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ASSOCIATION BETWEEN COMORBIDITY INDICES AND FUNCTIONAL AUTONOMY IN INDIVIDUALS WITH COGNITIVE IMPAIRMENT: A SYSTEMATIC REVIEW

 

M.N. Temedda1,2,3, A. Garnier-Crussard1,2,4, C. Mouchoux1,2,3,5, V. Dauphinot1,2

 

1. Clinical and Research Memory Centre of Lyon (CMRR), Geriatrics Unit, Research Clinical Centre (CRC) – VCF (Aging Brain Frailty), Hospices civils de Lyon, F-69100 Villeurbanne, France; 2. University Lyon 1, F-69000 Lyon, France; 3. Pharmaceutical Unit, Charpennes Hospital, University Hospital of Lyon, F-69100 Villeurbanne, France; 4. INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Brain Dynamics and Cognition Team, University Lyon 1, F-69000 Lyon, France;
5. Normandie Univ, UNICAEN, INSERM, U1237, PhIND «Physiopathology and Imaging of Neurological Disorders», NeuroPresage Team, Institut Blood and Brain @ Caen-Normandie, Cyceron, F-14000 Caen, France.

Corresponding Author: Dr. Virginie Dauphinot, Clinical and Research Memory Center, Hôpital des Charpennes, 27 rue Gabriel Péri, 69100 Villeurbanne, France. Tel: +33 (0) 472432203. Fax: +33 (0) 472432054, E-mail address: virginie.dauphinot@chu-lyon.fr

J Prev Alz Dis 2024;
Published online March 6, 2024, http://dx.doi.org/10.14283/jpad.2024.51

 


Abstract

This systematic review aimed to examine whether higher comorbidity burden, as assessed by comorbidity indices, was associated with a functional autonomy decline in individuals with cognitive impairment. The search was conducted in the following databases: PubMed/MEDLINE, ScienceDirect, Cochrane, and Embase. Both cross-sectional and longitudinal studies that examined the relationship between comorbidity indices and scales measuring activities of daily living (ADL) in individuals with cognitive impairment were included. The quality assessment tool for observational cohort and cross-sectional studies of the National Institutes of Health (NIH) was used. Overall, 12 studies were included, among which three were longitudinal. Significant association was frequently reported by cross-sectional designs (n=7 studies) and only one study reported a significant longitudinal association. This longitudinal study repeatedly assessed both comorbidity burden and functional autonomy, and considered comorbidity burden as a time-varying covariate. Considering comorbidity burden as a time varying covariate may deal with the dynamic nature of comorbidity burden over time, and conducting repeated assessments during the follow-up using both comorbidity index and ADL scales may increase their sensitivity to reliably measure comorbidity burden and functional autonomy decline over time. In conclusion, a higher comorbidity index was associated with a lower level of functional autonomy in people with cognitive impairment. This relationship seems to be dynamic over time and using comorbidity indices and ADL scales only once may not deal with the fluctuation of both comorbidity burden and functional autonomy decline. To cope with complexity of this relationship this review highlights some methodological approaches to be considered.

Key words: Comorbidity burden, comorbidity index, functional autonomy decline, dementia, cognition.


 

Introduction

According to the World Health Organization (WHO), the global prevalence of dementia was approximately 55 million in 2019, and it is expected to triple by 2050 (1). Individuals with dementia and mild cognitive impairment (MCI) are more likely to experience a higher comorbidity burden compared to normal cognitive individuals (NCI), which could hasten cognitive decline (2–5).
Studies have found that comorbidity burden in older NCI increases the risk of decline in function defined as an inability to perform autonomously activities of daily living (ADL) (6–9). Nevertheless, this relationship in the population with dementia remains inconsistent (10–12). For instance, according to a one-year prospective cohort reported by Slaughter et al. higher comorbidity burden measured by the Charlson comorbidity index (CCI) was associated with two separate dimensions of functional autonomy (walking and eating disability) in patients with MCI (10). Nelis et al. also did so in a study where the number of medical conditions included for the calculation of the CCI was used to describe comorbidity burden and separate three dimensions of functional autonomy (poor mobility, self-care, and usual activities) (11). However, no significant association was found in a 6-month prospective cohort reported by Hiroyuki et al. who measured comorbidity burden by the total number of endorsed categories of the cumulative illness rating score for geriatrics (CIRS-G) and functional autonomy level was measured by the Physical Self-Maintenance Scale (PSMS) (12).
The heterogeneity in the results of such studies may be related to the heterogeneity in the assessment of comorbidity burden but also functional autonomy. The most accurate description of comorbidities is based on indices, such as the CCI, that consider both number and severity of comorbidities when assessing comorbidities (13, 14), and using ADL scales to assess functional autonomy allows the evaluation of the effect on overall function (15). To the best of our knowledge only one systematic review conducted in January 2016 that investigated the association between comorbidity burden and functional autonomy among late-onset Alzheimer’s Disease (LOAD) (16). In this previous systematic review, seven studies were included (17–23), and among these one did not use a comorbidity index (23), and another used the Clinical Dementia Rating – sum of boxes that encompassed more than just ADL precluding conclusions as to the association between comorbidities and functional autonomy (20). Among the other studies one was a longitudinal study (did not find a significant association (22)) and four were cross-sectional (three of which found a significant association (18, 19, 21)), which did not allow to interpret causality link between higher comorbidity burden and functional autonomy decline.
We therefore conducted a systematic review to examine whether higher comorbidity burden, as assessed by comorbidity indices, was associated with a functional autonomy decline in individuals with cognitive impairment.

 

Methods

This systematic review is reported according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (24).

Search strategy and study selection

We conducted the search in November, 2022 using key words and MeSH terms, in the following databases: PubMed/MEDLINE, ScienceDirect, Cochrane, and Embase. However, search equations were adapted to each database (supplementary appendix 1). Although we distinguish comorbidity and multimorbidity, the use of the term “multimorbidity” during the search process was unavoidable; prior to 2018, both terms were used interchangeably (25). To cope with limitation of our search strategy and to ensure the exhaustivity of the search, a snowballing strategy gathering articles through the reference list of included papers was used (26).

Eligibility criteria for selection of studies

Studies involved individuals with any stage of cognitive impairment, regardless of etiologic diagnosis. The comorbidity burden had to be measured by a comorbidity index, which could be validated in either the dementia population, geriatric population, or general population. In contrast, studies used disease count, the total number of endorsed disease categories or comorbidity index specific to one particular medical condition, such as the diabetes complications severity index, were excluded. Functional autonomy level had to be measured by ADL scales including basic (b) ADL, instrumental (i) ADL, or a combination of the both (b/iADL). Studies that separately examined few dimensions of functional autonomy, such as walking or eating, were excluded. The review included both cross-sectional and longitudinal studies that examined the relationship between comorbidity index and functional autonomy using quantitative measures. Studies had to be written in English or French language. The screening of titles and abstracts as well as full-text reviewing were ensured independently by a PhD student (MNT) and an epidemiologist (VD) based on eligibility criteria. Disagreements were resolved through discussion with the research team.

Data extraction

For each study, the following data were extracted: general characteristics, population characteristics and information about the studied association. However, general characteristics of studies were the first author, year of publication, setting of study, design and the quality of study. Data related to characteristics of population were the total number of sample, mean age with the standard deviation (SD), percentage of female, and the etiologic diagnosis and/or severity stages of cognitive impairment. Regarding the studied association, comorbidity indices, ADL scales, statistical methods used for analysis and a quantitative indicator of the relationship between comorbidity index and ADL scale (odds ratio [OR], regression coefficient, correlation coefficient, p-value, etc.), were extracted. Authors were contacted if there were any ambiguities. Data were summarized in one table (table 1), and results were considered statistically significant if the p-value was less than 0.05.

Table 1. Summary of included studies (n = 12)

AD: Alzheimer’s Disease; VD: Vascular Dementia; PD: Parkinson’s Disease; MCI: Mild Cognitive Impairment; LBD: Lewy Body Dementia; CIRS-G: Cumulative Illness Rating Scale-Geriatric; CIRS: Cumulative Illness Rating Scale; CCI: Charlson Comorbidity Index; GMHR: General Medical Health Rating; DAD: Disability Assessment of Dementia; PGDRS-P: Psychogeriatric Dependency Rating Scale- Physical subscale; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living; BI: Barthel index; PSMS: Physical Self-Maintenance Scale; FAQ: Functional Activities Questionnaire; PPT: Physical Performance Test; HUI: Health Utilities Index; B: Unstandardized coefficient; β: Standardized coefficient; r: Correlation coefficient; OR: Odds Ratio; CI: Confidence Interval; MMSE: Mini-Mental State Examination; NR: Not Reported; NS: No significant; CI: Confident interval; SE: Standard error

 

Risk of bias in individual studies

The quality assessment tool for observational cohort and cross-sectional studies of the National Institutes of Health (NIH) was used (https://www.nhlbi.nih.gov/health-topics/study-quality-assessment-tools) (27). For each included study, the following criteria were evaluated: the clarity of objective, the definition of study population, the participation rate, the uniformity of subject selection, the sample size justification, whether assessment of exposure was prior to outcome, timing between exposure and outcome measurements, the analysis of exposure levels, the repeated assessment of exposure, validity and reliability of the assessment tool (exposure and outcome), blinding of outcome assessors, the loss to follow-up rate, and adjustment for potential confounding variables. This tool allowed us to determine whether the study was of poor (0-4), fair (5-10), or good (11-14) quality. Quality assessment and data extraction were conducted by MNT and verified by VD.

 

Results

Study selection

A total of 1092 records were identified, among which 83 were gathered by the snowballing strategy. After removing 316 duplicates, the titles and abstracts of 776 records were screened. All 267 reports were sought for retrieval were assessed for eligibility, and after full-text review 255 reports were excluded; 12 studies were included (figure 1).

Figure 1. Flow Chart

 

Study and population characteristics

Included studies were published between 1999 and 2022, and were most frequently conducted in the USA (n = 6). We excluded from the description of participants the patients included in the study reported by Oosterveld et al. (19) (n = 213) since they were also included in the study reported by Wubben et al. as a subgroup (28). The total number of participants was 3141; the mean ± SD age was 76.6 ± 5.7 years, and 64% were female (King et al. did not provide information about gender (29)). The sample size of studies ranged from 76 to 697 patients, and 7/11 studies (n = 2081) described the etiologic diagnosis of cognitive impairment (table 1); the most frequent etiologic diagnosis was Alzheimer’s disease (AD; n = 1567, 75.3%) and the least frequent was Parkinson’s disease (PD; n = 76, 3.7%).

Description of the application of comorbidity indices

The CIRS-G was most frequently used (n = 6); three studies used the CCI, two studies used the General Medical Health Rating (GMHR), and one study used the CIRS (table 1).
Four studies used the CIRS-G as a physical comorbidity index by scoring 13/14 items, excluding the item “psychiatric illnesses” (17–19, 28). They excluded this item as dementia is included in the psychiatric item of CIRS-G. Solomon et al. chose to use all 14 items of the score without considering dementia diagnosis for the item “psychiatric illnesses” (22). To establish association between CIRS-G and functional autonomy scales, three studies used the CIRS-G total score (the sum of individual organ system scores) (17, 19, 28) , two studies used the CIRS-G as a severity index (total score/ total number of endorsed categories) (18, 22), and one study used the two summary measures (29). One study, reported by Mariani et al., did not use the geriatric version of CIRS (CIRS-G) but the CIRS published by Parmalee et al. (30). Regarding the CCI, two studies used the original version (12, 31) and one study used the Van Doorn version (32) (table 1; supplementary appendix 2).

Functional outcomes

Nine functional autonomy scales were used by the included studies (table 1). The majority of scales (5/9) measured the bADL: Psychogeriatric Dependency Rating Scale- Physical subscale (PGDRS-P) (19, 28, 31), Katz bADL (22), self-care Health Utilities Index (HUI) (18), Physical Performance Test (PPT) (29), and Barthel index (BI) (32). Two scales measured the iADL: Lawton iADL (22, 30), and Functional Activities Questionnaire (FAQ) (17). Two scales measured both b/iADL: Disability Assessment of Dementia (DAD) (19) and PSMS (12).

Risk of bias

Two longitudinal studies were rated as having good quality according to the NIH assessment tool (22, 28), while the remaining studies were graded as fair. Only two studies used comorbidity indices as categorical variables (30, 31). Only one study performed multiple assessments of comorbidity indices over time (4 times) (28). Given the majority of the studies were cross-sectional (9/12), it was not possible to determine the proportion of participants lost to follow-up, and thus it was considered as not applicable for the NIH. However, one longitudinal study initially intended to span a duration of 12 months had to be prematurely stopped at 6 months due to a significant dropout rate, with more than 20% of participants who were lost to follow-up after at this point (12). Only two studies performed more than one assessment of functional autonomy: two time points in the study reported by Hiroyuki et al. (12), and four time points in the study reported by Wubben et al. (28).

Association between comorbidity indices and functional autonomy

Overall, cross-sectional associations were reported by 11 studies and longitudinal associations were reported by three studies. Nine studies used only cross-sectional association, one study used only longitudinal association and two studies used both. Seven studies reported a significant association between higher comorbidity indices and a lower functional autonomy level (table 1).

Studies used only cross-sectional association (n = 9 studies)

Four studies used CIRS-G score to evaluate comorbidity burden and three of them reported a significant association (18, 19, 29), a lower functional autonomy level was significantly associated with higher CIRS-G total score (19, 29) and CIRS-G considered as a severity index (18). One study reported by Mariani et al. did not report a significant association between CIRS and IADL (30).
Two studies used CCI to evaluate comorbidity burden and only one reported a significant association with a lower functional autonomy level (31). This significant association was found in the univariate linear regression model but it did not remain significant in the multivariable linear regression model.
Two studies used GMHR to evaluate comorbidity burden, and both reported a significant association between higher comorbidity burden (lower GMHR) and a lower functional autonomy level (21, 33). Specifically, one study examined this relationship across different stages of cognitive impairment, as measured by the Mini-Mental State Examination (MMSE), and found a significant association in all three stages: MMSE >20, MMSE 10-19, and MMSE <10 (21).

Studies used both cross-sectional and longitudinal association (n = 2 studies)

Two studies used both cross-sectional and longitudinal analysis (12, 28). In the study reported by Hiroyuki et al. a significant association was found only at cross-sectional level between lower functional autonomy and higher comorbidity burden measured by the CCI. Regarding the longitudinal analysis, a trend towards a significant association between CCI and subsequent decline in functional autonomy was reported after 6 months of follow-up with a β of -0.16 (95%CI [-0.76;0.03]) and p-value = 0.072.
In the study reported by Wubben et al. the CIRS-G total score was used to measure comorbidity burden (28). At a cross-sectional level, a significant association was found between lower functional autonomy and higher comorbidity burden. Regarding the longitudinal analysis, two associations were reported. When considering CIRS-G as a time invariant baseline covariate, it was significantly associated with functional autonomy decline over time with a B of -0.3 (SE = 0.12). Also, when considering CIRS-G as a time varying covariables, it was significantly associated with functional autonomy decline over time with B of -1.1 (SE = 0.23). However, the association was weaker when CIRS-G was considered as time invariant baseline covariate compared to when it was considered as time varying covariate.

Studies used only longitudinal association (n = 1 study)

The study reported by Solomon et al. examined the association between CIRS-G severity score and the relative annual rate of change in functional autonomy measured by both Katz bADL and Lawton iADL, and a trend towards a significant association was reported with respective OR of 2.7 (95%CI [0.7;9.6]) and 1.8 (95%CI [0.3;10.1]; p-value was not reported) (22).

 

Discussion

Overall, 12 studies reporting association between comorbidity index and functional autonomy were included in the systematic review, among which three were longitudinal. The majority of studies provide evidence that higher comorbidity index is associated with lower functional autonomy level in people with cognitive impairment.
Significant association was frequently reported by cross-sectional designs (12, 18, 19, 21, 28, 29, 33). When examining the studied relationship using longitudinal designs (12, 22, 28), findings varied. Associations that involved comorbidity burden as invariant over time to predict functional autonomy decline were either significant but weak (28) or found only a trend towards a significant effect (12, 22); in contrast, a longitudinal study reported by Wubben et al., in which both comorbidity burden and functional autonomy were repeatedly assessed and comorbidity burden was considered as a time-varying covariate, found a significant association (28). These findings suggest that the impact of comorbidity burden on functional autonomy may diminish over time, and therefore considering comorbidity burden as time-varying may provide a better explanation of its impact on functional autonomy decline over time. This is further supported by Leoutsakos et al. who investigated another outcome in AD, and who found in a longitudinal study that comorbidity burden when considered as time-varying may better explain cognitive decline than when it is considered as invariant (20).
Comorbidity burden and functional autonomy decline are both variable over time for various reasons (28, 34, 35). Interaction between diseases (36, 37) and the effect of treatments on the stability of each disease (38–41) (whether individuals are treated or not, and whether they receive an appropriate treatment or not) are among factors that may increase fluctuation of comorbidity burden over time. Specifically, in people with cognitive impairment some factors may contribute to the variability over time of comorbidity burden as the poor level of treatment adherence and the lower access to health services (42). In addition to the progressive functional decline related to cognitive impairment, functional decline may be further accentuated by the dynamic nature of comorbidity burden in two ways (8, 16, 34, 43, 44): on the one hand some comorbidities have been directly associated with lower functional autonomy level independently from the cognitive state (8, 43, 44), and, on the other hand, comorbidity burden can further reduce indirectly functional autonomy level by exacerbating the current cognitive decline (16, 45). However, the methodology used by Wubben et al. seems to take into account the complexity of this relationship by two aspects (28): considering comorbidity burden as a time varying covariate may deal with the dynamic nature of comorbidity burden over time, and conducting repeated assessments during the follow-up using both comorbidity index and ADL scales may increase their sensitivity to reliably measure comorbidity burden and functional autonomy decline over time and better explain their relationship.
Another important point reported by this review is that significant association was frequently found when measuring comorbidity burden by either CIRS-G (18, 19, 28, 29) or GMHR (21, 33) compared to one study reported significant association using the CCI (12). Both CIRS-G and GMHR can consider medication use, the stability of diseases and some geriatric syndromes (depression, urinary incontinence and medication number) (46) to determine the severity level of comorbidity burden. In contrast, the CCI considers only weight related to each condition to determine severity level of comorbidity burden. There is no consensus in the literature regarding the best comorbidity index to measure comorbidity burden, but we believe that considering medication, the stability of diseases and geriatric syndromes may reliably measure the comorbidity burden. However, the limited number of included studies did not allow us to suggest which comorbidity index may better explain functional autonomy decline.
The most important limitation of the present study was the heterogeneity between studies (population, comorbidity indices, b/iADL scales, and design) that did not encourage us to perform a meta-analysis, but this systematic review suggests that an appropriate assessment of comorbidity burden and functional autonomy can elucidate the negative impact of comorbidity burden on functional autonomy decline. Another limitation was that the report of the comorbidity may not be exhaustive in individuals with cognitive impairment, which may affect the use of comorbidity indices in some included studies. To cope with this limitation, future studies may ameliorate the assessment of comorbidity burden by using different sources of information to gather medical conditions (prescriptions, caregivers/families and health care providers) as the case of GMHR.
In conclusion, a higher comorbidity index was associated with a lower level of functional autonomy in people with cognitive impairment. This relationship seems to be dynamic over time and using comorbidity indices and ADL scales only once may not deal with the fluctuation of both comorbidity burden and functional autonomy decline. To cope with complexity of this relationship this review highlights some methodological approaches to be considered. Functional autonomy decline detrimentally impacts life quality and heightens mortality risk in individual with cognitive impairment. In contrast, comorbidity burden represents a potentially modifiable factor, and effective management of comorbidities may mitigate the severity stages of functional autonomy decline.

 

Conflict of Interest: The authors declare no competing interests; this manuscript has not been published or submitted elsewhere. All authors have contributed significantly, and all authors are in agreement with the content of the manuscript.

Authors’ contributions: Mohamed Nour Temedda: Conceptualization, Methodology, Data analysis, Writing – Original Draft. Antoine Garnier-Crussard: Writing – Review & Editing. Christelle Mouchoux: Conceptualization, Methodology, Analysis, Writing – Original Draft, Supervision. Virginie Dauphinot: Conceptualization, Methodology, Data analysis, Writing – Original Draft, Supervision.

Funding: None.

Acknowledgements: Mohamed Nour Temedda (PhD student) is supported by “France Médéric d’Alzheimer” (Research scholarship). We thank the Dr Philip Robinson (Hospices Civils de Lyon) for help in manuscript preparation.

Ethical standards: Not applicable.

 

SUPPLEMENTARY MATERIAL

 

References

1. The Lancet Public Health. Reinvigorating the public health response to dementia. Lancet Public Health. 2021;6(10):e696. doi:10.1016/S2468-2667(21)00215-2
2. Chen TB, Yiao SY, Sun Y, et al. Comorbidity and dementia: A nationwide survey in Taiwan. PLOS ONE. 2017;12(4):e0175475. doi:10.1371/journal.pone.0175475
3. Poblador-Plou B, Calderón-Larrañaga A, Marta-Moreno J, et al. Comorbidity of dementia: a cross-sectional study of primary care older patients. BMC Psychiatry. 2014;14:84. doi:10.1186/1471-244X-14-84
4. Kao SL, Wang JH, Chen SC, Li YY, Yang YL, Lo RY. Impact of Comorbidity Burden on Cognitive Decline: A Prospective Cohort Study of Older Adults with Dementia. Dement Geriatr Cogn Disord. 2021;50(1):43-50. doi:10.1159/000514651
5. Vance D, Larsen KI, Eagerton G, Wright MA. Comorbidities and cognitive functioning: implications for nursing research and practice. J Neurosci Nurs. 2011;43(4):215-224. doi:10.1097/JNN.0b013e3182212a04
6. Stuck AE, Walthert JM, Nikolaus T, Büla CJ, Hohmann C, Beck JC. Risk factors for functional status decline in community-living elderly people: a systematic literature review. Social Science & Medicine. 1999;48(4):445-469. doi:10.1016/S0277-9536(98)00370-0
7. Falvey JR, Gustavson AM, Price L, Papazian L, Stevens-Lapsley JE. Dementia, Comorbidity, and Physical Function in the Program of All-Inclusive Care for the Elderly. J Geriatr Phys Ther. 2019;42(2):E1-E6. doi:10.1519/JPT.0000000000000131
8. Aubert CE, Kabeto M, Kumar N, Wei MY. Multimorbidity and long-term disability and physical functioning decline in middle-aged and older Americans: an observational study. BMC Geriatrics. 2022;22(1):910. doi:10.1186/s12877-022-03548-9
9. Buckinx F, Peyrusqué E, Kergoat MJ, Aubertin-Leheudre M. Reference Standard for the Measurement of Loss of Autonomy and Functional Capacities in Long-Term Care Facilities. J Frailty Aging. 2023;12(3):236-243. doi:10.14283/jfa.2023.4
10. Slaughter SE, Hayduk LA. Contributions of environment, comorbidity, and stage of dementia to the onset of walking and eating disability in long-term care residents. J Am Geriatr Soc. 2012;60(9):1624-1631. doi:10.1111/j.1532-5415.2012.04116.x
11. Nelis SM, Wu YT, Matthews FE, et al. The impact of co-morbidity on the quality of life of people with dementia: findings from the IDEAL study. Age Ageing. 2019;48(3):361-367. doi:10.1093/ageing/afy155
12. Tanaka H, Nagata Y, Ishimaru D, Ogawa Y, Fukuhara K, Nishikawa T. Clinical factors associated with activities of daily living and their decline in patients with severe dementia. Psychogeriatrics. 2020;20(3):327-336. doi:10.1111/psyg.12502
13. Rozzini R, Frisoni GB, Ferrucci L, et al. Geriatric Index of Comorbidity: validation and comparison with other measures of comorbidity. Age Ageing. 2002;31(4):277-285. doi:10.1093/ageing/31.4.277
14. Aslam F, Khan NA. Tools for the Assessment of Comorbidity Burden in Rheumatoid Arthritis. Front Med (Lausanne). 2018;5:39. doi:10.3389/fmed.2018.00039
15. Marshall GA, Amariglio RE, Sperling RA, Rentz DM. Activities of daily living: where do they fit in the diagnosis of Alzheimer’s disease? Neurodegener Dis Manag. 2012;2(5):483-491. doi:10.2217/nmt.12.55
16. Haaksma ML, Vilela LR, Marengoni A, et al. Comorbidity and progression of late onset Alzheimer’s disease: A systematic review. PLOS ONE. 2017;12(5):e0177044. doi:10.1371/journal.pone.0177044
17. Tekin S, Fairbanks LA, O’Connor S, Rosenberg S, Cummings JL. Activities of daily living in Alzheimer’s disease: neuropsychiatric, cognitive, and medical illness influences. Am J Geriatr Psychiatry. 2001;9(1):81-86.
18. Doraiswamy PM, Leon J, Cummings JL, Marin D, Neumann PJ. Prevalence and impact of medical comorbidity in Alzheimer’s disease. J Gerontol A Biol Sci Med Sci. 2002;57(3):M173-177. doi:10.1093/gerona/57.3.m173
19. Oosterveld SM, Kessels RPC, Hamel R, et al. The influence of co-morbidity and frailty on the clinical manifestation of patients with Alzheimer’s disease. J Alzheimers Dis. 2014;42(2):501-509. doi:10.3233/JAD-140138
20. Leoutsakos JMS, Han D, Mielke MM, et al. Effects of general medical health on Alzheimer’s progression: the Cache County Dementia Progression Study. Int Psychogeriatr. 2012;24(10):1561-1570. doi:10.1017/S104161021200049X
21. Lyketsos CG, Galik E, Steele C, et al. The General Medical Health Rating: a bedside global rating of medical comorbidity in patients with dementia. J Am Geriatr Soc. 1999;47(4):487-491. doi:10.1111/j.1532-5415.1999.tb07245.x
22. Solomon A, Dobranici L, Kåreholt I, Tudose C, Lăzărescu M. Comorbidity and the rate of cognitive decline in patients with Alzheimer dementia. Int J Geriatr Psychiatry. 2011;26(12):1244-1251. doi:10.1002/gps.2670
23. Melis RJF, Marengoni A, Rizzuto D, et al. The influence of multimorbidity on clinical progression of dementia in a population-based cohort. PLoS One. 2013;8(12):e84014. doi:10.1371/journal.pone.0084014
24. PRISMA_update_protocol_20180214.pdf. Published online February 14, 2018. Accessed July 27, 2023. https://osf.io/https://osf.io/2v7mk
25. Nicholson K, Makovski TT, Griffith LE, Raina P, Stranges S, van den Akker M. Multimorbidity and comorbidity revisited: refining the concepts for international health research. J Clin Epidemiol. 2019;105:142-146. doi:10.1016/j.jclinepi.2018.09.008
26. Falagas ME, Pitsouni EI, Malietzis GA, Pappas G. Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. FASEB J. 2008;22(2):338-342. doi:10.1096/fj.07-9492LSF
27. Ma LL, Wang YY, Yang ZH, Huang D, Weng H, Zeng XT. Methodological quality (risk of bias) assessment tools for primary and secondary medical studies: what are they and which is better? Military Medical Research. 2020;7(1):7. doi:10.1186/s40779-020-00238-8
28. Wubben N, Haaksma M, Ramakers IHGB, et al. A comparison of two approaches for modeling dementia progression in a changing patient context. Int J Geriatr Psychiatry. 2022;37(5). doi:10.1002/gps.5706
29. King LA, Priest KC, Nutt J, et al. Comorbidity and functional mobility in persons with Parkinson disease. Arch Phys Med Rehabil. 2014;95(11):2152-2157. doi:10.1016/j.apmr.2014.07.396
30. Mariani E, Monastero R, Ercolani S, et al. Influence of comorbidity and cognitive status on instrumental activities of daily living in amnestic mild cognitive impairment: results from the ReGAl project. Int J Geriatr Psychiatry. 2008;23(5):523-530. doi:10.1002/gps.1932
31. van Rossum ME, Koek HL. Predictors of functional disability in mild cognitive impairment and dementia. Maturitas. 2016;90:31-36. doi:10.1016/j.maturitas.2016.05.007
32. Boltz M, Resnick B, Kuzmik A, et al. Pain Incidence, Treatment, and Associated Symptoms in Hospitalized Persons with Dementia. Pain Manag Nurs. 2021;22(2):158-163. doi:10.1016/j.pmn.2020.08.002
33. Samus QM, Mayer L, Onyike CU, et al. Correlates of functional dependence among recently admitted assisted living residents with and without dementia. J Am Med Dir Assoc. 2009;10(5):323-329. doi:10.1016/j.jamda.2009.01.004
34. Meghani SH, Buck HG, Dickson VV, et al. The Conceptualization and Measurement of Comorbidity: A Review of the Interprofessional Discourse. Nurs Res Pract. 2013;2013:192782. doi:10.1155/2013/192782
35. Wadley VG, Crowe M, Marsiske M, et al. Changes in everyday function among individuals with psychometrically defined Mild Cognitive Impairment in the ACTIVE Study. J Am Geriatr Soc. 2007;55(8):1192-1198. doi:10.1111/j.1532-5415.2007.01245.x
36. Quiñones AR, Markwardt S, Thielke S, Rostant O, Vásquez E, Botoseneanu A. Prospective Disability in Different Combinations of Somatic and Mental Multimorbidity. J Gerontol A Biol Sci Med Sci. 2018;73(2):204-210. doi:10.1093/gerona/glx100
37. Jiang X, Wang L, Morgenstern LB, Cigolle CT, Claflin ES, Lisabeth LD. New Index for Multiple Chronic Conditions Predicts Functional Outcome in Ischemic Stroke. Neurology. 2021;96(1):e42-e53. doi:10.1212/WNL.0000000000010992
38. Zhuang S, Wang HF, Li J, Wang HY, Wang X, Xing CM. Renin-angiotensin system blockade use and risks of cognitive decline and dementia: A meta-analysis. Neurosci Lett. 2016;624:53-61. doi:10.1016/j.neulet.2016.05.003
39. Tully PJ, Hanon O, Cosh S, Tzourio C. Diuretic antihypertensive drugs and incident dementia risk: a systematic review, meta-analysis and meta-regression of prospective studies. J Hypertens. 2016;34(6):1027-1035. doi:10.1097/HJH.0000000000000868
40. Fox C, Smith T, Maidment I, et al. Effect of medications with anti-cholinergic properties on cognitive function, delirium, physical function and mortality: a systematic review. Age Ageing. 2014;43(5):604-615. doi:10.1093/ageing/afu096
41. Liu L, Jia L, Jian P, et al. The Effects of Benzodiazepine Use and Abuse on Cognition in the Elders: A Systematic Review and Meta-Analysis of Comparative Studies. Front Psychiatry. 2020;11:00755. doi:10.3389/fpsyt.2020.00755
42. Bunn F, Burn AM, Goodman C, et al. Comorbidity and dementia: a scoping review of the literature. BMC Medicine. 2014;12(1):192. doi:10.1186/s12916-014-0192-4
43. Castellanos-Perilla N, Borda MG, Fernández-Quilez Á, Aarsland V, Soennesyn H, Cano-Gutiérrez CA. Factors associated with functional loss among community-dwelling Mexican older adults. Biomedica. 2020;40(3):546-556. doi:10.7705/biomedica.5380
44. Gardener EA, Huppert FA, Guralnik JM, Melzer D. Middle-aged and mobility-limited: prevalence of disability and symptom attributions in a national survey. J Gen Intern Med. 2006;21(10):1091-1096. doi:10.1111/j.1525-1497.2006.00564.x
45. Valletta M, Vetrano DL, Calderón-Larrañaga A, et al. Association of mild and complex multimorbidity with structural brain changes in older adults: A population-based study. Alzheimers Dement. Published online January 3, 2024. doi:10.1002/alz.13614
46. Canaslan K, Ates Bulut E, Kocyigit SE, Aydin AE, Isik AT. Predictivity of the comorbidity indices for geriatric syndromes. BMC Geriatr. 2022;22(1):440. doi:10.1186/s12877-022-03066-8

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