Does attention deficit and hyperactivity disorder increase risk of mortality?

October 13, 2020

Background and Purpose: Few studies have explored the risk of death from Attention-Deficit/Hyperactivity Disorder, or ADHD, and existing studies have found inconsistencies and lack information on individual causes. This study investigated the relationship between ADHD and the mortality rate in Taiwan. Method: A retrospective cohort study was conducted using the national health insurance database for the entire population of Taiwan. The ADHD group included 275,980 newly diagnosed ADHD patients aged 4 to 44 years between 1 January 2000 and 31 December 2012. The patients with ADHD were compared with 1,931,860 gender- and age-matched control groups. Suicide, accidental injury, homicide, and the mortality rate of natural causes were analyzed using a Cox regression model of competitive risk adjustment, and the relationship between ADHD and individual causes was discussed. Result: During the observation period, 727 people (0.26%) died in the ADHD group as opposed to 3,594 (0.19%) in the non-ADHD group. Of the ADHD group, 546 (75.1%) were male and 2,852 (79.4%) were male in the control group. After adjusting for the potential confounders, ADHD patients had higher total-cause mortality (adjusted danger ratio 1.07; 95%CI, 1.00-1.17) than non-ADHD groups. ADHD patients also had higher mortality rates than the control group from suicide (adjusted danger ratio 2.09; 95%CI, 1.62-2.71), accidental injury (adjusted danger ratio 1.30; 95%CI, 1.10-1.52) and homicide (adjusted danger ratio 2.00; 95%CI, 1.09-3.68). The two groups had similar risk of mortality from natural causes. Conclusion: In this study, patients with ADHD had higher rates of injury-related mortality, particularly from suicide, accidental injury, and homicide. Although the risk of injury death was significantly higher in ADHD patients than in non-ADHD groups, the absolute risk of death was low due to the fact that the cases included in this study were mostly young people.    Few studies have investigated the risk of death from Attention-Deficit/Hyperactivity Disorder, or ADHD, and existing studies have found inconsistencies and a lack of information on individual causes. Since ADHD originates in younger groups, the impact of ADHD on death is difficult to explore. It needs big data on individual cases and long-term tracking to find the evidences, especially if the individual causes of death (such as suicide) are also explored; in this case, sample size and tracking time must be even larger and longer. This study explored the relationship between ADHD and mortality rate in Taiwan, using the database of national health care of the entire Taiwan population for the retrospective cohort study. As far as we know, the study included the largest number of participants. So far, we have not only empirically found the evidence that ADHD would increase the overall mortality rate, but more importantly, we have discussed suicide, accidental injury, homicide, and natural causes of individual mortality rate through the analysis of big data. This study was published in a new open access journal published by JAMA network open (2019 impact factor=5.032, 19/165=11.5% in medicine general & internal) (1).  The ADHD group included 275,980 newly diagnosed ADHD patients aged 4 to 44 years (figure 1) between January 1st, 2000 and December 31st, 2012. The ADHD patients were compared with 1,931,860 gender- and age-matched control groups. The association between ADHD and mortality rate was analyzed using Cox regression analysis, and controlling gender, age, rural and urban insured coverage, insured salary, outpatient visits, birth defects, intelligence deficiency, depression, autism, substance abuse, behavioral disorders, and antagonism. Suicide, accidental injury, homicide, and the mortality rate of natural causes were analyzed using a Cox regression model of competitive risk adjustment, and the relationship between ADHD and specific death cause was investigated. The mean (standard deviation) age of the ADHD group and control group was 9.61 (5.74) years old. The majority of the cases were male (ADHD group 209,406; control group was 1,465,842; men in both groups were 75.88%). A total of 4,321 cases from two cohorts died during the observation period (15.1 million person-years), while 727 (0.26%) died in the ADHD group, as opposed to 3,594 (0.19%) in the ADHD group. Of the ADHD group, 546 (75.1%) were male and 2,852 (79.4%) were male in the control group. After adjusting for potential confounders, ADHD patients had higher total-cause mortality (adjusted hazard ratio 1.07; 95%CI, 1.00-1.17) than non-ADHD groups). ADHD patients also had higher mortality than the control group from suicide (adjusted danger ratio 2.09; 95%CI, 1.62-2.71), accidental injury (adjusted hazard ratio 1.30; 95%CI, 1.10-1.52) and homicide (adjusted hazard ratio 2.00; 95%CI, 1.09-3.68). The two groups had similar risk of death from natural causes. 　　In this study, patients with ADHD had higher rates of injury-related mortality, particularly from suicide, accidental injury, and mortality due to homicide. Although the risk of death from injury was significantly higher than in the non-ADHD group, the absolute risk of death was low due to the fact that the cases included in this study were mostly young people. Figure 1. Flow chart of data collection in this study.   Source： Attention-Deficit/Hyperactivity Disorder and Mortality Risk in Taiwan | Attention Deficit/Hyperactivity Disorders | JAMA Network Open | JAMA Network  Reference： CHEN V. C., CHAN H. L., WU S. I., LEE M., LU M. L., LIANG H. Y., DEWEY M. E., STEWART R., LEE C. T. Attention-Deficit/Hyperactivity Disorder and Mortality Risk in Taiwan, JAMA Netw Open 2019: 2: e198714 

The first article of "AI Medical Scientists'' analyzed the related comorbidities of ALS

October 13, 2020

Background and purpose: It is helpful to study the causes of diseases by discussing their cormorbidities. The cause of Amyotrophic Lateral Sclerosis (ALS), a deadly disease, remains unknown. The study used a medical database to explore the diseases associated with ALS.  Method: This study included 705 newly diagnosed ALS patients over 15 years old between January 1, 2007 and December 31, 2013; 14,100 controls matching in sex, age, residence, and insurance premium. The research data were drawn from the National Health Insurance Research Database and Serious Disabling Diseases database, which were designed as case-control studies for the entire population. The comorbidities diagnosed by physicians prior to the date of initial ALS diagnosis were analyzed during the observation period 1, 3, 5, 7, or 9 years before the first ALS diagnosis, respectively. Chi-square test or t test was used to test the demographic differences between ALS patients and the control group. The relationship between these diseases and ALS risk was studied by using the conditional logistic regression model and the stepwise selection method. The path analysis was used to assess the pathway relationship between the comorbidities and ALS before the initial diagnosis of ALS. Results: ALS was associated with 28 comorbidities prior to their initial diagnosis, including 17 positive and 11 negative comorbidities. Path analysis showed that these 11 negative associated diseases could be classified as diabetes and its complications. Seventeen positive associated diseases could be classified as metabolic syndrome, neuroinflammation, head trauma, motor injury, infection, and complications.  Conclusion: The results of this study supported the hypothesis that metabolic abnormalities occurred prior to ALS diagnosis. Metabolic disturbance might have an influence on the incidence of ALS, and energy metabolic defects might play a role in the pathogenesis of ALS.   Why do people get a major disease? This question has puzzled human beings for thousands of years. Many medical scientists have spent their lives searching for the answer to it, and their efforts have resulted in an accumulation of medical knowledge. However, the causes of many major and deadly diseases remain unknown, and there is no cure. The research team used a big data database and a self-developed statistical analysis program with the aim of providing information regarding the causes of the disease by means of machine learning, hoping to give the medical scientists a reference for a new research direction, to accelerate the understanding of the causes of specific diseases and the development of new treatments. How can we prove that the analysis program in this study is correct and effective? In addition to the theoretical and methodological exploration, the application of our analytical program (procedure) in the exploration of the causes of disease can preliminarily verify the effectiveness of the analytical program. The automatic results of the new machine learning analysis program must be largely in line with existing medical knowledge, and the newly discovered information must conform to basic medical theory. This study was the first in a series of "AI Medical Scientists" verification papers: Amyotrophic Lateral Sclerosis (ALS), also known as motor neuro disease (MND) or Lou Gehrig's disease, associated network analysis of comorbidities in those suffering from the disease. (1) A similar analysis program (procedure) has been successfully confirmed in late-onset Alzheimer's disease. (2) The verification results of such diseases as colorectal cancer, pancreatic cancer, systemic lupus erythematosus and rheumatoid arthritis have been submitted for review. This study included 705 newly diagnosed ALS patients over 15 years old between January 1, 2007 and December 31, 2013; the control group comprised 14,100 people who were categorized according to gender, age, the insured unit by urban and rural areas and insured premium matching. The research data were drawn from the National Health Insurance Research Database and Serious Disabling Diseases database, which were designed as case-control studies for the entire population. The comorbidities diagnosed by physicians prior to the date of initial ALS diagnosis were analyzed during the observation period 1, 3, 5, 7, or 9 years before the first ALS diagnosis, respectively. Chi-square test or t test was used to test the demographic differences between ALS patients and the control group. The relationship between these diseases and ALS risk was studied by using the conditional logistic regression model and the stepwise selection method. The path analysis was used to assess the pathway relationship between the comorbidities and ALS before the first diagnosis of ALS. ALS was associated with 28 comorbidities prior to their first ALS diagnosis, including 17 positive and 11 negative comorbidities. Path analysis showed that these 11 negative associated diseases could be classified as diabetes and its complications. Seventeen positive associated diseases could be classified as metabolic syndrome, neuroinflammation, head trauma, motor injury, infection, and complications (Figure 1).  The results of this study support the hypothesis that metabolic abnormalities occurred prior to ALS diagnosis. Metabolic disturbance might have an influence on the incidence of ALS, and energy metabolic defects might play a role in the pathogenesis of ALS.    Figure I: Path analysis model for prior diseases associated with amyotrophic lateral sclerosis (ALS).The red and blue lines indicated a positive (728, 354, 783, 333, 353, 920, 434, 722, 357, 355, 356, 682 and 717) and negative  (250, 709, 521, 532, 536, 414, 382, V67 and 924) direct links of prior diseases with ALS, respectively.. The International Classification of Diseases codes, the ninth revision, are displayed in the boxes.   Source：https://www.tandfonline.com/doi/abs/10.1080/21678421.2018.1522354