Abstract
Background:
Subjective cognitive decline (SCD) is increasingly recognized in some cases as an early clinical stage in the Alzheimer’s disease continuum, yet the factors that predict which individuals will progress to objective impairment remain poorly understood.
Objective:
We evaluated risk factor differences and cognitive domain markers associated with progression in participants with subjective cognitive decline (SCD) at baseline from the NYU Alzheimer’s Disease Research Center.
Methods:
We included SCD non-decliners (n = 27), who remained stable, and decliners (n = 24), who progressed to mild cognitive impairment or worse, between the second to sixth yearly follow-up visits. Adjusted mixed-effects models examined group differences and associations between demographic, APOE status, psychometric test performance and comorbidities with longitudinal-decline.
Results:
Overall, mean (SD) age was 67.4 (9.2) and total follow-up time was 5.1 (1.8) years. Lower education (14.9 (3.2) vs. 17.3 (2.1)), Hispanic ethnicity (50.0% vs. 11.0%), and hypercholesterolemia (aOR: 6.67) were risk factors for progression in SCD, p ≤ 0.05, whereas APOE status was not. Notably, SCD decliners were at increased risk for both amnestic and non-amnestic cognitive-decline with psychometric changes in memory, executive, and language domains (p < 0.001 for all).
Discussion:
These findings inform further work on SCD outcomes and related biomarkers, as well as preventive studies that target modifiable risk factors for SCD progression.
Keywords: Alzheimer’s disease, Cognition, Memory, Older adults, Subjective cognitive decline
Introduction
Subjective cognitive decline (SCD) may in some cases represent a preclinical stage of Alzheimer’s Disease (AD), identifying persons with normal performance on psychometric tests who progress to subsequent AD stages faster than cognitively normal individuals without subjective complaints. Consequently, SCD also associates with imaging and biofluids biomarkers of AD.1–4
Specifically, previous work from our center5 showed that SCD is a precursor of further decline to mild cognitive impairment (MCI) or dementia over a 7-year mean follow-up interval. Specifically, in a sample of 260 participants (166 cognitively normal with SCD and 47 cognitively normal without SCD), SCD participants were 4.5 times more likely to decline to mild cognitive impairment (MCI) or dementia. We subsequently studied features of decline in 98 SCD participants over a 2-year interval, noting worsening scores on the Digit Symbol Substitution Test and the Brief Cognitive Rating Scale for staging assessment, as well as a reduction in anxiety.6
Subsequently, we reported on the characteristics of a pre-SCD condition materializing in likely AD. In this study 47 cognitively normal healthy older participants (mean baseline age= 64.1±8.9 years) were followed for approximately 6.7±3.1 years. After controlling for age, sex, years of education, and follow-up time, there were significant group differences in the rate of decline and composite psychometric scores between future decliners (n=36) relative to non-decliners (n=11).7
As such, SCD can be a precursor to MCI or AD dementia, taking as long as 7 to 15 years for transition to occur. However, stage transition rates may differ even among SCD participants, as we have found in the pre-SCD interval8. Moreover, the specific drivers and nature of SCD progression remain unclear. Therefore, in this study we aimed to evaluate risk factor differences between SCD decliners and non-decliners related to demographics, genetics, and presence of comorbidities. We also investigated cognitive domain markers associated with progression over time in order to investigate the nature of progression in participants with SCD.
Methods
Study Design, Participants and Variables
Study Design
A retrospective longitudinal analysis was conducted on selected participants followed at the NYU Alzheimer’s Disease Research Center (ADRC). The NYU School of Medicine Institutional Review Board approved all aspects of this study, and written informed consent was obtained from all participants. Procedures involving experiments on human subjects are done in accord with the ethical standards of the Committee on Human Experimentation of the institution in which the experiments were done or in accord with the Helsinki Declaration of 1975.
Participants
Participants were community dwelling, over 55 years of age, recruited by public announcement or referral to participate in this longitudinal study on cognition and brain aging. Inclusion for the ADRC include (1) having a study partner, (2) fluency in English and Spanish (including their study partner), and (3) willingness to undergo ADRC Clinical Core evaluation that includes imaging and blood draw. Exclusion criteria include (1) history of moderate/severe psychiatric illness, significant neurological disease other than AD and related disorders, significant organ disease or transplant, autoimmune disease requiring treatment; malignancy within 5 years (other than low grade prostate cancer, non-melanotic skin cancer); (2) alcohol or drug abuse. Participants are followed yearly, and the ADRC Clinical Core evaluations include full clinical, neurological, psychiatric, neuropsychological, clinical laboratory, and neuroimaging evaluations according to the National Alzheimer’s Coordinating Center Uniform Data Set 3.0. For our analysis we analyzed a subset of Clinical Core participants that (1) had a consensus diagnosis of SCD at baseline by the Global Deterioration Scale9 and (2a) who remained cognitively and functionally normal for six annual visits, deemed “non-decliners” or (2b) who progressed to MCI or dementia between the second to sixth follow-up visits, deemed “decliners.” This resulted in an analysis cohort of 27 non-decliners and 24 decliners. Participants included in this analysis cohort were recruited between January 1, 2013 and December 31, 2015, followed at yearly intervals until December 31, 2021. Follow-up evaluations were completed without reference to the prior results.
Demographic variables
Demographic variables included age (calculated from reported date of birth), sex (biological variable as reported by subject), years of education (calculated based on highest reported completed diploma/degree), and body mass index (calculated from measured weight and height).
Genetic variable (APOE)
APOE genotype was characterized as having at least one APOE ℇ4 allele or at least one APOE ℇ2 allele.
Medical Co-morbidities
Clinical co-morbidities assessed included clinical history of hypertension, diabetes, anxiety, insomnia, obstructive sleep apnea, osteoarthritis, hypercholesterolemia, and thyroid disease. These conditions were reported by participants during clinical assessment.
Cognitive variables
Cognitive measures assessed in this study included the functional and cognitive assessments used in the National Alzheimer’s Coordinating Center’s Uniform Data Set 3.0: Clinical Dementia Rating (CDR) scale,10,11 Mini Mental State Exam (MMSE),12,13 Montreal Cognitive Assessment (MoCA),14,15 Digit Span Recall (Forwards, Backwards), Benton Visual Retention Tests, Craft story, Multilingual Naming Test (MINT), Vegetable and Animal naming, Trail Making Test Part A and Part B.16–22 The CDR is a clinician-rated staging tool used in this study to summarize cognitive and functional impairment, with global scores of 0 considered normal, 0.5 very mild impairment, 1 mild, 2 moderate, and 3 severe, such that higher scores reflect poorer performance and greater dementia severity. The MMSE is a 30-point global cognitive screener used in this study, where scores ≥24–27 (age/education-dependent) are often within normal limits and scores ≤23 suggest impairment, with lower scores indicating poorer performance. The MoCA is a 30-point screening measure used in this study for detecting mild cognitive impairment, where scores ≥26 (with +1 point for ≤12 years of education) are commonly considered normal and scores <26 indicate possible impairment, with lower scores reflecting poorer performance. Digit Span Forward and Backward used in this study assess attention and working memory respectively, with typical adult spans around 6–7 digits forward and 4–5 backward (age/education-adjusted), and spans or scaled scores ≥1–1.5 SD below norms indicating poor performance. The Benton Visual Retention Test used in this study evaluates visual memory, perception, and construction by immediate reproduction of designs, with performance judged against age/education norms (normal ≈ within 1 SD of the mean) and elevated error counts or low correct reproductions ≥1.5 SD below norms interpreted as poor visuospatial memory/constructive ability. The Craft Story (immediate and delayed recall) used in this study measures verbal episodic memory via story recall, with raw scores converted to age-adjusted norms (normal ≈ within 1 SD; impairment often ≤ −1.5 SD), such that lower recall relative to norms indicates poorer episodic memory. The MINT used in this study assesses confrontation naming, typically scored on a 32- or 68-item total where high scores near ceiling are normal in healthy adults (adjusted for language/education), and reduced totals or z-scores ≤ −1 to −1.5 suggest anomia and poorer language performance. Category fluency for Vegetables and Animals used in this study assesses semantic fluency and executive retrieval by naming as many items as possible in 60 seconds, with age/education-adjusted norms typically in the mid-teens or higher and outputs ≤1–1.5 SD below norms (often <12–15 items) indicating poor performance. Trail Making Test Part A used in this study measures visual scanning and processing speed via timed sequencing of numbers, with completion times within age-adjusted norms (often <40–60 seconds with few errors) considered normal, and prolonged times or many errors indicating poorer processing speed/attention. Trail Making Test Part B used in this study measures set-shifting and executive control by alternating numbers and letters, with age-adjusted normal completion times typically longer than Part A (often <90–150 seconds and using the B–A difference for interpretation), and markedly prolonged times, high error rates, or large B–A differences indicating poor executive function.
Statistical Analysis
All models included age, sex, education, self-reported Hispanic ethnicity, baseline psychometric data, and APOE genotype.
Comparison of baseline participant characteristics between SCD decliners vs. non-decliners
Generalized linear models examined group differences in continuous demographic risk factors. Differences in categorical variables were assessed with the Fisher exact test to compare proportions of participants between SCD groups.
Comparison of Psychometric test performance over time in SCD decliners vs. non-decliners
Analysis of Covariance (ANCOVA) was conducted to test whether significant differences in changes in mean cognitive scores existed between SCD groups over time. In the ANCOVA analysis, time was treated as continuous. Psychometric test performance associations with longitudinal decline, comparing decliners to non-decliners, were also investigated with linear mixed effects models.
Examining associations of medical comorbidities with longitudinal decline
To investigate the associations of medical comorbidities with longitudinal decline, comparing decliners to non-decliners, logistic (i.e., non-linear) mixed-effects models with random intercept and slope were used controlling for covariates/potential confounders, and their interactions with time (operationalized as years from baseline for each participant).
Results
Comparison of baseline participant characteristics between SCD decliners vs. non-decliners
At baseline, there were no significant differences between decliners and non-decliners in age, sex, clinical co-morbidities, APO-ε2 or APO-ε4 carrier status (Table 1). The mean (SD) age for decliners vs. non-decliners was 68.5 (11.3) vs. 66.3 (7.1) years (p =0.41). The proportion of females was 67% vs. 74%, and APOE4 was 29% vs. 33%, p≥0.70 for both. There were significant differences in follow-up time, years of education and Hispanic ethnicity. The mean (SD follow-up time was 3.7 (1.97) vs. 6.3 (1.60) years, (p <0.001) for decliners vs. non-decliners. Participants with lower years of education (14.9 (3.2) vs. 17.3 (2.1)) and those of Hispanic ethnicity (50% vs. 11%) were more likely to be decliners (p ≤ 0.004 for both). It is important to note that not all participants consented to the biomarkers and neuroimaging evaluations, so that was not included in this study. For this study, the focus was on clinical consensus diagnosis, not necessarily the biological definition of AD. Of the 24 participants that declined, etiology of progression was: AD (N=17), Vascular (N=1), frontotemporal dementia (FTD) (N=1), Depression (N=3) and Medical comorbidity (N=2).
Table 1:
GLM Comparison of Participant characteristics at Baseline of Subjective Cognitive Decline (SCD) Groups: Decliners vs. Non-Decliners, NYU Alzheimer's Disease Research Center
| Characteristics | All n=51 | SCD Group | P-value | |
|---|---|---|---|---|
| Decliners (n=24) | Non-Decliners (n=27) | |||
| Age, mean (SD), y | 67.4 (9.2) | 68.5 (11.3) | 66.3 (7.1) | 0.40 |
| Average Follow-up time, mean (SD), y** | 5.1 (1.8) | 3.7 (2.0) | 6.3 (1.6) | <.001 |
| Female, n *(%) | 36 (71) | 16 (67) | 20 (74) | 0.72 |
| Male, n *(%) | 15 (29) | 8 (33) | 7(26) | |
| Education, years (SD) | 16.1 (4.3) | 14.9 (3.2) | 17.3 (2.1) | 0.003 |
| Clinical co-morbid conditions | ||||
| Hypertension n *(%) | 23 (45) | 13 (54) | 14 (52) | 0.42 |
| SBP mm, Hg mean (SD), | 141.5 (17.6) | 142.8 (18.0) | 139.8 (16.2) | 0.33 |
| Diabetes n *(%) | 6 (12) | 3 (13) | 3 (11) | 0.44 |
| Body mass index, mean ± SD | 27.7 ± 4.8 | 27.9 ± 5.8 | 27.4 ± 5.0 | 0.23 |
| Current smoker, n *(%) | 4 (8) | 2 (8) | 2 (7) | 0.76 |
| Anxiety, n *(%) | 5 (10) | 2 (8) | 3 (11) | 0.73 |
| Atrial Fibrillation, n *(%) | 6 (12) | 2(8) | 4 (15) | 0.67 |
| Hypercholesterolemia, n *(%) | 23 (45) | 18 (75) | 5 (19) | 0.42 |
| Osteoarthritis, n *(%) | 6 (12) | 2(8) | 4 (15) | 0.89 |
| Hyposomnia, n *(%) | 7 (14) | 3 (13) | 4 (15) | 0.43 |
| Sleep apnea, n *(%) | 4 (8) | 2 (8) | 2 (7) | 0.99 |
| Thyroid Disease, n *(%) | 16 (31) | 8 (30) | 8 (30) | 0.18 |
| Average number of vascular risk factors (SD) | 1.5 (1.1) | 1.5 (1.3) | 1.3 (1.1) | 0.23 |
| APOE genotype | ||||
| Having at least one APOE ε4 allele, n *(%) | 15 (28) | 7 (29) | 9 (33) | 0.84 |
| Having at least one APOE ε2 allele, n *(%) | 6 (12) | 1 (4) | 5 (19) | 0.13 |
| Race and Ethnicity n *(%) | ||||
| Black/African-American | 5 (10) | 4 (17) | 1 (4) | 0.14 |
| White | 44 (86) | 18 (75) | 26 (96) | |
| Asian | 2 (4) | 2 (8) | 0 (0) | |
| Hispanic Ethnicity n *(%) | ||||
| No | 36 (71) | 12 (50) | 24 (89) | 0.004 |
| Yes | 15 (29) | 12 (50) | 3 (11) | |
| MMSE median (interquartile range) | 29 (28, 30) | 29 (27, 30) | 29 (27, 30) | 0.99 |
| CDR median (interquartile range) | 0 (0, 0) | 0 (0, 0) | 0 (0, 0) | 0.99 |
(%), represents column percent; APOE represents apolipoprotein E, GLM represents generalized linear models; mean (SD) represents mean (standard deviation), MMSE represents mini mental state examination; n represents sample size, SBP represents systolic blood pressure; SD, standard deviation. Significant P-value ≤ 0.05.
Average follow up time refers to average number of visits before cognitive decline is seen
Comparison of Psychometric test performance over time in SCD decliners vs. non-decliners
Results of the ANCOVA analysis are shown in Table 2. SCD decliners were at increased risk for psychometric changes in the Craft story (verbal short-term memory), Multilingual Naming Test (MINT), Vegetable and Animal semantic fluency tests, and the Trail Making Test Part A and Part B assessing for processing speed and executive function (p < 0.001 for all).
Table 2:
ANCOVA Comparisons of Psychometric test performance over time in SCD decliners vs. non-decliners
| Psychometric Test Score | Domain | SCD Group | P-value | |
|---|---|---|---|---|
| Decliners (n=24) | Non-Decliners (n=27) | |||
| CDRSUM, mean (SD) | Function | 0.453 (0.08) | 0.171 (0.07) | 0.003 |
| CDRGLOB, mean (SD) | Function | 0.199 (0.02) | 0.014 (0.01) | <0.001 |
| MMSE, mean (SD) | Global Cognition | 25.3 (0.29) | 28.99 (0.24) | 0.051 |
| MOCATOTS, mean (SD) | Global Cognition | 22.47 (2.79) | 27.33 (2.17) | <0.001 |
| CRAFTURS, mean (SD) | Verbal | 13.12 (4.07) | 16.49 (3.73) | <0.001 |
| UDSBENTC, mean (SD) | Visuospatial | 14.59 (1.34) | 15.7 (2.17) | 0.14 |
| DIGFORCT, mean (SD) | Attention | 8.19 (0.47) | 9.18 (0.38) | 0.09 |
| DIGFORSL, mean (SD) | Attention | 6.64 (0.22) | 7.09 (0.18) | 0.09 |
| DIGBACCT, mean (SD) | Attention | 6.58 (0.44) | 7.08 (0.35) | 0.36 |
| DIGBACLS, mean (SD) | Attention | 4.73 (0.23) | 5.21 (0.18) | 0.10 |
| ANIMALS, mean (SD) | Language: category fluency | 18.40 (0.95) | 22.19 (0.84) | 0.005 |
| VEG, mean (SD) | Language: category fluency | 12.92 (0.64) | 15.91 (0.54) | 0.004 |
| TRAILA, mean (SD) | Processing Speed | 41.4 (20.33) | 31.16 (11.27) | 0.001 |
| TRAILB, mean (SD) | Executive function | 118.2 (20.56) | 66.99 (17.75) | 0.051 |
| CRAFTDRE, mean (SD) | Memory | 11.49 (0.92) | 14.87 (0.74) | 0.005 |
| UDSBENRS, mean (SD) | Visuospatial/ Memory | 0.91 (0.28) | 0.93 (0.26) | 0.24 |
| MINTTOTS, mean (SD) | Language naming | 28.9 (3.78) | 30 (1.83) | <0.001 |
ANIMALS represents Animals — Total number of animals named in 60 seconds; ANOCOVA represents analysis of covariance, MMSE represents Mini Mental State Exam; CDR represents and the Clinical Dementia Rating scale; MOCA represents Montreal Cognitive Assessment; DIGFORCT represents Digit Span Recall, Forwards Number Span Test: Forward — Number of correct trials, DIGFORSL represents Digit Span Recall, Forwards Number Span Test: Forward — Longest Span Forward; DIGBACCT represents Digit Span Recall Number Span Test: Backward — Number of correct trials; DIGBACLS represents Digit Span Recall Number Span Test: Backward — Longest span backward; Digit Span Recall, Reverse [17], VEG represents Vegetables — Total number of vegetables named in 60 seconds MINTTOTS represents; Multilingual Naming Test —Total score; TRAILA and TRAILB represents Trail Making Test Part A and PART B respectively, CRAFTDRE represents Craft Story 21 Recall (Delayed) — Total story units recalled, paraphrase scoring; UDSBENRS represents Benson Complex Figure Recall — Recognized original stimulus among four options, CRAFTURS represents immediate recall.
Examining associations of medical comorbidities with longitudinal decline
Logistic mixed effects estimates examining associations of medical comorbidities with longitudinal decline are shown in Figure 1. Only hypercholesteremia had a significant association. Decliners were approximately 7 times more likely (aOR: 6.67; 95% CI: 1.30 – 33.33, p =0.002) to have hypercholesterolemia compared to non-decliners.
Figure 1: Logistic Mixed Effects Model Showing Effect Sizes Comparing Decliners vs. Non-Decliners over Time.
CI represents confidence interval, Significant P-value ≤ 0.05.
Discussion
Our findings indicate that lower education, Hispanic ethnicity, and hypercholesterolemia are risk factors for progression in SCD, whereas APOE-ε4 carriage is not. The nature of progression showed prospective decline in psychometric tests across multiple domains: memory, language (naming and semantic fluency), processing speed and executive function. Thus, SCD decliners are at increased risk for both amnestic and non-amnestic cognitive decline.
Regarding the demographics risks factors, education and related factors of educational quality and literacy are well established risk factors for dementia.23,24 Racial and ethnic minorities (e.g. Hispanic persons) have disproportionately higher rates of dementia and clinical AD,25 and vascular risk factors (e.g. hypercholesterolemia) lower the threshold for cognitive impairment.26,27 Moreover, such factors are synergistic in that racial and ethnic minorities with lower education, and associated social determinants of low income and reduced health care, have even higher risk of SCD28. Additionally, these challenges can be exacerbated when individuals fail to discuss cognitive decline with health care professionals. Thus, our results suggest that SCD screening, monitoring, and intervention in these groups is of high clinical importance.
Our finding of hypercholesterolemia as a risk factor for SCD progression is not only expected as a risk factor for vascular cognitive impairment, but it also has links to AD. Indeed, we have previously observed that hyperlipidemia is equivalently prevalent in MCI from neuropathologically-confirmed AD and microvascular disease29,30. Yet, imaging biomarkers of microvascular disease (e.g. white matter hyperintensities on MRI FLAIR sequence) are not driven by hypercholesterolemia in SCD31. However, hypercholesterolemia has been associated with elevated brain amyloid levels and hypometabolism in AD-susceptible brain regions32–35. As it is a modifiable factor, lipid control in persons with SCD may be an important intervention to prevent the onset of objective cognitive decline.
APOE-ε4 carriage was surprisingly not a risk factor for progression in SCD in our cohort, despite its well-known association with increase AD risk36 likely due to increased amyloid deposition37. In other studies of SCD, APOE-ε4 carriage has been associated with higher CSF p-tau, lower CSF AB42, and higher amyloid deposition38. Such abnormalities, particularly CSF AB42/tau ratio, are reliably predictive of SCD progression to MCI and AD dementia39. One possibility to explain this discrepancy, apart from cohort and SCD assessment differences, is that SCD progression may correlate with accelerated spread of tauopathy, which may be less driven by APOE-ε4.
Furthermore, since we do not have biomarkers assessments in this cohort, it is also possible that some of the progression is due to some other form of dementia. This may be supported by aspects of non-amnestic progression and hyperlipidemia as a risk factor. Nevertheless, as noted above, most of the decline was due to AD i.e., amnestic MCI predominant presentation (17/24) and overall AD/ADRD was in 19/24 cases. With the depression cases, while the diagnosis did not indicate AD, we do not know for certain that AD was not present since they can often co-occur and depression can be the first sign of AD as well. We did not utilize AD proteinopathy biomarkers for the diagnosis but rather MRI and clinical measures. Future work could potentially observe biofluids and/or imaging markers of amyloid, tau, and cerebrovascular disease in SCD participants to investigate their predictive value for amnestic vs. non-amnestic decline. Such work would help establish these biomarkers as complements to psychometric testing as predictors of disease status and progression and provide more objective clinical insights into SCD pathology.
Strengths of our study include the comprehensive psychometric assessments and the longitudinal follow-up of study participants. Limitations of this study include the small sample size, as well as the absence of established markers of AD neuropathology such as amyloid and tau. Additionally, it is possible that Hispanic ethnicity being a significant factor could be due to the small sample size and note caution in interpreting this statement.
In summary, our findings provide insights into the design of biomarker studies concerning SCD outcomes, as well as preventive studies of MCI and eventual AD development. Our research adds to prior preventative studies of MCI and eventual AD development by incorporating social determinants of health and group differences (e.g., education, low-income, race, access to healthcare) to examine the risk of MCI/AD progression in patients with SCD. As discussed above, the consideration of modifiable risk factors like education and vascular risk in clinical practices may be crucial in identifying SCD patients likely to progress to MCI/AD.
Acknowledgments
This study was made possible with funding from the National Institute on Aging [P30AG008051, P30AG066512, RF1AG072507, R21AG070880, R01AG082278, RF1AG083975, K23AG068534, L30-AG064670, R01AG072644, R01AG056031, and R01AG056531], Alzheimer’s Association [AARG-D- 21-848397, and Bright Focus Foundation [A2022033S] and the Louis J And June E Kay Foundation. The authors thank the patients, research participants, and study partners at the NYU Alzheimer’s Disease Research Center for their time, effort and dedication to research.
Funding
AVM is a Council member of Alzheimer’s Association International Research Grants Program, Steering Committee Member of the Alzheimer's Disease Cooperative Study and Editorial Board Member of Alzheimer's & Dementia: Translational Research & Clinical Interventions and Journal of Neuro-Ophthalmology. OMB received funding from NYU Alzheimer’s Disease Research Center Developmental Pilot [P30AG066512], Columbia University Alzheimer’s Disease RCMAR [P30AG059303], National Institute on Aging [R01AG082278, RF1AG083975, K23AG068534, and L30-AG064670], Alzheimer’s Association [AARG-D- 21-848397, and BrightFocus Foundation [A2022033S]. MAB and AB received funding from the NYU Alzheimer’s Disease Research Center [P30AG066512] through a Diversity Supplement. RSO received funding from the National Institute on Aging [P30AG066512, R01AG072644, R01AG056031, and R01AG056531]. AVM received funding from National Institute on Aging [P30AG008051, P30AG066512, RF1AG072507, and R21AG070880]
Footnotes
Ethical Considerations
The NYU School of Medicine Institutional Review Board approved all aspects of this study.
Consent to Participate
Written informed consent was obtained from all participants.
Declaration of Conflict of Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
Anonymized data may be made available to investigators once a review committee has approved application for use of data.
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Data Availability Statement
Anonymized data may be made available to investigators once a review committee has approved application for use of data.