Novel biomarkers for prediction of atonic postpartum hemorrhage among 'low-risk' women in labor.

Background: Postpartum hemorrhage (PPH) is the primary cause of maternal mortality globally, with uterine atony being the predominant contributing factor. However, accurate prediction of PPH in the general population remains challenging due to a lack of reliable biomarkers. Methods: Using retrospective cohort data, we quantified 48 cytokines in plasma samples from 40 women diagnosed with PPH caused by uterine atony. We also analyzed previously reported hemogram and coagulation parameters related to inflammatory response. The least absolute shrinkage and selection operator (LASSO) and logistic regression were applied to develop predictive models. Established models were further evaluated and temporally validated in a prospective cohort. Results: Fourteen factors showed significant differences between the two groups, among which IL2Rα, IL9, MIP1ß, TNFß, CTACK, prenatal Hb, Lymph%, PLR, and LnSII were selected by LASSO to construct predictive model A. Further, by logistic regression, model B was constructed using prenatal Hb, PLR, IL2Rα, and IL9. The area under the curve (AUC) values of model A in the training set, internal validation set, and temporal validation set were 0.846 (0.757-0.934), 0.846 (0.749-0.930), and 0.875 (0.789-0.961), respectively. And the corresponding AUC values for model B were 0.805 (0.709-0.901), 0.805 (0.701-0.894), and 0.901 (0.824-0.979). Decision curve analysis results showed that both nomograms had a high net benefit for predicting atonic PPH. Conclusion: We identified novel biomarkers and developed predictive models for atonic PPH in women undergoing "low-risk" vaginal delivery, providing immunological insights for further exploration of the mechanism underlying atonic PPH.

A Multimodal Feature Fusion Brain Fatigue Recognition System Based on Bayes-gcForest.

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers.

Occurrence, source apportionment and ecological risk of bisphenol analogues in river sediments in areas with different land use patterns.

Bisphenol analogues (BPs) have gained increasing attention in recent years due to their ubiquitousness and potential endocrine disrupting properties in environments. However, little information is available on their spatiotemporal distribution, source apportionment and ecological risk in river sediments, especially the case in river basins with a high population density and those typical regions with agricultural-urban gradient, where land use patterns and intensity of human activity are varying. In this study, field investigations of BPs in the sediment of the entire Qinhuai River Basin, a typical agricultural-suburban agricultural-urban gradient area, were conducted before and after the flood period. Thirty-two sites were sampled for six types of BPs, resulted in no significant difference in the concentration of ΣBPs between the two periods, with ΣBPs ranging from 3.92 to 151 ng/g and 2.16-59.0 ng/g, respectively. Bisphenol A (BPA) was the main contributor. Whereas a multivariate analysis of variance (MANOVA) suggested that the composition structure of BPs had been influenced by water periods. The land use patterns had an impact on the distribution of ΣBPs in river sediments, which was more significant in after the flood period, with ΣBPs in urban rivers was 1.85 times, 3.44 times, and 3.08 times higher than the suburban rivers, agricultural rivers, and reservoirs, respectively. Yet land use types did not significantly alter the composition structure of BPs. The correlation analysis between BPs and the physicochemical properties of sediments showed a significant positive correlation between BPA and total organic carbon (TOC). The positive matrix factorization model (PMF) suggested that BPs in sediments of the basin might be influenced by industrial coatings, textiles, electronics and biopharmaceuticals, as well as urban wastewater or solid waste generated from daily life. The ecological risk assessment posed by BPA, based on the risk quotient, indicated that the ecological risk of BPA in sediments was low for three indicator benthic organisms: crustaceans, worms, and mollusks. However, the risk of BPA in river sediments varied among different land use patterns, with the risk ranking as follows: reservoirs < agricultural rivers < suburban rivers < urban rivers.

Catalytic Asymmetric Redox-Neutral [3+2] Photocycloadditions of Cyclopropyl Ketones with Vinylazaarenes Enabled by Consecutive Photoinduced Electron Transfer.

Consecutive photoinduced electron transfer (ConPET) is a powerful and atom-economical protocol to overcome the limitations of the intrinsic redox potential of visible light-absorbing photosensitizers, thereby considerably improving the substrate and reaction types. Likely because such an exothermic single-electron transfer (SET) process usually does not require the aid of chiral catalysts, resulting in an inevitable racemic background reaction, notably, no enantioselective manifolds have been reported. Herein, we report on the viability of cooperative ConPET and chiral hydrogen-bonding catalysis for the [3+2] photocycloaddition of cyclopropyl ketones with vinylazaarenes. In addition to enabling the first use of olefins that preferentially interact with chiral catalysts, this catalysis platform paves the way for the efficient synthesis of pharmaceutically and synthetically important cyclopentyl ketones functionalized by azaarenes with high yields, ees and dr. The robust capacity of the method can be further highlighted by the low loading of the chiral catalyst (1.0 mol %), the good compatibility of both 2-azaarene and 3-pyridine-based olefins, and the successful concurrent construction of three stereocenters on cyclopentane rings involving an elusive but important all-carbon quaternary.

Evaluating infection risks in buses based on passengers' dynamic temporal and typical spatial scenarios: A case study of COVID-19.

Conventional buses, as an indispensable part of the urban public transport system, impose cross-infection risks on passengers. To assess differential risks due to dynamic staying durations and locations, this study considered four spatial distributions (i = 1-4) and six temporal scenarios (j = 1-6) of buses. Based on field measurements and a risk assessment approach combining both short-range and room-scale effects, risks are evaluated properly. The results showed that temporal asynchrony between infected and susceptible individuals significantly affects disease transmission rates. The Control Case assumes that infected and susceptible individuals enter and leave synchronously. However, ignoring temporal asynchrony scenarios, i.e., the Control Case, resulted in overestimation (+30.7 % to +99.6 %) or underestimation (-15.2 % to -69.9 %) of the actual risk. Moreover, the relative difference ratios of room-scale risks between the Control Case and five temporal scenarios are impacted by ventilation. Short-range risk exists only if infected and susceptible individuals have temporal overlap on the bus. Considering temporal and spatial asynchrony, a more realistic total reproduction number (R) can be obtained. Subsequently, the total R was assessed under five temporal scenarios. On average, for the Control Case, the total R was estimated to be +27.3 % higher than j = 1, -9.3 % lower than j = 2, +12.8 % higher than j = 3, +33.0 % lower than j = 4, and + 77.6 % higher than j = 5. This implies the need for a combination of active prevention and real-time risk monitoring to enable rigid travel demand and control the spread of the epidemic.

Spatiotemporal occurrence and characteristics of microplastics in the urban road dust in a megacity, eastern China.

The pervasive issue of microplastics pollution has garnered public attention, yet urban residents remain unaware of the threat within their living spaces. Urban road dust, as primary reservoirs for environmental microplastics, offers an insightful perspective into their occurrence and characteristics. This study investigated microplastics in the urban road dust in Nanjing, a megacity in eastern China, to reveal their spatiotemporal pattern. The abundance of microplastics in the road dust measured 143.3 ± 40.8 particles/m2, with predominant fragments and suspected tire wear particles, particularly those below 100 µm. Significant spatial variations were observed across urban functional zones (P < 0.05), with commercial and heavy industrial areas experiencing the highest microplastic pollution (up to 223.5 particles/m2). Infrared spectroscopy analysis identified 29 polymer types, with polystyrene (PS), polyamide (PA), and polyvinylidene difluoride (PVDF) prevailing. Light industrial zones exhibited slight contamination (mean = 93.4 particles/m2) but with diverse polymer components (24 types). Redundancy analysis and variation partitioning revealed that urban functional zoning, 7-day accumulated precipitation, and monthly PM2.5 primarily influenced the occurrence and characteristics of microplastics in urban road dust (P = 0.001). This study deepened our understanding of microplastics pollution in urban environments, providing novel insights for effective urban environmental management and improvement.

Exploring the primary magnetic parameters affecting chemical fractions of heavy metal(loid)s in lake sediment through an interpretable workflow.

The magnetic properties of lake sediments account for close relationships with heavy metal(loid)s (HMs), but little is known about their relationships with chemical fractions (CFs) of HMs. Establishing an effective workflow to predict HMs risk among various machine learning (ML) methods in conjunction with magnetic measurement remains challenging. This study evaluated the simulation efficiency of nine ML methods in predicting the risk assessment code (RAC) and ratio of the secondary and primary phases (RSP) of HMs with magnetic parameters in sediment cores of a shallow lake. The sediment cores were collected and sliced, and the total amount and CFs of HMs, as well as magnetic parameters, were determined. Support vector machine (SVM) outperformed other models, as evidenced by coefficient of determination (R2) > 0.8. Interpretable machine learning (IML) methods were employed to identify key indicators of RAC and RSP among the magnetic parameters. Values of χARM, HIRM, χARM/χ, and χARM/SIRM of sediments ranging in 220-500 × 10-8 m3/kg, 30-40 × 10-5Am2/kg, 15-25, and 0.5-1, respectively, indicated the potential ecological risks of Cd, Hg, and Sb. This study offers new perspectives on the risk assessment of HMs in lake sediments by combining magnetic measurement with IML workflow.

Tumor Microenvironment-Regulating Two-Photon Probe Based on Bimetallic Post-Coordinated MOF Facilitating the Dual-Modal and Deep Imaging-Guided Synergistic Therapies.

The intricate tumor microenvironment (TME) always brings about unsatisfactory therapeutic effects for treatments, although nanomedicines have been demonstrated to be highly beneficial for synergistic therapies to avoid the side effects caused by the complexity and heterogeneity of cancer. Developing nanotheranostics with the functionalities of both synergistic therapies and TME regulation is a good strategy but is still in its infancy. Herein, an "all-in-one" nanoplatform for integrated diagnosis and treatment, namely, Carrier@ICG@DOX@FA (CIDF), is constructed. Benefiting from the bimetallic coordination of Eu3+-HTHA (4,4,4-trifluoro-1-(9-hexylcarbazol-3-yl)-1,3-butanedione) and Fe3+ with the ligands in UiO-67, CIDF can simultaneously achieve two-photon fluorescence imaging, fluorescent lifetime imaging in deep tumors, and regulation of TME. Owing to its porosity, CIDF can encapsulate indocyanine green as photosensitizers and doxorubicin as chemotherapeutic agent, further realizing light-controlled drug release. Moreover, CIDF exhibited good biocompatibility and tumor targeting by coating with folic-acid-modified polymers. Both in vitro and in vivo experiments demonstrate the excellent therapeutic efficacy of CIDF through dual-modal-imaging-guided synergistic photothermal-, photodynamic-, and chemotherapy. CIDF provides a new paradigm for the construction of TME-regulated synergistic nanotheranostics and realizes the complete elimination of tumors without recurrence.

Spatiotemporal distribution, source apportionment, and ecological risk of bisphenol analogues in a highly urbanized river basin.

Bisphenol analogues (BPs), as one of the endocrine disruptors, have received wide attention due to their adverse impacts on ecosystems. However, the seasonal spatiotemporal distribution, source apportionment, and ecological risk of BPs in natural basins are poorly understood. Especially in highly urbanized river basins with the extensive economic development and anthropogenic activities threaten these critical but ecologically fragile regions. In this study, field investigations of BPs in the waters of the entire Qinhuai River Basin (QRB) were conducted in June (before the annual flood period) and August (after the annual flood period) 2023. The Qinhuai River, an important primary tributary of the lower Yangtze River, is located in eastern China and the QRB is characterized by a high population density and dense urbanization. Thirty-two sites were sampled for six types of BPs known to be ubiquitous in the surface water of the QRB. Significant differences in the concentrations of those BPs were found. Specifically, the concentration of total BPs (ΣBPs) was significantly higher before than after the flood period: 20.3-472 ng/L (mean = 146 ng/L) and 14.1-105 ng/L (mean = 35.9 ng/L), respectively. BPA was the main contributor to ΣBPs before the flood, and BPB followed by BPA after the flood. ΣBP concentrations were 12-241 % higher downstream than upstream of wastewater treatment plants (WWTPs). The results of a principal component analysis followed by multiple linear regression (PCA-MLR) suggested that untreated wastewater discharge from the WWTPs is an important source of BPs in the basin, with urban rainfall runoff as another potential source after the flood period. An assessment of the ecological risk of BPs, based on a calculation of the risk quotient, showed that BPA and BPS should be given due attention, and overall ecological risk of BPs pose a low risk to local algae but high and medium risks to invertebrates and fish, respectively.

Fluorinated Iron Metal-Organic Frameworks for Activatable 19F Magnetic Resonance Imaging and Synergistic Therapy of Tumors.

Due to their appealing physiochemical properties, metal-organic frameworks (MOFs) have been widely employed in biomedical fields. In this study, we utilize ferric ions and fluorine-containing organic ligands as both structural and functional units to develop a stimulus-responsive nanoagent, 19FIMOF-TA nanoparticles, for activatable 19F magnetic resonance imaging (MRI) and synergistic therapy of tumors. This nanoagent could respond to excess GSH in a tumor microenvironment, discharging fluorinated organic ligands and reduced ferrous ions. The release of these fluorine-containing small molecules results in boosting of the 19F MRI signals, which could be further enhanced by the photothermal effect of this nanoagent to achieve a responsive cascaded amplification of 19F MRI signals for tumor visualization. Meanwhile, ferroptosis promoted by the ferrous ions leads to significant tumor cell death, which is synergistically aggravated by the photothermal effect. The encouraging results illustrate the promising potential of our nanoagent for effective tumor imaging and combinative cancer therapy.

Urban surface classification using semi-supervised domain adaptive deep learning models and its application in urban environment studies.

High-resolution urban surface information, e.g., the fraction of impervious/pervious surface, is pivotal in studies of local thermal/wind environments and air pollution. In this study, we introduced and validated a domain adaptive land cover classification model, to automatically classify Google Earth images into pixel-based land cover maps. By combining domain adaptation (DA) and semi-supervised learning (SSL) techniques, our model demonstrates its effectiveness even when trained with a limited dataset derived from Gaofen2 (GF2) satellite images. The model's overall accuracy on the translated GF2 dataset improved significantly from 19.5% to 75.2%, and on the Google Earth image dataset from 23.1% to 61.5%. The overall accuracy is 2.9% and 3.4% higher than when using only DA. Furthermore, with this model, we derived land cover maps and investigated the impact of land surface composition on the local meteorological parameters and air pollutant concentrations in the three most developed urban agglomerations in China, i.e., Beijing, Shanghai and the Great Bay Area (GBA). Our correlation analysis reveals that air temperature exhibits a strong positive correlation with neighboring artificial impervious surfaces, with Pearson correlation coefficients higher than 0.6 in all areas except during the spring in the GBA. However, the correlation between air pollutants and land surface composition is notably weaker and more variable. The primary contribution of this paper is to provide an efficient method for urban land cover extraction which will be of great value for assessing the urban surface composition, quantifying the impact of land use/land cover, and facilitating the development of informed policies.

Glycan Metabolic Fluorine Labeling for In Vivo Visualization of Tumor Cells and In Situ Assessment of Glycosylation Variations.

The abnormality in the glycosylation of surface proteins is critical for the growth and metastasis of tumors and their capacity for immunosuppression and drug resistance. This anomaly offers an entry point for real-time analysis on glycosylation fluctuations. In this study, we report a strategy, glycan metabolic fluorine labeling (MEFLA), for selectively tagging glycans of tumor cells. As a proof of concept, we synthesized two fluorinated unnatural monosaccharides with distinctive 19 F chemical shifts (Ac4 ManNTfe and Ac4 GalNTfa). These two probes could undergo selective uptake by tumor cells and subsequent incorporation into surface glycans. This approach enables efficient and specific 19 F labeling of tumor cells, which permits in vivo tracking of tumor cells and in situ assessment of glycosylation changes by 19 F MRI. The efficiency and specificity of our probes for labeling tumor cells were verified in vitro with A549 cells. The feasibility of our method was further validated with in vivo experiments on A549 tumor-bearing mice. Moreover, the capacity of our approach for assessing glycosylation changes of tumor cells was illustrated both in vitro and in vivo. Our studies provide a promising means for visualizing tumor cells in vivo and assessing their glycosylation variations in situ through targeted multiplexed 19 F MRI.

Titanium-Based Superlattice with Fe(III)-Regulable Bandgap and Performance for Optimal and Synergistic Sonodynamic-Chemotherapy Guided by Magnetic Resonance Imaging.

Superlattices have considerable potential as sonosensitizers for cancer therapy because of their flexible and tunable band gaps, although they have not yet been reported. In this study, a Ti-based organic-inorganic superlattice with good electron-hole separation was synthesized, which consisted of orderly layered superlattices of 2,2'-bipyridine-5,5'-dicarboxylic acid (BPDC) and Ti-O layers. In addition, the superlattice was coordinated with Fe(III) and encapsulated doxorubicin (DOX) to prepare Ti-BPDC@Fe@DOX@PEG (TFDP) after biocompatibility modification. TFDP can realize the simultaneous generation of reactive oxygen species and release of DOX under ultrasound irradiation. Moreover, adjusting the Fe(III) content can effectively modulate the band gap of the superlattice and increase the efficiency of sonodynamic therapy (SDT). The mechanisms underlying this modulation were explored. TFDP with Fe(III) can also be used as a contrast agent for magnetic resonance imaging (MRI). Both in vitro and in vivo experiments demonstrated the ability of TFDP to precisely treat cancer using MRI-guided SDT/chemotherapy. This study expands the applications of superlattices as sonosensitizers with flexible and tailored modifications and indicates that superlattices are promising for precise and customized treatments.

Macrophage Reprogramming via Targeted ROS Scavenging and COX-2 Downregulation for Alleviating Inflammation.

Inflammation-related diseases affect large populations of people in the world and cause substantial healthcare burdens, which results in significant costs in time, material, and labor. Preventing or relieving uncontrolled inflammation is critical for the treatment of these diseases. Herein, we report a new strategy for alleviating inflammation by macrophage reprogramming via targeted reactive oxygen species (ROS) scavenging and cyclooxygenase-2 (COX-2) downregulation. As a proof of concept, we synthesize a multifunctional compound named MCI containing a mannose-based macrophage targeting moiety, an indomethacin (IMC)-based segment for inhibiting COX-2, and a caffeic acid (CAF)-based section for ROS clearance. As revealed by a series of in vitro experiments, MCI could significantly attenuate the expression of COX-2 and the level of ROS, leading to M1 to M2 macrophage reprogramming, as evidenced by the reduction and the elevation in the levels of pro-inflammatory M1 markers and anti-inflammatory M2 markers, respectively. Furthermore, in vivo experiments show MCI's promising therapeutic effects on rheumatoid arthritis (RA). Our work illustrates the success of targeted macrophage reprogramming for inflammation alleviation, which sheds light on the development of new anti-inflammatory drugs.

Multi-scale risk assessment and mitigations comparison for COVID-19 in urban public transport: A combined field measurement and modeling approach.

The outbreak of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused an unparalleled disruption to daily life. Given that COVID-19 primarily spreads in densely populated indoor areas, urban public transport (UPT) systems pose significant risks. This study presents an analysis of the air change rate in buses, subways, and high speed trains based on measured CO2 concentrations and passenger behaviors. The resulting values were used as inputs for an infection risk assessment model, which was used to quantitatively evaluate the effects of various factors, including ventilation rates, respiratory activities, and viral variants, on the infection risk. The findings demonstrate that ventilation has a negligible impact on reducing average risks (less than 10.0%) for short-range scales, but can result in a reduction of average risks by 32.1%-57.4% for room scales. When all passengers wear masks, the average risk reduction ranges from 4.5-folds to 7.5-folds. Based on our analysis, the average total reproduction numbers (R) of subways are 1.4-folds higher than buses, and 2-folds higher than high speed trains. Additionally, it is important to note that the Omicron variant may result in a much higher R value, estimated to be approximately 4.9-folds higher than the Delta variant. To reduce disease transmission, it is important to keep the R value below 1. Thus, two indices have been proposed: time-scale based exposure thresholds and spatial-scale based upper limit warnings. Mask wearing provides the greatest protection against infection in the face of long exposure duration to the omicron epidemic.

Improving the efficiency of machine learning in simulating sedimentary heavy metal contamination by coupling preposing feature selection methods.

Sediment cores were collected from Taihu Lake in China. The chronology was determined by radionuclide. Heavy metals and magnetic properties of each core slice were assessed, respectively. The concentrations of most heavy metals in sediments surged at 20 cm from the surface, accompanying the increase in the concentrations of single-domain magnetic particles. This may be resulted from the influence of anthropic activities on the lake's environment after the 1970s. Two feature selection methods, random forest (RF) and maximal information coefficient (MIC), were combined with support vector machine (SVM) model to simulate heavy metals, with the inclusion of selected magnetic and physicochemical parameters. Compared with the modeling results obtained with the full set of parameters, a reasonable simulation performance was obtained with RF and MIC. RF performed better than MIC by increasing the R2 of simulation models for Cd, Cr, Cu, Pb, and Sb. For heavy metals with high ecological risks (As, Cd, Cr, Hg, Pb, Sb), the correlation coefficients for observed and predicted data ranged from 0.73 to 0.97 with only 14-27% of the parameters selected by RF as input variables. The RF-RBF-SVM enabled heavy metal predictions based on the magnetic properties of the lake sediments.

Microplastics in infant milk powder.

Microplastics can be found almost everywhere and extensively expose to human. Infants are vulnerable, and the potential risks of microplastics may be greater for infants, however, infants' microplastic exposure is still poorly understood. Since milk powder makes up a significant portion of many infants' diets, we investigated microplastic pollution in infant milk powder and calculated the microplastic exposure from milk powder, feeding bottles and milk powder preparation. On one hand, we studied 13 different types of milk powder with different packaging, processing systems, and milk sources. The boxed milk powder (7 ± 3 items/100 g) was more polluted with microplastics than the canned milk powder (4 ± 3 items/100 g). The inner packaging of the boxed milk powder was plastic and aluminum foil laminated, it emitted 8 ± 2 to 17 ± 1 items/100 g of microplastics, and might be a main source of microplastics in boxed milk powder. On the other hand, we found that when infants consume milk powder, the microplastic exposure from milk powder itself is little, exposure from feeding bottles is 6.8 times higher than that from milk powder and milk powder preparation is 1.7 times higher.

A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake.

There is a great need for timely monitoring and rapid water quality assessment to control the algal blooms that often occur in eutrophic lakes. While algal cell density (ACD) is a critical indicator of algal growth, field monitoring is laborious and time-consuming, and rapid assessment of algal blooms based on ACD is often not possible. To address the limitations of conventional ACD detection, we proposed a soft sensor approach that uses surrogate indicators to simulate ACD in machine learning models. We conducted a case study using monitoring data from Chaohu Lake collected between 2016 and 2019. We found that ensemble learning models, especially extreme gradient boosting (XGBoost), outperformed traditional machine learning algorithms by comparing various machine learning algorithms. Also, considering the influence of input variable selection on model performance, we combined the results of different filter methods in the multi-stage variable selection process. Finally, we screened out seven key variables out of the 43 initial candidate variables, including dissolved oxygen (DO), chlorophyll-a (Chl-a), Secchi disk depth (SD), pH, permanganate index (CODMn), week of the year (WOY), and wind velocity (WV). Their inclusion substantially improved data accessibility and supported the development of a rapid simulation model. The final model was capable of reliable spatiotemporal generalization, with an overall R2 value of 0.761. On the theoretical side, our study makes a new attempt to simulate ACD values in a eutrophic lake. For practical purposes, the soft sensor can facilitate the rapid assessment of bloom conditions, which helps the local administration with emergency prevention and control.

Association of the visceral adiposity index with arterial stiffness in elderly Chinese population.

BACKGROUND: The visceral adiposity index (VAI) is a new marker of adipose dysfunction and related with cardiometabolic risk. The aim of this study is to investigate the association of VAI with arterial stiffness in elderly Chinese population. METHODS: A total of 1,707 elderly individuals over 60 years of age were recruited for this cross-sectional study. We measured body composition, anthropometrics, blood pressure, and lipid parameters. The arterial stiffness was measured by brachial-ankle pulse wave velocity (baPWV) and defined as baPWV ≥ 1400 cm/s. VAI‬ was calculated based on body mass index, waist circumference, triglyceride and high-density lipoprotein cholesterol. Multivariable regression analysis was performed to evaluate the association between VAI and baPWV. RESULTS: There were significant differences in VAI tertiles between low-baPWV and high-baPWV group (p = 0.008). Univariate analysis demonstrated that age, history of hypertension, SBP, DBP, total cholesterol, fasting glucose, the higher VAI tertiles were correlated with the existence of high-baPWV (p < 0.05). Participants in the higher VAI tertiles had higher OR (1.0 ≤ VAI < 1.74: OR= 2.89, 95% CI [1.44, 5.80]; VAI ≥ 1.75: OR = 4.23, 95% CI [1.45, 12.37], p for trend: 0.004) comparing with the lowest VAI tertile. Non-linear relationship was detected between VAI and baPWV. VAI was positively correlated with baPWV when VAI < 2.10. CONCLUSIONS: This study demonstrates that VAI is independently associated with the risk of arterial stiffness in elderly population.