Soluble CD14 and CD14 Variants, Other Inflammatory Markers, and Glucose Dysregulation in Older Adults: The Cardiovascular Health Study

Study Population

The design and rationale of CHS have been described before (18). Briefly, CHS is a prospective population-based cohort study of older adult men and women recruited from four U.S. field centers: Forsyth County, NC; Sacramento County, CA; Washington County, MD; and Pittsburgh, PA. Candidates were randomly sampled from Medicare eligibility lists, and those who were institutionalized, wheelchair-bound, in hospice, receiving treatment for cancer, not expected to remain in the area for the next 3 years, or unable to give informed consent were excluded. A total of 5,201 participants (original cohort) were recruited in 1988–1989 from random samples of Medicare eligibility lists. An additional 687 primarily black participants were recruited in 1992–1993 (supplemental cohort) for a total cohort of 5,888 individuals. During in-person visits, participants underwent evaluation for demographic and lifestyle factors, physical examination, and fasting blood collection for laboratory measurement and storage using standardized protocols, as previously described (18,19). After excluding individuals with treated diabetes (type 2, in this age range) at baseline, we had a data set of 5,380 participants. Of those, 5,296 individuals had measures of both fasting glucose and fasting insulin at baseline, who were included in analyses of insulin resistance measures. For these analyses, we had baseline measures of sCD14 in 4,999 participants and of IL-6, CRP, and WBC count in 4,927, 5,302, and 5,305 participants, respectively. After further exclusion of 515 individuals with prevalent type 2 diabetes at baseline by fasting glucose (≥126 mg/dL), random glucose (≥200 mg/dL), or antihyperglycemic medication, our study sample comprised 4,865 participants for assessment of the association with incident type 2 diabetes (hereafter referred to as diabetes). For this set of analyses, we had baseline measures of sCD14 in 4,558 participants and of IL-6, CRP, and WBC count in 4,502, 4,836, and 4,850 participants, respectively. For instrumental variable analyses of sCD14 in relation to insulin resistance measures using Mendelian randomization, which were stratified by race, genotypic data were available in 3,387 white and 422 African American participants.

Inflammatory Measures

sCD14 was measured in baseline plasma specimens using a commercial ELISA (R&D Systems, Minneapolis, MN) with an interassay coefficient of variation of 5.3–12.4% (20). CRP was measured using an ultra-sensitive ELISA developed at the CHS Central Laboratory at the University of Vermont with an interassay coefficient of variation of 5.5% (20). IL-6 was measured using commercial ELISA kits (Quantikine IL-6; R&D Systems, Minneapolis, MN) with an interassay coefficient of variation of 6.3%. Complete blood counts (including WBC counts) were measured at each of the participating clinical centers using the following instruments: Coulter Stack S cell counter (Coulter, Inc., Hialeah, FL) (University of Pittsburgh, Pittsburgh, PA, and University of California, Davis, Davis, CA) and the Sysmex NE8000 counter (Toa Electronics, Inc., Chicago, IL) (Wake Forest University, Winston-Salem, NC, and Johns Hopkins University, Baltimore, MD).

Genotyping

Genotyping done in this cohort has been described in detail (15). Fine-mapping of the CD14 locus was performed in 3,950 European American (3,660 with sCD14 measured) and 792 African American (683 with sCD14 measured) participants who were genotyped using the custom gene-centric IBCv2 genotyping array that contains high single nucleotide polymorphism (SNP) marker density and linkage disequilibrium coverage for various cardiovascular, metabolic, and inflammation-related genes, including CD14 (21). IBCv2 array used a “cosmopolitan” tagging approach to capture the genetic diversity across candidate genes in the multiple ethnic populations represented in the HapMap, including both Europeans and West Africans. Tag SNPs were selected to capture known variation with minor allele frequency >0.02 and an r² of at least 0.8 in HapMap populations. The top SNP in whites was identified as rs5744455, while that in African Americans was identified as rs5744451.

Insulin Resistance and Diabetes

Fasting glucose and insulin values were analyzed using standard enzymatic methods at the University of Vermont Central Laboratory. Measurements were performed in serum at baseline, as well as in all but one of the follow-up examinations, which used EDTA plasma. Specimens were frozen at −70°C <1 h after collection (22). Values were harmonized across examinations, as described previously (23). Two measures of insulin resistance were selected: 1) quantitative insulin-sensitivity check index (QUICKI), which is derived from a formula using fasting insulin and fasting glucose and correlates well with glucose clamp studies (24,25), and 2) HOMA 2 index of insulin resistance (HOMA2IR), which improves upon the original HOMA index by using a computer program (26,27).

Participants were followed up through year 2011 for incident diabetes, defined as a fasting glucose ≥126 mg/dL, random glucose ≥200 mg/dL, or use of antihyperglycemic medication. As previously described, after the 1989–1990 examination, glucose measurements were obtained in 1992–1993, 1994–1995, 1996–1997, 1998–1999, and, in a subset, 2005–2006 (23). All were on fasting samples, with the exception of 1994–1995, which were random.

Covariates

Hypertension was defined by systolic and diastolic cutoffs of 140 and 90 mmHg, respectively, or by self-report and antihypertensive therapy. Trained personnel performed anthropometric measurements in standardized fashion. Measurement of physical activity in kilocalories has been previously reported (23). Heavy alcohol use was defined as consumption of >14 drinks/week in men or >7 drinks/week in women. Prevalent atherosclerotic cardiovascular disease (ASCVD) consisted of coronary heart disease, stroke or transient ischemic attack, and peripheral arterial disease. Along with heart failure (HF), prevalent ASCVD components were ascertained at the 1989–1990 and 1992–1993 examinations and in the intervening period through a combination of CHS questionnaires, medical record review, and physician confirmation (19,28). Additionally, fasting baseline samples were used to measure creatinine and lipids (22). Estimated glomerular filtration rate (eGFR) was calculated using the Chronic Kidney Disease Epidemiology equation (29).

Statistical Methods

Baseline characteristics are presented as median (interquartile range) for continuous variables and count (percent) for categorical variables, both in the cohort overall and by quartiles of sCD14 level. We tested for trend across increasing quartiles of sCD14 by using contrasts in generalized linear models for continuous variables and the Cochran-Armitage test for categorical variables. The four biomarkers showed positive skew and were converted to base-2 logarithm to normalize their distributions and to evaluate their associations with outcomes uniformly per doubling of their levels. Pearson correlation coefficients were calculated between log-transformed inflammatory biomarkers. Using minimally and fully adjusted linear regression models, we tested the associations between the biomarkers and QUICKI and HOMA2IR. To avoid disproportionate influence of extreme upper values for HOMA2IR, this measure was winsorized at the 99th percentile. The initial model adjusted for age, sex, and race, while the full model additionally adjusted for BMI, systolic blood pressure, use of antihypertensive medication, smoking status, heavy alcohol use, physical activity, estrogen use, prevalent ASCVD, prevalent HF, and eGFR. A subsequent model examined whether additional adjustment for LDL cholesterol influenced the findings; as triglycerides and HDL cholesterol are influenced by insulin resistance, they were considered part of the outcome and therefore not included for adjustment. Additional sensitivity analyses evaluated the impact of further adjustment for use of statins, oral corticosteroids, and nonsteroidal anti-inflammatory drugs. In the second part of the analyses, participants with prevalent diabetes at baseline based on fasting glucose, random glucose, and medication use were excluded. Using Cox proportional hazards models with the same covariates for the initial and full models, we tested for association between the four biomarkers and incident diabetes. In both linear regression and Cox regression analyses, we assessed the functional form of the relationship between inflammatory markers and outcome measures using generalized additive model (GAM) plots. For analyses of insulin resistance and incident diabetes, we tested for interaction by age, sex, race, BMI, prevalent ASCVD, and prevalent HF by including appropriate cross-product terms in the full models. Last, we also tested for associations of the biomarkers with QUICKI, HOMA2IR, and incident diabetes independent of each other by including the four biomarkers simultaneously in the full model.

For the Mendelian randomization analysis, we used the two-stage least-squares method using sCD14 as the endogenous regressor; SNPs rs5744455, rs778584, and rs4914 in whites and rs5744451 in African Americans as instrumental variables; and QUICKI and HOMA2IR as the continuous outcomes.

We used SAS 9.4 (SAS Institute, Cary, NC) and Stata 15 (StataCorp, College Station, TX) for all analyses. A two-tailed P value <0.05 was considered statistically significant.