The Novara Cohort Study: Rationale, Objective and Preliminary Findings From an Italian Ageing Cohort Study

The Novara Cohort Study: Rationale, Objective and Preliminary Findings From an Italian Ageing Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Novara Cohort Study: Rationale, Objective and Preliminary Findings From an Italian Ageing Cohort Study Chiara Aleni, Silvia Cracas, Giulia Garro, Annamaria Antona, Jacopo Venetucci, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4939105/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background The Novara Cohort Study (NCS) is the first multidisciplinary cohort study on aging in Northern Italy. It is designed to explore aging trajectories and health outcomes in the general population. This study involves the collection of biological samples and extensive data, including socioeconomic, medical history, lifestyle habits, quality of life and physical function. Objective This paper outlines the rationale, objectives, and preliminary findings of the NCS pilot phase. It discusses the baseline characteristics, initial biological characterization, and identifies key areas for improvement to ensure the successful implementation of the full-scale study. Methods The NCS pilot phase enrolled participants aged 35 and older residing in Novara, Italy. The study involved the collection of biological samples, medical examinations, questionnaires and functional tests. Data were collected included demographic information, physical activity, sleep quality, diet, quality of life, mental health, medical history, and medication use. Key blood parameters were analyzed alongside clinical data. Results The pilot phase enrolled 123 participants, 68 (55.3%) females and 55 (44.7%) males with a median age of 65 years old. The NCS pilot participants had higher education levels, lower smoking rates, and higher physical activity levels than the general population. Blood biomarker profiling showed significant variability across participants, providing a solid foundation for effectively analyzing aging trajectories. Conclusions The NCS pilot provided crucial insights into participant characteristics and identified areas for study protocol enhancement throughout all phases. These findings will guide refinements to optimize future study processes and outcomes, ultimately aimed at investigating the biological, social, and environmental determinants of aging in the Northern Italy area population. geroscience biomarkers epidemiology longevity health trajectories longitudinal analysis predictive modeling age-related diseases aging dynamics Figures Figure 1 Figure 2 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 disability 1 , 2 . The expanding unhealthy ageing population presents a growing challenge, potentially leading to escalating social and medical costs 3 , 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 longevity 5 . The ageing process is influenced by a multifaceted interplay among various factors including the environment (exposome), lifestyle choices, socio-economic conditions, and individual susceptibility 6 . Given the intricate nature of these interactions, population-based prospective studies serve as a strategic resource for comprehending ageing and its various trajectories 7 . 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 inform 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 metabolomics 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 ageing 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 18 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 to the study is voluntary and a mass media campaign was established to 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, used for fresh hematological analyses, as well as for biobanking dedicated to nucleic acid extractions, peripheral blood mononuclear cells (PBMC) cryopreservation, and plasma proteomic and metabolomic analyses; ii) Lithium Heparin (LH) tubes, for plasma dedicated to biochemical analyses and biobanking; iii) Na-Citrate-containing tubes, for coagulation tests; iv) Gel serum separator tubes, for easy separation of serum from the clot, dedicated to serological and immunological analyses. 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 Collection tubes Volume (ml) Material Blood biomarkers on fresh samples Biobanking N° of aliquots Aliquots volume (µl) Storage temp. (°C) Serum gel (BD Vacutainer) 15 Serum CYSC, protein electrophoresis, TP 4 500 -180 K2-EDTA (BD Vacutainer) 16 EDTA plasma WBC, RBC, HGB, HCT, MCV, MCH, MCHC, RDW-SD, RDW-CV, PDW, MPV, P-LCR, PLT, NEU, LYMPHO, MONO, EOS, BASO, EB, IG, HbA1c 6 500 -180 EDTA buffy coat 1 300 -80 Whole blood in RNA later 2 1600 -20 PBMC 2 500 (2.0 mln) -180 Sodium-Citrate (BD Vacutainer) 3 Citrate plasma FBG, DD 4 500 -180 Citrate buffy coat 2 300 -80 Gel Lithium-Heparin (BD Vacutainer) 13.5 Heparin plasma ALT, AST, BIL, BUN, Ca, TC, CR, FER, FA, ALP, P, GGT, HDL, IL-6, LDL, CRP, K, Na, FT4, TRF, TG, TSH, UA, B12 6 500 -180 Vacutest (Securlab) 80–100 Urine 10 5000; 500 -80 Salivette (Sarstedt) 0.5 Saliva -80 ALT , Alanine Aminotransferase; ALP , Alkaline Phosphatase; AST , Aspartate Aminotransferase; BASO , Basophils; BIL , Bilirubin; BUN , Blood Urea Nitrogen; CA , Calcium; CR , Creatinine; CRP , C-Reactive Protein; CYSC , Cystatin C; DD , D-dimer; EB , Erythroblasts; EOS , Eosinophils; FA , Folic Acid; FBG , Fibrinogen; FER , Ferritin; FT4 , Free-thyroxine (FT4); GGT , Gamma-Glutamyl transferase; HbA1c , Glycated Hemoglobin; HDL , High-Density Lipoprotein; HCT , Hematocrit; HGB , Hemoglobin; IG , Immature Granulocytes; IL-6 , Interleukin-6; K , Potassium; LDL , Low-Density Lipoprotein; LYMPHO , Lymphocytes; MCV , Mean Corpuscular Volume; MCH , Mean Corpuscular Hemoglobin; MCHC , Mean Corpuscular Hemoglobin Concentration; MONO , Monocytes; MPV , Mean Platelet Volume; Na , Sodium; NEU , Neutrophils; P , Phosphorus; PDW , Platelet Distribution Width; PLT , Platelets; P-LCR , Platelet Larger Cell Ratio; RBC , Red Blood Cells; RDW-CV , Red Cell Distribution Width-Coefficient of Variation; RDW-SD , Red Cell Distribution Width-Standard Deviation; TSH , Thyroid Stimulating Hormone; TP , Total Proteins; TRF , Transferrin; UA , Uric Acid; WBC , White Blood Cells. 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 and validated by the NCS staff; v) information about diet, including alcohol consumption, examined using a modified version of the food-frequency EPIC questionnaire 11 ; vi) quality of life, including life satisfaction, physical and psychological health and social behavior; 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. 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 test, which measures upper body strength and muscle function; 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 Test 18 , 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 The NCS employs both passive and active follow-up methods. Passive follow-up consists of the periodical linkage with the clinical records and death register of the Novara Local Health Authority. Active follow-up is based on participants' age: those < 65 years undergo active follow-ups every 5 years, while those ≥ 65 years undergo active follow-ups every 3 years. 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. For this study, data from residents in the Local Health Authority (LHA) of Novara between 60–69 years old were retrieved. 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. 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; 45.5% of the participants 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. Most 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%). 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). Table 2 NCS pilot participant demographic characteristics NCS participants Men n = 55 Women n = 68 Total n = 123 Age (years)-mean (SD) 65.5 (10.0) 64.7 (8.1) 65.1 (9.0) Age ≤ 65 Mean (SD) 56.5 (7.2) 58.8 (6.1) 57.9 (6.6) Median (min-max) 57 (44–65) 61.5 (36–65) 61 (36–65) Age > 65 Mean (SD) 72.0 (5.7) 71.4 (4.0) 71.7 (4.9) Median (min-max) 71 (66–93) 71 (66–80) 71 (66–93) Education n (%) n (%) n (%) Primary or lower middle school 9 (16.4) 9 (13.2) 18 (14.7) High school 25 (45.4) 38 (55.9) 63 (51.2) University degree 21 (38.2) 21 (30.9) 42 (34.1) Marital status Single, divorced or widower 10 (18.2) 27 (39.7) 37 (30.1) Married 44 (80.0) 40 (58.8) 84 (68.3) Missing 1 (1.8) 1 (1.5) 2 (1.6) Cohabitation status Living alone 8 (14.5) 14 (20.6) 22 (17.9) Cohabiting 47 (85.5) 54 (79.4) 101 (82.1) Employment status Employed 18 (32.7) 15 (22.0) 33 (26.8) Unemployed 0 (0.0) 8 (11.8) 8 (6.5) Retired 37 (67.3) 45 (66.2) 82 (66.7) n , number; SD , standard deviation. Table 3 NCS pilot participant lifestyle and risk factors NCS participants Men n = 55 Women n = 68 Total n = 123 n (%) n (%) n (%) Smoking habits Never smoker 21 (38.2) 34 (50.0) 55 (44.7) Past smoker 21 (38.2) 31 (45.6) 52 (42.3) Current smoker 9 (16.3) 2 (2.9) 11 (8.9) Missing 4 (7.3) 1 (1.5) 5 (4.1) Alcohol consumption Never to seldom 11 (20.0) 23 (33.8) 34 (27.6) < 1 alcohol unit/day 28 (50.9) 33 (48.5) 61 (49.6) ≥ 1 alcohol unit/day 9 (16.4) 7 (10.3) 16 (13.0) Missing 7 (12.7) 5 (7.4) 12 (9.8) Physical activity Low 11 (20.0) 19 (27.9) 30 (24.4) Moderate 23 (41.9) 27 (39.7) 50 (40.7) High 16 (29.1) 17 (25.0) 33 (26.8) Missing 5 (9.1) 5 (7.4) 10 (8.1) Participation in screening programs No 25 (45.5) 2 (2.9) 27 (21.9) Yes 30 (54.5) 66 (97.1) 96 (78.1) BMI < 25 28 (50.9) 33 (48.6) 61 (49.6) 25-29.99 20 (36.4) 24 (35.3) 44 (35.8) ≥ 30 7 (12.7) 10 (14.6) 17 (13.8) Missing Diet - 1 (1.5) 1 (0.8) Omnivorous diet 51 (92.7) 61 (89.7) 111 (90.2) Vegetarian diet 0 4 (5.9) 4 (3.3) Other 1 (1.8) 1 (1.5) 2 (1.6) Missing 3 (5.5) 2 (2.9) 5 (4.1) Sleep Quality Poor 31 (56.4) 41 (60.3) 72 (58.6) Good 18 (32.7) 24 (35.3) 42 (34.1) Missing 6 (10.9) 3 (4.4) 9 (7.3) n , number; BMI , Body Mass Index 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 still exhibited blood pressure readings above the normal range (Data not shown). Forty percent 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 cardiovascular disease (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 .3% of women), and beta-blockers (10.9% of men and 10.3% of women) (data not shown) . Representativeness of the NCS pilot population compared to the general population To evaluate the representativeness of the NCS pilot sample, we compared the demographic and lifestyle characteristics of the NCS pilot study participants with those of residents aged 60–69 in the ASL Novara, as derived from the PASSI surveillance system 20 ( 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 ageing. 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. 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 A-B). 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 C-D). To fulfill the ultimate objective of the NCS project, we explored the distribution of blood analytes related to age. Toward this aim, we grouped participants into those over and under 65 years old. Statistically significant differences were identified in several variables, including a decrease in eGFR and increases in AST, DD, CysC, and HbA1c among participants over 65 years old ( Table S10 ). Variables with VIP scores greater than 1.4 characterizing the over-65 population included decreased eGFR and increased HbA1c, ferritin (FER), BUN, and CysC (Fig. 2 E-F). 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. 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) , the group of diabetic (in red) from the group of non-diabetic subjects (in green) (C) , and the group of over 65 years old (in red) from the group of under 65 years old (in green) individuals (E) . 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) , with and without diabetes (D) , and under and over 65 years old (F) . The classification achieves statistical significance for VIP scores > 1. DISCUSSION The Novara Cohort Study (NCS) aims to comprehensively investigate both longitudinal and cross-sectional aspects of ageing 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 participate 21 . This bias can limit the study's external validity, as volunteers may diverge significantly from the general population in ways that affect study outcomes 22 . The NCS pilot results highlight the active involvement of health-focused citizens committed to supporting scientific research. 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, participatory research included public events to promote community engagement. These activities raised trust, which is crucial for the success and sustainability of cohort studies 23 . 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 facilities 24 – 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 population 27 . Increased DBIL, shown in one in two participants, along with altered GGT, ALT, and ALP, suggests potential liver impairment in our cohort, supporting the presence of hepatic conditions frequent in older adults (e.g., hepatitis and bile duct obstructions) 28 . 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 population 29 . A reduction in TRF levels was observed in 65% of the NCS pilot participants. This condition is commonly seen in ageing 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 cancer 30 . Finally, a chronic inflammatory profile, represented by alteration of FBG, DD, and CRP inflammation markers, was clearly identified in our cohort. This enhanced inflammatory state, often referred to as "inflammaging", is associated with various age-related conditions, including cardiovascular diseases 31 – 33 . Validated in large cohort studies and longitudinal follow-ups, these biomarkers can help construct biological profiles that delineate ageing trajectories and identify health risk profiles. Studies such as the “Baltimore Longitudinal Study of Aging” (BLSA), which has been ongoing since 1958 34 , 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 ageing 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 ageing populations 36 , 37 . Similarly, the Italian cohort study Moli-SANI highlighted their importance in predicting cardiovascular diseases and diabetes 38 . 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 conditions 39 . 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 markers 28 . 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 levels 41 . These alterations reflect diabetes-linked metabolic dysregulations, where HbA1c is a critical marker for long-term glucose control 42 . To meet the primary aim of the NCS project, we investigated biomarker variation during aging. By comparing participants over and under 65 years, we identified a group of biomarkers associated with inflammation, metabolism, liver, and kidney function. Ageing is a multifaceted process that impairs various physiological systems. Kidney function naturally declines with age, primarily evidenced by a decrease in the eGFR, as reflected by elevated CR and BUN 43 , 44 . DD and CysC, which are known to increase with age and are linked to cardiovascular risk 45 , 46 , showed alterations in our cohort. Additionally, increased HbA1c, which is reported to rise with age even in the absence of diabetes or other metabolic diseases 47 , was observed. Overall, these preliminary results reveal a pattern typical of ageing that could form the basis for developing a comprehensive panel of biomarkers. Such a panel could predict biological ageing, thereby facilitating preventive and personalized interventions 5 . 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. In conclusion, the preliminary findings of NCS pilot gave researchers essential pieces of information: the risk of selection bias due to volunteer participation must be mitigated by adopting a strategy that involves a random sample of the population; the analysis of the panel of blood biomarkers is reassuring, demonstrating sufficient variability across participants to effectively investigate the ageing trajectories; and the Novara population exhibited a high level of participation and involvement, which is crucial for the success of population studies like the NCS. Declarations Funding This study was partially funded by the Italian Ministry of University and Research (MUR) program "Departments of Excellence 2023-2027," AGING Project – Department of Translational Medicine, Università del Piemonte Orientale, and is part of the AGE-IT project, which has received funding from the MUR – M4C2 1.3 of PNRR under grant agreement no. PE0000015. Acknowledgements We would like to express our gratitude to the Clinical Chemistry Laboratory of Maggiore della Carità University Hospital (Novara, Italy) for their invaluable support in the blood sample analysis. Furthermore, we thank Dr. Tiziana Cena (Unit of Epidemiology, Local Health Unit Vercelli – Italy) and Dr. Donatella Tiberti (SEREMI, Piedmont Region, Italy) for the provision and analysis of PASSI data. Declaration of Conflicting Interests We disclose the following potential conflicts of interest: Professor Daniela Capello, the study supervisor, is the scientific director of UPO Biobank (Center for Autoimmune and Allergic Diseases, Novara, Italy), which supports this research and collects the biological samples. Additionally, several collaborators involved in this study hold positions within the Biobank. Nonetheless, we affirm that comprehensive measures were implemented to maintain objectivity and uphold scientific integrity. Data Availability The datasets used and analyzed during the current study were extracted from the REDCap database, which contains data collected as part of the Novara Cohort Study. All participants in this study provided informed consent that specifically authorized the analysis of their data in aggregated form and allowed for the sharing of these data with other research institutions, subject to ethical approval. Due to the sensitive nature of the information and the privacy agreements in place, the raw data are not publicly available. However, the datasets can be made available to other researchers upon reasonable request. Interested researchers should contact the corresponding author with a clear rationale for their request. Data sharing will be subject to approval by the relevant ethics committees and will adhere to the terms of consent provided by the study participants. References World Health Organization. Ageing and Health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health United Nations. World Population Ageing 2019 . 2019. The Lancet Healthy Longevity. Ageing populations: unaffordable demography. Lancet Healthy Longev . Dec 2022;3(12):e804. doi:10.1016/S2666-7568(22)00272-0 Choi M, Sempungu JK, Lee EH, Lee YH. Living longer but in poor health: healthcare system responses to ageing populations in industrialised countries based on the Findings from the Global Burden of Disease Study 2019. BMC Public Health . Feb 22 2024;24(1):576. doi:10.1186/s12889-024-18049-0 Moqri M, Herzog C, Poganik JR, et al. Biomarkers of aging for the identification and evaluation of longevity interventions. Cell . Aug 31 2023;186(18):3758-3775. doi:10.1016/j.cell.2023.08.003 Li Y, Tian X, Luo J, Bao T, Wang S, Wu X. Molecular mechanisms of aging and anti-aging strategies. Cell Commun Signal . 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Reweighting UK Biobank corrects for pervasive selection bias due to volunteering. Int J Epidemiol . Apr 11 2024;53(3)doi:10.1093/ije/dyae054 Wilkins CH. Effective Engagement Requires Trust and Being Trustworthy. Med Care . Oct 2018;56 Suppl 10 Suppl 1(10 Suppl 1):S6-S8. doi:10.1097/MLR.0000000000000953 Ritt M, Jager J, Ritt JI, Sieber CC, Gassmann KG. Operationalizing a frailty index using routine blood and urine tests. Clin Interv Aging . 2017;12:1029-1040. doi:10.2147/CIA.S131987 Blodgett JM, Theou O, Howlett SE, Rockwood K. A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course. Geroscience . Aug 2017;39(4):447-455. doi:10.1007/s11357-017-9993-7 Hao Q, Sun X, Yang M, Dong B, Dong B, Wei Y. Prediction of mortality in Chinese very old people through the frailty index based on routine laboratory data. Sci Rep . Jan 18 2019;9(1):221. doi:10.1038/s41598-018-36569-9 Jung E, Kong SY, Ro YS, Ryu HH, Shin SD. Serum Cholesterol Levels and Risk of Cardiovascular Death: A Systematic Review and a Dose-Response Meta-Analysis of Prospective Cohort Studies. Int J Environ Res Public Health . Jul 6 2022;19(14)doi:10.3390/ijerph19148272 Guerra Ruiz AR, Crespo J, Lopez Martinez RM, et al. Measurement and clinical usefulness of bilirubin in liver disease. Adv Lab Med . Aug 2021;2(3):352-372. doi:10.1515/almed-2021-0047 Katzke V, Johnson T, Sookthai D, Hüsing A, Kühn T, Kaaks R. Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study. BMJ Open . 2020;10(3):e033532. doi:10.1136/bmjopen-2019-033532 Tian Y, Tian Y, Yuan Z, et al. Iron Metabolism in Aging and Age-Related Diseases. Int J Mol Sci . Mar 25 2022;23(7)doi:10.3390/ijms23073612 Stevenson AJ, McCartney DL, Harris SE, et al. Trajectories of inflammatory biomarkers over the eighth decade and their associations with immune cell profiles and epigenetic ageing. Clinical Epigenetics . 2018;10(1)doi:10.1186/s13148-018-0585-x Meier HCS, Mitchell C, Karadimas T, Faul JD. Systemic inflammation and biological aging in the Health and Retirement Study. GeroScience . 2023;45(6):3257-3265. doi:10.1007/s11357-023-00880-9 Saavedra D, Añé-Kourí AL, Barzilai N, et al. Aging and chronic inflammation: highlights from a multidisciplinary workshop. Immunity & Ageing . 2023;20(1)doi:10.1186/s12979-023-00352-w Shock NW, Greulich RC, Costa PT, Jr., et al. Normal Human Aging: The Baltimore Longitudinal Study on Aging. NIH Publication . 1984;doi:10.13016/SCLW-ICCQ Ferrucci L, Bandinelli S, Benvenuti E, et al. Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J Am Geriatr Soc . Dec 2000;48(12):1618-25. doi:10.1111/j.1532-5415.2000.tb03873.x Ferrucci L. The Baltimore Longitudinal Study of Aging (BLSA): A 50-Year-Long Journey and Plans for the Future. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences . 2008;63(12):1416-1419. doi:10.1093/gerona/63.12.1416 National Institute of Health. InCHIANTI Study. https://www.nia.nih.gov/inchianti-study Iacoviello L, Bonanni A, Costanzo S, et al. The Moli-Sani Project, a randomized, prospective cohort study in the Molise region in Italy; design, rationale and objectives. Italian Journal of Public Health . 2007;4(2)doi:10.2427/5886 World Health Organization. Noncommunicable diseases. https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases Bonaccio M, Di Castelnuovo A, Pounis G, et al. High adherence to the Mediterranean diet is associated with cardiovascular protection in higher but not in lower socioeconomic groups: prospective findings from the Moli-sani study. International Journal of Epidemiology . 2017;46(5):1478-1487. doi:10.1093/ije/dyx145 Istituto Superiore di Sanità. Il progetto Cuore. Dorcely B, Katz K, Jagannathan R, et al. Novel biomarkers for prediabetes, diabetes, and associated complications. Diabetes Metab Syndr Obes . 2017;10:345-361. doi:10.2147/DMSO.S100074 Zhang Y, Yu C, Li X. Kidney Aging and Chronic Kidney Disease. International Journal of Molecular Sciences . 2024;25(12):6585. doi:10.3390/ijms25126585 Noronha IL, Santa-Catharina GP, Andrade L, Coelho VA, Jacob-Filho W, Elias RM. Glomerular filtration in the aging population. Front Med (Lausanne) . 2022;9:769329. doi:10.3389/fmed.2022.769329 McDermott MM, Liu K, Green D, et al. Changes in D-dimer and inflammatory biomarkers before ischemic events in patients with peripheral artery disease: The BRAVO Study. Vasc Med . Feb 2016;21(1):12-20. doi:10.1177/1358863X15617541 West M, Kirby A, Stewart RA, et al. Circulating Cystatin C Is an Independent Risk Marker for Cardiovascular Outcomes, Development of Renal Impairment, and Long‐Term Mortality in Patients With Stable Coronary Heart Disease: The LIPID Study. Journal of the American Heart Association . 2022;11(5)doi:10.1161/jaha.121.020745 Masuch A, Friedrich N, Roth J, Nauck M, Müller UA, Petersmann A. Preventing misdiagnosis of diabetes in the elderly: age-dependent HbA1c reference intervals derived from two population-based study cohorts. BMC Endocrine Disorders . 2019;19(1)doi:10.1186/s12902-019-0338-7 Additional Declarations Competing interest reported. Declaration of Conflicting Interests We disclose the following potential conflicts of interest: Professor Daniela Capello, the study supervisor, is the scientific director of UPO Biobank (Center for Autoimmune and Allergic Diseases, Novara, Italy), which supports this research and collects the biological samples. Additionally, several collaborators involved in this study hold positions within the Biobank. Nonetheless, we affirm that comprehensive measures were implemented to maintain objectivity and uphold scientific integrity. Supplementary Files SupplementarymaterialSR1.docx Cite Share Download PDF Status: Published Journal Publication published 23 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 12 Nov, 2024 Reviews received at journal 08 Nov, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviews received at journal 09 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers invited by journal 07 Oct, 2024 Editor assigned by journal 01 Oct, 2024 Editor invited by journal 03 Sep, 2024 Submission checks completed at journal 03 Sep, 2024 First submitted to journal 19 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4939105","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361091100,"identity":"f6d2ca47-6947-4d6b-8fce-0810fee95223","order_by":0,"name":"Chiara Aleni","email":"","orcid":"","institution":"University of Eastern Piedmont Amadeo Avogadro","correspondingAuthor":false,"prefix":"","firstName":"Chiara","middleName":"","lastName":"Aleni","suffix":""},{"id":361091101,"identity":"affec788-338e-41f1-884a-a08c31e98730","order_by":1,"name":"Silvia 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Avogadro","correspondingAuthor":false,"prefix":"","firstName":"Daniela","middleName":"","lastName":"Capello","suffix":""}],"badges":[],"createdAt":"2024-08-19 13:51:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4939105/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4939105/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-02947-3","type":"published","date":"2025-05-23T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67099753,"identity":"f1b93a49-2d8e-42c6-8551-5cff58dcce95","added_by":"auto","created_at":"2024-10-21 08:05:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":710375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of the 67 blood parameters based on Pearson correlation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e","description":"","filename":"Figure1article.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4939105/v1/cfe359308bb0c9355d7a9215.jpg"},{"id":67099752,"identity":"e50c682a-49f0-465f-a12b-2fc4feab388b","added_by":"auto","created_at":"2024-10-21 08:05:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":504183,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePLS-DA analysis of blood biomarker distribution in NCS pilot participants based on diseases and age\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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) \u003cstrong\u003e(A)\u003c/strong\u003e, the group of diabetic (in red) from the group of non-diabetic subjects (in green) \u003cstrong\u003e(C)\u003c/strong\u003e, and the group of over 65 years old (in red) from the group of under 65 years old (in green) individuals \u003cstrong\u003e(E)\u003c/strong\u003e. 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 \u003cstrong\u003e(B)\u003c/strong\u003e, with and without diabetes \u003cstrong\u003e(D)\u003c/strong\u003e, and under and over 65 years old \u003cstrong\u003e(F)\u003c/strong\u003e. The classification achieves statistical significance for VIP scores \u0026gt;1.\u003c/p\u003e","description":"","filename":"Figure2article.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4939105/v1/bb3f8dbf1c7d100a5d6b9c41.jpg"},{"id":83459968,"identity":"acbf1071-bb69-4e1c-85d3-556860b6ce63","added_by":"auto","created_at":"2025-05-26 16:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2636729,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4939105/v1/a9d7758a-8f70-48aa-b90d-11538e1882c2.pdf"},{"id":67099754,"identity":"b93a82da-85f8-4318-8aea-5239c56919ee","added_by":"auto","created_at":"2024-10-21 08:05:03","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":70539,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarymaterialSR1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4939105/v1/ceeec9da8d81fb447c65d6bd.docx"}],"financialInterests":"Competing interest reported. Declaration of Conflicting Interests\n\nWe disclose the following potential conflicts of interest: Professor Daniela Capello, the study supervisor, is the scientific director of UPO Biobank (Center for Autoimmune and Allergic Diseases, Novara, Italy), which supports this research and collects the biological samples. Additionally, several collaborators involved in this study hold positions within the Biobank. Nonetheless, we affirm that comprehensive measures were implemented to maintain objectivity and uphold scientific integrity.","formattedTitle":"\u003cp\u003eThe Novara Cohort Study: Rationale, Objective and Preliminary Findings From an Italian Ageing Cohort Study\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eWhile 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 disability\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The expanding unhealthy ageing population presents a growing challenge, potentially leading to escalating social and medical costs\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. 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 longevity\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe ageing process is influenced by a multifaceted interplay among various factors including the environment (exposome), lifestyle choices, socio-economic conditions, and individual susceptibility\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Given the intricate nature of these interactions, population-based prospective studies serve as a strategic resource for comprehending ageing and its various trajectories\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe 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 inform 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 metabolomics 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 ageing processes and the development of risk indicators based on the identified profiles.\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eTarget population\u003c/h2\u003e \u003cp\u003eThe NCS is a longitudinal population study aiming to include a minimum of 10,000 participants aged 18 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eParticipation process\u003c/h2\u003e \u003cp\u003e The participation to the study is voluntary and a mass media campaign was established to 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.\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations and privacy\u003c/h2\u003e \u003cp\u003e The study protocol was approved by the local Ethical Committee (Comitato Etico Interaziendale AOU Maggiore della Carit\u0026agrave; 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Assessment Procedures\u003c/h2\u003e \u003cp\u003eParticipants 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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBiological samples\u003c/h2\u003e \u003cp\u003eBlood, 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): i) Ethylenediaminetetraacetic acid (EDTA) tubes, used for fresh hematological analyses, as well as for biobanking dedicated to nucleic acid extractions, peripheral blood mononuclear cells (PBMC) cryopreservation, and plasma proteomic and metabolomic analyses; ii) Lithium Heparin (LH) tubes, for plasma dedicated to biochemical analyses and biobanking; iii) Na-Citrate-containing tubes, for coagulation tests; iv) Gel serum separator tubes, for easy separation of serum from the clot, dedicated to serological and immunological analyses.\u003c/p\u003e \u003cp\u003eA 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\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Participants received the results of these tests, along with guidance on how to discuss them with their General Practitioner.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiospecimens: collection tubes, volumes, blood analyses on fresh samples, and biobanking\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCollection tubes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVolume (ml)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood biomarkers\u003c/p\u003e \u003cp\u003eon fresh samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBiobanking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u0026deg; of aliquots\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAliquots volume (\u0026micro;l)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStorage temp. (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum gel\u003c/p\u003e \u003cp\u003e(BD Vacutainer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCYSC, protein electrophoresis, TP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eK2-EDTA\u003c/p\u003e \u003cp\u003e(BD Vacutainer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEDTA plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWBC, RBC, HGB, HCT, MCV, MCH, MCHC, RDW-SD, RDW-CV, PDW, MPV, P-LCR, PLT, NEU, LYMPHO, MONO, EOS, BASO, EB, IG, HbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEDTA buffy coat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhole blood in RNA later\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePBMC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500 (2.0 mln)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium-Citrate\u003c/p\u003e \u003cp\u003e(BD Vacutainer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCitrate plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFBG, DD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCitrate buffy coat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGel Lithium-Heparin\u003c/p\u003e \u003cp\u003e(BD Vacutainer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeparin plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eALT, AST, BIL, BUN, Ca, TC, CR, FER, FA, ALP, P, GGT, HDL, IL-6, LDL, CRP, K, Na, FT4, TRF, TG, TSH, UA, B12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVacutest\u003c/p\u003e \u003cp\u003e(Securlab)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5000;\u003c/p\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalivette\u003c/p\u003e \u003cp\u003e(Sarstedt)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaliva\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eALT\u003c/b\u003e, \u003cem\u003eAlanine Aminotransferase;\u003c/em\u003e \u003cb\u003eALP\u003c/b\u003e, \u003cem\u003eAlkaline Phosphatase;\u003c/em\u003e \u003cb\u003eAST\u003c/b\u003e, \u003cem\u003eAspartate Aminotransferase;\u003c/em\u003e \u003cb\u003eBASO\u003c/b\u003e, \u003cem\u003eBasophils;\u003c/em\u003e \u003cb\u003eBIL\u003c/b\u003e, \u003cem\u003eBilirubin;\u003c/em\u003e \u003cb\u003eBUN\u003c/b\u003e, \u003cem\u003eBlood Urea Nitrogen;\u003c/em\u003e \u003cb\u003eCA\u003c/b\u003e, \u003cem\u003eCalcium;\u003c/em\u003e \u003cb\u003eCR\u003c/b\u003e, \u003cem\u003eCreatinine;\u003c/em\u003e \u003cb\u003eCRP\u003c/b\u003e, \u003cem\u003eC-Reactive Protein;\u003c/em\u003e \u003cb\u003eCYSC\u003c/b\u003e, \u003cem\u003eCystatin C;\u003c/em\u003e \u003cb\u003eDD\u003c/b\u003e, \u003cem\u003eD-dimer;\u003c/em\u003e \u003cb\u003eEB\u003c/b\u003e, \u003cem\u003eErythroblasts;\u003c/em\u003e \u003cb\u003eEOS\u003c/b\u003e, \u003cem\u003eEosinophils;\u003c/em\u003e \u003cb\u003eFA\u003c/b\u003e, \u003cem\u003eFolic Acid;\u003c/em\u003e \u003cb\u003eFBG\u003c/b\u003e, \u003cem\u003eFibrinogen;\u003c/em\u003e \u003cb\u003eFER\u003c/b\u003e, \u003cem\u003eFerritin;\u003c/em\u003e \u003cb\u003eFT4\u003c/b\u003e, \u003cem\u003eFree-thyroxine (FT4);\u003c/em\u003e \u003cb\u003eGGT\u003c/b\u003e, \u003cem\u003eGamma-Glutamyl transferase;\u003c/em\u003e \u003cb\u003eHbA1c\u003c/b\u003e, \u003cem\u003eGlycated Hemoglobin;\u003c/em\u003e \u003cb\u003eHDL\u003c/b\u003e, \u003cem\u003eHigh-Density Lipoprotein;\u003c/em\u003e \u003cb\u003eHCT\u003c/b\u003e, \u003cem\u003eHematocrit;\u003c/em\u003e \u003cb\u003eHGB\u003c/b\u003e, \u003cem\u003eHemoglobin;\u003c/em\u003e \u003cb\u003eIG\u003c/b\u003e, \u003cem\u003eImmature Granulocytes;\u003c/em\u003e \u003cb\u003eIL-6\u003c/b\u003e, \u003cem\u003eInterleukin-6;\u003c/em\u003e \u003cb\u003eK\u003c/b\u003e, \u003cem\u003ePotassium;\u003c/em\u003e \u003cb\u003eLDL\u003c/b\u003e, \u003cem\u003eLow-Density Lipoprotein;\u003c/em\u003e \u003cb\u003eLYMPHO\u003c/b\u003e, \u003cem\u003eLymphocytes;\u003c/em\u003e \u003cb\u003eMCV\u003c/b\u003e, \u003cem\u003eMean Corpuscular Volume;\u003c/em\u003e \u003cb\u003eMCH\u003c/b\u003e, \u003cem\u003eMean Corpuscular Hemoglobin;\u003c/em\u003e \u003cb\u003eMCHC\u003c/b\u003e, \u003cem\u003eMean Corpuscular Hemoglobin Concentration;\u003c/em\u003e \u003cb\u003eMONO\u003c/b\u003e, \u003cem\u003eMonocytes;\u003c/em\u003e \u003cb\u003eMPV\u003c/b\u003e, \u003cem\u003eMean Platelet Volume;\u003c/em\u003e \u003cb\u003eNa\u003c/b\u003e, \u003cem\u003eSodium;\u003c/em\u003e \u003cb\u003eNEU\u003c/b\u003e, \u003cem\u003eNeutrophils;\u003c/em\u003e \u003cb\u003eP\u003c/b\u003e, \u003cem\u003ePhosphorus;\u003c/em\u003e \u003cb\u003ePDW\u003c/b\u003e, \u003cem\u003ePlatelet Distribution Width;\u003c/em\u003e \u003cb\u003ePLT\u003c/b\u003e, \u003cem\u003ePlatelets;\u003c/em\u003e \u003cb\u003eP-LCR\u003c/b\u003e, \u003cem\u003ePlatelet Larger Cell Ratio;\u003c/em\u003e \u003cb\u003eRBC\u003c/b\u003e, \u003cem\u003eRed Blood Cells;\u003c/em\u003e \u003cb\u003eRDW-CV\u003c/b\u003e, \u003cem\u003eRed Cell Distribution Width-Coefficient of Variation;\u003c/em\u003e \u003cb\u003eRDW-SD\u003c/b\u003e, \u003cem\u003eRed Cell Distribution Width-Standard Deviation;\u003c/em\u003e \u003cb\u003eTSH\u003c/b\u003e, \u003cem\u003eThyroid Stimulating Hormone;\u003c/em\u003e \u003cb\u003eTP\u003c/b\u003e, \u003cem\u003eTotal Proteins;\u003c/em\u003e \u003cb\u003eTRF\u003c/b\u003e, \u003cem\u003eTransferrin;\u003c/em\u003e \u003cb\u003eUA\u003c/b\u003e, \u003cem\u003eUric Acid;\u003c/em\u003e \u003cb\u003eWBC\u003c/b\u003e, \u003cem\u003eWhite Blood Cells.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eQuestionnaires\u003c/h2\u003e \u003cp\u003eThe 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)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e; iii) sleeping habits, evaluated with the Pittsburgh Sleep Quality Index (PSQI)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and the Epworth Sleepiness Scale (ESS)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, 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 and validated by the NCS staff; v) information about diet, including alcohol consumption, examined using a modified version of the food-frequency EPIC questionnaire\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; vi) quality of life, including life satisfaction, physical and psychological health and social behavior; vii) mental health, encompassing depression, anxiety and attachment style, and measured using the Beck Depression Inventory-II (BDI)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, Beck Anxiety Inventory (BAI)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and Attachment Style Questionnaire (ASQ)\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMedical examination\u003c/h2\u003e \u003cp\u003eThe 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)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and the Montreal Cognitive Assessment (MOCA)\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. 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.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eFunctional tests\u003c/h2\u003e \u003cp\u003eSeveral physical tests were used to assess the functional status, physical capabilities, and mobility of participants. The tests included: i) handgrip test, which measures upper body strength and muscle function; ii) Timed Up and Go Test (TUG)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, to assess mobility and balance based on the time to stand, walk, turn, and sit; iii) 6-Minute Walking Test\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, to evaluate aerobic capacity and endurance by measuring walking distance; iv) Short Physical Performance Battery (SPPB)\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, that provides a composite score of balance, gait speed, and lower extremity strength.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up\u003c/h2\u003e \u003cp\u003eThe NCS employs both passive and active follow-up methods. Passive follow-up consists of the periodical linkage with the clinical records and death register of the Novara Local Health Authority. Active follow-up is based on participants' age: those\u0026thinsp;\u0026lt;\u0026thinsp;65 years undergo active follow-ups every 5 years, while those\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026ge;\u003c/span\u003e\u0026thinsp;65 years undergo active follow-ups every 3 years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData collection and analysis\u003c/h2\u003e \u003cp\u003eThe study utilized REDCap version 14.0.33 for secure pseudonymized data storage.\u003c/p\u003e \u003cp\u003eDescriptive 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\u0026rsquo;s chi-square test was used to verify associations between categorical variables.\u003c/p\u003e \u003cp\u003eNCS 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. For this study, data from residents in the Local Health Authority (LHA) of Novara between 60\u0026ndash;69 years old were retrieved.\u003c/p\u003e \u003cp\u003eThe Student\u0026rsquo;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\u0026thinsp;\u0026gt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDemographic and lifestyle characteristics of NCS pilot study participants\u003c/h2\u003e \u003cp\u003eAn overview of the NCS pilot participants' demographic characteristics is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, the NCS pilot study included 123 participants, with 68 (55.3%) women and 55 (44.7%) men; 45.5% of the participants 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. Most 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%).\u003c/p\u003e \u003cp\u003eLifestyle characteristics and risk factors were collected and analyzed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). 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).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNCS pilot participant demographic characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNCS participants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;55\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;68\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;123\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (years)-mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.5 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.7 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.1 (9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.8 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.9 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (44\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.5 (36\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61 (36\u0026ndash;65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.0 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.4 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.7 (4.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian (min-max)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (66\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (66\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71 (66\u0026ndash;93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary or lower middle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026nbsp;(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u0026nbsp;(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (14.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026nbsp;(45.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63 (51.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u0026nbsp;(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (34.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle, divorced or widower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (30.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (58.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84 (68.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCohabitation status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (17.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (79.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (82.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33 (26.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8 (6.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRetired\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (67.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003en\u003c/b\u003e, \u003cem\u003enumber;\u003c/em\u003e \u003cb\u003eSD\u003c/b\u003e, \u003cem\u003estandard deviation.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNCS pilot participant lifestyle and risk factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNCS participants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;55\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;68\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;123\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking habits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55 (44.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31 (45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52 (42.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u0026nbsp;(2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11 (8.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever to seldom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (27.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 alcohol unit/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (48.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61 (49.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;1 alcohol unit/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u0026nbsp;(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16 (13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u0026nbsp;(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (9.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30 (24.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50 (40.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (29.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (26.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10 (8.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParticipation in screening programs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27 (21.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (54.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96 (78.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (50.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61 (49.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25-29.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (36.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (35.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003cp\u003e\u003cb\u003eDiet\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOmnivorous diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (92.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e111 (90.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetarian diet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4 (3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep Quality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (56.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (60.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72 (58.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (34.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003en\u003c/b\u003e, \u003cem\u003enumber;\u003c/em\u003e \u003cb\u003eBMI\u003c/b\u003e, \u003cem\u003eBody Mass Index\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHealth status of the NCS pilot study participants\u003c/h2\u003e \u003cp\u003eCommon cardiovascular disease (CVD) risk factors were evaluated (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Almost one in two participants was overweight (BMI\u0026thinsp;\u0026ge;\u0026thinsp;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 (\u0026lt;\u0026thinsp;120mmHg). In contrast, diastolic BP remained within the normal range (\u0026lt;\u0026thinsp;80mmHg), averaging 76.4 mmHg for men and 77.3 mmHg for women.\u003c/p\u003e \u003cp\u003eAmong 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 still exhibited blood pressure readings above the normal range \u003cem\u003e(Data not shown).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eForty percent 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 cardiovascular disease (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 \u003cem\u003e(Data not shown).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eWomen 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 .3% of women), and beta-blockers (10.9% of men and 10.3% of women) \u003cem\u003e(data not shown)\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRepresentativeness of the NCS pilot population compared to the general population\u003c/h2\u003e \u003cp\u003eTo evaluate the representativeness of the NCS pilot sample, we compared the demographic and lifestyle characteristics of the NCS pilot study participants with those of residents aged 60\u0026ndash;69 in the ASL Novara, as derived from the PASSI surveillance system\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Compared with PASSI data, the NCS pilot participants had a significantly higher level of education (p\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003eAlcohol consumption data showed that NCS pilot participants drank more alcohol, with 13.0% consuming\u0026thinsp;\u0026ge;\u0026thinsp;1 alcohol unit/day compared to 5.2% in PASSI (p\u0026thinsp;=\u0026thinsp;0.009). Conversely, those consuming\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;\u0026lt;\u0026thinsp;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying biomarkers to predict aging trajectories: insights from the NCS pilot\u003c/h2\u003e \u003cp\u003e To assess the general health status of NCS participants, we identified a panel of 67 blood parameters through a literature review focusing on ageing. Descriptive statistics of these biomarkers are in \u003cb\u003eTable S3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eOverall, 13 parameters were deranged in over 15% of participants (\u003cb\u003eTable S4\u003c/b\u003e). 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 (\u0026lt;\u0026thinsp;90 mL/min/1.73 m\u0026sup2;), indicated by a decreased estimated Glomerular Filtration Rate (eGFR) based on serum creatinine (CR) levels, was reported in 63 participants (51.2%) (\u003cb\u003eTable S5\u003c/b\u003e) (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\u0026ndash;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 (\u003cb\u003eTable S6\u003c/b\u003e). This skewed distribution suggests early stages of biomarker dysregulation warranting closer monitoring and further investigation.\u003c/p\u003e \u003cp\u003eNext, 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; \u003cb\u003eTable S7\u003c/b\u003e) 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.\u003c/p\u003e \u003cp\u003eGiven 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 (\u003cb\u003eTable S8\u003c/b\u003e). 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\u0026thinsp;\u0026ge;\u0026thinsp;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003eParticipants 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) (\u003cb\u003eTable S9\u003c/b\u003e). Variables with VIP greater than 1.5 included HbA1c, TG, WBC, and HDL (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D).\u003c/p\u003e \u003cp\u003eTo fulfill the ultimate objective of the NCS project, we explored the distribution of blood analytes related to age. Toward this aim, we grouped participants into those over and under 65 years old. Statistically significant differences were identified in several variables, including a decrease in eGFR and increases in AST, DD, CysC, and HbA1c among participants over 65 years old (\u003cb\u003eTable S10\u003c/b\u003e). Variables with VIP scores greater than 1.4 characterizing the over-65 population included decreased eGFR and increased HbA1c, ferritin (FER), BUN, and CysC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 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) \u003cb\u003e(A)\u003c/b\u003e, the group of diabetic (in red) from the group of non-diabetic subjects (in green) \u003cb\u003e(C)\u003c/b\u003e, and the group of over 65 years old (in red) from the group of under 65 years old (in green) individuals \u003cb\u003e(E)\u003c/b\u003e. 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 \u003cb\u003e(B)\u003c/b\u003e, with and without diabetes \u003cb\u003e(D)\u003c/b\u003e, and under and over 65 years old \u003cb\u003e(F)\u003c/b\u003e. The classification achieves statistical significance for VIP scores\u0026thinsp;\u0026gt;\u0026thinsp;1.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe Novara Cohort Study (NCS) aims to comprehensively investigate both longitudinal and cross-sectional aspects of ageing within the Novara population. Dimensions considered include biological, physical, cognitive, environmental, social, and psychological aspects.\u003c/p\u003e \u003cp\u003eThis 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.\u003c/p\u003e \u003cp\u003eNCS 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 participate\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. This bias can limit the study's external validity, as volunteers may diverge significantly from the general population in ways that affect study outcomes\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe NCS pilot results highlight the active involvement of health-focused citizens committed to supporting scientific research. 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, participatory research included public events to promote community engagement. These activities raised trust, which is crucial for the success and sustainability of cohort studies\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe 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 facilities\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOverall, 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 population\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Increased DBIL, shown in one in two participants, along with altered GGT, ALT, and ALP, suggests potential liver impairment in our cohort, supporting the presence of hepatic conditions frequent in older adults (e.g., hepatitis and bile duct obstructions)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. 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 population\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. A reduction in TRF levels was observed in 65% of the NCS pilot participants. This condition is commonly seen in ageing 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 cancer\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Finally, a chronic inflammatory profile, represented by alteration of FBG, DD, and CRP inflammation markers, was clearly identified in our cohort. This enhanced inflammatory state, often referred to as \"inflammaging\", is associated with various age-related conditions, including cardiovascular diseases\u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eValidated in large cohort studies and longitudinal follow-ups, these biomarkers can help construct biological profiles that delineate ageing trajectories and identify health risk profiles. Studies such as the \u0026ldquo;Baltimore Longitudinal Study of Aging\u0026rdquo; (BLSA), which has been ongoing since 1958\u003csup\u003e34\u003c/sup\u003e, and the \u0026ldquo;Invecchiare in Chianti\u0026rdquo; (InCHIANTI)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, have extensively investigated a wide array of biomarkers related to cardiovascular, inflammatory, and metabolic conditions that provide insights into the ageing 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 ageing populations\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Similarly, the Italian cohort study Moli-SANI highlighted their importance in predicting cardiovascular diseases and diabetes\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAcknowledging that variations in biomarkers might be related to both diseases and/or ageing, we analyzed their distribution in relationship to the most prevalent chronic conditions\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. 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 markers\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. 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 \u0026ldquo;Progetto CUORE\u0026rdquo;, reporting elevated HbA1c, WBC, TG, and B1, alongside concurrent reductions in HDL and LDL levels\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. These alterations reflect diabetes-linked metabolic dysregulations, where HbA1c is a critical marker for long-term glucose control\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo meet the primary aim of the NCS project, we investigated biomarker variation during aging. By comparing participants over and under 65 years, we identified a group of biomarkers associated with inflammation, metabolism, liver, and kidney function. Ageing is a multifaceted process that impairs various physiological systems. Kidney function naturally declines with age, primarily evidenced by a decrease in the eGFR, as reflected by elevated CR and BUN\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. DD and CysC, which are known to increase with age and are linked to cardiovascular risk\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, showed alterations in our cohort. Additionally, increased HbA1c, which is reported to rise with age even in the absence of diabetes or other metabolic diseases\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, was observed.\u003c/p\u003e \u003cp\u003eOverall, these preliminary results reveal a pattern typical of ageing that could form the basis for developing a comprehensive panel of biomarkers. Such a panel could predict biological ageing, thereby facilitating preventive and personalized interventions\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. 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.\u003c/p\u003e \u003cp\u003eIn conclusion, the preliminary findings of NCS pilot gave researchers essential pieces of information: the risk of selection bias due to volunteer participation must be mitigated by adopting a strategy that involves a random sample of the population; the analysis of the panel of blood biomarkers is reassuring, demonstrating sufficient variability across participants to effectively investigate the ageing trajectories; and the Novara population exhibited a high level of participation and involvement, which is crucial for the success of population studies like the NCS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially funded by the Italian Ministry of University and Research (MUR) program \u0026quot;Departments of Excellence 2023-2027,\u0026quot; AGING Project \u0026ndash; Department of Translational Medicine, Universit\u0026agrave; del Piemonte Orientale, and is part of the AGE-IT project, which has received funding from the MUR \u0026ndash; M4C2 1.3 of PNRR under grant agreement no. PE0000015.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the Clinical Chemistry Laboratory of Maggiore della Carit\u0026agrave; University Hospital (Novara, Italy) for their invaluable support in the blood sample analysis. Furthermore,\u0026nbsp;we thank Dr. Tiziana Cena (Unit of Epidemiology, Local Health Unit Vercelli \u0026ndash; Italy) and Dr. Donatella Tiberti (SEREMI, Piedmont Region, Italy) for the provision and analysis of PASSI data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Conflicting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe disclose the following potential conflicts of interest: Professor Daniela Capello, the study supervisor, is the scientific director of UPO Biobank (Center for Autoimmune and Allergic Diseases, Novara, Italy), which supports this research and collects the biological samples. Additionally, several collaborators involved in this study hold positions within the Biobank. Nonetheless, we affirm that comprehensive measures were implemented to maintain objectivity and uphold scientific integrity.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study were extracted from the REDCap database, which contains data collected as part of the Novara Cohort Study. All participants in this study provided informed consent that specifically authorized the analysis of their data in aggregated form and allowed for the sharing of these data with other research institutions, subject to ethical approval. Due to the sensitive nature of the information and the privacy agreements in place, the raw data are not publicly available. However, the datasets can be made available to other researchers upon reasonable request. Interested researchers should contact the corresponding author with a clear rationale for their request. Data sharing will be subject to approval by the relevant ethics committees and will adhere to the terms of consent provided by the study participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Ageing and Health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health\u003c/li\u003e\n\u003cli\u003eUnited Nations. \u003cem\u003eWorld Population Ageing 2019\u003c/em\u003e. 2019. \u003c/li\u003e\n\u003cli\u003eThe Lancet Healthy Longevity. Ageing populations: unaffordable demography. \u003cem\u003eLancet Healthy Longev\u003c/em\u003e. Dec 2022;3(12):e804. doi:10.1016/S2666-7568(22)00272-0\u003c/li\u003e\n\u003cli\u003eChoi M, Sempungu JK, Lee EH, Lee YH. 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Kidney Aging and Chronic Kidney Disease. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e. 2024;25(12):6585. doi:10.3390/ijms25126585\u003c/li\u003e\n\u003cli\u003eNoronha IL, Santa-Catharina GP, Andrade L, Coelho VA, Jacob-Filho W, Elias RM. Glomerular filtration in the aging population. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e. 2022;9:769329. doi:10.3389/fmed.2022.769329\u003c/li\u003e\n\u003cli\u003eMcDermott MM, Liu K, Green D, et al. Changes in D-dimer and inflammatory biomarkers before ischemic events in patients with peripheral artery disease: The BRAVO Study. \u003cem\u003eVasc Med\u003c/em\u003e. Feb 2016;21(1):12-20. doi:10.1177/1358863X15617541\u003c/li\u003e\n\u003cli\u003eWest M, Kirby A, Stewart RA, et al. Circulating Cystatin C Is an Independent Risk Marker for Cardiovascular Outcomes, Development of Renal Impairment, and Long‐Term Mortality in Patients With Stable Coronary Heart Disease: The LIPID Study. \u003cem\u003eJournal of the American Heart Association\u003c/em\u003e. 2022;11(5)doi:10.1161/jaha.121.020745\u003c/li\u003e\n\u003cli\u003eMasuch A, Friedrich N, Roth J, Nauck M, M\u0026uuml;ller UA, Petersmann A. Preventing misdiagnosis of diabetes in the elderly: age-dependent HbA1c reference intervals derived from two population-based study cohorts. \u003cem\u003eBMC Endocrine Disorders\u003c/em\u003e. 2019;19(1)doi:10.1186/s12902-019-0338-7\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"geroscience, biomarkers, epidemiology, longevity, health trajectories, longitudinal analysis, predictive modeling, age-related diseases, aging dynamics","lastPublishedDoi":"10.21203/rs.3.rs-4939105/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4939105/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Novara Cohort Study (NCS) is the first multidisciplinary cohort study on aging in Northern Italy. It is designed to explore aging trajectories and health outcomes in the general population. This study involves the collection of biological samples and extensive data, including socioeconomic, medical history, lifestyle habits, quality of life and physical function.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis paper outlines the rationale, objectives, and preliminary findings of the NCS pilot phase. It discusses the baseline characteristics, initial biological characterization, and identifies key areas for improvement to ensure the successful implementation of the full-scale study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe NCS pilot phase enrolled participants aged 35 and older residing in Novara, Italy. The study involved the collection of biological samples, medical examinations, questionnaires and functional tests. Data were collected included demographic information, physical activity, sleep quality, diet, quality of life, mental health, medical history, and medication use. Key blood parameters were analyzed alongside clinical data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe pilot phase enrolled 123 participants, 68 (55.3%) females and 55 (44.7%) males with a median age of 65 years old. The NCS pilot participants had higher education levels, lower smoking rates, and higher physical activity levels than the general population. Blood biomarker profiling showed significant variability across participants, providing a solid foundation for effectively analyzing aging trajectories.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe NCS pilot provided crucial insights into participant characteristics and identified areas for study protocol enhancement throughout all phases. These findings will guide refinements to optimize future study processes and outcomes, ultimately aimed at investigating the biological, social, and environmental determinants of aging in the Northern Italy area population.\u003c/p\u003e","manuscriptTitle":"The Novara Cohort Study: Rationale, Objective and Preliminary Findings From an Italian Ageing Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 08:04:58","doi":"10.21203/rs.3.rs-4939105/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-12T06:25:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-08T08:37:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27008113856868244320901818517383943494","date":"2024-10-18T10:16:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-09T10:12:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276400734958803547232560731249999948505","date":"2024-10-08T09:52:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-07T07:18:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-01T11:29:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-03T16:06:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-03T16:03:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-19T13:50:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d737ab1-9ffa-49c3-a599-5c4fbec3ac33","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T15:59:48+00:00","versionOfRecord":{"articleIdentity":"rs-4939105","link":"https://doi.org/10.1038/s41598-025-02947-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-23 15:57:08","publishedOnDateReadable":"May 23rd, 2025"},"versionCreatedAt":"2024-10-21 08:04:58","video":"","vorDoi":"10.1038/s41598-025-02947-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-02947-3","workflowStages":[]},"version":"v1","identity":"rs-4939105","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4939105","identity":"rs-4939105","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}