Prognostic Value of Fasting vs. Non-Fasting Low Density...

BangaloreBethany Doran, Yu Guo, Jinfeng Xu, Howard Weintraub, Samia Mora, David J. Maron and Sripal

(NHANES-III)Long-term Mortality: Insight from the National Health and Nutrition Survey III Prognostic Value of Fasting vs. Non-Fasting Low Density Lipoprotein Cholesterol Levels on

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DOI: 10.1161/CIRCULATIONAHA.114.010001

1

Prognostic Value of Fasting vs. Non-Fasting Low Density Lipoprotein

Cholesterol Levels on Long-term Mortality: Insight from the National Health

and Nutrition Survey III (NHANES-III)

Running title: Doran et al.; Fasting vs. non-fasting lipids

Bethany Doran, MD, MPH1; Yu Guo, MA1; Jinfeng Xu, PhD1; Howard Weintraub, MD1;

Samia Mora, MD2; David J. Maron, MD3; Sripal Bangalore, MD, MHA1

1New York University School of Medicine, New York, NY; 2Brigham and Women’s Hospital,

Boston, MA; 3Stanford University, Stanford, CA

Address for Correspondence:

Sripal Bangalore, MD, MHA, FACC, FAHA, FSCAI

Director of Research, Cardiac Catheterization Laboratory

Director, Cardiovascular Outcomes Group

Associate Professor of Medicine, New York University School of Medicine

New York, NY 10016

Tel: 212-263-3540

Fax: 212-263-3988

E-mail: [email protected]

Journal Subject Code: Atherosclerosis:[90] Lipid and lipoprotein metabolism

Samia Mora, MD2; David J. Maron, MD3; Sripal Bangalore, MD, MHMHHAAA1

11NeNeNeww w YYoYorkrkrk UUniniivveversr ity School of Medicine, Neweww YYYork, NY; 2Brrrigii haam m m aand Women’s Hospital,

BoBoststtoonon, MAMAMA; 33SSttanffforrrd UUUnniiveersrsrsitity,y,y, SSStaannnfooord,d CCCAAA

Address forr r CoCoCorrrrrresesespopopondndn enenenccec ::

at Karolinska Inst on July 14, 2014http://circ.ahajournals.org/Downloaded from



2

Abstract

Background—National and international guidelines recommend fasting lipid panel measurement

for risk stratification of patients for prevention of cardiovascular (CV) events. Yet, the

prognostic value of fasting vs. non-fasting low density lipoprotein cholesterol (LDL-C) is

uncertain.

Methods and Results—Patients enrolled in the National Health and Nutrition Survey III

(NHANES-III), a nationally representative cross-sectional survey performed between 1988 to

1994, were stratified based on fasting status ( 8 hours or <8 hours) and followed for a mean of

14.0 (±0.22) years. Propensity score matching was used to assemble fasting and non-fasting

cohorts with similar baseline characteristics. The risk of outcomes as a function of LDL-C and

fasting status was assessed using receiver operating characteristic (ROC) curves and

bootstrapping methods. The interaction between fasting status and LDL-C was assessed using

Cox proportional hazards modeling. Primary outcome was all-cause mortality. Secondary

outcome was CV mortality. One-to-one matching based on propensity score yielded 4,299 pairs

of fasting and non-fasting individuals. For the primary outcome, fasting LDL-C yielded similar

prognostic value as non-fasting LDL-C [C-statistics=0.59 (95% CI 0.57-0.61) vs. 0.58 (95% CI

0.56-0.60; P=0.73], and LDL-C by fasting status interaction term in the Cox proportional hazard

model was not significant (Pinteraction= 0.11). Similar results were seen for the secondary outcome

[fasting vs. non-fasting C-statistics=0.62 (95% CI 0.60-0.66) vs. 0.62 (95% CI 0.60-0.66);

P=0.96; and Pinteraction=0.34].

Conclusions—Non-fasting LDL-C has similar prognostic value as that of fasting LDL-C.

National and international agencies should consider re-evaluating the recommendation that

patients fast before obtaining a lipid panel.

Key Words: cholesterol, mortality, fasting

fasting status was assessed using receiver operating characteristic (ROC) curvess ananand d d

bootstrapping methods. The interaction between fasting status and LDL-C was asasseseesssssededd uusissingnng

Cox proportional hazards modeling. Primary outcome was all-cause mortality. Secondary

ouutctccomommee wawawas CVVV mmmortality. One-to-one matchingngng bbased on propenennsityty ssscccoro e yielded 4,299 pairs

ofoff fffasasting andndd nnnoonon-f-fasasastititingngng iindndndivivividididuauaalslss.. FFFororr ttthhhee e prprimimimaryy y ouououtctct omomome,e,e fffasasastititinngng LDLDLDL-C-CC yyyieiei ldlddededed sssimimimilililar

prprrogggnon stic vvalaluee aass nooon--faststtininng g g LDLDL-LL CCC [[C-ssstaaatistiiicss=0=00.5.5.59 99 (9(( 555% CCII 0.557--0-0.6661)1)) vvs. 00.5588 (9995%%% CCCI

0.0 56566-0-0-0.6.660;0;0; PPP=0=0.7.773]3]3], , anananddd LDLDL L-L-L CCC bybyby ffasastititingngng sstatatatututusss inininteteterracacactititiononon ttteeerm mm ininin tthehehe CCCoxox ppprororopopoportrtioioonananal l l hahahazazazardrdr

model was nonoott t sisis gngngnififi iciccanana t (P(P(Pintinin erararactictict ononn== 0.0.0.11111).).. SiSiSimimimilalaar r r rer sususultltts ss wewewererere seeeeeen n n fofoforr r thththee sesesecococondndndara y outcomee




3

Introduction

Current national and international guidelines on cholesterol management recommend that lipid

panel measurement should be performed after an 8-12 hour fast.1-3 The reason often stated for

obtaining a fasting lipid panel is for greater precision for certain lipid parameters (especially

triglycerides), which can be variable, based on time and content of the last meal. From a practical

standpoint, it is cumbersome for patients to fast before obtaining a blood draw, and may delay

diagnosis and treatment of hyperlipidemia.

Prior data have shown that levels of total cholesterol (TC), low density lipoprotein

cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C) vary little with respect to

fasting time, while triglycerides may vary by up to 20-30%.4, 5 Recently, studies have suggested

that non-fasting lipids may be equivalent (and potentially superior) in predicting cardiovascular

(CV) outcomes, as the non-fasting state may more accurately reflect the body’s exposure to

circulating lipids.6-8 Studies have demonstrated no benefit, or even improved risk prediction,

with the use of non-fasting as compared to fasting triglycerides.9-12 No prior studies have

examined the relationship of fasting vs. non-fasting LDL-C and mortality. Our objective was to

use the National Health Examination and Nutrition Survey III (NHANES-III), a nationally

representative database of the US population, to evaluate the prognostic value of fasting versus

non-fasting LDL-C for prediction of all-cause mortality and CV mortality in men and women.

Methods

Study Population

We used the NHANES-III linked to the National Death Index (NDI), a nationally representative

civilian cohort of non-institutionalized individuals within the United States. Baseline data was

fasting time, while triglycerides may vary by up to 20-30%.4, 5 Recently, studiess hhhavvve susuggggggesesesteted

hat non-fasting lipids may be equivalent (and potentially superior) in predicting cardiovascular

CCV)V)V) oooutututcococommemes, aaasss tht e non-fasting state may mooorerer aaccurately reflflecee t ththhee e bbody’s exposure to

ciircccuulating lipipidsdsds.66--8 SSSttudididieseses hhhaavave e dededemmoonnnstraaateed nooo bbbennenefffitit,, orrr eevvennn imimppproovovededed rrisisk kk prprredededici tititiononn,, ffff

wiwiiththth tttheheh uusesese oof f nnnonn-n-fafaaststiiningg asass ccooompmpmparareeded tttooo fffastststinini ggg ttrrigigglylylycececerriridededes.s.s 99-19-122 NNNooo prprrioior rr ststtududu ieieiess s haaaveee

examined thee rrrelele atatatioioionsnsshihih p p ofofof fffassstititingngng vvs.s.s. nnnononon-f-ffasasasttinining gg LDLDL L-L-L CCC ananand d d momomortrtrtalala ititity.y.y. OOOururur ooobjbjb ececectitive was to




4

collected between 1988-1994 using a multistage stratified probability cluster sampling design

where certain groups were intentionally oversampled and participant weights were added to

reflect the demographics of the 1990 US census. Comprehensive data about the validation and

collection of data are available elsewhere.13 The inclusion criteria for this study were adults 18

years of age and older residing in the United States who had participated in the NHANES III

study with data on fasting time. We excluded those in whom LDL-C calculations was not

possible due to missing HDL-C, TC, or triglyceride levels and those with triglycerides 400

mg/dl in whom the Friedewald equation may not be accurate.

Data Collection

Participants were interviewed in their homes and examined in a mobile examination center

where blood samples were obtained and physical exam performed. If participants were unable to

attend an examination at a center, home exam was performed. Institutional Review Board (IRB)

approval and documented consent was obtained from individuals through the Centers for Disease

Control and Prevention.

Laboratory Methods

Blood samples were collected through venipuncture and shipped on dry ice to the laboratory

analyzing the sample. Serum HDL-C, triglyceride, and TC levels were measured enzymatically

at Johns Hopkins University Lipoprotein Analytical Laboratory using a Hitachi 704 Analyzer

(Boehringer Mannheim Diagnostics, Indianapolis, Indiana). Lipid collection and analyses were

standardized to Centers for Disease Control and Prevention criterion.14 LDL-C was derived using

the Friedewald formula [LDL-C=TC - HDL-C – (triglycerides/5)],15 with prior studies showing

excellent correlation between fasting direct and indirect methods of LDL-C measurement,16-18

and a 0.97 correlation coefficient between Friedewald and directly measured LDL-C in non-

Participants were interviewed in their homes and examined in a mobile examinatattioiion nn cceentntntererer

where blood samples were obtained and physical exam performed. If participants were unable tom

attteteendndnd aaann exexexaamminnnatatatioion at a center, home exam wwwaaas s pperformed. Innstss itututtioioionnnal Review Board (IRB)

appprprovo al and ddococcuumumenennteeeddd cococonsnnsenent tt wwawasss ooobtainini eed fffrororom iiinnddivivvididduauallss ttthrhrououugghh tthhehe CCenenntetersrsr ffforrr DDisii eeeasem

CoCoontntntrororoll anandd d PrPPrevevvennntitioonon. .

Laboratoryy MMMetete hohohodsdsds




5

fasting individuals.19

Variable Definitions

We classified individuals as fasting if they had fasted for at least 8 hours and stratified

individuals based on fasting status at the time of phlebotomy. The Adult Treatment Panel III

(ATP-III) guidelines define fasting time as 9-12 hours in the US, with new guidelines from the

ACC/AHA taskforce recommending obtaining fasting lipids but not specifying the duration of

fast.1, 20 We used 8 hours to define fasting in keeping with recent studies examining lipids4, 10, 21

and to reflect a more conservative fasting definition. Hypertension was defined as systolic blood

pressure of 140 mmHg, or diastolic blood pressure 90 mmHg as per the 2014 evidence based

hypertension guidelines.22 We defined diabetes as serum glycosylated hemoglobin of 6.5% as

per the World Health Organization’s updated definition of diabetes23 or self-reported history of

diabetes. We used enlarged waist circumference (defined as >88 cm for women and >102 cm for

men) as a proxy for obesity, as waist circumference has been shown to be more highly correlated

with mortality and reflective of central adiposity than BMI.24, 25

Outcome Measures

The primary outcome analyzed was mortality from all-causes and the secondary outcome was

CV mortality. Data on mortality was obtained using death records from the NDI cross-matched

to NHANES-III using probabilistic record matching. International Classification of Diseases,

Ninth Revision (ICD-9) and ICD-10 codes were recoded as underlying classification of death

(UCOD) within the NHANES III-NDI. Deaths from CV related diseases included deaths from

ischemic heart disease (I20-I25), heart failure (I50), essential hypertensive heart disease (I11-

I13), cerebrovascular disease (I60-I69) and atherosclerosis (I70-I71).

hypertension guidelines.22 We defined diabetes as serum glycosylated hemoglobibiin ofoo 6.6.6 5%5%5% aas

per the World Health Organization’s updated definition of diabetes23 or self-reported history of

diiababbeteteteeses. WWWeee ussededed eenlarged waist circumferenceee ((dddefined as >888 cccm fofoforr r wow men and >102 cm fo

mmenn)n) as a proxxy y fooor obobobesessititity,y, aasss wawaaisisst t ccirrccummmfeeerenncecee haasas bbeeeeenn shshowowown n totoo bbee momomorere hhhigigighlhlhly y cooorrrreele aaated

wiwiiththth mmmorortataalililitytyty aandndnd rrefefflelectctc ivive e ofoff cccenenenttrtralall aaadididipopoposisiitytyt ttthahaan BMBMMI.II.fff 244,, 2525

Outcome MeMeeasasasururreseses




6

Statistical Analysis

All analyses were performed using SAS software version 9.3 (SAS Institute Inc). We adjusted

for the complex, stratified study sampling design using survey weights for examination and

interview portions of survey as per the CDC recommendations.26 Sensitivity analyses were

performed without using survey weights.

Propensity Score Matching

We used propensity score matching to assemble a cohort of paired participants based on fasting

status with similar baseline characteristics. Propensity score was calculated using a non-

parsimonious multivariable logistic regression model with fasting status (dichotomized as yes or

no) as the dependent variable. CV risk factors were entered into the model as covariables to

control for possible confounders (including race, smoking history, prior CVD, cholesterol

medication use, diabetes, elevated TC, low HDL-C, hypertension, enlarged waist circumference

and low socioeconomic status). Matching was performed using SAS 9.3 and SAS macro

(GMATCH) with greedy matching in a 1 to 1 ratio without replacement, with caliper width of

0.2 times the standard deviation of the logit of the propensity scores. The discriminatory power

of the fasting and non-fasting LDL-C model was evaluated using the area under the receiver-

operator curve (ROC) using the Hosmer-Lemeshow C-statistic. Fasting and non-fasting ROC

curves were compared using bootstrapping methods to evaluate for a statistically significant

difference. Absolute standardized differences were calculated between the fasting and non-

fasting cohort before and after propensity score matching.

We generated Kaplan-Meier curves to assess survival functions in both fasting and non-

fasting cohorts. Primary analysis was performed on the matched cohort. The prognostic values of

fasting vs. non-fasting LDL-C measurement for primary and secondary outcomes were assessed

no) as the dependent variable. CV risk factors were entered into the model as covovvararriaiablblbleseses ttto o o

control for possible confounders (including race, smoking history, prior CVD, cholesterol

memedididicacacatititiononon uussse, dididiababa etes, elevated TC, low HDLLL-C-C- ,, hypertensionn, , enlalaargrgrgede waist circumference

anndd d lol w socioeoecocoonnnommimic stststatatatususus)).). MaMaMatctchhihinnng wwawas peeerffformememed d uuusinini gg SASASS 99.9 33 ananndd d SASASSS mmamacccroo o

GGGMAMAMATCTCT H)H)H) wwitithhh ggrgreeeeedydyy mmatatatchchhinining g g iinin aaa 111 ttto o o 1 rararatitiio wiwiiththouououtt t reeeplplp aacacemememenennt,, wwititith h cacac liliipepeper wwiwidtdtd h h ofoff

0.2 times the e stststanana dadadardrdrd dddeveviaiaiatititiononn ooof f thththe lolologigig t t t ofofof ththt eee prprpropopenenensisisitytyty ssscococoreees.s.s TTThehehe dddisisi crcrrimimminininatatatoro y power




7

using ROC curves. Sensitivity analysis to assess whether the prognostic significance of fasting

vs. non-fasting LDL-C varies by length of fast was performed on the unmatched cohort using

different cut-points to define fasting status (<4 vs. 4 hours, <8 vs. 8 hours, <12 vs. 12 hours).

We stratified by presence of diabetes to determine whether diabetic status influenced prognostic

significance of fasting in unmatched models. Further sensitivity analyses were performed

including patients with triglycerides 400 mg/dL. We conducted sensitivity analyses at different

follow up time cut-points (5, 10, and 15 years) to ensure that the significance of fasting status did

not vary by follow-up length. Analyses were also performed to evaluate the influence of fasting

vs. non-fasting TC and triglycerides levels on all-cause and CV mortality.

Cox proportional hazard models were used to evaluate the association of LDL-C levels

with outcomes after adjustment for potential confounders. Individuals were stratified by tertiles

of LDL-C levels (<100 mg/dL [referent], 100-130 mg/dL, and 130 mg/dL) with the lowest

tertile used as the reference group. Secondary analyses were performed using clinical cut points

(LDL-C levels <130 mg/dL, 130 mg/dL-160 mg/dL, and 160 mg/dL). Interaction between

fasting status and LDL-C was tested in both primary and secondary outcome models using

interaction terms for fasting state and LDL-C tertiles. Two tailed p-values of 0.05 or less were

considered statistically significant.

Results

Our initial dataset included 20,024 adults. As shown by Figure 1, we excluded those in whom

LDL-C calculation was not possible (n=699), those with triglycerides 400 mg/dL (n=440) (see

sensitivity analyses below), and those in whom fasting time was missing (n=2,723), or final

mortality status was missing (n=1). Thus, our final dataset included 16,161 individuals

Cox proportional hazard models were used to evaluate the association of f LDLDLDLL--CCC lelelevevevelsls

with outcomes after adjustment for potential confounders. Individuals were stratified by tertiles ff

off LLLDLDLDL CC-C lllevevvelss (<(<(<101 0 mg/dL [referent], 100-1131300 mg/dL, and 131 0 0 mgmgmg/d/ L) with the lowest

eerttiilile used as ththhee rrrefefef rerencncnceee grgrgroououp.p.p. SSSececcoonndarryry anaaalyyysesss wwwerere peperrffoorrmemed dd uususininng gg clclininnicicalalal cccututt ppooio nnnts

LLLDLDLDL-C-C-C llevevvelelelss <<<133030 mmmg/g/g/dLdL, 131313000 mgmmg//d/dL-L-L-1616160 00 mgmgmg/dddL,L, andndnd 1616160 0 mgmgmg/d/d/ LLL).. IIntntntererracactititioonon bbbetttweweeenen

fasting statuss aaandndn LLLDLDLDL-C-C wwwasasas ttesesestetet d d d innn bbbotototh h h prprp imimimararary y y ananndd d sesesecococondndndaraa y y y ouououtctctcomomomee momomodededelslsls uusing




8

representing 172,332,619 adults in the US population.

During a mean follow-up of 14.0 (+/- 0.22) years, there were a total of 3,788 deaths

(23.4%) and 1,454 (9.0%) CV deaths. Among the 16,161 individuals 10,023 (62.0%) participants

were fasting, and 6,138 (38.0%) individuals were non-fasting at the time of phlebotomy. Prior to

propensity score matching there were significant differences in the baseline variables between

the two groups (Table 1). Propensity score matching matched 4,299 (42.9% of fasting; 70.0% of

non-fasting) individuals with similar propensity scores. Post matching, there were no significant

differences between the baseline characteristics of the two groups and the absolute standardized

differences were <10% for all matched variables indicating an adequate match.27

All-Cause Mortality

In the unmatched cohort, there was an increased risk of all-cause mortality with increasing LDL-

C tertile [HRs 1 (referent), 1.57 (95% CI 1.34-1.83) (2nd tertile), 2.00 (95% CI 1.70-2.33) (3rd

tertile), respectively]. Test for interaction between fasting status and all-cause mortality was not

significant (Pinteraction = 0.64) indicating lack of association between fasting status and LDL-C

with all-cause mortality (eTable 1). Furthermore, the C-statistics for fasting vs. non-fasting

groups for predicting all-cause mortality were similar [0.58 (95% CI 0.57-0.60) vs. 0.58 (95% CI

0.56-0.59); P =0.55] (eFigure 1) suggesting similar prognostic value of fasting and non-fasting

LDL-C levels. Analyses including individuals with triglycerides of 400 mg/dL did not show a

significant difference between fasting vs. non-fasting C-statistics [0.58 (95% CI 0.57-0.60) vs.

0.57 (95% CI 0.55-0.59); P=0.34] (eFigure 2). Results were largely similar based on diabetic

status: fasting vs. non-fasting C-statistics in non-diabetics were not significantly different [0.59

(95% CI 0.57-0.60) vs. 0.59 (95% CI 0.57-0.61); P=0.79] nor were C-statistics in diabetics

[0.51(95% CI 0.46-0.56) vs. 0.51(95% CI 0.46-0.56); P=0.98] (eFigures 3 and 4).

All-Cause Mortality

n the unmatched cohort, there was an increased risk of all-cause mortality with increasing LDL-

C C teteertrtrtilililee [H[H[HRsRsR 1 (((rererefef rent), 1.57 (95% CI 1.34-1.8.8.83)) (2nd tertile), 222.0. 0 (9(9(955%5% CI 1.70-2.33) (3rd

eerttiilile), respecctitiivvvellly].]. TTeesesttt fofofor r r ininteteeraraactctioionn beeetwwweennn ffastttininng g ssstaatatusus anndnd aallll -ccauauusesee mmororortataaliliittyty wwwasass nnnot

iigngngnififficicicanantt (P(P(Pintintereraractitiionono === 000.6.64)4) iindndndicicicatatatiningg g lalalackckck oof ff asassososocic aaatiiononon bbbetetetweweweenenn fffasasstinngng ssstatatatutut s s ananand LDLDLDL-L-CCC

with all-causesee mmmororrtatatalilitytyty ((eTeTTababablelee 111).).. Fuuurtrtrthehehermrmrmororo e,e,e ttheheh CCC-s-sstatatatitit stststicicics s fofofor r r fafafastststinining g vsvsvs.. nononon-n-n faf sting




9

Sensitivity analysis using different cut-point definitions for fasting of <4 hours vs. 4

hours [0.58 (95% CI 0.57-0.59) vs. 0.60 (95% CI 0.56-0.64); P=0.37] or for <12 hours vs. 12

hours [C-statistics 0.57 (95% CI 0.56-0.59) vs. 0.59 (95% CI 0.57-0.60); P=0.37] (eFigures 5

and 6) showed largely concordant results with using an 8 hour fasting cut-point definition, and

did not show significant difference between fasting and non-fasting groups. Sensitivity analysis

using different follow up times did not show significant different between fasting and non-

fasting groups (data not shown).

Within the propensity score matched cohort, there was increased risk of all-cause

mortality by increasing LDL-C tertile [HRs 1 (referent), 1.61 (95% CI 1.25-2.08) (2nd tertile),

2.10 (95% CI 1.70-2.61) (3rd tertile), respectively]. There was no difference between fasting vs.

non-fasting LDL-C and all-cause mortality within each tertile of LDL-C (Figure 2). Test for

interaction between fasting status and all-cause mortality was not significant (Pinteraction = 0.11)

indicating lack of association between fasting status and LDL-C with all-cause mortality (Table

2). Similarly, the C-statistics for the fasting and non-fasting groups for predicting all-cause

mortality were similar [C-statistics 0.59 (95% CI 0.56-0.61) vs. 0.58 (95% CI 0.56-0.60); P=

0.73] (Figure 3).

In the unmatched cohort, C-statistics of triglyceride levels in fasting vs. non-fasting

groups for predicting all-cause mortality were not significantly different [(C-statistics 0.60 (95%

CI 0.59-0.62) vs. 0.61 (95% CI 0.59-0.62); P=0.96, respectively] (eFigure 7). Similarly, C-

statistics of TC level in fasting and non-fasting groups for predicting all-cause mortality were not

significantly different [(C-statistics 0.60 (95% CI 0.59-0.62) vs. 0.59 (95% CI 0.57-0.61);

P=0.31] (eFigure 8).

2.10 (95% CI 1.70-2.61) (3rd tertile), respectively]. There was no difference betwtwweeeen fafafastststinining g g vsv .

non-fasting LDL-C and all-cause mortality within each tertile of LDL-C (Figure 2). Test for

nnteteerararactctctiioionnn bebebetwweeeeeennn fasting status and all-cause momom rrtality was not t sis gnnififificicicant (Pinteraction = 0.11)

nndiiicac ting lack k ofoo aassssooociaiaiatititiononon bbebetwtwweeeeenn fafaastinnngg statttuuss andndnd LLDLDLDL--CC wwiwiththh aaalll-c-cauauaussese mmmororo tatat lililityyy (((TaTaTabbble

2)2).. SiSiSimimim lalarlrlrly,yy, tthhee CCC-s-sttattitiststiccs ss fofoforrr thththee fafaaststtinining gg anannd d d nnononn-ffafastststininingg g grgrgrououupspsps ffoorr pprprededdicicctitit ngngng aaalll-c-ccauauusese

mortality weereree sssimimmilillararr [[[C-C-ssstatatatitt ststticicicss 0.00 59599 (((959595% % % CICICI 000.5.556-6 0.0.0.616161) ) ) vsvsvs.. 0.00 585858 (((959595% % % CICIC 000.5.5.56-6-6 0.0.0 60); P=




10

Cardiovascular Mortality

Outcomes for CV mortality prior to propensity score matching similarly demonstrated increased

risk of CV mortality by increasing LDL-C tertile [HRs 1 (referent), 1.82 (95% CI 1.38-2.39) (2nd

tertile), 2.94 (95% CI 2.20-3.93) (3rd tertile)]. Test for interaction between fasting status and all-

cause mortality was not significant (Pinteraction = 0.11) indicating lack of association between

fasting status and LDL-C with CV mortality (eTable 1). Fasting vs. non-fasting C-statistics

were also similar [0.62 (95% CI 0.59-0.64) vs. 0.62 (95% CI 0.60-0.64); P=0.80] (eFigure 9)

suggesting similar prognostic value of fasting and non-fasting LDL-C levels on CV mortality.

Fasting vs. non-fasting C-statistics in non-diabetics were similar [0.62 (95% CI 0.60-0.65) vs.

0.64 (95% CI 0.61-0.67); P=0.42] as well as in diabetics [0.55 (95% CI 0.49-0.61) vs. 0.53 (95%

CI 0.47-0.60); P=0.67] (eFigures 10 and 11). Sensitivity analysis including individuals with

triglycerides of 400 showed largely concordant results with similar prognostic value of fasting

and non-fasting LDL-C levels [0.62 (95% CI 0.60-0.64) vs. 0.61 (95% CI 0.58-0.63); P=0.51]

(eFigure 12).

Sensitivity analysis using different cut-points for fasting of <4 hours vs. 4 hours [0.62

(95% CI 0.60-0.64) vs. 0.65 (95% CI 0.59-0.70); P=0.34] (eFigure 13), or for <12 hours vs. 12

hours [0.61 (95% CI 0.59-0.63) vs. 0.63 (95% CI 0.61-0.66); P=0.27] (eFigure 14) showed

largely concordant results with an 8 hour fasting cut-point definition, showing similar prognostic

value of fasting vs. non-fasting LDL-C. Sensitivity analysis using different follow up time cut-

points did not show significant difference between fasting and non-fasting groups (data not

shown).

In the propensity score matched cohort, there was increased risk of CV mortality by

increasing LDL-C tertile [HR 1 (referent), 1.68 (95% CI 1.13-2.51) (2nd tertile), 3.04 (95% CI

0.64 (95% CI 0.61-0.67); P=0.42] as well as in diabetics [0.55 (95% CI 0.49-0.66111) vvvs. 000.5.55333 (9(9(955%

CI 0.47-0.60); P=0.67] (eFigures 10 and 11). Sensitivity analysis including individuals with

rrigigglylylycecece iriridededes s oof 440400 0 showed largely concordannt t t reressults with simmilililar pprororogggnostic value of fasting

anndd d non n-fastinng g LDLDLDL-L-CC lelelevevelslsls [[0.0.626262 ((99595%% CCCI 00.6000-00.6444))) vsvs. 00.0.61611 ((9595%%% CCCI 0.0.0.58558-0-0.6.6.63)3)3 ;; P=P=P=000.5515 ]]]

eFeFFigigigururu ee 12122).)).

Sensititivivivititi y y y anananalala ysysy isss uuusiss ngngng ddififffef rererentntnt cccuuut-t-t popopoininntstss ffororr fffasasa titit ngngng ooof f <4<4<4 hhhououoursrsrs vvvs.s.s. 4 4 4 hohohours [0.62




11

2.00-4.62) (3rd tertile); respectively]. Test for interaction between fasting status and CV

mortality remained non-significant (Pinteraction = 0.34) (Table 2) indicating lack of association

between fasting status and LDL-C with CV mortality. Similarly, the C-statistics for the fasting

and non-fasting groups for predicting CV mortality were similar [0.62 (95% CI 0.60-0.66) vs.

0.62 (95% CI 0.60-0.66); P=0.96] (Figure 4) suggesting similar prognostic value of fasting and

non-fasting LDL-C.

In the unmatched cohort, C-statistics of triglyceride levels in fasting vs. non-fasting

groups predicting CV mortality were not significantly different [(C-statistics 0.62 (95% CI 0.60-

0.64) vs. 0.61 (95% CI 0.59-0.64); P=0.81, respectively] (eFigure 15). The C-statistics of TC

levels for CV mortality in fasting and non-fasting groups were similarly not significantly

different [C-statistics 0.64 (95% CI 0.62-0.66) vs. 0.63 (95% CI 0.60-0.65); P=0.49] (eFigure

16).

Sensitivity analyses without using survey weights yielded largely similar results for both

primary and secondary outcomes (data not shown).

Discussion

Every year millions of blood samples are drawn across the world for the measurement of lipid

panels, in particular LDL-C, with most national and international guidelines recommending a

fasting panel for such measurement. The results of this nationally representative cohort study

with 16,161 individuals followed for 14.0 years representing >172 million adults in the US

population show similar prognostic value of non-fasting LDL-C levels as compared to fasting

LDL-C levels for prediction of both all-cause mortality as well as CV mortality, thereby

questioning this traditional practice.

evels for CV mortality in fasting and non-fasting groups were similarly not signniifificiccannntltltly yy

different [C-statistics 0.64 (95% CI 0.62-0.66) vs. 0.63 (95% CI 0.60-0.65); P=0.49] (eFigure

166).).).

Sensitivivititityy annaala yysyseseses wwwiitithohoututut uussinnng suuurvvvey weweighghghttsts yyyieeldldeded llarargegeelyyy ssimimmiililarar rrresssulululttts ffforor bbboooth

prprimimimarararyy y ananndd d sesecocoonnddararyy oououtct oomomeseses (((dadad tataa nnnototot ssshooownwnwn).




12

Fasting Lipid Panel

The origin of the need for a fasting lipid panel is not entirely clear. It is known that certain lipid

parameters, especially triglycerides may be sensitive to fasting status and to the content of the

last meal (and in particular high fat loads). As such, fasting panels have been recommended to

provide accurate lipid measurements. However there are a number of drawbacks with this

approach including the need to reschedule a visit for a separate blood draw if patient is not

fasting thereby decreasing compliance and delaying treatment. Moreover, as individuals are in a

non-fasting state for the majority of time during the day, obtaining a fasting lipid panel may not

accurately reflect post-prandial abnormalities in lipid metabolism and thus obtaining a non-

fasting lipid panel may reflect a more relevant physiological state.7, 8 Obtaining a non-fasting

blood sample may also offer the opportunity to assess non-fasting blood glucose which may add

accuracy in identifying glucose intolerance.28, 29

Recently, several studies have questioned the need for fasting lipid profile, mostly

involving the use of non-fasting triglycerides in CV risk assessment. Although the role of

triglycerides as an independent CV risk factor is less clear than LDL-C, studies have shown that

postprandial triglycerides are similar or possibly even superior to fasting triglycerides in CV risk

prediction.9, 10, 30-32 Recent recommendations suggest potentially moving towards non-fasting

triglycerides for risk assessment, however further research is needed before definitive

recommendations can be made.33, 34

Fewer studies have addressed the use of non-fasting LDL-C in risk prediction. Numerous

animal, population based, and clinical studies have shown that LDL-C is associated with

increased CV mortality,35-37 with genetic studies also showing a causative mortality linkage.38-40

These studies have traditionally used fasting LDL-C as convention and thus recommendations

fasting lipid panel may reflect a more relevant physiological state.7, 8 Obtaining aaa nonoo -f-f-fassastititingngng

blood sample may also offer the opportunity to assess non-fasting blood glucose which may add

acccucuuraraaccycy iiinn n idididenntititiffyfying glucose intolerance.28, 299

Recentntlyly,, ssseveveeraral ll stststudududieieies s hhahavveve qquuuestiioi nnned thhhe nneneeeded fffoorr ffaaasttiingng lllipipidid ppproroofifilelee,, mmomossts lylyly

nnvovovolvlvlvinini gg g thththee e ususeee oofof nnononn-f-fasa tititingngng trtrtrigigiglylycceceririridededes ininin CCCVV V riiiskskk aaasssssesesssmsmsmenennt.t.t. AAAltthhohougugughh h ththe ee rrroleee ooof f

riglycerides s asasas aan n n ininndededepepep ndndndenenent CVCVCV rrrisi k k k fafafactcttororor iiis s lelelesssss ccleleeararar ttthahahan n n LDLDLDL-L-L-C,C,C, ssstututudidid esese hhhavavave e e shs own thatt




13

made by various agencies such as the ATP-III have generally been for obtaining fasting lipids.

However, multiple trials, including the Heart Protection Study and Anglo-Scandinavian Cardiac

Outcomes Trial included individuals who were not fasting during the time of phlebotomy when

analyzing effects of lipid lowering agents, suggesting that some of the data supporting lipid

lowering therapy actually springs from studies involving non-fasting individuals. 41, 42

Prior studies examining CV events have demonstrated increased CV risk by LDL-C level

for individuals in a non-fasting state,21, 43-45 but none have examined long-term mortality

outcomes in a representative sample. A recent population based study by Sidhu et al in 2012

showed that in a population based sample, lipid levels by subclass varied little with respect to

fasting time, and by less than 10% for LDL-C.4 Other studies have also shown little variation

with post-prandial LDL-C levels when compared with fasting levels.44, 46 These studies suggest

that the variation between fasting and non-fasting LDL-C levels, if any, is small. Our study is the

first to show that in a population-based sample, the association between LDL-C with CV and all-

cause mortality does not differ by fasting status. Our analyses also suggest that obtaining fasting

TC and triglyceride levels do not have improved prognostic significance over that of non-fasting

levels.

This provides further evidence that it may be unnecessary to use fasting lipid levels to

risk stratify patients. In our primary analyses we excluded patients with a triglyceride level 400

mg/dl, or roughly ~2% of the total population. However, the results were largely concordant in a

sensitivity analysis after including the above patients. Thus, our results are broadly applicable to

all patients undergoing blood draw to assess lipid panel and is applicable to LDL-C measurement

as well as triglyceride and TC measurement.

fasting time, and by less than 10% for LDL-C.4 Other studies have also shown liliittttlelee vvaraariaiaiatititionono

with post-prandial LDL-C levels when compared with fasting levels.44, 46 These studies suggest

hhatatt ttthehehe vvararariaiaiatttionnn bbbete ween fasting and non-fastingngng LLLDL-C levels, iiif f anny,y,y iiis s small. Our study is the

fifiirstt t to show ththatatt innn a a popooppupulalalatititioonon-b-bbaasasededd ssamplplp ee, thhhee assososociciaatatiooon n bbebetwtweeeenn LDLDDL-L-L-CC wwiwiththth CCCV V V anannd d aall-

caausususe ee momom rtrttalalaliitity y dododoeses nnnotott ddififfefefer bybyby fffaasastitiingngng ssstatatatuuus.s.s uOuOur anananalalalyysyseseses aallsl ooo susus gggggesese t t ththt atata ooobtbtbtaainniningngg ffaaastitiingn

TC and triglycycycerererididde e e leleevevevels dddooo nooot t t hahahavevv iiimpmpmprororoveveved d prprprogogognonoostststicicic ssigigigninin fiicacac ncncnce e e ovovoveer r thththatatat ooof f f non n-fastinggg




14

2013 ACC/AHA Guidelines and LDL-C Measurement

The recently published 2013 ACC/AHA guidelines recommend obtaining fasting lipids, however

the guidelines do not specify the length of time for fasting, nor cite data to support the need for

fasting LDL-C. The guidelines move away from recommending lowering LDL-C to specific

targets but recommend moderate to high intensity statin for patients with atherosclerotic CV

disease, an intensity of statins that would reduce baseline LDL-C by approximately 40-50%

which can easily be assessed with a non-fasting sample.

This has important implications in clinical practice. Requiring patients to fast causes

patients increased stress, potential hypoglycemia in patients with diabetes, increased

transportation costs, and potentially missed days of work. In addition, the inconvenience of

fasting may also delay treatment or diagnosis of hyperlipidemia if patients are unable to fast

before clinic visits. Enabling patients to obtain non-fasting lipid profiles would improve patient

satisfaction, and potentially avoid delays in detection and treatment of hyperlipidemia while at

the same time providing similar prognostic value as that of a non-fasting LDL-C value.

Limitations

The design of this study using data from an existing database limits our ability to prove that

fasting and non-fasting lipids have the same prognostic value. In addition, fasting and non-

fasting LDL-C were not collected on the same individuals. Moreover, in non-fasting patients

data was not available on the composition of patient meals.

Conclusions

In conclusion, the results of this study of 16,161 individuals followed for 14.0 years and

representative of the US population fail to show a superior prognostic value of fasting LDL-C

ransportation costs, and potentially missed days of work. In addition, the inconvnvvennnieiencncn e ee ofofof

fasting may also delay treatment or diagnosis of hyperlipidemia if patients are unable to fast f

beefofoforeree cclililinininicc vvisisiitststs. Enabling patients to obtain nonon nnn-fasting lipid prpp offililleeess s would improve patient

aatiissfsfaction, anand dd pootetennntiaiaalllllly y avavavoioid dd dededelalaayyss in deeetectttiooon aaanndnd ttrrreatatmmmenntnt ooff f hyyypepeerlrlrliipipididdemememiaia wwhihihileele aaatt

hhe e e sasaamemem ttimimimee prprrovvvididiinng g g sisimimiilalar rr prprprogoognonoostststicicic vvvalalaluuuee asss thhahattt ofofof a nnnononn-fffasasastitingngng LLDLDLDL-C-C vvvaalalueee.

Limitations




15

levels when compared with non-fasting LDL-C levels both for the prediction of all-cause

mortality as well as CV mortality. Our study suggests that a non-fasting LDL-C measurement

offers a more convenient method of phlebotomy while preserving the prognostic value of the

test. National and international guideline societies should re-consider the need for fasting LDL-

C. Similar results were seen for triglycerides and TC thus questioning the value of obtaining

fasting lipid profile.

Acknowledgments: Data analysis and statistical support were provided by New York University

School of Medicine Cardiovascular Outcomes Group. Author Contributions: Authors YG, BD,

and SB had full access to all of the data in the study and take responsibility for the integrity of

the data and the accuracy of the data analysis. Study concept and design: BD and SB.

Analysis and interpretation of data: YG, JX, BD and SB. Drafting of the manuscript: BD and

SB. Critical revision of the manuscript for important intellectual content: BD, SM, HW, DM and

SB. Statistical analysis: YG, JX, BD and SB

Conflict of Interest Disclosures: Dr. Mora reports research grants from Atherotech Diagnostics,

AstraZeneca and is on the advisory board for Quest Diagnostics, Cerenis Therapeutics,

Genzyme, Lilly. Dr. Bangalore is on the advisory board for Pfizer. The remaining authors have

no conflicts to disclose.

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21211. MMoMora SS, , RRRifafaii i N,N,N BBururu inining gg JEJE, , RiRiR dkdkerer PPM.M. FaFaststiinnggg coompmpmpararared wwwittthh nononfnfnfaastitingngng lllipipipidids s anannd d dappoollipi oproteinnss fofof rr prprededdicicictititingngng iinnciciidededenntt ccardidiiovvvasccucullar evevvenentsts.. CiCiircccululatattioionn. . 20202000808;1;1118188:9:9993933-1-100000 11.1

2222. JaJaamemem ss PAPAPA OOSSS, CCararrteeer r BLBLL,, CuCuCushshshmmamann n WCWCWC, DDeDennnnniiisoson-n-n HiHiHimmmmmmelelfafaf rbrbrb CC,, HHaHandndndleleer r J,J,J, LLLaccklklanana dd d DDTDT,LeFeFevrv e MLML, MMacKcKenenziee TDTD, OgOgededegbebe OO, SmSmitithh SCC JJr,r SSvevetktkeyy LLP,P, Talalerer SSJ,J TTowwnsn ennd d RRRR, , Wright JT Jrr,, NaNaNarvrvva a a ASASAS,, OrOrOrtitiiz zz E.E.E. 2201010 44 evevevidididenenencecece-b-bbasasasedede ggguiuiuidededeliiinenene fororor ttthehehe mmmanana agagagemememenenentt t of high blbloooodd prpresessusurere iinn adadulultsts:: ReRepoportrt frfromom tthehe ppananelel mmemembebersrs aappppoiointnteded ttoo ththee EiEighghthth JJoiointnt NNatatioionanalldd




18

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33. Miller M, Stone NJ, Ballantyne C, Bittner V, Criqui MH, Ginsberg HN, Golldbdbbererergg ACACAC,Howard WJ, Jacobson MS, Kris-Etherton PM. Triglycerides and cardiovascular disease a cientific statement from the American Heart Association. Circulation. 2011;123:2292-2333.

34344. KKaKannell WWWB,B,, VVVasasanann RRRS.S.S. TTririglglglycycycereridideses aass vavascs uululaar risisskkk fafafactc ororrsss: NNewew eeepip dedemimimiololo ogogicic iiinsnsn igigighthth ssffoor cuc rrent opininioioi nnn inin ccararardididiololologoogy.y. CCuCurrrr OOOpinnn CCCardddiiool. 22200000999;22424:3:345455.

3555. GoGoGordrdononn TTT, , KKaKannnnnelell WWWB,B CCCasasstetetelllllliii WWWP,P,P, DDDawawawbebeb r r TTRR. LiLiipopopoprprprotototeieiinsnss,, cacaardrddioiovavavascsccululararar ddissseaeaasese, , anannd ddeatath:h: TThehe FFraamim ngnghaham ststududy.y. ArArchc Intterernn MeMedd.. 198181;1;14141:1:112128..

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normal food intake on lipids, lipoproteins, apolipoproteins, and cardiovascular risk k prprp ededicictitionon. . Circulation. 2008;118:2047-2056.

45. Talmud PJ, Hawe E, Miller GJ, Humphries SE. Nonfasting apolipoprotein b and triglyceride evels as a useful ppredictor of coronary heart disease risk in middle-agged UK men. y Arterioscler

ThThrororombmbmb VVVasasascc Biiololol.. 202 02;22:1918-1923.

4466. VVan Dierenen SSS, NöNöththlililingngngs s s U,U,U VVVananan DDDeerr Scchhooouww YYY, SSSpipipijkjkeeermmamannn AA,A, RRuuuttteen n G,G,G SSluluuikikk DDD, WWeWeikikikeeert CC,C, JJJooo st H, BoBoeiingngg H,, BBBeulleenensss J. NNonono -f-faaastinngng lipiiddsss anannd d d riririsksks oof ccacarrdiovvavascscuuulaarar ddiseeaeasese inn n patttieeentswiwiiththth dddiaiai bebetetetess s memeelllititi usus. DDiDiababeetetololologogogiaiaia.. 22020111111;5;5;54:4::73737 --7-777.




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Table 1. Baseline Characteristics of Fasting and Non-Fasting LDL-C Cohorts, Pre and Post-Propensity Score Matching

Pre-match Post-matchClinical Variables Fasting

(n=10,023)**Non-fasting (n=6,138)** P Value

ASD(%)

Fasting(n=4,299)**

Non-fasting (n=4,299)** P value

ASD(%)

Age, mean (SE), y 42.68 (0.43) 45.43 (0.55) <0.001 6.4 42.7 (0.54) 43.43 (0.57) 0.20 1.8 Women (%) 51.35 54.07 0.02 5.5 51.66 53.13 0.36 2.9 White (%) 75.38 77.17 0.06 4.2 76.00 77.04 0.41 2.5 Enlarged waist circ (%) 34.44 37.54 0.01 6.5 33.03 34.59 0.27 3.3 Smoker (%) 52.23 53.24 0.29 2.0 52.69 52.55 0.93 0.3 DM (%) 4.93 8.39 <0.001 13.9 5.76 6.88 0.08 4.6 HTN (%) 15.98 18.82 <0.001 7.5 17.42 16.47 0.45 2.5 Elevated cholesterol (%) 26.62 28.66 0.07 4.6 25.89 26.78 0.52 2.0 Prior CVD (%) 4.93 6.30 <0.001 6.0 4.55 4.74 0.75 0.9 Cholesterol lowering med (%) 3.08 3.69 0.16 3.4 3.48 3.51 0.96 0.2 Low SES (%) 13.41 12.24 0.22 3.5 12.07 12.57 0.68 1.5 LDL-C, mean (SE), mg/dL* 125.04 (0.77) 123.71 (0.84) 0.07 1.9 118.55 118.33 0.84 0.3 Low HDL-C (%) 35.74 35.09 0.54 1.4 33.12 33.39 0.85 0.6 Abbreviations: ASD = absolute standardized difference; CVD = cardiovascular disease; med = medication; DM = diabetes mellitus; HDL-C = high density lipoprotein cholesterol; HTN = hypertension; circ = circumference; LDL-C = low density lipoprotein C; SES = socioeconomic status *To convert to mmol/L, multiply values by 0.0259 **N reported based on unweighted numbers; P-values based on weighted values

ged waist circ (%) 34.44 37.54 0.01 6.5 33.03 34.599 00.2.27 7 3ker (%) 52.23 53.24 0.29 2.0 52.69 52.2.555555 00.9.9333 0%) 4.93 8.39 <0.001 13.9 5.76 6 8.8888 0.00.080808 444(%) 15.98 18.82 <0.001 7.5 17.42 16.47 0.45 2

ated cholesterol (%) 26.62 28.66 0.07 4.6 25.89 26.78 0.52 2CVDD (%(%)) 4.93 6.30 <00.0. 01 6.0 4.55 4.74 0.75 0

essteteroror l l lololoweririringngn mm dded (((%)%)% 3.08 3.69 0.0 16 3.4 33.48 8 3.51 0.96 0ESESES S (%(%) ) 1313.4411 1212.2.24 4 0.0 22 33.55 1112.2 0707 112.2 5757 00.6. 8 1

C,CC mmmean (SE), mgmg/d/d/dL*L* 11252525.0.004 4 (0(0(0.77 )7)7) 11 3232 7.71 (0(0.8.8. )4)4 0.0 07 11.9.. 11818.5.5555 11111 888.333333 000 8.8.84 44 0HDHDH L-LL C (%) 5535.74 4 5353 .09 0.0 54 111.4.. 333.12 33. 9939 00.85 0vviaiaiationonons: ASD = aabsbsbsolutee tstana da drdr zizede dififfefef erencncn e;e CVDVDD == cc rarardio avascs uluu ar d sisi aeasese;; memem d d d == memm idid cationoo ; DMD = dd aiabebetetess memem llliti us; DHDHDL-C CC == high dd nnensityott ieieinn n hchcholololesestet rorol;l;l; HHHTNTN == hypypy erertetensn oioion;n cic rc == cirirircucucumfmfmfererenencecc ; LDLDLDL-L-L-C === lolol ww edensn ityy y ilipopopoprprproto ieinn n C;C;C SS SESES === ss cococio cece onon momomicic sstatatuutussonverert t toto mmmmomomol/l/l/L,LL mulultititi lplplyy vavalulueses bbbyy 0.0.0 020 599 eepopop rtrteded bbasaseded oonn ununweweigigghthteded nnumumbebersrs;; PP-vvalalueuess babasesed d onon wweieighghg teted d vavalulueses




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Table 2. Cox Proportional Hazards Model of All Cause and Cardiovascular Mortality by LDL-C Level in Fasting and Non-Fasting Cohorts in the Matched Participants

Fasting Non-FastingOutcome LDL-C range

(mg/dL)*Hazard Ratio

(95% CI) P Value LDL-C range (mg/dL) *

Hazard Ratio (95% CI) P Value

All-Cause Mortality LDL-C 1st tertile 99.60 1 (referent) 100.73 1 (referent) LDL-C 2nd tertile 99.60-129.68 1.61 (1.25-2.08) <0.001 100.73-129.22 1.21 (0.92-1.60) 0.17 LDL-C 3rd tertile 129.68-361.40 2.10 (1.70-2.61) <0.001 129.22-438.00 2.23 (1.76-2.83) <0.001 LDL-C x Fasting Status 0.11 CV Mortality LDL-C 1st tertile 99.60 1 (referent) 100.73 1 (referent) LDL-C 2nd tertile 99.60-129.68 1.68 (1.13-2.51) 0.012 100.73-129.22 1.59 (0.97-2.61) 0.063 LDL-C 3rd tertile 129.68-361.40 3.04 (2.00-4.62) <0.001 129.22-438.00 4.00 (2.58-6.19) <0.001 LDL-C x Fasting Status 0.34

Abbreviations: CV = cardiovascular mortality; LDL-C = low density lipoprotein cholesterol *To convert to mmol/L, multiply values by .0259

( ) ( )L-C 3rd tertile 129.68-361.40 2.10 (1.70-2.61) <0.001 129.22-438.00 2.23 (1.76-2 8.883)3)3) <<<00.0.0000 1L-C x Fasting Status 0.11 Mortality

L-C 1st tertile t 99.60 1 (referent) 100.73 1 (referent) L-C 2nd tertile 99.60-129.68 1.68 (1.13-2.51) 0.012 100.73-129.22 1.59 (0.97-2.61) 0.063 L-C 3rd tertile 129.68-361.40 3.04 (2.00-4.62) <<0.001 129.22-438.00 4.4 00 (2.58-6.19) <0.001L-C C xx x FaFaFastststinininggg StStStataa uss 0.0 433 vviaaiatiti nonons:s: CCV = cacardrdr ioi vascscululara mmorrtatalityty; ; LDL L-L C C = low w densn ity y lipop prototein n hcc lolo esterool lonvnn rrert t tot mmol/L,L, mmmultitiplplplyy y vavalulueseses bbby y y 0.0025252599




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Figure Legends:

Figure 1. Participant flowchart.

Figure 2. Kaplan-Meier curve for fasting vs. non-fasting LDL-C levels and all-cause mortality.

Figure 3. Prognostic value of fasting vs. non-fasting LDL-C levels on all-cause mortality in the

matched cohort.

Figure 4. Prognostic value of fasting vs. non-fasting LDL-C levels on cardiovascular mortality

in the matched cohort.

Figure 4. Prognostic value of fasting vs. non-fasting LDL-C levels on cardiovassccculalalar momomortrtrtalalaliitity y

n the matched cohort.



Figure 1 at Karolinska Inst on July 14, 2014http://circ.ahajournals.org/Downloaded from


1

SUPPLEMENTAL MATERIAL

Supplemental Table 1 – All Cause and Cardiovascular Mortality by LDL-C Level in Fasting and Non-

Fasting Cohorts Prior to Propensity Score Matching

Outcome

Fasting Non-Fasting

LDL-C

(mg/dL)*

Hazard Ratio

(95% CI) P Value

LDL-C

(mg/dL)*

Hazard Ratio

(95% CI) P Value

All-Cause Mortality

LDL-C 1st tertile

(reference) ≤106.41 1 (referent)

≤104.76 1 (referent).

LDL-C 2nd

tertile 106.41-138.28 1.57 (1.34-1.83) <0.001 104.76-137.11 1.28 (1.05-1.55) 0.015

LDL-C 3rd

tertile 138.28-380.00 2.00 (1.70-2.33) <0.001 137.11-438.00 2.07 (1.67-2.57) <0.001

LDL-C x Fasting

Status 0.11

CV Mortality

LDL-C 1st tertile

(reference) ≤106.41 1 (referent)

≤104.76 1 (referent)

LDL-C 2nd

tertile 106.41-138.28 1.82 (1.38-2.39) <0.001 104.76-137.11 1.62 (1.15-2.28) <0.001

LDL-C 3rd

tertile 138.28-380.00 2.94 (2.20-3.93) <0.001 137.11-438.00 6.18 (2.21-4.76) <0.001

LDL-C x Fasting

Status 0.64

Abbreviations: CV = cardiovascular; LDL-C = low density lipoprotein cholesterol

*To convert to mmol/L, multiply values by 0.0259

2

FIGURE LEGENDS

Figure 1 - Prognostic value of fasting vs. non-fasting LDL-C level on all-cause mortality in the

unmatched cohort

Figure 2 - Sensitivity analysis: Prognostic value of fasting vs. non-fasting LDL-C level

including patients with triglycerides ≥400 mg/dL on all-cause mortality in the

unmatched cohort

Figure 3 - Prognostic value of fasting vs. non-fasting LDL-C level on all-cause mortality in

patients without diabetes in the unmatched cohort

Figure 4 - Prognostic value of fasting vs. non-fasting LDL-C level on all-cause mortality in

diabetic patients in the unmatched cohort

Figure 5 – Sensitivity Analysis: Prognostic value of fasting (<4 hours) vs. non-fasting (≥4 hours)

LDL-C level on all-cause mortality in the unmatched cohort

Figure 6 - Sensitivity Analysis: Prognostic value of fasting (<12 hours) vs. non-fasting (≥12

hours) LDL-C level on all-cause mortality in the unmatched cohort

Figure 7 - Prognostic value of fasting vs. non-fasting triglyceride level on all-cause mortality in

the unmatched cohort

Figure 8 –Prognostic value of fasting vs. non-fasting total cholesterol level on all-cause

mortality in the unmatched cohort

Figure 9 - Prognostic value of fasting vs. non-fasting LDL-C level on cardiovascular mortality

in the unmatched cohort


in patients without diabetes in the unmatched cohort


in diabetic patients in the unmatched cohort

Figure 12 - Sensitivity analysis: Prognostic value of fasting vs. non-fasting LDL-C level

including those with triglycerides ≥400 mg/dL on cardiac mortality in the unmatched

cohort


hours) LDL-C level on cardiovascular mortality in the unmatched cohort


hours) LDL-C level on cardiovascular mortality in the unmatched cohort

Figure 15 - Prognostic value of fasting vs. non-fasting triglyceride level on cardiovascular


Figure 16 - Prognostic value of fasting vs. non-fasting cholesterol level on cardiovascular


3

Supplemental Figure 1 – Prognostic value of fasting vs. non-fasting LDL-C level

on all-cause mortality in the unmatched cohort

4

Supplemental Figure 2 – Sensitivity analysis: Prognostic value of fasting vs. non-

fasting LDL-C level including those with triglycerides ≥400 mg/dL on all-cause


5


on all-cause mortality in patients without diabetes in the unmatched cohort

6


on all-cause mortality in diabetic patients in the unmatched cohort

7

Supplemental Figure 5 – Sensitivity Analysis: Prognostic value of fasting (<4

hours) vs. non-fasting (≥4 hours) LDL-C level on all-cause mortality in the

unmatched cohort

8

Supplemental Figure 6 - Sensitivity Analysis: Prognostic value of fasting (<12

hours) vs. non-fasting (≥12 hours) LDL-C level on all-cause mortality in the

unmatched cohort

9

Supplemental Figure 7 –Prognostic value of fasting vs. non-fasting triglyceride

level on all-cause mortality in the unmatched cohort

10

Supplemental Figure 8 –Prognostic value of fasting vs. non-fasting total

cholesterol level on all-cause mortality in the unmatched cohort

11


on cardiovascular mortality in the unmatched cohort

12

Supplemental Figure 10 – Prognostic value of fasting vs. non-fasting LDL-C

level on cardiovascular mortality in patients without diabetes in the unmatched

cohort

13

Supplemental Figure 11 – Prognostic value of fasting vs. non-fasting LDL-C

level on cardiovascular mortality in diabetic patients in the unmatched cohort

14

Supplemental Figure 12 - Sensitivity analysis: Prognostic value of fasting vs.

non-fasting LDL-C level including those with triglycerides ≥400 mg/dL on

cardiovascular mortality in the unmatched cohort

15


hours) vs. non-fasting (≥4 hours) LDL-C level on cardiovascular mortality in the

unmatched cohort

16


hours) vs. non-fasting (≥12 hours) LDL-C level on cardiovascular mortality in the

unmatched cohort

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Supplemental Figure 15 –Prognostic value of fasting vs. non-fasting triglyceride

level on cardiovascular mortality in the unmatched cohort

18

Supplemental Figure 16 –Prognostic value of fasting vs. non-fasting total

cholesterol level on cardiovascular mortality in the unmatched cohort

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