corrected for age and sex, as IL-18 serum concentrations are associated with these covariates (12). Details differed in the methods for the measures, so we used ranked data from each study (SPPB in 12 ranks, walk speed in 30) to combine in pooled analyses. All pooled models were adjusted for age, sex, study, plus known versus unknown family origin for Iowa-EPESE (but included only those reporting being white to a question about race).

Genetic Analyses

We used data from the HapMap (phase I) (http:// www.hapmap.org/) project to select an optimum set of SNPs from the IL-18 gene with minor allele frequencies . 0.1. Three SNPs (rs5744256, rs543810, and rs1293344) were selected that correlated with a minimum r2   0.8 with all other SNPs in the IL-18 gene. During the study, HapMap phase II data became available, and reanalysis showed that the three SNPs correlated with 15 of 21 SNPs at r2 . 0.8 and 17 SNPs at r2 . 0.5.

We used a Mendelian randomization (22) approach, implemented with the instrumental variables (27) options in Stata v9 (StataCorp, College Station, TX), to obtain estimates of the associations between IL-18 serum concen- trations and performance measures free from reverse cau- sality or confounding. We compared the instrumental variable estimates of the association between IL-18 concen- tration and performance measures to those from ordinary linear regression using a Durbin–Wu–Hausman endogeneity test based on the C statistic. This is analogous to testing whether any observed associations between SNPs and functioning were consistent with the expected association, given (a) the association between SNPs and IL-18 serum concentrations and (b) the correlation between IL-18 con- centrations and functioning (Figure 1).

A previous study described two SNPs associated with IL- 18 serum concentrations (15). Neither of these SNPs occurs in HapMap II. To ensure that we captured the information from these variants, we sequenced 581 bp of IL-18 containing the two SNPs IL-18/Aþ183G (rs5744292) and IL-18/Tþ533C (rs4937100) (15) in the 90 European HapMap samples. We then compared patterns of linkage disequilibrium between HapMap SNPs and the sequenced SNPs.

With the InCHIANTI DNA samples we first performed whole genome amplification, including 6% blind duplicates. We used the modified TaqMan assays at KBiosciences (Hoddesdon, U.K.) to generate genotypes from both SNPs for all samples including duplicates. For the Iowa-EPESE samples, we used conventional TaqMan probes (Applied Biosystems, Foster City, CA).

R ESULTS

trations are shown in Figure 2. There was a highly sig- nificant decrease in IL-18 concentrations for each additional C allele of rs5744256: Mean serum concentrations were 76.8 pmol/mL (0.5 SD) lower in rare homozygotes (TT) compared to common homozygotes (CC) (linear regression p ¼ 1 3 10(cid:3)5). There were no significant associations with the other two SNPs (rs543810, p ¼ .31, rs1293344, p ¼ .11).

Statistical Analysis

Most physical functioning measures can be analyzed as continuous variables, but are really ordinal scores that are more sensitive in identifying poor function: Analyses, therefore, used both linear and logistic regression models. We used logistic regression to calculate odds ratios of being among the poor functioning groups given a 1 standard deviation (SD) increase in IL-18 concentrations, using z scores of logged serum concentrations. All analyses were

(p . .05), and the genotyping error rate, as assessed by blind duplicates, was , 1% for each SNP. Our sequencing analysis in HapMap samples showed that rs5744256 is also

Table 2. Associations of IL-18 Serum Levels (Log Transformed) With Measures of Function in the InCHIANTI Study (Ages 65–80 Years)

Odds Ratio for Being in Linear Regression p Value Poorest Functioning Group Coefficient (Regression Given a 1 SD Increase p Value for Performance Measure (95% CI) Coefficient) in IL-18 (95% CI) Odds Ratio Self-reported functioning ADLs 0.11 (–0.01 to 0.23) .076 1.27 (0.84–1.91) .26 Tested physical performance Summary Physical Performance Battery (cid:3)0.76 ((cid:3)1.25 to (cid:3)0.28) .002 1.52 (1.22–1.89) .00016 Walking test (7 m) 0.08 (0.03–0.13) .00096 1.45 (1.17–1.80) .00070 Tested cognitive function MMSE (cid:3)0.01 ((cid:3)0.04 to 0.02) .466 1.05 (0.86–1.28) .66

Notes: All models were adjusted for age and sex. Poorest functioning groups are defined as: 1 or more Activities of Daily Living (ADLs), lowest 20% of performance scores, slowest 20% of walking times, and lowest 20% Mini-Mental State Examination (MMSE) scores within the 65–80 age group. IL-18 ¼ interleukin-18; CI ¼ confidence interval; SD ¼ standard deviation.

strongly correlated (r2 ¼ 0.85) with the IL-18/Aþ183G (rs5744292) SNP reported by Tiret and colleagues (15) as associated with IL-18 concentrations.

Using both the InCHIANTI and Iowa-EPESE studies, we next assessed the role of the IL-18 rs5744256 SNP on the functional measures associated with IL-18 concentrations. We hypothesized that C allele carriers would have lower rates of limitation in physical functioning, given that the C allele reduces IL-18 concentrations and the positive correlation between IL-18 concentrations and functioning.

Using the InCHIANTI participants, we found associa- tions between IL-18 serum concentrations and the tested physical performance measures (SPPB and 7-meter walk- time) in both linear and logistic models (Table 2). A 1 SD increase in IL-18 serum concentrations was associated with an increased chance of being in the 20% of slowest walkers (odds ratio, 1.45; 95% confidence interval [CI], 1.17–1.80; p ¼ .0007) and 20% of poorest functioning participants based on the Summary Performance Scores (SPPB) (odds ratio, 1.52; 95% CI, 1.22–1.89; p ¼ .00016). There was no association with ADL (p ¼ .26) or Mini-Mental State Examination (p ¼ .66).