Predicting Suicides Among US Army Soldiers After Leaving Active Service.

Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. Objective: To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. Design, Setting, and Participants: In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. Main outcome and measures: The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Results: Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. Conclusions and relevance: These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

A framework for leveraging machine learning tools to estimate personalized survival curves.

The conditional survival function of a time-to-event outcome subject to censoring and truncation is a common target of estimation in survival analysis. This parameter may be of scientific interest and also often appears as a nuisance in nonparametric and semiparametric problems. In addition to classical parametric and semiparametric methods (e.g., based on the Cox proportional hazards model), flexible machine learning approaches have been developed to estimate the conditional survival function. However, many of these methods are either implicitly or explicitly targeted toward risk stratification rather than overall survival function estimation. Others apply only to discrete-time settings or require inverse probability of censoring weights, which can be as difficult to estimate as the outcome survival function itself. Here, we employ a decomposition of the conditional survival function in terms of observable regression models in which censoring and truncation play no role. This allows application of an array of flexible regression and classification methods rather than only approaches that explicitly handle the complexities inherent to survival data. We outline estimation procedures based on this decomposition, empirically assess their performance, and demonstrate their use on data from an HIV vaccine trial. Supplementary materials for this article are available online.

Carnivorous Nepenthes Pitchers with Less Acidic Fluid House Nitrogen-Fixing Bacteria.

Carnivorous pitcher plants are uniquely adapted to nitrogen limitation, using pitfall traps to acquire nutrients from insect prey. Pitcher plants in the genus Sarracenia may also use nitrogen fixed by bacteria inhabiting the aquatic microcosms of their pitchers. Here, we investigated whether species of a convergently evolved pitcher plant genus, Nepenthes, might also use bacterial nitrogen fixation as an alternative strategy for nitrogen capture. First, we constructed predicted metagenomes of pitcher organisms from three species of Singaporean Nepenthes using 16S rRNA sequence data and correlated predicted nifH abundances with metadata. Second, we used gene-specific primers to amplify and quantify the presence or absence of nifH directly from 102 environmental samples and identified potential diazotrophs with significant differential abundance in samples that also had positive nifH PCR tests. Third, we analyzed nifH in eight shotgun metagenomes from four additional Bornean Nepenthes species. Finally, we conducted an acetylene reduction assay using greenhouse-grown Nepenthes pitcher fluids to confirm nitrogen fixation is indeed possible within the pitcher habitat. Results show active acetylene reduction can occur in Nepenthes pitcher fluid. Variation in nifH from wild samples correlates with Nepenthes host species identity and pitcher fluid acidity. Nitrogen-fixing bacteria are associated with more neutral fluid pH, while endogenous Nepenthes digestive enzymes are most active at low fluid pH. We hypothesize Nepenthes species experience a trade-off in nitrogen acquisition; when fluids are acidic, nitrogen is primarily acquired via plant enzymatic degradation of insects, but when fluids are neutral, Nepenthes plants take up more nitrogen via bacterial nitrogen fixation. IMPORTANCE Plants use different strategies to obtain the nutrients that they need to grow. Some plants access their nitrogen directly from the soil, while others rely on microbes to access the nitrogen for them. Carnivorous pitcher plants generally trap and digest insect prey, using plant-derived enzymes to break down insect proteins and generate a large portion of the nitrogen that they subsequently absorb. In this study, we present results suggesting that bacteria living in the fluids formed by Nepenthes pitcher plants can fix nitrogen directly from the atmosphere, providing an alternative pathway for plants to access nitrogen. These nitrogen-fixing bacteria are only likely to be present when pitcher plant fluids are not strongly acidic. Interestingly, the plant's enzymes are known to be more active under strongly acidic conditions. We propose a potential trade-off where pitcher plants sometimes access nitrogen using their own enzymes to digest prey and at other times take advantage of bacterial nitrogen fixation.

A meta-analysis of postoperative outcomes of patients with and without chronic hepatitis C undergoing primary total hip or knee arthroplasty.

Purpose: This investigation provides a rigorous systematic review of the postoperative outcomes of patients with and without chronic hepatitis C who underwent total hip arthroplasty (THA) and total knee arthroplasty (TKA). Methods: We queried PubMed, Embase, Cochrane Database of Systematic Reviews, Scopus, Web of Science and the 'gray' literature, including supplemental materials, conference abstracts and proceedings as well as commentary published in various peer-reviewed journals from 1992 to present to evaluate studies that compared the postoperative outcomes of patients with and without chronic hepatitis C who underwent primary THA or TKA. This investigation was registered in the PROSPERO international prospective register of systematic reviews and follows the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. In our literature search, we identified 14 articles that met our inclusion criteria and were included in our fixed-effects meta-analysis. The postoperative outcomes analyzed included periprosthetic joint infection (PJI), aseptic revision, non-homebound discharge and inpatient mortality. Results: Our statistical analysis demonstrated a statistically significant increase in postoperative complications of patients with chronic hepatitis C who underwent primary THA or TKA including PJI (odds ratio (OR): 1.98, 95% CI: 1.86 - 2.10), aseptic revision (OR: 1.58, 95% CI: 1.50 - 1.67), non-homebound discharge (OR: 1.31, 95% CI: 1.28- 1.34) and inpatient mortality (OR: 9.37, 95% CI: 8.17 - 10.75). Conclusion: This meta-analysis demonstrated a statistically significant increase in adverse postoperative complications in patients with chronic hepatitis C who underwent primary THA or TKA compared to patients without chronic hepatitis C.

Sarracenia pitcher plant-associated microbial communities differ primarily by host species across a longitudinal gradient.

Plant-associated microbial communities can profoundly affect plant health and success, and research is still uncovering factors driving the assembly of these communities. Here, we examine how geography versus host species affects microbial community structure and differential abundances of individual taxa. We use metabarcoding to characterize the bacteria and eukaryotes associated with five, often co-occurring species of Sarracenia pitcher plants (Sarraceniaceae) and three natural hybrids along the longitudinal gradient of the U.S. Gulf Coast, as well as samples from S. purpurea in Massachusetts. To tease apart the effects of geography versus host species, we focus first on sites with co-occurring species and then on species located across different sites. Our analyses show that bacterial and eukaryotic community structures are clearly and consistently influenced by host species identity, with geographic factors also playing a role. Naturally occurring hybrids appear to also host unique communities, which are in some ways intermediate between their parent species. We see significant effects of geography (site and longitude), but these generally explain less of the variation among pitcher communities. Overall, in Sarracenia pitchers, host plant phenotype significantly affects the pitcher microbiomes and other associated organisms.

Elephant genotypes reveal the size and connectivity of transnational ivory traffickers.

Transnational ivory traffickers continue to smuggle large shipments of elephant ivory out of Africa, yet prosecutions and convictions remain few. We identify trafficking networks on the basis of genetic matching of tusks from the same individual or close relatives in separate shipments. Analyses are drawn from 4,320 savannah (Loxodonta africana) and forest (L. cyclotis) elephant tusks, sampled from 49 large ivory seizures totalling 111 t, shipped out of Africa between 2002 and 2019. Network analyses reveal a repeating pattern wherein tusks from the same individual or close relatives are found in separate seizures that were containerized in, and transited through, common African ports. Results suggest that individual traffickers are exporting dozens of shipments, with considerable connectivity between traffickers operating in different ports. These tools provide a framework to combine evidence from multiple investigations, strengthen prosecutions and support indictment and prosecution of transnational ivory traffickers for the totality of their crimes.

A Value-of-Information Framework for Personalizing the Timing of Surveillance Testing.

BACKGROUND: Patient surveillance using repeated biomarker measurements presents an opportunity to detect and treat disease progression early. Frequent surveillance testing using biomarkers is recommended and routinely conducted in several diseases, including cancer and diabetes. However, frequent testing involves tradeoffs. Although surveillance tests provide information about current disease status, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. Predictions based on patients' earlier biomarker values may be used to inform decision making; however, predictions are uncertain, leading to decision uncertainty. METHODS: We propose the Personalized Risk-Adaptive Surveillance (PRAISE) framework, a novel method for embedding predictions into a value-of-information (VOI) framework to account for the cost of uncertainty over time and determine the time point at which collection of biomarker data would be most valuable. The proposed sequential decision-making framework is innovative in that it leverages the patient's longitudinal history, considers individual benefits and harms, and allows for dynamic tailoring of surveillance intervals by considering the uncertainty in current information and estimating the probability that new information may change treatment decisions, as well as the impact of this change on patient outcomes. RESULTS: When applied to data from cystic fibrosis patients, PRAISE lowers costs by allowing some patients to skip a visit, compared to an "always test" strategy. It does so without compromising expected survival, by recommending less frequent testing among those who are unlikely to be treated at the skipped time point. CONCLUSIONS: A VOI-based approach to patient monitoring is feasible and could be applied to several diseases to develop more cost-effective and personalized strategies for ongoing patient care. HIGHLIGHTS: In many patient-monitoring settings, the complications and costs of frequent tests are not justified for patients who are at low risk of disease progression. Predictions based on patient history may be used to individualize the timing of patient visits based on evolving risk.We propose Personalized Risk-Adaptive Surveillance (PRAISE), a novel method for personalizing the timing of surveillance testing, where prediction modeling projects the disease trajectory and a value-of-information (VOI)-based pragmatic decision-theoretic framework quantifies patient- and time-specific benefit-harm tradeoffs.A VOI-based approach to patient monitoring could be applied to several diseases to develop more personalized and cost-effective strategies for ongoing patient care.

Serine biosynthesis defect due to haploinsufficiency of PHGDH causes retinal disease.

Macular telangiectasia type 2 (MacTel) is a progressive, late-onset retinal degenerative disease linked to decreased serum levels of serine that elevate circulating levels of a toxic ceramide species, deoxysphingolipids (deoxySLs); however, causal genetic variants that reduce serine levels in patients have not been identified. Here we identify rare, functional variants in the gene encoding the rate-limiting serine biosynthetic enzyme, phosphoglycerate dehydrogenase (PHGDH), as the single locus accounting for a significant fraction of MacTel. Under a dominant collapsing analysis model of a genome-wide enrichment analysis of rare variants predicted to impact protein function in 793 MacTel cases and 17,610 matched controls, the PHGDH gene achieves genome-wide significance (P = 1.2 × 10-13) with variants explaining ~3.2% of affected individuals. We further show that the resulting functional defects in PHGDH cause decreased serine biosynthesis and accumulation of deoxySLs in retinal pigmented epithelial cells. PHGDH is a significant locus for MacTel that explains the typical disease phenotype and suggests a number of potential treatment options.

Exome-Based Rare-Variant Analyses in CKD.

BACKGROUND: Studies have identified many common genetic associations that influence renal function and all-cause CKD, but these explain only a small fraction of variance in these traits. The contribution of rare variants has not been systematically examined. METHODS: We performed exome sequencing of 3150 individuals, who collectively encompassed diverse CKD subtypes, and 9563 controls. To detect causal genes and evaluate the contribution of rare variants we used collapsing analysis, in which we compared the proportion of cases and controls carrying rare variants per gene. RESULTS: The analyses captured five established monogenic causes of CKD: variants in PKD1, PKD2, and COL4A5 achieved study-wide significance, and we observed suggestive case enrichment for COL4A4 and COL4A3. Beyond known disease-associated genes, collapsing analyses incorporating regional variant intolerance identified suggestive dominant signals in CPT2 and several other candidate genes. Biallelic mutations in CPT2 cause carnitine palmitoyltransferase II deficiency, sometimes associated with rhabdomyolysis and acute renal injury. Genetic modifier analysis among cases with APOL1 risk genotypes identified a suggestive signal in AHDC1, implicated in Xia-Gibbs syndrome, which involves intellectual disability and other features. On the basis of the observed distribution of rare variants, we estimate that a two- to three-fold larger cohort would provide 80% power to implicate new genes for all-cause CKD. CONCLUSIONS: This study demonstrates that rare-variant collapsing analyses can validate known genes and identify candidate genes and modifiers for kidney disease. In so doing, these findings provide a motivation for larger-scale investigation of rare-variant risk contributions across major clinical CKD categories.

A new approach for rare variation collapsing on functional protein domains implicates specific genic regions in ALS.

Large-scale sequencing efforts in amyotrophic lateral sclerosis (ALS) have implicated novel genes using gene-based collapsing methods. However, pathogenic mutations may be concentrated in specific genic regions. To address this, we developed two collapsing strategies: One focuses rare variation collapsing on homology-based protein domains as the unit for collapsing, and the other is a gene-level approach that, unlike standard methods, leverages existing evidence of purifying selection against missense variation on said domains. The application of these two collapsing methods to 3093 ALS cases and 8186 controls of European ancestry, and also 3239 cases and 11,808 controls of diversified populations, pinpoints risk regions of ALS genes, including SOD1, NEK1, TARDBP, and FUS While not clearly implicating novel ALS genes, the new analyses not only pinpoint risk regions in known genes but also highlight candidate genes as well.

A case-control collapsing analysis identifies retinal dystrophy genes associated with ophthalmic disease in patients with no pathogenic ABCA4 variants.

PURPOSE: Variants in the ABCA4 gene are causal for a variety of retinal dystrophy phenotypes, including Stargardt disease (STGD1). However, 15% of patients who present with symptoms compatible with STGD1/ABCA4 disease do not have identifiable causal ABCA4 variants. We hypothesized that a case-control collapsing analysis in ABCA4-negative patients with compatible symptoms would provide an objective measure to identify additional disease genes. METHODS: We performed a genome-wide enrichment analysis of "qualifying variants"-ultrarare variants predicted to impact protein function-in protein-coding genes in 79 unrelated cases and 9028 unrelated controls. RESULTS: Despite modest sample size, two known retinal dystrophy genes, PRPH2 and CRX, achieved study-wide significance (p < 1.33 × 10-6) under a dominant disease model, and eight additional known retinal dystrophy genes achieved nominal significance (p < 0.05). Across these ten genes, the excess of qualifying variants explained up to 36.8% of affected individuals. Furthermore, under a recessive model, the cone-rod dystrophy gene CERKL approached study-wide significance. CONCLUSION: Our results indicate that case-control collapsing analyses can efficiently identify pathogenic variants in genes in non-ABCA4 retinal dystrophies. The genome-wide collapsing analysis framework is an objective discovery method particularly suitable in settings with overlapping disease phenotypes.

Improved Pathogenic Variant Localization via a Hierarchical Model of Sub-regional Intolerance.

Different parts of a gene can be of differential importance to development and health. This regional heterogeneity is also apparent in the distribution of disease-associated mutations, which often cluster in particular regions of disease-associated genes. The ability to precisely estimate functionally important sub-regions of genes will be key in correctly deciphering relationships between genetic variation and disease. Previous methods have had some success using standing human variation to characterize this variability in importance by measuring sub-regional intolerance, i.e., the depletion in functional variation from expectation within a given region of a gene. However, the ability to precisely estimate local intolerance was restricted by the fact that only information within a given sub-region is used, leading to instability in local estimates, especially for small regions. We show that borrowing information across regions using a Bayesian hierarchical model stabilizes estimates, leading to lower variability and improved predictive utility. Specifically, our approach more effectively identifies regions enriched for ClinVar pathogenic variants. We also identify significant correlations between sub-region intolerance and the distribution of pathogenic variation in disease-associated genes, with AUCs for classifying de novo missense variants in Online Mendelian Inheritance in Man (OMIM) genes of up to 0.86 using exonic sub-regions and 0.91 using sub-regions defined by protein domains. This result immediately suggests that considering the intolerance of regions in which variants are found may improve diagnostic interpretation. We also illustrate the utility of integrating regional intolerance into gene-level disease association tests with a study of known disease-associated genes for epileptic encephalopathy.

Convergence between the microcosms of Southeast Asian and North American pitcher plants.

The 'pitchers' of carnivorous pitcher plants are exquisite examples of convergent evolution. An open question is whether the living communities housed in pitchers also converge in structure or function. Using samples from more than 330 field-collected pitchers of eight species of Southeast Asian Nepenthes and six species of North American Sarracenia, we demonstrate that the pitcher microcosms, or miniature ecosystems with complex communities, are strikingly similar. Compared to communities from surrounding habitats, pitcher communities house fewer species. While communities associated with the two genera contain different microbial organisms and arthropods, the species are predominantly from the same phylogenetic clades. Microbiomes from both genera are enriched in degradation pathways and have high abundances of key degradation enzymes. Moreover, in a manipulative field experiment, Nepenthes pitchers placed in a North American bog assembled Sarracenia-like communities. An understanding of the convergent interactions in pitcher microcosms facilitates identification of selective pressures shaping the communities.

Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer's disease.

OBJECTIVE: The genetic bases of Alzheimer's disease remain uncertain. An international effort to fully articulate genetic risks and protective factors is underway with the hope of identifying potential therapeutic targets and preventive strategies. The goal here was to identify and characterize the frequency and impact of rare and ultra-rare variants in Alzheimer's disease, using whole-exome sequencing in 20,197 individuals. METHODS: We used a gene-based collapsing analysis of loss-of-function ultra-rare variants in a case-control study design with data from the Washington Heights-Inwood Columbia Aging Project, the Alzheimer's Disease Sequencing Project and unrelated individuals from the Institute of Genomic Medicine at Columbia University. RESULTS: We identified 19 cases carrying extremely rare SORL1 loss-of-function variants among a collection of 6,965 cases and a single loss-of-function variant among 13,252 controls (P = 2.17 × 10-8; OR: 36.2 [95% CI: 5.8-1493.0]). Age-at-onset was 7 years earlier for patients with SORL1 qualifying variant compared with noncarriers. No other gene attained a study-wide level of statistical significance, but multiple top-ranked genes, including GRID2IP,WDR76 and GRN, were among candidates for follow-up studies. INTERPRETATION: This study implicates ultra-rare, loss-of-function variants in SORL1 as a significant genetic risk factor for Alzheimer's disease and provides a comprehensive dataset comparing the burden of rare variation in nearly all human genes in Alzheimer's disease cases and controls. This is the first investigation to establish a genome-wide statistically significant association between multiple extremely rare loss-of-function variants in SORL1 and Alzheimer's disease in a large whole-exome study of unrelated cases and controls.

Statistical analysis of iron geochemical data suggests limited late Proterozoic oxygenation.

Sedimentary rocks deposited across the Proterozoic-Phanerozoic transition record extreme climate fluctuations, a potential rise in atmospheric oxygen or re-organization of the seafloor redox landscape, and the initial diversification of animals. It is widely assumed that the inferred redox change facilitated the observed trends in biodiversity. Establishing this palaeoenvironmental context, however, requires that changes in marine redox structure be tracked by means of geochemical proxies and translated into estimates of atmospheric oxygen. Iron-based proxies are among the most effective tools for tracking the redox chemistry of ancient oceans. These proxies are inherently local, but have global implications when analysed collectively and statistically. Here we analyse about 4,700 iron-speciation measurements from shales 2,300 to 360 million years old. Our statistical analyses suggest that subsurface water masses in mid-Proterozoic oceans were predominantly anoxic and ferruginous (depleted in dissolved oxygen and iron-bearing), but with a tendency towards euxinia (sulfide-bearing) that is not observed in the Neoproterozoic era. Analyses further indicate that early animals did not experience appreciable benthic sulfide stress. Finally, unlike proxies based on redox-sensitive trace-metal abundances, iron geochemical data do not show a statistically significant change in oxygen content through the Ediacaran and Cambrian periods, sharply constraining the magnitude of the end-Proterozoic oxygen increase. Indeed, this re-analysis of trace-metal data is consistent with oxygenation continuing well into the Palaeozoic era. Therefore, if changing redox conditions facilitated animal diversification, it did so through a limited rise in oxygen past critical functional and ecological thresholds, as is seen in modern oxygen minimum zone benthic animal communities.