Nej Mo a 1304501Aas

8/12/2019 Nej Mo a 1304501Aas

1/12

T he n e w e n g l a n d j o u r n a l o f medicine

n engl j med 370;3 nejm.org january 16, 2014 233

original article

Body-Mass Index and Mortality among

Adults with Incident Type 2 DiabetesDeirdre K. Tobias, Sc.D., An Pan, Ph.D., Chandra L. Jackson, Ph.D.,

Eilis J. OReilly, Sc.D., Eric L. Ding, Sc.D., Walter C. Willett, M.D., Dr.P.H.,JoAnn E. Manson, M.D., Dr.P.H., and Frank B. Hu, M.D., Ph.D.

From the Departments of Nutrition (D.K.T.,A.P., C.L.J., E.J.O., E.L.D., W.C.W., F.B.H.)

and Epidemiology (W.C.W., J.E.M., F.B.H.),Harvard School of Public Health, and theChanning Division of Network Medicine(E.J.O., E.L.D., W.C.W., F.B.H.) and theDivision of Preventive Medicine (J.E.M.),Department of Medicine, Brigham andWomens Hospital and Harvard MedicalSchool all in Boston; and Saw SweeHock School of Public Health and YongLoo Lin School of Medicine, National Uni-versity of Singapore and National Uni-versity Health System, Singapore (A.P.).Address reprint requests to Dr. Tobias atthe Department of Nutrition, HarvardSchool of Public Health, 665 HuntingtonAve., Boston, MA 02115, or at dbanel@

hsph.harvard.edu; or to Dr. Pan at the SawSwee Hock School of Public Health, Nation-al University of Singapore, 16 Medical Dr.,MD3, Singapore 117597, or at [email protected].

Drs. Tobias and Pan contributed equallyto this article.

This article was updated on April 3, 2014,at NEJM.org.

N Engl J Med 2014;370:233-44.DOI: 10.1056/NEJMoa1304501

Copyright 2014 Massachusetts Medical Society.

A B S T RA C T

Background

The relation between body weight and mortality among persons with type 2diabetes remains unresolved, with some studies suggesting decreased mortalityamong overweight or obese persons as compared with normal-weight persons(an obesity paradox).

Methods

We studied participants with incident diabetes from the Nurses Health Study(8970 participants) and Health Professionals Follow-up Study (2457 participants)who were free of cardiovascular disease and cancer at the t ime of a diagnosis ofdiabetes. Body weight shortly before diagnosis and height were used to calculatethe body-mass index (BMI, the weight in kilograms divided by the square of theheight in meters). Multivariable Cox models were used to estimate the hazard ratiosand 95% confidence intervals for mortality across BMI categories.

Results

There were 3083 deaths during a mean period of 15.8 years of follow-up. A J-shapedassociation was observed across BMI categories (18.5 to 22.4, 22.5 to 24.9 [reference],25.0 to 27.4, 27.5 to 29.9, 30.0 to 34.9, and 35.0) for all-cause mortality (hazardratio, 1.29 [95% confidence interval {CI}, 1.05 to 1.59]; 1.00; 1.12 [95% CI, 0.98 to1.29]; 1.09 [95% CI, 0.94 to 1.26]; 1.24 [95% CI, 1.08 to 1.42]; and 1.33 [95% CI,1.14 to 1.55], respectively). This relationship was linear among participants who hadnever smoked (hazard ratios across BMI categories: 1.12, 1.00, 1.16, 1.21, 1.36,and 1.56, respectively) but was nonlinear among part icipants who had ever smoked(hazard ratios across BMI categories: 1.32, 1.00, 1.09, 1.04, 1.14, and 1.21) (P = 0.04for interaction). A direct linear trend was observed among participants younger

than 65 years of age at the time of a diabetes diagnosis but not among those65 years of age or older at the time of diagnosis (P

8/12/2019 Nej Mo a 1304501Aas

2/12

Th e n e w e n g l a n d j o u r n a l o f medicine

n engl j med 370;3 nejm.org january 16, 2014234

Excess adiposity is a well-established

risk factor for premature death in thegeneral population, including death due

to cardiovascular disease or cancer.1-4However,a so-called obesity paradox (i.e., an associationbetween obesity, as compared with normalweight, and reduced mortality) has been reported

among patients with heart failure, end-stage renaldisease, or hypertension, and, recently, amongthose with type 2 diabetes.5-12 Most of thesestudies, however, have been limited by small sam-ples and suboptimal control for smoking statusand preexisting chronic conditions.

Smoking is a concern in analyses of bodyweight and mortality because it is associatedwith decreased body weight but an increasedrisk of death.13Statistical adjustment for smok-ing status (e.g., ever smoked vs. never smoked)is often insufficient to control for varying de-

grees of smoking duration and intensity. Thus,stratification according to smoking status canbe an important way to examine the associationbetween body weight and the risk of death; inaddition, the subgroup analysis among personswho have never smoked can reduce residual biasrelated to smoking.3,4,13-15An additional concernis reverse causation, whereby underlying chronicdisease or frailty both causes weight loss and ele-vates the risk of death. Exclusion of persons withknown illnesses at baseline and censoring of datafor patients who died early in the follow-up periodare routinely performed to reduce this bias.16

To address the limitations of previous analy-ses, we conducted a detailed analysis of the as-sociation between body-mass index (BMI) andthe risk of death among participants with inci-dent diabetes from two large prospective cohortstudies, the Nurses Health Study (NHS) and theHealth Professionals Follow-up Study (HPFS).

Methods

Study Population

The NHS was initiated in 1976 with the enroll-

ment of 121,700 female nurses 30 to 55 years of age.

The HPFS began in 1986, enrolling 51,529 male

health professionals between 40 and 75 years of

age. Questionnaires are administered biennially to

update medical, lifestyle, and other health-related

information.17,18 Cumulative follow-up exceeds

90% of potential person-time for both cohorts.

Our analyses included women and men report-

ing incident diabetes between baseline (1976 forthe NHS and 1986 for the HPFS) and January 1,2010 (Fig. S1 in the Supplementary Appendix, avail-able with the full text of this article at NEJM.org).We excluded participants reporting a history of dia-betes at baseline or reporting cardiovascular dis-ease (stroke, coronary heart disease, or coronary-

artery bypass graft surgery) or cancer before adiabetes diagnosis. Participants were excluded ifthey were underweight (BMI [the weight in kilo-grams divided by the square of height in meters]

8/12/2019 Nej Mo a 1304501Aas

3/12

Body-Mass Index, Mortality, and Type 2 Diabetes


analysis assessed the potential effect of error in

self-reported weight and height with the use of

the sex-specific calibration equations proposed

by Berrington de Gonzalez et al.3from the U.S.

National Health and Nutrition Evaluation Survey

sample (BMImeasured= 0.63 + [1.01 BMIself-reported] +

[0.0006 age in years] for women and 0.29 +

[0.98 BMIself-reported] + [0.012 age in years] for men).

Ascertainment of Deaths

The primary outcome was death from any cause

through January 1, 2012. Most deaths (>98%) were

identified from reports by the next of kin or postal

authorities or from searches of the National Death

Index.24,25The cause of death was determined by

physician review of medical records and death

certificates. The diagnostic codes of the Interna-

tional Classification of Diseases,8th Revision(ICD-8),

were used to classify deaths as due to cardio-

vascular disease (ICD-8 codes 390 through 459and 795), cancer (ICD-8 codes 140 through 207),

or other causes.

Assessment of Covariables

Detailed information on cigarette smoking, physi-

cal activity, menopausal status (for NHS partici-

pants only), and several lifestyle factors and health

outcomes were updated every 2 years. Marital

status and status with respect to a family history

of diabetes were assessed periodically. Dietary

information was collected from validated food-

frequency questionnaires approximately every

4 years. Diet quality was assessed with the use

of the 2010 Alternate Healthy Eating Index

(with scores ranging from 2.5 to 87.5 and higher

scores indicating a healthier diet).26The covari-

ables were derived from the most recent ques-

tionnaire before a diabetes diagnosis.

Statistical Analysis

Hazard ratios and 95% confidence intervals were

estimated from Cox proportional-hazards models,

with number of months since a diabetes diagno-sis as the time scale. Person-time was calculated

from the date of a diabetes diagnosis until death

or the end of follow-up (January 1, 2012). BMI

categories were defined as follows: 18.5 to 22.4,

22.5 to 24.9 (reference), 25.0 to 27.4, 27.5 to 29.9,

30.0 to 34.9, and 35.0 or higher. Multivariable

models were adjusted for race or ethnic group

(white, black, Asian American, Hispanic, or other),

smoking status (never smoked; previously smoked;

currently smokes 1 to 14, 15 to 24, or 25 cigarettes

per day; or not reported), alcohol consumption

(women: 0, 0.1 to 4.9, 5.0 to 14.9, or 15.0 g per day;

men: 0, 0.1 to 4.9, 5.0 to 29.9, or 30.0 g per day),

physical activity (

8/12/2019 Nej Mo a 1304501Aas

4/12



All-Cause Mortality

A total of 3083 deaths from all causes were ob-

served over a mean follow-up of 15.8 years, with

a maximum follow-up of 36 years among women

(mean, 16.2 years; 16.1 deaths per 1000 person-

years) and 26 years among men (mean, 14.5 years;

21.8 deaths per 1000 person-years). The mean

(SD) age at death was 74.67.8 years for womenand 78.78.7 years for men. In general, crude

rates of death were higher among participants

who had ever smoked than among those who had

never smoked, across BMI categories (Table 2).

A J-shaped association between BMI and all-cause mortality was observed among all the par-ticipants (Table 2) (P

8/12/2019 Nej Mo a 1304501Aas

5/12

8/12/2019 Nej Mo a 1304501Aas

6/12

8/12/2019 Nej Mo a 1304501Aas

7/12



the first 4 years of follow-up (a total of 227 par-ticipants: 78 who had never smoked and 149 whohad ever smoked) (Fig. 1B, 1D, and 1F, respectively)resulted in a monotonic positive association be-tween BMI and death among participants whohad never smoked, and the results did not differsubstantially from those of the primary analysis.

Exclusion of participants with weight loss of morethan 1 BMI unit before a diabetes diagnosis,adjustment for baseline hypertension or hyper-cholesterolemia, and adjustment for year of dia-betes diagnosis produced similar results (data notshown). The tests for the proportional-hazardsassumption did not indicate a violation in eithercohort. Age-adjusted survival curves are depictedin Figures S2A through S2D in the Supplemen-tary Appendix.

Using the traditional cutoff points for the BMIcategories of normal weight, overweight, and

obesity (Table S2 in the Supplementary Appendix)and correcting errors in self-reported weight andheight (Table S3 in the Supplementary Appendix)did not significantly change the results.

Cause-Specific Mortality

We assessed the relationship between BMI just be-

fore a diabetes diagnosis and deaths due to cardio-

vascular disease (941 deaths), cancer (784 deaths),

and other causes (e.g., respiratory diseases, re-

nal disease, suicide, and accidents; 1358 deaths)

(Fig. 2A, 2B, and 2C; and Table S4 in the Supple-

mentary Appendix). There was a significant di-

rect linear relationship between BMI and car-

diovascular mortality among all participants

(P

8/12/2019 Nej Mo a 1304501Aas

8/12

8/12/2019 Nej Mo a 1304501Aas

9/12

8/12/2019 Nej Mo a 1304501Aas

10/12



HazardRatio3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

A

DeathfromCardiovascularCa

uses

18.5

22.

422.5

24.9

25.0

27.4

2

7.52

9.9

30.0

34.9

35.0

AllParticipants

BMICategory

3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

18.5

22.4

22.5

24.

9

25.0

27.4

27.5

29.9

30.0

34.9

35.

0

NeverSmoked

3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

18.5

22.

422.5

24.9

25.0

27.4

27.5

29

.9

30.0

34.9

35.0

EverSm

oked

HazardRatio3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

B

DeathfromCancer

18.5

22.

422.5

24.9

25.0

27.4

2

7.52

9.9

30.0

34.9

35.0

AllParticipants

BMICategory

3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

18.5

22.4

22.5

24.

9

25.0

27.4

27.5

29.9

30.0

34.9

35.

0

NeverSmoked

3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

18.5

22.

422.5

24.9

25.0

27.4

27.5

29

.9

30.0

34.9

35.0

EverSm

oked

HazardRatio3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

C

DeathfromOtherCauses

18.5

22.

422.5

24.9

25.0

27.4

2

7.52

9.9

30.0

34.9

35.0

AllParticipants

BMICategory

3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

18.5

22.4

22.5

24.

9

25.0

27.4

27.5

29.9

30.0

34.9

35.

0

NeverSmoked

3.

00

2.

00

2.

50

1.

50

1.

00

0.

50

0.

00

18.5

22.

422.5

24.9

25.0

27.4

27.5

29

.9

30.0

34.9

35.0

EverSm

oked

1.0

2

1.

00

1.

07

1.

11

1.

28

1.

23

0.

98

1.

00

1.

21

1.

43

1.

57

1.7

0

1.

51

1.

00

1.

25

1.

10

1.

44

1.

33

0.

83

1.

00

1.

02

0.

86

1.

29

1.3

7

1.

00

1.

00

1.

00

1.

03

1.

16

1.

07

1.

87

1.

00

1.

36

1.

23

1.

51

1.

29

1.

37

1.

00

1.

09

1.

07

1.

07

1.

35

1.

44

1.

00

1.

20

1.

30

1.

30

1.5

8

1.3

0

1.

00

1.

03

0.

95

0.

94

1.

27

The New England Journal of Medicine

Downloaded from nejm.org on June 24, 2014. For personal use only. No other uses without permission.

Copyright 2014 Massachusetts Medical Society. All rights reserved.

8/12/2019 Nej Mo a 1304501Aas

11/12



nation across multiple BMI categories and keyanalyses to address potential biases. Prospec-tively measured body weight, documented justbefore or at the time of a diabetes diagnosis,prevents misclassification from weight changedue to early pharmacologic treatments or life-style changes shortly after diagnosis. Enroll-ment of health professionals has proved benefi-cial with respect to the reliability and validityof self-reported health-related exposures andoutcomes, and it reduces confounding by edu-cational and socioeconomic factors.

Limitations of the study include the reliance

on self-reported weight measures, although er-roneous reporting was shown to be very mini-mal in validation studies, and corrections oferrors in BMI measurement produced similarfindings. Information on weight was obtainedan average of 11 months before diagnosis as aproxy for the weight at diagnosis, but this is

unlikely to have resulted in appreciable error.Finally, the relative homogeneity of the NHSand HPFS cohorts may limit the generalizabil-ity of our findings to other racial and ethnicgroups.

In conclusion, our results indicate a J-shapedrelationship between BMI at the time of a dia-betes diagnosis and the risk of death from allcauses, with the lowest risk observed amongnormal-weight participants with a BMI of 22.5to 24.9. Among participants who had neversmoked, there was a direct linear relationship

between BMI and mortality, whereas a nonlin-ear relationship was observed among thosewho had ever smoked. There was no evidenceof a protective effect of overweight or obesityon mortality. In addition, given the relationshipof overweight and obesity to other critical pub-lic health end points (e.g., cardiovascular dis-ease and cancer), the maintenance of a healthybody weight should remain the cornerstone ofdiabetes management, irrespective of smokingstatus. Further evidence is needed to corrobo-rate our findings in other populations.

Supported by grants from the National Institutes of Health(UM1 CA167552, UM1 CA176726, P01 CA87969, R01 HL034594,P01 CA055075, DK58845, CA55075, CA87969, HL34594, P30DK46200, and 1U54CA155626-01) and the American DiabetesAssociation (7-12-MN-34).

Disclosure forms provided by the authors are available withthe full text of this article at NEJM.org.

We thank the participants in the Health Professionals Follow-upStudy and the Nurses Health Study for their continuing sup-port, as well as our colleagues working in these studies fortheir contributions.

Figure 2 (facing page).Hazard Ratios for Cause-SpecificMortality among Participants with Incident Type 2

Diabetes, According to BMI Shortly before Diagnosisof Type 2 Diabetes.

The results from the two cohorts (NHS and Health Pro-

fessionals Follow-up Study) were combined with the useof a fixed-effect meta-analysis. The numbers of person-

years were 179,081 for all participants, 79,546 for thosewho had never smoked, and 97,762 for those who had

ever smoked. Panel A shows cardiovascular mortality inthe total study population (941 deaths), among partici-

pants who had never smoked (340 deaths), and amongthose who had ever smoked (592 deaths). Panel B shows

cancer mortality in the total study population (784 deaths),among participants who had never smoked (266 deaths),

and among those who had ever smoked (514 deaths).Panel G shows mortality from other causes in the total

study population (1358 deaths), among participants

who had never smoked (561 deaths), and among thosewho had ever smoked (786 deaths). All estimates have

been adjusted for age, race, marital status, menopausalstatus (for the NHS cohort only), presence or absence of

a family history of diabetes, smoking status (among allparticipants and those who had ever smoked), alcohol

intake, and Alternate Healthy Eating Index score. A totalof 20 women and 79 men with missing data on smoking

status were excluded from the stratified analyses (24 totaldeaths, including 9 from cardiovascular causes, 4 from

cancer, and 11 from other causes). The bars represent

95% confidence intervals.

References

1. Adams KF, Schatzkin A, Harris TB, etal. Overweight, obesity, and mortality in alarge prospective cohort of persons 50 to71 years old. N Engl J Med 2006;355:763-78.2. Chen Z, Yang G, Offer A, et al. Bodymass index and mortality in China: a 15-year prospective study of 220 000 men. IntJ Epidemiol 2012;41:472-81.3. Berrington de Gonzalez A, Hartge P,Cerhan JR, et al. Body-mass index and mor-tality among 1.46 million white adults.N Engl J Med 2010;363:2211-9. [Erratum,N Engl J Med 2011;365:869.]

4. Zheng W, McLerran DF, Rolland B,et al. Association between body-mass in-dex and risk of death in more than 1 mil-lion Asians. N Engl J Med 2011;364:719-29.5. Schmidt D, Salahudeen A. The obesity-survival paradox in hemodialysis patients:why do overweight hemodialysis pat ientslive longer? Nutr Clin Pract 2007;22:11-5.6. Uretsky S, Messerli FH, Bangalore S,et al. Obesity paradox in patients with hy-pertension and coronary artery disease.Am J Med 2007;120:863-70.7. Lavie CJ, Milani RV, Ventura HO,

Romero-Corral A. Body composition andheart failure prevalence and prognosis:getting to the fat of the matter in theobesity paradox. Mayo Clin Proc 2010;85:605-8.8. Doehner W, Erdmann E, Cairns R, etal. Inverse relation of body weight andweight change with mortal ity and mor-bidity in patients with type 2 diabetes andcardiovascular co-morbidity: an analysisof the PROACTIVE study population. Int JCardiol 2012;162:20-6.9. McEwen LN, Karter AJ, Waitzfelder BE,


Downloaded from nejm.org on June 24, 2014. For personal use only. No other uses without permission.

Copyright 2014 Massachusetts Medical Society. All rights reserved.

8/12/2019 Nej Mo a 1304501Aas

12/12



et al. Predictors of mortality over 8 years intype 2 diabetic patients: Translating Re-search Into Action for Diabetes (TRIAD).Diabetes Care 2012;35:1301-9.10. Carnethon MR, De Chavez PJ, BiggsML, et al. Association of weight statuswith mortalit y in adults with incident dia-betes. JAMA 2012;308:581-90. [Erratum,JAMA 2012;308:2085.]

11. Logue J, Walker JJ, Leese G, et al. As-sociation between BMI measured with in ayear after diagnosis of type 2 diabetes andmortal ity. Diabetes Care 2013;36:887-93.12. Florez H, Castillo-Florez S. Beyondthe obesity paradox in diabetes: fitness,fatness, and mortality. JAMA 2012;308:619-20.13. Manson JE, Stampfer MJ, HennekensCH, Willett WC. Body weight and longevity:a reassessment. JAMA 1987;257:353-8.14. Manson JE, Willett WC, Stampfer MJ,et al. Body weight and mortality amongwomen. N Engl J Med 1995;333:677-85.15. Pischon T, Boeing H, Hoffmann K, etal. General and abdominal adiposity and

risk of death in Europe. N Engl J Med2008;359:2105-20. [Erratum, N Engl J Med2010;362:2433.]16. Hu FB. Obesity epidemiology. NewYork: Oxford University Press, 2008.17. Colditz GA, Manson JE, HankinsonSE. The Nurses Health Study: 20-year con-tribution to the understanding of healthamong women. J Womens Health 1997;6:49-62.18. Rimm EB, Giovannucci EL, WillettWC, et al. Prospective study of alcoholconsumption and risk of coronary diseasein men. Lancet 1991;338:464-8.

19. Classification and diagnosis of diabe-tes mellitus and other categories of glucoseintolerance. Diabetes 1979;28:1039-57.20. Report of the Expert Committee on theDiagnosis and Classification of DiabetesMellitus. Diabetes Care 1997;20:1183-97.21. Manson JE, Rimm EB, Stampfer MJ, etal. Physical activity and incidence of non-insulin-dependent diabetes mellitus in

women. Lancet 1991;338:774-8.22. Hu FB, Leitzmann MF, Stampfer MJ,Colditz GA, Willett WC, Rimm EB. Phys-ical activity and television watching inrelation to risk for type 2 diabetes mel-litus in men. Arch Intern Med 2001;161:1542-8.23. Rimm EB, Stampfer MJ, Colditz GA,Chute CG, Litin LB, Willett WC. Validityof self-reported waist and hip circumfer-ences in men and women. Epidemiology1990;1:466-73.24. Rich-Edwards JW, Corsano KA, Stamp-fer MJ. Test of the National Death Indexand Equifax Nationwide Death Search. AmJ Epidemiol 1994;140:1016-9.

25. Stampfer MJ, Willett WC, Speizer FE,et al. Test of the National Death Index.Am J Epidemiol 1984;119:837-9.26. Chiuve SE, Fung TT, Rimm EB, et al.Alternative dietary indices both stronglypredict risk of chronic disease. J Nutr2012;142:1009-18.27. Durrleman S, Simon R. Flexible re-gression models with cubic splines. StatMed 1989;8:551-61.28. American Diabetes Association. Stan-dards of medical care in diabetes 2013.Diabetes Care 2013;36:Suppl 1:S11-S66.29. Ross C, Langer RD, Barret t-Connor E.

Given diabetes, is fat better than thin?Diabetes Care 1997;20:650-2.30. Khalangot M, Tronko M, KravchenkoV, Kulchinska J, Hu G. Body mass indexand the risk of total and cardiovascularmortality among patients with type 2 dia-betes: a large prospective study in Ukraine.Heart 2009;95:454-60.31. Chaturvedi N, Fuller JH. Mortality

risk by body weight and weight change inpeople with NIDDM. Diabetes Care1995;18:766-74.32. Zoppini G, Verlato G, Leuzinger C, etal. Body mass index and the risk of mor-tality in t ype II diabetic patients from Ve-rona. Int J Obes Relat Metab Disord 2003;27:281-5.33. Calle EE, Thun MJ, Petrelli JM, Rod-riguez C, Heath CW Jr. Body-mass indexand mortality in a prospective cohort ofU.S. adults. N Engl J Med 1999;341:1097-105.34. Manson JE, Bassuk SS, Hu FB,Stampfer MJ, Colditz GA, Willett WC.Estimating the number of deaths due to

obesity: can the divergent findings bereconciled? J Womens Health (Larchmt)2007;16:168-76.35. Perry JR, Voight BF, Yengo L, et al.Stratifying type 2 diabetes cases by BMIidentifies genetic risk variants in LAMA1and enrichment for risk variants in leancompared to obese cases. PLoS Genet2012;8(5):e1002741.36. Conus F, Rabasa-Lhoret R, PronnetF. Characteristics of metabolically obesenormal-weight (MONW) subjects. ApplPhysiol Nutr Metab 2007;32:4-12.Copyright 2014 Massachusetts Medical Society.

SPECIALTIESANDTOPICSATNEJM.ORG

Specialty pages at the Journals website (NEJM.org) feature articles in cardiology,endocrinology, genetics, infectious disease, nephrology, pediatrics, and many othermedical specialties. These pages, along with collections of articles on clinical andnonclinical topics, offer links to interactive and multimedia content and feature

recently published articles as well as material from the NEJM archive (18121989).


Downloaded from nejm org on June 24 2014 For personal use only No other uses without permission

Documents

Nej Mo a 1304501Aas