Factors associated with severe cognitive decline in community-dwelling older persons in Cameroon (Sub-Saharan African)

Severe cognitive decline (SVD) is a major cause of dependency in older people. The aim of this study was to identify factors associated with severe cognitive decline, as assessed by the mini-mental state examination (MMSE), in community-dwelling adults aged 55 + in Cameroon.

Method: Data are from a cross-sectional survey carried out in Cameroon. The Cognitive status was assessed using the MMSE and a score of 18/30 or lower is considered as a proxy of severe cognitive decline

Result: A total of 403 adults participated in the study. Of these, 16 (3.9%) had an MMSE score < 18 and were considered to have severe cognitive decline. The rate of severe cognitive decline increased with rising age, from 2.1% in those aged 55 to 64 years, to 3.3% in those aged 65 to 74, and 11% in those aged 75 and older. The factors associated with severe cognitive decline (MMSE score < 18) by multivariate analysis in our population are level of education (OR 0.10 (95%CI 0.02–0.37), p < 0.01), body mass index (OR 0.88 (95%CI 0.78–0.99), p = 0.03). and IADL score (OR 0.12 (95%CI 0.03–0.38), p < 0.001).

Conclusion: The three main factors associated with cognitive decline were education, IADL dependency and BMI. This study shows that among older people in sub-Saharan Africa, the effect of BMI, IADL dependency and education on cognitive function appears similar to that observed in middle- and high-income countries.

Since the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) published by the American Psychiatric Association, the term “dementia” has been renamed “major neurocognitive disorder” (MNCD), because the term dementia was deemed to be stigmatizing [1]. MNCD is considered as a geriatric syndrome [2] associating both significant impairment of cognitive performance (of gradual onset) and a declining ability to perform the activities of daily living. It is one of the leading causes of dependency (estimated to account for around 11%) in persons aged 60 years and above. MNCD is a major public health challenge, because it has serious repercussions both for the healthcare system and for society as a whole [3]. The prevalence of MNCD increases with advancing age [4–6]. The World Alzheimer Report 2021, as well as several other studies have underlined that the economic impact of MNCD will outstrip that of non-communicable diseases, such as cancer, stroke or heart disease. [7–9]. In a study performed in Cameroon in 2019, the authors estimated that the overall prevalence of cognitive impairment in rural areas was around 33% in individuals aged 60 years and over. It is well established that MNCD are strongly associated with adverse health outcomes (hospitalization, nursing home admission, loss of autonomy, death), as well as impacting on the organisation of patient care, and more generally, healthcare delivery. Cameroon, in Sub-Saharan African, will experience rapid population ageing between now and 2050, by which time the proportion of inhabitants aged 60 years and over will rise from 5–15% [10]. In line with these demographic projections, there will be a commensurate rise in age-related health problems, notably an increased prevalence of chronic disease, including MNCD, and its attendant consequences, such as incident dependency, unscheduled hospitalization, and admission into long-term care [11].

Since 2019, the World Health Organization program for integrated care for older people (ICOPE), which aims to promote primary prevention initiatives, has highlighted cognitive status (defined by 10 items of the Mini Mental State Examination (MMSE)) as an intrinsic capacity that is necessary for successful ageing [12, 13]. The MMSE is the most widely used instrument worldwide to evaluate cognition. To the best of our knowledge, no study to date has investigated the risk factors for severe cognitive decline in community-dwelling adults with preserved functional status in Cameroon. The aim of this study was therefore to identify the factors associated with severe cognitive decline, as assessed by the MMSE, among community-dwelling adults aged 55 years and older who live in urban areas and are members of an association for older persons in Cameroon (Mutuelle des Personnes Agées du Cameroun, MUPAC, an apolitical, not-for-profit humanitarian association aimed at negotiating the provision of healthcare for older people).

The MUPAC Study is a population-based, cross-sectional study conducted in Cameroon. Its primary objective was to negotiate the provision of healthcare to older people. Details of the MUPAC study design have previously been published elsewhere [14]. Briefly, community-dwelling men and women aged 55 years or older were selected from among the members of MUPAC in the city of Douala, Cameroon, from 1 January to 31 May 2019. From a total of about 2000 members in Douala, 615 agreed to participate, yielding an acceptance rate of 31%. For the purposes of the present study, the following variables were extracted from the members’ files: socio-demographic data (age, sex and level of education), marital status, medical history, overall cognitive performance and functional status. Physical performance was assessed using the Short Physical Performance Battery (SPPB) [15] and the Study of Osteoporotic Fractures (SOF) index [16]. Functional status was assessed using Lawton’s Instrumental Activities of Daily Living (IADL) [17], and Katz’s Activities of Daily Living (ADL) [18]. Depression was investigated using the Center for Epidemiologic Studies-Depression scale (CES-D)[19], and falls and sensory impairment were self-reported.

A total of 403 adults participated in the study. Their characteristics are described in Table 1. Of these, 16 (3.9%) had an MMSE score < 18 and were considered to have severe cognitive decline. The rate of severe cognitive decline increased with rising age, from 2.1% in those aged 55 to 64 years, to 3.3% in those aged 65 to 74, and 11% in those aged 75 and older. There was no difference in cognitive impairment between sexes. Subjects with cognitive impairment generally had a lower socio-educational level (p = 0.001), and lower body weight (58 vs 79 kg). They were also more often dependent on IADLs, with impaired physical function according to the SOF index (frailty) and SPPB (risk of sarcopenia).

The factors associated with severe cognitive impairment (MMSE score < 18) by multivariate analysis in our population are displayed in Table 2, and were level of education (OR 0.10 (95%CI 0.02–0.37), p < 0.01), body mass index (OR 0.88 (95%CI 0.78–0.99), p = 0.03) and IADL score (OR 0.12 (95%CI 0.03–0.38), p < 0.001).

In this study among members of the MUPAC, comprising a population of urban community-dwelling older adults in Cameroon, the prevalence of severe cognitive decline was estimated at 3.9%. Despite the limitations of this study, this finding is in line with prevalence data for MNCD reported in other studies from Sub-Saharan Africa [23]. There is a paucity of research data reporting the prevalence of severe cognitive decline or dementia in Sub-Saharan Africa, with the result that it is difficult to establish an accurate picture of the situation in each individual country [6, 23–25]. Studies from Africa have generally reported heterogeneous, but usually lower prevalence of dementia than studies from Europe or America. However, the limitations of many African studies include the low quality of the methodology, varying types of study settings (i.e. in-patients, outpatients, nursing homes, and autopsy studies), and limited coverage of the different African regions.

In a report published in 2017, using the DSM-III/IV criteria and based on a total of 10 studies, experts from Alzheimer’s Disease International estimated the prevalence of MNCD in Sub-Saharan Africa to be 5.5% in persons aged 60 years and older, with a female predominance (the prevalence among women being twofold that in men of the same age) [26]. In a systematic review of the literature published in 2014 investigating the prevalence of dementia in Sub-Saharan Africa, Lekoubou et al analysed a total of 49 studies and found a prevalence of 9% [27]. In another study performed in the hospital setting in Cameroon, Kuate Tegueu et al estimated that the prevalence of severe cognitive disorders was 12% in patients aged 65 years and older. Our study provides complementary insights to the existing literature from Sub-Saharan Africa, by including urban community-dwelling adults, and by estimating the prevalence of moderate to severe cognitive decline, defined by an MMSE score < 18. We observed in our study that the rate of severe cognitive decline increased in line with age, to reach 11% in those aged 75 years and older. This is congruent with the literature [6].

In our study, we also observed an association between the level of education and cognitive performance, whereby a lower level of education was association with lower cognitive performance. This result is also in line with the large body of evidence coming from studies in high- and middle-income countries, and also from Sub-Saharan Africa. Indeed, Lekoubou et al also showed that one of the most consistent risk factors for dementia among older adults in Sub-Saharan Africa was having fewer than 6 years of education [27]. This is all the more pertinent considering that improvements in educational level have contributed to promoting healthy ageing [28]. Several potential mechanisms have been proposed to explain this link between education and the risk of dementia. It is now known that the level of education may play a protective role against neurodegeneration, promoting enhanced function of the neuronal system (whereby, when neurons die, others can perform similar functional tasks), thereby mitigating the signs of functional and cognitive deficiency [29]. For example, in the Personnes Agées Quid (PAQUID) cohort, individuals who had not completed primary school showed more rapid declines in measures of verbal fluency, psychomotor speed and episodic memory, compared with those who had completed primary school. It has further been shown that the first signs of cognitive decline appear up to 15 years before the onset of clinical disease [30, 31]. A second factor identified in our study as being related to cognitive function was BMI. In our study, low BMI was associated with lower cognitive performance. This has also been reported in other works [32, 33]. In a systematic review and meta-analysis of prospective studies, Yi Qu et al confirmed that overweight and obesity were positively associated with dementia in middle-aged adults, but negatively associated with dementia in older adults, and with cognitive disorders in both middle-aged and older adults [34]. Weight loss may result from pre-dementia apathy, or reduced olfactive function [35, 36]. Cova et al also reported in a prospective study that higher BMI was associated with a lower risk of progression to dementia and Alzheimer’s disease in patients with mild cognitive impairment, while low body weight was associated with 2.5-fold risk of progression to dementia within 2.4 years [40,41]. Our results provide a good opportunity to underline the utility of performing systematic evaluation of BMI (as a proxy of undernutrition) during home visits and primary care consultations, either by GPs or nurses. BMI can have a deleterious influence on cognitive status, and therefore, early management is essential, especially considering that BMI is often underdiagnosed in older adults. BMI is an effective, user-friendly metric that is easy to implement in routine practice. Furthermore, early intervention for nutritional status in older individuals, before it begins to affect cognition, is an objective that is closely aligned with the practices and goals of GPs in primary care. Regarding the link between loss of IADLs and cognitive decline, it is difficult to discern, in our study, whether the IADL impairment is the cause or the consequence of cognitive decline. Nevertheless, reduced scores on IADLs are associated with associated with lower cognitive performance, but seemingly, more as a consequence thereof. Roehr et al previously reported that the risk of dementia and Alzheimer’s disease was highest in individuals with both subjective cognitive decline and impaired IADLs [39].

Our study has some limitations, the main one being the use of the MMSE as a proxy to assess cognitive function. In the diagnostic work-up for MNCD, neuropsychological evaluation uses a battery of tests. Nevertheless, the prevalence of cognitive decline seems to be close to that reported for Sub-Saharan Africa. Some strengths of our study should also be noted. First, this is the first time that a study of this type has been conducted in the population of Cameroon and we included a relatively large sample size. Moreover, the quality of the data is reliable because the data were collected by clinicians trained in geriatric assessment. In addition, we achieved good data completeness, thereby maximizing the statistical power of the analyses.

Our study found a prevalence of severe cognitive decline of 3.9% in urban community-dwelling adults aged 55 years and over in Cameroon. The prevalence of severe cognitive decline increases with age. The three main factors associated with cognitive decline were level of education, IADLs and BMI. This study shows that among older populations in Sub-Saharan Africa, the effect of BMI, IADLs and education on cognitive function seems to be comparable to what is observed in middle- and high-income countries.

Conflict of interest statement 

The authors declare that they do not have any conflicts of interest 

Author contribution statement 

NST and M.T.-T. designed the study; NST, MTT and CKT collected the data; MTT, SM, NST and CKT developed the data analysis strategy; SM analyzed the data; SM, NST, MAM, LM, LL, JFD and MTT interpreted the results and drafted the manuscript. All authors have read and agreed to the published version of the manuscript. 

Data availability 

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. 

1. Han, J.W.; So, Y.; Kim, T.H.; Lee, D.Y.; Ryu, S.-H.; Kim, S.Y.; Kim, S.G.; Kim, S.-K.; Youn, J.C.; Jhoo, J.H.; et al. Prevalence Rates of Dementia and Mild Cognitive Impairment Are Affected by the Diagnostic Parameter Changes for Neurocognitive Disorders in the DSM-5 in a Korean Population. Dement. Geriatr. Cogn. Disord. 2017, 43, 193–203, doi:10.1159/000458408.
2. Tabue-Teguo, M.; Grasset, L.; Avila-Funes, J.A.; Genuer, R.; Proust-Lima, C.; Péres, K.; Féart, C.; Amieva, H.; Harmand, M.G.-C.; Helmer, C.; et al. Prevalence and Co-Occurrence of Geriatric Syndromes in People Aged 75 Years and Older in France: Results From the Bordeaux Three-City Study. J. Gerontol. A. Biol. Sci. Med. Sci. 2017, 73, 109–116, doi:10.1093/gerona/glx068.
3. Akpan, A.; Tabue-Teguo, M.; Fougère, B. Neurocognitive Disorders: Importance of Early/Timely Detection in Daily Clinical Practice. J. Alzheimers Dis. JAD 2019, 70, 317–322, doi:10.3233/JAD-180381.
4. Jia, L.; Du, Y.; Chu, L.; Zhang, Z.; Li, F.; Lyu, D.; Li, Y.; Li, Y.; Zhu, M.; Jiao, H.; et al. Prevalence, Risk Factors, and Management of Dementia and Mild Cognitive Impairment in Adults Aged 60 Years or Older in China: A Cross-Sectional Study. Lancet Public Health 2020, 5, e661–e671, doi:10.1016/S2468-2667(20)30185-7.
5. Reitz, C.; Brayne, C.; Mayeux, R. Epidemiology of Alzheimer Disease. Nat. Rev. Neurol. 2011, 7, 137–152, doi:10.1038/nrneurol.2011.2.
6. GBD 2016 Neurology Collaborators Global, Regional, and National Burden of Neurological Disorders, 1990–2016: A Systematic Analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019, 18, 459–480, doi:10.1016/S1474-4422(18)30499-X.
7. International, A.D.; University, M. World Alzheimer Report 2021: Journey through the Diagnosis of Dementia. 2021.
8. Akinyemi, R.O.; Yaria, J.; Ojagbemi, A.; Guerchet, M.; Okubadejo, N.; Njamnshi, A.K.; Sarfo, F.S.; Akpalu, A.; Ogbole, G.; Ayantayo, T.; et al. Dementia in Africa: Current Evidence, Knowledge Gaps, and Future Directions. Alzheimers Dement. J. Alzheimers Assoc. 2022, 18, 790–809, doi:10.1002/alz.12432.
9. Wimo, A.; Seeher, K.; Cataldi, R.; Cyhlarova, E.; Dielemann, J.L.; Frisell, O.; Guerchet, M.; Jönsson, L.; Malaha, A.K.; Nichols, E.; et al. The Worldwide Costs of Dementia in 2019. Alzheimers Dement. J. Alzheimers Assoc. 2023, doi:10.1002/alz.12901.
10. Metanmo, S.; Simo-Tabue, N.; Kuate-Tegueu, C.; Bonnet, M.; Gbessemehlan, A.; Metanmo, F.; Dramé, M.; Tabue-Teguo, M. Short Physical Performance Battery and Study of Osteoporotic Fractures Index in the Exploration of Frailty Among Older People in Cameroon. Int. J. Public Health 2023, 68, 1605900, doi:10.3389/ijph.2023.1605900.
11. Essomba, M.J.N.; Atsa, D.; Noah, D.Z.; Zingui-Ottou, M.; Paula, G.; Nkeck, J.R.; Noubiap, J.J.; Ashuntantang, G. Geriatric Syndromes in an Urban Elderly Population in Cameroon: A Focus on Disability, Sarcopenia and Cognitive Impairment. Pan Afr. Med. J. 2020, 37, 229, doi:10.11604/pamj.2020.37.229.26634.
12. Sum, G.; Lau, L.K.; Jabbar, K.A.; Lun, P.; George, P.P.; Munro, Y.L.; Ding, Y.Y. The World Health Organization (WHO) Integrated Care for Older People (ICOPE) Framework: A Narrative Review on Its Adoption Worldwide and Lessons Learnt. Int. J. Environ. Res. Public. Health 2022, 20, 154, doi:10.3390/ijerph20010154.
13. Rudnicka, E.; Napierała, P.; Podfigurna, A.; Męczekalski, B.; Smolarczyk, R.; Grymowicz, M. The World Health Organization (WHO) Approach to Healthy Ageing. Maturitas 2020, 139, 6–11, doi:10.1016/j.maturitas.2020.05.018.
14. Metanmo, S.; Kuate-Tegueu, C.; Gbessemehlan, A.; Dartigues, J.-F.; Ntsama, M.-J.; Nguegang Yonta, L.; Kengne, A.-P.; Simo-Tabue, N.; Tabue-Teguo, M. Self-Reported Visual Impairment and Sarcopenia among Older People in Cameroon. Sci. Rep. 2022, 12, 17694, doi:10.1038/s41598-022-22563-9.
15. Guralnik, J.M.; Simonsick, E.M.; Ferrucci, L.; Glynn, R.J.; Berkman, L.F.; Blazer, D.G.; Scherr, P.A.; Wallace, R.B. A Short Physical Performance Battery Assessing Lower Extremity Function: Association with Self-Reported Disability and Prediction of Mortality and Nursing Home Admission. J. Gerontol. 1994, 49, M85-94, doi:10.1093/geronj/49.2.m85.
16. Ensrud, K.E.; Ewing, S.K.; Taylor, B.C.; Fink, H.A.; Cawthon, P.M.; Stone, K.L.; Hillier, T.A.; Cauley, J.A.; Hochberg, M.C.; Rodondi, N.; et al. Comparison of 2 Frailty Indexes for Prediction of Falls, Disability, Fractures, and Death in Older Women. Arch. Intern. Med. 2008, 168, 382–389, doi:10.1001/archinternmed.2007.113.
17. Lawton, M.P.; Brody, E.M. Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living. The Gerontologist 1969, 9, 179–186.
18. Katz, S.; Ford, A.B.; Moskowitz, R.W.; Jackson, B.A.; Jaffe, M.W. STUDIES OF ILLNESS IN THE AGED. THE INDEX OF ADL: A STANDARDIZED MEASURE OF BIOLOGICAL AND PSYCHOSOCIAL FUNCTION. JAMA 1963, 185, 914–919, doi:10.1001/jama.1963.03060120024016.
19. Radloff, L.S. The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Appl. Psychol. Meas. 1977, 1, 385–401, doi:10.1177/014662167700100306.
20. Folstein, M.F.; Folstein, S.E.; McHugh, P.R. “Mini-Mental State”. A Practical Method for Grading the Cognitive State of Patients for the Clinician. J. Psychiatr. Res. 1975, 12, 189–198, doi:10.1016/0022-3956(75)90026-6.
21. Fekadu, B.; Tareke, M.; Tadesse, M.; Anbesaw, T. Neurocognitive Impairment and Associated Factors Among Elderly in the Bahir Dar City Administration, Northwest Ethiopia. Front. Aging Neurosci. 2022, 14, 888704, doi:10.3389/fnagi.2022.888704.
22. Boada, M.; López, O.L.; Olazarán, J.; Núñez, L.; Pfeffer, M.; Paricio, M.; Lorites, J.; Piñol-Ripoll, G.; Gámez, J.E.; Anaya, F.; et al. A Randomized, Controlled Clinical Trial of Plasma Exchange with Albumin Replacement for Alzheimer’s Disease: Primary Results of the AMBAR Study. Alzheimers Dement. J. Alzheimers Assoc. 2020, 16, 1412–1425, doi:10.1002/alz.12137.
23. Guerchet, M.; M’belesso, P.; Mouanga, A.M.; Bandzouzi, B.; Tabo, A.; Houinato, D.S.; Paraïso, M.N.; Cowppli-Bony, P.; Nubukpo, P.; Aboyans, V.; et al. Prevalence of Dementia in Elderly Living in Two Cities of Central Africa: The EDAC Survey. Dement. Geriatr. Cogn. Disord. 2010, 30, 261–268, doi:10.1159/000320247.
24. Adoukonou, T.; Yoro-Zohoun, I.; Gnonlonfoun, D.D.; Amoussou, P.; Takpara, C.; Agbetou, M.; Guerchet, M.; Preux, P.-M.; Houinato, D.; Ouendo, E.-M. Prevalence of Dementia among Well-Educated Old-Age Pensioners in Parakou (Benin) in 2014. Dement. Geriatr. Cogn. Disord. 2020, 49, 210–218, doi:10.1159/000508623.
25. Guerchet, M.; Houinato, D.; Paraiso, M.N.; von Ahsen, N.; Nubukpo, P.; Otto, M.; Clément, J.-P.; Preux, P.-M.; Dartigues, J.-F. Cognitive Impairment and Dementia in Elderly People Living in Rural Benin, West Africa. Dement. Geriatr. Cogn. Disord. 2009, 27, 34–41, doi:10.1159/000188661.
26. Dementia in Sub-Saharan Africa: Challenges and Opportunities.
27. Lekoubou, A.; Echouffo-Tcheugui, J.B.; Kengne, A.P. Epidemiology of Neurodegenerative Diseases in Sub-Saharan Africa: A Systematic Review. BMC Public Health 2014, 14, 653, doi:10.1186/1471-2458-14-653.
28. Grasset, L.; Brayne, C.; Joly, P.; Jacqmin-Gadda, H.; Peres, K.; Foubert-Samier, A.; Dartigues, J.-F.; Helmer, C. Trends in Dementia Incidence: Evolution over a 10-Year Period in France. Alzheimers Dement. J. Alzheimers Assoc. 2016, 12, 272–280, doi:10.1016/j.jalz.2015.11.001.
29. Liu, C.; Ma, Y.; Hofman, A.; Waziry, R.; Koton, S.; Pike, J.R.; Windham, B.G.; Power, M.C.; Sharrett, A.R.; Gottesman, R.F. Educational Attainment and Dementia: Mediation by Mid-Life Vascular Risk Factors. Ann. Neurol. 2023, 94, 13–26, doi:10.1002/ana.26647.
30. Amieva, H.; Jacqmin-Gadda, H.; Orgogozo, J.-M.; Le Carret, N.; Helmer, C.; Letenneur, L.; Barberger-Gateau, P.; Fabrigoule, C.; Dartigues, J.-F. The 9 Year Cognitive Decline before Dementia of the Alzheimer Type: A Prospective Population-Based Study. Brain J. Neurol. 2005, 128, 1093–1101, doi:10.1093/brain/awh451.
31. Lövdén, M.; Fratiglioni, L.; Glymour, M.M.; Lindenberger, U.; Tucker-Drob, E.M. Education and Cognitive Functioning Across the Life Span. Psychol. Sci. Public Interest J. Am. Psychol. Soc. 2020, 21, 6–41, doi:10.1177/1529100620920576.
32. Guerchet, M.; Mouanga, A.M.; M’belesso, P.; Tabo, A.; Bandzouzi, B.; Paraïso, M.N.; Houinato, D.S.; Cowppli-Bony, P.; Nubukpo, P.; Aboyans, V.; et al. Factors Associated with Dementia among Elderly People Living in Two Cities in Central Africa: The EDAC Multicenter Study. J. Alzheimers Dis. JAD 2012, 29, 15–24, doi:10.3233/JAD-2011-111364.
33. Anstey, K.J.; Cherbuin, N.; Budge, M.; Young, J. Body Mass Index in Midlife and Late-Life as a Risk Factor for Dementia: A Meta-Analysis of Prospective Studies. Obes. Rev. Off. J. Int. Assoc. Study Obes. 2011, 12, e426-437, doi:10.1111/j.1467-789X.2010.00825.x.
34. Qu, Y.; Hu, H.-Y.; Ou, Y.-N.; Shen, X.-N.; Xu, W.; Wang, Z.-T.; Dong, Q.; Tan, L.; Yu, J.-T. Association of Body Mass Index with Risk of Cognitive Impairment and Dementia: A Systematic Review and Meta-Analysis of Prospective Studies. Neurosci. Biobehav. Rev. 2020, 115, 189–198, doi:10.1016/j.neubiorev.2020.05.012.
35. Payne, M.; Manera, V.; Robert, P.; Vandersteen, C.; Beauchet, O.; Galery, K.; Sacco, G.; Fabre, R.; Gros, A. Olfactory Identification Disorders Due to Alzheimer’s Disease: A New Test from France to Quebec. PLoS ONE 2022, 17, e0265764, doi:10.1371/journal.pone.0265764.
36. Devanand, D.P.; Michaels-Marston, K.S.; Liu, X.; Pelton, G.H.; Padilla, M.; Marder, K.; Bell, K.; Stern, Y.; Mayeux, R. Olfactory Deficits in Patients with Mild Cognitive Impairment Predict Alzheimer’s Disease at Follow-Up. Am. J. Psychiatry 2000, 157, 1399–1405, doi:10.1176/appi.ajp.157.9.1399.
37. Cova, I.; Clerici, F.; Rossi, A.; Cucumo, V.; Ghiretti, R.; Maggiore, L.; Pomati, S.; Galimberti, D.; Scarpini, E.; Mariani, C.; et al. Weight Loss Predicts Progression of Mild Cognitive Impairment to Alzheimer’s Disease. PLOS ONE 2016, 11, e0151710, doi:10.1371/journal.pone.0151710.
38. Cova, I.; Clerici, F.; Maggiore, L.; Pomati, S.; Cucumo, V.; Ghiretti, R.; Galimberti, D.; Scarpini, E.; Mariani, C.; Caracciolo, B. Body Mass Index Predicts Progression of Mild Cognitive Impairment to Dementia. Dement. Geriatr. Cogn. Disord. 2016, 41, 172–180, doi:10.1159/000444216.
39. Roehr, S.; Riedel-Heller, S.G.; Kaduszkiewicz, H.; Wagner, M.; Fuchs, A.; van der Leeden, C.; Wiese, B.; Werle, J.; Bickel, H.; König, H.-H.; et al. Is Function in Instrumental Activities of Daily Living a Useful Feature in Predicting Alzheimer’s Disease Dementia in Subjective Cognitive Decline? Int. J. Geriatr. Psychiatry 2019, 34, 193–203, doi:10.1002/gps.5010.

Table I: Characteristics of the study population (N=403) according to presence of severe cognitive decline

MMSE, Mini Mental State Examination; BMI, body mass index; ADL, Katz’s Activities of Daily Living; IADL, Lawton’s Instrumental Activities of Daily Living; SPPB, Short Physical Performance Battery; CES-D, Center for Epidemiologic Studies-Depression scale; SOF, Study of Osteoporotic Fractures  

Table II: Factors associated with the presence of severe cognitive decline by multivariate logistic regression. 

*Age and SOH were forced in the model. 

BMI, Body Mass Index ; IADL : Lawton’s Instrumental Activities of Daily Living ;OR, Odds Ratio; CI, Confidence Interval.