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. 2024 Apr 25;19(4):e0302073. doi: 10.1371/journal.pone.0302073

Systemic immune-inflammatory indicators and bone mineral density in chronic kidney disease patients: A cross-sectional research from NHANES 2011 to 2018

Yuying Jiang 1, Xiaorong Bao 1,*
Editor: Ewa Tomaszewska2
PMCID: PMC11045113  PMID: 38662733

Abstract

Background

The purpose of this study was to look at the relationship between the Systemic Immune Inflammatory Index (SII) and bone mineral density (BMD) in the pelvis, left upper and lower limbs, lumbar spine, thoracic spine, and trunk in a chronic kidney disease (CKD) population in the United States.

Methods

The National Health and Nutrition Examination Survey (2011–2016) yielded 2302 people with CKD aged >18 years. CKD was defined as eGFR less than 90 ml/min/1.73 m2 or eGFR greater than 90 ml/min/1.73 m2 with urine ACR greater than 30 mg/L.SII was calculated as PC * (NC / LC) from platelet count (PC), neutrophil count (NC), and lymphocyte count (LC). Multiple logistic regression was used to examine the relationship between BMD and SII at different sites in CKD patients, smoothed curve-fitting and generalized weighting models were used to investigate non-linear relationships, and a two-tailed linear regression model was used to find potential inflection points in the model.

Results

We discovered a negative correlation between SII and pelvic BMD among 2302 participants after controlling for gender, age, and race [β = -0.008; 95% confidence value -0.008; 95% confidence interval (CI) -0.014, -0.002]. Lower PEBMD was related to increasing SII (trend p = 0.01125). After additional correction, only pelvic BMD remained adversely linked with SII [value -0.006; 95% CI -0.012, -0.000, p = 0.03368]. Smoothed curve fitting revealed a consistent inverse relationship between SII and pelvic BMD. Further stratified analyses revealed a substantial positive negative connection between SII and pelvic BMD in individuals who did not have hypertension, diabetes, a BMI of more than 30 kg/m2, or stage 2 CKD. The connection between SII and PEBMD in people without diabetes revealed a strong inverted U-shaped curve.

Conclusion

In individuals with CKD in the United States, there was a negative connection between the systemic immunoinflammatory index (SII) and pelvic BMD. The SII might be a low-cost and simple test for CKD-related BMD loss.

Background

More than 10% of the world’s population and more than 80 billion people are affected by chronic kidney disease (CKD), one of the world’s leading non-communicable causes of death [1]. By 2024, it is expected to rank as the fifth leading cause of life expectancy loss globally [2]. Patients with CKD frequently develop mineral bone disorders, such as osteoporosis and renal osteodystrophy, which worsen with deteriorating renal function, which is extremely common and harmful [3]. The 2017 KDIGO recommendations state that when evaluating the diagnostic and treatment requirements for osteoporosis in individuals with CKD, testing BMD may be preferred to doing a bone biopsy [4]. Furthermore, a 2022 meta-analysis showed that lower BMD values were associated with an increased risk of all-cause mortality in patients with CKD [5]. In patients with CKD, the clinical utility of BMD measurements for assessing bone loss and other related health conditions remains worth investigating due to the presence of various hormonal and metabolic alterations [6].

In fact, ongoing low-grade inflammation and immunological dysfunction are now recognized as distinguishing characteristics of CKD and are linked to patient death [7]. Numerous investigations have demonstrated that inflammation worsens renal function. White blood cell count, interleukin-6, his-CRP, and tumor necrosis factor-alpha receptor were found to have a favorable correlation with the outcome of CKD in a cross-sectional investigation by Shankar et al. [8]. Inflammation may be a sign of a poor prognosis in CKD patients, according to cohort research by Amdur RL et al. [9].

Due to their shared developmental ecological niche, the immune system and bone currently function as a tightly coupled functional unit (the bone immune system), with numerous anatomical and vascular sites of ongoing interaction between the two [10]. The role of the immune system in various skeletal pathologies is currently well established. Through direct or indirect influences on the physiological functions of bone cells, immune cells can eventually alter bone density [11, 12]. Some indices reflecting systemic immune and inflammatory status, such as the sex granulocyte-lymphocyte ratio [13], derived from immune cell counts, have also been correlated with BMD changes [14]. To better monitor the health of CKD patients, researchers are looking for novel indices based on immune cell counts to evaluate the risk of bone loss in patients.

A novel index that uses platelet, neutrophil, and lymphocyte counts called the Systemic Immune Inflammation Index (SII) can be used to measure the level of systemic inflammation [15, 16]. Growing data suggests that it gives insight into the body’s overall immunological and inflammatory status and may be used to forecast risk and evaluate prognosis in conditions such as tumors, coronary artery disease, and bone loss [17–20]. Additionally, Qin et al. noted that higher SII was linked to a higher incidence of albuminuria in adults [21]. The association between BMD and SII in CKD patients is still unclear due to the small number of studies, and further research is required to determine the function of SII in the problems of bone loss in CKD patients.

The purpose of this study was to evaluate the association between SII and BMD in CKD patients and to assess the correlation between SII and the risk of bone loss/osteoporosis in CKD patients based on the theoretical backdrop discussed above. We hypothesized that increasing SII is linked to a higher risk of osteoporosis and that SII is inversely correlated with bone mineral density (BMD).

Materials and methods

Study subjects

The National Health and Nutrition Examination Survey (NHANES), which is based on a cross-sectional cohort study and intended to evaluate the nutritional and health status of the general population in the United States (US), served as the source for all subject data. Every two years, NHANES is updated and is associated with the US Centers for Disease Control and Prevention. Data from the NHANES 2011–2016 (2011–2012, 2013–2014, and 2015–2016) were retrieved. The authors do not have access to information that could identify individual participants during data collection.

Out of the 29,903 participants, we eliminated 5,324 people because their lymphocyte, neutrophil, and platelet counts were missing; 13,366 people because their blood creatinine, sex, race, and BMD measurements were missing; 8,541 people because they did not have CKD; 370 people because they were under 18; and 2,302 people because there were no pregnant people among the sample. The inclusion and exclusion details of the study population in this study are shown in Fig 1.

Fig 1. The flow of participants through the study.

Fig 1

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Ethics statement

The NHANES procedure was approved by the National Center for Health Statistics Research Ethics Review Board, and written informed permission was acquired. The NHANES data were made publicly accessible after being anonymized. It allowed scholars to transform the data into a format that could be learned. To make sure that data were only utilized for statistical analyses and that all experiments were carried out in compliance with the relevant standards and laws, we agreed to abide by the study’s data usage rules.

SII calculations

Using automated hematology analytical tools, the lymphocyte, neutrophil, and platelet counts (expressed as 103 cells/L) were calculated. On the NHANES website, laboratory procedures for full blood count testing are described. Based on recent research [15], SII was calculated as PC * (NC / LC) for the platelet count (PC), neutrophil count (NC), and lymphocyte count (LC).

Diagnosis of CKD

eGFR 90 ml/min/1.73 m2 or eGFR >90 ml/min/1.73 m2 and urine ACR >30 mg/L were both considered signs of CKD [22]. Both urinary ACR and serum creatinine were measured. Serum creatinine, ethnicity, and gender were used to compute the estimated glomerular filtration rate (eGFR) using the CKD-EPI equation [23]. The Kidney Disease Guidelines for Improving Global Outcomes [24] were used to identify the stage of CKD.

Acquisition of bone density

In actuality, no one location or method can completely satisfy the therapeutic demands for bone mineral assessments. The evaluation of osteoporosis can be impacted by biological variations in bone composition that exist between populations and locations, as well as by technological inaccuracies in the accuracy of various measures [25]. The most used bone densitometry technology, DXA, is used to measure the mineral content of bone at several places across the skeleton, particularly those that are most likely to fracture [26]. Hip fractures are a serious consequence of osteoporosis in clinical terms [27]. The World Health Organization defines osteoporosis as having a femoral neck DXA score that is below normal [25]. The majority of bone loss in CKD patients comes from the cortical bone [28], and hip fracture risk is higher in ESRD and milder stages of CKD [29]. The frequency of poor BMD, however, varied from 50 to 80 percent in the radius, 16 to 47 percent in the femoral neck, and 13 to 29 percent in the lumbar spine in many investigations on hemodialysis patients [30]. In order to better link BMD with inflammation at various places, we included BMD of the pelvis, left upper and lower limbs, and lumbar spine in the current investigation. We also included BMD of the thoracic spine and trunk in the study.

A qualified radiographer used a Hologic QDR-4500A fan-beam densitometer (Hologic; Bedford, MA, USA) to perform dual-energy X-ray absorptiometry (DXA) exams on all participants, including those who were included in the final analyses. Using Hologic APEX software (version 4.0), all DXA examination data were analyzed. The NHANES website offers further details.

Covariates

This study additionally included covariates in the analysis to account for the possible impact of other variables on bone metabolism. Age, race, body mass index (BMI), smoking, hypertension, diabetes mellitus, blood alkaline phosphatase, serum uric acid, blood calcium, blood phosphorus, blood vitamin D, and blood triglycerides were eventually added as covariates based on other research [31, 32]. In this instance, the final blood pressure reading was calculated as the average of the body measures, and hypertension was defined as a mean systolic blood pressure of greater than 140 mmHg and/or a mean diastolic blood pressure of greater than 90 mmHg. Diabetes mellitus was deemed to exist when the hemoglobin A1c (HbA1c) level reached 6.5%. How these variables were estimated is fully described on the NHANES website (https://www.cdc.gov/nchs/nhanes/).

Analyses of statistics

With statistical significance set at P 0.05, we conducted all statistical analyses using R (http://www.r-project.org) and EmpowerStats (http://www.empowerstats.com). Model 1 included no covariate adjustments, Model 2 had ethnicity, age, and gender adjustments, and Model 3 included all of the covariate adjustments from Table 1. Additionally, subgroup analyses were performed. To deal with nonlinearities, the Generalised Additive Model (GAM) and smooth curve fitting were employed.

Table 1. Description of 2,302 participants included in the present study.

Characteristics Q1 Q2 Q3 Q4 P value
N 576 573 577 576
Age (years) 44.488 ± 11.117 44.412 ± 10.447 44.560 ± 10.922 44.724 ± 11.003 0.968
Total Calcium (mg/dL) 2.346 ± 0.091 2.356 ± 0.088 2.354 ± 0.086 2.359 ± 0.098 0.096
25OHD2+25OHD3 (nmol/L) 63.203 ± 28.906 65.243 ± 27.110 64.057 ± 25.920 66.087 ± 25.829 0.279
25OHD2 (nmol/L) 3.596 ± 10.980 3.557 ± 11.726 4.122 ± 12.854 3.783 ± 11.338 0.841
Albumin, urine (ug/mL) 72.630 ± 250.402 73.659 ± 357.259 88.322 ± 381.993 138.666 ± 667.685 0.038
Creatinine, urine (mg/dL) 131.101 ± 88.452 133.274 ± 83.573 136.662 ± 85.280 140.875 ± 90.268 0.250
Albumin creatinine ratio (mg/g) 66.559 ± 256.037 67.308 ± 286.152 80.224 ± 449.240 123.264 ± 610.823 0.081
Alkaline Phosphatase (ALP) (IU/L) 66.446 ± 23.509 65.461 ± 19.062 67.557 ± 21.268 69.542 ± 22.202 0.010
Phosphorus (mg/dL) 1.204 ± 0.184 1.223 ± 0.184 1.236 ± 0.183 1.223 ± 0.204 0.032
Uric acid (mg/dL) 323.144 ± 84.056 338.505 ± 90.126 344.375 ± 84.532 354.352 ± 89.122 <0.001
Creatinine (mg/dL) 0.978 ± 0.421 1.014 ± 0.509 1.001 ± 0.376 1.114 ± 0.912 <0.001
Cholesterol (mmol/L) 5.209 ± 1.136 5.108 ± 1.095 5.098 ± 1.088 5.024 ± 1.110 0.043
Blood Urea Nitrogen (mg/dL) 13.036 ± 5.102 13.736 ± 5.135 13.865 ± 5.620 14.705 ± 7.611 <0.001
GFR (mL/min/1.73m2) 88.430 ± 20.421 85.214 ± 17.720 85.691 ± 18.615 83.101 ± 21.066 <0.001
Left Arm BMD (g/cm2) 0.767 ± 0.103 0.788 ± 0.098 0.785 ± 0.106 0.792 ± 0.101 <0.001
Left Leg BMD (g/cm2) 1.161 ± 0.152 1.189 ± 0.145 1.174 ± 0.139 1.189 ± 0.140 0.001
Thoracic Spine BMD (g/ cm2) 0.822 ± 0.130 0.841 ± 0.123 0.827 ± 0.121 0.843 ± 0.132 0.013
Lumbar Spine BMD (g/ cm2) 1.043 ± 0.161 1.051 ± 0.157 1.036 ± 0.149 1.044 ± 0.167 0.477
Pelvis BMD (g/ cm2) 1.247 ± 0.173 1.263 ± 0.163 1.256 ± 0.169 1.244 ± 0.167 0.235
Trunk Bone BMD (g/ cm2) 0.895 ± 0.122 0.906 ± 0.113 0.894 ± 0.114 0.896 ± 0.115 0.256
Gender (%) <0.001
Men 241 (41.840%) 300 (52.356%) 296 (51.300%) 347 (60.243%)
Women 335 (58.160%) 273 (47.644%) 281 (48.700%) 229 (39.757%)
Race/Ethnicity (%) <0.001
Mexican American 61 (10.590%) 60 (10.471%) 72 (12.478%) 59 (10.243%)
Other Hispanic 43 (7.465%) 54 (9.424%) 68 (11.785%) 58 (10.069%)
Non-Hispanic White 158 (27.431%) 247 (43.106%) 260 (45.061%) 309 (53.646%)
Non-Hispanic Black 205 (35.590%) 116 (20.244%) 109 (18.891%) 95 (16.493%)
Non-Hispanic Asian 82 (14.236%) 63 (10.995%) 48 (8.319%) 33 (5.729%)
Other Race—Including Multi-Racial 27 (4.688%) 33 (5.759%) 20 (3.466%) 22 (3.819%)
Hypertension, n (%) 0.799
No 482 (85.159%) 482 (85.765%) 472 (83.986%) 473 (84.014%)
Yes 84 (14.841%) 80 (14.235%) 90 (16.014%) 90 (15.986%)
Smoke now recoded <0.001
No 103 (17.882%) 128 (22.339%) 101 (17.504%) 121 (21.007%)
Yes 102 (17.708%) 115 (20.070%) 131 (22.704%) 174 (30.208%)
Unknown 371 (64.410%) 330 (57.592%) 345 (59.792%) 281 (48.785%)
BMI (kg/m2) <0.001
< = 25 193 (33.682%) 148 (25.829%) 132 (23.037%) 134 (23.304%)
>25, < = 30 181 (31.588%) 184 (32.112%) 186 (32.461%) 184 (32.000%)
>30 199 (34.729%) 241 (42.059%) 255 (44.503%) 257 (44.696%)
Diabetes, n (%) 0.033
No 521 (90.451%) 510 (89.161%) 507 (87.868%) 490 (85.069%)
Yes 55 (9.549%) 62 (10.839%) 70 (12.132%) 86 (14.931%)
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BMI, body mass index; GFR, glomerular filtration rate. Mean ± sd. for continuous variables: P value was calculated using a weighted linear regression model. % for Categorical variables: P value was calculated by weighted chi-square test.

Results

Baseline characteristics of the SII-strategized population

2,302 people were examined in this study. Depending on their SII levels, the research sample was evenly split into Qs (Q1-Q4). Table 1 displays weighted demographic and medical information. The study had 2,302 adult participants in total. The Qs were statistically different (p < 0.05) for urinary albumin, blood alkaline phosphatase, blood phosphorus, blood uric acid, blood creatinine, blood cholesterol, blood urea nitrogen, glomerular filtration rate, bone density in the left arm, bone density in the left leg, bone density in the thoracic vertebrae, sex, ethnicity, whether or not they were smokers, BMI, and diabetes mellitus, whereas the age of There were no statistically significant differences in comparisons of total calcium, blood vitamin D, urinary creatinine, urinary albumin-creatinine ratio, lumbar spine bone density, pelvic bone density, trunk bone density, and hypertension. Additionally, we noticed that patients in the first quartile of SII had greater blood cholesterol and glomerular filtration rates compared to other subgroups, but lower blood creatinine and blood urea nitrogen.

The GFR values for the CKD patients in this study were computed using the CKD-EPI algorithm, and the staging of the participating patients was done in accordance with the KDIGO recommendations: In terms of lumbar spine bone mineral density and current smoking status across CKD subgroups, there was no statistically significant difference between individuals with different CKD stages (p> 0.05). The sample was mostly focused on CKD stage 2, and SII tended to rise as CKD advanced, despite statistically significant differences between the remaining variables. The result is shown in Table 2.

Table 2. Description of 2,302 participants included in the present study.

Characteristics 1 2 3 4 5 P-value
N 510 1673 97 9 13
Age (years) 39.492 ± 11.845 45.751 ± 10.178 49.835 ± 8.701 44.556 ± 7.333 48.231 ± 9.968 <0.001
Total Calcium (mg/dL) 2.338 ± 0.089 2.359 ± 0.087 2.368 ± 0.128 2.278 ± 0.109 2.323 ± 0.158 <0.001
25OHD2+25OHD3 (nmol/L) 53.294 ± 21.893 67.748 ± 27.198 71.280 ± 29.665 63.700 ± 42.276 61.969 ± 26.606 <0.001
25OHD2 (nmol/L) 2.934 ± 7.205 3.632 ± 11.922 6.009 ± 15.799 20.719 ± 28.458 24.976 ± 33.351 <0.001
Alkaline Phosphatase (ALP) (IU/L) 70.661 ± 22.296 65.589 ± 20.425 73.155 ± 24.410 65.000 ± 18.432 105.231 ± 51.357 <0.001
Phosphorus (mg/dL) 1.214 ± 0.185 1.218 ± 0.184 1.245 ± 0.198 1.392 ± 0.133 1.684 ± 0.352 <0.001
Uric acid (mg/dL) 312.226 ± 86.494 343.923 ± 81.646 402.994 ± 99.732 535.300 ± 231.601 336.754 ± 129.381 <0.001
Creatinine (mg/dL) 0.723 ± 0.154 1.036 ± 0.161 1.445 ± 0.337 2.838 ± 0.685 7.365 ± 3.291 <0.001
Cholesterol (mmol/L) 5.060 ± 1.195 5.130 ± 1.067 5.137 ± 1.259 5.138 ± 1.687 4.215 ± 0.983 0.038
Blood Urea Nitrogen (mg/dL) 11.316 ± 3.810 13.805 ± 4.145 20.660 ± 10.724 47.000 ± 20.609 42.769 ± 16.513 <0.001
GFR (mL/min/1.73m2) 113.434 ± 13.559 80.013 ± 8.254 51.928 ± 7.690 22.997 ± 4.006 8.912 ± 3.087 <0.001
Albumin, urine (ug/mL) 165.779 ± 490.847 27.149 ± 190.702 217.935 ± 1015.095 1214.790 ± 2029.890 983.027 ± 829.289 <0.00001
Creatinine, urine (mg/dL) 113.376 ± 77.705 133.638 ± 84.815 114.027 ± 82.924 85.810 ± 39.787 69.360 ± 30.377 0.00003
Albumin creatinine ratio (mg/g) 147.080 ± 390.210 23.879 ± 194.352 148.087 ± 603.868 1678.315 ± 2989.744 1403.805 ± 978.625 <0.00001
Left Arm BMD (g/ cm2) 0.755 ± 0.097 0.793 ± 0.102 0.766 ± 0.103 0.804 ± 0.083 0.712 ± 0.123 <0.001
Left Leg BMD (g/ cm2) 1.148 ± 0.142 1.190 ± 0.144 1.145 ± 0.127 1.207 ± 0.151 1.081 ± 0.222 <0.001
Thoracic Spine BMD (g/ cm2) 0.825 ± 0.128 0.836 ± 0.125 0.821 ± 0.136 0.930 ± 0.189 0.824 ± 0.156 0.049
Lumbar Spine BMD (g/ cm2) 1.036 ± 0.156 1.047 ± 0.158 1.025 ± 0.157 1.121 ± 0.204 1.055 ± 0.208 0.252
Pelvis BMD (g/ cm2) 1.224 ± 0.162 1.265 ± 0.166 1.201 ± 0.179 1.382 ± 0.312 1.088 ± 0.139 <0.001
Trunk Bone BMD (g/ cm2) 0.882 ± 0.112 0.905 ± 0.116 0.864 ± 0.111 0.978 ± 0.181 0.822 ± 0.126 <0.001
SII 1.936 ± 1.042 2.022 ± 1.118 2.347 ± 1.173 2.494 ± 1.496 2.541 ± 1.248 0.003
Gender (%) <0.001
Men 197 (38.627%) 933 (55.768%) 46 (47.423%) 2 (22.222%) 6 (46.154%)
Women 313 (61.373%) 740 (44.232%) 51 (52.577%) 7 (77.778%) 7 (53.846%)
Race/Ethnicity (%) <0.001
Mexican American 100 (19.608%) 139 (8.308%) 8 (8.247%) 4 (44.444%) 1 (7.692%)
Other Hispanic 60 (11.765%) 149 (8.906%) 13 (13.402%) 1 (11.111%) 0 (0.000%)
Non-Hispanic White 141 (27.647%) 791 (47.280%) 42 (43.299%) 0 (0.000%) 0 (0.000%)
Non-Hispanic Black 129 (25.294%) 360 (21.518%) 21 (21.649%) 4 (44.444%) 11 (84.615%)
Non-Hispanic Asian 59 (11.569%) 159 (9.504%) 7 (7.216%) 0 (0.000%) 1 (7.692%)
Other Race—Including Multi-Racial 21 (4.118%) 75 (4.483%) 6 (6.186%) 0 (0.000%) 0 (0.000%)
Hypertension, n (%) <0.001
No 384 (77.264%) 1431 (87.203%) 80 (86.022%) 5 (55.556%) 9 (69.231%)
Yes 113 (22.736%) 210 (12.797%) 13 (13.978%) 4 (44.444%) 4 (30.769%)
Smoke now recoded 0.253
No 86 (40.952%) 338 (47.740%) 22 (47.826%) 3 (50.000%) 4 (80.000%)
Yes 124 (59.048%) 370 (52.260%) 24 (52.174%) 3 (50.000%) 1 (20.000%)
BMI (kg/m2) <0.001
< = 25 152 (30.159%) 434 (25.972%) 17 (17.526%) 0 (0.000%) 4 (30.769%)
>25, < = 30 108 (21.429%) 594 (35.548%) 28 (28.866%) 0 (0.000%) 5 (38.462%)
>30 244 (48.413%) 643 (38.480%) 52 (53.608%) 9 (100.000%) 4 (30.769%)
Diabetes, n (%) <0.001
No 403 (79.020%) 1536 (91.866%) 74 (76.289%) 5 (55.556%) 10 (76.923%)
Yes 107 (20.980%) 136 (8.134%) 23 (23.711%) 4 (44.444%) 3 (23.077%)
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BMI, body mass index; GFR, glomerular filtration rate. Mean ± sd. for continuous variables: P value was calculated using a weighted linear regression model. % for Categorical variables: P value was calculated by weighted chi-square test.

Relationship between SII and BMD in CKD patients

Table 3 displays the outcomes of the multivariate regression analysis. We initially created an unadjusted model to investigate the relationship between SII and BMD at various locations. We discovered that the SII percentile was significantly linked with BMD in the left arm alone (0.006, 95% CI: 0.002, 0.009, p = 0.00370). However, after controlling for sex, age, and race in adjusted model I, SII was associated with pelvic BMD, suggesting that a higher SII percentile was associated with lower odds of pelvic BMD. This association also suggested that the higher SII percentile was associated with a trend towards decreasing pelvic BMD as the SII percentile increased (trend p = 0.01125). Age, sex, race, blood alkaline phosphatase, blood urea nitrogen, blood calcium, blood triglycerides, blood phosphorus, blood creatinine, blood uric acid, total active vitamin D, 2,5-hydroxyvitamin D2, presence of hypertension, diabetes mellitus, BMI, and calculated GFR were all taken into account in the adjusted Model II. Further analyses for pelvic BMD and SII were then carried out, and only pelvic BMD remained substantially linked with SII.

Table 3. Association of SII with GFR among 2,302 CKD patients, NHANES 2011–2016.

Characteristics Model 1 Model 2 Model 3
β (95% CI) P value a β (95% CI) P value b β (95% CI) P value c
Left Arm BMD (g/ cm2) 0.006 (0.002, 0.009) 0.00370 -0.001 (-0.004, 0.001) 0.28068 -0.001 (-0.004, 0.001) 0.36145
Left Leg BMD (g/ cm2) 0.005 (-0.000, 0.010) 0.07565 -0.002 (-0.006, 0.002) 0.33328 -0.002 (-0.006, 0.002) 0.38476
Thoracic Spine BMD (g/ cm2) 0.002 (-0.002, 0.007) 0.32539 0.000 (-0.004, 0.005) 0.91423 -0.001 (-0.006, 0.003) 0.58741
Lumbar Spine BMD (g/ cm2) -0.000 (-0.006, 0.005) 0.87219 0.001 (-0.005, 0.006) 0.85132 0.001 (-0.005, 0.007) 0.77892
Pelvis BMD (g/ cm2) -0.005 (-0.011, 0.001) 0.12507 -0.008 (-0.014, -0.002) 0.01125 -0.006 (-0.012, -0.000) 0.03368
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a Model 1, no covariates were adjusted.

b Model 2, Adjust for sex, age, and race.

c Model 3. Adjust for Age, sex, race, blood alkaline phosphatase, blood urea nitrogen, blood calcium, blood triglycerides, blood phosphorus, blood creatinine, blood uric acid, total active vitamin D, 2,5-hydroxyvitamin D2, presence of hypertension, diabetes mellitus, BMI, and calculated GFR. Generalized additive models were applied.

Fig 2 displays the smoothed curve fits and scatter plots. Model III was used to fit a smoothed curve to represent the nonlinear connection between SII and PEBMD. There were no statistically significant optimum breakpoints identified using a two-stage linear regression model. The detailed results of the threshold effect analysis are presented in Table 4. Overall, SII and PEBMD have a negative correlation, as can be observed.

Fig 2. Association between SII and Pelvis BMD.

Fig 2

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(A) Each black dot represents a sample. (B) The solid red line represents a smooth curve fit between the variables. Blue bands indicate 95% confidence intervals of the fit. SII, systemic immunoinflammatory index; PEBMD, pelvic bone mineral density.

Table 4. Threshold effect analysis of SII and pelvic BMD in CKD patients using Model 3.

Outcome b Pelvis BMD (g/ cm2) a
Model I
    one-line effect -0.006 (-0.012, -0.000) 0.0337
Model II
    Folding point (K) 0.912
    < K-segment effect 1 0.096 (-0.022, 0.215) 0.1100
    > K-segment effect 2 -0.007 (-0.013, -0.002) 0.0136
    The difference in effect between 2 and 1 -0.104 (-0.223, 0.016) 0.0885
    Predicted value of the equation at the breakpoint 1.260 (1.249, 1.270)
Log-likelihood ratio test 0.086
95 confidence interval at the breakpoint 0.838, 1.15
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a Table data: β (95%CI) P value / OR (95%CI) P value

b Exposure variable: SII

Adjustment variable. Age, sex, race, blood alkaline phosphatase, blood urea nitrogen, blood calcium, blood triglycerides, blood phosphorus, blood creatinine, blood uric acid, total active vitamin D, 2,5-hydroxyvitamin D2, presence of hypertension, diabetes mellitus, BMI, and calculated GFR

SII and pelvic bone density in CKD patients: Stratified analysis and investigation of the threshold effect

In order to analyze changes in pelvic BMD caused by SII in patients with CKD, the sample was stratified using the previously reported adjusted model. Because there weren’t enough stage 5 patients with CKD, stage 4 and stage 5 patients were analyzed together:

Our findings indicated that the negative connection between SII and pelvic BMD was independently and significantly positive in men [0.0008 (-0.023, -0.006)] but not statistically significant in the female models when subgroup analyses stratified by sex were performed. Independent statistical significance for different racial groups was absent. Patients without diabetes, hypertension, or a BMI of more than 30 kg/m2 demonstrated a substantial positive connection between SII and pelvic BMD rather than the expected negative correlation. Patients with CKD stage 2 showed a negative connection between SII and pelvic BMD when we grouped the patients based on CKD stage [0.0222 (-0.015, -0.001)]. The results are shown in Table 5.

Table 5. Stratified analysis of SII and pelvic BMD in patients with CKD.

Subgroups N Model 2 a Model 3 b
β (95% CI) P value β (95%CI) P value
Gender (%)
Men 1184 -0.014 (-0.023, -0.006) 0.0008
Women 1118 -0.002 (-0.010, 0.007) 0.6559
Race/Ethnicity (%)
Mexican American 252 -0.010 (-0.026, 0.005) 0.2012
Other Hispanic 223 0.002 (-0.019, 0.022) 0.8611
Non-Hispanic White 974 -0.008 (-0.017, 0.001) 0.0845
Non-Hispanic Black 525 0.002 (-0.011, 0.015) 0.7369
Non-Hispanic Asian 226 -0.015 (-0.040, 0.011) 0.2592
Other Race—Including Multi-Racial 102 -0.017 (-0.057, 0.024) 0.4151
Diabetes, n (%)
No 2028 -0.009 (-0.015, -0.003) 0.0051 -0.006 (-0.013, -0.000) 0.0407
Yes 273 0.001 (-0.019, 0.020) 0.9394 -0.005 (-0.026, 0.016) 0.6200
Hypertension, n (%)
No 1909 -0.008 (-0.015, -0.002) 0.0100 -0.007 (-0.013, -0.000) 0.0387
Yes 344 -0.007 (-0.024, 0.010) 0.4249 -0.009 (-0.026, 0.008) 0.3240
CKDS Stages
    1 510 -0.003 (-0.017, 0.010) 0.6223 -0.002 (-0.015, 0.011) 0.7798
    2 1673 -0.009 (-0.016, -0.003) 0.0071 -0.008 (-0.015, -0.001) 0.0222
    3 97 -0.005 (-0.035, 0.024) 0.7180 0.004 (-0.026, 0.033) 0.8110
    4+5 22 0.067 (-0.036, 0.170) 0.2202 0.040 (-0.137, 0.217) 0.6789
BMI (kg/m2)
< = 25 607 -0.006 (-0.016, 0.005) 0.2675 -0.004 (-0.014, 0.007) 0.4796
>25, < = 30 735 -0.001 (-0.011, 0.009) 0.8264 0.001 (-0.009, 0.011) 0.9030
>30 952 -0.015 (-0.024, -0.006) 0.0017 -0.014 (-0.024, -0.005) 0.0031
Open in a new tab

a Model 2, Adjust for sex, age, and race.

b Model 3. Adjust for Age, sex, race, blood alkaline phosphatase, blood urea nitrogen, blood calcium, blood triglycerides, blood phosphorus, blood creatinine, blood uric acid, total active vitamin D, 2,5-hydroxyvitamin D2, presence of hypertension, diabetes mellitus, BMI, and calculated GFR. Generalized additive models were applied.

Fig 3 displays the outcomes of further stratified analyses based on patient gender, the presence of diabetes, hypertension, and a BMI greater than 30 kg/m2, as well as smoothed curve fitting using model 3.

Fig 3. Model 3 dictated the stratification of the study’s participants based on gender, the existence of hypertension, the presence of diabetes, and a BMI of more than 30 kg/m2, after which smoothed curves fitting the model were shown.

Fig 3

Open in a new tab

The relationship between SII and PEBMD showed a significant inverted U-shaped curve with a fold point of 0.969 ((1,000 cells/l)) after stratifying the sample for patients who did not have diabetes mellitus, according to a threshold effect analysis using a two-stage linear regression model. The connection between SII and PEBMD likewise displayed an inverted U-shaped curve in individuals without and with hypertension, with fold points of 0.938 (1,000 cells/l) and 2.947 (1,000 cells/l), respectively. SII and PEBMD had a more complicated non-linear association in individuals with BMI more than 30 kg/m2, with no significant fold points. Tables 6 and 7 show the findings of the threshold effect investigation.

Table 6. Using Model 3, SII and pelvic BMD in CKD patients without diabetes were analyzed for threshold effects after smoothed curve fitting.

Outcome: Pelvis BMD (g/cm2)
Model I
    one-line effect -0.005 (-0.011, 0.001) 0.1207
Model II
    Folding point (K) 0.969
    < K-segment effect 1 0.113 (0.008, 0.218) 0.0355
    > K-segment effect 2 -0.007 (-0.013, -0.000) 0.0384
    The difference in effect between 2 and 1 -0.120 (-0.226, -0.013) 0.0279
    Predicted value of the equation at the breakpoint 1.261 (1.250, 1.272)
Log-likelihood ratio test 0.027
95 confidence interval at the breakpoint 0.833, 1.194
Open in a new tab

Table 7. Using Model 3, CKD patients were stratified according to whether they had hypertension or not, followed by a threshold effect analysis of their SII and pelvic BMD, respectively, after smoothing curve fitting.

Outcome Pelvis BMD (g/ cm2)
No hypertension Hypertension
Model I
    one-line effect -0.006 (-0.013, -0.000) 0.0386 -0.008 (-0.025, 0.008) 0.3129
Model II
    Folding point (K) 0.938 2.947
    < K-segment effect 1 0.107 (-0.006, 0.220) 0.0637 -0.032 (-0.061, -0.003) 0.0291
    > K-segment effect 2 -0.008 (-0.014, -0.002) 0.0129 0.019 (-0.013, 0.050) 0.2494
    The difference in effect between 2 and 1 -0.115 (-0.230, -0.001) 0.0490 0.051 (0.000, 0.102) 0.0508
    Predicted value of the equation at the breakpoint 1.261 (1.250, 1.272) 1.237 (1.196, 1.278)
Log-likelihood ratio test 0.047 0.042
Open in a new tab

Adjustment variable. Age, sex, race, blood alkaline phosphatase, blood urea nitrogen, blood calcium, blood triglycerides, blood phosphorus, blood creatinine, blood uric acid, total active vitamin D, 2,5-hydroxyvitamin D2, presence of hypertension, diabetes mellitus, BMI, and calculated GFR

Some hints are given by the variations in the association between SII and PEBMD in the various subgroups, but more research is required to validate these correlations and dive further into the underlying processes.

Discussion

Long recognized as a prognostic factor in a variety of illnesses, the immune system. According to recent findings from cross-sectional studies of the NHANES-III 2011–2016 database, SII levels in individuals with CKD correlate with pelvic BMD, which declines with rising SII.

To our knowledge, this is the first community-based, nationally representative cohort of people from the United States to evaluate the relationship between SII, a less expensive clinical measure, and reduced bone density, a typical consequence of CKD.

We discovered that SII levels were negatively correlated with pelvic BMD in patients with CKD by comparing regression models after unadjusted modeling, the addition of demographic variables, and the remaining pertinent variables. This finding corroborated the persistent finding that hip BMD can be used to predict fractures, as mentioned in the KDIGO guidelines [33]. Additionally, when examining the relationship between SII and PEBMD, the findings of stratified analyses revealed that men who were obese, male, and free of diabetes or hypertension had a stronger negative association between SII and PEBMD. These might serve as some benchmarks for further clinical choices.

According to a 2014 study by Hu [15] et al., SII is a thorough and unique inflammatory biomarker. Prior research has routinely employed SII as a predictor of the development of kidney transplant rejection as well as the incidence of acute or chronic renal injury [34–38]. Numerous studies connected to SII have been conducted in recent years, and they have thoroughly examined its clinical importance. Lai et al. [34] in a cohort study showed that elevated levels of SII prior to CAG were an important and independent risk factor for postoperative AKI, and Halpern et al. [39] in a single-center cohort study showed that elevated levels of SII were independently associated with increased survival only in post-transplant patients. For instance, Xie et al. [40] in 2022 found that elevated levels of SII were associated with hepatic steatosis but not with hepatic fibrosis. A cohort study was conducted by Halpern et al. in one center. Regarding prognosis, prospective cohort research by Shi [31] et al. showed that in CKD patients with ACS, higher SII was linked to poor cardiovascular outcomes. In a similar vein, Xie et al. [41] discovered a favorable correlation between SII and abdominal aortic calcification, a frequent CKD consequence. In a cohort investigation of critically ill AKI patients, Lan et al. [42] discovered a J-shaped relationship between SII and all-cause death in these patients.

Studying the onset of CKD’s consequences, however, can be more successful in enhancing the quality of patients’ survival because CKD is a long-term chronic disease. Long-term, chronic inflammation can also exacerbate renal anemia and renal function [43], as well as promote malnutrition, in the kidney, the site of the majority of renal disorders [44]. To preserve the kidneys, the body suppresses inflammation through autophagy [45]. The very diverse etiology of bone disease, as well as the restrictions and particular adverse effects of available treatments, make it difficult to diagnose and treat osteoporosis in individuals with severe CKD [46]. Therefore, it is crucial to identify osteoporosis early and take steps to avoid it. We opted to investigate inflammatory markers, a mechanism frequently present in BMD decrease and nephropathy, and ultimately found specific outcomes.

Our research has a few drawbacks. Firstly, since this research is cross-sectional, temporality cannot be established. Additionally, even after controlling for a number of pertinent confounders, we were unable to completely exclude the impact of other confounders, therefore it is important to proceed with care when interpreting our results. Third, while patients with CKD typically use oral drugs like hormones depending on the underlying condition, our findings could not accurately reflect the true situation because the NHANES database’s constraints prevented us from including individuals’ medication use as a covariate. Fourth, although the CKD-EPI equation is the most accurate GFR estimation equation that has been tested in a wide range of populations and is appropriate for general clinical use [47], its accuracy is still not comparable to filtered marker measurements. The degree of renal impairment in this study was extrapolated from the CKD-EPI equation.

Despite these drawbacks, our study provides a number of advantages. Our study is typical of the multiracial and gender-diverse adult population of the United States since we employed a nationally representative sample. Furthermore, the size of the sample used in our study allowed us to do a subgroup analysis.

Conclusion

In our study, pelvic bone density in CKD patients was associated with SII levels, and pelvic bone density reduced as SII increased. The change in BMD with SII in CKD patients without diabetes may be at a turning point. SII and pelvic bone density were substantially associated with individuals with stage 2 CKD. This may offer recommendations for the prevention and management of problems from osteoporosis in CKD patients. To support our findings, further comprehensive prospective cohort studies are still required.

Data Availability

Publicly available datasets were analyzed for this study from the NHANES database (www.cdc.gov/nchs/nhanes/).

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Ewa Tomaszewska

6 Mar 2024

PONE-D-24-02266Systemic Immune-inflammatory Indicators and Bone Mineral Density in Chronic Kidney Disease Patients: A Cross-sectional Research from NHANES 2011 to 2018PLOS ONE

Dear Dr. Bao,

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Reviewer #1: I reviewed the article sent to me for review - "Systemic markers of immune inflammation and bone mineral density in patients with chronic kidney disease: a cross-sectional study from NHANES 2011–2018"

The presented research work may not be groundbreaking in its assumptions, as it has been known for years that CKD and high levels of inflammatory parameters reduce bone mineral density, but the work meets the publication requirements.

I believe that the work is valuable due to the research and breadth of the group analyzed.

Reviewer #2: 1. eGFR needs superscripts for the 2's of the unit species.

2. line 58-59 '[value -0.008; 95% confidence

interval (CI) -0.014, -0.002]' Please change to [β=-0.008; 95% confidence

value -0.008; 95% confidence interval (CI) -0.014, -0.002]'.

3. From table 1 kind of it can be seen that there are very few participants with BMI<18, I suggest that the authors can combine them in the group with BMI<25 only, in addition, the square meters in the units of BMI need to be superscripted.

4. on what basis did the authors choose these covariates?

5. the authors excluded non-compliant participants from the initial group of about 20,000 participants and ended up with about 2,000 participants left to be included in the analysis. does this exclusion result in a selective bias in the sample?

6. It is recommended that the authors add a discussion of SII-related studies to the discussion:

I suggest citing the following literature:

1. doi: 10.3389/fimmu.2022.925690

2. doi: 10.1016/j.numecd.2023.04.015

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PLoS One. 2024 Apr 25;19(4):e0302073. doi: 10.1371/journal.pone.0302073.r002

Author response to Decision Letter 0

19 Mar 2024

Dear Editor and Reviewers,

We appreciate the opportunity to allow us to revise our manuscript and thanks for the reviewers’ constructive comments and suggestions. We would like to submit our revised manuscript, entitled ‘Systemic Immune-inflammatory Indicators and Bone Mineral Density in Chronic Kidney Disease Patients: A Cross-sectional Research from NHANES 2011 to 2018’ for consideration for publication. In the revised manuscript, we have carefully addressed all comments and questions raised by reviewers point-by-point. We greatly appreciate your time and efforts to improve our manuscript for publication.

Reply to Reviewers

Reviewer #1:

1． I reviewed the article sent to me for review - "Systemic markers of immune inflammation and bone mineral density in patients with chronic kidney disease: a cross-sectional study from NHANES 2011–2018" The presented research work may not be groundbreaking in its assumptions, as it has been known for years that CKD and high levels of inflammatory parameters reduce bone mineral density, but the work meets the publication requirements. I believe that the work is valuable due to the research and breadth of the group analyzed.

Reply: We sincerely appreciate your acknowledgment of the importance of our study and the paper. We selected SII, a novel parameter combining platelets, neutrophils, and lymphocytes, to quantitatively explore the clinical significance of the inflammatory response in the development of complications in CKD patients and to increase the confidence of the results by expanding the sample size, even though it is already widely known that the combined systemic inflammatory response in CKD patients reduces BMD. We will also investigate the precise mechanisms linking the two in more detail in later research to provide ground-breaking outcomes. Once again, thank you for your confirmation. Please don't hesitate to contact us if you have any further queries or require any clarification.

Reviewer #2:

Concerns:

1． eGFR needs superscripts for the 2's of the unit species.

Reply: We appreciate your insightful feedback, and we have updated the entire text to include all non-standard units of measurement. Going forward, we'll make sure to focus more on the pertinent aspects of our research.

2． line 58-59 '[value -0.008; 95% confidence interval (CI) -0.014, -0.002]' Please change to [β=-0.008; 95% confidence value -0.008; 95% confidence interval (CI) -0.014, -0.002]'.

Reply: Thank you very much for your valuable comments, we have revised this part according to the review comments.

3． From Table 1 kind of it can be seen that there are very few participants with BMI<18, I suggest that the authors combine them in the group with BMI<25 only, in addition, the square meters in the units of BMI need to be superscripted.

Reply: We appreciate your insightful feedback. We have superscripted the square meter needs in terms of BMI and merged participants with a BMI <18 kg/m2 in the BMI <25kg/m2 category. In our next job, we'll also take extra care with these aspects.

.

4． On what basis did the authors choose these covariates?

Reply: Thank you very much for pointing out this deficiency. During the inclusion of covariates, we considered three aspects that affect the duration of CKD, the body's inflammatory response, and changes in bone mineral density, so we reviewed the relevant literature and referred to our own experience in clinical practice to determine the covariates to be included in the study. We included gender, age, and race as demographic data because this affects the general condition of the patients. Urine albumin, urine creatinine, urine creatinine/albumin ratio, blood urea nitrogen, blood creatinine, blood uric acid, and GFR are often used to assess disease progression in patients with CKD. The presence of hypertension, diabetes mellitus, and obesity, on the other hand, have been shown to influence further deterioration of renal function. Blood alkaline phosphatase, blood calcium, blood triglycerides, blood phosphorus, and vitamin D are closely related to bone metabolism. Taking all these considerations into account, we finally included these covariates.

5． the authors excluded non-compliant participants from the initial group of about 20,000 participants and ended up with about 2,000 participants left to be included in the analysis. does this exclusion result in a selective bias in the sample?

Reply: We much appreciate your candid counsel. We did take into consideration the possibility that, had missing data and patients not diagnosed with CKD been removed from the study because non-randomization was used, there would have been differences between the study population and the target population represented, which would have diminished the validity of the study's conclusions. We increased the sample size by including 8 years of data, spanning from 2011 to 2018, to reduce this mistake. The website also suggests combining multiple years of data to increase the sample size and thereby reduce error, given the history of the NHANES database with the oversampling of certain subgroups and the limited analytical utility of a 2-year sample to provide estimates for subgroups with lower percentages of population distribution. To minimize mistakes, we took this action and included in the study all eligible patients. To lessen confounding in the study and lower the error in the conclusions, we also performed stratified analyses and built multivariate analytic models. We do think about utilizing more public datasets or conducting follow-up research using actual studies to better establish the study's legitimacy. Once again, I appreciate your thoughtful suggestions.

6． It is recommended that the authors add a discussion of SII-related studies to the discussion. I suggest citing the following literature:

doi: 10.3389/fimmu.2022.925690\\ doi: 10.1016/j.numecd.2023.04.015

Reply: We appreciate you pointing us in the direction of the well-researched literature, which goes into additional detail about the clinical importance of SII in various contexts. Based on your suggestion and after carefully reviewing the research, we have updated the discussion section to provide a fresh analysis of the significance of SII in the diagnosis and prognosis of other diseases. The citations for the sources you suggested are found in lines 342–359. Furthermore, it has been demonstrated that SII is related to abdominal aortic atherosclerosis, a typical consequence of advanced CKD, which offers suggestions for further research. Once again, I appreciate your advice and manuscript recommendation.

Line 342–357, fourth paragraph of the Discussion section:

According to a 2014 study by Hu [15] et al., SII is a thorough and unique inflammatory biomarker. Prior research has routinely employed SII as a predictor of the development of kidney transplant rejection as well as the incidence of acute or chronic renal injury [34–38]. Numerous studies connected to SII have been conducted in recent years, and they have thoroughly examined its clinical importance. Lai et al. [34] in a cohort study showed that elevated levels of SII before CAG were an important and independent risk factor for postoperative AKI, and Halpern et al. [40] in a single-center cohort study showed that elevated levels of SII were independently associated with increased survival only in post-transplant patients. For instance, Xie et al. [39] in 2022 found that elevated levels of SII were associated with hepatic steatosis but not with hepatic fibrosis. A cohort study was conducted by Halpern et al. in one center. Regarding prognosis, prospective cohort research by Shi [31] et al. showed that in CKD patients with ACS, higher SII was linked to poor cardiovascular outcomes. In a similar vein, Xie et al. [41] discovered a favorable correlation between SII and abdominal aortic calcification, a frequent CKD consequence. In a cohort investigation of critically ill AKI patients, Lan et al. [42] discovered a J-shaped relationship between SII and all-cause death in these patients.

All of these suggestions are insightful and help us polish our manuscript considerably. We made every effort to make the manuscript better. The paper's structure and content won't be impacted by these modifications. We sincerely thank the editors and reviewers for their hard work, and we hope that the corrections will be accepted. Kindly do not hesitate to get in touch with us if you have any more inquiries or suggestions.

I express my gratitude once more for your insightful remarks and recommendations.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0302073.s001.docx (23.4KB, docx)

Decision Letter 1

Ewa Tomaszewska

27 Mar 2024

Systemic Immune-inflammatory Indicators and Bone Mineral Density in Chronic Kidney Disease Patients: A Cross-sectional Research from NHANES 2011 to 2018

PONE-D-24-02266R1

Dear Dr. Xiaorong Bao,

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Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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I have no further comments.

Thank you so much for your efforts.

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Reviewer #2: No

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Acceptance letter

Ewa Tomaszewska

3 Apr 2024

PONE-D-24-02266R1

PLOS ONE

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    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0302073.s001.docx (23.4KB, docx)

    Data Availability Statement

    Publicly available datasets were analyzed for this study from the NHANES database (www.cdc.gov/nchs/nhanes/).

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