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Purpose: To examine the associations of healthy dietary patterns with cardiometabolic biomarkers and all-cause mortality among individuals with prediabetes. Methods: This cohort study included 8363 adults with prediabetes from the National Health and Nutrition Examination Survey 1999–2014. Healthy dietary patterns including Alternate Healthy Eating Index-2010 (AHEI-2010), Alternate Mediterranean Diet score (AMED), Dietary Approaches to Stop Hypertension score (DASH), and Healthy Eating Index-2015 (HEI-2015) were calculated based on 24-h dietary recall data. Mortality status was obtained by linkage to National Death Index records. Cardiometabolic biomarkers, including blood glucose, insulin, HbA1c, C-reactive protein (CRP), and lipids, were measured at recruitment. Results: During 61,991 person-years of follow-up, 991 deaths occurred. Comparing the extreme quartiles, the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality were 0.65 (0.49, 0.85) for AHEI-2010 (P-trend = 0.002), 0.68 (0.50, 0.92) for AMED (P-trend = 0.004), 0.72 (0.53, 0.98) for DASH (P-trend = 0.03), and 0.78 (0.58, 1.05) for HEI-2015 (P-trend = 0.08). Besides, the HRs (95% CIs) for all-cause mortality per 20-percentile increment in scores were 0.78 (0.67, 0.90) for AHEI-2010 (P = 0.001), 0.73 (0.62, 0.86) for AMED (P < 0.001), 0.84 (0.69, 1.02) for DASH (P = 0.08), and 0.86 (0.74, 1.00) for HEI-2015 (P = 0.04). In addition, higher dietary scores were associated with favorable blood glucose, insulin, HOMA-IR, blood lipids, and CRP (all P-trend < 0.05). The high-density lipoprotein cholesterol and CRP explained 1.53–9.21% of the associations between dietary patterns and all-cause mortality (P < 0.05). Conclusions: Diets with higher AHEI-2010, AMED, DASH, and HEI-2015 were associated with improved cardiometabolic factors and lower all-cause mortality among individuals with prediabetes. These findings suggest that multiple healthy dietary patterns instead of a one-size-fits-all diet plan might be beneficial and acceptable for individuals with prediabetes. [ABSTRACT FROM AUTHOR]

The goal of elucidating the primary mechanisms constraining the assembly and distribution of biodiversity remains among the central unresolved challenges facing the field of ecology. Simulation studies and experimental manipulations have focused on how patterns in community assembly result from bivariate relationships along productivity or environmental gradients. However, the joint influence of multiple resource gradients on the distribution of species richness in natural communities remains understudied. Using data from a large network of multiscale vegetation plots across forests and woodlands of the southeastern United States, we find significant evidence for the scale‐dependent, joint constraints of forest structure and soil resources on the distribution of vascular plant species richness. In addition to their significant partial effects on species richness, understory light levels and soil fertility positively interact, suggesting a trade‐off between the two limiting resources with species richness peaking both in high‐light, low‐fertility conditions as well as low‐light, high‐fertility settings. This finding provides a novel perspective on the biodiversity–productivity relationship that suggests a transition in limiting resources from soil nutrients to light availability when enhanced productivity results in reduced light resources for subordinate individuals. Results likewise have meaningful implications for our understanding of scale‐dependent community assembly processes as size‐asymmetric competition replaces environmental filtering as the primary assembly mechanism structuring temperate forest communities along an increasing soil fertility gradient. [ABSTRACT FROM AUTHOR]

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Predictions of above-ground biomass and the change in above-ground biomass require attachment of uncertainty due the range of reported predictions for forests. Because above-ground biomass is seldom measured, there have been no opportunities to obtain such uncertainty estimates. Standard methods involve applying an allometric equation to each individual tree on sample plots and summing the individual values. There is uncertainty in the allometry which leads to uncertainty in biomass at the tree level. Due to interdependence between competing trees, the uncertainty at the plot level that results from aggregating individual tree biomass in this way is expected to overestimate variability. That is, the variance at the plot level should be less than the sum of the individual variances. We offer a modeling strategy to learn about change in biomass at the plot level and model cumulative uncertainty to accommodate this dependence among neighboring trees. The plot-level variance is modeled using a parametric density-dependent asymptotic function. Plot-by-time covariate information is introduced to explain the change in biomass. These features are incorporated into a hierarchical model and inference is obtain within a Bayesian framework. We analyze data for the eastern United States from the Forest Inventory and Analysis (FIA) Program of the US Forest Service. This region contains roughly 25,000 FIA monitored plots from which there are measurements of approximately 1 million trees spanning more than 200 tree species. Due to the high species richness in the FIA data, we combine species into plant functional types. We present predictions of biomass and change in biomass for two plant functional types. [ABSTRACT FROM AUTHOR]

Density dependence could maintain diversity in forests, but studies continue to disagree on its role. Part of the disagreement results from the fact that different studies have evaluated different responses (survival, recruitment, or growth) of different stages (seeds, seedlings, or adults) to different inputs (density of seedlings, density or distance to adults). Most studies are conducted on a single site and thus are difficult to generalize. Using USDA Forest Service's Forest Inventory and Analysis data, we analyzed over a million seedling-tosapling recruitment observations of 50 species from the eastern United States, controlling for the effects of climate. We focused on the per-seedling recruitment rate, because it is most likely to promote diversity and to be identified in observational or experimental data. To understand the prevalence of density dependence, we quantified the number of species with significant positive or negative effects. To understand the strength of density dependence, we determined the magnitude of effects among con- and heterospecifics, and how it changes with overall species abundance. We found that density dependence is pervasive among the 50 species, as the majority of them have significant effects and mostly negative. Density-dependence effects are stronger from conspecific than heterospecfic adult neighbors, consistent with the predictions of the Janzen-Connell hypothesis. Contrary to recent reports, density-dependence effects are more negative for common than rare species, suggesting disproportionately stronger population regulation in common species. We conclude that density dependence is pervasive, and it is strongest from conspecific neighbors of common species. Our analysis provides direct evidence that density dependence regulates population dynamics of tree species in eastern U.S. forests. [ABSTRACT FROM AUTHOR]

Many applications involve count data from a process that yields an excess number of zeros. Zero-inflated count models, in particular, zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, along with Poisson hurdle models, are commonly used to address this problem. However, these models struggle to explain extreme incidence of zeros (say more than 80%), especially to find important covariates. In fact, the ZIP may struggle even when the proportion is not extreme. To redress this problem we propose the class of k-ZIG models. These models allow more flexible modeling of both the zero-inflation and the nonzero counts, allowing interplay between these two components. We develop the properties of this new class of models, including reparameterization to a natural link function. The models are straightforwardly fitted within a Bayesian framework. The methodology is illustrated with simulated data examples as well as a forest seedling dataset obtained from the USDA Forest Service's Forest Inventory and Analysis program. [ABSTRACT FROM AUTHOR]

Current evidence implicates a significant association between depression and obesity and related metabolic dysfunction. The weight-adjusted-waist index (WWI) was recently identified as an ideal index that integrates total body fat, muscle mass, and bone mass. This study investigated the relationship between WWI and depressive symptoms in adults. Participants from the National Health and Nutrition Examination Survey (2005–2018) were enrolled. Depressive symptom severity was measured with the Patient Health Questionnaire-9 (PHQ-9). Survey-weighted multivariable logistic regression, subgroup analysis, and generalized additive models were used to determine the relationship between WWI and depressive symptoms. A total of 34,575 participants were included, with a mean WWI of 11.01; 2,979 participants were suspected of having depressive symptoms (PHQ-9 score ≥ 10). A significant positive association was identified between WWI and depressive symptoms (odds ratio = 1.416, 95 % confidence interval: 1.303–1.539, P < 0.0001). Subgroup analyses suggested that the association between WWI and depressive symptoms was stronger in individuals who were female, overweight, divorced, middle-aged or older (over 40 years old), and had diabetes. Furthermore, the non-linear multivariable regression revealed an inflection point for the WWI at 11.438, and the association was only significant when the WWI was higher than this point. This study was retrospective and only included participants from the United States; therefore, further validation is needed from studies in other countries, especially middle-to-low-income countries, using longitudinal cohorts. This study identified a significant positive association between WWI and depressive symptoms. • Weight-adjusted-waist-index (WWI) was an integrated index that reflect the condition of fat, muscle and bone mass. • WWI is closely related to depression, particularly in females and individuals with marital difficulties or diabetes. • The association between WWI and depressive symptom was more significant when the score of WWI was higher than 11.438. [ABSTRACT FROM AUTHOR]

Background: Social determinants of health (SDHs) are the primary drivers of preventable health inequities, and the associations between SDHs and health outcomes among individuals with type 2 diabetes remain unclear. This study aimed to estimate the associations of combined SDHs with life expectancy and future health risks among adults with type 2 diabetes from the UK and USA., Methods: In an analysis of two nationwide cohort studies, adults with type 2 diabetes were identified from the UK Biobank from March 13, 2006, to Oct 1, 2010 (adults aged 37-73 years) and the US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 (adults aged ≥20 years). Participants with type 2 diabetes at baseline were included in our analysis. Participants without information on SDHs or follow-up were excluded. The UK Biobank assessed 17 SDHs and the US NHANES assessed ten SDHs, with each SDH dichotomised into advantaged and disadvantaged levels. The combined score of SDHs were calculated as the sum of the weighted scores for each SDH. Participants were then categorised into tertiles (favourable, medium, and unfavourable SDH groups). Primary outcomes were life expectancy and mortality in both cohorts, and incidences of cardiovascular disease, diabetes-related microvascular disease, dementia, and cancer in the UK Biobank. Outcomes were obtained from disease registries up until Dec 31, 2021, in the UK Biobank and Dec 31, 2019, in the US NHANES cohorts., Findings: We included 17 321 participants from the UK Biobank cohort (median age 61·0 years [IQR 56·0-65·0]; 6028 [34·8%] women and 11 293 [65·2%] men) and 7885 participants from the NHANES cohort (mean age 59·2 years [95% CI 58·7-59·6]; 3835 [49·1%, weighted] women and 4050 [50·9%, weighted] men) in our analysis. In the UK Biobank, 3235 deaths (median follow-up 12·3 years [IQR 11·5-13·2]), 3010 incident cardiovascular disease (12·1 years [10·8-13·0]), 1997 diabetes-related microvascular disease (8·0 years [7·1-8·9]), 773 dementia (12·6 years [11·8-13·5]), and 2259 cancer cases (11·3 years [10·4-12·2]) were documented; and the US NHANES documented 2278 deaths during a median follow-up of 7·0 years (3·7-11·2). After multivariable adjustment, compared with the favourable SDH group, the hazard ratio was 1·33 (95% CI 1·21-1·46) in the medium SDH group and 1·89 (1·72-2·07) in the unfavourable SDH group in the UK Biobank cohort; 1·51 (1·34-1·70) in the medium SDH group and 2·02 (1·75-2·33) in the unfavourable SDH group in the US NHANES cohort for all-cause mortality; 1·13 (1·04-1·24) in the medium SDH group and 1·40 (1·27-1·53) in the unfavourable SDH group for incident cardiovascular disease; 1·13 (1·01-1·27) in the medium SDH group and 1·41 (1·26-1·59) in the unfavourable SDH group for incident diabetes-related microvascular disease; 1·35 (1·11-1·64) in the medium SDH group and 1·76 (1·46-2·13) in the unfavourable SDH group for incident dementia; and 1·02 (0·92-1·13) in the medium SDH group and 1·17 (1·05-1·30) in the unfavourable SDH group for incident cancer in the UK Biobank cohort (p trend <0·010 for each category). At the age of 45 years, the mean life expectancy of participants was 1·6 years (0·6-2·3) shorter in the medium SDH group and 4·4 years (3·3-5·4) shorter in the unfavourable SDH group than in the favourable SDH group in the UK Biobank. In the US NHAHES cohort, the life expectancy was 1·7 years (0·6-2·7) shorter in the medium SDH group and 3·0 years (1·8-4·3) shorter in the unfavourable SDH group, compared with the favourable group., Interpretation: Combined unfavourable SDHs were associated with a greater loss of life expectancy and higher risks of developing future adverse health outcomes among adults with type 2 diabetes. These associations were similar across two nationwide cohorts from varied social contexts, and were largely consistent across populations with different demographic, lifestyle, and clinical features. Thus, assessing the combined SDHs of individuals with type 2 diabetes might be a promising approach to incorporate into diabetes care to identify socially vulnerable groups and reduce disease burden., Funding: The National Natural Science Foundation of China, the National Key R&D Program of China, and the Fundamental Research Funds for the Central Universities., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: Patients who leave against medical advice (AMA) face increased risks of negative health outcomes, presenting a challenge for healthcare systems. This study examines demographic and hospital course factors associated with patients leaving AMA after an upper extremity (UE) orthopaedic procedure., Methods: We analyzed 262,912 patients who underwent UE orthopaedic procedures between 2011 and 2020, using the Healthcare Cost and Utilization Project database. We then compared demographic and hospital course factors between patients who left AMA and those who did not leave AMA., Results: Of 262,912 UE orthopaedic patients, 0.45% (1,173) left AMA. Those more likely to leave AMA were aged 30 to 49 (OR, 5.953, P < 0.001), Black (OR, 1.708, P < 0.001), had Medicaid (OR, 3.436, P < 0.001), and were in the 1st to 25th income percentile (OR, 1.657, P < 0.001). Female patients were less likely to leave AMA than male patients (OR, 0.647, P < 0.001). Patients leaving AMA had longer stays (3.626 versus 2.363 days, P < 0.001) and longer recovery times (2.733 versus 1.977, P < 0.001)., Conclusion: We found that male, Black, younger than 49 years old, Medicaid-insured, and lowest income quartile patients are more likely to leave AMA after UE orthopaedic treatment., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Orthopaedic Surgeons.)

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO 2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO 2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: In the context of shifting population demographics in the United States, a diverse workforce in health care can lead to improved patient outcomes and enhance access to culturally competent care for minorities. The objective of this study was to analyze and quantify the relationship of gender, race, and academic rank, tenure status, and degree in American academic surgical faculty., Materials and Methods: A 12-y retrospective cross-sectional analysis of the data from the Association of American Medical Colleges was performed. The distribution of race and gender across academic ranks, tenure, and degree types were recorded from 2007-2018. Simple descriptive statistics and chi-square analysis was used to analyze the time trends and association between gender and race across academic rank, tenure status, and degree types., Results: When averaged over the 12 y of this study, there were significantly more whites (69.8%) and males (74.5%) among the academic surgery faculty compared with other races and females, respectively (P value <0.05). Asians and females experienced the greatest increase in proportional representation across all academic ranks with an absolute increase of 7% and 5% in full professor, 5% and 6% in associate professor, and 7% and 3% in chairperson (P value <0.05), respectively. No significant association was observed between gender and race with tenure status or degree type., Conclusions: Female and Asians are increasing in proportional representation; however, racial and gender disparities remain prevalent at higher academic ranks and positions of leadership, especially among black and Hispanic academic surgeons., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Tree species are predicted to track future climate by shifting their geographic distributions, but climate-mediated migrations are not apparent in a recent continental-scale analysis. To better understand the mechanisms of a possible migration lag, we analyzed relative recruitment patterns by comparing juvenile and adult tree abundances in climate space. One would expect relative recruitment to be higher in cold and dry climates as a result of tree migration with juveniles located further poleward than adults. Alternatively, relative recruitment could be higher in warm and wet climates as a result of higher tree population turnover with increased temperature and precipitation. Using the USDA Forest Service's Forest Inventory and Analysis data at regional scales, we jointly modeled juvenile and adult abundance distributions for 65 tree species in climate space of the eastern United States. We directly compared the optimal climate conditions for juveniles and adults, identified the climates where each species has high relative recruitment, and synthesized relative recruitment patterns across species. Results suggest that for 77% and 83% of the tree species, juveniles have higher optimal temperature and optimal precipitation, respectively, than adults. Across species, the relative recruitment pattern is dominated by relatively more abundant juveniles than adults in warm and wet climates. These different abundance-climate responses through life history are consistent with faster population turnover and inconsistent with the geographic trend of large-scale tree migration. Taken together, this juvenile-adult analysis suggests that tree species might respond to climate change by having faster turnover as dynamics accelerate with longer growing seasons and higher temperatures, before there is evidence of poleward migration at biogeographic scales.