ABSTRACT
Background
Amyotrophic lateral sclerosis (ALS) has a prolonged latency period, though its preclinical characteristics remain poorly understood. This study uses UK Biobank data to explore and compare ALS's pre‐diagnostic features, including symptoms and medication use, aiming to provide insights into the disease's underlying mechanisms.
Methods
Clinical symptoms and medications were identified from self‐reports, hospital records, and death registry data. Propensity score matching was used to match ALS with Alzheimer's disease (ad) and Parkinson's disease (PD), ensuring balance in socioeconomic factors to compare symptoms 0–5 years before diagnosis. Cox regression analysis was applied to assess the associations between medication use and the risk of incident ALS and mortality after ALS diagnosis.
Results
A total of 753 ALS cases were observed in 502 417 participants, with an incidence rate of 10.58 per 100 000 person‐years. In the ALS cohort, the male‐to‐female ratio was 2.9, with a median age at onset of 64.61 years (Interquartile range (IQR): 56.80–71.31) and a median survival time post‐diagnosis of 9.08 months (IQR: 3.18–18.98), while females (log‐rank p = 0.038) and individuals with earlier (< 64.61 years) disease onset (log‐rank p < 0.001) had longer survival periods. In the 5 years prior to diagnosis, ALS showed a higher incidence of falls compared to ad (11.3% vs. 3.2%, p < 0.001), but a lower incidence than PD (10.7% vs. 28.3%, p < 0.001). Additionally, ALS had a lower incidence of depression (4.6% vs. 25.6%, p < 0.001), anxiety (3.5% vs. 18.1%, p < 0.001), sleep disorders (1.4% vs. 7.2%, p < 0.001), hypotension (3.4% vs. 30.5%, p < 0.001), constipation (0.3% vs. 4.9%, p < 0.001), and urinary dysfunction (2.2% vs. 8.7%, p < 0.001) compared with PD. The use of calcium channel blockers may be a risk factor for incident ALS (adjusted HR 1.61, 95% CI: 1.22–2.12, p < 0.001).
Conclusions
Pre‐diagnostic presentations of falls are more frequent in ALS than in ad, but less frequent than in PD. However, ALS exhibits fewer psychiatric symptoms and autonomic dysfunction compared with PD. The use of calcium channel blockers may be associated with an increased risk of developing ALS in the future.
Keywords: amyotrophic lateral sclerosis, medications, prodromal symptoms
1. Introduction
Amyotrophic lateral sclerosis (ALS) is characterized by the progressive degeneration of neurons responsible for controlling voluntary muscle movement [1]. Due to the lack of effective therapeutic interventions, mortality often occurs within 3 years of the onset of symptoms, primarily due to respiratory failure [2]. Although genetics is a major risk factor for ALS [3], it does not fully account for the entire disease burden, as the causes of 85% of sporadic cases remain unclear [4]. A gene‐time‐environment hypothesis of ALS posits that the disease arises from an interplay of genetic predisposition, age‐associated cellular damage, and environmental exposure [5]. However, the complex interactions of multiple factors contributing to ALS and its heterogeneous manifestations make research on non‐genetic factors in ALS challenging [6]. On the other hand, early diagnosis of ALS remains a significant challenge. While ALS is still a relatively rare disease, its prevalence is expected to rise with ageing populations and improved diagnosis methods [7], maintaining a global incidence rate of only 1.68 per 100 000 person‐years [8]. Additionally, research on ALS‐specific early symptoms that can be easily differentiated from those of other neurodegenerative diseases is still insufficient, leading to delayed or missed diagnoses [9].
Research on the preclinical features of the disease may help provide some evidence. As a neurodegenerative disease, ALS is considered to have a prolonged preclinical phase, similar to disorders such as Parkinson's disease (PD), where symptoms can emerge 15 years earlier [10], and Alzheimer's disease (ad), marked by amyloid plaque build‐up up to 20 years before diagnosis [11]. Thus, it is reasonable to speculate that there may be specific pre‐disease features associated with ALS. Prior ALS research predominantly concentrates on the characteristics and treatments of diagnosed ALS patients, mainly through case–control or cross‐sectional studies. There is a lack of research on the preclinical stage of the disease, with limited evidence available, especially for sporadic cases, which constitute the majority of ALS cases [12].
Against this backdrop, we utilized the data from the UK Biobank's registry of over 500 000 individuals to conduct further research on ALS. The UK Biobank is a large‐scale biomedical database and research resource, containing in‐depth genetic and health information from half a million UK participants [13]. Using this valuable resource, our study aimed to explore: (1) the incidence, sociodemographic characteristics, and prognosis of ALS; (2) preclinical features of ALS and how they compare with those of individuals who later develop PD and ad; and (3) prospective analyses identifying clinical medications linked to ALS risk and its progression.
2. Method
2.1. Study Population and ALS Diagnosis
Among the 502 357 participants recruited by the UK Biobank, aged 37–73, from 22 assessment centres across England, Wales, and Scotland between 2006 and 2010 [14], a total of 753 were diagnosed with ALS. Additionally, the study included 55 292 healthy controls, 4809 individuals with PD, and 4445 individuals with ad. Healthy controls were defined based on the absence of International Classification of Diseases (ICD)‐coded diseases. Clinical outcomes were determined over a follow‐up period from 2007 to 2023 using algorithmically defined outcomes (ALS [field ID 42029], ad [field ID 42020], PD [field ID 42032]), derived from encoded data collected during the UK Biobank's initial assessment, hospital admission records, and mortality data, with the algorithm's accuracy validated in a study of 17 000 Biobank participants in England and used in several previous studies [15, 16]. This research was conducted using the UK Biobank Resource under Application Number 108832 and informed consent was obtained from all participants registered in the UK Biobank (https://www.ukbiobank.ac.uk/learn‐more‐about‐uk‐biobank/about‐us/ethics).
2.2. Study Design
As depicted in Figure 1, our study focuses on ALS and delves into its sociodemographic characteristics, clinical symptoms, and medications. We comprehensively analysed 753 ALS patients, detailing their age, gender, Townsend Deprivation Index (TDI), ethnicity, and education, among other aspects. In our exploration of ALS disease risk, we maintained focus on these 753 patients. For the post‐diagnosis survival analysis, we excluded patients reported only in death registries (n = 57) and those who self‐reported ALS (n = 51), due to potential inaccuracies in diagnosis timing. Patients without a recorded death event by the end of follow‐up were also excluded (n = 139), as their survival time remained indeterminate. Consequently, 506 patients were included in the descriptive analysis and Cox regression for survival post‐diagnosis.
FIGURE 1.
Research design flowchart. AD, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; ATC, Anatomical Therapeutic Chemical Classification System; BMI, body mass index; PD, Parkinson's disease; TDI, Townsend Deprivation Index.
2.3. Clinical Symptoms
The clinical features associated with ad and PD, as used in prior research [17], were thoroughly considered in the study. These include memory problems, sleep disorders, constipation, anxiety, depression, falls, hypotension, urinary dysfunction, and abnormal weight loss. All events were identified from baseline interviews and updated according to ICD‐10 coding up until the end of the follow‐up period (Table S1). The timing of the first occurrence of these events was determined using the corresponding dates in ICD‐10 codes or from the dates when questionnaires were conducted. The clinical features comparisons across ALS, PD, and ad were conducted among matched cohorts after propensity score matching. Matching factors included sex, age, qualifications, ethnicity, and TDI with a calliper value of 0.2 times the standard deviation of propensity scores.
2.4. Medications and Covariates
Medications were self‐reported (Field ID: 20003) and classified based on the Anatomical Therapeutic Chemical (ATC) classification system [18], encompassing all categories of antihypertensive drugs, laxatives, statins, and medications used to treat neurological disorders, including treatments for depression and anxiety conditions (Table S2). Additionally, we further classified the pharmacological mechanisms of the aforementioned drug categories, with the detailed classification listed in Table S3.
Covariates, including age, sex, ethnicity, qualifications, and TDI, were obtained during the initial assessment. Educational attainment was self‐reported and categorized into academic (college or university degree, A‐levels or equivalent, general certificate of secondary education general certificate of secondary education [GCSEs] or equivalent) and vocational (certificate of secondary education [CSEs] or equivalent, national vocational qualification [NVQ] or higher national diploma [HND] or higher national certificate [HNC] or equivalent, other vocational qualifications) education. The TDI was derived from national census data, including information on unemployment, vehicle ownership, household overcrowding, and occupation.
2.5. Statistical Analysis
Baseline and clinical characteristics were summarized as median (Interquartile range (IQR)) for continuous variables and as counts (percentages) for categorical variables. In the analysis of prodromal features, the cumulative incidence of each health condition across different time windows before and after ALS onset was used to demonstrate their progression over time. Propensity scores were generated based on covariates (sex, age, qualifications, ethnicity, and TDI) and matched using nearest‐neighbour matching with a calliper of 0.2 times the standard deviation to compare the occurrence rates of nine conditions among patients with different diseases. Additionally, the Chi‐squared test was used to compare categorical variables, particularly the occurrence rates of health conditions, in samples with n ≥ 40 and a theoretical frequency (T) ≥ 5. Associations between medications and ALS risk and prognosis were analysed using Cox proportional hazard models. In the time‐to‐death analysis, Kaplan–Meier curves were used to illustrate survival differences among various groups. The follow‐up period commenced from the date of the initial ALS diagnosis until the date of death. The proportional hazards assumptions were examined using Schoenfeld residuals. All analyses were adjusted for sex, age at admission, qualifications, ethnicity, and TDI. Missing covariate data (missing percentage: TDI‐0.12%, Qualifications‐19.00%) were imputed using the multiple imputation method with the ‘mice’ package in R.
All statistical analyses were performed with R software, version 4.2.1. Two‐sided p < 0.05 was considered statistically significant. Bonferroni correction was applied for multiple comparisons.
3. Results
3.1. Baseline Characteristics
The initial UK Biobank cohort, consisting of 502 417 participants, featured a diverse ethnic composition: 94.6% were White, 1.6% Black, 2.0% South Asian, with the remainder from other mixed backgrounds. The incidence of ALS in this cohort was 10.58 per 100 000 person‐years of follow‐up: 10.56 in the White cohort, 17.59 in the Black cohort, and 7.14 in the South Asian cohort. Table 1 presents the sociodemographic characteristics of 753 ALS patients. Of these, 645 were diagnosed through hospital records, 57 were identified via death registries, and 51 were confirmed through self‐reports. In the ALS cohort, which was predominantly white (93.9%), the patients had a median admission age of 57 years (IQR: 50–63, Birth year: 1937–1969). A significant majority (77.7%) held vocational qualifications, and the cohort displayed a wide range of socio‐economic statuses, as reflected by the median TDI score (−2.17, IQR: −3.68 to 0.36). Furthermore, a notable male predominance was evident in the cohort, with a male‐to‐female ratio of 2.9, and among those diagnosed in hospitals, the median age at onset was 64.61 years (IQR: 56.80–71.31). Additionally, compared with participants without ALS, the ALS cohort showed a higher male‐to‐female ratio, a greater proportion of individuals with vocational qualifications, and an older average age, as elaborated in Table S4.
TABLE 1.
Baseline characteristics of all included patients with ALS.
| Overall ALS (n = 753) | Hospitalization (n = 645) | Death (n = 57) | Self‐report (n = 51) | p value | |
|---|---|---|---|---|---|
| Sex, n (%) | |||||
| Female | 425 (56.4) | 366 (56.7) | 32 (56.1) | 27 (52.9) | |
| Male | 328 (43.6) | 279 (43.3) | 25 (43.9) | 24 (47.1) | 0.869 |
| Male to female ratio | 1.30 | 1.31 | 1.28 | 1.13 | |
| Year of birth, n (%) | 0.027 | ||||
| 1937–1940 | 95 (12.6) | 77 (11.9) | 13 (22.8) | 5 (9.8) | |
| 1940–1950 | 457 (60.7) | 397 (61.6) | 34 (59.6) | 26 (51.0) | |
| 1959–1960 | 164 (21.8) | 136 (21.1) | 10 (17.5) | 18 (35.3) | |
| 1960–1969 | 37 (4.9) | 35 (5.4) | 0 (0.0) | 2 (3.9) | |
| Age at admission | 57.00 (50.00–63.00) | 57.00 (50.00–63.00) | 56.00 (48.00–63.00) | 60.00 (51.50–63.00) | 0.668 |
| TDI | −2.17 (−3.68–0.36) | −2.25 (−3.73–0.28) | −1.95 (−3.67–0.35) | −1.74 (−3.27–1.45) | 0.388 |
| Qualification, n (%) | 0.454 | ||||
| Academic | 496 (77.7) | 432 (78.0) | 32 (71.1) | 32 (82.1) | |
| Vocational | 142 (22.3) | 122 (22.0) | 13 (28.9) | 7 (17.9) | |
| Ethnicity, n (%) | 0.673 | ||||
| White | 707 (93.9) | 605 (93.8) | 55 (96.5) | 47 (92.2) | |
| Black | 20 (2.7) | 16 (2.5) | 1 (1.8) | 3 (5.9) | |
| South Asian | 10 (1.3) | 10 (1.6) | 0 (0.0) | 0 (0.0) | |
| Other mix | 16 (2.1) | 14 (2.2) | 1 (1.8) | 1 (2.0) | |
| Age at ALS report, years | 63.92 (56.09–70.67) | 64.61 (56.80–71.31) | 66.21 (59.30–72.47) | 52.42 (42.77–57.40) | < 0.001 |
| Death, n (%) | 576 (76.5) | 506 (78.4) | 57 (100.0) | 13 (25.5) | < 0.001 |
| Age at death | 71.40 (65.70–75.60) | 71.10 (65.50–75.50) | 73.10 (68.10–76.40) | 66.50 (61.20–68.40) | 0.004 |
| Survival time, months | 7.75 (1.63–18.29) | 9.08 (3.18–18.98) | 0.00 (0.00–0.00) | 43.13 (30.47–72.43) | <0.001 |
Abbreviations: ALS, amyotrophic lateral sclerosis; TDI, Townsend Deprivation Index.
In the survival analysis, after excluding ALS patients diagnosed outside of hospitals and those with unknown survival times, a total of 506 ALS patients were included in the analysis. As shown in Figure S2, the median survival time post‐diagnosis was 9.08 months (IQR: 3.18–18.98). Regarding survival advantages, female (log‐rank p = 0.038) and those with earlier onset of the disease had longer survival periods (log‐rank p < 0.001). However, no significant differences in survival time were observed across different ages, ethnicities, or TDI scores.
3.2. Clinical Symptoms
The cumulative incidence of various symptoms across different time windows surrounding ALS report is illustrated in a Figure 2: starting 10 years prior to ALS diagnosis, the frequency of symptoms (memory problems, sleep disorders, constipation, anxiety, depression, falls, hypotension, urinary dysfunction, and abnormal weight loss) begins to rise, with a more rapid increase observed around the time of diagnosis. The prevalence of most symptoms in ALS patients was higher than that in healthy controls, including memory problems (1.1% vs. 0.0%, p = 0.023), depression (19.0% vs. 12.5%, p = 0.002), sleep disorders (10.4% vs. 4.2%, p < 0.001), abnormal weight loss (2.4% vs. 0.1%, p < 0.001), falls (32.0% vs. 20.0%, p < 0.001), urinary dysfunction (4.9% vs. 1.7%, p = 0.002), and constipation (3.1% vs. 0.2%, p < 0.001). However, no statistically significant difference was found in hypotension (9.7% vs. 4.6%, p = 0.058) and anxiety (12.5% vs. 8.9%, p = 0.054).
FIGURE 2.
Cumulative timeline summary of multiple clinical symptoms in patients with ALS. Abbreviations: ALS, amyotrophic lateral sclerosis.
Among nearest‐neighbour matching, coarsened exact matching (CEM), exact matching, and optimal matching, the nearest‐neighbour matching model was selected as the optimal approach based on standardized mean differences (SMD < 0.1) and sample size (Table S5). Using propensity score matching (PSM) based on covariates (sex, age, qualifications, ethnicity, and TDI), 441 ad patients and 597 PD patients were matched 1:1 with ALS patients. Additionally, using PSM based on covariates (sex, age, imputed qualifications, ethnicity, and imputed TDI), 560 ad patients and 716 PD patients were matched 1:1 with ALS patients. A detailed summary of the matching balance is summarized in Table S5 and Figure S1.
As shown in Figure 3 and Table 2, in the 0–5 years preceding diagnosis, falls were more common in ALS patients compared with those with ad (11.3% vs. 3.2%, p < 0.001). However, compared with PD, ALS patients had a lower prevalence of falls (10.7% vs. 28.3%, p < 0.001), depression (4.6% vs. 25.6%, p < 0.001), anxiety (3.5% vs. 18.1%, p < 0.001), hypotension (3.4% vs. 30.5%, p < 0.001), constipation (0.3% vs. 4.9%, p < 0.001), urinary dysfunction (2.2% vs. 8.7%, p < 0.001), and sleep disorders (1.4% vs. 7.2%, p < 0.001). PSM performed using sex, age, imputed qualifications, ethnicity, and imputed TDI yielded consistent results.
FIGURE 3.
Prevalence of health conditions in ALS cohorts compared with those with ad and PD within the 0–5 years preceding diagnosis. *Differences were considered significant after Bonferroni correction (p < 0.0056). Abbreviations: AD, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; PD, Parkinson's disease; PSM, propensity score matching.
TABLE 2.
Prevalence of clinical symptoms in ALS cohorts compared with ad and PD within the 0–5 years preceding diagnosis.
| Vs. ad | Vs. PD | |||||
|---|---|---|---|---|---|---|
| Controls | ALS | p value | Controls | ALS | p value | |
| PSM performed after deleting missing data | ||||||
| n | 441 | 441 | 597 | 597 | ||
| Fall | 12 (3.2) | 39 (11.3) | < 0.001 | 169 (28.3) | 49 (10.7) | < 0.001 |
| Depression | 5 (1.3) | 18 (4.7) | 0.010 | 153 (25.6) | 23 (4.6) | < 0.001 |
| Anxiety | 7 (1.7) | 11 (2.7) | 0.455 | 108 (18.1) | 19 (3.5) | < 0.001 |
| Memory loss | 3 (0.7) | 0 (0.0) | 0.250 | 5 (0.8) | 0 (0.0) | 0.075 |
| Hypotension | 7 (1.6) | 15 (3.7) | 0.107 | 182 (30.5) | 19 (3.4) | < 0.001 |
| Abnormal weight loss | 1 (0.2) | 4 (0.9) | 0.371 | 11 (1.8) | 5 (0.9) | 0.219 |
| Constipation | 3 (0.7) | 2 (0.5) | 0.977 | 29 (4.9) | 2 (0.3) | < 0.001 |
| Urinary dysfunction | 6 (1.4) | 10 (2.4) | 0.404 | 52 (8.7) | 12 (2.2) | < 0.001 |
| Sleep disorders | 9 (2.2) | 7 (1.6) | 0.760 | 43 (7.2) | 8 (1.4) | < 0.001 |
| PSM performed after imputing missing data | ||||||
| n | 560 | 560 | 716 | 716 | ||
| Fall | 19 (4.0) | 56 (13.1) | < 0.001 | 192 (29.5) | 63 (11.4) | < 0.001 |
| Depression | 11 (2.2) | 24 (4.9) | 0.032 | 164 (25.2) | 31 (5.1) | < 0.001 |
| Anxiety | 11 (2.2) | 24 (4.9) | 0.032 | 129 (19.8) | 22 (3.4) | < 0.001 |
| Memory loss | 9 (1.6) | 1 (0.2) | 0.026 | 9 (1.4) | 1 (0.1) | 0.018 |
| Hypotension | 5 (0.9) | 19 (3.6) | 0.006 | 184 (28.3) | 25 (3.7) | < 0.001 |
| Abnormal weight loss | 2 (0.4) | 5 (0.9) | 0.450 | 17 (2.6) | 6 (0.9) | 0.022 |
| Constipation | 7 (1.3) | 4 (0.7) | 0.508 | 25 (3.8) | 4 (0.6) | < 0.001 |
| Urinary dysfunction | 5 (0.9) | 13 (2.5) | 0.079 | 64 (9.8) | 12 (1.8) | < 0.001 |
| Sleep disorders | 9 (1.7) | 6 (1.1) | 0.595 | 52 (8.0) | 9 (1.3) | < 0.001 |
Note: Participants were matched using PSM based on sex, age, qualifications, ethnicity, and TDI.
Abbreviations: ad, Alzheimer's disease; ALS, amyotrophic lateral sclerosis; PD, Parkinson's disease; PSM, propensity score matching.
Additionally, we conducted a symptom comparison in ALS patients, categorizing them by the timing of disease onset, length of survival period, and sex. We found that early‐onset ALS patients exhibited higher rates of depression (22.0% vs. 16.0%, p = 0.043) and falls (35.5% vs. 28.5%, p = 0.045) compared with those with late‐onset ALS. Furthermore, patients with shorter survival periods were more likely to experience anxiety than those with longer survival periods (15.6% vs. 9.0%, p = 0.023) (Table S6).
3.3. Medication
As shown in Figure 4 and Table 3, calcium channel blockers (HR (95% CI) = 1.69 (1.31–2.17), p < 0.001), antihypercholesterolaemia drugs (HR (95% CI) = 1.36 (1.14–1.64), p = 0.001), and statins (HR (95% CI) = 1.39 (1.15–1.69), p = 0.001) may be risk factors for incident ALS. After adjusting for sex, age, education level, ethnicity, TDI, and body mass index (BMI), calcium channel blockers remained significant (HR (95% CI) = 1.61 (1.22–2.12), p = 0.001).
FIGURE 4.
Associations between medication use and the risk of incident ALS and mortality after ALS diagnosis. The Cox regression analysis was adjusted for sex, age, qualifications, ethnicity, TDI, and BMI. Red highlights indicate statistical significance after Bonferroni correction (p < 0.0013). Abbreviations: ACE, angiotensin‐converting enzyme; ALS, amyotrophic lateral sclerosis; ARB, angiotensin II receptor blocker; BMI, body mass index; HR, hazard ratio; MAOI, monoamine oxidase inhibitor; SGLT2, sodium‐glucose cotransporter‐2; SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant; TDI, Townsend Deprivation Index; TDZ, thiazolidinedione.
TABLE 3.
Medications use and incident ALS risk, excluding patients with follow‐up less than 2 years.
| Unadjusted | Adjusted | |||||
|---|---|---|---|---|---|---|
| Index | ALS case/n | HR (95% CI) | p value | ALS case/n | HR (95% CI) | p value |
| Antihypertension drugs | 630/55530 | 1.27 (1.07–1.51) | 0.005 | 539/49448 | 1.18 (0.98–1.42) | 0.078 |
| ACE inhibitors | 630/55530 | 1.03 (0.79–1.34) | 0.815 | 539/49448 | 0.94 (0.70–1.26) | 0.674 |
| ARB | 630/55530 | 1.50 (1.08–2.09) | 0.017 | 539/49448 | 1.19 (0.80–1.78) | 0.393 |
| Beta blockers | 630/55530 | 0.98 (0.71–1.35) | 0.897 | 539/49448 | 0.82 (0.57–1.20) | 0.305 |
| Calcium channel blockers | 630/55530 | 1.69 (1.31–2.17) | < 0.001* | 539/49448 | 1.61 (1.22–2.12) | 0.001* |
| Diuretics | 630/55530 | 1.34 (1.02–1.75) | 0.034 | 539/49448 | 1.23 (0.91–1.66) | 0.185 |
| Alpha blockers | 630/55530 | 0.71 (0.10–5.08) | 0.737 | 539/49448 | 0.78 (0.11–5.55) | 0.805 |
| Vasodilators | 630/55530 | 0 (0‐Inf) | 0.989 | 539/49448 | 0 (0‐Inf) | 0.990 |
| Renin inhibitors | 630/55530 | — | — | 539/49448 | — | — |
| Antihypercholesterolaemia drugs | 630/55530 | 1.36 (1.14–1.64) | 0.001* | 539/49448 | 1.32 (1.08–1.61) | 0.006 |
| Statins | 630/55530 | 1.39 (1.15–1.69) | 0.001* | 539/49448 | 1.30 (1.05–1.61) | 0.017 |
| Cholesterol absorption inhibitors | 630/55530 | 1.15 (0.81–1.64) | 0.435 | 539/49448 | 1.27 (0.88–1.83) | 0.204 |
| Bile acid sequestrants | 630/55530 | 2.80 (0.39–19.92) | 0.303 | 539/49448 | 3.21 (0.45–22.80) | 0.245 |
| Nicotinic nacid derivatives | 630/55530 | 0 (0‐Inf) | 0.985 | 539/49448 | 0 (0‐Inf) | 0.986 |
| Fibrates | 630/55530 | 0.61 (0.09–4.32) | 0.619 | 539/49448 | 0.69 (0.10–4.93) | 0.714 |
| Antidiabetes drugs | 630/55530 | 0.98 (0.63–1.52) | 0.918 | 539/49448 | 0.97 (0.60–1.57) | 0.887 |
| Stimulation of beta cells | 630/55530 | 1.04 (0.49–2.20) | 0.914 | 539/49448 | 1.22 (0.58–2.58) | 0.598 |
| Alpha glucosidase inhibition | 630/55530 | 0 (0‐Inf) | 0.989 | 539/49448 | 0 (0‐Inf) | 0.989 |
| Alpha amylase inhibition | 630/55530 | 0 (0‐Inf) | 0.989 | 539/49448 | 0 (0‐Inf) | 0.989 |
| SGLT2 inhibition | 630/55530 | — | — | 539/49448 | — | — |
| Metformin | 630/55530 | 0.93 (0.56–1.52) | 0.765 | 539/49448 | 0.95 (0.56–1.61) | 0.848 |
| TZD | 630/55530 | 0.40 (0.06–2.87) | 0.365 | 539/49448 | 0.49 (0.07–3.49) | 0.476 |
| Insulin | 630/55530 | 1.36 (0.68–2.73) | 0.387 | 539/49448 | 0.96 (0.40–2.32) | 0.933 |
| Anticonstipation drugs | 630/55530 | 1.26 (0.87–1.83) | 0.223 | 539/49448 | 0.98 (0.63–1.54) | 0.946 |
| Stimulant laxatives | 630/55530 | 3.00 (1.34–6.70) | 0.007 | 539/49448 | 2.36 (0.88–6.30) | 0.088 |
| Osmotic laxatives | 630/55530 | 1.63 (0.73–3.65) | 0.231 | 539/49448 | 0.90 (0.29–2.80) | 0.854 |
| Bulk forming laxatives | 630/55530 | 1.38 (0.62–3.07) | 0.437 | 539/49448 | 1.28 (0.53–3.09) | 0.582 |
| Lubricant laxatives | 630/55530 | 2.73 (0.68–10.96) | 0.155 | 539/49448 | 3.03 (0.75–12.14) | 0.118 |
| Prokinetics | 630/55530 | 0 (0‐Inf) | 0.989 | 539/49448 | 0 (0‐Inf) | 0.990 |
| Antidepression drugs | 630/55530 | 1.44 (1.11–1.86) | 0.006 | 539/49448 | 1.42 (1.07–1.89) | 0.015 |
| SSRI | 630/55530 | 1.46 (1.05–2.05) | 0.026 | 539/49448 | 1.64 (1.16–2.32) | 0.005 |
| TCA | 630/55530 | 1.20 (0.75–1.91) | 0.451 | 539/49448 | 1.01 (0.58–1.74) | 0.986 |
| MAOI | 630/55530 | 0 (0‐Inf) | 0.989 | 539/49448 | 0 (0‐Inf) | 0.989 |
| Other antidepressants | 630/55530 | 1.73 (0.95–3.14) | 0.071 | 539/49448 | 1.50 (0.74–3.01) | 0.257 |
| Antianxiety drugs | 630/55530 | 1.15 (0.86–1.55) | 0.343 | 539/49448 | 1.07 (0.77–1.49) | 0.674 |
| Benzodiazepines | 630/55530 | 0.45 (0.06–3.18) | 0.422 | 539/49448 | 0.50 (0.07–3.58) | 0.492 |
| Barbiturates | 630/55530 | — | — | 539/49448 | — | — |
| Other anxiolytics | 630/55530 | — | — | 539/49448 | — | — |
Note: The Cox regression analysis was adjusted for sex, age, qualifications, ethnicity, TDI, and BMI. Bold formatting has been applied exclusively to medication categories to denote classifications.
Abbreviations: ACE, angiotensin‐converting enzyme; ALS, amyotrophic lateral sclerosis; ARB, angiotensin II receptor blocker; BMI, body mass index; HR, hazard ratio; MAOI, monoamine oxidase inhibitor; SGLT2, sodium‐glucose cotransporter‐2; SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant; TDI, Townsend Deprivation Index; TDZ, thiazolidinedione.
Significance after Bonferroni corrections, with a corrected threshold of p < 0.0013.
The Cox regression results of medication use and mortality risk after ALS diagnosis are summarized in Figure 4 and Table 4. In the unadjusted model, antidiabetic drugs [HR (95% CI) = 0.57 (0.35–0.91), p = 0.018] and insulin [HR (95% CI) = 0.44 (0.21–0.94), p = 0.034] may be protective factors for prognosis, while bulk‐forming laxatives [HR (95% CI) = 3.49 (1.30–9.38), p = 0.013] and other antidepressants [HR (95% CI) = 2.78 (1.32–5.89), p = 0.007] may be risk factors. After adjusting for sex, age, education level, ethnicity, TDI, and BMI, antidiabetic drugs [HR (95% CI) = 0.59 (0.36–0.94), p = 0.028] and insulin [HR (95% CI) = 0.44 (0.21–0.93), p = 0.033] remained protective, while bulk‐forming laxatives [HR (95% CI) = 3.63 (1.34–9.80), p = 0.011] and other antidepressants [HR (95% CI) = 3.10 (1.46–6.59), p = 0.003] remained risk factors. However, after Bonferroni multiple correction, all of these associations lost significance.
TABLE 4.
Medications use and incident mortality risk after ALS diagnosis.
| Unadjusted | Adjusted | |||||
|---|---|---|---|---|---|---|
| Index | ALS case/n | HR (95% CI) | p value | ALS case/n | HR (95% CI) | p value |
| Antihypertension drugs | 506/506 | 0.88 (0.73–1.06) | 0.186 | 506/506 | 0.85 (0.70–1.03) | 0.095 |
| ACE inhibitors | 506/506 | 0.98 (0.74–1.30) | 0.885 | 506/506 | 0.95 (0.72–1.27) | 0.750 |
| ARB | 506/506 | 0.83 (0.57–1.20) | 0.314 | 506/506 | 0.80 (0.55–1.16) | 0.238 |
| Beta blockers | 506/506 | 0.95 (0.68–1.34) | 0.777 | 506/506 | 0.98 (0.69–1.37) | 0.886 |
| Calcium channel blockers | 506/506 | 0.95 (0.72–1.25) | 0.694 | 506/506 | 0.90 (0.68–1.19) | 0.443 |
| Diuretics | 506/506 | 0.84 (0.61–1.15) | 0.274 | 506/506 | 0.80 (0.57–1.10) | 0.169 |
| Alpha blockers | 506/506 | 0.31 (0.04–2.24) | 0.247 | 506/506 | 0.36 (0.05–2.57) | 0.308 |
| Vasodilators | 506/506 | — | — | 506/506 | — | — |
| Renin inhibitors | 506/506 | — | — | 506/506 | — | — |
| Antihypercholesterolaemia drugs | 506/506 | 0.90 (0.74–1.10) | 0.322 | 506/506 | 0.89 (0.73–1.09) | 0.255 |
| Statins | 506/506 | 0.89 (0.72–1.10) | 0.277 | 506/506 | 0.87 (0.70–1.07) | 0.193 |
| Cholesterol absorption inhibitors | 506/506 | 0.98 (0.66–1.47) | 0.939 | 506/506 | 1.00 (0.67–1.51) | 0.984 |
| Bile acid sequestrants | 506/506 | 6.26 (0.87–45.06) | 0.068 | 506/506 | 7.53 (1.03–55.26) | 0.047 |
| Nicotinic nacid derivatives | 506/506 | — | — | 506/506 | — | — |
| Fibrates | 506/506 | 1.14 (0.28–4.58) | 0.852 | 506/506 | 1.06 (0.26–4.28) | 0.936 |
| Antidiabetes drugs | 506/506 | 0.57 (0.35–0.91) | 0.018 | 506/506 | 0.59 (0.36–0.94) | 0.028 |
| Stimulation of beta cells | 506/506 | 1.13 (0.47–2.74) | 0.778 | 506/506 | 1.26 (0.52–3.07) | 0.606 |
| Alpha glucosidase inhibition | 506/506 | — | — | 506/506 | — | — |
| Alpha amylase inhibition | 506/506 | — | — | 506/506 | — | — |
| SGLT2 inhibition | 506/506 | — | — | 506/506 | — | — |
| Metformin | 506/506 | 0.69 (0.40–1.17) | 0.165 | 506/506 | 0.70 (0.41–1.19) | 0.184 |
| TZD | 506/506 | 1.03 (0.15–7.36) | 0.974 | 506/506 | 0.95 (0.13–6.80) | 0.960 |
| Insulin | 506/506 | 0.44 (0.21–0.94) | 0.034 | 506/506 | 0.44 (0.21–0.93) | 0.033 |
| Anticonstipation drugs | 506/506 | 1.28 (0.81–2.00) | 0.288 | 506/506 | 1.21 (0.77–1.90) | 0.411 |
| Stimulant laxatives | 506/506 | 0.89 (0.40–2.00) | 0.784 | 506/506 | 0.80 (0.35–1.80) | 0.585 |
| Osmotic laxatives | 506/506 | 1.31 (0.33–5.26) | 0.704 | 506/506 | 1.48 (0.36–6.01) | 0.587 |
| Bulk forming laxatives | 506/506 | 3.49 (1.30–9.38) | 0.013 | 506/506 | 3.63 (1.34–9.80) | 0.011 |
| Lubricant laxatives | 506/506 | — | — | 506/506 | — | — |
| Prokinetics | 506/506 | — | — | 506/506 | — | — |
| Antidepression drugs | 506/506 | 0.89 (0.66–1.19) | 0.424 | 506/506 | 0.90 (0.67–1.22) | 0.510 |
| SSRI | 506/506 | 0.89 (0.60–1.31) | 0.552 | 506/506 | 0.92 (0.62–1.36) | 0.676 |
| TCA | 506/506 | 0.71 (0.44–1.15) | 0.164 | 506/506 | 0.69 (0.43–1.12) | 0.138 |
| MAOI | 506/506 | — | — | 506/506 | — | — |
| Other antidepressants | 506/506 | 2.78 (1.32–5.89) | 0.007 | 506/506 | 3.10 (1.46–6.59) | 0.003 |
| Antianxiety drugs | 506/506 | 0.89 (0.64–1.22) | 0.461 | 506/506 | 0.86 (0.62–1.19) | 0.364 |
| Benzodiazepines | 506/506 | 0.43 (0.11–1.74) | 0.238 | 506/506 | 0.43 (0.10–1.77) | 0.244 |
| Barbiturates | 506/506 | — | — | 506/506 | — | — |
| Other anxiolytics | 506/506 | — | — | 506/506 | — | — |
Note: The Cox regression analysis was adjusted for sex, age, qualifications, ethnicity, TDI, and BMI. Bold formatting has been applied exclusively to medication categories to denote classifications.
Abbreviations: ACE, angiotensin‐converting enzyme; ALS, amyotrophic lateral sclerosis; ARB, angiotensin II receptor blocker; BMI, body mass index; HR, hazard ratio; MAOI, monoamine oxidase inhibitor; SGLT2, sodium‐glucose cotransporter‐2; SSRI, selective serotonin reuptake inhibitor; TCA, tricyclic antidepressant; TDI, Townsend Deprivation Index; TDZ, thiazolidinedione.
4. Discussion
Using data from over 500 000 UK Biobank participants, our study compared ALS during its pre‐diagnostic phase. We observed an ALS incidence of 10.58 per 100 000 person‐years and a marked male predominance (male‐to‐female ratio of 2.9); early‐onset ALS and female gender may confer a survival advantage. Additionally, calcium channel blocker use may be positively associated with ALS. Pre‐diagnostically, falls were more common in ALS than in ad but less frequent than in PD, while psychiatric symptoms and autonomic dysfunction were less pronounced compared with PD.
On the one hand, our study supplements the existing evidence. Globally, a previous systematic review reported ALS incidence rates ranging from 0.26 per 100 000 person‐years in Ecuador to 23.46 per 100 000 person‐years in Japan [19]. While our estimated incidence of 10.58 per 100 000 person‐years falls within this range, it is still relatively high, which may be attributed to the older age distribution and the multiethnic background of this cohort [20]. Additionally, while neuropsychological impairments are not included in the diagnostic criteria for ALS, it is noteworthy that ALS patients may also experience such cognitive and psychological challenges. These may encompass a decline in executive memory functions, a marked increase in apathy and irritability, a noticeable neglect of personal hygiene, alterations in dietary habits, and the emergence of psychological issues such as depression, anxiety, and disrupted sleep patterns [21, 22]. Although these symptoms have the potential to manifest in the early phases of ALS, they are more frequently observed and tend to be more pronounced during the more advanced stages of the disease [23]. Some studies suggest that these psychiatric abnormalities may play a role in the progression of motor deficits and the prognosis of ALS [24]. This aligns with our finding that the use of antidepressants is associated with an increased risk of mortality following an ALS diagnosis. Besides, memory loss in ALS patients was already higher than in normal controls before diagnosis. Considering that approximately 15% of ALS patients meet the diagnostic criteria for frontotemporal dementia [25], as reported in the literature, this suggests a shared aetiology between ALS and other neurodegenerative diseases. However, the specific factors that lead to the distinct progression and differentiation of ALS from other degenerative diseases remain unknown.
On the other hand, this study provides new evidence, particularly in one aspect: exploring the preclinical characteristics of ALS. In the cross‐disorder analysis of symptoms in ALS, PD, and ad, we found that ALS shares many symptoms common to other neurodegenerative diseases, though it lacks distinct, specific manifestations. The most frequent pre‐diagnostic symptom in ALS, compared with ad, was falls; however, falls, psychiatric symptoms, and autonomic dysfunction in ALS were less prominent than in PD. Consequently, general physicians and even specialist neurologists might not initially recognize a diagnosis of amyotrophic lateral sclerosis due to the overlap in disease presentation with other conditions [1]. Additionally, we found that the use of calcium channel blockers prior to diagnosis may be associated with an increased risk of ALS. Calcium ions play a crucial role in various physiological processes, including muscle contraction, nerve signal transmission, blood clotting, and the regulation of enzyme activity [26]. Calcium channel blockers can influence the nervous system by reducing calcium influx into neurons, which may help regulate neurotransmitter release, reduce neuronal excitability, and protect against neurodegeneration [27]. Calcium channel blockers have been studied for their potential neuroprotective effects in conditions like ad and PD [28, 29]. However, our results suggest that calcium channel blockers may be harmful in ALS. Previous studies have indicated that hypertension and antihypertensive drug use are associated with a reduced risk of ALS [30, 31]. This appears to be a contradictory finding, as hypertension and antihypertensive drugs are generally considered to have opposing effects, yet both seem to offer a protective role for ALS. Considering that some studies suggest hypertension may increase ALS risk [32], current population‐based evidence remains conflicting and inconclusive. In animal research, previous studies have observed downregulation of Ca2 + in skeletal muscle mitochondria in ALS mouse models [33] and hyperactive intracellular calcium signaling [34]. However, these studies primarily focus on skeletal muscle and lack exploration of the key pathological site in ALS—the motor neurons. Future animal and clinical studies should focus more on hypertension and antihypertensive drugs to explore their potential impact on ALS.
Our study has limitations. First, ALS diagnoses in the UK Biobank are based on hospital records, death registers, and self‐reports, which may introduce delays, biases, and a skew towards more severe cases. Given the rarity of ALS, managing data for over 500 000 individuals to capture in detail about 700 ALS cases is challenging. Therefore, in the post‐diagnosis ALS analysis, we included only hospital data to ensure greater accuracy. Second, in our cross‐disease comparison of symptom profiles, the differences in the TDI among ALS, ad, and PD within the UK Biobank cohort were relatively small (standardized mean differences before matching < 0.1). This suggests a degree of regional homogeneity within the UK Biobank cohort, which is limited to participants from England, Wales, and Scotland. Therefore, future research using multiregional or international cohorts is needed to further validate our findings. Besides, the UK Biobank does not include genotype and phenotype data, making it difficult to distinguish between familial and sporadic ALS. Third, although the UK Biobank cohort included multiple ethnicities, its predominantly white composition limits the generalizability of our findings. Race is an important factor influencing ALS, as different ethnic groups may have distinct genetic backgrounds. For example, the expansion of the GGGGCC repeat in the C9ORF72 gene, a leading genetic cause of familial ALS, is more commonly found in individuals of European descent, particularly Southern Europeans like Italians and Spaniards [35]. Mutations in the Superoxide Dismutase 1 (SOD1) gene, which encodes superoxide dismutase 1, are also a well‐known cause of familial ALS and are most prevalent in Caucasian populations [36]. Additionally, variations in the Valosin‐containing protein and Optineurin genes have been identified as risk factors for ALS in African populations [37]. Therefore, the lack of race and genotype‐based stratification limits the depth of ALS research. Future studies should incorporate primary care and genetic data for precise ALS diagnosis, differentiate familial from sporadic cases, and expand ethnically diverse cohorts to enhance generalizability.
5. Conclusion
Prediagnostic presentations of falls are more frequent in ALS than in ad, but less frequent than in PD. However, ALS exhibits fewer psychiatric symptoms and autonomic dysfunction compared with PD. The use of calcium channel blockers may be associated with an increased risk of developing ALS in the future.
Author Contributions
Chunyang Pang and Wen Cao contributed to data statistics and writing papers. Jiali Xie, Yaojia Li, Luyi Zhu, and Huan Yu participated in the data organization. Dongsheng Fan and Binbin Deng provided resources and designed the study.
Ethics Statement
UK—the North West Multi‐centre Research Ethics Committee approved the Biobank (Ref: 11/NW/0382), with all participants providing written informed consent to participate in the UK Biobank study. This research was conducted using the UK Biobank resource under Application Number 108832.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting information
Figure S1 Love plot showing the covariate balance before and after matching using nearest‐neighbour matching
Figure S2. Kaplan–Meier survival curves for 506 patients with amyotrophic lateral sclerosis since diagnosis from hospital
Table S1. Read codes and ICD‐10 codes used to identify health conditions
Table S2. ATC codes to identify treatment
Table S3. Medication classification based on ATC Codes
Table S4. Baseline characteristics of participants with and without ALS
Table S5. Covariate balance before and after matching across four models
Table S6. Prevalence of at least one occurrence during life course in ALS cohorts stratified by onset, survival, and sex.
Pang C., Cao W., Xie J., et al., “Prediagnosis Insights Into Amyotrophic Lateral Sclerosis: Clinical Symptoms and Medication Use,” Journal of Cachexia, Sarcopenia and Muscle 16, no. 4 (2025): e70003, 10.1002/jcsm.70003.
Funding: The study was supported by the National Natural Science Foundation of China (81901273) and the Natural Science Foundation of Zhejiang Province (ZCLY24H0903).
Data Availability Statement
The data supporting the findings of this study are available on the UK Biobank project site and are subject to successful registration and application processes. Further details are available at https://www.ukbiobank.ac.uk/. Our research data are available upon reasonable request. The R code for the statistical analysis of this study is available upon request from Professor Dongsheng Fan and Professor Binbin Deng.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Figure S1 Love plot showing the covariate balance before and after matching using nearest‐neighbour matching
Figure S2. Kaplan–Meier survival curves for 506 patients with amyotrophic lateral sclerosis since diagnosis from hospital
Table S1. Read codes and ICD‐10 codes used to identify health conditions
Table S2. ATC codes to identify treatment
Table S3. Medication classification based on ATC Codes
Table S4. Baseline characteristics of participants with and without ALS
Table S5. Covariate balance before and after matching across four models
Table S6. Prevalence of at least one occurrence during life course in ALS cohorts stratified by onset, survival, and sex.
Data Availability Statement
The data supporting the findings of this study are available on the UK Biobank project site and are subject to successful registration and application processes. Further details are available at https://www.ukbiobank.ac.uk/. Our research data are available upon reasonable request. The R code for the statistical analysis of this study is available upon request from Professor Dongsheng Fan and Professor Binbin Deng.