Analysis of Yield Spreads on Commercial Mortgage-Backed Securities

Yield spreads on commercial mortgage-backed securities (CMBS) are defined as the difference between the yield on CMBS and the yield on comparable-maturity Treasuries. CMBS yield spreads declined dramatically from 1992 until 1997, then increased in 1998 and 1999. The relationship between CMBS yield spreads and other variables is estimated in an effort to explain recent trends. Results identify several variables that are related to yield spreads on both fixed-rate and variable-rate CMBS. However, even after controlling for other observable factors, the yield spread on CMBS still declined from 1992 until 1997, then increased each of the next two years. Possible explanations for this phenomenon are explored.

CMBS Mortgage Pool Diversification and Yields: An Empirical Note: Working Paper Series--05-12

In the early years of the Commercial Mortgage-Backed Securities market, nearly all issues were backed by mortgages on a single property type. Beginning in the early 1990's, the composition of CMBS mortgage pools changed, and most mortgages on a variety of property types. This study examines the relationship between the diversification of the CMBS mortgage pool and yields on CMBS supported by the pool. Results indicate that yield spreads on AAA-rated CMBS backed by diversified pools are slightly lower than yield spreads on CMBS backed by undiversified pools. There is no evidence that yields on lower-rated CMBS are affected by pool diversification

Immune Response to SARS-CoV-2 Third Vaccine in Patients With Rheumatoid Arthritis Who Had No Seroconversion After Primary 2-Dose Regimen With Inactivated or Vector-Based Vaccines

Objective. The aim of this study was to assess the immune response after a third dose of SARS-CoV-2 vaccine in patients with rheumatoid arthritis (RA) with undetectable antibody titers after the primary regimen of 2 doses. Methods. Patients with RA with no seroconversion after 2 doses of SARS-CoV-2 vaccine and who received a third dose of either an mRNA or vector-based vaccine were included. Anti-SARS-CoV-2 IgG antibodies, neutralizing activity, and T cell responses were assessed after the third dose. Results. A total of 21 nonresponder patients were included. At the time of vaccination, 29% were receiving glucocorticoids and 85% biologic disease-modifying antirheumatic drugs (including 6 taking abatacept [ABA] and 4 taking rituximab [RTX]). The majority (95%) received the BNT162b2 vaccine and only one of them received the ChAdOx1 nCoV-19 vaccine. After the third dose, 91% of the patients presented detectable anti-SARS-CoV-2 IgG and 76% showed neutralizing activity. Compared to other treatments, ABA and RTX were associated with the absence of neutralizing activity in 4 out of 5 (80%) patients and lower titers of neutralizing antibodies (median 3, IQR 0-20 vs 8, IQR 4-128; P = 0.20). Specific T cell response was detected in 41% of all patients after the second dose, increasing to 71% after the third dose. The use of ABA was associated with a lower frequency of T cell response (33% vs 87%, P = 0.03). Conclusion. In this RA cohort, 91% of patients who failed to seroconvert after 2 doses of SARS-CoV-2 vaccine presented detectable anti-SARS-CoV-2 IgG after a third dose. The use of ABA was associated with a lower frequency of specific T cell response.Fil: Isnardi, Carolina A.. No especifíca;Fil: Cerda, Osvaldo L.. No especifíca;Fil: Landi, Margarita. Austral University Hospital; LiberiaFil: Cruces, Leonel Hernán. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Schneeberger, Emilce E.. No especifíca;Fil: Montoro, Claudia Calle. Austral University Hospital; LiberiaFil: Alfaro, María Agustina. No especifíca;Fil: Roldán, Brian M.. No especifíca;Fil: Gómez Vara, Andrea B.. No especifíca;Fil: Giorgis, Pamela. No especifíca;Fil: Ezquer, Roberto Alejandro. No especifíca;Fil: Crespo Rocha, María G. No especifíca;Fil: Reyes Gómez, Camila R.. No especifíca;Fil: de Los Ángeles Correa, Mária. No especifíca;Fil: Rosemffet, Marcos G.. No especifíca;Fil: Abarza, Virginia Carrizo. No especifíca;Fil: Pellet, Santiago Catalan. Austral University Hospital; LiberiaFil: Perandones, Miguel. No especifíca;Fil: Reimundes, Cecilia. Austral University Hospital; LiberiaFil: Longueira, Yesica Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Turk, Gabriela Julia Ana. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Quiroga, María Florencia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Laufer, Natalia Lorna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Biomédicas en Retrovirus y Sida. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Biomédicas en Retrovirus y Sida; ArgentinaFil: Quintana, Rosana Maris. No especifíca;Fil: de la Vega, María Celina. No especifíca;Fil: Kreplak, Nicolás. No especifíca;Fil: Pifano, Marina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Maid, Pablo. Austral University Hospital; LiberiaFil: Pons Estel, Guillermo J.. No especifíca;Fil: Citera, Gustavo. No especifíca

Multi-ethnic genome-wide association study for atrial fibrillation

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and has a complex heritability. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels