Wearable biomolecular sensing nanotechnologies in chronic disease management.

The integration of nanotechnologies with body-interfaced bioelectronics has ushered in a new era of proactive and personalized healthcare for managing chronic diseases. Our survey of molecular signatures across various chronic diseases has revealed a complex network of biomarkers that can provide valuable insights into disease progression and overall health status. Innovative strategies for real-time monitoring of these crucial biomolecules can be developed by leveraging nanoscale sensing systems that employ both biological and synthetic nanomaterials. The seamless integration of nanoscale molecular sensing systems with various unobtrusive body-interfaced form factors has enabled unprecedented access to real-time physiological data in diverse bodily environments. These technologies have the potential to revolutionize disease prevention, monitoring, and management by providing timely and accurate data to both patients and healthcare providers. Despite extensive academic research on continuous body-interfaced biochemical sensing, a notable translational gap remains, with only a few commercial successes to date, including Abott’s Freestyle Libre series, Medtronic’s Guardian series, and Dexcom’s G-series glucose monitors. The journey from technical innovation to commercialization of body-interfaced sensing technologies resembles a meticulous sieving process, wherein each stage imposes stringent requirements for advancement ( Fig. 4 ). Even with regulatory approvals, some products have been withdrawn from the market due to issues that could have been preemptively addressed during early development. As we look toward the future, it is crucial to examine the critical junctures where advancements in body-interfaced sensing technologies often falter and identify opportunities for improvement. In the early development stages, researchers should prioritize features that enable clinical translation, such as continuous monitoring and physiological compatibility. While point-of-care diagnostics have advanced home healthcare, medical-grade body-interfaced sensors can substantially improve patient compliance in chronic disease management; for example, studies have shown improved glycemic control with CGMs 112 . The design challenges for body‐interfaced sensors differ markedly from those encountered in conventional biosensor development where sample collection and signal stability can be well managed. Body-interfaced sensors must contend with variable biofluid composition, limited and fluctuating sample volumes, and motion artifacts. Implantable and ingestible devices present additional challenges: biocompatibility, immune response, biofouling, and long‐term powering, and data transmission in harsh internal environments are critical concerns. Nanomaterials provide promising strategies to overcome these hurdles in addition to sensing challenges. Nanostructured antifouling coatings prevent unwanted adsorption 113 , while nanomaterial-based energy harvesters offer a sustainable power solution 66 . These challenges must be overcome while maintaining high accuracy, sensitivity, and minimizing the occurrence of false positives and negatives to ensure robust sensor performance. Achieving continuous sensing with body-interfaced affinity sensors requires reagent-free approaches for direct in vivo sensing, reversible molecular interactions for repeated sensing, and small operational time gaps between readings to ensure continuous data flow with minimal hysteresis 4 , 114 . Additionally, overcoming form factor challenges, such as developing mechanically imperceptible interfaces to the body, is essential for user comfort and technology acceptance. Advances in nanostructured materials, such as engineered nanowires and bioinspired adhesives are critical in the development of ultra-flexible, conformal devices that seamlessly integrate with the body while maintaining high electrical performance 66 . Extensive validation of biomarker correlation to blood or disease conditions is also crucial, particularly for alternative biofluids like sweat or tears. For instance, the Google SCL project aimed to monitor glucose levels through tears but was discontinued due to an insufficient correlation between tear and blood glucose levels 115 . Preclinical research, which involves rigorous testing of sensor prototypes in laboratory environments and animal models, is crucial for evaluating the safety and efficacy of body-interfaced sensing technologies. Continuous intravenous glucose sensing, which garnered attention in the 2010s, was explored by several companies that subsequently failed; for example, Glumetrics’ GluCath system was shut down due to in vivo thrombus formation 116 . One major hurdle in the transition from lab-based prototypes to commercialization is the scalability and cost-effectiveness of fabrication methods. Ensuring that these methods can scale up to meet future market demand without compromising quality is crucial. The digital pill industry exemplifies this challenge, with FDA clearance for products like Proteus in the mid-2010s, which ultimately failed to gain traction due to cost concerns 117 . The extensive data generated by body-interfaced sensors requires robust data interpretation and storage approaches to extract meaningful insights. Current methods rely heavily on supervised learning, which demands costly and biased manual labeling, thereby limiting scalability. Future research should focus on self-supervised learning approaches, leveraging unlabeled data and advanced generative models to handle incomplete data streams effectively to enable reliable forecasting, and provide actionable healthcare insights. Beyond technical challenges, concerns about privacy infringement and surveillance paranoia can also lead to end-user reluctance and limited market confidence 118 . Moreover, continuous monitoring raises questions about user autonomy and the potential for discrimination if sensitive health data is misinterpreted or improperly shared. Companies like HealthVerity may play a key role in supporting ethical data practices by enabling privacy-protecting record linkage, ensuring Health Insurance Portability and Accountability Act (HIPAA) compliance, and facilitating secure, de-identified data sharing across healthcare systems 119 . By adopting technologies and frameworks that prioritize data integrity and patient confidentiality, researchers and developers can mitigate the risk of bias or misuse and foster greater public trust in body-interfaced biosensing systems. As new regulatory pathways for expedited approval, such as the Breakthrough Device pathway, become available, the deployment of innovative body-interfaced sensing technologies continues to accelerate. Still, regulatory approval remains a deciding factor in whether a new device makes it to market; for example, GlySure Ltd.’s GlySure system failed to received FDA approval, leading to the company going out of business in 2018 120 . Regulatory guidelines are expected to evolve to address the unique ethical challenges posed by these emerging technologies, which are not properly regulated under current guidelines 121 . Continuous monitoring of long-term adverse events and patient outcomes is essential to identify any potential risks or issues that may arise post-approval. The case of the GlucoWatch Biographer, discontinued due to reports of skin burns, highlights the necessity of post-market surveillance in ensuring the safety and effectiveness of body-interfaced sensing technologies 116 . Furthermore, regulatory approval of a device for monitoring a certain physiological parameter does not necessarily guarantee improved patient outcomes. Therefore, post-market surveillance serves as a critical mechanism to assess the real-world impact of these technologies on patient well-being, providing valuable insights into their effectiveness and applicability in chronic disease monitoring. Ultimately, seamless integration of emerging body-interfaced biomolecular sensors with existing healthcare infrastructure, under proper regulatory oversight, is essential for optimal utilization by healthcare providers. This integration will facilitate efficient data collection, analysis, and decision-making processes within established workflows, ensuring that these technologies effectively contribute to improved patient care.

Biomarkers are essential for diagnosing, monitoring, and managing chronic diseases, providing insights into disease progression and enabling personalized treatment 4 . Traditional biomarker analysis has primarily focused on disease-specific markers (e.g., troponin for cardiovascular disease); however, while these markers correlate with disease state, they often fail to capture the entirety of the disease. As medical science continues to elucidate the interconnectedness of the molecular world, it has become increasingly clear that diseases are best viewed through an eclectic approach that combines multiple body systems. For example, analysis of indirect surrogate biomarkers in other body systems (e.g., C-reactive protein (CRP), choline) can provide valuable insight into cardiovascular disease progression (e.g., vessel integrity) that can facilitate disease treatment and prevention. Here, we highlight key biomarkers relevant to chronic disease ( Fig. 1 ) and emphasize the importance of adopting a system-wide approach to disease monitoring and management. Cardiovascular disease, including coronary artery disease, heart valve disease, and heart failure, is the leading cause of death worldwide 5 . Monitoring cardiovascular health can help track disease progression and predict acute events such as heart attack and stroke 6 . Cardiovascular biomarkers include cardiac dysfunction and tissue damage indicators, including B-type natriuretic peptide (BNP) and its N-terminal partner fragment (NT-proBNP)—peptides generated through excessive heart muscle stretching—and troponin I (cTnI), troponin T (cTnT), and creatine kinase-myocardial band (CK-MB)—proteins released due to heart muscle degradation 6 , 7 . In the peripheral cardiovascular system, vessel stenosis can be caused by either atherosclerotic plaque formation, identified through lipid-based biomarkers such as low-density lipoproteins (LDL) and apolipoprotein B 8 , or thrombus formation, identified via biomarkers like thrombin and D-dimer proteins 9 . Surrogate biomarkers of the digestive (e.g., microbiota-derived trimethylamine- N -oxide (TMAO) and its precursor, choline 10 ) and skeletal (e.g., osteopontin, osteoprotegerin 11 ) systems are also emerging as useful indicators of vessel stenosis and calcification, respectively. Metabolic disorders, like diabetes mellitus, pose significant systemic risks to patients and often require diligent molecular monitoring (e.g., glycemic control) to avoid complications 13 . Glucose, glycated hemoglobin (HbA1c), and insulin are traditional biomarkers of diabetes; however, recent years have seen the emergence of alternative markers, including β-hydroxybutyrate (β-HB) 14 , hyocholic acid 15 , and leptin 16 related to obesity and diet that can inform diabetic progression. Biomolecular monitoring can also help manage autoimmune disorders, such as arthritis (e.g., rheumatoid arthritis, gout) and lupus, by tracking disease progression, predicting flare-ups, and guiding proactive treatment. Autoimmune biomarkers often include autoantibodies—for example, rheumatoid factor and anti-citrullinated protein antibodies (ACPAs) for arthritis 18 , and anti-DNA antibodies for lupus 19 . In addition to monitoring disease progression and activity, sensors may also be employed in management of chronic symptoms (e.g., chronic wounds 22 , kidney damage 23 ). Biomolecular monitoring of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis, can identify and track neurodegeneration, allowing clinicians to address neurological symptoms in a timely manner. For example, neuron-specific protein markers, like neurofilament light chain (NfL) 24 , 25 , can indicate neuron breakdown and predict neurological dysfunction. Aggregation of other markers such as amyloid beta (Aβ) and tau proteins in Alzheimer’s disease 26 , or α-synuclein in Parkinson’s disease 27 , can begin years or even decades before symptom onset and serve as key indicators of disease imminence. MicroRNAs (miRNAs) have also emerged as promising biomarkers of neurodegenerative diseases; for example, increased expression of miR-34a and miR-124 have been linked to the onset of Alzheimer’s and Parkinson’s, respectively 28 , 29 . Biomolecular monitoring is poised to revolutionize mental health care by enabling accurate diagnosis and personalized treatment of anxiety, depression, and bipolar disorders through comprehensive biomarker profiling 30 – 32 . Fluctuations in neurotransmitters like dopamine, serotonin, and gamma-aminobutyric acid (GABA) are typical identifiers of mental health conditions 33 ; however, these markers alone cannot adequately stratify patients with more complex issues (e.g., treatment-resistant depression) 34 . Emerging biomarkers, including brain-derived proteins (e.g., brain-derived neurotrophic factor (BDNF)), endocrine molecules (e.g., cortisol), and inflammatory markers (e.g., cytokines), have shown strong correlative relationships with mental health conditions and represent promising clinical indicators 32 . The importance of the gut-brain axis has also become increasingly clear, with microbial metabolites such as short-chain fatty acids (scFAs) and uremic toxins (e.g., indoxyl sulfate) being shown to significantly influence individual mental state 35 . Continuous monitoring of women’s health markers can enhance the management of menstruation, pregnancy, and menopause-related chronic conditions, many of which are currently underdiagnosed or overlooked. Menstruation-related ailments (e.g., polycystic ovary syndrome, endometriosis, dysmenorrhea) are associated with hormone (e.g., sex-hormone binding globulin (SHBG), estradiol), prostaglandin (e.g., prostaglandin E2 and F2α), and inflammatory (e.g., leukotriene B4) markers 36 , 37 . Pregnancy-related chronic conditions (e.g., preeclampsia, hyperemesis gravidarum, gestational diabetes) are associated with placental (e.g., placental protein 13 (PP-13)) and regulatory (e.g., soluble fms-like tyrosine kinase-1 (sFlt-1), soluble endoglin) proteins 38 . Menopause-related conditions, including premature ovarian insufficiency and postmenopausal osteoporosis, can be monitored through ovarian (e.g., follicle-stimulating hormone, inhibin B) and bone integrity (e.g., C-terminal telopeptide of type I collagen (CTX)) markers, respectively 39 . Inflammation monitoring has emerged as a promising surrogate for tracking chronic disease and infection progression due to the ubiquitous nature of inflammation in diseases 40 – 42 . Inflammation biomarkers are typically cytokines such as interleukins (e.g., interleukin 6 (IL-6)), tumor necrosis factors (e.g., tumor necrosis factor alpha (TNF-α)), and interferons (e.g., interferon gamma (IFN-γ)) 43 . Many cytokines exhibit polycausal fluctuations due to the systemic nature of inflammation, and ongoing research has focused on elucidating disease-associated diagnostic cytokine patterns 4 . For example, increased levels of IFN-γ, IL-1β, IL-6, and TNF-α are strongly correlated with atherosclerotic plaque formation in cardiovascular disease 40 . Exogenous markers, including externally introduced drugs and nutrients, can also prove valuable in the context of biomolecular sensing by allowing users to monitor materials entering their body and measure its subsequent effects 44 – 46 . Continuous drug monitoring, such as the sensing of chemotherapy agents, allows clinicians to track drug dosage over time after initial administration 45 . This analysis can be coupled with sensing of other health indicators (e.g., inflammation level) to simultaneously assess drug effectiveness and optimize dosing while minimizing deleterious effects. Monitoring diet-derived biomarkers (e.g., vitamin D, omega-3 fatty acids) can also guide preventive medicine approaches through the development of personalized nutrition regimens 45 , 47 . When identifying appropriate biomarkers, factors such as molecular relevance, specificity, and timescale must be carefully considered. Not all biomarkers are well-suited for continuous monitoring; for example, binary biomarkers such as the SARS-CoV-2 spike protein indicate a disease state based on presence or absence, making continuous assessment unnecessary 48 . Continuous monitoring is most valuable for biomarkers whose concentrations fluctuate meaningfully with disease progression. However, distinguishing which biomarkers fall into this category can be extremely challenging due to the body’s inherent complexity—numerous biomarkers fluctuate for various reasons unrelated to disease. Adding to this complexity, many biomarkers (e.g., cytokines) exhibit polycausal fluctuations, such that their changing levels are indicative of a general response (e.g., inflammation) not easily assigned to a specific disease state 4 , 49 . Furthermore, the same biomarker may fluctuate at drastically different timescales (i.e., minutes vs. hours) for different disease states, making it challenging to discern whether changes are due to acute or chronic processes 49 . This assessment underscores the importance of implementing robust clinical validation alongside sensor development 50 . A deeper understanding of temporal biomarker dynamics is still in progress, and continuous monitoring helps establish a cyclic system—allowing sensors to elucidate biological mechanisms that, in turn, can guide further sensor development and improve diagnostic and treatment capabilities 4 .

Early diagnosis and continuous monitoring of chronic diseases hinge on the accurate measurement of physicochemical signals and related biomarkers. The integration of nanomaterials and nanostructures into body-interfaced sensors has emerged as a powerful tool, significantly enhancing sensor performance through the use of multifunctional materials and optimized interactions with target analytes. Many physiological analytes of interest require detection at sub-nanomolar (nM) or even picomolar (pM) levels. Over the past few decades, extensive research has focused on developing high-resolution sensors that surpass the limitations of bulk and planar sensing platforms through the intervention of nanotechnology 51 . Nanoengineered devices enhance the signal-to-noise ratio to achieve superior sensitivity, down to the femtomolar (fM) level and even the single-molecule level, by leveraging their size-matched interaction with target molecules and often overcoming analyte transport limitations. This section highlights the synergistic combination of nanomaterials, nanostructures, and bioaffinity elements for the detection of minute targets, as well as the application of nanoscale materials and nanoengineered surfaces for in-situ and real-time monitoring of chronic disease biomarkers. It emphasizes various sensing approaches that facilitate their detection ( Box 1 ). Nanomaterials play a crucial role in improving sensor performance due to their unique, nanoscale-driven physicochemical properties. These materials possess large surface-to-volume ratios, high electron mobilities, and are easily modified with recognition elements to produce active target-recognition materials ( Fig. 2a ). Common nanomaterials employed in sensors include quantum dots (QDs), nanoparticles (NPs), nanowires (NWs), graphene, MXenes, and metal-organic frameworks (MOFs). These materials serve as important scaffolds for sensor functionalization, providing both versatile binding sites and structural stability. Nanomaterials can also play an active role in sensing systems, serving as conductive electrodes, active sensing layers, or functional additives that enhance target binding and signal transduction. Biological and synthetic recognition elements are also nanoscale entities and exhibit strong affinity or high catalytic activity for specific targets, enabling sensitive and specific recognition of key physiological markers. Nanoscale recognition elements encompass a diverse range of biological and synthetic molecules, including ionophores, peptides, nucleic acids, aptamers, antibodies, enzymes, molecularly imprinted polymers (MIPs), and whole cells. These components interact with specific target analytes, enabling detection of a wide range of substances through innovative sensing strategies. The interplay between nanomaterials and recognition elements enables the development of a highly robust sensing system capable of precise biomarker quantification, achieving sensitivities compatible with the targets of interest. For example, nanozymes–catalytically active nanomaterials–have emerged as promising alternatives to natural enzymes, overcoming the limitations of traditional enzymatic systems ( Fig. 2b ) 52 . Nanozymes offer enhanced stability, tunable activity, and lower cost, making them suitable for diverse applications; however, their substrate selectivity remains a challenge. Hybrid approaches that combine nanozymes with natural enzymes 53 , other nanozymes, or synthetic receptors, such as MIPs 54 , can enhance selectivity and functionality. In an example, core–shell nanoparticles with built-in dual functionality—featuring a MIP shell for customizable target recognition and a nickel hexacyanoferrate core for stable electrochemical transduction—were reported for continuous monitoring of circulating nutrients and therapeutic drugs 54 . Nanomaterials also play a crucial role in enhancing energy transfer for ultrasensitive optical detection. In Förster Resonance Energy Transfer (FRET)-based biosensors, nanomaterials like QDs and up-conversion nanoparticles serve as efficient donors, while AuNPs act as acceptors, enabling non-radiative energy transfer within 10 nm. Compared to organic dyes, these nanomaterials offer superior photostability, tunable spectra, chemical stability, low toxicity, and high quenching efficiency 55 ( Fig. 2c ). Engineered nanostructures facilitate electron and photon transfer, manipulating light-matter interaction to enhance sensor performance. A variety of nanostructures and patterns can be fabricated by multiple nanofabrication techniques: top-down and bottom-up approaches 56 . Top-down methods, such as photolithography (UV light), EBL (electron), and FIB (ion) enable precise construction of nanostructures, but are often limited by high costs and low throughput. Bottom-up approaches can create versatile patterns, ranging from self-assembly of simple hexagonal patterns using nanoparticles to complex arbitrary designs via DNA scaffolding (e.g., DNA origami) 57 ; however, these methods still face scalability challenges. High-throughput techniques such as roll-to-roll imprinting, roll transfer, and inkjet printing offer promising solutions for cost-effective, large-area sensor fabrication when coupled with robust nanomaterials-based formulations. The morphology at the sensor interface is critical for both electrochemical and photonic devices. The nanostructured surfaces enhance electric signals by modulating the electric double layer (EDL) 58 . Nanoporous structures enable sensing beyond traditional Debye length limitations, thereby reducing screening effects and enhancing charge transfer at the electrode ( Fig. 2d ). Furthermore, integrating a single nanowire or nanotube as the FET gate facilitates single-molecule detection through local surface potential changes during binding events. Furthermore, the sub-wavelength nanoengineered surfaces are employed for precise light control to amplify signals, enabling single-molecule-level detection. For example, surface plasmon resonance (SPR) on nanostructured metallic surfaces enables label-free, real-time biomolecular detection via enhanced light-matter interaction 59 . Plasmon-enhanced fluorescence (PEF) further amplifies the fluorescence of nearby fluorophores using metallic nanostructures, such as AuNPs and AgNPs, nanohole arrays, and nanorod arrays, thereby significantly boosting sensitivity in fluorescence-based sensors 60 . Advanced techniques, such as integration of plasmonic light-entrapping techniques (e.g., plasmonic tweezers) with SERS, achieve single-molecule resolution by confining and amplifying molecular signals at nanoscale hotspots 61 . The integration of nanomaterials, bio-affinity elements, and nanostructures have led to the development of highly sensitive biomolecular sensing platforms capable of detecting key biomarkers with remarkable advancement in sensitivity. Box 1 introduces popular and emerging transduction strategies for identifying specific molecular targets. Integrating sensors into daily life requires form factors that are unobtrusive for effective health monitoring. Building on the foundation of point-of-care devices, new body-interfaced technologies are emerging for the management of chronic diseases. While most remain laboratory prototypes ( Table 1 ), some, such as continuous glucose monitors (CGMs), have reached practical application. Here, we explore form factors that can be interfaced with various locations on the human body, discussing the design challenges and considerations associated with different biological samples and analytes of interest ( Fig. 3a ). Wearable sensing platforms enable non-invasive monitoring of chronic diseases with high adherence and data accessibility. Their integration with soft, stretchable materials enhances comfort and usability. On-skin sweat analysis has been achieved using form factors like skin patches, tattoos, earpieces, and socks 62 . Passive collection of natural sweat faces challenges like limited temporal resolution and accuracy issues due to sweat mixing and insufficient volumes for continuous sensing. To address this, hydrogels can be integrated into electronic interfaces for low-volume sampling 63 or detecting solid-state analytes 64 . Exercise or thermal stimuli can induce profuse sweating, but large variations in sweat rate complicate analyte quantitation. Controlled sweat induction and analysis via iontophoresis, which deliver cholinergic drugs loaded via small currents, allows for sustained local sweat stimulation and in situ sampling of biomarkers for monitoring conditions like metabolic syndrome, gout, and inflammation 42 , 46 , 65 . ISF closely mirrors blood in biomarker composition with less cellular interference 66 . ISF extraction is challenging due to hydraulic resistance from the extracellular matrix and limited quantity, but research into improved methods, such as microneedles and microdialysis, is ongoing 67 . CGMs–a commercial success–use an enzyme-coated sensor needle for glucose monitoring in ISF. However, these devices require frequent replacement due to sensor drift, foreign body rejection, and epidermal shedding. Microneedles offer minimally invasive ISF sensing, but inconsistencies in skin penetration can affect accuracy and reliability 68 . Reverse iontophoresis, which uses applied current to generate the electroosmotic flow of ISF through the epidermis, often results in highly diluted samples 69 and can preferentially extract charged or zwitterionic small molecules, further complicating analysis. Skin-interfaced devices like bandages and dressings can monitor exudate in chronic wounds linked to conditions such as diabetes, vascular diseases, or pressure injuries 51 . Soft microfluidic biomolecular sensors enable on-site monitoring of biomarkers in wound exudate, aiding clinical assessment and intervention 70 , 71 . Wearable wound sensors can also be integrated with therapeutic components like drug delivery and electrical or mechanical stimulation to accelerate healing 22 . However, wound exudate sampling in situ is complicated by potential contamination, limited sample volume, and a complex composition (including dead cells and debris), which can affect data accuracy. Devices like mouthguards and tooth tattoos enable direct monitoring of exogenous compounds such as food and drug intake 72 , and markers of localized diseases (e.g. dental caries) 73 and salivary biomarkers 74 . Current intraoral sensors primarily detect liquids, while neglecting solid foods; therefore, future research should focus on developing solid-state sensors. Stability challenges, due to daily wear-and-tear and salivary contamination, also need to be addressed. Breath analysis offers metabolomic and respiratory insights, detecting pathogen biomarkers and volatile organic compounds (VOCs) linked to disease onset and progression 75 . In situ analysis via facemasks provides real-time data while avoiding storage-related errors and contamination 76 . However, detecting gaseous analytes in breath requires complex sensor engineering for selectivity as few materials have successfully distinguished gaseous products in real human breath. Exhaled breath condensate (EBC), collected using fluid sampling or cooling modules integrated into facemasks, enables rapid analysis of pathogens (e.g., SARS-CoV-2 virus 77 ), nitrite, and ammonia 78 . However, EBC biomarker concentration varies with breathing route, respiration rate, and breath portion 79 , necessitating standardized collection protocols for reliable quantification. Tear analysis gained popularity in the 2010s, exemplified by Google’s smart contact lens (SCL) initiative. The easy accessibility of the eye presents an opportunity for real-time tracking of ocular biomarkers for diseases like glaucoma 80 and systemic metabolites for conditions like diabetes 81 . Eyeglasses-based tear analysis utilizes fluid sampling units at the nose pads for stimulated tear collection 82 . SCL-based biomolecular sensors offer a direct sample interface for continuous monitoring. However, integrating bulky batteries and electronic circuitry into a small ocular cavity remains a challenge. Miniaturized powering units, such as wireless power transmission and biofuels, coupled with wireless data transmission methods such as near-field communication (NFC) and radio frequency identification (RFID), could enable data transmission and real-time sensing 83 . The clinical use of SCLs for diabetes monitoring is hindered by discrepancies between tear and blood glucose levels. As such, proper sampling without stimulation and personalized lag time correction are crucial for accurate glucose analysis 84 . Ingestible devices provide real-time data on local biomarkers by passing through the digestive system without disrupting daily activities. Ingestible electronic pills that can monitor gastrointestinal (GI) gases, have revealed interindividual fermentative patterns in humans in response to different diets 85 . Additionally, ingestible capsules equipped with RFID chips, like the Proteus Discover system, monitor medication adherence by transmitting an identification code upon contact with gastric fluids 86 . Fecal analysis has demonstrated the diagnostic value of liquid-phase biomarkers (e.g., calprotectin, miRNA, microbiomes) for GI conditions linked to inflammation, infection, and cancer 87 . However, challenges in sensing liquid-phase markers include maintaining the stability of biological components in harsh environments, providing sustained power, and addressing potential GI tract obstruction due to size constraints 88 . These challenges are currently being addressed with materials-based solutions such as biofuel cell powering 89 and the use of durable, selective membranes that allow the incorporation of living genetic sensors in vivo 90 , 91 . Integrating nanomaterials into ingestible devices could enhance sensor sensitivity, stability, and power efficiency. Implantable devices interface directly with internal tissues or organs including blood, providing access to unique environments. They typically include a self-contained biomolecular sensor and a wearable data monitor. Key challenges include biocompatibility, mechanical compliance, sensor fouling, size and power limitations, and efficient data transmission. Additional complexities arise from specific absorption rate limits and implantation or retrieval procedures. Innovations such as functionalized nanomaterials facilitate long-term monitoring of metabolites 93 and neurotransmitters 92 , while bioresorbable polymer-based electronics eliminate the need for device retrieval 94 , enhancing their practicality. Strategies like microfluidic-enabled liquid-phase filtering and kinetic differential measurement (KDM) help reduce sensor fouling and correct signal drift in vivo 95 , 96 . A notable example is the Senseonics Eversense® CGM, the first fully integrated implantable biomolecular sensing system, which requires only biannual replacement of a small subcutaneous sensor implant. Integrating sensors into body-interfaced devices suitable for everyday use is crucial for commercialization. Rather than reinventing the wheel, researchers should expand existing architectures by adding new sensing modalities, lowering barriers to entry for novel biomedical devices. CGMs are the most successful commercially available body-interfaced sensors to date, having been extensively optimized in industry and validated in vivo . Whenever possible, these systems should serve as references, leveraging proven functional and design elements. Other existing biomedical technologies (e.g., pacemakers, stents) have long proven viable in vivo and have the potential to serve as platforms for biomolecular sensor integration 97 – 99 . For example, incorporating an oxygen sensor into an existing pacemaker system can simplify future medical testing and regulatory clearance by reducing the degree of innovation 97 . Body-interfaced biomolecular sensors offer transformative potential for chronic disease care by enabling real-time monitoring of health parameters directly from the body. This integration supports prevention, monitoring, and management strategies across a range of chronic conditions ( Fig. 3b ). Chronic disease prevention involves dietary choices, lifestyle changes, and risk factor management 100 . Body-interfaced sensors aid in tracking stress 101 , 102 , substance use 103 , and allergen exposure 104 . Fitness wearables and ingestibles can also monitor nutrition, motivating healthier behaviors and promoting better outcomes 105 . Real-time tracking of biomarkers like troponin and uric acid enhances organ health assessment while reducing the need for blood draws 6 , 20 . Peripheral biomarkers (e.g., inflammation, hormone levels, nutritional status) provide broader insights into physiological states 42 , 46 , 65 . Continuous monitoring helps differentiate between transient changes and chronic pathology, guiding treatment decisions and supporting holistic health management. For drugs with narrow therapeutic windows (e.g., antibiotics, immunosuppressants, anticoagulants), real-time monitoring ensures efficacy while minimizing side effects which is critical for cases during organ transplantation or mental health treatment 46 , 106 . Ingestible sensors can confirm medication intake and track physiological responses, thereby improving adherence and facilitating the management of complex regimens 107 . CGMs exemplify personalized treatment, enabling immediate dose adjustments and improving outcomes in diabetes. Continuous, non-invasive sensing is also transforming chronic disease research. Large-scale, long-term data enables the discovery of new biomarkers and metrics (e.g., glycemic variability in diabetes) 108 . Deep learning algorithms and time-series analysis can uncover hidden relationships between physiological patterns and outcomes, allowing early detection of complications and guiding timely interventions 109 , 110 . At the population level, these sensors provide real-world insights that inform public health strategies by integrating lifestyle, environmental, and genetic data 111 .

Chronic diseases, such as cardiovascular disease and diabetes, rank among the leading causes of premature deaths globally, placing immense strain on both patients and healthcare infrastructure 1 . The growing prevalence of these conditions, fueled by a rapidly growing aging population, necessitates more independent and affordable healthcare solutions. While early preventive measures and health promotion approaches can reduce the incidence of chronic diseases and ease the burden on the healthcare system, the ongoing challenge lies in effectively monitoring these diseases and tracking their progression. The fusion of nanotechnologies with wearable bioelectronics has catalyzed a paradigm shift toward proactive and personalized healthcare, with the potential to drastically improve chronic disease management. Traditionally, chronic disease management has relied heavily on the clinical evaluation of vital signs and specific biomarkers in blood. Recent advances in nano-sensing technologies and biomarker discovery have opened new avenues for more comprehensive condition-specific health assessments. This review begins with an overview of the global landscape of chronic diseases, highlighting key conditions and their associated molecular signatures. We then discuss body-interfaced sensing technologies relevant to the monitoring and management of these chronic conditions. Since body-interfaced physical sensors, such as those monitoring vital signs, have been extensively covered in existing literature 2 , 3 , this review focuses on the evolving landscape of biomolecular sensing. We focus our discussion on the role of nanomaterials in enhancing sensor sensitivity, while also highlighting their potential to improve flexibility and biocompatibility, key factors in overcoming limitations of conventional sensing platforms. We also examine the relevant form factors of body-interfaced biomolecular sensing devices, emphasizing the importance of design and engineering in optimizing their performance and user experience. Additionally, we outline the transformative potential of these sensors in chronic disease management and discuss the challenges and opportunities associated with their integration and commercialization. This review is not intended to be exhaustive, but instead aims to provide a focused perspective on how nanotechnology can address key sensing challenges and unlock new possibilities for continuous, body-interfaced monitoring of chronic diseases. Through selected examples and thematic analysis, we highlight both the promise and current limitations of these emerging technologies. We aim to emphasize the importance of translating these innovations into practical healthcare solutions that deliver tangible benefits to patients and healthcare systems alike.