The GLADEL Multinational Latin American Prospective
Inception Cohort of 1,214 Patients With Systemic
Lupus Erythematosus
Ethnic 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 Alarcón-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 populations. A prospective multinational inception cohort should prove
useful in identifying the influence of ethnicity on the clinical characteristics 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 mestizos, 507 were white, and 152 were African-Latin American (ALA).
From Servicio de Reumatologı́a (BAP-E), Hospital Escuela Eva Perón,
Granadero Baigorria, Rosario, Argentina; Sección Reumatologı́a (LJC,
ERS), Servicio de Clı́nica Médica Hospital Italiano, Buenos Aires,
Argentina; Departamento de Inmunologı́a y Reumatologı́a (MHC, DA-S)
and Unidad de Epidemiologı́a Clı́nica (ARV), Instituto Nacional de
Ciencias Médicas y Nutrición Salvador Zubirán, México DF, México;
Servicio de Reumatologı́a (SG), Hospital Provincial de Rosario, Rosario,
Argentina; Servicio de Reumatologı́a (IA), Centro Nacional de
Enfermedades Reumáticas, Hospital Universitario de Caracas, Caracas,
Venezuela; and Servicio de Reumatologı́a (FC, AA), Hospital Privado,
Centro Medico de Córdoba, Córdoba, 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
(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)
Abbreviations: ACR = American College of Rheumatology, ALA =
African-Latin American, SLE = systemic lupus erythematosus.
INTRODUCTION
S
ystemic lupus erythematosus (SLE) is a complex autoimmune disease that may result from the interplay of
genetic, hormonal, and environmental factors12. Although
its prognosis has improved remarkably in the past decades22,23,30, it remains a potentially serious condition. Several
studies have shown that some sociodemographic characteristics 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,
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Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
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, socioeconomic status, and disease-related variables in 3 ethnic groups
in clinical centers in the United States4–8,10,11,15,27,64,84. Important differences were detected in clinical and immunogenetic 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 derived from their language rather than their ethnic background, 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 characteristics 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, national, and sociodemographic variables.
PATIENTS AND METHODS
The GLADEL study held an investigator meeting in
1997 in Mexico City. During 4 days, participants developed
2
a common protocol, consensus definitions, and selected outcome measures, and received direct training in the database
software.
Center Selection
The 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 experience 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 Selection
In 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 rheumatologist 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 mandatory. After incorporating the initial 30 patients, each group
continued to include 1 new randomly selected patient per
month diagnosed within the previous 2 years.
Database
All 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 rheumatology 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 communication with the submitting center for any queries arising.
Clinical Information
Each 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
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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 Collaborating 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 Variables
Ethnic groups: An operational definition was necessary. It was developed by consensus including an expert in
immunogenetics. These definitions were determined according 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 evaluated 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 neighborhood quality. Each variable has 5 categories with independent and progressive scores. A final score classifies
subjects in 5 categories: high, medium-high, medium, medium-low, and low.
Type of medical care was divided into the following
categories:
Institutional: patients treated primarily in public institutions. 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 insurancepaid support. Without coverage: patients who pay for their
private care.
Education: we considered from 0 (illiterate) up to 20
years of formal education.
Lupus in Latin America: GLADEL Inception Cohort
Laboratory Studies
Studies were done in the standard routine laboratory at
each center. Autoantibodies and complement tests were performed 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 Analysis
Overall 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 assessed by means of the chi-square statistic. When a significant 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.
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Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
RESULTS
Composition of the GLADEL Cohort
At 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 Characteristics
Table 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 Data
Analysis 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 diagnosis 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 significantly 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 followup than whites, but again, this could reflect the earlier
entrance of several Argentine groups.
Clinical Features
Table 4 shows the clinical manifestations of the entire
cohort both at onset and cumulative to cutoff time. All
manifestations analyzed increased in frequency with followup. 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 significantly more frequent in ALA than in either whites or
mestizos. On the other hand, livedo reticularis was significantly more frequent in mestizos than in ALA. Xerophthalmia and sicca syndrome were less frequent in ALA than
in the other 2 groups.
Renal disease was significantly more frequent in mestizos 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
Country
Total
Argentina
Variable
No.
(%) No.
(%)
Gender
Female
Male
1091
123
89.9 284
10.1 32
90
10
Ethnic group
White
507 41.8 260
Mestizo
537 44.2 54
African-Latin 152 12.5
1
American
Other
18
1.5
1
Total
1,214 100.0 316
Brazil
Colombia
Cuba
Chile
No. (%) No. (%) No. (%) No.
(%)
Guatemala
Mexico
Peru
Venezuela
No. (%) No. (%) No. (%) No. (%)
188
19
91
9
133
17
89
11
25
2
93
7
88
7
93
7
25
4
86
14
218
30
88
12
55
7
89
11
75
5
94
6
82 124
17.1
3
0.3 77
60
1
37
34
92
20
23
61
13
22
0
5
81
0
18
33
61
0
35
1
64
28
0.0 0
3
97
0
8
236
0
3
95
0
1
55
1
2
89
2
24
8
48
30
10
60
0.3
3
26* 207
1
4
17* 150
3
0
12* 27
0
1
2* 95
1
8*
0
29
0
4
2* 248
2
5
20* 62
8
0
5* 80
0
7*
*Of the total cohort.
4
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Lupus in Latin America: GLADEL Inception Cohort
TABLE 2. Sociodemographic Characteristics of the GLADEL Cohort in the 3 Major Ethnic Groups
Ethnic Group
White
(n = 507)
Variable
Gender
Female
Male
Residence*
Urban
Rural
Marital Status*
Single
Separated/divorced/
widowed
Married
Socioeconomic status
Upper/upper middle
Middle
Lower middle/lower
Medical insurance*
Full medical coverage
Partial or no coverage
Education
Years of education
Mestizo
(n = 537)
ALA
(n = 152)
Pairwise Comparison (p)
Overall
Comparison (p)
No.
(%)
No.
(%)
No.
(%)
W vs. M
W vs. ALA
M vs. ALA
460
47
90.7
9.3
478
59
89.0
11.0
136
16
89.5
10.5
0.6578
0.4123
0.6387
1.0000
480
27
94.7
5.3
463
72
86.5
13.5
146
6
96.0
4.0
<0.0001
<0.0001
0.6716
0.0007
252
17
50.5
3.4
259
21
48.2
3.9
86
5
56.6
3.3
0.5270
0.7323
0.4155
0.2024
230
46.1
257
47.9
61
40.1
81
165
261
16.0
32.5
51.5
37
149
351
6.9
27.7
65.4
12
19
121
7.9
12.5
79.6
<0.0001
<0.0001
<0.0001
0.0002
360
146
Mean
10.4
71.1
28.9
SD
4.3
281
256
Mean
9.7
52.3
47.7
SD
3.8
101
51
Mean
9.2
66.4
33.6
SD
4.4
<0.0001
<0.0001
0.2685
0.0022
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.
and consequently the sum of renal manifestations was also
more frequent in mestizos. We observed that ALA had a significantly higher frequency of nephrotic syndrome than did
whites. Interestingly, nephrotic syndrome was not significantly 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 damage 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 from
Argentina and Mestizos from Mexico
Because of the aforementioned clinical differences observed 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 possible differences between a major ethnic group from 2 different countries, we compared mestizos from Argentina and
Mexico. The data are shown in Table 6, where sociodemographic 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 significantly 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 Scores
A 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
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Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
FIGURE 1.
Age distribution of patients at diagnosis in the 3 major ethnic groups.
MEX-SLEDAI did not reach significance between mestizo
and ALA patients. Stepwise multiple logistic regression analysis produced a large predictive model for disease activity,
defined as a score higher than 12 in SLEDAI and 8 in MEXSLEDAI. Results were similar with both indices, and therefore 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).
TABLE 3. Selected Epidemiologic Data of the GLADEL Cohort in the 3 Major Ethnic Groups
Ethnic Group
Total
(n = 1,214)
Variable
Age at
onset (yr)
Age at
diagnosis (yr)
Delay to
diagnosis (mo)
Disease
duration (mo)
Follow-up (mo)
White
(n = 507)
Mestizo
(n = 537)
ALA
(n = 152)
Pairwise Comparison
Overall
W vs.
Comparison (p) M
W vs.
ALA
M vs.
ALA
Mean
SD
Mean
SD
Mean
SD
Mean
SD
28
12
29.5
12.2
28.1
12.3
26.2
10.6
0.0077
0.0219
0.0058
0.2018
30
12
31.1
12.5
29.9
12.5
26.9
10.8
0.0010
0.0488
0.0003
0.0157
<0.0001
0.6872
<.0001
<.0001
Median Range Median Range Median Range Median Range
6
0.4–490
6.0 0.4–301
6.9 0.4–490
3.8 0.4–84
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
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.
6
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Lupus in Latin America: GLADEL Inception Cohort
TABLE 4. Clinical Manifestations of SLE Patients in the GLADEL Cohort. Comparison of the Cumulative Data between the 3 Major
Ethnic Groups
Ethnic Group
Total (n = 1,214)
Manifestation
General
Fever
Weight loss
Polyadenopathy
Systemic hypertension
Musculoskeletal
Arthralgia and/or
arthritis
Avascular bone
necrosis
Myalgia/myosis
Cutaneous
Alopecia
Photosensitivity
Malar rash (butterfly
lesions)
Discoid lesions
Oral/nasal ulcers
Livedo reticularis
Subacute cutaneous
lesions
Raynaud syndrome
Panniculitis
Any cutaneous
Ocular Lesions
Xerophthalmia
Sicca Syndrome
Uveitis/episcleritis/
scleritis
Cataracts
Respiratory
Pleuritis
Lupus pneumonitis
Pulmonary hemorrhage
Pulmonary embolism
Pulmonary hypertension
Any respiratory
Cardiovascular
Pericarditis
Myocardial involvement
Endocardial involvement
Pericardial tamponade
White
(n = 507)
Mestizo
(n = 537)
ALA
(n = 152)
Overall
At onset Cumulative Cumulative Cumulative Cumulative Comparison
(%)
(%)
(%)
(%)
(%)
(p*)
Pairwise
Comparison (p*)
W vs.
M
W vs.
ALA
M vs.
ALA
0.8507
0.6877
0.0416
0.0216
0.1966
0.0250
0.0203
0.7670
28.6
13.0
4.5
2.1
56.7
26.6
14.7
26.9
60.2
29.8
14.0
21.1
52.9
22.5
13.2
31.8
59.2
31.6
21.0
30.2
0.0492
0.0100
0.0485
0.0003
0.0208
0.0090
0.7189
<0.0001
67.3
93.2
93.5
92.5
94.1
0.7410
0.6275
1.0000 0.5953
0.0
1.1
1.2
0.9
1.3
0.8870
0.7677
1.0000 0.7530
7.6
17.5
18.5
17.3
13.2
0.3060
0.6285
0.1425 0.2642
20.3
24.5
23.6
57.6
56.1
61.3
55.0
59.8
63.3
59.0
51.8
59.0
61.2
59.2
63.2
0.2701
0.0243
0.3261
0.2109
0.0106
0.1627
0.1924 0.6414
0.9251 0.1175
1.0000 0.3991
5.3
10.5
1.8
0.7
11.8
41.7
9.9
2.8
11.2
40.6
9.5
3.3
10.4
43.2
11.5
2.2
19.7
40.1
5.9
2.6
0.006
0.6492
0.1084
0.5525
0.6911
0.4150
0.3133
0.3466
0.0092
0.9253
0.1916
0.7966
10.2
0.4
46.3
28.2
1.4
90.1
29.4
1.0
89.5
27.6
1.7
90.7
25.7
2.0
89.5
0.6335
0.5310
0.7811
0.5371
0.4235
0.6038
0.4132 0.6804
0.3944 0.7329
1.000 0.6413
0.9
1.5
0.3
6.5
8.3
1.1
7.5
9.7
1.2
5.2
8.6
1.1
1.3
3.3
0.7
0.0130
0.0440
1.0000
0.3426
0.5907
1.000
0.0013 0.0079
0.0108 0.0336
1.000
1.000
0.1
2.2
2.2
2.6
0.0
0.1380
0.6896
0.0769 0.0481
3.6
0.2
0.0
0.1
0.2
0.5
22.1
1.9
1.0
1.2
1.4
6.1
23.5
2.8
0.6
0.8
1.6
7.1
20.9
1.3
1.7
1.7
1.5
6.3
21.0
0.7
0.0
0.7
0.7
2.0
0.5755
0.1100
0.0880
0.4226
0.6910
0.0454
0.3324
0.1221
0.1457
0.2664
1.0000
0.6232
0.5831
0.2115
1.0000
1.0000
0.6924
0.0176
1.0000
1.0000
0.2184
0.6998
0.6919
0.0398
2.7
0.3
0.3
0.2
17.2
3.0
2.7
0.7
16.4
1.8
2.0
1.2
15.6
3.7
2.6
0.2
26.3
4.6
5.9
1.3
0.0070
0.0630
0.0320
0.1200
0.8001
0.0609
0.5406
0.0624
0.0087
0.0664
0.0222
1.0000
0.0039
0.6367
0.0685
0.1241
0.0034
0.5166
0.0489
0.7624
continued
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Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
TABLE 4. (continued )
Ethnic Group
Total (n = 1,214)
White
(n = 507)
Mestizo
(n = 537)
ALA
(n = 152)
Overall
At onset Cumulative Cumulative Cumulative Cumulative Comparison
(%)
(%)
(%)
(%)
(%)
(p*)
Manifestation
Vascular thrombosis
Any cardiovascular
Renal
Persistent proteinuria
and/or cellular casts
Nephrotic syndrome
Acute renal failure
Chronic renal failuire
Any renal
Neurologic
Psychosis
Seizures
Psychosis and/or seizures
Chorea
Organic brain syndrome
Transverse myelitis
Ischemic stroke
All strokes (ischemic
and/or hemorrhagic)
Cranial nerve disease
Polyneuritis
Mononeuritis multiplex
Lupus headache
Any neurologic
Digestive
Xerostomy
Peritoneal serositis
Gyneco-obstetricy
Amenorrhea
Pregnancy loss
Hematologic
Hemolytic anemia
Leukopenia
Lymphopenia
Thrombocytopenia
Any hematologic
Pairwise
Comparison (p*)
W vs.
M
W vs.
ALA
M vs.
ALA
1.4
6.3
5.6
42.9
5.3
37.1
6.3
47.1
3.9
47.4
0.5509
0.0023
0.5117
0.0011
0.6716 0.3288
0.0293 1.0000
4.5
46.0
36.7
53.3
50.7
<0.0001
<0.0001
0.0054 0.9236
1.1
0.4
0.2
5.3
6.7
3.2
1.7
51.7
5.7
1.8
0.8
43.6
6.7
4.3
2.4
58.3
10.5
3.9
2.0
55.3
0.1178
0.0483
0.0994
<0.0001
0.5447
0.0198
0.0488
<0.0001
0.0448
0.1253
0.2036
0.0123
0.1196
1.0000
1.0000
0.5164
0.5
1.6
2.1
0.1
0.0
0.1
0.6
0.6
4.0
8.1
11.4
0.4
1.9
0.6
1.6
2.8
3.0
7.9
10.3
0.6
1.2
0.4
2.0
3.3
5.2
8.6
12.7
0.2
2.6
0.7
1.5
2.6
3.9
6.6
9.9
0.7
1.3
0.7
0.7
1.3
0.1853
0.7495
0.4189
0.4470
0.2370
0.6658
0.6158
0.4374
0.0857
0.7361
0.2444
0.3605
0.1148
0.6874
0.6375
0.5850
0.5981
0.7273
1.0000
1.0000
1.0000
0.5452
0.4714
0.2706
0.6725
0.5036
0.3990
0.3928
0.5432
1.0000
0.6919
0.5432
0.2
0.3
0.1
0.2
4.1
3.6
1.2
1.1
4.4
26.4
3.7
2.2
1.2
3.7
26.4
4.1
0.7
1.1
4.7
27.7
2.0
0.0
0.7
6.6
21.7
0.5487
0.0390
0.8570
0.3162
0.3334
0.8737
0.0682
1.0000
0.5385
0.6761
0.4392
0.0769
1.0000
0.1731
0.2876
0.3246
0.5809
1.0000
0.4012
0.1456
0.7
0.1
4.0
1.3
4.3
1.4
4.1
1.3
2.6
0.7
0.6883
0.9371
0.8785
1.0000
0.4770 0.4797
0.6891 1.0000
0.4
1.6
5.3
9.3
5.6
10.9
5.4
8.2
4.4
7.3
0.8878
0.1811
0.8878
0.1811
0.6699 0.8272
0.2598 0.8589
2.4
5.1
5.9
5.2
12.5
11.8
42.3
59.3
19.2
72.5
13.0
39.6
51.1
19.1
68.2
11.5
42.6
63.5
18.2
74.3
9.9
46.7
70.4
23.0
80.3
0.5090
0.3455
<0.0001
0.4126
0.0064
0.5090 0.3277 0.6623
0.3455 0.1331 0.4046
<0.0001 <0.0001 0.1239
0.7508 0.2997 0.2008
0.0337 0.0042 0.1358
Abbreviations: See previous tables.
*Bold numbers indicate significant differences.
y
Percentages computed for the total number of women.
Damage Score
Despite the higher frequency of renal disease and higher
mean maximum disease activity indices in ALA, these patients achieved significantly lower damage scores than both
8
mestizos and whites. In the stepwise multiple logistic regression 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);
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Medicine Volume 83, Number 1, January 2004
Lupus in Latin America: GLADEL Inception Cohort
TABLE 5. Comparison of Ethnic Groups, Multivariate Models Obtained by Multiple Logistic Regression Analysis
Model*
Renal
Damage
Yes vs. No
(n = 558/
n = 655)
Model*
Lymphopenia
Yes 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)
OR
1.0
1.0
1.0
1.0
1.0
1.0
1.0
OR
95% CI
p
OR
95% CI
p
1.95
1.36–2.80
<0.0001
1.23
0.79–1.93
0.36
1.58
1.12–2.24
0.01
2.10
1.34–3.29
0.001
1.02
0.71–1.46
0.94
1.23
0.77–1.96
0.38
1.10
0.76–1.59
0.63
0.78
0.48–1.25
0.30
0.75
0.50–1.12
0.16
0.78
0.45–1.36
0.39
0.73
0.52–1.05
0.09
1.08
0.69–1.67
0.74
0.64
0.21–1.95
0.43
0.92
0.19–4.42
0.91
OR
95% CI
p
1.65
0.62–4.43
0.32
2.12
0.76–5.96
0.15
0.74
0.28–1.96
0.55
0.76
0.28–2.06
0.59
0.98
0.34–2.84
0.98
0.96
0.36–2.57
0.93
1.68
0.16–18.04
0.67
Ethnic Group
White
(n = 507)
Mestizo
(n = 537)
AfricanLatin
American
(n = 152)
Other
(n = 18)
*One missing value in this model.
y
Sixty-three missing values in this model.
z
Seventy missing values in this model.
x
One 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), marital
status (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).
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 Complement
Although 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 differences between the 3 major ethnic groups (Table 9). These
should, however, be taken with caution. Antinuclear antibodies 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.
Treatment
Cumulative 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 cyclophosphamide 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 significantly 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 availability in our countries (ie, chloroquine is less costly). Overall
immunosuppressive use was more frequent in nonwhites,
particularly significant when compared with mestizos.
Mortality
Thirty-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 socioeconomic 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.
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Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
TABLE 6. Comparison of Sociodemographic Characteristics and Clinical Manufestations of SLE Between White and Mestizos from
Argentina and Mestizos from Mexico
Ethnic Group
Variable
Gender
Female
Male
Socioeconomic status
Upper/upper middle
Middle
Lower middle/lower
Medical insurance
Full medical coverage
Partial or no coverage
Education
Years of education
(mean, SD)
Clinical manifestation
Fever
Polyadenopathy
Photosensitivity
Oral/nasal ulcers
Livedo reticularis
Sicca syndrome
Persistent proteinuria/
cell cast
Psychosis
Hemolytic anemia
Lymphopenia
Argentine
White
(n = 260)
Argentine
Mestizo
(n = 54)
Mexican
Mestizo
(n = 236)
(%)
(%)
(%)
Overall
Comparison
(p*)
Pairwise Comparison (p*)
AW vs.
AM
AW vs.
MM
AM vs.
MM
90.0
10.0
88.9
11.1
87.3
12.7
0.6280
0.8056
0.3945
1.000
11.5
44.6
43.9
0.0
11.1
88.9
8.0
19.5
72.5
<0.0001
<0.0001
<0.0001
0.0149
54.1
45.9
33.3
66.7
47.0
53.0
0.0154
0.0069
0.1266
0.0708
9.1 (3.6)
<0.0001
0.0017
<0.0001
0.9386
10.8 (4.1)
8.9 (3.2)
62.7
17.7
53.8
39.2
7.7
13.8
35.8
57.4
22.2
33.3
29.6
14.8
11.1
51.8
46.6
10.6
52.5
50.0
14.8
6.8
53.0
0.0014
0.0226
0.0201
0.0064
0.0273
0.0329
<0.0001
0.5385
0.4433
0.0070
0.2178
0.1135
0.8259
0.0317
0.0004
0.0288
0.7875
0.0186
0.0145
0.0122
0.0001
0.1735
0.0388
0.0153
0.0097
1.0000
0.2643
0.8813
1.5
15.8
48.1
7.4
18.5
64.8
5.9
6.8
66.9
0.0108
0.0018
<0.0001
0.0322
0.6849
0.0356
0.0140
0.0018
<0.0001
0.7537
0.0141
0.7520
Abbreviations: AW, Argentine white; AM, Argentine mestizo; MM, Mexican mestizo.
*Bold numbers indicate significant differences of clinical manifestaions.
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).
DISCUSSION
We describe the GLADEL cohort, a multicenter, multinational, 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
10
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 Portuguesespeaking groups. Throughout the study, a supervising group
conducted quality control of the data entered, facilitated by
built-in characteristics of the database that detect contradictions. 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
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Medicine Volume 83, Number 1, January 2004
Lupus in Latin America: GLADEL Inception Cohort
TABLE 7. Comparison of Argentine Whites and Mestizos and Mexican Mestizos, Multivariate Models Obtained by Multiple
Logistic Regression Analysis
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)
OR
1.0
1.0
1.0
1.0
1.0
1.0
1.0
OR
95% CI
p
OR
95% CI
p
1.68
0.86–3.28
0.13
1.93
1.27–2.92
0.002
2.37
1.23–4.60
0.01
2.36
1.57–3.53
<0.0001
0.77
0.39–1.53
0.45
0.87
0.56–1.34
0.52
0.54
0.27–1.09
0.09
1.30
0.84–2.00
0.24
0.97
0.48–1.95
0.93
0.71
0.45–1.13
0.15
0.53
0.27–1.01
0.06
0.57
0.38–0.85
0.007
2.97
0.71–12.4
0.14
0.41
0.12–1.33
0.14
Ethnic Group
Argentine
white
(n = 260)
Argentine
Mestizo
(n = 54)
Mexican
Mestizo
(n = 236)
*One missing value in this model.
y
Thirty-four missing values in this model.
z
Seventy-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), marital
status (single vs. all others), and socioeconomic status (lower middle/lower vs. all others).
addition, socioeconomic, educational, and demographic variations 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 Englishspeaking 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 8. Maximum Activity and Damage Scores in Patients in GLADEL Cohort. Comparisons Between the 3 Major Ethnic Groups
Total
White
Mestizo
ALA
Overall
Comparison
(p)
13.1 (8.3)
12 (7.9)
13.5 (8.5)
15.6 (8.9)
<0.0001
7.9 (4.8)
7.1 (4.8)
8.4 (4.6)
8.6 (4.9)
0.58 (1.1)
0.60 (1.1)
Ethnic Group
Variable
Maximum mean
SLEDAI*(SD)
Maximum mean
MEX-SLEDAI*(SD)
Maximum mean
SLICC/ACR*(SD)
0.59 (1)
0.42 (1)
Pairwise Comparison (p)
W vs. M
W vs. ALA
M vs. ALA
0.0033
<0.0001
0.0078
<0.0001
<0.0001
0.0014
0.9851
0.0252
0.6874
0.0175
0.0070
Abbreviations: See previous tables.
*Average of the maximum score for each patient.
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11
Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
TABLE 9. Cumulative Immunologic Findings of SLE Patients in the GLADEL Cohort. Total and By Ethnic Groups
Ethnic Group
White
Mestizo
ALA
(%)
(%)
(%)
(%)
1,137/1,161
664/942
236/459
267/552
247/506
141/483
59/194
262/518
177/452
97.9
70.5
51.4
48.4
48.8
29.2
30.4
50.6
39.2
99.4
67.2
49.3
47.1
50.2
26.1
38.1
55.0
41.4
95.9
74.6
54.2
48.8
46.5
31.4
24.7
48.7
42.6
99.3
69.5
52.2
50.0
47.5
35.0
8.3
41.4
27.5
<0.0001
0.0674
0.6539
0.8693
0.7552
0.2162
0.0411
0.1043
0.0783
431/875
462/859
239/418
671/975
49.3
53.8
57.2
68.8
53.6
60.7
59.7
70.1
42.1
42.3
43.5
63.7
59.4
64.9
65.1
80.3
<0.0001
<0.0001
0.0077
0.0013
Variable
Autoantibodies
ANA
High-anti-dsDNA
Anti-U1-nRNP
Anti-Sm
Anti-Ro (SSA)
Anti-LA (SSB)
Lupus anticoagulant
Anti-IgG anticardiolipin
Anti-IgM anticardiolipin
Complement
Low C3
Low C4
Low CH50
Low complement (any)
Total
+/tested
Pairwise Comparison*
Overall
Comparison*
W vs. M
W vs. ALA
M vs. ALA
0.0001
0.0249
0.3915
0.7680
0.5305
0.2883
0.0739
0.2108
0.8312
1.0000
0.6594
0.7075
0.6502
0.7247
0.1116
0.0543
0.0576
0.0472
0.0368
0.2834
0.7890
0.9028
0.8968
0.5764
0.2840
0.3286
0.0362
0.0018
<0.0001
0.0095
0.0566
0.3128
0.4822
0.4390
0.0289
0.0024
0.0003
0.0044
0.0006
*Bold Numbers indicate significant differences.
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 10. Comparison of Cumulative Treatment Regimens in the 3 Major Ethnic Group
Ethnic Group
White
Mestizo
ALA
Variable
Total
(%)
(%)
(%)
Overall
Comparison*
Corticosteroids
Oral corticosteroid
Pulses of methylprenisolone
Antimalarials
Hydroxicloroquine
Chloroquine
Immunosuppressive agents
Azathioprine
Methotrexate
Oral cyclophosphamide
IV cyclophasphamide
Dialysis
Peritoneal dialysis
Hemodialysis
Renal transplant
91.8
91.4
22.8
74.7
34.5
46.5
46.9
19.8
7.8
2.1
29.2
3.5
0.7
3.2
0.2
90.9
90.1
24.8
74.7
45.2
35.9
39.8
16.8
5.7
1.4
25.4
2.0
0.0
2.0
0.2
92.4
92.2
24.8
73.6
27.4
52.0
53.8
21.6
10.4
3.2
32.2
4.8
1.5
4.5
0.2
92.8
92.8
24.3
79.6
24.3
61.8
47.4
23.0
6.6
1.3
31.6
3.3
0.0
3.3
0.0
0.6503
0.4296
0.2026
0.3231
<0.0001
<0.0001
<0.0001
0.0737
0.0168
0.1399
0.0436
0.0370
0.0067
0.0725
0.8630
Pairwise Comparison*
W vs. M
0.43338
0.2750
0.0891
0.6719
<0.0001
<0.0001
0.0001
0.0499
0.0064
0.0635
0.0169
0.0164
0.0078
0.0240
1.0000
W vs. ALA
M vs. ALA
0.6215
0.4247
0.3092
0.6215
<0.0001
<0.0001
0.1108
0.0931
0.6964
1.0000
0.1446
0.3546
1.0000
1.0000
1.0000
0.1389
0.5333
0.0340
0.1684
0.7392
0.2105
0.2740
0.9218
0.5110
0.2106
0.6506
1.0000
0.3546
1.0000
Abbreviations: See previous tables.
*Bold Numbers indicate significant differences.
12
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Medicine Volume 83, Number 1, January 2004
Lupus in Latin America: GLADEL Inception Cohort
TABLE 11. General Characteristics of Patients in GLADEL Cohort Who Died
Variable
Dead (n = 34)
Mean age at time of death/last follow-up (yr)
Median of disease duration (mos)
Sociodemographic
Ethnic group
White (%)
Mestizo (%)
African-Latin American (%)
Other (%)
Female (%): Male (%)
Mean years of education (SD)
Lower socioeconomic level (lower & lower middle) (%)
Without medical coverage (%)
Index
Maximum mean SLEDAI (SD)
Maximum mean MEX-SLEDAI (SD)
Maximum mean SLICC/ACR (SD)
30
18.5
16 (47.1)
14 (41.2)
3 (8.8)
1 (2.9)
29 (85): 5 (15)
7.8 (3.7)
24 (70.6)
13 (38.2)
19.6 (13.3)
11.7 (7.9)
2.1 (2.6)
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 diagnoses despite their aforementioned deficiencies in accessibility 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
TABLE 12. Cause of Death of SLE Patients in the GLADEL
Cohort
Cause of Death
No.
(%)
SLE activity + infection
SLE activity
Infection
Neoplasia
Unknown
Total
15
12
5
1
1
34
(44)
(35)
(15)
(3)
(3)
(100)
Alive (n = 1,180)
31.9
32.5
491 (41.6)
523 (44.3)
149 (12.6)
17 (1.4)
1062 (90): 118 (10)
10 (4.1)
720 (61)
201 (17)
13 (8.1)
7.8 (4.6)
0.54 (1)
p
0.1473
0.0008
0.755
0.3807
0.0026
0.2886
0.0014
0.0041
0.0075
0.0003
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 migration 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.
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13
Medicine Volume 83, Number 1, January 2004
Pons-Estel et al
Although our cohort is still young, it is thought provoking 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 cardiovascular 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 treatment 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
14
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
Alarcón-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, Sección Reumatologı́a, Servicio de Clı́nica Médica Hospital Italiano,
Buenos Aires; Jorge A. Manni, Sebastián Grimaudo, and
Judith Sarano, Departamento de Inmunologı́a, Instituto de
Investigaciones Médicas ‘‘Alfredo Lanari’’, Buenos Aires;
José A. Maldonado-Cocco, Maria S. Arriola, and Graciela
Gómez, Servicio de Reumatologı́a, Instituto de Rehabilitación Psicofı́sica, Buenos Aires; Mercedes A. Garcı́a, Ana
Inés Marcos, and Juan Carlos Marcos, Servicio de Reumatologı́a, Hospital Interzonal General de Agudos General San
Martı́n, La Plata; Hugo R. Scherbarth, Pilar C. Marino, and
Estela L. Motta, Servicio de Reumatologı́a, Hospital
Interzonal General de Agudos ‘‘Dr. Oscar Alende’’ Mar
del Plata; Cristina Drenkard, Susana Gamron, and Carlos M.
Onetti, Servicio de Reumatologı́a, UHMI1, Hospital Nacional de Clı́nicas, Córdoba; Verónica Saurit, Servicio de
Reumatologı́a, Hospital Privado, Centro Medico de Córdoba,
Córdoba; Norberto Quagliatto, Alberto A. Gentiletti, and
Daniel Machado, Servicio de Reumatologı́a, Hospital
Provincial de Rosario, Rosario; Marcelo Abdala and Simón
Palatnik, Servicio de Reumatologı́a, Hospital Provincial del
Centenario, Rosario; Guillermo Berbotto and Carlos A.
Battagliotti, Servicio de Reumatologı́a Hospital Escuela Eva
Perón, Granadero Baigorria, Rosario, Argentina.
BRAZIL: Emilia Sato, Elaine M. C. Sella, and
Alexandre W. S. Souza, Disciplina de Reumatologı́a, Universidade Federal da São Paulo (UNIFESP), São Paulo; Lilian
T. Lavras Costallat, Manoel Barros Bertolo, and Ibsen Bellini
Coimbra, Divisao de Reumatologı́a, Faculdade de Ciencias
Medicas, Universidade Estadual da Campinas, Campinas;
Eduardo Ferreira Borba Neto and Eloisa Bonfá, Divisao de
Reumatologı́a, Faculdade da Medicina, Universidade da São
Paulo, São Paulo; João Carlos Tavares Brenol, Ricardo
Xavier, and João Marasca, Serviço de Reumatologı́a, Hospital
da Clinicas da Porto Alegre, Universidade Federal do Rio
Grande do Sul; Fernando de Souza Cavalcanti, Ângela Luzia
Branco Duarte, and Cláudia Diniz Lopes Marques, Disciplina
de Reumatologı́a, Centro de Ciencias da Saúde, Universidade
Federal da Pernambuco, Pernambuco; Nilzio Antonio Da
Silva, Ana Carolina de O. e Silva, and Tatiana Ferracine
Pacheco, Serviço da Reumatologı́a, Faculdade de Medicina,
Universidade Federal de Goias, Goiania.
COLOMBIA: José Fernando Molina-Restrepo, Servicio de Reumatologı́a, Hospital Pablo Tobon Uribe, and Javier
Molina-López, Sección de Reumatologı́a, Universidad de
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Medicine Volume 83, Number 1, January 2004
Antioquia, Hospital Universitario San Vicente de Paul,
Medellı́n; Antonio Iglesias-Gamarra, Facultad de Medicina,
Universidad Nacional de Colombia, and Antonio IglesiasRodrı́guez, Servicio de Reumatologı́a, Hospital San Juan de
Dios, Facultad de Medicina, Universidad Nacional, Bogotá;
Eduardo Egea-Bermejo, Departamento de Inmunologı́a,
Universidad del Norte, Barranquilla; Oscar Uribe-Uribe, Luis
A. Ramı́rez, and Oscar Felipe, Sección de Reumatologı́a,
Universidad de Antioquia, Hospital Universitario San Vicente
de Paul, Medellı́n; Renato A. Guzmán-Moreno and José F.
Restrepo-Suárez, Departamento de Medicina Interna e InmunoReumatologı́a, Clı́nica Saludcoop 104 y Hospital San Juan
de Dios, Facultad de Medicina, Universidad Nacional de
Colombia, Bogotá.
CUBA: Marlene Guibert-Toledano, Gil Alberto ReyesLlerena, and Alfredo Hernández-Martı́nez, Servicio de
Reumatologı́a, Centro de Investigaciones Médico Quirúrgicas (CIMEQ), La Habana.
CHILE: Loreto Massardo, Néstor Gareca, and Sergio
Jacobelli, Departamento de Inmunologı́a Clı́nica y Reumatologı́a, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago; Oscar J. Neira, Leonardo R. Guzmán,
and Marı́a A. Alvarado, Sección Reumatologı́a, Hospital
del Salvador, Facultad de Medicina, Universidad de Chile,
Santiago.
GUATEMALA: Abraham Garcı́a-Kutzbach, Ivette
Castro-Ampie, and Cesar Garcia, Servicio de Reumatologı́a,
Hospital Universitario Esperanza, Ciudad de Guatemala.
MÉXICO: Virginia Pascual-Ramos, Departamento de
Inmunologı́a y Reumatologı́a, Instituto Nacional de Ciencias
Médicas y Nutrición Salvador Zubirán, México DF; Leonor
A. Barile-Fabris and Juan Manuel Miranda-Limón, Departamento de Reumatologı́a, Hospital de Especialidades, Centro
Médico Nacional La Raza, Instituto Mexicano de Seguro
Social, México DF; Mary-Carmen Amigo and Luis H.
Silveira, Departamento de Reumatologı́a y Departamento de
Bioquı́mica, Instituto Nacional de Cardiologı́a Ignacio
Chávez, México DF; Ignacio Garcı́a De La Torre, Gerardo
Orozco-Barocio, and Magali L. Estrada-Contreras, Departamento de Inmunologı́a y Reumatologı́a, Hospital General de
Occidente de la Secretarı́a de Salud, Guadalajara, Jalisco;
Maria Josefina Sauza del Pozo, Laura E. Aranda Baca, and
Adelfia Urenda Quezada, Servicio de Reumatologı́a, Instituto Mexicano de Seguro Social, Hospital de Especialidades
No 25, Monterrey, NL; Guillermo F. Huerta-Yáñez, Servicio
de Reumatologı́a, Hospital de Especialidades Miguel Hidalgo, Aguascalientes.
PERU: Eduardo M. Acevedo-Vásquez, José Luis AlfaroLozano, and Jorge M. Cucho-Venegas, Servicio de Reumatologı́a, Hospital Nacional Guillermo Almenara Irigoyen,
ESSALUD, Lima; Maria Inés Segami, Cesar A. Ugarte, and
Felipe E. Becerra, Servicio de Reumatologı́a, Hospital
Nacional Edgardo Rebagliatti Martins, ESSALUD, Lima.
Lupus in Latin America: GLADEL Inception Cohort
VENEZUELA: Rosa Chacón-Dı́az and Soham Al Snih
Al Snih, Servicio de Reumatologı́a, Centro Nacional de
Enfermedades Reumáticas, Hospital Universitario de Caracas, Caracas; Maria H. Esteva-Spinetti and Jorge Vivas,
Unidad de Reumatologı́a, Hospital Central de San Cristóbal,
San Cristóbal.
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