Performance on the MMSE is moderated by demographic variables, with scores decreasing with advanced age and lower levels of education⁹. Although normative data stratified by age and education have been published^10-12, those studies have focused almost exclusively on the impact of lower levels of education, whereas there remains relatively little information available regarding appropriate cut-scores or interpretive strategies for highly educated individuals. This gap is particularly problematic given implications in the literature regarding cognitive reserve¹³. This literature demonstrates that, once diagnosed, patients with probable Alzheimer's disease who have higher levels of education tend to demonstrate a steeper slope of decline^{14, 15} and earlier mortality rates¹⁵. Identifying cognitive dysfunction in these individuals as early as possible is desirable so that appropriate treatment strategies can be implemented earlier in the course of the disease. To date, however, the authors are unaware of any published investigations that have specifically examined the utility of the MMSE in detecting cognitive dysfunction in highly educated individuals. The current investigation explored this question in individuals with at least 16 years of education. It was hypothesized that in highly educated patients, the frequently implemented MMSE cut-score of 24⁹ would not yield an adequate balance between sensitivity and specificity and that a higher cut-score would need to be utilized to achieve optimal estimates of diagnostic accuracy.

A total of 1141 individuals with 16 or more self-reported years of education were identified. The sample included 1064 (93%) individuals who self-identified as Caucasian and 77 (7%) who self-identified as African-American. Of the 1141 participants, 658 individuals (242 males and 416 females) had no dementia and were considered cognitively normal (see Ivnik et al.¹⁹ for full criteria used to define normal cognition). The remaining 307 (164 males and 143 females) carried diagnoses of dementia established via consensus among ADRC investigators and based on published diagnostic criteria. Diagnoses included 202 (66%) patients with probable Alzheimer's disease, 48 (16%) with dementia with Lewy bodies, 18 (6%) with frontotemporal dementia, 13 (4%) with vascular dementia, and 25 (8%) with other dementia etiologies. A sample of 176 patients (106 males and 70 females) diagnosed with Mild Cognitive Impairment (MCI) was also included for comparison purposes.

The total sample included 512 (45%) males and 629 (55%) females, with a mean age of 75.9 (SD=7.2) years and a mean self-reported education of 17.1 (SD=1.5) years. There were no significant between-group differences (dementia vs. no dementia) in terms of age, gender, or education.

An optimal balance between sensitivity (.89) and specificity (.91) was obtained with a cut-score of 27 (26 or below). This yielded only slight improvement in the overall correct classification rate (90.1%) but identified 70 of the 104 dementia patients who were missed using the traditional cutoff. The cut score of 27 yields a likelihood ratio of 9.6, indicating that college graduates with an MMSE score of 26 and with complaints of cognitive decline (self- or other-report) are nearly 10 times more likely to have dementia that those who obtain a score of 27 or better.

As expected, the improved sensitivity obtained when the cut score is raised to 27 is achieved at the sacrifice of specificity. As a result 61 (9%) non-demented patients fall below the higher cutoff, as compared to only 3 (<1%) false positive identifications with the traditional cut-score of 24.

Next, because clinicians regularly evaluate patients with cognitive dysfunction with and without dementia, the above-mentioned analyses were calculated on a cognitively impaired group (MCI and dementia) versus non-demented controls to determine if an appropriate cut-score could be obtained. Estimates of sensitivity and specificity are presented in Table 2. As can be seen from Table 2, the traditional cut-score of 24 yields a very poor sensitivity (.45) but perfect specificity (1.0). Raising the cut-score to 27 yields an increased sensitivity (.69) with a concomitant decline, though still impressive, specificity (.91).

The current analyses also demonstrate that, when MCI is entered into the equation, obtaining an optimal balance between SN and SP is very difficult indeed. Table 2 demonstrates that optimal balances between SN and SP are found at cut-scores of either 27 (SN=.69, SP=.91) or 28 (SN and SP = .78). One might note that the NPV and PPV for the cognitively impaired group using the traditional cut-score of 24 is quite impressive even at low base rates; however, this is a function of a perfect SP and the low base rates. What this translates to for practicing clinicians is a very high false negative rate (often 50% or more) meaning that, because of the small number of true cases in low base rate settings, a large portion of those individuals actually suffering from cognitive dysfunction will not be detected and referred on for a comprehensive evaluation and/or treatment. Table 3 allows the individual clinician to make the determination as to what cut-score(s) s/he wishes to implement given the nature of the clinic population (e.g., demographics, appropriate base rate), additional information obtained in the medical examination (i.e., screening for cognitive impairment versus dementia if information regarding functional change is obtained), as well as his/her preferences for potential diagnostic error (i.e., false negative and false positive rates).

The vast majority of the published literature examining the relationship between cognitive test performance and education focuses on lower educated populations without consideration to individuals who have obtained high levels of education. In fact, research suggests that lower cut-scores on the MMSE are appropriate when evaluating populations obtaining lower levels of education¹¹ and correction formulas have been published⁵. Educational attainment is often considered one manifestation of cognitive reserve, with higher education levels associated with greater reserve and greater ability to withstand neuropathological burden before exhibiting detectable signs of disease (see Stern¹³ for review). Individuals with greater cognitive reserve are believed to maintain higher levels of cognitive functioning in the early stages of degenerative dementia. By the time cognitive symptoms are first identified, these patients are believed to have significantly greater disease burden and faster subsequent decline. Identifying such individuals at an earlier stage of disease development and progression is desirable for both treatment and research purposes.

There was not enough data in the current sample to test the comparative accuracy of individual cut-scores among highly educated individuals across ethnic groups. Therefore, the current findings with Caucasian individuals must be tested within ethnic minority populations before generalizations can be made. Additionally, the sample is an English-speaking sample and caution must be used when attempting to generalize to English as a second language or non-English speaking individuals. It should also be noted that the MMSE was administered as part of the clinical examination and was not used as part of the inclusion/exclusion criteria for the study database. Therefore, the MMSE was not used as a screening measure of cognitive functioning in this sample and might perform differently when used in this context (e.g., epidemiological studies).