabolites associated with the OVOB and lean groups at t0 (ß[group]),
adjusting for sex and age (FDR < 0.1, 66 metabolites). Selected
differential metabolites represent metabolic pathways associated with
obesity at t0. Normalized peak intensities and standard deviations are
reported.
Metabolites significantly associated with BMI group at t0 (FDR < 0.1)
were selected for metabolite set enrichment analysis (MSEA) to identify
biological pathways enriched with obesity ([102]Figure S4). No pathways
reached an adjusted significance threshold, although Beta Oxidation of
Very Long Chain Fatty Acids was trending towards significantly enriched
at t0 (unadjusted p = 0.07).
As sex is associated with BMI percentile and glucose homeostasis
measures within this cohort ([103]Table S1), metabolites were
identified that were differential by sex at t0 during the Fasted Visit.
Using unpaired t-tests, 40 metabolites were associated with sex (FDR <
0.05), with higher levels of FA, SM, and PCs in females and higher
levels of short-chain ACs and amino acid metabolites (e.g., kynurenine
and 3-methyl-2-oxovalerate) in males ([104]Table S3). These
associations emphasize variations in fat and muscle mass in adolescents
during puberty, as previous findings detail differential metabolites
elevated in obese males and females [[105]7]. Sex-stratification will
be considered in additional analyses.
3.3. Metabolome Response to the OGTT in OVOB and Lean Participants
The response of the metabolome to an OGTT is represented in [106]Figure
4, stratifying individuals by state (Fasted and Random-Fed), time (t0
and t60), and group (lean and OVOB). Metabolites peak intensities were
centered across all samples and metabolites were grouped using
hierarchical clustering to identify groups of metabolites with similar
dynamics during the OGTT. Significant changes in metabolite levels from
t0 to t60 are reported, including alterations in 68% of metabolites in
lean during the Fasted OGTT Challenge ([107]Figure S5a), 84% of
metabolites in OVOB during the Fasted OGTT Challenge ([108]Figure S5b),
and 77% of metabolites in OVOB during the Random-Fed OGTT Challenge
([109]Figure S5c) (FDR < 0.05). Most metabolites decreased in response
to the OGTT, which may be attributed to the high abundance of lipids
within the annotated metabolites in this dataset. Metabolite classes
that consistently decreased in response to the OGTT include medium- and
long-chain ACs, FFA, and lipids, such as SMs, PCs, and DGs. The
metabolite with the largest increase was hippurate, increasing at
approximately 4 log2FC in each group. As reported by Shaham et al.
[[110]2], this likely reflects the metabolism of the preservative
benzoic acid, found in the glucola beverage used for the OGTT
[[111]30]. All paired t-tests are reported in [112]Table S4.
Figure 4.
[113]Figure 4
[114]Open in a new tab
Dynamic response of the metabolome to an oral glucose tolerance test.
Heatmap of normalized peak intensity for individual metabolites (p =
246) (mean 0, standard deviation 1). Metabolites are grouped by
hierarchical clustering (columns). Subjects are ordered by group (OVOB
and lean), time point after the OGTT (t0 and t60), and state (Fasted
and Random-Fed) (rows). OVOB, overweight and obese.
Using a fold change, differences in metabolite response between BMI
groups at the Fasted Visit was explored, identifying 15 significant
metabolites ([115]Figure S6). Five medium- and long-chain ACs (AC 8:0,
10:1, 12:1, 14:2, and 16:1), two fatty acid oxidation intermediates (FA
10:0-COOH and FA 16:0-COOH), and six FAs (FA 12:0, 12:2, 14:2, 20:0,
20:1, and 22:1) had a larger decrease in lean than OVOB. These results
suggest a more robust decrease in lipolysis and beta-oxidation in lean
in response to the glucose challenge.
3.4. Metabolome Differences between the Fasted and Random-Fed OGTT Challenge
in OVOB
OVOB participants returned to the clinic for a random-fed state OGTT
challenge. At t0, random-fed OVOB participants had significantly higher
glucose (p = 0.0052) and insulin levels (p = 3.28^−^20) than
individuals in the fasted state ([116]Table S5). At t60, random-fed
participants had significantly lower levels of glucose (p = 1.40^−21)
than individuals in the Fasted OGTT Challenge, although their insulin
levels were not significantly different (p = 0.657). These results
suggest individuals arriving to the clinic for the OGTT in a variety of
fed states have a primed insulin response, enabling the rapid response
to the glucose load. Metabolites were identified that were associated
with arriving to the OGTT Challenge fasted or random-fed at t0 and t60.
At t0, 155 metabolites (63% profiled) and at t60, 122 metabolites (49%
profiled) differed between OVOB-Fasted and OVOB-Random-Fed ([117]Table
S6). Grouping by super pathway, metabolites are represented indicating
direction of association (ß[state]) and significance (−log10 [p-value])
for t0 ([118]Figure 5A) and t60 ([119]Figure 5B).
Figure 5.
[120]Figure 5
[121]Open in a new tab
Differential metabolites between Fasted and Random-Fed in OVOB. Linear
regression analyses identified metabolites associated with OVOB-Fasted
and OVOB-Random-Fed (ßstate) at (A) t0 and (B) t60. On the top of the
plots, metabolites are reported that are higher in OVOB-Fasted (ßstate
< 0), with position indicated by −log10 (p-value). On the bottom of the
plots, metabolites are reported that are lower in OVOB-Fasted (ßstate >
0), with position indicated by −log10 (p-value). Metabolites are listed
in the same order in 5A and 5B. Colors indicate metabolite class.
Horizontal lines (dotted) signify FDR = 0.1. Several distinguishing
metabolite names are listed. Age and sex are included in the model.
At t0, almost all the FAs profiled (97%) were significantly higher at
the Fasted Visit compared to the Random-Fed Visit, as expected,
representing mobilization of energy substances (anabolism) from adipose
tissue during fasting. In parallel, additional lipids were higher at
the Fasted Visit, including all SMs and 94% of the profiled PCs (15
PCs). Lysophospholipids varied in their associations with state, with
five higher in the fasted state (LPC 16:0, LPC 17:0, LPC 18:1, LPC
20:0, LPC 23:0) and two higher in the random state (LPC 18:2 and LPE
18:2). Most medium- and long-chain ACs were higher at fasting,
paralleling the FA levels and representing increases in beta-oxidation
at fasting [[122]31]. Interestingly, three of the four dicarboxylic
fatty acids profiled were higher at the Random-Fed state at (FA
9:0-COOH, FA 10:0-COOH, and FA 11:0-COOH), suggesting increased omega
oxidation related to the fed state, perhaps due to increased
carbohydrate oxidation reducing the capacity to oxidize FA still
entering the system. Many amino acids were higher at the Random-Fed
Visit, including histidine, isoleucine/leucine, methionine, proline,
tryptophan, tyrosine, and valine. Several short-chain ACs were higher
in at the Random-Fed Visit, indicating increased BCAA metabolism. Bile
acids, including primary, secondary, and conjugate bile acids, were
higher at the Random-Fed Visit, representing bile acid and gut hormone
response to a meal [[123]32].
At t60, the metabolome represents the switch from an anabolic to a
catabolic state in response to the glucose challenge. A portion of the
differential metabolites at t0 normalized between OVOB-Fasted and
OVOB-Random-Fed, including most of the differential lipids (SMs and
PCs). At t60, 75% of the FAs and 78% of the medium- and long-chain ACs
remain higher at the Fasted Visit. Fatty acid oxidation intermediates
varied in their association with state at t60, with several higher in
the Fasted group (FA 9:0-COOH, FA 12:0-OH, FA 12:0-NH2, and FA 14:0-OH)
and several higher in the Random-Fed group (FA 10:0-OH and FA
10:0-COOH). Seven of the twelve lysophospholipids profiled were higher
at the Random-Fed Visit at t60. Amino acids and bile acids remained
higher in Random-Fed at t60.
3.5. Sex-Specific Associations of Metabolite Trajectories with Insulin
Resistance in Participants with Overweight and Obesity
In OVOB, metabolites were identified from the fasted (t0, t60, and fold
change) and random-fed (t0, t60, and fold change) glucose challenges
that were associated with IR, measured by HOMA-IR ([124]Figure S7). Sex
stratified linear models were used considering the differential glucose
and insulin responses between males and females ([125]Table S1). All
results are reported in [126]Table S7.
In males across all visits and time points, no metabolites were
significantly associated with HOMA-IR. The metabolite mesobilirubinogen
was trending towards positive association with HOMA-IR at t60 in the
Fasted and Random-Fed visit (FDR < 0.2).
In females, metabolites within multiple pathways were correlated with
HOMA-IR at t0 and t60 during the Fasted and Random-Fed visits
([127]Table 2). Consistently, diacylglycerides (DG 32:0, DG 32:1, DG
34:1, and DG 34:2) and the nucleotide urate were positively associated
with HOMA-IR, the latter supported by previous work establishing the
connection between hyperuricemia and IR [[128]33]. More specifically,
at t60 during the fasted visit, several amino acid metabolites
(isoleucine/leucine, AC 5:0-OH, proline, and glutamate) and lipids (DGs
and PCs) were positively associated with HOMA-IR. These results expand
upon previous work [[129]3], which found a blunted decrease in levels
of BCAAs and other amino acid metabolites in subjects with IR.
Represented by the fold change from the fasted visit, medium- and
long-chain ACs were positively associated with HOMA-IR, demonstrating
that a blunted decline in ACs in response to a glucose challenge is
associated with IR, which parallels the decline in FA following the
glucose challenge. Comparing the significant metabolites at Fasted t60
vs. the fold change during the fasted visit, only DG 32:0 and PC 32:1
were significantly associated with HOMA-IR in both models. Although
less metabolites were significantly associated with HOMA-IR during the
Random-Fed Visit, at t60, DGs, monoglycerides, glutamate, and urate
exhibited positive associations. No significant associations were
observed using the fold change from the Random-Fed.
Table 2.
Metabolites associated with HOMA-IR in females with overweight and
obesity at the Fasted and Random-Fed Visit. Beta coefficients and
standard errors from linear regression models are reported, adjusting
for age at the Fasted Visit (FDR < 0.1).
Metabolite Pathway Fasted t0 Fasted t60 Fasted Fold Change Random-Fed
t0 Random-Fed t60
AC 12:0 acylcarnitine 2.0 ± 0.3
AC 12:1 acylcarnitine 2.7 ± 0.5
AC 14:0 acylcarnitine 2.3 ± 0.8
AC 16:0 acylcarnitine 2.6 ± 0.6
AC 16:1 acylcarnitine 1.3 ± 0.7
AC 18:0 acylcarnitine 1.6 ± 0.2
AC 5:0-OH acylcarnitine 0.9 ± 0.3
AC 5:1 acylcarnitine 1.5 ± 1.6
AC 6:0 acylcarnitine 1.7 ± 0.6
gamma-glutamyltyrosine amino acid 0.8 ± 0.2 0.9 ± 0.3 0.9 ± 0.3
Glu-Phe amino acid 0.9 ± 0.2
glutamate amino acid 0.7 ± 0.3 0.9 ± 0.3
indole-3-methyl acetate amino acid 0.7 ± 0.3
L-gamma-glutamylisoleucine amino acid 0.8 ± 0.3
Leu-Ile amino acid 0.7 ± 0.5 0.9 ± 0.2
leucine+isoleucine amino acid 0.7 ± 0.3
N-acetylphenylalanine amino acid 0.7 ± 0.3
Phe-Phe amino acid −0.7 ± 0.3
Phe-Trp amino acid 0.7 ± 0.3
pipecolate amino acid −0.6 ± 1.1
proline amino acid 0.7 ± 0.3
cholate bile acid 0.8 ± 0.3
hyocholate bile acid 0.8 ± 0.3
indole-3-lactate carbohydrate 0.7 ± 0.3
caffeine exogenous 1.7 ± 0.6
FA 18:4 fatty acid 0.8 ± 0.3
FA 20:3 fatty acid 1.1 ± 0.3
FA 22:1 fatty acid 1.9 ± 0.5
3-hydroxyphenyl-valerate fatty acid intermediate 0.9 ± 0.6
DG 32:0 lipid 1.2 ± 0.2 1.2 ± 0.2 2.2 ± 0.4 1.1 ± 0.2 1.0 ± 0.2
DG 32:1 lipid 1.0 ± 0.2 1.1 ± 0.2 0.9 ± 0.2 0.8 ± 0.2
DG 34:1 lipid 1.0 ± 0.2 0.9 ± 0.2 0.9 ± 0.2
DG 34:2 lipid 1.1 ± 0.2 1.0 ± 0.2 0.9 ± 0.2 0.8 ± 0.2
DG 36:2 lipid 0.7 ± 0.2
DG 36:3 lipid 0.7 ± 0.3
MG 14:0 lipid 1.0 ± 0.2
MG 16:0 lipid 1.2 ± 0.3
MG 18:1 lipid 0.8 ± 0.2 0.9 ± 0.2 0.7 ± 0.3
LPC 16:0 lipid 1.1 ± 1.2
LPC 18:2 lipid 0.8 ± 1.7
PC 32:1 lipid 0.8 ± 0.2 1.0 ± 0.4
PC 34:3 lipid 0.7 ± 0.3
PC 34:4 lipid 0.7 ± 0.3
N2,N2-dimethylguanosine nucleotide 0.7 ± 0.3
urate nucleotide 0.8 ± 0.2 1.0 ± 0.2 1.0 ± 0.2 0.9 ± 0.2
[130]Open in a new tab
4. Discussion
In the present study, we have characterized the metabolome response
during an OGTT in OVOB (n = 228) and lean adolescents (n = 55). We
identified metabolites that change significantly during the glucose
challenge, highlighting the switch from FA to glucose oxidation at 60
min during the OGTT. We classified differential metabolites by BMI
status at baseline and during the OGTT, suggesting that at an early
age, obesity and its metabolic consequences may influence the
metabolome dynamics in response to a challenge. Subsequently,
overweight and obese adolescents returned to the clinic for a
random-fed glucose challenge to compare the fasted and random-fed
metabolome to degree of IR, and significant associations were found in
female participants but not in males. Our results are the first study
to deeply assess the fasted and random-fed metabolome response in
adolescents and will be used for future analyses predicting the
longitudinal risk of prediabetes development within the cohort.
4.1. Lipids, Fatty Acids, and Acylcarnitines
In response to the glucose challenge, most lipids, FAs, and FA
oxidation intermediates, including hydroxyl-FAs, dicarboxylic FAs, and
acylcarnitines, decreased. As observed in previous studies in adults,
these alterations in the metabolome are reflective of the switch from
FA oxidation to glucose oxidation and fat storage during the OGTT
[[131]34]. Acylcarnitines, biomarkers of mitochondrial beta-oxidation,
reflecting the relative utilization of FA to carbohydrate [[132]34] and
reflect the degree of IR [[133]35]. At the Fasted-Visit, several
medium- and long-chain ACs and dicarboxylic FAs were lower in OVOB
participants ([134]Figure 3) and had a blunted decline in OVOB
participants ([135]Figure S6). Furthermore, in OVOB females at the
fasted visit, the fold change of eight ACs (AC 5:1, 6:0, 12:0, 12:1,
14:0, 16:0, 16:1, and 18:0) was positively associated with HOMA-IR (FDR
< 0.1). These results suggest that starting at a young age, obesity and
IR influences metabolic flexibility in response to a glucose load
[[136]36]. In parallel, Nowak et al. [[137]19] in a group of older
males observed that AC 10:0 and AC 12:0 exhibited a smaller decline at
30 min in response to an OGTT, suggesting that the sustained elevation
of the AC may directly impair insulin sensitivity. Our findings suggest
that during adolescence, the prolonged insulin response ([138]Figure 2)
in OVOB females is also associated with insulin resistance.
At the fasted visit at baseline, lipids, including DGs and SMs,
exhibited positive associations with obesity ([139]Table S3), supported
by previous analyses [[140]7]. A primary question in these studies is
whether the non-fasted state could be used to identify changes in
metabolism in relation to IR. In females, at both the Fasted and
Random-Fed Visit at t0 and t60, diacylglycerides (DG 32:0, DG 32:1, DG
34:1, and DG 34:2) were positively associated with HOMA-IR, suggesting
that independent of fed-state, these lipids may provide predictive
ability for the progression of IR and T2D.
4.2. Amino Acids
In lean adolescents, approximately half of the profiled amino acids and
their metabolites decreased in response to the OGTT, including
leucine/isoleucine, methionine, histidine, serine, and glutamate,
representing a decrease in proteolysis [[141]2,[142]3]. Deviations in
amino acids response were observed between OVOB and lean, potentially
due to the elevated insulin response within OVOB ([143]Table 1,
[144]Figure 2). The larger insulin response in OVOB may act on skeletal
muscles to decrease protein degradation [[145]37], as evidenced by
significant decreases in amino acids, including trypthophan, lysine,
and glutamine, in only OVOB adolescents at t60. Comparing the
metabolome response to an OGTT in 14 obese and 6 lean adults,
Geidenstam et al. [[146]10] observed at 30 min post-OGTT that
asparagine, glutamate, taurine, tyrosine, and leucine/isoleucine
increased in obese adults, which was absent in lean. This effect was
not evident in our cohort, potentially because the metabolome was
profiled at a later timepoint (t60).
At t60 during the fasted visit, several amino acids
(leucine/isoleucine, glutamate, and proline) and amino acid metabolites
(gamma-glutamyltyrosine, L-gamma-glutamylisoleucine and
N-acetylphenylalanine) were associated with IR in females ([147]Table
2). Without stratifying by sex, Mullner et al. observed levels of BCAAs
associated with a heightened insulin response [[148]11]. Frequent
inconsistencies in the association between BCAA and IR in adolescents
are observed [[149]7,[150]38], due to study population differences in
age, sexual maturation, and degree of IR, representing major challenges
in paediatric prediction studies. Glutamate was associated with HOMA-IR
in the Fasted and Random-Fed OGTT at t0 and t60. In a rate-limiting TCA
cycle step, alpha-ketoglutarate is converted to glutamate-by-glutamate
dehydrogenase, allowing for a rescue pathway of excess TCA substrate.
Elevated levels of glutamate have been associated with an increased
risk of T2D [[151]39], as our results highlight the sensitivity between
IR and TCA cycle overload in females. Overall, the t60 metabolome at
the Fasted Visit had the largest number of significant amino acids with
IR in females, suggesting a lack of suppression of proteolysis with
reduced insulin sensitivity.
4.3. Bile Acids
Paralleling previous studies [[152]2,[153]34], we observed a dramatic
increase in several bile acids in response to the glucose challenge,
including glycocholate, glycodeoxycholate, glycohyocholate, and
taurocholate. In response to a meal, the gallbladder releases bile into
the small intestine, stimulated by gastric filling and the intestinal
hormone cholecystokinin (CCK). Post intestinal absorption and transport
to the liver, it is estimated that 10–30% of bile acids reach systemic
circulation [[154]40]. Our results and others [[155]2,[156]34] have
suggested that a bolus of glucose stimulates the release of bile acids
in the gallbladder, supported by Liddle et al. finding that glucose
ingesting stimulates CCK production [[157]41]. Previous work has
suggested a link between bile acid secretion and metabolism with
obesity and IR [[158]42]. At baseline, during the Fasted Visit, several
primary and secondary bile acids were associated with obesity,
including positive associations with chenodeoxycholate,
hyodeoxycholate, and deoxycholate and inverse associations with
glycocholate, glycohyocholate, taurocholate, and
tauro-alpha/beta-muricholate. Furthermore, at t60 during the Fasted
Visit, cholate and hyocholate were positively associated with IR in
females ([159]Table 2). Therefore, a blunted decrease of certain bile
acids may be associated with insulin resistance and metabolic
dysfunction.
4.4. Conclustions and Future Directions
The metabolome response to an OGTT may be associated with IR in a
sex-specific manner, due to the observed differences in insulin
response to an OGTT in adolescent males and females. In healthy and
metabolically unhealthy youth, insulin sensitivity decreases during
puberty [[160]43]. Furthermore, in a cohort of healthy children and
adolescents, girls in late puberty (Tanner’s Stage 4 or 5) have higher
insulin levels than boys [[161]44]. The sexual dimorphism observed in
late puberty is due, in part, to a higher growth hormone secretion in
pubertal girls [[162]45]. In our cohort, we observed higher levels of
glucose t120, insulin t90, and insulin t120 in females, suggesting that
females have a larger insulin response to the OGTT than males. Future
analyses must be conducted to determine if the associations between the
metabolome across visits and HOMA-IR in females is attributed to IR
shifts during puberty or the onset of metabolic dysfunction and
prediabetes.
The metabolome was comprehensively profiled using a liquid
chromatography/mass spectrometry-based platform, generating
approximately 250 metabolites. Our study utilized a well-powered sample
size, strongly complementing and elaborating on the only other study
assessing the metabolome response to an OGTT in adolescents [[163]11]
by incorporating both a fasted and a random-fed visit in the OVOB
participants. Our results emphasize the potential of analyzing the
metabolome response in a random glucose challenge for the prediction of
metabolic dysfunction, particularly in females. The results from this
study emphasize that the switch from FA to glucose metabolism in
response to a glucose challenge is associated with obesity and insulin
resistance. Future work will collect plasma samples in response to a
glucose challenge at more timepoints, such as 30 min, to assess more
subtle changes in the metabolites, similarly to Zhao et al. [[164]34].
Our study design presented two limitations regarding the Random-Fed
Visit. Firstly, we only recruited OVOB participants for the visit, not
allowing for a comparison between lean and OVOB. Secondly, we desired
to simulate a random glucose challenge that is typically performed in
practice for women being screened for gestational diabetes using 50-g
of glucose. The differences in grams of glucose solution administered
between the Fasted and Random-Fed Visit create challenges in the direct
comparison of the metabolome response. Our priority was to replicate
what is being practiced in the clinic. Future work should compare the
metabolome response in different fed-states utilizing the same glucose
load. A bioinformatic limitation in the study was the inability to map
individual significant metabolites to biological pathways using MSEA,
due to many metabolites with the pathways not being profiled in the
untargeted metabolomics platform and the lack of HMDBs for metabolites
that were significant. Future directions will incorporate a partial
correlation-based approach [[165]46] to assess alterations in the
relationship of metabolites at the fasted random-fed visit and if
subnetworks of metabolites are associated with insulin resistance
cross-sectionally and longitudinally.
Our results emphasize the utility of profiling multiple metabolic
pathways outside glucose metabolism in understanding the associations
between obesity, IR, and the response to a glucose challenge in
adolescents. Classifying the metabolism of lipids, amino acids, and
fatty acids, rather than solely glucose metabolism, deepens the
understanding of the pathophysiology of insulin resistance in
adolescents, with differences than adults due to pubertal development.
Future work will test if the highlighted metabolic pathways complement
or enhance the ability of glucose to predict the development of
prediabetes during adolescence.
Acknowledgments