Differing Von Hippel Lindau Genotype in Paired Primary and Metastatic Tumors in Patients with Clear Cell Renal Cell Carcinoma

In sporadic clear cell renal cell carcinoma (CCRCC), the von Hippel Lindau (VHL) gene is inactivated by mutation or methylation in the majority of primary (P) tumors. Due to differing effects of wild-type (WT) and mutant (MT) VHL gene on downstream signaling pathways regulating angiogenesis, VHL gene status could impact clinical outcome. In CCRCC, comparative genomic hybridization analysis studies have reported genetic differences between paired P and metastatic (M) tumors. We thus sequenced the VHL gene in paired tumor specimens from 10 patients to determine a possible clonal relationship between the P tumor and M lesion(s) in patients with CCRCC. Using paraffin-embedded specimens, genomic DNA from microdissected samples (>80% tumor) of paired P tumor and M lesions from all 10 patients, as well as in normal tissue from 6 of these cases, was analyzed. The DNA was used for PCR-based amplification of each of the 3 exons of the VHL gene. Sequences derived from amplified samples were compared to the wild-type VHL gene sequence (GenBank Accession No. AF010238). Methylation status of the VHL gene was determined using VHL methylation-specific PCR primers after DNA bisulfite modification. In 4/10 (40%) patients the VHL gene status differed between the P tumor and the M lesion. As expected, when the VHL gene was mutated in both the P tumor and M lesion, the mutation was identical. Further, while the VHL genotype differed between the primary tumor in different kidneys or multiple metastatic lesions in the same patient, the VHL germline genotype in the normal adjacent tissue was always wild-type irrespective of the VHL gene status in the P tumor. These results demonstrate for the first time that the VHL gene status can be different between paired primary and metastatic tissue in patients with CCRCC

Association of blood lead concentrations with mortality in older women: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>Blood lead concentrations have been associated with increased risk of cardiovascular, cancer, and all-cause mortality in adults in general population and occupational cohorts. We aimed to determine the association between blood lead, all cause and cause specific mortality in elderly, community residing women.</p> <p>Methods</p> <p>Prospective cohort study of 533 women aged 65–87 years enrolled in the Study of Osteoporotic Fractures at 2 US research centers (Baltimore, MD; Monongahela Valley, PA) from 1986–1988. Blood lead concentrations were determined by atomic absorption spectrometry. Using blood lead concentration categorized as < 8 μg/dL (0.384 μmol/L), and ≥ 8 μg/dL (0.384 μmol/L), we determined the relative risk of mortality from all cause, and cause-specific mortality, through Cox proportional hazards regression analysis.</p> <p>Results</p> <p>Mean blood lead concentration was 5.3 ± 2.3 μg/dL (range 1–21) [0.25 ± 0.11 μmol/L (range 0.05–1.008)]. After 12.0 ± 3 years of > 95% complete follow-up, 123 (23%) women who died had slightly higher mean (± SD) blood lead 5.56 (± 3) μg/dL [0.27(± 0.14) μmol/L] than survivors: 5.17(± 2.0) [0.25(± 0.1) μmol/L] (<it>p </it>= 0.09). Women with blood lead concentrations ≥ 8 μg/dL (0.384 μmol/L), had 59% increased risk of multivariate adjusted all cause mortality (Hazard Ratio [HR], 1.59; 95% confidence interval [CI], 1.02–2.49) (p = 0.041) especially coronary heart disease (CHD) mortality (HR = 3.08 [CI], (1.23–7.70)(p = 0.016), compared to women with blood lead concentrations < 8 μg/dL(< 0.384 μmol/L). There was no association of blood lead with stroke, cancer, or non cardiovascular deaths.</p> <p>Conclusion</p> <p>Women with blood lead concentrations of ≥ 8 μg/dL (0.384 μmol/L), experienced increased mortality, in particular from CHD as compared to those with lower blood lead concentrations.</p

Human Sirt-1: Molecular Modeling and Structure-Function Relationships of an Unordered Protein

BACKGROUND: Sirt-1 is a NAD+-dependent nuclear deacetylase of 747 residues that in mammals is involved in various important metabolic pathways, such as glucose metabolism and insulin secretion, and often works on many different metabolic substrates as a multifunctional protein. Sirt-1 down-regulates p53 activity, rising lifespan, and cell survival; it also deacetylases peroxisome proliferator-activated receptor-gamma (PPAR-gamma) and its coactivator 1 alpha (PGC-1alpha), promoting lipid mobilization, positively regulating insulin secretion, and increasing mitochondrial dimension and number. Therefore, it has been implicated in diseases such as diabetes and the metabolic syndrome and, also, in the mechanisms of longevity induced by calorie restriction. Its whole structure is not yet experimentally determined and the structural features of its allosteric site are unknown, and no information is known about the structural changes determined by the binding of its allosteric effectors. METHODOLOGY: In this study, we modelled the whole three-dimensional structure of Sirt-1 and that of its endogenous activator, the nuclear protein AROS. Moreover, we modelled the Sirt-1/AROS complex in order to study the structural basis of its activation and regulation. CONCLUSIONS: Amazingly, the structural data show that Sirt-1 is an unordered protein with a globular core and two large unordered structural regions at both termini, which play an important role in the protein-protein interaction. Moreover, we have found on Sirt-1 a conserved pharmacophore pocket of which we have discussed the implication

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation