obesity

reviews

doi: 10.1111/j.1467-789X.2006.00263.x

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

263

Blackwell Publishing LtdOxford, UKOBRobesity reviews1467-7881© 2006 The Authors; Journal compilation © 2006 The International Association for the Study of Obesity

2006

8

263276

Review Article

The CODA meta-analysis project S. Duval et al.

Address for correspondence: S Duval, Division

of Epidemiology and Community Health,

School of Public Health, University of

Minnesota, 1300 South Second St., Suite 300,

Minneapolis, MN 55454-1015, USA. E-mail:

duval@epi.umn.edu

*See Appendix.

The Collaborative Study of Obesity and Diabetes in Adults (CODA) project: meta-analysis design and description of participating studies

S. Duval, G. Vazquez, W. L. Baker and D. R. Jacobs Jr for the CODA Study Group*

Division of Epidemiology and Community

Health, School of Public Health, University of

Minnesota, Minneapolis, MN, USA

Received 15 November 2005; revised 20

February 2006; accepted 21 February 2006

Summary

The Collaborative Study of Obesity and Diabetes in Adults (CODA) project wasformed to establish an international database of studies of abdominal obesity andtype 2 diabetes mellitus (T2DM), and to provide analyses of these associationsusing individual participant data (IPD) meta-analytic techniques. The collabora-tion involves obtaining raw data from existing studies. The main objectives of thecollaboration are to assess which simple anthropometric indices most closelypredict the risk of T2DM in adults, and to investigate ethnicity and other factorsthat potentially modify that prediction. A second task related to primary preven-tion of diabetes subsequently evolved, the CODA-2 project, and is concerned withpopulation-based methods to identify people most likely to benefit from diabetesinterventions. This article describes the meta-analysis design and the studiesinvolved. The collaboration currently has 37 studies enrolled, providing data on260 000 participants. The proposed IPD meta-analyses will help resolve severaloutstanding issues in diabetes.

Keywords:

Diabetes, meta-analysis, obesity.

Introduction

In 1999, the US Centers for Disease Control and Prevention(CDC) organized a workshop to examine the use of standardcategories of body mass index (BMI) and waist circumfer-ence (WC) as indicators of health risk in adults (1). TheBMI and WC categories came from evidence-based obesityguidelines issued separately by the US National Institutesof Health (2), The World Health Organization (3), and theScottish Intercollegiate Guidelines Network (4). The datapresented confirmed that the BMI categories and the WCcategories were both able to identify risk for type 2 diabetesmellitus (T2DM) in a variety of populations worldwide.Despite such confirmation, which adds to a growing bodyof evidence in the medical literature, a major issue thatemerged from the workshop was that the value of thesetwo anthropometric indices as health indicators might differ

from each other and might vary depending on the popula-tion and the health condition examined. Given the relativesimplicity of measuring these two anthropometric indicesand their potential value as indicators of health risk inindividuals and populations, participants of the workshoprecommended that these issues be explored further.

In direct response to this workshop, the CollaborativeStudy of Obesity and Diabetes in Adults (CODA) project,a consortium of existing studies contributing raw data formeta-analysis, was established. This report describes theCODA project, giving general background informationabout the studies participating, with specific focus on thechallenges involved in establishing the collaborative effort,and on the methods implemented to ensure success.

The purposes of this paper are to document several prac-tical issues pertaining to assembling the meta-analytic data-base and to describe CODA and its database.

obesity

reviews (2007)

8

, 263–276

264

The CODA meta-analysis project

S. Duval

et al.

obesity

reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

Objectives of CODA

The objectives of the collaboration are: (i) to assess whichsimple anthropometric indices (WC, BMI, waist-to-hipratio [WHR]) most closely predict the risk of T2DM inadults; (ii) to investigate ethnicity and other factors thatpotentially modify that prediction; (iii) to determine thresh-olds for diabetes for the anthropometric measures, whichmay vary across populations; and (iv) to determine if theassociation of these anthropometric indices with cardiovas-cular disease morbidity and mortality are exacerbated bytheir association with T2DM.

After the inception of the CODA project, several relatedquestions were added to the scope of the project, and willbe referred to as the CODA-2 project. The CODA-2 objec-tives are related to primary prevention of diabetes andrevolve around population-based methods to identify peo-ple most likely to benefit from interventions. These are: (i)to determine whether it is possible to predict several dia-betes-related risk states (impaired fasting glucose [IFG] andimpaired glucose tolerance [IGT], collectively referred to asimpaired glucose metabolism [IGM], undiagnosed diabe-tes, and diabetes incidence) using non-invasive or mini-mally invasive methods; and (ii) should the screening toolsdiffer by ethnicity?

This question regarding screening has led to a new col-laboration between the CODA group and the DETECT-2group (5), who have goals similar to those of CODA-2.Rather than pursue similar questions independently in theUnited States and Denmark, the two groups are workingtogether to bring the most useful information to the diabe-tes community by incorporating additional studies as veri-fication for findings in either study group.

Methods

Identification of relevant studies

Several strategies were employed to establish the collabo-rative study. Among them were a literature search, screen-ing of abstracts of major international diabetesconferences, personal communication with experts in thefield, collaborators and colleagues, and collaboration withCDC and other international groups.

In the case of the literature search and screening ofabstracts to identify relevant studies, we developed a pro-tocol that focused on prospective studies and cross-sec-tional studies with the ability to discern newly diagnosedcases, and where abdominal obesity had been measured asa risk factor for diabetes. The collaborative approachincluded working together with experts in the field andinternational agencies such as WHO MONICA, and thecollaborative groups DECODE (6,7), and DECODA (6,8).This second approach was intended to increase identifica-

tion of studies as well as to increase successful recruitmentof studies. Incorporation of new studies identified after theinitial collaboration is ongoing as we develop new researchquestions.

Inclusion criteria

To be eligible for inclusion in either CODA or CODA-2,studies had to meet the following criteria: (i) prospectivestudies with T2DM as an outcome, or cross-sectional stud-ies able to determine newly diagnosed cases of IGT orT2DM; and (ii) at least one measure of obesity, with par-ticular emphasis on WC.

Outcomes

Incident diabetes was the principal outcome for the pro-spective studies. We collected time to event/date of event,diagnostic criteria and instrument used for diagnosis. Inaddition, we requested glucose measurements at follow-upwhere possible. For cross-sectional studies and at baselinefor prospective studies, we requested fasting glucose mea-surements, 2-h Oral Glucose Tolerance Test (OGTT), anddetails on those with known diabetes, based on either self-report or medications to distinguish between newly diag-nosed and known diabetes at baseline. In addition todiabetes, cause-specific mortality (International Classifica-tion of Diseases [ICD]-9 or ICD-10 codes) was requestedfor prospective studies.

Baseline covariates

Anthropometric variables were the initial focus of theCODA project. We collected information on weight,height, waist and hip, where available. In addition toanthropometric variables, we collected demographic infor-mation including ethnicity/race, metabolic measurementsand other established risk factors (9–13). Included vari-ables are WC (cm), body weight (kg), height (m), BMI(kg m

−

2

), hip circumference (cm), WHR, fasting glucose(mmol L

−

1

), 2-h OGTT (mmol L

−

1

), fasting total cho-lesterol (mmol L

−

1

), high-density lipoprotein (HDL)-cholesterol (mmol L

−

1

), triglycerides (mmol L

−

1

), fastinginsulin (uu mL

−

1

), 2-h insulin (uu mL

−

1

), diastolic and sys-tolic blood pressure (mmHg), consumption of alcohol, edu-cational attainment, smoking status, menopausal status,use of hormone replacement therapy, use of blood pressuremedication, history of hypertension, gestational diabetes,and first degree family history of diabetes.

Establishing the collaborative group

Initial communication from the CODA steering commit-tee (see Appendix) was by a letter of invitation, along

obesity

reviews

The CODA meta-analysis project

S. Duval

et al.

265

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

with a request for input into the collaborative effort, toworkshop delegates (1). Much interest was generated atthis stage, leading to a list of study names and principalinvestigators. Those studies were sent a questionnaire toverify eligibility, and permission forms were obtainedfrom each study to become part of CODA. For studiesthat were eligible and willing to join the collaboration, wesent a data request form with a suggested data-codingscheme, allowing flexibility in the format to ensure conve-nience to collaborators. Also at this stage, a full protocoland other supporting documentation were sent to all col-laborators.

Data acquisition, collection and checking

Study collaborators were asked to supply raw data as out-lined by the data request form. We implemented a flexibledata transfer method that included File Transfer Protocolto a secure site, email, or CD of any machine-readablemedium and in any format convenient to the collaborator.Once the original data file was received from the partici-pating study, data were transferred to SAS version 8, andthe original data were archived for backup purposes.

The original data were examined for completeness andconsistency using the following protocol: summary statis-tics for all variables were sent back to collaborators toverify categories, units of measurements, and protocol. Inaddition, we were able to verify consistency of data withinindividuals, highlight potential outliers, and identify miss-ing data. Any data queries were discussed and resolveddirectly with the responsible collaborator.

Harmonization

Implicit in the process of implementing a meta-analysis atthe level of the individual is the task of acquiring individ-ual-level data from a representative selection of existingstudies. Most often the studies identified for the meta-analysis will have data generated for similar but non-identical purposes, and with similar but non-identicalmethods. Obtaining a variable from the original sourcesthat is identical in meaning and structure across the studies

is ideal. When this is not possible an attempt must be madeto derive from an existing variable, or variables, a newvariable that is comparable across the studies.

We use the term ‘harmonize’ to refer to the process thatincludes thinking about the comparability of variablesacross studies, and then defining and deriving the codingscheme of a new variable that is as close as possible to theconceptual target and yet is still true to each study’s specificdata. Consideration is given to study design, variable-specific data collection protocols, units of measurement,response categories and variable names. Harmonizationalways involves compromises and is often, but not always,possible to achieve.

To harmonize variables, we collated information from allstudies and, on a variable-by-variable basis, produced atranslation table. The translation table contains eachstudy’s original coding scheme for each variable of interest,and the coding scheme for the new variable common to allstudies. Once the data translations were available, we cre-ated a set of common variables in each study and createda pooled SAS data file for analysis. Figure 1 describes theCODA data management process from the reception ofdata to the formation of the collated database.

For example, independent of definitions used in individ-ual studies, diabetes was computed using two differentcriteria: (i) following recommendations of the AmericanDiabetes Association (ADA) (14), cases were defined asfasting plasma glucose (FPG)

≥

7.0 mmol L

−

1

(

≥

126 mgdL

−

1

); and (ii) based on World Health Organization (WHO)recommendations (15), defined as FPG

≥

7.0 mmolL

−

1

(

≥

126 mg dL

−

1

) or 2-h OGTT

≥

11.1 mmol L

−

1

(

≥

200 mg dL

−

1

) for the subset of studies that reported bothFPG and 2-h OGTT. In cross-sectional studies and at base-line in longitudinal studies that included self-report of dia-betes, we then separated known from previously unknowncases and defined

newly diagnosed ADA

and

newly diag-nosed WHO

. These uniform renditions of the newly diag-nosed diabetes outcome in cross-sectional studies are lesssubject to behavioural changes that might have influencedanthropometric values such as BMI and WC.

We scrutinized anthropometric variables for outliers, andestablished criteria for trimming on a study-specific basis.

Figure 1

Flow chart of CODA data manage-ment process. CODA, The Collaborative Study of Obesity and Diabetes in Adults.

Original datasets from collaborators

Permanent SAS datasets

‘as is’

Harmonized SAS datasets

Master SAS CODA database

Check for internal consistency

Harmonize variables across studies

Combine SAS datasets Check for consistency

across studies

266

The CODA meta-analysis project

S. Duval

et al.

obesity

reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

Typically, anthropometric values were considered to be out-liers if they were more than 3.5 standard deviations fromthe mean, and consequently trimmed prior to analysis. Insubsequent analyses, we will use similar strategies for trim-ming outliers from continuous variables in the database.We obtained information from each study as to the site ofboth waist and hip measurement; WC was subsequentlyclassified into four categories, according to measurementsite (16). In cases where there was ambiguity, we contactedthe collaborator directly for clarification and agreementwith our coding.

Data confidentiality

Data received from each investigator remain the property ofthe original study group. Any data supplied are held securelyand treated as confidential. This study is based on anony-mized datasets already collected; therefore ethical consider-ations are those pertaining to secondary data analysis.

Statistical analysis

Any meta-analysis gains power at the expense of broaden-ing of definitions, as discussed under harmonization,above. Among advantages of an individual participant data(IPD) meta-analysis are that it allows more accurate time-to-event analysis, covariate adjustment, and subgroup anal-ysis, than does a literature-based meta-analysis; it can alsoinclude unpublished findings (17–19). It is also possible toperform an assessment of heterogeneity between studiesand subgroups (e.g. age group, gender, and race/ethnicity),often at both the pooled within study and between studylevels (17,20,21). In addition, sensitivity analysis takingaccount of factors including completeness of follow-up,methods of assessing outcomes, periods of follow-up, andthe use of repeated measurements of risk factors can beperformed to determine the robustness of the findings andthe appropriateness of analysis based on pooled datasets.

Analysis is based on two different strategies. The first isto calculate summary statistics for each study and combineresults using a standard meta-analytic approach. Randomeffects models, which take heterogeneity between studiesinto account, will be the main analytic technique (22,23).In addition, heterogeneity due to study-level populationcharacteristics and study design features will be analysedusing meta-regression methods (24). The second is to per-form a ‘pooled’ analysis, using hierarchical regression mod-els, with the study being incorporated as either a fixed orrandom effect and with adjustment for other individual-level covariates where indicated (17,20,21). The hierarchi-cal regression, or multilevel model, can be written as a two-level model with participants corresponding to level oneunits and studies to level two units, as described in Higgins

et al

. (18).

Project management

Study co-ordination and statistical analyses are performedin the Division of Epidemiology and Community Health inthe School of Public Health at the University of Minnesota.Collaborating investigators are welcomed to proposeprojects and to spend time at the University of Minnesotadoing data analysis.

Publication policy

The results of the study will be published in scientific peer-reviewed journals after the analyses are completed andpresented to all of the collaborators. Collaborators aregiven sufficient time for a full review of the results. Theprimary publications will be in the name of the collabora-tive group rather than individual authors.

Writing for these primary publications is by a writingcommittee from the research staff working at the analysiscentre, and those collaborators who have expressed interestin particular analyses. All participating studies and investi-gators contributing to this project will be listed at the endof each publication along with the members of the writingcommittee. The member of the group leading the analysisand writing effort will be designated as the correspondingauthor in the publication. Contributors have the right towithdraw their data from a given paper without giving areason, for example, if issues concerning local publicationcannot be reconciled. If multiple papers arise from thismeta-analysis, contributors may decline participation on apaper-by-paper basis.

Results

Collaboration characteristics

Figure 2 presents the history of the formation of the CODAdatabase. Until now, 110 studies have been approached,and communication established with 73. From those stud-ies, 10 studies were not eligible, two declined and in 10cases the communication lapsed after several attempts toestablish collaboration. The remaining 51 studies com-pleted a questionnaire and agreed to participate. However,six additional studies were not eligible, either because theydid not collect WC measurements or did not have glucosemeasurement at baseline (to be able to distinguish newlydiagnosed diabetes from known diabetes). Of the 110approached studies that are not currently included in thedatabase, 13 are prospective cohorts. The main regionsfrom which the non-participating studies derive are theUnited States (seven studies), Europe (38 studies), and Asia(14 studies). Thirty-seven datasets currently constitute theCODA database, with at least eight additional studies plan-ning to send data.

obesity

reviews

The CODA meta-analysis project

S. Duval

et al.

267

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

Characteristics of the studies

Table 1 provides a detailed description of the study levelcharacteristics. Collectively, these 37 studies involve atotal of more than 260 000 participants. Twenty-threestudies are prospective (62%) and 14 are cross-sectional.Prevalence analysis of diabetes and IGT can be performedin 35 of the studies that provided glucose measurements(64% of the sample). Studies are from 20 countries, mostof the participants being from the United States (58%),India (14%) and Europe (14%). Most studies are basedon population-based surveys that consisted of probabilis-tic samples of a geographical area. Several studies usedstratified random sampling, and, e.g. IRAS and JACDS,stratified on glucose status. We also have three occupa-tional studies, one study including an HMO cohort, and acohort of US veterans. The proportion of males is 33%and the ages of participants at baseline ranges from 18 to98 years.

Outcomes

Table 2 provides study level characteristics for CODA out-comes. Collectively, follow-up information is available formore than 150 000 participants, with a mean duration offollow-up of 9.9 years and a total follow-up of almost1 500 000 person-years, with more than 9000 incidentcases of diabetes and nearly 19 000 deaths. Fifteen studiesused only blood measurements, two studies used self-reportand one study medication records only. In addition, fivestudies used a combination of self-report, blood measure-ments and medical records.

Newly diagnosed diabetes was available for 35 studiesbased on ADA criteria and for 25 based on WHO criteria.Known diabetes could not be calculated in four studiesbecause the studies excluded subjects based on known

diabetes or because it was associated with the samplingscheme (e.g. IRAS). Prevalent cases of diabetes were calcu-lated from the sum of known and newly diagnosed cases.Prevalence of newly diagnosed diabetes was calculated asthe ratio of the newly diagnosed cases to those at risk,excluding the known cases. In addition, prevalence couldnot be computed for studies when known diabetes statuswas unavailable.

Baseline covariates

We collected baseline information on anthropometrics,demographics, metabolic risk factors, ethnicity/race andother risk factors. Table 3 presents availability of baselinevariables. Waist circumference was available in 34 andWHR in 32 of the studies. All anthropometrics were mea-sured at clinic visits, except for IOWA and NURSES.

Limitations

The IPD design does have some limitations. Not all relevantstudies have chosen to participate; this ‘study wide non-participation’ might result in ‘collaboration bias’, a termwe use in a somewhat analogous way to that of publicationbias in literature-based meta-analysis. However, there is noreason to believe that lack of participation is correlatedwith effect size, but is rather more likely linked with prag-matic issues, such as not enough monetary reward, lack ofinterest, or no time for such activities in an academic envi-ronment. On balance, we feel that the rewards from thisdesign outweigh these potential problems.

Conclusions

We have described CODA, a meta-analysis project includ-ing a collaborative group of diabetes studies and investiga-tors, and the CODA database. The substantive rationalefor the first set of analyses has been determined, and inves-tigators representing many parts of the world have com-mitted their time and knowledge to the combined effort.We have the large majority of cohort studies with a verylarge number of participants.

The proposed IPD meta-analyses from the CODA groupwill help resolve several outstanding questions in diabetesepidemiology.

Conflict of interest statement

No conflict of interest was declared.

Acknowledgements

The CODA Project is supported by the US Centers forDisease Control and Prevention, Division of Diabetes

Figure 2

Flow chart of CODA collaboration. CODA, The Collaborative Study of Obesity and Diabetes in Adults.

37

73

110

34

8

Collaboration invitation

Noresponse

Communication established

Not eligible

Declined

Not eligible

Datasets received

Failedcommunication

Completedquestionnaire

10

51

Pending response

Additional CODA-2

CODA-13

10

6

2

37

268

The CODA meta-analysis project

S. Duval

et al.

obesity

reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

Tab

le 1

Cha

ract

eris

tics

of s

tud

ies

incl

uded

in C

OD

A

Stu

dy

nam

eA

cron

ymC

ount

ryB

asel

ine

year

s%

m

ale

Ag

e ra

nge

% w

hite

non-

His

pan

ic*

Sam

ple

si

zeS

tud

y d

esig

nR

esp

onse

rate

(%

)

Cro

ss-s

ecti

on

al s

tud

ies

Aus

tral

ian

Ob

esity

, D

iab

etes

, an

d L

ifest

yle

Stu

dy

(25)

AU

SD

IAB

Aus

tral

ia19

99–2

000

4525

–95

11 2

47P

opul

atio

n-b

ased

sur

vey,

7g

eog

rap

hica

l are

as55

Cat

alon

ia S

tud

y (2

6)C

ATA

LON

IAS

pai

n19

9444

29–9

12

217

Pop

ulat

ion

bas

ed s

urve

y, 4

1 to

wns

67C

henn

ai U

rban

Pop

ulat

ion

Stu

dy

(27)

CU

PS

Ind

ia19

96–1

998

4420

–90

1 26

2P

opul

atio

n b

ased

sur

vey,

2 u

rban

com

mun

ities

90

Che

nnai

Urb

an R

ural

Ep

idem

iolo

gy

Stu

dy

(28)

CU

RE

SIn

dia

2001

–200

249

20–9

825

903

Pop

ulat

ion

bas

ed s

urve

y, C

henn

aina

Non

-com

mun

icab

le D

isea

ses

Sur

vey

in 1

998

(29)

GH

AN

AG

hana

1998

4525

–91

577

Pop

ulat

ion

bas

ed s

urve

y, 1

rur

al a

nd2

urb

an c

omm

uniti

esna

Car

dio

vasc

ular

dis

ease

ris

k fa

ctor

s Jo

rdan

Stu

dy

(30)

JOR

DA

NJo

rdan

1994

–199

637

18–9

02

818

Pop

ulat

ion

bas

ed s

urve

y, 4

sem

iurb

an c

omm

uniti

es71

Kau

nas

MO

NIC

A (

31)

KA

UN

AS

-MO

NLi

thua

nia

1992

–199

349

35–6

41

239

Pop

ulat

ion-

bas

ed s

urve

y

†

, K

auna

s58

Lille

MO

NIC

A (

32)

LILL

E-M

ON

Fran

ce19

95–1

996

5036

–67

1 19

5P

opul

atio

n-b

ased

sur

vey

†

, 86

tow

ns o

f no

rthe

rn F

ranc

e61

Third

Nat

iona

l Hea

lth a

nd N

utrit

ion

Exa

min

atio

n S

urve

y(3

3)N

HA

NE

S I

IIU

SA

1990

4718

–90

4219

618

Pop

ulat

ion

bas

ed s

urve

y,na

tionw

ide

77

Nat

iona

l Urb

an D

iab

etes

Stu

dy

(34)

NU

DS

Ind

ia19

99–2

000

4720

–96

11 2

15P

opul

atio

n b

ased

sur

vey,

6 c

ities

90P

olan

d-M

ON

ICA

Kra

kow

(35

)P

OL-

MO

NP

olan

d19

92–1

993

4743

–73

364

Pop

ulat

ion-

bas

ed s

urve

y

†

,se

miru

ral c

omm

uniti

es72

Sin

gap

ore

Nat

iona

l Hea

lth S

urve

y 19

98 (

36)

SIN

GA

PO

RE

Sin

gap

ore

1998

4618

–69

4 72

3P

opul

atio

n b

ased

sur

vey,

natio

nwid

e63

Tuni

s C

VD

Stu

dy

(37)

TUN

ISTu

nis

1995

4030

–93

862

Pop

ulat

ion

bas

ed s

urve

y, u

rban

com

mun

ities

47

Wor

kfor

ce D

iab

etes

Sur

vey

(38)

WO

RK

NZ

New

Zea

land

1988

–199

072

40–7

85

677

Wor

ksite

sur

vey,

2 c

omm

uniti

es67

Pro

spec

tive

stu

die

s w

ith

ou

t b

asel

ine

glu

cose

mea

sure

men

t

IOW

A W

omen

’s H

ealth

Stu

dy

(40)

IOW

AU

SA

1986

055

–69

100

41 8

36P

opul

atio

n b

ased

coh

ort,

Iow

a42

Nur

ses’

Hea

lth S

tud

y (4

1)N

UR

SE

SU

SA

1986

–198

70

39–6

610

052

468

Occ

upat

iona

l coh

ort,

natio

nwid

e90

Pro

spec

tive

stu

die

s w

ith

glu

cose

mea

sure

men

t

Ath

eros

cler

osis

Ris

k in

Com

mun

ities

Stu

dy

(42)

AR

ICU

SA

1986

–199

045

45–6

473

15 7

92P

opul

atio

n b

ased

coh

ort,

4co

mm

uniti

esna

Cor

onar

y A

rter

y R

isk

Dev

elop

men

t in

You

ng A

dul

tsS

tud

y (4

3)C

AR

DIA

US

A19

85–1

986

4618

–35

485

103

Pop

ulat

ion

bas

ed c

ohor

t, 4

urb

anco

mm

uniti

es67

MR

C E

ly S

tud

y (4

4)E

LYU

K19

90–1

992

4340

–68

1 12

2P

opul

atio

n b

ased

coh

ort,

Ely

74Fi

nlan

d-M

ON

ICA

(45

)FI

N-M

ON

Finl

and

1987

–199

247

25–6

411

997

Pop

ulat

ion

bas

ed c

ohor

t

†

, 3

rura

lan

d u

rban

com

mun

ities

80

Fram

ing

ham

Offs

prin

g S

tud

y (4

6)FR

AM

ING

HA

MU

SA

1995

4722

–79

100

3 19

7P

opul

atio

n b

ased

coh

ort,

Fram

ing

ham

69

*Ap

plie

s on

ly t

o U

S s

tud

ies.

†

MO

NIC

A p

roto

col.

‡

62%

of

the

trib

al c

omm

uniti

es p

opul

atio

n.C

OD

A,

The

Col

lab

orat

ive

Stu

dy

of O

bes

ity a

nd D

iab

etes

in A

dul

ts;

na,

not

avai

lab

le.

obesity

reviews

The CODA meta-analysis project

S. Duval

et al.

269

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

Pro

spec

tive

Pop

ulat

ion

Stu

dy

of W

omen

inG

othe

nbur

g (

39)

GO

TEB

OR

GS

wed

en19

68–1

970

038

–61

1 46

2P

opul

atio

n b

ased

coh

ort,

Got

henb

urg

90

OG

TT F

ollo

w u

p S

tud

y (4

7)H

IRO

SH

IMA

Jap

an19

94–1

996

4830

–80

907

Ato

mic

bom

b s

urvi

vors

, H

irosh

ima

24In

sulin

Res

ista

nce

Ath

eros

cler

osis

Stu

dy

(48)

IRA

SU

SA

1992

–199

444

40–6

938

1 62

42

HM

O c

ohor

ts, 2

pop

ulat

ion

bas

ed,

stra

tified

by

dia

bet

es s

tatu

s48

Jap

anes

e A

mer

ican

Com

mun

ity D

iab

etes

Stu

dy

(49)

JAC

DS

US

A19

83–1

988

5334

–75

0 6

58C

ohor

t of

vol

unte

ers

com

mun

ity-w

ide

recr

uitm

ent,

2nd

& 3

rd g

ener

atio

n, s

trat

ified

by

dia

bet

es s

tatu

s

95

Jap

anes

e-B

razi

lian

Dia

bet

es S

tud

y G

roup

(50

)JB

DS

GB

razi

l19

93–2

000

5330

–92

1 57

8P

opul

atio

n b

ased

coh

ort,

Jap

anes

e-B

razi

lian

com

mun

ity76

Mau

ritiu

s N

on-c

omm

unic

able

Dis

ease

Stu

dy

(13)

MA

UR

ITIU

SM

aurit

ius

1987

4725

–75

5 07

8P

opul

atio

n b

ased

coh

ort,

11co

mm

uniti

es86

Mex

ico

City

Dia

bet

es S

tud

y (5

1)M

EX

CIT

YM

exic

o19

90–1

992

4129

–67

2 28

2P

opul

atio

n b

ased

coh

ort,

6 lo

w-in

com

e co

mm

uniti

es64

Nor

mat

ive

Ag

ing

Stu

dy

(52)

NA

SU

SA

1961

–196

810

027

–85

100

1 91

7M

ale

vete

rans

, g

reat

er B

osto

n ar

ea86

Nau

ru S

tud

y (5

3)N

AU

RU

Nau

ru19

8744

19–8

1 8

68P

opul

atio

n b

ased

coh

ort

86N

orth

ern

Sw

eden

MO

NIC

A (

54)

NS

WE

D-M

ON

Sw

eden

1986

–199

949

25–7

46

947

Pop

ulat

ion-

bas

ed c

ohor

t

†

, to

wns

and

rur

al c

omm

uniti

es80

Oul

u 55

(55

)O

ULU

55Fi

nlan

d19

90–1

991

4455

831

Pop

ulat

ion

bas

ed c

ohor

t, O

ulu

78P

aris

Pro

spec

tive

Stu

dy

I (5

6)PA

RIS

Fran

ce19

67–1

972

100

43–5

57

746

Occ

upat

iona

l coh

ort,

Par

isR

anch

o B

erna

rdo

Stu

dy

(57)

RA

NC

HO

US

A19

84–1

987

4423

–96

100

2 48

0P

opul

atio

n b

ased

coh

ort,

mid

dle

and

up

per

mid

dle

cla

ss c

omm

uniti

es80

San

Ant

onio

Hea

rt S

tud

y (5

8)S

AH

SU

SA

1979

–198

843

24–6

964

5 15

8P

opul

atio

n b

ased

coh

ort,

low

,m

idd

le a

nd h

igh

inco

me

com

mun

ities

64

Stro

ng H

eart

Stu

dy

(59)

SH

SU

SA

1989

–199

241

44–7

50

4 54

9P

opul

atio

n b

ased

coh

orts

of

Am

eric

an I

ndia

ns in

Ariz

ona,

O

klah

oma

and

Nor

th/S

outh

Dak

ota

62

‡

Up

psa

la L

ong

itud

inal

Stu

dy

of A

dul

t M

en (

60)

ULS

AM

Sw

eden

1970

–197

310

050

2 32

2P

opul

atio

n b

ased

coh

ort,

Up

psa

la82

Tota

l33

18–9

826

6 83

9

Stu

dy

nam

eA

cron

ymC

ount

ryB

asel

ine

year

s%

m

ale

Ag

e ra

nge

% w

hite

non-

His

pan

ic*

Sam

ple

si

zeS

tud

y d

esig

nR

esp

onse

rate

(%

)

*Ap

plie

s on

ly t

o U

S s

tud

ies.

†

MO

NIC

A p

roto

col.

‡

62%

of

the

trib

al c

omm

uniti

es p

opul

atio

n.C

OD

A,

The

Col

lab

orat

ive

Stu

dy

of O

bes

ity a

nd D

iab

etes

in A

dul

ts;

na,

not

avai

lab

le.

Tab

le 1

Con

tinue

d

270

The CODA meta-analysis project

S. Duval

et al.

obesity

reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity.

obesity

reviews

8

, 263–276

Tab

le 2

Out

com

es in

the

CO

DA

dat

abas

e (g

rey

box

ind

icat

es d

ata

elem

ent

not

avai

lab

le in

dat

abas

e)

Stu

dy

Dia

bet

es o

utco

me

Mor

talit

yIn

cid

ent

CH

D

Cro

ss-s

ectio

nal

Inci

den

ceN

o.FU

yea

rsm

ean

(ran

ge)

Tota

l No.

even

tsC

ause

-sp

ecifi

c m

orta

lity

Kno

wn

%*

New

lyd

iag

nose

d%

†P

reva

lenc

e%

Ag

eon

set

No.

FU y

ears

mea

n(r

ang

e)

No.

FU

cont

acts

Dx

crite

riaD

xin

stru

men

tTo

tal

No.

even

ts

AD

A‡

WH

O‡

AU

SD

IAB

5.0

2.2

4.3

9.0

Yes

CAT

ALO

NIA

13.8

4.3

4.6

20.0

Yes

CU

PS

7.2

3.0

5.2

12.0

Yes

CU

RE

S5.

87.

913

.3Ye

sG

HA

NA

3.3

2.2

5.4

Yes

JOR

DA

N4.

010

.06.

614

.3K

AU

NA

S-M

ON

2.1

10.2

12.1

7 (0

–8)

76Ye

sLI

LLE

-MO

N7.

53.

411

.6Ye

sN

HA

NE

S I

II§7.

03.

114

.09.

0N

UD

S10

.58.

511

.220

.5Ye

sP

OL-

MO

N1.

43.

37.

89.

16

(0–7

)29

Yes

SIN

GA

PO

RE

3.6

4.4

6.8

10.1

Yes

TUN

IS13

.47.

311

.323

.7W

OR

KN

Z1.

92.

82.

94.

8Ye

sIO

WA

6.4

6.4

Yes

10 (

0–12

)4

na

�

2128

14 (

0–15

)77

28Ye

sN

oN

UR

SE

S2.

92.

9Ye

s12

(0–

12)

6na

�

1820

12 (

0–12

)30

03Ye

sYe

sA

RIC

7.7

4.4

11.8

7 (3

–9)

3A

DA

��

1537

11 (

0–13

)16

77Ye

sYe

sC

AR

DIA

0.7

0.4

1.1

Yes

13 (

2–16

)5

AD

A

�

156

9 (0

–13)

132

Yes

Yes

ELY

–5.

27.

1–

9 (3

–13)

2W

HO

�

5612

(2–

14)

92Ye

s

FIN

-MO

N§

1.9

1.9

3.1

3.2

Yes

9 (0

–12)

nana

�

154

8 (0

–11)

555

Yes

FRA

MIN

GH

AM

4.5

1.1

5.5

41

AD

A

�

81G

OTE

BO

RG

0.8

1.5

2.3

24 (

0–25

)3

na

��

7522

(0–

25)

514

Yes

Yes

–, e

xclu

sion

crit

eria

incl

uded

kno

wn

dia

bet

es;

�

, A

DA

new

cas

es in

80%

of

the

sam

ple

and

WH

O in

34%

;

�

, cl

inic

al m

easu

rem

ent;

�

, se

lf re

por

t;

�

, ho

spita

l rec

ord

; A

DA

(14

); W

HO

(15

).A

DA

, Am

eric

an D

iab

etes

Ass

ocia

tion;

CH

D, c

oron

ary

hear

t dis

ease

; CO

DA

, The

Col

lab

orat

ive

Stu

dy

of O

bes

ity a

nd D

iab

etes

in A

dul

ts; D

x, d

iag

nosi

s; F

U, f

ollo

w-u

p; n

a, n

ot a

pp

licab

le; W

HO

, Wor

ld H

ealth

O

rgan

izat

ion.

*Kno

wn

=

(se

lf-re

por

ted

+ on

med

icat

ion)

/par

ticip

ants

†New

ly d

iag

nose

d =

New

cas

es/(

par

ticip

ants

− k

now

n ca

ses)

.‡G

luco

se m

easu

rem

ents

wer

e ca

libra

ted

acc

ord

ing

to

met

hod

s d

escr

ibed

by

B C

arst

ense

n, J

Lin

dst

rom

, J

Sun

dva

ll, K

Bor

sch-

John

sen,

J T

uom

ileht

o, t

he D

PS

Stu

dy

Gro

up (

unp

ublis

hed

).§N

HA

NE

S I

II: A

DA

new

cas

es in

80%

of

sam

ple

, W

HO

in 3

4%;

FIN

-MO

N,

NS

WE

D-M

ON

: on

ly a

sub

gro

up h

ad g

luco

se m

easu

red

at

bas

elin

e.

obesity reviews The CODA meta-analysis project S. Duval et al. 271

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8, 263–276

HIR

OS

HIM

A3.

37.

415

.318

.1Ye

s8

(3–8

)1

WH

O�

104

7 (2

–8)

27Ye

sIR

AS

–9.

717

.0–

5 (5

–7)

1W

HO

��

143

3 (1

–6)

37Ye

sJA

CD

S–

2.9

8.2

–Ye

s10

(5–

13)

2W

HO

�94

14 (

0–17

)75

Yes

Yes

JBD

SG

11.6

13.8

17.6

27.5

7 (5

–7)

1W

HO

�85

6 (0

–8)

85Ye

sM

AU

RIT

IUS

5.9

5.8

9.0

14.4

Yes

9 (5

–11)

2W

HO

�60

6Ye

sM

EX

CIT

Y10

.43.

25.

215

.2Ye

s6

(5–9

)2

WH

O�

127

Yes

NA

S–

5.7

6.5

–13

(2–

20)

6W

HO

�86

11 (

2–20

)19

8Ye

sYe

sN

AU

RU

20.7

14.9

15.8

34.3

Yes

71

WH

O�

�10

43

(0–7

)17

2Ye

sYe

sN

SW

ED

-MO

N§

3.1

1.5

2.9

4.2

Yes

8 (0

–13)

1A

DA

��

112

11 (

5–17

)10

6Ye

sYe

sO

ULU

557.

314

.020

.126

.1Ye

s7

1W

HO

�35

3PA

RIS

3.7

2.6

6.3

Yes

4 (1

–9)

4A

DA

�30

921

(0–

27)

2226

Yes

RA

NC

HO

4.4

3.1

10.0

13.7

Yes

8 (5

–11)

1W

HO

�10

27

(0–1

3)74

8Ye

sYe

sS

AH

S7.

62.

64.

812

.0Ye

s8

(6–1

1)1

WH

O�

295

8 (0

–11)

160

Yes

Yes

SH

S34

.814

.719

.648

.7Ye

s7

(2–1

0)2

WH

O�

526

3 (0

–12)

1118

Yes

ULS

AM

0.8

10.4

11.2

20 (

7–31

)1

WH

O�

�20

828

(0–

31)

296

Yes

Yes

TOTA

L92

6119

054

Stu

dy

Dia

bet

es o

utco

me

Mor

talit

yIn

cid

ent

CH

D

Cro

ss-s

ectio

nal

Inci

den

ceN

o.FU

yea

rsm

ean

(ran

ge)

Tota

l No.

even

tsC

ause

-sp

ecifi

c m

orta

lity

Kno

wn

%*

New

lyd

iag

nose

d%

†P

reva

lenc

e%

Ag

eon

set

No.

FU y

ears

mea

n(r

ang

e)

No.

FU

cont

acts

Dx

crite

riaD

xin

stru

men

tTo

tal

No.

even

ts

AD

A‡

WH

O‡

–, e

xclu

sion

crit

eria

incl

uded

kno

wn

dia

bet

es; �

, A

DA

new

cas

es in

80%

of

the

sam

ple

and

WH

O in

34%

; �,

clin

ical

mea

sure

men

t; �

, se

lf re

por

t; �

, ho

spita

l rec

ord

; A

DA

(14

); W

HO

(15

).A

DA

, Am

eric

an D

iab

etes

Ass

ocia

tion;

CH

D, c

oron

ary

hear

t dis

ease

; CO

DA

, The

Col

lab

orat

ive

Stu

dy

of O

bes

ity a

nd D

iab

etes

in A

dul

ts; D

x, d

iag

nosi

s; F

U, f

ollo

w-u

p; n

a, n

ot a

pp

licab

le; W

HO

, Wor

ld H

ealth

O

rgan

izat

ion.

*Kno

wn

= (s

elf-

rep

orte

d +

on

med

icat

ion)

/par

ticip

ants

†New

ly d

iag

nose

d =

New

cas

es/(

par

ticip

ants

− k

now

n ca

ses)

.‡G

luco

se m

easu

rem

ents

wer

e ca

libra

ted

acc

ord

ing

to

met

hod

s d

escr

ibed

by

B C

arst

ense

n, J

Lin

dst

rom

, J

Sun

dva

ll, K

Bor

sch-

John

sen,

J T

uom

ileht

o, t

he D

PS

Stu

dy

Gro

up (

unp

ublis

hed

).§N

HA

NE

S I

II: A

DA

new

cas

es in

80%

of

sam

ple

, W

HO

in 3

4%;

FIN

-MO

N,

NS

WE

D-M

ON

: on

ly a

sub

gro

up h

ad g

luco

se m

easu

red

at

bas

elin

e.

Tab

le 2

Con

tinue

d

272 The CODA meta-analysis project S. Duval et al. obesity reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8, 263–276

Translation. Supported by a grant from the Centers forDisease Control, CDC/200-2001-00061. The authorsthank the collaborating investigators for data access andProfessor John Himes for expert advice on waist circum-ference protocols.

References

1. Seidell JC, Kahn HS, Williamson DF, Lissner L, Valdez R.Report from a Centers for Disease Control and Prevention work-shop on use of adult anthropometry for public health and primaryhealth care. Am J Clin Nutr 2001; 73: 123–126.

Table 3 Baseline measurement availability in the CODA database (grey box indicates available measure in database)

Anthropometrics Clinical measurements

WC* BW HT BMI HIP WHR FGLUC 2h-GLUC

TCHOL HDL-C TG FINS 2h-INS

BP

AUSDIAB 2

CATALONIA 2 #

CUPS 2

CURES 2 #

GHANA 2

JORDAN

KAUNAS-MON 2 #

LILLE-MON 2

NHANES III 3

NUDS 1 #

POL-MON 2

SINGAPORE 2

TUNIS 4

WORKNZ 4

IOWA 1X X X X X X

NURSES 4X X X X X X

ARIC 4

CARDIA 2

ELY 2

FIN-MON 2

FRAMINGHAM 4

GOTEBORG 2

HIROSHIMA

IRAS 1

JACDS 4

JBDSG 4

MAURITIUS 2

MEXCITY 4

NAS 4

NAURU 2

NSWED-MON 2

OULU55

PARIS 3

RANCHO 1

SAHS 4

SHS 4

ULSAM 2

WC, waist circumference; BW, body weight; HT, height; BMI, body mass index (kg m−2); HIP, hip circumference; WHR, waist-to-hip ratio; FGLUC, fasting glucose; 2h-GLUC, 2-h OGTT; TCHOL, fasting total cholesterol; HDL-C, high-density lipoprotein-cholesterol; TG, triglycerides; FINS, fasting insulin; 2h-INS, 2 h insulin; BP, diastolic and systolic blood pressure; ALC, consumption of alcohol; EDUC, educational attainment; SMOK, smoking status; MENOPAUS, menopausal status; HRT, use of hormone replacement therapy; HBP MED, use of blood pressure medication; HX HBP, history of hypertension; GEST DIAB, gestational diabetes; FH DIAB, First degree family history of diabetes.*Waist circumference measured based on the following protocols: 1, narrowest waist; 2, midpoint rib/crest; 3, just above crest; 4, umbilicus; X, self-reported; #, based on capillary samples.White box indicates data not available; Grey box indicates data available.

obesity reviews The CODA meta-analysis project S. Duval et al. 273

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8, 263–276

2. National Institutes of Health. Clinical guidelines on the identi-fication, evaluation, and treatment of overweight and obesity inadults: the evidence report. Obes Res 1998; 6: S51–S210.3. World Health Organization. Obesity Preventing andManaging the Global Epidemic: Report of the WHO Con-sultation of Obesity. Geneva: World Health Organization,1997.

4. Scottish Intercollegiate Guidelines Network. Obesity in Scot-land: Integrating Prevention with Weight Management. Edin-burgh: SIGN, 1996.5. Glumer C, Vistisen D, Borch-Johnsen K, Colagiuri S, on behalfof the DETECT–2 collaboration. Risk scores for type 2 diabetescan be applied in some populations but not all. Diabetes Care2006; 29: 410–414.

Covariates

Age Sex Race/ethnicity

ALC EDUC SMOK MENOPAUS HRT HBP MED

GEST DIAB

HX HBP

FH DIAB

AUSDIAB

CATALONIA

CUPS

CURES

GHANA

JORDAN

KAUNAS-MON

LILLE-MON

NHANES III

NUDS

POL-MON

SINGAPORE

TUNIS

WORKNZ

IOWA

NURSES

ARIC

CARDIA

ELY

FIN-MON

FRAMINGHAM

GOTEBORG

HIROSHIMA

IRAS

JACDS

JBDSG

MAURITIUS

MEXCITY

NAS

NAURU

NSWED-MON

OULU55

PARIS

RANCHO

SAHS

SHS

ULSAM

Table 3 Continued

274 The CODA meta-analysis project S. Duval et al. obesity reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8, 263–276

6. Nakagami T, Qiao Q, Carstensen B, Nhr-Hansen C, Hu G,Tuomilehto J, Balkau B, Borch-Johnsen K, The DECODE-DECODA Study Group: Age, body mass index and Type 2 diabe-tes associations modified by ethnicity. Diabetologia 2003; 46:1063–1070.7. DECODE Study Group. Age- and sex-specific prevalences ofdiabetes and impaired glucose regulation in 13 European cohorts.Diabetes Care 2003; 26: 61–69.8. The DECODA Study Group. Age- and sex-specific prevalencesof diabetes and impaired glucose regulation in 11 Asian cohorts.Diabetes Care 2003; 26: 1770–1780.9. Young TK, Harris SB. Risk of clinical diabetes in a NorthernNative Canadian cohort. Arctic Med Res 1994; 53: 64–70.10. Edelstein SL, Knowler WC, Bain RP, Andres R, Barrett-Connor EL, Dowse GK, Haffner SM, Pettitt DJ, Sorkin JD, MullerDC, Collins VR, Hamman RF. Predictors of progression fromimpaired glucose tolerance to NIDDM: an analysis of six prospec-tive studies. Diabetes 1997; 46: 701–710.11. Chen KT, Chen CJ, Gregg EW, Imperatore G, Narayan KMV.Impaired fasting glucose and risk of diabetes in Taiwan: follow-up over 3 years. Diabetes Res Clin Pract 2003; 60: 177–182.12. Shin CS, Yoo KY, Lee HK, Paik HY, Koh C-S, Park YS, KimYI, Yang BG, Shin YS. Risk factors for the development ofNIDDM in Yonchon County, Korea. Diabetes Care 1997; 20:1842–1846.13. Boyko EJ, Chitson P, de Courten M, Tuomilehto J, ZimmetPZ, Alberti KGMM. Features of the metabolic syndrome predicthigher risk of diabetes and impaired glucose tolerance. A pro-spective study of Mauritius. Diabetes Care 2000; 23: 1242–1248.14. Expert Committee on the Diagnosis and Classification of Dia-betes Mellitus. Follow-up report on the diagnosis of diabetes mel-litus. Diabetes Care 2003; 26: 3160–3167.15. World Health Organization. Definition, diagnosis and classi-fication of diabetes mellitus and its complications. I. Diagnosis andclassification of diabetes mellitus. Geneva: World Health Organi-zation, 1999.16. Wang J, Thornton JC, Bari S, Williamson B, Gallagher D,Heymsfield SB, Horlick M, Kotler D, Laferrere B, Mayer L, Pi-Sunyer FX, Pierson RN Jr. Comparisons of waist circumferencesmeasured at 4 sites. Am J Clin Nutr 2003; 77: 379–384.17. Bennett DA. Review of analytical methods for prospectivecohort studies using time to event data: single studies and impli-cations for meta-analysis. Stat Methods Med Res 2003; 12: 297–319.18. Higgins JPT, Whitehead A, Turner RM, Omar RZ, Thomp-son SG. Meta-analysis of continuous outcome data from individ-ual patients. Stat Med 2001; 20: 2219–2241.19. Whitehead A, Omar RZ, Higgins JPT, Savaluny E, TurnerRM, Thompson SG. Meta-analysis of ordinal outcomes usingindividual patient data. Stat Med 2001; 20: 2243–2260.20. Simmonds MC, Higgins JP, Stewart LA, Tierney JF, ClarkeMJ, Thompson SG. Meta-analysis of individual patient data fromrandomized trials: a review of methods used in practice. Clin Trials2005; 2: 209–217.21. Thompson SG, Turner RM, Warn DE. Multilevel models formeta-analysis, and their application to absolute risk differences.Stat Methods Med Res 2001; 10: 375–392.22. DerSimonian R, Laird N. Meta-analysis in clinical trials. Con-trol Clin Trials 1986; 7: 177–188.23. Normand S.L.T. Meta-analysis. formulating, evaluating com-bining and reporting. Stat Med 1999; 18: 321–359.24. Thompson SG, Higgins JP. How should meta-regression anal-yses be undertaken and interpreted? Stat Med 1998; 21: 1559–1573.

25. Dunstan DW, Zimmet PZ, Welborn TA, Cameron AJ, ShawJ, de Courten M, Jolley D, McCarty DJ, AusDiab Steering Com-mittee. The Australian Diabetes, Obesity and Lifestyle Study (Aus-Diab) – methods and response rates. Diabetes Res Clin Pract 2002;57: 119–129.26. Castell C, Tresserras R, Serra J, Goday A, Lloveras G, SallerasL. Prevalence of diabetes in Catalonia (Spain): an oral glucosetolerance test-based population study. Diabetes Res Clin Pract1999; 43: 33–40.27. Mohan V, Shanthirani S, Deepa R, Premalatha G, Sastry NG,Saroja R. Intra-urban differences in the prevaence of the metabolicsyndrome in southern India – the Chennai Urban Population Study(CUPS No. 4). Diabetic Medicine 2001; 18: 280–287.28. Deepa M, Pradeepa R, Rema M, Mohan A, Deepa R, Shan-thirani S, Mohan V. The Chennai Urban Rural EpidemiologyStudy (CURES) – study design and methodology (urban compo-nent) (CURES-I). J Assoc Physicians India 2003; 51: 863–870.29. Amoah AGB. Undiagnosed diabetes and impaired glucoseregulation in adult Ghanaians using the ADA and WHO diagnos-tic criteria. Acta Diabetol 2002; 39: 7–13.30. Ajlouni K, Jaddou H, Batieha A. Diabetes and impaired glu-cose tolerance in Jordan: prevalence and associated risk factors. JIntern Med 1998; 244: 317–323.31. Domarkiene S, Tamosiunas A, Reklaitiene R, Sidlauskiene D.Trends in main cardiovascular risk factors among middle-agedKaunas population between 1983 and 2002. Medicina (Kaunas)2003; 39: 1193–1199.32. Cottel D, Dallongeville J, Wagner A, Ruidavets JB, ArveilerD, Ferrieres J, Bingham A, Marecaux N, Ducimetiere P, AmouyelP. The North-East-South gradient of coronary heart disease mor-tality and case fatality rates in France is consistent with a similargradient in risk factor clusters. European Journal of Epidemiology2000; 16: 317–322.33. Okosun IS. Racial differences in rates of Type 2 diabetes inAmerican women: how much is due to differences in overall adi-posity? Ethn Health 2001; 6: 27–34.34. Ramachandran A, Snehalatha C, Kapur A, Vijay V, MohanV, Das AK, Rao PV, Yajnik CS, Prasanna KM, Nair JD, for theDiabetes Epidemiology Study Group in India (DESI). High preva-lence of diabetes and impaired glucose tolerance in India: nationalurban diabetes survey. Diabetologia 2001; 44: 1094–1101.35. Dennis BH, Pajak A, Pardo B, Davis CE, Williams OD,Piotrowski W. Weight gain and its correlates in Poland between1983 and 1993. Int J Obes Relat Metab Disord 2000; 24: 1507–1513.36. Cutter J, Tan BY, Chew SK. Levels of cardiovascular diseaserisk factors in Singapore following a national intervention pro-gramme. Bull World Health Organ 2001; 79: 908–915.37. Chihaoui M, Kanoun F, Ben Rehaiem B, Ben Brahim S, FtouhiB, Mekaouar A, Fekih M, Mbazaa A, Zouari B, Ben Khalifa F.Predictive risk factors for deterioration from normoglycemic stateto type 2 diabetes mellitus or impaired glucose tolerance in aTunisian urban population. Diabetes Metab 2001; 27: 487–495.38. Scragg R, Baker J, Metcalf P, Dryson E. Prevalence of diabetesmellitus and impaired glucose tolerance in a New Zealand multi-racial workforce. N Z Med J 1991; 104: 395–397.39. Bjorkelund C, Bondyr-Carlsson D, Lapidus L, Lissner L,Mansson J, Skoog I, Bengtsson C. Sleep disturbances in midlifeunrelated to 32-year diabetes incidence. The prospective popula-tion study of women in Gothenburg. Diabetes Care 2005; 28:2739–2744.40. Folsom AR, Kushi LH, Anderson KE, Mink PJ, Olson JE,Hong C-P. Associations of general and abdominal obesity withmultiple outcomes in older women. The Iowa Women’s HealthStudy. Arch Intern Med 2000; 160: 2117–2128.

obesity reviews The CODA meta-analysis project S. Duval et al. 275

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8, 263–276

41. Carey VJ, Walters EE, Colditz GA, Solomon CG, Willett WC,Rosner BA, Speizer FE, Manson JE. Body fat distribution and riskof non-insulin-dependent diabetes mellitus in women. The Nurse’sHealth Study. Am J Epidemiol 1997; 145: 614–619.42. ARIC investigators. The Atherosclerosis Risk in Communities(ARIC) Study: design and objectives. Am J Epidemiol 1989; 129:687–702.43. Friedman GD, Cutter GR, Donahue RP, Hughes GH, HulleySB, Jacobs DR, Liu K, Savage PJ. CARDIA: study design, recruit-ment, and some characteristics of the examined subjects. J ClinEpidemiol 1988; 41: 1105–1116.44. Williams DR, Wareham NJ, Brown DC, Byrne CD, ClarkPM, Cox BD, Cox LJ, Day NE, Hales CN, Palmer CR. Undiag-nosed glucose intolerance in the community: the Isle of Ely Dia-betes Project. Diabet Med 1995; 12: 30–35.45. Tuomilehto J, Marti B, Kartovaara L, Korhonen HJ, PietinenP. Body fat distribution, serum lipoproteins and blood pressure inmiddle-aged Finnish men and women. Rev Epidemiol Sante Pub-lique 1990; 38: 507–515.46. Meigs JB, Wilson PWF, Nathan DM, D’Agostino RB Sr,Williams K, Haffner SM. Prevalence and characteristics of themetabolic syndrome in the San Antonio Heart and FraminghamOffspring Studies. Diabetes 2003; 52: 2160–2167.47. Ito C, Maeda R, Ishida S, Harada H, Inoue N, Sasaki H.Importance of OGTT for diagnosing diabetes mellitus based onprevalence and incidence of retinopathy. Diabetes Res Clin Pract2000; 49: 181–186.48. Wagenknecht LE, Mayer EJ, Rewers M, Haffner S, Selby J,Borok GM, Henkin L, Howard G, Savage PJ, Saad MF. TheInsulin Resistance Atherosclerosis Study (IRAS). Objectives,design, and recruitment results. Ann Epidemiol 1995; 5: 464–472.49. Boyko EJ, Leonetti DL, Bergstrom RW, Newell-Morris L,Fujimoto WY. Low insulin secretion and high fasting insulin andC-peptide levels predict increased visceral adiposity. 5-year follow-up among initially nondiabetic Japanese–American men. Diabetes1996; 45: 1010–1015.50. Gimeno SG, Ferreira SR, Franco LJ, Iunes M. Comparison ofglucose tolerance categories according to World Health Organiza-tion and American Diabetes Association diagnostic criteria in apopulation-based study in Brazil. The Japanese-Brazilian DiabetesStudy Group. Diabetes Care 1998; 21: 1889–1892.

51. Haffner SM, Gonzalez C, Mykkanen L, Stern MP.Total immunoreactive proinsulin, immunoreactive insulin andspecific insulin in relation to conversion to NIDDM: theMexico City Diabetes Study. Diabetologia 1997; 40: 830–837.52. Cassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity andbody fat distribution in relation to the incidence of non-insulin-dependent diabetes mellitus. A prospective cohort study of men inthe Normative Aging Study. Am J Epidemiol 1992; 136: 1474–1486.53. Dowse GK, Zimmet PZ, Finch CF, Collins VR. Decline inincidence of epidemic glucose intolerance in Nauruans: implica-tions for the ‘thrifty genotype’. Am J Epidemiol 1991; 133: 1093–1104.54. Eliasson M, Asplund K, Nasic S, Rodu B. Influence of smok-ing and snus on the prevalence and incidence of type 2 diabetesamongst men: the northern Sweden MONICA study. J Intern Med2004; 256: 101–110.55. Rajala U, Laakso M, Qiao Q, Keinanen-Kiukaanniemi S.Prevalence of retinopathy in people with diabetes, impaired glu-cose tolerance, and normal glucose tolerance. Diabetes Care 1998;21: 1664–1669.56. Balkau B, Forhan A, Eschwege E. Two hour plasma glucoseis not unequivocally predictive for early death in men withimpaired fasting glucose: more results from the Paris ProspectiveStudy. Diabetologia 2002; 45: 1224–1230.57. Wedick NM, Mayer-Davis EJ, Wingard DL, Addy CL, Bar-rett-Connor E. Insulin resistance precedes weight loss in adultswithout diabetes: The Rancho Bernardo Study. Am J Epidemiol2001; 153: 1198–1205.58. Haffner SM, Hazuda HP, Mitchell BD, Patterson JK, SternMP. Increased incidence of type II diabetes mellitus in MexicanAmericans. Diabetes Care 1991; 14: 102–108.59. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le N-A, Oopik AJ,Cucchiara AJ, Savage PJ, Howard BV. The Strong Heart Study. Astudy of cardiovascular disease in American Indians: design andmethods. Am J Epidemiol 1990; 132: 1141–1155.60. Zethelius B, Byberg L, Hales CN, Lithell H, Berne C. Proin-sulin is an independent predictor of coronary heart disease: reportfrom a 27-year follow-up study. Circulation 2002; 105: 2153–2158.

Appendix

CODA study groupSteering committee: Sue Duval (PI), David R. Jacobs Jr (co-investigator), Karri Silventoinen (co-investigator/projectmanager), Jaakko Tuomilehto (co-investigator), MichaelStern (co-investigator), Rodolfo Valdez (co-investigator/CDC project officer).Data analyses and management: Sue Duval (PI), Karri Sil-ventoinen (project manager), Gabriela Vazquez (statisti-cian), David R. Jacobs Jr (co-investigator), William L.Baker (programmer), Amit Mahipal (research assistant),Khadeeja Pruett (research assistant).

Official study names, acronyms, and collaborators fromeach study in CODAAustralian Obesity, Diabetes, and Lifestyle Study (AUS-DIAB): J Shaw;

Atherosclerosis Risk in Communities (ARIC) Study: JStevens;Coronary Artery Risk Development in Young Adults(CARDIA) Study: P Schreiner, D. Jacobs;Catalonia Study (CATALONIA): C Castell;Chennai Urban Population Study (CUPS): V Mohan;Chennai Urban Rural Epidemiology Study (CURES): VMohan;MRC Ely Study (ELY): N Wareham;Finland-MONICA (FIN-MON): J Tuomilehto;Framingham Offspring Study (FRAMINGHAM): P Wilson;Non-communicable Diseases Survey in 1998 (GHANA): AAmoah;Prospective Population Study of Women in Gothenburg(GOTEBORG): L Lissner;

276 The CODA meta-analysis project S. Duval et al. obesity reviews

© 2007 The AuthorsJournal compilation © 2007 The International Association for the Study of Obesity. obesity reviews 8, 263–276

OGTT Follow up Study (HIROSHIMA): C Ito;Iowa Women’s Health Study (IOWA): A Folsom;Insulin Resistance Atherosclerosis Study (IRAS): LWagenknecht;Japanese American Community Diabetes Study (JACDS):E Boyko;Japanese-Brazilian Diabetes Study Group (JBDSG): S RFerreira, LJ Franco;Cardiovascular disease risk factors Jordan Study (JOR-DAN): A Batieha;Kaunas MONICA (KAUNAS-MON): S Domarkiene, ATamosiunas;Lille MONICA (LILLE-MON): P Amouyel, JDallongeville;Mauritius Non-communicable Disease Study (MAURI-TIUS): J Shaw;Mexico City Diabetes Study (MEXCITY): M Stern, CGonzalez;Normative Aging Study (NAS): P Cassano;

Nauru Study (NAURU): J Shaw;Third National Health and Nutrition Examination Survey(NHANES III): Public use dataset;Northern Sweden MONICA (NSWED-MON): M Eliasson;National Urban Diabetes Study (NUDS): A Ramachan-dran;Nurses’ Health Study (NURSES): GA Colditz, D Feskanich;Oulu 55 (OULU55): S Keinanen-Kiukaanniemi;Paris Prospective Study I (PARIS): MA Charles;POL-MONICA Krakow (POL-MON): A Pajak;Rancho Bernardo Study (RANCHO): E Barrett-Connor;San Antonio Heart Study (SAHS): M Stern;Strong Heart Study (SHS): BV Howard;Singapore National Health Survey 1998 (SINGAPORE): JCutter, CW Fong, SK Chew;Tunis CVD Study (TUNIS): F Harzallah;Uppsala Longitudinal Study of Adult Men (ULSAM): BZethelius;Workforce Diabetes Survey (WORKNZ): R Scragg