Circulating LysophosphatidylcholinesAreMarkers of aMetabolically BenignNonalcoholic Fatty LiverRAINER LEHMANN, PHD
1,2
HOLGER FRANKEN, PHD3
SASCHA DAMMEIER, PHD4
LARS ROSENBAUM, PHD3
KONSTANTINOS KANTARTZIS, MD1,2
ANDREAS PETER, MD1,2
ANDREAS ZELL, PHD3
PATRICK ADAM, MD5
JIA LI, PHD6
GUOWANG XU, PHD6
ALFRED KÖNIGSRAINER, MD7
J€URGEN MACHANN, PHD8
FRITZ SCHICK, MD, PHD8
MARTIN HRABÉ DE ANGELIS, PHD9
MATTHIAS SCHWAB, MD10
HARALD STAIGER, PHD1,2
ERWIN SCHLEICHER, PHD1,2
AMALIA GASTALDELLI, PHD11
ANDREAS FRITSCHE, MD1,2
HANS-ULRICH HÄRING, MD1,2
NORBERT STEFAN, MD1,2
OBJECTIVEdNonalcoholic fatty liver (NAFL) is thought to contribute to insulin resistanceand its metabolic complications. However, some individuals with NAFL remain insulin sensitive.Mechanisms involved in the susceptibility to develop insulin resistance in humans withNAFL arelargely unknown.We investigated circulatingmarkers andmechanisms of ametabolically benignand malignant NAFL by applying a metabolomic approach.
RESEARCH DESIGN AND METHODSdA total of 265 metabolites were analyzed be-fore and after a 9-month lifestyle intervention in plasma from 20 insulin-sensitive and 20 insulin-resistant subjects with NAFL. The relevant plasma metabolites were then tested for relationshipswith insulin sensitivity in 17 subjects without NAFL and in plasma from 29 subjects with livertissue samples.
RESULTSdThe best separation of the insulin-sensitive from the insulin-resistant NAFL groupwas achieved by a metabolite pattern including the branched-chain amino acids leucine andisoleucine, ornithine, the acylcarnitines C3:0-, C16:0-, and C18:0-carnitine, and lysophospha-tidylcholine (lyso-PC) C16:0 (area under the ROC curve, 0.77 [P = 0.00023] at baseline and 0.80[P = 0.000019] at follow-up). Among the individual metabolites, predominantly higher levels oflyso-PC C16:0, both at baseline (P = 0.0039) and at follow-up (P = 0.001), were found in theinsulin-sensitive compared with the insulin-resistant subjects. In the non-NAFL groups, nodifferences in lyso-PC C16:0 levels were found between the insulin-sensitive and insulin-resistantsubjects, and these relationships were replicated in plasma from subjects with liver tissue samples.
CONCLUSIONSdFrom a plasma metabolomic pattern, particularly lyso-PCs are able toseparate metabolically benign from malignant NAFL in humans and may highlight importantpathways in the pathogenesis of fatty liver–induced insulin resistance.
Diabetes Care 36:2331–2338, 2013
The prevalence of nonalcoholic fattyliver (NAFL) is increasing worldwideand is now affecting.30% of adults
and a considerable number of children indeveloped countries. NAFL represents astrong and independent predictor of type2 diabetes, the metabolic syndrome, andcardiovascular disease, findings that areprobably largely attributable to dysregu-lated hepatic metabolic signaling, result-ing in insulin resistance, which is oftenfound in fatty liver (1–8). In fact, in hu-mans, among several body fat compart-ments that are considered to play a majorrole in the pathogenesis of insulin resis-tance as visceral fat and intramyocellularfat, liver fat is most strongly associatedwith insulin resistance (9,10). However,a considerable amount of subjects withNAFL and without more advanced stagesof fatty liver–associated diseases remaininsulin sensitive (11).
The following question remains: Canbroad systematic blood screening help tofind mechanisms and/or markers for thedissociation of NAFL from insulin resis-tance in humans? Furthermore, are theseparameters also relevant in subjects with-out NAFL or can they even help toidentify NAFL-associated insulin resis-tance? To address these questions in thecurrent study, we used precise phenotyp-ing methods in humans that allowedcareful quantification of body fat com-partments, liver fat content, and glucoseand lipid metabolism and combined thissettingwith a targeted plasmametabolomicapproach.
c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c
From the 1Division of Endocrinology, Diabetology,Vascular Medicine, Nephrology, and ClinicalChemistry, Department of Internal Medicine,University Hospital T€ubingen, T€ubingen, Ger-many; the 2Institute of Diabetes Research andMetabolic Diseases, Member of the German Cen-ter for Diabetes Research, University of T€ubingen,T€ubingen, Germany; the 3Center for Bioinfor-matics, University of T€ubingen, T€ubingen, Ger-many; the 4Medical Proteome Center, Institute forOphthalmic Research, University of T€ubingen,T€ubingen, Germany; the 5Institute of Pathology,University of T€ubingen, T€ubingen, Germany; the6CAS Key Laboratory of Separation Science for
Analytical Chemistry, Dalian Institute of ChemicalPhysics, Chinese Academy of Sciences, Dalian,China; the 7Department of General, Visceral, andTransplant Surgery, University of T€ubingen,T€ubingen, Germany; the 8Section on Experimen-tal Radiology, University of T€ubingen, T€ubingen,Germany; the 9Institute of Experimental Genetics,Helmholtz-Zentrum,Munich, Germany; the 10Dr.Margarete Fischer-Bosch Institute for ClinicalPharmacology and University of T€ubingen, Stutt-gart, Germany; and the 11Institute of Clinical Physi-ology, National Research Council, Pisa, Italy.
Readers may use this article as long as the work isproperly cited, the use is educational and not forprofit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ fordetails.
care.diabetesjournals.org DIABETES CARE, VOLUME 36, AUGUST 2013 2331
P a t h o p h y s i o l o g y / C o m p l i c a t i o n sO R I G I N A L A R T I C L E
SubjectsCaucasians from the southern part ofGermany participated in the ongoingT€ubingen Lifestyle Intervention Program(12,13). Individuals were included in thestudy when they fulfilled at least one ofthe following criteria: a family history oftype 2 diabetes, a BMI .27 kg/m2, and aprevious diagnosis of impaired glucosetolerance and/or of gestational diabetes.They were considered healthy accordingto a physical examination and routine lab-oratory tests. The participants had no his-tory of liver disease and did not consumemore than two alcoholic drinks per day.Serum aminotransferase levels were,2.5times the upper limit of normal. In a firstapproach, out of 330 subjects who metthe aforementioned requirements, i.e.,had measurements of body fat distribu-tion and liver fat content, using magneticresonance techniques at baseline (12), werandomly selected 40 subjects who hadNAFL for metabolomics analysis. Wethen measured the identified metabolo-mic parameters in 17 subjects who under-went the same phenotyping strategies andwho did not have NAFL. Finally, we mea-sured the relevant metabolites in a thirdgroup of 29 subjects from whom liver tis-sue samples were available. Informedwritten consent was obtained from allparticipants after the nature and possibleconsequences of the studies were ex-plained and the local medical ethics com-mittee had approved the protocol.
Lifestyle interventionThe 57 subjects who underwent precisephenotyping participated in the 9-monthlifestyle intervention. After the baselinemeasurements, individuals underwent di-etary counseling and had up to 10 sessionswith a dietitian. Counseling was aimed toreduce bodyweight, intake of calories, and,particularly, intake of calories from fat andto increase intake of fibers. Individualswere asked to perform at least 3 h ofmoderate sports per week. Aerobic endur-ance exercise (e.g., walking or swimming)with an onlymoderate increase in the heartrate was encouraged (13).
Total body fat, body fatdistribution, and lean body massMeasurements of total body and visceral fatand lean body mass were performed by anaxial T1-weighted fast spin echo techniquewith a 1.5-T whole-body imager (14).
Noninvasive measurement of liverfat content and intramyocellularlipidsLiver fat content was noninvasively mea-sured by localized proton magnetic reso-nance (1HMR) spectroscopy as previouslydescribed (14). NAFL was defined as liverfat content .5.56% (15). Intramyocellularlipid content of the tibialis anterior musclewasdeterminedaspreviously described (14).
Oral glucose tolerance test andclinical chemical analysesThe 57 individuals who underwent precisephenotyping and the intervention alsounderwent a 75-g oral glucose tolerancetest (OGTT) at baseline and after 9 monthsof lifestyle intervention. Whole-body in-sulin sensitivity was calculated from glu-cose and insulin values during theOGTTasproposed by Matsuda and DeFronzo (16).In the 29 subjects fromwhom liver sampleswere available, the homeostatic model as-sessment of insulin resistance (HOMA-IR)was calculated from fasting blood samples(glucose [mmol/L] z insulin [mU/L]/22.5).Blood glucose was determined using a bed-side glucose analyzer (YSI, Yellow Springs,CO). Plasma insulin was determined onan ADVIA Centaur XP and all other routineparameters on an ADVIA 1800 clinicalchemistry system (Siemens HealthcareSystems, Erlangen, Germany). Serum plas-minogen activator inhibitor 1 was mea-sured by ELISA (Bender MedSystems),and fetuin-A was measured by an im-munoturbidimetic method (BioVendorLaboratory Medicine, Modreci, CzechRepublic) (17). The metabolic syndromewas estimated based on the recommen-dation of the Executive Summary of theThird Report of the National CholesterolEducation Program (NCEP) Expert Panelon Detection, Evaluation, and Treatmentof High Blood Cholesterol in Adults(Adult Treatment Panel III) (18).
Liver samplesThe 29 Caucasians (62.8 6 2.2 years ofage; sex, 22 male/7 female; BMI, 25.5 60.85 kg/m2) who underwent liver surgerywere fasted overnight prior to the collec-tion of blood samples and liver biopsies.Subjects tested negative for viral hepatitisand had no liver cirrhosis. Liver sampleswere taken from normal, nondiseased tis-sue during surgery, immediately frozen inliquid nitrogen, and stored at 2808C.
Metabolite profilingAt baseline and after 9 months of lifestyleintervention, 265 blood metabolites were
analyzed by the targeted IDQ metabolo-mics platform from Biocrates (Innsbruck,Austria). This platform combines flowinjection (acylcarnitines and glycero-phospholipids), liquid chromatographic(amino acids and bile acids), gas chro-matographic (free fatty acids), and massspectrometric approaches. For the repli-cation of the findings on palmitoyl lyso-phospatidylcholine (lyso-PC C16:0) inthe lifestyle intervention study, lyso-PCC16:0 levels were measured with anothermethod, targeted ultraperformance liquidchromatography mass spectrometry, inthe fasting blood samples of the subjectsfrom whom liver samples were available.
Statistical analysesFirst, 40 subjects with NAFL were ran-domly selected from a larger group solelybased on their liver fat content at baseline,when they had NAFL (liver fat content.5.56%). Subjects were then dividedinto two groups based on the median in-sulin sensitivity, which was measuredfrom the OGTT as proposed by Matsudaand DeFronzo (16) (insulin-sensitive andinsulin-resistant NAFL groups). This sim-ple two-step approach was specificallychosen to not introduce a bias that mayarise by an artificial matching process,e.g., when matching for age, sex, and oradiposity, because today it is not knownhow these parameters influence the rela-tionship of liver fat content with insulinsensitivity. The same approach was cho-sen for the 17 subjects without NAFL(insulin-sensitive and insulin-resistantnon-NAFL groups). Pearson correlationswere used to test for relationships be-tween continuous variables. Paired Stu-dent t tests were performed to comparedata obtained from the same experimentalgroup. When different groups were com-pared, two-sample Student t tests were ap-plied. In addition, repeated-measuresmultivariate ANOVA (MANOVA) wasused, which allowed us to test in a moregeneral aspect for differences in the param-eters between the insulin-sensitive andinsulin-resistant groups, as well as forchanges of the parameters within the sub-jects and to perform group 3 time inter-action tests. For the evaluation of the massspectrometric data, a signal-to-noise ratioof 3 was set as the limit of detection. If nototherwise stated, a significance thresholdof a = 0.01 was used as a reasonable com-promise between type I (false-positive)and type II (false-negative) error, as previ-ously suggested (19). Statistical analysiswas performed using Matlab version
2332 DIABETES CARE, VOLUME 36, AUGUST 2013 care.diabetesjournals.org
Circulating lysophosphatidylcholines and fatty liver
7.9.0.529 (R2009b) (The MathWorksInc.).
To assess the discriminative potentialof metabolite subsets in a multivariatemanner, we applied a wrapper-basedfeature subset selection approach resort-ing to six different heuristic optimizationtechniques implemented in the EvA2framework and six classification algo-rithms from the WEKA package (20).We applied the feature subset selectionprocedures individually and additionallyintegrated them into an ensemble to dis-cover particularly robust metabolite sub-sets (21). Comparable approaches havedemonstrated their usefulness in massspectrometry studies (22).
RESULTS
Characteristics of the subjects withNAFLAt baseline, the 20 insulin-sensitive and20 insulin-resistant subjects in the NAFLgroups had almost an identical mean liverfat content, whereas the mean insulinsensitivity differed largely based on theallocation of the subjects into the groups(Table 1). Therewas a dominance ofmales(75%) in the insulin-sensitive group andof females (65%) in the insulin-resistantgroup as well as a significant difference
in age between the groups. The insulin-resistant women were older than theinsulin-sensitive women (54 6 4 vs.466 2 years, P = 0.06), and it is possiblethat more women in the insulin-resistantgroup were postmenopausal. General ad-iposity, as estimated from the BMI andprecisely measured by magnetic reso-nance imaging, was lower in the insulin-sensitive group. No differences in plasmalevels were detected for aspartate trans-aminase, alanine transaminase, high-sensitivity C-reactive protein (hs-CRP),fasting glucose, or 2-h glucose (Table 1).Also, no differences were found betweenthe groups in the serum lipid profiles(Supplementary Table 1). Statisticallysignificant higher fetuin-A levels, whichmay indicate elevated hepatic inflamma-tion, were found in the insulin-resistantgroup. During the lifestyle intervention,liver fat content decreased significantlyand to a similar extent in bothNAFLgroups,and the difference in insulin sensitivityremained unaltered between the groupsat follow-up (Table 1). In the repeated-measures MANOVA, the statistical rela-tionships at baseline and at follow-up,which were observed between and withinthe groups, were largely similar to the re-sults of the Student t tests (data notshown).
Predictive effect of a metabolitepattern to separate insulin-sensitivefrom insulin-resistant subjects withNAFLAfter the initial evaluation of 265 plasmametabolite measurements, 85 metaboliteshad to be excluded based on low signalintensities (Supplementary Tables 2 and3). First, we hypothesized that a patternof metabolites may be able to separateinsulin-sensitive from insulin-resistantsubjects with NAFL. The detected patternincluded seven metabolites contributingto the separation of insulin-sensitivefrom insulin-resistant individuals withNAFL, both at baseline and at follow-up(Fig. 1A andD). We found that the sum ofthe levels of the metabolites of this pat-tern, which consisted of the branched-chain amino acids (BCAAs) leucine andisoleucine, ornithine, the acylcarnitinesC3:0-, C16:0-, and C18:0-carnitine, andlyso-PC C16:0, was higher in the insulin-sensitive compared with the insulin-resistant NAFL groups at baseline and atfollow-up (Fig. 1B and E). More impor-tantly, using a naive Bayes classifier, thismetabolite pattern at baseline separatedinsulin-sensitive from insulin-resistantsubjects with a relatively high discrimina-tory power at baseline (area under theROC curve, 0.77; positive predictive
Table 1dSelected characteristics of the 40 subjects with NAFL at baseline and after 9 months of lifestyle intervention
Values are means 6 SEM. ALT, alanine transaminase; AST, aspartate transaminase; AU, arbitrary units; IMCL, intramyocellular lipid; LBM, lean body mass; MRS,magnetic resonance spectroscopy; MRT, magnetic resonance tomography; PAI-1, plasminogen activator inhibitor 1. At follow-up, magnetic resonancemeasurementswere only available in 30 subjects. #x2 test. *P , 0.05 for change within each group.
care.diabetesjournals.org DIABETES CARE, VOLUME 36, AUGUST 2013 2333
Figure 1dA plasma metabolite pattern predicts insulin sensitivity in NAFL. Plasma pattern of seven metabolites consistent with lyso-PC C16:0,ornithine, leucine, isoleucine, and C3:0-, C16:0-, and C18:0-carnitine for insulin-sensitive (blue) and insulin-resistant (red) subjects with NAFL atbaseline (A) and after 9 months of lifestyle intervention (D). Each axis represents one metabolite, and all axes have their common origin in the centerof the circle. The circle has radius 1 and intersects all axes at 1 (at the arrowhead) and 21 (in the lower half). The blue and red stars denote theindividuals represented by the levels of the corresponding metabolites, projected to the axes after mean centering and scaling to unit variance. Anindividual’s metabolite level was combined by equally weighted linear combination. The connecting lines between the stars meet at the center (i.e.,mean) of all individuals of each group to indicate the separation of the groups in this projection. The closer the center of one of the groups is to 1, the
2334 DIABETES CARE, VOLUME 36, AUGUST 2013 care.diabetesjournals.org
Circulating lysophosphatidylcholines and fatty liver
value, 0.75; negative predictive value,0.88; sensitivity, 0.90; specificity, 0.70)(Fig. 1C). No such level of significancecould be observed for any single metabo-lite. Interestingly, although many param-eters changed during the intervention, themetabolite pattern measured at follow-upalso discriminated the insulin-sensitivefrom the insulin-resistant subjects at fol-low-up (positive predictive value, 0.83;negative predictive value, 0.77; sensitiv-ity, 0.75; specificity, 0.85) (Fig. 1E and F).
The metabolic fingerprints ofinsulin-sensitive and -resistantsubjects with NAFLNext, we investigated which of the in-dividual metabolites were different be-tween the insulin-sensitive and theinsulin-resistant subjects with NAFL.The plasma levels of total lyso-PCsreached the significance level P , 0.01at baseline (Fig. 2A). Within the metabo-lite group of lyso-PCs, lyso-PC C16:0 lev-els were different, with 19.4% lowerplasma levels in insulin-resistant subjects(mean, 70.42 vs. 86.34 mmol/L; P =0.0039) (Fig. 2B). Because there was asex difference between the NAFL groups,we also adjusted for sex using general lin-ear regression models, and the differencesbetween the groups for total lyso-PCs andlyso-PC C16:0 were not largely affected(P = 0.008 and 0.015, respectively). Inter-estingly, the relationships were also notaffected by the lifestyle intervention(e.g., for lyso-PC C16:0: insulin resistant,71.79 mmol/L vs. insulin sensitive,88.64 mmol/L), and the same differencesin the metabolite parameters were foundbetween the insulin-sensitive and theinsulin-resistant NAFL groups after theintervention (Fig. 2C and D).
Then, we investigated which metab-olites correlated with insulin sensitivity inall 40 subjects and in the 20 insulin-sensitive and in the 20 insulin-resistantNAFL groups analyzed separately. Atbaseline, the most robust correlations(significant relationships with insulinsensitivity were found in all subjects andin both NAFL groups analyzed sepa-rately) were present among the lyso-PCs(Supplementary Table 4). In all 40 sub-jects, for example, lyso-PC C16:0correlated not only positively with insulin
sensitivity (r = 0.38, P = 0.016) but alsonegatively with circulating hs-CRP (r =20.54, P = 0.0004), a marker of subclin-ical inflammation. During the lifestyle in-tervention, insulin sensitivity increased(P = 0.04) and hs-CRP levels decreased(P = 0.006), but lyso-PC C16:0 levelsdid not change (P = 0.37). Interestingly,at follow-up, the relationship of lyso-PCC16:0 levels with insulin sensitivity wassimilarly strong (r = 0.39, P = 0.014) com-pared with baseline; however, the rela-tionship of lyso-PC C16:0 levels withhs-CRP levels was no longer significant(r = 20.23, P = 0.16).
Relationships of lyso-PC C16:0 levelswith insulin sensitivity in subjectswithout NAFLWe then tested whether the blood lyso-PC C16:0 levels were also different be-tween the respective non-NAFL groups(Supplementary Table 5). No differencein the lyso-PC C16:0 levels was found (in-sulin sensitive, 85.5 6 3.3 mmol/L; insu-lin resistant, 84.1 6 6.7 mmol/L; P =0.86). Power analyses revealed that wehad a power of 0.97 to find the same dif-ference in lyso-PC C16:0 that we ob-served in the NAFL groups.
Relationships of lyso-PC C16:0 levelswith insulin sensitivity and hepaticinflammation in subjects with liversamplesFinally, we investigated whether similarrelationships were apparent in bloodfrom subjects who underwent liver tissuesampling. Subjects in the upper (n = 7;triglyceride content, 4.8 6 0.6%) andlower (n = 7; triglyceride content, 0.7 60.07%) quartiles of liver triglyceride con-tent were divided by the medians in fast-ing HOMA-IR. In the upper quartile, livertriglyceride content was not different(Supplementary Fig. 1A); however, theHOMA-IR was lower (Supplementary Fig.1B) and lyso-PC C16:0 levels were higher(Supplementary Fig. 1C) and a trend forless inflammation was found (Supplemen-tary Fig. 1D–F) in the insulin-sensitivecompared with the insulin-resistantgroup. No difference in lyso-PC C16:0 lev-els was seen in the respective groups ofthe lower quartile (74.5 6 2.4 vs. 64.8 62.4 mmol/L, P = 0.56).
CONCLUSIONSdConsidering thatamong body fat and ectopic lipid com-partments, liver fat content correlatesmost strongly with insulin sensitivity(9,10), the fact that some subjects can ac-cumulate large amounts of fat within theliver yet remain insulin sensitive is an un-expected finding for many researchers.However, there is increasing data sup-porting the existence of such a phenotype(11,12,23), and the investigation ofmechanisms determining this phenotypemay help to advance our knowledge, spe-cifically about signaling and detoxifica-tion pathways that are involved in theregulation of insulin sensitivity in NAFL.
In the current study, using a plasmametabolomic approach, we first foundthat a metabolite pattern including theBCAAs leucine and isoleucine, ornithine,the acylcarnitinesC3:0-,C16:0-, andC18:0-carnitine, and lyso-PC C16:0 was able toseparate the insulin-sensitive from theinsulin-resistant NAFL groups with a rela-tively high discriminative power. Interest-ingly, the same metabolomic patternseparated the NAFL groups after a lifestyleintervention, after liver fat content andinsulin sensitivity had changed, yet similarrelationships for liver fat and insulin sensi-tivity were still apparent between thegroups. When we then studied the individ-ual metabolites comprising this metabolo-mic pattern, we found lyso-PCs to moststrongly correlate with insulin sensitivityin subjects with NAFL. These findingssupport that a metabolomic pattern, partic-ularly one including lyso-PC C16:0, maybe a circulating marker of insulin sensitivityin NAFL.
The metabolomic pattern that wehave identified to separate the NAFLgroups includes BCAAs. They wereshown by interesting studies from New-gard et al., Gerszten, and others (19,24–26) to be important for the regulation ofglucose metabolism. In addition, Perse-ghin and colleagues (27) showed that infirst-degree relatives of type 2 diabetic in-dividuals, not only glucose and fatty acidmetabolism but also leucine metabolismshowed resistance to insulin. In our pres-ent study, we also detected correlations ofleucine and isoleucine with insulin sensi-tivity at baseline in our 40 subjects withNAFL. However, and in contrast to what
higher the levels of the metabolites in that group compared with the comparator group. Differences in this plasma metabolite pattern between insulin-sensitive and insulin-resistant subjects with NAFL at baseline (B) and after 9 months of lifestyle intervention (E); depicted P for differences between thegroups in two-sample Student t tests. P , 0.0001 for a group, P = 0.26 for time, and P = 0.55 for a group 3 time effect in the repeated-measuresmultivariate ANOVA test. Discriminating power of the plasma metabolite pattern to differentiate insulin-sensitive from insulin-resistant subjects withNAFL at baseline (C) and after 9 months of lifestyle intervention (F) (area under the ROC curve). AU, arbitrary units; AUC, area under the curve.
care.diabetesjournals.org DIABETES CARE, VOLUME 36, AUGUST 2013 2335
we had expected, these relationships werepositive. Interestingly, in the smallergroup of 17 subjects without NAFL, theserelationships were negative (both r ,20.42 and both P , 0.09). In addition,we detected a strong interaction effect ofthese circulating BCAAs with NAFL (bothP , 0.006) but not with BMI, to deter-mine insulin sensitivity. Although wehave no explanation for this novel ob-servation, and cannot rule out that therelatively small sample size may havebrought about this finding, it may be of
importance for further studies on the roleof BCAAs in the regulation of insulin sen-sitivity, particularly in the aspect ofNAFL.
What is known about the role of lyso-PCs in metabolism? Lyso-PCs in plasmaare primarily generated from PCs of lip-oproteins or from membrane-derived PCsby the action of secretory or lipoprotein-bound phospholipase A2. Anothersource of plasma lyso-PC levels is the pro-duction from PCs by endothelial lipaseas well as from HDL or oxidized LDL
by lecithin-cholesterol acyltransferase,which is secreted from the liver. On theother hand, lyso-PCs can also be reacyla-ted to PC by the action of lyso-PC acyl-transferase. Interestingly, most recently, astudy in mice identified palmitoyl-,stearoyl-, and oleoyl-lyso-PCs to be lowerin the serum of NASH animals comparedwith animals with simple steatosis andsuggested that high lyso-PC acyltransfer-ase activity is involved in this process(28). By the action of lysophospholipaseD/autotoxin plasma, lyso-PC can also betransformed to lysophosphatidic acidand sphingosine-1-phosphate, importantextracellular signaling lipids with multi-ple biological functions (29). Finally, thebioavailability of choline, which is re-quired for hepatic PC genesis, plays animportant role in the pathophysiologyof NAFL and its progression, andis regulated by the gut microbiome (30),may determine the plasma lyso-PC levels.Interestingly, recently lyso-PC C16:0 wasfound to enhance glucose uptake in aninsulin-independent and protein kinaseC-d–dependent manner in adipocytes(31). This finding was confirmed in vivoby glucose-lowering effects of lyso-PCC16:0 in type 1 and type 2 diabetesmouse models (31). Whether these ef-fects and putatively beneficial effects oflyso-PC C16:0 on glucose uptake in othertissues translate into a positive effect onwhole-body glucose uptake needs to beinvestigated in future studies.
In contrast to glucose metabolism,the role of lyso-PCs in the regulation ofinflammation has been studied more ex-tensively. It has been shown that lyso-PCC16:0 is able to inhibit reactive oxygenspecies production in stimulated poly-morphonuclear leukocytes (32,33). Asputative mechanisms of action, lyso-PCsactivate G protein–coupled receptors(GPR4, G2A, and GPR119) that are in-volved, for example, in migration and apo-ptosis of immune cells, osteoclastogenesisand angiogenesis, and insulin secretionfrom b-cells (29). Importantly, lyso-PCswere recently found to enhance the sup-pressive function of human CD4+CD25+
regulatory T cells (Tregs) (34). Tregs secreteanti-inflammatory signals, inhibit macro-phagemigration, and induce noninflamma-tory M2-like macrophage differentiation(35). In addition, defects in Treg functionare considered to play a role not only inautoimmune diseases, such as autoimmunehepatitis (36), but also in other causes ofhepatic inflammation (37,38). In agreementwith an anti-inflammatory property of
Figure 2dPlasma lysophosphatidylcholine (lyso-PC) levels in insulin-sensitive and insulin-resistant subjects with NAFL. Plasma levels of total lyso-PCs (A) and lyso-PC C16:0 (B) of theinsulin-sensitive group with NAFL and the insulin-resistant group with NAFL before the 9monthsof lifestyle intervention (baseline). Plasma levels of total lyso-PCs (C) and lyso-PC C16:0 (D)after the 9 months of lifestyle intervention of the insulin-sensitive group with NAFL and theinsulin-resistant group with NAFL; depicted P for differences between the groups in two-sampleStudent t tests. P = 0.001 for a group, P = 0.27 for a time, and P = 0.44 for a group3 time effect(total lyso-PCs) (A andC) and P = 0.0009 for a group, P = 0.35 for a time, and P = 0.65 for a group3 time effect (palmitoyl lyso-PCs) (B andD) in the repeated-measures multivariate ANOVA test.
2336 DIABETES CARE, VOLUME 36, AUGUST 2013 care.diabetesjournals.org
Circulating lysophosphatidylcholines and fatty liver
lyso-PCs, we detected strong negative rela-tionships of total lyso-PCs and lyso-PCC16:0 with hs-CRP levels in our subjectswith NAFL.
We then asked whether lyso-PC C16:0levels also differ between insulin-sensitiveand insulin-resistant subjects withoutNAFL. No such relationship was found.Importantly, the insulin-sensitive andinsulin-resistant subjects in the respectiveNAFL and non-NAFL groups had verysimilar insulin sensitivity (Fig. 3). Thisindicates that lyso-PC C16:0 levels maybe a good marker of subclinical inflam-mation and insulin resistance and mayputatively protect from inflammatory pro-cesses, specifically in NAFL. In support ofthis hypothesis, we found lyso-PCC16:0 tobe negatively associated with insulin resis-tance in subjects with high, but not low,liver tissue lipid content. Certainly, becauseof the very small sample size of this group,we cannot draw definite conclusions fromthis observation. Importantly, the replica-tion was not only done in a separatepopulation than the initial screening pop-ulation but another method of measure-ment of lyso-PC C16:0 levels was alsoapplied. Interestingly, in the large Relation-ship Between Insulin Sensitivity and Car-diovascular Risk (RISC) study, lyso-PCswere also found to negatively correlate
with estimated hepatic insulin resistance(39). Also in agreement with our presentdata, the correlations were stronger in thesubjects with NAFL compared with thesubjects without hepatic steatosis (A.G.,personal communication). Moreover, andin agreement with our hypothesis, our liverhistology data indicate that hepatic inflam-mation is present when plasma lyso-PCC16:0 levels are low.
In summary, from a panel of 180metabolites, the lyso-PC concentrationsdiffered most strongly between insulin-sensitive and insulin-resistant subjectswith NAFL. These relationships, as well asthe correlations with insulin sensitivity,were still apparent after 9 months oflifestyle intervention. Interestingly, lyso-PC levels did not change during thisintervention, indicating that they are ge-netically determined. The fact that thecorrelations of lyso-PC levels with insulinsensitivity and with systemic and hepaticinflammation were less strong or absentwhen liver fat content was low, and theknowledge about the signaling propertiesof lyso-PCs, supports the hypothesis that agenetically determined high lyso-PC avail-ability may protect specifically from fattyacid–induced insulin resistance and he-patic inflammation when NAFL is present.Alternatively, elevated circulating lyso-PCs
may not be functionally active in this pro-cess but represent activatedpathways in thesynthesis or metabolism of phospholipidsand phosphatidylcholine and, thus, in thegeneration of hepatic endoplasmic reticu-lum stress (40) (Supplementary Fig. 2).
In conclusion, in the future, a metab-olomic fingerprint, most robust in the classof lyso-PCs, may be able to differentiateinsulin-sensitive from insulin-resistant sub-jects with NAFL. Furthermore, as low lyso-PC levels appear to specifically be a markerof NAFL-associated insulin resistance,these findings may highlight novel andinteresting pathways for the studies on thepathogenesis of insulin resistance in NAFL.
AcknowledgmentsdThis work was sup-ported in part by the Competence Networkfor Diabetes Mellitus, funded by the FederalMinistry of Education and Research (FkZ 01GI 1104A), a grant from the German FederalMinistry of Education and Research to theGerman Center for Diabetes Research, a grantfrom the Sino-German Center for ResearchPromotion (Deutsche Forschungsgemeins-chaft [DFG] and Natural Science Foundationof China, GZ 753) to G.X. and R.L., and theDFG (KFO 114). N.S. is currently supportedby a Heisenberg Professorship from the DFG(STE 1096/3-1).The supporters had no influence on the
study design and on the collection, analysis,and interpretation of the data.No potential conflicts of interest relevant to
this article were reported.R.L., H.F., and N.S. researched data and
wrote the manuscript. S.D., A.Z., M.H.d.A.,M.S., H.S., and H.-U.H. designed the study,contributed to the discussion, and reviewedthe manuscript. L.R., K.K., A.P., P.A., J.L.,G.X., A.K., J.M., F.S., E.S., A.G., and A.F. re-searched data and edited the manuscript. Allauthors provided substantial contributions toconception and design, acquisition of data oranalysis and interpretation of data, draftingthe article or revising it critically for importantintellectual content, and final approval of theversion to be published. N.S. is the guarantorof this work and, as such, had full access to allthe data in the study and takes responsibilityfor the integrity of the data and the accuracy ofthe data analysis.Parts of this study were presented at the
72nd Scientific Sessions of the American Di-abetes Association, Philadelphia, Pennsylva-nia, 8–12 June 2012.
References1. Cohen JC, Horton JD, Hobbs HH. Human
fatty liver disease: old questions and newinsights. Science 2011;332:1519–1523
2. Roden M. Mechanisms of disease: hepaticsteatosis in type 2 diabetesdpathogenesis
Figure 3dRelationships of liver fat content with insulin sensitivity at the baseline visit. The insertdepicts the mean (SE) values for the respective groups and the level of statistical significance.*P, 0.05 for differences between the insulin-resistant and insulin-sensitive groups. P = 0.20 fordifferences between the insulin-sensitive and P = 0.18 for differences between the insulin-resistantgroups. #P, 0.05 for differences between the insulin-resistant NAFL group and all other groups.AU, arbitrary units.
care.diabetesjournals.org DIABETES CARE, VOLUME 36, AUGUST 2013 2337
4. Kotronen A, Yki-Järvinen H. Fatty liver:a novel component of the metabolic syn-drome. Arterioscler Thromb Vasc Biol2008;28:27–38
5. Perseghin G. Viewpoints on the way to aconsensus session: where does insulinresistance start? The liver. Diabetes Care2009;32(Suppl. 2):S164–S167
6. Cusi K. Role of obesity and lipotoxicity inthe development of nonalcoholic steato-hepatitis: pathophysiology and clinicalimplications. Gastroenterology 2012;142:711–725.e6
7. Gastaldelli A, Cusi K, Pettiti M, et al. Re-lationship between hepatic/visceral fat andhepatic insulin resistance in nondiabeticand type 2 diabetic subjects. Gastroenter-ology 2007;133:496–506
8. Larson-Meyer DE, Newcomer BR,Ravussin E, et al. Intrahepatic and intra-myocellular lipids are determinants ofinsulin resistance in prepubertal children.Diabetologia 2011;54:869–875
9. Stefan N, Kantartzis K, Machann J, et al.Identification and characterization ofmetabolically benign obesity in humans.Arch Intern Med 2008;168:1609–1616
10. Fabbrini E, Magkos F, Mohammed BS, et al.Intrahepatic fat, not visceral fat, is linkedwithmetabolic complications of obesity. ProcNatl Acad Sci USA 2009;106:15430–15435
11. Stefan N, Häring HU. The metabolicallybenign and malignant fatty liver. Diabetes2011;60:2011–2017
12. Kantartzis K, Peter A, Machicao F, et al.Dissociation between fatty liver and in-sulin resistance in humans carrying avariant of the patatin-like phospholipase 3gene. Diabetes 2009;58:2616–2623
13. Kantartzis K, Thamer C, Peter A, et al.High cardiorespiratory fitness is an in-dependent predictor of the reduction inliver fat during a lifestyle intervention innon-alcoholic fatty liver disease. Gut2009;58:1281–1288
14. Machann J, Thamer C, Stefan N, et al.Follow-up whole-body assessment of ad-ipose tissue compartments during a life-style intervention in a large cohort atincreased risk for type 2 diabetes. Radi-ology 2010;257:353–363
15. Szczepaniak LS, Nurenberg P, Leonard D,et al. Magnetic resonance spectroscopy tomeasure hepatic triglyceride content:prevalence of hepatic steatosis in thegeneral population. Am J Physiol Endo-crinol Metab 2005;288:E462–E468
16. Matsuda M, DeFronzo RA. Insulin sensitivityindices obtained from oral glucose tolerancetesting: comparisonwith theeuglycemic insulinclamp. Diabetes Care 1999;22:1462–1470
17. Stefan N, Fritsche A, Weikert C, et al.Plasma fetuin-A levels and the risk of type2 diabetes. Diabetes 2008;57:2762–2767
18. Expert Panel on Detection, Evaluation, andTreatment of High Blood Cholesterol inAdults. Executive Summary of The ThirdReport of The National Cholesterol Educa-tion Program (NCEP) Expert Panel on De-tection, Evaluation, And Treatment of HighBlood Cholesterol In Adults (Adult Treat-ment Panel III). JAMA2001;285:2486–2497
19. Wang TJ, Larson MG, Vasan RS, et al. Me-tabolite profiles and the risk of developingdiabetes. Nat Med 2011;17:448–453
21. Franken H, Lehmann R, Häring HU,Fritsche A, Stefan N, Zell A.Wrapper- andensemble-based feature subset selectionmethods for biomarker discovery in tar-geted metabolomics. In Pattern Recogni-tion in Bioinformatics. Loog M, Wessels L,Reinders M, Ridder D, Eds. Berlin/Hei-delberg, Springer, 2011, p. 121–132
23. Amaro A, Fabbrini E, Kars M, et al. Disso-ciation between intrahepatic triglyceridecontent and insulin resistance in familialhypobetalipoproteinemia. Gastroenterol-ogy 2010;139:149–153
24. Newgard CB, An J, Bain JR, et al. Abranched-chain amino acid-related meta-bolic signature that differentiates obese andlean humans and contributes to insulin re-sistance. Cell Metab 2009;9:311–326
25. Huffman KM, Shah SH, Stevens RD, et al.Relationships between circulating metabolicintermediates and insulin action in over-weight to obese, inactive men and women.Diabetes Care 2009;32:1678–1683
26. W€urtz P, Mäkinen VP, Soininen P, et al.Metabolic signatures of insulin resistancein 7,098 young adults. Diabetes 2012;61:1372–1380
27. Lattuada G, Sereni LP, Ruggieri D, et al.Postabsorptive and insulin-stimulatedenergy homeostasis and leucine turnoverin offspring of type 2 diabetic patients.Diabetes Care 2004;27:2716–2722
28. Tanaka N, Matsubara T, Krausz KW,Patterson AD, Gonzalez FJ. Disruption ofphospholipid and bile acid homeostasis inmice with nonalcoholic steatohepatitis.Hepatology 2012;56:118–129
29. Schmitz G, Ruebsaamen K. Metabolismand atherogenic disease association of ly-sophosphatidylcholine. Atherosclerosis2010;208:10–18
30. Corbin KD, Zeisel SH. Choline metabo-lism provides novel insights into non-alcoholic fatty liver disease and itsprogression. Curr Opin Gastroenterol2012;28:159–165
31. Yea K, Kim J, Yoon JH, et al. Lysophos-phatidylcholine activates adipocyte glu-cose uptake and lowers blood glucoselevels in murine models of diabetes. J BiolChem 2009;284:33833–33840
32. Lin P,Welch EJ, Gao XP,Malik AB, Ye RD.Lysophosphatidylcholine modulatesneutrophil oxidant production throughelevation of cyclic AMP. J Immunol 2005;174:2981–2989
33. M€uller J, Petkovi�c M, Schiller J, Arnold K,Reichl S, Arnhold J. Effects of lysophos-pholipids on the generation of reactive ox-ygen species by fMLP- and PMA-stimulatedhuman neutrophils. Luminescence 2002;17:141–149
34. Hasegawa H, Lei J, Matsumoto T, OnishiS, Suemori K, Yasukawa M. Lysophos-phatidylcholine enhances the suppressivefunction of human naturally occurringregulatory T cells through TGF-b pro-duction. Biochem Biophys Res Commun2011;415:526–531
35. Osborn O, Olefsky JM. The cellular andsignaling networks linking the immunesystem and metabolism in disease. NatMed 2012;18:363–374
36. Buckner JH. Mechanisms of impairedregulation by CD4(+)CD25(+)FOXP3(+)regulatory T cells in human autoimmunediseases. Nat Rev Immunol 2010;10:849–859
37. Speletas M, Argentou N, Germanidis G,et al. Foxp3 expression in liver correlateswith the degree but not the cause of in-flammation. Mediators Inflamm 2011;2011:827565
38. Peiseler M, Sebode M, Franke B, et al.FOXP3+ regulatory T cells in autoim-mune hepatitis are fully functional andnot reduced in frequency. J Hepatol 2012;57:125–132
39. Gastaldelli A, Natali A, Gall WE, et al.;RISC investigators. Metabolomic profileassociated with hepatic insulin resistancein nondiabetic subjects: results from theRISC Study. Diabetes 2012;61(Suppl. 1):A461
40. Fu S, Yang L, Li P, et al. Aberrant lipidmetabolism disrupts calcium homeostasiscausing liver endoplasmic reticulumstress in obesity. Nature 2011;473:528–531
2338 DIABETES CARE, VOLUME 36, AUGUST 2013 care.diabetesjournals.org
Circulating lysophosphatidylcholines and fatty liver
Supplementary Figure 1. Relationships of liver triglyceride (TG) content (A), insulin sensitivity (HOMA-IR) (B), palmitoyl lyso-PC (C) and liver histological parameters (D-F) in subjects in the upper quartile (N=7) of liver TG content (all N=29), who were divided by the median HOMA-IR.Panel D: p=0.08 (χ2 likelihood ratio) for having a cumulative value of mild, moderate, and advanced hepatic inflammation and ballooning. Mallory-Hyaline was not found in these subjects. Panel E: Liver histology of a patient representative for the insulin sensitive group (N=3). No inflammatory infiltrates are seen (original magnification 100x H&E). The hepatocytes show no ballooning and no Mallory-Hyaline (insert; original magnification 400x H&E). Panel F: Liver histology of a patient representative for the insulin resistant group (N=4). A mild lymphoid infiltrate, accentuated in the periportal tracts, but also in the lobules is shown (arrows; original magnification 100x H&E). Focally a cytoplasmic ballooning of hepatocytes is detected (insert; arrows; original magnification 400x H&E).
Supplementary Figure 2. Schematic working models of the potential role of lyso-PCs in NAFL- induced insulin resistance. Lyso-PCs may inhibit hepatic inflammation by regulating the function of naturally occurring CD4(+)CD25(+) regulatory T cells (Tregs) and, thereby, protect from insulin resistance (model 1). Alternatively, elevated circulating lyso-PCs may represent activated pathways in the synthesis or the metabolism of phospholipids and phosphatidylcholine and thus, in the generation of hepatic endoplasmatic reticulum stress (model2).
1
Circulating Lyso-Phosphatidylcholines are Markers of a metabolically benign Nonalcoholic Fatty Liver
Supplementary Table 1 Serum lipid levels of the 40 subjects with nonalcoholic fatty liver at baseline and after 9 months of lifestyle intervention
Supplementary Table 3 List of plasma metabolites that were excluded from data analysis. (Metabolites with >30% of the mass spectrometric signals below the limit of detection (LOD) were excluded from the data evaluation (LOD was defined as a signal-to-noise ratio of 3)).
���������
��" �����*���
��" �����*���
��" �����*���
��" �����*���
��" �����*���
����� � ���
�*���
�&'��(��&� �)��
�&� � ��
��*���
��&�&'���
��&� � �(�&'�&�)��
��*���
��*�&'���
��*���
���*���
���&'���
���*���
�����
���*�&� ���
���*�&� ���
���&� ���
���*���
���*�&� ���
���*�&� ���
���*�&� ���
�������������� ����������
��������*���
��������*���
�������*���
�������*���
��������*���
�������������� ���������������
��������*���
��������*���
���������������
���&��*���
���*���
���&���*��(�����")��
����&���*��(�")��
2&���*�4 ��(2&��& ����/�2��"�2)��
2&���*�4 ���
9
2&���*�4 ��(2&��&!� ��/�2��"�2)��
2&���*�4 ���
�&���*�4 ��(���"�����/�2)��
2&���*�4 �(�����/"��2)��
2&���*�4 ��
2&���*�4 ��(2&��6��&��2"�/��"�2)��
2&���*4 �(2&��6��6��&���*)��
2&���*4 ��(2&�6��6��&���*)��
2&���*4 ��(+ ��/)��
2&���*�4 ��
2&���*�4 ���
���*��(!����2"��"�2)��
���*��(5�!���2)��
2&���*�4 ��(���2�2)��
2&���*�4 ��(2&�6��&���*�)��
2&���*�4 ���
��*��(���2"��"�2)��
���*��(����"2���2)��
2&���*�4 ��(���7"��2)��
���� ��� ���
'" �+ ����
����+ �����
,����������
���!�"����&#��-".�/��
0���"&$��"����
��/�".�1����������
��/�".� �"�����
!������!���+ ����
����2�����
#�+ + ����2�/�+ ��!�����������
�"����/�+ ��!�����������
#��2"�����
#��"�"�����
# ��+ �/�����
# ��+ �����
$���������
��������
�!��"/�".�2!"��2��2�/�
'�".�2!"��2��2�/�
���2"/�".�2!"��2��2�/�
���2"���!"2!"��2��2�/�
���2"��"/�".�2!"��2��2�/�
���!"2!"��2��2�/�
$���"2!"��2��2�/�
$���"2!��"/�".�2!"��2��2�/�
$���"/�".�2!"��2��2�/�
$���"���!"2!"��2��2�/�
10
$���"��"/�".�2!"��2��2�/�
3�"/�".�2!"��2��2�/�
�
�
�
Supplementary Table 4 Significant correlations of metabolites with insulin sensitivity (ISI) in all 40 subjects with nonalcoholic fatty liver (NAFL) and the insulin sensitive (IS) and insulin resistant (IR) NAFL subgroups at baseline.
Metabolite class all IS IR Metabolite class all IS IR Lysophosphatidylcholines Phosphatidylcholines (acyl-alkyl)
Total lyso-PC + + PC ae C30:1 + Total lysoPC/PC + + PC ae C30:2 + lysoPC C16:0 + + PC ae C34:0 - lysoPC C17:0 + - + PC ae C34:2 - lysoPC C18:0 + + PC ae C36:2 - lysoPC C18:1 + + PC ae C36:3 - lysoPC C18:2 + + PC ae C38:2 - lysoPC C20:4 + PC ae C38:3 - lysoPC C28:1 + PC ae C40:1 - Amino acids PC ae C44:3 - Glu - - Fatty acids Ile + Total FFA - Leu + FFA (even) - Sphingomyelines FFA (�9) - Total SM + FFA (�10) - Total SM(non-OH) + total SFA - Total SM/(SM+PC) + + + total MUFA - SM (OH) C16:1 + PUFA/SFA + SM (OH) C22:2 + + C16:0 (palmitic) - SM (OH) C24:1 + C16:1�10 (sapienic) - SM C18:0 + + + C18:1�9 (oleic) - SM C18:1 + + C18:1�7 (vaccenic) - SM C26:1 + C18:3�6 (γ-linolenic) - Acylcarnitines C20:1�9 (c-11-eicosenoic) - C2 (acetyl) + C22:4�6 (adrenic) - C7-dicarboxylic (pimelyl) + + C8 (octanoyl) + C10 (decanoyl) + C12 (dodecanoyl) +
Phosphatidylcholines (diacyl)
Total PC - PC aa C32:2 - PC aa C32:3 - + PC aa C34:3 - PC aa C34:4 - PC aa C36:0 - PC aa C36:2 - PC aa C36:3 -
No metabolite out of the classes of bile acids and biogenic amines did show a significant correlation with ISI. Dark grey fields indicate p < 0.01, light grey fields indicate p < 0.05. A positive correlation is indicated by plus and a negative correlation by minus.
11
Supplementary Table 5 Selected characteristics of the 17 subjects without nonalcoholic fatty liver at baseline and after 9 months of lifestyle intervention
![Metabolically Competent Human Skin Models: Activation and Genotoxicity of Benzo[a]pyrene](https://static.documents.pub/doc/80x56/635e119da0f1eac29f0c8481/metabolically-competent-human-skin-models-activation-and-genotoxicity-of-benzoapyrene.jpg)
