Географическая связь множественного склероза и амиотропического бокового склероза.

Introduction

Amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) are both devastating, incurable, neurodegenerative diseases. Though bearing some obvious similarities (e.g., both diseases can result in both motor degeneration and cognitive impairment), they also have some notable differences that usually result in them not being considered related. MS, for example, is widely considered to be an autoimmune disease with a significant genetic component underlying its risk 1 – 4 ; ALS is typically not characterized as an autoimmune disease and the vast majority of ALS cases are considered to be sporadic, with only 10–15% of cases typically being attributed to a heritable gene (“familial ALS” or FALS) 5 – 8 , though it is possible that the role of genetics in ALS has been underestimated 9 , 10 . Furthermore, the prognosis for ALS is far worse than for MS. Most people with ALS will die within three to five years from onset of symptoms 11 , 12 . The progression of MS is much more heterogeneous in nature, with some people experiencing significant periods of relapses and remissions 13 . Treatment options for MS are both more numerous and more promising, and while the disease is not considered curable, some patients experience only a slow decline over decades 14 .

In general, the scientific community considers both diseases to be of unknown etiology. Though numerous causal factors have been proposed and explored, the scientific community is far from achieving widespread consensus about the cause of either disease. Progress on identifying causal factors has been better for MS than for ALS. For example, there has been significant progress in identifying genetic susceptibility for MS, and some evidence suggesting a possible role played by viruses 4 , 15 , 16 . However, the research stops short of concluding that the etiology of MS is understood. The research stops even shorter for ALS: While significant progress has been made in identifying key contributors like protein misfolding, mitochondrial dysfunction, and neuroinflammation, identifying what sets these mechanisms in motion has proven elusive 17 – 19 .

The study shows here that a closer examination of the geographic distribution of ALS and MS in the United States suggests that a significant association between the diseases has likely been overlooked. The mortality crude rates of ALS and MS exhibit a very strong and consistent geographic association, even after controlling for obvious factors such as race and latitude, suggesting that something else connects these diseases. The paper will begin with a very brief overview of the two diseases, followed by a description of the methods, results, and robustness test. The final section will discuss the findings, some possible explanations for the results obtained, limitations of the current study and future research opportunities.

ALS overview

ALS is a neurodegenerative disease in which motor neurons that control voluntary muscle movement and breathing begin to degenerate and die. Affected muscles become weak and atrophied, leading the individual to have trouble using their arms or legs, and eventually to have trouble chewing and breathing on their own 20 . Though not traditionally thought of as causing cognitive impairment, up to 50% of patients develop cognitive and/or behavioral symptoms over the course of the disease 21 .

Roughly 85% of the cases of ALS are considered sporadic, with only 10–15% of cases typically being attributed to a heritable gene (“familial ALS” or FALS) 1 , 22 , 23 . There is, however, relatively little consensus on the definition of FALS, and there are significant regional differences in whether and how ALS patients are tested for genetic involvement. There are more than 40 genes that have been associated with ALS, with significant variance in frequency, mode of inheritance and penetrance 24 . The role of genetics in ALS may turn out to be significantly underestimated 25 .

The research on ALS exhibits very little consensus about causation, with a wide range of putative risk factors that are considered, including (but not limited to) exposure to toxins such as agricultural chemicals 26 or heavy metals 27 , occupational exposures that include professional soccer 28 and service in the military 29 , 30 , genetics 31 , and viruses 32 . Almost all cases of ALS exhibit clumps of aggregated misfolded TDP-43 protein in the cells of disease-affected tissues, though it is not known to what degree these clumps are causative in the disease or a byproduct of the disease. Studies have also found that the disease is more common in athletes 33 – 35 , and is slightly more common in men than women 36 – 38 . The study of an extraordinary cluster of ALS in Guam at the end of World War II also gave rise to a hypothesis about the role of the neurotoxin β-methyl-amino-L-alanine (BMAA), produced by cyanobacteria, that could accumulate in the food chain 39 , 40 . Recent studies suggest that ALS, like MS, may have a north–south gradient in its distribution lending further support for the role of environmental factors in its development 41 – 43 .

There is no known cure for ALS or treatment to reverse its damage; however, several medications—including Riluzole, Edaravone, and most recently Tofersen (Qalsody), an antisense oligonucleotide therapy targeting SOD1 mutations—are used to slow the progression of the disease. ALS tends to strike in middle age (i.e., between the ages of 55 and 75) and most people with ALS die within three to five years from onset of symptoms. However, about 10% of those diagnosed with ALS experience a more slowly progressing version of the disease and may live for a decade or more (e.g., Stephen Hawking survived with the disease for 55 years) 44 .

MS overview

Multiple sclerosis is a chronic neurological disease whereby the immune system attacks the myelin sheaths protecting nerve fibers. This results in demyelinating lesions in the brain and spinal cord that lead to neurological deficits that can be motor, cognitive, or sensory in nature 45 . Progression of the disease results in shrinking of the cerebral cortex. The disease is called “multiple sclerosis” because of the distinctive sclerotic plaques and lesions that can be seen in affected areas of the nervous system 46 .

There are multiple theories about the causative or risk factors of multiple sclerosis but there is no consensus on why some people get the disease. Multiple sclerosis is known to strike women more of than men 47 , and while it is not thought to be inherited per se, susceptibility to the disease is thought to have a significant genetic component, as well as epigenetic and environmental components 48 , 49 . Hundreds of gene loci have been associated with susceptibility to the disease 50 . In fact, research suggests that dozens of genes might have a combined effect to increase the likelihood of MS 51 , 52 , and many of the genes identified are similar to those with other autoimmune diseases such as rheumatoid arthritis, lupus, and type 1 diabetes 53 , 54 . Furthermore, one line of research posits a relationship between multiple sclerosis and the Epstein-Barr virus (EBV) 55 . While EBV is a ubiquitous virus that infects most people at some time in their lives, those people who have never contracted it appear to be at lower risk of multiple sclerosis, and those who contract it in adolescence or adulthood (resulting in its significantly more infectious form, mononucleosis) have a higher risk of MS than people who contract the virus in early childhood 16 , 56 , 57 .

There is also evidence of a north–south gradient in geographic distribution of MS that has helped to foster a theory that the disease is related to sun exposure 58 – 60 , and several studies have shown that people with relatively higher levels of vitamin D are less likely to develop the disease or to have a milder version of it 61 – 63 . However, supplementation of patients with vitamin D has not yielded strong or consistent therapeutic results 64 .

MS often strikes in young adulthood (i.e., between 20 and 40 years of age) and the prognosis for the disease is highly variable. Some people will only have short periods of symptoms that punctuate long quiescent periods in which they might make a partial or full recovery. Other people have significant, protracted degeneration that results in decline of function and can lead to death. There is no cure for the disease, but there are several treatments used to reduce the severity and/or progression of the disease.

Exploring the geographic correlation between MS and ALS

Though there have been a handful of papers documenting a potential relationship between ALS and MS 65 , 66 , the diseases are typically considered to be unrelated to each other for myriad reasons: 1) They have different pathological markers, 2) the evidence for a genetic association has thus far been weak, 3) MS is considered to be an autoimmune disease whereas ALS has not typically been considered an autoimmune disease, and 4) the epidemiology of MS has frequently noted a strong north–south gradient that until recently was not typically described for ALS (notably, however, recent studies have begun to identify a north–south gradient in the incidence of ALS 41 , 42 ).

It should seem odd, then, that heat maps based on age-adjusted mortality crude rates for ALS and MS from the United States Centers for Disease Control and Prevention (CDC) WONDER database exhibit very visible similarities (see Fig. 1 ). The crude rates used to create the maps below are calculated using deaths from each disease per 100,000 person years, and age adjusted to account for differences in age distributions across states. Because there are race-related differences in ALS and MS (as will be discussed later in the paper), the heat maps below are generated using only data on people characterized as “White, Non-Hispanic or Latino Origin”. The north–south gradient oft-mentioned in the research on MS is visually detectable, but so too is an apparent north–south gradient in the distribution of ALS. The higher crude rates in the Northwest and the lower crude rates in the Southeast also appear striking. It is dangerous, however, to put too much faith in visual detection based on heat maps – more rigorous analysis is necessary. Fig. 1 Age-Adjusted Crude Rates in the U.S., by state, 2018 – 2022, People characterized as “White, Non-Hispanic or Latino” from CDC WONDER database. Source: CDC Wonder Database. Data represents age-adjusted crude death rates by state for people characterized as “White, Non-Hispanic or Latino”, as retrieved on March 26, 2025. Use of this data here does not imply endorsement by CDC, ATSDR, HHS or the United States Government of this research. This data and the map generating feature is publicly available on the CDC Wonder website ( https://wonder.cdc.gov/ ) for no charge. Furthermore, these maps may be freely used or reproduced without obtaining copyright permission. More information on the use of the CDC Wonder data and the map generating tool is available at .

By using an atypical (but more complete) source of data than most ALS or MS epidemiological studies, and controlling for race, gender, latitude, and economic effects, this study will show that a) there is a geographic pattern in ALS that is at least as strong as the geographic pattern in MS, and b) there is a very strong correlation in the geography of ALS and MS deaths even after controlling for the effect of race and latitude, suggesting that these diseases may have some sort of connection that is not yet understood. Collectively, the findings here could help uncover a relationship between the diseases that, in turn, could yield insight into what causes them, and how to prevent and treat them.

Methods

The study here utilizes mortality data and demographic data from the U.S. Centers for Disease Control WONDER database, and in a robustness test, uses mortality and demographic data from the World Health Organization. The study combines this data with latitude data from the U.S. Census Bureau and World Population Review, economic data from the U.S. Bureau of Labor Statistics and the World Inequality Database, and data on the number of neurologists working in each state hand collected from the U.S. News Health Directory.

Mortality data

Most epidemiological studies use surveys or data registries (e.g., the National Amyotrophic Lateral Sclerosis Registry, the North American Registry for Care and Research of Multiple Sclerosis) to identify cases. Physicians voluntarily report diseases to such registries, or patients can self-report. The great value of these surveys and registries is that they can help ascertain incidence rates (i.e., the number of cases of disease onset) or prevalence rates (i.e., the number of affected individuals in a population at a given time) of the disease, and they can collect ancillary information that may be useful in identifying risk factors. However, these sources of data are necessarily incomplete, and the voluntary nature of the data submission can make the data prone to systematic error or bias.

To overcome these limitations, this study instead uses mortality data reported on death certificates. Though mortality data has some limitations compared to incidence or prevalence rates for many research problems (it is, for example, of little use in studying a non-fatal illness), mortality data provides a unique set of advantages for this study. First, in the United States, the collection of mortality data is mandatory and standardized. When an individual dies in the United States, a certifier (e.g. physician, medical examiner or coroner) must file the cause(s) of death on a death certificate using the International Classification of Diseases (10 th ed) maintained by the World Health Organization. This classification contains codes for diseases, signs and symptoms, abnormal findings, complaints, social circumstances and external causes of injury. Despite its name, the classification includes medical diagnostic codes for both disease and non-disease causes of morbidity.

The CDC maintains a database of the information contained on these death certificates called the WONDER database. This database includes information on the underlying cause of death, multiple contributing causes of death (when multiple causes are indicated), and demographic data such as gender, age and race. This data is provided at multiple geographic levels of analysis (county, state, census region) and covers residents of all fifty states plus the District of Columbia 2 . This database is thus considerably more complete than surveys or registries.

The study here uses data coded with the ICD 10 code of G35 (multiple sclerosis) and G12.2 (motor neuron disease). With respect to ALS, it is important to note that the CDC WONDER database reports data on the ICD 10 classification of G12.2, which corresponds to motor neuron disease. Approximately eighty-five percent of motor neuron disease cases are of the ALS type 67 , 68 , and many doctors in the United States use the terms “motor neuron disease” and “ALS” interchangeably 69 . This data thus are not a perfect measure of ALS mortality as they also include non-ALS motor neuron disease deaths (e.g., progressive bulbar palsy, primary lateral sclerosis, progressive spinal muscle atrophy), however, the proportion of those is modest and is unlikely to bias the results, as noted in the Discussion section. In the United States it is also reasonable to assume that diagnosis of MS and ALS (or motor neuron disease) have a high (though not perfect) degree of accuracy and reliability.

From 2018 to 2022, the CDC WONDER database reports 33,563 deaths where ALS was the underlying cause and 20,342 deaths where MS was the underlying cause. The prevalence of MS is higher than the mortality rate; for diseases that are not always fatal or diseases that are of long duration, mortality data underestimates the number of cases. To assess the scale of this potential source of error, it is useful to compare the CDC WONDER data on “Multiple Causes of Death” (where a cause of death is listed as one of multiple causes on a death certificate) with the CDC WONDER “Underlying Cause of Death” data (the single disease or injury which initiated the train of morbid events leading directly to death). For those death certificates that listed ALS as one of the Multiple Causes of Death, 99.5% listed ALS as the Underlying Cause of Death. This high number is not surprising as ALS typically kills quickly. For MS, the percentage was 85.6%, and rises to 91.4% if COVID-19 deaths are removed from the data. Since the intention here is to identify all cases of ALS and MS irrespective of whether it is the ultimate cause of death, the CDC “Multiple Cause of Death” data is used here throughout the paper. The issue of potential bias in using mortality data for MS is discussed further in the Discussion section.

Crude death rates

The crude rates used are the number of deaths reported each state for each calendar year for the cause of death, normalized by the population of the state for that year as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Crude Rate}} = \left( {{\text{Count}}/{\text{Population}}} \right)*{1}00,000$$\end{document}

Age-adjusted rates

Where age-adjusted rates are used, the rates are weighted averages of the age-specific death rates, where the weights represent a fixed population by age. Age adjustment is a technique for “removing” the effects of age from crude rates, so as to allow meaningful comparisons across populations with different underlying age structures (e.g., states that have significantly different age distributions). Age-adjusted death rates in the CDC WONDER database are calculated by applying age-specific death rates (Ri) to the U.S. standard population age distribution. The year "2000 U.S. standard" is the default population selection for the calculation of age-adjusted rates. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^{\prime} = \sum i\left( { \, Psi/Ps \, } \right)R \, i$$\end{document} where Psi is the standard population for age group i and Ps is the total U.S. standard population (all ages combined) 3 .

Race and ethnicity

As indicated later in the sections describing the data, for most of the analyses data were used only for people characterized as “White, Non-Hispanic or Latino Origin” to control for racial differences in rates of MS and ALS. Race and Hispanic origin are reported separately on U.S. death certificates in accordance with standards set forth by the U.S. Office of Management and Budget. According to the CDC, information on the death certificate about the race and Hispanic ethnicity of the decedent is reported by the funeral director as provided by an informant, often the surviving next of kin, or, in the absence of an informant, on the basis of observation.

Gender

The data are also stratified by gender (male, female) as indicated on the death certificate.

Data for control variables

To control for other state-level covariates that might drive variance in neurodegenerative disease mortality, the mortality data are supplemented with other state-level statistics.

Population-centered latitude

Data on the population-centered latitude of each state were gathered from the United States Census Bureau to capture potential differences in ultraviolet (UV) exposure (indicated as potentially relevant by research on multiple sclerosis).

Real per-capita income

Data on real per capita income (based on constant 2017 dollars) for each state were obtained from the United States Bureau of Labor Statistics, to control for the potential influence of poverty or wealth on the mortality data 4

Number of neurologists in each state

Some states might have systematic differences in the likelihood of receiving a neurodegeneration diagnosis, either because of differences in access to doctors with the relevant expertise or differences in death reporting norms. To control for this, the study uses the number of neurologists in each state, hand collected from the U.S. News Health Directory.

Robustness test

For robustness testing, the patterns in the CDC WONDER data are compared with the World Health Organization mortality data as reported annually by countries that are “Member States” from their civil registration and vital statistics systems. This data is less complete, and more subject to differences in diagnosis reliability and reporting norms. The data also does not include race information. All of these omissions should, however, make it harder to identify the same associations in the WHO data as the CDC data and thus make conclusions based on such comparisons more conservative.

Because population-centered latitude for the countries in the WHO database was not available, the latitude for the geographic center point of each country as obtained from World Population Review was used instead. This data is provided in degrees and minutes; the study here converts the minutes into decimals by dividing the minutes by sixty (e.g., 24 degrees 30 min becomes 24.5).

To control for wealth at the nation level for the robustness test, a variable was constructed based on Average Adult Income in Purchase Price Parity (Constant 2017 dollars) from the World Inequality Database, averaged over the years 2017 – 2021 (because 2021 was the last year available from the dataset).

Analyses

The data are analyzed using Pearsson correlation coefficients and ordinary least squares multivariate regression with robust standard errors (StataSE 14). Scatterplots were also generated using Stata. The regressions include observations for each of the fifty states plus the District of Columbia, yielding a total of 51 observations per model, and each model is replicated for each gender to allow for the possibility that the relationship between the diseases and the controls varies by gender.

Results

Demographic differences

First, like many other studies on ALS and MS, the data here reveal significant differences in crude rates based on gender and race. As shown in Table 1 a, the crude rate of mortality for ALS is higher for males than females, and the crude rate of MS is significantly higher for females than males. Table 1 Demographic data on ALS and MS in the U.S. a . a The National Center for Health Statistics (NCHS) age-adjusts death rates using the direct method. That is, by applying age-specific death rates (Ri) to the U.S. standard population age distribution: R' = S i ( Psi / Ps ) R i. Where Psi is the standard population for age group i and Ps is the total U.S. standard population (all ages combined). For more details, see https://wonder.cdc.gov/wonder/help/mcd-expanded.html . **Crude Rate and Age Adjusted Rate not calculated due to unreliability. a) Comparison of Genders, All Races Combined (Obs: 102) Female Male Person-years 835,361,882 814,710,500 ALS Deaths 17,428 21,873 Crude rate 2.1 2.7 Age adjusted rate 1.5 2.3 MS Deaths 24,334 12,580 Crude rate 2.9 1.5 Age adjusted rate 2.2 1.3 b)Comparison of Race Differences, Genders Combined (Obs: 102) Single race 6 Person-years Deaths Crude rate Age adj. rate ALS American Indian or Alaska native 21,378,377 164 0.8 0.9 Asian 100,152,164 993 1 0.9 Black or African American 222,871,117 2,694 1.2 1.2 More than one race 47,444,030 193 0.4 0.8 Native Hawaiian or Other Pacific Islander 4,191,899 33 0.8 0.9 White 1,254,034,795 35,224 2.8 2 MS American Indian or Alaska native 21,378,377 106 0.5 0.5 Asian 100,152,164 145 0.1 0.1 Black or African American 222,871,117 4,432 2 1.9 More than one race 47,444,030 159 0.3 0.6 Native Hawaiian or Other Pacific Islander 4,191,899 13 ** ** White 1,254,034,795 32,059 2.6 1.9 c) Comparison of Hispanic origin, Genders combined (Obs: 102) Hispanic origin Person-years Deaths Crude rate Age adjusted rate ALS Hispanic or Latino 269,345,779 2471 0.9 1.2 Not Hispanic or Latino 984,689,016 32,709 3.3 2.2 MS Hispanic or Latino 269,345,779 1227 0.5 0.6 Not Hispanic or Latino 984,689,016 30,765 3.1 2.1

Also similar to prior research, the data indicate that the crude rates for mortality for ALS and MS are significantly higher for people characterized as “white” versus the other five race categories included in the CDC database (see Table 1 b). It is also interesting to note that both ALS and MS are more common among people characterized as Black or African American than Asian.

In the US, a significant number of people classified as “White” are of Hispanic origin. Since it has been observed in the literature that ALS and MS may not be as common in people of Hispanic origin 70 , 71 , Table 1 c further details the numbers of deaths, crude rate, and age-adjusted crude rate between people classified as “White, Hispanic or Latino origin” and “White, Non-Hispanic or Latino Origin.” As shown, the crude mortality rates for both diseases are dramatically higher for people classified as “White, Non-Hispanic or Latino Origin” compared to those classified as “White, Hispanic or Latino Origin.”

The significant differences in crude rates for ALS and MS by gender and race have significant implications for how the geographic analyses are handled. In fact, not controlling for gender and race can mask significant associations in interesting and misleading ways. For example, if state level crude rates for ALS and MS are used with the data on males and females combined in a correlation analysis (see Table 2 a), no significant relationship is obtained between ALS and MS (-0.03, p = 0.736). When the data are separated by gender, however, very different results are revealed: a very large and significant positive relationship for ALS and MS for both females and males (0.75, p < 0.001, and 0.70, p < 0.001 respectively) (see Table 2 b). Table 2 Pairwise correlation between state-level age-adjusted mortality crude rates for ALS and MS in the United States, people characterized as “White, Non-Hispanic or Latino” only, 2018 – 2022, CDC WONDER multiple cause data. a) Genders combined (Obs: 102) ALS MS -0.03 (p = 0.736) b) Genders separated Females (Obs: 51) Males (Obs: 51) ALS ALS MS 0.75 0.70 (p < 0.001) (p < 0.001)

Scatterplots of the data that distinguish between males and females reveal why these contrasting effects are obtained. In each of the scatterplots in Fig. 2 , a blue line shows the line fitted for males, a pink line shows the line fitted for females, and a black line shows the line fitted for data when gender is pooled. As shown, there are positive pairwise correlations in the mortality crude rates between the diseases when one controls for gender (as shown by the upward sloping blue and pink lines), but if the data are pooled across genders (the black line is fitted to the pooled data) the relationship between ALS and MS is obfuscated by a Simpson’s Paradoxwhereby a trend appears in several groups of data but disappears or reverses when those groups are combined 72 , 73 . Fig. 2 Scatterplot of ALS and MS age-adjusted crude rates using CDC WONDER multiple cause data on “White, Non-Hispanic or Latino origin” only, by gender.

The preceding suggests two very important things: 1) There are strong associations in the geographical distribution of deaths from ALS and MS that are not typically recognized or addressed in the research on these diseases, potentially overlooking some important clues for their causes, risk factors and treatments, and 2) It is extremely important that epidemiological research that examines these diseases accounts for both gender and race. Failure to do so could significantly alter the results obtained and the conclusions drawn.

To further tease apart the state variation in crude mortality rates for ALS and MS, StataSE 14 was used to run multivariate ordinary least-squares regressions with robust standard errors, that incorporate state-level controls for latitude ( population-centered latitude of the state ), wealth (estimated with state-level real per-capita income ), and access to neurological healthcare ( number of neurologists in the state ) 5 (Tables 3 , 4 ). Table 3 Amyotrophic lateral sclerosis age-adjusted crude rates, regressions with robust errors, “White, Non-Hispanic or Latino” only, 2018 – 2022, state-level (Obs: 51), CDC WONDER multiple cause data. Females Males Coef Robust SE t P > t Coef Robust SE t P > t a) ALS with state-level controls only PC latitude 0.037 0.023 1.58 0.121 0.048 0.021 2.30 0.026 Neurologists 0.000 0.000 0.87 0.389 0.000 0.000 0.80 0.429 RPC income -0.000 0.000 -0.71 0.484 -0.000 0.000 -1.34 0.153 Constant 1.166 1.026 1.14 0.262 3.101 1.200 2.82 0.007 F(3, 47) 0.880 1.990 Prob > F 0.457 0.129 R-squared 0.217 0.344 Root MSE 0.379 0.408 b) ALS with state-level controls and MS PC Latitude 0.004 0.008 0.53 0.600 0.008 0.007 1.10 0.276 Neurologists 0.000 0.000 1.48 0.147 0.000 0.000 1.17 0.247 RPC income -0.000 0.000 -0.55 0.582 -0.000 0.000 -2.22 0.032 MS crude rate 0.370 0.083 4.44 0.000 0.633 0.158 4.00 0.000 Constant 0.857 0.573 1.50 0.141 3.231 0.683 4.73 0.000 F(4, 46) 5.640 13.880 Prob > F 0.001 0.000 R-squared 0.587 0.639 Root MSE 0.278 0.306 Table 4 Multiple sclerosis age adjusted crude rates, regressions with robust errors, “White, Non-Hispanic or Latino” only, 2018 – 2022, state-level (Obs: 51), CDC WONDER multiple cause data. Females Males Coef Robust SE t P > t Coef Robust SE t P > t a) MS with state-level controls only PCLatitude 0.087 0.043 2.02 0.049 0.063 0.026 2.45 0.018 Neurologists -0.000 0.000 -0.13 0.899 -0.000 0.000 -0.12 0.907 RPC income -0.000 0.000 -0.81 0.424 -0.000 0.000 -0.56 0.578 Constant 0.835 1.553 0.54 0.593 -0.205 0.943 -0.22 0.828 F(3, 47) 2.220 2.900 Prob > F 0.099 0.045 R-squared 0.338 0.423 Root MSE 0.704 0.432 b) MS with state-level controls and ALS PCLatitude 0.040 0.027 1.49 0.142 0.029 0.018 1.57 0.123 Neurologists -0.000 0.000 -1.57 0.124 -0.000 0.000 -0.99 0.329 RPC income -0.000 0.000 -0.57 0.571 0.000 0.000 1.31 0.197 ALS crude rate 1.276 0.239 5.34 0.000 0.711 0.143 4.97 0.000 Constant -0.652 0.621 -1.05 0.300 -2.410 0.659 -3.65 0.001 F(4, 46) 49.340 39.470 Prob > F 0.000 0.000 R-squared 0.650 0.682 Root MSE 0.517 0.324

Because of the significant differences in crude rates for the neurodegenerative diseases by race, and because the U.S. data is far more robust for people characterized as “White, Non-Hispanic or Latino Origin” than any other race due to the demographics of the U.S., the following regressions include observations only for people characterized as “White, Non-Hispanic or Latino Origin”. Furthermore, the data are partitioned by gender to allow for the possibility that there is a different relationship between the diseases, or between the diseases and the state-level controls for each gender. For each of the diseases, a model is first run with state-level controls only, followed by a model that adds the other neurodegenerative disease variable.

Table 3 a,b show the results for ALS. Table 3 a shows the results for ALS with state-level controls only. The overall fit of the models is strong and significant (p < 0.001) with an R-squared of 0.22 for the model based on females and 0.34 for the model based on males, but the only individual variable that is strongly significant is population-centered latitude for the model with males, suggesting a north–south gradient for ALS for men, where ALS is more likely in more northerly states in the US. There is no significant relationship with the number of neurologists or real per capita income in the state for either females or males.

Table 3 b shows the results for ALS when MS is included as a regressor. These models have a much stronger overall fit, achieving an R-square of 0.59 for females, and 0.64 for males, and suggesting that this model explains a very large portion of the variance in the state level distribution of mortality crude rates of ALS. In this model, the coefficient for population-centered latitude is no longer significant for either gender, likely because MS has a stronger association with latitude than ALS, and potentially indicating that the relationship between ALS and latitude for males is mediated by MS (i.e., ALS may appear to have a relationship with latitude if it has a relationship with MS and MS has a relationship with latitude). As in the previous model, this model does not indicate a significant association with the number of neurologists, but for males, it appears that the distribution of mortality crude rates for ALS have a negative association with average real per capita income in the state. These results also show a very strong positive coefficient for MS – each unit increase in MS mortality crude rate is associated with 0.37 (females) or 0.63 (males) units of increase in ALS crude rate.

Tables 4 a,b show the results for MS. In Table 4 a, the models for MS with state-level controls achieve a strong significant fit, with an R-squared of 0.34 for the model with females and 0.42 for the model for males. In these models, population-centered latitude is positive and significant for both genders, indicating that MS crude rates are significantly higher in more northerly states, consistent with prior research. None of the other controls are significant.

Table 4 b adds the ALS variable, and the R-squareds again go up dramatically for both the model for females (0.65) and for males (0.68), suggesting that adding ALS to the model explains significantly more variance in the distribution of MS than just the state-level controls. Intriguingly, population-centered latitude is no longer significant in the models once ALS is added. To examine whether multicollinearity between population-centered latitude and ALS is dampening the ability to detect their mutual effects, the variance-inflation factors for the explanatory variables were analyzed. These turned out to be 2.12 for population-centered latitude, 1.35 for real per capita income, 1.15 for neurologists, and 1.73 for ALS, suggesting that the explanatory variables are only moderately correlated and unlikely to be problematic. It is an intriguing result that suggests the possibility that the appearance of the north–south gradient might be due to something that connects the diseases to each other, and that connection between the diseases may be more significant than either disease’s connection to latitude. The models also indicate large, significant and positive coefficients for ALS – each additional unit of increase in mortality crude rate for ALS is associated with a 1.28 unit increase in MS mortality crude rate for females or a 0.71 increase for males.

Overall, the results here indicate a strong and surprising association in the geographic distribution of ALS and MS that is not typically recognized, and that could have significant implications for understanding these diseases.

Robustness test: replicating with the world health organization data

To further evaluate the robustness of the findings in Tables 3 , 4 , the study here replicates the previous analyses using mortality data available from the World Health Organization, as reported annually by Member States from their civil registration and vital statistics systems. At the time of this study, data were available for cause of death based on the tenth revision of the ICD (International Classification of Diseases) – the same as that used for the CDC data – for 2017 through 2022. Both deaths and crude rates are indicated for each gender in each country. WHO also provides the person-years used to calculate crude rates.

There are several limitations associated with using this data. First, both ALS and MS are challenging to accurately diagnose, and introducing multinational variance into an analysis of these diseases is likely to amplify those measurement problems 74 , 75 . Second, there are significant racial differences in the mortality crude rates of ALS and MS, and race indicators are not provided in the WHO mortality data.

Third, whereas it is mandated by law that all deaths in the U.S. be accompanied by a death certificate, and that those death certificates indicate cause(s) of death using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (ICD) coding system, this level of standardization may not hold for all the member countries of the World Health Organization. Notably, some of the member countries report ICD codes at the three-digit level of detail rather than the four-digit level of detail (i.e., they report data aggregated to higher level categories, such as G12 spinal muscular atrophy and related syndromes (SMA) versus G12.2 Motor Neuron Disease). While this is not a problem for MS which has a three-digit level classifications (G35), it is a problem for MND which has the classification G12.2. Fortunately, an analysis of the G12 classifications based on the US CDC Multiple Cause data from 2018 – 2022 (see Table 5 ) indicates that of the 39,946 deaths where a G12 (SMA) cause of death was indicated, an overwhelming number of those (39,301 deaths; 98%) were attributed to G12.2 (Motor Neuron Disease), suggesting that the analyses on the WHO data can be conducted at the three-digit level with little loss of information. Table 5 Analysis of US CDC G12 ICD classification data based on CDC WONDER data. ICD 12 Classifications ICD code Deaths Crude rate Age adjusted rate Infantile spinal muscular atrophy, type I [Werdnig-Hoffman] G12.0 43 .00 .00 Other inherited spinal muscular atrophy G12.1 75 .00 .00 Motor neuron disease (ALS) G12.2 39,301 2.40 1.90 Spinal muscular atrophy, unspecified G12.9 527 .00 .00

All these sources of divergence between these two datasets should make it harder to obtain similar patterns of results across the two datasets, making it less likely that we will conclude that this robustness test enhances our faith in the reliability of the findings. Because some countries do not report any deaths from neurodegenerative diseases, only countries that reported at least one death for each of ALS and MS are included in the analysis.

Table 6 shows the pairwise correlations between ALS and MS, by gender, obtained using the WHO mortality data. As shown, like the CDC data, the WHO data indicates very high positive correlations (p < 0.001) in the geographic distribution of crude rates of ALS and MS. Table 6 Pairwise correlation of crude rates for ALS and MS based on WHO mortality data, 2017–2022, by gender. Females (Obs: 65) Males (Obs: 66) ALS MS 0.72 0.70 (p < 0.001) (p < 0.001)

Next, the study attempts to replicate the multivariate regression models incorporating latitude and wealth controls. The results for all the models are presented in (Tables 7 , 8 ), and for ease of comparison, Table 9 presents a summary comparing the results of the models that use CDC data versus the WHO data. Table 7 ALS crude rates, regressions with robust errors, country-level, all races, 2017 – 2022, with country-level controls only (Obs: 54), WHO mortality data. Females Obs: 54 Males Obs:55 Coef Robust SE t P > t Coef Robust SE t P > t a) ALS with county-level controls only Latitude 0.002 0.001 1.36 0.180 0.002 0.001 1.01 0.317 PPP income 0.000 0.000 0.48 0.632 0.000 0.000 0.56 0.578 Constant 0.086 0.060 1.42 0.161 0.119 0.076 1.56 0.124 F(2, 51) 0.96 F(2,52) 0.59 Prob > F 0.391 0.557 R-squared 0.065 0.039 Root MSE 0.137 0.172 b) ALS with county-level controls and MS Latitude 0.001 0.001 0.56 0.578 -0.000 0.001 -0.12 0.906 PPP income 0.000 0.000 0.94 0.352 0.000 0.000 1.41 0.163 MS crude rate 1.018 0.215 4.72 0.000 2.096 0.446 4.70 0.000 Constant 0.009 0.029 0.32 0.750 0.019 0.044 0.44 0.664 F(3, 50) 18.67 F(3,51) 13.84 Prob > F 0.000 .000 R-squared 0.492 .432 Root MSE 0.102 .134 Table 8 Multiple sclerosis crude rates, regressions with robust errors, country-level, all races, 2017 – 2022 (Obs: 54), WHO mortality data. Females Obs: 54 Males Obs: 55 Coef Robust SE t P > t Coef Robust SE t P > t a) MS with county-level controls only Latitude 0.001 0.001 1.69 0.098 0.001 0.000 2.13 0.038 PPP income -0.000 0.000 -0.19 0.853 -0.000 0.000 -0.65 0.520 Constant 0.075 0.034 2.21 0.032 0.047 0.020 2.42 0.019 F(2, 51) 1.43 F(2,52) 2.38 Prob > F 0.248 0.102 R-squared 0.055 0.082 Root MSE 0.091 0.053 b) MS with county-level controls and ALS Latitude 0.000 0.000 0.87 0.388 0.001 0.000 1.78 0.081 PPP income -0.000 0.000 -0.59 0.557 -0.000 0.000 -1.20 0.237 ALS crude rate 0.449 0.061 7.36 0.000 0.195 0.028 7.07 0.000 Constant 0.037 0.021 1.73 0.090 0.024 0.013 1.78 0.081 F(3, 50) 19.94 F(3,51) 21.46 Prob > F 0.000 0.000 R-squared 0.487 0.457 Root MSE 0.068 0.041 Table 9 Summary of comparison of regression results using CDC data versus WHO data. CDC WHO Females Males Females Males ALS/SMA with MS R squared 0.587 0.639 0.492 0.432 Latitude Not significant Not significant Not significant Not significant Per capita income Not significant Not significant Not significant Not significant MS Positive significant Positive significant Positive significant Positive significant MS with ALS/SMA R squared 0.650 0.682 0.487 0.457 Latitude Not significant Not significant Not significant Not significant Per capita income Not significant Not significant Not significant Not significant ALS/SMA Positive significant Positive significant Positive significant Positive significant

Despite the differences in the datasets, the congruence between the results is extraordinary. The results using the WHO data mirror the results using the CDC data, and in every model, a significant and positive relationship was found between ALS and MS.

Discussion

This study compares the geographic distribution of death rates of ALS and MS while controlling for gender, race, wealth, latitude, and access to neurological health care. The primary analyses are conducted using U.S. data obtained from the Centers for Disease Control WONDER database, and a robustness test repeats the analysis using data from the World Health Organization mortality data based on data reported by member countries. Both sets of analyses indicate a very strong positive correlation in the geographic distribution of ALS and MS. Furthermore, the multivariate analyses indicate that the mortality crude rate for ALS explains a very high portion of the variance in MS mortality crude rates (and vice versa) even after controlling for latitude, economic factors, race and gender.

This result is surprising because it has not previously been identified in the literature before – the diseases are typically considered to be quite different 76 and studies exploring a relationship between them are rare 77 . Furthermore, studies that have looked for a link between MS and ALS have typically concluded there was no evidence for a genetic or mechanistic link between the diseases 78 . For example, a study of genome-wide data from 4088 MS patients, 3762 ALS patients and 12,030 healthy controls concluded that “Genome-wide association meta-analysis of SNPs [single-nucleotide polymorphisms] as well as polygenic analyses fails to provide evidence in favor of an overlap in genetic susceptibility between MS and ALS” 79 . However, as noted previously, researchers are becoming increasingly aware of a north–south gradient in ALS that is strikingly similar to MS. Furthermore, the data here suggest that the diseases have a much stronger geographic association with each other than either does with latitude, and these results hold even after controlling for gender, race, wealth and access to neurological healthcare.

There are (at least) three potential explanations for these results worthy of future study. First, there may be a genetic link between the diseases that is difficult to detect in first-order or second-order relatives and thus has not been noted in the genetic comparisons. For example, a genetic mutation with low penetrance could exist in a large number of related people while only rarely being observed as a disease. Some possibilities to consider here are genes that interact with the ubiquitin proteasome pathway (and thus the body’s ability to clear damaged proteins), or genes that interact with autoimmune sensitivity. For the genetic link explanation to hold up, however, we would also expect to see a north–south gradient in the distribution of the genotype, or we would expect to find an epigenetic interaction with some element of the external environment that varies with the north–south gradient.

This brings us to the second potential explanation worth further study: Perhaps there is an environmental attribute that is associated with both diseases. In addition to the UV and Vitamin D explanations historically pursued for MS, it would make sense to consider the distribution of elements of both the natural environment that could vary with the north–south gradient (viruses, parasites, algae, molds, etc.), as well as the distribution of man-made elements or practices that are likely to vary with the north–south gradient (use of heating oil, agricultural practices, industrial practices, mining, chemical contamination of fisheries, etc.) The list of suspects is admittedly long, but comparing across geographies could help to narrow the hunt.

The third potential explanation is more discouraging but also more easily addressable: There could be some source of systematic error in the data. For example, suppose the true distribution of MS is highly skewed in a north–south gradient and the true distribution of ALS is evenly spread across latitudes. If some percentage of the time (e.g., 20%) MS is misdiagnosed as ALS, the skewed data for MS will introduce skew into the ALS distribution, creating a correlation between them. Most doctors consider misdiagnosis of MS as ALS to be rare, but the true rate of misdiagnosis is unknown. To ascertain whether this or other data errors are driving the results, it would be useful to compare these results with a geographically stratified sample whereby ALS and MS diagnoses were obtained in a precise and standardized way across a wide geographical range. If a pattern is upheld that is similar to the pattern identified here, then we will be more reassured that data error is not somehow to blame.

There are a number of limitations of the research here. First, as noted previously, the data used as an indicator of ALS mortality rates is from the CDC WONDER database that uses ICD 10 code G12.2. As such, it modestly inflated by deaths that are motor neuron diseases that would not have been categorized as ALS. However, the percentage of motor neuron disease cases that are not ALS is low (generally estimated at 15% or less), and the correlation between the G12.2 death rate and MS death rate is extremely high (0.70 for males and 0.75 for females). It would thus be unlikely for non-ALS motor neuron cases to drive this relationship, and if they are, that would be an interesting finding in itself.

Second, mortality data is an imperfect measure of the incidence of MS as both the fatality risk and length of time from prognosis to death is highly variable in MS 80 . Numerous studies suggest that patients with MS experience an average reduction in life expectancy of 7–14 years compared to the general population 81 – 85 . However, the survivability of multiple sclerosis varies by numerous factors, including age of onset, gender, treatment with interferon-β-1b, regional differences, and more 80 . Scalfari et al. (2013) report that just over half of MS patients have MS recorded as the underlying cause of death on their death certificates 80 , and as noted previously here, in U.S. cases where MS is listed as one of the multiple causes of death, 91.4% of the death certificates lists MS at the main underlying cause of death. It is thus reasonable to assume that mortality data is not as good of a proxy for incidence in MS as in ALS, and it certainly underestimates the incidence of MS. It would be preferable to assess this relationship using high-quality incidence data. Recent studies indicate that medico-administrative databases such as the French SNDS or Swedish SNPR may enable large-scale, population-based estimation of disease incidence without relying on voluntary case reporting. With validated identification algorithms, these databases might offer more accurate assessments of incidence 41 , 86 .

Thirdly, the results here are primarily based on data from the U.S. context. The global data from the World Health Organization are significantly limited by the inability to control for race. Without controlling for race (which exhibits a very strong relationship with ALS and MS), it is difficult to know how much to infer from the relationships found between ALS and MS, and between the diseases and geography. Because the WHO data draws from many different nations, it also undoubtedly has more variance in its comprehensiveness and diagnosis criteria used. This limits both the value of using the WHO data in the robustness test and limits the generalizability of the findings. It would be very interesting to replicate the WHO data analysis incorporating race data if a reliable source of such data could be obtained.

The data here do not enable us to pinpoint the nature of the relationship between ALS and MS, but they do suggest that there is something that has somehow, up until now, been overlooked, and they point to several potentially fruitful research directions as briefly outlined above. If we can identify a common factor driving the geographic distribution of these diseases, we are likely to greatly further our understanding of their causes and potential treatments.

Of the cases that are genetic, about 25–40% are thought to be caused by a defect in the C9orf72 gene that is involved in a range of cellular processes, including lysosome homeostasis, autophagy, and inflammatory responses. Another 12–20% of genetic cases are thought to be caused by a defect in the SOD1 gene, which produces an enzyme that breaks down superoxide radicals in the body.

Deaths of nonresidents (e.g. nonresident aliens, nationals living abroad, residents of Puerto Rico, Guam, the Virgin Islands, and other territories of the U.S.) and fetal deaths are excluded.

For more information on how the CDC WONDER database creates age-adjusted rates for the Multiple Cause of Death data, please see https://wonder.cdc.gov/wonder/help/mcd-expanded.html#Age-Adjusted%20Rates

Given the heterogeneity in income within a state it would have been preferable to have income data at the individual level, but unfortunately this data is not provided on death certificates.

All three of these variables are admittedly coarse, but no way yet exists to match the mortality data to physical address and wealth at the individual-level. The mortality, latitude and economic data can be obtained at the county level, but the mortality data becomes less reliable at this level, with many counties either not reporting or suppressing data on deaths by these neurodegenerative diseases. I thus concluded that it made more sense to start at the state level, though future research could evaluate to what degree the results change when assessed at the county level.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Acknowledgements

I would like to thank William H. Greene, Sagar Shamkant Bhavsar, Ray Niaura, Airi Pajunen, Ruben Cauchi, Eino Solje, and Paul Cox for their invaluable suggestions and support.

Author contributions

MS wrote the manuscript text, analyzed the data and prepared the figures.

Data availability

The data on U.S. crude mortality rates were obtained from the publicly accessible data base at https://wonder.cdc.gov/ . Data were retrieved March 26, 2024. The data on global crude mortality rates were obtained from the publicly available WHO Mortality Database at https://www.who.int/data/data-collection-tools/who-mortality-database . Data were retrieved March 26, 2024. Data on population-centered latitude of each U.S. state was obtained at the publicly available database at https://www.census.gov/geographies/reference-files/time-series/geo/centers-population.html Data were retrieved May 20, 2024. Latitude by country was obtained from the publicly available dataset at https://worldpopulationreview.com/country-rankings/latitude-by-country Data were retrieved May 20, 2024. Real per-capita income (based on constant 2017 dollars) at the U.S. state level was obtained from the publicly available data at https://www.bls.gov/cps/earnings.htm Data were retrieved May 20, 2024. Real per-capita income at the national level was obtained from the publicly available dataset at the World Inequality Database at https://wid.world/ Data were retrieved May 20, 2024. Data on the number of neurologists by U.S. state was collected by searching the U.S. News Health Directory at Find a Doctor – Book Online – Review Ratings | US News. Data were retrieved August 26, 2024.

Declarations

Competing interests

The authors declare no competing interests.

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