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. 2020 May 22;15(5):e0233460. doi: 10.1371/journal.pone.0233460

Cornea verticillata and acroparesthesia efficiently discriminate clusters of severity in Fabry disease

Wladimir Mauhin 1,2,*, Olivier Benveniste 2,3, Damien Amelin 2, Clémence Montagner 1, Foudil Lamari 4,5, Catherine Caillaud 6,7, Claire Douillard 8, Bertrand Dussol 9,10, Vanessa Leguy-Seguin 11, Pauline D'Halluin 12, Esther Noel 13, Thierry Zenone 14, Marie Matignon 15,16, François Maillot 17,18, Kim-Heang Ly 19, Gérard Besson 20, Marjolaine Willems 21, Fabien Labombarda 22, Agathe Masseau 23, Christian Lavigne 24, Didier Lacombe 25,26, Hélène Maillard 27, Olivier Lidove 1,2
Editor: Maria Vittoria Cubellis28
PMCID: PMC7244174  PMID: 32442237

Abstract

Backgroud

Fabry disease (OMIM #301 500), the most prevalent lysosomal storage disease, is caused by enzymatic defects in alpha-galactosidase A (GLA gene; Xq22.1). Fabry disease has historically been characterized by progressive renal failure, early stroke and hypertrophic cardiomyopathy, with a diminished life expectancy. A nonclassical phenotype has been described with an almost exclusive cardiac involvement. Specific therapies with enzyme substitution or chaperone molecules are now available depending on the mutation carried. Numerous clinical and fundamental studies have been conducted without stratifying patients by phenotype or severity, despite different prognoses and possible different pathophysiologies. We aimed to identify a simple and clinically relevant way to classify and stratify patients according to their disease severity.

Methods

Based on data from the French Fabry Biobank and Registry (FFABRY; n = 104; 54 males), we applied unsupervised multivariate statistics to determine clusters of patients and identify clinical criteria that would allow an effective classification of adult patients. Thanks to these criteria and empirical clinical considerations we secondly elaborate a new score that allow the severity stratification of patients.

Results

We observed that the absence of acroparesthesia or cornea verticillata is sufficient to classify males as having the nonclassical phenotype. We did not identify criteria that significantly cluster female patients. The classical phenotype was associated with a higher risk of severe renal (HR = 35.1; p <10−3) and cardiac events (HR = 4.8; p = 0.008) and a trend toward a higher risk of severe neurological events (HR = 7.7; p = 0.08) compared to nonclassical males. Our simple, rapid and clinically-relevant FFABRY score gave concordant results with the validated MSSI.

Conclusion

Acroparesthesia and cornea verticillata are simple clinical criteria that efficiently stratify Fabry patients, defining 3 different groups: females and males with nonclassical and classical phenotypes of significantly different severity. The FFABRY score allows severity stratification of Fabry patients.

Introduction

Fabry disease (FD; OMIM #301 500) is an X-linked lysosomal storage disease caused by an enzymatic defect of the hydrolase alpha-galactosidase A (AGAL-A), resulting in the accumulation of glycosphingolipids, mainly globotriaosylceramide (Gb3) and its deacetylated form globotriaosylsphingosine (lysoGb3), the latter being commonly used as a surrogate biomarker [1–3]. FD has historically been characterized by acral pain, angiokeratoma, cerebral strokes, progressive renal failure and cardiomyopathy, with a diminished life-expectancy [4]. However, the clinical presentation and incidence of FD are changing as the diagnostic approach is moving from clinicobiochemical algorithms to genetic screenings. Indeed, the first estimations based on clinical ascertainment before 2000 evaluated the incidence of FD between 1:40,000–117,000 live births [5,6], whereas three recent newborn screening studies observed incidences greater than 1:10,000 [7–10]. Since 1990, a nonclassical or late-onset phenotype of FD has been described, with higher residual AGAL-A activity and predominant, if not isolated, cardiac manifestations [11]. The majority of the individuals detected by genetic screenings carry galactosidase A alpha (GLA) variants that are usually associated with this nonclassical phenotype of FD [7,12]. The classical and nonclassical phenotypes have been empirically determined on the basis of the presence or absence of characteristic symptoms (usually neuropathic pain, angiokeratoma, and cornea verticillata (CV)), GLA enzyme activity and/or the GLA genetic variant, though without any consensus [12,13]. As the prognosis of the different phenotypes is markedly different, there is a need to determine reproducible classification criteria to improve the reliability of therapeutic studies and to personalize the bedside management of FD. Some scoring systems already exist, and they have been elaborated with empirical considerations; these scoring systems include many nonobjective criteria with several items that make them difficult to use in a daily practice. Moreover, existing scoring systems do not differentiate nonclassical from classical phenotypes of the disease whereas a growing literature suggests the need for personalized management [14–16]. In this study, we employed unsupervised multivariate statistics for clinical data to identify simple and objective criteria that would allow an effective classification of adult patients. Additionally, we propose a new and simple scoring system based on this classification to assess the clinical severity and facilitate the management of FD patients.

Materials and methods

Patients, clinical data and biological samples

We analyzed data from patients prospectively included in the multicenter cohort FFABRY with an enzymatic and/or genetic diagnosis of FD from December 2014 to May 2017. Written consent were obtained after written and verbal information. The present study was approved by the local ethics committee (Comité de Protection des Personnes VI—Pitié Salpêtrière) and the Comité consultatif sur le traitement de l’information en matière de recherche dans le domaine de la santé, according to the relevant French legislation. Clinical data were prospectively collected through a standardized online form. Cardiac hypertrophy was defined as diastolic interventricular septum thickness > 13 mm by cardiac echocardiography or magnetic resonance imaging (MRI). Arrhythmia was defined as the presence of cardiac conduction defect or rhythm trouble. Estimation of the glomerular filtration rate (eGFR) was based on the CKD-EPI equation [17]. Glomerular hyperfiltration was defined as eGFR > 135ml/min/1.73m2 [18]. Proteinuria was positive if above 0.3 g/24 h or if the proteinuria/creatininuria ratio was > 50 mg/ mmol. Cornea verticillata was assessed via slit-lamp examination. If not mentioned in the medical records, the patients were considered to not have a history of the following items: cerebral stroke, movement disorder, seizure, renal or cardiac transplantation, dialysis, need of a pacemaker (PM), and cardiac failure. All other items were considered missing if not mentioned. The Mainz Severity Score Index (MSSI) was calculated automatically according to the scoring system established by Whybra et al. [19].

Blood samples were collected at the time of inclusion. Plasma was isolated by centrifugation using BD Vacutainer™ serum tubes with an increased silica act clot activator and BD Vacutainer™ heparin tubes before storage at -80°C. All patients were screened for the presence of anti-agalsidase antibodies, as previously described [20]. LysoGb3 concentrations were measured in available plasma samples (n = 36) by ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS), as previously described [20].

Statistical analyses

Males and females were analyzed separately due to the known phenotype differences [12]. We performed ascending hierarchical clustering on principal components (HCPC) after multiple correspondence analysis (MCA) for the following categorical variables: presence or history of CV, angiokeratoma, history of Fabry acral pain, hypertrophic cardiomyopathy (HCM), arrhythmia, eGFR </> 45 ml/min/1.73 m2, renal transplant, ischemic stroke and hearing loss and GLA variant type (missense vs. others). All the categorical variables were used as active and included in the clinical clustering except the GLA variant type used as illustrative. Cerebral MRI abnormalities were excluded due to missing data. Age was considered an illustrative variable and was not included in the clustering. MCA and HCPC were performed with R software version 3.4.0 and the package FactoMineR. Patients with missing data were excluded from this analysis. The correlation between variable and dimension was considered significant at p < 0.02. We defined the best algorithm to meet the previous clusters using ROC curves. After verification for normal distribution and equality of variances with Shapiro-Wilk and Levene tests, respectively, we employed parametric tests such as the t-test with and without Welch’s correction for unequal variances, Pearson correlation and linear regression for Gaussian values, or nonparametric tests such as Kruskal-Wallis (KW) and Mann-Whitney (MW) comparison tests and the Spearman correlation test. We used the log-rank test with Kaplan-Meier to analyze the survival distribution. We applied logistic regression with stepwise selection based on p-values for discrete variables and Fisher’s exact t-test for contingency. The p-value for the alpha-risk in all tests, except for MCA and HCPC, was 0.05. GraphPad Prism 5.0 and the EZR plugin version 1.35v [21] packages for R software were used.

Results

From December 2014 to May 2017, 104 patients (54 males) were prospectively included in the FFABRY cohort. Their general characteristics are described in Table 1.

Table 1. Characteristics of patients (*time under enzyme replacement therapy included).

Males (n = 54) Females (n = 50)
Exposure to treatment None Agalsidase alpha Agalsidase beta Agalsidase alpha and beta Migalastat (+/- agalsidase) None Agalsidase alpha Agalsidase beta Agalsidase alpha and beta Migalastat (+/- agalsidase)
Exposed (n) 10 12 18 11 3 25 9 8 6 2
Currently treated (%) - 44 (81%) - 25 (50%)
Median age (Q1Q3) 27.2 years (20.8–41.0) 46.1 years (34.3–53.6) 48.7 years (43.4–60.4) 42.7 years (31.9–47.8) 43.3 years (32.7–52.1) 43.2 years (36.1–53.2) 52,7 years (48,0–54,6) 58,5 years (47,6–63,0) 54,9 years (49,5–61,2) 52.4 years +/- 8.2 years
Median follow-up time (Q1Q3) 4.1 months (1.9–5.4) 5.7 years (3.7–7.7) 7.1 years (2.8–13.8) 10.7 years (5.2–16.2) 4.2 years* (3.9–8.7) 3.0 years (0.6–5.5) 5.8 years (3.0–12.2) 14.8 years (11.6–15.9) 8.6 years (6.2–10.3) 9.8 years* +/- 8.1 years
Median cumulative exposure to specific treatment (Q1Q3) - 4.0 years (1.3–6.4) 4.6 years (0.3–9.3) 10.6 years (4.0–12.7) 4.2 years (0.6–8.2)* - 2.5 years (2.4–9.9) 6.7 years (2.2–11.4) 6.4 years (5.2–10.0) 2.4 years* +/- 1.6 years
Mean age at visit (mean +/- SD) 43.4 +/- 14.7 years 48.9 +/- 14.5 years
Mean +/- SD cumulative exposure to specific treatment 7.0 +/- 4.8 years 6.1 +/- 4.5 years
Median MSSI Neurological [Q1-Q3] (max 20) 5 [1.3–8] 4.5 [1.3–6.0]
MSSI Cardiac (max 20) 6 [0.0–11.8] 2 [0.0–10.8]
MSSI Renal (max 18) 0 [0–8] 0 [0–8]
MSSI General (max 18) 4 [2–6] 2 [1–4]
MSSI Global (max 76) 20.5 [12.5–28.0] 14.5 [7.0–21.8]
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Clinical clustering in males

Multiple component analysis (MCA) and hierarchical clustering on principal components (HCPC) were performed with data for 41 male patients who had available complete data (Fig 1). Their mean age was 44.4 years-old. We assume that treatment with ERT or chaperone therapy does not modify the overall phenotype of patients. Six patients were untreated at the inclusion (mean age 34.6 years-old; min–max 17.1–58.1 y.). Mean duration of treatment was 6.5 years in treated patients (min—max: 0.26–15.5 y.). The first 2 dimensions of MCA expressed 53.7% of the total inertia. An ascending HCPC performed on the first 5 dimensions identified 3 different clusters (Fig 2). Group 1 (mean age = 50.5 +/- 11.2 years; n = 21) was characterized by the absence of CV (p<10−7), the absence of angiokeratoma (p<10−4), the absence of acral pain (p<0.001), a missense mutation (p<0.02), eGFR > 45 ml/min/1.73 m2 (p<0.02), and the absence of renal transplant (p = 0.02). Group 2 (mean age = 32.3 +/- 9.9 years; n = 13) was characterized by the absence of hypertrophic cardiomyopathy (HCM) (p<10−3), the presence of CV (p = 0.001), the presence of angiokeratoma (p = 0.003), the presence of acral pain (p = 0.007), and eGFR > 45 ml/min/1.73 m2. Group 3 (mean age = 48.8 +/- 9.5 years; n = 7) was characterized by eGFR < 45 ml/min/1.73 m2 (p<10−6), a history of renal transplant (p<10−4) and the presence of CV (p = 0.002). On the basis of these characteristics, we considered group 1 to be the nonclassical phenotype and groups 2 and 3 to be the classical phenotype, with younger and older patients, respectively. When considering the absence of acral pain or CV as criteria for the nonclassical phenotype, we met the previously defined clusters with a sensitivity of 89.5%, a specificity of 91.0%, a positive predictive value of 89.5%, a negative predictive value of 91.0%, and an area under the receiver operating characteristic (ROC) curve of 0.9.

Fig 1.

Fig 1

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a: Variable factor map obtained by multiple component analysis using data for 41 males with complete data (AK: angiokeratoma; CV: cornea verticillata; eGFR: estimated glomerular filtration rate in ml/min/1.73 m2; HCM: hypertrophic cardiomyopathy; KT kidney transplantation). b: Ascending hierarchical classification of individuals using the first 5 dimensions of multiple component analysis performed using data for 41 males with complete data. Three clusters were identified. Group 1, characterized by the absence of cornea verticillata (p<10−7), the absence of angiokeratoma (p<10−4), the absence of acral pain (p<0.001) and the absence of renal disease, was referred to as the nonclassical cluster. Groups 2 and 3, characterized by the presence of cornea verticillata (p<0.002), were referred to as classical groups with younger (mean age 32.3 +/- 9.9 years) and older (mean age 48.8 +/- 9.5 years) patients, respectively.

Fig 2. Hierarchical clustering on the first 5 dimensions of the multiple component analysis performed on data of the 41 males with complete data.

Fig 2

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Clinical clustering in females

We applied the same approach for females using the same variables for patients with complete data (n = 36). MCA and HCPC revealed 3 different clusters without any clinical significance (Fig 3). Therefore, we considered that clinical clustering was not appropriate for females.

Fig 3. Ascending hierarchical classification of individuals (using the first 5 dimensions of multiple component analysis performed using data for 36 females with complete data) did not reveal any significant groups.

Fig 3

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FFABRY score

As already mentioned, the morbidity of FD relies on renal, cardiac and central nervous system involvement. Hence, the prognosis of FD depends on the clinical phenotype of patients. Based on the results of the previous clustering, we introduce the first severity scoring system that takes into account the clinical phenotype of FD. The FFABRY score is therefore constructed with 4 variables: the overall clinical phenotype, the kidney disease score, the heart disease score and the central nervous system score as followed:

Phenotype

  • Males with the classical phenotype: past or actual acroparesthesia and cornea verticillata

  • Males with the nonclassical phenotype: no past or actual acroparesthesia or no cornea verticillata

  • Females

Kidney disease: K score from K0 to K5

FD renal involvement is progressive and characterized by glomerular hyperfiltration and proteinuria followed by a decrease in glomerular function [22,23]. We propose the scale as follows:

K0: No proteinuria and 90 > eGFR > 135 ml/min/1.73 m2

K1: Hyperfiltration such as eGFR ≥ 135ml/min/1.73m2 without proteinuria

K2: 60 < eGFR ≤ 90 ml/min/1.73 m2 OR proteinuria > 0.3g/24h or 50 mg/ mmol

K3: 30 < eGFR ≤ 60 ml/min/1.73 m2 +/- proteinuria.

K4: 15 < eGFR ≤ 30 ml/min/1.73 m2 +/- proteinuria.

K5: eGFR ≤ 15 ml/min/1.73 m2 or dialysis or renal transplant.

Heart disease: H score from H0 to H4

FD is a cause of HCM, progressively leading to diastolic dysfunction, ischemia or obstructive cardiac failure [24]. FD is also characterized by arrhythmia, which has become the leading cause of death [25,26]. An interventricular septum thickness (IST) > 30 mm has been associated with a high risk for sudden death [24]. Additionally, we propose the following staging:

H0: No HCM. No cardiac symptomatology.

H1: HCM such as 13 < IST ≤ 30 mm and/or QRS interval on ECG ≥ 200 msec and/or ventricular hypertrophy on ECG without cardiac symptoms and no need of antiarrhythmic or beta-blockers.

H2: H1 + need of antiarrhythmic or beta-blockers*.

H3: 35% < Left ventricular ejection function (LVEF) ≤ 50% and/or a need of pacemaker (PM) implantation and/or angina.

H4: Need of heart transplant or LVEF ≤ 35% or IST > 30 mm.

* according to guidelines on management of HCM [27]

Central nervous system involvement: N score from N0 to N2

In a logistic regression model, we observed that chronic headaches were associated with a higher risk of stroke in males (OR 16.2, p = 0.01), independent of renal and cardiac diseases. Moreover, a trend toward a higher risk of cerebral stroke was observed in males with cochlear disorder defined by the presence of tinnitus or hearing loss (OR 7.8, p = 0.054), independent of age. Additionally, we propose the following staging:

N0: No chronic headache and no cochlear disorder and no history of stroke.

N1: Chronic headache and/or tinnitus and/or hearing loss and no history of stroke.

N2: History of cerebral stroke.

Total FFABRY score from T0 to T11

The total (T) score was defined as the sum of the K-, H- and N-scores.

Description of the FFABRY cohort clustered by sex, cornea verticillata and acroparesthesia

Using our clustering criteria, we distinguished 29 males with the classical phenotype, 25 males with the nonclassical phenotype and 50 females. Their clinical and biochemical characteristics are described extensively in Table 2. Briefly, among males, those with the nonclassical phenotype were diagnosed at an older age (45.4 vs. 28.6 years; p < 10−3) and had a milder phenotype including a less steep eGFR slope (-1.7 vs. -2.4 ml/min/1.72m2/ years; p < 0.05), a lower risk of renal transplantation (log-rank, HR = 0.07; p < 10−3) and a lower risk of cerebral stroke (1/25 vs. 4/29; non-significant). Surprisingly, ENT involvement was frequent in both male groups, being observed in 70.0% of classical and 52.0% of nonclassical cases (p = ns). Of note, one of the most commonly reported symptoms was anxiety, which was observed in 70.3% of the male patients, with suicide attempts reported for 10.5% of the male cohort. Females exhibited a contrasting globally milder phenotype (Fig 4 and Table 2). However, 18.0% of them had a history of ischemic stroke, with a median age at diagnosis of 45.1 years, and the event was significantly associated with the existence of cardiac rhythm problems (OR: 15.3; p = 0.046). Such an association with rhythm problems was not observed in men.

Table 2. Characteristics of patients stratified by clinical phenotype defined with the FFABRY scoring system.

Phenotype Classical Males Nonclassical Males Females
n 29 25 50
Median age [IQR] (years) 39.8 [29.5–46.3] 51.8 [43.0–60.8] 51 [38.0–58.5]
Median age at diagnosis (years) 28.6 45.4 42.8
Treatment received (N =) 25 19 25
Agalsidase alpha (n =) 8 4 9
Agalsidase beta (n =) 8 10 8
Both agalsidases successively (n =) 8 3 6
Migalastat (n =) 1 2 2
Mean cumulative duration of treatment (+/- SD; years) 10.3 +/- 8.5 13.1 +/- 5.1 6.1 +/- 4.0
Renal disease
R-score > 0 15 19 24
Median R-score 1 0.5 0
Median renal MSSI (IQR) 4 (0–18) 0 (0–8)
eGFR slope in ml/min/1.73 m2/y (r2; p) -2.4 (0.66; p < 0.0001) -1.7 (0.61; p < 0.0001) -0.7 (0.19; p < 0.003)
ESRD (n) 81 0 1
Median survival without renal transplantation 48.6 years NA NA
ACE blocker (n) 14 14 16
ACE blockers in patients with R-score >0 67.9% 50%
Cardio-vascular disease
HCM2 (n =) 15 213 19
Median survival without HCM 46.3 years 57.6 years 4 62.0 years
Pacemaker (n =) 1 6 5 1
Median survival without severe cardiac event (H-score ≥ 3) 50.8 years 60.8 years6 71.2 years
Neurological disease
Dyshidrosis (n) 18/29 6/25 (p = 0.005) 34/50
Heat intolerance (n) 17/23 8/23 (p = 0.008) 9/40
Ischemic strokes (n; %) 4; 13.8% 1; 4.0% 9; 18.0%
Median age [IQR]; years 27.6 [15.0–33.4] 34.0 45.1 [45.1–61.9]
ENT involvement
Tinnitus 12/22 (54.5%) 7/22 (31.8%) 15/44 (34.1%)
Hearing loss 17/24 (70.8%) 13/25 (52.0%) 20/48 (41.7%)
Ophthalmological involvement
Cataract (median age at diagnosis; years) 5/12 (44.5) 4/20 (64.3) 6/42 (60.1)
Cornea verticillata 19/19 0/23 28/42
Other
Angiokeratoma 23 (79.3%) 8 (33.3%)7 18/47 (38.3%)
Abdominal pain 14/27 (51.8%) 5/17 (29.4%) 8/42 (24.4%)
Mental health
Anxiety 12/38 (70.3%) 16/34 (47.1%)
Depression symptoms 11/38 (28.9%) 15/44 (44.1%)
Suicide attempt 4/38 0
Mutations c.137A>G* c.334C>T* c.123del*
c.169C>T c.337T>C* c.125T>G*
c.233C>G c.486G>C* c.214del (n = 2)*
c.334C>T (n = 3)* c.522T>A* c.233C>G (n = 2)
c.424T>C c.644A>G (n = 9)* c.334C>T (n = 5)*
c.486G>C c.692A>G* c.424T>C*
c.539del* c.713G>A(n = 2)* c.427G>A (n = 2)*
c.548G>C* c.758T>C c.486G>C*
c.680G>A c.802-3_802-2del* c.504A>C*
c.729G>C* c.847C>T (n = 2)* c.548G>C*
c.798T>A (n = 2)* c.902G>A* c.655A>C*
c.802-3_802-2del* c.1010T>C* c.680G>A*
c.806T>C* c.1016T>G* c.695T>C*
c.847C>T* c.1087C>T (n = 2)* c.718_719del*
c.875C>T c.729G>C*
c.884T>G c.798T>A
c.901C>T (n = 2)* c.802-3_802-2del (n = 4)*
c.902G>A* c.840A>T
c.1010T>C* c.884T>G (n = 3)*
c.1069_1079del* c.901C>T (n = 6)*
c.1246C>T* c.902G>A*
no data (n = 4) c.1277_1278del (n = 2)*
c.1021del
c.1024C>T
c.1087C>T
c.1168G>A
no data (n = 6)
LysoGb3 (median in ng/ml; IQR; n)
In treated patients 18.9 (11.6–32.3; n = 17) 6.25 (2.6–21.9; n = 17)8 4.5 (2.7–6.2; n = 15)
In untreated patients 101.8 (n = 2) 8.5 (3.0–16.7; n = 5) 2.6 (1.7–3.8; n = 22)
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eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; ACE-blocker: angiotensin conversion enzyme blocker; HCM: hypertrophic cardiomyopathy; PM: pacemaker; NA: not available. (1) HR vs. nonclassical 14.4; log-rank p = 0.0003; (2) in Cox regression, HCM is influenced by age at diagnosis (HR 0.81; p<10–7) and cumulative exposure to treatment (HR 0.71; p < 0.0001); (3) in Cox regression, influenced by the phenotype: HR nonclassical vs. classic: 0.19; p = 0.006); (4) log-rank classical vs. nonclassical; p < 0.003; (5) log-rank classical vs. nonclassical; p = ns; (6) log-rank classical vs. nonclassical; p = 0.01; (7) Fisher-exact t-test classical vs. nonclassical; p<0.001; (8) Mann-Whitney, p = 0.01

*variants included in the MCA and HCPC analyses (exhaustive data). One 46.6-year-old female, a p.A143T carrier, was included in the MCA and HCPC analyses. She had been treated for 2 years with migalastat for acral and abdominal pain and angiokeratoma but had no renal, cardiac or cerebrovascular involvement (MSSI = 13; plasma lysoGb3 1.1nM). Another female p.A143T carrier, aged 43.0 years, had no dermatological, ophthalmological, neurological, renal or cardiological symptoms (MSSI = 1; plasma lysoGb3 1.3nM). She received no specific treatment and was not included in the MCA and HCPC analyses due to missing data.

Fig 4. Renal, cardiac and neurological severe event-free survival curves (black: Classical group males, green: Nonclassical group males, red: Females).

Fig 4

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Performances of clustering with MSSI and FFABRY scores in the entire FFABRY cohort

Clustering appeared clinically relevant using both MSSI and FFABRY scores, with significant differences between groups stratified by classes of ages (Table 3). The classical phenotype was associated with a higher risk of severe renal events (K-score ≥ 3; Cox analysis: HR (classical/ nonclassical) = 35.1; p <10−3; HR (classical/ females) = 100; p < 10−5; Fig 4) and a higher risk of severe cardiac events (H-score ≥ 3; Cox analysis: HR (classical vs. nonclassical) = 4.8; p = 0.008; HR (classical/ females) = 16.7; p < 10−4; Fig 4), and there was a trend toward a higher risk of severe neurological events (N-score = 2) in males with the classical compared to the nonclassical phenotype (Cox analysis; HR = 7.7; p = 0.08, Fig 4). There was no significant difference among females (HR = 2.5; p = 0.2). T-score evolution illustrated that the overall severity increased with time in the classical group compared to the nonclassical group (Fig 5).

Table 3. FFABRY and MSSI scores (comparison with Mann-Whitney test p*: Classical versus nonclassical males; p**: Males versus females).

Males Females
Classical Nonclassical p* p**
n 29 25 50
Age total 39.84 [29.49, 46.29] 51.76 [42.96, 60.76] 0.001 51.00 [37.99, 58.51] 0.001
< 35 y n (median [IQR]) 14 (28.69 [21.69, 32.51]) 6 (29.20 [24.90, 34.35]) 0.509 10 (27.16 [25.09, 32.51]) 0.766
35–50 y n (median [IQR]) 11 (44.50 [43.52, 46.30]) 6 (47.46 [44.28, 47.66]) 0.228 13 (43.14 [38.39, 46.56]) 0.233
> 50 y n (median [IQR]) 4
(51.58 [50.93, 53.93])		 13 (60.76 [58.10, 67.58]) 0.024 27 (58.22 [52.95, 65.26]) 0.074
FFABRY Heart score 1.00 [0.00, 2.00] 2.00 [1.00, 3.00] 0.085 1.00 [0.00, 2.00] 0.025
< 35 y (median [IQR]) 0.00 [0.00, 1.00] 1.00 [0.25, 1.75] 0.130 0.00 [0.00, 0.00] 0.213
35–50 y (median [IQR]) 1.00 [1.00, 2.50] 1.50 [0.25, 2.75] 0.716 0.00 [0.00, 2.00] 0.113
> 50 y (median [IQR]) 3.50 [3.00, 4.00] 3.00 [2.00, 3.00] 0.040 2.00 [1.00, 2.00] 0.001
FFABRY Kidney score 1.00 [0.00, 5.00] 0.50 [0.00, 2.00] 0.214 0.00 [0.00, 2.00] 0.228
< 35 y (median [IQR]) 0.00 [0.00, 0.75] 0.00 [0.00, 0.75] 1.000 0.00 [0.00, 0.00] 0.902
35–50 y (median [IQR]) 2.00 [1.00, 5.00] 0.00 [0.00, 0.00] 0.024 1.00 [0.00, 2.00] 0.026
> 50 y (median [IQR]) 4.50 [4.00, 5.00] 2.00 [1.50, 3.00] 0.004 2.00 [0.00, 2.00] 0.004
FFABRY Neurological score 1.00 [0.00, 1.00] 1.00 [0.00, 1.00] 0.340 1.00 [0.00, 1.00] 0.669
< 35 y (median [IQR]) 0.50 [0.00, 1.00] 0.00 [0.00, 0.00] 0.165 1.00 [0.00, 1.00] 0.231
35–50 y (median [IQR]) 1.00 [0.25, 1.00] 1.00 [0.25, 1.00] 0.859 0.00 [0.00, 1.00] 0.743
> 50 y (median [IQR]) 1.00 [1.00, 1.25] 1.00 [0.00, 1.00] 0.076 1.00 [0.00, 1.00] 0.311
FFABRY Total score 3.00 [1.00, 7.00] 4.00 [2.00, 6.00] 0.882 2.50 [1.00, 4.75] 0.312
< 35 y (median [IQR]) 1.00 [1.00, 1.75] 1.50 [1.00, 2.00] 0.724 1.00 [0.00, 1.00] 0.487
35–50 y (median [IQR]) 5.50 [3.25, 7.00] 2.50 [2.00, 3.75] 0.043 2.00 [1.00, 4.25] 0.030
> 50 y (median [IQR]) 9.00 [8.00, 10.25] 6.00 [4.00, 6.00] 0.003 3.00 [2.00, 5.00] 0.002
MSSI cardiac 2.00 [0.00, 9.00] 10.00 [3.00, 14.00] 0.013 2.00 [0.00, 10.75] 0.025
< 35 y (median [IQR]) 0.50 [0.00, 2.75] 4.50 [0.00, 9.00] 0.505 0.00 [0.00, 1.50] 0.348
35–50 y (median [IQR]) 6.00 [2.00, 10.50] 6.50 [1.50, 9.25] 0.724 0.00 [0.00, 2.00] 0.047
> 50 y (median [IQR]) 1.50 [0.00, 6.75] 14.00 [13.00, 16.00] 0.190 9.00 [1.50, 14.00] 0.067
MSSI general 5.00 [3.00, 8.00] 2.00 [1.00, 4.00] 0.001 2.00 [1.00, 4.00] <0.001
< 35 y (median [IQR]) 4.50 [2.00, 6.00] 2.00 [1.25, 2.00] 0.054 1.50 [1.00, 4.75] 0.167
35–50 y (median [IQR]) 5.00 [4.00, 8.50] 5.50 [3.25, 7.00] 0.577 1.00 [1.00, 3.00] 0.005
> 50 y (median [IQR]) 6.50 [5.00, 9.25] 1.00 [1.00, 2.00] 0.006 2.00 [1.50, 4.00] 0.006
MSSI renal 4.00 [0.00, 18.00] 0.00 [0.00, 8.00] 0.052 0.00 [0.00, 8.00] 0.111
< 35 y (median [IQR]) 0.00 [0.00, 0.00] 0.00 [0.00, 0.00] 0.342 0.00 [0.00, 0.00] 0.558
35–50 y (median [IQR]) 8.00 [6.00, 18.00] 0.00 [0.00, 0.00] 0.015 4.00 [0.00, 4.00] 0.010
> 50 y (median [IQR]) 13.00 [8.00, 18.00] 8.00 [0.00, 8.00] 0.017 0.00 [0.00, 8.00] 0.030
MSSI neurological 7.00 [3.00, 10.00] 2.00 [0.00, 5.00] 0.001 4.50 [1.25, 6.00] 0.002
< 35 y (median [IQR]) 6.00 [2.25, 8.00] 4.00 [2.25, 5.75] 0.534 5.50 [5.00, 7.50] 0.640
35–50 y (median [IQR]) 6.00 [3.50, 12.00] 4.00 [0.75, 7.25] 0.362 5.00 [1.00, 6.00] 0.348
> 50 y (median [IQR]) 9.00 [7.75, 10.75] 1.00 [0.00, 2.00] 0.004 3.00 [0.00, 5.50] 0.004
MSSI total 24.00 [14.00, 33.00] 20.00 [12.00, 24.00] 0.080 14.50 [7.00, 21.75] 0.017
< 35 y (median [IQR]) 14.00 [8.75, 18.75] 10.50 [4.75, 18.50] 0.535 11.00 [5.50, 18.50] 0.704
35–50 y (median [IQR]) 29.00 [24.50, 32.50] 16.50 [12.00, 24.75] 0.039 13.00 [7.00, 15.00] 0.002
> 50 y (median [IQR]) 35.50 [33.75, 37.25] 22.00 [18.00, 24.00] 0.005 19.00 [9.00, 26.00] 0.014
Open in a new tab

Fig 5. T-score evolution in the classical group compared to the nonclassical group (with associated linear regression curve).

Fig 5

Open in a new tab

LysoGb3 plasma levels, anti-agalsidase antibodies and FFABRY

LysoGb3 plasma levels were higher in males with the classical phenotype compared to those with the nonclassical phenotype or females in both treatment-naïve (respective medians 101.8, 5.8 and 3.2; Kruskal Wallis p < 0.04) and in treated (respective medians 18.9, 6.7 and 4.5; p < 10−4) patients. As expected, there was no correlation between lysoGb3 plasma levels and FFABRY scores after stratification by phenotype among treated patients. Two female and 18 male patients were positive for anti-agalsidase antibodies. If considering only males who had been exposed to agalsidase, the presence of antibodies was significantly associated with the classical phenotype (56% vs. 20%, p < 0.02). Their characteristics have already been reported [20].

Genotype-phenotype correlation

Among the 30 different genetic variants observed in males, nonsense variants were associated with the classical phenotype (9/13 patients with nonsense variants). The four patients classified in the nonclassical group had no cornea verticillata: a c.802-3_802-2del carrier with a K1H3N0 score at 17.1 years, two c.847C>T carriers with K0H1N0 and K0H1N1 scores at 33.4 and 47.3 years, and a c.522T>A carrier with a K2H0N0 score at 47.7 years. Five variants were observed in both the classical and nonclassical groups: c.334C>T, c.847C>T, c.802-3_802-2del, c. 902G>A and c.1010T>C. Their characteristics are described in Table 4.

Table 4. Characteristics of patients with genetic variants observed in both classical and nonclassical groups (eGFR: Estimated glomerular filtration by CKD-EPI equation in ml/min/1.73m2; RT: Renal transplant).

Variant Classical patient Nonclassical patient
age Treatment duration (y) FFABRY score eGFR age Treatment duration (y) FFABRY score eGFR
c.334C>T 34.4 6.2 K5 H1 N1 T7 RT 48.1 13.4 K0 H3 N0 T3 105
45.2 3.4 K3 H3 N1 T7 52
59.4 5.6 K4 H3 N1 T8 23
c.802-3_802-2del 50.6 1.3 K4 H3 N1 T8 25 17.1 0 K1 H3 N0 T4 136
c.847C>T 49.8 11.1 K5 H1 N0 T6 RT 33.4 13.3 K0 H1 N0 T1 116
47.3 13 K0 H1 N1 T2 102
c.902G>A 17.5 0.4 K1 H0 N0 T1 136 24.9 0 K0 H2 N0 T2 121
c.1010T>C 46.3 4.3 K2 H3 N0 T5 81 60.9 4.8 K3 H2 N1 T6 57
Open in a new tab

Discussion

The nonclassical phenotype of FD has become the most prevalent [7,12]. However, most clinical and fundamental research studies addressing with FD have not stratified patients based on phenotype. Here, we propose a simple algorithm to distinguish patients. We demonstrate that CV assessed by slit-lamp and acral pain, but not angiokeratoma, are statistically sufficient to distinguish males with the classical phenotype from those with the nonclassical phenotype, with significant differences in terms of severity. As expected, males with the classical phenotype have more severe renal disease than do males with the nonclassical phenotype, but the former also experience cardiomyopathy earlier, and some of them also have stroke early, independent of arrhythmia. Females cannot be separated into classical and nonclassical phenotypes, likely due to the X-inactivation status in the different organs.

Hemangiomas, which are frequent, can easily be misdiagnosed as angiokeratoma, which may explain the reduced specificity of angiokeratoma for the classical phenotype. In contrast, CV is a pivotal criterion in our classification. Although CV can be related to exposure to amiodarone, its association with the severity of FD has already been described, as observed in 94% of classical Fabry cases [28]. A recent paper reported that CV is often underdiagnosed in FD using the slit-lamp approach [29]. The present work concerned 4 males whose phenotype was not mentioned and 10 females. However, whether a more sensitive approach using in vivo corneal confocal microscopy (IVCM) may reveal minimal deposits in nonclassical patients and females is not the question. Hence, we suggest that an unquestionable CV, with obvious deposits assessed by slit-lamp, is associated with a classical FD phenotype. Similarly, our second pivotal criterion is the existence past or actual of acral pain and not the proof of small fiber neuropathy assessed by paraclinical exams.

Our study was based on data for 104 adult patients; a small number of patients is inherent with the rarity of FD. However, FFABRY is a multicenter database including patients from different pedigrees, different medical specialties (nephrology, cardiology, internal medicine, and genetics) and different locations in France, which allows for clinical and genetic heterogeneity (42 different variants) that benefits analyses. We used a linear regression model to assess the slope of eGFR evolution in the cohort, which is limited by the heterogeneity of patients, especially females. Analysis of X-inactivation may allow for a better classification of the female phenotype and help in stratifying the risk of severe events; unfortunately, this analysis remains unavailable in routine practice [30]. As experts managing lysosomal diseases in a dedicated tertiary center, we assume that treatment with ERT or chaperone therapy has not modified the overall natural history of the disease or the prognosis of patients. Nonetheless, to the best of our knowledge, regression but no disappearance of CV has ever been reported [31].

In the era of evidence-based medicine, severity scores have become mandatory for evaluating therapeutics. With FFABRY, we propose a clinically and statistically objective severity scoring system for FD. The FFABRY score was developed based on the natural history of FD and the severe clinically relevant events we observed in our center of expertise for lysosomal diseases. Our N-score highlights the importance of cochlear disorders and headaches, which are associated with the risk of cerebral stroke in males, whereas white matter lesions (WMLs) were not related to any specific symptom.

FFABRY allowed us to establish a portrait of current Fabry patients, distinguishing males with classical and nonclassical phenotypes and females with their proper clinical specificity. Interestingly, we observed no systematic genotype-phenotype correlation. Some patients sharing the same genetic variant were classified into two different groups. The FFABRY scores as well as individual clinical criteria demonstrated that our classification performed better than genotype for describing disease severity. Other scoring systems have already been developed for FD. The MSSI, which has been the most commonly used scoring system, was developed on the basis of data from 24 males and 15 females registered in the Fabry Outcome Survey (FOS) database (Shire-Takeda)[19]. The MSSI includes 26 variables, empirically weighted, among which hemorrhoids, facial appearance and subjective fitness assessment are notable; however, the MSSI does not include renal transplantation. The Disease Severity Scoring System (DS-3), which was elaborated by experts from the Fabry Registry (Sanofi-Genzyme), also includes nonobjective items such as the “patient reported domain” or sweating capacity. WMLs are included, though they are asymptomatic and not associated with poorer outcomes. Finally, the different items and their weightings have been empirically and not statistically established in these two scoring systems. Moreover, the number of items makes them challenging to use and time consuming. The Fabry International Prognostic Index (FIPI) appears to be much more robust, as it has been developed on the basis of multivariate analyses of data from 1483 patients from the FOS registry [32]. Although FIPI appears to be an effective prediction tool, it does not allow assessment of the actual clinical severity. Indeed, five of the six variables included in the cardiac item refer to extracardiac symptoms (eGFR, proteinuria, deafness, vertigo, and angiokeratoma). The online Fabry Stabilization index (FASTEX) was recently validated [33], and the authors established an attractive tool for personal follow-up of individuals. Nevertheless, clinical phenotypes are not distinguished in this system, which could be misleading for interindividual comparisons, making the scoring system useless in group studies. After stratification according to phenotype, FFABRY scores allowed a rapid and clinically relevant evaluation of disease severity. Analyses of the K score highlight the suboptimal management of FD females in our cohort. Our study also emphasizes windows of opportunity for the diagnosis and introduction of specific treatments, as follows: before 30 years old in males with the classical phenotype, 45 years old in males with the nonclassical classical phenotype and 50 years old in females. Regarding the obviously different prognoses observed with the FFABRY score, we believe that the classical and nonclassical phenotypes should be considered as two different subtypes of FD in males, with different management strategies. As for Niemann-Pick disease A and B or Gaucher types 1, 2 and 3, it would be useful to rename the phenotypes for male patients, with classical as type 1 and nonclassical as type 2, to fully take into account such obvious clinical differences and possible pathophysiological differences.

Identifying acral pain and cornea verticillata is a rapid and simple approach that statistically discriminates Fabry phenotypes.

Supporting information

S1 Data. Clinical and biological data of included patients.

(XLSX)

Click here for additional data file. (146.8KB, xlsx)

Acknowledgments

We gratefully thank the Société Nationale Française de Médecine Interne and Vaincre les maladies lysosomales patient association for their support as well as Isabelle Citerne and Epiconcept’s team for their help.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

  • 1.Brady RO, Gal AE, Bradley RM, Martensson E, Warshaw AL, Laster L. Enzymatic defect in Fabry’s disease. Ceramidetrihexosidase deficiency. N Engl J Med. 1967;276: 1163–1167. 10.1056/NEJM196705252762101 [DOI] [PubMed] [Google Scholar]
  • 2.Smid BE, van der Tol L, Biegstraaten M, Linthorst GE, Hollak CEM, Poorthuis BJHM. Plasma globotriaosylsphingosine in relation to phenotypes of Fabry disease. J Med Genet. 2015;52: 262–268. 10.1136/jmedgenet-2014-102872 [DOI] [PubMed] [Google Scholar]
  • 3.Young-Gqamana B, Brignol N, Chang H-H, Khanna R, Soska R, Fuller M, et al. Migalastat HCl reduces globotriaosylsphingosine (lyso-Gb3) in Fabry transgenic mice and in the plasma of Fabry patients. PloS One. 2013;8: e57631 10.1371/journal.pone.0057631 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Schiffmann R, Warnock DG, Banikazemi M, Bultas J, Linthorst GE, Packman S, et al. Fabry disease: progression of nephropathy, and prevalence of cardiac and cerebrovascular events before enzyme replacement therapy. Nephrol Dial Transplant Off Publ Eur Dial Transpl Assoc—Eur Ren Assoc. 2009;24: 2102–2111. 10.1093/ndt/gfp031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Meikle PJ, Hopwood JJ, Clague AE, Carey WF. Prevalence of lysosomal storage disorders. JAMA. 1999;281: 249–254. 10.1001/jama.281.3.249 [DOI] [PubMed] [Google Scholar]
  • 6.Poorthuis BJ, Wevers RA, Kleijer WJ, Groener JE, de Jong JG, van Weely S, et al. The frequency of lysosomal storage diseases in The Netherlands. Hum Genet. 1999;105: 151–156. 10.1007/s004399900075 [DOI] [PubMed] [Google Scholar]
  • 7.Spada M, Pagliardini S, Yasuda M, Tukel T, Thiagarajan G, Sakuraba H, et al. High incidence of later-onset fabry disease revealed by newborn screening. Am J Hum Genet. 2006;79: 31–40. 10.1086/504601 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Burton BK, Charrow J, Hoganson GE, Waggoner D, Tinkle B, Braddock SR, et al. Newborn Screening for Lysosomal Storage Disorders in Illinois: The Initial 15-Month Experience. J Pediatr. 2017;190: 130–135. 10.1016/j.jpeds.2017.06.048 [DOI] [PubMed] [Google Scholar]
  • 9.Hopkins PV, Klug T, Vermette L, Raburn-Miller J, Kiesling J, Rogers S. Incidence of 4 Lysosomal Storage Disorders From 4 Years of Newborn Screening. JAMA Pediatr. 2018;172: 696–697. 10.1001/jamapediatrics.2018.0263 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Wittmann J, Karg E, Turi S, Legnini E, Wittmann G, Giese A-K, et al. Newborn screening for lysosomal storage disorders in hungary. JIMD Rep. 2012;6: 117–125. 10.1007/8904_2012_130 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.von Scheidt W, Eng CM, Fitzmaurice TF, Erdmann E, Hübner G, Olsen EG, et al. An atypical variant of Fabry’s disease with manifestations confined to the myocardium. N Engl J Med. 1991;324: 395–399. 10.1056/NEJM199102073240607 [DOI] [PubMed] [Google Scholar]
  • 12.Arends M, Wanner C, Hughes D, Mehta A, Oder D, Watkinson OT, et al. Characterization of Classical and Nonclassical Fabry Disease: A Multicenter Study. J Am Soc Nephrol. 2017;28: 1631–1641. 10.1681/ASN.2016090964 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.van der Tol L, Smid BE, Poorthuis BJHM, Biegstraaten M, Deprez RHL, Linthorst GE, et al. A systematic review on screening for Fabry disease: prevalence of individuals with genetic variants of unknown significance. J Med Genet. 2014;51: 1–9. 10.1136/jmedgenet-2013-101857 [DOI] [PubMed] [Google Scholar]
  • 14.Ortiz A, Germain DP, Desnick RJ, Politei J, Mauer M, Burlina A, et al. Fabry disease revisited: Management and treatment recommendations for adult patients. Mol Genet Metab. 2018;123: 416–427. 10.1016/j.ymgme.2018.02.014 [DOI] [PubMed] [Google Scholar]
  • 15.Citro V, Cammisa M, Liguori L, Cimmaruta C, Lukas J, Cubellis MV, et al. The Large Phenotypic Spectrum of Fabry Disease Requires Graduated Diagnosis and Personalized Therapy: A Meta-Analysis Can Help to Differentiate Missense Mutations. Int J Mol Sci. 2016;17 10.3390/ijms17122010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Oder D, Nordbeck P, Wanner C. Long Term Treatment with Enzyme Replacement Therapy in Patients with Fabry Disease. Nephron. 2016;134: 30–36. 10.1159/000448968 [DOI] [PubMed] [Google Scholar]
  • 17.Chen F, Du M, Blumberg JB, Chui KKH, Ruan M, Rogers G, et al. Association Between Dietary Supplement Use, Nutrient Intake, and Mortality Among US Adults: A Cohort Study. Ann Intern Med. 2019;170: 604–613. 10.7326/M18-2478 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Tonneijck L, Muskiet MHA, Smits MM, van Bommel EJ, Heerspink HJL, van Raalte DH, et al. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment. J Am Soc Nephrol JASN. 2017;28: 1023–1039. 10.1681/ASN.2016060666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Whybra C, Kampmann C, Krummenauer F, Ries M, Mengel E, Miebach E, et al. The Mainz Severity Score Index: a new instrument for quantifying the Anderson-Fabry disease phenotype, and the response of patients to enzyme replacement therapy. Clin Genet. 2004;65: 299–307. 10.1111/j.1399-0004.2004.00219.x [DOI] [PubMed] [Google Scholar]
  • 20.Mauhin W, Lidove O, Amelin D, Lamari F, Caillaud C, Mingozzi F, et al. Deep characterization of the anti-drug antibodies developed in Fabry disease patients, a prospective analysis from the French multicenter cohort FFABRY. Orphanet J Rare Dis. 2018;13: 127 10.1186/s13023-018-0877-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Kanda Y. Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant. 2013;48: 452–458. 10.1038/bmt.2012.244 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Tøndel C, Kanai T, Larsen KK, Ito S, Politei JM, Warnock DG, et al. Foot process effacement is an early marker of nephropathy in young classic Fabry patients without albuminuria. Nephron. 2015;129: 16–21. 10.1159/000369309 [DOI] [PubMed] [Google Scholar]
  • 23.Riccio E, Sabbatini M, Bruzzese D, Annicchiarico Petruzzelli L, Pellegrino A, Spinelli L, et al. Glomerular Hyperfiltration: An Early Marker of Nephropathy in Fabry Disease. Nephron. 2019;141: 10–17. 10.1159/000493469 [DOI] [PubMed] [Google Scholar]
  • 24.Marian AJ, Braunwald E. Hypertrophic Cardiomyopathy: Genetics, Pathogenesis, Clinical Manifestations, Diagnosis, and Therapy. Circ Res. 2017;121: 749–770. 10.1161/CIRCRESAHA.117.311059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Baig S, Edward NC, Kotecha D, Liu B, Nordin S, Kozor R, et al. Ventricular arrhythmia and sudden cardiac death in Fabry disease: a systematic review of risk factors in clinical practice. Eur Eur Pacing Arrhythm Card Electrophysiol J Work Groups Card Pacing Arrhythm Card Cell Electrophysiol Eur Soc Cardiol. 2018;20: f153–f161. 10.1093/europace/eux261 [DOI] [PubMed] [Google Scholar]
  • 26.Mehta A, Clarke JTR, Giugliani R, Elliott P, Linhart A, Beck M, et al. Natural course of Fabry disease: changing pattern of causes of death in FOS—Fabry Outcome Survey. J Med Genet. 2009;46: 548–552. 10.1136/jmg.2008.065904 [DOI] [PubMed] [Google Scholar]
  • 27.Authors/Task Force members, Elliott PM, Anastasakis A, Borger MA, Borggrefe M, Cecchi F, et al. 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy: the Task Force for the Diagnosis and Management of Hypertrophic Cardiomyopathy of the European Society of Cardiology (ESC). Eur Heart J. 2014;35: 2733–2779. 10.1093/eurheartj/ehu284 [DOI] [PubMed] [Google Scholar]
  • 28.van der Tol L, Sminia ML, Hollak CEM, Biegstraaten M. Cornea verticillata supports a diagnosis of Fabry disease in non-classical phenotypes: results from the Dutch cohort and a systematic review. Br J Ophthalmol. 2016;100: 3–8. 10.1136/bjophthalmol-2014-306433 [DOI] [PubMed] [Google Scholar]
  • 29.Leonardi A, Carraro G, Modugno RL, Rossomando V, Scalora T, Lazzarini D, et al. Cornea verticillata in Fabry disease: a comparative study between slit-lamp examination and in vivo corneal confocal microscopy. Br J Ophthalmol. 2019. 10.1136/bjophthalmol-2019-314249 [DOI] [PubMed] [Google Scholar]
  • 30.Echevarria L, Benistan K, Toussaint A, Dubourg O, Hagege AA, Eladari D, et al. X-chromosome inactivation in female patients with Fabry disease. Clin Genet. 2016;89: 44–54. 10.1111/cge.12613 [DOI] [PubMed] [Google Scholar]
  • 31.Prominent regression of corneal deposits in Fabry disease 16 years after initiation of enzyme replacement therapy.—PubMed—NCBI. [cited 27 Nov 2019]. Available: https://www.ncbi.nlm.nih.gov/pubmed/?term=Prominent+regression+ofcorneal+deposits+in+Fabrydisease+16+years+afterinitiation+of+enzymereplacement+therapy [DOI] [PubMed]
  • 32.Hughes DA, Malmenäs M, Deegan PB, Elliott PM, Ginsberg L, Hajioff D, et al. Fabry International Prognostic Index: a predictive severity score for Anderson-Fabry disease. J Med Genet. 2012;49: 212–220. 10.1136/jmedgenet-2011-100407 [DOI] [PubMed] [Google Scholar]
  • 33.Cairns T, Wanner C. Will the FAbry STabilization indEX make its way to everyday clinical practice? Clin Kidney J. 2019;12: 61–64. 10.1093/ckj/sfy126 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Maria Vittoria Cubellis

24 Mar 2020

PONE-D-19-35853

Cornea verticillata and acroparesthesia efficiently discriminate clusters of severity in Fabry disease

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Cornea verticillata and acroparesthesia efficiently discriminate clusters of severity in Fabry disease

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"WM received honoraria, congress fees and travel assistance from Shire-Takeda, Amicus and Sanofi-Genzyme.

FLam has received travel support from Amicus Therapeutics., Shire and Sanofi-Genzyme. He received lecture fees from Actelion Pharmaceuticals.

OL has received travel support and lecture fees from Amicus Therapeutics, Shire, and Sanofi-Genzyme.

DL has received honoraria and travel assistance from Sanofi-Genzyme and has participated on boards with Amicus.

HM received honoraria and travel assistance from Sanofi-Genzyme and Amicus and has participated on boards with Amicus and Shire.

BD has received honoraria from Amicus (member of the scientific board) and Novartis (lectures) and travel fees from Genzyme-Sanofi.

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EN has received travel fees from Shire and Sanofi-Genzyme and an honorarium from Amicus.

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CC has received consultant honoraria and congress fees from Biomarin and Sanofi-Genzyme and has participated in editorial activity with Takeda-Shire.

TZ has received congress fees and travel assistance from Sanofi-Genzyme.

FM has received honoraria from Shire and travel assistance from Sanofi-Genzyme.

CL has received honoraria from Sanofi-Genzyme and travel assistance from Sanofi-Genzyme and Shire.

CD has received travel assistance from Shire, Sanofi-Genzyme, Sobi, Orphan Europe, Nutricia, Lucane Pharma, Amicus, and Ultragenyx and honoraria from Amicus and has participated on boards with Ultragenyx and Sanofi.

GB has received travel assistance from Shire, Genzyme-Sanofi and Amicus.

OB, AD, PDH, KHL, FLab, MM, CM, and MW declare no conflict of interest."

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Reviewers' comments:

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Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #1: I Don't Know

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Many thanks for submitting the manuscript PONE-D-19-35853 for review. The authors Mauhin and colleagues present an informative study that aims to clinically classify Fabry patients. The authors develop a new scoring system to classify FD patients and conclude that acral pain (experienced in the past or presence) and cornea verticillata suffice to discriminate classical from nonclassical FD patients.

The strength of the study lies in the very well documented patient cohort of 104 male and female FD patients from various centers in France. The evaluations and statistical tests used are, in the opinion of this reviewer, correctly chosen.

Nevertheless, I think that the study is quite ambitious and in the current version confusing at times. I will specify my criticism in the following points so that the authors can make changes if necessary, because I think that clarity needs to be improved in order to reach a broader readership.

Major points:

1. Abstract: In general, and this should perhaps be addressed in the abstract, I am unsure about the message and merit of the study. In the abstract the "methods" describe very briefly the statistical evaluation. Would it not be more appropriate to mention the approach of introducing a new score to validate the patient clusters obtained. I think that would summarize the article better. Is a completely new score even suitable to validate the clusters? Or would you maybe call it a “comparison”. As I said I am quite unsure what to do with this approach even though I think it is interesting. Since you use also the approved MSSI score for "validation" or correlation of the data (Table 3), maybe this can be named here as well. Since this should be described in more detail, the incredibly detailed "Results" section could then be shortened by a few of the given numbers and only mention general findings. Detailed results should then be named in the actual results section.

2. Introduction: A further question of understanding arises from the last sentence of the introduction, page 7, lines 122-ff. How do the authors transmit from the clinical clustering to the FFABRY score? What is the relationship between the score, as strangely enough introduced in the results section starting on page 13, and the clustering data shown above (Figures 1 and 2)? If I understand it correctly, clustering is being conducted qualitatively, binary, yes/no, but the score suddenly comes up with 6 K-classes for kidney disease, 5 H classes for heart disease and 3 N classes for neurological involvement. Of course I understand the introduction of a scoring system that is as precise as possible. The scores of the parameters are added to form a total score, but how does this fit to the clustering approach into basically 2(!) patient groups (or 3, if I consider the females) introduced before? I feel a logical disruption here, 2 different subjects being mixed up. The score results from the existing patient data and the clustering also, but should the score result from the clustering data either? That connection is missing, especially since all 54 male individuals obtain an FFABRY score, but were not used for the clustering. The title of Table 2 states: “Characteristics of patients stratified by clinical phenotype defined with the FFABRY scoring system.” If the FFABRY score is exclusively used to classify the patients, there should not be an overlap in FFABRY scores in the classical and nonclassical group. In this case, I think it is better to indicate the min-max than the IQR, because IQR confusingly suggests an intersection of the groups.

3. Results: About the clustering criteria. If I understand this correctly, each criterion can be answered in binary form. Acral pain/no acral pain, CV/no CV and so on. Since it is mentioned in the text as a distinguishing criterion for the nonclassical group 1 (line 182) I searched for "no missense mutation" or similar in figure 1a, but I did not find it. Maybe it helps to sum up all criteria in a table.

4. Results: Table 3 contains a lot of information, but is hardly mentioned or explained in the text. You just state (page 18, line 300-f.): “Clustering appeared clinically relevant using both MSSI and FFABRY scores, with significant differences between groups stratified by classes of ages (table 3).“ However, the statistical assessment was carried out between the 3 groups classical, nonclassical and female. Here is the question, what do we learn from this? Statistical significance rarely seems to be the case here. If age is a significant factor, then surely statistics should have been displayed between age groups within each patient group? That confuses me a bit?

5. Discussion: I would be interested how many patients in this cohort were initially classified as classical before the score was applied? Did the score obtained match the original assessment? In this regard it is important to note whether the physical examinations for the renal, cardiac and neurological parameters have been performed by the same physician in all 104 patients? And was the MSSI score taken as a basis? As you write, it is subject to a certain amount of subjectivity.

6. Discussion: I am unsure about this point (it relates to point 5 above), but are the numerical thresholds used for the FFABRY score objective? As can be seen from the biomarker, patients with higher lyso-Gb3 levels are not necessarily more severely affected. In other words, how objectively can one say that a patient with 60 < eGFR ≤ 90 ml/min/1.73 m² is less severely affected than a patient with 30 < eGFR ≤ 60 ml/min/1.73 m²? This should also be discussed I think. In the end, the overall condition of the patient always tells us how severely he/she is affected or am I wrong?

7. Discussion: What is the overall benefit to use the Score developed in this study? Maybe you could give examples to clarify this. I give an example: A study that discovers a missense mutation that is amenable to chaperone therapy has a direct impact on a patient with such mutation. The clinical phenotype of the patient plays a minor role. The main indicator is the knowledge about the genetics of the patient. I mean, certainly in nonclassical cases a decision on therapy will be approached more cautiously than in classical cases, where the start of therapy must not be delayed. But does this study contribute anything to that? Don't get me wrong, I think the new score is very informative and objectifies the issue somehow, maybe it should be mentioned in the title of the study? But as you write, there is hardly a novelty in the finding that CV discriminates the Fabry patient groups, I quote from the article (page 23, line 350-ff.): "Although CV can be related to exposure to amiodarone, its association with the severity of FD has already been described, as observed in 94% of classical Fabry cases (22)". In other words, isn´t there any evidence that the classification can be helpful in therapy decisions as to start an ERT or PCT for example? This topic should be discussed.

8. Figures: Basically all figures, but especially Figure 4 require strong editing. The statements made cannot be reconstructed at all on the basis of the figure, as the caption is not legible. The legend for Figure 4 does not provide much information either. I would recommend the use of panel "A", "B", and "C" for renal, cardiac, and neurological. Do the numbers in Figure 3 have a meaning? They are not readable. Do they denote patient IDs as in Figure 1b? Figure 5 reads the French term "Linéaire". This should be translated into English.

Minor points:

Page 4, l. 75: GLA gene should be written in italics (holds true throughout the text)

Page 6, l. 106-ff.: The sentence on page 6, starting at line 106 reports 3 recent newborn studies. First, the references for the low incidence should be placed after "live births [3,4],..." and the references [5-7] should be placed as is. However, the authors omit a fourth study by Wittmann and colleagues (JIMD Rep. 2012;6:117-25. doi: 10.1007/8904_2012_130.), which reaches a similar result in a newborn study in Hungary and should also be cited here.

Page 12, line 177-ff.: It is not indicated whether the 41 subjects for this analysis also included untreated patients. Apart from the split between the sexes, I consider this to be an essential point to consider. Even though this following statement is made on page 24, line 368-ff.: “As experts managing lysosomal diseases in a dedicated tertiary center, we assume that treatment with ERT or chaperone therapy has not modified the overall natural history of the disease or the prognosis of patients. Nonetheless, to the best of our knowledge, regression but no disappearance of CV has ever been reported (25)." This must be introduced directly at this point, as it otherwise contributes to the confusing structure of the article.

Page 16, line 267: Should not the 41 males and 36 females with complete data be used for this analysis? Would the result change?

Page 23, line 366-ff. and page 24, line 371-ff. Redundant information is given.

Reviewer #2: The manuscript Cornea verticillata and acroparesthesia efficiently discriminate clusters of severity in

Fabry disease by Wladimir MAUHIN and coworkers, concerns with Fabry disease and specifically with the search for “a simple and clinically relavent way to classify patients according to their disease severity”.

Overall, the manuscript is well organized and described.

I think that it deserves to be published. I would suggest few minor revisions.

1 Line 100-102: The authors state: “Fabry disease (FD; OMIM #301 500) is an X-linked lysosomal storage disease caused by an enzymatic defect of the hydrolase alpha-galactosidase A (AGAL-A), resulting in the accumulation of glycosphingolipids, mainly globotriaosylceramide (Gb3) (1)”.

Please shortly cite and discuss LysoGb3. Despite the fact that Gb3 accumulates, LysoGb3 is the biomarker usually measured, in fact the authors show and discuss results concerning LysoGb3 (for example see the paragraph “LysoGb3 plasma levels, anti-agalsidase antibodies and FFABRY” and table2).

The following references could be useful:

Smid, B.E.; van der Tol, L.; Biegstraaten, M.; Linthorst, G.E.; Hollak, C.E.; Poorthuis, B.J. Plasma globotriaosylsphingosine in relation to phenotypes of Fabry disease. J. Med. Genet. 2015, 52, 262–268.

Young-Gqamana, B.; Brignol, N.; Chang, H.H.; Khanna, R.; Soska, R.; Fuller, M.; Sitaraman, S.A.;Germain, D.P.; Giugliani, R.; Hughes, D.A.; et al. Migalastat hcl reduces globotriaosylsphingosine (lyso-Gb3) in Fabry transgenic mice and in the plasma of Fabry patients. PLoS ONE 2013, 8, e57631

2 Lines 119-120. The authors state: “As the prognosis of the different phenotypes is markedly different, there is a need to determine reproducible classification criteria to improve the reliability of therapeutic studies and to personalize the bedside management of FD. Some scoring systems already exist, and they have been elaborated with empirical considerations; these scoring systems include many nonobjective criteria with several items that make them difficult to use in a daily practice”.

It would be useful to introduce some references. For example:

Fabry disease revisited: Management and treatment recommendations for adult patients. Ortiz A, Germain DP, Desnick RJ, Politei J, Mauer M, Burlina A, Eng C, Hopkin RJ, Laney D, Linhart A, Waldek S, Wallace E, Weidemann F, Wilcox WR. Mol Genet Metab. 2018 Apr;123(4):416-427. doi: 0.1016/j.ymgme.2018.02.014. Epub 2018 Feb 28. Review.

The Large Phenotypic Spectrum of Fabry Disease Requires Graduated Diagnosis and Personalized Therapy: A Meta-Analysis Can Help to Differentiate Missense Mutations. Citro V, Cammisa M, Liguori L, Cimmaruta C, Lukas J, Cubellis MV, Andreotti G. Int J Mol Sci. 2016 Dec 1;17(12). pii: E2010. Review.

Long Term Treatment with Enzyme Replacement Therapy in Patients with Fabry Disease. Oder D, Nordbeck P, Wanner C. Nephron. 2016;134(1):30-6. doi: 10.1159/000448968. Epub 2016 Aug 27. Review.

Fabry disease: Review and experience during newborn screening. Hsu TR, Niu DM. Trends Cardiovasc Med. 2018 May;28(4):274-281. doi: 10.1016/j.tcm.2017.10.001. Epub 2017 Oct 20. Review.

3 Table 3. Please specify the meaning of “p”

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PLoS One. 2020 May 22;15(5):e0233460. doi: 10.1371/journal.pone.0233460.r002

Author response to Decision Letter 0

4 May 2020

Response to Editor

#1 We revised documents to fulfill with PLOS ONE’s style requirement

#2 & #3 : We added/ modified informations in the manuscript and the online submission information. In the manuscript now appears “Written consent were obtained after written and verbal information. The present study was approved by the local ethics commitee (Comité de Protection des Personnes VI - Pitié Salpêtrière) and the Comité consultatif sur le traitement de l’information en matière de recherche dans le domaine de la santé, according to the relevant French legislation. »

#4 Our ethics statement appears in the Methods section of the manuscript.

#5 We have inquired about updated conflicts of interests. No modification has been done. Also we confirm and mention that they do “not alter our adherence to PLOS ONE policies on sharing data and materials”

#6. We had caption of the supporting information file at the end of the manuscript

Answers to reviewers:

Reviewer #1

#1 We first thank the reviewer for the constructive comments on our manuscript. We have taken in account the feeling of the reviewer about the lack of clarity. We modified the abstract to this end, notably by mentioning the new score we introduce in the manuscript. Nevertheless, we could not explicit the whole statistical protocol in the abstract and invite readers to refer to the method section due to the lack of space. We had to shorten the “Results” part as proposed by the reviewer to allow the modifications. We propose the following modifications:

In the “Methods” section: Thanks to these criteria and empirical clinical considerations we secondly elaborate a new score that allow the severity stratification of patients.

In the “Results”section: The classical phenotype was associated with a higher risk of severe renal (HR = 35.1; p <10-3) and cardiac events (HR = 4.8 ; p = 0.008) and a trend toward a higher risk of severe neurological events (HR = 7.7; p = 0.08) compared to nonclassical males. Our simple, rapid and clinically-relevant FFABRY score gave concordant results with the validated MSSI.

#2 We understood this remark. As mentioned in the introduction, some facts have to be noticed:

1. “the prognosis of the different phenotypes is markedly different”

2. “there is a need to determine reproducible classification criteria” for phenotype

3. “scoring systems already exist” but they do not differentiate nonclassical from classical phenotypes of the disease. Finally they “include many nonobjective criteria with several items that make them difficult to use in a daily practice”.

Hence, existing scores cannot be used to stratify patients.

We aimed first to easily classify the clinical phenotype of patients and performed hierarchical clustering to this end. Secondly, we aimed to elaborate a simple score that take into account this previous classification in order to stratify patients by groups of comparable prognosis. The phenotype classification is the starting point and the fundamental characteristic of the FFABRY score.

We modified the text to specifically explicit this point.

If I understand it correctly, clustering is being conducted qualitatively, binary, yes/no, but the score suddenly comes up with 6 K-classes for kidney disease, 5 H classes for heart disease and 3 N classes for neurological involvement. Of course I understand the introduction of a scoring system that is as precise as possible. The scores of the parameters are added to form a total score, but how does this fit to the clustering approach into basically 2(!) patient groups (or 3, if I consider the females) introduced before? I feel a logical disruption here, 2 different subjects being mixed up. The score results from the existing patient data and the clustering also, but should the score result from the clustering data either? That connection is missing, especially since all 54 male individuals obtain an FFABRY score, but were not used for the clustering.

We propose the FFABRY score as an extension of the statistical work that should not be considered as a mix up. As mentioned in the introduction we propose a “scoring system based on this [phenotype] classification to assess the clinical severity” of patients. The prognosis of Fabry disease is related to kidney failure, cardiac hypertrophy and cerebral stroke. Therefore, we proposed unquestionable empirical criteria of severity in terms of renal, cardiac and central nervous system involvement based on the existent literature. Criteria are well defined in the manuscript. The interest and the novelty of our FFABRY score lie on the phenotype classification defined by the previous statistical clustering. It allows an unbiased stratification of severity, organ by organ, according to the clinical phenotypes. The FFABRY score has not been introduced to validate our statistical work. It has been introduced to serve as a new simple tool to stratify Fabry disease patients.

We have added 2 sentences to introduce the specific paragraph in order to clarify this point: “As already mentioned, the morbidity of FD relies on renal, cardiac and central nervous system involvement. Hence, the prognosis of FD depends on the clinical phenotype of patients. Based on the results of the previous clustering, we introduce the first severity scoring system that takes into account the clinical phenotype of FD. The FFABRY score is therefore constructed with 4 variables: the clinical phenotype, the kidney disease score, the heart disease score and the central nervous system score as followed: …”

The title of Table 2 states: “Characteristics of patients stratified by clinical phenotype defined with the FFABRY scoring system.” If the FFABRY score is exclusively used to classify the patients, there should not be an overlap in FFABRY scores in the classical and nonclassical group. In this case, I think it is better to indicate the min-max than the IQR, because IQR confusingly suggests an intersection of the groups.

There may be a misunderstanding: the clinical phenotype is a fundamental variable of the scoring system. We have tried to correct this misunderstanding with the introduction added to the score paragraph: “The FFABRY score is therefore constructed with 4 variables: the overall clinical phenotype, the kidney disease score, the heart disease score and the central nervous system score as followed:..”.

We definitely think that IQR is the most effective illustration of dispersion in such non-normal Gaussian distributions.

#3. It is more complicated. Multiple correspondence analysis allows to analyze categorical variables expressed in binary form. The list is already mentioned in the Method section “multiple correspondence analysis (MCA) for the following categorical variables: presence or history of CV, angiokeratoma, history of Fabry acral pain, hypertrophic cardiomyopathy (HCM), arrhythmia, eGFR </> 45 ml/min/1.73 m², renal transplant, ischemic stroke, hearing loss and GLA variant type (missense vs. others).” Each variable can be active, participating into the dispersion map, or illustrative, not included in the calculation: clusters do not take into account illustrative variables. However, illustrative variables can be used to describe clusters.

Among the categorical variables, the GLA variant type (missense vs. others) was considered as an illustrative variable because we aimed to elaborate a clinical clustering. Age was also considered as an illustrative variable because of the bias it could introduce. Therefore, we can describe the prevalence of missense mutations and the age distribution in clusters but these variables were not weighted in the calculation and therefore did not appear on the dispersion map figure 1. We add the following sentence in the Method section to clarify: “All the categorical variables were used as active and included in the clinical clustering except the GLA variant type used as illustrative”

#4. First of all, yes, the age is a fundamental factor in Fabry disease. Fabry disease is a degenerative disease in which older patients are necessarily more severe. As already described in the methods, the age was used as an illustrative variable in the statistics because including it as an active variable would have led to mix not yet severe young classical patients and non severe old nonclassical patients. This is illustrated in the figure 1 in which groups 2 and 3 share common characteristics such as cornea verticillata being considered as classical patients, but mean ages of the groups are different, the youngest being the less severe.

Including the age as an active variable for clustering could have been possible in a very large cohort that is impossible for Fabry disease that remains a rare disease.

The table 3 illustrates first the comparability of the validated but very time consuming MSSI score and our simple FFABRY score. Also, it shows the importance of considering both ages and clinical phenotypes to stratify patients.

#5. The FFABRY cohort was initiated in 2014. It gathers data from 17 tertiary centers in France. The physical examinations were performed locally by different practitioners, guided with a standardized form that does not mention the clinical phenotype. There was therefore no classification before the clustering. Although empirically validated, the MSSI score takes into account a lot of (very) subjective (“characteristic facial appearance”, fatigue, depression, diaphoresis) and some inconsistent variables such as the presence of hemorrhoids… With FFABRY, we aimed to elaborate a score based on statistics and strong, objective and reproducible evaluation.

It is important to note that the phenotype is not defined by the score itself but by the absence or presence of cornea verticillata or acral pain. The FFABRY score is supplementary tool, based on this classification and elaborate to stratify patients taking into account these clinical phenotypes.

#6. We may not understand your question. The clinical phenotype is essential, especially for young patients. The presence of cornea verticillata and/or acral pain will lead us to be more aggressive in the treatment and surveillance than for other patients. Once more, the clinical phenotype classification is different from the score.

This is what we wanted to illustrate in the discussion with the sentence “Regarding the obviously different prognoses observed with the FFABRY score, we believe that the classical and nonclassical phenotypes should be considered as two different subtypes of FD in males, with different management strategies.”

However, your remark seems to address to a larger problem than the one of the FFABRY score but to the validity of thresholds in general. Also, we agree that a patient with an eGFR of 58ml/min.1.73m² is not so different from another with 63ml/min/1.73m²… The fact is that we try to elaborate a reproducible stratifying score, and we need thresholds to do so. The different thresholds used for the FFABRY score have been chosen on the basis of already validated scales such as the international KDIGO classification for renal disease or on existing literature.

The FFABRY score is not elaborated to manage patients at bedside. It has not been elaborated to substitute the overall clinical evaluation. It has been elaborated to stratify patients mainly in clinical research to allow the comparison of comparable patients.

#7. We may have answered this point in the previous one at some point.

We agree that there is no individual benefit for the patient to be evaluated with the FFABRY score, no more than with the MSSI or the FIPI. The clinical phenotype is essential, especially for young patients. The presence of cornea verticillata and/or acral pain will lead us to be more aggressive in the treatment and surveillance than for other patients.

Once more, the clinical phenotype classification is different from the score. Also, the FFABRY score has been elaborated to stratify patients mainly for clinical research to allow the comparison of comparable patients. As we observe such different prognoses between phenotypes, it is essential to stratify patients upon clinical phenotypes to evaluate the different therapeutical strategies properly. Secondly, we already know that the preexistence of organ involvement, such as proteinuria or cardiac fibrosis, before treatment is determinant for the overall prognosis. That is what we try to illustrate with our sentence:

“clinical phenotypes are not distinguished in [the existing scoring systems], which could be misleading for interindividual comparisons, making the scoring system useless in group studies. After stratification according to phenotype, FFABRY scores allowed a rapid and clinically relevant evaluation of disease severity.”

#8. We edited figures 3, 4 and 5 to improve readability.

Numbers in Fig3 referred to IDs. We modified the numbers as they appear in the database to improve readability. It does not change the meaning of the figure that clustering is not relevant for women patients.

We have moved the fig4 to the paragraph “Performances of clustering with MSSI and FFABRY scores in the entire FFABRY cohort » where it appears more appropriate and comprehensible.

Minor points:

Page 4, l. 75: GLA gene should be written in italics (holds true throughout the text)

>> We modified all the occurrences.

Page 6, l. 106-ff.: The sentence on page 6, starting at line 106 reports 3 recent newborn studies. First, the references for the low incidence should be placed after "live births [3,4],..." and the references [5-7] should be placed as is. However, the authors omit a fourth study by Wittmann and colleagues (JIMD Rep. 2012;6:117-25. doi: 10.1007/8904_2012_130.), which reaches a similar result in a newborn study in Hungary and should also be cited here.

>> Thank you for this remark. We did the modifications and added the reference.

Page 12, line 177-ff.: It is not indicated whether the 41 subjects for this analysis also included untreated patients. Apart from the split between the sexes, I consider this to be an essential point to consider. Even though this following statement is made on page 24, line 368-ff.: “As experts managing lysosomal diseases in a dedicated tertiary center, we assume that treatment with ERT or chaperone therapy has not modified the overall natural history of the disease or the prognosis of patients. Nonetheless, to the best of our knowledge, regression but no disappearance of CV has ever been reported (25)." This must be introduced directly at this point, as it otherwise contributes to the confusing structure of the article.

>> Thank you for this remark. As you observed we already mentioned in the discussion that we assumed that the effect of treatment is not sufficient to modify the overall prognosis of patients. In order to improve clarity and quality, we have added to the text the following sentences : “Their mean age was 44.4 years-old. We assume that treatment with ERT or chaperone therapy does not modify the overall phenotype of patients. Six patients were untreated at the inclusion (mean age 34.6 years-old). Mean duration of treatment was 6.5 years in treated patients.“

Page 16, line 267: Should not the 41 males and 36 females with complete data be used for this analysis? Would the result change?

>>Indeed it would have improved the description of the cohort. Nevertheless, in the table 2, we excluded missing data for each corresponding item as mentioned by denominator. We have decided to include all the patients in the description of the cohort because in such rare diseases we think that patient with incomplete data can bring important information without introducing bias.

Page 23, line 366-ff. and page 24, line 371-ff. Redundant information is given.

Thank you for this remark, indeed we think that X-inactivation analysis would be very very interesting. We corrected this point.

Reviewer #2:

#1 We thank the reviewer 2 for his review and his remarks. We modified the manuscript to introduce the lysoGb3 as a surrogate biomarker in Fabry disease. Although we used it, we did not go into further details because the role of biomarker is still debated.

Hence, we have added the following sentence: “resulting in the accumulation of glycosphingolipids, mainly globotriaosylceramide (Gb3) and its deacetylated form globotriaosylsphingosine (lysoGb3), the latter being commonly used as a surrogate biomarker »

We also have added the 2 references mentioned.

#2 Thank you for the proposition. We have added the 3 first references from Ortiz et al, Citro et al and Oder et al. because they illustrate well the difficulty we can meet with the management of adult Fabry patients with the problem of clinical phenotypes that we try to address in this study. We introduce them with the following sentence: “Moreover, existing scoring systems do not differentiate nonclassical from classical phenotypes of the disease whereas a growing literature suggests the need for personalized management [14–16]. »

#3 Thank you for this remark. We have added the meaning in the legend: “comparison with Mann-Whitney test p*: classical versus Nonclassical males; p**: males versus females”

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Decision Letter 1

Maria Vittoria Cubellis

6 May 2020

Cornea verticillata and acroparesthesia efficiently discriminate clusters of severity in Fabry disease

PONE-D-19-35853R1

Dear Dr. MAUHIN,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Maria Vittoria Cubellis

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Maria Vittoria Cubellis

13 May 2020

PONE-D-19-35853R1

Cornea verticillata and acroparesthesia efficiently discriminate clusters of severity in Fabry disease

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