Background: Subjective questionnaires used for the diagnosis of pre-mild cognitive impairment (pre-MCI) and conventional criteria for MCI might mainly result in false-positive diagnostic errors. The integrated criteria based on demographically adjusted total and process Z scores on neuropsychological tests have been validated to be sensitive for measuring pre-MCI, MCI, and MCI subtypes. However, the underrepresentativity of Chinese populations and the complexity in some tests limit the use of the established Z scores in the elderly Chinese population. Objective: The aim of this study was to develop a useful Z score calculator to assess individual cognitive performance after adjustment of the scores on each of the neuropsychological tests according to sex, age, and education and to establish preliminary norms for the objective assessment of cognition function in elderly Chinese individuals. Methods: The neuropsychological test battery consists of measures of performance on different cognitive domains, including episodic memory, language, executive function, processing speed, and attention. Data were obtained from 220 clinically cognitively normal Chinese volunteers aged 60 years or older from the cohort of the Shanghai study of health promotion among frail elderly individuals. Regression models were used to investigate the impact of age, education, and sex on test scores. Z scores were estimated for the different scores by subtracting the predicted mean from the raw score and dividing it by the root mean square error term for any given linear regression model. Results: Preliminary analyses indicated that age, sex, or level of education significantly affected test scores. A series of linear regression models was constructed for all instruments, i.e.: the Trail-Making Test A and B, to assess executive function or attention; the Boston Naming Test and animal list generation, to assess language; delayed free correct responses and the Hopkins Verbal Learning Test-Revised (HVLT-R) recognition, as well as three process scores, i.e., intrusion errors, learning slope, and retroactive interference, from the HVLT-R, to assess memory, by adjusting for the covariates of age, sex, and level of education concurrently. The Z scores on all neuropsychological tests were estimated based on the corresponding regression coefficients. Conclusions: We constructed a multivariable regression-based normative Z score method for the measurement of cognition among older Chinese individuals in the community. The normative score will be useful for the accurate diagnosis of different subtypes of pre-MCI and MCI after preliminary rapid screening in the community.

Subjective cognitive decline (SCD) is unspecific and highly prevalent in older populations. SCD is a core criterion of mild cognitive impairment (MCI) and the pre-MCI stage of Alzheimer’s disease (AD) or preclinical AD stage; it has also been reported in many conditions, including normal aging, psychiatric, neurological and medical comorbidities, medications and personality characteristics [1]. SCD in individuals with unimpaired performance on cognitive tests, as a pre-MCI SCD condition, could last approximately 15 years before the appearance of clinical symptoms [1, 2]. At this stage, the brain still has sufficient functional compensation for the mild neuronal damage that characterizes it [3]. If subjectively reported cognitive decline of pre-MCI SCD occurs in the memory domain, rather than other domains of cognition, the pre-MCI SCD is proposed as a preclinical AD SCD, which is characterized by increasing compensatory cognitive effects and subtle cognitive decline [1]. After this stage, the threshold of neuropsychological tests is below normal age-, sex-, and education-adjusted performance, as well as the stage at which MCI or preclinical AD occurs. Pre-MCI SCD and MCI are also the important components of both reversible cognitive frailty and potential reversible cognitive frailty [4]. Although cerebrospinal fluid biomarkers and neuroimaging are important criteria for diagnosing of pre-MCI and preclinical AD, these methods are not available in major settings [4-6]. Therefore, it is important to objectively differentiate target populations with subtle cognitive decline by demographically corrected normative scores on the neuropsychological test battery which is critical for prevention and timely intervention.

Although the working group of the Subjective Cognitive Decline Initiative developed a conceptual framework for research on SCD and established research criteria for SCD in individuals with pre-MCI and preclinical AD SCD [1], the diagnosis of pre-MCI SCD is based mainly on various self-reported measures [7]. Furthermore, approximately 60% of the items surveyed are related to the memory domain, while only 16% pertain to executive function and 11% address the attention domain. These subjective self-reported measures resulted in a high rate of false-positive diagnostic errors. Similarly, the conventional criteria used to assess MCI are also susceptible to false-positive errors in the identification of MCI and MCI subtypes [8].

The scores on the neuropsychological test battery can objectively show cognitive impairment in different domains; however, age, sex, race, level of education, and other potential confounds, such as reliability, practice effects, measurement error, and relevant clinical factors may affect cognitive scores. Different statistical methods, including simple standard deviation difference methods [9], reliable change index scores [10-12] and regression-based change score norms [9, 13-15] have been used to account for these confounds. Among these methods, normative reliable change index scores and multivariate regression-based Z scores for the neuropsychological battery of the UNIFORM data set had been developed to interpret the longitudinal change and cognitive performance below the normal standard [10, 13-15]. Since 2005, the neuropsychological battery of the UNIFORM data set [9], as well as its new version [10], have been developed from the 3,602 cognitively normal participants in the National Alzheimer Coordinating Center database. The predicted mean value of each neuropsychological test as a function of sex, age, and education could be estimated according to the coefficients for these variables in the multivariate regression model. The corresponding normative online Z scores could be calculated according to the raw score, predicted population mean score, and root mean square error of the regression equation [15]. The neuropsychological test battery includes general cognitive measure, as well as domain-specific tests, episodic memory, language, visuospatial, immediate attention, working memory and executive attention tests. According to demographically corrected normative scores on the neuropsychological test battery, cognitive performance in dementia and MCI due to AD can be measured.

To objectively differentiate MCI, pre-MCI SCD or subtle cognitive decline from cognitively normal individuals, researchers have recently developed Z scores of 6 neuropsychological tests, including 2 language measures, 30-item Boston Naming Test and Animal Fluency, 2 memory measures, Rey Auditory Verbal Learning Test (AVLT) delayed free recall correct responses and AVLT recognition, and 2 attention/executive function tests, Trail-Making Test (TMT) Part A and Part B, based on regression coefficients derived from a sample of the Alzheimer’s Disease Neuroimaging Initiative cognitively normal participants (n = 381) [8]. Participants were classified as late pre-MCI SCD if they performed >1 SD below the age/sex/education-adjusted mean on two measures across different cognitive domains and were classified as MCI if they had impaired total scores on two measures in the same domain, or one impaired score in each of the three cognitive domains [16]. Furthermore, they also developed three neuropsychological process scores from AVLT, including intrusion errors, learning slope, and retroactive interference [17]. Participants were classified as early pre-MCI SCD if they performed >1 SD below the demographically adjusted mean on two measures across three process scores, or one impaired process score and one impaired total score of three cognitive domains. The converted total Z scores on neuropsychological tests integrated into the MCI criteria [8, 16] and the process Z scores integrated into subtle cognitive decline criteria [16, 17] exhibited significantly improved sensitivity and specificity regarding the identification of MCI, MCI subtypes, and pre-MCI SCD among cognitively normal older adults.

Until now, the most representative normative score of the neuropsychological test battery was the UNIFORM data set. The complete UNIFORM data set contains diverse volunteers with respect to demographics, sex, age, education, and race. The participants were mainly white, female, and highly educated. Therefore, the normative data for underrepresented samples, including the Chinese people, need to be expanded. In addition, it is difficult to administer AVLT with 7 trials and a 15-item word list when measuring for the elderly. In the present study, one of the aims is to develop a normative Z score of the neuropsychological test battery in community-dwelling Chinese elderly, including the memory, executive function, language, processing speed, and attention domains. The other aim of this study is to create objective diagnosis criteria of pre-MCI SCD and MCI in community-dwelling Chinese elderly according to previous reported demographically corrected total Z scores of 6 neuropsychological tests [16, 17], only replaced AVLT with the Hopkins Verbal Learning Test-Revised (HVLT-R).

Study Sample

Older volunteers were recruited directly from the rapid-screening setting of communities via newsletters and phone calls. Participants (or an authorized representative) gave their written informed consent, and the study protocol was approved by the committee on human research of Huadong Hospital. The subjects were deemed clinically cognitively normal during the initial assessment, based on the following criteria: (1) a Clinical Dementia Rating [18] global score of 0 and a Mini Mental Status Examination (MMSE) score >26 points (middle school), >22 (primary school) or >19 (illiteracy) [19]; (2) a Functional Assessment Questionnaire [20] score <5; (3) no other indications of SCD, cognitive decline, dementia, and depression based on information from supplemental questionnaires, including the Spanish SCD questionnaire MyCog and TheirCog scores [21], and the 15-item geriatric depression scale; (4) maintenance of the functional and cognitive statuses for 1 year; and (5) availability of a complete set of demographic data, including age, education, and sex. We excluded those participants who were <60 years or >90 years old, those with an uncontrolled medical, neurological, or psychiatric condition, along with severe hearing and vision loss, and those who were using psychotropic medications. Among all of the Chinese volunteers, 220 met the criteria described above.

Neuropsychological Tests

The study included the following 9 neuropsychological test scores and 3 neuropsychological process scores (Table 1), which were widely used in the uniform data set and a sample of the Alz-heimer’s Disease Neuroimaging Initiative cognitively normal participants. We only replaced AVLT with a shorter word list and less repetitious HVLT-R [19, 22], which is a 12-item (4 words from 3 semantic categories) word list learning and memory test that includes 3 learning trials (List A, Trials 1–3), an interference trial with a different list (List B), a short-delay free recall (Trial 4) for List A, a long-delay free recall (Trial 5) for List A performed 25 min later, and delayed recognition of 24 words (i.e., 12 List A words: 6 “same” categories of related non-List A words and 6 “other” categories of unrelated words). The delayed free correct responses and HVLT-R recognition scores were used for memory assessment. The three process scores from the HVLT-R included: learning slope ([List A Trial 3 – List A Trial 1]/3), retroactive interference (List A Trial 4/List A Trial 3), and intrusion errors (total number of extra-list intrusion errors across all recall trials). The Wechsler Adult Intelligence Scale-Revised (WAIS-R) Digit Symbol Coding subtest [13], the WAIS-R Digit Span Forward score and the TMT Part A were used for the assessment of processing speed/attention [23, 24]; the WAIS-R Digit Span Backward score and the TMT Part B were used for the assessment of executive function [23, 24], and the total score of the Animal List Generation [8, 13] and the total correct responses on the Boston Naming Test [8, 13] were used to assess language performance. The task in the TMT Part B is to connect numbers and letters in an alternate sequence (1-A-2-B-3-C…13-L). We replaced the letters “A, B, C...” with ─, ═, ☰.…” The time (number of seconds) required to complete the task was scored, with lower scores indicating better cognitive function.

Table 1.

Neuropsychological tests used to demographically establish corrected normative Z scores of cognitively normal Chinese participants

Neuropsychological tests used to demographically establish corrected normative Z scores of cognitively normal Chinese participants
Neuropsychological tests used to demographically establish corrected normative Z scores of cognitively normal Chinese participants

Statistical Analysis

First, we described the demographics of the cohort (age, education, and sex). The mean, median, 25th and 75th percentiles, and ranges of scores in the overall sample are presented. The mean scores and SDs for each test are provided according to age (divided into three groups: 60–69, 70–79, and 80–90 years) and education (divided into two groups: ≤10 and ≥11 years). We created a multiple regression equation that was specific to demographic variables and used regression coefficients from the multivariable model (gender, age, and education combined). Multivariable models were run using all three demographic variables (age, gender, and education) for each neuropsychological measure.

Calculation of Z Scores

We used an equation published previously [15] to calculate Z score estimates for individual subjects on the neuropsychological test battery described above:

where Y is the raw score for an individual subject obtained from performance on of the regression equation, which was used as a replacement of the estimate of the population’s standard deviation. The following formula was used to calculate the RMSE:

where Y is the observed neuropsychological test score, Yˆ is the predicted neuropsychological test score, n is the number of observations, and k is the number of predictors/covariates (i.e., age, education, and sex).

Despite the fact that all participants met our eligibility criteria, individuals with scores in the top 1% on the TMT A and B and intrusion errors on the HVLT-R (because a lower score on these tests indicates better performance) and individuals in the bottom 1% of the range on other measures were eliminated from further Z score analysis. We used regression coefficients from the multivariable model (sex, age, and education combined) to predict the mean score on each neuropsychological test of the theoretical population for an individual subject with the same age (coded as 0 = 60–69 years), education (coded as 0 = ≤10 years), and sex (coded as 0 = female).

The cohort used in the preliminary study included 220 cognitively normal Chinese participants aged over 60 years (Table 2; 52.73% of the sample were women). The majority of the sample (65.45%) was aged between 70 and 90 years and was highly educated (64.09%). The cognitive domain, maximum score, actual sample number, mean and 25th, 50th, and 75th percentiles, and score ranges for each neuropsychological test are indicated in Table 3.

Table 2.

Sample distribution of cognitively normal Chinese participants according to sex, age, and education

Sample distribution of cognitively normal Chinese participants according to sex, age, and education
Sample distribution of cognitively normal Chinese participants according to sex, age, and education
Table 3.

Summary statistics of clinically cognitively normal Chinese participants

Summary statistics of clinically cognitively normal Chinese participants
Summary statistics of clinically cognitively normal Chinese participants

To evaluate the effect of age, sex, and education on each neuropsychological test and neuropsychological process, a series of linear regression models with three demographic variables was constructed. The output of these models contained regression coefficients and their 95% confidence intervals (Table 4). After adjusting for the three demographic variables simultaneously, gender was significantly associated with the Boston Naming Test total score and the Animal Fluency total score and marginally significantly associated with Digit Span Backward total trials (p = 0.082). Age was significantly associated with the results of nearly all tests, with the exception of two process scores, i.e., intrusion errors and retroactive interference from the HVLT-R. Finally, education was also significantly associated with major tests and marginally significantly associated with Delayed Free Recall scores from the HVLT-R (p = 0.080), Boston Naming Test total scores (p = 0.065), and Digit Span Backward total correct trials (p = 0.083); however, education was not significantly associated with TMT A and two process scores, i.e., intrusion errors and retroactive interference from the HVLT-R.

Table 4.

Multivariate linear regression coefficients and 95% confidence intervals (CIs) for gender, age, and education

Multivariate linear regression coefficients and 95% confidence intervals (CIs) for gender, age, and education
Multivariate linear regression coefficients and 95% confidence intervals (CIs) for gender, age, and education

The predicting mean score Yˆ of each neuropsychological test was calculated by establishing a regression equation based on the intercepts to obtain regression coefficients for the variables in the multivariable regression model (Table 5). RMSE was calculated by the above formula. Finally, adjusted Z scores for all three demographic variables (age, sex, and education) were calculated for each test. To improve the distribution of residuals and better satisfy model assumptions, Z scores in the top or bottom 1% [13], or 5 SD outside of the mean of Z score on any particular test was excluded [14]. Correspondingly, individuals with Z scores in the top 1% on TMT A and B, intrusion errors in the HVLT-R, and Z scores in the bottom 1% range on other measures were further excluded from the study. The mean Z scores and SDs for each test are indicated in Table 6.

Table 5.

Regression equations for demographically corrected predicting mean scores of neuropsychological tests

Regression equations for demographically corrected predicting mean scores of neuropsychological tests
Regression equations for demographically corrected predicting mean scores of neuropsychological tests
Table 6.

Summary statistics of the uniform Z score of the neuropsychological test battery for Chinese participants with normal cognition

Summary statistics of the uniform Z score of the neuropsychological test battery for Chinese participants with normal cognition
Summary statistics of the uniform Z score of the neuropsychological test battery for Chinese participants with normal cognition

The converted neuropsychological test total Z scores could assess objectively the impairment of the corresponding cognitive domain. Therefore, these total Z scores were integrated into the MCI criteria [8, 16, 17] and late subtle cognitive decline [16, 17], which were used to identify MCI, pre-MCI, SCD, and MCI subtypes. The conventional MCI criteria are based mainly on MMSE scores and abnormal performance of the memory domain based on a single memory score on the neuropsychological test (delayed recall of story A from WMS-R Logical Memory) [8, 25]. Although the MMSE allows a general, global statement of the cognitive performance of wide cognitive domains, such as orientation, memory, attention, language comprehension, and visuoconstruction on a minimum dimension, the test is unsuitable for the early identification of dementia or MCI among healthy controls [14, 26]. A global Clinical Dementia Rating score of 0.5 failed to identify the severity of MCI and MCI subtypes, which led to a high rate of false-positive errors [8, 27, 28]. In addition, story memory is less sensitive than verbal list learning tasks regarding the identification of MCI [29], and a single low memory score is observed in approximately 39% of healthy older adults [30]. Until now, the diagnosis of SCD has mainly been based on various self-reported measures [6], with memory-domain-related items accounting for approximately 60% of the items [6]. Because the alteration in process scores on learning and memory tests was associated with an obvious decline in the total memory scores [31, 32], the process Z scores were integrated into the subtle cognitive decline criteria [14, 15] and could identify objectively the early stage of pre-MCI SCD or subtle cognitive decline and predict progression to MCI.

This article reports the development of normative data and Z score calculator for the neuropsychological test battery in older Chinese adults. We reported the normative total Z scores in each domain on at least two neuropsychological tests and three process scores from the HVLT-R. The normative calculator is a convenient tool to convert to age-, education-, and sex-adjusted Z scores in a Chinese older adult cohort according to the regression coefficients obtained in this study. Moreover, MCI subtypes and pre-MCI with deficits in different cognitive domains could be identified based on the mean and standard derivation values calculated in this study. Based on the demographically adjusted mean score value and standard deviation on each test performed by cognitively normal Chinese elderly (Table 5), we can determine that an individual has an impaired total score in each test and/or process scores of HVLT-R if the person has higher than 1 SD Z scores of the norm on TMT A, TMT B, and intrusion errors, or less than 1 SD Z scores of the norm on other measures. Meanwhile, we create objective diagnostic criteria for MCI, MCI subtypes, pre-MCI, and early or late stage of pre-MCI SCD according to the criteria reported in the previous literature [16, 17]. A clinician can use our norm or criteria to classify patients with different cognitive status, including pre-MCI SCD, MCI, and the subtypes of MCI in cross-sectional studies and predict the progress of cognitive decline in longitudinal elderly Chinese cohort studies. Pre-MCI SCD or MCI usually occur simultaneously with physical frailty, which is termed as reversible cognitive or potentially reversible cognitive frailty [6]. The diagnosis of cognitive frailty subtypes depends mainly on various questionnaires [33, 34] and rapid screening tools [35] that may result in false-positive errors. To integrate our established norm into the diagnosis criteria of cognitive frailty, a clinician can objectively diagnose cognitive frailty subtypes or revalidate these high-risk older people with cognitive frailty in the community assessed by rapid screening tools, and this will be efficient regarding resources and time.

The neuropsychological test battery reported in this study contains a set of tests that target early deficits of learning and memory and different cognitive domains, including attention, processing speed, and executive and memory performance. We chose neuropsychological tests that were widely used in the uniform data set and a sample of the Alzheimer’s Disease Neuroimaging Initiative cognitively normal participants, to meet our research aims. Here, we only replaced the AVLT with a relatively brief measure, the HVLT-R. The HVLT-R, with a short word list and less repetition is easy to administer to elderly people. Nonetheless, the main disadvantage of the method of normative norm and Z score is that it is difficult to assess meaningful changes in test scores following a given time interval or a treatment intervention, and it excludes the influence of test-retest reliability, practice effects, score fluctuations due to error, and relevant clinical factors. Another important normative method, reliable change index scores and standardized regression-based change score norms, can make up the deficiency [9, 12].

The demographics analyzed in this study represent better the community-dwelling older Chinese volunteers. A beneficial endeavor was made to establish normative data and a Z score integrated into preliminary diagnosis criteria for pre-MCI SCD, MCI, and cognitive frailty. Preliminary analyses indicated that the demographic variables of sex, age, and education affected test scores. Similarly to the results of a previous study performed using a cohort with different ethnicities, younger age and a higher level of education were associated with scores that indicated better cognitive performance compared with older age and a lower level of education. However, males and females only differed significantly on two measures of the language domain. These different findings might stem from the small size of the sample used in our study. The participants were mainly Chinese who were ≥60 years or ≤90 years old, the normative data were underrepresented when individuals’ ages were outside the range. Additional studies to expand the normative data to underrepresented individuals and even to population-based cohorts are needed. Finally, the findings of this study require further validation using biomarkers from cerebrospinal fluid, neuroimaging, or circulating exosomes.

The authors would like to thank all older volunteers and the health care staff members who have made substantive contributions to the research or the manuscript.

The research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. Subjects (or their parents or guardians) have given their written informed consent, and the study protocol was approved by the committee on human research of Huadong Hospital.

The authors have no conflicts of interest to declare.

This work received financial support from grants from the Medical Science and Technology Support Project of Shanghai Science and Technology Commission (grant No.18411962200) and the Shanghai clinical key discipline construction (grant No. 2017ZZ02010). The funding source played no role in the design or conduct of the study, the collection, management, analysis, or interpretation of the data, or the preparation, review, or approval of the manuscript.

R.Q., Y.Z.: study design. R.Q., Y.Z.: writing the manuscript. R.Q., X.F., G.K., Z.W.: analysis and interpretation of data. All authors contributed to experimental investigation and gave final approval of this version of the paper.

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