Predictors of objective cognitive impairment and subjective cognitive complaints in patients with Fabry disease

This study investigates the relationship between objective cognitive impairment (OCI), subjective cognitive complaints and depressive symptoms in men and women with classical and non-classical Fabry disease (FD). Cognitive functioning was assessed using a neuropsychological test battery, subjective cognitive complaints using a structured interview and depressive symptoms using a depression scale (CESD). Eighty-one patients were included (mean age 44.5 ± 14.3, 35% men, 74% classical). Subjective cognitive complaints were reported by 64% of all patients. OCI was present in thirteen patients (16%), predominantly in men with classical FD. Thirty-one patients (38%) had a high score (≥16) on the CESD scale. Male sex (OR, 6.8; 95%CI, 1.6–39.8; p = 1.6 * 10−2) and stroke (OR, 6.4; 95% CI, 1.1–41.0; p = 3.7 * 10−2) were independently positively associated with OCI, and premorbid IQ (one IQ point increase: OR, 0.91; 95%CI, 0.82–0.98; p = 3.8 * 10−2) was independently negatively associated with OCI. The CESD-score (one point increase: OR, 1.07; 95% CI, 1.02–1.13; p = 3.3 * 10−3) and a history of depression (OR, 2.7; 95% CI, 1.1–7.3; p = 3.9 * 10−2) were independently positively associated with subjective cognitive complaints. OCI is present in 16% of FD patients, warranting referral for neuropsychological assessment. Nevertheless, subjective cognitive complaints are related to depressive symptoms, emphasizing the importance of recognition and treatment of the latter.

SCIENTIfIC REPORTS | (2019) 9:188 | DOI: 10.1038/s41598-018-37320-0 on the relationship between subjective cognitive complaints and depressive symptoms and/or OCI can have significant implications for the therapeutic measures indicated to address these complaints. The objective of this study was to investigate the prevalence of OCI, subjective cognitive complaints, depressive symptoms and to explore their risk factors and interrelation in patients with FD in general as well as in subgroups defined by sex and FD phenotype.

Results
Baseline characteristics. Of the 154 known FD patients in the AMC, ten patients were not considered eligible because of comorbidity known to influence the neuropsychological test results (autism (n = 2), blindness (n = 1), intellectual and developmental disabilities (n = 3), severe aphasia (n = 1)) or because of insufficient knowledge of the Dutch language (n = 3).
Of the 144 contacted patients, 63 patients were not willing to participate (not interested (n = 29), time constraints (n = 8) and participation being too strenuous (n = 26)). There were no significant differences between participants and non-participants in sex, phenotype, age, TIA, stroke or median Fazekas score (see Supplemental  Table E-4 for data on non-participants). A total of 81 patients were included with a mean age of 44.5 ± 14.3 years (range: 19-76 years) ( Table 1). Fifty-three patients were women (65%) and 60 patients had classical FD phenotype (74%). Twenty-two patients (27%) reported a history of or a current depression, as diagnosed by their general practitioner, psychologist or psychiatrist, without statistically significant differences between subgroups split by sex and phenotype (p = 6.6 * 10 −1 ).
Subjective cognitive complaints. Fifty-two patients (64%) experienced subjective cognitive complaints in at least one domain, without statistically significant differences between subgroups split by sex and phenotype (p = 3.0 * 10 −1 ) ( Table 2).
Objective cognitive impairment. There were no signs of underachievement or lack of motivation in any of the patients based on the TOMM score.
A total of 13 (16%) patients had any OCI, of which four (5%) had severe OCI (Table 2). Seven men with classical FD (41%) had any OCI of which two (12%) had severe OCI. For men with non-classical FD this was three (27%) and one (9%), respectively, and in women with classical FD this was three (7%) and one (2%), respectively. OCI did not occur in women with non-classical FD.
Most abnormal T-scores (T-scores ≤33) were found in the attention and executive functioning domain ( Table 2). Decreased T-scores were found in the attention and executive functioning domain in men with classical FD (T-score, 45.6; p = 1.4 * 10 −2 ) and men with non-classical FD (T-score, 46.6; p = 1.2 * 10 −2 ) ( Table 3). None of the patients scored <24 on the MMSE, suggesting lack of sensitivity for the detection of OCI in FD using this cut-off. After post-hoc correction there were no differences in premorbid IQ between the subgroups divided by sex and phenotype (Table 3). However, there was a difference looking at sex only: men had a lower premorbid IQ compared to women (W = 468.5, p = 6.6 * 10 −3 ).
Questionnaires. Thirty-one patients (38.3%) scored ≥16 on the CESD, indicating the presence of depressive symptoms (Table 4), with comparable scores in all subgroups. MSSI scores were higher in men and women with classical FD and men with non-classical disease compared to women with non-classical disease, indicating less severe disease in women with non-classical disease. BPI, MCS and PCS scores were comparable in all subgroups, indicating no differences in pain, mental QoL and physical QoL in all subgroups, respectively. Almost half of all patients (n = 39) experienced poor sleep quality.
Cerebral involvement. Ten patients had a history of stroke as diagnosed by a neurologist, none of them were women with non-classical FD (Table 5). Seventy-three patients (90%) had an MRI brain before the neuropsychological assessment. WMLs were present in 43 patients (58.9%) and were most often mild (total Fazekas score of 1 or 2) (n = 27) (median time between MRI and assessment: 0.7 years).

Discussion
In this large sample of Dutch patients with FD we have, for the first time, shown a relationship between sex, phenotype and risk for OCI. OCI was present in 41% of men with classical disease, affecting mostly the executive functioning domain. In addition, OCI was found in a significant number (27%) of men with non-classical FD. In women with classical FD, however, the prevalence of OCI was markedly lower (7%) and none of the women with non-classical FD had OCI. The risk of OCI in patients with FD was independently related to male sex, a history of stroke and to premorbid IQ.
In a healthy population of male veterans from the United States (n = 4371), slightly younger compared to our cohort (38.4 ± 2.5 years), OCI was found in ~6-7% of this cohort when using similar criteria 8 . In a second healthy mixed control group (n = 138) of a study on the cognitive effects of type 1 diabetes, with comparable age (49 ± 7 years) to our study population, the prevalence of OCI was 5% 9 . This indicates that OCI in our population of FD patients is much higher in male patients than would be expected in this age group. In women with FD the prevalence of OCI is comparable to that in the general population. Loeb et al. 7 found OCI in 30% of patients with FD, but used different criteria to define OCI and a smaller reference population (n = 80), possibly explaining the differences. The impaired executive domain found in our study is in accordance with previous studies in FD 3 . T≤33, n (%) T≤33, n (%) T≤33, n (%) T≤33, n (%) T≤33, n (%)

Memory
RAVLT ir 6 (7.4%) 4 (23.5%)  Moreover, our study confirms the preliminary finding of a study by Sigmundsdottir et al. 5 that men with FD are more likely to get OCI, especially in those with classical disease. In addition, a relationship between the extent of the WMLs (Fazekas score) and the presence of severe OCI was established. In a previous study a subgroup of patients with markedly increased volumes of white matter lesions showed more cognitive deficits compared to patients with lower lesion volumes 6 . In the general population, as was found in our study, the positive relationship between WMLs and OCI is not very strong 10 . It has been postulated that a threshold of WML severity needs to be surpassed before OCI develops 10 .

Attention and executive functioning
The relation between stroke and OCI in the general population has been more firmly established 11 . Likewise, in our study the relation between a history of stroke and OCI in FD is clearly present. This was also observed in previous studies in FD, albeit using univariate analyses 5,7 . The positive relationship between (premorbid) IQ and neuropsychological test scores has been firmly established in the general population 8,12 . It has been theorized that a higher premorbid IQ reflects a greater "cognitive reserve", thus more decline has to take place before OCI occurs 13,14 . The observation that higher T-scores in FD patients with higher premorbid IQ lower the chance of OCI fits this hypothesis. We also found a lower median premorbid IQ in men compared to women. Despite this difference, male sex was related to a higher risk of OCI independently of premorbid IQ. Premorbid IQ therefore does not fully explain the differences in prevalence of OCI between men and women with FD. A new finding is that almost two-thirds of our cohort of FD patients experienced subjective cognitive complaints, without significant differences in prevalence between all subgroups. Interestingly, in our study the subjective cognitive complaints were not related to OCI, but showed a clear relation with both depression in the past and current depressive symptoms. In the general population, the relation between OCI and subjective cognitive complaints is still controversial 15 . More thoroughly established is that patients with depression have a higher prevalence of subjective cognitive complaints 16 , as was also previously shown by Loeb et al. 7 in a population of FD patients. The relation of depressive symptoms to subjective cognitive complaints further emphasizes the importance of recognizing these symptoms. Conversely, Loeb et al. 7 concluded that in patients with FD prevalence of subjective cognitive complaints is not increased. It seems that, in our cohort of FD patients, subjective cognitive complaints were highly prevalent. Of these, subjective memory complaints were present in 46% of our cohort (data not shown), while these are found in 22% of the general population 17 , indicating that the prevalence in patients with FD could be more than twice as high. The difference to our study might be caused by the difference in assessment (structured interview versus questionnaire) as well as the use of a high cutoff (mean +2 SD compared to a healthy population) for detecting subjective cognitive complaints in the Loeb et al. 7 study. The high prevalence (38%) of depressive symptoms in our study is in line with the previously found prevalence of 46% from a mixed cohort of 186 patients with FD 18 . In populations with chronic diseases or chronic pain various treatments have been shown to improve depressive complaints 19,20 . Unfortunately, treatment effects and risk factors for depression in FD are largely unknown. Only one small, uncontrolled study (n = 15) looked at the effect of psychological counseling and found all FD patients improving 21 . Therefore, efficacy of treatment options for depressive symptoms should be topic of further research in FD patients.
This study has some strengths and limitations. Strengths are the precise phenotyping of the studied cohort and the use of a reliable cognitive test battery. Moreover, this study is the first to combine data on subjective cognitive complaints, depressive symptoms, cognitive functioning and MRI brain parameters in subgroups of patients divided by sex and FD phenotype.
We included 81 patients, more than half of the known patients in the Netherlands, a fairly large group for a rare disease like FD. However, we cannot rule out inclusion bias: patients with subjective cognitive complaints might be more interested in participation. Conversely, patients with severe cognitive impairment might not participate due to participation begin to strenuous. Nevertheless, we found no significant differences in patient characteristics, nor in the presence of TIA, stroke or the height of the Fazekas score between participants and non-participants in this study.
MRIs were made using the same standardized protocol, as part of routine follow up. This means that sometimes there was a time gap between the MRI and the neuropsychological assessment, but in most patients this was not more than a year and WMLs are known to increase slowly. Of some patients MRI of the brain was not available, mostly due to the presence of non-MRI compatible ICD/pacemakers. Excluding these patients, however, might also lead to a bias towards less affected patients.
We did not assess a healthy control group ourselves. Instead, we used large normative datasets (median sample size: 471) compiled of healthy control groups from multiple studies. Furthermore, most neuropsychological test results were corrected for age, sex and education level, although not for (premorbid) IQ.
Lastly, we used the Fazekas-scale to assess WMLs in this study. It has been shown that visual rating scales have a lower ceiling compared to volumetric measurements of WMLs 22 . Perhaps, the use of volumetric measurement of WMLs would have strengthened the relation to OCI.  In conclusion, OCI is present in one-sixth of FD patients, predominantly in men with classical disease. The relation between a history of stroke and OCI in this study re-emphasizes the importance of prevention of stroke in patients with FD. Moreover, the presence of stroke or other clinical indications of OCI warrants referral of FD patients for neuropsychological assessment. The high prevalence of subjective cognitive complaints, equally distributed over the phenotypes and sexes, was not explained by OCI, but showed a clear relation with current or historical depressive complaints. Evaluation of subjective cognitive complaints in patients with FD should therefore include a psychological evaluation and healthcare professionals should focus on recognition and treatment of depressive symptoms.

Methods
Study design and phenotype. The baseline data of an ongoing prospective cohort study on neuropsychological functioning in adult patients (≥18 years) with a definite diagnosis of FD 23 are presented. All known adult Fabry patients (n = 154) at the Academic Medical Centre (AMC), the national referral centre for patients with FD in the Netherlands, were screened for eligibility.
All patients were phenotypically characterized as having classical or non-classical FD 2 , see Supplemental File 1 for criteria. Demographic parameters, clinical and disease characteristics of all patients were gathered from the local Fabry database containing prospectively collected data as well as from medical records.
This study was approved by the Human Research Ethics Committee of the AMC and conducted in accordance with the Declaration of Helsinki in 2013 24 . All participants provided informed consent prior to inclusion and all experiments were performed in accordance with relevant guidelines and regulations. The datasets generated and analyzed during the current study are not publicly available. Because of the rarity of the disease, even anonymized can be linked to a specific individual. In case of a specific scientific question, requests to make part of the dataset available will be reviewed.

Neuropsychological assessment. All included patients completed a neuropsychological test battery
assessing language skills, memory, visuospatial perception, processing speed and executive functioning. Language skills were assessed using the Boston Naming Test (BNT) 25 and the Wechsler Adult Intelligence Scale IV: Similarities (WAIS-IV: S) 26 . Memory was assessed using the Dutch version of The Rey Auditory Verbal Learning Test (RAVLT) 27 and the Rivermead Behavioural Memory Test (RBMT): Storytelling 28 , both assessing immediate recall (ir) and delayed recall (dr). Visuospatial perception was assessed using the WAIS IV: Block Design (BD) and the Judgement of Line Orientation (JLO) 26,29 . Processing speed was assessed using the Trail Making Test part A (TMTA) 30 , Stroop Word (W) and Stroop Colour (C) 31 . Executive functioning was assessed using the TMT part    Table 6. Univariate relations to subjective cognitive complaints, objective cognitive impairment and cognitive domains. Univariate relations to subjective cognitive complaints (SCC) and objective cognitive impairment (OCI) were tested using kendalls tau-b. Univariate relations to the combined T-scores of the cognitive domains were tested using generalized linear models and are presented as beta's. For binary variables kendalls tau-b and beta's were calculated with the presented value coded as 1 (men, classical, classical men, employment, single, alcohol/drug abuse, ERT use, presence of fatigue, history of depression and history of stroke) and the other value coded as 0 (e.g. women, non-classical, unemployment). For continuous variables beta's were calculated for a data sample size was 471 healthy participants (range: 121-1000). All normative data were corrected for age and most subtests also for sex and educational level. See Supplemental  34 . Motivation and underachievement were assessed using the Test of Memory Malingering (TOMM) 35 . An estimation of intelligence was done using the Dutch Adult Reading Test (DART), the Dutch version of the National Adult Reading which correlates strongly with intellectual ability and is relatively resistant to change by neurological impairment 36 .
The neuropsychological test battery was preceded by a structured interview assessing subjective cognitive complaints, addressing the following domains: memory, attention and executive complaints. This resulted in a score ranging from 0 to 3: no subjective cognitive complaints (0), subjective cognitive complaints in one domain (1), in two domains (2) or in all three domains (3), see Supplemental File 2.
Objective cognitive impairment. OCI was defined as a T-score ≤33 (<5 th percentile, −1.67 SD) on at least two neuropsychological tests, resembling statistical significance of two one-tailed tests with p < 0.05. Severe OCI was defined as a T-score ≤30 (<2.3 rd percentile, −2 SD) on at least two neuropsychological tests, resembling statistical significance of two two-tailed tests with p < 0.05. We choose the cutoff of −1.67 SD to prevent high rates of false positives, a strategy which has been recommended for other diseases as well 37 . To decrease the family-wise error rate, one or more T-scores ≤33 or ≤30 in the following combination of tests assessing a similar cognitive domain, were regarded as a single abnormal T-score: Animal Fluency and/or Occupational Fluency and/or Letter Fluency, RAVLT ir and/or RBMT ir, RAVLT dr and/or RBMT dr, TMTA and/or Stroop W and/or Stroop C, TMTB and/or Stroop CW, WAIS-IV: BD and/or JLO.

Questionnaires.
Depressive symptoms were measured using The Centre for Epidemiological Studies -Depression scale (CESD) 38 . Twenty items on depressive symptoms experienced in the last week are scored on a four point Likert scale (range 0 to 3). Total scores range from 0 to 60 points and patients with scores ≥16 were classified as having depressive symptoms 39 .
Pain was measured using the Brief Pain Inventory (BPI) which is divided in three separate sub scores: 1) worst pain, 2) average pain and 3) pain interference with life. The latter is an average score of the influence of pain on general activity, mood, walking, work, enjoyment of life, relations and sleep. Pain scores are graded from 0 (absence of pain) to 10 (worst possible pain) 40 .
Quality of life (QoL) was obtained using the Dutch version of the Short Form-36 Health Survey (SF-36). The SF-36 assesses eight domains of QoL which are calculated using Dutch normative data 41 . The eight domains can be grouped into two summary scores: the Physical Component Summary (PCS) and the Mental Component Summary (MCS) that are standardized using normative data from the US population and are presented as T-scores 42 .
Sleep quality was measured using The Pittsburgh Sleep Quality Index (PSQI) 43 . The PSQI assesses seven domains which are graded from 0 to 3, resulting in a score ranging from 0 to 21. PSQI scores >5 are indicative for poor sleep quality.
Brain MRI. MRI of the brain was performed yearly or biannually as part of routine follow-up on a 3-T system (Philips Ingenia, Philips Medical Systems, Best, The Netherlands). All MRIs of the brain were made using the same standardized protocol, see Supplemental Table E-2 more information on MRI settings per sequence. Eight patients had no MRI of the brain, six because of the presence of an MRI non-compatible pacemaker or ICD and one because of claustrophobia. In one patient the brain MRI was made in a different hospital. MRIs were re-evaluated by two neuroradiologists, (MRL evaluated basilar artery pathology, MGL evaluated infarctions, WMLs and atrophy), blinded for all patient characteristics. White matter lesions (WMLs) were rated on axial FLAIR using the Fazekas scale, ranging from 0 (no WMLs) to 6 (confluent periventricular and deep WMLs) 45 . Presence and number of (lacunar) infarctions was rated on DWI, axial T2 and FLAIR images. Basilar artery diameter (BAD) was rated on axial T2 images 46 . Atrophy of medial and temporal lobe were rated on T1 3D GRE images using the Medial Temporal lobe Atrophy rating scale (MTA) 47 . See Supplemental Table E-3 for additional information on the scales. Statistical methods. Data are presented as median and range or mean ± SD where appropriate. R (version 3.3.1) and SPSS version 24.0 (SPSS Inc. Chicago, Illinois, USA) were used for statistical analysis. Between subgroup differences were compared using the Kruskall-Wallis test, one way ANOVA and Fisher's exact test where appropriate. Post hoc analyses were done with the Dunn test, Tukeys test and 2 × 2 Fisher's exact tests with Bonferroni-Holm correction for abovementioned tests, respectively. Included and excluded patients were compared as a whole group and per subgroup divided for sex and phenotype. To check if neuropsychological test results differed from the average from the reference cohort, T-scores were compared to a T-score of 50 using a one-sided sign test.
T-scores per cognitive domain were obtained calculating mean T-scores of all tests addressing this domain. Variables were included in the univariate analyses if they were deemed as potentially related to OCI trough literature search in the general population or previous studies on OCI and FD. Next, the univariate models were used to identify variables for multivariate models. Linear regression was used to analyze the univariate relation between variables and T-scores per cognitive domain. Kendall's tau-b (r τ ) was used to analyze the univariate relation between variables and subjective cognitive complaints (range 0-3) and between variables and OCI (binary, SCIENTIfIC REPORTS | (2019) 9:188 | DOI:10.1038/s41598-018-37320-0 yes/no) ( Table 6). Bootstrapping was used to calculate bias corrected accelerated 95% confidence intervals (CI) of Kendall's tau-b, stratified for sex. To minimize the effect of multiple testing, the relation between a variable and OCI was first tested. If the relation between a variable and OCI was (very) weak (≈r τ < 0.1, ≈p > 0.25) a relation between the variable and T-scores per cognitive domain was not tested. To correct for false discovery rate we used the Benjamini-Hochberg procedure in Table 6 48 .
A multiple logistic regression model was used to check which variables are independent risk factors of OCI and a proportional odds model was used to check which variables are independent risk factors of subjective cognitive complaints. Both models were iteratively built selecting variables from univariate models (inclusion if p < 0.10). The Akaike Information Criterion (AIC) was used to optimize the models. We chose the simplest models and compared them to our theoretical concepts. Variance inflation factor was used to explore potential multicollinearity in the logistic regression model. A likelihood ratio test was used to test proportionality of odds, comparing the goodness of fit of the proportional odds model to its multinomial counterpart.
P-values < 0.05 were considered statistically significant. If multiple tests were carried out regarding a single hypothesis, the results were corrected using the Bonferroni-Holm correction, to control for the family wiser error rate 49 .
Results were reported in accordance with the STROBE guidelines 50 .