Operationalizing Frailty in the Atherosclerosis Risk in Communities Study Cohort

Background: Factors that may contribute to the development of frailty in late life have not been widely investigated. The Atherosclerosis Risk in Communities (ARIC) Study cohort presents an opportunity to examine relationships of midlife risk factors with frailty in late life. However, we first present findings on the validation of an established frailty phenotype in this predominantly biracial population of older adults. Methods: Among 6,080 participants, we defined frailty based upon the Cardiovascular Health Study (CHS) criteria incorporating measures of weight loss, exhaustion, slow walking speed, low physical activity, and low grip strength. Criterion and predictive validity of the frailty phenotype were estimated from associations between frailty status and participants' physical and mental health status, physiologic markers, and incident clinical outcomes. Results: A total of 393 (6.5%) participants were classified as frail and 50.4% pre-frail, similar to CHS (6.9% frail, 46.6% pre-frail). In age-adjusted analyses, frailty was concurrently associated with depressive symptoms, low self-rated health, low medication adherence, and clinical biomarker levels (ie, cholesterol, hemoglobin A1c, white blood cell count, C-reactive protein, and hemoglobin). During 1-year follow-up, frailty was associated with falls, low physical ability, fatigue, and mortality. Conclusions: These findings support the validity of the CHS frailty phenotype in the ARIC Study cohort. Future studies in ARIC may elucidate early-life exposures that contribute to late-life frailty

Characteristics of the diffuse astrophysical electron and tau neutrino flux with six years of IceCube high energy cascade data

We report on the first measurement of the astrophysical neutrino flux using particle showers (cascades) in IceCube data from 2010 -- 2015. Assuming standard oscillations, the astrophysical neutrinos in this dedicated cascade sample are dominated ($\sim 90 \%$) by electron and tau flavors. The flux, observed in the sensitive energy range from $16\,\mathrm{TeV}$ to $2.6\,\mathrm{PeV}$, is consistent with a single power-law model as expected from Fermi-type acceleration of high energy particles at astrophysical sources. We find the flux spectral index to be $\gamma=2.53\pm0.07$ and a flux normalization for each neutrino flavor of $\phi_{astro} = 1.66^{+0.25}_{-0.27}$ at $E_{0} = 100\, \mathrm{TeV}$, in agreement with IceCube's complementary muon neutrino results and with all-neutrino flavor fit results. In the measured energy range we reject spectral indices $\gamma\leq2.28$ at $\ge3\sigma$ significance level. Due to high neutrino energy resolution and low atmospheric neutrino backgrounds, this analysis provides the most detailed characterization of the neutrino flux at energies below $\sim100\,{\rm{TeV}}$ compared to previous IceCube results. Results from fits assuming more complex neutrino flux models suggest a flux softening at high energies and a flux hardening at low energies (p-value $\ge 0.06$). The sizable and smooth flux measured below $\sim 100\,{\rm{TeV}}$ remains a puzzle. In order to not violate the isotropic diffuse gamma-ray background as measured by the Fermi-LAT, it suggests the existence of astrophysical neutrino sources characterized by dense environments which are opaque to gamma-rays.Comment: 4 figures, 4 tables, includes supplementary materia

IceCube Search for Neutrinos Coincident with Compact Binary Mergers from LIGO-Virgo's First Gravitational-Wave Transient Catalog

Using the IceCube Neutrino Observatory, we search for high-energy neutrino emission coincident with compact binary mergers observed by the LIGO and Virgo gravitational wave (GW) detectors during their first and second observing runs. We present results from two searches targeting emission coincident with the sky localization of each gravitational wave event within a 1000 second time window centered around the reported merger time. One search uses a model-independent unbinned maximum likelihood analysis, which uses neutrino data from IceCube to search for point-like neutrino sources consistent with the sky localization of GW events. The other uses the Low-Latency Algorithm for Multi-messenger Astrophysics, which incorporates astrophysical priors through a Bayesian framework and includes LIGO-Virgo detector characteristics to determine the association between the GW source and the neutrinos. No significant neutrino coincidence is seen by either search during the first two observing runs of the LIGO-Virgo detectors. We set upper limits on the time-integrated neutrino emission within the 1000 second window for each of the 11 GW events. These limits range from 0.02-0.7 $\mathrm{GeV~cm^{-2}}$. We also set limits on the total isotropic equivalent energy, $E_{\mathrm{iso}}$, emitted in high-energy neutrinos by each GW event. These limits range from 1.7 $\times$ 10$^{51}$ - 1.8 $\times$ 10$^{55}$ erg. We conclude with an outlook for LIGO-Virgo observing run O3, during which both analyses are running in real time

The genetic architecture of type 2 diabetes

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of heritability. To test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole genome sequencing in 2,657 Europeans with and without diabetes, and exome sequencing in a total of 12,940 subjects from five ancestral groups. To increase statistical power, we expanded sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support a major role for lower-frequency variants in predisposition to type 2 diabetes