the gladel multinational latin american prospective inception cohort of 1,214 patients with systemic...

The GLADEL Multinational Latin American ProspectiveInception Cohort of 1,214 Patients With Systemic

Lupus ErythematosusEthnic and Disease Heterogeneity Among ‘‘Hispanics’’

Bernardo A. Pons-Estel, MD, Luis J. Catoggio, MD, Mario H. Cardiel, MD, MSc,

Enrique R. Soriano, MD, Silvana Gentiletti, MD, Antonio R. Villa, MD, MSc, Isaac Abadi, MD,

Francisco Caeiro, MD, Alejandro Alvarellos, MD, and Donato Alarcon-Segovia, MD, PhD,

on behalf of the Grupo Latinoamericano de Estudio del Lupus (GLADEL)

Abstract: Clinical and laboratory manifestations and outcome of

systemic lupus erythematosus (SLE) may vary in different pop-

ulations. A prospective multinational inception cohort should prove

useful in identifying the influence of ethnicity on the clinical char-

acteristics of SLE. We therefore analyzed clinical, laboratory, and

prognostic variables in Latin American SLE patients with disease

of recent onset who were entered into a prospective cohort, and

compared these variables in the cohort’s 3 major ethnic groups.

Thirty-four centers from 9 Latin American countries participated by

randomly incorporating SLE patients within 2 years of diagnosis

into a standardized database. Participating centers were selected for

their expertise in diagnosing and managing SLE. We were then able

to evaluate prospectively socioeconomic variables, ethnicity, type

of medical care, clinical and laboratory features, disease activity,

damage, and mortality at each site. A coordinating center controlled

the quality of the information submitted.Of the 1,214 SLE patients included in the cohort, 537 were mes-

tizos, 507 were white, and 152 were African-Latin American (ALA).

(There were also small numbers of pure Amerindian and oriental

individuals.) Significant differences were found between them in

socioeconomic characteristics, type of care, and level of education

favoring whites. Mestizos and ALA were younger at onset. Delay to

diagnosis and disease duration was shorter in ALA. Fever was more

frequent in whites; discoid lesions in ALA; renal disease and

lymphopenia in mestizos and ALA. Although we found differences in

background variables between ethnic groups from different

countries, mestizos from 2 distant countries (Argentina and Mexico)

were clinically akin and showed similar differences to whites.

Mortality was associated with lower education, poor medical

coverage, and shorter follow-up. In an exploratory model nonwhite

ethnicity was associated with renal disease and lymphopenia,

damage, and cumulative American College of Rheumatology

criteria. These differences in clinical, prognostic, socioeconomic,

educational, and access to medical care features in Latin American

lupus patients of 3 major ethnic groups from 9 different countries

may have an impact on the patients’ disease. ‘‘Hispanics,’’ as they

have come to be generically termed on the basis of language, actually

constitute a markedly heterogeneous group of subjects.

(Medicine 2004;83:1–17)

INTRODUCTION

Systemic lupus erythematosus (SLE) is a complex auto-

immune disease that may result from the interplay of

genetic, hormonal, and environmental factors12. Although

its prognosis has improved remarkably in the past deca-

des22,23,30, it remains a potentially serious condition. Several

studies have shown that some sociodemographic character-

istics such as ethnicity, gender, age, income, education, and

access to health care are important variables associated with

the outcome of SLE21,29,35,43,44,56,63,68,72,75,80,82. Disease activity,

Abbreviations: ACR = American College of Rheumatology, ALA =

African-Latin American, SLE = systemic lupus erythematosus.

Medicine � Volume 83, Number 1, January 2004 1

From Servicio de Reumatologıa (BAP-E), Hospital Escuela Eva Peron,

Granadero Baigorria, Rosario, Argentina; Seccion Reumatologıa (LJC,

ERS), Servicio de Clınica Medica Hospital Italiano, Buenos Aires,

Argentina; Departamento de Inmunologıa y Reumatologıa (MHC, DA-S)

and Unidad de Epidemiologıa Clınica (ARV), Instituto Nacional de

Ciencias Medicas y Nutricion Salvador Zubiran, Mexico DF, Mexico;

Servicio de Reumatologıa (SG), Hospital Provincial de Rosario, Rosario,

Argentina; Servicio de Reumatologıa (IA), Centro Nacional de

Enfermedades Reumaticas, Hospital Universitario de Caracas, Caracas,

Venezuela; and Servicio de Reumatologıa (FC, AA), Hospital Privado,

Centro Medico de Cordoba, Cordoba, Argentina.

Supported in part by grants from the Pan American League of Associations

for Rheumatology (PANLAR).

Address reprint requests to: Bernardo A. Pons-Estel, MD, Avenida del

Huerto 1375, Piso 24, (2000) Rosario, Argentina. E-mail: baponsestel@

buenaventuraguarani.com.ar.

Copyright n 2004 by Lippincott Williams & Wilkins

ISSN: 0025-7974/04/8301-0001

DOI: 10.1097/01.md.0000104742.42401.e2

Copyr ight © Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

organ damage, infection, and treatment have also been identified

asfactorsinfluencingtheprognosis2,3,13,14,19,20,39,54,57,61,71,76,77,83.

The LUMINA study has compared clinical, socioeco-

nomic status, and disease-related variables in 3 ethnic groups

in clinical centers in the United States4–8,10,11,15,27,64,84. Im-

portant differences were detected in clinical and immuno-

genetic variables that could help identify associations with

clinical manifestations, disease activity, and physician’s global

scores. The epidemiology of SLE has been evaluated mainly

in North America2–11,21,27,29,48,54–56,64,76,77,81,84 and in some

European countries17,19,20,38–40,42,46, but little information is

available from Latin America1,16,22,23,26,36,37,41,47,49,51,52,65,79.

For obvious reasons, the Latin American studies make few

comparisons between ethnic groups, although some have

shown a poor prognosis and a high prevalence of infections in

African-Latin American (ALA) SLE patients, both of which

may relate to socioeconomic variables.

Several studies from the United States have included

Latin American patients, usually referring to them as

‘‘Hispanics’’5–7,9–11,27,39,60,64,81,84, a term that is mainly de-

rived from their language rather than their ethnic back-

ground, which can vary between and within Latin American

countries. Notwithstanding, the so-called Hispanics in the

United States have been shown to have more severe disease

and poorer outcomes than whites, often equating African

Americans, whose lupus tends to have poor prognosis11,64.

Interpreting and comparing those studies has also been

difficult due to the inclusion of diverse proportions of

hospital patients with varying degrees of disease duration.

Using a prospective cohort enables researchers to avoid

under-registry of information, evaluate characteristics both at

baseline and throughout the clinical course of the disease,

and ascertain the influence of diverse treatment strategies

and comorbid states. The incorporation of patients early after

diagnosis also minimizes the exclusion of early deaths, an

important variable in a chronic disease such as SLE.

These considerations were taken into account in the

development of the Grupo Latinoamericano de Estudio del

Lupus (GLADEL) cohort, started in 1997 as a multinational

inception prospective cohort in Latin American centers

having expertise in the diagnosis and management of SLE.

For this task we used a computer database available to all

groups and interconnected among them.

Herein we describe the cohort and the general char-

acteristics of the first 1,214 Latin American SLE patients

with recent-onset SLE incorporated into the predetermined

database and followed prospectively for a mean of 20

months. We analyze the potential differences by ethnic, na-

tional, and sociodemographic variables.

PATIENTS AND METHODSThe GLADEL study held an investigator meeting in

1997 in Mexico City. During 4 days, participants developed

a common protocol, consensus definitions, and selected out-

come measures, and received direct training in the database

software.

Center SelectionThe 34 centers participating in the GLADEL cohort

are distributed among 9 Latin American countries. To be

included, they had to meet the following criteria: have ex-

perience in SLE (referral centers with a lupus clinic, an

academic profile, and a rheumatology training program);

have a genuine interest in the research project; and have an

identified leader, as well as adequate human, technical, and

communication facilities.

Patient SelectionIn order to have a balanced representation of centers

in the initial cohort, each center was asked to incorporate a

minimum of 20 and a maximum of 30 randomly selected

patients. Randomization was done locally in each center. The

first patients were entered in October 1997, and to insure

their recent onset they could only be included if the diagnosis

of SLE had been made after 1 January 1996 by a rheu-

matologist or a qualified internist with experience in SLE.

Fulfillment of 4 American College of Rheumatology (ACR)

1982 SLE criteria73 at the time of diagnosis was not man-

datory. After incorporating the initial 30 patients, each group

continued to include 1 new randomly selected patient per

month diagnosed within the previous 2 years.

DatabaseAll groups started using ARTHROS 2.058 as a common

database for collection of information and moved on to the

new version ARTHROS 6.0. This is a user-friendly rheu-

matology database developed by Argentine rheumatologists

using a Windows platform. One of its many advantages is the

lack of language barriers: for example, data collected in

Spanish can be retrieved by an English-speaking investigator

since all characteristics are coded.

In order to obtain reliable information all investigators

were trained in a similar fashion. Patient data were collected

by a clinician trained in the program in small groups and

personalized sessions with 1 of the developers of the program.

At a coordinator center, strict control and supervision of the

data received was undertaken, with permanent communica-

tion with the submitting center for any queries arising.

Clinical InformationEach patient was interviewed and her or his clinical

chart information was validated. Investigators were asked to

establish precisely the dates of disease onset, diagnosis, and

fulfillment of ACR SLE criteria. They also were asked to

capture all relevant clinical and laboratory evaluations as

clinically indicated. Disease features were defined according

to ACR or other well-accepted criteria62. The clinical course

2 n 2004 Lippincott Williams & Wilkins

Pons-Estel et al Medicine � Volume 83, Number 1, January 2004


can be described as seen by both patient and physician at the

time of each visit (that is, same, better, worse). Disease

activity using both SLEDAI18 and MEX-SLEDAI34 were

measured in all patients at the time of entry and every

6 months thereafter. Systemic Lupus International Collabo-

rating Clinics/American College of Rheumatology (SLICC/

ACR) Damage Index for systemic lupus erythematosus31,32

was measured yearly. All researchers followed local

regulations according to their institutional review boards.

Definition of Demographic VariablesEthnic groups: An operational definition was neces-

sary. It was developed by consensus including an expert in

immunogenetics. These definitions were determined accord-

ing to the parents’ and all 4 grandparents’ self-reported

ethnicity5. Patients were questioned as to their place of birth,

as well as to that of their parents and grandparents. They

were thus classified as the following:

White: individuals with all white European ancestors;

Mestizo: individuals born in Latin America who had

both Amerindian and white ancestors;

African-Latin Americans (ALA): individuals born in

Latin America with at least 1 African ancestor irrespective of

whether other ancestors were white or Amerindian.

Pure Amerindians were those individuals who had all

autochthonous ancestors.

Final assignment of patients was the prerogative of the

clinician, who considered anthropomorphic characteristics

for this.

Socioeconomic status: Socioeconomic status was eval-

uated using the Graffar method33, a validated scale

previously used in Latin America78. The Graffar scale takes

into account 5 variables: parent’s occupation, parent’s level

of education, main source of income, housing, and neigh-

borhood quality. Each variable has 5 categories with in-

dependent and progressive scores. A final score classifies

subjects in 5 categories: high, medium-high, medium, me-

dium-low, and low.

Type of medical care was divided into the following

categories:

Institutional: patients treated primarily in public in-

stitutions. Partial coverage: patients who receive limited

support toward medical care expenses. Complete coverage:

patients who have all expenses paid for. Without coverage:

patients who have no economic support and have to pay for

all their expenses for medical care.

Private: patients cared for in private institutions or

practice. With coverage: patients with prepaid or insurance-

paid support. Without coverage: patients who pay for their

private care.

Education: we considered from 0 (illiterate) up to 20

years of formal education.

Laboratory StudiesStudies were done in the standard routine laboratory at

each center. Autoantibodies and complement tests were per-

formed at each center and the cutoff values were considered

valid. Standardization of immunologic tests between centers

is being incorporated but was not yet available at the time

of the current study.

Statistical AnalysisOverall comparisons of the clinical, sociodemographic,

and immunologic categorical variables among the major

ethnic groups (white, mestizo, ALA, and other) were

performed using cross tabulations, and their significance as-

sessed by means of the chi-square statistic. When a sig-

nificant result was found, bivariated comparisons were

performed to identify groups that were statistically different

by the Fisher exact test. A similar analysis was applied when

comparing the 3 selected ethnic groups: Argentine white,

Argentine mestizo, and Mexican mestizo.

For continuous variables (age at onset; age at diagnosis;

delay to diagnosis—defined as time between onset of disease

and diagnosis; disease duration; follow-up; positive results in

immunologic tests; and scores of SLEDAI, MEX-SLEDAI, and

SLICC) the comparison between ethnic groups was established

by Kruskal-Wallis test, and the comparison for 2 samples was

done using Mann-Whitney U test. Thus, ethnic group was

considered the main independent variable. To test the main

effect of this variable over clinical outcomes, we factorized it in

2 ways. In the first, we built 3 dummy variables taking the major

categories of the variable ethnic group: white vs. mestizo, white

vs. ALA, and white vs. other. In the second, we established the

comparison between 2 dummy variables: Argentine white vs.

Argentine mestizo, and Argentine white vs. Mexican mestizo.

The multivariate models were adjusted by gender (female vs.

male), education (<10 yr vs. �10 yr), medical coverage (partial

or no coverage vs. full medical coverage), age at SLE diagnosis

(>27 yr vs.�27 yr), delay to SLE diagnosis (�6 mo vs. <6 mo),

follow-up (�20 mo vs. <20 mo), number of hospitalizations

(�1 vs. 0), marital status (single vs. all others), socioeconomic

status (lower middle/lower vs. all others), and country

(Argentina vs. the rest). All these variables were entered into

the models to diminish differences in the heterogeneity in both

clinical and sociodemographic variables of our populations.

The criterion to stratify the continuous variables was based in

the median value. Clinical outcomes as dependent variables

were built and tested in the different multivariate models. We

showed selected models. All the multivariate analyses were

conducted by means of unconditional logistic regression to

derive the odds ratio as association measure adjusted by

multiple covariates. Multicollinearity was probed in all models

by means of the covariance matrix. All statistical analyses were

performed separately with SAS v. 870, and with SPSS/PC v.

10.069, and the data then compared.

n 2004 Lippincott Williams & Wilkins 3

Medicine � Volume 83, Number 1, January 2004 Lupus in Latin America: GLADEL Inception Cohort


RESULTS

Composition of the GLADEL CohortAt the defined cutoff date of 1 January 2000, the

GLADEL cohort included 1,214 patients from 34 centers

from 9 Latin American countries. Table 1 shows the gender

and ethnic distribution by country of origin. Ninety percent

of the patients were female, without differences between

countries or ethnic groups. There were 537 mestizos (44%),

507 whites (42%) and 152 ALA (13%). There were small

numbers of pure Amerindian and oriental individuals.

Whites predominated in Argentina and Cuba and to a lesser

degree in Brazil. Mestizos predominated in Guatemala,

Mexico, and Peru. ALA patients were more prevalent in

Venezuela, Brazil, and Colombia.

Sociodemographic CharacteristicsTable 2 shows the sociodemographic characteristics of

the GLADEL cohort as found in the 3 major ethnic groups.

Although most patients lived in urban areas, mestizos lived

significantly more frequently in rural areas than did whites

(13.5% vs. 5.3%; p = 0.0001) or ALA. There were no

differences in marital status between groups. Significant

differences were detected in socioeconomic status, type of

medical care, and level of formal education favoring white

patients. Mestizos showed better socioeconomic status and

medical coverage than ALA.

Selected Epidemiologic DataAnalysis of the 3 major ethnic groups by decade of age at

diagnosis is shown in Figure 1. In all 3 ethnic groups the

majority was between 11 and 40 years of age. Table 3 shows

that for the entire group, the mean age at disease onset was 28

years, and that at diagnosis was 30. The median delay to di-

agnosis was 6 months. The median disease duration was 32

months, and the median follow-up to cutoff date was 20 months.

Mestizos and ALA were significantly younger at onset

than whites. Similarly, age at diagnosis was significantly

lower in ALA and mestizos than in whites. Delay to

diagnosis was significantly shorter in ALA than in mestizos

and whites. Disease duration (onset to last visit) was sig-

nificantly shorter for ALA. However, this could reflect the

later entrance of Brazil and Cuba, 2 countries with a large

ALA population, into the study. Mestizos had shorter follow-

up than whites, but again, this could reflect the earlier

entrance of several Argentine groups.

Clinical FeaturesTable 4 shows the clinical manifestations of the entire

cohort both at onset and cumulative to cutoff time. All

manifestations analyzed increased in frequency with follow-

up. Analysis between ethnic groups was performed with the

cumulative data. There were interesting differences. Fever

was significantly more frequent in whites than in mestizos.

Weight loss was less frequent in mestizos than in whites and

ALA. Of the cutaneous manifestations, photosensitivity was

less frequent in mestizos, while discoid lesions were sig-

nificantly more frequent in ALA than in either whites or

mestizos. On the other hand, livedo reticularis was signi-

ficantly more frequent in mestizos than in ALA. Xeroph-

thalmia and sicca syndrome were less frequent in ALA than

in the other 2 groups.

Renal disease was significantly more frequent in mes-

tizos and ALA than in whites. Other consequences of lupus

nephropathy such as acute or chronic renal failure and

hypertension were more frequent in mestizos than in whites,

TABLE 1. Gender and Ethnic Distributions of the GLADEL Cohort by Country

Variable

Country

Total Argentina Brazil Colombia Cuba Chile Guatemala Mexico Peru Venezuela

No. (%) No. (%) No. (%) No. (%) No. (%) No. (%) No. (%) No. (%) No. (%) No. (%)

Gender

Female 1091 89.9 284 90 188 91 133 89 25 93 88 93 25 86 218 88 55 89 75 94

Male 123 10.1 32 10 19 9 17 11 2 7 7 7 4 14 30 12 7 11 5 6

Ethnic group

White 507 41.8 260 82 124 60 34 23 22 81 33 35 1 3 8 3 1 2 24 30

Mestizo 537 44.2 54 17.1 3 1 92 61 0 0 61 64 28 97 236 95 55 89 8 10

African-LatinAmerican

152 12.5 1 0.3 77 37 20 13 5 18 0 0.0 0 0 0 0 1 2 48 60

Other 18 1.5 1 0.3 3 1 4 3 0 0 1 1 0 0 4 2 5 8 0 0

Total 1,214 100.0 316 26* 207 17* 150 12* 27 2* 95 8* 29 2* 248 20* 62 5* 80 7*

*Of the total cohort.




and consequently the sum of renal manifestations was also

more frequent in mestizos. We observed that ALA had a sig-

nificantly higher frequency of nephrotic syndrome than did

whites. Interestingly, nephrotic syndrome was not signifi-

cantly more frequent in mestizos than in whites.

Ethnic group was probed as a main effect in 7 different

models by logistic regression multivariate analysis (Table 5).

After controlling for clinical and sociodemographic variables

as well as for country of origin (Argentina vs. the rest), both

mestizos and ALA were statistically associated with a higher

probability of lymphopenia, and mestizos with renal damage,

than whites. In a similar comparison, cumulative clinical dam-

age as measured by SLICC and the probability of achieving

6 or more ACR criteria were less probable in mestizos than in

whites, although without statistical significance.

Comparison of Whites and Mestizos fromArgentina and Mestizos from Mexico

Because of the aforementioned clinical differences ob-

served in the 3 major ethnic groups included in the whole

cohort, we decided to compare whites and mestizos from a

country whose participating centers included an important

proportion of both ethnic groups. Also, to determine possi-

ble differences between a major ethnic group from 2 dif-

ferent countries, we compared mestizos from Argentina and

Mexico. The data are shown in Table 6, where socio-

demographic differences of the 3 ethnic/national groups also

can be seen. There were differences in some of the variables

analyzed between Mexican and Argentine mestizos. Renal

involvement and lymphopenia were again found to be sig-

nificantly more frequent in mestizos from either country than

in Argentine whites.

Multivariate models were obtained, and their results are

shown in Table 7. It is interesting that mestizos had similar

findings independent of their country of origin. Mestizos of

either country had more renal disease and lymphopenia, and a

trend to lower MEX-SLEDAI and cumulated ACR criteria,

than did Argentine whites. Contradictory findings were seen

in death prediction.

Activity ScoresA trend for higher disease activity indices was observed

from ALA, to mestizos, to whites (Table 8). While there were

significant differences in SLEDAI among all groups, the

TABLE 2. Sociodemographic Characteristics of the GLADEL Cohort in the 3 Major Ethnic Groups

Ethnic Group

Overall

Comparison (p)

White

(n = 507)

Mestizo

(n = 537)

ALA

(n = 152) Pairwise Comparison (p)

Variable No. (%) No. (%) No. (%) W vs. M W vs. ALA M vs. ALA

Gender

Female 460 90.7 478 89.0 136 89.5 0.6578 0.4123 0.6387 1.0000

Male 47 9.3 59 11.0 16 10.5

Residence*

Urban 480 94.7 463 86.5 146 96.0 <0.0001 <0.0001 0.6716 0.0007

Rural 27 5.3 72 13.5 6 4.0

Marital Status*

Single 252 50.5 259 48.2 86 56.6 0.5270 0.7323 0.4155 0.2024

Separated/divorced/widowed

17 3.4 21 3.9 5 3.3

Married 230 46.1 257 47.9 61 40.1

Socioeconomic status

Upper/upper middle 81 16.0 37 6.9 12 7.9 <0.0001 <0.0001 <0.0001 0.0002

Middle 165 32.5 149 27.7 19 12.5

Lower middle/lower 261 51.5 351 65.4 121 79.6

Medical insurance*

Full medical coverage 360 71.1 281 52.3 101 66.4 <0.0001 <0.0001 0.2685 0.0022

Partial or no coverage 146 28.9 256 47.7 51 33.6

Education Mean SD Mean SD Mean SD

Years of education 10.4 4.3 9.7 3.8 9.2 4.4 0.0075 0.0316 0.0048 0.1232

Abbreviations: W, white; M, mestizo; ALA, African-Latin American; SD, standard deviation.*There were 2 missing values for residence for the mestizo ethnic group. Also, there were 8 missing values for marital status, and 1 missing value for

medical insurance for the white ethnic group.




MEX-SLEDAI did not reach significance between mestizo

and ALA patients. Stepwise multiple logistic regression anal-

ysis produced a large predictive model for disease activity,

defined as a score higher than 12 in SLEDAI and 8 in MEX-

SLEDAI. Results were similar with both indices, and there-

fore we present only the SLEDAI data. Variables associated

with higher disease activity were formal education of less

than 10 years odds ratio [OR], 1.5; 95% confidence interval

[CI], 1.1–1.9); partial or no medical coverage (OR, 1.4;

95% CI, 1.1–1.8), age older than 27 years (OR, 1.6; 95%

CI, 1.2–2.1), time of follow-up �20 months (OR, 1.6; 95%

CI, 1.2–2.2), delay to diagnosis �6 months (OR, 0.6; 95% CI,

0.5–0.8), and disease duration �32 months (OR, 0.7; 95% CI,

0.5–0.9) (data not shown).

FIGURE 1. Age distribution of patients at diagnosis in the 3 major ethnic groups.

TABLE 3. Selected Epidemiologic Data of the GLADEL Cohort in the 3 Major Ethnic Groups

Ethnic Group

Total

(n = 1,214)

White

(n = 507)

Mestizo

(n = 537)

ALA

(n = 152)Overall W vs. W vs. M vs.

Variable Mean SD Mean SD Mean SD Mean SD Comparison (p) M ALA ALA

Age atonset (yr)

28 12 29.5 12.2 28.1 12.3 26.2 10.6 0.0077 0.0219 0.0058 0.2018

Age atdiagnosis (yr)

30 12 31.1 12.5 29.9 12.5 26.9 10.8 0.0010 0.0488 0.0003 0.0157

Median Range Median Range Median Range Median Range

Delay todiagnosis (mo)

6 0.4–490 6.0 0.4–301 6.9 0.4–490 3.8 0.4–84 <0.0001 0.6872 <.0001 <.0001

Diseaseduration (mo)

32 0.9–534 34.2 0.9–333 30.9 1.5–534 27.2 2.4–111 0.0002 0.0851 <.0001 0.0041

Follow-up (mo) 19.9 0.0–162 22.3 0.0–53 17.8 0.0–162 19.9 0.0–52 0.0299 0.0112 0.1379 0.5341

Abbreviations: See previous table.

Pairwise Comparison




TABLE 4. Clinical Manifestations of SLE Patients in the GLADEL Cohort. Comparison of the Cumulative Data between the 3 MajorEthnic Groups

Ethnic Group

Total (n = 1,214)

White

(n = 507)

Mestizo

(n = 537)

ALA

(n = 152)Overall

Pairwise

Comparison (p*)

Manifestation

At onset

(%)

Cumulative

(%)

Cumulative

(%)

Cumulative

(%)

Cumulative

(%)

Comparison

(p*)W vs.

M

W vs.

ALA

M vs.

ALA

General

Fever 28.6 56.7 60.2 52.9 59.2 0.0492 0.0208 0.8507 0.1966

Weight loss 13.0 26.6 29.8 22.5 31.6 0.0100 0.0090 0.6877 0.0250

Polyadenopathy 4.5 14.7 14.0 13.2 21.0 0.0485 0.7189 0.0416 0.0203

Systemic hypertension 2.1 26.9 21.1 31.8 30.2 0.0003 <0.0001 0.0216 0.7670

Musculoskeletal

Arthralgia and/orarthritis

67.3 93.2 93.5 92.5 94.1 0.7410 0.6275 1.0000 0.5953

Avascular bonenecrosis

0.0 1.1 1.2 0.9 1.3 0.8870 0.7677 1.0000 0.7530

Myalgia/myosis 7.6 17.5 18.5 17.3 13.2 0.3060 0.6285 0.1425 0.2642

Cutaneous

Alopecia 20.3 57.6 55.0 59.0 61.2 0.2701 0.2109 0.1924 0.6414

Photosensitivity 24.5 56.1 59.8 51.8 59.2 0.0243 0.0106 0.9251 0.1175

Malar rash (butterflylesions)

23.6 61.3 63.3 59.0 63.2 0.3261 0.1627 1.0000 0.3991

Discoid lesions 5.3 11.8 11.2 10.4 19.7 0.006 0.6911 0.0092 0.0034

Oral/nasal ulcers 10.5 41.7 40.6 43.2 40.1 0.6492 0.4150 0.9253 0.5166

Livedo reticularis 1.8 9.9 9.5 11.5 5.9 0.1084 0.3133 0.1916 0.0489

Subacute cutaneouslesions

0.7 2.8 3.3 2.2 2.6 0.5525 0.3466 0.7966 0.7624

Raynaud syndrome 10.2 28.2 29.4 27.6 25.7 0.6335 0.5371 0.4132 0.6804

Panniculitis 0.4 1.4 1.0 1.7 2.0 0.5310 0.4235 0.3944 0.7329

Any cutaneous 46.3 90.1 89.5 90.7 89.5 0.7811 0.6038 1.000 0.6413

Ocular Lesions

Xerophthalmia 0.9 6.5 7.5 5.2 1.3 0.0130 0.3426 0.0013 0.0079

Sicca Syndrome 1.5 8.3 9.7 8.6 3.3 0.0440 0.5907 0.0108 0.0336

Uveitis/episcleritis/scleritis

0.3 1.1 1.2 1.1 0.7 1.0000 1.000 1.000 1.000

Cataracts 0.1 2.2 2.2 2.6 0.0 0.1380 0.6896 0.0769 0.0481

Respiratory

Pleuritis 3.6 22.1 23.5 20.9 21.0 0.5755 0.3324 0.5831 1.0000

Lupus pneumonitis 0.2 1.9 2.8 1.3 0.7 0.1100 0.1221 0.2115 1.0000

Pulmonary hemorrhage 0.0 1.0 0.6 1.7 0.0 0.0880 0.1457 1.0000 0.2184

Pulmonary embolism 0.1 1.2 0.8 1.7 0.7 0.4226 0.2664 1.0000 0.6998

Pulmonary hypertension 0.2 1.4 1.6 1.5 0.7 0.6910 1.0000 0.6924 0.6919

Any respiratory 0.5 6.1 7.1 6.3 2.0 0.0454 0.6232 0.0176 0.0398

Cardiovascular

Pericarditis 2.7 17.2 16.4 15.6 26.3 0.0070 0.8001 0.0087 0.0039

Myocardial involvement 0.3 3.0 1.8 3.7 4.6 0.0630 0.0609 0.0664 0.6367

Endocardial involvement 0.3 2.7 2.0 2.6 5.9 0.0320 0.5406 0.0222 0.0685

Pericardial tamponade 0.2 0.7 1.2 0.2 1.3 0.1200 0.0624 1.0000 0.1241

continued




Damage ScoreDespite the higher frequency of renal disease and higher

mean maximum disease activity indices in ALA, these pa-

tients achieved significantly lower damage scores than both

mestizos and whites. In the stepwise multiple logistic re-

gression analysis, damage defined as SLICC/ACR � 1 was

associated with medium socioeconomic status in comparison

with high socioeconomic status (OR, 1.4; 95% CI, 1.1–1.9);

TABLE 4. (continued )

Ethnic Group

Total (n = 1,214)

White

(n = 507)

Mestizo

(n = 537)

ALA

(n = 152)Overall

Pairwise

Comparison (p*)

Manifestation

At onset

(%)

Cumulative

(%)

Cumulative

(%)

Cumulative

(%)

Cumulative

(%)

Comparison

(p*)

W vs.

M

W vs.

ALA

M vs.

ALA

Vascular thrombosis 1.4 5.6 5.3 6.3 3.9 0.5509 0.5117 0.6716 0.3288

Any cardiovascular 6.3 42.9 37.1 47.1 47.4 0.0023 0.0011 0.0293 1.0000

Renal

Persistent proteinuriaand/or cellular casts

4.5 46.0 36.7 53.3 50.7 <0.0001 <0.0001 0.0054 0.9236

Nephrotic syndrome 1.1 6.7 5.7 6.7 10.5 0.1178 0.5447 0.0448 0.1196

Acute renal failure 0.4 3.2 1.8 4.3 3.9 0.0483 0.0198 0.1253 1.0000

Chronic renal failuire 0.2 1.7 0.8 2.4 2.0 0.0994 0.0488 0.2036 1.0000

Any renal 5.3 51.7 43.6 58.3 55.3 <0.0001 <0.0001 0.0123 0.5164

Neurologic

Psychosis 0.5 4.0 3.0 5.2 3.9 0.1853 0.0857 0.5981 0.6725

Seizures 1.6 8.1 7.9 8.6 6.6 0.7495 0.7361 0.7273 0.5036

Psychosis and/or seizures 2.1 11.4 10.3 12.7 9.9 0.4189 0.2444 1.0000 0.3990

Chorea 0.1 0.4 0.6 0.2 0.7 0.4470 0.3605 1.0000 0.3928

Organic brain syndrome 0.0 1.9 1.2 2.6 1.3 0.2370 0.1148 1.0000 0.5432

Transverse myelitis 0.1 0.6 0.4 0.7 0.7 0.6658 0.6874 0.5452 1.0000

Ischemic stroke 0.6 1.6 2.0 1.5 0.7 0.6158 0.6375 0.4714 0.6919

All strokes (ischemicand/or hemorrhagic)

0.6 2.8 3.3 2.6 1.3 0.4374 0.5850 0.2706 0.5432

Cranial nerve disease 0.2 3.6 3.7 4.1 2.0 0.5487 0.8737 0.4392 0.3246

Polyneuritis 0.3 1.2 2.2 0.7 0.0 0.0390 0.0682 0.0769 0.5809

Mononeuritis multiplex 0.1 1.1 1.2 1.1 0.7 0.8570 1.0000 1.0000 1.0000

Lupus headache 0.2 4.4 3.7 4.7 6.6 0.3162 0.5385 0.1731 0.4012

Any neurologic 4.1 26.4 26.4 27.7 21.7 0.3334 0.6761 0.2876 0.1456

Digestive

Xerostomy 0.7 4.0 4.3 4.1 2.6 0.6883 0.8785 0.4770 0.4797

Peritoneal serositis 0.1 1.3 1.4 1.3 0.7 0.9371 1.0000 0.6891 1.0000

Gyneco-obstetricy

Amenorrhea 0.4 5.3 5.6 5.4 4.4 0.8878 0.8878 0.6699 0.8272

Pregnancy loss 1.6 9.3 10.9 8.2 7.3 0.1811 0.1811 0.2598 0.8589

Hematologic

Hemolytic anemia 2.4 11.8 13.0 11.5 9.9 0.5090 0.5090 0.3277 0.6623

Leukopenia 5.1 42.3 39.6 42.6 46.7 0.3455 0.3455 0.1331 0.4046

Lymphopenia 5.9 59.3 51.1 63.5 70.4 <0.0001 <0.0001 <0.0001 0.1239

Thrombocytopenia 5.2 19.2 19.1 18.2 23.0 0.4126 0.7508 0.2997 0.2008

Any hematologic 12.5 72.5 68.2 74.3 80.3 0.0064 0.0337 0.0042 0.1358

Abbreviations: See previous tables.*Bold numbers indicate significant differences.yPercentages computed for the total number of women.




partial or no medical coverage (OR, 1.6; 95% CI, 1.2–2.1);

and disease duration (OR, 1.5; 95% CI, 1.2–2). On the other

hand, urban residence was protective (OR, 0.6; 95% CI, 0.4–0.9).

Autoantibodies and ComplementAlthough autoantibodies and complement levels have

not been tested systematically within the cohort up to now,

and the results have not been subjected yet to interlaboratory

control, the results give some insight into potential differ-

ences between the 3 major ethnic groups (Table 9). These

should, however, be taken with caution. Antinuclear anti-

bodies were significantly less prevalent in mestizos than in

whites and ALA. Antidouble-stranded DNA antibodies were

significantly more frequent in mestizos than in whites,

and IgM anticardiolipin antibodies were significantly less

frequent in ALA than in whites and mestizos. Both ALA and

whites had low complement levels, including C3 and C4,

more frequently than mestizos.

TreatmentCumulative treatment regimens are described in

Table 10. Steroids were used in 92% and antimalarials

in 75% of patients in the cohort. Immunosuppressive agents

were received by 47% of patients with intravenous cy-

clophosphamide predominating, followed by azathioprine.

Forty-two (3.45%) patients entered dialysis and 2 received

renal transplants. When this was analyzed for the different

ethnic groups, differences appeared in the use of antimalarials

and immunosuppressive agents. Chloroquine was significant-

ly more frequently used in mestizos and ALA compared with

whites, and the reverse was true for hydroxychloroquine.

Some of these differences may reflect cost and drug availabil-

ity in our countries (ie, chloroquine is less costly). Overall

immunosuppressive use was more frequent in nonwhites,

particularly significant when compared with mestizos.

MortalityThirty-four patients (2.8%) died within the GLADEL

cohort. Their general characteristics are presented in

Table 11. The survival rate at 4 years was 95%. Because

of the small number of patients who died, we compared

survival rates between whites and the rest of the cohort

(nonwhites) rather than between the 3 major ethnic groups.

There were no significant differences in survival rates

between whites and nonwhites. However, nonwhites who

died had lower age at disease onset and lower age at the time

of death than whites (p = 0.05 and p = 0.03, respectively).

Patients who died had lower education level, lower socio-

economic status, and poorer medical coverage. Obviously

they had higher mean activity indices and SLICC/ACR

scores than those who survived. The causes of death are

presented in Table 12.

TABLE 5. Comparison of Ethnic Groups, Multivariate Models Obtained by Multiple Logistic Regression Analysis

Ethnic Group

Model*

RenalDamage

Yes vs. No

(n = 558/n = 655)

Model*

LymphopeniaYes vs. No

(n = 720/n = 493)

Modely

SLEDAI

�12 vs. <12

(n = 584/

n = 567)

Modelz

MEX-SLEDAI

�8 vs. <8

(n = 567/

n = 577)

Modelx

SLICC

�1 vs. <0

n = 353/

n = 690

Model*

ACR Criteria

�6 vs. <6

(n = 653/

n = 560)

Model*

Death

Yes vs. No

(n = 34/

n = 1179)

White(n = 507)

OR 1.0 1.0 1.0 1.0 1.0 1.0 1.0

Mestizo(n = 537)

OR 1.95 1.58 1.02 1.10 0.75 0.73 0.6495% CI 1.36–2.80 1.12–2.24 0.71–1.46 0.76–1.59 0.50–1.12 0.52–1.05 0.21–1.95

p <0.0001 0.01 0.94 0.63 0.16 0.09 0.43

African-LatinAmerican(n = 152)

OR 1.23 2.10 1.23 0.78 0.78 1.08 0.9295% CI 0.79–1.93 1.34–3.29 0.77–1.96 0.48–1.25 0.45–1.36 0.69–1.67 0.19–4.42

p 0.36 0.001 0.38 0.30 0.39 0.74 0.91

Other(n = 18)

OR 1.65 2.12 0.74 0.76 0.98 0.96 1.6895% CI 0.62–4.43 0.76–5.96 0.28–1.96 0.28–2.06 0.34–2.84 0.36–2.57 0.16–18.04

p 0.32 0.15 0.55 0.59 0.98 0.93 0.67

*One missing value in this model.ySixty-three missing values in this model.zSeventy missing values in this model.xOne hundred seventy-one missing values in this model.All of the models adjusted by: Gender (female vs. male), education (<10 vs. �10 yr.), medical coverage (partial or no coverage vs. full medical coverage),

age at SLE diagnosis (>27 vs. �27 yr), delay to SLE diagnosis (�6 vs. <6 mo) follow-up (�20 vs. <20 mo), number of hospitalizations (�1 vs. 0), maritalstatus (married or free union vs. all others), socioeconomic status (lower middle/lower vs. all others), and country (factored as dummy variables: Argentina vs.Mexico, Argentina vs. Brazil, Argentina vs. Colombia, Argentina vs. Chile, and Argentina vs. other countries).




Mortality could be predicted in a stepwise logistic

regression model by the following: education (<10 yr vs.

�10 yr; OR, 3.2; 95% CI, 1.3–7.6), SLICC score (�1 vs. 0;

OR, 2.8; 95% CI, 1.2–6.4), time of follow-up (�20 mo vs.

<20 mo; OR, 0.26; 95% CI, 0.10–0.65), marital status (single

vs. others; OR, 2.4; 95% CI, 1.0–5.7), medical coverage

(partial or no coverage vs. full medical coverage; OR, 2.7;

95% CI, 1.1–6.5), and country (Argentina vs. the rest; OR,

3.0; 95% CI, 1.3–7.1).

DISCUSSIONWe describe the GLADEL cohort, a multicenter, mul-

tinational, prospective inception cohort of Latin American

SLE patients seen in their countries of origin and treated by

their local physicians. Both the size and origin of this cohort

make it unique. An effort was made to keep equilibrium so

no single group with a large number of patients would

predominate and introduce a bias. Data were entered into a

user-friendly database that requires no writing and crosses

language barriers, thus allowing participation of Portuguese-

speaking groups. Throughout the study, a supervising group

conducted quality control of the data entered, facilitated by

built-in characteristics of the database that detect contra-

dictions. In addition, individuals coordinating the cohort were

in regular communication and had periodic meetings to set

policies and define variables and terms. The ultimate size of

the cohort will now be predetermined in order to have patient

representation from each country according to its population.

Latin America is a large subcontinent rich in the

variety of racial admixtures between and within countries. In

TABLE 6. Comparison of Sociodemographic Characteristics and Clinical Manufestations of SLE Between White and Mestizos fromArgentina and Mestizos from Mexico

Ethnic Group

Argentine

White

(n = 260)

Argentine

Mestizo

(n = 54)

Mexican

Mestizo

(n = 236) Overall

Comparison AW vs. AW vs. AM vs.

Variable (%) (%) (%) (p*) AM MM MM

GenderFemale 90.0 88.9 87.3 0.6280 0.8056 0.3945 1.000

Male 10.0 11.1 12.7

Socioeconomic statusUpper/upper middle 11.5 0.0 8.0 <0.0001 <0.0001 <0.0001 0.0149

Middle 44.6 11.1 19.5

Lower middle/lower 43.9 88.9 72.5

Medical insuranceFull medical coverage 54.1 33.3 47.0 0.0154 0.0069 0.1266 0.0708

Partial or no coverage 45.9 66.7 53.0

EducationYears of education

(mean, SD)10.8 (4.1) 8.9 (3.2) 9.1 (3.6) <0.0001 0.0017 <0.0001 0.9386

Clinical manifestationFever 62.7 57.4 46.6 0.0014 0.5385 0.0004 0.1735

Polyadenopathy 17.7 22.2 10.6 0.0226 0.4433 0.0288 0.0388

Photosensitivity 53.8 33.3 52.5 0.0201 0.0070 0.7875 0.0153

Oral/nasal ulcers 39.2 29.6 50.0 0.0064 0.2178 0.0186 0.0097

Livedo reticularis 7.7 14.8 14.8 0.0273 0.1135 0.0145 1.0000

Sicca syndrome 13.8 11.1 6.8 0.0329 0.8259 0.0122 0.2643

Persistent proteinuria/cell cast

35.8 51.8 53.0 <0.0001 0.0317 0.0001 0.8813

Psychosis 1.5 7.4 5.9 0.0108 0.0322 0.0140 0.7537

Hemolytic anemia 15.8 18.5 6.8 0.0018 0.6849 0.0018 0.0141

Lymphopenia 48.1 64.8 66.9 <0.0001 0.0356 <0.0001 0.7520

Abbreviations: AW, Argentine white; AM, Argentine mestizo; MM, Mexican mestizo.*Bold numbers indicate significant differences of clinical manifestaions.

Pairwise Comparison (p*)




addition, socioeconomic, educational, and demographic var-

iations are prominent, and these sometimes are related to

ethnic groups through their economic predominance. Thus,

the apparent homogeneity of Latin Americans is a myth, and

within the subcontinent lies great diversity. The reason for

the apparent homogeneity is the predominance of 2 related

languages, Spanish and Portuguese, which has resulted in

the unfortunate terminology of ‘‘Hispanic’’ based mainly

on the former language being spoken by many Latin

Americans and their descendants now living in English-

speaking North America.

The influences of ethnic, social, and demographic

variables on the clinical characteristics of SLE patients have

already been demonstrated by other studies. Thus, in series

from both the United States and Europe, more severe disease

was noticed in nonwhite patients5,10,28,29,45,56,63,72,74,75,80. In

our study, both ALA and mestizos had more severe disease

than did whites, as evidenced by a higher frequency of renal

disease, pericarditis, polyadenopathy, and discoid lesions in

ALA. In addition, both ALA and mestizos had higher

maximum disease activity indices than whites, but this was

lost when controlled by country. However, damage scores

tended to be lower in ALA than in both mestizos and whites,

a surprising finding that might be explained by shorter

disease duration or by the more recent incorporation of

Brazilian and Cuban groups into the study. Longer follow-up

in the GLADEL cohort may help determine if ethnicity does

actually play a role in the resulting damage from SLE.

A peculiar observation was that of a significantly lower

frequency of both xerophthalmia and sicca syndrome (both

TABLE 7. Comparison of Argentine Whites and Mestizos and Mexican Mestizos, Multivariate Models Obtained by MultipleLogistic Regression Analysis

Ethnic Group

Model*

Renal

Damage

Yes vs. No

(n = 245/

n = 304)

Model*

Lymphopenia

Yes vs. No

(n = 318/

n = 231)

Modely

SLEDAI

�12 vs. <12

(n = 217/

n = 299)

Modely

MEX-SLEDAI

�8 vs. <8

(n = 234)/

n = 282

Modelz

SLICC

�1 vs. 0

(n = 184/

n = 292)

Model*

ACR Criteria

�6 vs. <6

(n = 284/

n = 265)

Model*

Death

Yes vs. No

(n = 20/

n = 529)

Argentinewhite(n = 260)

OR 1.0 1.0 1.0 1.0 1.0 1.0 1.0

ArgentineMestizo(n = 54)

OR 1.68 2.37 0.77 0.54 0.97 0.53 2.9795% CI 0.86–3.28 1.23–4.60 0.39–1.53 0.27–1.09 0.48–1.95 0.27–1.01 0.71–12.4

p 0.13 0.01 0.45 0.09 0.93 0.06 0.14

MexicanMestizo(n = 236)

OR 1.93 2.36 0.87 1.30 0.71 0.57 0.4195% CI 1.27–2.92 1.57–3.53 0.56–1.34 0.84–2.00 0.45–1.13 0.38–0.85 0.12–1.33

p 0.002 <0.0001 0.52 0.24 0.15 0.007 0.14

*One missing value in this model.yThirty-four missing values in this model.zSeventy-four missing values in this model.All of the models adjusted by: gender (female vs. male), education (<10 vs. �10 yr), medical coverage (partial or no coverage vs. full medical coverage),

age at SLE diagnosis (>27 vs. �27 yr), delay to SLE diagnosis (�6 vs. <6 mo), follow-up (�20 vs. <20 mo), number of hospitalizations (�1 vs. 0), maritalstatus (single vs. all others), and socioeconomic status (lower middle/lower vs. all others).

TABLE 8. Maximum Activity and Damage Scores in Patients in GLADEL Cohort. Comparisons Between the 3 Major Ethnic Groups

Ethnic Group Overall

ComparisonPairwise Comparison (p)

Variable Total White Mestizo ALA (p) W vs. M W vs. ALA M vs. ALA

Maximum meanSLEDAI*(SD)

13.1 (8.3) 12 (7.9) 13.5 (8.5) 15.6 (8.9) <0.0001 0.0033 <0.0001 0.0078

Maximum meanMEX-SLEDAI*(SD)

7.9 (4.8) 7.1 (4.8) 8.4 (4.6) 8.6 (4.9) <0.0001 <0.0001 0.0014 0.9851

Maximum meanSLICC/ACR*(SD)

0.58 (1.1) 0.60 (1.1) 0.59 (1) 0.42 (1) 0.0252 0.6874 0.0175 0.0070

Abbreviations: See previous tables.*Average of the maximum score for each patient.




xerophthalmia and xerostomia) in ALA than in mestizos and,

particularly, in whites. This was not apparently related to a

lower frequency of anti-Ro and anti-La in ALA.

Mestizos and ALA had lower socioeconomic status,

fewer years of formal education, and less accessibility to

medical care than did whites, and these socioeconomic factors

TABLE 9. Cumulative Immunologic Findings of SLE Patients in the GLADEL Cohort. Total and By Ethnic Groups

Variable

Total

+/tested

Ethnic Group

White Mestizo ALAOverall

Pairwise Comparison*

(%) (%) (%) (%) Comparison* W vs. M W vs. ALA M vs. ALA

Autoantibodies

ANA 1,137/1,161 97.9 99.4 95.9 99.3 <0.0001 0.0001 1.0000 0.0368

High-anti-dsDNA 664/942 70.5 67.2 74.6 69.5 0.0674 0.0249 0.6594 0.2834

Anti-U1-nRNP 236/459 51.4 49.3 54.2 52.2 0.6539 0.3915 0.7075 0.7890

Anti-Sm 267/552 48.4 47.1 48.8 50.0 0.8693 0.7680 0.6502 0.9028

Anti-Ro (SSA) 247/506 48.8 50.2 46.5 47.5 0.7552 0.5305 0.7247 0.8968

Anti-LA (SSB) 141/483 29.2 26.1 31.4 35.0 0.2162 0.2883 0.1116 0.5764

Lupus anticoagulant 59/194 30.4 38.1 24.7 8.3 0.0411 0.0739 0.0543 0.2840

Anti-IgG anticardiolipin 262/518 50.6 55.0 48.7 41.4 0.1043 0.2108 0.0576 0.3286

Anti-IgM anticardiolipin 177/452 39.2 41.4 42.6 27.5 0.0783 0.8312 0.0472 0.0362

Complement

Low C3 431/875 49.3 53.6 42.1 59.4 <0.0001 0.0018 0.3128 0.0024

Low C4 462/859 53.8 60.7 42.3 64.9 <0.0001 <0.0001 0.4822 0.0003

Low CH50 239/418 57.2 59.7 43.5 65.1 0.0077 0.0095 0.4390 0.0044

Low complement (any) 671/975 68.8 70.1 63.7 80.3 0.0013 0.0566 0.0289 0.0006

*Bold Numbers indicate significant differences.

TABLE 10. Comparison of Cumulative Treatment Regimens in the 3 Major Ethnic Group

Variable Total

Ethnic Group

White Mestizo ALAOverall

Pairwise Comparison*

(%) (%) (%) Comparison* W vs. M W vs. ALA M vs. ALA

Corticosteroids 91.8 90.9 92.4 92.8 0.6503 0.43338 0.6215 1.0000

Oral corticosteroid 91.4 90.1 92.2 92.8 0.4296 0.2750 0.4247 1.0000

Pulses of methylprenisolone 22.8 24.8 24.8 24.3 0.2026 0.0891 0.3092 1.0000

Antimalarials 74.7 74.7 73.6 79.6 0.3231 0.6719 0.6215 0.1389

Hydroxicloroquine 34.5 45.2 27.4 24.3 <0.0001 <0.0001 <0.0001 0.5333

Chloroquine 46.5 35.9 52.0 61.8 <0.0001 <0.0001 <0.0001 0.0340

Immunosuppressive agents 46.9 39.8 53.8 47.4 <0.0001 0.0001 0.1108 0.1684

Azathioprine 19.8 16.8 21.6 23.0 0.0737 0.0499 0.0931 0.7392

Methotrexate 7.8 5.7 10.4 6.6 0.0168 0.0064 0.6964 0.2105

Oral cyclophosphamide 2.1 1.4 3.2 1.3 0.1399 0.0635 1.0000 0.2740

IV cyclophasphamide 29.2 25.4 32.2 31.6 0.0436 0.0169 0.1446 0.9218

Dialysis 3.5 2.0 4.8 3.3 0.0370 0.0164 0.3546 0.5110

Peritoneal dialysis 0.7 0.0 1.5 0.0 0.0067 0.0078 0.2106

Hemodialysis 3.2 2.0 4.5 3.3 0.0725 0.0240 0.3546 0.6506

Renal transplant 0.2 0.2 0.2 0.0 0.8630 1.0000 1.0000 1.0000

Abbreviations: See previous tables.*Bold Numbers indicate significant differences.




may have a bearing on the more severe disease found in

nonwhites. The possible association of more severe disease

and social inequalities has also been recorded in ‘‘Hispanics’’

in the United States10 and North American Indians50,53.

Possible genetic factors associated with ethnicity such

as FcgRIIA gene alleles could be influencing this, since

these may be a risk factor for lupus nephritis in African

Americans67. The finding made here that ALA have

significantly shorter delay to diagnosis than both mestizos

and whites may indicate that they have an inherently more

severe disease from early onset, thus prompting earlier diag-

noses despite their aforementioned deficiencies in accessi-

bility to medical care and socioeconomic status.

Health disparities in SLE have led to the analysis of the

interplay of socioeconomic status, ethnicity, education, and

psychosocial and behavioral variables in contributing to poor

outcome10,25,29,43,44,56,72,82. A proposal has been made to

identify mediators to target interventions designed to reduce

such health disparities in SLE, and the observations recorded

here and more yet to arise from the continued follow-up of

the GLADEL cohort may contribute to this goal.

Physicians and health authorities in Latin America, as

well as those of the United States, Canada, and European

countries where Latin American individuals live, should be

aware that social inequities may result in increased severity of

a disease that, due to its pathogenic complexity, might seem

remote from these seemingly mundane factors. It is already a

well-known fact that an epidemiologic transition has occurred

in Latin America, where infectious and parasitic diseases,

once commonly thought to be more prevalent, have been

surpassed by chronic diseases such as SLE. Interest in SLE by

Latin American physicians and investigators thus is not

gratuitous and is supported by the findings presented herein.

An interesting observation was obtained from the

comparison between Argentine mestizos and whites and their

comparison with Mexican mestizos. The geographic distance

between the 2 countries and the lack of a significant mi-

gration between the 2 indicate that their resulting similarities

may be truly ethnic and probably of an ancient origin, albeit

influenced by socioeconomic similarities. Except for lower

frequencies of polyadenopathy, serositis, and hemolytic

anemia in Mexican mestizos, these tended to be more akin

to Argentine mestizos than to Argentine whites.

Another interesting observation from our study resulted

from multiple logistic regression analysis that provided a

model for maximal disease activity in SLE. It included lesser

formal education, partial or lack of medical coverage, older

age at onset, and longer follow-up. Conversely, longer delay

to diagnosis and longer disease duration seemed to have a

protective effect, possibly by reflecting milder disease and

a decrease in disease activity with time, respectively.

TABLE 11. General Characteristics of Patients in GLADEL Cohort Who Died

Variable Dead (n = 34) Alive (n = 1,180) p

Mean age at time of death/last follow-up (yr) 30 31.9 0.1473

Median of disease duration (mos) 18.5 32.5 0.0008

Sociodemographic

Ethnic group

White (%) 16 (47.1) 491 (41.6) 0.755

Mestizo (%) 14 (41.2) 523 (44.3)

African-Latin American (%) 3 (8.8) 149 (12.6)

Other (%) 1 (2.9) 17 (1.4)

Female (%): Male (%) 29 (85): 5 (15) 1062 (90): 118 (10) 0.3807

Mean years of education (SD) 7.8 (3.7) 10 (4.1) 0.0026

Lower socioeconomic level (lower & lower middle) (%) 24 (70.6) 720 (61) 0.2886

Without medical coverage (%) 13 (38.2) 201 (17) 0.0014

Index

Maximum mean SLEDAI (SD) 19.6 (13.3) 13 (8.1) 0.0041

Maximum mean MEX-SLEDAI (SD) 11.7 (7.9) 7.8 (4.6) 0.0075

Maximum mean SLICC/ACR (SD) 2.1 (2.6) 0.54 (1) 0.0003

TABLE 12. Cause of Death of SLE Patients in the GLADELCohort

Cause of Death No. (%)

SLE activity + infection 15 (44)

SLE activity 12 (35)

Infection 5 (15)

Neoplasia 1 (3)

Unknown 1 (3)

Total 34 (100)




Although our cohort is still young, it is thought pro-

voking that patients who died within the GLADEL cohort

had both poorer education and poorer medical coverage than

those who are still alive. As in other series2,3,24,57,66,76,77,82,

infection was an important cause of death of lupus patients in

the GLADEL cohort. A bimodal mortality pattern has been

shown in systemic lupus erythematosus76. Our inception

cohort has a median follow-up of 32 months. This was a

limiting factor for reporting events that occur later in the

disease course (second peak of mortality) such as cardio-

vascular mortality. The study of this would be among the

objectives of continued follow-up of this cohort. In addition,

SLE is a paradigm of complex disease where predisposing

disease-modifying and pharmacodynamic-influencing genes

interplay with environmental and hormonal factors to cause

an extraordinary heterogeneity12. Continued analysis of a

cohort with the characteristics described herein, ideally with

the concurrent study of participating genes, may help us to

dissect and, in time, understand the role of each in its

diversity. A recent observation of the association with SLE

of allele A of the SNP PD-1.3 of the PD-1 gene in the 2q37.3

chromosome region in Europeans, less frequent in Mexicans

and practically absent in African-Americans, suggests it to

be a recent mutation affecting mostly Europeans and, to a

lesser extent, populations admixed with them59. Analyses of

European populations and their admixtures within the Latin

American subcontinent as included in the GLADEL cohort

for associations with this and other genetic markers may help

determine the role of the various lupus-predisposing genes in

the clinical spectrum of SLE.

Our analysis of 3 major ethnic groups in this Latin

American lupus cohort gives us further insight into the role

of ethnicity and the associated social inequalities on the

clinical manifestations and outcomes of lupus. We also see

clearly that, at least as pertaining to SLE, ‘‘Hispanics’’ is not

a homogenous group.

The differences between Hispanic whites, mestizos,

and ALA are well recognized in patients living in Latin

America. However the differences should be of particular

relevance for Latin American patients living in North

America or Europe, where most of these patients would be

grouped together as Hispanics. The differences found in the

GLADEL cohort should be helpful for diagnosis and treat-

ment of these patients, since their clinical behavior may

be different. When a physician encounters a ‘‘Hispanic’’

patient with SLE, further efforts to characterize his or her

country of origin and ethnic background should provide

useful information.

ACKNOWLEDGMENTS

The authors express their gratitude to Daniel Wojdyla

for assistance in handling the database and statistical analysis

of the GLADEL cohort and to Daniel Villalba and Leonardo

Grasso for assistance with the software ARTHROS 6.0.

APPENDIX: GLADEL CO-AUTHORS

Coordinators: Bernardo A. Pons-Estel and Donato

Alarcon-Segovia. The following participants are members of

Grupo Latinoamericano de Estudio del Lupus (GLADEL)

and have incorporated at least 20 patients into the database.

ARGENTINA: Patricia M. Imamura, Seccion Reuma-

tologıa, Servicio de Clınica Medica Hospital Italiano,

Buenos Aires; Jorge A. Manni, Sebastian Grimaudo, and

Judith Sarano, Departamento de Inmunologıa, Instituto de

Investigaciones Medicas ‘‘Alfredo Lanari’’, Buenos Aires;

Jose A. Maldonado-Cocco, Maria S. Arriola, and Graciela

Gomez, Servicio de Reumatologıa, Instituto de Rehabilita-

cion Psicofısica, Buenos Aires; Mercedes A. Garcıa, Ana

Ines Marcos, and Juan Carlos Marcos, Servicio de Reuma-

tologıa, Hospital Interzonal General de Agudos General San

Martın, La Plata; Hugo R. Scherbarth, Pilar C. Marino, and

Estela L. Motta, Servicio de Reumatologıa, Hospital

Interzonal General de Agudos ‘‘Dr. Oscar Alende’’ Mar

del Plata; Cristina Drenkard, Susana Gamron, and Carlos M.

Onetti, Servicio de Reumatologıa, UHMI1, Hospital Nacio-

nal de Clınicas, Cordoba; Veronica Saurit, Servicio de

Reumatologıa, Hospital Privado, Centro Medico de Cordoba,

Cordoba; Norberto Quagliatto, Alberto A. Gentiletti, and

Daniel Machado, Servicio de Reumatologıa, Hospital

Provincial de Rosario, Rosario; Marcelo Abdala and Simon

Palatnik, Servicio de Reumatologıa, Hospital Provincial del

Centenario, Rosario; Guillermo Berbotto and Carlos A.

Battagliotti, Servicio de Reumatologıa Hospital Escuela Eva

Peron, Granadero Baigorria, Rosario, Argentina.

BRAZIL: Emilia Sato, Elaine M. C. Sella, and

Alexandre W. S. Souza, Disciplina de Reumatologıa, Uni-

versidade Federal da Sao Paulo (UNIFESP), Sao Paulo; Lilian

T. Lavras Costallat, Manoel Barros Bertolo, and Ibsen Bellini

Coimbra, Divisao de Reumatologıa, Faculdade de Ciencias

Medicas, Universidade Estadual da Campinas, Campinas;

Eduardo Ferreira Borba Neto and Eloisa Bonfa, Divisao de

Reumatologıa, Faculdade da Medicina, Universidade da Sao

Paulo, Sao Paulo; Joao Carlos Tavares Brenol, Ricardo

Xavier, and Joao Marasca, Servico de Reumatologıa, Hospital

da Clinicas da Porto Alegre, Universidade Federal do Rio

Grande do Sul; Fernando de Souza Cavalcanti, Angela Luzia

Branco Duarte, and Claudia Diniz Lopes Marques, Disciplina

de Reumatologıa, Centro de Ciencias da Saude, Universidade

Federal da Pernambuco, Pernambuco; Nilzio Antonio Da

Silva, Ana Carolina de O. e Silva, and Tatiana Ferracine

Pacheco, Servico da Reumatologıa, Faculdade de Medicina,

Universidade Federal de Goias, Goiania.

COLOMBIA: Jose Fernando Molina-Restrepo, Servi-

cio de Reumatologıa, Hospital Pablo Tobon Uribe, and Javier

Molina-Lopez, Seccion de Reumatologıa, Universidad de




Antioquia, Hospital Universitario San Vicente de Paul,

Medellın; Antonio Iglesias-Gamarra, Facultad de Medicina,

Universidad Nacional de Colombia, and Antonio Iglesias-

Rodrıguez, Servicio de Reumatologıa, Hospital San Juan de

Dios, Facultad de Medicina, Universidad Nacional, Bogota;

Eduardo Egea-Bermejo, Departamento de Inmunologıa,

Universidad del Norte, Barranquilla; Oscar Uribe-Uribe, Luis

A. Ramırez, and Oscar Felipe, Seccion de Reumatologıa,

Universidad de Antioquia, Hospital Universitario San Vicente

de Paul, Medellın; Renato A. Guzman-Moreno and Jose F.

Restrepo-Suarez, Departamento de Medicina Interna e Inmuno-

Reumatologıa, Clınica Saludcoop 104 y Hospital San Juan

de Dios, Facultad de Medicina, Universidad Nacional de

Colombia, Bogota.

CUBA: Marlene Guibert-Toledano, Gil Alberto Reyes-

Llerena, and Alfredo Hernandez-Martınez, Servicio de

Reumatologıa, Centro de Investigaciones Medico Quirurg-

icas (CIMEQ), La Habana.

CHILE: Loreto Massardo, Nestor Gareca, and Sergio

Jacobelli, Departamento de Inmunologıa Clınica y Reuma-

tologıa, Escuela de Medicina, Pontificia Universidad Cato-

lica de Chile, Santiago; Oscar J. Neira, Leonardo R. Guzman,

and Marıa A. Alvarado, Seccion Reumatologıa, Hospital

del Salvador, Facultad de Medicina, Universidad de Chile,

Santiago.

GUATEMALA: Abraham Garcıa-Kutzbach, Ivette

Castro-Ampie, and Cesar Garcia, Servicio de Reumatologıa,

Hospital Universitario Esperanza, Ciudad de Guatemala.

MEXICO: Virginia Pascual-Ramos, Departamento de

Inmunologıa y Reumatologıa, Instituto Nacional de Ciencias

Medicas y Nutricion Salvador Zubiran, Mexico DF; Leonor

A. Barile-Fabris and Juan Manuel Miranda-Limon, Departa-

mento de Reumatologıa, Hospital de Especialidades, Centro

Medico Nacional La Raza, Instituto Mexicano de Seguro

Social, Mexico DF; Mary-Carmen Amigo and Luis H.

Silveira, Departamento de Reumatologıa y Departamento de

Bioquımica, Instituto Nacional de Cardiologıa Ignacio

Chavez, Mexico DF; Ignacio Garcıa De La Torre, Gerardo

Orozco-Barocio, and Magali L. Estrada-Contreras, Departa-

mento de Inmunologıa y Reumatologıa, Hospital General de

Occidente de la Secretarıa de Salud, Guadalajara, Jalisco;

Maria Josefina Sauza del Pozo, Laura E. Aranda Baca, and

Adelfia Urenda Quezada, Servicio de Reumatologıa, Insti-

tuto Mexicano de Seguro Social, Hospital de Especialidades

No 25, Monterrey, NL; Guillermo F. Huerta-Yanez, Servicio

de Reumatologıa, Hospital de Especialidades Miguel Hidal-

go, Aguascalientes.

PERU: Eduardo M. Acevedo-Vasquez, Jose Luis Alfaro-

Lozano, and Jorge M. Cucho-Venegas, Servicio de Reumato-

logıa, Hospital Nacional Guillermo Almenara Irigoyen,

ESSALUD, Lima; Maria Ines Segami, Cesar A. Ugarte, and

Felipe E. Becerra, Servicio de Reumatologıa, Hospital

Nacional Edgardo Rebagliatti Martins, ESSALUD, Lima.

VENEZUELA: Rosa Chacon-Dıaz and Soham Al Snih

Al Snih, Servicio de Reumatologıa, Centro Nacional de

Enfermedades Reumaticas, Hospital Universitario de Cara-

cas, Caracas; Maria H. Esteva-Spinetti and Jorge Vivas,

Unidad de Reumatologıa, Hospital Central de San Cristobal,

San Cristobal.

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the gladel multinational latin american prospective inception cohort of 1,214 patients with systemic...

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