Descriptive statistics for demographic and clinical characteristics are provided in Table for all 93 participants. As expected, motor and functional capacity scores indicated that participants were in the mild-to-moderate stages of disease. Average motor scores suggested that this sample had notable motor impairments and the majority of the sample (n = 68) reported functional decline to some degree. Delayed memory scores suggested that almost half of the sample (n = 44) also had clinically significant memory deficits (i.e., 2 SD below the mean of normal individuals). Depression scores were positively skewed, as most participants (n = 53) did not report any depressed mood.
Demographic and clinical characteristics of HD participants (N = 93)
Age, education, and sex were not related to each other; however, there were differences in the education level based on race (F2,90= 5.32, p =.007), with Native Americans having fewer years of education (M= 9.0, SD= 1.41) than African American (M =13.0, SD= 3.61) or Caucasian (M= 13.80, SD =2.05) participants. Age and race were the only demographic factors not associated with any of the clinical characteristics of HD (i.e., TFC, TMS, UHDRS depression item, and delayed memory scores). There were sex differences—t(91) = −2.33, p= .022—in delayed memory scores with men having lower z-scores (M= −2.08, SD= 1.30) than women (M= −1.41, SD= 1.40). Similarly, years of education was related to delayed memory scores—t(91) = 2.12, p= .037—with college graduates having higher z-scores (M= −1.16, SD= 1.31) than those with <16 years of education (M= −1.88, SD= 1.38).
Table presents participants according to the standard scores derived from the WRAT Reading subtest and the Barona formula. A t-test comparing WRAT-3 and WRAT-4 Reading subtest standard scores indicated that there was no statistical difference between the two forms—t(85) = 1.36, p = .18; therefore, the two WRAT Reading forms were collapsed into a single WRAT Reading subtest variable. WRAT Reading scores were normally distributed with a mean of 92.9 (SD = 11.2). The mean Barona score was higher at 107.5 (SD = 7.54). The distribution of the Barona scores was negatively skewed with almost half the sample (n = 45) having an estimated IQ >110. Accordingly, the Barona formula provided estimates of premorbid intelligence that were higher than WRAT Reading estimates for 89.3% (n = 83) of the participants. The average discrepancy between estimates was nearly 1SD with a mean difference of −14.6 (SD= 11.0) standard score points. For the 10.7% (n = 10) of participants with higher WRAT Reading scores than the Barona, WRAT Reading scores were an average of 4.60 (SD= 3.91) points above the Barona estimate, with discrepancies ranging from 0.02 to 13.72 points. On HD symptom measures, there were no statistically significant differences between the group who obtained higher WRAT Reading scores than the Barona compared with the group who obtained higher Barona estimates than WRAT Reading scores.
The number of participants with HD in each standard score group on the WRAT-Reading subtest and Barona IQ estimation formula (N = 93)
Bivariate correlations between demographic variables, clinical measures, and premorbid intelligence estimates are found in Table . Poorer performances on delayed memory and TMS were related to lower WRAT Reading scores. Delayed memory scores, regardless of the measure used (i.e., HVLT-R vs. RBANS), accounted for a significant portion of the variance in WRAT Reading scores (r2= .24 for the entire sample). TMS also contributed to the variance in WRAT Reading scores, although to a lesser degree (r2= .06). In contrast to WRAT Reading scores, Barona scores had a weak relationship with delayed memory and were not related to TMS. UHDRS depression scores and total functional capacity were not related to either estimate of premorbid intelligence. Interestingly, delayed memory was the only clinical variable that correlated with discrepancies between the two measures, as it accounted for 11% of the variability in the discrepancy score. The Barona formula and the WRAT–Barona discrepancy score also correlated with demographic factors, which is to be expected since demographic variables are a key component of the Barona formula.
Bivariate correlations between demographic and HD clinical characteristics with estimates of premorbid intelligence (N = 93)
Because both motor dysfunction and memory scores were associated with WRAT Reading, we conducted brief follow-up analyses to examine group differences in WRAT Reading performance based on motor and delayed memory scores. Since there are no established cutoff scores for differing levels of motor impairment, we divided the sample into two extreme groups (i.e., highest and lowest 25%). Differences between the upper and lower quartile of participants based on motor score were not significant for the Barona, but were significant for WRAT Reading—t(46) = 2.50, p= .016. There was a spread of nearly eight standard score points between the quartiles on WRAT Reading (lower quartile M= 97.4, SD= 10.4; upper quartile M= 89.6, SD= 11.3) with an effect size of d= 0.72. We also examined group differences based on delayed memory scores. The sample was separated into those with scores indicative of memory impairment (z= −2.00) versus the remainder of the sample. Again, there were no differences between the groups’ Barona estimates; however, WRAT Reading scores were markedly different—t(91) = 5.52, p ≤ .0001—and approximately 11 points lower for participants below the cut point (nonimpaired M= 98.2, SD= 8.9; impaired M= 87.0, SD= 10.7) with an effect size of d= 1.14.