Fossil traces of the bone-eating worm Osedax in early Oligocene whale bones

Osedax is a recently discovered group of siboglinid annelids that consume bones on the seafloor and whose evolutionary origins have been linked with Cretaceous marine reptiles or to the post-Cretaceous rise of whales. Here we present whale bones from early Oligocene bathyal sediments exposed in Washington State, which show traces similar to those made by Osedax today. The geologic age of these trace fossils (∼30 million years) coincides with the first major radiation of whales, consistent with the hypothesis of an evolutionary link between Osedax and its main food source, although older fossils should certainly be studied. Osedax has been destroying bones for most of the evolutionary history of whales and the possible significance of this “Osedax effect” in relation to the quality and quantity of their fossils is only now recognized.     

The mediated effects of maternal depression and infant temperament on maternal role

We examined prenatal depression, postpartum depression, and infant temperament, respectively, in a mediated process model to predict maternal role. Using a prospective, observational design, we surveyed 168 women during pregnancy and then in postpartum. Data analyses supported the contribution of each variable in an ascending fashion (ab?=??0.01, SE?=?0.004, 95 % CI [?0.021, ?0.004]), such that infant temperament had the strongest effects (sr ² ?=?.124, p?<?.001). Further, postpartum depression was found to influence maternal role with both direct effects and indirect effects via infant temperament. These results highlighted the significant impact postpartum depression may have on maternal role. Future interventions targeting mothers experiencing or who are at risk for depression may consider tools to improve mother-baby interactions. The effects of such intervention may subsequently improve both infant temperament and maternal role evaluation.     

Large common deletions associate with mortality at old age

Copy-number variants (CNVs) are a source of genetic variation that increasingly are associated with human disease. However, the role of CNVs in human lifespan is to date unknown. To identify CNVs that influence mortality at old age, we analyzed genome-wide CNV data in 5178 participants of Rotterdam Study (RS1) and positive findings were evaluated in 1714 participants of the second cohort of the Rotterdam Study (RS2) and in 4550 participants of Framingham Heart Study (FHS). First, we assessed the total burden of rare (frequency <1%) and common (frequency >1%) CNVs for association with mortality during follow-up. These analyses were repeated by stratifying CNVs by type and size. Secondly, we assessed individual common CNV regions (CNVR) for association with mortality. We observed that the burden of common but not of rare CNVs influences mortality. A higher burden of large (≥ 500 kb) common deletions associated with 4% higher mortality [hazard ratio (HR) per CNV 1.04, 95% confidence interval (CI) 1.02-1.07, P = 5.82 × 10(-5)] in the 11 442 participants of RS1, RS2 and FHS. In the analysis of 312 individual common CNVRs, we identified two regions (11p15.5; 14q21.3) that associated with higher mortality in these cohorts. The 11p15.5 region (combined HR 1.59, 95% CI 1.31-1.93, P = 2.87 × 10(-6)) encompasses 41 genes, of which some have previously been related to longevity, whereas the 14q21.3 region (combined HR 1.57, 95% CI 1.19-2.07, P = 1.53 × 10(-3)) does not encompass any genes. In conclusion, the burden of large common deletions, as well as common CNVs in 11p15.5 and 14q21.3 region, associate with higher mortality.     

Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study

Background

Family studies and heritability estimates provide evidence for a genetic contribution to variation in the human life span.

Methods

We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participants from 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards, logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate ≥80%, minor allele frequency ≥10%, Hardy-Weinberg test p ≥ 0.001).

Results

In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10^-5). The two sets of SNPs were in high linkage disequilibrium (minimum r² = 0.58). The top 30 SNPs for generalized estimating equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002), SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983 near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a and rs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.

Conclusion

Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of the associations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication as more genes and biologic pathways are proposed as contributing to longevity and healthy aging.

     

Histopathological correlation of the kiel with the original rappaport classification of malignant non-hodgkin lymphomas

Summary Using the Kiel and the Rappaport classifications, a comparative histopathological analysis of 486 cases with non-Hodgkin lymphomas from a prospective study of the Kiel Lymphoma Study Group, still in progress, was performed. The greater part of Rappaport's classical lymphoma entities was found to be inhomogeneous and to include tumors of considerable prognostic heterogeneity, as shown by differences in actuarial survival. Some of the Kiel lymphoma entities have been identified in several lymphoma types of the Rappaport classification, indicating that translation of one scheme into the other is difficult or impossible. In addition, centrocytic lymphoma of the Kiel classification may not be homogeneous.On the whole, the Kiel classification appears to be superior to the original Rappaport classification in categorizing the various prognostically diverse types of non-Hodgkin lymphomas.Abbreviations Kiel classification CLL Chronic lymphocytic leukaemia - IC Lymphoplasmacytic/lymphoplasmacytoid lymphoma (LP immunocytoma) - CC Centrocytic lymphoma - CB-CC Centroblasticcentrocytic lymphoma - CB Centroblastic Iymphoma - IB Immunoblastic lymphoma - LB Lymphoblastic lymphoma Abbreviations Rappaport classification DWDL Diffuse well-differentiated lymphocytic lymphoma - DPDL Diffuse poorly differentiated lymphocytic lymphoma - DLH (DM) Diffuse lymphocytic-histiocytic (mixed) lymphoma - DH Diffuse histiocytic lymphoma - NPDL Nodular poorly differentiated lymphocytic lymphoma - NLH (NM) Nodular lymphocytie-histiocytie (mixed) lymphoma - NH Nodular histiocytic lymphoma Supported by the Deutsche Krebshilfe e. V., BonnPresented in part at the 5th Meeting of the International Society of Haematology, European and African Division, Hamburg, August 26–31, 1979     

Identification of homogeneous genetic architecture of multiple genetically correlated traits by block clustering of genome‐wide associations

Genome‐wide association studies (GWAS) using high‐density genotyping platforms offer an unbiased strategy to identify new candidate genes for osteoporosis. It is imperative to be able to clearly distinguish signal from noise by focusing on the best phenotype in a genetic study. We performed GWAS of multiple phenotypes associated with fractures [bone mineral density (BMD), bone quantitative ultrasound (QUS), bone geometry, and muscle mass] with approximately 433,000 single‐nucleotide polymorphisms (SNPs) and created a database of resulting associations. We performed analysis of GWAS data from 23 phenotypes by a novel modification of a block clustering algorithm followed by gene‐set enrichment analysis. A data matrix of standardized regression coefficients was partitioned along both axes—SNPs and phenotypes. Each partition represents a distinct cluster of SNPs that have similar effects over a particular set of phenotypes. Application of this method to our data shows several SNP‐phenotype connections. We found a strong cluster of association coefficients of high magnitude for 10 traits (BMD at several skeletal sites, ultrasound measures, cross‐sectional bone area, and section modulus of femoral neck and shaft). These clustered traits were highly genetically correlated. Gene‐set enrichment analyses indicated the augmentation of genes that cluster with the 10 osteoporosis‐related traits in pathways such as aldosterone signaling in epithelial cells, role of osteoblasts, osteoclasts, and chondrocytes in rheumatoid arthritis, and Parkinson signaling. In addition to several known candidate genes, we also identified PRKCH and SCNN1B as potential candidate genes for multiple bone traits. In conclusion, our mining of GWAS results revealed the similarity of association results between bone strength phenotypes that may be attributed to pleiotropic effects of genes. This knowledge may prove helpful in identifying novel genes and pathways that underlie several correlated phenotypes, as well as in deciphering genetic and phenotypic modularity underlying osteoporosis risk. © 2011 American Society for Bone and Mineral Research.     

Diabetes and Deficits in Cortical Bone Density,Microarchitecture, and Bone Size: Framingham HR‐pQCT Study

Older adults with type 2 diabetes (T2D) tend to have normal or greater areal bone mineral density (aBMD), as measured by DXA, than those who do not have diabetes (non‐T2D). Yet risk of fracture is higher in T2D, including 40% to 50% increased hip fracture risk. We used HR‐pQCT to investigate structural mechanisms underlying skeletal fragility in T2D. We compared cortical and trabecular bone microarchitecture, density, bone area, and strength in T2D and non‐T2D. In secondary analyses we evaluated whether associations between T2D and bone measures differed according to prior fracture, sex, and obesity. Participants included 1069 members of the Framingham Study, who attended examinations in 2005 to 2008 and underwent HR‐pQCT scanning in 2012 to 2015. Mean age was 64 ± 8 years (range, 40 to 87 years), and 12% (n = 129) had T2D. After adjustment for age, sex, weight, and height, T2D had lower cortical volumetric BMD (vBMD) (p < 0.01), higher cortical porosity (p = 0.02), and smaller cross‐sectional area (p = 0.04) at the tibia, but not radius. Trabecular indices were similar or more favorable in T2D than non‐T2D. Associations between T2D and bone measures did not differ according to sex or obesity status (all interaction p > 0.05); however, associations did differ in those with a prior fracture and those with no history of fracture. Specifically, cortical vBMD at the tibia and cortical thickness at the radius were lower in T2D than non‐T2D, but only among those individuals with a prior fracture. Cortical porosity at the radius was higher in T2D than non‐T2D, but only among those who did not have a prior fracture. Findings from this large, community‐based study of older adults suggest that modest deterioration in cortical bone and reductions in bone area may characterize diabetic bone disease in older adults. Evaluation of these deficits as predictors of fracture in T2D is needed to develop prevention strategies in this rapidly increasing population of older adults. © 2017 American Society for Bone and Mineral Research.     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).