Frailty-based screening tool may help flag people at higher risk of ALS

A modified frailty index based on five self-reported health measures may help identify people at higher risk of developing amyotrophic lateral sclerosis (ALS), according to a new study.

The model, which included falls, whole-body pain, long-standing illness or disability, self-rated health, and tiredness or lethargy in the previous two weeks, showed moderate ability to distinguish between people who later developed ALS and those who did not.

Researchers said the tool is not meant to diagnose ALS, but could potentially be used as a first step to identify people who may benefit from additional testing.

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“To a certain extent, this model can serve as a potential tool for identifying high-risk groups of ALS,” the researchers wrote in the study, “A modified frailty index to identify high-risk groups for amyotrophic lateral sclerosis,” published in Frontiers in Neurology.

ALS is a progressive neurodegenerative disease marked by the loss of motor neurons, the nerve cells that control movement. Because there is no definitive diagnostic test for ALS, patients often wait months for a diagnosis, delaying treatment initiation.

Identifying people at high risk could potentially help support earlier detection. Some risk-stratification models based on blood or cerebrospinal fluid biomarkers have shown promise, but the need to measure multiple biomarkers limits their usefulness for screening people in the general population.

Here, researchers in China evaluated whether frailty scores — which reflect biological aging and vulnerability to health problems by tallying accumulated health deficits — could serve as a screening tool to identify people at risk of ALS.

The team analyzed data from 500,033 adults, ages 40 to 69, who participated in the UK Biobank and were recruited between 2006 and 2010. Participants were followed through the end of 2021, during which 628 were diagnosed with ALS.

Researchers first calculated a standard frailty index (FI) using 49 health deficits. Five of these deficits — falls, whole-body pain, long-standing illness or disability, self-rated health, and tiredness or lethargy in the past two weeks — were significantly associated with ALS and were combined into a modified frailty index (MFI).

Higher frailty scores were linked to greater risk of ALS

Results showed that higher scores on both the standard FI and the MFI were associated with a higher risk of ALS. The MFI association remained significant after researchers excluded people who reported poor health at the study’s start, an analysis used to assess potential reporting bias. It also remained significant after they excluded ALS cases diagnosed within the first three, five, or eight years of follow-up, but was no longer significant after cases diagnosed within 10 years were excluded. These later analyses were used to assess the possibility of reverse causation, in which frailty reflects early, undiagnosed ALS rather than future disease risk.

Researchers then combined the MFI with age, sex, and body mass index (BMI, a measure based on weight relative to height) to create a risk-stratification model.

In the validation group, the model had an area under the curve (AUC) of 0.696. AUC ranges from 0 to 1, with higher values indicating a better ability to distinguish between two outcomes — in this case, people who developed ALS and those who did not. The researchers characterized the model’s performance as moderate. Its AUC was slightly higher than the 0.688 for a model based on the standard FI, age, and sex, although the difference was not statistically significant.

When participants were divided into 10 groups according to their estimated risk, ALS incidence increased almost steadily across the groups. In the validation group, the highest-risk group had an ALS incidence (number of new cases) of about 317 cases per 100,000 people, and its relative risk was 15.84 times that of the lowest-risk group.

Overall, these findings suggest the MFI-based model may help identify people more likely to develop ALS, but it cannot determine whether any one person will develop the disease. Because ALS is rare, being classified as higher risk would still not mean a person is likely to develop the disease.

The researchers also noted that the model was developed and validated only in UK Biobank participants, who tend to be healthier and more advantaged than the general population. They said the model needs to be validated in more diverse populations.

“This suggests that the MFI could serve as a first-step screening tool, and that coupling it with biomarker testing, such as NfL [neurofilament light chain, a biomarker of nerve cell damage], in high-scoring individuals may help improve early detection and shorten diagnostic delays,” the scientists concluded.