Caregiving for a Spouse with Cognitive Impairment: Effects on Nutrition and Other Lifestyle Factors

Abstract

Background:

Being a spousal caregiver (SCG) for a patient with cognitive impairment is well known to be associated with increased risk for dementia and cognitive decline.

Objective:

This study examined the impact of the care-recipient’s cognitive status on lifestyle factors influencing cognitive decline in SCGs, focusing on nutritional status and blood biomarkers.

Methods:

Fifty-one SCGs participated (mean age 73.5±7.0 years) in this study. All participants underwent clinical assessment including the Mini Nutritional Assessment (MNA), Geriatric Depression Scale, Pittsburgh Sleep Quality Index, and International Physical Activity Questionnaire to evaluate lifestyle factors, and the Mini-Mental State Examination to assess global cognition. Also, nutritional blood biomarkers were measured.

Results:

SCGs caring for a demented spouse showed significantly higher depression scores (t = –3.608, p = 0.001) and malnutrition risk (t = 2.894, p = 0.006) compared to those caring for a non-demented spouse. Decreased care recipients’ cognition was significantly correlated with higher GDS (β= –0.593, t = –4.471, p < 0.001) and higher MNA scores (β= 0.315, t = 2.225, p = 0.031) and lower level of high-density lipoprotein (HDL) cholesterol (β= 0.383, t = 2.613, p = 0.012) in their SCGs. Gender had moderating effects on association of care-recipients’ cognition with sleep quality (B[SE] = 0.400[0.189], p = 0.041) and HDL cholesterol (B[SE] = –1.137[0.500], p = 0.028) among SCGs. Poorer care-recipient’s cognition was associated with worse sleep quality and low HDL cholesterol among wives but not husband caregivers.

Conclusion:

This study provides substantial evidence that SCGs are at risk for depression and malnutrition, which can further affect cognitive decline. As such, these factors should be well assessed and monitored among SCGs for patient with cognitive impairment.

Keywords

Dementia depression HDL cholesterol malnutrition spousal caregiver

INTRODUCTION

Among old adults, caring for a spouse with cognitive decline is well-known to be associated with significant risk to the caregiver’s health such as cardiovascular disease [1, 2], depression [3 –5], and, especially, cognitive impairment [1 , 7]. Spousal caregivers (SCGs) can, in effect, become secondary patients. A previous population-based study reported a sixfold greater risk of dementia in SCGs whose spouse had dementia (hereafter, dementia SCGs) relative to non-dementia SCGs [8]. Therefore, it is important to understand the caregiving-related risk factors for caregiver’s cognitive decline.

Caring for a progressively dementing spouse is a prototypic chronic stressor [9]. As care-recipient’s cognitive function decline, most SCGs are exposed to increasing physical and psychosocial demands. This exposure to chronic stress can lead to psychosocial distress and unhealthy behaviors such as poor sleep quality, physical inactivity, and limited positive social interaction [10]. These caregiving-related consequences are potentially modifiable risk factors for cognitive decline in SCGs [11 –13]. Also, as the care-recipient’s cognitive function declines, the SCG may find it difficult to maintain a healthy diet in the face of the increased caregiving burden [14]. Diet and nutritional factors have been studied as modifiable risk factors for cognitive decline in late life. Several cross-sectional [15] and longitudinal studies [16 –18] found significant associations between poor nutritional status and worse cognitive status.

Although SCGs may initially be cognitively normal, malnutrition is likely to cause cognitive decline among older adults [18]. However, most previous research has examined how the nutritional status of patients with cognitive decline affects the patients’ cognitive function [15 –17]. No study has examined the nutritional status and biomarkers of SCGs according to the care-recipient’s cognition.

Therefore, in this study, we examined the impact of the care-recipient’s cognitive status on the lifestyle factors (malnutrition, sleep, depression, and physical activity) influencing cognitive decline among SCGs, focusing on nutritional status and blood biomarkers (serum albumin and lipid profile) which contribute to SCG’s cognitive decline. A previous review reported gender differences in caregiving [19], but evidence related to gender differences in nutrition are lacking. Thus, we further investigated gender differences in the impact of caregiving on cognition-related lifestyle factors and nutritional blood biomarkers for SCGs.

METHODS

Study design and participants

The present study included 51 wife–husband couples recruited from Chungnam National University Geriatric Psychiatry Clinic between May 2020 and May 2021. Of the caregivers in these couples, 31 were SCGs of persons with dementia, and 20 were SCGs of persons with mild cognitive impairment (MCI) or normal cognition (CN) (hereafter, non-dementia SCGs). The inclusion criteria for SCGs were as follows: 1) aged 55–90 years; 2) acting as the primary caregiver for their spouse/care recipient; 3) able to function independently; and 4) not diagnosed with dementia. CN was defined by a Clinical Dementia Rating score (CDR) of 0. The score on the Korean version of the Mini-Mental State Examination (MMSE) [20] was ≥27. Care recipients with CN were treated for anxiety disorder and insomnia. The care recipients with MCI met the core clinical criteria for MCI diagnosis recommended by the National Institute of Aging and Alzheimer’s Association guidelines [21]. Dementia was diagnosed according to the diagnostic criteria of DSM-IV (American Psychiatric Association, 1994). SCGs underwent comprehensive clinical assessments by a trained neuropsychological technician and a research nurse. The current research was approved by the Ethical Review Committee of Chungnam National University Hospital with the committee’s reference number 2020-05-002. Before investigation, informed consent was signed and obtained by each participant.

Clinical assessments

Nutritional status was assessed using the Korean version of the Mini Nutritional Assessment (MNA) [22]. The MNA was developed to determine the risk of malnutrition in older adults [23] and has been employed to detect nutritional deficiencies in institutional settings [24] as well as in healthy general populations [25]. The MNA consists of 18 items related to body mass index (BMI), weight loss, arm and calf circumference, appetite, general and cognitive health, dietary matters, and a subjective judgement of protein energy malnutrition. With a total of 30 points possible, the MNA uses the following validated cutoff scores to indicate malnourishment, risk for malnutrition, and well-nourished status: < 17, 17–23.5, and 24–30, respectively. These cutoffs were created based on validation studies against clinical status as judged by physicians using a comprehensive evaluation of anthropomorphic, dietary, and biological markers of malnutrition [26].

Each participant’s global cognition was assessed using the Korean version of the MMSE [20]. The Korean version of the Geriatric Depression Scale (GDS) [27] was implemented to assess the current severity of depressive symptoms in each participant. To evaluate sleep quality, the Korean version of the Pittsburgh Sleep Quality Index (PSQI) was used [28]. On the PSQI, each question is assigned a score ranging from 0 to 3 points, and the total score ranges from 0 to 21. Subjects scoring > 5 have poor sleep quality. The Korean version of the International Physical Activity Questionnaire (IPAQ) was used to evaluate participants’ physical activity level [29]. The seven items of the IPAQ identify the total time (min) spent on physical activities of moderate–vigorous intensity over the last 7 days, including walking, and inactivity. Replies were converted to metabolic equivalent task (MET) minutes per week (MET-min/week) according to the IPAQ scoring method [30]. An average MET score was derived for each type of activity. The following MET values were used: walking = 3.3 METs, moderate physical activity = 4.0 METs, and vigorous physical activity = 8.0 METs. The total physical activity was calculated as the sum of the MET-min/week values derived from walking, moderate activity, and vigorous activity.

Comorbid vascular risk factors (VRFs) other than hyperlipidemia, such as hypertension, diabetes mellitus, coronary artery disease, transient ischemic attack, and stroke, were assessed. Each comorbidity was defined by either a documented medical history of the disease or treatment with medication. The classification was based on data collected via a thorough interview with participants and reliable information provided by a trained nurse. A VRF score (VRS) was calculated for the number of VRFs observed and is reported as a percentage [31].

BMI was the weight in kilograms divided by the square of the height in meters. Research nurses measured the height and body weight of all participants and calculated the BMI.

Blood sampling and laboratory assessments

A blood sample was obtained by venipuncture in the early morning following an overnight fast and collected in K2 EDTA and SST tubes. Serum albumin [32, 33] and lipid profile [34 –36] (triglyceride, high-density lipoprotein [HDL] cholesterol, and low-density lipoprotein [LDL] cholesterol), which are known to be related to cognitive function and reflect participants’ nutritional status, were evaluated. Genomic DNA was extracted from whole blood samples, and apolipoprotein E (APOE) genotyping was performed as previously described [37]. APOE ɛ4 (APOE4) positivity was coded if at least one ɛ4 allele was present.

Statistical analysis

To compare non-dementia SCGs and dementia SCGs in terms of the variables in Table 1, we examined group differences in continuous variables using Mann–Whitney U-tests, and categorical variables were evaluated using χ² and Fisher exact tests.

Table 1

Comparisons of demographic characteristics, lifestyle factors and nutritional blood biomarker between non-dementia SCG and dementia SCG

Variables	Non-dementia SCG (n = 20)	Dementia SCG (n = 31)	Total sample (N = 51)	p
SCG factors
Age	72.1±6.4	74.4±7.4	73.5±7.0	0.248
Sex, F (%)	13 (65.0%)	20 (64.5%)	33 (64.7%)	0.998
Education, y	9.3±4.9	8.7±4.6	8.9±4.7	0.691
Household, n (%)	0.999
- without other family members	20 (100.0%)	30 (96.8%)	50 (98.0%)
- with other family members	0 (0.0%)	1 (3.2%)	1 (2.0%)
Types of care, caring alone (%)	20 (100.0%)	25 (80.7%)	45 (88.2%)	0.112
MMSE (SCG)	25.4±3.0	25.0±3.2	25.2±3.1	0.620
APOE4 positivity	4 (21.1%)	4 (12.9%)	8 (16.0%)	0.715
BMI	25.3±4.1	23.8±3.2	19.5±5.8	0.156
VRS (%)	15.0±14.3	21.3±17.1	18.8±16.2	0.528
Care-recipient factors
MMSE	24.5±2.9	16.4±4.8	19.5±5.8	< 0.001
Global CDR	< 0.001
- 0	7 (35.0%)	0 (0.0%)	7 (13.7%)
- 0.5	13 (65.0%)	7 (22.6%)	20 (39.2%)
- 1	0	16 (51.6%)	16 (31.4%)
- 2	0	8 (25.8%)	8 (15.7%)
Lifestyle factors
GDS	9.2±5.8	15.7±7.3	13.2±7.4	0.001
MNA	22.0±2.3	19.5±3.3	20.4±3.1	0.006
- < 17	0 (0%)	7 (23.3%)	7 (13.7%)
- 17∼23.5	16 (80%)	21 (70.0%)	37 (72.5%)
- >= 24	4 (20%)	2 (6.7%)	6 (11.8%)
PSQI	7.4±3.9	9.3±3.4	8.5±3.7	0.069
IPAQ (MET×min./week)	3,034.2±3,054.0	2,763.2±2,599.3	2,871.6±2,762.8	0.738
Nutritional blood marker
Serum albumin	4.8±0.3	4.6±0.2	4.6±0.2	0.975
Triglyceride	146.5±62.3	174.9±129.4	163.7±108.3	0.301
HDL cholesterol	56.2±10.2	48.5±10.2	51.5±10.8	0.011
LDL cholesterol	104.8±32.7	98.7±33.5	101.1±33.0	0.523

Data are presented as mean±SD or n (%). SCG, Spousal caregiver; MMSE, Mini-Mental Status Examination; APOE, Apolipoprotein E; VRS, Vascular risk factor score; CDR, Clinical Dementia Rating; GDS, Geriatric Depression Scale; MNA, Mini-Nutritional Assessment; PSQI, Pittsburgh Sleep Quality Index; IPAQ, International Physical Activity Questionnaire; HDL, High-density lipoprotein; LDL, Low-density lipoprotein.

Multiple linear regression analyses were performed to examine the association among care-recipients’ MMSE score with lifestyle factors influencing cognitive decline (GDS, MNA, PSQI, and IPAQ) and nutritional blood biomarker in SCGs after controlling for age and gender of SCG, and for care-recipient education years. The Bonferroni correction was applied to multiple comparisons using p < 0.05/no. of analyses within each dependent variable (lifestyle factors influencing cognitive decline and nutritional blood biomarkers). For the analyses of nutritional blood biomarker, we additionally adjusted for physical activity, VRS, BMI, and APOE4 genotyping which could affect blood lipid profile.

To explore interactions of gender with care-recipient’s MMSE score on other variables, those variables that had associations of p < 0.1 with the MMSE score of care recipient were selected for the next analyses. Then, after adjusting for age of SCG, care-recipient’s education years, care recipient’s MMSE score, and selected variables, we performed multiple linear regression analyses with interaction terms (care-recipient’s MMSE score×gender) as independent variables and the selected variable as the dependent variable (Table 3). Additional subgroup analyses were performed according to gender to assess the main effects of care-recipient’s cognition on lifestyle factors and nutritional biomarkers. All analyses were performed using SPSS 21 software (SPSS, Inc.; Chicago, IL), and p-values < 0.05 (two-sided) were considered to indicate statistical significance. No correction for multiple comparisons was done for interaction analyses due to the exploratory nature of this study and to minimize the risk of type II errors.

Table 3

Interaction analyses of the moderating effect of the gender on associations of MMSE of care-recipient with lifestyle factors and nutritional biomarkers

Variables of SCGs	B	SE	t	p ^a
Lifestyle factors
MMSE^b x gender → GDS	0.201	0.347	0.578	0.566
MMSE^b x gender → MNA	– 0.111	0.183	– 0.605	0.548
MMSE^b x gender → PSQI	0.400	0.189	2.113	0.041^*
Nutritional blood biomarker
MMSE^b x gender → Triglyceride	3.831	5.504	0.696	0.490
MMSE^b x gender → HDL cholesterol	– 1.137	0.500	– 2.275	0.028^*

^aAdjusted for age of SCG and education years of care-recipient. ^bMMSE of care-recipient. ^*p < 0.05 (before Bonferroni correction). MMSE, Mini-Mental Status Examination; SCG, Spousal caregiver; GDS, Geriatric Depression Scale; MNA, Mini-Nutritional Assessment; PSQI, Pittsburgh Sleep Quality Index; IPAQ, International Physical Activity Questionnaire; HDL, High-density lipoprotein; LDL, Low-density lipoprotein.

RESULTS

Characteristics of the study population

Table 1 provides descriptive statistics of our sample. Of the total sample, 60.8% (n = 31) of participants were dementia caregivers; 64.7%(n = 33) of those were wives. There were no differences in age, education years, and MMSE scores between non-dementia SCGs and dementia SCGs. There was no significant correlation between care-recipient’s MMSE score and SCG’s one (r = 0.159, p = 0.276). Except for one couple, all participants lived alone as a married older couple without other family members. All participants were primary caregivers for their spouses.

In the group comparison, relative to non-dementia SCGs, dementia SCGs showed significantly higher depression scores (t = –3.608, p = 0.001) and malnutrition risk (t = 2.894, p = 0.006). When the Bon-ferroni-corrected p-value (p < 0.05/4 = 0.013) was applied, the relationships of care-recipient cognition with GDS and MNA scores remained significant. The GDS scores of dementia SCGs (15.7±7.3) suggested clinically significant depression. The mean MNA score of the dementia SCG group was 19.5±3.3, indicating a risk of malnutrition. In our sample, the nutritional status of dementia SCGs was often reduced. Indeed, our results showed that only 6.7%(n = 2) of SCGs exhibited good nutritional status; 70%were at risk of malnutrition, and 23.3%showed poor nutritional status (Table 1).

Correlation of lifestyle factors and nutritional biomarkers with care-recipient’s cognition

The care-recipient’s cognition was significantly correlated with GDS (β= –0.593, t = –4.471, p < 0.001) and MNA scores (β= 0.315, t = 2.225, p =0.031) and showed a weak association with the PSQI score (β= –0.287, t = –1.890, p = 0.065) among SCGs (Table 2). When the Bonferroni-corrected p-value (p < 0.05/4 = 0.013) was applied, the relationship between the care-recipient’s cognition and GDS score remained significant, and the association of the MNA score with the care-recipient’s MMSE score approached significance.

Table 2

Regression for the MMSE of care-recipient versus lifestyle factors and nutritional blood biomarker

	MMSE of care-recipient
Variable of SCGs	β	T	p ^a	p ^b
Lifestyle factors
GDS	–0.593	–4.471	< 0.001^*†c
MNA	0.315	2.225	0.031^*,c
PSQI	–0.287	–1.890	0.065^c
IPAQ	0.214	1.366	0.179
Nutritional blood biomarker
Serum albumin	–0.169	–1.106	0.275	0.292
Triglyceride	–0.310	–2.155	0.036^*c	0.079
HDL cholesterol	0.383	2.613	0.012^*†c	0.044^*
LDL cholesterol	0.064	0.399	0.692	0.525

^aAdjusted for age and sex of SCG and for education years of care-recipient. ^bAdjusted for age, sex, physical activity, VRS, BMI, and APOE4 genotyping of SCG and for education years of care-recipient. ^cVariables with a p value < 0.1 according to linear regression analyses were selected for interaction analyses in Table 3. ^*p < 0.05 (before Bonferroni correction). ^†p < 0.013 (Bonferroni-corrected p < 0.05/4 = 0.013 was used as a statistical threshold. MMSE, Mini-Mental Status Examination; SCG, Spousal caregiver; GDS, Geriatric Depression Scale; MNA, Mini-Nutritional Assessment; PSQI, Pittsburgh Sleep Quality Index; IPAQ, International Physical Activity Questionnaire; HDL, High-density lipoprotein; LDL, Low-density lipoprotein; PA, physical activity; VRS, Vascular risk factor score; APOE, Apolipoprotein E.

Also, the care-recipients’ cognitive levels were significantly associated with SCG’s HDL cholesterol (β= 0.383, t = 2.613, p = 0.012) and triglyceride (β= –0.310, t = –2.155, p = 0.036) (Table 2). When the Bonferroni-corrected p-value (p < 0.05/4 = 0.013) was applied, the positive association between the care-recipient’s MMSE score and the SCG’s HDL cholesterol remained significant. These significant associations with HDL cholesterol (p = 0.044), but not with triglyceride (p = 0.079), persisted after adjustment for physical activity, VRS, BMI, and APOE4 genotyping which could affect the lipid profile (Table 2).

Interaction between care-recipient’s cognition and gender in relation to lifestyle factors and nutritional biomarkers

According to the multiple linear regression model including the interaction term (care-recipient‘s MMSE score×gender), gender significantly moderated the impact of care-recipient cognition on SCG’s PSQI (B[SE] = 0.400[0.189], t = 2.113, p = 0.041) and HDL cholesterol (B[SE] = –1.137[0.500], t =–2.275, p = 0.028) (Table 3). In additional subgroup analyses, poor care-recipient’s cognition was associated with poor sleep quality and low HDL cholesterol among the wives group, but not among the husbands (Fig. 1).

Fig. 1

Plots to demonstrate the moderation effect of gender on the relationship between cognition of care-recipient and (A) HDL cholesterol and (B) PSQI in SCGs. HDL, High-density lipoprotein; PSQI, Pittsburgh Sleep Quality Index; MMSE, Mini-Mental Status Examination; SCG, Spousal caregiver.

DISCUSSION

The present study investigated the association between decreased care-recipient’s cognition with higher levels of depression and malnutrition risk among SCGs, which might contribute to SCGs’ cognitive decline. In addition, poorer cognition among care recipients has been associated with decreased HDL cholesterol and sleep quality for SCGs, with gender having a significant moderating effect on these relationships.

In this study, dementia SCGs showed more risk of malnutrition and low HDL cholesterol than did non-dementia SCGs after adjusting for multiple comparisons. Furthermore, poor cognition among care recipients was significantly correlated with low HDL cholesterol among SCGs after adjusting for age, gender, care-recipient’s education years, physical activity, VRS, BMI, and APOE4 genotyping. As the cognitive functioning of care-recipients declines, due to the increased caregiving burden, SCGs may simplify food preparation and rely more on fast food and highly processed meals at the expense of more nutritious alternatives [14]. In a previous study [38] using the same MNA questionnaire and conducted with Korean adults aged 65 years or older who visited a health care center, 88%were well nourished, whereas only 11.8%of those in the present study were well nourished. This suggests that SCGs are more likely to be at risk of malnutrition if their spouse have cognitive impairment, or even other problems such as insomnia and anxiety disorder.

Nutritional status has been studied extensively as a modifiable risk factor for cognitive decline in late life [15 , 18]. A previous study reported that the SCG’s nutritional status have significant association with the nutritional status of the care recipient [39]. Malnutrition as measured by the MNA predicted a faster rate of cognitive, functional, and neuropsychiatric impairment [16 , 41] in patients with dementia, which could increase the caregiving burden of SCGs. This association underlines the importance of nutritional monitoring in the context of dementia caregiving because malnutrition can co-occur with other morbidities and care difficulties and may compromise the quality of life and cognitive decline of both older people with cognitive impairment and SCGs [42]. It is also likely that many SCGs have shared their spouse’s lifestyle, especially dietary pattern, for three decades or longer [43] and this trend has continued throughout their life [43]. This unhealthy dietary pattern, which already resulted in one spouse’s cognitive impairment, is likely to continue after the other spouse becomes a caregiver. So, it might seem to correlate with care-recipient’s cognitive decline and the SCG’s malnutrition risk. However, this does not paint the entire picture, since the MNA Questionnaire has many questions that reflect changes in the last 3 months.

We found that worse cognition among care recipients was significantly associated with decreased HDL cholesterol in SCGs. Consistent with our findings, previous studies reported that caregiving-related chronic stress might also result in decreased HDL cholesterol [44, 45]. HDL cholesterol is a powerful lipid predictor of cardiovascular disease [46] which significantly affect cognitive decline [47, 48]. Furthermore, low HDL cholesterol was associated with cognitive decline over a 4-year follow-up in CN older adults [49] as well as low gray matter volume in CN older adults [50]. Taken together, these findings suggest that low HDL cholesterol may serve as a risk factor for cognitive decline in SCGs.

The interaction between care recipient’s cognition and gender had a significant effect on HDL cholesterol and PSQI. Only in the wives’ group was care-recipients’ cognition significantly associated with SCGs’ PSQI and HDL cholesterol. Wives reported more caregiving distress compared to husbands [51], which seems to affect sleep quality [52]. Consistent with this finding, a previous study revealed that poor sleep quality (long and short sleep duration) had a significant association with low HDL cholesterol among women, but not among men [53]. SCGs may experience poor sleep as a result of the care recipient‘s sleep disturbances such as insomnia, sundowning, and behavioral problems [54]. Taken together, these findings suggest a possible relationship among the care recipient’s cognition with the HDL cholesterol and poor sleep quality of SCGs. Furthermore, greater attention should be paid to the potential for cognitive deterioration and cardiovascular risk in wives who are SCGs. However, since we did not perform a correction for multiple comparisons in the moderation analysis of gender, caution must be exercised when interpreting the actual significance of these correlations, and confirmation of these results with larger and independent data sets is warranted.

The lower the care-recipient’s cognitive functioning, the greater the depression and caregiving burden for SCGs. Depression is a common mood disturbance among dementia caregivers [5]. Consistent with our results, a naturalistic 6-year longitudinal cohort study reported that dementia SCGs were four times as likely to experience depression as non-caregivers [55]. Depressive symptoms and subjective stress have been associated with caregiver risk for cardiovascular disease [2, 56] and poor cognitive functioning [12, 57]. SCGs may reduce participation in community activities because of caregiving demands, and social interactions with other family and friends may also decrease in response to the care-recipient’s behavioral and cognitive problems [10, 11].

Strengths and limitations

This study has several strengths. Fist, we used variables that could be easily assessed in the clinic. Second, we measured several lifestyle variables that could be modified in later life. Thus, it will be possible to check the results easily and quickly and intervene in malnutrition and other lifestyle factors influencing cognitive decline in SCGs.

Several limitations of this study should be noted. First, the subjects in this analysis were physically and cognitively healthy and functionally independent enough to come to a university hospital with a patient with cognitive decline. This may reduce the generalizability of our results to more diverse populations. It could also result in potentially biasing the results toward the null. Therefore, the risk for malnutrition and lifestyle factors influencing cognitive decline may potentially be underestimated. Second, this study had a relatively small sample size. However, we found significant associations even after adjusting for various cardiovascular risk factors, BMI, physical activity, and other variables that could affect HDL cholesterol. Third, we did not consider other factors that may have an effect on nutritional status (e.g., use of dentures, difficulty in chewing food etc.) in older adults. But, since we only included subjects who were physically and cognitively healthy, this effect might be negligible.

In summary, the present study found that the worse care recipients’ cognitive function became, the higher was the risk that SCGs might develop depression and malnutrition, which could potentially contribute to their own cognitive decline. It provides substantial indications that depression and malnutrition which could affect cognitive decline should be well assessed and monitored among SCGs for patient with cognitive impairment.

Footnotes

ACKNOWLEDGMENTS

This Study funded by the Ministry of Science and ICT, Republic of Korea (grant no.: NRF-2019M3A9F3065867) and the Jisan Cultural Psychiatry Research Fund (2020) from the Korean Neuropsychiatric Research Foundation.

The authors thank the extraordinary participants and families of this study who made our work possible. We also acknowledge the great work of all the research assistants and study staff.

Authors’ disclosures available online ().

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