Normal weight obesity (NWO) in young women: hematological patterns and their relationship to body composition and cardiometabolic risk factors.

Normal weight obesity (NWO) in young women: hematological patterns and their relationship to body composition and cardiometabolic risk factors. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Normal weight obesity (NWO) in young women: hematological patterns and their relationship to body composition and cardiometabolic risk factors. Waldemar Pluta, Anna Lubkowska, Wioleta Dudzińska This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8481327/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 15 You are reading this latest preprint version Abstract Normal Weight Obesity (NWO) is characterised by excessive body fat with a normal BMI. This study aimed to assess whether young women with the NWO phenotype exhibit a different hematological profile compared to their peers with a normal body composition. The study included 176 young women aged 18–24 years with a normal BMI (18.5–24.9 kg/m²). Based on a previously established cut-off point for percentage body fat (PBF), participants were assigned to the NWO group (PBF ≥ 35.78%) and the control group (PBF < 35.78%). Complete blood counts and biochemical parameters were analysed. It was demonstrated that women in the NWO group had significantly higher values ​​of leukocytes, lymphocytes, and platelets compared to the control group. Significantly higher platelet hematocrit and large platelet counts, as well as higher hs-CRP levels, were also observed. Significant positive correlations were found between body fat percentage and the hematological parameters examined. Based on the results, the NWO phenotype in young, clinically healthy women is associated with subclinical inflammation and prothrombotic potential. Routine hematological parameters may be a cost-effective and readily available tool for early assessment of cardiometabolic risk in individuals with a normal body weight. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Endocrinology Health sciences/Medical research Biological sciences/Physiology normal weight obesity hematological patterns DXA percent body fat INTRODUCTION Obesity and its associated metabolic disorders constitute a rapidly escalating global health challenge. While Body Mass Index (BMI) remains the traditional standard for weight classification, its diagnostic limitations are increasingly recognised—specifically its inability to distinguish between lean muscle mass and adipose tissue. Consequently, reliance on BMI alone may lead to the misclassification of cardiometabolic risk, particularly in individuals presenting with the NWO phenotype. This specific condition, characterised by excess body fat accumulation despite a normal BMI, represents a "hidden" form of obesity associated with a significantly elevated risk of metabolic dysregulation [ 1 ]. Adipose tissue is no longer viewed merely as an energy reservoir but as a dynamic endocrine organ [ 2 ]. In states of excess adiposity, such as NWO, adipose tissue becomes dysfunctional and secretes proinflammatory cytokines (e.g., IL-6, TNF-α) and adipokines. This low-grade chronic inflammation is a key mechanism linking obesity to cardiovascular disease [ 3 ]. Importantly, systemic inflammation directly influences hematopoiesis in the bone marrow, potentially leading to alterations in peripheral blood cell counts [ 4 ]. Therefore, hematological parameters—widely available through routine Complete Blood Count (CBC) tests—could serve as early, cost-effective biomarkers of the metabolic dysregulation associated with NWO. White blood cell (WBC) count is a well-established marker of systemic inflammation and an independent predictor of cardiovascular events [ 5 , 6 ] and type 2 diabetes [ 7 ]. Similarly, platelet indices, such as Mean Platelet Volume (MPV) and the increasingly recognised Platelet Large Cell Count (P-LCC), reflect platelet activation and thrombotic potential [ 8 ]. Despite this, research specifically analysing the relationship between detailed body composition and these hematological markers in young, apparently healthy women remains limited. Most studies focus on older populations or individuals with diagnosed metabolic syndrome [ 9 , 10 ], leaving a gap in our understanding of early subclinical changes in the NWO phenotype. This study is a continuation of our previous research, in which we focused on the analysis of body composition and metabolic parameters in young women (18–24 years old) from north-western Poland who had a normal BMI (18.5–24.9 kg/m²). Using dual-energy X-ray absorptiometry (DXA), we identified the phenotype of metabolic obesity with normal body weight (NWO) in 27.3% of the studied women. The key achievement was establishing a new cut-off point for percentage body fat (PBF) at 35.78%, effectively identifying individuals with increased cardiometabolic risk. Women with the NWO phenotype were characterised by higher insulin values (13.4 vs. 10.4 µIU/ml), HOMA-IR index (2.3 vs. 1.7), and an unfavourable lipid profile, including increased LDL cholesterol (2.1 vs. 1.6 mM), triglycerides (0.8 vs. 0.6 mM) and decreased HDL (1.1 vs. 1.2 mM) compared to the control group. A particularly important finding was the android-to-gynoid adipose tissue distribution ratio (A/G), which showed correlations with insulin resistance (r = 0.21) and HDL concentration (r = -0.32) [ 1 ]. The present study aimed to evaluate whether young women who meet this NWO criterion exhibit distinct hematological profiles compared with their lean peers. Specifically, we investigated the association between PBF, lipid profile, and a comprehensive panel of hematological indices—including inflammatory (WBC, lymphocytes) and thrombotic markers (PLT, P-LCC)—to determine their utility in early cardiometabolic risk stratification. MATERIALS AND METHODS The study was conducted in accordance with the principles of the Declaration of Helsinki, after obtaining prior approval from the Bioethics Committee of the Pomeranian Medical University in Szczecin (no. KB-0012/13/2021).Written informed consent was obtained from all participants. Volunteers The study is a continuation of our previous project [ 1 ]. Using the previously established PBF cut-off point (35.78%), 88 individuals were selected for the NWO group (BMI 18.5–24.9 kg/m², PBF ≥ 35.78%) and matched with 88 individuals for the control group (normal weight; BMI 18.5–24.9 kg/m², PBF < 35.78%) from the original volunteer cohort Research Procedures Anthropometric measurements Anthropometric data were retrieved from our existing database described in our previous manuscript [ 1 ]; no new measurements were performed for this specific analysis. Blood collection and analysis of hematological parameters Similar to anthropometric measurements, hematological data were retrieved from the dataset obtained in the first stage of the project [ 1 ]. Hematological parameters were measured in whole blood using a BM HEM-3 hematology analyser (Biomaxima S.A., Lublin, Poland). The following parameters were examined: white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin mass (MCH), mean corpuscular hemoglobin concentration (MCHC), platelets (PLT), platelet critical value (PCT), mean platelet volume (MPV), platelet distribution width (PDWs, PDWc), red blood cell distribution width (RDWs, RDWc), lymphocytes (LYM), medium-sized cells (MID), granulocytes (GRA), percentage of lymphocytes (LYM%), percentage of medium-sized cells (MID%), percentage of granulocytes (GRA%), number of large platelets (P-LCC) and large platelet index (P-LCR). STATISTICAL ANALYSIS The minimal size of the study sample was estimated using the G*Power 3.1.9.4 software. The effect size was set at 0.5, the error probability α was 0.05, and the power was 0.95. The non-central parameter δ was 3.317. The total sample size was 176 and the actual power was 0.951. Statistical analysis was performed using Statistica 13.3 (Statistica PL, StatSoft, Krakow, Poland) software. The Shapiro-Wilk test was used to check the normality of the distribution. Since the data distribution was not normal, nonparametric tests were used for detailed statistical analyses. Results were presented as median and interquartile range (25%– 75%; Q25 – Q75). The Mann-Whitney U test was used to show the significance of differences. Spearman correlation coefficients were used to describe the relationships between continuous variables. The level of significance was set at p < 0.05. RESULTS Group Characteristics The median age is the same for both groups (20 years). Women in the NWO group had significantly higher anthropometric parameters compared to the control group. Body weight was significantly higher (62 vs. 58 kg, p < 0.0001), as were waist circumference (WC) (73 vs. 69 cm, p < 0.0001), hip circumference (HC) (99 vs. 94 cm, p < 0.0001) and percent body fat (PBF) (37.9 vs 32.1%, p < 0.0001). Body mass index (BMI) (22.7 vs. 20.4 kg/m², p < 0.0001) and waist-to-height ratio (WHtR) (43 vs. 41, p < 0.0001) were also significantly higher in the NWO group. Detailed values of the anthropometric parameters analysed are shown in Table 1 . Table 1 Anthropometric characteristics of the subjects. All (n = 176) NW (n = 88) NWO (n = 88) P-value median Q 25 –Q 75 median Q 25 –Q 75 median Q 25 –Q 75 Age [years] 20 19–22 20 19–22 20 19–22 0.500 Weight [kg] 60 55–65 58 54–63 62 57–67 p < 0.0001 Height [cm] 168 163–172 169 164–173 167 163–172 0.132 BMI [kg/m 2 ] 21.4 20.2–23.1 20.4 19.5–21.7 22.7 21.3–23.7 p < 0.0001 PBF [%] 35.2 32.1–38 32.1 29.5–33.4 37.9 36.7–39.4 p < 0.0001 WC [cm] 70 67–74 69 66–73 73 69–75 p < 0.0001 HC [cm] 97 91–100 94 90–97 99 96–103 p < 0.0001 WHR 0.7 0.70–0.76 0.7 0.71–0.76 0.7 0.69–0.76 0.550 WHtR 42 40–44 41 39–43 43 41–45 p < 0.0001 Legend : BMI – body mass index; PBF – percent body fat; WC – waist circumference; HC – hip circumference; WHR – waist to hip ratio; WHtR – waist to height ratio Analysis of body composition showed that women in the NWO group had significantly higher body fat. These differences were evident in both relative (percent body fat (PBF), fat mass (FM), appendicular fat mass (AFM) and absolute (fat mass index (FMI), appendicular fat mass ratio (AFMI)) values of fatness indices (p < 0.001 for all comparisons). A higher fat mass ratio (FMR) further confirmed increased fat accumulation in the NWO group (p < 0.001). The android to gynoid adipose tissue ratio (A/G) was significantly higher in women in the NWO group (p < 0.001). Analysis of visceral adipose tissue deposition, expressed by visceral fat area (VFA), weight (VFM) and volume (VFV), showed significantly higher values in the NWO group (p < 0.001 for all parameters). Analysis of fat-free deposit showed lower values for all indices in the NWO group., with only appendicular fat-free mass (AFFM) and appendicular lean mass (ALM) values being statistically significantly lower (p < 0.05) compared to the control group. Detailed values for body composition parameters are shown in the supplementary material ( Table S1 . ). The analysis of hematological parameters showed that the values of red cell distribution width (RDWc), mid-sized cells (MID), mid-sized cell percentage (MID%), and granulocyte percentage (GRA%) were higher in the control group than in the study group (NWO), but these differences were not statistically significant (p > 0.05). Among the remaining parameters, significantly higher values in the NWO group compared to the control group were observed for white blood cells (WBC) (6.3 vs. 5.7 x 10 9 /l; p = 0.045), plateletcrit (PCT) (0.21 vs. 0.18%; p = 0.001), lymphocytes (LYM) (2.1 vs. 1.8 x 10 9 /l; p = 0.005) and the number of large platelets (P-LCC) (67 vs. 57 x 10 9 /l; p = 0.003). In addition, the NWO group was characterised by a significantly higher platelet count (PLT) (244 vs 222 x 10 9 /l; p = 0.010). The NWO group was characterised by a significantly higher hs-CRP concentration (0.63 vs 0.6 mg/l; p = 0.014) with a lower (but not statistically significant) NO concentration (6.44 vs 7.72 µM; p = 0.479) compared to the control group. Detailed values of the analysis of hematological and biochemical parameters are presented in Table 2 . Table 2 Hematological and biochemical parameters of study participants All (n = 176) NW (n = 88) NWO (n = 88) P-value median Q 25 –Q 75 median Q 25 –Q 75 median Q 25 –Q 75 WBC [x 10 9 /l] 6.1 5–7.3 5.7 4.8–6.0 6.3 5.3–7.5 0.045 RBC [x 10 9 /l] 4.5 4.3–4.8 4.5 4.3–4.8 4.6 4.3–4.9 0.331 HGB [g/l] 126.5 119–133 124.5 118–131 128.3 120–134.8 0.112 HCT [%] 36.9 35.4–39 36.5 35.3–38.6 37.4 35.4–39.6 0.069 MCV [fl] 83 77–86 81 76–85.5 84 78–86.5 0.075 MCH [pg] 28.5 26.6–29.3 28 26.1–29.2 28.7 27–29.8 0.112 MCHC [g/l] 341 334–349 340 333–348 342 336.3–349.5 0.664 PLT [x 10 9 /l] 244 199– 279 222 185.5–265 244 199–279 0.010 PCT [%] 0.19 0.17–0.22 0.18 0.16–0.2 0.21 0.17–0.26 0.001 MPV [fl] 8.5 7.1–9.8 8.1 7–9.8 8.8 7.4–9.9 0.093 PDWs [fl] 11.4 9.2–13.9 11.1 8.7–13 11.8 9.6–14 0.091 PDWc [%] 38 37.2–39.9 37.7 36.1–39.8 38.5 37.3–40 0.092 RDWs [fl] 39.6 35.4–42.6 39.1 35.4–42.2 39.6 36–42.7 0.919 RDWc [%] 15.4 14.6–16.2 15.6 14.6–16.6 15.3 14.5–15.8 0.160 LYM [x 10 9 /l] 2.04 1.6–2.4 1.8 1.5–2.3 2.1 1.8–2.7 0.005 MID [x 10 9 /l] 0.32 0.21–0.48 0.34 0.21–0.45 0.31 0.21–0.53 0.964 GRA [x 10 9 /l] 3.6 2.8–4.5 3.3 2.7–4.6 3.7 3.1–4.5 0.169 LYM% [%] 34.6 26.5–40.5 31.1 25–39.6 36.3 28.7–40.8 0.059 MID% [%] 5.8 3.7–8.5 6 3.8–8.9 5.7 3.7–7.5 0.341 GRA% [%] 59.7 53–67.5 61.5 53.4–67.8 57.8 52.6–66.5 0.329 P-LCC [x 10 9 /l] 61 49–77 57 44.5–68.5 67 54.8–85.5 0.003 P-LCR [%] 26.3 22.2–34.4 25.4 20.6–33.9 27.7 22.8–34.7 0.180 hs-CRP [mg/l] 0.6 0.6–1.1 0.6 0.6–1 0.63 0.6–1.2 0.014 NO [µM] 7.3 4.6–12.3 7.7 4.6–13.8 6.4 4.8–11.9 0.479 Legend : WBC - white blood cells; RBC - red blood cells; HGB – hemoglobin; HCT – hematocrit; MCV - mean corpuscular volume; MCH - mean corpuscular hemoglobin; MCHC - mean corpuscular hemoglobin concentration; PLT – platelets; PCT – plateletcrit; MPV - mean platelet volume; PDWs - platelet distribution width (SD); PDWc - platelet distribution width (CV); RDWs - red cell distribution width; RDWc - red cell distribution width; LYM – lymphocytes; MID - mid-sized cells; GRA – granulocytes; LYM% - lymphocyte percentage; MID% - mid-sized cell percentage; GRA% - granulocyte percentage; P-LCC - platelet large cell count; P-LCR - platelet large cell ratio Correlation analysis Correlation analysis showed a statistically significant weak, positive correlation between WBC and PBF, TG/G (r = 0.20) and TG (r = 0.21) (p < 0.05). PCT correlated weakly, positively with HDL-C, HOMA-IR (r = 0.22), LAP (r = 0.20) (p < 0.05), PBF, Non-HDL (r = 0.25), TC, glucose (r = 0.27), LDL-C (r = 0.24), TG (r = 0.28), TG/G (r = 0.29) (p < 0.01). In the case of LYM, a weak positive correlation was shown with PBF (r = 0.21), TG (r = 0.22), TG/G (r = 0.20) (p < 0.05), LDL-C, Non-HDL (r = 0.24), TC (r = 0.27) and HDL-C (r = 0.25) (p < 0.01). P-LCC statistically significantly correlated with PBF, HOMA-IR (r = 0.21), LAP (r = 0.20) (p < 0.05), with HDL-C (r = 0.30), LDL-C (r = 0.27), Non-HDL (r = 0.28), TG (r = 0.29), TG/G (r = 0.31) (p < 0.01) and with TC, glucose (r = 0.32 p < 0.001). Detailed results of the correlation analysis are presented in Table 3 and in the supplementary material (Table S2.). Table 3 Correlations between hematological parameters and percent body fat, lipid profile, and cardiometabolic risk indices. WBC PCT LYM P-LCC PBF [%] 0.20* 0.25** 0.21* 0.21* TC [mM] 0.13 0.27** 0.27** 0.32*** HDL-C [mM] 0.10 0.22* 0.25** 0.30** LDL-C [mM] 0.11 0.24** 0.24** 0.27** Non-HDL [mM] 0.13 0.25** 0.24** 0.28** TG [mM] 0.21* 0.28** 0.22* 0.29** Glucose [mM] 0.11 0.27** 0.14 0.32*** Insulin [µlU/ml] -0.01 0.12 0.02 0.07 HOMA-IR 0.10 0.22* 0.08 0.21* TG/HDL-C 0.08 0.09 -0.02 0.02 TG/G 0.20* 0.29** 0.20* 0.31** VAI 0.05 0.05 -0.09 -0.01 LAP 0.13 0.20* 0.05 0.20* CMI 0.08 0.09 -0.06 0.02 Legend : WBC - white blood cells; PLT – platelets; PCT – plateletcrit; LYM – lymphocytes; P-LCC - platelet large cell count; PBF - percent body fat; TC – total cholesterol; HDL-C – high-density lipoprotein cholesterol; LDL-C – low-density lipoprotein cholesterol; Non-HDL – non-high-density lipoprotein cholesterol; TG – triglycerides; HOMA-IR - homeostatic model assessment for insulin resistance; TG/HDL-C – triglycerides to HDL-C ratio; TG/G – triglycerides to glucose ratio; VAI – visceral adiposity index; LAP – lipid accumulation product; CMI – cardiometabolic index; * p < 0.05, ** p < 0.01, *** p < 0.001 Discussion There is evidence of a link between NWO obesity and an abnormal cardiometabolic profile. We have demonstrated this in our previous studies [ 1 ]. The current study, representative of the same population of young women with NWO obesity, showed that higher PBF levels were associated with higher levels of hematological parameters, including WBC, LYM, PCT, and P-LCC. Our results indicate that, in young women, despite having a normal body weight (as determined by BMI), hematological changes are observed that, when measured in a commonly used and widely available manner, may predict the NWO phenotype, associated inflammation, and increased risk of thrombosis. Among many hematological indicators, WBC and LYM counts are widely used to diagnose systemic inflammation [ 11 ]. It is elevated in obesity, is significantly correlated with insulin resistance, strongly predicts the development of type 2 diabetes in obese and non-obese individuals, metabolic syndrome, and is a predictor of mortality from coronary heart disease, regardless of traditional risk factors for cardiovascular disease [ 4 , 10 , 12 – 15 ]. It has also been shown that a high WBC count can predict future cardiovascular events in healthy individuals [ 16 ]. In this study, we found for the first time that WBC and LYM counts were significantly higher in women with NOW (6.3 vs. 5.7 x 10 9 /l, p = 0.045 and 2.1 vs. 1.8 x 10 9 /l, p = 0.005, respectively). While the mean values did not exceed the accepted physiological range, there was relative leukocytosis and lymphocytosis, with a progressive increase dependent on PBF. This suggests that the observed changes may be mediated in part by an increase in adipose tissue content and the release of inflammatory markers from it. The association between inflammation and NWO has been highlighted in several studies and recently summarised in a systematic review and meta-analysis [ 17 ]. The presence of a chronic, low-grade inflammatory response in the NWO phenotype was primarily associated with elevated levels of IL-6 and CRP, which are potent inducers of leukocytosis [ 4 ]. Our current study confirms these previous findings. We observed a significant increase in hs-CRP levels in the NWO group (0.63 vs. 0.60 mg/dl; p = 0.014). Although hs-CRP levels were within the reference ranges in both groups, a significant difference remained between them. Previous studies have shown that concomitant exposure to elevated levels of proinflammatory markers, such as CRP and WBC, correlated with the development of several disease states in obese individuals [ 18 – 20 ]. Our study extends these findings to young non-obese women, which has important implications for their health risks. Interestingly, PBF is associated with hs-CRP and WBC counts independently of BMI. These changes cannot be explained by factors or diseases that typically increase CRP and WBC counts. We observed these associations in healthy, very young women, so subclinical disease is unlikely to explain our results. These data suggest that young women with the NWO phenotype have a state of low-grade systemic inflammation, as evidenced by increased WBC counts and hs-CRP. Elevated hs-CRP levels within the reference range in apparently healthy men and women have been associated with an increased risk of developing cardiovascular disease, diabetes, and nonalcoholic fatty liver disease [ 21 – 23 ]. Numerous clinical studies have linked CRP with an increased risk of myocardial infarction, stroke, peripheral artery disease, and sudden death [ 24 ]. Furthermore, elevated CRP levels are one of the strongest predictors of progressive vascular disease [ 25 ]. Moreover, CRP can activate endothelium by modulating the endothelial nitric oxide synthase-nitric oxide pathway. Elevated CRP levels are associated with a significant decrease in NO production, which is a consequence of CRP's action to reduce endothelial nitric oxide synthase mRNA stability and protein levels [ 26 , 27 ]. In our group of women with NWO, we observed a non-significantly lower plasma NO concentration compared to the control group (6.44 vs. 7.72 µM; p = 0.479), which, although not statistically significant, is consistent with the extensive literature describing reduced NO bioavailability in obesity [ 28 – 31 ]. Such endothelial dysfunction promotes the activation and adhesion of PLTs and WBCs, as well as the release of proinflammatory cytokines. These phenomena increase vascular permeability to oxidised lipoproteins and inflammatory mediators, ultimately causing structural changes in the arterial wall, including smooth muscle cell proliferation and the initiation of atherosclerotic plaque development [ 32 ]. Platelet activation is also increased in NO-deficient states, contributing to thrombosis and increasing the risk of acute cardiovascular events such as myocardial infarction or stroke [ 33 ]. The results of this study provided another interesting observation. It concerns the association of WBC and LYM counts with several cardiovascular risk factors, including TG levels and the TG/G ratio. This is consistent with the findings of Ohshita et al. [ 30 ] and Targher et al. [ 34 ]. They reported that in individuals with normal fasting glucose, an elevated, yet normal, WBC count is associated with a set of metabolic abnormalities typical of the insulin resistance syndrome and is associated with some components of the metabolic syndrome, such as BMI, WHR, and TG levels. The impact of TG on cardiovascular risk has long been well documented [ 35 ]. It is also known that combined exposure to high WBC counts and TG levels that are consistently within the reference range is associated with a more than threefold increased risk of cardiovascular mortality, independent of traditional risk factors [ 36 ]. In our analysis, WBC count and previously assessed TG concentration (0.8 vs. 0.6 mM; p = 0.01; [ 1 ] were significantly higher in the NWO group, and these variables were associated. These data provide new evidence suggesting that combined exposure to high TG concentrations and WBC counts may be associated with an increased risk of cardiovascular disease in young women with the NWO phenotype. This finding is plausible in our cohort. TG concentration has been shown to be closely associated with inflammation, including elevated WBC counts [ 37 ]. This association is thought to occur through indirect mediators such as adipose tissue or insulin resistance [ 38 , 39 ]. Our group of women with NWO has increased PBF and is insulin resistant (HOMA-IR: 2.3 vs. 1.7; p = 0.002) [ 1 ]. Therefore, the results may be consistent with the hypothesis that individuals with high WBC counts, and high TG concentrations are more insulin-resistant, which further increases their cardiovascular risk. One result of this study is a significant increase in PLT, P-LCC, and PCT counts in women with NWO. We also found a positive association between PLT and P-LCC counts, and between PLT and PBF. Although PLT counts are important for quantifying the coagulation process, they are significantly higher in obesity and metabolic syndrome [ 8 , 31 , 40 ]. They are also associated with the risk of arterial and venous thrombosis in obese individuals [ 4 ] and the risk of type 2 diabetes [ 41 ]. Nevertheless, we believe that, in addition to PLT and PCT, the importance of markers that measure the number of larger, more active platelets should be emphasised. P-LCC is such an indicator. We found no other studies in the literature that assessed P-LCC in women with NWO. In our research, we found that P-LCC was significantly higher in this group of subjects (67 vs. 57 × 10 9 /l; p = 0.003). This result was also consistent with significantly higher PLT counts (244 vs. 222 x 10 9 /l; p = 0.010). Larger platelets are more reactive and have a greater ability to form clots, increasing the risk of thrombosis [ 42 ]. They are considered immature, and their number increases with factors influencing platelet turnover. Inflammation is considered the most important factor for increased platelet turnover [ 8 , 43 , 44 ]. Exposure to inflammatory signals can accelerate the production of larger, more reactive PLTs [ 45 ]. We hypothesise that, in women with NWO, due to inflammation, there may be a subclinical increase in platelet formation and a corresponding acceleration of their entry into peripheral blood, which may explain the high PLT and P-LCC counts in our study. Furthermore, PCT, which measures the total platelet volume as a percentage of the volume occupied in the blood, plays an effective role in detecting quantitative abnormalities. PCT correlates with PLT count and has comparable clinical significance. Limitations The present study has several notable strengths. First, the participants were recruited from a well-characterised, homogeneous ethnic group. Second, the narrow age range (18–24 years) is a significant advantage, as it minimises age-related variability and eliminates confounding factors associated with aging and co-morbidities that could otherwise skew the analysis. This homogeneity allows for a more precise evaluation of the specific impact of adiposity on hematological parameters in young, apparently healthy women. However, the authors are also aware of the study's limitations. The primary limitation is its cross-sectional design, which precludes the determination of causal relationships between adipose tissue distribution and the observed hematological alterations. Longitudinal studies are required to confirm these findings and establish the directionality of the associations. Regarding methodological limitations, complete blood count was performed using a 3-part differential analyser, which prevented the specific quantification of neutrophils and the calculation of the neutrophil-to-lymphocyte ratio (NLR), a validated marker of systemic inflammation. Furthermore, hs-CRP C was measured at a single time point; given its biological variability, this may not fully reflect long-term inflammatory status. We also lacked data on serum IL-6 and other proinflammatory cytokines or adipokines, which would have allowed for direct verification of the specific pathways linking adipose tissue to the observed leukocytosis. Finally, although we excluded participants with hormonal disorders, the specific phase of the menstrual cycle was not standardised. Since estrogen fluctuations (e.g., in the follicular phase) can influence platelet count, future research should account for the menstrual cycle phase to minimise this variability. Future Research Directions Future research should focus on several key areas to validate and expand upon these findings. First, prospective longitudinal studies are essential to evaluate whether the observed hematological alterations in young NWO women translate into a higher incidence of cardiovascular events or type 2 diabetes in later life. Second, future investigations should include a broader panel of bioactive mediators, specifically proinflammatory cytokines (e.g., IL-6, TNF-α) and adipokines, to confirm the molecular mechanisms linking adipose tissue accumulation with systemic leukocytosis. Third, to minimise physiological variability, future protocols should standardise blood sampling by menstrual cycle phase and utilise 5-part differential hematology analysers to assess specific inflammatory indices, such as the neutrophil-to-lymphocyte ratio (NLR). Finally, interventional studies are warranted to determine whether lifestyle modifications (diet or physical activity) aimed at reducing body fat can reverse these unfavorable hematological profiles in women with the NWO phenotype. Conclusion In conclusion, this study provides evidence that the NWO phenotype in young, apparently healthy women is associated with early, unfavourable changes in hematological parameters. We identified a specific profile characterised by elevated markers of systemic inflammation (WBC, LYM) and thrombotic potential (PLT, PCT, and notably P-LCC), which significantly correlate with body fat percentage and lipid disturbances. These findings suggest that despite maintaining a normal body weight, women with excess adiposity exhibit a subclinical proinflammatory and prothrombotic state similar to that observed in overt obesity. The observed correlations between red blood cell and platelet indices (such as MCV and MPV) and the lipid profile further underscore the systemic impact of hidden metabolic dysregulation on blood cell morphology. Clinically, these results challenge the sufficiency of BMI as a standalone diagnostic tool. Routine hematological analysis—particularly focusing on leukocyte counts and novel platelet indices like P-LCC—may serve as an accessible, cost-effective screening method to identify young women at increased cardiometabolic risk who would otherwise be overlooked. Early detection of these subtle hematological deviations offers a critical window for targeted lifestyle interventions to prevent the progression to symptomatic cardiovascular disease. Declarations Institutional Review Board Statement The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (the Bioethics Committee of the Pomeranian Medical University; Ref. KB-0012/13/2021). Conflicts of Interest: The authors declare no conflict of interest. Funding: Financed by the Minister of Science under the "Regional Excellence Initiative" Program. Agreement No. RID/SP/0045/2024/01.This research was funded by grant from the Ministry of Science and Higher Education obtained by the Faculty of Health Sciences of the Pomeranian Medical University in Szczecin [WNoZ-318/S/2026]. Author Contribution Conceptualisation, A.L., W.D. and W.P.; methodology, A.L. and W.D.; software, W.P.; formal analysis, A.L., W.D. and W.P.; investigation, A.L., W.D. and W.P.; resources, A.L.; data curation, W.P.; writing—original draft preparation, A.L., W.D. and W.P.; writing—review and editing, A.L., W.D. and W.P.; visualisation, W.D. and W.P.; supervision, A.L. and WD.; project administration, A.L.; funding acquisition, A.L. Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Pluta, W., Lubkowska, A. & Dudzińska, W. Diagnostic and prognostic value of adipose tissue content and distribution indicators for normal weight obesity in young women. Sci. Rep. 15 , 33995 (2025). Kahn, C. R., Wang, G. & Lee, K. Y. Altered adipose tissue and adipocyte function in the pathogenesis of metabolic syndrome. J. Clin. Invest. 129 , 3990–4000 (2019). Ahmed, B., Sultana, R. & Greene, M. W. Adipose tissue and insulin resistance in obese. Biomed. Pharmacother. 137 , 111315 (2021). Purdy, J. C. & Shatzel, J. J. The Hematologic Consequences of Obesity. Eur. J. Haematol. 106 , 306–319 (2021). Okada, K. et al. Association between baseline white blood cell count and future cardiovascular events in patients with stable coronary artery disease- Sub-analysis of the REAL-CAD trial. Am. J. Prev. Cardiol. 23 , 101052 (2025). Jiang, W. et al. White blood cell counts can predict 4-year cardiovascular disease risk in patients with stable coronary heart disease: a prospective cohort study. Front. Cardiovasc. Med. 11 , 1358378 (2024). Seo, I. H. & Lee, Y. J. Usefulness of Complete Blood Count (CBC) to Assess Cardiovascular and Metabolic Diseases in Clinical Settings: A Comprehensive Literature Review. Biomedicines 10 , 2697 (2022). Vauclard, A. et al. Obesity: Effects on bone marrow homeostasis and platelet activation. Thromb. Res. 231 , 195–205 (2023). Jeong, H. R., Lee, H. S., Shim, Y. S. & Hwang, J. S. Positive Associations between Body Mass Index and Hematological Parameters, Including RBCs, WBCs, and Platelet Counts, in Korean Children and Adolescents. Child. (Basel) . 9 , 109 (2022). Li, Z., Yao, Z. & Liu, Q. The association between white blood cell counts and metabolic health obesity among US adults. Front Nutr 12 , (2025). Babio, N. et al. White blood cell counts as risk markers of developing metabolic syndrome and its components in the PREDIMED study. PLoS One . 8 , e58354 (2013). Vozarova, B. et al. High white blood cell count is associated with a worsening of insulin sensitivity and predicts the development of type 2 diabetes. Diabetes 51 , 455–461 (2002). Zhang, H. et al. White blood cell subtypes and risk of type 2 diabetes. J. Diabetes Complications . 31 , 31–37 (2017). Gu, Z. et al. Obesity and lipid-related parameters for predicting metabolic syndrome in Chinese elderly population. Lipids Health Dis. 17 , 289 (2018). Brown, D. W., Giles, W. H. & Croft, J. B. White blood cell count: an independent predictor of coronary heart disease mortality among a national cohort. J. Clin. Epidemiol. 54 , 316–322 (2001). Madjid, M., Awan, I., Willerson, J. T. & Casscells, S. W. Leukocyte count and coronary heart disease: implications for risk assessment. J. Am. Coll. Cardiol. 44 , 1945–1956 (2004). Mohammadian Khonsari, N. et al. Association of normal weight obesity phenotype with inflammatory markers: A systematic review and meta-analysis. Front. Immunol. 14 , 1044178 (2023). Ellulu, M. S., Patimah, I., Khaza’ai, H., Rahmat, A. & Abed, Y. Obesity and inflammation: the linking mechanism and the complications. Arch. Med. Sci. 13 , 851–863 (2017). Jung, U. J. & Choi, M. S. Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int. J. Mol. Sci. 15 , 6184–6223 (2014). van Wijk, D. F. et al. C-Reactive Protein Identifies Low-Risk Metabolically Healthy Obese Persons: The European Prospective Investigation of Cancer-Norfolk Prospective Population Study. J. Am. Heart Assoc. 5 , e002823 (2016). Koenig, W. et al. C-Reactive protein, a sensitive marker of inflammation, predicts future risk of coronary heart disease in initially healthy middle-aged men: results from the MONICA (Monitoring Trends and Determinants in Cardiovascular Disease) Augsburg Cohort Study, 1984 to 1992. Circulation 99 , 237–242 (1999). Lee, J., Yoon, K., Ryu, S., Chang, Y. & Kim, H. R. High-normal levels of hs-CRP predict the development of non-alcoholic fatty liver in healthy men. PLOS ONE . 12 , e0172666 (2017). Ridker, P. M., Hennekens, C. H., Buring, J. E. & Rifai, N. C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl. J. Med. 342 , 836–843 (2000). Kuller, L. H., Tracy, R. P., Shaten, J. & Meilahn, E. N. Relation of C-reactive protein and coronary heart disease in the MRFIT nested case-control study. Multiple Risk Factor Intervention Trial. Am. J. Epidemiol. 144 , 537–547 (1996). Van Der Meer, I. M. et al. C-reactive protein predicts progression of atherosclerosis measured at various sites in the arterial tree: the Rotterdam Study. Stroke 33 , 2750–2755 (2002). Venugopal, S. K., Devaraj, S., Yuhanna, I., Shaul, P. & Jialal, I. Demonstration that C-reactive protein decreases eNOS expression and bioactivity in human aortic endothelial cells. Circulation 106 , 1439–1441 (2002). Verma, S. et al. A self-fulfilling prophecy: C-reactive protein attenuates nitric oxide production and inhibits angiogenesis. Circulation 106 , 913–919 (2002). Pradhan, A. D. et al. Tissue plasminogen activator antigen and D-dimer as markers for atherothrombotic risk among healthy postmenopausal women. Circulation 110 , 292–300 (2004). Nieto-Vazquez, I. et al. Insulin resistance associated to obesity: the link TNF-alpha. Arch. Physiol. Biochem. 114 , 183–194 (2008). Ohshita, K. et al. Elevated white blood cell count in subjects with impaired glucose tolerance. Diabetes Care . 27 , 491–496 (2004). Park, B. J. et al. The relationship of platelet count, mean platelet volume with metabolic syndrome according to the criteria of the American Association of Clinical Endocrinologists: a focus on gender differences. Platelets 23 , 45–50 (2012). Widmer, R. J. & Lerman, A. Endothelial dysfunction and cardiovascular disease. Glob Cardiol. Sci. Pract. 2014 , 291–308 (2014). Solanki, K. et al. The expanding roles of neuronal nitric oxide synthase (NOS1). PeerJ 10, e13651 (2022). Targher, G. et al. The white blood cell count: its relationship to plasma insulin and other cardiovascular risk factors in healthy male individuals. J. Intern. Med. 239 , 435–441 (1996). Miller, M. et al. Triglycerides and Cardiovascular Disease: A Scientific Statement From the American Heart Association. Circulation 123 , 2292–2333 (2011). Shankar, A., Mitchell, P., Rochtchina, E. & Wang, J. J. The association between circulating white blood cell count, triglyceride level and cardiovascular and all-cause mortality: population-based cohort study. Atherosclerosis 192 , 177–183 (2007). Fang, J. X. et al. Potential causal and temporal relationship between plasma triglyceride levels and circulating leukocyte. J. Lipid Res. 65 , 100662 (2024). Mahdiani, A., Kheirandish, M. & Bonakdaran, S. Correlation Between White Blood Cell Count and Insulin Resistance in Type 2 Diabetes. Curr. Diabetes Rev. 15 , 62–66 (2019). Ahmed, F., Al-Habori, M., Al-Zabedi, E. & Saif-Ali, R. Impact of triglycerides and waist circumference on insulin resistance and β-cell function in non-diabetic first-degree relatives of type 2 diabetes. BMC Endocr. Disord . 21 , 124 (2021). Raghavan, V., Gunasekar, D. & Rao, K. R. Relevance of Haematologic Parameters in Obese Women with or without Metabolic Syndrome. J. Clin. Diagn. Res. 10 , EC11–EC16 (2016). Hwang, J. Y., Kwon, Y. J., Choi, W. J. & Jung, D. H. Platelet count and 8-year incidence of diabetes: The Korean Genome and Epidemiology Study. Diabetes Res. Clin. Pract. 143 , 301–309 (2018). Han, S. et al. Associations of Platelet Indices with Body Fat Mass and Fat Distribution. Obes. (Silver Spring) . 26 , 1637–1643 (2018). Koupenova, M., Kehrel, B. E., Corkrey, H. A. & Freedman, J. E. Thrombosis and platelets: an update. Eur. Heart J. 38 , 785–791 (2017). Benova, A. & Tencerova, M. Obesity-Induced Changes in Bone Marrow Homeostasis. Front. Endocrinol. (Lausanne) . 11 , 294 (2020). Couldwell, G. & Machlus, K. R. Modulation of megakaryopoiesis and platelet production during inflammation. Thromb. Res. 179 , 114–120 (2019). Additional Declarations No competing interests reported. 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factors.","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eObesity and its associated metabolic disorders constitute a rapidly escalating global health challenge. While Body Mass Index (BMI) remains the traditional standard for weight classification, its diagnostic limitations are increasingly recognised\u0026mdash;specifically its inability to distinguish between lean muscle mass and adipose tissue. Consequently, reliance on BMI alone may lead to the misclassification of cardiometabolic risk, particularly in individuals presenting with the NWO phenotype. This specific condition, characterised by excess body fat accumulation despite a normal BMI, represents a \"hidden\" form of obesity associated with a significantly elevated risk of metabolic dysregulation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdipose tissue is no longer viewed merely as an energy reservoir but as a dynamic endocrine organ [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In states of excess adiposity, such as NWO, adipose tissue becomes dysfunctional and secretes proinflammatory cytokines (e.g., IL-6, TNF-α) and adipokines. This low-grade chronic inflammation is a key mechanism linking obesity to cardiovascular disease [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Importantly, systemic inflammation directly influences hematopoiesis in the bone marrow, potentially leading to alterations in peripheral blood cell counts [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, hematological parameters\u0026mdash;widely available through routine Complete Blood Count (CBC) tests\u0026mdash;could serve as early, cost-effective biomarkers of the metabolic dysregulation associated with NWO.\u003c/p\u003e \u003cp\u003eWhite blood cell (WBC) count is a well-established marker of systemic inflammation and an independent predictor of cardiovascular events [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and type 2 diabetes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, platelet indices, such as Mean Platelet Volume (MPV) and the increasingly recognised Platelet Large Cell Count (P-LCC), reflect platelet activation and thrombotic potential [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite this, research specifically analysing the relationship between detailed body composition and these hematological markers in young, apparently healthy women remains limited. Most studies focus on older populations or individuals with diagnosed metabolic syndrome [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], leaving a gap in our understanding of early subclinical changes in the NWO phenotype.\u003c/p\u003e \u003cp\u003eThis study is a continuation of our previous research, in which we focused on the analysis of body composition and metabolic parameters in young women (18\u0026ndash;24 years old) from north-western Poland who had a normal BMI (18.5\u0026ndash;24.9 kg/m\u0026sup2;). Using dual-energy X-ray absorptiometry (DXA), we identified the phenotype of metabolic obesity with normal body weight (NWO) in 27.3% of the studied women. The key achievement was establishing a new cut-off point for percentage body fat (PBF) at 35.78%, effectively identifying individuals with increased cardiometabolic risk. Women with the NWO phenotype were characterised by higher insulin values (13.4 vs. 10.4 \u0026micro;IU/ml), HOMA-IR index (2.3 vs. 1.7), and an unfavourable lipid profile, including increased LDL cholesterol (2.1 vs. 1.6 mM), triglycerides\u003c/p\u003e \u003cp\u003e(0.8 vs. 0.6 mM) and decreased HDL (1.1 vs. 1.2 mM) compared to the control group. A particularly important finding was the android-to-gynoid adipose tissue distribution ratio (A/G), which showed correlations with insulin resistance (r\u0026thinsp;=\u0026thinsp;0.21) and HDL concentration (r = -0.32) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study aimed to evaluate whether young women who meet this NWO criterion exhibit distinct hematological profiles compared with their lean peers. Specifically, we investigated the association between PBF, lipid profile, and a comprehensive panel of hematological indices\u0026mdash;including inflammatory (WBC, lymphocytes) and thrombotic markers (PLT, P-LCC)\u0026mdash;to determine their utility in early cardiometabolic risk stratification.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki, after obtaining prior approval from the Bioethics Committee of the Pomeranian Medical University in Szczecin (no. KB-0012/13/2021).Written informed consent was obtained from all participants.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVolunteers\u003c/h2\u003e \u003cp\u003eThe study is a continuation of our previous project [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Using the previously established PBF cut-off point (35.78%), 88 individuals were selected for the NWO group\u003c/p\u003e \u003cp\u003e(BMI 18.5\u0026ndash;24.9 kg/m\u0026sup2;, PBF\u0026thinsp;\u0026ge;\u0026thinsp;35.78%) and matched with 88 individuals for the control group (normal weight; BMI 18.5\u0026ndash;24.9 kg/m\u0026sup2;, PBF\u0026thinsp;\u0026lt;\u0026thinsp;35.78%) from the original volunteer cohort\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch Procedures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric measurements\u003c/h2\u003e \u003cp\u003eAnthropometric data were retrieved from our existing database described in our previous manuscript [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]; no new measurements were performed for this specific analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBlood collection and analysis of hematological parameters\u003c/h3\u003e\n\u003cp\u003eSimilar to anthropometric measurements, hematological data were retrieved from the dataset obtained in the first stage of the project [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHematological parameters were measured in whole blood using a BM HEM-3 hematology analyser (Biomaxima S.A., Lublin, Poland). The following parameters were examined: white blood cell count (WBC), red blood cell count (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), mean corpuscular hemoglobin mass (MCH), mean corpuscular hemoglobin concentration (MCHC), platelets (PLT), platelet critical value (PCT), mean platelet volume (MPV), platelet distribution width (PDWs, PDWc), red blood cell distribution width (RDWs, RDWc), lymphocytes (LYM), medium-sized cells (MID), granulocytes (GRA), percentage of lymphocytes (LYM%), percentage of medium-sized cells (MID%), percentage of granulocytes (GRA%), number of large platelets (P-LCC) and large platelet index (P-LCR).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e \u003cp\u003eThe minimal size of the study sample was estimated using the G*Power 3.1.9.4 software. The effect size was set at 0.5, the error probability α was 0.05, and the power was 0.95. The non-central parameter δ was 3.317. The total sample size was 176 and the actual power was 0.951.\u003c/p\u003e \u003cp\u003eStatistical analysis was performed using Statistica 13.3 (Statistica PL, StatSoft, Krakow, Poland) software. The Shapiro-Wilk test was used to check the normality of the distribution. Since the data distribution was not normal, nonparametric tests were used for detailed statistical analyses. Results were presented as median and interquartile range (25%\u0026ndash; 75%; Q25 \u0026ndash; Q75). The Mann-Whitney U test was used to show the significance of differences. Spearman correlation coefficients were used to describe the relationships between continuous variables.\u003c/p\u003e \u003cp\u003eThe level of significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGroup Characteristics\u003c/h2\u003e \u003cp\u003eThe median age is the same for both groups (20 years). Women in the NWO group had significantly higher anthropometric parameters compared to the control group. Body weight was significantly higher\u003c/p\u003e \u003cp\u003e(62 vs. 58 kg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), as were waist circumference (WC) (73 vs. 69 cm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), hip circumference (HC) (99 vs. 94 cm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and percent body fat (PBF) (37.9 vs 32.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Body mass index (BMI) (22.7 vs. 20.4 kg/m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and waist-to-height ratio (WHtR) (43 vs. 41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were also significantly higher in the NWO group. Detailed values of the anthropometric parameters analysed are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric characteristics of the subjects.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNW\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNWO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003emedian\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003emedian\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003emedian\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge [years]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u0026ndash;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight [kg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55\u0026ndash;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u0026ndash;63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57\u0026ndash;67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight [cm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e168\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u0026ndash;172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e169\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u0026ndash;173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e167\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e163\u0026ndash;172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI [kg/m\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e21.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2\u0026ndash;23.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e20.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.5\u0026ndash;21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e22.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.3\u0026ndash;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePBF [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e35.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.1\u0026ndash;38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.5\u0026ndash;33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e37.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36.7\u0026ndash;39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC [cm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66\u0026ndash;73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHC [cm]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u0026ndash;97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e99\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e96\u0026ndash;103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u0026ndash;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u0026ndash;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.69\u0026ndash;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e41\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39\u0026ndash;43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e41\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eLegend\u003c/b\u003e: BMI \u0026ndash; body mass index; PBF \u0026ndash; percent body fat; WC \u0026ndash; waist circumference; HC \u0026ndash; hip circumference;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eWHR \u0026ndash; waist to hip ratio; WHtR \u0026ndash; waist to height ratio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnalysis of body composition showed that women in the NWO group had significantly higher body fat. These differences were evident in both relative (percent body fat (PBF), fat mass (FM), appendicular fat mass (AFM) and absolute (fat mass index (FMI), appendicular fat mass ratio (AFMI)) values of fatness indices (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all comparisons). A higher fat mass ratio (FMR) further confirmed increased fat accumulation in the NWO group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The android to gynoid adipose tissue ratio (A/G) was significantly higher in women in the NWO group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Analysis of visceral adipose tissue deposition, expressed by visceral fat area (VFA), weight (VFM) and volume (VFV), showed significantly higher values in the NWO group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all parameters). Analysis of fat-free deposit showed lower values for all indices in the NWO group., with only appendicular fat-free mass (AFFM) and appendicular lean mass (ALM) values being statistically significantly lower (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) compared to the control group. Detailed values for body composition parameters are shown in the supplementary material (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe analysis of hematological parameters showed that the values of red cell distribution width (RDWc), mid-sized cells (MID), mid-sized cell percentage (MID%), and granulocyte percentage (GRA%) were higher in the control group than in the study group (NWO), but these differences were not statistically significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Among the remaining parameters, significantly higher values in the NWO group compared to the control group were observed for white blood cells (WBC) (6.3 vs. 5.7 x 10\u003csup\u003e9\u003c/sup\u003e/l; p\u0026thinsp;=\u0026thinsp;0.045), plateletcrit (PCT) (0.21 vs. 0.18%; p\u0026thinsp;=\u0026thinsp;0.001), lymphocytes (LYM) (2.1 vs. 1.8 x 10\u003csup\u003e9\u003c/sup\u003e/l; p\u0026thinsp;=\u0026thinsp;0.005) and the number of large platelets (P-LCC) (67 vs. 57 x 10\u003csup\u003e9\u003c/sup\u003e/l; p\u0026thinsp;=\u0026thinsp;0.003). In addition, the NWO group was characterised by a significantly higher platelet count (PLT) (244 vs 222 x 10\u003csup\u003e9\u003c/sup\u003e/l; p\u0026thinsp;=\u0026thinsp;0.010). The NWO group was characterised by a significantly higher hs-CRP concentration (0.63 vs 0.6 mg/l; p\u0026thinsp;=\u0026thinsp;0.014) with a lower (but not statistically significant) NO concentration (6.44 vs 7.72 \u0026micro;M; p\u0026thinsp;=\u0026thinsp;0.479) compared to the control group. Detailed values of the analysis of hematological and biochemical parameters are presented in\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHematological and biochemical parameters of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;176)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNW\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNWO\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003emedian\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003emedian\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003emedian\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u0026ndash;7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.8\u0026ndash;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.3\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.3\u0026ndash;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.3\u0026ndash;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.3\u0026ndash;4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHGB [g/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u0026ndash;133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e118\u0026ndash;131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120\u0026ndash;134.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCT [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.3\u0026ndash;38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35.4\u0026ndash;39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV [fl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u0026ndash;86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76\u0026ndash;85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78\u0026ndash;86.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCH [pg]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.6\u0026ndash;29.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.1\u0026ndash;29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u0026ndash;29.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCHC [g/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334\u0026ndash;349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e333\u0026ndash;348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e336.3\u0026ndash;349.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLT [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e199\u0026ndash; 279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e185.5\u0026ndash;265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e199\u0026ndash;279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCT [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.17\u0026ndash;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u0026ndash;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17\u0026ndash;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV [fl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.1\u0026ndash;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u0026ndash;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.4\u0026ndash;9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDWs [fl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.2\u0026ndash;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u0026ndash;13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.6\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePDWc [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.2\u0026ndash;39.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.1\u0026ndash;39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37.3\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDWs [fl]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.4\u0026ndash;42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.4\u0026ndash;42.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e36\u0026ndash;42.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDWc [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.6\u0026ndash;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.6\u0026ndash;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.5\u0026ndash;15.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.6\u0026ndash;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u0026ndash;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8\u0026ndash;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMID [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026ndash;0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u0026ndash;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.21\u0026ndash;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRA [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8\u0026ndash;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.7\u0026ndash;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1\u0026ndash;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLYM% [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5\u0026ndash;40.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u0026ndash;39.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.7\u0026ndash;40.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMID% [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7\u0026ndash;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.8\u0026ndash;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.7\u0026ndash;7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGRA% [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53\u0026ndash;67.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.4\u0026ndash;67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.6\u0026ndash;66.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-LCC [x 10\u003csup\u003e9\u003c/sup\u003e/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49\u0026ndash;77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.5\u0026ndash;68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54.8\u0026ndash;85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-LCR [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.2\u0026ndash;34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.6\u0026ndash;33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.8\u0026ndash;34.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP [mg/l]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u0026ndash;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6\u0026ndash;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO [\u0026micro;M]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u0026ndash;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u0026ndash;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.8\u0026ndash;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eLegend\u003c/b\u003e: WBC - white blood cells; RBC - red blood cells; HGB \u0026ndash; hemoglobin; HCT \u0026ndash; hematocrit; MCV - mean corpuscular volume; MCH - mean corpuscular hemoglobin; MCHC - mean corpuscular hemoglobin concentration; PLT \u0026ndash; platelets;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003ePCT \u0026ndash; plateletcrit; MPV - mean platelet volume; PDWs - platelet distribution width (SD); PDWc - platelet distribution width (CV);\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eRDWs - red cell distribution width; RDWc - red cell distribution width; LYM \u0026ndash; lymphocytes; MID - mid-sized cells;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eGRA \u0026ndash; granulocytes; LYM% - lymphocyte percentage; MID% - mid-sized cell percentage; GRA% - granulocyte percentage;\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP-LCC - platelet large cell count; P-LCR - platelet large cell ratio\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation analysis\u003c/h3\u003e\n\u003cp\u003eCorrelation analysis showed a statistically significant weak, positive correlation between WBC and PBF, TG/G (r\u0026thinsp;=\u0026thinsp;0.20) and TG (r\u0026thinsp;=\u0026thinsp;0.21) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003ePCT correlated weakly, positively with HDL-C, HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.22), LAP (r\u0026thinsp;=\u0026thinsp;0.20) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), PBF, Non-HDL (r\u0026thinsp;=\u0026thinsp;0.25), TC, glucose (r\u0026thinsp;=\u0026thinsp;0.27), LDL-C (r\u0026thinsp;=\u0026thinsp;0.24), TG (r\u0026thinsp;=\u0026thinsp;0.28), TG/G (r\u0026thinsp;=\u0026thinsp;0.29) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eIn the case of LYM, a weak positive correlation was shown with PBF (r\u0026thinsp;=\u0026thinsp;0.21), TG (r\u0026thinsp;=\u0026thinsp;0.22), TG/G (r\u0026thinsp;=\u0026thinsp;0.20) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), LDL-C, Non-HDL (r\u0026thinsp;=\u0026thinsp;0.24), TC (r\u0026thinsp;=\u0026thinsp;0.27) and HDL-C (r\u0026thinsp;=\u0026thinsp;0.25) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eP-LCC statistically significantly correlated with PBF, HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.21), LAP (r\u0026thinsp;=\u0026thinsp;0.20) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with HDL-C (r\u0026thinsp;=\u0026thinsp;0.30), LDL-C (r\u0026thinsp;=\u0026thinsp;0.27), Non-HDL (r\u0026thinsp;=\u0026thinsp;0.28), TG (r\u0026thinsp;=\u0026thinsp;0.29), TG/G (r\u0026thinsp;=\u0026thinsp;0.31) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and with TC, glucose (r\u0026thinsp;=\u0026thinsp;0.32 p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eDetailed results of the correlation analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and in the supplementary material \u003cb\u003e(Table S2.).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between hematological parameters and percent body fat, lipid profile, and cardiometabolic risk indices.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePCT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLYM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-LCC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePBF [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTC [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHDL-C [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLDL-C [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNon-HDL [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGlucose [mM]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInsulin [\u0026micro;lU/ml]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG/HDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTG/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.20*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eLegend\u003c/b\u003e: WBC - white blood cells; PLT \u0026ndash; platelets; PCT \u0026ndash; plateletcrit; LYM \u0026ndash; lymphocytes; P-LCC - platelet large cell count;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePBF - percent body fat; TC \u0026ndash; total cholesterol; HDL-C \u0026ndash; high-density lipoprotein cholesterol; LDL-C \u0026ndash; low-density lipoprotein cholesterol; Non-HDL \u0026ndash; non-high-density lipoprotein cholesterol; TG \u0026ndash; triglycerides; HOMA-IR - homeostatic model assessment for insulin resistance; TG/HDL-C \u0026ndash; triglycerides to HDL-C ratio; TG/G \u0026ndash; triglycerides to glucose ratio; VAI \u0026ndash; visceral adiposity index; LAP \u0026ndash; lipid accumulation product; CMI \u0026ndash; cardiometabolic index; * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere is evidence of a link between NWO obesity and an abnormal cardiometabolic profile. We have demonstrated this in our previous studies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The current study, representative of the same population of young women with NWO obesity, showed that higher PBF levels were associated with higher levels of hematological parameters, including WBC, LYM, PCT, and P-LCC. Our results indicate that, in young women, despite having a normal body weight (as determined by BMI), hematological changes are observed that, when measured in a commonly used and widely available manner, may predict the NWO phenotype, associated inflammation, and increased risk of thrombosis.\u003c/p\u003e \u003cp\u003eAmong many hematological indicators, WBC and LYM counts are widely used to diagnose systemic inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is elevated in obesity, is significantly correlated with insulin resistance, strongly predicts the development of type 2 diabetes in obese and non-obese individuals, metabolic syndrome, and is a predictor of mortality from coronary heart disease, regardless of traditional risk factors for cardiovascular disease [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. It has also been shown that a high WBC count can predict future cardiovascular events in healthy individuals [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this study, we found for the first time that WBC and LYM counts were significantly higher in women with NOW (6.3 vs. 5.7 x 10\u003csup\u003e9\u003c/sup\u003e/l, p\u0026thinsp;=\u0026thinsp;0.045 and\u003c/p\u003e \u003cp\u003e2.1 vs. 1.8 x 10\u003csup\u003e9\u003c/sup\u003e/l, p\u0026thinsp;=\u0026thinsp;0.005, respectively). While the mean values did not exceed the accepted physiological range, there was relative leukocytosis and lymphocytosis, with a progressive increase dependent on PBF. This suggests that the observed changes may be mediated in part by an increase in adipose tissue content and the release of inflammatory markers from it. The association between inflammation and NWO has been highlighted in several studies and recently summarised in a systematic review and meta-analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The presence of a chronic, low-grade inflammatory response in the NWO phenotype was primarily associated with elevated levels of IL-6 and CRP, which are potent inducers of leukocytosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Our current study confirms these previous findings. We observed a significant increase in hs-CRP levels in the NWO group (0.63 vs. 0.60 mg/dl; p\u0026thinsp;=\u0026thinsp;0.014). Although hs-CRP levels were within the reference ranges in both groups, a significant difference remained between them. Previous studies have shown that concomitant exposure to elevated levels of proinflammatory markers, such as CRP and WBC, correlated with the development of several disease states in obese individuals [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our study extends these findings to young non-obese women, which has important implications for their health risks. Interestingly, PBF is associated with\u003c/p\u003e \u003cp\u003ehs-CRP and WBC counts independently of BMI. These changes cannot be explained by factors or diseases that typically increase CRP and WBC counts. We observed these associations in healthy, very young women, so subclinical disease is unlikely to explain our results. These data suggest that young women with the NWO phenotype have a state of low-grade systemic inflammation, as evidenced by increased WBC counts and hs-CRP.\u003c/p\u003e \u003cp\u003eElevated hs-CRP levels within the reference range in apparently healthy men and women have been associated with an increased risk of developing cardiovascular disease, diabetes, and nonalcoholic fatty liver disease [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Numerous clinical studies have linked CRP with an increased risk of myocardial infarction, stroke, peripheral artery disease, and sudden death [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, elevated CRP levels are one of the strongest predictors of progressive vascular disease [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Moreover, CRP can activate endothelium by modulating the endothelial nitric oxide synthase-nitric oxide pathway. Elevated CRP levels are associated with a significant decrease in NO production, which is a consequence of CRP's action to reduce endothelial nitric oxide synthase mRNA stability and protein levels [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In our group of women with NWO, we observed a non-significantly lower plasma NO concentration compared to the control group (6.44 vs. 7.72 \u0026micro;M; p\u0026thinsp;=\u0026thinsp;0.479), which, although not statistically significant, is consistent with the extensive literature describing reduced NO bioavailability in obesity [\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Such endothelial dysfunction promotes the activation and adhesion of PLTs and WBCs, as well as the release of proinflammatory cytokines. These phenomena increase vascular permeability to oxidised lipoproteins and inflammatory mediators, ultimately causing structural changes in the arterial wall, including smooth muscle cell proliferation and the initiation of atherosclerotic plaque development [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Platelet activation is also increased in NO-deficient states, contributing to thrombosis and increasing the risk of acute cardiovascular events such as myocardial infarction or stroke [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of this study provided another interesting observation. It concerns the association of WBC and LYM counts with several cardiovascular risk factors, including TG levels and the TG/G ratio. This is consistent with the findings of Ohshita et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and Targher et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. They reported that in individuals with normal fasting glucose, an elevated, yet normal, WBC count is associated with a set of metabolic abnormalities typical of the insulin resistance syndrome and is associated with some components of the metabolic syndrome, such as BMI, WHR, and TG levels. The impact of TG on cardiovascular risk has long been well documented [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. It is also known that combined exposure to high WBC counts and TG levels that are consistently within the reference range is associated with a more than threefold increased risk of cardiovascular mortality, independent of traditional risk factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In our analysis, WBC count and previously assessed TG concentration (0.8 vs. 0.6 mM; p\u0026thinsp;=\u0026thinsp;0.01; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] were significantly higher in the NWO group, and these variables were associated. These data provide new evidence suggesting that combined exposure to high TG concentrations and WBC counts may be associated with an increased risk of cardiovascular disease in young women with the NWO phenotype. This finding is plausible in our cohort. TG concentration has been shown to be closely associated with inflammation, including elevated WBC counts [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This association is thought to occur through indirect mediators such as adipose tissue or insulin resistance [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our group of women with NWO has increased PBF and is insulin resistant (HOMA-IR: 2.3 vs. 1.7; p\u0026thinsp;=\u0026thinsp;0.002) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, the results may be consistent with the hypothesis that individuals with high WBC counts, and high TG concentrations are more insulin-resistant, which further increases their cardiovascular risk.\u003c/p\u003e \u003cp\u003eOne result of this study is a significant increase in PLT, P-LCC, and PCT counts in women with NWO. We also found a positive association between PLT and P-LCC counts, and between PLT and PBF. Although PLT counts are important for quantifying the coagulation process, they are significantly higher in obesity and metabolic syndrome [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. They are also associated with the risk of arterial and venous thrombosis in obese individuals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and the risk of type 2 diabetes [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Nevertheless, we believe that, in addition to PLT and PCT, the importance of markers that measure the number of larger, more active platelets should be emphasised. P-LCC is such an indicator. We found no other studies in the literature that assessed P-LCC in women with NWO. In our research, we found that P-LCC was significantly higher in this group of subjects (67 vs. 57 \u0026times; 10\u003csup\u003e9\u003c/sup\u003e/l; p\u0026thinsp;=\u0026thinsp;0.003). This result was also consistent with significantly higher PLT counts (244 vs. 222 x 10\u003csup\u003e9\u003c/sup\u003e/l; p\u0026thinsp;=\u0026thinsp;0.010). Larger platelets are more reactive and have a greater ability to form clots, increasing the risk of thrombosis [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. They are considered immature, and their number increases with factors influencing platelet turnover. Inflammation is considered the most important factor for increased platelet turnover [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Exposure to inflammatory signals can accelerate the production of larger, more reactive PLTs [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. We hypothesise that, in women with NWO, due to inflammation, there may be a subclinical increase in platelet formation and a corresponding acceleration of their entry into peripheral blood, which may explain the high PLT and P-LCC counts in our study. Furthermore, PCT, which measures the total platelet volume as a percentage of the volume occupied in the blood, plays an effective role in detecting quantitative abnormalities. PCT correlates with PLT count and has comparable clinical significance.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThe present study has several notable strengths. First, the participants were recruited from a well-characterised, homogeneous ethnic group. Second, the narrow age range (18\u0026ndash;24 years) is a significant advantage, as it minimises age-related variability and eliminates confounding factors associated with aging and co-morbidities that could otherwise skew the analysis. This homogeneity allows for a more precise evaluation of the specific impact of adiposity on hematological parameters in young, apparently healthy women.\u003c/p\u003e \u003cp\u003eHowever, the authors are also aware of the study's limitations. The primary limitation is its cross-sectional design, which precludes the determination of causal relationships between adipose tissue distribution and the observed hematological alterations. Longitudinal studies are required to confirm these findings and establish the directionality of the associations. Regarding methodological limitations, complete blood count was performed using a 3-part differential analyser, which prevented the specific quantification of neutrophils and the calculation of the neutrophil-to-lymphocyte ratio (NLR), a validated marker of systemic inflammation. Furthermore, hs-CRP C was measured at a single time point; given its biological variability, this may not fully reflect long-term inflammatory status. We also lacked data on serum IL-6 and other proinflammatory cytokines or adipokines, which would have allowed for direct verification of the specific pathways linking adipose tissue to the observed leukocytosis. Finally, although we excluded participants with hormonal disorders, the specific phase of the menstrual cycle was not standardised. Since estrogen fluctuations (e.g., in the follicular phase) can influence platelet count, future research should account for the menstrual cycle phase to minimise this variability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFuture Research Directions\u003c/h2\u003e \u003cp\u003eFuture research should focus on several key areas to validate and expand upon these findings. First, prospective longitudinal studies are essential to evaluate whether the observed hematological alterations in young NWO women translate into a higher incidence of cardiovascular events or type 2 diabetes in later life. Second, future investigations should include a broader panel of bioactive mediators, specifically proinflammatory cytokines (e.g., IL-6, TNF-α) and adipokines, to confirm the molecular mechanisms linking adipose tissue accumulation with systemic leukocytosis. Third, to minimise physiological variability, future protocols should standardise blood sampling by menstrual cycle phase and utilise 5-part differential hematology analysers to assess specific inflammatory indices, such as the\u003c/p\u003e \u003cp\u003eneutrophil-to-lymphocyte ratio (NLR). Finally, interventional studies are warranted to determine whether lifestyle modifications (diet or physical activity) aimed at reducing body fat can reverse these unfavorable hematological profiles in women with the NWO phenotype.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides evidence that the NWO phenotype in young, apparently healthy women is associated with early, unfavourable changes in hematological parameters. We identified a specific profile characterised by elevated markers of systemic inflammation (WBC, LYM) and thrombotic potential (PLT, PCT, and notably P-LCC), which significantly correlate with body fat percentage and lipid disturbances.\u003c/p\u003e \u003cp\u003eThese findings suggest that despite maintaining a normal body weight, women with excess adiposity exhibit a subclinical proinflammatory and prothrombotic state similar to that observed in overt obesity. The observed correlations between red blood cell and platelet indices (such as MCV and MPV) and the lipid profile further underscore the systemic impact of hidden metabolic dysregulation on blood cell morphology.\u003c/p\u003e \u003cp\u003eClinically, these results challenge the sufficiency of BMI as a standalone diagnostic tool. Routine hematological analysis\u0026mdash;particularly focusing on leukocyte counts and novel platelet indices like\u003c/p\u003e \u003cp\u003eP-LCC\u0026mdash;may serve as an accessible, cost-effective screening method to identify young women at increased cardiometabolic risk who would otherwise be overlooked. Early detection of these subtle hematological deviations offers a critical window for targeted lifestyle interventions to prevent the progression to symptomatic cardiovascular disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eInstitutional Review Board Statement\u003c/h2\u003e \u003cp\u003eThe study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (the Bioethics Committee of the Pomeranian Medical University; Ref. KB-0012/13/2021).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eFinanced by the Minister of Science under the \"Regional Excellence Initiative\" Program. Agreement No. RID/SP/0045/2024/01.This research was funded by grant from the Ministry of Science and Higher Education obtained by the Faculty of Health Sciences of the Pomeranian Medical University in Szczecin [WNoZ-318/S/2026].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualisation, A.L., W.D. and W.P.; methodology, A.L. and W.D.; software, W.P.; formal analysis, A.L., W.D. and W.P.; investigation, A.L., W.D. and W.P.; resources, A.L.; data curation, W.P.; writing\u0026mdash;original draft preparation, A.L., W.D. and W.P.; writing\u0026mdash;review and editing, A.L., W.D. and W.P.; visualisation, W.D. and W.P.; supervision, A.L. and WD.; project administration, A.L.; funding acquisition, A.L.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePluta, W., Lubkowska, A. \u0026amp; Dudzińska, W. 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(Lausanne)\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 294 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCouldwell, G. \u0026amp; Machlus, K. R. Modulation of megakaryopoiesis and platelet production during inflammation. \u003cem\u003eThromb. Res.\u003c/em\u003e \u003cb\u003e179\u003c/b\u003e, 114\u0026ndash;120 (2019).\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"normal weight obesity, hematological patterns, DXA, percent body fat","lastPublishedDoi":"10.21203/rs.3.rs-8481327/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8481327/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNormal Weight Obesity (NWO) is characterised by excessive body fat with a normal BMI. This study aimed to assess whether young women with the NWO phenotype exhibit a different hematological profile compared to their peers with a normal body composition. The study included 176 young women aged 18\u0026ndash;24 years with a normal BMI (18.5\u0026ndash;24.9 kg/m\u0026sup2;). Based on a previously established cut-off point for percentage body fat (PBF), participants were assigned to the NWO group (PBF\u0026thinsp;\u0026ge;\u0026thinsp;35.78%) and the control group (PBF\u0026thinsp;\u0026lt;\u0026thinsp;35.78%). Complete blood counts and biochemical parameters were analysed. It was demonstrated that women in the NWO group had significantly higher values ​​of leukocytes, lymphocytes, and platelets compared to the control group. Significantly higher platelet hematocrit and large platelet counts, as well as higher hs-CRP levels, were also observed. Significant positive correlations were found between body fat percentage and the hematological parameters examined. Based on the results, the NWO phenotype in young, clinically healthy women is associated with subclinical inflammation and prothrombotic potential. Routine hematological parameters may be a cost-effective and readily available tool for early assessment of cardiometabolic risk in individuals with a normal body weight.\u003c/p\u003e","manuscriptTitle":"Normal weight obesity (NWO) in young women: hematological patterns and their relationship to body composition and cardiometabolic risk factors.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-20 11:23:36","doi":"10.21203/rs.3.rs-8481327/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-16T05:32:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-06T17:00:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-05T13:02:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-04T08:05:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T10:38:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39408579845616755694741440831218340746","date":"2026-01-16T10:56:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"296482206641446581688045433519256729261","date":"2026-01-16T09:50:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"193704915689030333475413155338400193304","date":"2026-01-16T08:22:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189268579974640820602471635434655690655","date":"2026-01-16T08:17:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236508446501626795715235530844186062350","date":"2026-01-16T08:02:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-16T07:37:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-06T15:05:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-31T11:24:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-31T11:22:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-30T12:22:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"988fe6e9-2927-4fa6-9f1b-cf3386029ee3","owner":[],"postedDate":"January 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":61241727,"name":"Health sciences/Biomarkers"},{"id":61241728,"name":"Health sciences/Diseases"},{"id":61241729,"name":"Health sciences/Endocrinology"},{"id":61241730,"name":"Health sciences/Medical research"},{"id":61241731,"name":"Biological sciences/Physiology"}],"tags":[],"updatedAt":"2026-05-04T16:06:08+00:00","versionOfRecord":{"articleIdentity":"rs-8481327","link":"https://doi.org/10.1038/s41598-026-48634-9","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-28 15:57:24","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2026-01-20 11:23:36","video":"","vorDoi":"10.1038/s41598-026-48634-9","vorDoiUrl":"https://doi.org/10.1038/s41598-026-48634-9","workflowStages":[]},"version":"v1","identity":"rs-8481327","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8481327","identity":"rs-8481327","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}