Analyzing Longitudinal Health Screening Data with Feature Ensemble and Machine Learning Techniques: Investigating Diagnostic Risk Factors of Metabolic Syndrome for Chronic Kidney Disease Stages 3a to 3b.

Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.

What Elements Really Intercalate into Pd Lattice When Heated in Dimethylformamide?

Palladium hydrides (PdHx) are pivotal in both fundamental research and practical applications across a wide spectrum. PdHx nanocrystals, synthesized by heating in dimethylformamide (DMF), exhibit remarkable stability, granting them widespread applications in the field of electrocatalysis. However, this stability appears inconsistent with their metastable nature. The substantial challenges in characterizing nanoscale structures contribute to the limited understanding of this anomalous phenomenon. Here, through a series of well-conceived experimental designs and advanced characterization techniques, including aberration-corrected scanning transmission electron microscopy (AC-STEM), in situ X-ray diffraction (XRD), and time-of-flight secondary ion mass spectrometry (TOF-SIMS), we have uncovered evidence that indicates the presence of C and N within the lattice of Pd (PdCxNy), rather than H (PdHx). By combining theoretical calculations, we have thoroughly studied the potential configurations and thermodynamic stability of PdCxNy, demonstrating a 2.5:1 ratio of C to N infiltration into the Pd lattice. Furthermore, we successfully modulated the electronic structure of Pd nanocrystals through C and N doping, enhancing their catalytic activity in methanol oxidation reactions. This breakthrough provides a new perspective on the structure and composition of Pd-based nanocrystals infused with light elements, paving the way for the development of advanced catalytic materials in the future.

Stable anchoring of single rhodium atoms by indium in zeolite alkane dehydrogenation catalysts.

Maintaining the stability of single-atom catalysts in high-temperature reactions remains extremely challenging because of the migration of metal atoms under these conditions. We present a strategy for designing stable single-atom catalysts by harnessing a second metal to anchor the noble metal atom inside zeolite channels. A single-atom rhodium-indium cluster catalyst is formed inside zeolite silicalite-1 through in situ migration of indium during alkane dehydrogenation. This catalyst demonstrates exceptional stability against coke formation for 5500 hours in continuous pure propane dehydrogenation with 99% propylene selectivity and propane conversions close to the thermodynamic equilibrium value at 550°C. Our catalyst also operated stably at 600°C, offering propane conversions of >60% and propylene selectivity of >95%.

Confinement-Induced Indium Oxide Nanolayers Formed on Oxide Support for Enhanced CO2 Hydrogenation Reaction.

An enclosed nanospace often shows a significant confinement effect on chemistry within its inner cavity, while whether an open space can have this effect remains elusive. Here, we show that the open surface of TiO2 creates a confined environment for In2O3 which drives spontaneous transformation of free In2O3 nanoparticles in physical contact with TiO2 nanoparticles into In oxide (InOx) nanolayers covering onto the TiO2 surface during CO2 hydrogenation to CO. The formed InOx nanolayers are easy to create surface oxygen vacancies but are against over-reduction to metallic In in the H2-rich atmospheres, which thus show significantly enhanced activity and stability in comparison with the pure In2O3 catalyst. The formation of interfacial In-O-Ti bonding is identified to drive the In2O3 dispersion and stabilize the metastable InOx layers. The InOx overlayers with distinct chemistry from their free counterpart can be confined on various oxide surfaces, demonstrating the important confinement effect at oxide/oxide interfaces.

Generating active metal/oxide reverse interfaces through coordinated migration of single atoms.

Identification of active sites in catalytic materials is important and helps establish approaches to the precise design of catalysts for achieving high reactivity. Generally, active sites of conventional heterogeneous catalysts can be single atom, nanoparticle or a metal/oxide interface. Herein, we report that metal/oxide reverse interfaces can also be active sites which are created from the coordinated migration of metal and oxide atoms. As an example, a Pd1/CeO2 single-atom catalyst prepared via atom trapping, which is otherwise inactive at 30 °C, is able to completely oxidize formaldehyde after steam treatment. The enhanced reactivity is due to the formation of a Ce2O3-Pd nanoparticle domain interface, which is generated by the migration of both Ce and Pd atoms on the atom-trapped Pd1/CeO2 catalyst during steam treatment. We show that the generation of metal oxide-metal interfaces can be achieved in other heterogeneous catalysts due to the coordinated mobility of metal and oxide atoms, demonstrating the formation of a new active interface when using metal single-atom material as catalyst precursor.

Using a Decision Tree Algorithm Predictive Model for Sperm Count Assessment and Risk Factors in Health Screening Population.

Purpose: Approximately 20% of couples face infertility challenges and struggle to conceive naturally. Despite advances in artificial reproduction, its success hinges on sperm quality. Our previous study used five machine learning (ML) algorithms, random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting, to model health data from 1375 Taiwanese males and identified ten risk factors affecting sperm count. Methods: We employed the CART algorithm to generate decision trees using identified risk factors to predict healthy sperm counts. Four error metrics, SMAPE, RAE, RRSE, and RMSE, were used to evaluate the decision trees. We identified the top five decision trees based on their low errors and discussed in detail the tree with the least error. Results: The decision tree featuring the least error, comprising BMI, UA, ST, T-Cho/HDL-C ratio, and BUN, corroborated the negative impacts of metabolic syndrome, particularly high BMI, on sperm count, while emphasizing the link between good sleep and male fertility. Our study also sheds light on the potentially significant influence of high BUN on spermatogenesis. Two novel risk factors, T-Cho/HDL-C and UA, warrant further investigation. Conclusion: The ML algorithm established a predictive model for healthcare personnel to assess low sperm counts. Refinement of the model using additional data is crucial for improved precision. The risk factors identified offer avenues for future investigations.

CeO2/Cu2O/Cu Tandem Interfaces for Efficient Water-Gas Shift Reaction Catalysis.

Metal-oxide interfaces on Cu-based catalysts play very important roles in the low-temperature water-gas shift reaction (LT-WGSR). However, developing catalysts with abundant, active, and robust Cu-metal oxide interfaces under LT-WGSR conditions remains challenging. Herein, we report the successful development of an inverse copper-ceria catalyst (Cu@CeO2), which exhibited very high efficiency for the LT-WGSR. At a reaction temperature of 250 °C, the LT-WGSR activity of the Cu@CeO2 catalyst was about three times higher than that of a pristine Cu catalyst without CeO2. Comprehensive quasi-in situ structural characterizations indicated that the Cu@CeO2 catalyst was rich in CeO2/Cu2O/Cu tandem interfaces. Reaction kinetics studies and density functional theory (DFT) calculations revealed that the Cu+/Cu0 interfaces were the active sites for the LT-WGSR, while adjacent CeO2 nanoparticles play a key role in activating H2O and stabilizing the Cu+/Cu0 interfaces. Our study highlights the role of the CeO2/Cu2O/Cu tandem interface in regulating catalyst activity and stability, thus contributing to the development of improved Cu-based catalysts for the LT-WGSR.

Improving the Hydrogen Oxidation Reaction Rate of Ru by Active Hydrogen in the Ultrathin Pd Interlayer.

Enhancing the catalytic activity of Ru metal in the hydrogen oxidation reaction (HOR) potential range, improving the insufficient activity of Ru caused by its oxophilicity, is of great significance for reducing the cost of anion exchange membrane fuel cells (AEMFCs). Here, we use Ru grown on Au@Pd as a model system to understand the underlying mechanism for activity improvement by combining direct in situ surface-enhanced Raman spectroscopy (SERS) evidence of the catalytic reaction intermediate (OHad) with in situ X-ray diffraction (XRD), electrochemical characterization, as well as DFT calculations. The results showed that the Au@Pd@Ru nanocatalyst utilizes the hydrogen storage capacity of the Pd interlayer to "temporarily" store the activated hydrogen enriched at the interface, which spontaneously overflows at the "hydrogen-deficient interface" to react with OHad adsorbed on Ru. It is the essential reason for the enhanced catalytic activity of Ru at anodic potential. This work deepens our understanding of the HOR mechanism and provides new ideas for the rational design of advanced electrocatalysts.

Long-wave-infrared pulse production at 11 µm via difference-frequency generation driven by femtosecond mid-infrared all-fluoride fiber laser.

We present an ultrafast long-wave infrared (LWIR) source driven by a mid-infrared fluoride fiber laser. It is based on a mode-locked Er:ZBLAN fiber oscillator and a nonlinear amplifier operating at 48 MHz. The amplified soliton pulses at ∼2.9 µm are shifted to ∼4 µm via the soliton self-frequency shifting process in an InF3 fiber. LWIR pulses with an average power of 1.25-mW centered at 11 µm with a spectral bandwidth of ∼1.3 µm are produced through difference-frequency generation (DFG) of the amplified soliton and its frequency-shifted replica in a ZnGeP2 crystal. Soliton-effect fluoride fiber sources operating in the mid-infrared for driving DFG conversion to LWIR enable higher pulse energies than with near-infrared sources, while maintaining relative simplicity and compactness, relevant for spectroscopy and other applications in LWIR.

Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.

In many countries, especially developed nations, the fertility rate and birth rate have continually declined. Taiwan's fertility rate has paralleled this trend and reached its nadir in 2022. Therefore, the government uses many strategies to encourage more married couples to have children. However, couples marrying at an older age may have declining physical status, as well as hypertension and other metabolic syndrome symptoms, in addition to possibly being overweight, which have been the focus of the studies for their influences on male and female gamete quality. Many previous studies based on infertile people are not truly representative of the general population. This study proposed a framework using five machine learning (ML) predictive algorithms-random forest, stochastic gradient boosting, least absolute shrinkage and selection operator regression, ridge regression, and extreme gradient boosting-to identify the major risk factors affecting male sperm count based on a major health screening database in Taiwan. Unlike traditional multiple linear regression, ML algorithms do not need statistical assumptions and can capture non-linear relationships or complex interactions between dependent and independent variables to generate promising performance. We analyzed annual health screening data of 1375 males from 2010 to 2017, including data on health screening indicators, sourced from the MJ Group, a major health screening center in Taiwan. The symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error were used as performance evaluation metrics. Our results show that sleep time (ST), alpha-fetoprotein (AFP), body fat (BF), systolic blood pressure (SBP), and blood urea nitrogen (BUN) are the top five risk factors associated with sperm count. ST is a known risk factor influencing reproductive hormone balance, which can affect spermatogenesis and final sperm count. BF and SBP are risk factors associated with metabolic syndrome, another known risk factor of altered male reproductive hormone systems. However, AFP has not been the focus of previous studies on male fertility or semen quality. BUN, the index for kidney function, is also identified as a risk factor by our established ML model. Our results support previous findings that metabolic syndrome has negative impacts on sperm count and semen quality. Sleep duration also has an impact on sperm generation in the testes. AFP and BUN are two novel risk factors linked to sperm counts. These findings could help healthcare personnel and law makers create strategies for creating environments to increase the country's fertility rate. This study should also be of value to follow-up research.

A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques.

With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b.

Mid-IR pulse amplification to ∼millijoule energies in a single transverse mode using large core Er:ZBLAN fibers operating at 2.8µm.

We demonstrate single transverse mode and high energy nanosecond pulse amplification at ∼2.8-µm using large core Er:ZBLAN fibers. The highest energies achieved are 0.75mJ from a 50 µm core, and 420µJ from a 30 µm core fibers respectively, seeded with 95 ns long pulses generated by a ring-cavity Q-switched Er:ZBLAN fiber laser. Nearly diffraction-limited beams with M2 = 1.2-1.3 were obtained using a single-mode excitation technique of multi-mode core fibers. Achieved pulse energies exceed by approximately an order of magnitude the previously reported highest pulse energies in a single transverse mode from a fiber laser or amplifier at these mid-IR wavelengths.

Layered Pd oxide on PdSn nanowires for boosting direct H2O2 synthesis.

Hydrogen peroxide (H2O2) has the wide range of applications in industry and living life. However, the development of the efficient heterogeneous catalyst in the direct H2O2 synthesis (DHS) from H2 and O2 remains a formidable challenge because of the low H2O2 producibility. Herein, we develop a two-step approach to prepare PdSn nanowire catalysts, which comprises Pd oxide layered on PdSn nanowires (PdL/PdSn-NW). The PdL/PdSn-NW displays superior reactivity in the DHS at zero Celcius, presenting the H2O2 producibility of 528 mol kgcat-1·h-1 and H2O2 selectivity of >95%. A layer of Pd oxide on the PdSn nanowire generates bi-coordinated Pd, leading to the different adsorption behaviors of O2, H2 and H2O2 on the PdL/PdSn-NW. Furthermore, the weak adsorption of H2O2 on the PdL/PdSn-NW contributes to the low activation energy and high H2O2 producibility. This surface engineering approach, depositing metal layer on metal nanowires, provides a new insight in the rational designing of efficient catalyst for DHS.

Structure and Properties of Ultrathin SiOx Films on Cu(111).

The metal-oxide interface plays a crucial role in catalysis, and it has attracted increasing interest in recent years. Cu/SiO2, as a common copper-based catalyst, has been widely used in industrial catalysis. However, it is still a challenge to clarify the structures of the interface of Cu-SiOx and the effect on catalytic performance. Herein, we prepared ultrathin SiOx films by evaporating Si onto a Cu(111) surface followed by annealing in an O2 atmosphere, which were characterized by various surface science techniques. The results showed that a SiOx film could grow nearly layer-by-layer on the Cu(111) surface in the present condition. Both X-ray photoelectron spectroscopy (XPS) and high-resolution electron energy loss spectroscopy (HREELS) results confirmed the presence of Cu-O-Si and Si-O-Si species. Thermal stability experiments illustrated that the well-ordered silica films were stable under annealing in vacuum. The feature of CO adsorption suggested a CO-Cuδ+ species induced from the Cuδ+-O-Si. Low-energy ion scattering spectroscopy (LEIS) and XPS results demonstrated that some Cu2O appeared on the surface when the 1 ML SiOx/Cu(111) was exposed in O2 at 353 K.

Integrating Health Data-Driven Machine Learning Algorithms to Evaluate Risk Factors of Early Stage Hypertension at Different Levels of HDL and LDL Cholesterol.

PURPOSE: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. METHODS: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods-stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)-to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. RESULTS: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. CONCLUSION: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.

Evaluating the comparative efficiency of medical centers in Taiwan: a dynamic data envelopment analysis application.

BACKGROUND: People in Taiwan enjoy comprehensive National Health Insurance coverage. However, under the global budget constraint, hospitals encounter enormous challenges. This study was designed to examine Taiwan medical centers' efficiency and factors that influence it. METHODS: We obtained data from open sources of government routine publications and hospitals disclosed by law to the National Health Insurance Administration, Ministry of Health and Welfare, Taiwan. The dynamic data envelopment analysis (DDEA) model was adopted to estimate all medical centers' efficiencies during 2015-2018. Beta regression models were used to model the efficiency level obtained from the DDEA model. We applied an input-oriented approach under both the constant returns-to-scale (CRS) and variable returns-to-scale (VRS) assumptions to estimate efficiency. RESULTS: The findings indicated that 68.4% (13 of 19) of medical centers were inefficient according to scale efficiency. The mean efficiency scores of all medical centers during 2015-2018 under the CRS, VRS, and Scale were 0.85, 0.930, and 0.95,respectively. Regression results showed that an increase in the population less than 14 years of age, assets, nurse-patient ratio and bed occupancy rate could increase medical centers' efficiency. The rate of emergency return within 3-day and patient self-pay revenues were associated significantly with reduced hospital efficiency (p < 0.05). The result also showed that the foundation owns medical center has the highest efficiency than other ownership hospitals. CONCLUSIONS: The study results provide information for hospital managers to consider ways they could adjust available resources to achieve high efficiency.

Cell Membrane-Camouflaged Nanocarriers with Biomimetic Deformability of Erythrocytes for Ultralong Circulation and Enhanced Cancer Therapy.

Despite considerable advancements in cell membrane-camouflaged nanocarriers to leverage natural cell functions, artificial nanocarriers that can accurately mimic both the biological and physical properties of cells are urgently needed. Herein, inspired by the important effect of the stiffness and deformability of natural red blood cells (RBCs) on their life span and flowing through narrow vessels, we report the construction of RBC membrane-camouflaged nanocarriers that can mimic RBCs at different life stages and study how the deformability of RBC-derived nanocarriers affects their biological behaviors. RBC membrane-coated elastic poly(ethylene glycol) diacrylate hydrogel nanoparticles (RBC-ENPs) simulating dynamic RBCs exhibited high immunocompatibility with minimum immunoglobulin adsorption in the surface protein corona, resulting in reduced opsonization in macrophages and ultralong circulation. Furthermore, RBC-ENPs can deform like RBCs and achieve excellent diffusion in tumor extracellular matrix, leading to improved multicellular spheroid penetration and tumor tissue accumulation. In mouse cancer models, doxorubicin-loaded RBC-ENPs demonstrated superior antitumor efficacy to the first-line chemotherapeutic drug PEGylated doxorubicin liposomes. Our work highlights that tuning the physical properties of cell membrane-derived nanocarriers may offer an alternative approach for the bionic design of nanomedicines in the future.

Tumour-derived extracellular vesicle membrane hybrid lipid nanovesicles enhance siRNA delivery by tumour-homing and intracellular freeway transportation.

Extracellular vesicles (EVs) have been proved a promising small interfering RNA (siRNA) delivery vehicle to mediate gene-silencing. Tumour-derived extracellular vesicles (TDEVs) as genetic exchange vectors in the tumour microenvironment, enable intercellular communication for a wide range of endogenous cargo molecules, such as RNAs and proteins. However, the oncogenic cargo of TDEVs limits their application in siRNA delivery for cancer therapy. Herein, we isolated TDEVs from hepatocellular carcinoma (HCC) cells and derived TDEV membranes by abandoning their content. Innovative TDEV membrane hybrid lipid nanovesicles (LEVs) were then fabricated by fusion of TDEV membranes and phospholipids to realize precise delivery to tumours and highly efficient transfection of siRNA. The TDEV membranes endow LEVs with 'homing' targeting ability, facilitating specific internalisation into parent HCC cells primarily through heparan sulfate proteoglycan-mediated pathways. Unlike conventional lipid-based nanovesicles, LEVs can bypass the endosomal degradation pathway, boost the delivery of siRNA through the Golgi and endoplasmic reticulum (ER) intracellular 'freeway' transportation, achieving a 1.7-fold improvement in siRNA transfection efficiency compared with liposomes. Additionally, siRNA loaded LEVs were demonstrated to enhance the antitumour efficacy in HCC bearing mice through effective gene silencing in the tumour sites. Our results highlight the potential application of the TDEV membrane-derived nanovesicles as an advanced siRNA delivery strategy for cancer therapy.

A Simple and Efficient Embolization-Combined Therapy for Solid Tumors by Smart Poly(amino acid)s Nanocomposites.

Interventional embolization and minimally invasive thermal ablation are common clinical methods for treatment of unresectable solid tumors, but they both have many insurmountable disadvantages. Inspired by pH-responsive drug delivery systems, we report the tumor microenvironment-gelled nanocomposites with poly[(l-glutamic acid-ran-l-tyrosine)-b-l-threonine-b-l-cysteine]s (PGTTCs) coating nanoparticles (NPs, Au or Fe3O4) for noninterventional targeted embolization combined with noninvasive thermal ablation therapy of solid tumors by intravenous injection without catheter use. The results of the animal trial in vivo with tumor-bearing mice and rabbits showed superior targeted embolization and therapy and fluorescence/single-photon emission computed tomography/magnetic resonance multimodal imaging effects. Tumors treated with NPs@PGTTCs were shrunken and necrotized within 30 days, the long-term survival rate was more than 80%, and the same effects can be achieved within 15 days when combined with thermal ablation. The method is so simple and efficient for many hard-to-treat tumors within an acidic microenvironment, which is not only a great improvement and innovation in tumor theranostics but also an important development in nanomedicine.

Risk assessment of metabolic syndrome prevalence involving sedentary occupations and socioeconomic status.

OBJECTIVES: To determine whether occupation type, distinguished by socioeconomic status (SES) and sedentary status, is associated with metabolic syndrome (MetS) risk. METHODS: We analysed two data sets covering 73 506 individuals. MetS was identified according to the criteria of the modified Adult Treatment Panel III. Eight occupational categories were considered: professionals, technical workers, managers, salespeople, service staff, administrative staff, manual labourers and taxi drivers; occupations were grouped into non-sedentary; sedentary, high-SES; and sedentary, non-high-SES occupations. A multiple logistic regression was used to determine significant risk factors for MetS in three age-stratified subgroups. R software for Windows (V.3.5.1) was used for all statistical analyses. RESULTS: MetS prevalence increased with age. Among participants aged ≤40 years, where MetS prevalence was low at 6.23%, having a non-sedentary occupation reduced MetS risk (OR=0.88, p<0.0295). Among participants aged >60 years, having a sedentary, high-SES occupation significantly increased (OR=1.39, p<0.0247) MetS risk. CONCLUSIONS: The influence of occupation type on MetS risk differs among age groups. Non-sedentary occupations and sedentary, high-SES occupations decrease and increase MetS risk, respectively, among younger and older adults, respectively. Authorities should focus on individuals in sedentary, high-SES occupations.