Search Results (501)

Diabetes management depends on regular glucose monitoring, yet conventional blood-based methods are invasive and can reduce user comfort. This review presents an overview of wearable glucose monitoring technologies, with emphasis on non-invasive approaches. It first distinguishes invasive, minimally invasive, and non-invasive monitoring and discusses the use of interstitial fluid, sweat, saliva, tears, urine, and breath as alternative sensing media. The review then summarizes optical, electrochemical, electrical/electromagnetic, and nanotechnology-enabled sensing methods, together with representative wearable and commercially reported devices. At the end, textile-based systems are compared with non-textile platforms in terms of comfort, flexibility, signal reliability, durability, and practical integration. Across these approaches, major limitations include variable relationships between alternative biofluids and blood glucose, interference from physiological and environmental factors, calibration requirements, motion artifacts, limited durability, and insufficient clinical validation. Future development requires more reliable sensing, improved wearable integration, standardized testing, and validation under real-world conditions. Full article

Background and Objective: Maximal oxygen uptake (VO₂max) is a fundamental indicator of cardiorespiratory fitness in sports medicine, essential for athletic profiling and training prescription. However, traditional direct measurements require exhaustive physical testing, which induces considerable physiological stress, limits testing frequency, and increases the risk of injury. To address this problem, this study aimed to eliminate the need for exhaustive protocols by developing a highly precise, non-invasive digital prediction model for VO₂max utilizing readily available anthropometric data and acute metabolic biomarkers (blood glucose and lactate kinetics). Methods: To overcome the limitations of a small initial empirical sample (n = 16) and prevent model overfitting, the original dataset was statistically augmented to create a robust synthetic cohort (n = 200) using Multivariate Normal Distribution and k-Nearest Neighbors (k-NN) algorithms. Three different machine learning models (Multiple Linear Regression [MLR], Random Forest [RF], and Support Vector Regression [SVR]) were trained using such parameters as sex, height, weight, baseline/pre-exercise, and net (Δ) glucose and lactate concentrations. Evaluation of the performance of the models included tenfold cross-validation and Bland–Altman analysis as a measure of clinical agreement. Results: Among the three algorithms used, the highest correlation coefficient (R² = 0.939) was observed for SVR, along with the lowest error metrics (RMSE = 1.442 mL/kg/min, MAPE = 2.78%). Moreover, SVR showed remarkable performance in predicting VO₂max of female (R² = 0.890) and male (R² = 0.702) athletes separately. Also, Bland–Altman analysis proved almost zero-bias estimation with 95% limits of agreement ranging between −4.40 and 4.43 mL/kg/min. In order to bypass the black-box problem of complex algorithms for practical application in the field, the MLR model was used for the development of the Beta-Weighted Lacto-Glycemic Equation (β-LGE). Conclusions: In this work, a unique dual-model approach was introduced, with the SVR algorithm serving as a high-performance backend of a digital tool for sport technologists and Beta-Weighted LGE providing a practical calculation formula for coaches. This innovative approach allowed for a completely non-exhaustive profiling of an athlete’s VO₂max using only minimally invasive metabolic measurements. Full article

Consumer smartwatches are increasingly used to monitor health, physical activity, rehabilitation, and performance in real-world environments. Although these devices provide continuous and scalable data, many user-facing outputs are not direct physiological measurements, but estimates generated from sensor signals, proprietary algorithms, user characteristics, and contextual assumptions. This review article provides a practical framework for evaluating smartwatch-derived metrics by distinguishing between relatively direct sensor measurements and higher-level algorithmic outputs. We review how common and emerging metrics are generated, including cardiovascular measures, energy expenditure, aerobic capacity, sleep and readiness scores, body composition, movement mechanics, cuffless blood pressure, sweat loss and hydration, and non-invasive glucose monitoring. Across these domains, validity varies substantially by device, algorithm, population, activity type, environment, and intended application. Smartwatch-derived data may be most useful for tracking within-person trends and complementing laboratory, clinical, or self-reported assessments, but caution is warranted when using these outputs for precise physiological quantification, diagnostic classification, or cross-device comparisons. Future progress will require stronger validation frameworks, greater algorithmic transparency, standardized reporting, harmonized data infrastructure, and careful alignment between wearable metrics and meaningful health, rehabilitation, and performance decisions. Full article

Noninvasive blood glucose monitoring using impedance spectroscopy faces challenges due to high-dimensional, redundant data and complex nonlinear relationships with glucose concentration. To address these challenges, LASSO regression was applied for feature selection, followed by ensemble learning models incorporating multiple base learners. Among them, a Stacking ensemble framework—combining Support Vector Regression (SVR), Random Forest (RF), and LightGBM—achieved the best overall regression accuracy (MAE = 0.9662, RMSE = 1.3040, $R^2$ = 0.9414). Compared to the best individual base learner (MLP), the S1 ensemble improved overall MAE by 9.7% and RMSE by 7.6%. However, in the clinically critical hyperglycemic range, the individual MLP substantially outperformed S1 (RMSE: 0.7570 vs. 1.1716), indicating that S1’s superiority reflects a global statistical average rather than uniform improvement across all glycemic subranges. Therefore, while the proposed framework demonstrates the efficacy of combining feature selection with ensemble learning, the MLP model may be preferable for clinical applications prioritizing hyperglycemic risk identification. Full article

Smart contact lens sensors integrate biochemical sensing elements, flexible electronics, power modules, and wireless readout components onto optically transparent contact lens platforms, enabling non-invasive and potentially continuous analysis of tear-derived biomarkers and ocular physiological signals. This review focuses on the translation pathway from contact lens materials and fabrication methods to sensing mechanisms, tear biomarker interpretation, and clinical deployment. We synthesize recent progress in substrate engineering, manufacturing processes, power delivery, and representative sensing strategies for intraocular pressure, glucose, electrolytes, pH, cortisol, cholesterol, and inflammatory cytokines. Instead of treating these systems as isolated examples, we compare optical/colorimetric, electrochemical, field-effect transistor, microfluidic, and wireless resonant approaches in terms of sensitivity, response time, power/readout requirements, and clinical relevance. Finally, we discuss persistent barriers, including biocompatibility, interface stability, tear-sample variability, calibration, sterilization, regulatory validation, data privacy, and compatibility with commercial contact lens manufacturing. Full article

Non-invasive blood glucose estimation from exhaled breath has been proposed as a painless alternative to repeated capillary measurements; however, performance evaluation remains challenging in small-sample settings. This study investigates the estimation of blood glucose from human breath using volatile organic compound (VOC) signals acquired with an electronic nose. Responses from three metal-oxide sensor channels sensitive to CO, alcohol, and acetone were collected from 58 individuals, with one measurement per subject, and analyzed using strictly patient-level five-fold cross-validation, in which test folds comprised only real subjects. Two experimental factors were examined. First, model performance was evaluated with and without an additional interpretable alcohol–acetone log-ratio capturing relative variation between compounds. Second, model training was performed using either real data only or fold-wise tabular synthetic augmentation generated via a Gaussian copula fitted exclusively on training subjects, while evaluation remained strictly real-only. Under real-only training, classical machine learning models achieved the lowest prediction errors (approximately 6–7 mg/dL), whereas under synthetic augmentation FTTransformer was the best-performing deep learning model. This findings should be understood as a constrained proof-of-concept analysis rather than as evidence of diagnostic capability or clinical readiness. Full article

Introduction: Transcranial photobiomodulation (t-PBM) is a non-invasive metabolic neuromodulation technique intended to enhance cerebral bioenergetics by stimulating mitochondrial activity. To characterize both baseline metabolic vulnerability and real-time metabolic engagement during stimulation, this preliminary study integrated phosphorus magnetic resonance spectroscopy (³¹P-MRS) with resting-state fMRI. Methods: Eleven individuals with mild cognitive impairment (MCI) or early Alzheimer’s disease underwent ³¹P-MRS to quantify baseline cerebral metabolism (PCr/Pi, pH), followed by MRI sessions during which t-PBM was applied over bilateral frontal sites. Fractional amplitude of low-frequency fluctuations (fALFF), a resting-state index strongly associated with cerebral glucose metabolism, was used as a real-time proxy of metabolic change during stimulation. Results: Linear regression analyses indicated that lower baseline PCr/Pi and lower pH, markers of impaired oxidative metabolism, predicted greater increases in fALFF during t-PBM, most prominently in the right frontal pole (FP2) and, to a lesser extent, right dorsolateral prefrontal cortex (F4). While greater dementia severity also predicted larger fALFF responses in select regions, our findings suggest that t-PBM can boost metabolism in some brain regions where it is compromised, but that this may be independent of cognitive function in early AD/MCI. These findings suggest that t-PBM may preferentially engage brain regions with reduced metabolic capacity to exhibit stronger acute responses. Discussion: Overall, these hypothesis-generating results support the combined use of ³¹P-MRS and fALFF as complementary biomarkers to quantify baseline metabolic status and real-time target engagement. A single session of t-PBM produced neural activity changes consistent with partial metabolic normalization in vulnerable cortical regions. As these results are preliminary, ongoing longitudinal work with a larger cohort will determine whether baseline metabolic profiles and acute fALFF responses predict clinical outcomes after repeated t-PBM treatment. Full article

Background: Viral replication is a major factor in chronic hepatitis B (CHB). Still, the extent to which host metabolic dysfunction contributes to fibrosis risk remains unclear, particularly in studies that follow patients over time. Because most research relies solely on baseline assessments, it may overlook how metabolic changes and fibrosis interact as the disease progresses. Methods: We conducted a retrospective longitudinal cohort study of 304 adults with CHB using electronic medical records collected across 4 visits over 18 months. Repeated metabolic parameters and non-invasive fibrosis indices were examined using population-averaged and mixed-effects models. The associations we observed represent time-specific co-variation between exposures and outcomes measured at the same time point, rather than earlier predictors of later outcomes. Results: Across 1216 person-visits, 421 visit-level fibrosis risk events were recorded (34.6%). Incident clustered metabolic abnormalities occurred at a rate of 21.43 per 100 person-years. Among the metabolic syndrome components, insulin resistance showed the most consistent independent association with visit-level fibrosis risk status. In contrast, after adjustment, longitudinal trends in BMI, lipid measures, and transaminases did not independently distinguish patients with fibrotic progression. A practical clinical model based on age, AST, platelet count, and fasting glucose demonstrated moderate discrimination across risk strata (AUC = 0.772). Conclusions: In CHB, insulin resistance is consistently linked to visit-level fibrosis risk status. Longitudinal metabolic monitoring using routine clinical data provides a practical, scalable way to assess fibrosis risk, especially in resource-limited settings. These findings support incorporating time-based metabolic assessment into CHB care pathways alongside virological factors. Full article

This study presents a novel biosensor for non-invasive glucose detection in saliva using sol colloids of erbium phthalocyanine (ErPc) and polyvinyl acetate (PVAc). The sensors were manufactured by depositing thin films on glass substrates and characterized via optical transmission spectroscopy in the UV-Vis range. The detection signal was based on variations in the transmission spectra amplitude after glucose intake. Results showed that the transmission response effectively distinguished between three health conditions: a regular individual, an athlete, and a prediabetic patient. Specifically, the relative transmission increased significantly in the prediabetic subject compared to the healthy individuals, demonstrating the biosensor’s capability to track glucose fluctuations non-invasively. Full article

Background: Cardiometabolic multimorbidity (CMM) has emerged as a significant global health challenge. The triglyceride-glucose (TyG) index and a body shape index (ABSI), which are markers of insulin resistance and central obesity, respectively, have each been associated with CMM, but their combined utility remains unclear. This study analyzed the association of cumulative and single-point TyG-ABSI with CMM. Methods: This study included 5334 participants from the China Health and Retirement Longitudinal Study (CHARLS), covering the period from 2011 to 2020. Cumulative TyG-ABSI was derived from data collected in 2011 and 2015. Incident CMM was defined as having at least two of the following: heart disease, stroke, and diabetes. Cox regression models were used to estimate hazard ratios, and restricted cubic splines (RCS) were employed to examine nonlinear associations. Time-dependent receiver operating characteristic (ROC) curves were constructed to compare predictive performance. Subgroup and sensitivity analyses were also performed. Results: Over 9 years of follow-up, 424 participants (7.95%) developed CMM. Those in the highest TyG-ABSI quartile had a 2.46-fold higher risk than those in the lowest quartile. The association varied by sex: cumulative TyG-ABSI showed a nonlinear relationship with a threshold effect in females, whereas in males, only cumulative TyG-ABSI (not the single-point measure) was significant. Cumulative TyG-ABSI demonstrated better 5-year predictive performance than single-point measures. Conclusions: Cumulative TyG-ABSI independently predicts CMM risk across sexes, whereas single-point TyG-ABSI is only predictive in females. Quantifying long-term metabolic burden using cumulative TyG-ABSI offers a practical, noninvasive tool for chronic risk stratification in aging populations. Routine assessment of cumulative TyG-ABSI in clinical and community settings could facilitate early identification of high-risk individuals, enabling targeted preventive interventions to reduce the growing burden of CMM. Full article

Diabetic chronic wounds, characterized by persistent inflammation and a complex microenvironment, pose a major challenge to global healthcare. Traditional dressings act merely as passive physical barriers, lacking the ability to sense biochemical fluctuations or respond to dynamic pathological changes. Therefore, developing smart platforms for in situ, continuous, and non-invasive monitoring is crucial for early warning and precision intervention. This review systematically explores recent advances in high-fidelity wound monitoring, focusing on the deep integration of “front-end interface engineering” and “back-end data analysis”. We first analyze the specific physicochemical and biochemical abnormalities of the diabetic wound microenvironment. Next, we discuss how advanced material designs, such as active fluid management, anti-biofouling zwitterionic networks, and nanozyme-based reactive oxygen species (ROS) scavenging, ensure the long-term stability of sensing interfaces against complex microenvironmental interference. Building on this hardware foundation, we summarize in situ sensing strategies and multiparameter decoupling techniques tailored for key biomarkers, including pH, temperature, glucose, ROS, and MMP-9. Furthermore, we highlight cutting-edge developments in signal digitization, emphasizing the pivotal role of portable devices and machine learning algorithms in extracting high-dimensional features and translating complex multimodal signals into objective clinical metrics. By outlining this comprehensive technological closed-loop, this review aims to provide a systematic theoretical framework for the development and clinical translation of next-generation smart wound monitoring platforms. Full article

Sweat is a valuable biofluid for non-invasive health monitoring, as it contains electrolytes, metabolites, and organic compounds that can correlate with blood levels, making it highly attractive for wearable sensing. Building on advances in low-cost, portable electrochemical sensors, sweat analysis enables tracking of hydration status, metabolic stress, and energy availability via key markers such as sodium, potassium, lactate, and glucose. In the sports context, such wearable platforms can support performance optimization and recovery by assessing fluid loss and electrolyte balance in real time. Here, a multiplexed wearable sweat patch is developed to simultaneously monitor temperature, pH, ammonium, sodium, and sweat rate. The integrated platform demonstrates sensitivities of 10.1 mV/ln[NH₄⁺], 9.1 mV/ln[K⁺], 1.11 mV/ln[Na⁺], 14 mV/pH, 0.19% °C⁻¹, and approximately −1.0 mA (mL/min)⁻¹ for sweat rate, with stable signals and linear calibration responses over relevant physiological ranges. The sensor is implemented on a lightweight, biocompatible laser-scribed graphene on a PDMS substrate suitable for prolonged skin contact and mechanical deformation. In addition, a custom PDMS adhesive patch with optimized suction-cup microstructures is engineered to improve skin adhesion under both dry and wet conditions. Finally, the design of the platform was inspired by an adaptive cycling marathon across Saudi Arabia, where an earlier prototype of a wearable patch was deployed for real-time monitoring during a 30-day campaign. Full article

As a promising class of catalysts for enzymatic glucose sensors, Prussian blue analogues (PBAs) exhibit exceptional biomimetic activity. However, their performance is often constrained by poor intrinsic conductivity, which typically limits their sensitivity. To address this limitation, this study presents an effective approach using direct in situ growth of PBAs on the electrode substrates, which enables the effective integration of PBA-based electrochemical systems. A porous Ni framework was first electrodeposited onto a screen-printed gold electrode substrate, followed by the reduction of Pt onto the porous Ni. Subsequently, NiFe PBA was synthesized in situ using the porous Pt/Ni structure as a sacrificial template. Functionalized with glucose oxidase (GO_x), the PBA/Pt/Ni biosensor exhibited excellent performance for glucose detection in buffer solution, with a high sensitivity of 262.6 μA mM⁻¹·cm⁻² and an ultra-low detection limit of 1.45 μM (calculated at a signal-to-noise ratio of 3, S/N = 3). Notably, its sensitivity corresponds to a two-fold enhancement relative to the electrodes modified with commercial Prussian blue using the conventional drop-casting method. Even when tested in human sweat samples, the biosensor achieved a high sensitivity of 236.4 μA mM⁻¹·cm⁻² and a linear detection range of 20–1000 μM, with the broad sensing range fully encompassing the typical physiological concentrations of glucose in human sweat. This excellent performance arises from the high specific surface area of the porous Pt/Ni structure and the tight connection between PBA and the sacrificial Ni anode. This research presents a promising design strategy for advanced, wearable, and non-invasive health-monitoring platforms. Full article

Introduction: Diabetes mellitus (DM) is associated with multiple systemic complications, yet its effects on respiratory muscle structure remain insufficiently characterized. This study aimed to evaluate diaphragmatic morphology in patients with type 2 DM and to determine whether glycemic control is associated with diaphragmatic thickness. Methods: A total of 120 participants were enrolled, including 60 patients with type 2 DM and 60 healthy controls. Demographic, biochemical, and diaphragmatic ultrasound parameters were assessed, including right and left diaphragm thickness (DT) during inspiration and expiration, diaphragmatic excursion (DE), and costophrenic angle (CPA). All patients with DM underwent electromyography for evaluation of peripheral neuropathy. Diabetic participants were further stratified according to glycemic control using an HbA1c threshold of 7%. Correlation analyses and multivariable linear regression models adjusted for age and body mass index (BMI) were performed to examine the association between HbA1c and diaphragmatic parameters. Results: Compared with controls, patients with DM were older and had higher fasting glucose levels but lower total cholesterol, whereas BMI and other biochemical parameters were comparable. Peripheral neuropathy was identified in 28.3% of patients with DM, but was not associated with significant differences in DT, DE, or CPA. In the three-group analysis, right and left DT measured during both inspiration and expiration differed significantly among well-controlled DM, poorly controlled DM, and control groups, whereas DE and CPA remained similar. Within the DM cohort, higher HbA1c levels were significantly associated with lower right and left DT values. These inverse associations remained independent after adjustment for age and BMI, while no independent associations were observed between HbA1c and either DE or CPA. Conclusions: Poorer glycemic control was associated with reduced diaphragmatic thickness in patients with T2DM. Ultrasonographic assessment may offer a non-invasive approach for detecting early respiratory muscle involvement, although its clinical and functional relevance should be validated in future prospective studies. Full article

Coronary microvascular dysfunction (CMD) has emerged as a crucial contributor to cardiovascular morbidity and mortality, particularly in patients with ischemia and non-obstructive coronary arteries (INOCA). The condition arises from a complex interplay of structural and functional abnormalities within the small coronary vessels, driven by underlying molecular mechanisms including endothelial nitric oxide synthase (eNOS) uncoupling, oxidative stress, and chronic inflammation. Lipid metabolism plays a central role in this pathology, especially in the setting of elevated low-density lipoprotein cholesterol (LDL-C). Furthermore, the protective capacity of high-density lipoprotein (HDL) is increasingly understood to depend on its functionality rather than absolute levels, as it can become dysfunctional and pro-inflammatory in pathological states. Emerging evidence has identified lipoprotein(a) [Lp(a)] and triglyceride-rich lipoproteins as significant, independent contributors to microvascular injury. Comprehensive clinical assessment of microvascular dysfunction therefore requires integration of advanced lipid profiling, including apolipoprotein B (ApoB), [Lp(a)], and the triglyceride-glucose (TyG) index with invasive and non-invasive measures of coronary flow reserve to more precisely stratify risk. In this narrative review, we synthesize current observational, mechanistic, and early interventional data linking diverse lipid phenotypes to coronary microvascular dysfunction. We propose a concept of lipid-driven CMD endotypes, such as ApoB-/particle overload, dysfunctional HDL, Lp(a)-mediated risk, and metabolic/TyG-high states, and map these to a practical, mechanism-informed management framework. While intensive LDL-C lowering with high-intensity statins and combination therapy remains guideline-directed care for high-risk patients, evidence for dedicated microvascular benefit from newer lipid and cardiometabolic agents is still largely hypothesis-generating. A personalized approach that aligns lipid phenotyping, CMD endotyping, and existing guideline-based therapies may help refine risk assessment and inform future trials. Full article