Abstract
Background
Coronary artery calcification (CAC) is a major contributor to cardiovascular disease (CVD) in patients undergoing maintenance hemodialysis (MHD). However, its association with body composition and longitudinal changes in body composition remains understudied to date. This study aimed to investigate the associations of body composition and its longitudinal changes with CAC progression in MHD patients.
Methods
This prospective observational study included 209 Chinese MHD patients. Body composition was measured via bioelectrical impedance analysis, and CAC was assessed using the Agatston scoring system. Data were collected at baseline and 6 months later. The annualized change in CAC score (ΔCACS > 100) was used to define CAC progression. Unadjusted and adjusted binary logistic regression models were employed to evaluate the associations of body composition and its longitudinal changes with CAC progression, with adjustments for various clinical, biochemical, and demographic confounders.
Results
Of the 209 patients, 152 (72.73%) were classified into the CAC group (CACS > 0) and 57 (27.27%) into the non-CAC group at baseline. After 6 months, 193 patients completed two CT calcium scoring assessments; among these, 59 (30.57%) were assigned to the CAC progression group and 134 (69.43%) to the non-CAC progression group. Age, fat tissue index (FTI), extracellular-to-intracellular water ratio (E/I) and diabetes mellitus were significantly associated with an increased risk of CAC progression. Multivariate logistic regression analysis confirmed that age was an independent risk factor for CAC progression (OR = 1.033, 95% CI: 1.006–1.061, P = 0.018). Meanwhile, fat increase (follow-up FTI – baseline FTI > 0) during the follow-up period was an independent protective factor (OR = 0.435, 95% CI: 0.207–0.912, P = 0.028). Fat increase consistently exerted a protective effect against CAC progression (P < 0.05) independent of other covariates. Patients in the fat increase group had significantly higher muscle-related indices, including lean tissue index, lean tissue mass, and body cell mass (all P < 0.05). In contrast, the fat increase group had significantly lower BMI and fat-related indices, including FTI, total body fat (FAT), and adipose tissue mass (all P < 0.05).
Conclusions
Dynamic monitoring of body composition indicated that underweight MHD patients with fat increase and maintained higher muscle mass had a lower tendency for CAC progression.
Keywords: Body composition, Fat tissue index, Coronary artery calcification, Progression, Hemodialysis
Introduction
Chronic kidney disease (CKD) is a major and increasingly serious global public health challenge. As CKD advances to end-stage kidney disease (ESKD), patients require renal replacement therapy, including hemodialysis, peritoneal dialysis, or kidney transplantation [1]. According to estimates from the Global Burden of Disease Study, approximately 132 million individuals in China are affected by CKD [2]. As of December 2022, the number of patients receiving dialysis in China exceeded one million, with about 82.4% undergoing hemodialysis as the primary treatment modality [3]. Although hemodialysis has substantially improved survival and quality of life, overall mortality remains high, with a 5-year survival rate of only around 40% after dialysis initiation [4]. Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with CKD, and its risk in dialysis patients is 20–40 times higher than that in the general population [5, 6].
Vascular calcification (VC) is a major contributor to CVD and mortality in this population. It is a complex pathological process characterized by calcium–phosphate deposition in soft tissues, including the vascular wall, myocardium, and cardiac valves, and its presence markedly increases cardiovascular risk [7]. Coronary artery calcification (CAC), a specific manifestation of VC, is a sensitive marker of coronary atherosclerosis and is associated with various cardiovascular complications. CAC is commonly quantified using the Agatston scoring method [8].Cardiovascular calcification is increasingly recognized as a systemic condition among patients with CKD [9]. In China, CAC is the most prevalent cardiovascular complication among hemodialysis patients, affecting approximately 70% of this population [10]. Identifying factors associated with CAC progression is therefore of great clinical importance [11].
Previous studies have reported that visceral adiposity [12], malnutrition [13], and reduced muscle mass [14] are linked to the progression of coronary calcification. Body composition analysis is a valuable tool for distinguishing fat and muscle compartments and is widely used to assess nutritional status, obesity, and sarcopenia [15]. Although research has examined the relationship between CAC and clinical characteristics in dialysis patients, findings remain inconsistent due to geographical differences, varying follow-up durations, and methodological heterogeneity [16]. Nevertheless, the existing evidence is largely derived from studies assessing baseline or single-time-point measurements of body composition and nutritional status. For example, Yaprak et al. [17] investigated the associations of baseline body mass index (BMI), mid-upper arm circumference, lean tissue index (LTI), fat tissue index (FTI), and nutritional indicators such as the geriatric nutritional risk index (GNRI) with mortality in hemodialysis (HD) patients, while a retrospective analysis [18] from the CAC Consortium explored the relationship between BMI categories and coronary artery calcification. However, data remain scarce regarding how specific components of body composition—particularly skeletal muscle and adipose tissue—and, more importantly, their longitudinal changes may influence the initiation and progression of CAC. The mechanisms underlying CAC progression are not yet fully clarified. Investigating the association between baseline body composition and its longitudinal changes with CAC progression in Chinese hemodialysis patients may provide important insights for improving risk stratification and clinical outcomes.
Therefore, this study aimed to investigate the presence of CAC and evaluate its progression during follow-up, with the objective of identifying factors associated with the development and advancement of coronary artery calcification. In particular, we focused on body composition parameters to determine whether adipose tissue, muscle mass, and their longitudinal changes influence the onset and progression of CAC in hemodialysis patients. By emphasizing the dynamic, longitudinal changes in body composition rather than static measurements alone, this study highlights a key methodological strength and represents a meaningful contribution to the growing body of evidence on how dynamic nutritional and metabolic alterations influence cardiovascular outcomes in patients undergoing hemodialysis. The findings of this study may provide important evidence to support the prevention and management of vascular calcification and improve cardiovascular outcomes in this population.
Methods
Participants
This study was a prospective cross-sectional investigation that enrolled 209 patients with end-stage kidney disease (ESKD) undergoing maintenance hemodialysis at four tertiary hospitals in Zhengzhou between January and June 2017. Data were collected at baseline and again after 6 months. The inclusion criteria were: (1) age > 18 years; (2) diagnosis of ESKD with an estimated glomerular filtration rate < 5 mL·min− 1·1.73 m− 2 and receipt of maintenance hemodialysis for more than 3 months; and (3) ability to comply with study assessments. Exclusion criteria were: (1) unstable clinical conditions within the preceding 3 months, including acute coronary syndrome, decompensated heart failure, ischemic or hemorrhagic stroke, or severe infection; (2) current use of glucocorticoids; and (3) presence of malignant tumors.
The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, and written informed consent was obtained from all participants. Of the 209 patients enrolled at baseline, 193 completed the 6-month follow-up. Among those lost to follow-up, 4 died, 9 withdrew consent, and 3 discontinued participation after receiving kidney transplantation.
Data collection
Demographic and clinical information
Basic patient information was obtained from the hospital information system and supplemented through face-to-face interviews. Collected data included sex, age, educational level, etiology of ESKD, history of diabetes, hypertension, and coronary artery disease, as well as smoking and alcohol consumption status.
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Laboratory measurements
Blood samples were collected within two weeks before or after the 64-slice multidetector CT examination. The following laboratory parameters were assessed: Complete blood count: white blood cell count, hemoglobin, platelet count; Liver function tests: alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase; Renal function: serum creatinine, blood urea nitrogen, uric acid; Lipid profile: total cholesterol, triglycerides; Electrolytes: serum potassium, calcium, phosphorus; Other biomarkers: intact parathyroid hormone.
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Body composition assessment
Body composition was evaluated using the Body Composition Monitor (BCM; Fresenius Medical Care), a validated device designed to assess hydration status, lean tissue mass, fat tissue mass, and other body composition parameters in patients undergoing dialysis or with kidney disease [19]. All measurements were performed before dialysis. Patients were positioned supine and rested for several minutes to minimize posture-related fluid shifts. Electrodes were attached to all four limbs, ensuring clean and dry skin, secure adhesion, and good conductivity. After confirming patient information (e.g., height, weight, sex, and dialysis status), the device was activated, and measurements were initiated. The BCM then automatically applied multi-frequency electrical currents to assess bioimpedance.
Upon completion, the device generated the following parameters: intracellular water (ICW), extracellular water (ECW), total body water (TBW), overhydration (OH, in liters), lean tissue mass (LTM), and adipose tissue mass (ATM). In this study, body composition indices included the lean tissue index (LTI = LTM/height²) and fat tissue index (FTI = ATM/height²). Both LTI and FTI are internally calculated by the BCM and are theoretically unaffected by hydration status [19]. Patients were categorized based on changes in body composition during follow-up: Fat Increase group: follow-up FTI – baseline FTI > 0; Non-Fat Increase group: follow-up FTI – baseline FTI ≤ 0; Muscle loss group: follow-up LTI – baseline LTI < 0; Non-muscle loss group: follow-up LTI – baseline LTI ≥ 0. Body fat percentage (BFP) was calculated as adipose tissue mass (ATM) divided by dry body weight. The change in BFP was defined as the difference between the follow-up and baseline values, while the relative change in BFP was calculated as the change in body fat percentage divided by the baseline value. The extracellular-to-intracellular water ratio (E/I) has been recognized as an indicator reflecting both volume status and nutritional status in patients undergoing hemodialysis [20]. An increase in E/I was defined as a follow-up E/I value greater than the baseline E/I (i.e., follow-up E/I minus baseline E/I > 0).
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Coronary artery calcification assessment
Coronary artery calcification was assessed using a 64-slice multidetector computed tomography (CT) scanner. All images were interpreted by an experienced radiologist. The degree of calcification was quantified using the Agatston scoring method [21]. A calcified lesion was defined as an area ≥ 1 mm2 with a CT attenuation > 130 Hounsfield units (HU). The Agatston score for each lesion was calculated as the product of the lesion area and a weighted density factor determined by peak CT attenuation.
The total coronary artery calcification score (CACS) was obtained by summing the scores of calcified lesions in the left main coronary artery, left anterior descending artery, left circumflex artery, and right coronary artery. Patients were categorized into a non-calcification group (CACS = 0) and a calcification group (CACS > 0). CAC progression was defined as an annualized increase in CACS of more than 100 Agatston units. The annualized change in CACS was calculated as: ΔCACS = (follow-up CACS – baseline CACS) / follow-up duration (years) [22], a method commonly used to standardize CAC progression across different follow-up intervals.
Statistical analysis
Statistical analyses were performed using SPSS version 25.0. Continuous variables with a normal distribution were expressed as mean ± standard deviation (‾X ± S), and comparisons between groups were conducted using the independent-samples t-test. Non-normally distributed continuous variables were presented as median (Q1, Q3), and the Wilcoxon rank-sum test was applied for between-group comparisons. Categorical variables were expressed as frequencies and percentages [n (%)], and group differences were analyzed using the chi-square test. Logistic regression analyses were used to evaluate factors associated with coronary artery calcification. Four models were constructed: Model 1: unadjusted; Model 2: adjusted for age, sex, dialysis vintage, and diabetes; Model 3: adjusted for variables in Model 2 plus pre-dialysis blood urea nitrogen, serum phosphorus, triglycerides, and albumin; Model 4: adjusted for variables in Model 3 plus lean tissue index, fat tissue index, and extracellular water, E/I, E/I increase, body fat percentage, and relative change in BFP. A two-sided P value < 0.05 was considered statistically significant.
Results
Among the 209 hemodialysis patients enrolled, the mean age was 51.33 ± 14.05 years, the median dialysis vintage was 36 (16, 60) months, 121 (57.89%) were male, and 72 (34.45%) had diabetes. A total of 152 patients (72.73%) were classified into the coronary artery calcification (CAC) group (CACS > 0), while 57 patients (27.27%) were included in the non-CAC group. Baseline comparisons showed that patients in the CAC group were significantly older than those in the non-CAC group (55.11 ± 13.09 vs. 41.25 ± 11.37 years, P < 0.001). The CAC group also had higher systolic blood pressure (151.44 ± 22.77 vs. 138.09 ± 18.21 mmHg, P < 0.001), serum potassium (5.13 ± 0.83 vs. 4.73 ± 0.75 mmol/L, P = 0.001), and serum phosphorus levels (1.88 ± 0.55 vs. 1.72 ± 0.45 mmol/L, P = 0.043). Body mass index (BMI) was similarly higher in the CAC group (24.37 ± 3.78 vs. 22.86 ± 3.54 kg/m², P = 0.010). With respect to body composition, fat-related indices—including fat tissue index (FTI), total body fat (FAT), adipose tissue mass (ATM) and body fat percentage (BFP)—were all significantly elevated in the CAC group compared with the non-CAC group (FTI: 9.85 ± 4.37 vs. 8.15 ± 4.20 kg/m²; FAT: 19.71 ± 8.59 vs. 16.04 ± 7.86 kg; ATM: 26.81 ± 11.67 vs. 21.82 ± 10.71 kg; BFP: 0.41 ± 0.13 vs. 0.36 ± 0.15; P = 0.012, 0.005, 0.005, 0.017, respectively). In addition, extracellular water (ECW) and the ECW/ICW ratio were significantly higher in the CAC group (16.86 ± 3.04 vs. 15.52 ± 3.55 L; 0.95 ± 0.11 vs. 0.87 ± 0.11; P = 0.008, < 0.001, respectively). The proportions of male patients (63.16% vs. 43.86%; P = 0.018) and those with diabetes (43.42% vs. 10.53%; P < 0.001) were both significantly higher in the CAC group. No statistically significant differences were observed between the two groups regarding dialysis vintage, hemoglobin levels, C-reactive protein (CRP), intact parathyroid hormone (iPTH), blood urea nitrogen, creatinine, albumin, cholesterol, triglycerides, or muscle-related parameters such as lean tissue index (LTI), lean tissue mass (LTM), body cell mass (BCM), or intracellular water (ICW) (all P > 0.05). Detailed results are presented in Table 1.
Table 1.
Comparison of baseline characteristics between the coronary artery calcification and non-CAC groups
| Characteristics | Total, n = 209 | non-CAC group, n = 57 | CAC group, n = 152 | t/Z/χ² | P |
|---|---|---|---|---|---|
| Age, years | 51.33 ± 14.05 | 41.25 ± 11.37 | 55.11 ± 13.09 | -7.059 | 0.000 |
| Males, N (%) | 121 (57.90%) | 25(43.86%) | 96(63.16%) | 5.566 | 0.018 |
| Females, N (%) | 88 (42.10%) | 32(56.14%) | 56(36.84%) | ||
| Diabetes, N (%) | 72 (34.45%) | 6(10.53%) | 66(43.42%) | 18.434 | 0.000 |
| Duration of hemodialysis (months) | 36.00 (16.00, 60.00) | 38.00 (29.00, 47.50) | 43.00 (12.00, 76.75) | -1.652 | 0.100 |
| Pre-SBP, mmHg | 147.78 ± 22.39 | 138.09 ± 18.21 | 151.44 ± 22.77 | -3.970 | 0.000 |
| Pre-DBP, mmHg | 82.00 ± 13.57 | 84.18 ± 13.19 | 81.16 ± 13.67 | 1.430 | 0.154 |
| WBC, *10 9/L | 5.75 ± 1.84 | 5.58 ± 1.98 | 5.81 ± 1.79 | -0.793 | 0.429 |
| Hb, g/L | 108.06 ± 17.20 | 106.04 ± 17.26 | 108.83 ± 17.17 | -1.045 | 0.297 |
| PLT, *10 9/L | 157.18 ± 54.63 | 154.40 ± 61.73 | 158.18 ± 52.05 | -0.432 | 0.666 |
| Blood urea nitrogen, mmol/L | 23.25 ± 5.57 | 22.12 ± 4.90 | 23.68 ± 5.76 | -1.811 | 0.072 |
| Creatinine, umol/L | 887.70 ± 247.33 | 926.77 ± 239.61 | 873.22 ± 249.35 | 1.387 | 0.167 |
| Albumin, g/L | 41.43 ± 25.44 | 46.40 ± 48.13 | 39.57 ± 4.40 | 1.070 | 0.289 |
| Total cholesterol, mmol/L | 4.10 ± 1.11 | 4.01 ± 0.95 | 4.13 ± 1.17 | -0.689 | 0.492 |
| Triglyceride, mmol/L | 1.91 ± 1.41 | 1.83 ± 1.19 | 1.95 ± 1.49 | -0.543 | 0.588 |
| Potassium, mmol/L | 5.02 ± 0.83 | 4.73 ± 0.75 | 5.13 ± 0.83 | -3.218 | 0.001 |
| Calcium, mmol/L | 2.15 ± 0.20 | 2.18 ± 0.18 | 2.13 ± 0.20 | 1.626 | 0.105 |
| Phosphorus, mmol/L | 1.84 ± 0.53 | 1.72 ± 0.45 | 1.88 ± 0.55 | -2.046 | 0.043 |
| ALP, U/L | 116.50 (74.00, 161.00) | 129.00 (104.00, 154.00) | 125.00 (74.50, 163.75) | -1.534 | 0.125 |
| CRP, mg/L | 1.80 (1.00, 4.40) | 1.95 (0.63, 6.90) | 1.80 (1.10, 4.20) | -0.112 | 0.911 |
| iPTH, pg/ml | 351.70 (182.70, 686.03) | 174.3 (148.65, 218.10) | 239.40 (102.30, 395.25) | -1.036 | 0.300 |
| BMI, kg/m2 | 23.96 ± 3.77 | 22.86 ± 3.54 | 24.37 ± 3.78 | -2.607 | 0.010 |
| LTI, kg/m2 | 13.72 ± 2.80 | 14.07 ± 3.10 | 13.59 ± 2.67 | 1.103 | 0.271 |
| FTI, kg/m2 | 9.39 ± 4.38 | 8.15 ± 4.20 | 9.85 ± 4.37 | -2.528 | 0.012 |
| LTM, kg | 37.79 ± 9.73 | 38.5 ± 11.16 | 37.52 ± 9.17 | 0.645 | 0.519 |
| FAT, kg | 18.7 ± 8.53 | 16.04 ± 7.86 | 19.71 ± 8.59 | -2.807 | 0.005 |
| BFP | 0.39 ± 0.14 | 0.36 ± 0.15 | 0.41 ± 0.13 | -2.408 | 0.017 |
| ATM, kg | 25.45 ± 11.61 | 21.82 ± 10.71 | 26.81 ± 11.67 | -2.811 | 0.005 |
| BCM, kg | 20.99 ± 6.48 | 21.57 ± 7.39 | 20.77 ± 6.11 | 0.795 | 0.428 |
| TBW, L | 34.50 ± 6.78 | 33.55 ± 7.72 | 34.86 ± 6.39 | -1.239 | 0.217 |
| ECW, L | 16.49 ± 3.24 | 15.52 ± 3.55 | 16.86 ± 3.04 | -2.697 | 0.008 |
| ICW, L | 18.01 ± 3.91 | 18.03 ± 4.5 | 18.01 ± 3.67 | 0.036 | 0.971 |
| E/I | 0.93 ± 0.12 | 0.87 ± 0.11 | 0.95 ± 0.11 | -4.229 | 0.000 |
Abbreviations: Pre-SBP, pre-hemodialysis systolic blood pressure; Pre-DBP, pre-hemodialysis diastolic blood pressure; WBC, white blood cell; Hb, hemoglobin; PLT, platelet; ALP, alkaline phosphatase; CRP, C-reactive protein; iPTH, intact parathyroid hormone; BMI, body mass index; LTI, lean tissue index; FTI, fat tissue index; LTM, lean tissue mass; FAT, total body fat; BFP, body fat percentage; ATM, adipose tissue mass; BCM, body cell mass; TBW, total body water; ECW, extracellular water; ICW, intracellular water; E/I, extracellular-to-intracellular water ratio
Among the 209 hemodialysis patients enrolled, 193 completed two assessments of coronary artery CT calcium scoring. At baseline, 139 patients (72.02%) had coronary vascular calcification (CVC), which increased to 145 patients (75.13%) after six months. Interestingly, calcification scores reverted to zero in four patients, while ten patients developed new-onset CVC during follow-up. Based on the annualized change in calcification score, 59 patients (30.57%) were classified into the CVC progression group, and 134 (69.43%) were classified into the non-CVC progression group, using an annual increase of more than 100 Agatston units as the threshold for progression. Compared with the non-CVC progression group, patients in the CVC progression group were older (55.90 ± 12.06 vs. 48.60 ± 14.54 years, P = 0.001) and had higher body mass index (BMI) (24.88 ± 4.11 vs. 23.42 ± 3.50 kg/m², P = 0.013). Regarding body composition, fat-related indices—including FTI, FAT, ATM, and BFP—were all significantly higher in the progression group (FTI: 10.32 ± 5.38 vs. 8.67 ± 3.83 kg/m²; FAT: 20.72 ± 10.53 vs. 17.36 ± 7.47 kg; ATM: 28.18 ± 14.31 vs. 23.62 ± 10.17 kg; BFP: 0.42 ± 0.14 vs. 0.37 ± 0.13; P = 0.036, 0.031, 0.031, 0.046, respectively). The ECW/ICW ratio was also significantly elevated in the progression group (0.95 ± 0.12 vs. 0.90 ± 0.11, P = 0.008). The prevalence of diabetes was higher among patients with calcification progression compared with those without progression (42.37% vs. 26.12%; P = 0.025). Additionally, a significantly lower proportion of patients in the progression group experienced fat gain during follow-up compared with the non-progression group (56.90% vs. 76.52%; P = 0.006). No statistically significant differences were observed between the two groups in terms of sex, dialysis vintage, blood pressure, hemoglobin, C-reactive protein, iPTH, blood urea nitrogen, creatinine, albumin, cholesterol, triglycerides, serum potassium, serum phosphorus, or muscle-related parameters (including LTI, LTM, BCM, and ICW). In addition, there were no significant between-group differences in the prevalence of muscle loss or in the proportion of patients with an increase in the extracellular-to-intracellular water ratio (E/I). Similarly, no significant differences were observed in either the absolute or the relative change in body fat percentage between the two groups (all P > 0.05). Detailed results are summarized in Table 2.
Table 2.
Comparison between the calcification non-progression and progression groups
| Characteristics | non-CVC progression group, n = 134 | CVC progression group, n = 59 | t/Z/χ² | P |
|---|---|---|---|---|
| Age, years | 48.60 ± 14.54 | 55.90 ± 12.06 | -3.379 | 0.001 |
| Males, N (%) | 74 (55.22%) | 39 (66.10%) | ||
| Females, N (%) | 60 (44.78%) | 20 (33.90%) | 1.997 | 0.204 |
| Diabetes, N (%) | 35 (26.12%) | 25 (42.37%) | 5.051 | 0.025 |
| Duration of hemodialysis, months | 44.00 (25.00, 65.00) | 37.00 (12.00, 92.50) | 0.494 | 0.621 |
| Pre-SBP, mmHg | 145.34 ± 23.23 | 151.24 ± 19.64 | -1.689 | 0.093 |
| Pre-DBP, mmHg | 82.50 ± 13.96 | 80.86 ± 12.67 | 0.759 | 0.449 |
| WBC, *10 9/L | 5.61 ± 1.93 | 6.02 ± 1.76 | -1.398 | 0.164 |
| Hb, g/L | 107.46 ± 16.99 | 109.58 ± 18.37 | -0.777 | 0.438 |
| PLT, *10 9/L | 151.72 ± 57.16 | 163.69 ± 47.64 | -1.398 | 0.164 |
| Blood urea nitrogen, mmol/L | 22.87 ± 4.86 | 24.33 ± 6.74 | -1.692 | 0.092 |
| Creatinine, umol/L | 898.96 ± 229.5 | 879.64 ± 277.22 | -1.495 | 0.139 |
| Albumin, g/L | 40.21 ± 4.33 | 39.00 ± 5.62 | 1.635 | 0.104 |
| Total cholesterol, mmol/L | 4.03 ± 0.94 | 4.04 ± 1.11 | -0.097 | 0.923 |
| Triglyceride, mmol/L | 1.80 ± 1.29 | 2.15 ± 1.64 | -1.577 | 0.117 |
| Potassium, mmol/L | 4.95 ± 0.80 | 5.13 ± 0.79 | -1.446 | 0.150 |
| Calcium, mmol/L | 2.15 ± 0.18 | 2.16 ± 0.24 | -0.409 | 0.683 |
| Phosphorus, mmol/L | 1.80 ± 0.48 | 1.92 ± 0.62 | -1.261 | 0.210 |
| ALP, U/L | 164.63 ± 201.76 | 106.83 ± 54.79 | 1.331 | 0.189 |
| CRP, mg/L | 2.20 (1.10, 8.30) | 1.70 (0.80, 4.10) | 0.617 | 0.537 |
| iPTH, pg/ml | 195.90 (161.60, 434.30) | 234.40 (92.80, 315.10) | -0.897 | 0.370 |
| OH, L | 2.11 ± 1.55 | 2.44 ± 2.05 | -1.226 | 0.222 |
| Vurea, L | 32.52 ± 6.62 | 33.04 ± 5.91 | -0.520 | 0.604 |
| BMI, kg/m2 | 23.42 ± 3.50 | 24.88 ± 4.11 | -2.516 | 0.013 |
| LTI, kg/m2 | 13.93 ± 2.68 | 13.62 ± 2.92 | 0.705 | 0.482 |
| FTI, kg/m2 | 8.67 ± 3.83 | 10.32 ± 5.38 | -2.126 | 0.036 |
| LTM, kg | 38.46 ± 9.66 | 37.34 ± 9.46 | 0.748 | 0.456 |
| FAT, kg | 17.36 ± 7.47 | 20.72 ± 10.53 | -2.201 | 0.031 |
| BFP | 0.37 ± 0.13 | 0.42 ± 0.14 | -2.010 | 0.046 |
| ATM, kg | 23.62 ± 10.17 | 28.18 ± 14.31 | -2.200 | 0.031 |
| BCM, kg | 21.45 ± 6.38 | 20.69 ± 6.39 | 0.772 | 0.441 |
| TBW, L | 34.40 ± 6.84 | 34.96 ± 6.20 | -0.542 | 0.588 |
| ECW, L | 16.24 ± 3.19 | 16.95 ± 3.08 | -1.458 | 0.147 |
| ICW, L | 18.16 ± 3.97 | 18.02 ± 3.52 | 0.241 | 0.810 |
| E/I | 0.90 ± 0.11 | 0.95 ± 0.12 | -2.671 | 0.008 |
| E/I Increase | 64 (47.76%) | 31 (52.54%) | 0.375 | 0.540 |
| Absolute change in BFP | 0.05 ± 0.07 | 0.04 ± 0.09 | 0.981 | 0.329 |
| Relative change in BFP | 0.11 (0.03, 0.29) | 0.05 (-0.05, 0.28) | -1.123 | 0.261 |
| Fat Increase | 101 (76.52%) | 33 (56.90%) | 7.461 | 0.006 |
| Muscle Loss | 94 (70.68%) | 35 (59.32%) | 2.390 | 0.122 |
Abbreviations: Pre-SBP, pre-hemodialysis systolic blood pressure; Pre-DBP, pre-hemodialysis diastolic blood pressure; WBC, white blood cell; Hb, hemoglobin; PLT, platelet; ALP, alkaline phosphatase; CRP, C-reactive protein; iPTH, intact parathyroid hormone; OH, overhydration; Vurea, volume of urea distribution; BMI, body mass index; LTI, lean tissue index; FTI, fat tissue index; LTM, lean tissue mass; FAT, total body fat; BFP, body fat percentage; ATM, adipose tissue mass; BCM, body cell mass; TBW, total body water; ECW, extracellular water; ICW, intracellular water; E/I, extracellular-to-intracellular water ratio
E/I Increase: follow-up E/I – baseline E/I > 0; Fat Increase: follow-up FTI – baseline FTI > 0; Muscle loss: follow-up LTI – baseline LTI < 0
Univariate logistic regression analysis was performed to identify factors associated with coronary artery calcification progression. Age, BMI, FTI, BFP, E/I, and the presence of diabetes were significantly associated with an increased risk of calcification progression. Specifically, older age (OR = 1.038, 95% CI: 1.015–1.062, P = 0.001), higher BMI (OR = 1.107, 95% CI: 1.019–1.203, P = 0.016), elevated FTI (OR = 1.086, 95% CI: 1.014–1.164, P = 0.019), higher BFP (OR = 1.024, 95% CI: 1.000–1.047, P = 0.048), elevated E/I (OR = 1.037, 95% CI: 1.008–1.066, P = 0.011), and diabetes (OR = 2.080, 95% CI: 1.092–3.962, P = 0.026) were identified as risk factors. In contrast, an increase in FTI during follow-up appeared to confer a protective effect against calcification progression (OR = 0.405, 95% CI: 0.21–0.782, P = 0.007). Multivariate logistic regression analysis was then conducted to adjust for potential confounders. To avoid potential multicollinearity between BMI and FTI, BMI was excluded from the multivariable logistic regression analysis. The results confirmed that age remained an independent risk factor for the progression of coronary artery calcification (OR = 1.033, 95% CI: 1.006–1.061, P = 0.018). Meanwhile, fat increase over the follow-up period continued to serve as an independent protective factor (OR = 0.435, 95% CI: 0.207–0.912, P = 0.028). These findings suggest that while older age increases susceptibility to calcification progression, longitudinal increases in fat tissue index may have a protective role in hemodialysis patients. Table 3.
Table 3.
Univariate and multivariate analysis of factors associated with calcification progression
| OR (95%CI) | P | OR (95%CI) | P | |
|---|---|---|---|---|
| Age, years | 1.038 (1.015,1.062) | 0.001 | 1.033 (1.006,1.061) | 0.018 |
| Pre-SBP, mmHg | 1.012 (0.998,1.026) | 0.094 | ||
| Blood urea nitrogen, mmol/L | 1.048 (0.992,1.108) | 0.095 | ||
| Creatinine, umol/L | 1.000 (0.998,1.001) | 0.614 | ||
| Albumin, g/L | 0.950 (0.890,1.014) | 0.125 | ||
| Triglyceride, mmol/L | 1.180 (0.955,1.458) | 0.125 | ||
| Potassium, mmol/L | 1.327 (0.902,1.952) | 0.150 | ||
| BMI, kg/m2 | 1.107 (1.019,1.203) | 0.016 | ||
| FTI, kg/m2 | 1.086 (1.014,1.164) | 0.019 | 1.227 (0.979,1.538) | 0.076 |
| Diabetes | 2.080 (1.092,3.962) | 0.026 | 1.146 (0.539,2.438) | 0.723 |
| Fat Increase | 0.405 (0.210,0.782) | 0.007 | 0.435 (0.207,0.912) | 0.028 |
| Muscle Loss | 0.605 (0.319,1.147) | 0.124 | ||
| ECW, L | 1.074 (0.975,1.183) | 0.148 | ||
| E/I, % | 1.037 (1.008,1.066) | 0.011 | 1.027 (0.989,1.066) | 0.167 |
| BFP, % | 1.024 (1.000,1.047) | 0.048 | 0.934 (0.886,1.008) | 0.078 |
Abbreviations: Pre-SBP, pre-hemodialysis systolic blood pressure; BMI, body mass index; FTI, fat tissue index; ECW, extracellular water; E/I, extracellular-to-intracellular water ratio; BFP, body fat percentage. Fat Increase: follow-up FTI–baseline FTI > 0; Muscle loss: follow-up LTI – baseline LTI < 0
Further analysis of the relationship between fat increase and coronary artery calcification (CAC) demonstrated that fat increase consistently acted as a protective factor against CAC progression (P < 0.05), independent of other covariates. This protective effect was observed across all models, including Model 1 (unadjusted), Model 2 (adjusted for age, sex, dialysis vintage, and diabetes), Model 3 (further adjusted for pre-dialysis blood urea nitrogen, serum phosphorus, triglycerides, and albumin), and Model 4 (further adjusted for lean tissue index, fat tissue index, extracellular water, E/I, E/I increase, body fat percentage, and relative change in BFP), indicating that the association between fat increase and reduced CAC progression remained robust after accounting for potential confounders (Table 4).
Table 4.
Correlation between fat increase and calcification progression
| OR (95%CI) | P | ||
|---|---|---|---|
| Model 1 | Fat Increase | 0.405 (0.210,0.782) | 0.007 |
| Model 2 | Fat Increase | 0.395 (0.196,0.795) | 0.009 |
| Model 3 | Fat Increase | 0.364 (0.173,0.766) | 0.008 |
| Model 4 | Fat Increase | 0.410 (0.175,0.963) | 0.041 |
Fat Increase: follow-up FTI–baseline FTI > 0
Variables entered in Model 2, based on Model 1: age, sex, dialysis vintage, diabetes
Variables entered in Model 3, based on Model 2: blood urea nitrogen, serum phosphorus, triglycerides, and albumin
Variables entered in Model 4, based on Model 3: lean tissue index, fat tissue index, extracellular water, E/I, E/I increase, body fat percentage, and relative change in BFP
To further explore the relationship between fat increase and body composition, baseline parameters were compared between patients who experienced fat increase during follow-up and those who did not. No significant differences were observed between the two groups in demographic characteristics, including age, sex, and dialysis vintage, or in laboratory parameters such as hemoglobin, pre-dialysis blood urea nitrogen, lipid profile, albumin, C-reactive protein, and iPTH (all P > 0.05). However, patients in the fat increase group exhibited significantly higher muscle-related indices, including lean tissue index (14.19 ± 2.64 vs. 12.92 ± 2.69 kg/m², P = 0.002), lean tissue mass ( 39.30 ± 9.42 vs. 35.06 ± 8.91 kg, P < 0.001), and body cell mass (22.03 ± 6.25 vs. 19.13 ± 5.93 kg, P = 0.003) compared with the non-fat increase group. Conversely, the fat increase group had significantly lower BMI (23.48 ± 3.18 vs. 25.31 ± 4.76 kg/m², P = 0.009) and fat-related indices, including fat tissue index (FTI: 8.38 ± 3.47 vs. 11.56 ± 5.43 kg/m², P < 0.001), total body fat (FAT: 16.91 ± 7.07 vs. 22.7 ± 10.35 kg, P < 0.001), adipose tissue mass (ATM: 23.01 ± 9.62 vs. 30.88 ± 14.08 kg, P < 0.001), and body fat percentage (BFP: 0.36 ± 0.12 vs. 0.45 ± 0.15, P < 0.001). The extracellular-to-intracellular water ratio (E/I) was significantly lower in the fat increase group compared with the non-fat increase group (0.91 ± 0.11 vs. 0.95 ± 0.12, P = 0.031). Correspondingly, changes in BFP were significantly greater in the fat increase group, both in absolute BFP change [0.08 (0.04, 0.11) vs. −0.03 (− 0.07, − 0.00), P < 0.001] and in the relative change in BFP [0.22 (0.08, 0.36) vs. −0.06 (− 0.17, − 0.00), P < 0.001]. The proportion of patients with an increase in E/I was significantly higher in the fat increase group than in the non-fat increase group (55.07% vs. 35.09%; P = 0.011). These results are summarized in Table 5.
Table 5.
Baseline comparison of patients with and without fat increase
| Characteristics | Non-Fat Increase, n = 58 | Fat Increase, n = 138 | t/Z/χ² | P |
|---|---|---|---|---|
| Age, years | 50.88 ± 14.4 | 50.92 ± 14.03 | -0.019 | 0.985 |
| Males, N (%) | 32 (55.17%) | 82 (59.42%) | ||
| Females, N (%) | 26 (44.83%) | 56 (40.58%) | 0.153 | 0.695 |
| Diabetes, N (%) | 23 (39.66%) | 40 (28.99%) | 1.670 | 0.195 |
| Duration of hemodialysis, months | 48.43 ± 36.97 | 42.46 ± 35.06 | 1.070 | 0.286 |
| Pre-SBP, mmHg | 147.95 ± 19.51 | 146.93 ± 23.69 | 0.287 | 0.774 |
| Pre-DBP, mmHg | 83.40 ± 15.2 | 81.40 ± 12.98 | 0.927 | 0.355 |
| WBC, *10 9/L | 5.69 ± 1.59 | 5.79 ± 1.98 | -0.315 | 0.753 |
| Hb, g/L | 110.98 ± 16.92 | 106.89 ± 17.34 | 1.517 | 0.131 |
| PLT, *10 9/L | 158.55 ± 54.16 | 156.44 ± 56.01 | 0.240 | 0.811 |
| Blood urea nitrogen, mmol/L | 24.13 ± 5.91 | 22.83 ± 5.23 | 1.524 | 0.129 |
| Creatinine, umol/L | 885.01 ± 277.13 | 889.73 ± 229.92 | -0.123 | 0.902 |
| Albumin, g/L | 39.31 ± 5.24 | 40.04 ± 4.57 | -0.979 | 0.329 |
| Total cholesterol, mmol/L | 3.91 ± 1.24 | 4.14 ± 0.97 | -1.390 | 0.166 |
| Triglyceride, mmol/L | 1.97 ± 1.20 | 1.90 ± 1.47 | 0.345 | 0.730 |
| Potassium, mmol/L | 4.99 ± 0.74 | 5.05 ± 0.87 | -0.455 | 0.650 |
| Calcium, mmol/L | 2.17 ± 0.20 | 2.14 ± 0.20 | 0.744 | 0.458 |
| Phosphorus, mmol/L | 1.83 ± 0.50 | 1.83 ± 0.54 | -0.071 | 0.943 |
| ALP, U/L | 112.00 ± 40.05 | 162.08 ± 209.55 | -1.151 | 0.256 |
| CRP, mg/L | 4.64 ± 5.88 | 3.73 ± 5.58 | 0.664 | 0.508 |
| iPTH, pg/ml | 529.59 ± 520.79 | 533.22 ± 522.51 | -0.044 | 0.965 |
| OH, L | 2.03 ± 1.66 | 2.30 ± 1.75 | -0.977 | 0.330 |
| Vurea, L | 31.76 ± 5.97 | 33.16 ± 6.60 | -1.397 | 0.164 |
| BMI, kg/m2 | 25.31 ± 4.76 | 23.48 ± 3.18 | 2.678 | 0.009 |
| LTI, kg/m2 | 12.92 ± 2.69 | 14.19 ± 2.64 | -3.069 | 0.002 |
| FTI, kg/m2 | 11.56 ± 5.43 | 8.38 ± 3.47 | 4.120 | 0.000 |
| LTM, kg | 35.06 ± 8.91 | 39.30 ± 9.42 | -2.921 | 0.004 |
| FAT, kg | 22.70 ± 10.35 | 16.91 ± 7.07 | 3.893 | 0.000 |
| ATM, kg | 30.88 ± 14.08 | 23.01 ± 9.62 | 3.891 | 0.000 |
| BCM, kg | 19.13 ± 5.93 | 22.03 ± 6.25 | -3.006 | 0.003 |
| TBW, L | 33.55 ± 6.28 | 35.10 ± 6.81 | -1.493 | 0.137 |
| ECW, L | 16.24 ± 3.01 | 16.64 ± 3.30 | -0.792 | 0.429 |
| ICW, L | 17.31 ± 3.55 | 18.47 ± 3.89 | -1.956 | 0.052 |
| E/I | 0.95 ± 0.12 | 0.91 ± 0.11 | 2.178 | 0.031 |
| E/I Increase | 20 (35.09%) | 76 (55.07%) | 6.446 | 0.011 |
| BFP | 0.45 ± 0.15 | 0.36 ± 0.12 | 4.093 | 0.000 |
| Absolute change in BFP | -0.03 (-0.07,-0.00) | 0.08 (0.04,0.11) | 10.150 | 0.000 |
| Relative change in BFP | -0.06 (-0.17,-0.00) | 0.22 (0.08,0.36) | 10.225 | 0.000 |
Abbreviations: Pre-SBP, pre-hemodialysis systolic blood pressure; Pre-DBP, pre-hemodialysis diastolic blood pressure; WBC, white blood cell; Hb, hemoglobin; PLT, platelet; ALP, alkaline phosphatase; CRP, C-reactive protein; iPTH, intact parathyroid hormone; OH, overhydration; Vurea, volume of urea distribution; BMI, body mass index; LTI, lean tissue index; FTI, fat tissue index; LTM, lean tissue mass; FAT, total body fat ; ATM, adipose tissue mass; BCM, body cell mass; TBW, total body water; ECW, extracellular water; ICW, intracellular water; E/I, extracellular-to-intracellular water ratio; BFP, body fat percentage. E/I Increase: follow-up E/I – baseline E/I > 0
Discussion
The coronary artery calcium score (CACS) is closely associated with the risk of cardiovascular events and can improve risk stratification beyond traditional cardiovascular risk factors [23]. A positive correlation between CACS and cardiovascular event risk has been well established [24, 25]. Previous studies have identified older age [26], a history of diabetes [27], higher mean arterial pressure [28], and elevated time-averaged serum phosphorus levels [29] as independent risk factors for accelerated CAC progression and increased mortality. In this multicenter prospective study, we monitored body composition and coronary artery calcification in 209 hemodialysis patients. The baseline cross-sectional analysis revealed that patients with coronary artery calcification were older, had a higher proportion of males and individuals with diabetes, and exhibited higher systolic blood pressure, serum potassium, and phosphorus levels—factors that are consistent with those reported in prior studies.
Regarding body composition, both the calcification group and the calcification progression group exhibited higher BMI values. Although multivariate regression did not reveal a statistically significant association, this may be attributable to the lack of detailed stratification of body weight in the present study. Previous research has suggested that elevated BMI (overweight and obese individuals) is associated with an increased risk of cardiovascular calcification, although mortality rates are not significantly higher among overweight patients [18]. In the hemodialysis population, overweight or obese individuals often show a lower risk of cardiovascular mortality compared with patients of normal or low body weight, a phenomenon referred to as the “obesity paradox” [30, 31]. However, BMI has inherent limitations, as it cannot distinguish between fat and muscle mass, and reliance solely on BMI may obscure important differences in body composition. Previous studies [32] investigating the associations between body composition–related parameters—such as the visceral adiposity index, fat tissue index, lean tissue index, and body shape index—and Framingham risk scores, which estimate the 10-year risk of cardiovascular mortality, have also suggested that detailed body composition measurements provide more refined risk stratification than body mass index alone. Bioelectrical impedance analysis provides a more precise assessment by differentiating muscle and fat compartments. In this study, no statistically significant differences were observed in muscle-related indices (LTI, LTM, BCM) between the calcification groups, whereas fat-related indices (FTI, FAT, ATM, BFP) differed significantly. Consistent with previous studies [15, 33], these findings suggest a detrimental effect of excessive adiposity on vascular calcification, highlighting the potential role of adipose tissue in the coronary artery calcification.
Although the calcification group exhibited higher fat tissue index (FTI) values in the intergroup comparison, regression analysis did not identify LTI or FTI as independent predictors of coronary artery calcification. This may be attributable to the relatively small sample size and the fact that FTI in this study represents total body fat without distinguishing between visceral and subcutaneous fat. Previous studies have indicated that visceral adiposity is a risk factor for coronary artery calcification, as it secretes pro-inflammatory molecules and cytokines that promote the calcification process [33]. While FTI derived from multi-frequency bioelectrical impedance analysis cannot differentiate between subcutaneous and visceral fat, it offers practical advantages in terms of convenience and speed, and simultaneously provides reference values for assessing volume status, making it suitable for use in patients with chronic kidney disease. In addition, this study found that patients in the calcification group had higher extracellular water (ECW) and ECW/ICW ratios. Elevated ECW, reflecting volume overload, is closely associated with increased vascular wall tension and activation of inflammatory responses [34]. An increased ECW/ICW ratio may also indicate malnutrition and reduced cellular volume [35].Collectively, these factors can promote the osteogenic phenotypic transformation of vascular smooth muscle cells, thereby exacerbating coronary artery calcification.
Although CAC is traditionally considered a slowly progressive process, patients undergoing maintenance hemodialysis exhibit markedly accelerated vascular calcification. Previous studies have reported detectable and clinically relevant progression of CAC within short follow-up periods [36], including as early as 6 months, in this high-risk population. The rapid progression is thought to be driven by abnormalities in calcium–phosphate metabolism, chronic inflammation, and uremia-related vascular smooth muscle cell osteogenic transformation [37]. Therefore, a 6-month interval may be sufficient to capture meaningful changes in CAC in hemodialysis patients. Previous studies have identified sarcopenia as an independent risk factor for the progression of vascular calcification. Research by Ji Eun Jun et al. [14]conducted in adults without cardiovascular disease, demonstrated that a low skeletal muscle index independently predicted the development and progression of cardiovascular calcification, irrespective of other cardiometabolic risk factors. Skeletal muscle serves as the primary site for systemic insulin-mediated glucose uptake; thus, reduced muscle mass can impair glucose utilization and exacerbate insulin resistance [38]. Chronic hyperglycemia promotes oxidative stress, leading to increased formation of advanced glycation end products, which further amplifies oxidative stress and triggers inflammatory responses, ultimately causing cellular damage [39, 40]. The metabolic dysregulation and pro-inflammatory microenvironment associated with sarcopenia are closely linked to vascular calcification in patients with chronic kidney disease [41]. In contrast, individuals with preserved muscle mass appear less prone to developing coronary artery calcification.
In this study, both univariate and multivariate logistic regression analyses indicated that an increase in fat tissue index (FTI) was a protective factor against calcification progression. This association remained statistically significant after adjustment across multiple models, representing an intriguing and seemingly paradoxical observation. Further comparative analysis showed that patients in the fat increase group had lower BMI, higher muscle-related components (LTI, LTM, BCM), and lower fat-related components (FTI, FAT, ATM, BFP). A possible explanation is that the “fat increase group” does not represent an obese population, but rather individuals with relatively low adipose tissue and adequate muscle mass. The higher muscle mass in this group may provide a robust metabolic foundation that mitigates the risk of coronary artery calcification. On this foundation, a moderate increase in fat may not be detrimental and could even appear protective. In the present study, fat increase was defined as the longitudinal change in the fat tissue index rather than pathological or unfavorable fat accumulation. In patients with chronic kidney disease undergoing dialysis, weight gain is predominantly driven by an increase in fat mass. Over time, increases in fat mass have been associated with improved survival in hemodialysis patients, which appears to be attributable to gains in adipose tissue rather than lean mass [42]. Supporting this notion, Barac-Nieto M. reported that fat gain can be an early and important indicator of nutritional improvement, providing the energy necessary for subsequent muscle mass accretion [43]. However, we also observed a higher proportion of patients with an increased ECW/ICW ratio (E/I) in the fat increase group, which may be related to higher extracellular water levels. We speculate that patients in the fat increase group may have greater dietary intake, leading to a corresponding increase in extracellular water. A previous study by Yajima, T. [44] reported that annual changes in the E/I ratio were a risk factor for mortality. In contrast, in our study, an increase in E/I was not identified as a determinant of coronary artery calcification progression. Notably, the E/I ratio cannot distinguish whether an increased value reflects extracellular fluid expansion due to better appetite or nutritional intake, or intracellular fluid loss associated with nutritional deterioration. In addition, the relatively short follow-up duration may have influenced our findings. Therefore, the role of E/I and its longitudinal changes in vascular calcification warrants further investigation in larger-scale and long-term studies.
This study has several limitations. First, the relatively small sample size and short follow-up period meant that changes in body composition and coronary calcification were modest, which may limit the statistical reliability of the findings, and longer longitudinal studies are warranted to further validate the long-term progression of CAC. Second, confounding factors related to diet and medication use were unavoidable due to individual patient differences and compliance issues. Third, the use of fat tissue index (FTI) as a body composition indicator reflects total body fat but cannot accurately differentiate between subcutaneous and visceral adipose tissue. Fourth, the observational longitudinal design precluded strict control over changes in body composition. Future studies with larger sample sizes and longer follow-up periods are needed to better elucidate the relationship between body composition, its dynamic changes, and vascular calcification, thereby providing more robust evidence to optimize cardiovascular management in dialysis patients.
Our study further suggests that, although fat-related metrics and BMI were identified as risk factors for coronary artery calcification progression in univariate analyses, they did not retain independent significance in multivariate models. Importantly, dynamic monitoring of body composition revealed that underweight patients who experienced fat gain and maintained higher muscle mass demonstrated a lower tendency for calcification progression. These findings indicate that fat gain may serve as a marker of favorable nutritional status. For patients undergoing hemodialysis, maintaining adequate muscle mass and implementing targeted nutritional interventions may be crucial strategies to mitigate the progression of cardiovascular calcification.
Author contributions
Y.Z. wrote the main manuscript text and L.H. provided statistical analysis. All authors reviewed the manuscript.
Funding
This research was funded by the Three New Project of Affiliated Yijishan Hospital of Wannan Medical College (grant number Y24062) and the Wannan Medical College Young and Middle-aged Research Fund (grant number WK2024ZQNZ49).
Data availability
The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.
Declarations
Ethical approval and consent to participate
Ethical approval for this research in accordance with the Declaration of Helsinki was acquired from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University, and written informed consent was obtained from all participants.
Consent for publication
Not applicable. This manuscript does not contain any identifying images, personal information, or clinical details of research participants that could compromise their anonymity.
Generative AI statement
The author(s) declare that no Gen AI was used in the creation of this manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.