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1. Continuous Glucose Monitoring Metrics Predict All-Cause Mortality in Diabetes: A Real-world Long-term Study

   https://doi.org/10.2337/dc25-0716  

   Okuno, Tomoki; Macwan, Sharon A; Norman, Gregory J; Miller, Donald R; Reaven, Peter D; Zhou, Jin J (August 2025, Diabetes Care)

   OBJECTIVEInvestigate the association between continuous glucose monitoring (CGM)-derived glucose metrics and all-cause mortality in patients with type 1 or type 2 diabetes (T1D or T2D). RESEARCH DESIGN AND METHODSWe analyzed data from 2,752 adults (≥21 years old) with diabetes (65% T2D) from the Veterans Affairs Healthcare System who received Dexcom CGM between 2015 and 2020. All participants had ≥10 days of CGM data over landmark (LM) periods (14 days, 3 months, and 6 months) merged with electronic health records. All-cause mortality was assessed over 5 years from CGM initiation. Cox models evaluated associations between mortality and CGM metrics: mean glucose (MG, mg/dL), time in range (TIR, %), time above range (TAR, %), coefficient of variation (CV), and glycemic risk index (GRI, %). RESULTSMean age at CGM initiation was 64 years, and median CGM use was nearly 3 years. There were 407 deaths. In separate multivariable Cox models (adjusting for mortality-related variables), higher MG, TAR, CV, and GRI and lower TIR during the 6-month LM were associated with 5-year mortality (hazard ratios: MG 1.18, TAR 1.20, GRI 1.23, CV 1.18, and TIR 0.83; all P ≤ 0.01) and those associations remained significant after adjusting for LM HbA1c. Results were similar with shorter CGM LM observation windows. The association between CV and mortality was independent of other CGM metrics and appeared strongest in those with lower HbA1c levels. CONCLUSIONSCGM-derived metrics were associated with all-cause mortality in patients with diabetes and may better capture long-term risk associated with glucose fluctuations and periods of hypo- and hyperglycemia than HbA1c. 

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2. The 2022 Aotearoa New Zealand National Seismic Hazard Model: Process, Overview, and Results

   https://doi.org/10.1785/0120230182  

   Gerstenberger, Matthew C.; Bora, Sanjay; Bradley, Brendon A.; DiCaprio, Chris; Kaiser, Anna; Manea, Elena F.; Nicol, Andy; Rollins, Chris; Stirling, Mark W.; Thingbaijam, Kiran K.; et al (December 2023, Bulletin of the Seismological Society of America)

   Abstract The 2022 revision of Aotearoa New Zealand National Seismic Hazard Model (NZ NSHM 2022) has involved significant revision of all datasets and model components. In this article, we present a subset of many results from the model as well as an overview of the governance, scientific, and review processes followed by the NZ NSHM team. The calculated hazard from the NZ NSHM 2022 has increased for most of New Zealand when compared with the previous models. The NZ NSHM 2022 models and results are available online. 

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3. The 2023 US 50-State National Seismic Hazard Model: Overview and implications

   https://doi.org/10.1177/87552930231215428  

   Petersen, Mark D; Shumway, Allison M; Powers, Peter M; Field, Edward H; Moschetti, Morgan P; Jaiswal, Kishor S; Milner, Kevin R; Rezaeian, Sanaz; Frankel, Arthur D; Llenos, Andrea L; et al (February 2024, Earthquake Spectra)

   The US National Seismic Hazard Model (NSHM) was updated in 2023 for all 50 states using new science on seismicity, fault ruptures, ground motions, and probabilistic techniques to produce a standard of practice for public policy and other engineering applications (defined for return periods greater than ∼475 or less than ∼10,000 years). Changes in 2023 time-independent seismic hazard (both increases and decreases compared to previous NSHMs) are substantial because the new model considers more data and updated earthquake rupture forecasts and ground-motion components. In developing the 2023 model, we tried to apply best available or applicable science based on advice of co-authors, more than 50 reviewers, and hundreds of hazard scientists and end-users, who attended public workshops and provided technical inputs. The hazard assessment incorporates new catalogs, declustering algorithms, gridded seismicity models, magnitude-scaling equations, fault-based structural and deformation models, multi-fault earthquake rupture forecast models, semi-empirical and simulation-based ground-motion models, and site amplification models conditioned on shear-wave velocities of the upper 30 m of soil and deeper sedimentary basin structures. Seismic hazard calculations yield hazard curves at hundreds of thousands of sites, ground-motion maps, uniform-hazard response spectra, and disaggregations developed for pseudo-spectral accelerations at 21 oscillator periods and two peak parameters, Modified Mercalli Intensity, and 8 site classes required by building codes and other public policy applications. Tests show the new model is consistent with past ShakeMap intensity observations. Sensitivity and uncertainty assessments ensure resulting ground motions are compatible with known hazard information and highlight the range and causes of variability in ground motions. We produce several impact products including building seismic design criteria, intensity maps, planning scenarios, and engineering risk assessments showing the potential physical and social impacts. These applications provide a basis for assessing, planning, and mitigating the effects of future earthquakes. 

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4. How well do global ocean biogeochemistry models simulate dissolved iron distributions?

   https://doi.org/10.1002/2015gb005289  

   Tagliabue, Alessandro; Aumont, Olivier; DeAth, Ros; Dunne, John P; Dutkiewicz, Stephanie; Galbraith, Eric; Misumi, Kazuhiro; Moore, J Keith; Ridgwell, Andy; Sherman, Elliot; et al (February 2016, Global Biogeochemical Cycles)

   Abstract Numerical models of ocean biogeochemistry are relied upon to make projections about the impact of climate change on marine resources and test hypotheses regarding the drivers of past changes in climate and ecosystems. In large areas of the ocean, iron availability regulates the functioning of marine ecosystems and hence the ocean carbon cycle. Accordingly, our ability to quantify the drivers and impacts of fluctuations in ocean ecosystems and carbon cycling in space and time relies on first achieving an appropriate representation of the modern marine iron cycle in models. When the iron distributions from 13 global ocean biogeochemistry models are compared against the latest oceanic sections from the GEOTRACES program, we find that all models struggle to reproduce many aspects of the observed spatial patterns. Models that reflect the emerging evidence for multiple iron sources or subtleties of its internal cycling perform much better in capturing observed features than their simpler contemporaries, particularly in the ocean interior. We show that the substantial uncertainty in the input fluxes of iron results in a very wide range of residence times across models, which has implications for the response of ecosystems and global carbon cycling to perturbations. Given this large uncertainty, iron fertilization experiments based on any single current generation model should be interpreted with caution. Improvements to how such models represent iron scavenging and also biological cycling are needed to raise confidence in their projections of global biogeochemical change in the ocean. 

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5. Intercomparison of regional loss estimates from global synthetic tropical cyclone models

   https://doi.org/10.1038/s41467-022-33918-1  

   Meiler, Simona; Vogt, Thomas; Bloemendaal, Nadia; Ciullo, Alessio; Lee, Chia-Ying; Camargo, Suzana J.; Emanuel, Kerry; Bresch, David N. (December 2022, Nature Communications)

   Abstract Tropical cyclones (TCs) cause devastating damage to life and property. Historical TC data is scarce, complicating adequate TC risk assessments. Synthetic TC models are specifically designed to overcome this scarcity. While these models have been evaluated on their ability to simulate TC activity, no study to date has focused on model performance and applicability in TC risk assessments. This study performs the intercomparison of four different global-scale synthetic TC datasets in the impact space, comparing impact return period curves, probability of rare events, and hazard intensity distribution over land. We find that the model choice influences the costliest events, particularly in basins with limited TC activity. Modelled direct economic damages in the North Indian Ocean, for instance, range from 40 to 246 billion USD for the 100-yr event over the four hazard sets. We furthermore provide guidelines for the suitability of the different synthetic models for various research purposes. 

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