Ching-Piao Tsai1,2, Chenyu Hu3, Charles Tzu-Chi Lee3. 1. a Department of Biotechnology , Asia University , Taichung , Taiwan. 2. b Beito Health Management Hospital , Taipei , Taiwan. 3. c Department of Health Promotion and Health Education , National Taiwan Normal University , Taipei , Taiwan.
Abstract
OBJECTIVE: To investigate diseases associated with amyotrophic lateral sclerosis (ALS) by using a total population-based medical database. METHODS: This study included 705 ALS patients aged older than 15 years diagnosed from January 1, 2007, to December 31, 2013, along with 14,100 controls matching in sex, age, residence, and insurance premium. Data from the National Health Insurance Research Database (NHIRD) and Serious Disabling Diseases (SDD) database in Taiwan were used to conduct a total population-based case-control study. Prior diseases were categorized as being diagnosed 1, 3, 5, 7, or 9 years before first ALS diagnosis. Chi-square or t test was used to examine differences in demographic characteristics between the new patients with ALS and controls. Previous diseases were screened using a conditional logistic regression model. Multivariate analysis was performed using stepwise selection to evaluate the association between these diseases and the risk of ALS. The path analysis was conducted to analyze the pathway between prior diseases and ALS. RESULTS: In total, 28 diseases were associated with ALS, including 17 positive associations and 11 negative associations. The path analysis revealed that the 11 negatively associated diseases could be attributed to diabetes mellitus and its comorbidities. The 17 positively associated diseases could be categorized as metabolic syndrome, neuroinflammation, head trauma, sports injuries, infections, and their comorbidities. CONCLUSIONS: Our results support the hypothesis that diseases developing prior to ALS diagnoses are hypermetabolic disorders. Hypometabolic disorders may have a beneficial effect on ALS incidence. Defective energy metabolism may play a role in ALS pathogenesis.
OBJECTIVE: To investigate diseases associated with amyotrophic lateral sclerosis (ALS) by using a total population-based medical database. METHODS: This study included 705 ALSpatients aged older than 15 years diagnosed from January 1, 2007, to December 31, 2013, along with 14,100 controls matching in sex, age, residence, and insurance premium. Data from the National Health Insurance Research Database (NHIRD) and Serious Disabling Diseases (SDD) database in Taiwan were used to conduct a total population-based case-control study. Prior diseases were categorized as being diagnosed 1, 3, 5, 7, or 9 years before first ALS diagnosis. Chi-square or t test was used to examine differences in demographic characteristics between the new patients with ALS and controls. Previous diseases were screened using a conditional logistic regression model. Multivariate analysis was performed using stepwise selection to evaluate the association between these diseases and the risk of ALS. The path analysis was conducted to analyze the pathway between prior diseases and ALS. RESULTS: In total, 28 diseases were associated with ALS, including 17 positive associations and 11 negative associations. The path analysis revealed that the 11 negatively associated diseases could be attributed to diabetes mellitus and its comorbidities. The 17 positively associated diseases could be categorized as metabolic syndrome, neuroinflammation, head trauma, sports injuries, infections, and their comorbidities. CONCLUSIONS: Our results support the hypothesis that diseases developing prior to ALS diagnoses are hypermetabolic disorders. Hypometabolic disorders may have a beneficial effect on ALS incidence. Defective energy metabolism may play a role in ALS pathogenesis.
Authors: Tommaso Filippini; Maria Fiore; Marina Tesauro; Carlotta Malagoli; Michela Consonni; Federica Violi; Elisa Arcolin; Laura Iacuzio; Gea Oliveri Conti; Antonio Cristaldi; Pietro Zuccarello; Elisabetta Zucchi; Letizia Mazzini; Fabrizio Pisano; Ileana Gagliardi; Francesco Patti; Jessica Mandrioli; Margherita Ferrante; Marco Vinceti Journal: Int J Environ Res Public Health Date: 2020-01-30 Impact factor: 3.390