# HF-Sensor: A non-invasive biosensor system for heart failure diagnosis and monitoring at the point-of-care

Miguel A.D. Neves, Andrea Cruz, Pedro Melo, Evelyn Santos, Francisco

Vasques-Nóvoa, Ana Beatriz Baptista, João Barbosa-Breda, Fernando Friões,

Jorge Almeida, João Pedro Ferreira, et al.

► To cite this version:

Miguel A.D. Neves, Andrea Cruz, Pedro Melo, Evelyn Santos, Francisco Vasques-Nóvoa, et al. HF-Sensor: A non-invasive biosensor system for heart failure diagnosis and monitoring at the point-of-care. Biosensors and Bioelectronics, 2026, 298, pp.118427. 10.1016/j.bios.2026.118427. hal-05484256

HAL Id: hal-05484256

https://hal.univ-lorraine.fr/hal-05484256v1

Submitted on 29 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
# HF-Sensor: A non-invasive biosensor system for heart failure diagnosis and monitoring at the point-of-care

Miguel A.D. Neves<sup>a,b,●</sup>, Andrea Cruz<sup>c</sup>, Pedro Melo<sup>a,b</sup>, Evelyn Santos<sup>a,b</sup>, Francisco Vasques-Nóvoa<sup>d,e</sup>, Ana Beatriz Baptista<sup>d</sup>, João Barbosa-Breda<sup>d</sup>, Fernando Friões<sup>e</sup>, Jorge Almeida<sup>e</sup>, João Pedro Ferreira<sup>d,e,f</sup>, Adelino Leite-Moreira<sup>d</sup>, Amândio Rocha-Sousa<sup>d</sup>, Inês Mendes Pinto<sup>a,b,c,∗</sup>

## ABSTRACT

Heart failure (HF) is a major public health challenge, contributing to high morbidity, mortality, and significant economic burden worldwide. Early diagnosis and accurate risk stratification are critical for improving patient outcomes and guiding clinical decisions. N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a well-established blood biomarker of ventricular overload, widely used for diagnosing HF and assessing its severity. Beyond diagnosis, NT-proBNP provides valuable prognostic insights into treatment response and can predict adverse cardiovascular events, such as HF-related hospitalizations and mortality, thus playing a key role in personalized care and therapeutic management. Despite its clinical significance, current CE/FDA-certified methods for NT-proBNP measurement face critical limitations, particularly in point-of-care settings and for ongoing, minimally or non-invasive monitoring. These constraints hinder the ability to perform remote (e.g. community medical clinic) monitoring—a crucial capability for revolutionizing heart failure management. To address this challenge, we report the development and proof-of-concept validation of an ultrasensitive (compared to gold standard enzyme-linked immunosorbent assays), miniaturized electrochemical biosensor for fast and minute sample detection of NT-proBNP in tears from a well-characterized cohort of heart failure patients. Correlation analysis of NT-proBNP concentrations in tear fluid and blood plasma/serum, together with key clinical parameters, demonstrates that tear fluid is a promising non-invasive matrix for heart failure diagnosis and continuous monitoring in point-of-care settings. This approach provides a transformative pathway to advance heart failure management by enabling remote, point-of-care diagnostic monitoring outside of centralized hospitals and laboratories.

## 1. Introduction

Heart failure (HF) affects over 26 million people globally, and its diagnosis is often challenging due to the broad range of insidious and non-specific clinical manifestations (Forsyth et al., 2023; van der Meer et al., 2019; Truby and Rogers, 2020). This complexity, combined with the rising mortality and morbidity rates associated with cardiovascular diseases, underscores the urgent need for rapid, portable, and cost-effective technologies to screen for cardiovascular diseases, particularly at the point-of-care (POC) (Nachman et al., 2021).

Current diagnostic approaches for HF rely on the presence of clinical signs, cardiac structural alterations, and increased levels of blood

biomarkers, primarily, N-terminal pro-brain natriuretic peptide (NT-proBNP) (Truby and Rogers, 2020; Borlaug et al., 2023). NT-proBNP, a cleavage product of pro-BNP, is secreted by ventricular myocytes in response to increased wall stress and volume overload, making it a powerful biomarker for diagnosing HF, monitoring its progression, and guiding treatment (Hall, 2005; McKie and Burnett, 2016). NT-proBNP is released in excess early in the pathophysiology of HF, often before clinical symptoms manifest, making it a valuable early indicator of the condition (Hall, 2005; McKie and Burnett, 2016; Palazzuoli, 2010; Oremus et al., 2014). However, conventional NT-proBNP testing methods—primarily based on sandwich immunoassays, including Enzyme-Linked Immunosorbent Assays (ELISAs)—require invasive blood

<footnote><sup>a</sup> Institute for Research and Innovation in Health (i3S), University of Porto, Porto, 4200-135, Portugal</footnote>

<footnote><sup>b</sup> Molecular and Analytical Medicine Laboratory, Department of Biomedicine, Faculty of Medicine, University of Porto, Porto, 4200-319, Portugal</footnote>

<footnote><sup>c</sup> International Iberian Nanotechnology Laboratory (INL), Braga, 4715-330, Portugal</footnote>

<footnote><sup>d</sup> RISE-Health, Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, 4200-319, Portugal</footnote>

<footnote><sup>e</sup> Department of Internal Medicine, Unidade Local de Saúde São João, Porto, 4200-319, Portugal</footnote>

<footnote><sup>f</sup> Université de Lorraine, INSERM, Centre d'Investigations Cliniques 1433, CHRU de Nancy, Inserm 1116 and INI-CRCT (Cardiovascular and Renal Clinical Trialists) F-CRIN Network, Nancy, France</footnote>

<footnote>This article is part of a special issue entitled: Biosensors 2025 published in Biosensors and Bioelectronics.</footnote>

<footnote>* Corresponding author. Institute for Research and Innovation in Health (i3S), University of Porto, Porto, 4200-135, Portugal. E-mail address: ines.pinto@i3s.up.pt (I. Mendes Pinto).</footnote>
sampling and specialized laboratory analysis (Vieira et al., 2022; Woster et al., 2008; Mueller et al., 2003). Investigational point-of-care biosensors have also been developed for the detection of NT-proBNP (Pan et al., 2026; Pollok et al., 2020), however, these devices typically require blood serum, and therefore still depend on invasive patient sampling. These limitations create significant burden on both patients and national healthcare systems, highlighting the need for alternative, non-invasive methods to detect and monitor NT-proBNP levels in fast, decentralized, low-cost, and user-friendly settings.

While no CE- or FDA-approved technologies currently exist to quantify NT-proBNP in non-invasive biofluids with direct relevance to cardiovascular diseases, the hypothesis that NT-proBNP can be detected in such fluids is supported by its systemic distribution and the ability of small peptides to diffuse into biofluids beyond blood (Vieira et al., 2022; Pusparajah et al., 2016; Youngblood et al., 2019). For example, biomarkers such as glucose and inflammatory cytokines have been successfully detected in tears, suggesting that NT-proBNP may also be measurable in this biofluid (Vieira et al., 2022; Park et al., 2024). NT-proBNP, with a molecular weight of approximately 8.5 kDa, is small enough to diffuse into various biofluids, including tears. Its systemic distribution implies that NT-proBNP levels in tears could reflect early changes in cardiac function, even before overt symptoms develop.

Tears, produced by highly vascularized lacrimal glands in close proximity to the conjunctival and corneal vasculature, which could provide an ideal medium for NT-proBNP detection (Vieira et al., 2022). This anatomical configuration allows NT-proBNP to transudate or diffuse into tears as it circulates in the bloodstream. Furthermore, tears are a dynamic biofluid that responds quickly to systemic changes, making them highly sensitive for detecting early biomarkers. Tear sampling offers several advantages over blood sampling, such as a reduced risk of pre-analytical errors like hemolysis, making it more suitable for decentralized, outpatient, and point-of-care diagnostic applications (Vieira et al., 2022).

Detecting NT-proBNP in tears before the onset of overt HF symptoms could enable earlier diagnosis and intervention, ultimately improving patient outcomes. To address these needs, we have developed a portable electrochemical immunosensor platform capable of selectively quantifying NT-proBNP in tears and other biological fluids, such as serum and plasma. This platform demonstrates analytical performance that correlates with blood biomarker levels and clinical parameters of HF, offering a non-invasive alternative for HF diagnostics and monitoring. This biosensor has the potential to support early diagnosis of heart failure (HF), detect disease exacerbations, monitor disease progression, and enhance HF management through biomarker-driven risk stratification, thereby enabling timely and individualized interventions at the point of care. As a non-invasive approach, it represents a promising advancement for the management of HF, providing a more convenient and reliable method for early detection and continuous monitoring.

## 2. Methods

Unless otherwise specified, all reagents were purchased from Sigma-Aldrich® and used as received. Furthermore, all buffers and other aqueous solutions were prepared using ultrapure distilled deionized water (DI) with a measured resistivity ≥18.0 MΩ cm.

### 2.1. Fabrication of electrochemical biosensors

Electrochemical biosensors were built upon three electrode micro screen-printed electrodes (SPEs) – (Metrohm DropSens) consisting of Au working (WE) and counter (CE) electrodes with an Ag pseudo-reference electrode (RE). Following our previously established and experimentally validated surface chemistry protocol (Moreira et al., 2024; Abreu et al., 2020; Cruz et al., 2019, 2022) SPEs were initially cleaned by rinsing (3x) with Isopropanol followed by DI water (3x). Following cleaning SPE WEs were modified with a sulfosuccinimidyl 6-(3'-(2-pyridyldithio)

propionamido) hexanoate), (Sulfo-LC-SPDP – ThermoFisher Scientific), organothiol adlayer. The WE was treated with 1 µL of 10 mg/mL Sulfo-SPDP in PB buffer (10 mM 10 mM NaHPO₃, 5 % glycerol, pH = 7.4) and incubated for 20 min at RT in humidified chamber protected from light. The WEs were then rinsed (3x) with PB and allowed to air dry. Next, the Sulfo-LC-SPDP modified WEs were functionalized with an Anti-NTptoBNP antibody (R&D Systems, Clone #2258B),1 µL of 0.4 mg/mL antibody in PB buffer was then placed on the WE and incubated for overnight at 4 °C in a humidified chamber protected from light. The WEs were then rinsed (3x) with PB and allowed to air dry. The antibody functionalized WEs were then blocked with BSA to help prevent non-specific binding to the electrode surface, 1 µL of 0.3 % BSA in PB buffer was incubated on the WE surface for 30 min at RT protected from light and then rinsed (3x) with PB buffer. Following fabrication the SPEs were used immediately.

### 2.2. Electrochemical measurements

Analytical measurements were performed using an EmStat Pico (PalmSens BV) potentiostat which connects to a smartphone or computer via USB and to screen printed electrodes through a three-pin port. Electrochemical impedance spectroscopy (EIS) experiments were initiated from an Android or Windows-based device using PS Touch or PS Trace software (PalmSens BV), respectively. EIS measurements were conducted in an electrolyte solution containing 5 mM each of [Fe(CN)₆]³⁻/⁴⁻ in PBS buffer (10 mM NaHPO₃, 138 mM NaCl, 2.7 mM KCl, pH 7.4), at a fixed potential of +0.125 V, with sinusoidal perturbation with an amplitude of 5 mV and a frequency range of 0.1–1 × 10⁵ Hz (Moreira et al., 2024; Abreu et al., 2020). The impedance data was acquired and fitted to a Randles equivalent circuit using MultiTrace 4.5 software (PalmSens BV) and to determine the charge transfer resistance (R<sub>CT</sub>).

### 2.3. Standard and unknown sample analysis

Standards, serum and tear samples were analysed by determining the normalized change in R<sub>CT</sub> (ΔR<sub>CT</sub>, equation (1)), which requires the EIS measurement of the functionalized and BSA blocked SPE before (R<sub>CT</sub> BSA) and after addition of the standard/sample (R<sub>CT</sub> Sample) (Moreira et al., 2024; Abreu et al., 2020). To determine R<sub>CT</sub> Sample 1 µL of each sample/standard was incubated on a SPE WE for 30 min at RT in a humidified chamber protected from light, followed by rinsing 3x with PB buffer, prior to EIS measurement. The calibration curve was constructed by plotting ΔR<sub>CT</sub> of the NT-proBNP standards (2.5, 5, 10, and 15 pg/mL) against log (NT-proBNP concentration). Serum and tear sample NT-proBNP concentrations were determined by interpolating the sample ΔR<sub>CT</sub> using the linear equation of the calibration curve. Serum and tear samples were analysed either undiluted or diluted in PBS as required to ensure that the measured ΔR<sub>CT</sub> values remained within the calibration range. Calibration standards were prepared in PBS.

$$ \Delta R_{CT} = \frac{[R_{CT}(sample) - R_{CT}(BSA)]}{R_{CT}(BSA)} \qquad (Equation 1) $$

### 2.4. Enzyme linked immunosorbent assay

Plasma and serum samples were analysed by the enzyme linked immunosorbent assay (ELISA) kit, Human NT-Pro-BNP DuoSet ELISA (R&D systems, Cat N° DY3604) which contains the same capture antibody that was functionalized onto SPE biosensors. The kit was used according to the manufacturer's instructions.

### 2.5. Patient cohort

The NETDIAMOND (NEw Targets in DIAstolic heart failure: from coMoRbidities to persoNalizeD medicine) study is a single-centre, ongoing prospective cohort designed for mechanistic and multiomics-
driven discovery of novel pathophysiological mechanisms and therapeutic targets in HFpEF, including comorbidity-adjusted control participants, with inclusion and exclusion criteria described previously (Pintalhao et al., 2022) and clinical, analytical and echocardiographic characteristics listed in Table S1. Group A (N = 16) comprised serum collected at enrolment from 8 HFpEF patients and 8 comorbidity-matched controls. Group B (N = 15) included only HFpEF patients and consisted of paired samples collected at the same visit, including serum, plasma, and tears, after a protocol amendment enabled tear collection. The NETDiamond study complies with the Declaration of Helsinki. The Centro Hospitalar Universitário São João (CHUSJ) Ethical Committee approved the protocol (CES 35-17) and all subjects gave written, informed consent.

## 2.6. Whole-blood sample-collection and processing

Venous blood samples were collected using sterile vacuum collection tubes (BD Vacutainer® EDTA [K2] or SST™, for plasma and serum, respectively). Plasma and serum were separated by centrifugation at 5.000 rpm for 15 min at 4 °C, and stored at -80 °C until further analysis.

## 2.7. Tear collection and isolation

Tear samples were collected using sterile Merocel® sponges (BVI Merocel, ref. 400115, Medicalmix). A sponge segment was placed in the inferior conjunctival forix of each participant and left in place for 5 min to absorb tear fluid. No anesthetic drops or lubricants were applied before insertion. Placement and removal were performed using forceps. After collection, the sponges were placed in 200 µL PCR microtubes with a perforated bottom, which were then positioned within a custom-made opening in the cap of a 1.5 mL Eppendorf tubes. The assembly was subsequently centrifuged at 21.000×g for 15 min at 4 °C to extract the absorbed tear fluid.

## 2.8. Calculation of the limit of detection, correlations and statistical analysis

The concentration limit of detection ($C_{LOD}$) was determined by analyzing the residual standard deviation of the least squares regression data obtained from the linear region of the concentration-response curve as previously described (Moreira et al., 2024; Neves et al., 2015). Pearson correlations were used to analyze parametric data and Spearman's rank correlation for non-parametric data (Schober et al., 2018). Statistical analysis, correlations and data fitting was performed using GraphPad Prism 8 (GraphPad Software Inc.).

# 3. Results

## 3.1. Fabrication and operation of the HF-Sensor

The miniaturized HF-Sensor (Fig. 1a) consists of two main components: a portable potentiostat with a USB C interface and disposable screen-printed three-electrode arrays functionalized for the molecular recognition of NT-proBNP (Fig. 1b). The potentiostat consist of an emStat Pico (PalmSens) module with a port to insert a screen-printed electrode and USB C ports, which can be connected to either an Android or Windows-based device using PS Touch or PS Trace software, respectively. The screen-printed electrodes used for the HF-Sensor feature gold working (WE) and counter electrodes (CE), combined with a silver reference electrode (RE). To enable detection of NT-proBNP, the WE was functionalized with an anti-NT-proBNP antibody according to the generalized reaction scheme shown in Fig. 1b, following our previously established and characterized surface chemistry protocol (Moreira et al., 2024; Abreu et al., 2020; Cruz et al., 2019, 2022). The organothiol linking molecule sulfosuccinimidyl 6-[3'-(2-pyridyldithio)propionamido]hexanoate (PDT-NHS) was reacted

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** The Heart Failure (HF)-Sensor. a) Image of the miniaturized portable HF-Sensor in operation. b) Schematic of the HF-Sensor depicting the functionalization of the working electrode for NT-proBNP Detection.</figcaption></figure>

with cleaned Au electrodes to form a self-assembled adlayer on the WE surface. Anti-NT-proBNP antibodies were covalently attached to the organothiol adlayer by the chemical reaction between antibody primary lysine residues and terminal adlayer sulfosuccinimidyl moieties. The detection of NT-proBNP is based on faradaic electrochemical impedance spectroscopy (EIS) measurements taken before and after introducing an NT-proBNP-containing sample. This method assesses the capture of NT-proBNP by the antibody immobilized on the WE.

## 3.2. HF-Sensor for ultrasensitive NT-proBNP detection

The analytical performance of HF-Sensor was evaluated against ELISA for the detection of NT-proBNP. To ensure that the sensitivity of the analytical methods was being compared and not affinity differences between capture probes, the capture antibody employed for ELISA and the HF-Sensor were the same. Both HF-Sensor and ELISA were evaluated in PBS buffer where increasing concentrations of NT-proBNP were measured with both methods. Fig. 2a depicts the EIS Nyquist plots to increasing concentrations of NT-proBNP, from which the $R_{ct}$ was extracted and plotted against concentration, the resulting calibration curve of the linear working range of HF-Sensor is depicted in Fig. 2b, while the calibration curve of the linear working range of ELISA is depicted in Fig. 2c. The HF-Sensor was found to have a 6-fold linear working range and a limit of detection (LoD) of 0.33 ± 0.17 pg/mL while the ELISA linear working range covered a 7.5-fold concentration range the LoD was 32 ± 9 pg/mL, ~100 fold greater than HF-Sensor (Fig. 2d). The sensitivity of an analytical method is defined as the specific sensor response to a specific concentration of analyte, meaning the greater the slope of the linear calibration curve in the positive direction the more sensitive a method. HF-Sensor was found to have 150 fold greater sensitivity for NT-proBNP than ELISA (Fig. 2e). Overall HF-Sensor greatly outperforms the conventional “gold-standard” ELISA assay.

## 3.3. Clinical relevance of HF-Sensor for NT-proBNP quantification in different biological samples

The future vision of HF-Sensor is to provide point-of-care quantification to provide physicians information so that timely and accurate clinical decisions can be made in outpatient and remote clinical settings.

To validate the HF-Sensor two sample groups of the NETDiamond (NEw Targets in DIAstolic heart failure: from coMOrbities to
<figure><img src="image_3.png" /><figcaption>**Fig. 2. Analytical Performance of HF-Sensor.** a) Nyquist plots demonstrating the response of the HF-Sensor to different concentrations of NT-proBNP. Calibration curves depicting the linear working range of b) EIS HF-Sensor and c) ELISA. Both the HF-Sensor and ELISA employ the same capture antibody for NT-proBNP. d) The EIS sensor was found to have a 100x lower limit of detection (LoD) and e) be 150x more sensitive to changes in concentration when compared to conventional “gold standard” ELISA. “m” corresponds to the slope of the respective calibration curves, representing the sensitivity of the analytical signal to the biomarker concentration. The values are presented as mean ± SEM, with 6 replicates per condition.</figcaption></figure>

persoNalizeD medicine) study were made available (Pintalhao et al., 2022). Patients enrolled in NETDiamond have been diagnosed with heart failure with preserved ejection fraction (HFpEF) and all samples

collected are from HF patients. The first sample group is a retrospective cohort (16 Patients) which consisted of serum samples (N = 16) and was firstly used to compare sensor measurements with ELISA, whereas a

<figure><img src="image_4.png" /><figcaption>**Fig. 3. Correlation between NT-proBNP levels determined in serum samples from HF patients.** a) Depicts the correlation between all samples tested and b) depicts the individual correlations of the prospective and retrospective patient cohort NTproBNP measurements. The values are presented as mean ± SEM, with 3–6 replicates per condition. Insets to the graphs show the Pearson correlation coefficient (r) with the associated p-value.</figcaption></figure>
prospective cohort (N = 15) collected later to compare NT-proBNP levels across plasma, serum and tear samples obtained simultaneously. Currently, clinical guidance is based on NT-proBNP levels measured in plasma. However, since plasma and serum levels are known to overlap (Peake et al., 2012), we aimed to confirm whether NTproBNP levels measured in plasma matched those measured in serum, Fig. S1. The data shows that there is a very strong statistical Pearson correlation of r = 0.9789 (note; r = 0.7 - 0.89 are considered strong and r = 0.9-1 are considered very strong (Schober et al., 2018)) between plasma and serum values, suggesting that the two measurements provide comparable results.

Currently, all CE and FDA certified assays for NT-proBNP quantification rely on sandwich immunoassays, hence HF-Sensor will be directly compared against a “gold standard” ELISA assay. The serum NT-proBNP levels from both sample groups were measured with HF-Sensor and ELISA with the correlation data of both cohorts combined presented in Fig. 3a and individual cohorts in Fig. 3b. The data demonstrates a very strong direct statistically significant Pearson correlation (r = 0.9480, for both cohorts combined) between the serum values determined with HF-Sensor and ELISA, furthermore, t-test analysis indicated that none of the individual patient mean NT-proBNP levels were statistically different from one another between the two analytical methods.

Tears are not a conventional sample medium for clinical analysis, however, increasing evidence indicates that tears may be a useful non-invasive biological sample for the evaluation of disease specific biomarkers. As such the levels of NT-proBNP in tears were measure from the patient group B and correlated against NT-proBNP levels in serum and plasma, determined from both ELISA and HF-Sensor, Fig. 4. It is important to note that the volume of tears collected from each individual patient was on the order of 1–2 µL, which was insufficient to perform ELISA, even after dilution. The data depicted in Fig. 4 demonstrates a strong statistically relevant Spearman correlation between NT-proBNP in tears and that in serum and plasma, suggesting that tears may hold promise for the clinical monitoring of heart failure.

### 3.4. Correlation of HF-Sensor NT-proBNP levels with clinical parameters

Troponin is a protein released by the cardiomyocytes during stress and injury. High-sensitivity quantification of Troponin I and T are widely used in the diagnosis of acute myocardial injury and ischemia, conditions in which they typically show significant elevations. However, smaller variations in these biomarkers are also valuable for assessing long-term clinical outcomes in stable patients with chronic HF. High-sensitivity Troponin I levels were determined in patients from the prospective cohort within the context of the NETDiamond study, the measurements performed in the centralized hospital diagnostic laboratory

using a clinical analyzer (Roche Elecsys, Roche Diagnostics). The Troponin I levels were correlated with NT-proBNP levels determined in serum and plasma using ELISA (Fig. 5A) and in serum and tears using the HF-Sensor (Fig. 5B). The data depicts a positive statistical Spearman correlation between the levels of NT-proBNP with and high-sensitivity Troponin I for plasma, serum and tears.

Pulmonary artery systolic pressure (PASP) is frequently elevated in HF and may indicate increased left ventricle filling pressures, increased pulmonary arterial resistance, or both, and is associated ventricular

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5. Correlation of NT-proBNP with classical cardiac biomarker troponin. Correlation between the levels of blood troponin and NT-proBNP in a) plasma and serum determined with ELISA and b) serum and tears measured with HF-Sensor. The data depicts a positive statistical correlation between the levels of NT-proBNP and troponin. The values are presented as mean ± SEM, with 3–6 replicates per condition. Insets to the graphs show the Spearman's rank correlation coefficient (r) with the associated p-value.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 4. Non-invasive HF-Sensor measurement of NT-proBNP in tears. Tear samples from Heart Failure Patients in the prospective cohort were measured using the HF-Sensor and correlated against NT-proBNP levels measured from a) plasma and b) serum with ELISA and c) plasma from the HF-Sensor. The data depicts a strong positive statistical correlation between the levels of NT-proBNP in tears with serum and plasma. The values are presented as mean ± SEM, with 3–6 replicates per condition. Insets to the graphs show the Spearman's rank correlation coefficient (r) with the associated p-value.</figcaption></figure>
ooverload and increased HF-related mortality and morbidity. PASP was non-invasively estimated by transthoracic echocardiography as part of the NETDiamond cohort characterization. Doppler assessment of the tricuspid regurgitation jet was used to calculate PASP following standard clinical guidelines. Correlation of PASP against NT-proBNP levels determined in serum and tears with HF-Sensor are depicted in Fig. 5. The data depicts a strong positive statistical Spearman correlation (r = 0.7714) between the levels of tear NT-proBNP determined with HF-Sensor while the levels of NT-proBNP and Troponin I in serum and plasma only show a trend with PASP and do not achieve statistically relevant correlation.

## 4. Discussion

Currently CE marked and FDA approved NT-proBNP assays rely on venous blood samples, requiring trained personnel for phlebotomy and often causing patient discomfort. The samples are then sent to a centralized hospital or clinical lab and processed to plasma for analysis. Due to the dynamic nature of HF, frequent testing is required, which increases the number of phlebotomies a patient will need to undergo (Booth et al., 2014). The world health organization lists phlebotomy as the most common invasive procedure performed in modern healthcare and although routine, can still pose risks to patients and healthcare

workers including infection, disease and even death (Committee, 2010). For remote and recurrent monitoring, this invasive sampling approach becomes a significant barrier. To date, molecular profiling to identify NT-proBNP in venous blood has been performed using techniques such as mass spectrometry, lateral flow or immunoassay-based methods, however, such technologies have not been used to quantify NT-proBNP in other non-invasive fluids, such as tears. More effective, point-of-use and lower-cost technologies, with low sample volume requirements and low limits of detection, are required for the demonstration of NT-proBNP in non-invasive biofluids with analytical relevance for heart failure diagnosis and monitoring.

Herein, we introduce the handheld point-of-care electrochemical HF-Sensor the first report of a non-invasive portable point-of-care biosensor shown to quantitatively profile NT-proBNP in tear and blood serum samples as small as 1 µL. The HF-Sensor (Fig. 1A) outperformed the “gold-standard” ELISA assay for the detection of NT-proBNP providing higher sensitivity and a lower limit of detection (Fig. 2D and E). Furthermore, HF-Sensor was found to be clinically relevant as quantification of NT-proBNP levels in HF-Patient samples with both “gold-standard” ELISA and HF-Sensor were very-strongly statistically correlated (Fig. 3). Furthermore, NT-proBNP levels quantified in plasma (measured with ELISA) or serum (measured with ELISA and HF-Sensor) and tears were strongly correlated (Fig. 4). However, a direct correlation

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Correlation between biomarkers and clinical parameters. Elevated pulmonary artery systolic pressure (PASP) is associated with increased mortality and morbidity in the general population and in cardiac patients, especially those with heart failure. The levels of NT-proBNP and troponin were correlated with PASP. Correlation of PASP against NT-proBNP levels determined in a) serum and tears with EIS HF-Sensor and b) plasma and serum with ELISA. c) correlation of PASP with blood troponin. The data demonstrates a positive trend between PASP vs. troponin and PASP vs. NT-proBNP levels in serum and plasma. However, a statistically relevant positive correlation is observed between PASP and NT-proBNP levels in tears. The values are presented as mean ± SEM, with 3–6 replicates per condition. Insets to the graphs show the Spearman's rank correlation coefficient (r) with the associated p-value.</figcaption></figure>
of tear NT-proBNP levels was not possible due to tear sample volume constraints. Interestingly, tear NT-proBNP levels were found to be of similar magnitude to that of plasma and serum. Some may be expected that tear levels be lower than that of circulation, however, recent studies have demonstrated that levels of some biomarkers such as vascular endothelial growth factor (VEGF) and tumour necrosis factor alpha (TNF-α) are higher in tears than that of the circulation (Bai et al., 2024); it is worth noting that literature studies that quantify biomarkers in tears are limited. This proof-of-concept study demonstrates that NT-proBNP levels measured in tears not only correlate with plasma levels but are also associated with an exacerbated disease profile (Figs. 5 and 6); however, further validation in a much larger and more diverse cohort is required to fully assess HF-Sensor performance and to establish optimal tear NT-proBNP thresholds for predicting heart failure prognosis. Particularly as NT-proBNP reference levels ranges are highly variable with regards to patient age and co-morbidities. Current clinical/scientific literature indicates that particular care is needed when interpreting elevated NT-proBNP levels in patients with multiple comorbidities, to identify the cause of the elevation (Palazzuoli, 2010; Omland, 2008).

Currently, there are CE and FDA certified assays for NT-proBNP quantification labelled as point-of-care, such as the COBAS H232 from ROCHE, however, these assays require a minimum of 120 µL of anti-coagulated venous blood obtained by phlebotomy, limiting its applications to hospital urgent care settings and not for primary or remote care settings which would benefit from recurrent patient monitoring. HF-Sensor on the other hand is compatible with these applications in terms of both sample volume and minimally invasive sampling. HF-Sensor's compatibility with fingerpick blood or tear sampling eliminates the need for already sick HF-patients to be exposed to the risks associated with repeated phlebotomies, as repeated monitoring of NT-proBNP levels is the 'gold-standard' biomarker to monitor HF disease progression which enhances clinical management by optimizing medical therapies and contributing to improved overall patient survival (Pascual-Figal et al., 2024).

In conclusion HF-Sensor demonstrates a potential leap forward for the diagnosis and monitoring of HF by allowing sensitive and non-invasive NT-proBNP monitoring in remote and primary care clinical settings without the need for visits to hospitals and centralized clinical diagnostic laboratories.

### CRediT authorship contribution statement

**Miguel A.D. Neves:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Andrea Cruz:** Validation, Investigation, Formal analysis. **Pedro Melo:** Validation, Methodology, Formal analysis. **Evelyn Santos:** Investigation, Formal analysis. **Francisco Vasques-Nóvoa:** Writing – review & editing, Validation, Conceptualization. **Ana Beatriz Baptista:** Resources, Investigation. **João Barbosa-Breda:** Resources, Investigation. **Fernando Frioes:** Resources. **Jorge Almeida:** Resources. **João Pedro Ferreira:** Validation, Investigation. **Adelino Leite-Moreira:** Validation, Supervision, Resources, Methodology, Formal analysis, Conceptualization. **Amândio Rocha-Sousa:** Validation, Resources, Methodology, Formal analysis, Conceptualization. **Inês Mendes Pinto:** Writing – review & editing, Writing – original draft, Supervision, Resources, Project administration, Methodology, Investigation, Formal analysis, Conceptualization.

### Funding

This work was supported by funding from the La Caixa Foundation through the HF-Chip project (Grant Nº CI20-00248). MADN acknowledges the funding provided by the Fundação para a Ciência e a Tecnologia (FCT), Portugal (Grant No, 2023.09208.CEECIND/CP2844/CT0001). IMP and MADN acknowledge funding from the CARE-IN-

### HEALTH project.

EU Funding Statement: CARE-IN-HEALTH is funded by the European Union within HORIZON EUROPE Health Framework Programme under grant agreement Nº 101095413. Views and opinions expressed are however those of the authors and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency.

### Declaration of competing interest

The authors declare the following competing financial interest(s): Parts of this report were included in Portuguese provisional patent application (n° 20252007241902). The authors declare no other competing interests.

### Acknowledgments

The authors would like to thank Diana Pinho, Denis Santos, and Paulo Freitas (INL), as well as João Relvas, Hugo Prazeres, Bárbara Macedo, Daniel Moreira, and Duarte Carvalho (i3S) for their valuable preliminary assistance and insightful discussions.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bios.2026.118427.

### Data availability

Data will be made available on request.

### References

Abreu, C.M., et al., 2020. Non-invasive molecular assessment of human embryo development and implantation potential. Biosens. Bioelectron. 157, 112144.

Bai, W., et al., 2024. Recent advances in the study of tear biomarkers and contact lens-based biosensors. Chem. Eng. J. 499.

Booth, R.A., et al., 2014. Performance of BNP and NT-proBNP for diagnosis of heart failure in primary care patients: a systematic review. Heart Fail. Rev. 19, 439–451.

Borlaug, B.A., Sharma, K., Shah, S.J., Ho, J.E., 2023. Heart failure with preserved ejection fraction: JACC scientific statement. J. Am. Coll. Cardiol. 81, 1810–1834.

Committee, W.G.R. (Ed.), 2010. WHO Guidelines on Drawing Blood: Best Practices in Phlebotomy. World Health Organization, Geneva, Switzerland, p. 109.

Cruz, A., et al., 2019. Electrochemical immunosensor for TNFalpha-Mediated inflammatory disease screening. ACS Chem. Neurosci. 10, 2676–2682.

Cruz, A., et al., 2022. Non-invasive diagnosis and monitoring tool of children's mental health: a point-of-care immunosensor for IL-6 quantification in saliva samples. Front. Neurosci. 16, 919551.

Forsyth, P., Beezer, J., Bateman, J., 2023. Holistic approach to drug therapy in a patient with heart failure. Heart 109, 1183–1191.

Hall, C., 2005. NT-ProBNP: the mechanism behind the marker. J. Card. Fail. 11, S81–S83.

McKie, P.M., Burnett Jr., J.C., 2016. NT-proBNP: the Gold standard biomarker in heart failure. J. Am. Coll. Cardiol. 68, 2437–2439.

Moreira, D.C., et al., 2024. Multi-Parametric Decision System for Analytical Performance Assessment of Electrochemical (Bio)Sensors, vol. 9. Adv Mater Technol-Us, 2301464.

Mueller, T., Gegenhuber, A., Peoel, W., Haltmayer, M., 2003. Comparison of the Biomedica NT-proBNP enzyme immunoassay and the Roche NT-proBNP chemiluminescence immunoassay: implications for the prediction of symptomatic and asymptomatic structural heart disease. Clin. Chem. 49, 976–979.

Nachman, D., et al., 2021. In search of clinical impact: advanced monitoring technologies in daily heart failure care. J. Clin. Med. 10.

Neves, M.A., Blaszykowski, C., Bokhari, S., Thompson, M., 2015. Ultra-high frequency piezoelectric aptasensor for the label-free detection of cocaine. Biosens. Bioelectron. 72, 383–392.

Omland, T., 2008. Advances in congestive heart failure management in the intensive care unit: B-type natriuretic peptides in evaluation of acute heart failure. Crit. Care Med. 36, S17–S27.

Oremus, M., et al., 2014. A systematic review of BNP and NT-proBNP in the management of heart failure: overview and methods. Heart Fail. Rev. 19, 413–419.

Palazzuoli, A., 2010. Natriuretic peptides (BNP and NT-proBNP): measurement and relevance in heart failure. Vasc. Health Risk Manag. 6.

Pan, T.-M., Chen, C.-H., Weng, W.-C., 2026. Rapid and label-free detection of BNP and NT-proBNP in human serum using Ti-doped MoTex film-based extended-gate FET biosensors for heart failure diagnosis. Sensor. Actuator. B Chem. 448.

Park, W., et al., 2024. In-depth correlation analysis between tear glucose and blood glucose using a wireless smart contact lens. Nat. Commun. 15, 2828.
Pascual-Figal, D.A., et al., 2024. N-terminal pro-B-type natriuretic peptide post-discharge monitoring in the management of patients with heart failure and preserved ejection fraction - a randomized trial: the NICE study. Eur. J. Heart Fail. 26, 776-784.

Peake, R.W., et al., 2012. Comparison of sample types for N-terminal pro-B-type natriuretic peptide measured on the siemens immulite 2500 and dimension vista LOCI methods. Ann. Clin. Biochem. 49, 494-496.

Pintalhao, M., et al., 2022. Relaxin-2, pathophysiological insights and outcomes in heart failure with preserved ejection fraction: findings from the NETDiamond cohort. Int. J. Cardiol. 365, 87-90.

Pollok, N.E., et al., 2020. Electrochemical detection of NT-proBNP using a metalloimmunoassay on a paper electrode platform. ACS Sens. 5, 853-860.

Pusparajah, P., Lee, L.H., Abdul Kadir, K., 2016. Molecular markers of diabetic retinopathy: potential screening tool of the future? Front. Physiol. 7, 200.

Schober, P., Boer, C., Schwarte, L.A., 2018. Correlation coefficients: appropriate use and interpretation. Anesth. Analg. 126, 1763-1768.

Truby, L.K., Rogers, J.G., 2020. Advanced heart failure: epidemiology, diagnosis, and therapeutic approaches. JACC Heart Fail 8, 523-536.

van der Meer, P., Gaggin, H.K., Dec, G.W., 2019. ACC/AHA versus ESC guidelines on heart failure: JACC guideline comparison. J. Am. Coll. Cardiol. 73, 2756-2768.

Vieira, M., et al., 2022. Lab-on-a-chip technologies for minimally invasive molecular sensing of diabetic retinopathy. Lab Chip 22, 1876-1889.

Worster, A., et al., 2008. Diagnostic accuracy of BNP and NT-proBNP in patients presenting to acute care settings with dyspnea: a systematic review. Clin. Biochem. 41, 250-259.

Youngblood, H., Robinson, R., Sharma, A., Sharma, S., 2019. Proteomic biomarkers of retinal inflammation in diabetic retinopathy. Int. J. Mol. Sci. 20.