Introduction

While people worldwide are experiencing increased longevity, this positive trend is accompanied by a rise in the portion of life spent dealing with chronic diseases and disability1,2. The expanding unhealthy aging population presents a growing challenge, potentially leading to escalating social and medical costs3,4. Consequently, there is a need to develop interventions capable of preventing or delaying the onset of frailty and disease, extending the duration of a healthy lifespan. Accelerating the development of such interventions can be achieved through predictive information on subjects’ biological age and by understanding determinants associated with healthy longevity5.

The aging process is influenced by a multifaceted interplay among various factors including the environment (exposome), lifestyle choices, socio-economic conditions, and individual susceptibility6. Given the intricate nature of these interactions, population-based prospective studies serve as a strategic resource for comprehending aging and its various trajectories7.

The Novara Cohort Study (NCS) is the first population-based, multidisciplinary longitudinal study on aging in Northern Italy, established to identify the biological, social, and economic determinants of aging trajectories. The findings will contribute to informing strategies for stakeholders and policymakers, guiding the design of interventions aimed at promoting healthier ageing in the Novara area and beyond. To achieve this goal, the NCS will gather biological samples alongside a diverse range of data, including medical history, lifestyle, habits, quality of life, and physical function assessments from a minimum of 10,000 participants aged 35 or older residing in the Novara province, located in Northern Italy. Biological samples will undergo a comprehensive array of analyses encompassing serological, genetic, epigenetic, proteomic, and metabolomic profiling. Utilizing advanced computational techniques, these datasets will be integrated with questionnaire information and health outcomes to unveil the complex relationships among the investigated determinants, thus contributing to a deeper understanding of aging processes and the development of risk indicators based on the identified profiles.

This paper aims to describe the baseline characteristics of the population enrolled in the pilot study, evaluate its representativeness, and lay the groundwork for identifying biomarkers capable of revealing subclinical deficits to predict different aging trajectories.

Methods

Target population

The NCS is a longitudinal population study aiming to include a minimum of 10,000 participants aged 35 or older, representative of the Novara Province residents. Situated in northwest Italy within the industrialized region known as the ‘Pianura Padana’, Novara Province spans 88 municipalities and is positioned between two of Italy’s largest and most polluted metropolises, Milan and Turin. As of January 2023, the Novara Province had 362,502 residents, with a demographic breakdown of 48.8% male and 51.2% female, an average age of 46.9 years, and an aging index of 201. Approximately 25% of the population is over 65 years old, including 8% who are over 80 years old.

Participation process

The participation in the study is voluntary and a mass media campaign was established inviting the population of the Novara Province. NCS investigators met with stakeholders, including general practitioners, healthcare professionals, municipality council representatives, and citizens’ associations, to present the study and its objectives. Public meetings were also organized to engage with the general population, allowing citizens to express their willingness to participate directly at these events or by applying through the website.

The pilot study commenced in November 2022 and continued until May 2023, including subjects aged over 35 years. From November 2023, the NCS entered in the full implementation phase.

Ethical considerations and privacy

The study protocol was approved by the local Ethical Committee (Comitato Etico Interaziendale AOU Maggiore della Carità di Novara, Protocol Number CE137/2022). Participants were required to provide informed consent, ensuring their voluntary participation and understanding of the study’s purpose and procedures. All data collection and management procedures adhere to the guidelines outlined in the EU General Data Protection Regulation (GDPR) 2016/679.

Participant assessment procedures

Participants in the pilot study underwent a comprehensive assessment, which encompassed the collection of biological samples, questionnaires, medical examinations, and functional and cognitive tests, with the entire process taking up to four hours. The assessment team consisted of a medical doctor, a nurse practitioner, and three researchers of the NCS staff. The testing center was in Novara, within the rooms of the University of Piemonte Orientale (UPO) research biobank (UPO Biobank). The use of the UPO Biobank outpatient clinic and laboratories ensured adherence to appropriate ethical and quality standards for all protocol procedures and the collection of high-quality biological samples and data.

Biological samples

Blood, saliva, and urine samples were collected, processed, and stored in UPO Biobank. Participants autonomously collected their saliva and urine samples, while a trained nurse collected the blood samples following standardized procedures. Blood collection occurred after the participants had fasted overnight. Upon completion of the biospecimen collection, breakfast was offered to all participants. Approximately 50 ml of blood was drawn from each participant, using a variety of tubes to accommodate different analyses and storage requirements (Table 1): (i) Ethylenediaminetetraacetic acid (EDTA) tubes, utilized for fresh hematological analyses and biobanking purposes, including nucleic acid extraction, peripheral blood mononuclear cell (PBMC) cryopreservation, as well as plasma for proteomic and metabolomic studies; (ii) Lithium Heparin (LH) tubes, used for plasma intended for biochemical analyses and biobanking; (iii) Sodium Citrate (Na-Citrate) tubes, for coagulation testing and biobanking of plasma and buffy coat samples; (iv) Gel serum separator tubes, facilitating the separation of serum from the clot, specifically for fresh serological and immunological analyses, as well as for biobanking.

A portion of the fresh blood samples was immediately subjected to a series of hematological and biochemical assessments. The panel includes biomarkers associated with electrolyte balance, inflammation, cardiovascular disease risk, as well as indicators of renal damage, bone marrow, thyroid, and liver function (Table 1). Participants received the results of these tests, along with guidance on how to discuss them with their General Practitioner.

Table 1 Biospecimens: collection tubes, volumes, blood analyses on fresh samples, and biobanking.
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Questionnaires

The dimensions investigated in the questionnaires included: (i) demographic information: age, sex, place of birth, ethnicity, education level, marital status, occupational history and occupational exposure; (ii) physical activity, assessed using the International Physical Activity Questionnaire (IPAQ)8; (iii) sleeping habits, evaluated with the Pittsburgh Sleep Quality Index (PSQI)9 and the Epworth Sleepiness Scale (ESS)10, investigating snoring frequencies, nocturnal apnea, daytime sleepiness, frequency of unrested sleep and the presence of obstructive sleep apnea syndrome (OSAS); (iv) smoking habits, investigated using a novel questionnaire developed by the NCS staff; (v) information about diet, including alcohol consumption, examined using a modified version of the food-frequency EPIC questionnaire11; (vi) quality of life, encompassing life satisfaction, physical and psychological health, as well as social behavior, was assessed using a custom-designed internal questionnaire; (vii) mental health, encompassing depression, anxiety and attachment style, and measured using the Beck Depression Inventory-II (BDI)12, Beck Anxiety Inventory (BAI)13 and Attachment Style Questionnaire (ASQ)14 respectively. All questionnaires were self-administered by participants who were comfortable using a computer. Two trained members of the NCS staff administered the questionnaires to those requiring assistance. The staff members underwent standardized training and adhered to a uniform protocol to ensure consistency in the administration process.

Medical examination

The medical examination included an anamnestic interview and a physical assessment, conducted by healthcare professionals. The anamnestic interview explored personal and family medical histories, with diseases classified according to the International Classification of Diseases, 10th Revision (ICD-10). Information on current medication use was collected and coded using the Anatomical Therapeutic Chemical (ATC) classification system. Physical and cognitive assessments were performed using the Clinical Frailty Scale (CFS)15 and the Montreal Cognitive Assessment (MoCA)16. Anthropometric measurements such as height, weight, body mass index (BMI), waist-to-hip ratio (WHR), as well as vital signs including blood pressure, heart rate, respiratory rate, and blood oxygen saturation, were also recorded.

Functional tests

Several physical tests were used to assess the functional status, physical capabilities, and mobility of participants. The tests included: (i) Handgrip Strength, which assesses upper body strength and muscle function. The test is performed on both hands, and the average of the two measurements is calculated; (ii) Timed Up and Go Test (TUG)17, to assess mobility and balance based on the time to stand, walk, turn, and sit; (iii) 6-Minute Walking Test18, to evaluate aerobic capacity and endurance by measuring walking distance; (iv) Short Physical Performance Battery (SPPB)19, that provides a composite score of balance, gait speed, and lower extremity strength.

Follow-up

At the time of publication, the study has transitioned into its next phase, with a target of 1,500 participants by December 2025. Recruitment will maintain a similar age range distribution to ensure consistency and comparability across the cohort until 2030. The active follow-up phase is scheduled to commence in November 2025. At that point, both new recruitment and follow-up assessments for existing participants will proceed simultaneously, ensuring a continuous flow of data collection. Active follow-up intervals will be based on participants’ age: individuals under 65 will be followed up every 5 years, while those aged 65 and older will be followed up every 3 years. This overlapping approach is designed to maintain a robust dataset for longitudinal analysis while optimizing the study’s timeline. In parallel with the active follow-up, passive follow-up will be conducted using participants’ clinical health records. This will allow for the collection of data on new diagnoses, medication use, hospitalizations, mortality, and other health-related information. At the time of enrollment, participants were thoroughly informed and provided with specific and distinct options regarding active and passive follow-up. This allowed them the opportunity to choose whether to consent to passive follow-up.

Data collection and analysis

The study utilized REDCap version 14.0.33 for secure pseudonymized data storage. Descriptive analyses presented categorical variables as counts and percentages, while continuous variables were reported using mean and standard deviations (SD) for normally distributed variables or median and interquartile ranges (IQR) for skewed variables. Pearson’s chi-square test was used to verify associations between categorical variables.

NCS pilot demographic and lifestyle characteristics were compared to those obtained from the PASSI (Progressi delle Aziende Sanitarie per la Salute in Italia) surveillance system. PASSI employs a random sampling technique for collecting information about lifestyle and risk factors for non-communicable diseases (NCDs) among the general adult population. As over 80% of the NCS participants were aged 60 or older, with a mean age of 65.1 years (65.5 for men and 64.7 for women), the NCS cohort was assessed against PASSI data from residents in Novara Local Health Authority (LHA) for the 60–69 age range. Given that the gender distribution within this specific age group in our cohort closely aligns with that reported in the PASSI surveillance, and the sample size is too small to support statistically stable comparisons by sex, the data were analyzed in aggregate form without stratification by gender.

The Student’s t-test and Mann-Whitney U test were employed to analyze the differences in quantitative variables between NCS subgroups, depending on the normality of the distributions as determined by the Shapiro-Wilk test. The Fisher-Pearson skewness test assessed the symmetry of the variables within their normal ranges. The Pearson coefficient was calculated to investigate relationships between variables after Z-score normalization. Partial least squares-discriminant analysis (PLS-DA) identified blood biomarkers distinguishing subjects with cardiovascular disease, diabetes, and different age groups. Variable Importance in Projection (VIP) analysis, with a threshold of VIP > 1, was conducted to identify the most influential variables driving the observed separation in the model. Statistical analyses were performed using STATA version 17, R version 4.4.0, and MetaboAnalyst 6.0. All tests were two-tailed, with a type I error set to 0.05.

Quality assurance and monitoring procedure

To ensure the quality and efficiency of the study, a robust monitoring system that includes weekly staff meetings and monthly internal audits was established. These regular evaluations enable the team to promptly identify and address logistical challenges, fostering continuous improvement to enhance the participant experience while upholding the integrity of the study protocol. Additionally, monthly data exports are conducted to systematically check for missing values, inconsistencies, and other potential issues.

Results

Demographic and lifestyle characteristics of NCS pilot study participants

An overview of the NCS pilot participants’ demographic characteristics is provided in Table 2. Overall, the NCS pilot study included 123 participants, with 68 (55.3%) women and 55 (44.7%) men. Of these, 45.5% were under 65, while 54.5% were 65 or older. Most participants had an upper secondary school diploma, with 45.4% of men and 55.9% of women receiving this qualification. The majority of participants were married (men 80%, women 58.8%), living with someone (men 85.5%, women 79.4%), and retired (men 67.3%, women 66.2%). No statistically significant differences were observed between genders across these variables.

Lifestyle characteristics and risk factors were collected and analyzed (Table 3). Most participants identified themselves as either non-smokers or former smokers (76.4% of men and 95.6% of women). Almost all identified as omnivorous (92.7% of men and 89.7% of women). Adherence to a vegetarian diet was reported by 5.9% of women. Regarding alcohol consumption, the majority of NCS pilot participants (70.9% of men and 82.3% of women) reported consuming less than one alcoholic unit daily. Data on physical activity using the IPAQ questionnaire indicated that most of the NCS pilot population was at least moderately active (71.0% of men and 64.7% of women). The Pittsburgh Sleep Quality Index (PSQI) revealed that most NCS pilot participants experienced poor sleep quality (56.4% of men and 60.3% of women). No statistically significant differences were observed between genders across these variables.

Table 2 NCS pilot participant demographic characteristics.
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Table 3 NCS pilot participant lifestyle and risk factors.
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Health status of the NCS pilot study participants

Common cardiovascular disease (CVD) risk factors were evaluated (Table 3; Supplementary Table 1). Almost one in two participants was overweight (BMI ≥ 25), in particular 49.1% of men and 49.9% of women. The average systolic blood pressure (BP) recorded during the visits was 122.9 (13.6) mmHg for men and 123.8 (15.8) mmHg for women, both slightly above the recommended level (< 120mmHg). In contrast, diastolic BP remained within the normal range (< 80mmHg), averaging 76.4 mmHg for men and 77.3 mmHg for women.

Among the participants, 29.1% of men and 42.6% of women, reported a diagnosis of hypertension. Despite all of them claiming full adherence to antihypertensive therapy, elevated blood pressure readings were found in 47.0% of men and 55.2% of women during the visit. On the other hand, of those who did not report having hypertension, 42.2% of men and 41.0% of women exhibited blood pressure readings above the normal range (Data not shown). 40% of men and 27.9% of women reported having one chronic disease, while 36.4% of men and 42.7% of women declared having two or more conditions. The most frequent conditions were endocrine and metabolic diseases (34.5% of men and 57.4% of women), followed by CVD (30.9% of men and 25% of women), and neoplasia (16.4% of men and 22.1% of women). Osteoporosis was reported by 1.8% of men and 29.4% of women (data not shown). Women reported a higher (83.9%), albeit not statistically significant, use of over-the-counter (OTC) and prescription medications compared to men (70.9%). The most used drug classes were antihypertensives (27.3% of men and 35.3% of women), endocrine therapy (14.5% of men and 22.1% of women), lipid-lowering drugs (10.9% of men and 11.8% of women), antithrombotic drugs (16.4% of men and 10.3% of women), and beta-blockers (10.9% of men and 10.3% of women) (data not shown). No statistically significant differences were observed between genders across all the variables analyzed.

Representativeness of the NCS pilot population compared to the general population

To evaluate the representativeness of the NCS pilot sample, the demographic and lifestyle characteristics of the participants were compared with those of the Novara LHA residents using data from the PASSI surveillance system20 for the 60–69 age range and without stratification by gender (see Material and Methods; Supplementary Table 2). Compared with PASSI data, the NCS pilot participants had a significantly higher level of education (p < 0.001). Specifically, only 14.7% of NCS participants had primary or lower middle school education compared to 55.5% in PASSI; 51.2% had high school education compared to 34.9% in PASSI; and 34.1% had a university degree compared to 9.6% in PASSI. In terms of smoking habits, the NCS pilot participants exhibited a significantly lower prevalence of current smokers (8.9% vs. 17.0%, p = 0.04).

Alcohol consumption data showed that NCS pilot participants drank more alcohol, with 13.0% consuming ≥ 1 alcohol unit/day compared to 5.2% in PASSI (p = 0.009). Conversely, those consuming < 1 alcohol unit/day were more prevalent in PASSI than in the NCS pilot (77.2% vs. 94.8%). Physical activity levels revealed a significantly higher proportion of individuals with low physical activity in the PASSI group (45.1%, p < 0.001) compared to the NCS pilot group (24.4%). Moderate activity levels showed no significant difference between the groups (40.7% in NCS vs. 32.4% in PASSI), nor did high activity levels (26.8% in NCS vs. 22.5% in PASSI). There were no significant differences between the NCS pilot and PASSI data in terms of marital status, cohabitation, employment status, participation in cancer screening programs, or BMI category distribution.

Identifying biomarkers to predict aging trajectories: insights from the NCS pilot

To assess the general health status of NCS participants, we identified a panel of 67 blood parameters through a literature review focusing on aging. Descriptive statistics of these biomarkers are in Table S3.

Overall, 13 parameters were deranged in over 15% of participants (Table S4). Hypercholesterolemia (TC) was the most prevalent condition, affecting 72/123 (58.5%) subjects (mean: 229 mg/dL, SD: 21.9); 6 of them (8.3%) had a previous report, while 66 (91.7%) were newly identified. Mildly reduced renal function (< 90 mL/min/1.73 m²), indicated by a decreased estimated Glomerular Filtration Rate (eGFR) based on serum creatinine (CR) levels, was reported in 63 participants (51.2%) (Table S5) (median: 79.0, IQR: 67.0-86.5). Among these, 32 participants (50.8%) also exhibited abnormal cystatin C (CysC) and/or blood urea nitrogen (BUN) values (data not shown). Conjugated (direct) hyperbilirubinemia (DBIL) was observed in 51/123 (41.5%) individuals (median: 0.3 mg/dL; IQR: 0.3–0.4 mg/dL). Twelve (23.5%) participants also presented alteration in gamma-glutamyl transferase (GGT) and alkaline phosphatase (ALP) hepatic markers (data not shown). Alterations in inflammatory biomarkers, including low transferrin (TRF), high interleukin-6 (IL-6), and/or high fibrinogen (FBG), were observed in 52.8%, 26.0%, and 19.5% of participants, respectively. Among these, 22.8% exhibited abnormalities in at least two out of the three parameters. Notably, among the biomarkers most affected, the distribution of values in individuals within the normal range frequently clustered close to their upper (e.g., TC, FBG, DBIL, and CysC) or lower limits (e.g., eGFR) of the normal range (Table S6). This skewed distribution suggests early stages of biomarker dysregulation warranting closer monitoring and further investigation.

Next, to explore interactions within biological systems and uncover underlying relationships among blood parameters, we conducted pairwise evaluations to identify potential direct or inverse associations between variables. The Pearson correlation heatmap (Fig. 1; Table S7) displayed positive correlations among markers of liver function (GGT, ALP, and alanine aminotransferase/ALT), kidney function (CysC and CR) and hematological parameters, including hemoglobin (HGB), hematocrit (HCT), and red blood cells (RBC). We also noted positive correlations among inflammatory markers, including C-reactive protein (CRP), FBG, D-dimer (DD), alpha-1 (A1), alpha-2 (A2), and beta-1 (B1) proteins. These findings highlight the complex interplay and clinical relevance of these relationships, with implications for predicting aging quality and age-related disease risk, underscoring the need for further investigation in NCS follow-up.

Fig. 1
figure 1

Heatmap of the 67 blood parameters based on Pearson correlation. The graph visually represents the relationships between each pair of variables. The strength of each correlation is indicated by the Pearson correlation coefficient. Blue values denote negative correlations, while red values signify positive correlations. Lighter colors represent weaker correlations.

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Given that blood-based biomarker changes may be linked to various diseases in our population and/or aging, we examined the distribution of blood parameters concerning prevalent chronic diseases categorized by the ICD-10 classification system. Specifically, lower platelet levels (PLT) and ALP, alongside increased monocyte (MONO) and albumin (ALB) levels, were significantly associated with CVD, excluding hypertension (Table S8). Further analysis using PLS-DA and VIP scores identified MONO, PLT, vitamin B12 (B12), and ALP as key analytes contributing to the discrimination of this group of individuals (VIP score ≥ 2) (Fig. 2).

Fig. 2
figure 2

PLS-DA analysis of blood biomarker distribution in NCS pilot participants based on diseases and age. The scatter plot visually represents the separation of subjects based on the variables that distinguish them: the CVD group (in red) from the NO-CVD group (in green) (A), and the group of diabetic (in red) from the group of non-diabetic subjects (in green) (C). Each data point corresponds to a participant, and the placement of these points shows their projection onto the discriminant components. Ordered list included, showing analytes with higher discriminative power based on variable importance in projection (VIP) scores for distinguishing individuals with and without CVD (B), and with and without diabetes (D). The classification achieves statistical significance for VIP scores > 1.

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Participants with diabetes were characterized, as expected, by elevated levels of glycated hemoglobin (HbA1c). Additionally, they showed elevated white blood cell counts (WBC), including neutrophils (NEU) and lymphocytes (LYMPH), as well as triglycerides (TG) and B1, alongside reduced levels of high-density lipoproteins (HDL) and low-density lipoproteins (LDL) (Table S9). Variables with VIP greater than 1.5 included HbA1c, TG, WBC, and HDL (Fig. 2).

Discussion

The Novara Cohort Study (NCS) aims to comprehensively investigate both longitudinal and cross-sectional aspects of aging within the Novara population. Dimensions considered include biological, physical, cognitive, environmental, social, and psychological aspects.

This pilot represents the first wave of the NCS study, which is instrumental in identifying areas for improvement across all phases of the protocol. These include refining public engagement and participation procedures, determining the most appropriate questionnaires and functional tests to administer, and optimizing biobanking and sample analysis procedures. Moreover, this initial phase allowed researchers to evaluate the representativeness of the population involved, as well as the predictive potential of blood biomarkers profiling.

NCS pilot participants, compared to the general population, had significantly higher education levels and physical activity. This is known as self-selection or volunteer bias, which is common in research involving volunteers, where individuals with higher education are more likely to participate21. This bias can limit the study’s external validity, as volunteers may diverge significantly from the general population in ways that affect study outcomes22. Since external validity is a key objective of the NCS study, in the next phase a strategy to invite a random sample of the population will be implemented. On the other hand, the NCS pilot results highlight the active involvement of health-focused citizens committed to supporting scientific research. Participatory research efforts include public events to foster community engagement and build trust, a crucial factor for the success and sustainability of cohort studies23.

Understanding participant satisfaction and perceptions of study procedures is essential for evaluating the feasibility and acceptability of the research protocol and identifying areas for improvement. During this pilot study, feedback was collected orally immediately after participation. Overall, participants reported high satisfaction and found the questionnaires and functional tests helpful in reflecting on previously overlooked aspects of their health. While some initially expressed concerns about blood sampling, adequate information about the research objectives, a supportive environment, and nursing staff assistance helped them consent willingly. Personalized feedback from blood test results further enhanced participants’ engagement, making them feel closely monitored and valued. Following the pilot study, a brief written satisfaction questionnaire was introduced to gather more structured feedback immediately after participation. A dedicated project is now underway to assess participant satisfaction and the impact of the NCS on habits and lifestyle choices. This questionnaire explores their overall experience, challenges, expectations, and willingness to recommend the study. Findings will guide improvements to the study design and offer insights into its broader influence on participants’ lives, enhancing future engagement and satisfaction.

All questionnaires were self-administered by participants, with support from an NCS staff member if needed. We acknowledge that self-reported data may have significant drawbacks. For example, information regarding diseases, diagnoses, surgical interventions, and medications may lack detail or accuracy due to participants’ memory lapses or incomplete understanding of their medical history. Although resolving some of these challenges completely may be difficult, the pilot study played a crucial role in identifying and addressing these issues. Specifically, the questionnaires have been revised for clarity to improve their feasibility and reliability.

This preliminary analysis revealed a notable discrepancy between self-reported hypertension diagnoses and blood pressure measurements taken at enrollment, highlighting concerns about the reliability of self-reported questionnaires compared to clinical assessments. This inconsistency may be attributed to well-recognized sources of variability, including patient-related factors, procedural deviations, equipment inconsistencies, and observer bias43. These findings point to potential gaps in adherence to standardized blood pressure measurement protocols, underscoring the need for their evaluation and improvement.

The NCS pilot also provided an initial overview of the potential value of blood-based biomarkers by analyzing a panel of 67 biochemical parameters related to organ function, electrolyte balance, and inflammation. These parameters were chosen for their value in assessing frailty, their common use in clinical practice, and the availability of routine analytical procedures in hospital facilities24,25,26.

Overall, our findings indicate that a significant proportion of the study participants exhibited alterations in biomarkers related to liver function, cardiometabolic health, and inflammatory conditions, with TC, DBIL, TRF, and eGFR being the most affected parameters. Hypercholesterolemia emerged as the most prevalent marker, indicating a considerable burden of subclinical or clinical cardiovascular conditions. These findings align with the Moli-SANI cohort, which highlighted a burden of cardiovascular conditions in the general population27. Increased DBIL, along with altered GGT, ALT, and ALP, suggests the presence of hepatic conditions (e.g., hepatitis and bile duct obstructions)28 also frequent in older adults. Alterations in these parameters have also been associated with higher incidence rates of ischemic heart disease and other cardiovascular conditions, supporting these markers as useful for managing and predicting cardiovascular risk in the general population29. A reduction in TRF levels was observed in 52.8% of the participants. This condition is commonly seen in aging and is linked to liver dysfunction, iron disorders, and chronic inflammation. These issues tend to be more pronounced in older individuals and contribute to a range of health problems, including cardiovascular disease and cancer30. Finally, a chronic inflammatory profile, represented by alteration of FBG, DD, and CRP inflammation markers, was identified in our cohort. This enhanced inflammatory state, often referred to as “inflammaging”, is associated with various age-related conditions, including cardiovascular diseases31,32,33.

Validated in large cohort studies and longitudinal follow-ups, these biomarkers can help construct biological profiles that delineate aging trajectories and identify health risk profiles. Studies such as the “Baltimore Longitudinal Study of Aging” (BLSA), which has been ongoing since 195834, and the “Invecchiare in Chianti” (InCHIANTI)35, have extensively investigated a wide array of biomarkers related to cardiovascular, inflammatory, and metabolic conditions that provide insights into the aging process and disease progression. For example, elevated levels of inflammatory markers like CRP and metabolic markers like HbA1c have been consistently associated with poor health outcomes and increased mortality in aging populations36,37. Similarly, the Italian cohort study Moli-SANI highlighted their importance in predicting cardiovascular diseases and diabetes38.

Acknowledging that variations in biomarkers might be related to both diseases and/or ageing, we analyzed their distribution in relationship to the most prevalent chronic conditions39. Unexpectedly, the biomarkers found significantly associated with CVD in our cohort - PLT, MONO, ALP, and ALB - differ from those identified in other Italian studies. In the Moli-SANI, CRP, fibrinogen, and glucose levels were the most deregulated markers40. This discrepancy is possibly due to the low sample size of our study or the inclusion of a different representation of cardiovascular conditions. Conversely, biomarkers associated with diabetes were in line with other Italian studies like the “Progetto CUORE”, reporting elevated HbA1c, WBC, TG, and B1, alongside concurrent reductions in HDL and LDL levels41. These alterations reflect diabetes-linked metabolic dysregulations, where HbA1c is a critical marker for long-term glucose control42,43. Overall, these preliminary results reveal a pattern typical of aging that could form the basis for developing a comprehensive panel of biomarkers. Such a panel could predict biological aging, thereby facilitating preventive and personalized interventions5. By integrating biomarker discovery through omics analyses, we can expand and refine this panel, making it more precise for identifying subclinical deficits and uncovering new therapeutic targets.

We acknowledge the limitation of the relatively small sample size in this preliminary analysis and emphasize that the findings should be interpreted as exploratory and intended to inform the design and implementation of the larger cohort phase. The primary objective of the pilot study was to evaluate the feasibility and acceptability of study procedures, including participant questionnaires. This phase was instrumental in identifying areas for protocol refinement and optimizing the study design to align more effectively with our research objectives while enhancing the participant experience. The preliminary analyses of biomarkers and clinical variables provided valuable initial insights into their potential relevance and informative capacity. These findings underscore the importance of an integrated analysis, which will be conducted in a subsequent phase of the study with a larger cohort. This expanded dataset, expected soon, will deepen and broaden our analysis, contributing to a more comprehensive understanding of aging, its determinants, and associated biomarkers.

In conclusion, the preliminary and exploratory findings of the NCS pilot provided key insights for the implementation of the larger cohort phase. First, the risk of selection bias due to volunteer participation should be addressed by adopting a strategy based on random sampling of the population. Second, the analysis of the blood biomarker panel was reassuring, showing sufficient variability across participants to enable the investigation of aging trajectories. Third, the population showed a high level of participation and engagement, a critical factor for the success of the subsequent study phases.