Body Composition As A Frailty Marker For The Elderly Community.

Body composition (BC) in the elderly has been associated with diseases and mortality; however, there is a shortage of data on frailty in the elderly. To investigate the association between BC and frailty, and identify BC profiles in nonfrail, prefrail, and frail elderly people. A cross-sectional study comprising 235 elderly (142 females and 93 males) aged ≥65 years, from the city of Amparo, State of São Paulo, Brazil, was undertaken. Sociodemographic and cognitive features, comorbidities, medication, frailty, body mass index (BMI), muscle mass, fat mass, bone mass, and fat percent (%) data were evaluated. Aiming to examine the relationship between BC and frailty, the Mann-Whitney and Kruskal-Wallis nonparametric tests were applied. The statistical significance level was P<0.05. The nonfrail elderly showed greater muscle mass and greater bone mass compared with the prefrail and frail ones. The frail elderly had greater fat % than the nonfrail elderly. There was a positive association between grip strength and muscle mass with bone mass (P<0.001), and a negative association between grip strength and fat % (P<0.001). Gait speed was positively associated with fat mass (P=0.038) and fat % (P=0.002). The physical activity level was negatively associated with fat % (P=0.022). The weight loss criterion was positively related to muscle mass (P<0.001), bone mass (P=0.009), fat mass (P=0.018), and BMI (P=0.003). There was a negative association between fatigue and bone mass (P=0.008). Frailty in the elderly was characterized by a BC profile/phenotype with lower muscle mass and lower bone mass and with a higher fat %. The BMI was not effective in evaluating the relationship between BC and frailty. The importance of evaluating the fat % was verified when considering the tissue distribution in the elderly BC.101661-166

Quantifying and mapping species threat abatement opportunitiesto support national target setting

The successful implementation of the Convention on Biological Diversity’s post-2020Global Biodiversity Framework will rely on effective translation of targets from global tonational level and increased engagement across diverse sectors of society. Species conserva-tion targets require policy support measures that can be applied to a diversity of taxonomicgroups, that link action targets to outcome goals, and that can be applied to both global andnational data sets to account for national context, which the species threat abatement andrestoration (STAR) metric does. To test the flexibility of STAR, we applied the metric to vascular plants listed on national red lists of Brazil, Norway, and South Africa. The STARmetric uses data on species’ extinction risk, distributions, and threats, which we obtainedfrom national red lists to quantify the contribution that threat abatement and habitatrestoration activities could make to reducing species’ extinction risk. Across all 3 coun-tries, the greatest opportunity for reducing plant species’ extinction risk was from abatingthreats from agricultural activities, which could reduce species’ extinction risk by 54% inNorway, 36% in South Africa, and 29% in Brazil. Species extinction risk could be reducedby a further 21% in South Africa by abating threats from invasive species and by 21% inBrazil by abating threats from urban expansion. Even with different approaches to red-listing among countries, the STAR metric yielded informative results that identified wherethe greatest conservation gains could be made for species through threat-abatement andrestoration activities. Quantifiably linking local taxonomic coverage and data collection toglobal processes with STAR would allow national target setting to align with global targetsand enable state and nonstate actors to measure and report on their potential contributionsto species conservation. habitat restoration, national red lists, species’ extinction risk, threat reduction, threatened species, vascular plantspublishedVersio

Genotype and phenotype landscape of MEN2 in 554 medullary thyroid cancer patients: the BrasMEN study

Multiple endocrine neoplasia type 2 (MEN2) is an autosomal dominant genetic disease caused by RET gene germline mutations that is characterized by medullary thyroid carcinoma (MTC) associated with other endocrine tumors. Several reports have demonstrated that the RET mutation profile may vary according to the geographical area. In this study, we collected clinical and molecular data from 554 patients with surgically confirmed MTC from 176 families with MEN2 in 18 different Brazilian centers to compare the type and prevalence of RET mutations with those from other countries. The most frequent mutations, classified by the number of families affected, occur in codon 634, exon 11 (76 families), followed by codon 918, exon 16 (34 families: 26 with M918T and 8 with M918V) and codon 804, exon 14 (22 families: 15 with V804M and 7 with V804L). When compared with other major published series from Europe, there are several similarities and some differences. While the mutations in codons C618, C620, C630, E768 and S891 present a similar prevalence, some mutations have a lower prevalence in Brazil, and others are found mainly in Brazil (G533C and M918V). These results reflect the singular proportion of European, Amerindian and African ancestries in the Brazilian mosaic genome

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