Ceftazidime/avibactam versus polymyxin B in carbapenem-resistant Klebsiella pneumoniae infections: a propensity score-matched multicenter real-world study.

OBJECTIVES: In this retrospective observational multicenter study, we aimed to assess efficacy and mortality between ceftazidime/avibactam (CAZ/AVI) or polymyxin B (PMB)-based regimens for the treatment of Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections, as well as identify potential risk factors. METHODS: A total of 276 CRKP-infected patients were enrolled in our study. Binary logistic and Cox regression analysis with a propensity score-matched (PSM) model were performed to identify risk factors for efficacy and mortality. RESULTS: The patient cohort was divided into PMB-based regimen group (n = 98, 35.5%) and CAZ/AVI-based regimen group (n = 178, 64.5%). Compared to the PMB group, the CAZ/AVI group exhibited significantly higher rates of clinical efficacy (71.3% vs. 56.1%; p = 0.011), microbiological clearance (74.7% vs. 41.4%; p < 0.001), and a lower incidence of acute kidney injury (AKI) (13.5% vs. 33.7%; p < 0.001). Binary logistic regression revealed that the treatment duration independently influenced both clinical efficacy and microbiological clearance. Vasoactive drugs, sepsis/septic shock, APACHE II score, and treatment duration were identified as risk factors associated with 30-day all-cause mortality. The CAZ/AVI-based regimen was an independent factor for good clinical efficacy, microbiological clearance, and lower AKI incidence. CONCLUSIONS: For patients with CRKP infection, the CAZ/AVI-based regimen was superior to the PMB-based regimen.

Trends in cardiovascular risk factor prevalence, treatment, and control among US adolescents aged 12 to 19 years, 2001 to March 2020.

BACKGROUND: Early-life cardiovascular risk factors (CVRFs) are known to be associated with target organ damage during adolescence and premature cardiovascular morbidity and mortality during adulthood. However, contemporary data describing whether the prevalence of CVRFs and treatment and control rates have changed are limited. This study aimed to examine the temporal trends in the prevalence, treatment, and control of CVRFs among US adolescents over the past 2 decades. METHODS: This is a serial cross-sectional study using data from nine National Health and Nutrition Examination Survey cycles (January 2001-March 2020). US adolescents (aged 12 to 19 years) with information regarding CVRFs (including hypertension, elevated blood pressure [BP], diabetes, prediabetes, hyperlipidemia, obesity, overweight, cigarette use, inactive physical activity, and poor diet quality) were included. Age-adjusted trends in CVRF prevalence, treatment, and control were examined. Joinpoint regression analysis was performed to estimate changes in the prevalence, treatment, and control over time. The variation by sociodemographic characteristics were also described. RESULTS: A total of 15,155 US adolescents aged 12 to 19 years (representing ≈ 32.4 million people) were included. From 2001 to March 2020, there was an increase in the prevalence of prediabetes (from 12.5% [95% confidence interval (CI), 10.2%-14.9%] to 37.6% [95% CI, 29.1%-46.2%]) and overweight/obesity (from 21.1% [95% CI, 19.3%-22.8%] to 24.8% [95% CI, 21.4%-28.2%]; from 16.0% [95% CI, 14.1%-17.9%] to 20.3% [95% CI, 17.9%-22.7%]; respectively), no improvement in the prevalence of elevated BP (from 10.4% [95% CI, 8.9%-11.8%] to 11.0% [95% CI, 8.7%-13.4%]), diabetes (from 0.7% [95% CI, 0.2%-1.2%] to 1.2% [95% CI, 0.3%-2.2%]), and poor diet quality (from 76.1% [95% CI, 74.0%-78.2%] to 71.7% [95% CI, 68.5%-74.9%]), and a decrease in the prevalence of hypertension (from 8.1% [95% CI, 6.9%-9.4%] to 5.5% [95% CI, 3.7%-7.3%]), hyperlipidemia (from 34.2% [95% CI, 30.9%-37.5%] to 22.8% [95% CI, 18.7%-26.8%]), cigarette use (from 18.0% [95% CI, 15.7%-20.3%] to 3.5% [95% CI, 2.0%-5.0%]), and inactive physical activity (from 83.0% [95% CI, 80.7%-85.3%] to 9.5% [95% CI, 4.2%-14.8%]). Sex and race/ethnicity affected the evolution of CVRF prevalence differently. Whilst treatment rates for hypertension and diabetes did not improve significantly (from 9.6% [95% CI, 3.5%-15.8%] to 6.0% [95% CI, 1.4%-10.6%]; from 51.0% [95% CI, 23.3%-78.7%] to 26.5% [95% CI, 0.0%-54.7%]; respectively), BP control was relatively stable (from 75.7% [95% CI, 56.8%-94.7%] to 73.5% [95% CI, 40.3%-100.0%]), while glycemic control improved to a certain extent, although it remained suboptimal (from 11.8% [95% CI, 0.0%-31.5%] to 62.7% [95% CI, 62.7%-62.7%]). CONCLUSIONS: From 2001 to March 2020, although prediabetes and overweight/obesity increased, hypertension, hyperlipidemia, cigarette use, and inactive physical activity decreased among US adolescents aged 12 to 19 years, whereas elevated BP, diabetes, and poor diet quality remained unchanged. There were disparities in CVRF prevalence and trends across sociodemographic subpopulations. While treatment and control rates for hypertension and diabetes plateaued, BP control were stable, and improved glycemic control was observed.

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase of transaminase levels while undergoing VPA therapy for 1995 epilepsy patients. The study employed the two-tailed T test, Chi-square test, and binary logistic regression analysis, selecting six clinical parameters, including age, stature, leukocyte count, Total Bilirubin, oral dosage of VPA, and VPA concentration. These variables were used to build a risk prediction model using "H2O" autoML platform, achieving the best performance (AUC training = 0.855, AUC test = 0.789) in the training and testing data set. The model also exhibited robust accuracy (AUC valid = 0.742) in an external validation set, underscoring its credibility in anticipating VPA-induced transaminase abnormalities. The significance of the six variables was elucidated through importance ranking, partial dependence, and the TreeSHAP algorithm. This novel model offers enhanced versatility and explicability, rendering it suitable for clinicians seeking to refine parameter adjustments and address imbalanced data sets, thereby bolstering classification precision. To summarize, the personalized prediction model for VPA-treated epilepsy, established with an autoML model, displayed commendable predictive capability, furnishing clinicians with valuable insights for fostering pharmacovigilance.

Efficacy of omadacycline in the treatment of Legionella pneumonia: a case report.

Legionella, one of the main pathogens that causes community-acquired pneumonia, can lead to Legionella pneumonia, a condition characterized predominantly by severe pneumonia. This disease, caused by the bacterium Legionella pneumophila, can quickly progress to critical pneumonia and is often associated with damage to multiple organs. As a result, it requires close attention in terms of clinical diagnosis and treatment. Omadacycline, a new type of tetracycline derivative belonging to the aminomethylcycline class of antibiotics, is a semi-synthetic compound derived from minocycline. Its key structural feature, the aminomethyl modification, allows omadacycline to overcome bacterial resistance and broadens its range of effectiveness against bacteria. Clinical studies have demonstrated that omadacycline is not metabolized in the body, and patients with hepatic and renal dysfunction do not need to adjust their dosage. This paper reports a case of successful treatment of Legionella pneumonia with omadacycline in a patient who initially did not respond to empirical treatment with moxifloxacin. The patient also experienced electrolyte disturbance, as well as dysfunction in the liver and kidneys, delirium, and other related psychiatric symptoms.

Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.

Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi-depth phase-only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward-backward-diffraction framework to compute multi-depth diffraction fields, along with a layer-by-layer replacement method (L2RM) to handle occlusion relationships. The diffraction fields computed by the former are fed into the carefully designed FCN, which leverages its powerful non-linear fitting capability to generate multi-depth holograms of 3D scenes. The latter can smooth the boundaries of different layers in scene reconstruction by complementing information of occluded objects, thus enhancing the reconstruction quality of holograms. The proposed method can generate a multi-depth 3D hologram with a PSNR of 31.8 dB in just 90 ms for a resolution of 2160 × 3840 on the NVIDIA Tesla A100 40G tensor core GPU. Additionally, numerical and experimental results indicate that the generated holograms accurately reconstruct clear 3D scenes with correct occlusion relationships and provide excellent depth focusing.

An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data.

DNA-based data storage is a new technology in computational and synthetic biology, that offers a solution for long-term, high-density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns. Therefore, this study proposes an Effective DNA Storage (EDS) approach for archiving medical MRI data. The EDS approach incorporates three key components (i) a novel fraction strategy to address the critical issue of rotating encoding, which often leads to data loss due to single base error propagation; (ii) a novel rule-based quaternary transcoding method that satisfies bio-constraints and ensure reliable mapping; and (iii) an indexing technique designed to simplify random search and access. The effectiveness of this approach is validated through computer simulations and biological experiments, confirming its practicality. The EDS approach outperforms existing methods, providing superior control over bio-constraints and reducing computational time. The results and code provided in this study open new avenues for practical DNA storage of medical MRI data, offering promising prospects for the future of medical data archiving and retrieval.

Optimizing hydropower scheduling through accurate power load prediction: A practical case study.

Hydropower stations that are part of the grid system frequently encounter challenges related to the uneven distribution of power generation and associated benefits, primarily stemming from delays in obtaining timely load data. This research addresses this issue by developing a scheduling model that combines power load prediction and dual-objective optimization. The practical application of this model is demonstrated in a real-case scenario, focusing on the Shatuo Hydropower Station in China. In contrast to current models, the suggested model can achieve optimal dispatch for grid-connected hydropower stations even when power load data is unavailable. Initially, the model assesses various prediction models for estimating power load and subsequently incorporates the predictions into the GA-NSGA-II algorithm, specifically an enhanced elite non-dominated sorting genetic algorithm. This integration is performed while considering the proposed objective functions to optimize the discharge flow of the hydropower station. The outcomes reveal that the CNN-GRU model, denoting Convolutional Neural Network-Gated Recursive Unit, exhibits the highest prediction accuracy, achieving R-squared and RMSE (i.e., Root Mean Square Error) values of 0.991 and 0.026, respectively. The variance between scheduling based on predicted load values and actual load values is minimal, staying within 5 (m3/s), showcasing practical effectiveness. The optimized scheduling outcomes in the real case study yield dual advantages, meeting both the demands of ship navigation and hydropower generation, thus achieving a harmonious balance between the two requirements. This approach addresses the real-world challenges associated with delayed load data collection and insufficient scheduling, offering an efficient solution for managing hydropower station scheduling to meet both power generation and navigation needs.

The Burgeoning Significance of Liquid-Liquid Phase Separation in the Pathogenesis and Therapeutics of Cancers.

Liquid-liquid phase separation (LLPS) is a physiological phenomenon that parallels the mixing of oil and water, giving rise to compartments with diverse physical properties. Biomolecular condensates, arising from LLPS, serve as critical regulators of gene expression and control, with a particular significance in the context of malignant tumors. Recent investigations have unveiled the intimate connection between LLPS and cancer, a nexus that profoundly impacts various facets of cancer progression, including DNA repair, transcriptional regulation, oncogene expression, and the formation of critical membraneless organelles within the cancer microenvironment. This review provides a comprehensive account of the evolution of LLPS from the molecular to the pathological level. We explore the mechanisms by through which biomolecular condensates govern diverse cellular physiological processes, encompassing gene expression, transcriptional control, signal transduction, and responses to environmental stressors. Furthermore, we concentrate on potential therapeutic targets and the development of small-molecule inhibitors associated with LLPS in prevalent clinical malignancies. Understanding the role of LLPS and its interplay within the tumor milieu holds promise for enhancing cancer treatment strategies, particularly in overcoming drug resistance challenges. These insights offer innovative perspectives and support for advancing cancer therapy.

Few-Shot Relation Extraction With Automatically Generated Prompts.

Relation extraction (RE) tends to struggle when the supervised training data is few and difficult to be collected. In this article, we elicit relational and factual knowledge from large pretrained language models (PLMs) for few-shot RE (FSRE) with prompting techniques. Concretely, we automatically generate a diverse set of natural language templates and modulate PLM's behavior through these prompts for FSRE. To mitigate the template bias which leads to unstableness of few-shot learning, we propose a simple yet effective template regularization network (TRN) to prevent deep networks from over-fitting uncertain templates and thus stabilize the FSRE models. TRN alleviates the template bias with three mechanisms: 1) an attention mechanism over mini-batch to weight each template; 2) a ranking regularization mechanism to regularize the attention weights and constrain the importance of uncertain templates; and 3) a template calibration module with two calibrating techniques to modify the uncertain templates in the lowest-ranked group. Experimental results on two benchmark datasets (i.e., FewRel and NYT) show that our model has robust superiority over strong competitors. For reproducibility, we will release our code and data upon the publication of this article.

NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods.

Neural View Synthesis (NVS) has demonstrated efficacy in generating high-fidelity dense viewpoint videos using a image set with sparse views. However, existing quality assessment methods like PSNR, SSIM, and LPIPS are not tailored for the scenes with dense viewpoints synthesized by NVS and NeRF variants, thus, they often fall short in capturing the perceptual quality, including spatial and angular aspects of NVS-synthesized scenes. Furthermore, the lack of dense ground truth views makes the full reference quality assessment on NVS-synthesized scenes challenging. For instance, datasets such as LLFF provide only sparse images, insufficient for complete full-reference assessments. To address the issues above, we propose NeRF-NQA, the first no-reference quality assessment method for densely-observed scenes synthesized from the NVS and NeRF variants. NeRF-NQA employs a joint quality assessment strategy, integrating both viewwise and pointwise approaches, to evaluate the quality of NVS-generated scenes. The viewwise approach assesses the spatial quality of each individual synthesized view and the overall inter-views consistency, while the pointwise approach focuses on the angular qualities of scene surface points and their compound inter-point quality. Extensive evaluations are conducted to compare NeRF-NQA with 23 mainstream visual quality assessment methods (from fields of image, video, and light-field assessment). The results demonstrate NeRF-NQA outperforms the existing assessment methods significantly and it shows substantial superiority on assessing NVS-synthesized scenes without references. An implementation of this paper are available at https://github.com/VincentQQu/NeRF-NQA.

Effects of Fe-N co-modified biochar on methanogenesis performance, microbial community, and metabolic pathway during anaerobic co-digestion of alternanthera philoxeroides and cow manure.

The performance of anaerobic digestion (AD) is susceptible to disturbances in feedstock degradation, intermediates accumulation, and methanogenic archaea activity. To improve the methanogenesis performance of the AD system, Fe-N co-modified biochar was prepared under different pyrolysis temperatures (300,500, and 700 °C). Meanwhile, pristine and Fe-modified biochar were also derived from alternanthera philoxeroides (AP). The aim was to compare the effects of Fe-N co-modification, Fe modification, and pristine biochar on the methanogenic performance and explicit the responding mechanism of the microbial community in anaerobic co-digestion (coAD) of AP and cow manure (CM). The highest cumulative methane production was obtained with the addition of Fe-N-BC500 (260.38 mL/gVS), which was 42.37 % higher than the control, while the acetic acid, propionic acid, and butyric acid concentration of Fe-N-BC were increased by 147.58 %, 44.25 %, and 194.06 % compared with the control, respectively. The co-modified biochar enhanced the abundance of Chloroflexi and Methanosarcina in the AD system. Metabolic pathway analysis revealed that the increased methane production was related to the formation and metabolism of volatile fatty acids and that Fe-N-BC500 enhanced the biosynthesis of coenzyme A and the cell activity of microorganisms, accelerating the degradation of propionic acid and enhancing the hydrogenotrophic methanogenesis pathway. Overall, Fe-N co-modified biochar was proved to be an effective promoter for accelerated methane production during AD.

Association of flavonoid intake with coronary artery disease risk in the older population based on the National Health and Nutrition Examination Survey.

We investigated the association between flavonoid intake and coronary artery disease (CAD) risk in older adults. Data were extracted from the National Health and Nutrition Examination Survey (age ≥ 70 years; 2007-2010 and 2017-2018; n = 2 417). The total flavonoid and flavonoid subclass intake was calculated using validated food frequency questionnaires. The association between flavonoid intake and CAD risk was examined using generalized linear models with restricted cubic spline models. After multivariate adjustment, anthocyanin intake was positively associated with CAD risk; no significant associations were observed between other flavonoid subcategories and endpoint outcomes. Anthocyanins exhibited a non-linear association with CAD risk, and threshold effect analysis showed an inflection point of 15.8 mg/day for anthocyanins. Per unit increase in anthocyanins, the odds of CAD on the left of the inflection point decreased by 2%, while the odds on the right increased by 35.8%. Excessive flavonoid intake may increase CAD risk in the older population.

Personalized application of antimicrobial drugs in pediatric patients with augmented renal clearance: a review of literature.

The effect of renal functional status on drug metabolism is a crucial consideration for clinicians when determining the appropriate dosage of medications to administer. In critically ill patients, there is often a significant increase in renal function, which leads to enhanced drug metabolism and potentially inadequate drug exposure. This phenomenon, known as augmented renal clearance (ARC), is commonly observed in pediatric critical care settings. The findings of the current study underscore the significant impact of ARC on the pharmacokinetics and pharmacodynamics of antimicrobial drugs in critically ill pediatric patients. Moreover, the study reveals a negative correlation between increased creatinine clearance and blood concentrations of antimicrobial drugs. The article provides a comprehensive review of ARC screening in pediatric patients, including its definition, risk factors, and clinical outcomes. Furthermore, it summarizes the dosages and dosing regimens of commonly used antibacterial and antiviral drugs for pediatric patients with ARC, and recommendations are made for dose and infusion considerations and the role of therapeutic drug monitoring. CONCLUSION:  ARC impacts antimicrobial drugs in pediatric patients. WHAT IS KNOWN: • ARC is inextricably linked to the failure of antimicrobial therapy, recurrence of infection, and subtherapeutic concentrations of drugs. WHAT IS NEW: • This study provides an updated overview of the influence of ARC on medication use and clinical outcomes in pediatric patients. • In this context, there are several recommendations for using antibiotics in pediatric patients with ARC: 1) increase the dose administered; 2) prolonged or continuous infusion administration; 3) use of TDM; and 4) use alternative drugs that do not undergo renal elimination.

Equilibrative nucleotide transporter ENT3 (SLC29A3): A unique transporter for inherited disorders and cancers.

As a crucial gene associated with diseases, the SLC29A3 gene encodes the equilibrative nucleoside transporter 3 (ENT3). ENT3 plays an essential regulatory role in transporting intracellular hydrophilic nucleosides, nucleotides, hydrophilic anticancer and antiviral nucleoside drugs, energy metabolism, subcellular localization, protein stability, and signal transduction. The mutation and inactivation of SLC29A3 are intimately linked to the occurrence, development, and prognosis of various human tumors. Moreover, many hereditary human diseases, such as H syndrome, pigmentary hypertrichosis and non-autoimmune insulin-dependent diabetes mellitus (PHID) syndrome, Faisalabad histiocytosis (FHC), are related to SLC29A3 mutations. This review explores the mechanisms of SLC29A3 mutations and expression alterations in inherited disorders and cancers. Additionally, we compile studies on the inhibition of ENT3, which may serve as an effective strategy to potentiate the anticancer activity of chemotherapy. Thus, the synopsis of genetics, permeant function and drug therapy of ENT3 provides a new theoretical and empirical foundation for the diagnosis, prognosis of evaluation and treatment of various related diseases.

Room-Temperature Synthesis of Carbon-Nanotube-Interconnected Amorphous NiFe-Layered Double Hydroxides for Boosting Oxygen Evolution Reaction.

The oxygen evolution reaction (OER) is a key half-reaction in electrocatalytic water splitting. Large-scale water electrolysis is hampered by commercial noble-metal-based OER electrocatalysts owing to their high cost. To address these issues, we present a facile, one-pot, room-temperature co-precipitation approach to quickly synthesize carbon-nanotube-interconnected amorphous NiFe-layered double hydroxides (NiFe-LDH@CNT) as cost-effective, efficient, and stable OER electrocatalysts. The hybrid catalyst NiFe-LDH@CNT delivered outstanding OER activity with a low onset overpotential of 255 mV and a small Tafel slope of 51.36 mV dec-1, as well as outstanding long-term stability. The high catalytic capability of NiFe-LDH@CNT is associated with the synergistic effects of its room-temperature synthesized amorphous structure, bi-metallic modulation, and conductive CNT skeleton. The room-temperature synthesis can not only offer economic feasibility, but can also allow amorphous NiFe-LDH to be obtained without crystalline boundaries, facilitating long-term stability during the OER process. The bi-metallic nature of NiFe-LDH guarantees a modified electronic structure, providing additional catalytic sites. Simultaneously, the highly conductive CNT network fosters a nanoporous structure, facilitating electron transfer and O2 release and enriching catalytic sites. This study introduces an innovative approach to purposefully design nanoarchitecture and easily synthesize amorphous transition-metal-based OER catalysts, ensuring their cost effectiveness, production efficiency, and long-term stability.

Hydropower station scheduling with ship arrival prediction and energy storage.

Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues, this paper proposes a multi-objective real-time scheduling model. The proposed model incorporates energy storage and ship arrival prediction. An energy storage mechanism is introduced to stabilize power generation by charging the power storage equipment during surplus generation and discharging it during periods of insufficient generation at the hydropower stations. To facilitate the scheduling with the eneragy storage mechanism, the arrival time of ships to the stations are predicted. We use the maximization of generation minus grid load demand and the maximization of navigability assurance rate as two objective functions in the scheduling process. The model uses the Non-Dominated Sorting Beluga Whale Optimization (NSBWO) algorithm to optimize and solve the real-time discharge flow scheduling of the hydropower stations in different time periods. The NSBWO algorithm combines the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Beluga Whale Optimization (BWO). The experimental results show that the proposed method has advantages in predicting the expected arrival time of ships and scheduling the discharge flow. The prediction using XGBoost model reaches accuracy with more than 0.9, and the discharged flow obtained from scheduling meets the demand of hydropower stations grid load while also improves the navigation benefits. This study provides theoretical analysis with its practical applications in a real hyropower station as a case study for solving hydropower scheduling problems.

Low lean mass with obesity in older adults with hypertension: prevalence and association with mortality rate.

BACKGROUND: The influence of sarcopenic obesity (SO) on overall survival in older adults with hypertension has not been addressed. The aim of this study was to investigate the prevalence and mortality predictive value of various body composition phenotypes, focusing mainly on SO, in older adults with hypertension. METHODS: We included 1105 hypertensive patients aged ≥ 60 years from the National Health and Nutrition Examination Survey 1999-2004. Sarcopenia was broadly defined based on low lean mass (LLM; as measured by dual-energy X-ray absorptiometry), and was defined using appendicular lean mass (ALM) divided by height squared (ALM/height2), weight (ALM/weight), and body mass index (BMI; ALM/BMI), respectively. Obesity was defined as BMI ≥ 30 kg/m2, body fat percentage ≥ 30/42%, or waist circumference ≥ 102/88 cm. The prevalence of LLM with obesity was estimated according to each ALM index (ALMI). Multivariable Cox regression analysis and sensitivity analysis were used to examine the association between various body composition phenotypes and all-cause mortality. RESULTS: In older adults with hypertension, the prevalence of LLM with obesity by the ALM/height2 index (9.8%) was lower relative to the ALM/weight (11.7%) and ALM/BMI indexes (19.6%). After a median follow-up of 15.4 years, 642 deaths occurred. In the fully adjusted models, LLM with obesity was significantly associated with a higher risk of all-cause mortality (hazard ratio [HR] 1.69, 95% confidence interval [CI] 1.14-2.49, P = 0.008; HR 1.48, 95% CI 1.04-2.10, P = 0.028; HR 1.30, 95% CI 1.02-1.66, P = 0.037; respectively) compared with the normal body phenotype, with no statistical differences found in individuals with LLM or obesity alone. Sensitivity analysis confirmed the robustness of the results. CONCLUSIONS: The prevalence of LLM with obesity markedly differed in older adults with hypertension according to the 3 different ALMIs, varying from 9.8%, 11.7%, to 19.6%. Patients with both LLM and obesity had a higher risk of all-cause mortality. Further large, prospective, cohort studies are warranted to validate these findings and uncover underlying mechanisms.