The Mini Mental State Examination -- an Up to Date Review

Introduction

The bulk of older adults have adequate cognitive function to manage their daily activities of living. Still, the aging process causes numerous morphological changes to the brain that vary in severity among individuals (Messe´, Rudrauf, Benali, & Marrelec, 2014), which places older adults at higher risk to develop dementia and other neurological diseases (He et al., 2010). In 2015, 46.viii million individuals worldwide were estimated to be living with dementia. This is expected to near double by 2030 due to the increase in life expectancy (Ali, Guerchet, Wu, Prince, & Prina, 2015).

A variety of neuropsychological tests have been used to determine cerebral function including the xxx point Mini-Mental State Test (MMSE), Rowland Universal Dementia Assessment Calibration (RUDAS) (Naqvi, Haider, Tomlinson, & Alibhai, 2015) and Montreal Cognitive Assessment (MoCA) (Davis et al., 2015). Since 1975 the MMSE is the best known and most often used screening examination for dementia (Arevalo-Rodriguez et al., 2015; Folstein, Folstein, & McHugh, 1975; Tsoi, Chan, Hirai, Wong, & Kwok, 2015), which has been proven a valid tool in unlike populations such as Hispanics (Arévalo, Kress, & Rodriguez, 2019) and Asians (Rosli, Tan, Grey, Subramanian, & Mentum, 2016). The MMSE tests orientation to time, and place, repetition, exact retrieve, attention and calculation, language, and visual construction (Folstein et al., 1975). The MMSE is well regarded for its translatability to other languages, loftier reliability, and easy use for comparative analysis and assistants beyond populations (Nieuwenhuis-Mark, 2010). The MMSE cutoff score of 24 points for normal cognition has loftier validity, with 87% for sensitivity and 82% for specificity (G. F. Folstein et al., 1975; Nieuwenhuis-Mark, 2010). The MMSE is, nonetheless, found to be sensitive for confounders such as age and didactics that account for 12% variance in MMSE scores (Gluhm et al., 2013; Nieuwenhuis-Mark, 2010). The 100 point Modified Mini-Mental State Examination (3 MSE), with a cutoff score of 78 points or higher for normal cognition, is an alternative to the standard examination and has an additional four sections that test word fluency, similarity recognition, delayed retrieve of words, and retentivity of engagement and place of nascency (Teng & Chui, 1987; Tombaugh, McDowell, Kristjansson, & Hubley, 1996). Though a higher reliability coefficient (0.82 and 0.88 vs. 0.62 and 0.81) has been obtained with the iii MSE than MMSE, both the standard and modified versions of the test screen for similar cerebral domains, and scales are relatively proportional (Tombaugh et al., 1996). The changes in MMSE scores in individuals with dementia against historic period take been reported in many studies (Arevalo-Rodriguez et al., 2015).

Lilliputian is known about the trajectory of cognition during lifetime measured using the MMSE in general populations. Therefore, nosotros systematically reviewed the literature to map MMSE score trajectories over individual'due south lifespan in general populations.

Methods

Search strategy and report selection

The protocol of the systematic review was performed according to PRISMA standards (Moher, Liberati, Tetzlaff, & Altman, 2009). The search for the literature was conducted using four electronic databases MEDLINE, EMBASE, Cochrane, and PsycINFO via Ovid, for articles published from January 1, 2007 to May 25, 2017 (Appendix Table I). This catamenia was chosen to include more recent data to account for possible cohort effects (Matthews, Marioni, & Brayne, 2012). Key search terms for the study selection included "crumbling," "MMSE", and "cohort."

Conference abstracts and duplicates were removed before title and abstruse screening by contained investigators (JMN, AD). A third reviewer (SS) resolved any disagreements between the investigators. Articles were included if they met the following criteria: human articles, published in English, not-patient population or participants without neuropsychological diseases, longitudinal study design with a follow upwardly fourth dimension greater than or equal to 5 years for cohort studies with a mean age of participants less than 90 years at the baseline. Cohort studies including participants 90 years or older often have limited follow-upwardly periods; therefore, these studies were included if at to the lowest degree one MMSE information indicate was reported (including a cantankerous-sectional report pattern). Cohort data published multiple times were included in one case, presenting data with the longest follow-upwards catamenia.

Data extraction and quality cess

Two investigators (JMN, AD) independently extracted the post-obit data: writer, year of publication, study pattern, state of population, study population, sample size, age, sex, baseline, and follow-up MMSE scores, and follow-upwardly fourth dimension. Discrepancies were discussed with two other investigators where necessary (SS, ABM).

The take chances of bias and study quality was accessed independently by two investigators (JMN, AD) using the Newcastle-Ottawa scale (NOS), consisting of iii sections; selection, comparability, and exposure. A high score is indicative of depression study bias and skillful report quality (Lo, Mertz, & Loeb, 2014).

Data synthesis

Historic period in years was extracted in the following formats: mean (SD); median (IQR); mean (SE); hateful (range); median (range); and values as integer when no summary statistics were reported. Age presented equally median (IQR) and median (range) were converted to mean (SD) values (Hozo, Djulbegovic, & Hozo, 2005). Standard Error of Hateful (SE) was also converted to Standard Deviation (SD) (Barde & Barde, 2012). Integer values representing the overall age of the cohort were used equally a mean age. MMSE scores were extracted in the following formats: mean (SD); median (IQR); mean (SE); mean (range); median (range) and 95% Conviction Intervals (CI). MMSE scores presented every bit mean (SD) or hateful (SE) were converted to median (IQR) due to non-normality observed in the data (Barde & Barde, 2012; Hozo et al., 2005). Manufactures presenting MMSE scores as indicate estimates and 95% CI values were also converted to median (IQR) values (Krzywinski & Altman, 2013).

A meta-regression analysis was conducted to identify the trend in MMSE scores over chronological age. The mean (SD) age and median MMSE scores were used with IQR values of MMSE scores converted to median absolute deviation (MAD) (Pham-Gia & Hung, 2001) for calculation of variance for the meta-regression model. Articles that did non report any measure of dispersion for the estimated MMSE score were excluded from the meta-regression analysis. Three mixed-result models (Model 1: age, Model 2: historic period + age², Model iii: age + age² + historic period³) were developed. A meta-analysis model without age was also developed to place if age has any influence on MMSE scores and how much of the heterogeneity can exist explained past age. A alter-point analysis was conducted to place the age values with changes in MMSE scores.

The descriptive statistics were calculated using the IBM SPSS Statistics version 24 program (IBM, Armonk, New York, United states of america). Mapping of trajectories was conducted using GraphPad (GraphPad Prism five for Windows, Version v.01, La Jolla, CA, GraphPad Software Inc.). The R (R Foundation for Statistical Computing, version 3.3.iii, Vienna, Austria, R Cadre Team) parcel "Metafor" (Muggeo, 2003; Viechtbauer, 2010) and "Segmented" (Muggeo, 2003) were used for performing meta-regression and change-point assay.

Results

Figure 1 shows the PRISMA flowchart of study selection. A full of 4196 manufactures were found, of which 947 were duplicates and 566 conference abstracts, giving 2683 articles for championship and abstract screening. 2 hundred and eighty-vii articles were accepted for full-text screening. Information of 45 manufactures were included, consisting of 37 articles using the standard MMSE and 8 articles using the 3MSE.

Figure one. Flow diagram of article selection process.

Appendix Tabular array Two displays the descriptive characteristics of the included articles. Thirty-two cohorts had a longitudinal pattern and iii cohorts were cross-exclusive, with a mean (SD) baseline age of the 58,939 participants of 75.1 (12.viii) years with 61.2% females and median follow-up catamenia of 8 years (range 0.5–22). A total of 222 MMSE point estimates were extracted from cohorts of 14 countries. Nigh half of the cohorts were located in the USA (44%) and participants were predominantly Caucasian (89%), followed by a predominantly Hispanic (7%) and Asian (4%) origin.

Appendix Table III presents the MMSE/3MSE values for the cohorts at baseline and follow-up. Figure 2 presents the trajectory of standard MMSE scores expressed as median IQR (Figure 2a) and fitted values from the meta-regression model (Figure 2b) confronting hateful (SD) chronological age. Of the three mixed-effect models, the model with age and a 2nd and tertiary-caste polynomial of age provided the best fit (Akaike Information Criteria (AIC): Model 1 (634.47), Model 2 (614.85), Model iii (602.26)). A low AIC value is indicative of the best trade-off between the model fit and complexity of the model. When compared with the model without the historic period variable, an increase in AIC (672.8481) was observed. The meta-regression model demonstrated a meaning decrease in MMSE score with each year increment in age (regression coefficient of age: −0.10 (−0.15, −0.05)). The alter-betoken analysis identified the get-go refuse in MMSE score at the age of 41 years followed by another decline at age of 84 years. The rate of decline beneath age 41 years is −0.17 (95% CI: −0.35, 0.004), −0.04 (95% CI: −0.05, −0.03) from 41 years to younger than 84 years, and −0.53 (95% CI: −0.55, −0.50) from 84 years and onwards.

Figure 2. Trajectory of cognition measured by MMSE over chronological age in general populations depicted as median, IQR (A) and meta-regression fitted values (estimate and 95% CI) (B).

Figure three presents the trajectories of 3MSE (median; IQR) scores against hateful age. The majority (72%) of 3MSE score estimates were obtained from individuals anile seventy–80 years. None of the articles reported 3MSE scores in participants aged below 65 years and above 95 years. Due to the limited age range and number of score estimates, further statistics was omitted.

Figure 3. Trajectory of noesis measured by 3MSE over chronological age in general populations depicted every bit median, IQR.

Tabular array one displays the NOS scores for the included articles. The median (Bruce et al.) score was 7 points (range 6–9). Two articles obtained full scores for all three sections.

Tabular array 1. Newcastle-Ottawa Scale (NOS) scores of included manufactures.

Discussion

The trajectory of standard MMSE scores over a lifespan of general populations showed a gradual decline of the MMSE score with chronological historic period. The change-point analysis estimated a minimal decline in MMSE scores upwardly to the age of 84 years followed past a significant decline of half a betoken per year at higher ages.

At college chronological ages (range 84–105 years), the decline in MMSE scores was more pronounced compared to younger ages reaching abnormal scores, defined as cutoff score of 24 points (Folstein et al., 1975; Tombaugh & McIntyre, 1992). A review of cognitive functioning of individuals anile 90 years and older without dementia revealed a MMSE cutoff score of 23.3 points as abnormal (Legdeur et al., 2017). Applying these cutoff scores to the cohorts of our systematic review demonstrates the high prevalence of cognitive impairment at older age, which is in line with previously published prevalence data of dementia (Kochhann, Varela, Lisboa, & Chaves, 2010; Lin et al., 2013).

Similar rates of cerebral decline have been reported in a previous review, with a greatest rate of cognitive decline occurring 3–xv years before expiry (Karr, Graham, Hofer, & Muniz-Terrera, 2018). This accelerated cognitive reject is not only explained past the onset of age-related diseases such as Alzheimer's disease and vascular dementia (Ravona-Springer et al., 2011), merely also past non-brain-related changes, such as sensory impairment (Bathini, Brai, & Auber, 2019; Ray, Dening, & Crosbie, 2019). Age, apolipoprotein E ε4 allele, and diabetes have been shown to be associated with faster cerebral decline (Lipnicki et al., 2019). However, run a risk factors for cognitive pass up differ betwixt age groups; vascular disorders are a prominent risk factor for dementia at younger ages, only it is not at high age (Legdeur et al., 2018, 2019), suggesting differences in the pathophysiology (Denver & McClean, 2018; Rizzi, Rosset, & Roriz-Cruz, 2014). Furthermore, cognitive decline is often accompanied past a refuse of other organ systems, such as the musculoskeletal organization resulting in physical impairment (van Dam et al., 2018). This suggests mutual pathophysiological pathways related to aging, but the time dependency of the pass up of different organ systems has yet to be established (Legdeur et al., 2019; Stijntjes et al., 2017).

The MMSE values betwixt the seventh and ninth decade were much more heterogeneous than at younger or extreme ages. This is likely due to the heterogeneity of included cohorts, existence unlike in country of inclusion, ethnicity, socioeconomic status, lifestyle, and medical weather condition. Ethnicity, cultural differences, and education, as well as language, may bear on the accuracy of the MMSE test results (Dong et al., 2010; Ibrahim et al., 2009; Mathuranath et al., 2007; Sosa et al., 2012). The MMSE results may, therefore, be underestimated in populations with low literacy. In an analysis of private data of 20 cohorts including individuals aged 72.seven years (hateful), the apolipoprotein E ε4 allele, lower levels of education, current smoking, lower physical activity, and medical conditions such every bit low, diabetes, and stroke were associated with poorer cognitive performance (Lipnicki et al., 2019).

The MMSE is one of the most used screening tools for cognitive function in clinical practice, therefore mapping the trajectory of the MMSE was deliberately chosen (Tombaugh, 2005), even if other tools like the RUDAS and MoCA are too often clinically used. Yet, the MMSE has a well-known ceiling event (Houx et al., 2002) and may non detect subtle changes in cognitive refuse (Kim et al., 2014). Young and clinically cognitively healthy individuals obtain shut to the highest possible score. Individuals may begin accelerated cognitive refuse years before a change in MMSE is observed. Therefore, the decline in MMSE is representing an end of the ceiling event rather than the truthful onset of cognitive refuse. Therefore, even minimal decline in MMSE scores at younger ages might be indicative for cerebral reject.

This review is not without limitations. Articles published from 2007 onwards were included in this systematic review, which has limited the number of cohorts being included. This strategy made it more probable to include data of more recently performed cerebral testings and therewith inclusion of more than recent birth cohorts, minimizing cohort effects (Glenn, 1976). The results might non be generalizable to all ethnicities as the majority of cohorts consisted of White people. A more balanced variety of ethnicities would be highly warranted (Jüni, Witschi, Bloch, & Egger, 1999). Furthermore, the prevalence of Alzheimer'southward illness is higher in females than males (Hebert, Scherr, McCann, Beckett, & Evans, 2001). It was not possible to stratify the trajectories by sex, but the touch on the analyzes are estimated to be small equally the incidence of mild cerebral damage has been reported to be independent of sex (Au, Dale-McGrath, & Tierney, 2017).

Furthermore, the included articles described longitudinal cohorts with varying follow-up durations (range 0.5–22 years), number of completed measurements (range: twenty–65970), and intervals between measurements (range 0.21–12 years). Attrition is common in longitudinal cohorts, peculiarly among older individuals who feel cognitive decline or dice during follow-upwardly. Cohorts with long intervals between measurement waves may not detect the onset of cognitive impairment due to attrition, but include data of previous measurements. Including cohorts with varying measurement intervals may increment the heterogeneity of results being the outcome of discrepant cohort designs. Simply cohorts of the general population were included, excluding cohorts focusing on cerebral function in disease-specific cohorts. Therefore, this review gives a comprehensive overview of the cognitive function of general populations.

Decision

In general populations, the MMSE score declines minimally in immature and middle-aged cohorts and declines significantly (half a point of the score per twelvemonth) between 84 and 105 years.

Clinical implications

The Standard 30 indicate Mini-Mental State Examination (MMSE) is a normally used screening tool for cognitive function.
Between the age of 29 and 105 years, MMSE scores decline in full general populations.
From the age 84 years onwards the cognitive decline amounts to half a MMSE point per yr.

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Source: https://www.tandfonline.com/doi/full/10.1080/07317115.2020.1756021

The Mini Mental State Examination -- an Up to Date Review

Introduction

Methods

Search strategy and report selection

Data extraction and quality cess

Data synthesis

Results

Published online:

Published online:

Published online:

Published online:

Tabular array 1. Newcastle-Ottawa Scale (NOS) scores of included manufactures.

Discussion

Decision

Clinical implications

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