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P Petrossians et al.
LAS Database: acromegaly
at diagnosis
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Acromegaly at diagnosis in 3173
patients from the Liège Acromegaly
Survey (LAS) Database
Patrick Petrossians1, Adrian F Daly1, Emil Natchev2, Luigi Maione3, Karin Blijdorp4,
Mona Sahnoun-Fathallah5, Renata Auriemma6, Alpha M Diallo7, Anna-Lena Hulting8,
Diego Ferone9, Vaclav Hana Jr10, Silvia Filipponi11, Caroline Sievers12,
Claudia Nogueira13, Carmen Fajardo-Montañana14, Davide Carvalho15, Vaclav Hana10,
Günter K Stalla12, Marie-Lise Jaffrain-Réa11, Brigitte Delemer7, Annamaria Colao6,
Thierry Brue5, Sebastian J C M M Neggers4, Sabina Zacharieva2, Philippe Chanson3
and Albert Beckers1
1Department
of Endocrinology, CHU de Liège, University of Liège, Belgium
Centre of Endocrinology and Gerontology, Medical University, Sofia, Bulgaria
3APHP Endocrinology and Reproductive Diseases, Paris Sud University, Le Kremlin-Bicêtre, France
4Section of Endocrinology, Department of Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
5Department of Endocrinology, Centre de Référence des Maladies Rares d’Origine Hypophysaire, Hôpital de la
Timone, Marseille, France
6Dipartimento Di Medicina Clinica e Chirurgia, Sezione di Endocrinologia, University “Federico II”, Naples, Italy
7Department of Endocrinology, CHU de Reims, France
8Department of Molecular Medicine and Surgery, Karolinska University Hospital, Stockholm, Sweden
9Department of Internal Medicine, University of Genoa, Genova, Italy
10Third Department of Internal Medicine, 1st Faculty of Medicine, Charles University, Prague, Czech Republic
11Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, L’Aquila, Italy and
Neuromed, IRCCS, Pozzilli, Italy
12Department of Internal Medicine, Endocrinology and Clinical Chemistry, Max Planck Institute of Psychiatry,
Munich, Germany
13Department of Internal Medicine, Endocrinology, Diabetes and Metabolism Unit, Centro Hospitalar de Trás-osMontes e Alto Douro, Portugal
14Department of Endocrinology, Hospital Universitario de la Ribera, Alzira, Spain
15Department of Endocrinology, Diabetes and Metabolism, Centro Hospitalar S. João, Faculty of Medicine, Instituto
de Investigação e Inovação em Saúde, University of Porto, Porto, Portugal
Endocrine-Related Cancer
2Clinical
Correspondence
should be addressed
to A Beckers
Email
albert.beckers@chu.ulg.ac.be
Abstract
Acromegaly is a rare disorder caused by chronic growth hormone (GH) hypersecretion.
While diagnostic and therapeutic methods have advanced, little information exists
on trends in acromegaly characteristics over time. The Liège Acromegaly Survey (LAS)
Database, a relational database, is designed to assess the profile of acromegaly patients
at diagnosis and during long-term follow-up at multiple treatment centers. The
following results were obtained at diagnosis. The study population consisted of 3173
acromegaly patients from ten countries; 54.5% were female. Males were significantly
younger at diagnosis than females (43.5 vs 46.4 years; P < 0.001). The median delay from
first symptoms to diagnosis was 2 years longer in females (P = 0.015). Ages at diagnosis
and first symptoms increased significantly over time (P < 0.001). Tumors were larger in
males than females (P < 0.001); tumor size and invasion were inversely related to patient
age (P < 0.001). Random GH at diagnosis correlated with nadir GH levels during OGTT
(P < 0.001). GH was inversely related to age in both sexes (P < 0.001). Diabetes mellitus was
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Key Words
f acromegaly
f comorbidity
f database
f data mining
f diagnosis
f growth hormone
f IGF-1
f pituitary adenoma
f symptoms
This work is licensed under a Creative Commons
Attribution 3.0 Unported License.
Research
P Petrossians et al.
LAS Database: acromegaly
at diagnosis
present in 27.5%, hypertension in 28.8%, sleep apnea syndrome in 25.5% and cardiac
hypertrophy in 15.5%. Serious cardiovascular outcomes like stroke, heart failure and
myocardial infarction were present in <5% at diagnosis. Erythrocyte levels were increased
and correlated with IGF-1 values. Thyroid nodules were frequent (34.0%); 820 patients
had colonoscopy at diagnosis and 13% had polyps. Osteoporosis was present at diagnosis
in 12.3% and 0.6–4.4% had experienced a fracture. In conclusion, this study of >3100
patients is the largest international acromegaly database and shows clinically relevant
trends in the characteristics of acromegaly at diagnosis.
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Endocrine-Related Cancer
Introduction
Acromegaly is caused by chronic hypersecretion of
growth hormone (GH) and insulin-like growth factor-1
(IGF-1), usually due to a GH-secreting pituitary adenoma
(somatotropinoma) (Melmed 2017). Acromegaly is a
rare disorder; modern epidemiological data from various
population-based (Daly et al. 2006, Fernandez et al. 2010)
and insurance database studies (Burton et al. 2016) are
available and suggest that acromegaly has a prevalence
of 2.8–13.7 cases/100,000 and an incidence of 0.2–1.1
cases/100,000 (Lavrentaki et al. 2016).
Chronically elevated GH and insulin-like growth
factor-1 (IGF-1) levels lead to a complex spectrum of
signs that include acral overgrowth, facial changes,
musculoskeletal disease or gigantism if the GH
hypersecretion occurs before epiphyses have fused
(Melmed 2017). Patients with active acromegaly also
suffer from cardiovascular and metabolic abnormalities,
including hypertension, arrhythmia, cardiomegaly,
diabetes mellitus and dyslipidemia (Melmed et al. 2013).
Together these lead to increased morbidity and mortality
in acromegaly, predominantly due to cardiovascular and
respiratory disease (Stewart & Sherlock 2012, Ritvonen
et al. 2015, Ramos-Leví & Marazuela 2017). Bringing
GH/IGF-1 levels within the normal range returns
mortality to that of the general population, although the
precise threshold at which risk normalization occurs is
debated (Holdaway et al. 2008, Sherlock et al. 2010).
Methods for the management of acromegaly have
evolved over the past 40 years and for most approaches,
the efficacy and safety profiles are well documented.
Neurosurgical techniques have been refined from the
first trans-sphenoidal operations to new endoscopic
techniques, while medical therapies now involve a
range of options from somatostatin analogs (SSA) and
somatostatin receptor ligands (SRL) to the growth hormone
(GH) receptor antagonist pegvisomant and dopamine
agonists (Melmed 2016). Radiotherapy techniques have
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undergone significant developments leading to the
gamma-knife used today. Modern acromegaly therapy is
guided by recommendations from consensus publications,
with primary neurosurgery potentially offering cure
in pituitary tumors that are smaller or uncomplicated
(Giustina et al. 2010, Katznelson et al. 2014). In many
patients, multimodal therapy is needed, particularly for
those with aggressive disease or non-resectable tumors.
As a rare disease, studies on acromegaly have generally
focused on relatively small populations or have addressed
regional or national cohorts and patients enrolled in
treatment-specific safety databases (Jenkins et al. 1995,
Sherlock et al. 2009, Trainer 2009, Tritos et al. 2014). Data
from such studies have provided valuable information
about acromegaly and have contributed to improvements
in patient management. Large international studies of
the clinical characteristics and therapeutic evolution
of unselected groups of acromegaly patients do not
exist. We were interested in studying multiple aspects
of acromegaly, including detailed assessments of large
numbers of data points covering hormonal, pathological,
genetic, clinical and therapeutic measures and how these
factors are interrelated. We developed and deployed a
relational database, the Liège Acromegaly Survey (LAS)
Database, for the analysis of data collected from large
populations of patients with acromegaly. Following
preliminary studies to validate the data collection and
analysis potential of the LAS Database (Theodoropoulou
et al. 2009, Petrossians et al. 2012, Franck et al. 2017), we
report the first comprehensive study of 3173 acromegaly
in patients from 14 participating centers across Europe.
Methods
The study included patients with an established diagnosis of
acromegaly at the 14 study centers across Belgium (Centre
Hospitalier Universitaire de Liège), Bulgaria (Medical
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P Petrossians et al.
University, Sofia), Czech Republic (Charles University,
Prague), France (Paris Sud University, Le Kremlin-Bicêtre;
Hôpital de la Timone, Marseille, Centre Hospitalier
Universitaire de Reims), Germany (Max Planck Institute of
Psychiatry, Munich), Italy (Federico II University, Naples;
University of Genoa, Genoa; University of L’Aquila; and
Neuromed, Pozzilli), the Netherlands (Erasmus University
Medical Center, Rotterdam), Portugal (Centro Hospitalar S.
João, Porto), Spain (Hospital Universitario de la Ribera, Alzira)
and Sweden (Karolinska University Hospital, Stockholm).
The LAS Database is a relational database that permits
the analysis of comprehensive arrays of data covering
laboratory values, dose adaptation of treatment and
clinical evolution. The goal of the LAS Database was to
design a framework to capture available data on >2000
acromegaly patients and to permit statistically robust
analysis of clinically relevant topics. The database
management system was kept separate from the data
capture interface. The open source mySQL server (Oracle,
USA) was used to store the data, while the data capture
interface used locally at each participating center was
programmed using the Delphi RAD system. The initial
development and validation of the framework is described
in Petrossians and coworkers (Petrossians et al. 2012).
The current study ran from 30 September, 2012, to
1 January, 2015, and data cutoff for this analysis was
1 October, 2016. All patients with a diagnosis of acromegaly
at the participating centers up to 1 January 2015 were
valid for inclusion. Those with valid demographic data
and at least one post-diagnosis/baseline follow-up
dataset were included in the statistical analysis. There
was no upper or lower limit to the duration of follow-up,
number of treatments or treatment adaptations, drug
dose alterations or hormonal/clinical/radiological results
recorded over time. Complete data on the 147 variables
that were collected over the course of the patient’s clinical
follow-up were to be entered; when an assessment had
not been performed (e.g. cardiac ultrasound, colonoscopy,
polysomnography), these individuals were not included
in the statistical analysis for that particular parameter.
Hormonal data have evolved over time due to refinements
in assay methodologies, which can lead to inconsistencies
when comparing values. The LAS Database accounted for
changes in GH assay reference ranges by automatically
converting values in ng/mL to µU/mL based on the
date and reference used in the center at that time. For
IGF-1, absolute measured values were encoded along
with the upper limit of normal for age and sex based on
the assay used at the center. Results were then expressed
as percent of upper limit of the normal value (%ULN).
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Radiological data for the maximal tumor diameter were
used to calculate the proportion of patients with micro
(<10 mm) and macroadenomas (≥10 mm) on MRI scans at
diagnosis. Nodular thyroid disease was considered present
when a solitary thyroid nodule or a multinodular goiter
was confirmed on ultrasound. Diabetes was considered
as being present when a diagnosis had been made in the
medical history of the patient and/or a recorded glucose
value of ≥200 mg/dL was found at 120 min during a
standard oral glucose tolerance test (OGTT). Genetic
studies were not performed specifically over the course of
this study and only information on familial diseases or
previously established genetic diagnoses was collected.
The study was performed under a central Ethics
Committee approval covering all centers from the
Centre Hospitalier Universitaire de Liège, while each
individual center complied with their individual local
ethics requirements and procedures. Data were encoded
locally using the LAS Database data capture interface and
each patient entered was assigned an anonymous study
identifier. Patient identifying information was never
shared with the central database where information from
participating centers was pooled for analysis.
Statistics
To examine the evolution of factors over time the study
population was divided by study center, gender and decade
of diagnosis. Data were analyzed using the R software
package (R Core Team 2015; http://www.R-project.org)
and graphics were plotted using the Lattice package
(Lattice, Sarkar D. New York (2008). For continuous
variables, data were plotted and tested for normality. As
none of the variables had a normal distribution, data were
expressed as median and interquartile range (IQR) from
the first to the third quartile (25th and 75th percentiles).
Data distribution was represented graphically with
density graphs using Gaussian kernel smoothing with
individual data points plotted at the abscissa (‘rug’). Data
spreads were drawn using boxplots showing the medians
and interquartile ranges, while the whiskers represented
1.5 times the interquartile ranges. Statistical comparisons
were performed using the Mann–Whitney test. Single
and multiple regression analyses were performed using
generalized additive models. Count variables were
compared using the χ2 test. Time data were analyzed
either continuously for regression models or divided into
four groups (before 1990, 1990–1999, 2000–2009, 2010
and after). The earliest date (pre/post 1990) was chosen
as it represents a period when new diagnostic (MRI)
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B
Center (median age) and sex
A
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Valencia (49.4)
F
M
Rotterdam (44.8)
F
M
Reims (48.9)
F
M
Stockholm (47.2)
F
M
Liège (44.2)
F
M
Bicêtre (40.8)
F
M
Naples (44.4)
F
M
Prague (47.9)
F
M
Munich (43.6)
F
M
Marseilles (46)
F
M
L'Aquila/
F
M
Pozzilli (39.8)
Sofia (46.2)
F
M
Genova (45)
F
M
Porto (43.7)
F
M
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<1990
1990's
2000's
2010's
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1990's
2000's
2010's
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E
Figure 1
(A) Dot plot showing the sex ratio (M/F) and the number (n) of patients in the LAS Database and for individual centers. Centers were sorted based on the
sex ratio, in decreasing order. (B) Median age of patients at diagnosis represented as separate boxplots for males and females. Centers were sorted based
on the median age of diagnosis of all patients for each center (values in parenthesis). (C) Evolution of median age at diagnosis over time. (D) Estimated
delay between the first symptoms of acromegaly as reported by patients and the diagnosis of acromegaly, and displayed by the decade of diagnosis.
(E) Proportions of LAS Database patients diagnosed by different medical (generalist, specialist) or health care workers and non-medical individuals.
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P Petrossians et al.
and therapeutic (somatostatin analogs) modalities
were becoming generally available. Patient ages were
also analyzed either as continuous values for regression
and Mann–Whitney tests or grouped into categories:
0–29 years, 30–49 years, 50–64 years and ≥65 years.
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All
M
F
0.05
0.04
density
0.03
Results
0.02
0.01
Study population and demographics
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Maximal tumor diameter (mm)
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The study population consisted of 3173 patients with
a diagnosis of acromegaly. There was a slight female
predominance (F = 1729; 54.5%) across the total
population, and this tended to decrease over time from
57.3% in those diagnosed before 1990 to 50.6% of those
diagnosed after 2010. The male-to-female ratio (0.84)
varied across the centers from 0.43 to 1.4 (Fig. 1A and B).
A total of 468 cases underwent 777 genetic tests related
to acromegaly; 73 patients had known genetic/inherited
or syndromic features, 28 had an AIP gene mutation, 13
were from other AIP-negative familial isolated pituitary
adenomas (FIPA) kindreds, 11 had McCune Albright
syndrome, seven had multiple endocrine neoplasia type
1 (MEN1) and two had Carney complex. Five patients
had acromegaly secondary to ectopic growth hormonereleasing hormone (GHRH) secreting tumors.
The median age of diagnosis was 45.2 years
(IQR: 34.9–55.0 years) and was significantly younger in
males (43.5 years (IQR: 34.2–53.1)) than that in females
(46.4 years (IQR: 35.6–56.1); P < 0.001). The median
age at first symptoms of acromegaly was 33.5 years
(IQR: 23.6–44.5 years) overall and did not differ
significantly between the sexes. The median delay in
diagnosis was, however, significantly longer for females
(10 years (IQR: 4.0–18.0)) as compared with males (8 years
(IQR: 4.0–15.0); P = 0.015). The age at diagnosis increased
over time in both sexes, with those in the most recent
group (post-2010) being nearly 7 years older than the pre1990 group (48.79 (39.3–58.9) vs 41.8 (32.5–52) P < 0.001;
Fig. 1C). The median age at first symptoms of acromegaly
(as recalled by the patient) also increased over time with
patients diagnosed in the current decade being 17.1 years
older than those diagnosed pre-1990 (41.7 (32.6–50.5)
vs 24.6 (14–33.8); P < 0.001). Over time, however,
acromegaly was associated with a shorter delay between
first symptoms and diagnosis (Fig. 1D).
Acromegaly was most frequently diagnosed by
endocrinologists (44.9%), general/family practitioners
(17.5%) or internists (13.2%). Other diagnostic settings
included rheumatologists/orthopedic specialists (3.6%),
60
40
20
MICRO
MACRO
Tumor type
N
Y
Invasion
Figure 2
(A) Density plot and box plot representing the maximal diameter of
tumor at diagnosis. Data for the whole population (black line), male
(blue line) and female patients (red line) are shown. Individual patients
are represented below the density plot (‘rug’). (B) Maximal tumor
diameter in groups of patients based on the age at diagnosis. (C) Age of
patients at diagnosis in those with micro/macro adenomas and in those
with tumor invasion.
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neurologists (3.3%) and ophthalmologists (2.3%), while
in 2.3% of cases, the diagnosis was made by the patient
themself or their family/friends (Fig. 1E). The most frequent
signs/symptoms leading to presentation with acromegaly
were changes in physical appearance, with 21.5% reporting
dysmorphic features and 13.6% enlarged extremities. Other
presenting signs included headache (7.5%), fatigue/asthenia
(5.9%), sweating (2.0%) and sleep apnea (1.0%). In 8.4% of
female patients, menstrual disturbances were among the
symptoms leading to presentation with acromegaly.
A
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Radiological characteristics
0−29y
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50−64y
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Linear regression
Local regression
GH at diagnosis (ng/ml)
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At diagnosis, pituitary imaging data were available in 2545
cases, of which 1691 had an MRI and 854 had a CT scan.
The median tumor size at diagnosis was 15 mm (Fig. 2A)
and 71.8% of cases had a macroadenoma. In 4.6% of
cases, no pituitary tumor was visualized. Males had
larger tumors at diagnosis than females (P < 0.001), while
tumor size at diagnosis was inversely related to patient
age (Fig. 2B). Hence, patients with macroadenomas were
significantly younger (P < 0.001; Fig. 2C) and had more
frequent cavernous sinus invasion at diagnosis (P < 0.001).
The difference in tumor size between males and females
was due to patients under 30 years of age at diagnosis
(P = 0.002) as there was no significant difference between
the sexes in tumor size in older patients (data not shown).
In keeping with larger tumor size, younger patients had a
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Figure 3
(A) GH levels in groups of patients based on the age at diagnosis. (B)
Scatterplot of GH levels at diagnosis vs maximal tumor diameter. The
dotted line is the linear regression between these two variables, whereas
the continuous line is the result of a loess (locally weighted least squares
regression) smoothing. The latter shows the lack of a correlation
between tumor size and GH secretion for larger tumors. (C) Scatterplot
and regression line between GH nadir concentration during OGTT vs
random GH measurement.
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0
N
Y
N
Y
PRL cosecretion
Figure 4
Age of male and female patients at diagnosis based on prolactin (PRL)
co-secretion by the adenoma.
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Hormonal profiles
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GH (ng/ml)
At each age group studied, there was no difference between
males and females in terms of GH level at diagnosis. GH
levels at diagnosis were inversely related to patient age in
both sexes (P < 0.001; Fig. 3A). A linear regression analysis
between GH at diagnosis and maximal tumor diameter at
diagnosis showed an increase of GH values with the size
of tumor, but only up to a maximum tumor diameter of
20 mm; thereafter, no correlation with GH values existed
(Fig. 3B). Random GH at diagnosis correlated closely with
nadir GH levels during OGTT (P < 0.001, Fig. 3C). Over
time, GH levels at diagnosis fell significantly; this was
mainly driven by lower GH at diagnosis among females
over time from pre-1990 to the current decade (P < 0001).
As there was also a weak association between the date of
diagnosis and the GH level, it cannot be excluded that
changes in assay ranges could also contribute to this
finding.
IGF-1 levels (%ULN) were higher at diagnosis
among younger acromegaly patients; this difference was
significant for the study population overall and male
patients but not females (P < 0.001). IGF-1 (%ULN) also
correlated with tumor size (P = 0.04). Prolactin co-secretion
occurred in 10% of cases, while among surgically operated
patients, mixed GH/PRL staining was described in 26.3%
of tumors. Patients with prolactin co-secretion were
significantly younger at diagnosis than other acromegaly
patients (Fig. 4). Additionally, patients with GH and
prolactin co-secretion had significantly larger tumors
(P < 0.001) that were more likely to be invasive at diagnosis
than other patients. Co-secretion of hormones other than
prolactin was rarely seen at diagnosis (ACTH: 0.41%, TSH:
0.16%, gonadotropins: 0.13%).
Comorbidities at diagnosis
Metabolic system
Figure 5
Scatter plots and regression lines of basal glucose (A) and glucose at
120 min during OGTT (B) vs GH levels in non-diabetic patients.
higher rate of chiasmal compression at diagnosis, which
was 23.0% in those aged <30 years but only 10.0% in
those aged >65 years at diagnosis; this was present in both
sexes (P < 0.001). The proportions of patients with micro/
macroadenomas did not change over time. Invasion was
present in 47.6% of tumors at baseline (Fig. 2C); there was
no difference between the sexes and no change was seen
in the percentage of cases with invasion over time.
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At diagnosis of acromegaly, 24.5% of patients had type 2
diabetes, while three individuals had type 1 diabetes. In
addition, when 120-min glucose values on OGTT were
assessed, a further 24 patients not previously diagnosed
with diabetes had glucose values >200 mg/dL at 120 min.
Including all these patients, the prevalence of diabetes
mellitus at diagnosis in acromegaly patients was 27.5%.
In non-diabetic patients, glucose values (basal or at OGTT)
did not correlate with GH levels (P = 0.19; Fig. 5A and B).
Glucose levels did, however, correlate significantly with
IGF-1 values when expressed in absolute terms (P < 0.01) and
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Figure 6
Scatter plots and regression lines of glucose vs IGF-1 levels in non-diabetic patients. (A) Basal glucose vs measured IGF-1. (B) Glucose at 120 min during
OGTT vs measured IGF-1. (C) Basal glucose vs IGF-1 expressed as a percentage of the upper limit of normal (% ULN). (D) Glucose at 120 min during OGTT
vs IGF-1 expressed as % of ULN.
as %ULN (P = 0.038; Fig. 6A, B, C and D). The median total
cholesterol level was 183.2 mg/dL (IQR: 134.0–216.2 mg/dL).
Total cholesterol levels were higher in females than those
in males at diagnosis: 189.0 mg/dL (IQR: 139.9–221.4) vs
178.0 mg/dL (IQR: 133.0–205.0). Males were nearly twice
as likely to be current smokers as females at the time of
diagnosis (22.1 vs 11.9%, respectively).
Cardiovascular system
As cardiovascular disease is an important cause of
morbidity/mortality in acromegaly, we were interested
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in determining the prevalence of important variables
at diagnosis, in addition to diabetes and lipid profiles
described previously. Among these, hypertension was the
most frequent, occurring in 28.8% of patients overall at
diagnosis, and this remained relatively constant across
time of inclusion into the study. Cardiac hypertrophy
was reported in 15.5% of patients at diagnosis. Other
important cardiovascular morbidities were less frequent
at diagnosis: stroke (4.5%), arrhythmia (3.6%), ischemic
heart disease (3.5%), myocardial infarction (3.0%) and
heart failure (1.6%). Patients with hypertension, cardiac
hypertrophy, cardiac failure, ischemic heart disease
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and arrhythmia at diagnosis were all significantly older
at diagnosis than those without these cardiovascular
comorbidities (P < 0.001). Sleep apnea syndrome had
been diagnosed in 25.5% of the cohort. In centers
where polysomnography was systematically performed,
sleep apnea syndrome was detected in 69.0% of
tested subjects.
Red blood cell counts (analyzed separately for males
and females) did not show any correlation with random
GH or GH nadir during OGTT (P = 0.46), but RBC count
increased with absolute IGF-1 values (P = 0.046) and %ULN
values (P < 0.001). Similarly, hemoglobin concentration
was not correlated with GH levels but was positively
correlated with absolute IGF-1 values (P = 0.017) and IGF1
%ULN (P < 0.001).
Endocrine-Related Cancer
Other comorbidities
Overall, 34.0% of patients had a thyroid nodule or goiter
reported at diagnosis. There was no relationship between
other demographic or hormonal factors and the presence
of thyroid nodules. At diagnosis, 13% of patients who
had a colonoscopy (n = 820) had colonic polyps identified.
There was no difference in GH and IGF-1 levels between
patients with and without polyps. Four patients had
been diagnosed with colorectal cancer at diagnosis. In
total, 64 patients had a diagnosis of cancer, the most
common of which were breast (n = 16), thyroid (n = 11)
and skin (n = 10). At diagnosis, 12.3% of patients had been
diagnosed with osteoporosis. A hip fracture had occurred
by the time of diagnosis in 4.4% of acromegaly patients,
whereas 4.3% had suffered a vertebral fracture and 0.6%
a wrist fracture. There was only a significant relationship
between age at diagnosis and the presence of any fracture
in female patients (P = 0.012).
Discussion
Acromegaly is a rare endocrine disorder that is due to
chronic GH hypersecretion, usually from a pituitary
adenoma. It is usually diagnosed and managed in
expert referral centers, but due to its rarity even
pituitary specialists might see only a couple of hundred
cases over their full careers. One way to improve our
understanding of rare disorders is by pooling data from
many centers using patient registries. In acromegaly,
this has been done extensively on a regional and
national basis in Europe and Mexico (Jenkins et al. 1995,
Mestron et al. 2004, Reincke et al. 2006, Bex et al. 2007,
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Portocarrero-Ortiz et al. 2016, Maione et al. 2017).
Commercial entities support clinical databases to detect
and report on the safety of medical therapies, such as,
the pegvisomant ACROSTUDY program (van der Lely
et al. 2012, Freda et al. 2015, Bernabeu et al. 2016). The
data obtained from these registries have stimulated
ideas on aspects of morbidity, hormonal control and
medical treatment patterns that have later been proven
in independent clinical trials. National registries do
have limitations in terms of patient numbers and
the applicability of data to treatment norms in other
countries. International acromegaly databases with a
common underlying data capture methodology have
been long called for (Stewart 2004).
The LAS Database was originally developed and validated
as a single-center study tool (Petrossians et al. 2012), and
thereafter, was expanded across multiple European centers
in the current study; it has been used successfully to
facilitate analyses of disease characteristics and treatment
responses in various centers (Theodoropoulou et al. 2009,
Franck et al. 2017). The LAS Database provides some
specific advantages in that it is not limited to a national
dataset nor does it deal with patients managed with a single
treatment modality. The programming of the LAS Database
is a relational database that permits integrated statistical
analyses of independent variables, which is a challenge
for other registry-based listing. The LAS Database variables
(nearly 150 in total)were chosen based on extensive input
from acromegaly specialists in order to permit clinically
relevant questions and changes in criteria over time to be
addressed with robust statistical methods.
In the cohort, there was a small female predominance
overall (54.5%), which is in keeping with results from
other national centers in Europe and elsewhere (Sesmilo
et al. 2012, Portocarrero-Ortiz et al. 2016, Lesén et al.
2017, Maione et al. 2017). Over time, though, the sex
prevalence changed, such that those patients diagnosed
post-2010 were nearly evenly balanced (M:F 49.4%:
50.6%). Acromegaly usually has an occult onset and a
long period of symptoms can occur before a diagnosis is
made. In a two-center study in the United States, Reid and
coworkers suggested that delayed diagnosis contributed
to acromegaly patients presenting with similar disease
characteristics over the period 1981–2006 (Reid et al.
2010). In the LAS Database, first symptoms were seen in
the mid-30s in both sexes. However, it took significantly
longer (2 years) for females to achieve a diagnosis than
males, which is clinically relevant and indicates improved
awareness of acromegaly in women is needed. As the delay
between first symptoms and diagnosis decreased over
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the course of the study, this suggests that the efficiency
of referral and diagnosis is improving. This may be due to
much wider access to MRI and other specialist techniques
and better emphasis on concentrating pituitary expertise
in regional referral centers; improved awareness of
acromegaly may itself play a part in decreasing the delay
before diagnosis. It is interesting to note that the age at
diagnosis in the cohort overall increased by nearly seven
years from 1990 to the current decade. It has long been
noted that older patients with acromegaly can have milder
disease features and hormonal abnormalities (van der Lely
et al. 1992). More recently, it has been noted that a group
of patients with ‘normal’ GH and elevated IGF-1 exists,
that are older and have smaller tumors than acromegaly
patients with typically raised GH and IGF-1 parameters
(Dimaraki et al. 2002, Butz et al. 2016). It may be that the
wider access to MRI and greater awareness noted above is
also leading to increased pick-up of a milder phenotype
of acromegaly in an older population. In support of this,
hormonal data from the current cohort show a fall in GH
at diagnosis over time, due mainly to female acromegaly
patients. The correlations between patient age, tumor size
and GH secretion suggest an apparent triangular relation
among these three variables. The later the age at diagnosis,
the smaller the tumors and the lower the diagnostic GH
values; the reverse situation was also true. This raises
different possible interpretations. Is milder disease simply
being overlooked in younger patients or are older patients
more sensitive to small increases in GH secretion? It is
more likely, however, that acromegaly is heterogeneous,
and there are distinct phenotypes that can be identified.
A number of pathological features might explain this
difference, including genetic causes, such as AIP mutations
that predominately affect younger males (Daly et al. 2010).
Over representation of AIP-mutated cases among younger
subgroups of the current cohort could have influenced
the tumor size characteristics. As only a minority of
patients underwent tumoral or germline genotyping, this
hypothesis remains speculative. GH values at diagnosis
decreased with patient age and increased with tumor
size, although this later linear relation was not present for
tumors measuring more than 20 mm in diameter. This may
be explained by tumoral necrosis in bigger tumors or by
two different populations of tumors with the bigger tumors
being aggressive tumors secreting relatively low levels of
GH that appear as hyper-intense lesions on T2-weighted
MRI sequences (Potorac et al. 2015, 2016). Further studies
comparing T2 imaging signal, histologic features and
tumoral secretion may shed more light on this observation.
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Improvements in diagnosis of acromegaly can come
from greater awareness among those who first see the
patient. In the LAS Database cohort, the initial diagnosis
of acromegaly was made by an endocrinologist in nearly
45% of cases. As shown in Fig. 1, the variety of nonendocrine specialists that make acromegaly diagnoses
is quite marked. Given the range of potential signs/
symptoms and the specific problems caused by a pituitary
adenoma, it is crucial that awareness of pituitary tumors
continues to be widened across medical specialties and
related groups (Surchi et al. 2017). Delays in diagnosis
in patients that attend with multiple symptoms of
acromegaly still occur as illustrated clearly by De and
Foucault, leading to unnecessary exposure of excessive
GH/IGF-1 (De & Foucault 2014). Interestingly, in the age
of widespread Internet searching related to medicine,
as many people or friends/family diagnosed himself
or herself with acromegaly as did ophthalmologists.
Improved understanding of the pattern of signs and
symptoms suggestive of acromegaly is still needed among
both the health care sector and the general public.
Studies in acromegaly routinely use random GH
measurements, whereas the nadir of GH during OGTT
is considered as the ‘gold standard’ of GH assessment.
In this cohort of >3100 patients, a linear regression
between nadir GH and random GH showed a good
correlation between these two measures suggesting that
using random measurement of GH is a clinically valid
practice, as suggested by others (Bajuk Studen & Barkan
2008). Despite extensive clinical research, the question
still arises as to which hormonal measurement, GH or
IGF-1 (or both), is the best representation of the activity
of acromegaly. Indeed in clinical practice, patients
with high levels of GH and comparatively low (albeit
elevated) levels of IGF-1 are seen, contrasting with other
patients with slightly increased or normal levels of GH
but markedly elevated levels of IGF-1. Which of these
patients should be considered as being the most exposed
to active acromegaly? One pointer may come from
comparing other biological markers like glucose. Detailed
study of acromegaly patients with diabetes is limited since
these patients are already receiving treatment, and they
may show different compliance toward their diet and
therapy. Therefore, we assessed the effect of hormonal
secretion in non-diabetic patients. Glucose levels in
acromegaly patients increased with rising levels of IGF1, whereas no correlation was found with GH. Although
GH induces insulin resistance and raises glucose, in the
clinical setting, IGF-1 may represent a better marker of the
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metabolic burden of acromegaly; this point is echoed in
other national cohort analyses (Alexopoulou et al. 2008).
Acromegaly is associated with increased mortality
when hormonal levels are not controlled (Dekkers
et al. 2008). The presence of important comorbidities
contributes to this and the range of pathologies seen
in acromegaly patients is extensive (Pivonello et al.
2017). The actual contribution of the different major
classes of comorbidity to disease burden and death in
acromegaly is not clear. Traditionally, cardiovascular
disease, respiratory disease and cancer have been the
main causes of increased mortality in acromegaly. With
respect to cardiovascular and metabolic risk factors in the
current cohort, we confirm that diabetes is a common
problem in acromegaly, affecting more than a quarter
of patients at diagnosis, in keeping with other studies
(Hannon et al. 2017). Hypertension was also frequent,
being present in about 29% at diagnosis. Structural heart
disease is an important component of acromegaly, and
already 15.5% of patients had hypertrophy at diagnosis.
We noted that sleep apnea syndrome, a classical
acromegaly feature, that itself has a negative impact
on cardiorespiratory morbidity is seen in a quarter of
acromegaly patients at diagnosis. This figure is likely to
be an underestimate, as with strict polysomnography, the
rate of obstructive sleep apnea syndrome in acromegaly
can be as high as nearly 70% (Attal & Chanson 2010).
Acromegaly patients are not screened uniformly at
diagnosis for sleep apnea or other associated problems,
so the true prevalence rates of different comorbidities are
uncertain. An important factor to consider is the effect
of age on cardiovascular comorbidities, as we noted
that patients with hypertension, cardiac hypertrophy
and heart failure at diagnosis were significantly older at
diagnosis (6–13 years) than those without cardiovascular
complications. This raises the question as to what role
acromegaly plays in the cardiovascular health of the aging
patient? This is particularly of relevance as the current
study has shown that more aged patients with acromegaly
are being diagnosed. In this situation, it becomes difficult
to attribute a causative role for GH hypersecretion to
cardiac morbidities in acromegaly, and as patients age, the
presence of acromegaly may simply represent one of the
many contributory risk factors.
In the case of colonoscopy that is recommended for
surveillance of acromegaly patients, this was performed in
820 patients at diagnosis. While incomplete with respect
to the total cohort size, it is still one of the largest datasets
on colonic findings at diagnosis in acromegaly; 13%
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of patients had polyps but only four cases of colorectal
cancer were already present at diagnosis. Indeed, the rate
of recorded cancer cases either overall or by specific types
(e.g. breast cancer in women) does not appear as being
markedly elevated in the LAS Database patients in relation
to general European populations (Lutz et al. 2003). The
prevalence of thyroid nodules was high in acromegaly at
diagnosis; and 11 cases of thyroid cancer were identified
at that time. The prevalence of thyroid nodules was
probably an underestimation as ultrasound examinations
were not performed routinely at diagnosis of acromegaly.
There were some interesting findings regarding emerging
comorbidities. Red blood cell count and hemoglobin
concentrations were also raised in acromegaly, and
we confirmed that these increased with IGF-1 levels
but not GH. Again, this suggests that IGF-1 levels may
be a better representation of the activity of acromegaly
overall. The role of excessive GH-IGF-1 hypersecretion on
erythropoiesis in acromegaly is a recognized but relatively
neglected subject (Grellier et al. 1996, Teramoto &
Ouchi 1997, Zoppoli et al. 2011); however, in pediatric
and adult GH deficiency, it is well established that GH
replacement can lead to increased red blood cell measure
and correction of anemia (Christ et al. 1997, Valerio
et al. 1997, Bergamaschi et al. 2006, Esposito et al. 2016).
The role of increased red cell counts and potentially
other hematological measures in relation to respiratory
pathology (e.g. sleep apnea syndrome), cardiovascular
disease and outcomes is a potentially valuable avenue of
future research.
The LAS Database is the first international relational
database used to study acromegaly following a standard
methodological design. This first report of >3100 enrolled
patients at diagnosis shows that the clinical and hormonal
characteristics of acromegaly are evolving over time. While
acromegaly affects slightly more females than males,
female patients have a significantly longer delay before
diagnosis; this may be due in part to males having larger
tumors than females and these occur at a younger age.
The age at first symptoms and at diagnosis of acromegaly
is rising over time, indicating that improvements in
diagnostic measures are detecting a greater proportion
of older patients. In keeping with this, the LAS Database
cohort also shows a triangular relationship between age,
tumor size and GH secretion, with older patients having
smaller tumors and lower GH secretion. Cardiometabolic
comorbidities of acromegaly were frequently present at
diagnosis, such as diabetes mellitus (29.6%), hypertension
(28.8%), while cardiac hypertrophy was seen in 15.5%.
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Endocrine-Related Cancer
Thyroid nodules (34.0%), sleep apnea syndrome (25.5%)
and colonic polyps (13%) were also frequent but detailed
specific screening for these was less consistent at diagnosis.
The LAS Database provides a standardized platform
for combining large datasets across multiple centers
internationally and forthcoming analyses will address
important aspects of treatment responses and outcomes in
acromegaly.
Declaration of interest
Patrick Petrossians has undertaken consulting and has received travel
grants from Novartis, Ipsen and Pfizer. Adrian F Daly holds stock in Amryt
Pharma. Annamaria Colao has been principal investigator of research
studies from Novartis, Ipsen, Pfizer and Lilly, has received research
grants from Ferring, Lilly, Ipsen, Merck-Serono, Novartis, Novo-Nordisk
and Pfizer, has been a consultant for Novartis, Ipsen and Pfizer and has
received fees and honoraria from Ipsen, Novartis and Pfizer. Renata S
Auriemma has been a consultant for Novartis and has received fees and
honoraria from Novartis. Sebastian Neggers has received research grants
from Ipsen, Pfizer and Novartis and has been a consultant for Pfizer and
Ipsen. Vaclav Hana has received speaker fees and has served on Advisory
Boards for Pfizer, Novartis and Ipsen. Albert Beckers has received research
grants from Ipsen, Pfizer and Novartis and has served on Advisory
Boards for Ipsen.
Funding
This study was supported by an unrestricted educational grant from Ipsen.
The study funder had no role in the collection of data, had no access to the
data and had no involvement in the writing of this manuscript.
Acknowledgements
The authors would like to thank all of the physicians and scientists who
formed part of the ‘LAS Club’, and through the various planning and
discussion meetings gave their time and inspiration to the final LAS
Database structure. They thank Barbara Zabl for her help with testing of
the initial version of the LAS Database software, Dr. Maria Tichomirowa
for her work on the initial Liège cohort described in Petrossians et al. 2012
and Dr Marily Theodoroupolou for her input and ideas during the LAS
Club sessions. The authors dedicate this study to the memory of the late
Professor Franco Minuto, who was an early and enthusiastic contributor to
the LAS Club and who generously contributed his experience and ideas to
the Liège Acromegaly Study Database.
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Received in final form 19 July 2017
Accepted 21 July 2017
http://erc.endocrinology-journals.org
DOI: 10.1530/ERC-17-0253
24:10
Published by Bioscientifica Ltd.