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. Author manuscript; available in PMC: 2023 Nov 1.
Published in final edited form as: Appl Neuropsychol Adult. 2021 Sep 23;30(6):749–756. doi: 10.1080/23279095.2021.1976778

Examining racial disparities in the diagnosis of mild cognitive impairment

Andrew M Kiselica a, Ellen Johnson a,b,*, Kaleea R Lewis c,d, Kate Trout e
PMCID: PMC8940745  NIHMSID: NIHMS1771827  PMID: 34554020

Abstract

Black individuals are less likely to receive an accurate diagnosis of mild cognitive impairment (MCI) than their White counterparts, possibly because diagnoses are typically made by a physician, often without reference to objective neuropsychological test data. We examined racial differences in actuarial MCI diagnoses among individuals diagnosed with MCI via semi-structured clinical interview (the Clinical Dementia Rating) to examine for possible biases in the diagnostic process. Participants were drawn from the National Alzheimer’s Coordinating Center Uniform Data Set and included 491 individuals self-identifying as Black and 2,818 individuals self-identifying as White. Chi-square tests were used to examine racial differences in rates of low scores for each cognitive test (domains assessed included attention, processing speed/executive functioning, memory, language, and visual skills). Next, we tested for racial differences in probability of meeting actuarial criteria for MCI by race. Compared to Black participants diagnosed with MCI via clinical interview, White individuals diagnosed with MCI via clinical interview demonstrated significantly higher rates of low demographically-adjusted z-scores on tests of memory, attention, processing speed, and verbal fluency. Furthermore, White individuals were significantly more likely to meet actuarial criteria for MCI (71.60%) than Black individuals (57.90%). Results suggest there may be bias in MCI classification based on semi-structured interview, leading to over diagnosis among Black individuals and/or under diagnosis among White individuals. Examination of neuropsychological test data and use of actuarial approaches may reduce racial disparities in the diagnosis of MCI. Nonetheless, issues related to race-based norming and differential symptom presentations complicate interpretation of results.

Keywords: Cognition, diagnosis, mild cognitive impairment, neuropsychology, racial differences

Dementia is characterized by cognitive and behavioral declines with associated functional dependence (McKhann et al., 2011), and it is one of the most pressing healthcare and economic challenges of modern times (Alzheimer’s Association, 2020). There are 5 million individuals in the United States living with dementia, and care for these individuals amounts to over $300 billion per year (Alzheimer’s Association, 2020). Given that there are no available treatments that reverse the cognitive and functional problems of of dementia, there is increasing interest in identification of earlier stages of decline, during which risk mitigation approaches (e.g., diet, exercise, etc.) may reduce dementia risk (Knopman et al., 2015). Mild cognitive impairment (MCI) is thought to be a prodromal stage of cognitive decline, wherein individuals are at risk for developing dementia (Jack et al., 2018; Petersen, 2004). It is characterized by greater than expected (for demographic background) cognitive declines that do not yet impact functional performance (Albert et al., 2011).

Population studies suggest that Black individuals are at higher risk for mild cognitive impairment (MCI) than their White counterparts (Perales-Puchalt et al., 2021). However, findings of racial differences in MCI prevalence may be clouded by the fact that Black individuals are less likely to receive an accurate diagnosis (Gianattasio et al., 2019). Little research has been done to explain misdiagnosis of MCI in minority groups. In the majority of cases, the initial diagnosis of MCI is made by a primary care provider or other physician (e.g., geriatrician, neurologist) based on clinical interview, without consideration of comprehensive neuropsychological data (Cho et al., 2014). Inadequate consideration of neuropsychological test findings may help explain racial disparities in MCI misdiagnosis.

A variety of studies have been published putting forth actuarial approaches to the diagnosis of MCI, which rely on algorithms derived from neuropsychological data (Bondi et al., 2014; Edmonds et al., 2014; Edmonds et al., 2016; Jak et al., 2016; Kiselica et al., 2020; Oltra-Cucarella et al., 2018; Thomas et al., 2019). They have shown that in comparison to other methods (based on interviews, subjective reports, and/or cognitive screens), neuropsychologically-based actuarial approaches help to (1) avoid diagnosing MCI in individuals who lack objective evidence of cognitive impairment (i.e., reduce false positives); (2) avoid missing diagnoses in people who underreport symptoms (i.e., reduce false negatives); (3) produce groups of individuals who are less likely to revert to being cognitively unimpaired; (4) produce groups of individuals who are more likely to accumulate biomarkers of neurodegenerative disease; and (5) produce groups of individuals who are more likely to convert to dementia. In summary, this work convincingly demonstrates that actuarial approaches to MCI diagnoses improve diagnostic accuracy and predictive validity.

To date, research has yet to apply actuarial approaches to investigate racial disparities in MCI diagnosis. Specifically, incongruence between interview-based MCI diagnoses and actuarial MCI diagnoses may suggest systematic biases in diagnostic practices. There are three competing hypotheses, which could be supported by the data.

  1. Hypothesis 1: There are no racial disparities in the diagnosis of MCI by clinical interview. This hypothesis would be supported if Black and White individuals diagnosed with MCI by clinical interview demonstrated similar rates of MCI diagnoses when actuarial criteria are applied.

  2. Hypothesis 2: Clinical interview-based methods result in over diagnosis of White persons and/or under diagnosis of Black persons. This hypothesis would be supported if White individuals diagnosed with MCI based on clinical interview demonstrated a lower probability of meeting actuarial criteria for MCI than White individuals. In other words, many White individuals without objective evidence of cognitive impairment were diagnosed with MCI via clinical interview, and/or many Black individuals with objective evidence of cognitive impairment failed to be diagnosed with MCI via clinical interview.

  3. Hypothesis 3: Clinical interview-based methods result in under diagnosis for White persons and/or over diagnosis for Black persons. This hypothesis would be supported if White individuals diagnosed with MCI based on clinical interview demonstrated a higher probability of meeting actuarial MCI criteria than Black individuals. In other words, many Black individuals without objective evidence of cognitive impairment were diagnosed with MCI via clinical interview, and/or many White individuals with objective evidence of cognitive impairment failed to be diagnosed with MCI via clinical interview.

We consider this third hypothesis to be the most likely. Although little research has examined false positive MCI interview-based diagnoses among Black individuals, there is a history of over diagnosing Black persons with serious mental health conditions (Schwartz & Blankenship, 2014). Furthermore, past research indicates that Black individuals have an increased risk for false positive MCI diagnoses based on cognitive screens (Milani et al., 2018). To investigate these competing hypotheses, we examined racial differences in actuarial MCI diagnoses among individuals diagnosed with MCI using the Clinical Dementia Rating (CDR) Dementia Staging Instrument® (Morris, 1993) in the National Alzheimer’s Coordinating Center Uniform Data Set (UDS). Findings are important to better understand racial disparities in MCI diagnosis and have critical implications for clinical research and practice, epidemiological and public health studies, and quality of life for persons with suspected cognitive impairment.

Methods

Sample

We requested data through the National Alzheimer’s Coordinating Center online portal in November of 2020. Data were provided on November 19, 2020 and contained information dating to the September 2020 data freeze. The initial database included 43,343 participants with baseline data available. Because many of the measures in the UDS include a language component, the database was restricted to English speaking individuals (n = 39,673). The sample was then limited to individuals receiving the most recent version of the UDS neuropsychological battery, version 3.0 (UDS3NB), at baseline (n = 9,564). Next, since we were interested in studying individuals at risk for MCI, the sample was limited to individuals ages 50 and older (n = 9,264). While MCI is typically seen in old age, increasing evidence suggests a high prevalence in middle-age individuals as well (e.g., Kremen et al., 2014), justifying this approach. We then restricted the sample to participants without dementia (i.e., only individuals with CDR global score <1 were included in the sample) because we were interested in deriving normative data in a cognitively unimpaired group and applying this data to better understand diagnosis of MCI (n = 7,711). Finally, because we wanted to make comparisons across non-Hispanic Black/African American and White groups, we restricted the sample to individuals self-identifying as such. The final sample included baseline data for 7,201 participants from 30 Alzheimer’s Disease Research Centers, with study visits occurring from March 2015 through August 2020.

The cognitively normal individuals (CDR = 0; n = 3,892) formed a normative sample, from which demographically-adjusted z-scores were derived (see below for details on how z-scores were calculated). Inferential analyses were performed on the sample of individuals diagnosed with MCI (CDR = .5; n = 3,309). The research was conducted in accordance with the Helsinki Declaration and the guidelines of the University of Missouri Institutional Review Board.

Neuropsychological measures

Measures for the UDS 3.0 have been described in detail elsewhere (Besser et al., 2018; Weintraub et al., 2018). For the current study, we used seven core tests, which yield 12 scores (Kiselica et al., 2020). They included (1) a learning/memory measure, the Craft Story (Craft et al., 1996); (2) two measures of language—a semantic fluency (animal and vegetable trials) task and the Multilingual Naming Test (Gollan et al., 2012; Ivanova et al., 2013), a confrontation naming test; (3) a measure of visual construction and recall, the Benson Figure (Possin et al., 2011); (4) a measure of attention, the Number Span Task (includes forward and backward digit repetition); and (5) a measure of processing speed/executive functioning, the Trail Making Test parts A & B (Partington & Leiter, 1949).

Actuarial MCI diagnosis

Actuarial MCI diagnoses were made according to procedures developed by (Oltra-Cucarella et al., 2018), who defined a base rate of low scores approach to MCI diagnosis. They found this approach to be superior to other methods (e.g., Petersen criteria, Jak/Bondi criteria) in predicting progression to dementia due to Alzheimer’s disease. In the base rate of low scores approach, the number of scores below a certain cutoff is assessed within a normative sample. Then the number of low scores that best approximates the bottom 10% of the normal curve is set as the cutoff for evidence of objective cognitive impairment.

Low scores

We defined low scores as those falling below the 9th percentile (z = −1.3408), which is consistent with scores falling below the average range, per the American Academy of Clinical Neuropsychology consensus statement on uniform labeling of scores (Guilmette et al., 2020). We followed procedures from Weintraub et al. (2018) to create demographically adjusted normative scores for each neuropsychological test. Specifically, in the subsample of individuals classified as cognitively normal (CDR global score = 0), each test score was regressed onto age, sex, education, and race. Results of regression analyses are provided in Supplemental Table 1. The subsample included 80% individuals identifying as White and 20% of individuals identifying as Black/African American. Regression weights and standard errors of the estimates were then used to create regression-based z-scores (Shirk et al., 2011). Finally, scores were categorized as low if z was less than −1.3408.

Base rates of low scores and actuarial MCI

Base rates for numbers of low scores for the normative group are presented in Supplemental Table 2. The number of low scores that best approximated the bottom 10% of the normal distribution was 2+. Thus, individuals were categorized as meeting actuarial criteria for MCI if they had two or more demographically-adjusted z-scores below the 9th percentile.

Amnestic and Non-Amnestic MCI

Although not a primary focus of the paper, MCI can be broken down into subcategories based on symptom presentation, with the most common distinction being between amnestic and non-amnestic MCI (Petersen, 2004). Amnestic MCI is characterized by cognitive impairment in the memory domain, whereas non-amnestic MCI is characterized by cognitive impairment in domains other than memory (e.g., language or executive functioning). Individuals were classified with amnestic MCI if they had two or more low scores across the neuropsychological battery and at least two of the low scores occurred on memory tests (Craft Story, Benson Figure recall). Individuals were classified as non-amnestic MCI if they had two or more low scores and no more than one low score on a memory test. The amnestic versus non-amnestic distinction was used in post hoc analyses, which are presented in the discussion.

Analyses

In the subsample of individuals diagnosed as MCI by the CDR (global score = .5), we assessed rates of low scores by race on each test in the UDS3NB using chi-square tests of independence. We also examined racial differences in the overall number of low scores by race using an independent samples t-test. Finally, we tested for racial differences in probability of meeting actuarial criteria for MCI by race using a chi-square test of independence. We expected results to support hypothesis 3: Clinical interview-based methods result in under diagnosis for White persons and/or over diagnosis for Black persons. This hypothesis would be supported if White individuals diagnosed with MCI based on clinical interview demonstrated a higher probability of meeting actuarial MCI criteria than Black individuals.

Results

Demographics

Demographic information for the sample is provided in Table 1. Compared with Black individuals, White individuals on average had more years of education, were older, were less likely to be female, and were more likely to be diagnosed with MCI when using the CDR.

Table 1.

Sample demographic information and race comparisons.

Total Sample (n = 7,201) White (n = 5,933) Black/African American (n = 1,268) t or χ2, p
Age: M (SD) 70.45 (8.24) 70.54 (8.34) 70.02 (7.76) 2.13, .034
Education: M (SD) 16.24 (2.60) 16.47 (2.52) 15.16 (2.68) 15.96, <.001
% Female (n) 58.90% (4238) 55.10% (2666) 76.60% (971) 199.65, <.001
% Mild Cognitive Impairment* 46.00% (3,309) 47.50% (2818) 38.70% (491) 32.39, <.001
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*

Based on Clinical Dementia Rating global score.

Low Demographically-Adjusted scores by race

The proportion of individuals scoring at or below the 9th percentile on each neuropsychological test by race is shown in Table 2. White individuals were more likely to demonstrate low scores on tests of memory (Craft Story immediate and delayed recall, Benson Figure recall), attention (Number Span Forward and Backward), processing speed (Trailmaking Part A), and semantic fluency (Animal and Vegetable Fluency tests). Overall, White individuals classified with MCI demonstrated about one additional low score on testing on average (M = 3.63, SD = 2.84) when compared with Black individuals (M = 2.39, SD = 2.16), t(809.76) = 11.13, p < .001, d = .49.

Table 2.

Proportion of low (<9th Percentile) demographically adjusted test scores by race among participants diagnosed with mild cognitive impairment by the clinical dementia rating.

Proportion of Individuals with a Low Score
Cognitive Test White Black χ2, p
Benson Figure Copy 17.90% 17.40% 0.70, .791
Benson Figure Recall 45.80% 28.90% 47.67, <.001
Trailmaking Test Part A 27.70% 22.60% 5.44, .020
Trailmaking Test Part B 30.70% 31.90% 0.23, .629
Letter Fluency 24.70% 21.20% 2.70, .100
Number Span Forward 19.10% 14.90% 4.79, .029
Number Span Backward 28.20% 14.90% 37.55, <.001
Vegetable Fluency 35.50% 15.50% 75.19, <.001
Animal Fluency 33.90% 10.10% 110.24, <.001
Multilingual Naming Test 24.50% 21.30% 2.35, .126
Craft Story Immediate Recall 40.00% 20.40% 66.97, <.001
Craft Story Delayed Recall 46.40% 24.60% 78.39, <.001
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Base rate of actuarially defined MCI by race

Among individuals diagnosed with MCI using the CDR, White individuals were significantly more likely to meet actuarial criteria for MCI (71.60%) than Black individuals (57.90%), χ2(1) = 36.36, p < .001.

Discussion

We examined racial differences in likelihood of meeting actuarial MCI criteria among individuals diagnosed with MCI by semi-structured clinical interview among participants in the Uniform Data Set. With regard to individual neuropsychological test performance, White individuals were more likely to demonstrate low scores on measures of memory, verbal fluency, and attention. This finding conflicts with prior research, which showed lower memory performance among Black individuals with MCI compared to White individuals (McDougall et al., 2007). However, this prior study used a cognitive screener to classify individuals with MCI, in contrast to our study, which defined MCI by clinical interview. Future studies should examine the impact of different classification methods for MCI on likelihood of diagnosis in diverse groups.

Base rate of meeting actuarial MCI criteria by race

There was a clear racial disparity in the proportion of individuals meeting actuarial criteria for MCI in the subsample of individuals diagnosed with MCI by semi-structured clinical interview. Specifically, White individuals were 13.70% more likely than Black individuals to meet actuarial criteria. This finding is consistent with hypothesis 3: Clinical interview-based methods result in under diagnosis for White persons and/or over diagnosis for Black persons.

This pattern could emerge if Black participants and study partners reported a higher degree of cognitive symptoms, whereas White participants and study partners underreported symptoms. This explanation is at odds with prior research, which suggests that Black individuals are less concerned about the possibility of developing dementia (Roche et al., 2021), are more likely to view thinking problems as a normal part of the aging process (Alzheimer’s Association, 2021), and tend to self-report fewer cognitive problems (Rovner et al., 2012). Rather, a more plausible explanation is that clinical interview-based methods of diagnosis introduce systematic racial biases in the diagnostic process, such that reported symptoms of Black participants are overweighted, while those of White participants are underrated when compared to an actuarial approach. Similar reasoning has been used to explain overdiagnosis of psychosis in minoritized groups (Schwartz & Blankenship, 2014).

An alternative explanation may be that neuropsychological tests are biased. However, it is widely accepted that neuropsychological tests tend to be biased in favor of White participants (Ighodaro et al., 2017; Manly et al., 1998; Sachdev et al., 2014). Consequently, one would expect to see worse performance for Black individuals than White individuals within the same diagnostic category, and such a pattern has been found in previous research on dementia (Shadlen et al., 1999). We found the opposite pattern, lending further credence to the explanation that interview-based diagnoses are biased in such a way that leads to over diagnosis of Black individuals and/or under diagnosis of White individuals.

Another alternative consideration was that Black participants presented with symptoms that are more likely to be detected via clinical interview when compared with White individuals. To examine this possibility, we conducted post hoc analyses of racial differences in meeting actuarial criteria for amnestic (two or more low scores, with at least two low memory scores) versus non-amnestic (two or more low scores, with no more than one low memory score) MCI among those diagnosed with MCI via semi-structured clinical interview. Black individuals were more likely to present with non-amnestic MCI (60.90%) than White individuals (38.60%), whereas White individuals were more likely to present with amnestic MCI (61.40%) than Black individuals (39.10%), χ2(1) = 50.02, p < .001. Thus, racial disparities in MCI diagnosis by clinical interview may be driven in part by differences in symptom presentation.

Again, though, one would expect to see under diagnosis of Black individuals based on interview alone, given these differences in presentation. Indeed, an amnestic presentation fits with the “classic” Alzheimer’s disease symptom profile (McKhann et al., 2011). Furthermore, memory complaints are typically more likely to be reported than difficulties in other cognitive domains (La Joie et al., 2016; Miebach et al., 2019). Thus, it seems logical that White individuals, who more often presented as objectively amnestic, would be the ones to be over diagnosed with MCI via clinical interview, but we found evidence for the opposite pattern.

Implications of racial disparities in MCI diagnosis

If there are systematic inaccuracies in semi-structured interview diagnoses across race, they may be remedied by withholding diagnoses until a comprehensive neuropsychological evaluation is completed. Certainly, careful consideration of objective cognitive data and application of an empirical approach to MCI diagnosis may increase diagnostic accuracy (Bondi et al., 2014). In particular, using an actuarial approach that controls for race may be necessary to reduce the likelihood of false positives among Black individuals. To better understand findings, we completed post hoc analyses using norms that did not control for race and found the opposite pattern of results. That is, Black individuals diagnosed with MCI via clinical interview were slightly more likely to meet actuarial criteria for MCI (74.40%) than similarly diagnosed White individuals (69.00%), χ2(1) = 5.79, p = .016. This result fits with previous research, which indicated that controlling for race reduced the likelihood of meeting actuarial MCI criteria (Rotblatt et al., 2021). Such results are not surprising, as race (or better stated, the sociocultural and socioeconomic factors associated with race) explains a significant proportion of variance in cognitive performance, and controlling for race is likely to increase specificity. Taken together, our findings and prior literature suggest that racial disparities in MCI diagnoses may be reduced by using race-adjusted norms.

It is important to note, however, that such race-based norming approaches may obfuscate underlying factors that truly explain racial differences, in particular the quality of educational opportunities (Chin et al., 2012; Manly et al., 2002, 2004). Furthermore, one must be careful to only apply race-based norms in situations wherein the risks of false positives outweigh the risks of false negatives (Manly & Echemendia, 2007). In clinical practice, a diagnosis of MCI can be confusing for patients and caregivers and create significant anxiety (Blatchford & Cook, 2020). Furthermore, false positive diagnoses might lead to loss of independence and concerns over decisional capacity. Finally, lack of an MCI diagnosis does not preclude incorporating preventative measures, such as cognitive strategy training, exercise, social activity, and treatment of dementia risk factors (hypertension, depression, etc.), into the care plan (Livingston et al., 2020). Thus, in our view, the risk/benefit calculus in purely clinical environments favors avoiding false positive MCI diagnoses over avoiding false negative diagnoses, suggesting that race-based norms are favorable in these contexts. In other situations where services may be denied due to a false negative diagnosis, such as in disability evaluations, race-based norms are likely to be less appropriate (and possibly harmful).

Findings of potential bias in interview-based diagnoses lead to important considerations for public health. Race-based calculators and algorithms have been used to inform medical decisions and treatment plans with aims of providing risk-adjusted care. While well intended, race-adjusted plans have had many unforeseen consequences, such as inequitable access to care, reinforcement of implicit racial biases, and perpetuation of cycles of health disparities (Vyas et al., 2020). Incorporating biased interview-based estimates of MCI prevalence and incidence would only serve to exacerbate this problem. Instead, epidemiological methods should work toward identifying multidimensional and synergistic factors related to race that impact the observed difference in MCI diagnoses and make necessary adjustments to increase reliability and validity of classification methods.

Evidence of bias in interview-based diagnoses also has important implications for the lived experiences of Black individuals with MCI and their families. The dearth of research investigating the impact bias may have on the lives of Black adults and other older minoritized populations living with MCI illustrates how and why avoidable and preventable health disparities exist within these populations (Feagin & Bennefield, 2014). Critically examining how bias operates within diagnosis of MCI among Black adults can begin the process of dismantling the mechanisms that undermine the overall health and life satisfaction of these individuals and perpetuate racial health disparities.

Limitations

Of course, the implications of this research must be considered in light of certain limitations. First, there is no agreed upon empirical procedure for diagnosing MCI. While diagnosis of MCI is often done based on a single clinical interview (in this study, a semi-structured interview, the CDR), interdisciplinary diagnostic decisions are not uncommon. These practices may improve diagnostic accuracy and reduce the likelihood of racial disparities. Second, prior research suggests that Black patients perceive their care to be better with Black providers (Shen et al., 2018), such that the race of the examiner is important to consider when assessing for misdiagnosis. However, the UDS does not include this information. Future studies of the impact of provider-patient race concordance are therefore critical. Third, it must be acknowledged that while we based our definition of a low score on current consensus guidelines (Guilmette et al., 2020), the choice of an operationalization for a low score is admittedly arbitrary, and results may vary depending on the cut point selected. Fourth, we used an established number of low scores approach to make actuarial MCI diagnoses, and it must be acknowledged that other approaches exist (Jak et al., 2016). Finally, it should be noted that race-based normative approaches do not capture important sociocultural and socioeconomic factors that may influence scores, and we were unable to account for these factors, as they are not captured in the UDS.

Conclusions

These limitations notwithstanding, the current research provides important insights into racial differences in the diagnoses of MCI. Results indicate that there may be systematic racial biases in diagnoses based solely on semi-structured clinical interview. Specifically, findings suggest the possibility of over diagnosis of MCI among Black individuals and/or under diagnosis of MCI among White individuals. The work reinforces the importance of taking into account neuropsychological test data when diagnosing MCI. Further research is needed to investigate other factors contributing to racial disparities in the diagnosis of MCI and ways to improve consistency of classification across racial groups.

Supplementary Material

Supplementary Table 1
Supplementary Table 2
Supplementary Table 3

Acknowledgements

NACC data are contributed by the NIA-funded ADCs: P30 AG019610 (PI Eric Reiman, MD), P30 AG013846 (PI Neil Kowall, MD), P30 AG062428-01 (PI James Leverenz, MD) P50 AG008702 (PI Scott Small, MD), P50 AG025688 (PI Allan Levey, MD, PhD), P50 AG047266 (PI Todd Golde, MD, PhD), P30 AG010133 (PI Andrew Saykin, PsyD), P50 AG005146 (PI Marilyn Albert, PhD), P30 AG062421-01 (PI Bradley Hyman, MD, PhD), P30 AG062422-01 (PI Ronald Petersen, MD, PhD), P50 AG005138 (PI Mary Sano, PhD), P30 AG008051 (PI Thomas Wisniewski, MD), P30 AG013854 (PI Robert Vassar, PhD), P30 AG008017 (PI Jeffrey Kaye, MD), P30 AG010161 (PI David Bennett, MD), P50 AG047366 (PI Victor Henderson, MD, MS), P30 AG010129 (PI Charles DeCarli, MD), P50 AG016573 (PI Frank LaFerla, PhD), P30 AG062429-01(PI James Brewer, MD, PhD), P50 AG023501 (PI Bruce Miller, MD), P30 AG035982 (PI Russell Swerdlow, MD), P30 AG028383 (PI Linda Van Eldik, PhD), P30 AG053760 (PI Henry Paulson, MD, PhD), P30 AG010124 (PI John Trojanowski, MD, PhD), P50 AG005133 (PI Oscar Lopez, MD), P50 AG005142 (PI Helena Chui, MD), P30 AG012300 (PI Roger Rosenberg, MD), P30 AG049638 (PI Suzanne Craft, PhD), P50 AG005136 (PI Thomas Grabowski, MD), P30 AG062715-01 (PI Sanjay Asthana, MD, FRCP), P50 AG005681 (PI John Morris, MD), P50 AG047270 (PI Stephen Strittmatter, MD, PhD).

Funding

This work was supported by an Alzheimer’s Association Research Fellowship [2019-AARF-641693, PI Andrew Kiselica, PhD]. The NACC database is funded by NIA/NIH Grant U01 AG016976.

Footnotes

Supplemental data for this article can be accessed at publisher’s website.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data used for this study are made publicly available through the National Alzheimer’s Coordinating Center: https://naccdata.org/requesting-data/submit-data-request/

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1
NIHMS1771827-supplement-Supplementary_Table_1.docx (12.5KB, docx)
Supplementary Table 2
NIHMS1771827-supplement-Supplementary_Table_2.docx (17.8KB, docx)
Supplementary Table 3
NIHMS1771827-supplement-Supplementary_Table_3.docx (16.6KB, docx)

Data Availability Statement

Data used for this study are made publicly available through the National Alzheimer’s Coordinating Center: https://naccdata.org/requesting-data/submit-data-request/

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