Does intradialytic oral nutrition impact hemodialysis patients' quality of Life, appetite, and safety? A pilot study of a crossover clinical trial.

INTRODUCTION: due to the catabolic characteristics of hemodialysis (HD), patients should consume foods or supplements during this treatment to meet their energy requirements and maintain a neutral nitrogen balance; however, there are some outcomes in which the effect of intradialytic oral nutrition (ION) is scarcely known. OBJECTIVES: this study aims to evaluate the effect of two types of ION (liquid and solid) on QoL, appetite, and safety in HD patients. METHODS: a pilot randomized, crossover clinical trial was performed in 18 patients on chronic HD. One group received ION for 18 HD sessions, after the crossover continued for 18 more sessions in the control group, and vice versa. We recorded QoL, appetite, systolic blood pressure (SBP), and intradialytic hypotension (IH) events. RESULTS: clinical improvement was observed for most QoL components. Regardless of the consistency of supplementation, SBP increased to 4.10 mmHg. Both study groups reported a "very good-to-good" appetite. CONCLUSION: favorable clinical changes were observed in QoL scores during the study. Five of six IH events were reported for patients in the ION group, and SBP increased within the safe range (≤ 10 mmHg); appetite remained stable in both groups. Therefore, we concluded that this strategy, regardless of implementation consistency, is safe to be used in stable patients.

Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort.

Background: Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the USA and has a greater observed prevalence among those who identify as Black or Hispanic. Methods: This study aimed to assess T2D racial and ethnic disparities using the All of Us Research Program data and to measure associations between genetic ancestry (GA), socioeconomic deprivation, and T2D. We used the All of Us Researcher Workbench to analyze T2D prevalence and model its associations with GA, individual-level (iSDI), and zip code-based (zSDI) socioeconomic deprivation indices among participant self-identified race and ethnicity (SIRE) groups. Results: The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n = 2311), Black (n = 16,282), Hispanic (n = 16,966), and White (n = 50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest levels of socioeconomic deprivation, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation, both iSDI and zSDI, are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with iSDI and zSDI on T2D. Higher levels of iSDI and zSDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of iSDI and zSDI are negatively associated with T2D at high levels of African and Native American ancestry. Conclusions: Socioeconomic deprivation is associated with a higher prevalence of T2D in Black and Hispanic minority groups, compared to the majority White group. Nonetheless, socioeconomic deprivation is associated with reduced T2D risk within the Black and Hispanic groups. These results are paradoxical and have not been reported elsewhere, with possible explanations related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.

Population Pharmacogenomics for Health Equity.

Health equity means the opportunity for all people and populations to attain optimal health, and it requires intentional efforts to promote fairness in patient treatments and outcomes. Pharmacogenomic variants are genetic differences associated with how patients respond to medications, and their presence can inform treatment decisions. In this perspective, we contend that the study of pharmacogenomic variation within and between human populations-population pharmacogenomics-can and should be leveraged in support of health equity. The key observation in support of this contention is that racial and ethnic groups exhibit pronounced differences in the frequencies of numerous pharmacogenomic variants, with direct implications for clinical practice. The use of race and ethnicity to stratify pharmacogenomic risk provides a means to avoid potential harm caused by biases introduced when treatment regimens do not consider genetic differences between population groups, particularly when majority group genetic profiles are assumed to hold for minority groups. We focus on the mitigation of adverse drug reactions as an area where population pharmacogenomics can have a direct and immediate impact on public health.

Ancestry-attenuated effects of socioeconomic deprivation on type 2 diabetes disparities in the All of Us cohort.

Background: Diabetes is a common disease with a major burden on morbidity, mortality, and productivity. Type 2 diabetes (T2D) accounts for roughly 90% of all diabetes cases in the United States and has greater observed prevalence among those who identify as Black or Hispanic. Methods: The aims of this study were to determine whether T2D racial and ethnic disparities can be observed in data from the All of Us Research Program and to measure associations of genetic ancestry (GA) and socioeconomic deprivation with T2D. The All of Us Researcher Workbench was used to calculate T2D prevalence and to model T2D associations with GA, individual-level (iSDI) and zip code-based (zSDI) socioeconomic deprivation indices within and between participant self-identified race and ethnicity (SIRE) groups. Results: The study cohort of 86,488 participants from the four largest SIRE groups in All of Us: Asian (n=2,311), Black (n=16,282), Hispanic (n=16,966), and White (n=50,292). SIRE groups show characteristic genetic ancestry patterns, consistent with their diverse origins, together with a continuum of ancestry fractions within and between groups. The Black and Hispanic groups show the highest median SDI values, followed by the Asian and White groups. Black participants show the highest age- and sex-adjusted T2D prevalence (21.9%), followed by the Hispanic (19.9%), Asian (15.1%), and White (14.8%) groups. Minority SIRE groups and socioeconomic deprivation are positively associated with T2D, when the entire cohort is analyzed together. However, SIRE and GA both show negative interaction effects with SDI on T2D. Higher levels of SDI are negatively associated with T2D in the Black and Hispanic groups, and higher levels of SDI are negatively associated with T2D at high levels of African and Native American ancestry. Conclusion: Socioeconomic deprivation is positively associated with the SIRE group T2D disparities observed here but negatively associated with T2D within the Black and Hispanic groups that show the highest T2D prevalence. These results are paradoxical and have not been reported elsewhere. We discuss possible explanations for this paradox related to the nature of the All of Us data along with SIRE group differences in access to healthcare, diet, and lifestyle.

Race, Ethnicity, and Pharmacogenomic Variation in the United States and the United Kingdom.

The relevance of race and ethnicity to genetics and medicine has long been a matter of debate. An emerging consensus holds that race and ethnicity are social constructs and thus poor proxies for genetic diversity. The goal of this study was to evaluate the relationship between race, ethnicity, and clinically relevant pharmacogenomic variation in cosmopolitan populations. We studied racially and ethnically diverse cohorts of 65,120 participants from the United States All of Us Research Program (All of Us) and 31,396 participants from the United Kingdom Biobank (UKB). Genome-wide patterns of pharmacogenomic variation-6311 drug response-associated variants for All of Us and 5966 variants for UKB-were analyzed with machine learning classifiers to predict participants' self-identified race and ethnicity. Pharmacogenomic variation predicts race/ethnicity with averages of 92.1% accuracy for All of Us and 94.3% accuracy for UKB. Group-specific prediction accuracies range from 99.0% for the White group in UKB to 92.9% for the Hispanic group in All of Us. Prediction accuracies are substantially lower for individuals who identified with more than one group in All of Us (16.7%) or as Mixed in UKB (70.7%). There are numerous individual pharmacogenomic variants with large allele frequency differences between race/ethnicity groups in both cohorts. Frequency differences for toxicity-associated variants predict hundreds of adverse drug reactions per 1000 treated participants for minority groups in All of Us. Our results indicate that race and ethnicity can be used to stratify pharmacogenomic risk in the US and UK populations and should not be discounted when making treatment decisions. We resolve the contradiction between the results reported here and the orthodoxy of race and ethnicity as non-genetic, social constructs by emphasizing the distinction between global and local patterns of human genetic diversity, and we stress the current and future limitations of race and ethnicity as proxies for pharmacogenomic variation.

The landscape of health disparities in the UK Biobank.

The UK Biobank (UKB), a large-scale biomedical database that includes demographic and electronic health record data for more than half a million ethnically diverse participants, is a potentially valuable resource for the study of health disparities. However, publicly accessible databases that catalog health disparities in the UKB do not exist. We developed the UKB Health Disparities Browser with the aims of (i) facilitating the exploration of the landscape of health disparities in the UK and (ii) directing the attention to areas of disparities research that might have the greatest public health impact. Health disparities were characterized for UKB participant groups defined by age, country of residence, ethnic group, sex and socioeconomic deprivation. We defined disease cohorts for UKB participants by mapping participant International Classification of Diseases, Tenth Revision (ICD-10) diagnosis codes to phenotype codes (phecodes). For each of the population attributes used to define population groups, disease percent prevalence values were computed for all groups from phecode case-control cohorts, and the magnitude of the disparities was calculated by both the difference and ratio of the range of disease prevalence values among groups to identify high- and low-prevalence disparities. We identified numerous diseases and health conditions with disparate prevalence values across population attributes, and we deployed an interactive web browser to visualize the results of our analysis: https://ukbatlas.health-disparities.org. The interactive browser includes overall and group-specific prevalence data for 1513 diseases based on a cohort of >500 000 participants from the UKB. Researchers can browse and sort by disease prevalence and prevalence differences to visualize health disparities for each of the five population attributes, and users can search for diseases of interest by disease names or codes. Database URL https://ukbatlas.health-disparities.org/.

Rye: genetic ancestry inference at biobank scale.

Biobank projects are generating genomic data for many thousands of individuals. Computational methods are needed to handle these massive data sets, including genetic ancestry (GA) inference tools. Current methods for GA inference do not scale to biobank-size genomic datasets. We present Rye-a new algorithm for GA inference at biobank scale. We compared the accuracy and runtime performance of Rye to the widely used RFMix, ADMIXTURE and iAdmix programs and applied it to a dataset of 488221 genome-wide variant samples from the UK Biobank. Rye infers GA based on principal component analysis of genomic variant samples from ancestral reference populations and query individuals. The algorithm's accuracy is powered by Metropolis-Hastings optimization and its speed is provided by non-negative least squares regression. Rye produces highly accurate GA estimates for three-way admixed populations-African, European and Native American-compared to RFMix and ADMIXTURE (${R}^2 = \ 0.998 - 1.00$), and shows 50× runtime improvement compared to ADMIXTURE on the UK Biobank dataset. Rye analysis of UK Biobank samples demonstrates how it can be used to infer GA at both continental and subcontinental levels. We discuss user consideration and options for the use of Rye; the program and its documentation are distributed on the GitHub repository: https://github.com/healthdisparities/rye.

Ethnic disparities in mortality and group-specific risk factors in the UK Biobank.

Despite a substantial overall decrease in mortality, disparities among ethnic minorities in developed countries persist. This study investigated mortality disparities and their associated risk factors for the three largest ethnic groups in the United Kingdom: Asian, Black, and White. Study participants were sampled from the UK Biobank (UKB), a prospective cohort enrolled between 2006 and 2010. Genetics, biological samples, and health information and outcomes data of UKB participants were downloaded and data-fields were prioritized based on participants with death registry records. Kaplan-Meier method was used to evaluate survival differences among ethnic groups; survival random forest feature selection followed by Cox proportional-hazard modeling was used to identify and estimate the effects of shared and ethnic group-specific mortality risk factors. The White ethnic group showed significantly worse survival probability than the Asian and Black groups. In all three ethnic groups, endoscopy and colonoscopy procedures showed significant protective effects on overall mortality. Asian and Black women show lower relative risk of mortality than men, whereas no significant effect of sex was seen for the White group. The strongest ethnic group-specific mortality associations were ischemic heart disease for Asians, COVID-19 for Blacks, and cancers of respiratory/intrathoracic organs for Whites. Mental health-related diagnoses, including substance abuse, anxiety, and depression, were a major risk factor for overall mortality in the Asian group. The effect of mental health on Asian mortality, particularly for digestive cancers, was exacerbated by an observed hesitance to answer mental health questions, possibly related to cultural stigma. C-reactive protein (CRP) serum levels were associated with both overall and cause-specific mortality due to COVID-19 and digestive cancers in the Black group, where elevated CRP has previously been linked to psychosocial stress due to discrimination. Our results point to mortality risk factors that are group-specific and modifiable, supporting targeted interventions towards greater health equity.

Proteína C reactiva y su progresión en el tiempo para la detección de las fugas anastomóticas / C-reactive protein and its progression over time for the detection of anastomotic leaks

Introducción. Las fugas anastomóticas son una complicación común y crítica en cirugía gastrointestinal, por lo que su identificación y tratamiento temprano son necesarios para evitar resultados adversos. El uso convencional con un valor límite de la proteína C reactiva ha demostrado una utilidad limitada. El objetivo de este estudio fue determinar la utilidad de la medición seriada de la proteína C reactiva en la detección de fugas anastomóticas. Métodos. Revisión prospectiva de base de datos retrospectiva de pacientes sometidos a cirugía abdominal mayor con al menos una anastomosis intestinal. Se midió la proteína C reactiva al tercer y quinto día posoperatorio. Las complicaciones se categorizaron según la clasificación de Clavien-Dindo. La precisión diagnóstica fue evaluada por el área bajo la curva. Resultados. Se incluyeron 157 pacientes, el 52 % mujeres. La edad promedio fue de 63,7 años. El mayor número de cirugías correspondió a gastrectomía (36,3 %), resección anterior de recto (15,3 %) y hemicolectomía derecha (13,4 %). El 25,5 % tuvieron alguna complicación postoperatoria y el 32,5 % (n=13) presentaron fuga en la anastomosis. El aumento de la proteína C reactiva tuvo un área bajo la curva de 0,918 con un punto de corte de aumento en 1,3 mg/L, sensibilidad de 92,3 % (IC95% 78 ­ 100) y una especificidad de 92,4 % (IC95% 88 ­ 96). Conclusiones. El aumento de 1,3 mg/L en la proteína C reactiva entre el día de la cirugía y el quinto día fue un predictor preciso de fugas anastomóticas en pacientes con cirugía abdominal mayor

Introduction. Anastomotic leaks are a common and critical complication in gastrointestinal surgery. Their identification and early treatment are necessary to avoid adverse results, and conventional use with a cutoff value of C-reactive protein has shown limited utility. The objective of this study was to determine the usefulness of serial measurement of C-reactive protein in the detection of anastomotic leaks. Methods. Prospective review of a retrospective database of patients undergoing major abdominal surgery with at least one intestinal anastomosis. C-reactive protein was measured on the third and fifth postoperative days. Complications were classified according to the Clavien-Dindo classification. Diagnostic accuracy was evaluated by the area under the curve.Results. 157 patients were included, 52% were females. The average age was 63.7 years. The largest number of surgeries corresponded to gastrectomies (36.3%), anterior resection of the rectum (15.3%) and right hemicolectomies (13.4%). 25.5% had some postoperative complication and 32.5% (n=13) had anastomosis leaks. The increase in C-reactive protein had an area under the curve of 0.918 with an increase cut-off point of 1.3 mg/L, sensitivity of 92.3% (95% CI 78-100) and specificity of 92.4%. (95% CI 88-96). Conclusions. The 1.3 mg/L increase in C-reactive protein between the day of surgery and the fifth day was an accurate predictor of anastomotic leaks in patients with major abdominal surgery

Sick individuals, sick populations revisited: a test of the Rose hypothesis for type 2 diabetes disparities.

Introduction: The Rose hypothesis predicts that since genetic variation is greater within than between populations, genetic risk factors will be associated with individuals' risk of disease but not population disparities, and since socioenvironmental variation is greater between than within populations, socioenvironmental risk factors will be associated with population disparities but not individuals' disease risk. Methods: We used the UK Biobank to test the Rose hypothesis for type 2 diabetes (T2D) ethnic disparities in the UK. Our cohort consists of 26 912 participants from Asian, black and white ethnic groups. Participants were characterised as T2D cases or controls based on the presence or absence of T2D diagnosis codes in electronic health records. T2D genetic risk was measured using a polygenic risk score (PRS), and socioeconomic deprivation was measured with the Townsend Index (TI). The variation of genetic (PRS) and socioeconomic (TI) risk factors within and between ethnic groups was calculated using analysis of variance. Multivariable logistic regression was used to associate PRS and TI with T2D cases, and mediation analysis was used to analyse the effect of PRS and TI on T2D ethnic group disparities. Results: T2D prevalence differs for Asian 23.34% (OR=5.14, CI=4.68 to 5.65), black 16.64% (OR=3.81, CI=3.44 to 4.22) and white 7.35% (reference) ethnic groups in the UK. Both genetic and socioenvironmental T2D risk factors show greater within (w) than between (b) ethnic group variation: PRS w=64.60%, b=35.40%; TI w=71.18%, b=28.19%. Nevertheless, both genetic risk (PRS OR=1.96, CI=1.87 to 2.07) and socioeconomic deprivation (TI OR=1.09, CI=1.08 to 1.10) are associated with T2D individual risk and mediate T2D ethnic disparities (Asian PRS=22.5%, TI=9.8%; black PRS=32.0%, TI=25.3%). Conclusion: A relative excess of within-group versus between-group variation does not preclude T2D risk factors from contributing to T2D ethnic disparities. Our results support an integrative approach to health disparities research that includes both genetic and socioenvironmental risk factors.

Comparison of US Firearm-Related Deaths Among Children and Adolescents by Race and Ethnicity, 1999-2020.

This study assesses racial and ethnic differences in overall burden of firearm-related mortality and in change in firearm-related mortality among youths from 1999 to 2020.

The Apportionment of Pharmacogenomic Variation: Race, Ethnicity, and Adverse Drug Reactions.

Fifty years ago, Richard Lewontin found that the vast majority of human genetic variation falls within (~85%) rather than between (~15%) racial groups. This result has been replicated numerous times since and is widely taken to support the notion that genetic differences between racial groups are trivial and thus irrelevant for clinical decision-making. The aim of this study was to consider how the apportionment of pharmacogenomic variation within and between racial and ethnic groups relates to risk disparities for adverse drug reactions. We confirmed that the majority of pharmacogenomic variation falls within (97.3%) rather than between (2.78%) the three largest racial and ethnic groups in the United States: Black, Hispanic, and White. Nevertheless, pharmacogenomic variants showing far greater within than between-group variation can have high predictive value for adverse drug reactions, particularly for minority racial and ethnic groups. We predicted excess adverse drug reactions for minority Black and Hispanic groups, compared to the majority White group, and considered these results in light of the apportionment of genetic variation within and between groups. For 85% within and 15% between group variation, there are 700 excess adverse drug reactions per 1,000 patients predicted for a recessive effect model and 300 for a dominant model. We found high numbers of predicted Black and Hispanic excess adverse drug reactions for widely prescribed platinum chemotherapy compounds, such as cisplatin and oxaliplatin, as well as controlled narcotics, including fentanyl and tramadol. Our results indicate that race and ethnicity, while imprecise proxies for genetic diversity, correlate with patterns of pharmacogenomic variation in a way that is clearly relevant to medical treatment decisions. The effects of this variation is particularly pronounced for Black and Hispanic minority groups, owing to genetic differences from the majority White group. Treatment decisions that are made based on (assumed) White pharmacogenomic variant frequencies can be harmful for minority groups. Ignoring clinically relevant genetic differences among racial and ethnic groups, however well-intentioned, will exacerbate rather than ameliorate health disparities.

Comorbidities and ethnic health disparities in the UK biobank.

Objective: The goal of this study was to investigate the relationship between comorbidities and ethnic health disparities in a diverse, cosmopolitan population. Materials and Methods: We used the UK Biobank (UKB), a large progressive cohort study of the UK population. Study participants self-identified with 1 of 5 ethnic groups and participant comorbidities were characterized using the 31 disease categories captured by the Elixhauser Comorbidity Index. Ethnic disparities in comorbidities were quantified as the extent to which disease prevalence within categories varies across ethnic groups and the extent to which pairs of comorbidities co-occur within ethnic groups. Disease-risk factor comorbidity pairs were identified where one comorbidity is known to be a risk factor for a co-occurring comorbidity. Results: The Asian ethnic group shows the greatest average number of comorbidities, followed by the Black and then White groups. The Chinese group shows the lowest average number of comorbidities. Comorbidity prevalence varies significantly among the ethnic groups for almost all disease categories, with diabetes and hypertension showing the largest differences across groups. Diabetes and hypertension both show ethnic-specific comorbidities that may contribute to the observed disease prevalence disparities. Discussion: These results underscore the extent to which comorbidities vary among ethnic groups and reveal group-specific disease comorbidities that may underlie ethnic health disparities. Conclusion: The study of comorbidity distributions across ethnic groups can be used to inform targeted group-specific interventions to reduce ethnic health disparities.

Genetic Ancestry Inference for Pharmacogenomics.

Genetic ancestry inference can be used to stratify patient cohorts and to model pharmacogenomic variation within and between populations. We provide a detailed guide to genetic ancestry inference using genome-wide genetic variant datasets, with an emphasis on two widely used techniques: principal components analysis (PCA) and ADMIXTURE analysis. PCA can be used for patient stratification and categorical ancestry inference, whereas ADMIXTURE is used to characterize genetic ancestry as a continuous variable. Visualization methods are critical for the interpretation of genetic ancestry inference methods, and we provide instructions for how the results of PCA and ADMIXTURE can be effectively visualized.

Effects of genetic ancestry and socioeconomic deprivation on ethnic differences in serum creatinine.

The inclusion of ethnicity in equations for estimating the glomerular filtration rate (eGFR) from serum creatinine levels has been challenged since ethnicity is socially defined and therefore a poor proxy for biological differences. We hypothesized that genetic ancestry (GA) would be more strongly associated with creatinine levels among healthy individuals than self-identified ethnicity. We studied a diverse cohort of 35,590 participants characterized as part of the UK Biobank, grouped by self-reported ethnicity: Black, East Asian, Mixed, Other, South Asian, and White. We used multivariable modeling to test for associations between ethnicity, GA, socioeconomic deprivation, and serum creatinine levels, including covariates for age, sex, height, and body mass index. Model fit comparisons and relative importance analysis were used to compare the effects of ethnicity and GA on creatinine levels. Black ethnicity shows a positive effect on participant serum creatinine levels (ß = 9.36 ± 0.38), whereas East Asian (ß = -1.80 ± 0.66) and South Asian (ß = -0.28 ± 0.36) ethnicity show negative effects on creatinine. Male sex (ß = 17.69 ± 0.34) and height (ß = 0.13 ± 0.02) also show high positive associations with creatinine levels, while socioeconomic deprivation (ß = -0.04 ± 0.04) shows no significant association. African ancestry has the highest association (ß = 13.81 ± 0.52) with creatinine levels. Overall, GA (9.06%) explains significantly more of the variation in creatinine levels than ethnicity (4.96%), with African ancestry (6.36%) alone explaining more of the variation than ethnicity. We found that GA explains more of the variation in serum creatinine levels than socioeconomic deprivation, suggesting the possibility that ethnic differences in creatinine are shaped by genetic rather than social factors.

Novel land uses shape meta-community structures in neighbouring native forests: Dataset across Uruguay.

The presented datasets relate to the research article entitled "Native forest meta-community structures in Uruguay shaped by novel land use types in their surroundings" [Ramírez and Säumel; Ecology and Evolution, 2022]. The datasets include field survey data on woody species presence and absence from 384 plots at 32 permanent monitoring sites of native forests across the Oriental Republic of Uruguay (South America). We compiled different methods from meta-community studies, remote sensing and landscape ecology to explore how woody species communities are influenced by land use change from local to regional scale. We describe the diverse woody species composition in native forests across Uruguay and structure of metacommunities of woody species. Data on woody species diversity inform landscape planning, land-use management, policy and governance and can be used for further meta-analysis with other local, regional or global data sets.

Native forest metacommunity structures in Uruguay shaped by novel land-use types in their surroundings.

We explore the effect of land-use change from extensively used grasslands to intensified silvi- and agricultural monocultures on metacommunity structure of native forests in Uruguay. We integrated methods from metacommunity studies, remote sensing, and landscape ecology to explore how woody species distribution was influenced by land-use change from local to regional scale. We recorded richness and composition of adult and juvenile woody species from 32 native forests, created land-use maps from satellite image to calculate spatial metrics at landscape, class, and patch levels. We also analyzed the influence of land use pattern, climate, topography, and geographic distance between sites (d) on metacommunity, and created maps to visualize species richness and (dis)similarity between communities across the country. Woody species communities were distributed in a discrete pattern across Uruguay. Precipitation and temperature seasonality shaped species distribution pattern. Species richness and community dissimilarity increased from West to East. Latitude did not influence these patterns. Number of patches, landscape complexity, and interspersion and juxtaposition indexes determine woody species distribution at landscape level. Increasing areas covered by crops and timber plantation reduced species richness and increased community dissimilarity. The spatial metrics of native forest fragments at patch level did not influence metacommunity structure, species richness, and community dissimilarity. In conclusion, Uruguayan native forests display a high range of dissimilarity. Pressure of neighborhood land uses was the predominant factor for species assemblages. Conserving landscape structures that assure connectivity within and among native forest patches is crucial. On sites with rare target species, the creation of alliances between governmental institution and landowner complemented by incentives for biodiversity conservation provides opportunities to advance in species protection focused on those less tolerant to land-use change.

Association of Genetic Ancestry and Molecular Signatures with Cancer Survival Disparities: A Pan-Cancer Analysis.

While overall cancer mortality has steadily decreased in recent decades, cancer health disparities among racial and ethnic population groups persist. Here we studied the relationship between cancer survival disparities (CSD), genetic ancestry (GA), and tumor molecular signatures across 33 cancers in a cohort of 9,818 patients. GA correlated with race and ethnicity but showed observable differences in effects on CSD, with significant associations identified in four cancer types: breast invasive carcinoma (BRCA), head and neck squamous cell carcinoma (HNSCC), kidney renal clear cell carcinoma (KIRC), and skin cutaneous carcinoma (SKCM). Differential gene expression and methylation between ancestry groups associated cancer-related genes with CSD, of which, seven protein-coding genes [progestin and adipoQ receptor family member 6 (PAQR6), Lck-interacting transmembrane adaptor 1 (LIME1), Sin3A-associated protein 25 (SAP25), MAX dimerization protein 3 (MXD3), coiled-coil glutamate rich protein 2 (CCER2), refilin A (RFLNA), and cathepsin W (CTSW)] significantly interacted with GA and exacerbated observed survival disparities. These findings indicated that regulatory changes mediated by epigenetic mechanisms have a greater contribution to CSD than population-specific mutations. Overall, we uncovered various molecular mechanisms through which GA might impact CSD, revealing potential population-specific therapeutic targets for groups disproportionately burdened by cancer. SIGNIFICANCE: This large-cohort, multicancer study identifies four cancer types with cancer survival disparities and seven cancer-related genes that interact with genetic ancestry and contribute to disparities.