Introduction

Bioelectrical Impedance Analysis (BIA) assesses body composition by measuring the relationship between impedance and body water content. Phase angle (PhA) is an essential indicator of inflammation. It is calculated by taking the ratio of the raw measures of resistance (R) and reactance (Xc) obtained from BIA at a frequency of 50 kHz1. PhA is inversely related to the extracellular water to total body water ratio (ECW/TBW) derived from BIA2. Both PhA and water cell distribution ratios are used to evaluate cell integrity and how it is affected by body fat. Higher body fat leads to a reduction in PhA and an increase in ECW/TBW3. Multiple studies have confirmed that PhA is a crucial indicator of inflammation in the body4 and is linked to various health outcomes. For instance, in patients with cardiovascular diseases, PhA was negatively associated with C-reactive protein (CRP) and tumor necrosis factor alpha (TNF-α) in 80% and 100% of the related studies, respectively5.

Inflammatory markers are essential for diagnosing and monitoring various medical conditions. They fall into two main categories: serum and hematological inflammatory markers, each serving distinct roles in clinical practice. Serum inflammatory markers are substances found in the blood that indicate inflammation. Common examples include cytokines, CRP, Procalcitonin (PCT), Interleukin-6 (IL-6), and Serum amyloid A (SAA)6. On the other hand, measuring peripheral blood cells, the source of some of these cytokines, by complete blood count (CBC) is a practical, cost-effective, and rapid procedure that can provide valuable insights into the body’s inflammatory status7. Hematological inflammatory markers, derived from the CBC, include white blood cell (WBC) count, platelet counts, and some ratios. These markers are often used for acute conditions8.

The evidence suggests that PhA is significantly inversely associated with various serum inflammatory markers, including IL-1β, CRP, IL-6, albumin, IL-10, TNF-α, and multisystem inflammatory syndrome (MIS)9. There have been fewer studies on the association between PhA and CBC parameters than serum inflammatory markers. Hori et al. found that hemoglobin (Hgb) and hematocrit (Hct) increased with higher PhA in diabetic adults. However, ECW/TBW had an inverse association with Hgb and Hct10. While one study demonstrated a higher lymphocyte count in individuals with higher PhA11, another study did not find an association between WBC count and PhA12. Our study aimed to investigate the relationship between PhA and ECW/TBW, which serve as indicators of cellular health and hematological inflammatory markers. We also sought to determine if this relationship varies among individuals with different BMI classifications, including normal-weight and over-weight participants.

Methods

Study population

In our current research, we conducted an analysis using data from the PERSIAN Organizational Cohort Study in Mashhad (POCM), which involved a significant study population of 12,000 employees from Mashhad University of Medical Sciences across 35 organizational categories, including 14 hospitals, seven faculties, seven vice-chancellors, five health centers, three research institutes, and the central administrative department13. The study population comprised 5305 participants (2939 females and 2366 males). Among them, 1654 individuals had a normal weight (18.5 < body mass index (BMI) ≤ 24.99) with an average age of 44.13 ± 8.80, while 3651 individuals were classified as overweight or obese (BMI ≥ 25) with an average age of 46.31 ± 9.09. Inclusion criteria required participants to have complete biochemical and body composition data and a normal-weight, overweight, or obese BMI, excluding those underweight.

All methods were conducted in accordance with the Declaration of Helsinki. Ethical approval was secured from the Ethics Committee of Mashhad University of Medical Sciences (MUMS) (4031222), thereby ensuring the prioritization of participants’ rights and welfare throughout the research process.

Assessment of phase angle and water cell distribution ratios

We collected bioelectrical impedance analysis data using the Inbody 770 device from Inbody Corporation, Seoul, Korea. This device applies a low-level alternating current (50 to 1000 kHz) that encounters resistance from the body’s tissues. Participants stood barefoot on the footplate, held hand electrodes, and extended their arms. The key body composition measurements obtained included intracellular and extracellular water, total body water, body fat percentage, BMI, and PhA. Additionally, weight, measured in kilograms, height in centimeters, and waist circumferences (WC) were recorded following the protocols established by the US National Institutes of Health14, with all values recorded to the nearest decimal point.

Assessment of hematological markers

In the Mashhad Persian cohort study, participants were required to fast for 10 to 12 h and refrain from dyeing or cutting their hair and nails for at least two weeks. Approximately 25 ml of blood was drawn using one 9 ml clot aspirator and three 6 ml EDTA aspirators (Greiner Bio-One International GmbH, Kremsmünster, Austria). The blood samples were then processed to obtain whole blood, plasma, and serum samples. Cell count tests (NK Alpha, Nihon Kohden, Tokyo, Japan) and biochemical analyses (plate_number_1 automatic analyzer, Biotecnica Instruments, Rome, Italy) were conducted to evaluate the lipid profile, including total cholesterol, high-density lipoprotein cholesterol (HDL-C), and triglycerides. The following hematological inflammatory markers were calculated using the formulas provided:

$${\text{PHR = }}\frac{{{\text{Platelet}}}}{{{\text{HDL}}}}$$
$${\text{PLR}} = \frac{{{\text{Platelet}}}}{{{\text{Lymphocyte}}}}$$
$${\text{MHR}} = \frac{{{\text{Monocyte~}}}}{{{\text{HDL}}}}$$
$${\text{LHR}} = \frac{{{\text{Lymphocyte}}}}{{{\text{HDL}}}}$$
$${\text{RLR}} = \frac{{{\text{RDW}}}}{{{\text{Lymphocyte}}}}$$
$${\text{RPR}} = \frac{{{\text{RDW}}}}{{{\text{Platelet}}}}$$
$${\text{GLR}} = \frac{{{\text{Granulocyte~}}}}{{{\text{Lymphocyte}}}}$$

Statistical analysis

All statistical analyses were conducted precisely and accurately using the SPSS statistical package (version 26.0 for Windows). Continuous data were presented as mean ± standard deviation, and overall differences were assessed using either the Student’s t-test or analysis of variance (ANOVA). We checked the normality of the variables using the Kolmogorov–Smirnov test. We conducted a nonparametric analysis using the Mann–Whitney test for variables not showing a normal distribution. We used linear regression to examine the relationship between PhA and ECW/TBW with hematological markers in crude and adjusted models. We controlled for factors such as age, sex, energy intake, smoking habits, drug use, and alcohol consumption, as well as the presence of diabetes and hypertension, to identify any significant differences among the groups. The degree of variation or similarity between and within groups was determined based on the p-value, with a significance threshold set at p < 0.05. Additionally, scatterplots were generated using SPSS for the indicators that showed statistical significance in the linear regression analysis. PhA, or ECW/TBW, was plotted as an independent variable on the X-axis, while the dependent variables were represented on the Y-axis.

Results

Characteristics of subjects

A total of 5,305 participants were divided into two groups: those with a normal BMI (N = 1,654) and those classified as overweight or obese (N = 3,651). The physical and laboratory characteristics of the study subjects are detailed in Table 1. In the normal BMI category, 96.4% reported non-smoking status, with an average physical activity level of 39.0 ± 5.0 and a waist circumference of 88.6 ± 7.2 cm. In the overweight or obese group, 95% of participants were non-smokers, with an average physical activity level of 38.4 ± 5.7 and a waist circumference of 100.1 ± 9.1 cm. Comparative analysis showed significantly higher means of indices such as WBC, Hgb, Hct, Plt, RDW, PHR, PLR, MHR, LHR, TBW, and PhA in the BMI ≥ 25 groups. Additionally, the prevalence of diabetes or hypertension was higher in this group compared to the normal weight group. Conversely, the GLR, RPR, and HDL levels were higher in the normal weight group than in their overweight or obese counterparts.

Table 1 Baseline characteristics according to BMI categories.
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Associations between BIA parameters (PhA, ECW/TBW) and hematological parameters in the crude model

The results in Table 2 present the ECW/TBW ratio and PhA associations across two BMI categories in the crude model. In the BMI ≥ 25 group, PhA exhibited significant correlations with all examined markers. In the normal-weight group, PhA demonstrated significant correlations with all examined factors, except for lymphocytes, RLR, and GLR. Similarly, ECW/TBW was associated with nearly all hematological markers analyzed; however, it did not show significant correlations with Plt and RLR in the normal-weight group. In individuals with a BMI ≥ 25, ECW/TBW was not correlated with monocytes and WBC. Additionally, neither BMI category showed a correlation between ECW/TBW and RPR.

Table 2 Association between phase angle, ECW/TBW and hematological markers: A crude linear regression analysis.
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Associations between BIA parameters (PhA, ECW/TBW) and hematological parameters in the adjusted model

In Table 3, the adjusted regression results reveal a positive association between PhA and WBC (normal-weight: β: 0.090; BMI ≥ 25: β: 0.068), Hgb (normal-weight: β: 0.171; BMI ≥ 25: β: 0.123), and Hct (normal-weight: β: 0.153; BMI ≥ 25: β: 0.136) in both groups. Furthermore, there is a positive correlation between PhA and PHR (β: 0.052), Lymphocytes (β: 0.056), and Plt (β: 0.167) observed only in the BMI ≥ 25 group, while an inverse relationship with RPR was noted in both groups (normal-weight: β: −0.074; BMI ≥ 25: β: =−0.066). However, no significant associations were found between PhA and the other markers.

Table 3 Association between phase angle, ECW/TBW and hematological markers: an adjusted linear regression analysis.
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Regarding ECW/TBW associations with hematological indicators in the BMI ≥ 25 groups, elevated ECW/TBW were correlated positively with RLR (β: 0.065), RPR (β: 0.069), GLR (β: 0.059), and Granulocyte (β: 0.075). In both weight groups, an inverse association was found between ECW/TBW and Hgb (normal-weight: β: −0.199; BMI ≥ 25: β: −0.173) as well as Hct (normal-weight: β: −0.178; BMI ≥ 25: β: −0.176). An inverse relationship with Plt (β: − 0.055), and Lymphocytes (β: −0.083) were noted only in the overweight and obese group and with WBC (β: −0.096) only in the normal weight group. No significant correlations were observed between ECW/TBW and the other markers. Finally, Figs. 1 (association between phase angle and A) WBC, B) Hgb, C) Hct, D) Lymphocyte, E) Platelet, and F) PHR) and 2 (association between ECW/TBW and A) WBC, B) Hgb, C) Hct, D) Lymphocyte, E) Platelet, F) RPR, G) Granulocyte, and H) GLR) illustrate the scatter plots depicting the statistically significant relationships identified in our analysis.

Fig. 1
figure 1

Scatterplots of the Association Between Phase Angle and Significant Hematological Markers in Linear Regression Analysis. (A) WBC, (B) Hgb, (C) Hct, (D) Lymphocyte, (E) Platelet, (F) PHR.

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Fig. 2
figure 2

Scatterplots of the Association Between ECW/TBW and Significant Hematological Markers in Linear Regression Analysis. (A) WBC, (B) Hgb, (C) Hct, (D) Lymphocyte, (E) Platelet, (F) RPR, (G) Granulocyte, (H) GLR.

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Discussion

The current research was the first to comprehensively investigate the relationship between PhA and ECW/TBW with hematological markers. After accounting for the influence of confounding factors, most of the associations were no longer significant, highlighting the substantial role of these confounders in the levels of these hematological markers. Our findings revealed that PhA is directly associated with WBC, Hgb, and Hct in both BMI categories and with PHR, Platelet, and Lymphocytes only in the overweight group. Unlike PhA, ECW/TBW, in addition to the aforementioned markers, showed a direct relationship with granulocyte, RLR, RPR, and GLR markers (Fig. 3).

Fig. 3
figure 3

Relationship Between Phase Angle, ECW/TBW Ratio, and Hematological Markers Across BMI Categories.

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Hemoglobin and hematocrit levels

The evidence consistently shows a correlation between the destruction of red blood cells (RBC) and changes in PhA. For example, Varlet-Marie et al. found a link between the flexibility of RBCs and whole-body impedance at 50 kHz15. Similarly, Tran et al. observed a significant correlation between the release of cytoplasm after RBC hemolysis and the changes in the electrical properties of blood cells16. Our current study also discovered a positive connection between PhA and Hgb and Hct and a negative connection between ECW/TBW and Hgb and Hct in both BMI groups. Interestingly, these correlations were more pronounced in individuals with a normal BMI compared to those with a BMI ≥ 25. Similarly, Hori et al. identified a direct relationship between PhA with Hgb and Hct in adults with diabetes10. In a pilot study, Khatun et al. observed an inverse correlation between PhA and glycated hemoglobin (HbA1c), suggesting the potential of PhA as a valuable biomarker for monitoring metabolic health17. While our findings revealed a positive link between PhA and Hgb levels, it is essential to note that a study by Kim et al. indicated that PhA values are associated with the median value of hemoglobin rather than its variability in hemodialysis patients18. This suggests that stable average hemoglobin levels may reflect nutritional status, as indicated by PhA, but PhA may not influence fluctuations in hemoglobin in the same way.

In line with our research, reports indicate a connection between fluid retention and anemia in chronic kidney disease patients19 and those with heart failure19,20. Also, Hori et al. showed that higher ECW/TBW, Hgb, and Hct values decreased in adults with diabetes10. This association is likely linked to hydration and nutritional levels impacting erythropoiesis. Studies on animals have demonstrated that erythropoiesis is disrupted in dehydrated conditions21,22.

Platelet count

Our research indicates that PhA, as well as ECW/TBW, is linked to increased platelet levels. Additionally, we found that PhA is associated with elevated RHR, whereas ECW/TBW is linked to increased PLR. Devarasu et al. also noted a connection between PhA and increased platelets in children with dengue fever23. An increase in PhA indicates improved cellular integrity and overall health, including platelet health24,25. Moreover, a higher PhA may signify better nutritional status, supporting optimal platelet function and production. On the other hand, platelet-activating factor (PAF) is recognized as a potent inflammatory lipid mediator that plays a crucial role in inflammation, thrombosis, and disease pathophysiology15,16,17,18,19,20. PAF is produced by various cells, including endothelial cells, platelets, macrophages, monocytes, neutrophils, and mast cells, and is activated by various triggers26. Notably, PAF levels are inversely associated with PhA27. Furthermore, several studies have indicated an inverse relationship between PAF and platelet count. In certain inflammatory conditions, PAF can cause platelet sequestration in the microcirculation, particularly in organs like the lungs. For instance, a study demonstrated that allergen exposure in asthma patients led to decreased platelet count correlated with increased PAF28.

WBC and its component counts

Our study demonstrated higher WBC counts in higher PhA levels. Conversely, Lee et al. found no link between PhA and WBC in critically ill patients12. Lee et al.‘s study used PhA as a surveillance tool for postoperative infection in 221 patients admitted to the ICU after abdominal surgery12. Therefore, our different results can be attributed to our larger sample size and our target population, which consists of healthy adults. Also, we found a direct association between ECW/TBW and WBC in the normal-weight group. This is expected because elevated ECW/TBW indicates inflammation, and WBC levels typically increase as part of the immune response29. ECW/TBW, as a marker of inflammation, also affects WBC concentration through changes in fluid balance. During inflammation, ECW increases due to fluid leakage from blood vessels into surrounding tissues. This leads to a relative decrease in plasma volume, which can increase the concentration of WBCs in the bloodstream30.

The study observed a direct relationship between PhA and Lymphocyte count in overweight participants. To support this finding, Vannini et al. also demonstrated a direct association between PhA and Lymphocytes in Hemodialysis patients11. In the present study, ECW/TBW was directly associated with granulocytes while inversely associated with lymphocyte count in overweight participants. This disparity in the relationship between ECW/TBW and these two WBC components can be attributed to their function. When faced with inflammation (indicated by elevated ECW/TBW), lymphocytes migrate to the inflamed tissue31, potentially leading to a decrease in their presence in the bloodstream. Conversely, granulocytes respond to inflammation by increasing in the bloodstream as the initial reaction to infection or injury. The subsequent decrease in granulocytes triggered by inflammatory stimuli could result from a specific expansion of granulocytic progenitors or a general enhancement of the bone marrow’s ability to support hematopoiesis32.

The relationship between PhA and ECW/TBW in normal-weight individuals was more robust than in individuals with a BMI ≥ 25. It is important to note that obesity is associated with chronic inflammation and changes in PhA33. Moreover, obesity can elevate Hgb, Hct levels34, platelet counts35, WBC, and specific components like neutrophils36. Consequently, this factor may impact the linear relationship between cell health indicators and hematological markers. Notably, we observed that ECW/TBW, compared to PhA, was more strongly linked to changes in hematological markers. ECW and TBW provide more general information about bodily fluid distribution and do not specifically indicate cellular health or membrane integrity as PhA does37. Furthermore, the relationship between PhA or ECW/TBW with WBC components (including lymphocytes and granulocytes) was only identified in individuals with BMI ≥ 25. The higher circulating WBC and lymphocyte counts in obese participants could account for this discovery38. A recent study demonstrated higher values of early activated T and B lymphocytes in obese individuals than those of normal-weight39. These findings have significant implications for understanding the health effects of obesity on cellular health and immune response.

Implication

BIA may be a more valuable tool in clinical settings than traditional hematological laboratory tests due to its simplicity. Traditional tests can be prone to errors, and the blood collection process can encounter complications40. The BIA method, being non-invasive, is particularly advantageous for individuals who experience difficulties with blood draws. Our findings reveal a significant association between PhA and the ECW/ICW ratio with Hgb, Hct, and WBC, suggesting potential for indirect estimation methods using BIA measurements, which could benefit both clinical practice and broader applications41. The study emphasizes monitoring PhA and ECW/TBW ratios across various BMI categories. In overweight and obese individuals, a higher PhA is associated with increased platelets and lymphocytes, indicating its potential in managing obesity-related inflammation. Additionally, lower ECW/TBW ratios within this group correlate with further markers, such as granulocyte levels, PLR, RPR, and GLR, suggesting the estimation potential of water distribution in the leukocyte numbers and some rations.

Strengths and limitations

The study comprehensively analyzed the relationship between PhA and ECW/TBW as essential nutritional and cellular health indicators in conjunction with hematological inflammatory markers for the first time. Additionally, we gained specific insights into this relationship across different BMI categories by categorizing participants into two BMI groups. The study delved into indicators related to RBC, including Hgb and Hct, WBC, and its components and ratios. The substantial sample size enabled us to establish these relationships more reliably. However, the study had some limitations. The lack of data on participants’ drug use prevented us from adjusting for drugs that may have impacted the body’s inflammatory state in the regression model. Additionally, the absence of data on specific components of granulocytes (basophils, neutrophils, and eosinophils) restricted our analysis to the overall amount of granulocytes. As a result, we could not calculate specific ratios, such as the neutrophil-to-lymphocyte ratio (NLR), which has been found to have an inverse association with PhA in hemodialysis patients42. Lastly, the sample size of participants with a BMI of 18.5 or less was very small, so we could not investigate the associations in underweight participants. Further studies focusing on this BMI category are suggested.

Conclusion

The results of the current study showed that PhA and ECW/TBW, two markers obtained from BIA, which are usually indicative of inflammation in the body, were correlated with hematological markers. These hematological markers exhibit changes during inflammatory conditions. PhA demonstrated a general association with elevated Hgb, Hct, Platelet, WBC, lymphocyte, and PHR levels. Conversely, ECW/TBW displayed an inverse correlation with Hgb, Hct, Platelet, WBC, and Lymphocyte and a direct correlation with granulocyte and PLR, RPR, and GLR ratios. Overall, the results indicated that ECW/TBW, compared to PhA, exhibited a stronger relationship with hematological markers, possibly due to its capacity to signify inflammation in the body and represent the fluid balance, potentially impacting the concentration of these hematological markers. Therefore, the stronger association with all hematological markers seems reasonable.

Moreover, these connections were more evident in people with a normal weight than those with a BMI ≥ 25. As a result, it is proposed that obesity should be recognized as a potential factor influencing the linear correlation between PhA and ECW/TBW with hematological markers. It is worth noting that PhA and ECW/TBW measures were explicitly linked to lymphocytes and granulocytes but not monocytes. Furthermore, this correlation was only observed in individuals with a BMI ≥ 25. Future research should investigate the potential underlying mechanisms for these disparities.