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Novel Metabolic Markers for theRisk of Diabetes Development
inAmerican IndiansDiabetes Care 2015;38:220–227 | DOI:
10.2337/dc14-2033
OBJECTIVE
To identify novel metabolic markers for diabetes development in
AmericanIndians.
RESEARCH DESIGN AND METHODS
Using an untargeted high-resolution liquid chromatography–mass
spectrometry,we conducted metabolomics analysis of study
participants who developed inci-dent diabetes (n = 133) and those
who did not (n = 298) from 2,117 normoglycemicAmerican Indians
followed for an average of 5.5 years in the Strong Heart
FamilyStudy. Relative abundances of metabolites were quantified in
baseline fastingplasma of all 431 participants. Prospective
association of each metabolite withrisk of developing type 2
diabetes (T2D) was examined using logistic regressionadjusting for
established diabetes risk factors.
RESULTS
Seven metabolites (five known and two unknown) significantly
predict the risk ofT2D. Notably, one metabolite matching
2-hydroxybiphenyl was significantly as-sociatedwith an increased
risk of diabetes, whereas fourmetabolites matching PC(22:6/20:4),
(3S)-7-hydroxy-29,39,49,59,8-pentamethoxyisoflavan, or
tetrapeptideswere significantly associated with decreased risk of
diabetes. Amultimarker scorecomprising all seven metabolites
significantly improved risk prediction beyondestablished diabetes
risk factors including BMI, fasting glucose, and
insulinresistance.
CONCLUSIONS
The findings suggest that these newly detected metabolites may
represent novelprognostic markers of T2D in American Indians, a
group suffering from a dispro-portionately high rate of T2D.
Type 2 diabetes (T2D) is a metabolic disorder characterized by
hyperglycemia re-sulting from impaired insulin secretion and
increased insulin resistance (1). Thepathogenesis of T2D is
complex, involving both genetic and environmental factors,but the
precise mechanisms underlying T2D development remain incompletely
un-derstood. Traditional risk factors such as age, sex, obesity,
fasting glucose, andinsulin resistance contribute considerably to
disease risk and have therefore beenwidely used for routine
diagnosis or risk stratification, but most of thesemarkers failto
capture the complexity of disease etiology and thus have
limitations in detectingearly metabolic abnormalities that may
occur years or even decades before theonset/diagnosis of overt T2D.
Characterization of metabolic profiles and perturbed
1Department of Epidemiology, Tulane UniversitySchool of Public
Health, New Orleans, LA2Department of Biostatistics, School of
PublicHealth, University of North Carolina at ChapelHill, Chapel
Hill, NC3Division of Pulmonary Medicine, Emory Univer-sity School
of Medicine, Atlanta, GA4Department of Biostatistics and
Bioinformatics,EmoryUniversity School ofPublicHealth,
Atlanta,GA5Center for American Indian Health Research,University of
Oklahoma Health Sciences Center,Oklahoma City, OK6Medstar Health
Research Institute and George-town and Howard Universities Centers
for Trans-lational Sciences, Washington, DC
Corresponding author: Jinying Zhao, [email protected].
Received 25 August 2014 and accepted 31October 2014.
This article contains Supplementary Data onlineat
http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc14-2033/-/DC1.
© 2015 by the American Diabetes Association.Readers may use this
article as long as the workis properly cited, the use is
educational and notfor profit, and the work is not altered.
See accompanying articles, pp. 186,189, 197, 206, 213, and
228.
Jinying Zhao,1 Yun Zhu,1 Noorie Hyun,2
Donglin Zeng,2 Karan Uppal,3
ViLinh T. Tran,3 Tianwei Yu,4 Dean Jones,3
Jiang He,1 Elisa T. Lee,5 and
Barbara V. Howard6
220 Diabetes Care Volume 38, February 2015
HEA
LTHDISPARITIESIN
DIABETES
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metabolic pathways implicated in T2Ddevelopment will not only
provide novelinsights into disease pathophysiologybut also provide
instrumental data forrisk prediction and for developing effec-tive
therapeutic and preventive strate-gies against
diabetes.Metabolomics is an emerging analyt-
ical technology that simultaneouslyquantifies many metabolites
in bio-fluids. These metabolites represent theend products of
cellular metabolism inresponse to intrinsic and extrinsic stim-uli
and thus may reflect the metabolicchanges at earlier stages of
disease.Cross-sectional analyses have reportedassociations of
altered metabolites withobesity (2), insulin resistance (3),
predi-abetes, and overt T2D (4–7). Thesechanges included
acylcarnitines (6,8),amino acids (2,8), sugars (5,7), and
dif-ferent lipid species (5,8,9). Higherplasma levels of
branched-chain aminoacids (BCAAs) and aromatic amino acidswere
associated with an increased riskof T2D in the FraminghamOffspring
study(10). Another study found that
increaseddiacyl-phosphatidylcholines and reducedacyl-alkyl- and
lyso-phosphatidylcholinesaswell as sphingomyelinswere
associatedwith diabetes in a European population(11). More
recently, a-hydroxybutyrateand linoleoylglycerophosphocholinewere
also found to predict the develop-ment of dysglycemia and T2D in
Euro-peans (12). These findings derived fromEuropean populations,
however, maynot represent metabolic alterations inother ethnic
groups. Moreover, most ex-isting studies used a targeted
metabolo-mics approach by focusing on a subset ofpreselected
metabolites and thus mayhave limited ability in discovering
noveldisease-related metabolic changes. Theclinical utility of
previously detected me-tabolites in risk prediction was either
notreported or was minimal over conven-tional clinical factors.The
goal of this study is to identify
predictive metabolic markers for futurerisk of T2D in American
Indians, a minor-ity group suffering from a disproportion-ately
high rate of T2D. Metabolicprofiles of diabetes development
wereexamined in normoglycemic partici-pants using fasting plasma
samples col-lected prior to disease occurrence. Theutility of novel
metabolic markers in riskprediction beyond established diabetesrisk
factors was also investigated.
RESEARCH DESIGN AND METHODSStudy PopulationParticipants included
in the currentstudy were selected from the StrongHeart Family Study
(SHFS), a family-based prospective study designed toidentify
genetic factors for cardiovascu-lar disease (CVD), diabetes, and
theirrisk factors in American Indians residingin Arizona, North and
South Dakota, andOklahoma. A detailed description forthe study
design and methods of theSHFS had been reported previously(13,14).
In brief, a total of 3,665 tribalmembers (aged 14 years and
older)from 94 multiplex families (65 three-generation and 29
two-generation fami-lies, average family size 38) were recruitedand
examined in 2001–2003. All livingparticipants were followed and
reex-amined between 2006 and 2009. TheSHFS protocol was approved by
the in-stitutional review boards from the IndianHealth Service and
the participatingstudy centers. All participants gave in-formed
consent.
According to the American DiabetesAssociation 2003 criteria
(15), diabeteswas defined as fasting plasma glucose$7.0 mmol/L or
hypoglycemic medica-tions. Impaired fasting glucose was de-fined as
a fasting glucose of 6.1–6.9mmol/L and no
hypoglycemicmedications,and normal fasting glucose (NFG) wasdefined
as fasting glucose ,6.1 mmol/L.Incident cases of T2D were definedas
normal fasting glucose at baseline(2001–2003) and development of
newT2D by the end of follow-up (2006–2009).
Participants included in the currentanalysis have to meet the
following cri-teria: 1) attended clinical examinationsat both
baseline (2001–2003) andfollow-up (2006–2009), 2) had NFG
atbaseline, 3) were free of overt CVD andhypoglycemic medications
at baseline,and 4) had available fasting plasma sam-ple at baseline
for the proposed met-abolomic analysis. Participants withmissing
information for fasting glucoseor antidiabetes medication at
eitherbaseline or follow-up were also excludedfrom the current
analysis.
A total of 2,324 participants free ofovert CVD at baseline
attended bothclinical visits and had available fastingplasma
samples for the proposed analy-sis. Of these, 2,117 normoglycemic
par-ticipants met all of the criteria listed
above. After an average 5.5 years offollow-up, 197 participants
(9.3%) de-veloped incident T2D. Among thosewho did not develop T2D
(n = 1,920),159 participants (7.5%) progressed toimpaired fasting
glucose, whereas theother individuals (n = 1,761) remainedwith
stable NFG by the end of follow-up. The current metabolomics
analysismeasured metabolite levels in fastingplasma of 431
participants, including133 incident cases randomly selectedfrom
participants who developed newT2D (n = 197) and 298 control
subjectsrandomly selected from those who didnot develop T2D (n =
1,920). Supple-mentary Table 1 shows the comparisonof baseline
clinical characteristics be-tween participants who were selectedand
those not selected.
Assessments of Diabetes Risk FactorsFasting plasma glucose,
insulin, lipids,lipoproteins, and inflammatory bio-markers were
measured by standardlaboratory methods (14,16). BMI wascalculated
as body weight in kilogramsdivided by the square of height in
me-ters. Hypertension was defined as bloodpressure levels $140/90
mmHg or useof antihypertensive medications. Insulinresistance was
assessed using HOMA ac-cording to the following formula: HOMAof
insulin resistance (HOMA-IR) = fastingglucose (mg/dL)3 insulin
(mU/mL)/405(17). Renal function was assessed usingthe estimated
glomerular filtration rate(eGFR) calculated by the MDRD
equation(18). For cigarette smoking, subjectswereclassified as
current smokers, formersmokers, and nonsmokers. Alcohol
con-sumption was determined by self-reported history of alcohol
intake, thetype of alcoholic beverages consumed,frequency of
alcohol consumption, andaverage quantity consumed per day andper
week. Participants are classified ascurrent drinkers, former
drinkers, andnever drinkers. Dietary intake was as-sessed using the
block food frequencyquestionnaire (19).
Metabolic Profiling by High-Resolution Liquid
Chromatography–Mass SpectrometryRelative abundance of fasting
plasmametabolites was determined usinghigh-resolution liquid
chromatography–mass spectrometry (LC-MS). Detailed lab-oratory
protocols have previously beendescribed (20,21). Briefly, 65 mL
plasma
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sample aliquots were treated with ace-tonitrile, spiked with
internal standardmix, and centrifuged at 13,000g for10min at 48C to
remove proteins. Super-natant (130 mL) was removed andloaded into
autosampler vials. Anion ex-change (AE) columns (both C18 and
AEcolumns) were equilibrated to the initialcondition for 1.5 min
prior to the nextsample injection. Mass spectral datawere collected
with a 10-min gradienton a Thermo LTQ-Velos Orbitrap
massspectrometer (Thermo Fisher, San Diego,CA) to collect data from
mass/charge ra-tio (m/z) 85–2,000 in a positive ionizationmode.
Three technical replicates wererun for each sample using a
dual-columnchromatography procedure with C18and an AE column.
Pooled plasma sam-ples were included in each batch (n = 23)for
quality control. Peak extraction, dataalignment, and feature
quantificationwere performed using the adaptive pro-cessing
software (apLCMS) (22,23), acomputer package designed for
high-resolution metabolomics data analysis.Feature and sample
quality assessmentwas performed based on coefficient ofvariation
(CV) and Pearson correlation,respectively, based on the technical
rep-licates using xMSanalyzer (24). Metabo-lites with CV .50% in
our samples wereexcluded from further analyses. Potentialmetabolite
identitieswere determined byperforming an online search (10 ppmmass
accuracy) against the Metlin data-base (25), the Human Metabolomics
Da-tabase (26), and the LIPIDMAPS structuredatabase (27). Data
filtering, normaliza-tion, diagnostics, and summarizationwere
performed using the computerpackage MSPrep (28). Missing data
wereimputed using the half of the minimumobserved value within each
metaboliteacross all samples. Batch effect was cor-rected using the
algorithm ComBat (29)implemented in MSPrep.
Statistical AnalysisPrior to analysis, metabolites data werelog
transformed and standardized tounit variance and zero mean (z
scores).Continuous variables were also con-verted to standard
normal distribu-tions with corresponding mean andSD. Pearson
partial correlation coeffi-cients were calculated between
identi-fied metabolites and establishedclinical factors, adjusting
for age, sex,and study site.
To identify metabolic predictors andto estimate their effects on
the risk ofdeveloping T2D, we constructed a Coxproportional hazards
frailty model, inwhich time to event was the dependentvariable and
the level of each metabo-lite was the independent variable.
Thefrailty model was used here to accountfor the relatedness among
family mem-bers. The proportional hazards assump-tion was tested
using the Schoenfeldresiduals, and it shows that the
propor-tionality assumption holds in our data.For estimation of
metabolic effects thatare independent of traditional risk fac-tors,
the Cox frailty model was adjustedfor age, sex, site, BMI, eGFR,
HDL, triglyc-erides, fasting glucose, and insulin re-sistance
(assessed by HOMA-IR) atbaseline. Given the potential high
corre-lations among detected metabolites, weused the q value method
to adjustfor multiple testing (30), and a q value,0.05 was
considered statisticallysignificant.
To examine the combined effects ofmetabolites on diabetes risk,
weconstructed a multimarker metabolitesscore based on metabolites
that are sig-nificantly predictive of diabetes risk byfitting a
model according to the follow-ing formula: b1X1 + b2X2+ b3X3, where
Xidenotes the z score of the i-th metabo-lite and bi denotes the
regression coef-ficient from the logistic regressionmodel
containing the indicated metab-olites. The joint predictive ability
of me-tabolites was assessed using logisticregression by including
all clinical riskfactors (age, sex, study site, BMI, eGFR,HDL,
triglycerides, fasting glucose, andHOMA-IR) plus the multimarker
metab-olite score compared with the model in-cluding clinical risk
factors only. Wecalculated the area under the receiveroperating
characteristic curve (AUC),the net reclassification
improvement(NRI), and the integrated discriminationimprovement
(IDI) to assess the incre-mental value of the metabolic markersfor
risk prediction beyond classical riskfactors. Because our analysis
was basedon a regression model with no cross-validation or external
validation, it islikely that our model could be overfit-ted. To
avoid or minimize bias due tooverfitting, we conducted a
bootstrapestimation (1,000 reps) for coefficientsby SAS to obtain
bias-corrected esti-mates ofmetabolites on risk of diabetes.
To identify metabolic profiles associ-ated with risk of
diabetes, we conductedsparse partial least-squares
discriminantanalysis (sPLS-DA) using the computerpackage mixOmics
implemented in R.The sPLS-DA is a supervised, multivari-ate
technique to determine metabolicgroups associated with disease
risk.The sPLS-DA analysis included only me-tabolites showing
significant associa-tions with risk of diabetes. For ease
ofvisualization, we presented a Manhat-tan plot (2log10 P vs.
metabolic feature)to show the significance of individualmetabolites
according to status of inci-dent cases at follow-up using raw P
val-ues obtained from multivariate logisticregression analysis
(false discovery rateat q = 0.05 with a horizontal line).
RESULTS
Table 1 presents the characteristics ofthe study participants at
baseline(2001–2003) according to diabetes sta-tus at the end of
follow-up (2006–2009).The average follow-up period was 5.5years.
Compared with participants whodid not develop T2D, those who
devel-oped incident T2D had higher levels ofBMI, triglycerides,
fasting glucose, fast-ing insulin, and insulin resistance(HOMA-IR)
but lower level of HDL atbaseline. We also compared partici-pants
who were selected (n = 431) ver-sus those not selected (n = 1,686)
forthis study. It shows that, except forBMI and eGFR, selected
participantswere not appreciably different fromthose not selected
(SupplementaryTable 1).
Our untargeted high-resolution LC-MSdetected 11,628 distinct
ions (m/z) withCV #10%, of which 2,093 m/z featuresmatched known
compounds in availablemetabolomics databases. Among all11,628
features, altered levels of sevenmetabolites (five matching known
metab-olites and two unknown) were signifi-cantly associated with
risk of diabetesafter adjustment for clinical factors andmultiple
testing. Specially, a metabolitematching 2-hydroxybiphenyl
(2HBP)and an unknown chemical (m/z ratio1,178.804 [named X-1178])
were signif-icantly associated with an increased riskof diabetes,
whereas five metabo-lites matching phosphatidylcholine
(PC22:6/20:4), (3S)-7-hydroxy-29,39,49,59,8-pentamethoxyisoflavan
(HPMF), two tet-rapeptides (Met-Glu-Ile-Arg [MEIR] and
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Leu-Asp-Tyr-Arg [LDYR]), and an un-known metabolite (m/z ratio
490.816[named X-490]) were significantly asso-ciated with a
decreased risk of diabetes.These associations are independent
ofclinical factors including fasting glucoseand insulin resistance.
Per-SD increase inthe log-transformed levels of matching2HBP and
X-1178 was associated with80% and 89%, respectively, increasedrisk
of T2D. By contrast, per-SD increasein the log-transformed levels
of match-ing PC (22:6/20:4), HPMF, tetrapeptides,and X-490 was
associated with 32–42%decreased risk of T2D. In
themultivariatemodel categorizing metabolites as ter-tiles,
participants in the top tertile of2HBP and X-1178 had a hazard
ratio(HR) of 2.80 (95% CI 1.19–6.60) and2.87 (95% CI 1.08–7.60) for
developingincident T2D, respectively, comparedwith those in the
lowest tertile. In con-trast, participants in the top tertile of
PC(22:6/20:4), HPMF, MEIR, LDYR, andX-490 had an HR of 0.45 (95% CI
0.21–0.97), 0.38 (95% CI 0.18–0.80), 0.44 (95%CI 0.20–0.96), 0.37
(95% CI 0.16–0.87),and 0.46 (95% CI 0.21–0.97) for develop-ing T2D,
respectively, comparedwith thosein the lowest tertile of these
metabolites.To estimate the joint effects of metab-
olites on risk of diabetes development,we calculated HRs across
tertiles of thecombined metabolites comprising allseven significant
metabolites. For thetwo risk metabolites (2HBP and X-1178),the HR
for risk of developing incidentT2D by comparing the top with the
bot-tom tertiles of the summed metaboliteswas 6.89 (95% CI
2.63–18.08). For thefive protective metabolites (PC [22:6/20:4],
HPMF, MEIR, LDYR, and X-490),the HR of the top compared with
thebottom tertiles of summed metaboliteswas 0.23 (95% CI
0.10–0.51). Multivari-ate associations of each individual
me-tabolite along with their combinedeffects on diabetes risk are
shown inTable 2. Of note, regression coefficientslisted in Table 2
were corrected for po-tential overfitting by bootstrapping andthus
should represent unbiased esti-mates of metabolic effects on risk
ofT2D. For ease of visual inspection,Fig. 1 shows a Manhattan plot
(2log10P vs. metabolic feature) of all metabolitesusing raw P
values obtained from multi-variate regression analysis.
Metabolitessignificantly predictive of diabetes riskare shown at
the level of q = 0.05.
To investigate whether these detectedmetabolites improve risk
prediction, weadded the weighted multimarker scorecomprising all
seven metabolites to thefully adjusted statistical model.
Resultsshow that addition of the metabolitescore resulted in
significant improve-ment for diabetes risk prediction as as-sessed
by all three measures: the AUCvalue increased from 0.763 to 0.822(P
= 0.006), the NRI was 0.623 (95% CI0.427–0.819; P, 1025), and the
IDI was0.117 (95% CI 0.083–0.151; P , 1025).This indicates that the
newly detectedmetabolic markers significantly improve
risk prediction of T2D beyond estab-lished diabetes risk
factors. The fivematching known metabolites belong tothe classes of
glycerophosphocholine,flavonoids, and polypeptides (Supple-mentary
Table 2). Partial correlations ofthese matching metabolites with
clinicalrisk factors are shown in SupplementaryTable 3. Apart from
some weak correla-tions of 2HBP with fasting insulin or in-sulin
resistance, PC (22:6/20:4) with BMI,or LDYR with lipid levels, most
metabo-lites were not correlated with estab-lished diabetes
factors. The matchingmetabolites HPMF, MEIR, and the
Table 1—Characteristics of the study participants at baseline
(2001–2003)
Participants whodeveloped T2D
Participants who didnot develop T2D P*
n 133 298
Age, years 35.45 6 12.2 33.36 6 13.88 0.1208
Female sex, % 67.67 63.42 0.3885
BMI, kg/m2 36.74 6 7.96 31.11 6 8.00 ,0.0001
Current smoker, % 33.83 36.58 0.7266
Current drinker, % 63.16 68.79 0.5034
Systolic blood pressure, mmHg 120.88 6 15.34 118.87 6 12.96
0.1868
Diastolic blood pressure, mmHg 77.39 6 11.80 75.63 6 10.46
0.1222
HDL, mg/dL 47.52 6 14.41 52.44 6 14.63 0.0016
LDL, mg/dL 100.92 6 29.32 96.06 6 28.57 0.1062
Total triglyceride, mg/dL 167.20 6 99.12 132.16 6 65.47
,0.0001
Total cholesterol, mg/dL 180.70 6 34.16 174.75 6 33.48
0.0923
eGFR, mL/min/1.73 m2 104.56 6 21.41 105.18 6 24.84 0.7917
Fasting glucose, mg/dL 94.30 6 7.81 89.55 6 6.41 ,0.0001
Fasting insulin, mU/mL 20.52 6 13.08 14.14 6 11.47 0.0001
Insulin resistance (HOMA-IR) 4.80 6 3.07 3.15 6 2.60 ,0.0001
Total caloric intake, kcal/day 2,887.59 6 2,079.25 2,812.91 6
2,117.20 0.7409
Total dietary protein, g/day 97.51 6 82.98 94.99 6 81.77
0.7768
Total dietary fat, g/day 126.39 6 99.66 123.71 6 98.08
0.8017
Data are mean6 SD unless otherwise indicated. *Adjusting for
family relatedness by generalizedestimating equation.
Table 2—Multivariate association of baseline fasting plasma
metabolites with riskof developing T2D in American Indians by Cox
proportional hazards frailtymodel‡
Matching metabolitesMetabolite as continuous
variable*Metabolite as categorical
variable†*
Protective metabolitesPC (22:6/20:4) 0.68 (0.52–0.88) 0.45
(0.21–0.97)HPMF 0.58 (0.43–0.79) 0.38 (0.18–0.80)MEIR 0.61
(0.47–0.78) 0.44 (0.20–0.96)LDYR 0.63 (0.47–0.85) 0.37
(0.16–0.87)X-490 0.65 (0.50–0.84) 0.46 (0.21–0.97)Combined
protective effects 0.43 (0.31–0.59) 0.23 (0.10–0.51)
Risk metabolites2HBP 1.80 (1.26–2.57) 2.80 (1.19–6.60)X-1178
1.89 (1.29–2.77) 2.87 (1.08–7.60)Combined risk effects 2.56
(1.71–3.84) 6.89 (2.63–18.08)
Data are HR (95% CI). ‡Adjusted for age, sex, site, BMI, eGFR,
HDL, triglycerides, fasting glucose,and HOMA-IR. *HR per SD change
in log-transformed metabolite level. †Tertile 3 vs. tertile 1.
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unknown compound (X-490) were notcorrelated with any of the
known riskfactors for diabetes.To identify metabolic profiles
associ-
ated with risk of diabetes development,we conducted sPLS-DA
using the sevenmetabolites that were significantly pre-dictive of
disease risk. Fig. 2 demonstratesthat participants who developed
T2D andthose who did not were separated intotwo distinct groups,
suggesting that thesemetabolites could be used as discrimina-tory
markers for T2D risk stratification.This observation is consistent
with ourresults obtained by risk prediction analy-ses (i.e., AUC,
NRI, and IDI). Additionaladjustments for dietary intake of fat,
pro-tein, and caloric intake did not attenuatethe observed
associations (data notshown).
CONCLUSIONS
In this prospective investigation usingan untargeted
high-resolution metabolo-mic approach, we found that seven
me-tabolites independently predict futureonset of T2D in American
Indians, a groupwith a high rate of diabetes. Of the fivechemicals
matching known metabolites,two were lipids in the classes of
glycero-phosphocholine (PC) and flavonoid. Itshould be noted that
there are manyisobaric lipids, so the precise
structuralidentifications will require additional re-search.
Theobserved associationwithstoodadjustments for multiple clinical
indica-tors including age, sex, study site, BMI,eGFR, HDL,
triglycerides, fasting glucose,
and insulin resistance (HOMA-IR). Thecombination of these
metabolites signif-icantly improves risk prediction
beyondestablished diabetes risk factors. Thesemetabolites have not
been reported inprevious studies of European individu-als or other
ethnic groups and thusshould represent putative prognostic
markers of diabetes specific to Ameri-can Indians.
We found that a metabolite matching2HBP was associated with 80%
in-creased risk of developing T2D inde-pendent of classical risk
factors. Themechanism by which this metabolite af-fects diabetes
risk is unclear. However,
Figure 1—Manhattan plot of 11,628m/z features comparing
participants who developed incident T2D versus those who did not.
The negative log Pvalue was plotted against the m/z features. The
x-axis represents m/z of the detected features, ordered in
increasing value from 85 (left) to 1,800(right). A total of seven
metabolites significantly differed between the two groups at the
threshold of q = 0.05 (above the horizontal gray line).
Figure 2—Separation of study participants who developed incident
T2D and those who did notduring follow-up by sPLS-DA using a
multimarker metabolite score comprising all seven metab-olites
showing significant associations with incident T2D listed in Table
2.
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2HBP is known to be an environmentaltoxin that is widely used as
industrialantimicrobials, agricultural fungicide,and disinfectants.
2HBP was reportedto be mutagenic in human cells (31)and
carcinogenic in animal models(32,33). In addition,
hydroxybiphenylchemicals can be degraded by bacteriathrough the
biphenyl catabolic pathway(34). It is thus plausible to
hypothesizethat, apart from the possible direct toxiceffects of
2HBP on pancreas or periph-eral tissues, 2HBP may also
negativelyaffect diabetes through a yet unknownhost-gut microbiota
mechanism.Glycerophosphocholines are impor-
tant structural components of plasmalipoproteins and cell
membranes withdiverse biological functions. In thisstudy, we found
that elevated plasmalevel of matching PC (22:6/20:4) wasassociated
with 37% reduced risk ofT2D in our study population. This is
inagreement with a previous study dem-onstrating lowerplasmaor
serum levels ofPC species in diabetic patients than in con-trol
subjects (5). Moreover, reduced levelsof multiple
acyl-glycerophosphocholinespecies were highly correlated with
in-sulin resistance as measured by the eu-glycemic clamp (35),
lending furthersupport for a potential role of PCs in di-abetes
etiology. In the current investi-gation, another metabolite
matchingknown (3S)-7-hydroxy-29,39,49,59,8-pentamethoxyisoflavan
(named HPMF)was also significantly predictive of a de-creased risk
of diabetes. This metabolitebelongs to the class of flavonoids
thatare known to have a wide range ofbiological and pharmacological
activi-ties. Dietary flavonoid intakes havebeen associated with
reduced risk ofT2D in both human (36–38) and animalstudies (39). In
support of these findings,participants with a higher plasma
level(top tertile) of HPMF exhibited over60% reduced risk of T2D
compared withthosewith a lower level (bottom tertile) inour
analysis. While the precise mecha-nism underlying this association
awaitsfurther investigation, it is possible thatHPMFmay decrease
diabetes risk throughits potential antioxidant properties (40).
Itis also likely that HPMF may exert benefi-cial effects on energy
balance and lipidmetabolism (41) or anti-inflammatory ef-fects
through the nuclear factor-kB or theAMPK signaling pathways, which
play acentral role in the regulation of glucose
and lipid metabolism (42,43). In addition,flavonoids have been
shown to have anti-diabetes effects through enhancedpancreatic
b-cell function in animal ex-periments (44). The favorable effect
ofthis flavonoid chemical has not beenpreviously reported. Its
biological prop-erties should be investigated in
futureresearch.
In addition to the altered profiles ofPC and flavonoid, elevated
levels of twometabolites matching tetrapeptides(MEIR and LDYR) were
associated with;40% reduced risk of diabetes. Al-though the
mechanisms linking thesepeptides to diabetes remain to be
de-termined, peptides are known to be es-sential in regulating
lipid metabolism inkey insulin-target tissues and in main-taining
energy homeostasis and insulinsensitivity. They may also function
aspotent peptide hormones regulatingglucose metabolism in diabetes
(45). Inaddition to the five knownmatchingme-tabolites, two unknown
compoundswere also significantly predictive of di-abetes
development. These unknownchemicals might be not new but merelynot
yet identified. The structure andfunction of these unannotated
chemicalsshould be examined in future research.
Previous evidence has linked raisedcirculating levels of BCAAs
with insulinresistance (2,46,47) or diabetes (10,47).Our study,
however, did not find a sig-nificant association of BCAAs with risk
ofT2D development. This lack of replica-tion may not necessarily
representtrue negative findings because our anal-ysis accounted for
multiple testing of.11,000 m/z features with a stringentcriterion,
which could result in inappro-priate exclusion for a large number
ofmetabolites (false negatives). The dis-crepancy could also
represent genuinedifference between American Indiansand other
ethnic groups included in pre-vious studies because the unique
char-acteristics of American Indians, e.g.,genetic background and
lifestyle, couldpotentially lead to population-specificmetabolic
signatures. Future large-scalemetabolomics studies should
addressthis discrepancy.
In search of the origin of the interin-dividual variation, we
calculated partialcorrelations of metabolite relativeabundance with
standard risk indicatorsof diabetes, expecting that, for
example,higher BMI or fasting glucose should
correspondwith higher levels of riskme-tabolites or lower levels
of protectivemetabolites. However, in our study co-hort, most of
the detected matchingmetabolites were not correlated withclassical
risk factors, such as BMI, fastingglucose, and insulin resistance,
but thecombination of thesemetabolites signif-icantly improved risk
prediction beyondstandard risk factors. This is importantbecause
the fundamental task of risk pre-diction is to identify predictive
markersthat are sufficiently uncorrelated with es-tablished risk
factors so that they can beused to improve risk prediction over
andabove conventional clinical factors. Thesenewly detected
metabolic markers willprovide valuable information regardingthe
pathophysiology of diabetes develop-ment and also potential
therapeutic tar-gets for novel treatment options.
Our study has several limitations. First,although our
high-resolution LC-MS de-tected .11,000 distinct features, itshould
be noted that only 18% of thecompounds detected had a match in
thecurrent metabolomics database. Thesecompounds were unable to be
pursuedowing to the large number of possibleisomers and a lack of
available standards.However, these currently unannotatedmetabolites
may represent dietary,microbiome-related, or environmentalchemicals
associated with diabetes.With the advancement of
metabolomicresearch, we expect that the majority ofthese
unidentified chemicals will ulti-mately be annotated and their
associa-tions with disease will be determined.Additionally, manym/z
features matchedto therapeutic drugs and nutritional sup-plements,
but owing to their wide use bydiabetic patients, wewere unable to
eval-uate their contributions to the alteredmetabolic profiles.
Second, althoughhighly correlated, relative abundancesbut not
absolute concentrations wereused as a surrogate for
plasmametabolitelevels. Third, although we were able tocontrol many
of the known risk factors,the possibility of potential
confoundingby other factors such as diet and gut mi-crobiota cannot
be entirely excluded.Fourth, participants in the current studyare
young to middle-aged American Indi-ans who may have a high
propensity forthe development of T2D; therefore, gen-eralization of
our findings to other popu-lations should be approached
cautiously.However, given the rising tide of T2D in
care.diabetesjournals.org Zhao and Associates 225
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almost all ethnic groups worldwide, webelieve that our results
could be applica-ble to other populations. Finally, our re-sults
need to be replicated in large-scale,prospective metabolomic
analysis ofAmerican Indians and other ethnicgroups.Nonetheless,
this is the first prospec-
tive study to report novel predictive met-abolic markers and
altered metabolicprofiles associated with development ofT2D in
American Indians, aminority groupsuffering from a
disproportionately highrate of T2D. The SHFS has phenotypic
lon-gitudinal data available that allowed usto accurately classify
participants as inci-dent cases of diabetes. The
untargetedhigh-resolution metabolomics approachallowed us to
identify previously unde-scribed metabolic markers that may
bespecific to the population of American In-dians, whose genetic
makeup and/or life-style could be distinct from that ofindividuals
of European ancestry.In summary, this study identified sig-
nificant metabolic predictors of T2D inAmerican Indians above
and over estab-lished diabetes indicators. Targeting bi-ological
pathways that involve thesenewly detected metabolites wouldhelp to
develop early preventive andtherapeutic strategies tailored to
Amer-ican Indians, an ethnically important buttraditionally
understudied minoritypopulation.
Acknowledgments. The authors thank theSHFS participants, Indian
Health Service facili-ties, and participating tribal communities
fortheir extraordinary cooperation and involve-ment, which has
contributed to the success ofthe SHFS.Funding. This study was
supported by NationalInstitutes of Health grants
R01DK091369,K01AG034259, and R21HL092363 and coopera-tive agreement
grants U01HL65520, U01HL41642,U01HL41652, U01HL41654, and
U01HL65521.
The views expressed in this article are those ofthe authors and
do not necessarily reflect thoseof the Indian Health
Service.Duality of Interest. No potential conflicts ofinterest
relevant to this article were reported.Author Contributions. J.Z.
conceived thestudy, supervised the statistical analyses, andwrote
the manuscript. Y.Z., N.H., and D.Z.conducted statistical analyses.
K.U. and V.T.T.collected LC-MS data and conducted metabolo-mic
analyses. T.Y. and D.J. supervised metab-olomic data analyses.
J.H., E.T.L., and B.V.H.contributed to study design, data
interpreta-tion, and discussion and reviewed and editedthe
manuscript. J.Z. is the guarantor of thiswork and, as such, had
full access to all of thedata in the study and takes responsibility
for
the integrity of the data and the accuracy of thedata
analysis.
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