Next Article in Journal
Optimal Perfusion Pressure Enhances Donor Heart Preservation During Normothermic Ex Situ Perfusion in a Rat Transplantation Model
Previous Article in Journal
Comparison of Bilateral Rectus Sheath Block and Thoracic Epidural Analgesia for Postoperative Pain Control After Open Gastrectomy: A Randomized Controlled Trial
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Medicina
Volume 61
Issue 9
10.3390/medicina61091694
medicina-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Beyond Motor Decline in ALS: Patient-Centered Insights into Non-Motor Manifestations

by
Anca Moțățăianu
1,2,
Sebastian Andone
1,2,*,
Smaranda Maier
1,2,
Rareș Chinezu
3,
Medeea Roman
2,
Mihai Dumitreasă
2,
Rodica Bălașa
1,2 and
Ioana Ormenișan
2
1
Department of Neurology, University of Medicine, Pharmacy, Science and Technology of Târgu Mureș ‘George Emil Palade’, 540142 Târgu Mureș, Romania
2
Neurology Clinic, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania
3
Department of Neurosurgery, University of Medicine, Pharmacy, Science and Technology of Târgu Mureș ‘George Emil Palade’, 540142 Târgu Mureș, Romania
*
Author to whom correspondence should be addressed.
Medicina 2025, 61(9), 1694; https://doi.org/10.3390/medicina61091694
Submission received: 8 August 2025 / Revised: 3 September 2025 / Accepted: 15 September 2025 / Published: 18 September 2025
(This article belongs to the Special Issue Recent Advances in the Diagnosis and Treatment of Neurodegenerative Diseases)
Browse Figure
Versions Notes

Abstract

Background and Objectives: Traditionally regarded as a purely motor disorder, amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease characterized by the degeneration of upper and lower motor neurons. However, it is increasingly recognized as a condition with a broader clinical spectrum, encompassing a variety of non-motor symptoms (NMS) that significantly impact patients’ quality of life and may influence disease progression and prognosis. Materials and Methods: The study included 44 patients diagnosed with probable or definite ALS and 35 healthy controls (HC). Functional neurological status, non-motor manifestations, and cognitive and affective domains were evaluated using the revised ALS Functional Rating Scale (ALSFRS-R), the Non-Motor Symptoms Questionnaire (NMSQuest), the Frontal Assessment Battery (FAB), and the Beck Depression Inventory (BDI), respectively. Results: A majority of ALS patients exhibited non-motor symptoms (NMS). Significant associations were identified between specific NMS domains and ALSFRS-R subdomains: sleep disturbances were associated with lower fine motor, respiratory, and total scores; digestive symptoms with lower bulbar, respiratory, and total scores; cardiovascular symptoms with lower total scores; urinary symptoms with higher bulbar subscores and a significantly slower progression rate (ΔPR); and sensory symptoms with higher gross motor subscores. BDI scores were negatively correlated with respiratory and bulbar functions, whereas FAB scores showed positive correlations with both bulbar and total ALSFRS-R scores. Conclusions: Non-motor symptoms are highly prevalent in this ALS cohort. These symptoms do not consistently correlate with greater motor impairment, as urinary and somatosensory involvement may occur independently of functional decline. Cognitive, affective, and behavioral alterations co-exist with motor symptoms and are associated with poorer overall functional performance.

1. Introduction

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder primarily characterized by the degeneration of upper and lower motor neurons, leading to muscle weakness, atrophy, and ultimately respiratory failure. Traditionally considered a purely motor disease, increasing evidence over the past two decades has revealed a broader clinical spectrum, including a range of non-motor symptoms that significantly affect quality of life and may also influence disease progression and prognosis [1,2].
Recent studies have shown that cognitive impairment, depressive symptoms, and behavioral changes are common in ALS patients, yet these manifestations are often underrecognized in routine clinical care. Non-motor symptoms may present early in the disease course and include mood disorders, executive dysfunction, sleep disturbances, autonomic symptoms, and fatigue [3,4]. Cognitive and behavioral impairments—especially those affecting executive functions—are now understood to lie on a continuum with frontotemporal dementia, occurring in up to 50% of ALS cases [5]. Depression is another frequent comorbidity, with a prevalence ranging from 30% to 50%, and may influence both quality of life and survival [6,7].
Despite the growing recognition of these features, non-motor symptoms remain insufficiently explored in clinical research, particularly through standardized instruments. There is a lack of systematic evaluations using validated tools that can capture the breadth and severity of NMS in ALS. Furthermore, the prognostic significance of these symptoms is not yet fully understood.
In this study, we aimed to systematically assess non-motor symptoms in ALS patients using a combination of validated tools: the Non-Motor Symptoms Questionnaire (NMS Quest), the Beck Depression Inventory (BDI) for depressive symptoms, and the Frontal Assessment Battery (FAB) for evaluating executive dysfunction. We also explored the relationship between these symptoms and clinical characteristics, including their potential association with disease progression.

2. Materials and Methods

This observational, cross-sectional study included 44 patients that were diagnosed with ALS in the last 12 months in a tertiary care neurology center (Neurology Department of Mures University Clinical Emergency Hospital) [8]. The patients were included in the study from 1 August 2022 to 1 November 2022. Additionally, we enrolled a control group of 35 healthy subjects.
All patients included in the study were diagnosed with probable or definite ALS according to the revised El Escorial criteria [8]. Exclusion criteria comprised the following: 1. diagnosis of other neurodegenerative diseases; 2. subjects whose Mini-Mental State Examination (MMSE) scores were <24; 3. comorbid conditions with potential impact on autonomic nervous system function, such as diabetes mellitus or glucose intolerance, vitamin deficiencies, impaired hepatic or renal function, endocrinopathies, autoimmune disorders, and heart failure; and 4. treatment with medication potentially affecting the autonomic nervous system or patients on sedatives, antidepressants, or anxiolytics.

2.1. Neurological Assessment

At baseline, each patient underwent a complete neurological and neurophysiological examination evaluating disease severity and progression. The functional neurological status was assessed by using the revised ALS Functional Rating Scale (ALSFRS-R) [9,10]. The ALSFRS-R assessment included four subscores calculated for bulbar function (ALSFRS-R-B), lower limb function (ALSFRS-R-LL), upper limb function (ALSFRS-R-UL), and respiratory function (ALSFRS-R-R), with a maximum total score of 48 points [9]. The initial ALSFRS-R scores were recorded at the time of diagnosis, and the second one was recorded at the baseline visit. The ALSFRS-R progression rate (ΔPR) was calculated as the change in ALSFRS-R score from diagnosis to the date of study visit, using the following formula: 48 − [(ALSFRS-R at diagnosis − ALSFRS-R at study visit)/duration of symptoms (in months)] [11].
At the time of diagnosis, patients were categorized into three distinct ALS phenotypes based on clinical presentation and electromyographic findings: 1. classical or typical ALS, characterized by predominant lower motor neuron (LMN) signs in at least two body regions, accompanied by upper motor neuron (UMN) involvement in at least one region; 2. LMN-predominant ALS, encompassing phenotypic variants such as flail arm and flail leg syndromes; and 3. bulbar-onset ALS, defined by the early and progressive manifestation of bulbar symptoms, including dysarthria and dysphagia [12]. Patients were also classified into spinal or bulbar ALS subtypes based on the site of symptom onset. The spinal subtype included cases with initial limb weakness (upper or lower), while the bulbar subtype was defined by the early presence of bulbar symptoms such as dysarthria or dysphagia.
The ALS progression pattern was evaluated by the extension of muscle atrophy or weakness beyond the initial site of symptom onset, based on clinical examination, electrophysiological studies, and patient history. Patients were subsequently classified into two groups according to the direction of motor neuron degeneration: 1. a horizontal spreading pattern (HSP), defined by disease progression from the cervical region to the contralateral cervical region or from the lumbar region to the contralateral lumbar region; and 2. a vertical spreading pattern (VSP), characterized by disease extension from the cervical or lumbar region to the ipsilateral limb, from the bulbar region to the cervical or lumbar segments, or vice versa [13].

2.2. Evaluation of Non-Motor Symptoms

Non-motor symptoms were systematically assessed using the Non-Motor Symptoms Questionnaire (NMS Quest), a validated 30-item screening tool designed to identify a broad range of non-motor manifestations [14,15]. Although originally developed for Parkinson’s disease, it was employed in this study as a structured screening tool to capture the non-motor symptoms relevant to ALS.
The questionnaire is structured into few domains, including cognitive (memory deficits, attention/concentration deficit), psychiatric (apathy, sadness, anxiety, perceptual problems, hallucinations), sleep/fatigue (tiredness, insomnia, vivid dreams, sleep-talking), sensory (pain, discomfort in legs, leg swelling), cardiovascular (dizziness/lightheadedness, falls), urinary (urinary urgency, nocturia), sexual (interest in sex, sexual dysfunction), gastrointestinal (dribbling, difficulty swallowing, vomiting/nausea, constipation, bowel incontinence, incomplete bowel emptying, loss of/change in taste/smell), and miscellaneous(weight change, excessive sweating, double vision) [16]. In this study, the NMS Quest was administered through structured interviews conducted by a trained examiner. This approach ensured standardized data collection, minimized reporting bias, and allowed clarification of any ambiguous patient responses, including distinguishing falls due to weakness from falls caused by impaired cardiac autonomic control (i.e., orthostatic hypotension). The information obtained was used to determine the presence and distribution of non-motor symptoms across the cohort. Responses were analyzed for prevalence.
Prior to the NMS Quest interview, all participants received a document outlining the study’s objectives and ensuring the confidentiality of their data. Written informed consent was obtained from each participant, in accordance with the protocol approved by the hospital’s ethics committee, before any data were recorded in the study database.

2.3. Neuropsychological Assessment

Cognitive and affective functions were assessed using the Frontal Assessment Battery (FAB), the Beck Depression Inventory (BDI), and the Mini-Mental State Examination (MMSE). Executive function was assessed with the FAB, a brief neuropsychological tool designed to evaluate frontal lobe functions through six subtests, including conceptualization, mental flexibility, motor programming, sensitivity to interference, inhibitory control, and environmental autonomy [17,18].
Affective symptoms, particularly depressive features, were measured using the Beck Depression Inventory II (BDI-II), a validated self-report questionnaire comprising 21 items rated on a four-point scale that quantify the severity of depressive symptoms. BDI-II, the Romanian version, was completed by all the study participants. BDI-II assesses the presence and severity of the depressive symptoms and evaluates the cognitive, motivational, autonomic, and somatic domains [19].
The MMSE was administered as a global cognitive screening tool, evaluating orientation, registration, attention and calculation, recall, and language abilities [20]. All neuropsychological assessments were performed in a structured manner by trained examiners during the clinical evaluation. In this study, higher BDI scores were interpreted as indicative of more severe depressive symptoms, while lower FAB scores reflected greater executive dysfunction. BDI and FAB scores obtained by the ALS patients were compared to those in the HC population enrolled. Subsequent correlation analyses were performed to explore the relationship between these neuropsychological variables and patients’ functional status, as measured by the ALSFRS-R. The interpretation of FAB and BDI scores was integral to the evaluation of the interplay between mood, cognitive decline, and physical disability in ALS. FAB and BDI allowed a structured evaluation of cognitive and affective domains, which are both recognized as integral to the broader spectrum of NMS in ALS.

2.4. Statistical Analysis

Descriptive statistics were performed on the demographics, clinical characteristics, and non-motor symptoms of the ALS patient cohort. Continuous variables such as age and ALSFRS-R scores were expressed as means ± standard deviations (SD), while categorical variables, including ALS subtype, phenotype, and non-motor symptoms, were reported as frequencies and percentages.
The impact of non-motor symptoms on the ALS Functional Rating Scale-Revised (ALSFRS-R) subscores—respiratory, bulbar, fine motor, and gross motor functions—as well as on the disease progression rate (ΔPR), was evaluated using ANOVA test, with statistical significance set at p < 0.05.
Correlation analyses were conducted using Pearson’s correlation coefficient to investigate relationships between neuropsychological measures and functional status. Statistical significance for correlations was set at p < 0.05.
We used Shapiro–Wilk to test the normality of the continuous variables. As distributions did not significantly deviate from normality, we chose ANOVA to compare functional scores between groups.
To evaluate the relationship between non-motor symptoms and functional status in ALS patients, an ANOVA analysis was conducted. Given the exploratory nature of the study and the relatively small sample size, p-values were reported without correction for multiple comparisons. As such, the results should be interpreted cautiously and considered hypothesis-generating.
All statistical tests and analyses were using IBM SPSS Statistics, Version 26 (IBM Corp., Armonk, NY, USA). Tables and figures were created using Microsoft Excel 2019 (Microsoft Corp., Redmond, WA, USA).

3. Results

3.1. Demographics and Clinical Characteristics of the Study Participants

HC and patient demographics and clinical characteristics, including ALSFRS-R scores at the time of the study visit are detailed in Table 1.

3.2. Description of Non-Motor Symptoms

A wide range of non-motor manifestations was reported, reflecting the multisystem involvement of ALS. Cognitive symptoms were identified in 7 patients (15.9%), including memory impairment and attention deficits. Psychiatric symptoms were observed in 28 patients (63.3%), most commonly apathy, depression, anxiety, and perceptual disturbances. Sleep disorders were highly prevalent, affecting 35 patients (79.5%), and included insomnia, vivid dreams, and sleep-talking. Sensory symptoms were reported by 37 patients (84%), such as pain, discomfort in the lower limbs, and sensations of leg swelling. Cardiovascular symptoms, including dizziness and light-headedness, were noted in 30 patients (68.2%). Genitourinary complaints were present in 32 patients (72.7%) and consisted of urinary urgency, nocturia, and sexual dysfunction. Gastrointestinal symptoms were reported by 36 patients (81.8%) and included dribbling, dysphagia, vomiting, constipation, and bowel incontinence. The Non-Motor Symptoms Questionnaire results per domain are summed up in Table 2.

3.3. Association Between Non-Motor Symptoms and Functional Status in ALS Patients Assessed by ALSFRS-R Scores and Disease Progression Rate

To evaluate the relationship between non-motor symptoms and functional status in ALS patients, an ANOVA analysis was conducted using ALSFRS-R subdomain scores and ΔPR as outcome variables, as shown in Table 3 below. Significant associations were identified for several non-motor symptom categories. Patients with sleep disorders had significantly lower ALSFRS-R fine motor scores (p = 0.014) and total scores (p = 0.008), along with a reduced respiratory subscore (p = 0.030), suggesting a more pronounced functional decline. Similarly, the presence of digestive symptoms was associated with significantly lower bulbar (p = 0.026) and respiratory (p = 0.044) subscores, as well as lower ALSFRS-R total scores (p = 0.013). Cardiovascular symptoms were also linked to lower ALSFRS-R total scores (p = 0.027). Interestingly, urinary symptoms were associated with higher bulbar subscores (p = 0.012) and a significantly slower progression rate (ΔPR) (p = 0.045). Additionally, patients with sensory symptoms had significantly higher gross motor subscores (p = 0.039). Other non-motor symptoms, such as psychiatric symptoms, cognition/behavior changes, and sexual dysfunction, did not show statistically significant differences in ALSFRS-R scores or ΔPR.

3.4. Correlation Between Affective Symptoms, Frontal Executive Function, and Functional Status in ALS Patients

The correlation analysis between the Beck Depression Inventory (BDI), Frontal Assessment Battery (FAB), and ALSFRS-R scores revealed significant associations. Negative correlations were observed between BDI scores and both respiratory (r = −0.217, p = 0.023) and bulbar (r = −0.209, p = 0.028) subscores of ALSFRS-R, indicating that higher depressive symptoms were linked to lower functional performance in these areas. A more pronounced negative correlation was found with the fine motor subscore (r = −0.430, p < 0.0001) and total ALSFRS-R score (r = −0.422, p < 0.0001), suggesting that affective symptoms interrelate closely with fine motor skills and overall disease severity. In contrast, the FAB showed a positive correlation with the bulbar (r = 0.363, p < 0.0001) and total ALSFRS-R scores (r = 0.339, p < 0.0001), indicating that better frontal executive function was associated with better motor performance in these domains. However, no significant correlations were observed either between FAB and the respiratory, gross motor, or fine motor subscores, or between BDI and the gross motor subscore.
The correlation matrix between BDI, FAB and ALSFRS-R scores is detailed in Figure 1.

4. Discussion

Recent evidence has increasingly recognized ALS as a multisystem neurodegenerative disorder. Although the motor system remains the primary area of involvement, there is growing acknowledgment of NMS in the disease’s clinical manifestation [21]. Impairments in the autonomic and sensory nervous systems, along with cognitive and psychiatric disturbances, contribute to the broader clinical profile of ALS patients. We acknowledge that there is currently no universally accepted system for categorizing NMS in ALS, as the field is still evolving. To facilitate interpretation and enhance clinical relevance, we grouped the original nine NMS Quest domains into four broader categories. We hereby propose four overarching NMS domains in ALS: autonomic dysfunction (encompassing gastrointestinal, genitourinary, cardiovascular dysfunction), sensory dysfunction, cognitive impairment and frontotemporal dysfunction, and respectively, sleep disturbances and affective symptoms.

4.1. Autonomic Dysfunction

Similar to other neurodegenerative diseases, like Parkinson’s disease, emerging evidence highlights the role of the gut–brain axis in ALS pathophysiology. Gut dysbiosis may contribute to neuroinflammation through immune activation, blood–brain barrier disruption, and aberrant protein accumulation (i.e., amyloid) [22]. In ALS, gut dysbiosis and increased intestinal permeability may facilitate neuroinflammation via immune activation and neurotoxin translocation, contributing to motor neuron degeneration. Microbiota-derived metabolites also appear to influence disease progression by modulating immune responses [23,24,25].
A 2022 genome-wide association study examining gut flora and ALS susceptibility identified a higher relative abundance of Sutterella, Lactobacillales, and elevated levels of gamma-glutamyl-phenylalanine—an intermediate in the gamma-glutamyl pathway—as being associated with increased ALS risk [26]. In transgenic SOD1G93A mice, myenteric neurons exhibit aggregated pathological SOD1, leading to neuronal loss and impaired intestinal barrier integrity, marked by enteric glial activation and inflammatory cell infiltration. Notably, oral administration of a multistrain probiotic improved locomotor function and survival and reduced aberrant SOD1 accumulation in spinal cord neurons [27]. These findings support a bidirectional relationship between gut microbial dysbiosis and ALS pathogenesis, though causality remains unclear Future prospective studies including human models are needed.
Gastrointestinal dysfunction represents a relevant yet underrecognized feature of ALS. Several studies have examined digestive symptoms in relation to disease severity and phenotype, with constipation and early satiety frequently reported across clinical subtypes, irrespective of onset form [28]. In another study assessing upper gastrointestinal motor function in relation to ALS onset phenotype (bulbar vs. spinal) and disease severity, no significant correlation was observed between digestive symptoms—such as delayed gastric emptying or esophageal dysmotility—and ALSFRS-R scores or muscle strength measured by the Medical Research Council scale [29]. In our cohort, the majority of patients exhibited gastrointestinal symptoms—such as dribbling, difficulty swallowing, vomiting, constipation, bowel incontinence—and demonstrated significantly lower total ALSFRS-R scores, along with reduced respiratory and bulbar subscores, compared to the asymptomatic group. In addition, a high prevalence of constipation has been reported in the literature, several studies highlighting its association with lower functional scores. One such study further identified constipation as an independent risk factor for survival in patients with ALS [30].
Urinary dysfunction in ALS includes symptoms like incontinence, urgency, nocturia, and incomplete bladder emptying. Several studies confirmed the presence of urinary dysfunction among ALS patients. Given the interplay of supra-spinal lesions, spasticity and low mobility, there is still a debate regarding its pathophysiology. Urodynamic findings suggest that neurogenic bladder, with an overactive detrusor and high urethral resistance due to a contracted external sphincter, is the cause of urinary impairment [31]. Furthermore, neuropathological studies showed gliosis of the periaqueductal gray (PAG) and striatum of a 75-year-old ALS patient, both structures controlling micturition and defecation [32]. Urinary retention in these patients was also hypothesized to be associated with spasticity of the pelvic floor muscles, Lopes et al. finding a correlation between the Ashworth Scale, measured on the lower limbs, and the bladder post-void residual [33].
The relationship between urinary symptoms and ALS clinical status or disease progression remains inconsistent across studies. One of such studies reported no significant association between the clinical profile—phenotype, disease severity, time from symptom onset—and lower urinary tract or neurogenic bladder symptoms. In contrast, findings from Dubbioso’s study indicate that urinary complaints were an independent predictor of shorter survival [28,34]. Interestingly, in our ALS cohort, patients presenting with urinary symptoms exhibited slower rates of disease progression, and demonstrated higher bulbar subscores. However, this univariate analysis does not control for potential confounders, like age. Further multivariate analyses and longitudinal studies are warranted to better understand the complex interplay between NMS and functional impairment in ALS.
Conversely, no significant difference was found for the sexual domain in our study. This result agrees with the findings of several other studies investigating this domain in ALS populations [28,35]. Even though sexuality-related issues are common among ALS patients, a previous study reported that more than 75% of clinicians were unaware of any strategies or interventions to support their patients in this regard [36].
Cardiovascular dysregulation is a common feature in neurodegenerative diseases. In ALS, impaired cardiovascular neural regulation has been reported, presenting with palpitations, dizziness, light-headedness, and falls [37]. Neurally mediated cardiovascular disorders, such as orthostatic and postprandial hypotension or vasovagal syncope, are potential contributors to falls and associated morbidity and mortality in ALS patients [38].
The underlying pathophysiological processes proposed to date in ALS patients include reduced baroreceptor sensitivity, cardiac sympathetic hyperactivity at rest [39], or parasympathetic deterioration [37]. A decrease in the vagal tone and autonomic imbalance has been observed in ALS patients, where parasympathetic hypoactivity was demonstrated in ALS patients with bulbar signs [40]. Parasympathetic impairment in ALS patients was documented by several other studies [4,28,41].
Dizziness, lightheadedness or falls are symptoms pertaining to orthostatic hypotension (OH), which is another indicator of autonomicreflex failure. In a large ALS population, Quarracino et al. showed that out of the 1240 patients included, 10% presented with neurogenic OH, which is higher than the prevalence reported in the healthy middle-aged population [42].
More than half of our patients presented with cardiovascular symptoms. Dizziness, lightheadedness and falls were reported by 30 out of the 44 patients included. In addition, significantly lower total ALSFRS-R scores were recorded in this group compared to the patients without cardiovascular complaints. In agreement with other authors, we emphasize the need to address these dysautonomia symptoms as risk factors for cardiovascular events entailing increased morbidity and mortality. Given the supporting evidence that subclinical cardiac autonomic control dysfunction may be present early in the course of the disease [37], and that a decrease in life expectancy in ALS patients is due to cardiovascular collapse or sudden cardiac death [43], focused cardiovascular workup or cardiac function monitoring of ALS patients becomes more and more relevant.

4.2. Sensory Dysfunction

Despite its probable role in bulbar dysfunction, impaired dexterity and gait, somatosensory involvement is often neglected in motor neuron diseases. Clinical care is solely focused on the critical aspects of ALS, namely the preservation of motor independence and managing respiratory and bulbar symptoms [44], ignoring the large proportion of ALS patients who report pain or minor sensory symptoms throughout the course of the disease [45].
From the available clinical, imaging, histopathology or neurophysiology data, the literature provides vast evidence that the somatosensory system is not spared [44]. The majority of our ALS patients presented with sensory symptoms (pain, discomfort in legs, swollen legs). Interestingly, they had significantly higher scores on the gross motor ALSFRS-R subscore. We can safely infer that these symptoms are not due to reduced mobility or physical inactivity following severe physical impairment. Furthermore, several studies documented the presence of sensory signs and symptoms early in the course of the disease, sometimes as a subclinical finding, without significant concomitant motor complications [46,47].
Following skin biopsy and nerve conduction studies, sensory system impairment in ALS has been well documented in the literature [46,48,49,50,51,52,53,54,55]. In the European multicenter ESTEEM study, 20 of 88 ALS patients (22.7%) demonstrated sensory nerve conduction abnormalities, including reduced velocities and action potential amplitudes [48]. To assess large fiber sensory involvement, distal (antidromic dorsal sural and orthodromic medial plantar) and conventional (unilateral median and bilateral sural) sensory nerve conduction studies (NCS) were performed in 18 ALS patients and 31 controls. Abnormal amplitude and velocity values were found in 44.4% of patients on conventional NCS, and in 66.7% on distal NCS [50].
Multiple levels of the sensory pathway appear to be affected in ALS. Vaughan et al. reported degeneration of proprioceptive nerve endings at muscle spindles in ALS mouse models [52]. Additionally, satellite glial cells were identified as early targets of cytoplasmic mutant SOD1G93A accumulation in presymptomatic mice, suggesting a neurotoxic mechanism contributing to primary sensory alterations [53]. In a study using skin punch biopsies from 18 ALS patients and 18 controls, Ren Y. et al. found TDP-43 deposition in all samples, while phosphorylated TDP-43 (pTDP-43) was detected in 33.3% of ALS cases and correlated with higher Small-Fiber Neuropathy Symptoms Inventory Questionnaire (SFN-SIQ) scores, implicating small fiber involvement [54]. Additionally, cultured sensory neurons from ALS mouse models expressing TDP43A315T or SOD1G93A showed impaired neurite outgrowth compared to wild-type controls, supporting the presence of sensory neurodegeneration in ALS [55].
There are numerous neuroimaging studies backing sensory involvement in ALS, demonstrating reduced gray matter volume in the left postcentral gyrus and corticospinal tract demyelination at the cervical level in spinal onset ALS [56,57], gray matter atrophy in the left postcentral gyrus and glucose hypometabolism in the bilateral thalamus [58] or even spinal cervical cord gray and white matter atrophy [59].
Given its progressive nature and the aggregation and propagation of the specific SOD1 and TDP-43 proteins, a prion-like mechanism underlying the ALS pathophysiology has been recently promoted in the literature [60], defining ALS as a process starting focally and progressing to contiguous anatomic regions, affecting non-motor neurons [40]. There are authors suggesting that patient groups with sensory nerve dysfunction may represent a variant of ALS and that it may be part of the primary disease [48]. What becomes increasingly obvious is that abnormal sensory findings are compatible with the diagnosis of ALS and that they are not solely indicative of ALS-Plus or ALS-Mimic syndromes [8].

4.3. Cognitive Impairment and Frontotemporal Dysfunction

Acknowledging ALS as a multisystem disease that extends beyond motor neurons degeneration, the presence of cognitive and behavioral changes was demonstrated by numerous studies [61]. It is now recognized that frontotemporal dysfunction (FTD) accompanies motor symptoms in ALS. Cognitive impairment in ALS is clinically characterized by behavioral disinhibition, poor judgment, impairments in executive function, verbal praxis and immediate recall [62]. Furthermore, frontotemporal dementia and ALS, which lie on a shared clinical and pathological spectrum, exhibit overlapping genetic and neuropathological features [63]. Both are associated with TDP-43 dysregulation and mutations in genes involved in lysosomal and autophagy pathways [63,64].
The term frontotemporal spectrum disorder (ALS-FTSD) is proposed to better reflect the complexity of FTD. According to the Strong criteria, a diagnosis of ALS-FTD requires progressive cognitive and/or behavioral decline with loss of insight and/or psychotic features, or language impairment consistent with semantic dementia or non-fluent variant primary progressive aphasia. In the absence of full FTD criteria, cognitive or behavioral changes are classified as ALS with cognitive impairment (ALSci) or ALS with behavioral impairment (ALSbi). Furthermore, the ALS–dementia variant includes patients developing dementia not pertaining to FTD [65]. The FAB proved to be a reliable and accurate tool in detecting frontal lobe dysfunction in ALS patients [66].
More than 50% of ALS patients exhibit changes in language, behavior, memory, or social cognition [67]. Common cognitive deficits include impaired attention, concentration, verbal fluency, and FTD-spectrum cognitive impairment [68]. Increasing evidence indicates that cognitive and behavioral changes may accompany or even precede motor symptoms [69], with some studies suggesting a parallel decline of motor and cognitive functions [70]. Moreover, bulbar-onset ALS has been associated with a higher risk of cognitive impairment [71].
In our study, the time interval from disease onset to disease diagnosis was longer in patients presenting with cognitive impairment. Furthermore, we found moderate positive correlations of the FAB scores with the bulbar dysfunction and respectively, the global motor dysfunction. Deterioration of the physical functions may thus occur alongside the cognitive and behavioral changes pertaining to frontal lobe dysfunction. Intriguingly, Benbrika S. et al. suggest that cognitive function may be preserved throughout the course of the disease in ALS patients presenting with intact cognition at disease onset [72].
Given that comorbid FTD is considered a negative prognostic factor in ALS [73], its correlation with delays in disease diagnosis, severity and progression remains to be established.

4.4. Sleep Disturbances and Affective Symptoms

Sleep disturbances are reported early in the course of neurodegenerative diseases and the relationship between the two is deemed bidirectional, poor sleep may both contribute to and result from disease progression [74]. Although poorly acknowledged, it is recognized that newly diagnosed ALS patients have poor sleep quality [75], disorders of sleep being reported by 50–63% of patients with ALS [76]. In a prospective cohort study conducted on 396,918 individuals from the UK Biobank, data showed that organic sleep disorders—insomnia, hypersomnia, sleep–wake rhythm disorders, and sleep apnea—were associated with an increased risk of ALS and ALS-FTSD [77]. Polysomnographic studies conducted in ALS patients revealed decreased sleep efficiency, reduced REM and slow-wave sleep, and increased wakefulness [78]. Hypothalamic atrophy and pTDP-43 deposition may underlie these disturbances [79]. REM sleep reduction may also impair amygdala function, potentially contributing to mood disorders [80].
Sleep disturbances—difficulty falling asleep, vivid dreams, sleep-talking—are a common finding in our study. Moreover, the patients complaining of disrupted sleep exhibited greater global and fine motor dysfunction. This finding is supported by another study showing that patients reporting poor sleep at baseline recorded lower functional scores [81]. Silva et al. suggest that sleep disruption—independent of respiratory events—and poor sleep quality are associated with reduced survival in ALS patients [82]. Further research is needed to clarify the role of sleep disturbances as a risk factor and their impact on disease progression.
The prevalence of depression in ALS patients ranges from 10% to 45% [83,84]. However, it is frequently underdiagnosed given the overlapping cognitive and behavioral changes that confound depressive symptoms in these patients [69]. Nonspecific features such as insomnia, hypersomnia, and fatigue—often linked to physical impairment—further complicate diagnosis [83]. While fatigue and depression may overlap, evidence suggests that fatigue is a distinct clinical entity in this population [85]. Anhedonia, a key feature of depression, and apathy, marked by reduced motivation and empathy, are both common in ALS. Apathy is included in ALSbi criteria and often overlaps with depressive symptoms [65,86,87]. The distinction between apathy and depression is thus extremely relevant considering the treatment options available for the latter, with potential impact on quality of life and survival. We emphasize it is important that clinicians recognize affective symptoms and diagnose depression early in ALS patients.
A meta-analysis of 46 studies investigating overall depression in a total of 7171 ALS patients reported a summary prevalence of depressive symptoms of 34% [83]. Furthermore, in a case–control study including 1752 patients, patients with ALS were at higher risk of receiving a clinical diagnosis of depression and using antidepressants within the first year after diagnosis compared to controls [88]. A consistent relationship between the rates of distress or depression and ALSFRS-R scores was shown in a multicenter cohort assessing 329 ALS patients. In total, 12% of the patients included were diagnosed with depression and recorded lower scores on the total ALSFRS-R, fine motor and gross motor function [6], similar to other study results [84].
Our data support these findings; the results show a negative correlation between the BDI scores and disease severity, and thus indicate that affective symptoms are associated with lower functional performance in our patients. As for the relationship between survival and depression in ALS patients, the severity of depression may influence survival of these patients [7].
Few studies have evaluated non-motor symptoms in ALS patients using standardized scales. In a cohort of 58 patients, Jaafar et al. reported that every individual experienced at least two NMS. Most reported symptoms were fatigue, depression, dysphagia, anxiety and constipation, which closely followed our observations [89].
Our findings show a high burden of non-motor symptoms in ALS patients. The strength of our study consists in the large number of ALS patients included. Moreover, it is the first study conducted on Romanian ALS patients that assesses non-motor symptoms.
This study has several limitations. The data were collected and processed from questionnaires administered through structured interviews, which are more subjective and thus entail validity issues. However, such interviews are accessible and applicable in daily practice, and they also allow clarifications and minimize reporting bias. We agree that future studies should integrate objective clinical assessments (i.e., neuropsychological testing, polysomnography, urodynamic evaluation) to validate and complement patient-reported outcomes.
We also acknowledge the relatively small sample size of both the patient and the control groups, and the challenges inherent in recruiting patients with a rapidly progressive neurodegenerative disease. Moreover, the control group was younger than the study group, this imbalance reflecting the practical challenges of recruiting healthy, age-matched volunteers, particularly in older age groups. The difference in group characteristics is recognized as a limitation, and future studies with larger, age-matched cohorts are warranted.
Another limitation is the ΔPR formula, which was originally validated in prospective cohorts, while in our case they were retrospectively extracted. Although the average time from onset to diagnosis was short, alternative approaches, such as late slope models, may be more appropriate for future studies.
While this study shows a high prevalence of non-motor symptoms in ALS patients, we acknowledge that the observational cross-sectional design limits causal inferences. Moreover, uncorrected p-values increase the risk of Type I error so that the associations observed should therefore be interpreted with caution. However, the observed associations between specific NMS and ALFSFRS-R subscores further suggest that these symptoms are not incidental, but most likely reflective of ALS-related pathophysiological processes, and, as emerging evidence suggests, they may influence prognosis and disease trajectory. Future research should aim to clarify the prognostic value of non-motor symptoms in ALS through longitudinal, multicenter studies with larger and more demographically diverse cohorts. Such designs would allow assessment of how specific non-motor features influence functional decline, quality of life, and survival. Integrating objective evaluations (i.e., heart rate variability tests, sleep studies, neuropsychological testing) with patient-reported measures would strengthen the validity of findings and reduce self-report bias. Moreover, the development and validation of ALS-specific instruments would provide a more accurate framework for quantifying the burden of non-motor symptoms.

5. Conclusions

The impact of NMS on ALS patient’s lives is profound, extending well beyond the classical motor deficits. Recent advances in ALS research have prompted a paradigm shift, recognizing the disease as a multidimensional disorder encompassing motor, cognitive, behavioral, and systemic manifestations. Expanding research efforts have highlighted the clinical relevance of NMS, and our findings—despite being based on self-reported data from structured interviews and validated questionnaires—offer valuable insight into patients’ lived experiences.
Our study highlights the significant burden of non-motor symptoms in ALS and reveals distinct patterns of association with motor function, affective state and disease progression. Including a healthy control group allowed us to contextualize the BDI and FAB scores observed in patients, enabling the identification of disease-specific alterations in mood and executive function.
To summarize, there are 3 key findings in our study:
  • There is a high prevalence of NMS in our ALS study patients, with the majority of them presenting with more than one non-motor symptom according to the survey results.
  • The presence of NMS is not consistently reported by patients with greater functional impairment, as they do not uniformly correlate with lower ALS functional scores—somatosensory and urinary involvement may occur independently of the extent of motor disability.
  • Affective, cognitive and behavioral changes co-occur with motor symptoms, and they are expected to be more prominent in patients with more severe motor impairment.
In conclusion, broadening the clinical focus beyond motor symptoms is essential for improving diagnostic precision and developing targeted, patient-centered interventions. Systematic identification and management of cognitive, emotional, and behavioral symptoms can support more comprehensive care, ultimately enhancing quality of life. Given the substantial psychological burden on patients and caregivers, a compassionate, multidisciplinary approach should be integrated into standard ALS care.

Author Contributions

Conceptualization, A.M. and S.A.; methodology, A.M.; software, S.A.; validation, A.M., S.A. and I.O.; formal analysis, S.A.; investigation, A.M., S.A., M.D., M.R., R.C. and I.O.; resources, A.M.; data curation, A.M., S.M. and S.A.; writing—original draft preparation, A.M., I.O. and S.A.; writing—review and editing, A.M., S.A., I.O. and R.B.; visualization, S.A.; supervision, A.M.; project administration, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a grant of the Ministry of Research, Innovation, and Digitization, CNCS–UEFISCDI, project number PN-III-P1-1.1-TE-2021-0960, within PNCDI III.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Clinical County Emergency Hospital Mures (Approval No. 13472/21 June 2022) and by the University Ethics Board of UMFST “George Emil Palade” (Approval No. 1841/21 July 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author but will not be publicly available due to restrictions from our funding contract.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Goutman, S.A.; Hardiman, O.; Al-Chalabi, A.; Chió, A.; Savelieff, M.G.; Kiernan, M.C.; Feldman, E.L. Recent advances in the diagnosis and prognosis of amyotrophic lateral sclerosis. Lancet Neurol. 2022, 21, 480–493. [Google Scholar] [CrossRef]
  2. Feldman, E.L.; Goutman, S.A.; Petri, S.; Mazzini, L.; Savelieff, M.G.; Shaw, P.J.; Sobue, G. Amyotrophic lateral sclerosis. Lancet 2022, 400, 1363–1380. [Google Scholar] [CrossRef]
  3. Fang, T.; Jozsa, F.; Al-Chalabi, A. Nonmotor Symptoms in Amyotrophic Lateral Sclerosis: A Systematic Review. Int. Rev. Neurobiol. 2017, 134, 1409–1441. [Google Scholar] [CrossRef]
  4. Piccione, E.A.; Sletten, D.M.; Staff, N.P.; Low, P.A. Autonomic system and amyotrophic lateral sclerosis. Muscle Nerve 2015, 51, 676–679. [Google Scholar] [CrossRef] [PubMed]
  5. Nguyen, C.; Caga, J.; Mahoney, C.J.; Kiernan, M.C.; Huynh, W. Behavioural changes predict poorer survival in amyotrophic lateral sclerosis. Brain Cogn. 2021, 150, 105710. [Google Scholar] [CrossRef]
  6. Rabkin, J.G.; Goetz, R.; Factor-Litvak, P.; Hupf, J.; McElhiney, M.; Singleton, J.; Mitsumoto, H.; Als Cosmos Study Group. Depression and wish to die in a multicenter cohort of ALS patients. Amyotroph. Lateral Scler. Front. Degener. 2015, 16, 265–273. [Google Scholar] [CrossRef] [PubMed]
  7. Wei, Q.; Zheng, Z.; Guo, X.; Ou, R.; Chen, X.; Huang, R.; Yang, J.; Shang, H. Association between depression and survival in Chinese amyotrophic lateral sclerosis patients. Neurol. Sci. 2016, 37, 557–563. [Google Scholar] [CrossRef] [PubMed]
  8. Brooks, B.R.; Miller, R.G.; Swash, M.; Munsat, T.L. World Federation of Neurology Research Group on Motor Neuron Diseases. El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. Other Mot. Neuron Disord. 2000, 1, 293–299. [Google Scholar] [CrossRef]
  9. Cedarbaum, J.M.; Stambler, N.; Malta, E.; Fuller, C.; Hilt, D.; Thurmond, B.; Nakanishi, A. The ALSFRS-R: A revised ALS functional rating scale that incorporates assessments of respiratory function. BDNF ALS Study Group (Phase III). J. Neurol. Sci. 1999, 69, 13–21. [Google Scholar] [CrossRef]
  10. Great Lakes ALS Study Group. A comparison of muscle strength testing techniques in amyotrophic lateral sclerosis. Neurology 2003, 61, 1503–1507. [Google Scholar] [CrossRef]
  11. Labra, J.; Menon, P.; Byth, K.; Morrison, S.; Vucic, S. Rate of disease progression: A prognostic biomarker in ALS. J. Neurol. Neurosurg. Psychiatry 2016, 87, 628–632. [Google Scholar] [CrossRef]
  12. Ravits, J.; Appel, S.; Baloh, R.H.; Barohn, R.; Brooks, B.R.; Elman, L.; Floeter, M.K.; Henderson, C.; Lomen-Hoerth, C.; Macklis, J.D.; et al. Deciphering amyotrophic lateral sclerosis: What phenotype, neuropathology and genetics are telling us about pathogenesis. Amyotroph. Lateral Scler. Front. Degener. 2013, 14 (Suppl. 1), 5–18. [Google Scholar] [CrossRef]
  13. Milella, G.; Zoccolella, S.; Urso, D.; Nigro, S.; Tamburrino, L.; Gnoni, V.; Filardi, M.; Logroscino, G. Different patterns of spreading direction and motor neurons involvement in a cohort of limb-onset amyotrophic lateral sclerosis patients from Southern Italy: Potential implication on disease course or progression? Brain Behav. 2023, 13, e2899. [Google Scholar] [CrossRef]
  14. Chaudhuri, K.R.; Martinez-Martin, P.; Schapira, A.H.; Stocchi, F.; Sethi, K.; Odin, P.; Brown, R.G.; Koller, W.; Barone, P.; MacPhee, G.; et al. International multicenter pilot study of the first comprehensive self-completed nonmotor symptoms questionnaire for Parkinson’s disease: The NMSQuest study. Mov. Disord. 2006, 21, 916–923. [Google Scholar] [CrossRef]
  15. Chaudhuri, K.R.; Martinez-Martin, P. Quantitation of non-motor symptoms in Parkinson’s disease. Eur. J. Neurol. 2008, 15 (Suppl. 2), 2–7. [Google Scholar] [CrossRef] [PubMed]
  16. Chaudhuri, K.R.; Naidu, Y. Early Parkinson’s disease and non-motor issues. J. Neurol. 2008, 255 (Suppl. 5), 33–38. [Google Scholar] [CrossRef]
  17. Ahn, S.W.; Kim, S.H.; Kim, J.E.; Kim, S.M.; Kim, S.H.; Sung, J.J.; Lee, K.W.; Hong, Y.H. Frontal assessment battery to evaluate frontal lobe dysfunction in ALS patients. Can. J. Neurol. Sci. 2011, 38, 242–246. [Google Scholar] [CrossRef] [PubMed]
  18. Aiello, E.N.; Solca, F.; Torre, S.; Carelli, L.; Ferrucci, R.; Priori, A.; Verde, F.; Ticozzi, N.; Silani, V.; Poletti, B. Feasibility and diagnostics of the Frontal Assessment Battery (FAB) in amyotrophic lateral sclerosis. Neurol. Sci. 2023, 44, 587–592. [Google Scholar] [CrossRef]
  19. Beck, A.T.; Steer, R.A.; Ball, R.; Ranieri, W. Comparison of Beck Depression Inventories-IA and -II in psychiatric outpatients. J. Pers. Assess. 1996, 67, 588–597. [Google Scholar] [CrossRef]
  20. Folstein, M.F.; Folstein, S.E.; McHugh, P.R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 1975, 12, 189–198. [Google Scholar] [CrossRef] [PubMed]
  21. Masrori, P.; Van Damme, P. Amyotrophic lateral sclerosis: A clinical review. Eur. J. Neurol. 2020, 27, 1918–1929. [Google Scholar] [CrossRef]
  22. Heravi, F.S.; Naseri, K.; Hu, H. Gut Microbiota Composition in Patients with Neurodegenerative Disorders (Parkinson’s and Alzheimer’s) and Healthy Controls: A Systematic Review. Nutrients 2023, 15, 4365. [Google Scholar] [CrossRef]
  23. Boddy, S.L.; Giovannelli, I.; Sassani, M.; Cooper-Knock, J.; Snyder, M.P.; Segal, E.; Elinav, E.; Barker, L.A.; Shaw, P.J.; McDermott, C.J. The gut microbiome: A key player in the complexity of amyotrophic lateral sclerosis (ALS). BMC Med. 2021, 19, 13. [Google Scholar] [CrossRef]
  24. Jin, M.; Günther, R.; Akgün, K.; Hermann, A.; Ziemssen, T. Peripheral proinflammatory Th1/Th17 immune cell shift is linked to disease severity in amyotrophic lateral sclerosis. Sci. Rep. 2020, 10, 5941. [Google Scholar] [CrossRef] [PubMed]
  25. Özaydin Aksun, Z.; Erdoğan, S.; Kalkanci, A.; Şahin, E.A.; Çuhadar, T.; Şener, H.Ö. Is gut microbiota of patients with ALS different from that of healthy individuals? Turk. J. Med. Sci. 2024, 54, 579–587. [Google Scholar] [CrossRef] [PubMed]
  26. Zhang, L.; Zhuang, Z.; Zhang, G.; Huang, T.; Fan, D. Assessment of bidirectional relationships between 98 genera of the human gut microbiota and amyotrophic lateral sclerosis: A 2-sample Mendelian randomization study. BMC Neurol. 2022, 22, 8. [Google Scholar] [CrossRef] [PubMed]
  27. Xin, Z.; Xin, C.; Huo, J.; Liu, Q.; Dong, H.; Li, R.; Liu, Y. Neuroprotective Effect of a Multistrain Probiotic Mixture in SOD1G93A Mice by Reducing SOD1 Aggregation and Targeting the Microbiota-Gut-Brain Axis. Mol. Neurobiol. 2024, 61, 10051–10071. [Google Scholar] [CrossRef]
  28. Dubbioso, R.; Provitera, V.; Pacella, D.; Santoro, L.; Manganelli, F.; Nolano, M. Autonomic dysfunction is associated with disease progression and survival in amyotrophic lateral sclerosis: A prospective longitudinal cohort study. J. Neurol. 2023, 270, 4968–4977. [Google Scholar] [CrossRef]
  29. Ribichini, E.; Pallotta, N.; Badiali, D.; Carlucci, M.; Ceccanti, M.; Cambieri, C.; Libonati, L.; Corazziari, E.S.; Ruoppolo, G.; Inghilleri, M. Assessment of upper GI motor activity and GI symptoms in patients with amyotrophic lateral sclerosis: An observational study. Front. Neurol. 2025, 15, 1509917. [Google Scholar] [CrossRef]
  30. Niu, T.; Wang, P.; Zhou, X.; Liu, T.; Liu, Q.; Li, R.; Yang, H.; Dong, H.; Liu, Y. An overlap-weighted analysis on the association of constipation symptoms with disease progression and survival in amyotrophic lateral sclerosis: A nested case-control study. Ther. Adv. Neurol. Disord. 2025, 18, 17562864241309811. [Google Scholar] [CrossRef]
  31. Arlandis, S.; Vázquez-Costa, J.F.; Martínez-Cuenca, E.; Sevilla, T.; Boronat, F.; Broseta, E. Urodynamic findings in amyotrophic lateral sclerosis patients with lower urinary tract symptoms: Results from a pilot study. Neurourol. Urodyn. 2017, 36, 626–631. [Google Scholar] [CrossRef]
  32. Nakamura, M.; Nakayama, K.; Murakami, A.; Morise, S.; Kaneko, S.; Kusaka, H.; Yakushiji, Y. Early presentation of lower urinary tract and bowel dysfunction in sporadic amyotrophic lateral sclerosis: A case report. eNeurologicalSci 2022, 28, 100413. [Google Scholar] [CrossRef]
  33. Lopes de Carvalho, M.L.; Motta, R.; Battaglia, M.A.; Brichetto, G. Urinary disorders in amyotrophic lateral sclerosis subjects. Amyotroph. Lateral Scler. 2011, 12, 352–355. [Google Scholar] [CrossRef] [PubMed]
  34. Vázquez-Costa, J.F.; Arlandis, S.; Hervas, D.; Martínez-Cuenca, E.; Cardona, F.; Pérez-Tur, J.; Broseta, E.; Sevilla, T. Clinical profile of motor neuron disease patients with lower urinary tract symptoms and neurogenic bladder. J. Neurol. Sci. 2017, 378, 130–136. [Google Scholar] [CrossRef]
  35. Hirayama, T.; Shibukawa, M.; Yanagihashi, M.; Warita, H.; Atsuta, N.; Yamanaka, K.; Kano, O. Investigation of non-motor symptoms in patients with amyotrophic lateral sclerosis. Acta Neurol. Belg. 2023, 123, 1797–1804. [Google Scholar] [CrossRef] [PubMed]
  36. Shahbazi, M.; Holzberg, S.; Thirunavukkarasu, S.; Ciani, G. Perceptions of sexuality in individuals with Amyotrophic Lateral Sclerosis (ALS) and their treating clinicians. NeuroRehabilitation 2017, 41, 331–342. [Google Scholar] [CrossRef]
  37. Pavlovic, S.; Stevic, Z.; Milovanovic, B.; Milicic, B.; Rakocevic-Stojanovic, V.; Lavrnic, D.; Apostolski, S. Impairment of cardiac autonomic control in patients with amyotrophic lateral sclerosis. Amyotroph. Lateral Scler. 2010, 11, 272–276. [Google Scholar] [CrossRef] [PubMed]
  38. Carey, B.J.; Potter, J.F. Cardiovascular causes of falls. Age Ageing 2001, 30 (Suppl. 4), 19–24. [Google Scholar] [CrossRef]
  39. Oey, P.L.; Vos, P.E.; Wieneke, G.H.; Wokke, J.H.; Blankestijn, P.J.; Karemaker, J.M. Subtle involvement of the sympathetic nervous system in amyotrophic lateral sclerosis. Muscle Nerve 2002, 25, 402–408. [Google Scholar] [CrossRef]
  40. Merico, A.; Cavinato, M. Autonomic dysfunction in the early stage of ALS with bulbar involvement. Amyotroph. Lateral Scler. 2011, 12, 363–367. [Google Scholar] [CrossRef] [PubMed]
  41. Silveira, A.C.; Moraes, Í.A.P.; Vidigal, G.P.; Simcsik, A.O.; Rosa, R.M.; Favero, F.M.; Fernandes, S.M.S.; Garner, D.M.; Araújo, L.V.; Massa, M.; et al. Cardiac Autonomic Modulation in Subjects with Amyotrophic Lateral Sclerosis (ALS) during an Upper Limb Virtual Reality Task: A Prospective Control Trial. Biomed. Res. Int. 2022, 2022, 4439681. [Google Scholar] [CrossRef]
  42. Kim, M.J.; Farrell, J. Orthostatic Hypotension: A Practical Approach. Am. Fam. Physician 2022, 106, 365. [Google Scholar]
  43. Pimentel, R.M.M.; Macedo, H., Jr.; Valenti, V.E.; Rocha, F.O.; Abreu, L.C.; de MMonteiro, C.B.; Ferreira, C. Decreased Heart Rate Variability in Individuals with Amyotrophic Lateral Sclerosis. Respir. Care 2019, 64, 1088–1095. [Google Scholar] [CrossRef]
  44. Kleinerova, J.; Chipika, R.H.; Tan, E.L.; Yunusova, Y.; Marchand-Pauvert, V.; Kassubek, J.; Pradat, P.F.; Bede, P. Sensory Dysfunction in ALS and Other Motor Neuron Diseases: Clinical Relevance, Histopathology, Neurophysiology, and Insights from Neuroimaging. Biomedicines 2025, 13, 559. [Google Scholar] [CrossRef] [PubMed]
  45. Riancho, J.; Paz-Fajardo, L.; López de Munaín, A. Clinical and preclinical evidence of somatosensory involvement in amyotrophic lateral sclerosis. Br. J. Pharmacol. 2021, 178, 1257–1268. [Google Scholar] [CrossRef] [PubMed]
  46. Hammad, M.; Silva, A.; Glass, J.; Sladky, J.T.; Benatar, M. Clinical, electrophysiologic, and pathologic evidence for sensory abnormalities in ALS. Neurology 2007, 69, 2236–2242. [Google Scholar] [CrossRef]
  47. Theys, P.A.; Peeters, E.; Robberecht, W. Evolution of motor and sensory deficits in amyotrophic lateral sclerosis estimated by neurophysiological techniques. J. Neurol. 1999, 246, 438–442. [Google Scholar] [CrossRef]
  48. Pugdahl, K.; Fuglsang-Frederiksen, A.; de Carvalho, M.; Johnsen, B.; Fawcett, P.R.; Labarre-Vila, A.; Liguori, R.; Nix, W.A.; Schofield, I.S. Generalised sensory system abnormalities in amyotrophic lateral sclerosis: A European multicentre study. J. Neurol. Neurosurg. Psychiatry 2007, 78, 746–749. [Google Scholar] [CrossRef]
  49. Nolano, M.; Provitera, V.; Manganelli, F.; Iodice, R.; Caporaso, G.; Stancanelli, A.; Marinou, K.; Lanzillo, B.; Santoro, L.; Mora, G. Non-motor involvement in amyotrophic lateral sclerosis: New insight from nerve and vessel analysis in skin biopsy. Neuropathol. Appl. Neurobiol. 2017, 43, 119–132. [Google Scholar] [CrossRef]
  50. Isak, B.; Tankisi, H.; Johnsen, B.; Pugdahl, K.; Torvin MØLler, A.; Finnerup, N.B.; Christensen, P.B.; Fuglsang-Frederiksen, A. Involvement of distal sensory nerves in amyotrophic lateral sclerosis. Muscle Nerve 2016, 54, 1086–1092. [Google Scholar] [CrossRef] [PubMed]
  51. Heads, T.; Pollock, M.; Robertson, A.; Sutherland, W.H.; Allpress, S. Sensory nerve pathology in amyotrophic lateral sclerosis. Acta Neuropathol. 1991, 82, 316–320. [Google Scholar] [CrossRef] [PubMed]
  52. Vaughan, S.K.; Kemp, Z.; Hatzipetros, T.; Vieira, F.; Valdez, G. Degeneration of proprioceptive sensory nerve endings in mice harboring amyotrophic lateral sclerosis-causing mutations. J. Comp. Neurol. 2015, 523, 2477–2494. [Google Scholar] [CrossRef]
  53. Ruiz-Soto, M.; Riancho, J.; Tapia, O.; Lafarga, M.; Berciano, M.T. Satellite Glial Cells of the Dorsal Root Ganglion: A New “Guest/Physiopathological Target” in ALS. Front. Aging Neurosci. 2020, 12, 595751. [Google Scholar] [CrossRef]
  54. Ren, Y.; Liu, W.; Li, Y.; Sun, B.; Li, Y.; Yang, F.; Wang, H.; Li, M.; Cui, F.; Huang, X. Cutaneous somatic and autonomic nerve TDP-43 deposition in amyotrophic lateral sclerosis. J. Neurol. 2018, 265, 1753–1763. [Google Scholar] [CrossRef]
  55. Vaughan, S.K.; Sutherland, N.M.; Zhang, S.; Hatzipetros, T.; Vieira, F.; Valdez, G. The ALS-inducing factors, TDP43A315T and SOD1G93A, directly affect and sensitize sensory neurons to stress. Sci. Rep. 2018, 8, 16582. [Google Scholar] [CrossRef]
  56. Chen, Z.Y.; Liu, M.Q.; Ma, L. Gray Matter Volume Changes over the Whole Brain in the Bulbar- and Spinal-onset Amyotrophic Lateral Sclerosis: A Voxel-based Morphometry Study. Chin. Med. Sci. J. 2018, 33, 20–28. [Google Scholar] [CrossRef]
  57. Rasoanandrianina, H.; Grapperon, A.M.; Taso, M.; Girard, O.M.; Duhamel, G.; Guye, M.; Ranjeva, J.P.; Attarian, S.; Verschueren, A.; Callot, V. Region-specific impairment of the cervical spinal cord (SC) in amyotrophic lateral sclerosis: A preliminary study using SC templates and quantitative MRI (diffusion tensor imaging/inhomogeneous magnetization transfer). NMR Biomed. 2017, 30, e3801. [Google Scholar] [CrossRef]
  58. Buhour, M.S.; Doidy, F.; Mondou, A.; Pélerin, A.; Carluer, L.; Eustache, F.; Viader, F.; Desgranges, B. Voxel-based mapping of grey matter volume and glucose metabolism profiles in amyotrophic lateral sclerosis. EJNMMI Res. 2017, 7, 21. [Google Scholar] [CrossRef] [PubMed]
  59. Olney, N.T.; Bischof, A.; Rosen, H.; Caverzasi, E.; Stern, W.A.; Lomen-Hoerth, C.; Miller, B.L.; Henry, R.G.; Papinutto, N. Measurement of spinal cord atrophy using phase sensitive inversion recovery (PSIR) imaging in motor neuron disease. PLoS ONE 2018, 13, e0208255. [Google Scholar] [CrossRef] [PubMed]
  60. McAlary, L.; Yerbury, J.J.; Cashman, N.R. The prion-like nature of amyotrophic lateral sclerosis. Prog. Mol. Biol. Transl. Sci. 2020, 175, 261–296. [Google Scholar] [CrossRef]
  61. Cavallo, M.; Adenzato, M.; Macpherson, S.E.; Karwig, G.; Enrici, I.; Abrahams, S. Evidence of social understanding impairment in patients with amyotrophic lateral sclerosis. PLoS ONE 2011, 6, e25948. [Google Scholar] [CrossRef]
  62. Yang, W.; Sopper, M.M.; Leystra-Lantz, C.; Strong, M.J. Microtubule-associated tau protein positive neuronal and glial inclusions in ALS. Neurology 2003, 61, 1766–1773. [Google Scholar] [CrossRef]
  63. Root, J.; Merino, P.; Nuckols, A.; Johnson, M.; Kukar, T. Lysosome dysfunction as a cause of neurodegenerative diseases: Lessons from frontotemporal dementia and amyotrophic lateral sclerosis. Neurobiol. Dis. 2021, 154, 105360. [Google Scholar] [CrossRef] [PubMed]
  64. Ling, S.C.; Polymenidou, M.; Cleveland, D.W. Converging mechanisms in ALS and FTD: Disrupted RNA and protein homeostasis. Neuron 2013, 79, 416–438. [Google Scholar] [CrossRef]
  65. Strong, M.J.; Abrahams, S.; Goldstein, L.H.; Woolley, S.; Mclaughlin, P.; Snowden, J.; Mioshi, E.; Roberts-South, A.; Benatar, M.; HortobáGyi, T.; et al. Amyotrophic lateral sclerosis-frontotemporal spectrum disorder (ALS-FTSD): Revised diagnostic criteria. Amyotroph. Lateral Scler. Front. Degener. 2017, 18, 153–174. [Google Scholar] [CrossRef]
  66. Barulli, M.R.; Fontana, A.; Panza, F.; Copetti, M.; Bruno, S.; Tursi, M.; Iurillo, A.; Tortelli, R.; Capozzo, R.; Simone, I.L.; et al. Frontal assessment battery for detecting executive dysfunction in amyotrophic lateral sclerosis without dementia: A retrospective observational study. BMJ Open 2015, 5, e007069. [Google Scholar] [CrossRef]
  67. Goldstein, L.H.; Abrahams, S. Changes in cognition and behaviour in amyotrophic lateral sclerosis: Nature of impairment and implications for assessment. Lancet Neurol. 2013, 12, 368–380. [Google Scholar] [CrossRef]
  68. Ringholz, G.M.; Appel, S.H.; Bradshaw, M.; Cooke, N.A.; Mosnik, D.M.; Schulz, P.E. Prevalence and patterns of cognitive impairment in sporadic ALS. Neurology 2005, 65, 586–590. [Google Scholar] [CrossRef]
  69. Moțățăianu, A.; Ormenișan, I.; Bălașa, R. Apathy as Non-Motor Manifestation in Amyotrophic Lateral Sclerosis. Acta Marisiensis Ser. Medica 2021, 68, 3–5. [Google Scholar] [CrossRef]
  70. Chiò, A.; Moglia, C.; Canosa, A.; Manera, U.; Vasta, R.; Brunetti, M.; Barberis, M.; Corrado, L.; D’Alfonso, S.; Bersano, E.; et al. Cognitive impairment across ALS clinical stages in a population-based cohort. Neurology 2019, 93, e984–e994. [Google Scholar] [CrossRef] [PubMed]
  71. Yang, T.; Hou, Y.; Li, C.; Cao, B.; Cheng, Y.; Wei, Q.; Zhang, L.; Shang, H. Risk factors for cognitive impairment in amyotrophic lateral sclerosis: A systematic review and meta-analysis. Neurol. Neurosurg. Psychiatry 2021, 92, 688–693. [Google Scholar] [CrossRef]
  72. Benbrika, S.; Doidy, F.; Carluer, L.; Mondou, A.; Pélerin, A.; Eustache, F.; Viader, F.; Desgranges, B. Longitudinal Study of Cognitive and Emotional Alterations in Amyotrophic Lateral Sclerosis: Clinical and Imaging Data. Front. Neurol. 2021, 12, 620198. [Google Scholar] [CrossRef]
  73. Elamin, M.; Phukan, J.; Bede, P.; Jordan, N.; Byrne, S.; Pender, N.; Hardiman, O. Executive dysfunction is a negative prognostic indicator in patients with ALS without dementia. Neurology 2011, 76, 1263–1269. [Google Scholar] [CrossRef] [PubMed]
  74. Abbott, S.M.; Videnovic, A. Chronic sleep disturbance and neural injury: Links to neurodegenerative disease. Nat. Sci. Sleep. 2016, 8, 55–61. [Google Scholar] [CrossRef]
  75. Diaz-Abad, M.; Buczyner, J.R.; Venza, B.R.; Scharf, S.M.; Kwan, J.Y.; Lubinski, B.; Russell, J.W. Poor Sleep Quality in Patients with Amyotrophic Lateral Sclerosis at the Time of Diagnosis. J. Clin. Neuromuscul. Dis. 2018, 20, 60–68. [Google Scholar] [CrossRef]
  76. Lucia, D.; McCombe, P.A.; Henderson, R.D.; Ngo, S.T. Disorders of sleep and wakefulness in amyotrophic lateral sclerosis (ALS): A systematic review. Amyotroph Lateral Scler. Front. Degener. 2021, 22, 161–169. [Google Scholar] [CrossRef]
  77. Yang, T.; Pang, D.; Huang, J.; Xiao, Y.; Li, C.; Wei, Q.; Ou, R.; Cheng, Y.; Lin, J.; Che, N.; et al. Association between sleep and ALS-FTSD: A Prospective Cohort Study based on 396,918 UK biobank participants. Transl. Psychiatry 2025, 15, 123. [Google Scholar] [CrossRef] [PubMed]
  78. Lo Coco, D.; Puligheddu, M.; Mattaliano, P.; Congiu, P.; Borghero, G.; Fantini, M.L.; La Bella, V.; Ferri, R. REM sleep behavior disorder and periodic leg movements during sleep in ALS. Acta Neurol. Scan 2017, 135, 219–224. [Google Scholar] [CrossRef]
  79. Gnoni, V.; Zoccolella, S.; Giugno, A.; Urso, D.; Tamburrino, L.; Filardi, M.; Logroscino, G. Hypothalamus and amyotrophic lateral sclerosis: Potential implications in sleep disorders. Front. Aging Neurosci. 2023, 15, 1193483. [Google Scholar] [CrossRef]
  80. Anghel, L.; Ciubară, A.; Nechita, A.; Nechita, L.; Manole, C.; Baroiu, L.; Ciubară, A.B.; Mușat, C.L. Sleep Disorders Associated with Neurodegenerative Diseases. Diagnostics 2023, 13, 2898. [Google Scholar] [CrossRef] [PubMed]
  81. Li, X.; Liu, Q.; Niu, T.; Jia, H.; Liu, T.; Xin, Z.; Li, Z.; Zhou, X.; Li, R.; Liu, Y.; et al. Sleep Disturbances as a Potential Risk Factor for Deterioration of Respiratory Function in Patients with Amyotrophic Lateral Sclerosis. Ann. Indian Acad. Neurol. 2023, 26, 754–760. [Google Scholar] [CrossRef] [PubMed]
  82. Silva, F.; Silva, J.; Salgueira, S.; Mendes, A.; Matos, E.; Conde, B. Sleep Disturbances in Amyotrophic Lateral Sclerosis and Prognostic Impact-A Retrospective Study. Life 2024, 14, 1284. [Google Scholar] [CrossRef] [PubMed]
  83. Heidari, M.E.; Nadali, J.; Parouhan, A.; Azarafraz, M.; Tabatabai, S.M.; Irvani, S.S.N.; Eskandari, F.; Gharebaghi, A. Prevalence of depression among amyotrophic lateral sclerosis (ALS) patients: A systematic review and meta-analysis. J. Affect. Disord. 2021, 287, 182–190. [Google Scholar] [CrossRef] [PubMed]
  84. Anca, M.; Sebastian, A.; Cristina, R.; Zoltan, B.; Laura, B.; Adrian, B.; Septimiu, V.; Adina, S.; Smaranda, M. Predictors of Depression in Caucasian Patients with Amyotrophic Lateral Sclerosis in Romania. Brain Sci. 2020, 10, 470. [Google Scholar] [CrossRef] [PubMed]
  85. McElhiney, M.C.; Rabkin, J.G.; Gordon, P.H.; Goetz, R.; Mitsumoto, H. Prevalence of fatigue and depression in ALS patients and change over time. J. Neurol. Neurosurg. Psychiatry 2009, 80, 1146–1149. [Google Scholar] [CrossRef]
  86. Rascovsky, K.; Hodges, J.R.; Knopman, D.; Mendez, M.F.; Kramer, J.H.; Neuhaus, J.; van Swieten, J.C.; Seelaar, H.; Dopper, E.G.; Onyike, C.U.; et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011, 134 Pt 9, 2456–2477. [Google Scholar] [CrossRef]
  87. Rabinowitz, J.A.; Ellis, J.D.; Strickland, J.C.; Hochheimer, M.; Zhou, Y.; Young, A.S.; Curtis, B.; Huhn, A.S. Patterns of demoralization and anhedonia during early substance use disorder treatment and associations with treatment attrition. J. Affect. Disord. 2023, 335, 248–255. [Google Scholar] [CrossRef]
  88. Roos, E.; Mariosa, D.; Ingre, C.; Lundholm, C.; Wirdefeldt, K.; Roos, P.M.; Fang, F. Depression in amyotrophic lateral sclerosis. Neurology 2016, 86, 2271–2277. [Google Scholar] [CrossRef]
  89. Jaafar, N.; Malek, E.; Ismail, H.; Salameh, J. Nonmotor Symptoms in Amyotrophic Lateral Sclerosis and Their Correlation with Disease Progression. J. Clin. Neuromuscul. Dis. 2021, 23, 1–6. [Google Scholar] [CrossRef] [PubMed]
Medicina 61 01694 g001
Figure 1. Pearson’s correlation analysis between BDI, FAB and ALSFRS-R scores. * Statistically significant correlation set at p < 0.05. BDI: Beck Depression Inventory. FAB: Frontal Assessment Battery.
Figure 1. Pearson’s correlation analysis between BDI, FAB and ALSFRS-R scores. * Statistically significant correlation set at p < 0.05. BDI: Beck Depression Inventory. FAB: Frontal Assessment Battery.
Medicina 61 01694 g001
Table 1. Participants demographics and clinical characteristics.
Table 1. Participants demographics and clinical characteristics.
Patients (N = 44)HC (N = 35)p
Age (mean ± SD) (years) o58.39 (±12.52)53.11 (±9.84)0.044
Sex ratio (M:F)28:1623:120.847
BDI (mean ± SD) o10.75 (±8.59)5.49 (±3.04)<0.001
FAB (mean ± SD) o13.45 (±4.69)17.46 (±1.01)<0.001
ALSFRS-R scores o
Respiratory subscore (±SD)11.4 (±0.9)NANA
Bulbar subscore (±SD)10.1 (±2.4)NANA
Fine Motor subscore (±SD)8.8 (±2.7)NANA
Gross Motor subscore (±SD)6.3 (±3.4)NANA
Total score37.6 (±6.3)NANA
ALS subtype ∞
Bulbar9 (20.5%)NANA
Spinal35 (79.6%)NANA
ALS phenotype ∞
Classic25 (56.8%)NANA
LMN12 (27.3%)NANA
Bulbar7 (15.9%)NANA
Progression pattern ∞
Horizontal23 (52.3%)NANA
Vertical21 (47.7%)NANA
ΔPR (range)0.5 (0.3–1.3)NANA
Time from onset to diagnosis (months) *9.5 (5.75–24.25) *NANA
Abbreviation: BDI: Beck Depression Inventory; FAB: Frontal Assessment Battery; HC: healthy controls; LMN: lower motor neuron; ΔPR: disease progression rate; ∞ categorical variables reported as frequencies and percentages; o parametric distribution reported as mean (±SD); * non-parametric distribution reported as median (IQR).Compared to HC, patients had significantly higher BDI scores (10.75 ± 8.59 vs. 5.49 ± 3.04, p < 0.001) and lower FAB scores (13.45 ± 4.69 vs. 17.46 ± 1.01, p < 0.001), although the mean scores in both groups are within the normal range (BDI cutoff < 13; FAB cutoff > 12).
Table 2. Non-motor symptoms questionnaire results.
Table 2. Non-motor symptoms questionnaire results.
N = 44N (%) *
Cognitive changes7 (15.9%)
memory impairment4.5%
attention/concentration deficit13.6%
Psychiatric symptoms28 (63.3%)
apathy18.2%
sadness56.8%
anxiety47.7%
perceptual problems2.3%
hallucinations0%
Sleep disorders/fatigue35 (79.6%)
tiredness2.3%
insomnia72.7%
vivid dreams65.9%
sleep-talking11.4%
Sensory symptoms37 (84%)
pain61.4%
discomfort in legs77.3%
swollen legs20.5%
Cardiovascular symptoms30 (68.2%)
dizziness/light-headedness68.2%
Falls50%
Urinary symptoms16 (36.4%)
urinary urgency22.7%
nocturia31.8%
Sexual symptoms27 (61.4%)
interest in sex56.8%
sexual dysfunction56.8%
Gastrointestinal symptoms36 (81.8%)
dribbling31.8%
difficulty swallowing34.1%
vomiting/nausea22.7%
constipation75%
bowel incontinence18.2%
incomplete bowel emptying29.5%
Miscellaneous31 (70.5%)
loss of/change in taste/smell25%
weight change31.8%
excessive sweating13.6%
double vision0%
* All categorical variables reported as frequencies and percentages.
Table 3. ANOVA test for ALSFRS-R scores and ΔPR grouped by non-motor symptoms.
Table 3. ANOVA test for ALSFRS-R scores and ΔPR grouped by non-motor symptoms.
Mean (±SD)Mean (±SD)p *
Cognitive changes
YESNO
ALSFRS-R Respiratory11.6 (±0.5)11.4 (±0.9)0.618
ALSFRS-R Bulbar8.6 (±2.4)10.4 (±2.3)0.062
ALSFRS-R Gross Motor6.6 (±4.0)6.2 (±3.4)0.819
ALSFRS-R Fine Motor8.9 (±3.3)8.7 (±2.6)0.910
ALSFRS-R Total36.6 (±6.2)37.8 (±6.4)0.647
ΔPR1.0 (±1.9)0.9 (±0.9)0.753
Time from onset to diagnosis29.6 (±20.0)13.5 (±12.0)0.006
Psychiatric symptoms
YESNO
ALSFRS-R Respiratory11.5 (±0.9)11.2 (±0.9)0.233
ALSFRS-R Bulbar9.8 (±2.5)10.6 (±2.2)0.322
ALSFRS-R Gross Motor6.6 (±3.7)5.8 (±3.0)0.431
ALSFRS-R Fine Motor9.1 (±2.6)8.1 (±2.8)0.201
ALSFRS-R Total38.2 (±6.3)36.5 (±6.5)0.393
ΔPR0.9 (±1.1)1.0 (±0.9)0.607
Time from onset to diagnosis18.7 (±15.8)11.5 (±11.2)0.117
Sleep disorders
YESNO
ALSFRS-R Respiratory11.3 (±0.9)12 (±0.0)0.030
ALSFRS-R Bulbar9.7 (±2.5)11.4 (±0.9)0.053
ALSFRS-R Gross Motor6.4 (±3.6)5.9 (±3.1)0.695
ALSFRS-R Fine Motor8.3 (±2.7)10.7 (±1.4)0.014
ALSFRS-R Total36.3 (±6.4)42.4 (±2.9)0.008
ΔPR1.0 (±1.1)0.6 (±0.6)0.317
Time from onset to diagnosis16.54 (±14.8)14.22 (±14.4)0.675
Sensory symptoms
YESNO
ALSFRS-R Respiratory11.3 (±1.0)11.9 (±0.4)0.164
ALSFRS-R Bulbar10.0 (±2.5)10.9 (±1.6)0.355
ALSFRS-R Gross Motor6.8 (±3.4)3.9 (±2.2)0.039
ALSFRS-R Fine Motor8.5 (±2.8)10.0 (±1.5)0.181
ALSFRS-R Total36.8 (±6.2)41.6 (±5.5)0.069
ΔPR1.0 (±1.1)0.4 (±0.2)0.154
Time from onset to diagnosis17.3 (±14.9)9.6 (±10.9)0.202
Cardiovascular symptoms
YESNO
ALSFRS-R Respiratory11.2 (±1.0)11.8 (±0.4)0.064
ALSFRS-R Bulbar9.7 (±2.6)10.9 (±1.7)0.109
ALSFRS-R Gross Motor5.8 (±3.6)7.3 (±2.9)0.194
ALSFRS-R Fine Motor8.3 (±2.6)9.7 (±2.7)0.103
ALSFRS-R Total36.2 (±5.8)40.6 (±6.4)0.027
ΔPR0.9 (±0.9)0.9 (±1.3)0.817
Time from onset to diagnosis18.9 (±15.2)9.9 (±11.5)0.055
Urinary symptoms
YESNO
ALSFRS-R Respiratory11.7 (±0.6)11.3 (±1.0)0.132
ALSFRS-R Bulbar11.2 (±1.3)9.4 (±2.6)0.012
ALSFRS-R Gross Motor5.6 (±3.2)6.8 (±3.5)0.289
ALSFRS-R Fine Motor8.8 (±2.2)8.8 (±2.9)0.908
ALSFRS-R Total38.8 (±4.9)36.9 (±7.0)0.338
ΔPR0.5 (±0.5)1.2 (±1.2)0.045
Time from onset to diagnosis19.1 (±14.7)14.4 (±14.5)0.309
Sexual dysfunction
YESNO
ALSFRS-R Respiratory11.5 (±0.8)11.3 (±1.1)0.518
ALSFRS-R Bulbar9.8 (±2.6)10.6 (±1.8)0.273
ALSFRS-R Gross Motor6.6 (±3.3)5.8 (±3.6)0.475
ALSFRS-R Fine Motor8.8 (±2.7)8.7 (±2.8)0.932
ALSFRS-R Total37.3 (±6.2)38.5 (±6.7)0.702
ΔPR1.0 (±1.2)0.8 (±0.7)0.501
Time from onset to diagnosis18.7 (±15.7)11.9 (±11.8)0.138
Digestive symptoms
YESNO
ALSFRS-R Respiratory11.3 (±0.9)12.0 (±0.0)0.044
ALSFRS-R Bulbar9.7 (±2.5)11.8 (±0.7)0.026
ALSFRS-R Gross Motor6.4 (±3.5)5.6 (±3.1)0.547
ALSFRS-R Fine Motor8.4 (±2.8)10.1 (±1.8)0.109
ALSFRS-R Total36.5 (±6.4)42.5 (±3.2)0.013
ΔPR0.9 (±1.1)0.6 (±0.8)0.341
Time from onset to diagnosis17.1 (±14.4)11.6 (15.5)0.347
* Statistically significant variation set at p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Moțățăianu, A.; Andone, S.; Maier, S.; Chinezu, R.; Roman, M.; Dumitreasă, M.; Bălașa, R.; Ormenișan, I. Beyond Motor Decline in ALS: Patient-Centered Insights into Non-Motor Manifestations. Medicina 2025, 61, 1694. https://doi.org/10.3390/medicina61091694

AMA Style

Moțățăianu A, Andone S, Maier S, Chinezu R, Roman M, Dumitreasă M, Bălașa R, Ormenișan I. Beyond Motor Decline in ALS: Patient-Centered Insights into Non-Motor Manifestations. Medicina. 2025; 61(9):1694. https://doi.org/10.3390/medicina61091694

Chicago/Turabian Style

Moțățăianu, Anca, Sebastian Andone, Smaranda Maier, Rareș Chinezu, Medeea Roman, Mihai Dumitreasă, Rodica Bălașa, and Ioana Ormenișan. 2025. "Beyond Motor Decline in ALS: Patient-Centered Insights into Non-Motor Manifestations" Medicina 61, no. 9: 1694. https://doi.org/10.3390/medicina61091694

APA Style

Moțățăianu, A., Andone, S., Maier, S., Chinezu, R., Roman, M., Dumitreasă, M., Bălașa, R., & Ormenișan, I. (2025). Beyond Motor Decline in ALS: Patient-Centered Insights into Non-Motor Manifestations. Medicina, 61(9), 1694. https://doi.org/10.3390/medicina61091694

Article Metrics

Back to TopTop