ALS Progression Rate Before Diagnosis Fails to Predict Individual Disease Course, Landmark Study Finds

A sweeping new analysis of nearly 4,000 people with amyotrophic lateral sclerosis (ALS) has delivered an uncomfortable message to neurologists and drug developers alike: the single most widely used measure of how fast the disease was moving before diagnosis tells clinicians surprisingly little about how quickly an individual patient will deteriorate afterward. The study, published in the Journal of Neurology, drew on two large population-based registries in Italy and found that although the pre-diagnostic rate of decline does carry real prognostic information at the level of patient groups, it explains only a tiny fraction of the variability seen between one patient’s future trajectory and another’s.

Amyotrophic lateral sclerosis is a relentlessly progressive neurodegenerative disease in which motor neurons in the brain and spinal cord die, producing progressive weakness, respiratory failure and, typically, death within two to three years of symptom onset. Because the disease moves at strikingly different speeds in different people, and even within the same person over time, clinicians have long sought a metric that captures an individual’s progression rate early enough to guide counseling, treatment planning and trial enrollment. The instrument they rely on most is the revised ALS Functional Rating Scale, or ALSFRS-R, a 48-point questionnaire covering speech, swallowing, fine motor tasks, walking, breathing and self-care that is administered at virtually every clinic visit.

From this scale, researchers derive what is known as the pre-diagnostic ALSFRS-R slope, often called the preslope. It is calculated by subtracting the patient’s ALSFRS-R score at diagnosis from the maximum of 48 points and dividing by the number of months between symptom onset and diagnosis. The resulting figure, expressed in points lost per month, has become embedded in ALS practice and research: it has repeatedly been shown to predict survival and later functional decline, and it has been written into the design of randomized clinical trials both as an eligibility criterion and as a stratification variable intended to balance treatment groups. In the clinic, many neurologists use it to give newly diagnosed patients a rough forecast of what lies ahead.

The implicit assumption behind all of this is that the rate of decline before diagnosis continues, more or less, after diagnosis. The new study, led by Rosario Vasta and Adriano Chiò of the ALS Center at the University of Turin, together with colleagues in Modena, Novara and across the Emilia-Romagna region, put that assumption to a rigorous test. Their primary cohort came from the Piemonte and Valle d’Aosta Register for ALS, known as PARALS, a prospective population-based registry covering roughly 4.3 million people in northwestern Italy. Of 2,895 patients diagnosed there between 2000 and 2022, 2,222 had an ALSFRS-R assessment at diagnosis and entered the analysis. Every finding was then independently replicated in the Emilia-Romagna Registry for ALS, or ERRALS, which covers about 4.5 million people in northeastern Italy and contributed 1,543 patients diagnosed between 2009 and 2023.

The results were consistent across both cohorts and across several complementary statistical approaches. The correlation between the pre-diagnostic slope and the rate of decline measured 6, 12 or 18 months after diagnosis was modest at best, with Spearman’s rank correlation coefficients ranging from 0.19 to 0.30 in PARALS and from 0.23 to 0.24 in ERRALS. More striking was how little variance the preslope explained: 6.1 percent of the variability in the post-diagnostic slope at 6 months in PARALS, falling to just 0.4 percent by 18 months, and only 3.2 percent at 6 months in ERRALS, dropping to 0.4 percent thereafter. Scatter plots of pre- versus post-diagnostic slopes showed wide dispersion across the entire range, a visual confirmation that knowing how fast a patient declined before diagnosis leaves the future largely undetermined.

The team then asked whether the preslope added anything beyond established prognostic factors such as sex, age at onset and site of onset, and whether it outperformed other clinical measures. In regression models, adding the pre-diagnostic slope to a base model raised the explained variance by 6.1 percentage points at 6 months, but only 2.8 points at 12 months and a statistically insignificant 0.6 points at 18 months. When baseline lung function, measured as forced vital capacity, and the percentage of body mass lost since the premorbid state were also included, the preslope’s unique contribution shrank further, to 5.2, 1.9 and 0.08 percentage points at the three time points. Notably, at 6 months the preslope still outperformed both respiratory function and weight loss, but by 12 and 18 months forced vital capacity had become the stronger predictor, suggesting that a global rate reconstructed from symptom onset loses value faster than a direct snapshot of respiratory reserve.

Because patients who die or undergo tracheostomy drop out of follow-up, and because that attrition is itself tied to faster progression, the researchers also fitted sophisticated joint longitudinal-survival models that explicitly link a patient’s underlying functional trajectory to their risk of death. These models confirmed the overall pattern. In PARALS, accounting for informative attrition barely changed the estimates, while in ERRALS it attenuated the large late-follow-up differences, indicating that some of the apparent long-term predictive power of the preslope in that cohort reflected the selective disappearance of the sickest patients rather than genuine trajectory prediction.

The mixed-effects analyses, which used all 9,670 ALSFRS-R assessments recorded during the first 18 months after diagnosis in 1,743 PARALS patients, added an important nuance. A higher pre-diagnostic slope was associated with faster early decline, but also with a subsequent deceleration: the disease’s pace after diagnosis was not linear, and the shape of that curvature depended on the preslope itself. Patients in the highest quartile of pre-diagnostic slope declined about 2.04 points more on the ALSFRS-R at 12 months than those in the lowest quartile in PARALS, with a comparable 2.47-point gap in ERRALS. Yet even with both linear and quadratic interaction terms included, the preslope-related terms raised the marginal R-squared by only 0.43 percentage points in PARALS and 1.68 percentage points in ERRALS. In other words, the average difference between groups was meaningful, but the metric was nearly useless for distinguishing one individual’s future from another’s.

The authors are careful to frame this as a refinement rather than a refutation. The pre-diagnostic slope remains a legitimate group-level prognostic marker, consistent with earlier work using the PRO-ACT database, which found a moderate correlation of about 0.40 between pre- and post-baseline slopes, and with a smaller Japanese registry study that found an even weaker association. The new findings also dovetail with the recent PRECISION-ALS study, which characterized the natural history of ALSFRS-R trajectories and included many of the same PARALS patients. What this study adds is a quantification of just how little the preslope contributes to explaining the heterogeneity of individual futures, a distinction with direct consequences for how the metric should be used.

The implications ripple outward in two directions. For clinical trials, using the preslope as a stratification or eligibility variable may balance groups on average but does little to reduce the individual-level heterogeneity that inflates sample size requirements, and trial designers may want to weigh respiratory function and other measures more heavily at later time points. For patients and clinicians, the message is one of calibrated humility: a fast preslope does signal a worse average outlook, but it cannot tell any single person whether their own decline will track that average or diverge from it. The study also carries the usual caveats, including reliance on patient-reported symptom onset dates, the ordinal structure of the ALSFRS-R itself, and the fact that both cohorts were Italian, which may limit generalizability. Still, the replication across two independent, population-based registries gives the central conclusion unusual weight: in ALS, the past rate of decline is a weak prophet of the future, and the search for biomarkers that truly predict individual trajectories remains wide open.

Subject of Research: Predictive value of the pre-diagnostic ALSFRS-R slope for subsequent functional decline in amyotrophic lateral sclerosis

Article Title: Pre-diagnostic ALSFRS-R slope provides limited incremental information about the variability in subsequent functional decline in amyotrophic lateral sclerosis: a population-based study

Article References: Vasta, R., Calvo, A., Moglia, C., Canosa, A., Manera, U., Gianferrari, G., Zucchi, E., Martinelli, I., Mora, G., Cabras, S., Palumbo, F., Matteoni, E., Maccabeo, A., Pellegrino, G., Minerva, E., Pascariu, D., Callegaro, S., D’Ovidio, F., Mazzini, L., … Chiò, A. (2026). Pre-diagnostic ALSFRS-R slope provides limited incremental information about the variability in subsequent functional decline in amyotrophic lateral sclerosis: a population-based study. Journal of Neurology, 273(10), Article 651. https://doi.org/10.1007/s00415-026-14177-2

Image Credits: AI Generated

DOI: 10.1007/s00415-026-14177-2

Keywords: amyotrophic lateral sclerosis, ALSFRS-R, disease progression, prognosis, population-based registry, clinical trials, predictive markers, forced vital capacity, longitudinal modeling, neurodegeneration, PARALS, ERRALS