Lifestyle İntervention İmproves Metabolic Parameters But Does Not Modify PCSK9 Levels İn Prediabetes: A Prospective Cohort Study

Lifestyle İntervention İmproves Metabolic Parameters But Does Not Modify PCSK9 Levels İn Prediabetes: A Prospective 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 Research Article Lifestyle İntervention İmproves Metabolic Parameters But Does Not Modify PCSK9 Levels İn Prediabetes: A Prospective Cohort Study Yunus Saglar, Husamettin Durmus, Funda Eren, Betul Erismis, Enes Seyda Sahiner This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396849/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Prediabetes is a critical stage in the progression to type 2 diabetes mellitus, where early lifestyle interventions can significantly alter disease trajectory. Proprotein convertase subtilisin/kexin type 9 (PCSK9), a central regulator of LDL cholesterol metabolism, has also been implicated in glucose homeostasis and inflammation. However, whether PCSK9 reflects short-term metabolic improvements following lifestyle modification remains uncertain. Methods In this prospective, single-center cohort study, individuals aged 18–65 years with prediabetes and healthy controls were enrolled. Prediabetes was defined according to American Diabetes Association criteria. Participants with prediabetes underwent individualized medical nutrition therapy and a structured exercise program. Anthropometric and biochemical parameters, including PCSK9 levels, were measured at baseline and after 3 months. Statistical analyses included parametric and non-parametric tests, correlation analyses, and regression modeling. Results A total of 132 participants (91 with prediabetes and 41 controls) were included. After 3 months, significant improvements were observed in body weight, body mass index, fasting plasma glucose, HbA1c, triglycerides, and TyG index (all p < 0.05). Despite these marked metabolic improvements, PCSK9 levels remained unchanged (11.369 vs. 11.649 ng/mL; p = 0.10). PCSK9 levels were independently associated with age, alanine aminotransferase, and albumin levels, and demonstrated a negative correlation with changes in inflammatory indices. Notably, PCSK9 levels were significantly lower in prediabetic individuals compared to controls (p < 0.001). Conclusion While lifestyle interventions lead to significant metabolic improvement in prediabetes, they do not modify PCSK9 levels in the short term. These findings suggest that PCSK9 may not serve as a dynamic biomarker of short-term metabolic response, but rather reflect more stable metabolic or inflammatory states. This dissociation highlights the complexity of PCSK9 biology beyond lipid metabolism and underscores the need for longitudinal studies to clarify its role in glucose regulation. Trial Registration ISRCTN10701142. Registered on 23 April 2026 (retrospectively registered). Prediabetes PCSK9 Lifestyle intervention Insulin resistance Glucose metabolism INTRODUCTION Diabetes mellitus is a major global health concern associated with substantial morbidity, mortality, and increasing healthcare burden worldwide [ 1 , 2 ]. The global prevalence of diabetes has risen markedly over recent decades, highlighting the urgent need for effective preventive strategies [ 3 ]. Prediabetes represents an intermediate metabolic state between normal glucose homeostasis and overt diabetes, characterized by impaired fasting glucose, impaired glucose tolerance, and/or elevated glycated hemoglobin levels [ 4 ]. Individuals with prediabetes have an annual progression rate to type 2 diabetes of approximately 5–10%, underscoring the importance of early identification and timely intervention [ 5 ]. Lifestyle modification, including dietary changes and increased physical activity, remains the cornerstone of prediabetes management [ 6 ]. These low-cost and widely accessible interventions have consistently been shown to improve glycemic control, reduce body weight, and delay or prevent progression to type 2 diabetes [ 7 , 8 ]. Moreover, lifestyle interventions have strong real-world applicability and represent key components of public health strategies aimed at reducing the burden of diabetes [ 9 ]. However, despite well-established clinical benefits, the underlying biochemical and molecular mechanisms through which these interventions exert their effects are not fully understood [ 10 ]. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a hepatic protease that plays a central role in lipid metabolism by promoting the degradation of low-density lipoprotein (LDL) receptors [ 11 ]. Beyond its established role in cholesterol homeostasis, accumulating evidence suggests that PCSK9 may also be involved in glucose metabolism, insulin resistance, and inflammatory pathways [ 12 , 13 ]. Several studies have reported associations between PCSK9 levels and metabolic parameters such as fasting glucose and insulin resistance indices [ 14 ]. However, the relationship between PCSK9 and glucose metabolism remains controversial, with conflicting findings reported in the literature [ 15 , 16 ]. Furthermore, the effect of lifestyle interventions on PCSK9 levels is not clearly defined. While some studies suggest that physical activity may influence PCSK9 concentrations, existing evidence is limited and inconsistent, particularly in prediabetic populations [ 17 ]. It remains unclear whether PCSK9 reflects short-term metabolic improvements induced by lifestyle modification or represents a more stable biomarker of long-term metabolic or inflammatory status [ 18 ]. Therefore, the present study aimed to evaluate the effects of dietary and exercise interventions on PCSK9 levels in individuals with prediabetes. Additionally, we aimed to investigate the relationship between PCSK9 and metabolic and inflammatory parameters. By addressing this gap, our study seeks to provide clinically relevant insights into the metabolic role of PCSK9 and to clarify its potential utility as a biomarker in the early stages of diabetes progression. A total of 132 participants were included in the study, comprising 91 individuals with prediabetes and 41 healthy controls. Participants in the prediabetes group were enrolled in a structured lifestyle intervention program and were followed for 3 months. Follow-up assessments were completed in participants who attended the scheduled control visit at the end of the intervention period. METHODS Study design and participants This prospective, single-center cohort study was conducted at a tertiary care hospital between December 27, 2023 and November 7, 2024. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The study population consisted of individuals aged 18–65 years who were diagnosed with prediabetes, along with a group of healthy controls. Prediabetes was defined according to the American Diabetes Association criteria as glycated hemoglobin (HbA1c) levels between 5.7% and 6.4%. The control group included individuals with HbA1c levels below 5.7% and fasting plasma glucose levels < 100 mg/dL. Participants were consecutively recruited from outpatient clinics. All participants underwent a comprehensive baseline evaluation, including demographic, clinical, anthropometric, and biochemical assessments. Inclusion criteria Age between 18 and 65 years Diagnosis of prediabetes based on ADA criteria (HbA1c 5.7–6.4%) Willingness to participate and provide informed consent Exclusion criteria Diagnosis of type 2 diabetes mellitus Use of lipid-lowering medications (including PCSK9 inhibitors or statins) Chronic liver or kidney disease Active infection or inflammatory disease Malignancy Pregnancy or lactation A total of 132 participants were included in the study, comprising 91 individuals with prediabetes and 41 healthy controls. Participants in the prediabetes group were enrolled in a structured lifestyle intervention program and were followed for 3 months. Follow-up assessments were completed in participants who attended the scheduled control visit at the end of the intervention period. Data collection and variables All data were prospectively collected at the time of admission using a standardized data collection form. Demographic, clinical, anthropometric, and laboratory variables were recorded for all participants. Demographic and clinical variables Demographic data included age and sex. Clinical parameters recorded at baseline included presenting symptoms, vital signs, comorbidities, and medication history. Anthropometric measurements Anthropometric measurements included body weight, height, body mass index (BMI), waist circumference, hip circumference, and body fat percentage. Body weight and body composition were measured using a calibrated bioelectrical impedance analysis device. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m²). Laboratory parameters Venous blood samples were obtained from all participants at baseline after an overnight fast whenever feasible. Laboratory parameters included hemoglobin, hematocrit, platelet count, fasting plasma glucose, glycated hemoglobin (HbA1c), blood urea nitrogen, creatinine, liver function tests (including alanine aminotransferase and aspartate aminotransferase), lipid profile (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides), and inflammatory markers. Serum PCSK9 levels were measured from blood samples collected at baseline prior to initiation of any intervention. All laboratory analyses were performed in the hospital’s central laboratory using standardized methods. Derived indices To further evaluate metabolic status, additional indices were calculated. The triglyceride–glucose (TyG) index was calculated using the following formula: TyG index = ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]. The homeostatic model assessment for insulin resistance (HOMA-IR) was calculated in participants with available insulin measurements using the following formula: HOMA-IR = [fasting insulin (µU/mL) × fasting glucose (mg/dL)] / 405. Follow-up assessment Participants in the prediabetes group were re-evaluated after 3 months following lifestyle intervention. At follow-up, anthropometric measurements and laboratory parameters were reassessed using the same standardized procedures. Lifestyle intervention (diet and exercise) Participants in the prediabetes group underwent a structured lifestyle intervention program consisting of individualized medical nutrition therapy and a progressive exercise regimen over a 3-month period. Dietary intervention All participants received individualized dietary counseling delivered by a registered dietitian in accordance with current clinical guidelines for prediabetes management. The primary goal of the nutritional intervention was to achieve moderate weight loss (target: 5–7% of baseline body weight) and improve glycemic control. Daily caloric intake was individualized based on age, sex, and baseline body mass index. The macronutrient composition was planned to include approximately 45–55% carbohydrates, 25–35% fats, and 15–20% protein. Participants were advised to limit saturated fat intake to < 10% of total daily energy intake, avoid refined carbohydrates and sugar-sweetened beverages, and increase dietary fiber intake to ≥ 15 g per 1000 kcal. A Mediterranean-style dietary pattern was recommended, emphasizing the consumption of vegetables, fruits, whole grains, legumes, nuts, and olive oil. Dietary recommendations were tailored to individual preferences to enhance adherence. Exercise intervention Participants were instructed to perform regular physical activity in accordance with established recommendations. The exercise program consisted of at least 150 minutes per week of moderate-intensity aerobic activity (equivalent to 3–6 metabolic equivalents [METs]), such as brisk walking. The exercise program was progressive and individualized. Participants initially engaged in low-intensity activities, followed by gradual increases in duration and intensity based on tolerance. Each exercise session included a warm-up period (5–10 minutes), an active phase (20–40 minutes), and a cool-down period (5–10 minutes). Participants were encouraged to maintain regular physical activity throughout the study period and to incorporate exercise into their daily routines. Monitoring and adherence Adherence to the lifestyle intervention was monitored through both follow-up visits and structured telephone contacts. Participants were contacted via telephone at regular intervals during the intervention period to reinforce adherence to dietary and exercise recommendations and to provide ongoing support. Compliance with the intervention was evaluated at the end of the 3-month period based on attendance at follow-up visits and self-reported adherence to prescribed lifestyle modifications. Outcomes The primary and secondary outcomes of the study were predefined to evaluate the effects of lifestyle intervention on metabolic, inflammatory, and biochemical parameters in individuals with prediabetes. Primary outcome The primary outcome of the study was the change in serum PCSK9 levels from baseline to 3 months in the prediabetes group following lifestyle intervention. Secondary outcomes Secondary outcomes included changes in anthropometric, metabolic, and inflammatory parameters between baseline and 3 months. Anthropometric outcomes Body weight Body mass index (BMI) Body fat percentage Waist circumference Hip circumference Glycemic and metabolic outcomes Fasting plasma glucose Glycated hemoglobin (HbA1c) Triglyceride levels Lipid profile parameters (total cholesterol, LDL-C, HDL-C) TyG index HOMA-IR (when available) Inflammatory and biochemical outcomes Systemic immune-inflammation index (SII) Alanine aminotransferase (ALT) Aspartate aminotransferase (AST) Albumin levels Exploratory outcomes Exploratory analyses included: Associations between PCSK9 levels and metabolic parameters Correlations between changes in PCSK9 and changes in SII and other metabolic indices Comparisons of PCSK9 levels between prediabetes and control groups Statistical analysis All statistical analyses were performed using IBM SPSS Statistics for Windows, version 24.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation (SD) or median (interquartile range [IQR]), depending on the distribution of the data, while categorical variables were presented as frequencies and percentages. The normality of distribution was assessed using the Kolmogorov–Smirnov test and visual inspection of histograms. For comparisons between two independent groups (prediabetes vs. control), the independent samples t-test was used for normally distributed variables, and the Mann–Whitney U test was used for non-normally distributed variables. Within-group comparisons (baseline vs. 3 months) were performed using the paired samples t-test for normally distributed variables and the Wilcoxon signed-rank test for non-normally distributed variables. Categorical variables were compared using the chi-square test or Fisher’s exact test, as appropriate. Correlation analyses were performed using Pearson or Spearman correlation coefficients, depending on data distribution. Univariate regression analysis was conducted to evaluate the association between selected variables and the presence of prediabetes. Variables with clinical relevance and statistical significance were included in the analysis. A two-tailed p-value of < 0.05 was considered statistically significant. Clinical trial number not applicable. Ethics approval and consent to participate The study was approved by the Clinical Research Ethics Committee No. 2 of Ankara City Hospital (approval number: E2-23-5663). All procedures were performed in accordance with the ethical standards of the institutional and national research committee and with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. RESULTS A total of 132 participants were included in the study between December 27, 2023 and November 7, 2024, comprising 91 individuals with prediabetes and 41 healthy controls. Among the prediabetes group, 47 participants (51.6%) completed the 3-month follow-up assessment. The mean age of the study population was 45.3±12.0 years, and 52.8% (n=71) were female. None of the participants had chronic diseases, regular medication use, smoking, or alcohol consumption. The mean body weight was 79.5±13.9 kg and the mean BMI was 28.5±4.7 kg/m². At baseline, the median PCSK9 level was 11.625 ng/mL, the median fasting plasma glucose was 89.5 mg/dL, and the median HbA1c was 5.9%. Additional anthropometric and biochemical parameters are presented (Table 1). Table 1. Baseline demographic, clinical, and laboratory characteristics of all participants Variable Value Age (years) 45.3 ± 12.0 Sex (female), n (%) 71 (52.8) Control, n (%) 41 (31.1) Prediabetes, n (%) 91 (68.9) Height (cm) 167.0 (159.5–174.0) Weight (kg) 79.5 ± 13.9 BMI (kg/m²) 28.5 ± 4.7 Body fat (%) 29.1 ± 8.6 Waist (cm) 98.5 ± 7.6 Hip (cm) 110.8 ± 8.1 Waist/hip ratio 0.90 (0.83–0.94) PCSK9 (ng/mL) 11.625 (11.192–12.459) FPG (mg/dL) 89.5 (83.0–100.5) Insulin (mU/L) 10.7 (7.0–17.0) HOMA-IR 2.4 (1.6–4.0) HbA1c (%) 5.9 (5.6–6.1) Triglycerides (mg/dL) 146.0 (101.5–196.0) HDL (mg/dL) 41.5 (35.5–50.0) LDL (mg/dL) 123.9 ± 32.4 Total cholesterol (mg/dL) 199.3 ± 37.2 AST (U/L) 15.0 (11.0–20.0) ALT (U/L) 24.0 (18.0–35.0) GGT (U/L) 25.0 (17.0–35.0) LDH (U/L) 202.0 (180.0–219.5) Albumin (g/L) 45.0 (44.0–47.0) Creatinine (mg/dL) 0.81 (0.69–0.94) GFR 100.0 (89.0–110.0) WBC (×10⁹/L) 7.22 (6.33–8.46) Hemoglobin (g/dL) 14.0 (12.9–15.4) Platelets (×10⁹/L) 274.0 ± 64.8 CRP (mg/L) 3.0 (1.0–4.6) ESR (mm/h) 8.0 (5.5–13.0) VAI 4.40 (2.95–6.81) SII 476.35 (371.09–623.69) NLR 1.78 (1.48–2.28) MHR 0.010 (0.007–0.013) TyG index 4.74 (4.54–4.94) ABR 77.89 (57.05–103.23) ALBI score −3.22 (−3.36–−3.03) Systolic BP (mmHg) 127.0 (118.0–133.0) Diastolic BP (mmHg) 74.0 (67.5–79.0) Heart rate (beats/min) 69.5 (64.0–77.0) Values are presented as mean ± standard deviation or median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; HDL: high-density lipoprotein; LDL: low-density lipoprotein; GFR: glomerular filtration rate; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; VAI: visceral adiposity index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; MHR: monocyte-to-HDL ratio; TyG: triglyceride–glucose index; ABR: albumin–bilirubin ratio; ALBI: albumin–bilirubin score. Among the 47 participants who completed follow-up, significant reductions were observed in several anthropometric parameters, including body weight (−2.0 kg), BMI (−0.7 kg/m²), body fat percentage (−1.4%), and waist circumference (−1.0 cm). Similarly, improvements were noted in key metabolic parameters, with decreases in fasting plasma glucose (−4.0 mg/dL), HbA1c (−0.1%), triglyceride levels (−30 mg/dL), and TyG index (−0.12). In contrast, the change in serum PCSK9 levels was minimal and did not reach statistical significance (Table 2). Table 2. Changes in continuous variables over 3 months in the prediabetes group (n=47) Variable Change (median [IQR]) HbA1c (%) -0.1 (-0.2–0.0) PCSK9 (ng/mL) 0.133 (-0.286–0.950) Weight (kg) -2.0 (-3.5–-0.8) BMI (kg/m²) -0.7 (-1.3–-0.2) Body fat (%) -1.4 (-2.5–-0.1) Waist (cm) -1.0 (-3.0–0.0) Hip (cm) -1.0 (-2.0–0.0) FPG (mg/dL) -4.0 (-9.5–2.0) Insulin (mU/L) -0.8 (-3.0–2.4) HOMA-IR -0.2 (-0.9–0.4) Triglycerides (mg/dL) -30.0 (-78.5–-1.5) HDL (mg/dL) 0.0 (-4.0–2.5) LDL (mg/dL) -1.0 (-18.5–19.0) Total cholesterol (mg/dL) -7.0 (-29.5–9.0) AST (U/L) 2.0 (-2.5–4.5) ALT (U/L) 1.0 (-4.0–6.0) LDH (U/L) -31.0 (-73.0–-12.0) Albumin (g/L) 0.0 (-2.0–1.0) Creatinine (mg/dL) 0.0 (-0.06–0.06) WBC (×10⁹/L) 0.11 (-0.77–0.60) Hemoglobin (g/dL) 0.0 (-0.3–0.4) Platelets (×10⁹/L) 17.0 (-18.5–31.0) CRP (mg/L) 0.0 (-1.3–1.2) VAI -0.33 (-1.53–0.03) SII 16.39 (-52.08–115.25) NLR 0.01 (-0.17–0.21) MHR -0.001 (-0.003–0.000) TyG index -0.12 (-0.27–0.01) ABR -8.95 (-16.91–11.57) ALBI score 0.07 (-0.06–0.21) Values are presented as median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; FPG: fasting plasma glucose; HDL: high-density lipoprotein; LDL: low-density lipoprotein; AST: aspartate aminotransferase; ALT: alanine aminotransferase; LDH: lactate dehydrogenase; CRP: C-reactive protein; VAI: visceral adiposity index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; MHR: monocyte-to-HDL ratio; TyG: triglyceride–glucose index; ABR: albumin–bilirubin ratio; ALBI: albumin–bilirubin score. Significant reductions were observed in anthropometric parameters, including body weight (p<0.001), BMI (p<0.001), body fat percentage (p=0.006), waist circumference (p=0.001), and hip circumference (p<0.001). Similarly, key metabolic parameters showed significant improvement, with decreases in fasting plasma glucose (p=0.02), HbA1c (p=0.001), triglyceride levels (p<0.001), TyG index (p<0.001), and VAI (p=0.002). In addition, systolic blood pressure was significantly reduced (p<0.001). In contrast, serum PCSK9 levels did not change significantly over time (p=0.10). Likewise, inflammatory parameters, including SII, remained stable and did not show a statistically significant change (p=0.25) (Table 3). Table 3. Comparison of baseline and 3-month values in the prediabetes group (n=47) Variable Baseline 3 months p-value Anthropometric measurements Weight (kg) 82.0 (75.1–91.8) 80.9 (70.8–89.5) <0.001 BMI (kg/m²) 29.6 (27.1–32.9) 28.8 (26.2–31.7) <0.001 Body fat (%) 32.3 (26.4–41.5) 31.2 (24.8–40.4) 0.006 Waist (cm) 101.0 (97.5–106.0) 100.0 (96.0–103.5) 0.001 Hip (cm) 110.0 (107.0–113.5) 108.0 (107.0–112.0) <0.001 Waist-to-hip ratio 0.92 (0.87–0.96) 0.92 (0.87–0.96) 0.67 Laboratory parameters PCSK9 (ng/mL) 11.369 (11.087–11.698) 11.649 (11.302–12.293) 0.10 Fasting plasma glucose (mg/dL) 98.0 (90.5–106.5) 92.0 (85.5–100.0) 0.02 Insulin (mU/L) 9.6 (5.7–14.6) 9.3 (6.5–12.1) 0.28 HOMA-IR 2.0 (1.3–3.0) 2.0 (1.5–2.9) 0.21 HbA1c (%) 6.0 (5.9–6.2) 5.9 (5.7–6.1) 0.001 Triglycerides (mg/dL) 176.0 (130.0–238.5) 127.0 (98.5–176.5) <0.001 HDL (mg/dL) 40.0 (36.0–48.0) 40.0 (35.0–48.5) 0.37 LDL (mg/dL) 111.0 (100.0–143.5) 120.0 (98.5–176.5) 0.81 Total cholesterol (mg/dL) 202.0 (169.5–218.0) 190.0 (163.5–216.0) 0.07 AST (U/L) 14.0 (10.0–19.5) 16.0 (11.5–21.0) 0.32 ALT (U/L) 24.0 (20.5–33.0) 26.0 (19.0–30.0) 0.63 GGT (U/L) 25.0 (18.0–35.0) 29.0 (25.0–35.5) 0.047 LDH (U/L) 202.0 (179.5–219.5) 160.0 (132.0–189.0) <0.001 Albumin (g/L) 45.0 (44.0–48.0) 45.0 (43.0–47.0) 0.11 Creatinine (mg/dL) 0.82 (0.71–0.94) 0.79 (0.72–0.95) 0.83 GFR 97.0 (89.5–105.0) 96.0 (84.5–104.0) 0.56 WBC (×10⁹/L) 7.20 (6.60–8.31) 7.10 (6.26–8.57) 0.69 Hemoglobin (g/dL) 13.9 (12.6–14.9) 13.9 (12.5–15.3) 0.93 Platelets (×10⁹/L) 265.0 (223.5–317.5) 271.0 (228.5–315.0) 0.16 CRP (mg/L) 3.0 (2.0–4.8) 3.0 (1.7–4.8) 0.94 ESR (mm/h) 8.0 (6.0–14.5) 9.0 (5.0–14.0) 0.72 VAI 3.25 (2.42–4.69) 2.57 (1.91–3.73) 0.002 SII 456.29 (343.76–588.05) 473.94 (386.20–596.66) 0.25 NLR 1.80 (1.44–2.25) 1.70 (1.40–2.21) 0.64 MHR 0.010 (0.008–0.013) 0.010 (0.007–0.012) 0.001 TyG index 4.87 (4.70–5.05) 4.65 (4.53–4.87) <0.001 ABR 78.18 (57.05–100.98) 73.77 (55.61–101.07) 0.20 ALBI score −3.23 (−3.33–−3.05) −3.15 (−3.32–−2.94) 0.04 Vital signs Systolic BP (mmHg) 124.0 (117.0–131.5) 122.0 (115.0–128.0) <0.001 Diastolic BP (mmHg) 71.0 (66.0–76.0) 72.0 (68.0–75.0) 0.21 Heart rate (beats/min) 74.0 (67.5–82.5) 73.0 (68.5–78.0) 0.07 Values are presented as median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; HDL: high-density lipoprotein; LDL: low-density lipoprotein; GFR: glomerular filtration rate; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; VAI: visceral adiposity index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; MHR: monocyte-to-HDL ratio; TyG: triglyceride–glucose index; ABR: albumin–bilirubin ratio; ALBI: albumin–bilirubin score; BP: blood pressure. Participants with prediabetes had significantly higher age (p<0.001), BMI (p=0.001), body fat percentage (p<0.001), waist-to-hip ratio (p<0.001), fasting plasma glucose (p<0.001), HbA1c (p<0.001), triglyceride levels (p<0.001), GGT (p=0.01), CRP (p=0.007), ESR (p=0.003), TyG index (p<0.001), and blood pressure values (p<0.001). Conversely, the control group had significantly higher PCSK9 levels (p<0.001), glomerular filtration rate (p<0.001), SII (p=0.03), and NLR (p=0.04) (Table 4). Table 4. Comparison between control and prediabetes groups Variable Control (n=41) Prediabetes (n=91) p-value Age (years) 37.0 (28.0–44.0) 50.0 (41.5–58.0) <0.001 BMI (kg/m²) 26.0 (22.0–30.0) 28.7 (26.5–32.0) 0.001 Body fat (%) 23.8 (17.8–32.2) 29.2 (25.1–37.1) <0.001 Waist-to-hip ratio 0.84 (0.81–0.92) 0.91 (0.86–0.95) <0.001 FPG (mg/dL) 84.0 (79.0–87.0) 96.0 (87.0–105.0) <0.001 HbA1c (%) 5.3 (5.2–5.5) 6.0 (5.8–6.2) <0.001 Triglycerides (mg/dL) 106.0 (80.0–144.0) 163.0 (115.0–215.0) <0.001 GGT (U/L) 20.0 (14.0–33.0) 27.0 (18.5–35.0) 0.01 CRP (mg/L) 1.9 (0.5–4.0) 3.0 (1.8–5.0) 0.007 ESR (mm/h) 7.0 (4.0–9.0) 9.0 (6.0–15.0) 0.003 TyG index 4.56 (4.39–4.71) 4.83 (4.65–4.99) <0.001 Systolic BP (mmHg) 120.0 (112.0–126.0) 130.0 (123.5–136.0) <0.001 Diastolic BP (mmHg) 71.0 (65.0–75.0) 77.0 (70.0–81.0) <0.001 PCSK9 (ng/mL) 12.487 (11.947–13.589) 11.369 (11.045–11.947) <0.001 GFR 110.0 (100.0–114.0) 97.0 (87.0–104.5) <0.001 SII 578.10 (384.45–731.98) 456.64 (362.48–586.93) 0.03 NLR 1.99 (1.66–2.34) 1.66 (1.45–2.14) 0.04 Values are presented as median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; FPG: fasting plasma glucose; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; GGT: gamma-glutamyl transferase; TyG: triglyceride–glucose index; GFR: glomerular filtration rate; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; BP: blood pressure. In the prediabetes group, PCSK9 levels were positively correlated with age (r=0.24, p=0.02) and negatively correlated with ALT (r=−0.23, p=0.03) and albumin levels (r=−0.21, p=0.04). In the control group, PCSK9 levels demonstrated a significant negative correlation with uric acid levels (r=−0.37, p=0.02). Furthermore, changes in PCSK9 levels were negatively correlated with changes in white blood cell count (r=−0.32, p=0.03) and SII (r=−0.31, p=0.04), indicating a potential association between PCSK9 dynamics and inflammatory markers (Table 5). Table 5. Correlation between PCSK9 levels and selected variables Variable Control r Control p Prediabetes r Prediabetes p Age 0.12 0.44 0.24 0.02 ALT -0.21 0.19 -0.23 0.03 Albumin -0.10 0.54 -0.21 0.04 Uric acid -0.37 0.02 -0.10 0.36 WBC (change) -0.32 0.03 SII (change) -0.31 0.04 Values are presented as correlation coefficient (r) and p-value. ALT: alanine aminotransferase; WBC: white blood cell count; SII: systemic immune-inflammation index. Participants without an increase in PCSK9 levels had significantly higher fasting plasma glucose (p=0.01), HbA1c (p=0.02), and heart rate (p=0.03), as well as lower platelet counts (p=0.03). Changes in HbA1c were positively correlated with changes in body weight (r=0.45, p=0.001), BMI (r=0.41, p=0.004), waist circumference (r=0.39, p=0.007), fasting plasma glucose (r=0.37, p=0.01), ALT (r=0.34, p=0.02), creatinine (r=0.30, p=0.04), and hemoglobin (r=0.31, p=0.03). In univariate regression analysis, BMI (OR: 1.19, p<0.001), body fat percentage (OR: 1.10, p<0.001), fasting plasma glucose (OR: 1.14, p<0.001), and TyG index (OR: 1.63, p<0.001) were significantly associated with the presence of prediabetes. In contrast, the association between PCSK9 levels and prediabetes was borderline non-significant (p=0.05) (Table 6). Table 6. Univariate logistic regression analysis for predictors of prediabetes Variable Odds Ratio (OR) 95% CI p-value BMI 1.19 1.08–1.31 <0.001 Body fat percentage 1.10 1.05–1.16 <0.001 Fasting plasma glucose 1.14 1.08–1.20 <0.001 PCSK9 (ng/mL) 0.82 0.68–1.00 0.05 TyG index 1.63 1.34–1.99 <0.001 SII 0.998 0.997–1.000 0.06 NLR 0.64 0.35–1.17 0.15 Values are presented as odds ratio (OR) with 95% confidence interval (CI). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; TyG: triglyceride–glucose index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio. DISCUSSION In this prospective cohort study, we evaluated the effects of a structured lifestyle intervention on serum PCSK9 levels and metabolic parameters in individuals with prediabetes. The main finding of our study is that, although significant improvements were achieved in anthropometric and metabolic parameters following the intervention, serum PCSK9 levels did not change significantly over the 3-month period. This finding suggests a dissociation between short-term metabolic improvement and PCSK9 regulation. Prediabetes is a clinically important condition associated with an increased risk of progression to type 2 diabetes mellitus and cardiovascular disease [4,5]. Lifestyle interventions, including dietary modification and increased physical activity, have consistently been shown to improve glycemic control and reduce cardiometabolic risk factors [6–9]. In line with these findings, our study demonstrated significant reductions in body weight, BMI, body fat percentage, waist circumference, fasting plasma glucose, HbA1c, triglyceride levels, and TyG index. These results reinforce the effectiveness of structured, low-cost, and non-pharmacological interventions in the management of prediabetes [6–10]. Despite these improvements, PCSK9 levels remained unchanged. PCSK9 is primarily known as a key regulator of lipid metabolism through its role in LDL receptor degradation [11]. However, increasing evidence suggests that PCSK9 may also be involved in glucose metabolism, insulin resistance, and inflammatory pathways [12–14,18]. Based on these findings, PCSK9 has been proposed as a potential biomarker reflecting metabolic dysfunction. Nevertheless, our results indicate that PCSK9 does not behave as a dynamic biomarker of short-term metabolic improvement in individuals with prediabetes. The relationship between PCSK9 and glucose metabolism remains controversial. Some studies have reported positive associations between circulating PCSK9 levels and fasting glucose, insulin resistance, and components of metabolic syndrome [12,14], whereas others have found no significant association with glycemic status or diabetes risk [15,16]. Our findings are more consistent with studies suggesting that PCSK9 is not directly influenced by short-term metabolic changes. These discrepancies may be explained by differences in study populations, duration of follow-up, and underlying metabolic characteristics [12–16]. Several mechanisms may explain the lack of change in PCSK9 levels observed in our study. First, PCSK9 may reflect a relatively stable metabolic phenotype rather than acute metabolic fluctuations. While parameters such as fasting glucose and triglycerides can improve rapidly following lifestyle modification, PCSK9 levels may be influenced by longer-term hepatic regulation and genetic determinants [11,14–16]. Therefore, the 3-month duration of our intervention may have been insufficient to induce measurable changes in PCSK9 concentrations. Second, PCSK9 regulation appears to be more closely linked to lipid metabolism and hepatic cholesterol homeostasis than to short-term changes in glucose metabolism [11]. Although prediabetes is characterized by insulin resistance and early metabolic dysregulation, these changes may not be sufficient to significantly alter PCSK9 expression or secretion in the short term. This may explain why substantial improvements in metabolic parameters were not paralleled by changes in PCSK9 levels. Interestingly, PCSK9 levels were significantly higher in the control group compared to the prediabetes group. This finding suggests that PCSK9 may be associated with baseline metabolic status rather than disease progression alone. Previous studies have also reported variable associations between PCSK9 levels and metabolic phenotypes, indicating that its role may differ depending on the metabolic context [14–16]. In addition, PCSK9 levels were associated with age, ALT, and albumin levels in the prediabetes group. These findings are biologically plausible, as PCSK9 is predominantly synthesized in the liver [11]. The observed association with ALT may reflect hepatic metabolic activity, while the association with albumin may indicate a relationship with nutritional and metabolic status. Furthermore, changes in PCSK9 levels were negatively correlated with changes in inflammatory markers, including white blood cell count and SII. Inflammation is a key component in the pathophysiology of insulin resistance and progression from prediabetes to diabetes [5,18]. Emerging evidence suggests that PCSK9 may interact with inflammatory pathways and contribute to vascular inflammation [18]. Our findings support the hypothesis that PCSK9 may be involved in metabolic–inflammatory interactions, although the underlying mechanisms require further investigation. From a clinical perspective, our findings indicate that lifestyle intervention remains highly effective in improving metabolic health in individuals with prediabetes [6–10]. However, PCSK9 does not appear to be a suitable biomarker for monitoring short-term response to lifestyle interventions. This is particularly relevant given the ongoing search for reliable biomarkers in early metabolic disease. Our results suggest that PCSK9 may reflect a more stable metabolic or inflammatory state rather than acute metabolic changes. The strengths of our study include its prospective design, inclusion of both prediabetic and control groups, and the use of a structured lifestyle intervention program supported by dietary counseling and follow-up. Additionally, the comprehensive evaluation of anthropometric, metabolic, and inflammatory parameters provides a broader understanding of the role of PCSK9 in metabolic regulation. However, several limitations should be acknowledged. First, the study was conducted at a single center, which may limit generalizability. Second, the relatively short follow-up period may not have been sufficient to detect long-term changes in PCSK9 levels. Third, not all participants completed follow-up, which may have reduced statistical power. Fourth, adherence to lifestyle intervention was partly based on self-reported data, which may introduce bias. Finally, the observational design limits the ability to establish causality. Future studies should include larger, multicenter cohorts with longer follow-up periods to better evaluate the temporal relationship between lifestyle intervention and PCSK9 levels. In addition, further research is needed to clarify the role of PCSK9 in glucose metabolism and its interaction with inflammatory pathways. In conclusion, lifestyle intervention in individuals with prediabetes leads to significant improvements in metabolic parameters but does not significantly alter PCSK9 levels in the short term. These findings suggest that PCSK9 may not serve as a dynamic biomarker of short-term metabolic response, but rather reflects a more stable metabolic or inflammatory state. CONCLUSION Lifestyle intervention significantly improves metabolic parameters in individuals with prediabetes but does not affect PCSK9 levels in the short term. PCSK9 may reflect a more stable metabolic or inflammatory state rather than acute metabolic changes. Further long-term studies are needed to clarify its clinical role. LIST OF ABBREVIATIONS ABR Albumin–bilirubin ratio ALT Alanine aminotransferase AST Aspartate aminotransferase BMI Body mass index CRP C-reactive protein FPG Fasting plasma glucose GFR Glomerular filtration rate HbA1c Glycated hemoglobin HDL High-density lipoprotein LDL Low-density lipoprotein MHR Monocyte-to-HDL ratio NLR Neutrophil-to-lymphocyte ratio PCSK9 Proprotein convertase subtilisin/kexin type 9 SII Systemic immune-inflammation index TyG Triglyceride–glucose index VAI Visceral adiposity index DECLARATIONS Ethics approval and consent to participate The study was approved by the Clinical Research Ethics Committee No. 2 of Ankara City Hospital (approval number: E2-23-5663). All procedures were performed in accordance with the ethical standards of the institutional and national research committee and with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study. Clinical trial number ISRCTN10701142 Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The authors received no specific funding for this work . Acknowledgements Not applicable. REFERENCES Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections. Diabetes Res Clin Pract. 2022;183:109119. 10.1016/j.diabres.2021.109119 . Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045. Diabetes Res Clin Pract. 2019;157:107843. 10.1016/j.diabres.2019.107843 . GBD 2021 Diabetes Collaborators. Global burden of diabetes, 1990–2021. Lancet. 2023. 10.1016/S0140-6736(23)01301-6 . American Diabetes Association. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes—2024. Diabetes Care. 2024;47(Suppl 1):S20–42. 10.2337/dc24-S002 . Tabák AG, Herder C, Rathmann W, Brunner EJ, Kivimäki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279–90. 10.1016/S0140-6736(12)60283-9 . Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention. N Engl J Med. 2002;346(6):393–403. 10.1056/NEJMoa012512 . Tuomilehto J, Lindström J, Eriksson JG, Valle TT, Hämäläinen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle. N Engl J Med. 2001;344(18):1343–50. 10.1056/NEJM200105033441801 . Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM. Diabetes Care. 1997;20(4):537–44. 10.2337/diacare.20.4.537 . Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, et al. The long-term effect of lifestyle interventions to prevent diabetes. Lancet Diabetes Endocrinol. 2014;2(6):474–80. 10.1016/S2213-8587(14)70057-9 . Eckel RH, Jakicic JM, Ard JD, Miller NH, Hubbard VS, Nonas CA, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk. Circulation. 2014;129(25 Suppl 2):S76–99. 10.1161/01.cir.0000437740.48606.d1 . Seidah NG, Awan Z, Chrétien M, Mbikay M. PCSK9: a key modulator of cardiovascular health. Circ Res. 2014;114(6):1022–36. 10.1161/CIRCRESAHA.114.301621 . Cariou B, Le Bras M, Langhi C, Le May C, Guyomarc’h-Delasalle B, Krempf M, et al. Association between PCSK9 and glucose homeostasis. Diabetes. 2010;59(4):949–55. 10.2337/db09-1039 . Awan Z, Seidah NG, MacFadyen JG, Benjannet S, Chasman DI, Ridker PM, et al. Rosuvastatin, PCSK9, and incident diabetes. Circulation. 2012;126(5):575–84. 10.1161/CIRCULATIONAHA.111.103655 . Lakoski SG, Lagace TA, Cohen JC, Horton JD, Hobbs HH. Genetic and metabolic determinants of plasma PCSK9 levels. J Clin Endocrinol Metab. 2009;94(7):2537–43. 10.1210/jc.2009-0141 . Schmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM, et al. PCSK9 genetic variants and risk of type 2 diabetes. Lancet Diabetes Endocrinol. 2017;5(2):97–105. 10.1016/S2213-8587(16)30396-5 . Ference BA, Robinson JG, Brook RD, Catapano AL, Chapman MJ, Neff DR, et al. Variation in PCSK9 and risk of diabetes. J Am Coll Cardiol. 2016;68(3):263–74. 10.1016/j.jacc.2016.04.052 . Ridker PM, MacFadyen JG, Glynn RJ, Koenig W, Inflammation. PCSK9, and cardiovascular risk. Circulation. 2019;139(2):177–86. 10.1161/CIRCULATIONAHA.118.035052 . Tang ZH, Li TH, Peng J, Zheng J, Li TT, Liu LS, et al. PCSK9: a novel inflammation modulator in atherosclerosis. Atherosclerosis. 2019;282:1–9. 10.1016/j.atherosclerosis.2018.12.020 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 May, 2026 Reviewers agreed at journal 17 May, 2026 Reviews received at journal 13 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 05 May, 2026 Editor invited by journal 27 Apr, 2026 Submission checks completed at journal 26 Apr, 2026 First submitted to journal 26 Apr, 2026 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-9396849","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639911191,"identity":"b7710527-7b25-4bfd-ab88-6ea46a600aaa","order_by":0,"name":"Yunus Saglar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBADGQb2BiBlYEG8Fh4GngMgLRKkaJFIANFEaDG4kXtM8sefWh7zmc+vbvhRIMHA396dQEBLXpo0b9txHpnbOWU3e4AOkzhzdgMBLTlm0owNx3gkpHPSbvAAtRhI5BLWAnQYUIvkmbSbf4jVIsHDVsMjIcF+7DZRtkieeWNszdt2gEeCJ4fttoyBBA9Bv/AdzzG8+eNPnZwE+/FnN9/8sZHjb+/Fr0XhAJg6DMQ8BiAWD17lICDfAKbqgJj9AUHVo2AUjIJRMDIBAMagRIcmoeHrAAAAAElFTkSuQmCC","orcid":"","institution":"Ankara City Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yunus","middleName":"","lastName":"Saglar","suffix":""},{"id":639911192,"identity":"8afda128-2d2f-4eb0-88af-ed3c85fa1b54","order_by":1,"name":"Husamettin Durmus","email":"","orcid":"","institution":"Bandirma Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Husamettin","middleName":"","lastName":"Durmus","suffix":""},{"id":639911193,"identity":"c0ecbe31-3e78-437f-8b8b-b5b8b842e27c","order_by":2,"name":"Funda Eren","email":"","orcid":"","institution":"Ankara City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Funda","middleName":"","lastName":"Eren","suffix":""},{"id":639911194,"identity":"637c757d-d311-47c6-acc6-f1d247d878b5","order_by":3,"name":"Betul Erismis","email":"","orcid":"","institution":"Ankara City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Betul","middleName":"","lastName":"Erismis","suffix":""},{"id":639911195,"identity":"313d717a-9bac-42e6-a136-4a1a621c9d7a","order_by":4,"name":"Enes Seyda Sahiner","email":"","orcid":"","institution":"Ankara City Hospital","correspondingAuthor":false,"prefix":"","firstName":"Enes","middleName":"Seyda","lastName":"Sahiner","suffix":""}],"badges":[],"createdAt":"2026-04-12 21:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9396849/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9396849/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109268113,"identity":"46426171-a99c-4fd3-86b7-337816070df5","added_by":"auto","created_at":"2026-05-14 13:10:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":396778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9396849/v1/de98a320-45f8-4712-8507-7d65ef9e7eec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Lifestyle İntervention İmproves Metabolic Parameters But Does Not Modify PCSK9 Levels İn Prediabetes: A Prospective Cohort Study","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eDiabetes mellitus is a major global health concern associated with substantial morbidity, mortality, and increasing healthcare burden worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The global prevalence of diabetes has risen markedly over recent decades, highlighting the urgent need for effective preventive strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Prediabetes represents an intermediate metabolic state between normal glucose homeostasis and overt diabetes, characterized by impaired fasting glucose, impaired glucose tolerance, and/or elevated glycated hemoglobin levels [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Individuals with prediabetes have an annual progression rate to type 2 diabetes of approximately 5\u0026ndash;10%, underscoring the importance of early identification and timely intervention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLifestyle modification, including dietary changes and increased physical activity, remains the cornerstone of prediabetes management [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These low-cost and widely accessible interventions have consistently been shown to improve glycemic control, reduce body weight, and delay or prevent progression to type 2 diabetes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Moreover, lifestyle interventions have strong real-world applicability and represent key components of public health strategies aimed at reducing the burden of diabetes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, despite well-established clinical benefits, the underlying biochemical and molecular mechanisms through which these interventions exert their effects are not fully understood [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProprotein convertase subtilisin/kexin type 9 (PCSK9) is a hepatic protease that plays a central role in lipid metabolism by promoting the degradation of low-density lipoprotein (LDL) receptors [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Beyond its established role in cholesterol homeostasis, accumulating evidence suggests that PCSK9 may also be involved in glucose metabolism, insulin resistance, and inflammatory pathways [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Several studies have reported associations between PCSK9 levels and metabolic parameters such as fasting glucose and insulin resistance indices [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the relationship between PCSK9 and glucose metabolism remains controversial, with conflicting findings reported in the literature [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the effect of lifestyle interventions on PCSK9 levels is not clearly defined. While some studies suggest that physical activity may influence PCSK9 concentrations, existing evidence is limited and inconsistent, particularly in prediabetic populations [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. It remains unclear whether PCSK9 reflects short-term metabolic improvements induced by lifestyle modification or represents a more stable biomarker of long-term metabolic or inflammatory status [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to evaluate the effects of dietary and exercise interventions on PCSK9 levels in individuals with prediabetes. Additionally, we aimed to investigate the relationship between PCSK9 and metabolic and inflammatory parameters. By addressing this gap, our study seeks to provide clinically relevant insights into the metabolic role of PCSK9 and to clarify its potential utility as a biomarker in the early stages of diabetes progression.\u003c/p\u003e \u003cp\u003eA total of 132 participants were included in the study, comprising 91 individuals with prediabetes and 41 healthy controls. Participants in the prediabetes group were enrolled in a structured lifestyle intervention program and were followed for 3 months. Follow-up assessments were completed in participants who attended the scheduled control visit at the end of the intervention period.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis prospective, single-center cohort study was conducted at a tertiary care hospital between December 27, 2023 and November 7, 2024. The study was reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. The study population consisted of individuals aged 18\u0026ndash;65 years who were diagnosed with prediabetes, along with a group of healthy controls.\u003c/p\u003e \u003cp\u003ePrediabetes was defined according to the American Diabetes Association criteria as glycated hemoglobin (HbA1c) levels between 5.7% and 6.4%. The control group included individuals with HbA1c levels below 5.7% and fasting plasma glucose levels\u0026thinsp;\u0026lt;\u0026thinsp;100 mg/dL.\u003c/p\u003e \u003cp\u003eParticipants were consecutively recruited from outpatient clinics. All participants underwent a comprehensive baseline evaluation, including demographic, clinical, anthropometric, and biochemical assessments.\u003c/p\u003e \u003cp\u003eInclusion criteria\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAge between 18 and 65 years\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDiagnosis of prediabetes based on ADA criteria (HbA1c 5.7\u0026ndash;6.4%)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWillingness to participate and provide informed consent\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eExclusion criteria\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDiagnosis of type 2 diabetes mellitus\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUse of lipid-lowering medications (including PCSK9 inhibitors or statins)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eChronic liver or kidney disease\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eActive infection or inflammatory disease\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMalignancy\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePregnancy or lactation\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eA total of 132 participants were included in the study, comprising 91 individuals with prediabetes and 41 healthy controls. Participants in the prediabetes group were enrolled in a structured lifestyle intervention program and were followed for 3 months. Follow-up assessments were completed in participants who attended the scheduled control visit at the end of the intervention period.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection and variables\u003c/h3\u003e\n\u003cp\u003eAll data were prospectively collected at the time of admission using a standardized data collection form. Demographic, clinical, anthropometric, and laboratory variables were recorded for all participants.\u003c/p\u003e\n\u003ch3\u003eDemographic and clinical variables\u003c/h3\u003e\n\u003cp\u003eDemographic data included age and sex. Clinical parameters recorded at baseline included presenting symptoms, vital signs, comorbidities, and medication history.\u003c/p\u003e\n\u003ch3\u003eAnthropometric measurements\u003c/h3\u003e\n\u003cp\u003eAnthropometric measurements included body weight, height, body mass index (BMI), waist circumference, hip circumference, and body fat percentage. Body weight and body composition were measured using a calibrated bioelectrical impedance analysis device. BMI was calculated as weight in kilograms divided by the square of height in meters (kg/m\u0026sup2;).\u003c/p\u003e\n\u003ch3\u003eLaboratory parameters\u003c/h3\u003e\n\u003cp\u003eVenous blood samples were obtained from all participants at baseline after an overnight fast whenever feasible. Laboratory parameters included hemoglobin, hematocrit, platelet count, fasting plasma glucose, glycated hemoglobin (HbA1c), blood urea nitrogen, creatinine, liver function tests (including alanine aminotransferase and aspartate aminotransferase), lipid profile (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides), and inflammatory markers.\u003c/p\u003e \u003cp\u003eSerum PCSK9 levels were measured from blood samples collected at baseline prior to initiation of any intervention. All laboratory analyses were performed in the hospital\u0026rsquo;s central laboratory using standardized methods.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDerived indices\u003c/h2\u003e \u003cp\u003eTo further evaluate metabolic status, additional indices were calculated.\u003c/p\u003e \u003cp\u003eThe triglyceride\u0026ndash;glucose (TyG) index was calculated using the following formula:\u003c/p\u003e \u003cp\u003eTyG index\u0026thinsp;=\u0026thinsp;ln [fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL) / 2].\u003c/p\u003e \u003cp\u003eThe homeostatic model assessment for insulin resistance (HOMA-IR) was calculated in participants with available insulin measurements using the following formula:\u003c/p\u003e \u003cp\u003eHOMA-IR = [fasting insulin (\u0026micro;U/mL) \u0026times; fasting glucose (mg/dL)] / 405.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFollow-up assessment\u003c/h3\u003e\n\u003cp\u003eParticipants in the prediabetes group were re-evaluated after 3 months following lifestyle intervention. At follow-up, anthropometric measurements and laboratory parameters were reassessed using the same standardized procedures.\u003c/p\u003e\n\u003ch3\u003eLifestyle intervention (diet and exercise)\u003c/h3\u003e\n\u003cp\u003eParticipants in the prediabetes group underwent a structured lifestyle intervention program consisting of individualized medical nutrition therapy and a progressive exercise regimen over a 3-month period.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDietary intervention\u003c/h2\u003e \u003cp\u003e All participants received individualized dietary counseling delivered by a registered dietitian in accordance with current clinical guidelines for prediabetes management. The primary goal of the nutritional intervention was to achieve moderate weight loss (target: 5\u0026ndash;7% of baseline body weight) and improve glycemic control.\u003c/p\u003e \u003cp\u003eDaily caloric intake was individualized based on age, sex, and baseline body mass index. The macronutrient composition was planned to include approximately 45\u0026ndash;55% carbohydrates, 25\u0026ndash;35% fats, and 15\u0026ndash;20% protein. Participants were advised to limit saturated fat intake to \u0026lt;\u0026thinsp;10% of total daily energy intake, avoid refined carbohydrates and sugar-sweetened beverages, and increase dietary fiber intake to \u0026ge;\u0026thinsp;15 g per 1000 kcal.\u003c/p\u003e \u003cp\u003eA Mediterranean-style dietary pattern was recommended, emphasizing the consumption of vegetables, fruits, whole grains, legumes, nuts, and olive oil. Dietary recommendations were tailored to individual preferences to enhance adherence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExercise intervention\u003c/h2\u003e \u003cp\u003eParticipants were instructed to perform regular physical activity in accordance with established recommendations. The exercise program consisted of at least 150 minutes per week of moderate-intensity aerobic activity (equivalent to 3\u0026ndash;6 metabolic equivalents [METs]), such as brisk walking.\u003c/p\u003e \u003cp\u003eThe exercise program was progressive and individualized. Participants initially engaged in low-intensity activities, followed by gradual increases in duration and intensity based on tolerance. Each exercise session included a warm-up period (5\u0026ndash;10 minutes), an active phase (20\u0026ndash;40 minutes), and a cool-down period (5\u0026ndash;10 minutes).\u003c/p\u003e \u003cp\u003eParticipants were encouraged to maintain regular physical activity throughout the study period and to incorporate exercise into their daily routines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMonitoring and adherence\u003c/h2\u003e \u003cp\u003eAdherence to the lifestyle intervention was monitored through both follow-up visits and structured telephone contacts. Participants were contacted via telephone at regular intervals during the intervention period to reinforce adherence to dietary and exercise recommendations and to provide ongoing support.\u003c/p\u003e \u003cp\u003eCompliance with the intervention was evaluated at the end of the 3-month period based on attendance at follow-up visits and self-reported adherence to prescribed lifestyle modifications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003eThe primary and secondary outcomes of the study were predefined to evaluate the effects of lifestyle intervention on metabolic, inflammatory, and biochemical parameters in individuals with prediabetes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrimary outcome\u003c/h2\u003e \u003cp\u003eThe primary outcome of the study was the change in serum PCSK9 levels from baseline to 3 months in the prediabetes group following lifestyle intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSecondary outcomes\u003c/h2\u003e \u003cp\u003eSecondary outcomes included changes in anthropometric, metabolic, and inflammatory parameters between baseline and 3 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric outcomes\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBody weight\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBody mass index (BMI)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBody fat percentage\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWaist circumference\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHip circumference\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGlycemic and metabolic outcomes\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFasting plasma glucose\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGlycated hemoglobin (HbA1c)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTriglyceride levels\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLipid profile parameters (total cholesterol, LDL-C, HDL-C)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHOMA-IR (when available)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInflammatory and biochemical outcomes\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSystemic immune-inflammation index (SII)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlanine aminotransferase (ALT)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAspartate aminotransferase (AST)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eAlbumin levels\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eExploratory outcomes\u003c/h2\u003e \u003cp\u003eExploratory analyses included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAssociations between PCSK9 levels and metabolic parameters\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCorrelations between changes in PCSK9 and changes in SII and other metabolic indices\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eComparisons of PCSK9 levels between prediabetes and control groups\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using IBM SPSS Statistics for Windows, version 24.0 (IBM Corp., Armonk, NY, USA).\u003c/p\u003e \u003cp\u003eContinuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range [IQR]), depending on the distribution of the data, while categorical variables were presented as frequencies and percentages.\u003c/p\u003e \u003cp\u003eThe normality of distribution was assessed using the Kolmogorov\u0026ndash;Smirnov test and visual inspection of histograms.\u003c/p\u003e \u003cp\u003eFor comparisons between two independent groups (prediabetes vs. control), the independent samples t-test was used for normally distributed variables, and the Mann\u0026ndash;Whitney U test was used for non-normally distributed variables.\u003c/p\u003e \u003cp\u003eWithin-group comparisons (baseline vs. 3 months) were performed using the paired samples t-test for normally distributed variables and the Wilcoxon signed-rank test for non-normally distributed variables.\u003c/p\u003e \u003cp\u003eCategorical variables were compared using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate.\u003c/p\u003e \u003cp\u003eCorrelation analyses were performed using Pearson or Spearman correlation coefficients, depending on data distribution.\u003c/p\u003e \u003cp\u003eUnivariate regression analysis was conducted to evaluate the association between selected variables and the presence of prediabetes. Variables with clinical relevance and statistical significance were included in the analysis.\u003c/p\u003e \u003cp\u003eA two-tailed p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Clinical Research Ethics Committee No. 2 of Ankara City Hospital (approval number: E2-23-5663).\u003c/p\u003e\n\u003cp\u003eAll procedures were performed in accordance with the ethical standards of the institutional and national research committee and with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eA total of 132 participants were included in the study between December 27, 2023 and November 7, 2024, comprising 91 individuals with prediabetes and 41 healthy controls. Among the prediabetes group, 47 participants (51.6%) completed the 3-month follow-up assessment.\u003c/p\u003e\n\u003cp\u003eThe mean age of the study population was 45.3\u0026plusmn;12.0 years, and 52.8% (n=71) were female. None of the participants had chronic diseases, regular medication use, smoking, or alcohol consumption. The mean body weight was 79.5\u0026plusmn;13.9 kg and the mean BMI was 28.5\u0026plusmn;4.7 kg/m\u0026sup2;. At baseline, the median PCSK9 level was 11.625 ng/mL, the median fasting plasma glucose was 89.5 mg/dL, and the median HbA1c was 5.9%. Additional anthropometric and biochemical parameters are presented\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline demographic, clinical, and laboratory characteristics of all participants\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e45.3 \u0026plusmn; 12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eSex (female), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e71 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eControl, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e41 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003ePrediabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e91 (68.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e167.0 (159.5\u0026ndash;174.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e79.5 \u0026plusmn; 13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e28.5 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eBody fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e29.1 \u0026plusmn; 8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eWaist (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e98.5 \u0026plusmn; 7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHip (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e110.8 \u0026plusmn; 8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eWaist/hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e0.90 (0.83\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003ePCSK9 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e11.625 (11.192\u0026ndash;12.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eFPG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e89.5 (83.0\u0026ndash;100.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eInsulin (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e10.7 (7.0\u0026ndash;17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e2.4 (1.6\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e5.9 (5.6\u0026ndash;6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e146.0 (101.5\u0026ndash;196.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e41.5 (35.5\u0026ndash;50.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eLDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e123.9 \u0026plusmn; 32.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e199.3 \u0026plusmn; 37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e15.0 (11.0\u0026ndash;20.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e24.0 (18.0\u0026ndash;35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e25.0 (17.0\u0026ndash;35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e202.0 (180.0\u0026ndash;219.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e45.0 (44.0\u0026ndash;47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e0.81 (0.69\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e100.0 (89.0\u0026ndash;110.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e7.22 (6.33\u0026ndash;8.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e14.0 (12.9\u0026ndash;15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003ePlatelets (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e274.0 \u0026plusmn; 64.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e3.0 (1.0\u0026ndash;4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eESR (mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e8.0 (5.5\u0026ndash;13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e4.40 (2.95\u0026ndash;6.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e476.35 (371.09\u0026ndash;623.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e1.78 (1.48\u0026ndash;2.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eMHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e0.010 (0.007\u0026ndash;0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e4.74 (4.54\u0026ndash;4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eABR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e77.89 (57.05\u0026ndash;103.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eALBI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e\u0026minus;3.22 (\u0026minus;3.36\u0026ndash;\u0026minus;3.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e127.0 (118.0\u0026ndash;133.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e74.0 (67.5\u0026ndash;79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 288px;\"\u003e\n \u003cp\u003e69.5 (64.0\u0026ndash;77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as mean \u0026plusmn; standard deviation or median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; HDL: high-density lipoprotein; LDL: low-density lipoprotein; GFR: glomerular filtration rate; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; VAI: visceral adiposity index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; MHR: monocyte-to-HDL ratio; TyG: triglyceride\u0026ndash;glucose index; ABR: albumin\u0026ndash;bilirubin ratio; ALBI: albumin\u0026ndash;bilirubin score.\u003c/p\u003e\n\u003cp\u003eAmong the 47 participants who completed follow-up, significant reductions were observed in several anthropometric parameters, including body weight (\u0026minus;2.0 kg), BMI (\u0026minus;0.7 kg/m\u0026sup2;), body fat percentage (\u0026minus;1.4%), and waist circumference (\u0026minus;1.0 cm). Similarly, improvements were noted in key metabolic parameters, with decreases in fasting plasma glucose (\u0026minus;4.0 mg/dL), HbA1c (\u0026minus;0.1%), triglyceride levels (\u0026minus;30 mg/dL), and TyG index (\u0026minus;0.12). In contrast, the change in serum PCSK9 levels was minimal and did not reach statistical significance (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2. Changes in continuous variables over 3 months in the prediabetes group (n=47)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChange (median [IQR])\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.1 (-0.2\u0026ndash;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePCSK9 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.133 (-0.286\u0026ndash;0.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-2.0 (-3.5\u0026ndash;-0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.7 (-1.3\u0026ndash;-0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eBody fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-1.4 (-2.5\u0026ndash;-0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eWaist (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-1.0 (-3.0\u0026ndash;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHip (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-1.0 (-2.0\u0026ndash;0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eFPG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-4.0 (-9.5\u0026ndash;2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eInsulin (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.8 (-3.0\u0026ndash;2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.2 (-0.9\u0026ndash;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-30.0 (-78.5\u0026ndash;-1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.0 (-4.0\u0026ndash;2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-1.0 (-18.5\u0026ndash;19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-7.0 (-29.5\u0026ndash;9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e2.0 (-2.5\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e1.0 (-4.0\u0026ndash;6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-31.0 (-73.0\u0026ndash;-12.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.0 (-2.0\u0026ndash;1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.0 (-0.06\u0026ndash;0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.11 (-0.77\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.0 (-0.3\u0026ndash;0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003ePlatelets (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e17.0 (-18.5\u0026ndash;31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.0 (-1.3\u0026ndash;1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.33 (-1.53\u0026ndash;0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e16.39 (-52.08\u0026ndash;115.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.01 (-0.17\u0026ndash;0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eMHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.001 (-0.003\u0026ndash;0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-0.12 (-0.27\u0026ndash;0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eABR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e-8.95 (-16.91\u0026ndash;11.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eALBI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e0.07 (-0.06\u0026ndash;0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as median (interquartile range).\u003c/p\u003e\n\u003cp\u003eBMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; FPG: fasting plasma glucose; HDL: high-density lipoprotein; LDL: low-density lipoprotein; AST: aspartate aminotransferase; ALT: alanine aminotransferase; LDH: lactate dehydrogenase; CRP: C-reactive protein; VAI: visceral adiposity index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; MHR: monocyte-to-HDL ratio; TyG: triglyceride\u0026ndash;glucose index; ABR: albumin\u0026ndash;bilirubin ratio; ALBI: albumin\u0026ndash;bilirubin score.\u003c/p\u003e\n\u003cp\u003eSignificant reductions were observed in anthropometric parameters, including body weight (p\u0026lt;0.001), BMI (p\u0026lt;0.001), body fat percentage (p=0.006), waist circumference (p=0.001), and hip circumference (p\u0026lt;0.001). Similarly, key metabolic parameters showed significant improvement, with decreases in fasting plasma glucose (p=0.02), HbA1c (p=0.001), triglyceride levels (p\u0026lt;0.001), TyG index (p\u0026lt;0.001), and VAI (p=0.002). In addition, systolic blood pressure was significantly reduced (p\u0026lt;0.001). In contrast, serum PCSK9 levels did not change significantly over time (p=0.10). Likewise, inflammatory parameters, including SII, remained stable and did not show a statistically significant change (p=0.25) (Table 3).\u003c/p\u003e\n\u003cp\u003eTable 3. Comparison of baseline and 3-month values in the prediabetes group (n=47)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBaseline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3 months\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAnthropometric measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e82.0 (75.1\u0026ndash;91.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e80.9 (70.8\u0026ndash;89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e29.6 (27.1\u0026ndash;32.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e28.8 (26.2\u0026ndash;31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBody fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e32.3 (26.4\u0026ndash;41.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e31.2 (24.8\u0026ndash;40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eWaist (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e101.0 (97.5\u0026ndash;106.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e100.0 (96.0\u0026ndash;103.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHip (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e110.0 (107.0\u0026ndash;113.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e108.0 (107.0\u0026ndash;112.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.92 (0.87\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.92 (0.87\u0026ndash;0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLaboratory parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePCSK9 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e11.369 (11.087\u0026ndash;11.698)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e11.649 (11.302\u0026ndash;12.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFasting plasma glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e98.0 (90.5\u0026ndash;106.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e92.0 (85.5\u0026ndash;100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eInsulin (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9.6 (5.7\u0026ndash;14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9.3 (6.5\u0026ndash;12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.0 (1.3\u0026ndash;3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.0 (1.5\u0026ndash;2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6.0 (5.9\u0026ndash;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e5.9 (5.7\u0026ndash;6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e176.0 (130.0\u0026ndash;238.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e127.0 (98.5\u0026ndash;176.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e40.0 (36.0\u0026ndash;48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e40.0 (35.0\u0026ndash;48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLDL (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e111.0 (100.0\u0026ndash;143.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e120.0 (98.5\u0026ndash;176.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e202.0 (169.5\u0026ndash;218.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e190.0 (163.5\u0026ndash;216.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e14.0 (10.0\u0026ndash;19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e16.0 (11.5\u0026ndash;21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e24.0 (20.5\u0026ndash;33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e26.0 (19.0\u0026ndash;30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e25.0 (18.0\u0026ndash;35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e29.0 (25.0\u0026ndash;35.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eLDH (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e202.0 (179.5\u0026ndash;219.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e160.0 (132.0\u0026ndash;189.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAlbumin (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e45.0 (44.0\u0026ndash;48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e45.0 (43.0\u0026ndash;47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.82 (0.71\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.79 (0.72\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e97.0 (89.5\u0026ndash;105.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e96.0 (84.5\u0026ndash;104.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.20 (6.60\u0026ndash;8.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.10 (6.26\u0026ndash;8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e13.9 (12.6\u0026ndash;14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e13.9 (12.5\u0026ndash;15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePlatelets (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e265.0 (223.5\u0026ndash;317.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e271.0 (228.5\u0026ndash;315.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.0 (2.0\u0026ndash;4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.0 (1.7\u0026ndash;4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eESR (mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e8.0 (6.0\u0026ndash;14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9.0 (5.0\u0026ndash;14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.25 (2.42\u0026ndash;4.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2.57 (1.91\u0026ndash;3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e456.29 (343.76\u0026ndash;588.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e473.94 (386.20\u0026ndash;596.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.80 (1.44\u0026ndash;2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.70 (1.40\u0026ndash;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eMHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.010 (0.008\u0026ndash;0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.010 (0.007\u0026ndash;0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.87 (4.70\u0026ndash;5.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.65 (4.53\u0026ndash;4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eABR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e78.18 (57.05\u0026ndash;100.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e73.77 (55.61\u0026ndash;101.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eALBI score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026minus;3.23 (\u0026minus;3.33\u0026ndash;\u0026minus;3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026minus;3.15 (\u0026minus;3.32\u0026ndash;\u0026minus;2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVital signs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e124.0 (117.0\u0026ndash;131.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e122.0 (115.0\u0026ndash;128.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e71.0 (66.0\u0026ndash;76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e72.0 (68.0\u0026ndash;75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHeart rate (beats/min)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e74.0 (67.5\u0026ndash;82.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e73.0 (68.5\u0026ndash;78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; HDL: high-density lipoprotein; LDL: low-density lipoprotein; GFR: glomerular filtration rate; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; VAI: visceral adiposity index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; MHR: monocyte-to-HDL ratio; TyG: triglyceride\u0026ndash;glucose index; ABR: albumin\u0026ndash;bilirubin ratio; ALBI: albumin\u0026ndash;bilirubin score; BP: blood pressure.\u003c/p\u003e\n\u003cp\u003eParticipants with prediabetes had significantly higher age (p\u0026lt;0.001), BMI (p=0.001), body fat percentage (p\u0026lt;0.001), waist-to-hip ratio (p\u0026lt;0.001), fasting plasma glucose (p\u0026lt;0.001), HbA1c (p\u0026lt;0.001), triglyceride levels (p\u0026lt;0.001), GGT (p=0.01), CRP (p=0.007), ESR (p=0.003), TyG index (p\u0026lt;0.001), and blood pressure values (p\u0026lt;0.001). Conversely, the control group had significantly higher PCSK9 levels (p\u0026lt;0.001), glomerular filtration rate (p\u0026lt;0.001), SII (p=0.03), and NLR (p=0.04) (Table 4).\u003c/p\u003e\n\u003cp\u003eTable 4. Comparison between control and prediabetes groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eControl (n=41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePrediabetes (n=91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e37.0 (28.0\u0026ndash;44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e50.0 (41.5\u0026ndash;58.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e26.0 (22.0\u0026ndash;30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e28.7 (26.5\u0026ndash;32.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBody fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e23.8 (17.8\u0026ndash;32.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e29.2 (25.1\u0026ndash;37.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.84 (0.81\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.91 (0.86\u0026ndash;0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFPG (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e84.0 (79.0\u0026ndash;87.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e96.0 (87.0\u0026ndash;105.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eHbA1c (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e5.3 (5.2\u0026ndash;5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6.0 (5.8\u0026ndash;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e106.0 (80.0\u0026ndash;144.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e163.0 (115.0\u0026ndash;215.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eGGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e20.0 (14.0\u0026ndash;33.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e27.0 (18.5\u0026ndash;35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.9 (0.5\u0026ndash;4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e3.0 (1.8\u0026ndash;5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eESR (mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.0 (4.0\u0026ndash;9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9.0 (6.0\u0026ndash;15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.56 (4.39\u0026ndash;4.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e4.83 (4.65\u0026ndash;4.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e120.0 (112.0\u0026ndash;126.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e130.0 (123.5\u0026ndash;136.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e71.0 (65.0\u0026ndash;75.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e77.0 (70.0\u0026ndash;81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePCSK9 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e12.487 (11.947\u0026ndash;13.589)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e11.369 (11.045\u0026ndash;11.947)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e110.0 (100.0\u0026ndash;114.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e97.0 (87.0\u0026ndash;104.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e578.10 (384.45\u0026ndash;731.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e456.64 (362.48\u0026ndash;586.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.99 (1.66\u0026ndash;2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.66 (1.45\u0026ndash;2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as median (interquartile range). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; FPG: fasting plasma glucose; CRP: C-reactive protein; ESR: erythrocyte sedimentation rate; GGT: gamma-glutamyl transferase; TyG: triglyceride\u0026ndash;glucose index; GFR: glomerular filtration rate; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio; BP: blood pressure.\u003c/p\u003e\n\u003cp\u003eIn the prediabetes group, PCSK9 levels were positively correlated with age (r=0.24, p=0.02) and negatively correlated with ALT (r=\u0026minus;0.23, p=0.03) and albumin levels (r=\u0026minus;0.21, p=0.04). In the control group, PCSK9 levels demonstrated a significant negative correlation with uric acid levels (r=\u0026minus;0.37, p=0.02). Furthermore, changes in PCSK9 levels were negatively correlated with changes in white blood cell count (r=\u0026minus;0.32, p=0.03) and SII (r=\u0026minus;0.31, p=0.04), indicating a potential association between PCSK9 dynamics and inflammatory markers (Table 5).\u003c/p\u003e\n\u003cp\u003eTable 5. Correlation between PCSK9 levels and selected variables\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eControl r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eControl p\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003ePrediabetes r\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003ePrediabetes p\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eUric acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eWBC (change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eSII (change)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3326%;\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.3328%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as correlation coefficient (r) and p-value. ALT: alanine aminotransferase; WBC: white blood cell count; SII: systemic immune-inflammation index.\u003c/p\u003e\n\u003cp\u003eParticipants without an increase in PCSK9 levels had significantly higher fasting plasma glucose (p=0.01), HbA1c (p=0.02), and heart rate (p=0.03), as well as lower platelet counts (p=0.03). Changes in HbA1c were positively correlated with changes in body weight (r=0.45, p=0.001), BMI (r=0.41, p=0.004), waist circumference (r=0.39, p=0.007), fasting plasma glucose (r=0.37, p=0.01), ALT (r=0.34, p=0.02), creatinine (r=0.30, p=0.04), and hemoglobin (r=0.31, p=0.03). In univariate regression analysis, BMI (OR: 1.19, p\u0026lt;0.001), body fat percentage (OR: 1.10, p\u0026lt;0.001), fasting plasma glucose (OR: 1.14, p\u0026lt;0.001), and TyG index (OR: 1.63, p\u0026lt;0.001) were significantly associated with the presence of prediabetes. In contrast, the association between PCSK9 levels and prediabetes was borderline non-significant (p=0.05) (Table 6).\u003c/p\u003e\n\u003cp\u003eTable 6. Univariate logistic regression analysis for predictors of prediabetes\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.08\u0026ndash;1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eBody fat percentage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.05\u0026ndash;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eFasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.08\u0026ndash;1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePCSK9 (ng/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.68\u0026ndash;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTyG index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1.34\u0026ndash;1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.997\u0026ndash;1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.35\u0026ndash;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are presented as odds ratio (OR) with 95% confidence interval (CI). BMI: body mass index; PCSK9: proprotein convertase subtilisin/kexin type 9; TyG: triglyceride\u0026ndash;glucose index; SII: systemic immune-inflammation index; NLR: neutrophil-to-lymphocyte ratio.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this prospective cohort study, we evaluated the effects of a structured lifestyle intervention on serum PCSK9 levels and metabolic parameters in individuals with prediabetes. The main finding of our study is that, although significant improvements were achieved in anthropometric and metabolic parameters following the intervention, serum PCSK9 levels did not change significantly over the 3-month period. This finding suggests a dissociation between short-term metabolic improvement and PCSK9 regulation.\u003c/p\u003e\n\u003cp\u003ePrediabetes is a clinically important condition associated with an increased risk of progression to type 2 diabetes mellitus and cardiovascular disease [4,5]. Lifestyle interventions, including dietary modification and increased physical activity, have consistently been shown to improve glycemic control and reduce cardiometabolic risk factors [6\u0026ndash;9]. In line with these findings, our study demonstrated significant reductions in body weight, BMI, body fat percentage, waist circumference, fasting plasma glucose, HbA1c, triglyceride levels, and TyG index. These results reinforce the effectiveness of structured, low-cost, and non-pharmacological interventions in the management of prediabetes [6\u0026ndash;10].\u003c/p\u003e\n\u003cp\u003eDespite these improvements, PCSK9 levels remained unchanged. PCSK9 is primarily known as a key regulator of lipid metabolism through its role in LDL receptor degradation [11]. However, increasing evidence suggests that PCSK9 may also be involved in glucose metabolism, insulin resistance, and inflammatory pathways [12\u0026ndash;14,18]. Based on these findings, PCSK9 has been proposed as a potential biomarker reflecting metabolic dysfunction. Nevertheless, our results indicate that PCSK9 does not behave as a dynamic biomarker of short-term metabolic improvement in individuals with prediabetes.\u003c/p\u003e\n\u003cp\u003eThe relationship between PCSK9 and glucose metabolism remains controversial. Some studies have reported positive associations between circulating PCSK9 levels and fasting glucose, insulin resistance, and components of metabolic syndrome [12,14], whereas others have found no significant association with glycemic status or diabetes risk [15,16]. Our findings are more consistent with studies suggesting that PCSK9 is not directly influenced by short-term metabolic changes. These discrepancies may be explained by differences in study populations, duration of follow-up, and underlying metabolic characteristics [12\u0026ndash;16].\u003c/p\u003e\n\u003cp\u003eSeveral mechanisms may explain the lack of change in PCSK9 levels observed in our study. First, PCSK9 may reflect a relatively stable metabolic phenotype rather than acute metabolic fluctuations. While parameters such as fasting glucose and triglycerides can improve rapidly following lifestyle modification, PCSK9 levels may be influenced by longer-term hepatic regulation and genetic determinants [11,14\u0026ndash;16]. Therefore, the 3-month duration of our intervention may have been insufficient to induce measurable changes in PCSK9 concentrations.\u003c/p\u003e\n\u003cp\u003eSecond, PCSK9 regulation appears to be more closely linked to lipid metabolism and hepatic cholesterol homeostasis than to short-term changes in glucose metabolism [11]. Although prediabetes is characterized by insulin resistance and early metabolic dysregulation, these changes may not be sufficient to significantly alter PCSK9 expression or secretion in the short term. This may explain why substantial improvements in metabolic parameters were not paralleled by changes in PCSK9 levels.\u003c/p\u003e\n\u003cp\u003eInterestingly, PCSK9 levels were significantly higher in the control group compared to the prediabetes group. This finding suggests that PCSK9 may be associated with baseline metabolic status rather than disease progression alone. Previous studies have also reported variable associations between PCSK9 levels and metabolic phenotypes, indicating that its role may differ depending on the metabolic context [14\u0026ndash;16].\u003c/p\u003e\n\u003cp\u003eIn addition, PCSK9 levels were associated with age, ALT, and albumin levels in the prediabetes group. These findings are biologically plausible, as PCSK9 is predominantly synthesized in the liver [11]. The observed association with ALT may reflect hepatic metabolic activity, while the association with albumin may indicate a relationship with nutritional and metabolic status.\u003c/p\u003e\n\u003cp\u003eFurthermore, changes in PCSK9 levels were negatively correlated with changes in inflammatory markers, including white blood cell count and SII. Inflammation is a key component in the pathophysiology of insulin resistance and progression from prediabetes to diabetes [5,18]. Emerging evidence suggests that PCSK9 may interact with inflammatory pathways and contribute to vascular inflammation [18]. Our findings support the hypothesis that PCSK9 may be involved in metabolic\u0026ndash;inflammatory interactions, although the underlying mechanisms require further investigation.\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, our findings indicate that lifestyle intervention remains highly effective in improving metabolic health in individuals with prediabetes [6\u0026ndash;10]. However, PCSK9 does not appear to be a suitable biomarker for monitoring short-term response to lifestyle interventions. This is particularly relevant given the ongoing search for reliable biomarkers in early metabolic disease. Our results suggest that PCSK9 may reflect a more stable metabolic or inflammatory state rather than acute metabolic changes.\u003c/p\u003e\n\u003cp\u003eThe strengths of our study include its prospective design, inclusion of both prediabetic and control groups, and the use of a structured lifestyle intervention program supported by dietary counseling and follow-up. Additionally, the comprehensive evaluation of anthropometric, metabolic, and inflammatory parameters provides a broader understanding of the role of PCSK9 in metabolic regulation.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations should be acknowledged. First, the study was conducted at a single center, which may limit generalizability. Second, the relatively short follow-up period may not have been sufficient to detect long-term changes in PCSK9 levels. Third, not all participants completed follow-up, which may have reduced statistical power. Fourth, adherence to lifestyle intervention was partly based on self-reported data, which may introduce bias. Finally, the observational design limits the ability to establish causality.\u003c/p\u003e\n\u003cp\u003eFuture studies should include larger, multicenter cohorts with longer follow-up periods to better evaluate the temporal relationship between lifestyle intervention and PCSK9 levels. In addition, further research is needed to clarify the role of PCSK9 in glucose metabolism and its interaction with inflammatory pathways.\u003c/p\u003e\n\u003cp\u003eIn conclusion, lifestyle intervention in individuals with prediabetes leads to significant improvements in metabolic parameters but does not significantly alter PCSK9 levels in the short term. These findings suggest that PCSK9 may not serve as a dynamic biomarker of short-term metabolic response, but rather reflects a more stable metabolic or inflammatory state.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eLifestyle intervention significantly improves metabolic parameters in individuals with prediabetes but does not affect PCSK9 levels in the short term. PCSK9 may reflect a more stable metabolic or inflammatory state rather than acute metabolic changes. Further long-term studies are needed to clarify its clinical role.\u003c/p\u003e"},{"header":"LIST OF ABBREVIATIONS","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlbumin\u0026ndash;bilirubin ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eALT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlanine aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAspartate aminotransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody mass index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFasting plasma glucose\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGFR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlomerular filtration rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHbA1c\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlycated hemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-density lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMHR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMonocyte-to-HDL ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCSK9\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eProprotein convertase subtilisin/kexin type 9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTyG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTriglyceride\u0026ndash;glucose index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVAI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVisceral adiposity index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Clinical Research Ethics Committee No. 2 of Ankara City Hospital (approval number: E2-23-5663). All procedures were performed in accordance with the ethical standards of the institutional and national research committee and with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eISRCTN10701142\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors received no specific funding for this work\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections. Diabetes Res Clin Pract. 2022;183:109119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diabres.2021.109119\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2021.109119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045. Diabetes Res Clin Pract. 2019;157:107843. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.diabres.2019.107843\u003c/span\u003e\u003cspan address=\"10.1016/j.diabres.2019.107843\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Diabetes Collaborators. Global burden of diabetes, 1990\u0026ndash;2021. Lancet. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)01301-6\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)01301-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Diabetes Association. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes\u0026mdash;2024. Diabetes Care. 2024;47(Suppl 1):S20\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/dc24-S002\u003c/span\u003e\u003cspan address=\"10.2337/dc24-S002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTab\u0026aacute;k AG, Herder C, Rathmann W, Brunner EJ, Kivim\u0026auml;ki M. Prediabetes: a high-risk state for diabetes development. Lancet. 2012;379(9833):2279\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(12)60283-9\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(12)60283-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention. N Engl J Med. 2002;346(6):393\u0026ndash;403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMoa012512\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa012512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuomilehto J, Lindstr\u0026ouml;m J, Eriksson JG, Valle TT, H\u0026auml;m\u0026auml;l\u0026auml;inen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle. N Engl J Med. 2001;344(18):1343\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJM200105033441801\u003c/span\u003e\u003cspan address=\"10.1056/NEJM200105033441801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM. Diabetes Care. 1997;20(4):537\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/diacare.20.4.537\u003c/span\u003e\u003cspan address=\"10.2337/diacare.20.4.537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, et al. The long-term effect of lifestyle interventions to prevent diabetes. Lancet Diabetes Endocrinol. 2014;2(6):474\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-8587(14)70057-9\u003c/span\u003e\u003cspan address=\"10.1016/S2213-8587(14)70057-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckel RH, Jakicic JM, Ard JD, Miller NH, Hubbard VS, Nonas CA, et al. 2013 AHA/ACC guideline on lifestyle management to reduce cardiovascular risk. Circulation. 2014;129(25 Suppl 2):S76\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/01.cir.0000437740.48606.d1\u003c/span\u003e\u003cspan address=\"10.1161/01.cir.0000437740.48606.d1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeidah NG, Awan Z, Chr\u0026eacute;tien M, Mbikay M. PCSK9: a key modulator of cardiovascular health. Circ Res. 2014;114(6):1022\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCRESAHA.114.301621\u003c/span\u003e\u003cspan address=\"10.1161/CIRCRESAHA.114.301621\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCariou B, Le Bras M, Langhi C, Le May C, Guyomarc\u0026rsquo;h-Delasalle B, Krempf M, et al. Association between PCSK9 and glucose homeostasis. Diabetes. 2010;59(4):949\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2337/db09-1039\u003c/span\u003e\u003cspan address=\"10.2337/db09-1039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwan Z, Seidah NG, MacFadyen JG, Benjannet S, Chasman DI, Ridker PM, et al. Rosuvastatin, PCSK9, and incident diabetes. Circulation. 2012;126(5):575\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.111.103655\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.111.103655\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLakoski SG, Lagace TA, Cohen JC, Horton JD, Hobbs HH. Genetic and metabolic determinants of plasma PCSK9 levels. J Clin Endocrinol Metab. 2009;94(7):2537\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/jc.2009-0141\u003c/span\u003e\u003cspan address=\"10.1210/jc.2009-0141\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt AF, Swerdlow DI, Holmes MV, Patel RS, Fairhurst-Hunter Z, Lyall DM, et al. PCSK9 genetic variants and risk of type 2 diabetes. Lancet Diabetes Endocrinol. 2017;5(2):97\u0026ndash;105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2213-8587(16)30396-5\u003c/span\u003e\u003cspan address=\"10.1016/S2213-8587(16)30396-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerence BA, Robinson JG, Brook RD, Catapano AL, Chapman MJ, Neff DR, et al. Variation in PCSK9 and risk of diabetes. J Am Coll Cardiol. 2016;68(3):263\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacc.2016.04.052\u003c/span\u003e\u003cspan address=\"10.1016/j.jacc.2016.04.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRidker PM, MacFadyen JG, Glynn RJ, Koenig W, Inflammation. PCSK9, and cardiovascular risk. Circulation. 2019;139(2):177\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIRCULATIONAHA.118.035052\u003c/span\u003e\u003cspan address=\"10.1161/CIRCULATIONAHA.118.035052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang ZH, Li TH, Peng J, Zheng J, Li TT, Liu LS, et al. PCSK9: a novel inflammation modulator in atherosclerosis. Atherosclerosis. 2019;282:1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.2018.12.020\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2018.12.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prediabetes, PCSK9, Lifestyle intervention, Insulin resistance, Glucose metabolism","lastPublishedDoi":"10.21203/rs.3.rs-9396849/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9396849/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrediabetes is a critical stage in the progression to type 2 diabetes mellitus, where early lifestyle interventions can significantly alter disease trajectory. Proprotein convertase subtilisin/kexin type 9 (PCSK9), a central regulator of LDL cholesterol metabolism, has also been implicated in glucose homeostasis and inflammation. However, whether PCSK9 reflects short-term metabolic improvements following lifestyle modification remains uncertain.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this prospective, single-center cohort study, individuals aged 18\u0026ndash;65 years with prediabetes and healthy controls were enrolled. Prediabetes was defined according to American Diabetes Association criteria. Participants with prediabetes underwent individualized medical nutrition therapy and a structured exercise program. Anthropometric and biochemical parameters, including PCSK9 levels, were measured at baseline and after 3 months. Statistical analyses included parametric and non-parametric tests, correlation analyses, and regression modeling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 132 participants (91 with prediabetes and 41 controls) were included. After 3 months, significant improvements were observed in body weight, body mass index, fasting plasma glucose, HbA1c, triglycerides, and TyG index (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Despite these marked metabolic improvements, PCSK9 levels remained unchanged (11.369 vs. 11.649 ng/mL; p\u0026thinsp;=\u0026thinsp;0.10). PCSK9 levels were independently associated with age, alanine aminotransferase, and albumin levels, and demonstrated a negative correlation with changes in inflammatory indices. Notably, PCSK9 levels were significantly lower in prediabetic individuals compared to controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWhile lifestyle interventions lead to significant metabolic improvement in prediabetes, they do not modify PCSK9 levels in the short term. These findings suggest that PCSK9 may not serve as a dynamic biomarker of short-term metabolic response, but rather reflect more stable metabolic or inflammatory states. This dissociation highlights the complexity of PCSK9 biology beyond lipid metabolism and underscores the need for longitudinal studies to clarify its role in glucose regulation.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eISRCTN10701142. 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