C-reactive protein (CRP) is a circulating biomarker indicative of systemic inflammation. We aimed to evaluate genetic associations with CRP levels among non-European-ancestry populations through discovery, fine-mapping and conditional analyses. A total of 30 503 non-European-ancestry participants from 6 studies participating in the Population Architecture using Genomics and Epidemiology study had serum high-sensitivity CRP measurements and ∼200 000 single nucleotide polymorphisms (SNPs) genotyped on the Metabochip. We evaluated the association between each SNP and log-transformed CRP levels using multivariate linear regression, with additive genetic models adjusted for age, sex, the first four principal components of genetic ancestry, and study-specific factors. Differential linkage disequilibrium patterns between race/ethnicity groups were used to fine-map regions associated with CRP levels. Conditional analyses evaluated for multiple independent signals within genetic regions. One hundred and sixty-three unique variants in 12 loci in overall or race/ethnicity-stratified Metabochip-wide scans reached a Bonferroni-corrected P-value <2.5E-7. Three loci have no (HACL1, OLFML2B) or only limited (PLA2G6) previous associations with CRP levels. Six loci had different top hits in race/ethnicity-specific versus overall analyses. Fine-mapping refined the signal in six loci, particularly in HNF1A. Conditional analyses provided evidence for secondary signals in LEPR, IL1RN and HNF1A, and for multiple independent signals in CRP and APOE. We identified novel variants and loci associated with CRP levels, generalized known CRP associations to a multiethnic study population, refined association signals at several loci and found evidence for multiple independent signals at several well-known loci. This study demonstrates the benefit of conducting inclusive genetic association studies in large multiethnic populations.
Genome wide association studies (GWAS) have identified numerous loci associated with BMI, a major risk factor for cardiometabolic disease. Yet, genetic associations with trajectory of weight change have been less studied, particularly in highly vulnerable populations such as racially diverse older women. We conducted a GWAS analysis on longitudinal weight among African American participants in the Women’s Health Initiative SNP Health Association Resource (WHI SHARe) study to better understand the genetic architecture of weight change during the post-menopausal period of the life course. Between 1993 and 2005, we collected longitudinal data on weight in 6,852 African American women between the ages of 50 and 79 at baseline. The average weight at first measure was 81 kg (16) with a range of 46 to 129 kg. The average change in weight per year was 0.09 kg (0.36) ranging from -3.3 to 5.7 kg. The average follow-up duration was 5.3 years (range: 1 to 11). We used time-varying, repeated weight measures across the trial period to perform a growth curve analysis for weight change. Using a mixed model with an unstructured covariance matrix, weight was regressed on year since randomization for both fixed and random effects to derive each individual’s weight change slope. Individuals were genotyped using Affymetrix 6.0 array and ~ 3.2 million SNPs were imputed using MACH (v1.0.16) and a 50(CEU):50(YRI) HapMap 2 reference panel. We performed association analyses with weight change residuals adjusted for clinical covariates and principal components, using an additive genetic model. While no associations reached genome wide significance (p<5E-8), several SNPs reached suggestive significance. Our three most significant signals at 5q22.1, 10q22.1, and 12q15 (all p<8E-7), lie near likely candidate genes involved in lipid breakdown, transport, and/or cholesterol homeostasis (e.g. STARD4, PSAP, IFNG). Interestingly, we observed a high level of phenotypic variance, which we attribute to complex weight fluctuation among post-menopausal women. We are currently conducting sensitivity analyses incorporating information on age since menopause and investigating different methods of longitudinal modeling in an attempt to minimize our trait variability, thereby improving our power to detect genetic effects. Also, we are replicating our top findings in other large longitudinal studies of African American women. While our preliminary analyses did not detect genome-wide significant associations with weight change among the participants of WHI SHARe, many SNPs with near genome wide significance are close to strong biological candidate genes and warrant further exploration. Future studies are needed to further characterize the genetic architecture of weight change at this vulnerable period of the life cycle for women so that effective interventions to prevent weight gain can be implemented.
Introduction: As health care systems strive to meet the growing needs of seriously ill patients with high symptom burden and functional limitations, they need evidence about how best to deliver home-based palliative care (HBPC). We compare a standard HBPC model that includes routine home visits by nurses and prescribing clinicians with a tech-supported model that aims to promote timely interprofessional team coordination using video consultation with the prescribing clinician while the nurse is in the patient's home. We hypothesize that tech-supported HBPC will be no worse compared with standard HBPC. Methods: This study is a pragmatic, cluster randomized noninferiority trial conducted across 14 Kaiser Permanente sites in Southern California and the Pacific Northwest. Registered nurses (n = 102) were randomized to the two models so that approximately half of the participating patient–caregiver dyads will be in each study arm. Adult English or Spanish-speaking patients (estimate 10,000) with any serious illness and a survival prognosis of 1–2 years and their caregivers (estimate 4800) are being recruited to the HomePal study over ∼2.5 years. The primary patient outcomes are symptom improvement at one month and days spent at home. The primary caregiver outcome is perception of preparedness for caregiving. Study Implementation—Challenges and Contributions: During implementation we had to balance the rigors of conducting a clinical trial with pragmatic realities to ensure responsiveness to culture, structures, workforce, workflows of existing programs across multiple sites, and emerging policy and regulatory changes. We built close partnerships with stakeholders across multiple representative groups to define the comparators, prioritize and refine measures and study conduct, and optimize rigor in our analytical approaches. We have also incorporated extensive fidelity monitoring, mixed-method implementation evaluations, and early planning for dissemination to anticipate and address challenges longitudinally. Trial Registration: ClinicalTrials.gov: NCT#03694431.
Numerous common genetic variants that influence plasma high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-C), and triglyceride distributions have been identified via genome-wide association studies (GWAS). However, whether or not these associations are age-dependent has largely been overlooked. We conducted an association study and meta-analysis in more than 22,000 European Americans between 49 previously identified GWAS variants and the three lipid traits, stratified by age (males: <50 or ≥50 years of age; females: pre- or postmenopausal). For each variant, a test of heterogeneity was performed between the two age strata and significant Phet values were used as evidence of age-specific genetic effects. We identified seven associations in females and eight in males that displayed suggestive heterogeneity by age (Phet < 0.05). The association between rs174547 (FADS1) and LDL-C in males displayed the most evidence for heterogeneity between age groups (Phet = 1.74E-03, I(2) = 89.8), with a significant association in older males (P = 1.39E-06) but not younger males (P = 0.99). However, none of the suggestive modifying effects survived adjustment for multiple testing, highlighting the challenges of identifying modifiers of modest SNP-trait associations despite large sample sizes.
