Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing * E-mail: Yejin.Kim@uth.tmc.edu Affiliations School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Roles Formal analysis, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing Affiliation School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Roles Conceptualization, Formal analysis, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing Affiliation Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center at Houston, Houston, Texas, United States of America ⨯
Roles Data curation, Investigation, Software, Writing – original draft Affiliation School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States of America
Roles Conceptualization, Investigation, Supervision, Validation, Writing – original draft, Writing – review & editing Affiliation Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, United States of America ⨯
Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing Affiliations School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, Texas, United States of America, Institute for Stroke and Cerebrovascular Disease, University of Texas Health Science Center at Houston, Houston, Texas, United States of America ⨯
Alzheimer’s disease and related dementias (ADRD) is a multifactorial disease that involves several different etiologic mechanisms with various comorbidities. There is also significant heterogeneity in the prevalence of ADRD across diverse demographics groups. Association studies on such heterogeneous comorbidity risk factors are limited in their ability to determine causation. We aim to compare counterfactual treatment effects of various comorbidity in ADRD in different racial groups (African Americans and Caucasians). We used 138,026 ADRD and 1:1 matched older adults without ADRD from nationwide electronic health records, which extensively cover a large population’s long medical history in breadth. We matched African Americans and Caucasians based on age, sex, and high-risk comorbidities (hypertension, diabetes, obesity, vascular disease, heart disease, and head injury) to build two comparable cohorts. We derived a Bayesian network of 100 comorbidities and selected comorbidities with potential causal effect to ADRD. We estimated the average treatment effect (ATE) of the selected comorbidities on ADRD using inverse probability of treatment weighting. Late effects of cerebrovascular disease significantly predisposed older African Americans (ATE = 0.2715) to ADRD, but not in the Caucasian counterparts; depression significantly predisposed older Caucasian counterparts (ATE = 0.1560) to ADRD, but not in the African Americans. Our extensive counterfactual analysis using a nationwide EHR discovered different comorbidities that predispose older African Americans to ADRD compared to Caucasian counterparts. Despite the noisy and incomplete nature of the real-world data, the counterfactual analysis on the comorbidity risk factors can be a valuable tool to support the risk factor exposure studies.
Alzheimer’s disease (AD) is the sixth leading cause of death in the United States, affecting 6 million Americans aged 65 and older. AD risk develops over the long course of a lifetime and involves various etiologies such as genetic, vascular, and psychosocial factors, of which the complex biological mechanisms are still under investigation. Putative risk factors include race/ethnicity, low educational attainment, socioeconomic status, and comorbidities (hypertension, diabetes). These risk factors may interact with each other and further increase the risk of AD. Most studies find older African Americans are more likely than older Whites to develop AD. Comorbidity risk factors and socioeconomic status are believed to partially account for these differences, as they are more prevalent in African Americans. To disentangle the multifactorial effects of factors predisposing older adults to AD, we quantified counterfactual effect of high-risk comorbidities mediating the AD risk using nationwide electronic health records. We particularly focused on differential counterfactual effects between matched African Americans and Caucasians. Our extensive counterfactual analysis discovered different comorbidities that predispose older African Americans to AD compared to Caucasian counterparts. This differential risk between racial groups will contribute to developing targeted treatment to AD.
Citation: Kim Y, Zhang K, Savitz SI, Chen L, Schulz PE, Jiang X (2022) Counterfactual analysis of differential comorbidity risk factors in Alzheimer’s disease and related dementias. PLOS Digit Health 1(3): e0000018. https://doi.org/10.1371/journal.pdig.0000018
Editor: Dukyong Yoon, Yonsei University College of Medicine, REPUBLIC OF KOREA
Received: July 13, 2021; Accepted: January 21, 2022; Published: March 15, 2022
Copyright: © 2022 Kim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: We used Cerner Health Facts, a clinical database covering electronic health records (EHRs) from Cerner client hospitals. The Cerner Health Facts database contains a de-identified EHR and is subscribed by the University of Texas Health Science Center for research use. To apply, contact https://www.cerner.com/ap/en/solutions/data-research.
Funding: YK is supported in part by UTHealth startup, UT Stars award, and the National Institute of Health (NIH) under award number R01AG066749. SIS is supported by the Frank M. Yatsu Chair in Neurology. PES is supported by the Weston Brain Institute, The Kleberg Foundation, donations, and numerous pharmaceutical companies. XJ is CPRIT Scholar in Cancer Research (RR180012), and he was supported in part by Christopher Sarofim Family Professorship, UT Stars award, UTHealth startup, the National Institute of Health (NIH) under award number R01AG066749 and U01TR002062. The funders had no role in the design, methods, subject recruitments, data collections, analysis, and preparation of the paper.
Competing interests: The authors have declared that no competing interests exist.
Alzheimer’s disease (AD) is the 6th leading cause of death in the United States and it is the only one of the top 10 leading causes of death that cannot be cured [1–3]. Alzheimer’s disease and related dementias (ADRD) is a multifactorial disease that involves several different etiologic mechanisms with highly heterogeneous phenotypes [4,5]. Moreover, prior studies suggest that there is significant heterogeneity in the prevalence of ADRD across diverse demographic groups [6,7]. For example, most studies find that older African Americans are more likely than older non-Hispanic Caucasians to be diagnosed with ADRD [7–10]. Comorbidity risk factors such as cardiovascular disease, diabetes, and obesity, as well as socioeconomic status, are believed to account for these differences, as they are more prevalent in African Americans [1,2].
Association studies on such multifactorial and heterogeneous comorbidity risk factors are limited in their ability to determine causation. For example, although obesity is associated with increasing ADRD risk [11], its effect may be mediated by comorbidities such as hypertension, cardiovascular disease, and diabetes [12–14]. Counterfactual analysis, on the other hand, uses a methodology to estimate the outcome for an individual who had been exposed to a risk factor (factual) under alternative exposure scenarios (counterfactual) of if the individual had not been exposed. A confounder is a variable causing exposure to the risk factors and also outcomes. It is a major source of bias that can mislead us to draw wrong conclusions that the risk factor causes the outcome when it does not [15]. The gold standard to avoid such bias is to randomize the exposure in randomized clinical trials, but such a randomized study is not feasible in studying risk factors, particularly when the exposure is unethical (e.g., exposing subjects to putative risk factors) [16,17]. Alternatively, the counterfactual analysis with observational data aims to reduce the bias by adjusting the distribution of conditions that affect the exposure to the risk factor, such as via propensity score matching or weighting [18].
The statistical inferences to estimate the causality and counterfactuals from observational data in medicine have been long discussed but not yet widely used [16,18–25]. For example, previous research proposes a framework for emulating randomized trials from big observational data [17,18,26]. This framework simulates randomized clinical trials via controlling baseline characteristics, identifying time zero (baseline) to the outcome, adjusting the confounders by matching, and estimating treatment effect using potential outcome models [17]. A challenge here is that latent confounders can lead to selection bias; the causal structure can delineate the relationship between these confounders and help reduce the selection bias [27]. Indeed there is a separate line of causal analysis studies utilizing causal structure learning to investigate conditional independence among comorbid conditions [28–33], mainly with a few predetermined selected variables due to super-exponential complexity in structure learning. To date, comorbidity risk factor studies in ADRD often focus on the association [1,2], rather than causation [24,34]. Similar to the emulation of randomized clinical trials [17], the goal of this study was to investigate the counterfactual effect of comorbidity risk factors in ADRD, particularly focusing on racial heterogeneity (Fig 1).
PowerPoint slide larger image original image Fig 1. Study overview.Our goal is to assess the counterfactual effect of comorbidities that predispose each racial group to ADRD. We focused on African Americans and non-Hispanic Caucasians. (a) We used Cerner EHRs from more than 600 Cerner client hospitals. (b,c) To select cohort, we matched age and sex in ADRD and non-ADRD subjects. To make African American and Caucasian cohorts comparable, we matched race on the known ADRD risk factors (age, sex, hypertension, diabetes, obesity, heart disease, vascular disease, and head injury). Hidden confounders (such as socioeconomic status) were presented for clarification. (d) Age is the strongest risk factor in ADRD. We matched the age of ADRD and non-ADRD subjects. (e) To disentangle the multifactorial effect of comorbidities, we derived a Bayesian network of comorbidities and ADRD using constraint-based algorithms. (f) We used inverse probability of treatment weighting to estimate the counterfactual effect of comorbidities that have a direct edge to ADRD in each racial group’s Bayesian network. We performed the permutation test to validate the counterfactual effect. (g) We examined the difference in comorbidities paths of the Bayesian network.
One challenge to counterfactual comorbidity risk factor studies is the lack of data capturing comprehensive health conditions before ADRD onset. As ADRD is a heterogeneous disease with various etiologies, counterfactual analysis requires an extensive set of comorbidities to investigate how one disease might contribute to ADRD. Voluminous electronic health records (EHRs) from nationwide hospitals are a rich source for providing comprehensive data on the risk of ADRD. Nationwide EHRs also have larger sample sizes even in the minority populations compared to the sample size in data in clinical trials or observational studies, in which a participation rate is significantly low in the minority populations [35,36]. EHR data, however, are mainly collected for billing, not for scholarly study, and thus diagnosis billing codes in EHRs are sometimes incomplete and lack important details such as socioeconomic factors (e.g., education, literacy, life course exposures), which are one of the main causes of racial disparities in ADRD [37,38]. Despite the potential limitation due to these unobserved confounders, EHRs can extensively cover a large population’s long medical history in breadth and provide us a unique opportunity to investigate the counterfactual effect of comorbidity risk factors for ADRD. We undertook this study to provide unbiased insights on the racial differences of comorbidity risk factors in ADRD.
We utilized Cerner Health Facts, a large clinical database covering EHRs from more than 600 Cerner client hospitals, from 2000–2017, with a total of 49,826,000 inpatients and outpatients (Fig 1A) [39]. The Cerner Health Facts database contains a de-identified EHR and is subscribed by the University of Texas Health Science Center for research use [39]. These nationwide multi-center EHRs can increase generalizability of our findings.
We summarized our study design in Table 1. We included subjects with observation after the age of 65 and observations longer than 6 months. Age is the strongest risk factor for ADRD. Non-ADRD subjects were either not old enough to have ADRD onset (e.g., average ADRD onset age was 79.99 for African Americans and 81.57 for Caucasians) or old enough but censored (e.g., median observation length was 1.0 years). To avoid bias caused by different age distributions in ADRD and non-ADRD subjects, we matched age in their observation period (Fig 1B, 1C Matching 1). That is, for the ADRD subjects, the observation window started from when any diagnosis code was first recorded and ended when the first ADRD onset was recorded (Fig 1D). For the non-ADRD subjects, we selected subjects that had the closest age at the observation starts and ends. We truncated non-ADRD observations after the age when matched ADRD observations ended (Fig 1D).
PowerPoint slide larger image original image Table 1. Summary of the study design based on Target Trial framework [17].We performed the analysis for African American and Caucasian counterparts, respectively.
Comorbidities of interest were all the diseases (identified as diagnosis codes) that were diagnosed within the observation period, which might have potential risk to predispose to ADRD onset. EHRs are inherently noisy and sparse. To accurately identify the most direct putative risk factors and better understand root causes, it is important to condense the sparse diagnosis codes into clinically meaningful comorbidities and disentangle the effects of multifactorial comorbidities. Our approach to addressing this challenge was to group ICD9 or ICD 10 diagnosis codes by PheWas hierarchy to increase clinical relevance of the billing codes [40]. PheWas code is a hierarchical grouping of ICD codes based on statistical co-occurrence, code frequency, and human review. For more detail, see reference [40]. We included 100 PheWas disease codes that appeared within the observation window in more than 5% of the subjects. We counted the occurrence of each disease code that appears during the observation and converted them to a logarithm scale (i.e., log2(1+counts)) as the count distributions are skewed.
The outcome of interest was ADRD onset, which we detected as having either ADRD diagnosis code or medication. The ADRD diagnosis codes were PheWas codes for 290.11 (Alzheimer’s disease); 290.12 (Frontotemporal dementia, Pick’s disease, Senile degeneration of brain); 290.13 (Senile dementia); and 290.16 (Vascular dementia, Vascular dementia with delirium/delusions/depressed mood). The ADRD medications were acetylcholinesterase inhibitors (Donepezil, Galantamine, and Rivastigmine) or memantine. The definition of ADRD in EHRs can be controversial considering the fact that EHRs are for billing purposes. We compared our definition of ADRD onset with other potential definitions and discussed our rationale behind choosing our definition considering racial bias in S1 Text A.
We investigated and compared differences in the comorbidities predisposing each racial group to ADRD. Our approach for counterfactual effects analysis has three steps: i) cohort selection by matching sex, age, and known comorbidity risk factors, ii) disentangle dependency among comorbidities and ADRD to identify core skeleton that accounts for increasing ADRD risk by Bayesian network, and iii) measure and validate the counterfactual effects of the identified comorbidities with a direct relationship to ADRD by inverse probability of treatment weighting. Code is publicly available at https://github.com/yejinjkim/treatment-effect.
We matched African Americans and non-Hispanic Caucasians in terms of age, sex, and known comorbidity risk factors to create comparable racial cohorts (Fig 1C). Direct comparison on the risk of ADRD between African Americans and Caucasians would produce biases due to confounders in the large-scale heterogeneous EHRs [41,42]. For example, in Cerner Health Facts EHRs, 68.1% were female in African Americans, whereas 62.8% were female in Caucasians (Table 2). Careful cohort matching is needed to build comparable cohorts that are similar in terms of known risk factors. Theoretically, this cohort matching is not necessary for the Bayesian network (discussed in the next section) because the Bayesian network captures local interaction between variables, but we found that this cohort matching is helpful to the unconfoundedness assumption [16].
PowerPoint slide larger image original image Table 2. Cohort demographics.Distribution of comorbidities with a known risk before and after matching between African Americans and Caucasians. Standardized bias = difference in the mean of a given variable between African Americans and Caucasians divided by the standard deviation in African Americans.
We first matched ADRD subjects and non-ADRD subjects based on age and sex for each racial group. The comorbidities (with either known or unknown risk) incidence differs by race. Our focus is to identify the effect of comorbidities with an unknown risk that disproportionately affects racial groups because we already know the differential effect of comorbidities with known risk. The comorbidities that are known to increase ADRD risk disproportionately among racial groups were hypertension, diabetes, obesity, heart disease, vascular disease, and head injury (specific definition of each comorbidity is in S1 Table) [1,2]. So, we matched African Americans and Caucasians based on the comorbidities with known risk using propensity scores (Matching 2 in Fig 1C), so that the remaining potential risk factors are isolated. We also matched African Americans and Caucasians based on age and sex. We used the nearest neighbor matching with radius and caliper [43]. We reported standardized bias to evaluate the balance between the two groups. Detailed cohort selection process with the structural equation is available in S1 Text B.
Using the matched cohorts of African Americans and non-Hispanic Caucasians, we aimed to identify comorbidities that predispose each racial group to ADRD with potential causal effects. We derived the Bayesian network (Fig 1E), a directed acyclic graph of comorbidities and ADRD that has directed edges implying causation. Learning Bayesian networks is a principled approach to identifying and analyzing multifactorial effects, as it takes other confounders into account to determine possible causal effects via considering conditional independence. We built two Bayesian networks for African Americans and Caucasian counterparts respectively. Nodes were all comorbidities and ADRD. We set three tiers: tier1 = comorbidities with known risk, tier2 = comorbidities with unknown risk, and tier3 = ADRD. The comorbidities in tier1 were mostly chronic diseases that do not have a direct or immediate effect on ADRD (e.g., hypertension, diabetes) but have an indirect effect on ADRD by mediating through other subsequent comorbidities. The PC algorithm is one of the principled causal structure learning algorithms (by Peter and Clark) that can be applied to find Bayesian Networks (details in S1 Text C). [44,45] PC has been implemented by various software/libraries such as TETRAD [46], pcalg,[46,47] bnlearn,[48] and speed-up versions by Zarebavani and Zhang [49,50].
The causal structure, however, can vary by input data, which hinders the robustness of the structure. Bootstrapping and graph combination methods are usually adopted to increase robustness of the inference results [51,52,53]. We used the voting-based causal graph combination to obtain a robust and unbiased estimation of the causal graph, which significantly reduce the false positives and increase the overall robustness of the graph learning [54]. We leveraged the majority voting technique by randomly splitting the entire data into ten sub-datasets while withholding 10% of the original data each time. We applied the PC algorithm (with a significance level of 0.05) on each of the ten sub-datasets and aggregated the ten results. A directed edge presented in the final ensembled causal graph if it appears in more than half of all the causal graphs. We repeated the same procedure on each racial group and derived the final causal graphs of the two racial groups.
After we obtained the robust causal graphs, we investigated whether the two causal graphs are distinct enough. To compare the difference of the causal graphs, we used two metrics: structural hamming distance (SHD) and the graph edit distance (GED). The SHD measures the number of edges in which the two compared graphs do not coincide. The GED measures length of the shortest graph edit path, which is a sequence of node and edge edit operations (including substitutions, deletions, and insertions) transforming graph G1 to graph isomorphic to G2 [55].
After we identified unique comorbidities that predispose older African Americans and Caucasian counterparts to ADRD respectively, we quantified the causal effect of the identified comorbidities on ADRD risk by measuring the average treatment effect on increasing (or decreasing) ADRD risk (Fig 1F). That is, we would like to answer this question: “If a subject who had been exposed to the comorbidity in fact did not have the comorbidity (counterfactual), would the subject have had a lower level of ADRD risk?” The treatment effect analysis is to identify the difference in potential outcomes (e.g., ADRD risk) when the subject is exposed and not exposed to the comorbidity. Let us denote Yi(1) subject i’s outcome (i.e., ADRD onset) when the subject has certain comorbidity (Ti = 1) and Yi(0) subject i’s outcome when the subject does not have the comorbidity (Ti = 0). The treatment effect τi of having this comorbidity is then defined as: τi = Yi(1)−Yi(0) based on Neyman-Rubin’s potential outcome models [56]. Obviously, it is impossible to observe factual and counterfactual outcomes at the same time (e.g., it is impossible that a subject does and does not have the comorbidity, Yi(1) and Yi(0)). An approach to mitigate this missing counterfactual outcome is to average out the potential outcomes in the exposed and the unexposed respectively and estimate the average treatment (ATE) effect by E[Y(1)]−E[Y(0)]. The ATE is an unbiased estimate of the treatment effect (i.e., effect of comorbidity exposure) if the subjects are randomly assigned to either exposure or control group (such as in randomized clinical trials). However, in real-world data the exposure to the comorbidity is not random; subjects with and without the comorbidity differ systematically. The way to reduce the bias between the two groups is to match the subjects or weight their outcomes Y based on the likelihood of having the comorbidity T so that the likelihood distributions are similar [19,20]. Here we denote the likelihood of having the comorbidity T given subject’s condition X as propensity score. Several strategies to estimate the unbiased treatment effect include propensity score matching (to directly obtain counterfactual outcome by identifying propensity-matched neighbors), propensity score stratification (to stratify subjects into groups with a similar level of propensity score and directly compare the outcomes within each group), and inverse probability of treatment weighting (IPTW) [19,20]. Specifically we used the IPTW [57], which down-weights over-sampled patients and up-weights under-sampled subjects so that the two groups with and without the comorbidity are similar. The ATE can be then estimated as where e(Xi) is the propensity scores at Ti = 1 given subject’s features Xi. We are more interested in treatment effect among those who already had the comorbidity Ti = 1, which is the so-called average treatment effect among treated (ATT). We can estimate ATT by: where n1 refers to the number of subjects with Ti = 1. We can similarly define the average treatment effect among untreated or control (ATC) among those who did not have the comorbidity Ti = 0. In all, the treatment effect measures the amount of difference in outcome Y due to exposure T given the similarly weighted conditions X. For example, ATE = τ>0 means that the outcome when subjects are intervened to have the comorbidity is greater by τ than the outcomes when subjects are intervened not to have the comorbidity, implying the comorbidity exposure increases the outcomes on average. Similarly, ATT = τ>0 means that the outcome of subjects already having the comorbidity is greater by τ than the outcomes when the subjects are intervened not to have the comorbidity, implying the subjects with the comorbidity would have decreased the outcome (ADRD onset) by τ if they are without the comorbidity.
We obtained the propensity score e(Xi) of having the comorbidity T given subject’s features X using logistic regression. To avoid high variance of propensity scores due to overfitting, we used the self-normalized propensity estimator (or Hàjek estimator) [58]. The subject’s features Xi to infer propensity scores were the all other remaining comorbidities that have directed edge to T (the comorbidity of interest) and Y (ADRD) in the derived Bayesian network. Here we can also measure ATE and ATT of all the pairs of comorbidities with the directed edges in the Bayesian network to quantify the causal effect of one comorbidity to the others. We calculated the 95% confidence interval of the ATE and ATT with 100 bootstraps by random selection. We measure the ATE and ATT for each racial group. We used Dowhy, a publicly available package to measure the treatment effects [59].
We confirmed the estimated treatment effect via the permutation test (Fig 1F) [60,61]. The permutation test (or randomization test) is to assess if an ATE estimate is statistically significant by testing for Fisher’s Sharp Null, H0: Yi(1) = Yi(0), ∀i, which states that there is no treatment effect for all subjects [62]. A rejection of Fisher’s Sharp Null means there is a significant treatment effect [61]. We randomly shuffled the treatment variable (binary indicator whether the subject has the comorbidity or not) to make the treatment variable independent and observed the treatment effect as repeating the random permutation. We set 100 repetitions and used Dowhy to implement the permutation tests [59].
We first built matched cohorts of ADRD and non-ADRD subjects. Of the 49,826,000 patients, there were 157,620 subjects with ADRD diagnosis codes; 163,320 subjects with ADRD medication codes; and 235,912 subjects with either the diagnosis or medication codes. After excluding subjects without diagnosis/medication codes, timestamp, and observation length less than 6 months, we selected 138,026 ADRD subjects and matched 138,026 non-ADRD subjects based on age and sex.
We then matched African American and non-Hispanic Caucasian groups to build comparable cohorts of the two racial groups. After the extensive matching and reducing confounding effects, the final cohort was 7,662 ADRD and 7,418 non-ADRD for African Americans; 7,869 ADRD and 7,357 non-ADRD for non-Hispanic Caucasians. We calculated the standardized bias of each variable between African Americans and Caucasians to check whether the variables are balanced. The standardized bias < 0.10 was used as the cutoff value to confirm the balance after matching [63]. As a result, the age and comorbidity distributions were similar between the racial groups (Table 2, Fig 2); Most variables had standardized bias PowerPoint slide larger image original image Fig 2. Subject’s age and known risk factor distributions.
(a) Onset age distribution of ADRD patients after matching Caucasians to African Americans. The onset age of (original) Caucasians tends to be older than that of African Americans. The matched Caucasian follows a similar distribution. (b) Distribution of known risk factors after matching. The matched Caucasian follows a similar distribution with African Americans in terms of the confounding risk factors. Ca = Caucasians, Paired Ca = Caucasians that are matched to African Americans. AF = African Americans.
We derived the Bayesian network of comorbidities and ADRD for each racial group separately (Fig 3, S2 Table). The two causal structures from African Americans and their matched Caucasian cohort shared similar but also distinctive comorbidities. The SHD and GED of the two structures was 1,277 and 895, respectively. Among all the edges in the Caucasian causal graph, only 46.81% edges are present in the African American causal graph; and only 36.59% of the edges in the African American causal graph are present in the Caucasian causal graph. We focused on the comorbidities that have edges to ADRD in all ten bootstraps (Table 3) and measured the counterfactual treatment effect of the comorbidities to ADRD (Table 4, Fig 3). The identified comorbidities were grouped into three types: cerebrovascular disease, mental disorders, and inflammation/infection.