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  1. Talukder A, Shaid S, Hwang S, Alam E, Islam K, Kamruzzaman M
    Sci Rep, 2025 Apr 02;15(1):11343.
    PMID: 40175532 DOI: 10.1038/s41598-025-96446-0
    This study aims to enhance the precision of climate simulations by optimizing a multi-model ensemble of General Circulation Models (GCMs) for simulating precipitation, maximum temperature (Tmax), and minimum temperature (Tmin). Bangladesh, with its susceptibility to rapid seasonal shifts and various forms of flooding, is the focal point of this research. Historical simulations of 19 CMIP6 GCMs are meticulously compared with ERA5 data for 1986-2014. The bilinear interpolation technique is used to harmonize the resolution of GCM data with the observed grid points. Seven distinct error metrics, including Kling-Gupta Efficiency and normalized root mean squared error, quantify the grid-to-grid agreement between GCMs and ERA5 data. The metrics are integrated into the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for seasonal and annual rankings of GCMs. Finally, the ensemble means of top-performing models are estimated using Bayesian Model Averaging (BMA) and Arithmetic Mean (AM) for relative comparison. The outcomes of this study underscore the variability in GCM performance across different seasons, necessitating the development of an overarching ranking system. Results reveal ACCESS.CM2 is the preeminent GCM for precipitation, with an overall rating matric of 0.99, while INM.CM4.8 and UKESM1.0.LL excel in replicating Tmax and Tmin, with rating matrices of 1.0 and 0.88. In contrast, FGOALS.g3, KACE.1.0.G, and CanESM5 are the most underperformed models in estimating precipitation, Tmx, and Tmn, respectively. Overall, there are five models, ACCESS.ESM1.5, ACCESS.CM2, UKESM1.0.LL, MRI.ESM2.0, EC.Earth3 performed best in simulating both precipitation and temperature. The relative comparison of the ensemble means of the top five models revealed that the accuracy of BMA with Kling Gupta Efficiency (KGE) of 0.82, 0.65, and 0.82 surpasses AM with KGE of 0.59, 0.28, and 0.45 in capturing the spatial pattern of precipitation, Tmax and Tmin, respectively. This study offers invaluable insights into the selection of GCMs and ensemble methodologies for climate simulations in Bangladesh. Improving the accuracy of climate projections in this region can contribute significantly to climate science.
  2. GBD 2023 LDL Cholesterol Collaborators, Razo C, DeCleene NK, Johnson CO, Stark BA, LeGrand K, et al.
    JAMA, 2026 Aug 25;336(8):673-698.
    PMID: 42525403 DOI: 10.1001/jama.2026.8628
    IMPORTANCE: Elevated low-density lipoprotein cholesterol (LDL-C) is a modifiable risk factor for cardiovascular disease, the leading cause of premature death worldwide. Assessing the LDL-C-related burden is critical for guiding prevention and treatment strategies.

    OBJECTIVES: To estimate the global, regional, and national burden of ischemic heart disease and ischemic stroke attributable to elevated LDL-C (relative to 35-54 mg/dL) from 1990 to 2023 and to quantify the contributions of population growth, aging, risk-deleted burden, and exposure changes to burden trends.

    DESIGN, SETTING, AND POPULATION: This comparative risk assessment, part of the Global Burden of Disease Study 2023, estimated population-level LDL-C exposure and associated health loss in 204 countries and territories. Mean LDL-C levels were estimated using spatiotemporal gaussian process regression based on 806 studies across 161 countries. Relative risks were derived from meta-analyses of 38 randomized clinical trials. Population-attributable fractions for deaths and disability-adjusted life-years (DALYs) were estimated by age and sex for adults aged 25 years or older from 1990 to 2023, with 95% uncertainty intervals.

    EXPOSURE: Population-level LDL-C concentrations.

    MAIN OUTCOMES AND MEASURES: Population-attributable fractions, counts, and rates (all ages and age standardized per 100 000) of LDL-C-attributable deaths and DALYs from ischemic heart disease and ischemic stroke, with uncertainty intervals.

    RESULTS: In 2023, elevated LDL-C accounted for 3.6 million deaths (95% uncertainty interval, 2.2-5.4 million; 6.0% of global mortality) and 90.7 million DALYs (95% uncertainty interval, 58.9-123.3 million; 3.2% of DALYs). Although global all-ages rates remained stable, age-standardized death and DALY rates decreased by 45.6% and 39.5%, respectively, since 1990. In 2023, age-standardized LDL-C-attributable DALY rates were highest in Eastern Europe and lowest in high-income Asia-Pacific. One-third of the global LDL-C burden occurred in India and China. Population growth and aging drove the increasing burden, with notable regional disparities in LDL-C exposure and risk-deleted DALY rates shifting toward middle-sociodemographic settings.

    CONCLUSIONS AND RELEVANCE: Despite declining age-standardized rates, the absolute LDL-C burden has increased since 1990 due to demographic changes and has shifted toward middle-sociodemographic countries. Measurement and surveillance gaps persist. Strengthened prevention, diagnosis, and treatment access strategies are essential to mitigate the health burden of LDL-C.

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