Lipid Profiling Reveals DifferentTherapeutic Effects of Metforminand Glipizide in Patients WithType 2 Diabetes and CoronaryArtery DiseaseDiabetes Care 2014;37:2804–2812 | DOI: 10.2337/dc14-0090

OBJECTIVE

We recently demonstrated a beneficial effect of metformin compared with glipi-zide in type 2 diabetic patients regarding cardiovascular outcomes for 3-yeartreatment in the SPREAD-DIMCAD study. However, the potential mechanismfor the clinical effects remains unclear. Here, we performed a comprehensivelipidomics study to evaluate the different lipid metabolites in serum samplesobtained from participants in this study.

RESEARCH DESIGN AND METHODS

Liquid chromatography–quadrupole time of flight–mass spectrometry was usedto evaluate the different lipid metabolites in serum samples obtained from theparticipants (21 patients in glipizide group and 23 patients in metformin group)before and after each year of treatment (at 0 [baseline], 1, 2, and 3 years of studydrug administration).

RESULTS

A total of 118 serum lipid molecular species was identified and quantified. Duringtreatment, metformin induced a substantially greater change in serum lipid spe-cies comparedwith glipizide, especially at the 2- and 3-year time points (with 2, 11,and 12 lipid species being significantly different between the groups after eachyear of treatment [1, 2, or 3 years], P< 0.05). Among the significantly changed lipidspecies, three lipid metabolites were linked to long-term composite cardiovascu-lar events (adjusted P < 0.05). After treatment, triacylglycerols (TAGs) of a rela-tively higher carbon number showed a clearly increased trend inmetformin groupcompared with the glipizide group, whereas the changes in TAGs with differentdouble bonds were minimal.

CONCLUSIONS

Our findings revealed the differential therapeutic effects of metformin and glipi-zide on comprehensive lipidomics, which were comparable with their differentlong-term effects on cardiovascular outcomes.

1Ruijin Hospital, Shanghai Jiao-Tong UniversitySchool of Medicine, Shanghai, China2Key Laboratory of Separation Science for Ana-lytical Chemistry, Dalian Institute of ChemicalPhysics, Chinese Academy of Sciences, Dalian,China3Johns Hopkins School of Medicine, Baltimore,MD4Xinhua Hospital, Shanghai Jiao-Tong UniversitySchool of Medicine, Shanghai, China5Second Xiangya Hospital of Central South Uni-versity, Changsha, China6Shandong Provincial Hospital Affiliated to Shan-dong University, Jinan, China7Chang Hai Hospital, Second Military MedicalUniversity, Shanghai, China8First Affiliated Hospital of Wenzhou MedicalCollege, Zhejiang Province, China9Sir Run Run Shaw Hospital, Zhejiang Province,China10Jiangsu Province Hospital, Jiangsu Province,China11Shanghai Tenth People’s Hospital of TongjiUniversity, Shanghai, China12Nanfang Hospital, Guangdong Province, China13Nanjin Drum Tower Hospital, Jiangsu Province,China

Corresponding author: Guang Ning, guangning@medmail.com.cn, or Guowang Xu, xugw@dicp.ac.cn.

Received 12 January 2014 and accepted 19 June2014.

This article contains Supplementary Data onlineat http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc14-0090/-/DC1.

Y.Z., C.H., and J.H. contributed equally to thiswork.

© 2014 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.

Yifei Zhang,1 Chunxiu Hu,2 Jie Hong,1

Jun Zeng,2 Shenghan Lai,3 Ankang Lv,1

Qing Su,4 Yan Dong,4 Zhiguang Zhou,5

Weili Tang,5 Jiajun Zhao,6 Lianqun Cui,6

Dajin Zou,7 Dawang Wang,8 Hong Li,9

Chao Liu,10 Guoting Wu,11 Jie Shen,12

Dalong Zhu,13 Weiqing Wang,1

Weifeng Shen,1 Guang Ning,1 and

Guowang Xu2

2804 Diabetes Care Volume 37, October 2014

CARDIOVASC

ULA

RANDMETABOLICRISK

Cardiovascular disease (CVD) is themostcommon complication and the leadingcause of mortality in patients with type2 diabetes (1,2). In past decades, sulfo-nylureas and metformin, either alone orin combination, have been the corner-stones of drug therapy for type 2 diabe-tes (3). However, it remains unclearwhether these two drugs have differenteffects on cardiovascular risk in additionto their ability to lower glucose (4–6).We recently evaluated the different ef-fects of glipizide (a commonly usedsulfonylurea) and metformin on thecardiovascular events and mortality intype 2 diabetic patients with coronaryartery disease (CAD) in a multicenterinterventional study (Study on the Prog-nosis and Effect of Anti-diabetic Drugs onType-2 Diabetes Mellitus With CoronaryArtery Disease [SPREAD-DIMCAD]) (7).The study results demonstrated thatmetformin therapy for 3 years substan-tially reduced the incidence of major car-diovascular events in amedian follow-upof 5.0 years compared with glipizide, butno differences were identified betweenthe two groups in terms of clinical riskfactors, including plasma glucose, gly-cated hemoglobin, serum lipids, andblood pressure. Therefore, the potentialbenefit of metformin therapy on cardio-vascular outcomeswas not fully explainedby changes in these conventional cardio-vascular biomarkers.However, several recent metabolo-

mics and lipidomics studies have dem-onstrated that the high-throughputmetabolite quantification by using emerg-ing technologies can identify novel bio-markers associated with the risk offuture diabetes and CAD (8–21).Wang et al. (9,14,15) and Wurtz et al.(16) reported metabolic signaturesthat associated branched-chain andaromatic amino acids with future dia-betes, insulin resistance, and other met-abolic risk factors in humans. Floegelet al. (17) and Wang-Sattler et al. (18)found an independent association be-tween certain metabolic alterations, in-cluding sugar metabolites, amino acids,and choline-containing phospholipids,and a higher risk of type 2 diabetes(andprediabetes). Fernandez et al. (11) de-scribed an association between eight lipidspecies and the incidence of CVD. Berniniet al. (13) reported that novel metabolites(e.g., 3-hydroxybutyrate, a-ketoglutarate,threonine, and dimethylglycine), in

addition to the common lipid markers,might be related to the biochemistry ofCVD risk. These results suggested thatidentifying different metabolite profileswill provide us valuable information con-cerning drug action on metabolism andenable us to elucidate molecular mech-anisms of action. Here, we performed acomprehensive lipidomics study usingliquid chromatography–quadrupole timeof flight–mass spectrometry (LC-QTOF/MS) to evaluate the different lipid me-tabolites in serum samples from partic-ipants in the SPREAD-DIMCAD studyand to elucidate the differential drug-inducedeffects onglobal lipidmetabolitesand their potential mechanisms of actionregarding cardiovascular end points.

RESEARCH DESIGN AND METHODS

ChemicalsSynthetic lipid standards, includinglyso-phosphocholines [LPC (12:0) andLPC (19:0)], phosphatidylethanolamines[PE (30:0) and PE (34:0)], and phosphati-dylcholines [PC (34:0) and PC (38:0)],were obtained from Avanti Polar Lipids,Inc. (Alabaster, AL). Triacylglycerols [TAG(45:0) and TAG (51:0)] were purchasedfrom Sigma-Aldrich Shanghai Trading Co.Ltd (Shanghai, China).

Distilled water was purified using aMilli-Q system (Millipore, Bedford, MA).Dichloromethane (CH2Cl2), acetonitrile,methanol (MeOH), and isopropanol ofhigh-performance liquid chromatography(LC) grade were purchased from Tedia(Fairfield,OH).Analytical-gradeammoniumformate and 98% formic acid were pur-chased from Sigma-Aldrich (St. Louis, MO).

Clinical SamplesThe detailed design of the SPREAD-DIMCAD study has previously beenpublished (7). Briefly, the study was amulticenter, randomized, double-blind,and placebo-controlled clinical trialwith a total enrollment of 304 patients.Participants were randomly assigned toreceive either glipizide (30 mg daily) ormetformin (1.5 g daily) for 3 years. Thestudy was initiated in 1 June 2004 with amedian follow-up period of 5.0 years(range 3.7–5.7). The primary end pointswere time to the composite of recurrentcardiovascular events, including deathfrom a cardiovascular cause, deathfrom any cause, nonfatal myocardial in-farction, nonfatal stroke, or arterial re-vascularization. The follow-up for the

primary end points began at randomiza-tion and continued until the end of thestudy. The original protocol intended tocollect serum samples from all the pa-tients for metabolomic analysis duringthe 3 years of study drug administration.At study closure, serum samples at all fourtime pointsd0 (baseline), 1, 2, and 3 yearson the study drug administrationdhadbeen obtained from 21 glipizide-treatedpatients and 23 metformin-treated pa-tients; these samples were used for thefinal lipidomics analysis (SupplementaryFig. 1). The detailed characteristics ofthese 44 patients at baseline and atthe end of follow-up are summarized inTable 1. The main reasons for excludingthe original participants from the presentanalysis were the following: 1) prematuretermination of study drug administration(31 in the glipizide group and 32 inthe metformin group), 2) withdrawingconsent for serum sample collection atany point during follow-up each year (71in the glipizide group and 79 in the met-formin group), 3) death before the endof 3 years on study drug administration(8 in the glipizide group and 4 in themetformin group), or 4) other reasons(17 in the glipizide group and 18 in themetformin group).

All the serum samples were collectedin themorning after an overnight fastingfor 10–12 h and no smoking. The sam-ples were frozen immediately andstored at2808C until assayed. The studywas approved by the institutional re-view board of Ruijin Hospital, and writ-ten informed consent was obtainedfrom each patient. The study was con-ducted in accordancewith the principlesof the Declaration of Helsinki.

Serum Sample Preparation

Lipid Extraction

Serum lipids were extracted by a modi-fied Bligh/Dyer extraction procedure aspreviously described (22,23). Briefly, 30mL internal standard mixture (see Sup-plementary Table 1 for details) wasadded to 30 mL serum followed by theaddition of 540 mL CH2Cl2/MeOH (2:1,v/v). After thoroughly vortexing theresulting mixture, 120 mL water wasadded to form a two-phase system.The lipids were located in the bottomorganic phase. After centrifugation at6,000g for 10 min at 108C, 100 mL ofeach lipid extract was transferred forfurther LC–mass spectrometry (MS)

care.diabetesjournals.org Zhang and Associates 2805

analysis. Before injection, the lipid ex-tracts were diluted fivefold with acetoni-trile/isopropanol/water (65:30:5, v/v/v),and 10 mL of each diluted lipid extractwas loaded for the lipid profiling analysis.

Instrumental Analysis

LC-MS Analysis

The LC-MS lipidomics analysis wasperformed on a Q-TOF/MS (Agilent)coupled to an ultra-fast LC system(Agilent). An Ascentis Express C8 column(2.7-mm particle size, 2.1 3 150 mm;

Sigma-Aldrich, Munich, Germany) wasused for the LC separation. The LC sepa-ration conditions were identical to thosedescribed previously (22). The autosam-pler plates were maintained at 128C. Se-rum lipid profiling was performed on anAgilent Q-TOF/MS equipped with a dual-electrospray ion source. Full MS scanswere acquired in the positive-ionmode. The ion-source temperaturewas set at 3008C. The voltage of thecapillary was set at 4 kV. The voltagesof the fragmentor and skimmer were

set at 230 V and 65 V, respectively.The flow rate of the drying gas was11 L z min21. The analytes were acquiredusing a mixture of 10 mmol/L purine(m/z 121.0508) and 2 mmol/L hexakisphosphazine (m/z 922.0097) as lockmasses to ensure mass accuracy and re-producibility. The data were collectedat a mass range of m/z 400–1100 withan ion scan duration of 20 ms using LC-MS solution software (Agilent). All thestudy samples were randomly analyzed.Quality-control samples generated by

Table 1—Characteristics of the patients for metabolomic analysis at baseline and end of follow-up

Baseline End of Follow-up

P†Glipizide Metformin P* Glipizide Metformin P*

n 21 23 21 23

Age (years) 64.6 6 10.2 62.0 6 7.9 0.343

Sex 0.126Male 15 (71.4) 21 (91.3)Female 6 (28.6) 2 (8.7)

Time since diagnosis of diabetes (years) 7.0 6 7.4 3.6 6 4.6 0.076

Time since diagnosis of CAD (years) 3.8 6 8.6 1.9 6 2.4 0.297

Current smokers 6 (28.6) 7 (30.4) 0.989

Alcohol use 0 (0) 4 (17.4) 0.118

Body weight (kg) 66.3 6 11.3 69.7 6 9.3 0.291 68.1 6 11.7 69.5 6 10.0 0.671 0.044

BMI (kg/m2) 24.6 6 3.4 24.7 6 2.5 0.922 25.3 6 3.4 24.5 6 2.6 0.418 0.026

Waist circumference (cm) 88 6 10 88 6 8 0.856 92 6 10 88 6 6 0.186 0.006

Blood glucose controlGlycated hemoglobin (%) 7.6 6 1.4 7.5 6 2.0 0.874 7.1 6 1.2 7.1 6 0.9 0.943 0.930Glycated hemoglobin (mmol/mol) 60 6 15.3 58 6 21.9 54 6 13.1 54 6 9.8Fasting plasma glucose (mmol/L) 7.3 6 1.4 7.5 6 2.4 0.638 6.9 6 1.2 6.9 6 1.2 0.976 0.812Postload 2-h plasma glucose (mmol/L) 12.2 6 3.0 13.1 6 4.2 0.434 11.7 6 2.7 11.0 6 2.7 0.395 0.093

Blood pressure (mmHg)Systolic 124.8 6 16.8 133.1 6 21.8 0.164 132.0 6 14.2 127.0 6 13.6 0.252 0.075Diastolic 77.1 6 10.5 78.7 6 9.2 0.581 75.6 6 11.5 75.6 6 6.4 0.994 0.812

Fasting serum cholesterol (mmol/L)Total 4.65 6 1.30 4.71 6 1.12 0.878 5.04 6 1.62 4.52 6 1.34 0.284 0.329LDL 2.62 6 0.89 2.76 6 0.96 0.625 2.40 6 0.83 2.60 6 0.83 0.423 0.426HDL 1.18 6 0.26 1.16 6 0.31 0.849 1.28 6 0.38 1.23 6 0.33 0.256 0.356

Fasting serum triglyceride (mmol/L) 2.12 6 2.06 1.85 6 1.24 0.616 2.77 6 4.18 1.83 6 1.04 0.323 0.541

MedicationsGlucose-lowering drugSulfonylurea 12 (57.1) 12 (52.2) 0.741Metformin 7 (33.3) 9 (39.1) 0.690Thiazolidinedione 1 (4.8) 1 (4.3) 0.947Acarbose 8 (38.1) 6 (26.1) 0.393Glinide 0 (0) 0 (0) 1.000Insulin 4 (19.0) 0 (0) 0.028 10 (47.6) 4 (17.4) 0.032

Other drugsAspirin 20 (95.2) 18 (78.3) 0.170 15 (71.4) 16 (69.6) 0.630ACE inhibitor 10 (47.6) 13 (56.5) 0.451 10 (47.6) 9 (39.1) 0.738ARB 3 (14.3) 1 (4.3) 0.272 0 (0) 2 (8.7) 0.146b-Blocker 9 (42.9) 9 (39.1) 0.897 12 (57.1) 7 (30.4) 0.095Calcium-channel blocker 7 (33.3) 4 (17.4) 0.255 10 (47.6) 6 (28.6) 0.229Diuretic 2 (9.5) 1 (4.3) 0.522 2 (9.5) 0 (0) 0.146Statin 19 (90.5) 11 (47.8) 0.004 12 (57.1) 7 (30.4) 0.095

Data are means6 SD or n (%). Statistical significances were determined using a Student t test (for data normally distributed) or aMann-Whitney test(for data not normally distributed) and x2 test (for categorical variables). ARB, angiotensin receptor blocker. *P values are for the difference betweenthe groups at baseline or at the end of follow-up. †P value refers to comparison between the glipizide and themetformin group after treatment usingANCOVA.

2806 Effects of Metformin and Glipizide on CAD Diabetes Care Volume 37, October 2014

pooling all of the serum samples wereregularly inserted in the sequence tomonitor the response of the LC-MSsystem and to assess the lipid profil-ing platform.

Data Analysis

Statistical Analysis and Pattern Recognition

The data are presented as the meansand SD or as the median with the inter-quartile range. Univariate statisticalanalyses were performed with SPSS forWindows, version 13.0, software (SPSS,Chicago, IL). P values ,0.05 were con-sidered to be statistically significant.Within-group comparisons were per-formed using paired-sample t tests toevaluate the differences from baselinein each group. Student t test (for datathat were normally distributed) or theMann-Whitney test (for data that werenot normally distributed) and a one-wayANOVA with the two-sided Dunnettpost hoc test for multiple comparisonswere performed to investigate the dif-ferences between groups. During thelipid profiles analysis, the appropriatelipid internal standard was used to cor-rect the signal intensities of each lipid.Multivariate statistical analyses wereperformed using SIMCA-P software (ver-sion 11.5; Umetrics AB, Umea, Sweden).A Cox regression model adjusted for theduration of diabetes, the duration ofCAD, age, sex, and smoking history atbaseline was used to calculate the haz-ard ratios (HRs) of composite cardiovas-cular events for individual lipids in eachfollow-up year (using baseline values asthe reference).

SMART Analysis

A modeling strategy called scaled-to-maximum, aligned, and reduced trajec-tories (SMART) analysis was performedto visualize the multivariate responsesimilarity and to facilitate the interpre-tation of the lipidomics data (24).Briefly, the average spectrum of thebaseline measurement for each drugtreatment was subtracted from all ofthe spectra for each study (pretreat-ment subtraction). Then, the vectormagnitudes were calculated as thesquare root of the sum of the squaresof the obtained values for each adjustedspectrum. The average magnitude ateach time point for each drug treatmentgroup was determined. The largest mag-nitude for eachdrug treatment groupwasused to evaluate the overall magnitude of

the therapy-related effect and to scalethe treatment group data to a commonmagnitude. In the end, the average spec-tra at each time point were analyzed.

RESULTS

Clinical Subject CharacteristicsDuring the 3-year study drug adminis-tration, serum samples were obtainedfrom 21 glipizide-treated patients and23 metformin-treated patients at eachtime point (0 [baseline], 1, 2 and 3 yearsof study drug administration) and sub-jected to the lipidomics analyses. De-tailed characteristics of the 44 patientsat baseline and at the end of follow-upare summarized in Table 1 and Supple-mentary Tables 2 and 3. BMI, bodyweight, and waist circumference weresignificantly lower in the metformingroup than in the glipizide group aftertreatment (Table 1); there were no sig-nificant differences in the other clinicalcharacteristics between the glipizideandmetformin groups at either baselineor the end of the 3-year treatment. Thedistribution and doses of the glucose-lowering agents at baseline and aftertreatment were generally similar, ex-cept for the use of insulin, and therewere no significant differences betweenthe two groups regarding other concom-itant medications, except for statin useat baseline (Table 1).

Metformin Significantly InfluencedLipid Metabolism in Type 2 DiabeticPatients With CAD Compared WithGlipizideLipid profiling was performed on fastingserum samples obtained at baseline andat 1, 2, and 3 years of drug treatment forall the study subjects. A total of 118 se-rum lipid molecular species were identi-fied and quantified using the LC-MSlipidomics approach. To compare thetreatment effects of the two drugs onthe serum lipidome over time, SMARTwas used to analyze the lipidomicsdata from the two parallel drug treat-ment groups at different time points.Figure 1 illustrates the SMART analysisof the serum LC-MS data after 0, 1, 2,and 3 years of interventionwith glipizideand metformin. The two trajectoriesclearly point in similar directions, indi-cating that the lipidmetabolic responseswere similar. However, the metformintreatment trajectory was more openthan the glipizide treatment trajectory,

suggesting that metformin substantiallyaffected serum lipid metabolism in type 2diabetic patients with CAD, whereas theeffect of glipizide on the serum lipid profilewas limited compared with the baseline.SMART has been demonstrated to signifi-cantly reduce the risk of misinterpretingthe results of principal component analysis(24). One-way ANOVA of the LC-MS lipido-mics data for the entire cohort revealedthat a total of 10 lipid species were signif-icantly changed in the glipizide group andthat 23 lipids were significantly changed inthe metformin group (Table 2). Amongthese significantly changed lipids, bothdrug treatments significantly increased PClipids and significantly decreased sphin-gomyelin (SM) lipid species comparedwith baseline.

For further investigation of the lipidmetabolic differences between metfor-min and glipizide, all the lipids wereanalyzed in the metformin-glipizidecomparison after eliminating individ-ual differences in lipid metabolism (i.e.,the differences in the baseline lipid pro-files for each treatment were elimi-nated). Specifically, the amount of eachlipid species in the serum samples frompatients at 1, 2, and 3 years of drug treat-ment was first normalized to the lipidconcentration at baseline (i.e., serumlipid data from patients at 1, 2, and 3years of drug treatment were dividedby the baseline data). Then, the geomet-ric ratio of each analyzed lipid con-centration after normalization wascalculated in the fasting serum samplesfrom those who received metforminversus those who received glipizide atthe 1-, 2-, and 3-year time points. Withuse of this approach, differences be-tween the two medications and specificlipid species (i.e., potential lipid bio-markers) that may contribute to the ob-served clinical effects over time could beidentified. The results of the metformin-glipizide comparison for all the lipidanalytes are presented in Fig. 2A–C (1,2, and 3 years of therapy, respectively).The differences in the lipid metabolitesbetween these two treatments are di-rectly reflected by the P values plottedon the y-axis. The data indicated that thedifference between the glipizide andmetformin groups was limited at 1year, whereas the treatment differencesat 2 and 3 years were larger. In detail,only PC (36:5) (metformin vs. glipizide

care.diabetesjournals.org Zhang and Associates 2807

geometric ratio = 1.91, P = 0.046) and PC(O-36:3) (metformin vs. glipizide geomet-ric ratio = 1.29, P = 0.020) were signifi-cantly different between the groupsafter 1 year of treatment. However, 11lipid species, including cholesterol ester(20:4) (metformin vs. glipizide geometricratio = 0.62, P = 0.043), PC (34:3) (0.68,0.030), PC (O-34:1) (0.45, 0.003), PC (O-34:2) (1.72, 0.030), PC (O-36:4) (2.67,,0.001), PC (O-38:5) (0.76, 0.038), PC(O-38:6) (0.67, 0.039), SM (d18:1-14:0)(0.17, 0.014), SM (d18:1-16:1) (0.27,,0.001), TAG (44:0) (1.92, 0.018), andTAG (44:1) (1.90, 0.024), were signifi-cantly different after 2 years of treatment.Twelve lipid species, including LPC (16:1)(metformin vs. glipizide geometric ratio =0.69, P = 0.032), LPC (18:1) (0.67, 0.006),LPC (20:3) (0.74, 0.028), LPC (20:4) (0.59,0.004), LPC (22:6) (0.73, 0.029), PC (34:0)(0.77, 0.035), PC (O-34:2) (2.35,,0.001),PC (O-36:4) (2.14, ,0.001), PE (36:4)(0.76, 0.013), PE (38:6) (0.63, 0.034), SM(d18:1-14:0) (0.23, ,0.001), and SM(d18:1-16:1) (0.34,,0.001), were signifi-cantly different after 3 years of treat-ment. The lipid profiling data indicatedthatmetformin had a significantly greatereffect on serum lipid metabolism than

glipizide in type 2 diabetic patients withCAD in our study. When all the signifi-cantly changed lipid species in the 44patients were analyzed together, threelipid metabolites, PC (O-34:1) (HR28.673 [95% CI 1.373–598.997], P =0.030), SM (d18:0-24:1) (3.797 [1.120–12.880], P = 0.032), and SM (d18:1-20:1) (73.040 [3.183–1675.890], P =0.007), at different follow-up points (at1 year, 1 year, and 2 year, respectively),were associated with long-term compos-ite cardiovascular events with use of theadjusted Cox regression model.

Metformin Had a Beneficial Effect onTAGs in Type 2 Diabetic Patients WithConcomitant CADIt has been reported that TAG profilingcan improve the outcome prediction fordiabetic patients (8). To further explorewhich medication was more beneficialfor the treatment of CAD and diabetes,the TAG pattern associated with the re-ported diabetes risk was investigated bycomparing the metformin and glipizidegroup data for the TAG lipid analytesmeasured in the current study. Briefly,the geometric mean ratios of the TAGlevels after normalization (the TAG

levels in treated patients were normal-ized to those at baseline) in metformin-treated patients and glipizide-treatedpatients were compared with a focuson the acyl chain carbon number andthe double bond content to determinethe lipid effects of glipizide and metfor-min on serum TAGs in these patients.The results are presented in Fig. 3A–C(at 1, 2, and 3 years of therapy, respec-tively). Fig. 3 shows a clear trend of re-duced TAG lipids with a relatively lowercarbon number and of increased TAGwith a relatively higher carbon numberin the metformin group versus the glip-izide group during the 3-year treatmentperiod. There were minimal changes inTAG molecules with a different numberof double bonds (a slight increasing pat-tern was only observed for 0–3 doublebonds) in themetformin versus the glip-izide group throughout the 3-year treat-ment (Fig. 3). The observed patternchanges, regarding both the numberof acyl chain carbons and double bonds,were noted among TAGs but not amongother lipid classes (SupplementaryFig. 2).

CONCLUSIONS

The present lipidomic analyses werebased on a previously reported long-term interventional study, which is likelythe first report on cardiovascular out-comes comparing the long-term effectsof glipizide and metformin on the majorcardiovascular events in type 2 diabeticpatients with a history of CAD (7,25,26).The results demonstrated a beneficial ef-fect of metformin on cardiovascularoutcomes comparedwith glipizide. How-ever, because no differences were iden-tified between the two groups aftertreatment regarding traditional clinicalrisk factors, such as plasma glucose, gly-cated hemoglobin, serum lipids, andblood pressures, the potential effectsof metformin on CVD could not be ex-plained by the clinical findings.

Because dyslipidemia is strongly asso-ciated with cardiovascular events, espe-cially in the diabetic population (27), lipidmanagement in patients with type 2diabetes has the potential to reduce therisk of concomitant CAD. Moreover, lipidsare the fundamental components ofcellular membranes and are essentialin lipidomics because they representthe biochemical activity signature duringlipid metabolism and are therefore

Figure 1—The SMART analysis of the serum LC-MS lipidomics data obtained after 0, 1, 2, and 3years of intervention with glipizide and metformin revealed that metformin had a substantialeffect on serum lipid metabolism in type 2 diabetic patients with CAD, but the effects of glipizideon serum lipids were limited compared with the baseline lipid levels. The error bars representthe SE of the average at each time point. yr, year.

2808 Effects of Metformin and Glipizide on CAD Diabetes Care Volume 37, October 2014

Table

2—Lipid

itemssig

nifica

ntly

changedin

resp

onse

toglip

izideandmetfo

rmin

therapy

Lipid

items

Glipizid

e(n

=21)

Metfo

rmin

(n=23)

Baselin

e1year

2year

3year

Baselin

e1year

2year

3year

LPC(18:1)

4.596

0.314.64

60.26

3.956

0.325.68

60.39**

5.756

0.375.59

60.41

5.246

0.525.51

60.41

PC(32:0)

0.906

0.071.11

60.13

1.056

0.061.08

60.12

1.036

0.061.43

60.12**

1.096

0.050.97

60.06

PC(32:1)

0.396

0.040.59

60.12

0.676

0.10.57

60.12

0.526

0.070.83

60.12**

0.766

0.11**0.41

60.03

PC(34:2)

14.76

0.717.9

61.7

18.026

1.6713.4

60.92

14.66

0.716.9

60.9*

15.86

1.013.0

60.7

PC(36:2)

9.166

0.5810.78

61.32

10.46

0.87.97

60.51

9.266

0.3611.2

60.8*

9.166

0.618.31

60.41

PC(36:6)

0.106

0.010.13

60.02

0.106

0.010.07

60.01*

0.106

0.010.13

60.02

0.106

0.010.06

60.005**

PC(38:4)

3.456

0.323.43

60.5

3.516

0.382.89

60.31

3.656

0.324.35

60.43

3.276

0.312.62

60.2*

PC(38:5)

0.56

0.050.56

60.07

0.606

0.060.44

60.04

0.536

0.050.92

60.11**

0.696

0.060.49

60.04

PC(40:5)

0.546

0.060.69

60.11

0.656

0.060.44

60.05

0.636

0.060.98

60.16

0.656

0.050.42

60.03**

PC(40:7)

0.326

0.030.36

60.05

0.326

0.020.25

60.02

0.316

0.020.43

60.05*

0.306

0.030.25

60.02

PC(O-34:1)

0.286

0.020.17

60.02*

0.266

0.020.25

60.02

0.276

0.010.21

60.03

0.236

0.020.25

60.02

PC(O-34:2)

0.356

0.040.71

60.09***

0.736

0.06***0.55

60.05***

0.306

0.020.72

60.05***

0.596

0.05***0.50

60.04***

PC(O-36:3)

0.106

0.010.11

60.01

0.126

0.010.09

60.01

0.106

0.010.14

60.01***

0.116

0.010.10

60.01

PC(O-36:4)

0.396

0.021.22

60.14***

1.056

0.13***0.48

60.03

0.406

0.031.13

60.11***

0.946

0.10***0.45

60.03

PC(O-36:5)

0.556

0.070.64

60.07

0.766

0.070.54

60.05

0.636

0.050.85

60.08*

0.666

0.060.48

60.03*

PC(O-38:4)

0.326

0.020.37

60.02

0.446

0.03**0.29

60.03

0.336

0.020.45

60.03**

0.376

0.030.30

60.03

PC(O-38:7)

0.096

0.010.11

60.01

0.106

0.010.07

60.005

0.086

0.0040.11

60.01**

0.096

0.010.07

60.005

SM(d18:0-24:1)

5.936

0.734.5

60.31**

3.476

0.3***4.39

60.43**

5.546

0.715.5

60.76

4.316

0.424.74

60.55

SM(d18:1-14:0)

1.386

0.120.35

60.05***

0.516

0.12***0.95

60.12*

1.606

0.140.40

60.06***

0.416

0.09***0.94

60.12***

SM(d18:1-16:1)

1.046

0.110.49

60.06***

0.466

0.04***0.4

60.03***

1.576

0.170.43

60.03***

0.406

0.02***0.41

60.03***

SM(d18:1-16:0)

9.016

0.5711.33

61.34

10.066

0.638.13

60.64

10.246

0.7714.21

61.27**

10.26

0.69.92

60.66

SM(d18:1-20:1)

0.466

0.030.55

60.06

0.496

0.050.36

60.02

0.476

0.030.57

60.05

0.486

0.040.39

60.02*

ChE(18:1)

4.586

0.263.25

60.21***

3.166

0.24***3.37

60.31***

4.726

0.373.65

60.26

3.336

0.223.83

60.40

Data

aremean

s6

SE.ChE,ch

olestero

lester.*P

,0.05;

**P,

0.01;***P

,0.001

vs.baselin

eat

1,2,and3years

oftw

ostu

dydrugadministratio

n,resp

ectively.

care.diabetesjournals.org Zhang and Associates 2809

closely related to observable pheno-types. With this knowledge, lipidomicscan be viewed as the process of definingmultivariate lipid metabolic trajectoriesthat represent the systemic response(i.e., holistic lipid metabolic changes)of a living system to pharmaceutical in-terventions over time.In the current study, the results of the

SMART analysis (Fig. 1) indicated thatmetformin had a substantially greatereffect on serum lipid metabolism thanglipizide. Such a metabolic trajectoryanalysis is essential for depicting thetime-dependent metabolic behaviorsresulting from a specific interventionand is useful in clinical diagnosis. Fur-thermore, by using LC-QTOF/MS lipidprofiling to analyze 118 molecular lipidspecies across 7 lipid classes and 10 sub-classes, we identified a panel of individ-ual lipid species that were significantlyaltered in response to drug treatmentand that were significantly different be-tween the two treatment groups. Thealtered individual lipid species mainlybelonged to the LPC, PC, PC-O, TAG,SM, and PE lipid classes. The role ofthese lipid classes in the pathogenesisand progression of diabetes and athero-sclerosis has recently been recognized(28–37). Moreover, PC (O-34:1), SM

(d18:0-24:1), and SM (d18:1-20:1)were associated with an increased riskof long-term composite cardiovascularevents in our study. Although furtherstudies are needed to elucidate the bi-ological mechanism accounting for thelink between these specific lipid molec-ular species and the risk of cardiovascu-lar events, these findings indicated thatthe systematic analysis of serum lipidspecies, rather than lipid classes as awhole, may reveal the differential ef-fects of antidiabetes agents on futureCVD risk beyond the improvement ofclinical biomarkers.

By viewing acyl chains in their naturalcontext across distinct macromolecularspecies, we demonstrated an increasingtrend in TAG acyl chain carbon numberand a slight increase in TAGs with 0–3double bonds in the metformin groupcompared with the glipizide group. Al-though the risk pattern for the progres-sion of CVD indicated by acyl chaincarbon number and double bond con-tent remains unclear, several recentstudies have demonstrated a relation-ship between plasma TAGs and futurediabetic risk or insulin action (8,38). Inthe Framingham Heart Study cohort(FHS), Rhee et al. (8) found that TAGsof lower carbon number and double

bond content were associated with anincreased risk of type 2 diabetes,whereas TAGs of higher carbon numberand double bond content were associ-ated with a decreased risk of type 2 di-abetes. Furthermore, they found thatTAGs of lower carbon number and dou-ble bond content were elevated in thesetting of insulin resistance and thatTAGs of higher carbon number and dou-ble bond content had the weakest cor-relation with insulin resistance. Basedon these observations, our findings ofincreased TAG lipid species with highercarbon number in metformin-treatedversus glipizide-treated patients duringthe 3-year treatment period indicatethat long-term metformin treatmentwould be more beneficial for patientswith both type 2 diabetes and CAD.Schwab et al. (38) also determinedthat a sustained increase in insulin sen-sitivity was associated with a similar pat-tern in TAG changes. All of these resultscombined with our current findingsmight indicate a beneficial effect of cer-tain TAG species in diabetic patientswith future cardiovascular risk.

The major strength of our findings isthe LC-MS–based lipidomic analysis andthe breadth of the lipids that were ana-lyzed. Because the lipidomic profilingwas based on our previous multicenter,randomized clinical trial, the results pro-vide new evidence regarding the under-lying mechanisms of disease progressionand the effects of different drug therapieson cardiovascular outcomes in thesehigh-risk patients. However, our studyhad several limitations. First, serum sam-ples at all four timepointswere only avail-able for 44 study participants out of atotal of 304 patients owing to various rea-sons described above. However, thebaseline characteristics of the 44 partici-pants were similar to those of the totalparticipant population (SupplementaryTable 4) and of the remaining participantswho were not involved in the presentanalysis (data not shown). Regardingthe lipidomic profiling, there was nosignificant difference in the relativeconcentration of individual lipid metab-olites at baseline between the 44 partic-ipants involved in the lipid analysis andthe general population for whom baselinelipidomics data were available (n = 116)(data not shown). Furthermore, weperformed a validation analysis usingthe participants with available serum

Figure 2—Metformin-glipizide treatment comparison for all the analyzed lipids in the SPREAD-DIMCAD study. A: The geometric mean ratio of each analyzed lipid concentration in the met-formin versus the glipizide group after 1 year of treatment. B: The geometric mean ratio of eachlipid level in the metformin versus the glipizide group after 2 years of treatment. C: The geo-metric mean ratio of each lipid level in the metformin versus the glipizide group after 3 years oftreatment. For each graph, the P values are plotted on the y-axis, and each data point representsa distinct lipid entity. The x-axis represents the geometric mean ratio of each lipid level afternormalization in the metformin versus the glipizide group after different treatment periods, andthe y-axis represents the P value of each lipid level after normalization in the metformin versusthe glipizide group after the same treatment period. yr, year.

2810 Effects of Metformin and Glipizide on CAD Diabetes Care Volume 37, October 2014

sample at baseline and at 1 year of studydrug administration to confirm our cur-rent findings (n = 51 in the glipizide group,and n = 58 in the metformin group). Asexpected (Supplementary Fig. 3), the re-sultswere similar to those obtained in thecurrent analysis (i.e., TAGs with a highercarbon number were increased in themetformin group vs. the glipizide groupafter 1 year of treatment) (Fig. 3). There-fore, although the sample size was small,it was representative of the general pa-tient population. Second, statin use atbaseline was different between the twogroups (90.5% in the glipizide group vs.47.8% in the metformin group, P =0.004). However, comparing statin usein the two groups revealed that from 1year on, therewas no difference between

the twogroups at each follow-up year (P =0.062, 0.146, and 0.095 at 1, 2, and 3years of study drug administration, re-spectively) and that the percentage of pa-tients using statins in both groupsdecreased during the intervention.More-over, the concentrations of total choles-terol, cholesterol ester, and lipoproteinswere not different between the groups atbaseline and after 3 years of treatment,suggesting aminor influence of the differ-ent treatment with statins at baseline.Thus, although we could not exclude theimpact of statins on the lipid results, theconfounding effects of statins were notsupported by our data. Nevertheless,caution shall be exercised in interpretingthe current findings owing to the rela-tively small sample size.

In conclusion, by applying LC-MS–based lipidomics and measuring bio-chemical parameters, we revealed thedifferential therapeutic effects of met-formin and glipizide on comprehensivelipidomics, which were comparable withtheir different long-term effects on car-diovascular outcomes. Metformin moresubstantially changed serum lipid speciescompared with glipizide. Among the al-tered lipid species, three lipidmetaboliteswere linked to long-term composite car-diovascular events. Furthermore, aftertreatment, TAGs of higher carbon num-ber increased in the metformin groupcompared with the glipizide group. Basedon these findings, we speculated that alipidomics approachmay be useful in elu-cidating the complex mechanism of ac-tion of particular drugs and presents auseful tool for probing the mechanismsof progression of long-term CVDs. Futurestudies will be required to precisely evalu-ate the predictive findings in additionalcohorts and to determine whether wehave identified an early marker and/oran effector of cardiovascular disease andits associated therapeutics.

Funding. This study was supported by theMinistry of Science, Technology and Innova-tion Fund and projects (No. 2011YQ030114),the National Basic Research Program (No.2012CB517506) from the State Ministry ofScience and Technology of China, the grants(No. 81170784, No. 21175132) and the creativeresearch group project (No. 21321064) from theNational Natural Science Foundation of China,the Program for Innovative Research Team ofShanghai Municipal Education Commission, theSector Funds of Ministry of Health (No.201002002), the National Key New Drug Crea-tion and Manufacturing Program of Ministry ofScience and Technology (No. 2012ZX09303006-001), the Shanghai Committee on Science andTechnology (10dz1920802), and the Fund ofShanghai Municipal Health Bureau (No. 2012-244).Duality of Interest. No potential conflicts ofinterest relevant to this article were reported.Author Contributions. Y.Z. and J.H. contrib-uted to the study design, data analysis, andprimary drafting of the manuscript. C.H. con-tributed to the lipid profiling, statistical analysis,and primary drafting of the manuscript. J.Ze.contributed to the sample processing and lipidprofiling. S.L. reviewed and edited the manuscript.A.L., D.Zh., W.W., and W.S. contributed to thedesign and implementation of the protocol andto the discussion. Q.S., Y.D., Z.Z., W.T., J.Zh., L.C.,D.Zo., D.W., H.L., C.L., G.W., and J.S. contributedto the implementation of the protocol and co-ordinated the discussion. G.N. designed andimplemented the protocol and was the lead

Figure 3—Metformin-glipizide treatment comparison in the SPREAD-DIMCAD study based onTAGs. A: The geometric mean ratio of TAG levels in the metformin group versus that in theglipizide group after 1-year treatment. B: The geometric mean ratio of TAG levels in metforminversus the glipizide group after 2 years of treatment. C: The geometricmean ratio of TAG levels inthe metformin versus the glipizide group after 3 years of treatment. yr, year.

care.diabetesjournals.org Zhang and Associates 2811

author. G.X. contributed to the study design, dataanalysis, and review of themanuscript. G.N. is theguarantor of thiswork and, as such, had full accessto all the data in the study and takes responsibilityfor the integrity of the data and the accuracy ofthe data analysis.

References1. Gu K, Cowie CC, Harris MI. Mortality in adultswith and without diabetes in a national cohortof the U.S. population, 1971-1993. DiabetesCare 1998;21:1138–11452. Bertoni AG, Krop JS, Anderson GF, BrancatiFL. Diabetes-related morbidity and mortalityin a national sample of U.S. elders. DiabetesCare 2002;25:471–4753. DeFronzo RA. Pharmacologic therapy fortype 2 diabetes mellitus. Ann Intern Med1999;131:281–3034. Meinert CL, Knatterud GL, Prout TE, Klimt CR.A study of the effects of hypoglycemic agents onvascular complications in patients with adult-onset diabetes. II. Mortality results. Diabetes1970;19(Suppl.):789–8305. The University Group Diabetes Program. TheUniversity Group Diabetes Program. A study ofthe effects of hypoglycemic agents on vascularcomplications in patients with adult-onset dia-betes. V. Evaluation of pheniformin therapy.Diabetes 1975;24(Suppl. 1):65–1846. UK Prospective Diabetes Study (UKPDS)Group. Effect of intensive blood-glucose controlwith metformin on complications in overweightpatients with type 2 diabetes (UKPDS 34). Lan-cet 1998;352:854–8657. Hong J, Zhang Y, Lai S, et al.; SPREAD-DIMCADInvestigators. Effects of metformin versus glipi-zide on cardiovascular outcomes in patients withtype 2 diabetes and coronary artery disease. Di-abetes Care 2013;36:1304–13118. Rhee EP, Cheng S, Larson MG, et al. Lipid pro-filing identifies a triacylglycerol signature of insu-lin resistance and improves diabetes prediction inhumans. J Clin Invest 2011;121:1402–14119. Wang TJ, Larson MG, Vasan RS, et al. Metab-olite profiles and the risk of developing diabe-tes. Nat Med 2011;17:448–45310. Rhee EP, Gerszten RE. Metabolomics andcardiovascular biomarker discovery. Clin Chem2012;58:139–14711. Fernandez C, Sandin M, Sampaio JL, et al.Plasma lipid composition and risk of developingcardiovascular disease. PLoS ONE 2013;8:e7184612. Heather LC, Wang X, West JA, Griffin JL. Apractical guide to metabolomic profiling as adiscovery tool for human heart disease. J MolCell Cardiol 2013;55:2–11

13. Bernini P, Bertini I, Luchinat C, Tenori L,Tognaccini A. The cardiovascular risk of healthyindividuals studied by NMR metabonomics ofplasma samples. J Proteome Res 2011;10:4983–499214. Cheng S, Rhee EP, Larson MG, et al. Metab-olite profiling identifies pathways associatedwith metabolic risk in humans. Circulation2012;125:2222–223115. McCormack SE, Shaham O, McCarthy MA,et al. Circulating branched-chain amino acidconcentrations are associated with obesityand future insulin resistance in children andadolescents. Pediatr Obes 2013;8:52–6116. Wurtz P, Soininen P, Kangas AJ, et al.Branched-chain and aromatic amino acids arepredictors of insulin resistance in young adults.Diabetes Care 2013;36:648–65517. Floegel A, Stefan N, Yu Z, et al. Identificationof serum metabolites associated with risk oftype 2 diabetes using a targeted metabolomicapproach. Diabetes 2013;62:639–64818. Wang-Sattler R, Yu Z, Herder C, et al. Novelbiomarkers for pre-diabetes identified by me-tabolomics. Mol Syst Biol 2012;8:61519. Ferrannini E, Natali A, Camastra S, et al.Early metabolic markers of the developmentof dysglycemia and type 2 diabetes and theirphysiological significance. Diabetes 2013;62:1730–173720. Gall WE, Beebe K, Lawton KA, et al.; RISCStudy Group. alpha-hydroxybutyrate is an earlybiomarker of insulin resistance and glucose in-tolerance in a nondiabetic population. PLoSONE 2010;5:e1088321. Juraschek SP, Steffes MW, Miller ER 3rd,Selvin E. Alternative markers of hyperglycemiaand risk of diabetes. Diabetes Care 2012;35:2265–227022. Hu C, van Dommelen J, van der Heijden R,et al. RPLC-ion-trap-FTMS method for lipid pro-filing of plasma: method validation and applica-tion to p53 mutant mouse model. J ProteomeRes 2008;7:4982–499123. Wang C, Kong H, Guan Y, et al. Plasma phos-pholipidmetabolic profiling andbiomarkers of type2 diabetes mellitus based on high-performanceliquid chromatography/electrospray mass spec-trometry and multivariate statistical analysis.Anal Chem 2005;77:4108–411624. Keun HC, Ebbels TM, Bollard ME, et al. Geo-metric trajectory analysis of metabolic re-sponses to toxicity can define treatmentspecific profiles. Chem Res Toxicol 2004;17:579–58725. Lund SS, Gong Y. Comment on Hong et al.Effects of metformin versus glipizide on cardio-vascular outcomes in patients with type 2 dia-betes and coronary artery disease. Diabetes

care 2013;36:1304-1311. Diabetes Care 2014;37:e19–e2026. Hong J, Zhang Y, Lai S, Ning G. Response tocomment on Hong et al. Effects of metforminversus glipizide on cardiovascular outcomes inpatients with type 2 diabetes and coronary ar-tery disease. Diabetes care 2013;36:1304-1311.Diabetes Care 2014;37:e2127. Brown AS. Lipid management in patientswith diabetes mellitus. Am J Cardiol 2005;96:26E–32E28. Quinn MT, Parthasarathy S, Steinberg D.Lysophosphatidylcholine: a chemotactic factorfor human monocytes and its potential role inatherogenesis. Proc Natl Acad Sci U S A 1988;85:2805–280929. Schmitz G, Ruebsaamen K. Metabolism andatherogenic disease association of lysophos-phatidylcholine. Atherosclerosis 2010;208:10–1830. Quehenberger O, Dennis EA. The humanplasma lipidome. N Engl J Med 2011;365:1812–182331. Zhang J, Tu K, Xu Y, et al. Sphingomyelin inerythrocytemembranes increases the total cho-lesterol content of erythrocyte membranes inpatients with acute coronary syndrome. CoronArtery Dis 2013;24:361–36732. Makinen VP, Tynkkynen T, Soininen P, et al.Sphingomyelin is associatedwith kidney diseasein type 1 diabetes (The FinnDiane Study). Me-tabolomics 2012;8:369–37533. Yeboah J, McNamara C, Jiang XC, et al. As-sociation of plasma sphingomyelin levels andincident coronary heart disease events in anadult population: Multi-Ethnic Study of Athero-sclerosis. Arterioscler Thromb Vasc Biol 2010;30:628–63334. Wang Z, Klipfell E, Bennett BJ, et al. Gutflora metabolism of phosphatidylcholine pro-motes cardiovascular disease. Nature 2011;472:57–6335. Graessler J, Schwudke D, Schwarz PE,Herzog R, Shevchenko A, Bornstein SR. Top-down lipidomics reveals ether lipid deficiencyin blood plasma of hypertensive patients. PLoSONE 2009;4:e626136. Gaikwad NW. Ultra performance liquidchromatography-tandem mass spectrometrymethod for profiling of steroid metabolome inhuman tissue. Anal Chem 2013;85:4951–496037. Weir JM, Wong G, Barlow CK, et al. Plasmalipid profiling in a large population-based co-hort. J Lipid Res 2013;54:2898–290838. Schwab U, Seppanen-Laakso T, Yetukuri L,et al.; GENOBIN Study Group. Triacylglycerolfatty acid composition in diet-induced weight lossin subjects with abnormal glucose metabolism–the GENOBIN study. PLoS ONE 2008;3:e2630

2812 Effects of Metformin and Glipizide on CAD Diabetes Care Volume 37, October 2014