Background

The hormone klotho, encoded by the gene klotho, is primarily expressed in the kidney and choroid plexus of the brain. Higher klotho concentrations have been linked to better physical performance; however, it is unknown whether klotho relates to frailty status in older adults.

Methods

Plasma klotho was measured in 774 participants aged ≥65 years enrolled in InCHIANTI, a prospective cohort study comprising Italian adults. Frailty status was assessed at 3 and 6 years after enrollment. Frailty was defined as presence of at least three out of five criteria of unintentional weight loss, exhaustion, sedentariness, muscle weakness, and slow walking speed; prefrailty was defined as presence of one or two criteria; and robustness was defined as zero criteria. We assessed whether plasma klotho concentrations measured at the 3-year visit related to frailty.

Results

Each additional natural logarithm of klotho (pg/mL) was associated with lower odds of frailty versus robustness after adjustment for covariates (odds ratio [OR] 0.46; 95% confidence interval 0.21, 0.98; p-value = .045). Higher klotho was particularly associated with lower odds of exhaustion (OR 0.57; 95% CI 0.36, 0.89; p-value = .014). Participants with higher klotho also had lower estimated odds of weight loss and weakness, but these findings were not statistically significant.

Conclusions

Higher plasma klotho concentrations were associated with lower likelihoods of frailty and particularly exhaustion. Future studies should investigate modifiable mechanisms through which klotho may affect the frailty syndrome.

Objective

Lower health literacy is associated with higher rates of mortality and chronic disease. It remains unclear whether health literacy is associated with diabetes and/or hyperglycemia in older adults, and if this relationship differs by sex.

Research design and methods

We performed a cross-sectional analysis of 2510 older adults in the Health, Aging and Body Composition (Health ABC) Study who had both a Rapid Estimate of Adult Literacy in Medicine (REALM) measurement and diabetes status available. Sex-stratified logistic regression models were used to analyze the relationship of health literacy categories (low, medium, and high) to diabetes status, adjusting for key covariates. Secondary analyses examined the relationship of health literacy to glycemic markers (A1C, fasting blood glucose).

Results

Among participants in the Health ABC cohort, 429 had diabetes. Mean age was 76years old and 45% were female. Men with diabetes more commonly had low health literacy levels than men without diabetes (10.1% versus 9.3%, p=0.02). Similar results were seen among women (14.7% versus 6.1%, p<0.01). In a model adjusting for age, race, income, education, BMI, smoking, and alcohol use, women with low versus high health literacy had a two-fold higher likelihood of diabetes (OR=2.2; 95% CI 1.1-4.3). No significant relationship was observed in men. Progressively lower categories of health literacy were associated with higher age-adjusted mean A1C and fasting blood glucose levels in women (both p for trend <0.01) but not men.

Conclusions

In this large, ethnically diverse sample of community-dwelling older adults, lower health literacy level is related to a greater likelihood of diabetes and higher A1C and fasting blood glucose levels in women-but not in men-after adjusting for age, race, and other demographic and lifestyle factors. Future studies are needed to assess mechanisms underlying this relationship and if interventions to improve health literacy are effective in reducing the burden of diabetes, particularly in women.

Following regulatory guidance set forth in 2008 by the US Food and Drug Administration for new drugs for type 2 diabetes mellitus, many large randomized, controlled trials have been conducted with the primary goal of assessing the safety of antihyperglycemic medications on the primary end point of major adverse cardiovascular events, defined as cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke. Heart failure (HF) was not specifically mentioned in the US Food and Drug Administration guidance and therefore it was not a focus of these studies when planned. Several trials subsequently showed the impact of antihyperglycemic drugs on HF outcomes, which were not originally specified as the primary end point of the trials. The most impressive finding has been the substantial and consistent risk reduction in HF hospitalization seen across 4 trials of sodium glucose cotransporter 2 inhibitors. However, to date, these results have not led to regulatory approval of any of these drugs for a HF indication or a recommendation for use by US HF guidelines. It is therefore important to explore to what extent persuasive treatment effects on nonprimary end points can be used to support regulatory claims and guideline recommendations. This topic was discussed by researchers, clinicians, industry sponsors, regulators, and representatives from professional societies, who convened on the US Food and Drug Administration campus on March 6, 2019. This report summarizes these discussions and the key takeaway messages from this meeting.

Abstract

Acute pancreatitis (AP) is a disease characterized by an acute inflammatory phase followed by a convalescent phase. Diabetes mellitus (DM) was historically felt to be a transient phenomenon related to acute inflammation; however, it is increasingly recognized as an important late and chronic complication. There are several challenges that have prevented precisely determining the incidence rate of DM after AP and understanding the underlying mechanisms. The DREAM (Diabetes RElated to Acute Pancreatitis and its Mechanisms) Study is a prospective cohort study designed to address these and other knowledge gaps to provide the evidence needed to screen for, prevent, and treat DM after AP. In the following article, we summarize literature regarding the epidemiology of DM after AP and provide the rationale and an overview of the DREAM study.

Anthropometric traits, measuring body size and shape, are highly heritable and significant clinical risk factors for cardiometabolic disorders. These traits have been extensively studied in genome-wide association studies (GWASs), with hundreds of genome-wide significant loci identified. We performed a whole-exome sequence analysis of the genetics of height, body mass index (BMI) and waist/hip ratio (WHR). We meta-analyzed single-variant and gene-based associations of whole-exome sequence variation with height, BMI, and WHR in up to 22,004 individuals, and we assessed replication of our findings in up to 16,418 individuals from 10 independent cohorts from Trans-Omics for Precision Medicine (TOPMed). We identified four trait associations with single-nucleotide variants (SNVs; two for height and two for BMI) and replicated the LECT2 gene association with height. Our expression quantitative trait locus (eQTL) analysis within previously reported GWAS loci implicated CEP63 and RFT1 as potential functional genes for known height loci. We further assessed enrichment of SNVs, which were monogenic or syndromic variants within loci associated with our three traits. This led to the significant enrichment results for height, whereas we observed no Bonferroni-corrected significance for all SNVs. With a sample size of ∼20,000 whole-exome sequences in our discovery dataset, our findings demonstrate the importance of genomic sequencing in genetic association studies, yet they also illustrate the challenges in identifying effects of rare genetic variants.

In this study, we investigated low-frequency and rare variants associated with blood pressure (BP) by focusing on a linkage region on chromosome 16p13. We used whole genome sequencing (WGS) data obtained through the NHLBI Trans-Omics for Precision Medicine (TOPMed) program on 395 Cleveland Family Study (CFS) European Americans (CFS-EA). By analyzing functional coding variants and non-coding rare variants with CADD score > 10 residing within the chromosomal region in families with linkage evidence, we observed 25 genes with nominal statistical evidence (burden or SKAT p < 0.05). One of the genes is RBFOX1, an evolutionarily conserved RNA-binding protein that regulates tissue-specific alternative splicing that we previously reported to be associated with BP using exome array data in CFS. After follow-up analysis of the 25 genes in ten independent TOPMed studies with individuals of European, African, and East Asian ancestry, and Hispanics (N = 29,988), we identified variants in SLX4 (p = 2.19 × 10^-4) to be significantly associated with BP traits when accounting for multiple testing. We also replicated the associations previously reported for RBFOX1 (p = 0.007). Follow-up analysis with GTEx eQTL data shows SLX4 variants are associated with gene expression in coronary artery, multiple brain tissues, and right atrial appendage of the heart. Our study demonstrates that linkage analysis of family data can provide an efficient approach for detecting rare variants associated with complex traits in WGS data.

Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.

Few studies have demonstrated reproducible gene-diet interactions (GDIs) impacting metabolic disease risk factors, likely due in part to measurement error in dietary intake estimation and insufficient capture of rare genetic variation. We aimed to identify GDIs across the genetic frequency spectrum impacting the macronutrient-glycemia relationship in genetically and culturally diverse cohorts. We analyzed 33,187 participants free of diabetes from 10 National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program cohorts with whole-genome sequencing, self-reported diet, and glycemic trait data. We fit cohort-specific, multivariable-adjusted linear mixed models for the effect of diet, modeled as an isocaloric substitution of carbohydrate for fat, and its interactions with common and rare variants genome-wide. In main effect meta-analyses, participants consuming more carbohydrate had modestly lower glycemic trait values (e.g., for glycated hemoglobin [HbA1c], -0.013% HbA1c/250 kcal substitution). In GDI meta-analyses, a common African ancestry-enriched variant (rs79762542) reached study-wide significance and replicated in the UK Biobank cohort, indicating a negative carbohydrate-HbA1c association among major allele homozygotes only. Simulations revealed that >150,000 samples may be necessary to identify similar macronutrient GDIs under realistic assumptions about effect size and measurement error. These results generate hypotheses for further exploration of modifiable metabolic disease risk in additional cohorts with African ancestry.

Article highlights

We aimed to identify genetic modifiers of the dietary macronutrient-glycemia relationship using whole-genome sequence data from 10 Trans-Omics for Precision Medicine program cohorts. Substitution models indicated a modest reduction in glycemia associated with an increase in dietary carbohydrate at the expense of fat. Genome-wide interaction analysis identified one African ancestry-enriched variant near the FRAS1 gene that may interact with macronutrient intake to influence hemoglobin A1c. Simulation-based power calculations accounting for measurement error suggested that substantially larger sample sizes may be necessary to discover further gene-macronutrient interactions.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.