Association between systemic immune-inflammatory index and body mass index in cancer patients: A cross-sectional study from NHANES 2013 to 2018

ObjectiveTo explore correlations between body mass index (BMI), preoperative systemic immune-inflammation index (SII) and endocrine therapy resistance, and evaluate BMI and SII as predictors of resistance, in patients with luminal breast cancer.MethodsThis retrospective study included patients with luminal breast cancer who underwent endocrine therapy at Hebei General Hospital. Relationships between BMI and SII subgroups, and clinicopathological parameters were analysed using χ2-tests. Disease-free survival was assessed using Log-rank statistics. Multivariate analysis of factors related to disease progression were analysed using Cox proportional hazards model.ResultsOut of 161 patients, those with normal BMI and low SII had significantly lower endocrine resistance rates versus those with high BMI and SII, and BMI was significantly positively correlated with SII. High BMI or SII was associated with significantly lower disease-free survival rates. Hazard ratios for disease progression risk were 6.036, 3.508 and 1.733, for SII, BMI and TNM stage, respectively.ConclusionIn patients with luminal breast cancer, high BMI (>23 kg/m2) and SII (>518 × 109/L) levels may predict high endocrine resistance rates. BMI, SII and TNM stage were independent prognostic factors for endocrine therapy resistance.

In the U.S. general population, there is a lack of understanding regarding the association between the systemic immune inflammation (SII) index and estimated pulse wave velocity (ePWV), atherogenic index of plasma (AIP), and triglyceride-glucose (TyG) index and cardiovascular disease (CVD). As a result, the objective of our research was to investigate the association between the SII index and ePWV, AIP, and TyG index and incident CVD. We used the National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018 to conduct this study. The correlation between the SII index and ePWV, AIP, and TyG index was examined using generalized additive models with smooth functions. In addition, the association between SII index and triglyceride (TC), high-density lipoprotein cholesterol (HDL-C), and fast glucose (FBG) also were explored. Finally, we further performed multivariable logistic regression analysis, restricted cubic spline (RCS) plots, and subgroup analysis to study the connection between the SII index and CVD. Our analysis included 17389 subjects from the NHANES database. A substantial positive association existed between SII, WV, and the TyG index. In addition, with the increase of the SII index, AIP showed a trend of decreasing first, then rising, and then decreasing. The SII index was inversely and linearly associated with triglyceride (TG), while positively and linearly associated with fast glucose (FBG). However, high-density lipoprotein cholesterol (HDL-C) had a tendency of first declining, then climbing, and finally falling with the rise in the SII index. After adjusting for potential confounders, compared with the lowest quartiles, the odds ratios with 95% confidence intervals for CVD across the quartiles were 0.914 (0.777, 1.074), 0.935 (0.779, 1.096), and 1.112 (0.956, 1.293) for SII index. The RCS plot showed an inverse U-shaped curve relationship between the SII index and CVD. Overall, this study found a strong correlation between a higher SII index and ePWV and the TyG index. Additionally, these cross-sectional data also revealed a U-shaped connection between the SII index and CVD.

Background: Depression is one of the most common diseases in the world. Earlier research on the link between body mass index (BMI) and depression has been contentious. This study seeks to investigate the connection between BMI and depression among individuals with nonalcoholic fatty liver disease (NAFLD).Methods: All data were extracted from the National Health and Nutrition Examination Survey (NHANES) database 2017–2018. The Cox regression technique was employed to analyze the link between BMI and depression. To analyze the potential nonlinear connection between BMI and depression, Cox proportional hazards regression incorporating cubic spline functions and smooth curve fitting was utilized. In addition, a two-segment Cox proportional hazards regression model was used to pinpoint the inflection point at which BMI impacts the likelihood of depression. The Patient Health Questionnaire (PHQ-9) was the primary measure of depressive symptoms.Results: The mean age of the 1426 participants was 56.05 years with a standard deviation of 15.63 years, and approximately 49.30% of the sample were male. After controlling for confounding variables, BMI demonstrated a positive association with depression (OR:1.05, 95% CI:1.02–1.09, p < 0.004). The two-piecewise Cox proportional hazards regression model identified an inflection point for BMI at 29.8 kg/m2. Below this inflection point (BMI ≤ 29.8 kg/m2), BMI was positively correlated with an increased risk of depression (OR:1.23, 95% CI:1.04–1.45, p < 0.014). Conversely, when BMI exceeded 29.8 kg/m2, the association was not statistically significant (OR: 1.02, 95% CI: 0.98–1.07, p = 0.305).Conclusion: There is a nonlinear relationship between BMI and depression among patients with NAFLD. BMI was positively related to depression when BMI is less than 29.8 kg/m2.

The systemic immune-inflammation index (SII) is a newly identified marker of inflammation., and the relationship between chronic bronchitis (CB) and inflammation is closely associated. However, the influence of SII on CB remains unclear at present.This cross-sectional study was conducted using data from individuals with complete SII and CB records from the 2001–2018 National Health and Nutrition Examination Survey (NHANES). Binary weighted logistic regression was employed to investigate the relationship between SII and CB risk. Additionally, restricted cubic spline regression models and segmented regression models were used to examine nonlinear relationships and threshold effects. Receiver operating characteristic (ROC) curves were adopted to evaluate the predictive value of SII for CB. Stratified analysis was adopted to assess the association between SII and CB in different populations. After adjusting for all covariables, there was a significant positive relevance observed between log-transformed SII (log (SII)) with CB (OR = 1.52, 95% CI: 1.27–1.82, P < 0.001). A nonlinear dose–response relationship with the threshold of 8.14 was observed between log (SII) and CB risk. When log (SII) exceeded 8.14, each unit increase in log (SII) was associated with a 1.31-fold increase in the risk of CB (OR = 1.31, 95% CI: 1.22–1.40, P < 0.001). Furthermore, ROC curves revealed strong predictive capability of SII for CB (AUC = 0.729). Elevated SII levels are associated with an increased prevalence of CB. Furthermore, a non-linear association exists between SII and the increased risk of CB.

Inflammation and obesity have been widely recognized to play a key role in Diabetes mellitus (DM), and there exists a complex interplay between them. We aimed to clarify the relationship between inflammation and DM, as well as the mediating role of obesity in the relationship. Based on the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Univariate analyses of continuous and categorical variables were performed using t-test, linear regression, and χ2 test, respectively. Logistic regression was used to analyze the relationship between Systemic Immune-Inflammatory Index (SII) or natural logarithm (Ln)-SII and DM in three different models. Mediation analysis was used to determine whether four obesity indicators, including body mass index (BMI), waist circumference (WC), visceral adiposity index (VAI) and lipid accumulation product index (LAP), mediated the relationship between SII and DM. A total of 9,301 participants were included, and the levels of SII and obesity indicators (BMI, WC, LAP, and VAI) were higher in individuals with DM (p < 0.001). In all three models, SII and Ln-SII demonstrated a positive correlation with the risk of DM and a significant dose-response relationship was found (p-trend <0.05). Furthermore, BMI and WC were associated with SII and the risk of DM in all three models (p < 0.001). Mediation analysis showed that BMI and WC mediated the relationship between SII with DM, as well as Ln-SII and DM, with respective mediation proportions of 9.34% and 12.14% for SII and 10.23% and 13.67% for Ln-SII (p < 0.001). Our findings suggest that increased SII levels were associated with a higher risk of DM, and BMI and WC played a critical mediating role in the relationship between SII and DM.

The association between Body Mass Index (BMI), frailty index (FI), and dietary supplement in cancer survivors has been a subject of growing interest. This study investigates the relationship of BMI and FI with mortality in American cancer survivors and explores the impact of dietary supplement usage on different BMI and FI groups. Three thousand nine hundred and thirty-two cancer patients from the National Health and Nutrition Examination Survey (NHANES) database were included in the analyses. BMI, FI, and supplement usage were obtained through the NHANES structured survey and the 49-item FI tool. Weighted logistic and Cox proportional hazards models, Kaplan-Meier survival analyses, and propensity score matching (PSM) were used to elucidate the relationships between BMI, FI, dietary supplement, and mortality outcomes. The study found significant associations between higher BMI and increased frailty (Odds ratio [OR] = 1.04, 95% confidence interval [95% CI], 1.02-1.06). BMI < 25 kg/m2 and FI > 0.2 are associated with an increased mortality rate. Dietary supplement use can reduce all-cause and cancer mortality in cancer patients with BMI < 25 kg/m2 (Hazard ratio [HR] = 0.63, 95% CI, 0.47-0.84; HR = 0.48, 95% CI, 0.29-0.80) or FI ≤ 0.2 (HR = 0.77, 95% CI, 0.60-0.99; HR = 0.59, 95% CI, 0.39-0.89). In cancer patients with BMI < 25 kg/m2 and FI ≤ 0.2, dietary supplement users had lower all-cause and cancer mortality (HR = 0.49, 95% CI, 0.30-0.79; HR = 0.25, 95% CI, 0.10-0.60). The study revealed a negative correlation between BMI and the FI among the cancer patient cohort as well as their complex impact on mortality and highlighted the role of dietary supplement in cancer prognosis, indicating benefits for non-frail patients with BMI < 25 kg/m2.

The atherogenic index of plasma (AIP) is used to evaluate the risk of atherosclerosis, while the systemic immune-inflammation index (SII) measures inflammation. The AIP and SII are indicators used to predict diseases in various areas. This study aims to explore the relationship between AIP and SII. A cross-sectional study design was used to recruit 70,190 participants from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018, excluding AIP missing data, SII missing data, participants under 20 years of age, and participants with missing covariates to eventually include 8163 participants. We used weighted multiple linear regression analysis, trend test, smooth curve fitting and threshold effect analysis to examine the relationship between AIP and SII. Among the 8163 participants included in the study, the mean (± SD) age was 48.412 ± 16.842 years. The mean SII (± SD) for all participants was 519.910 ± 316.974. In a model adjusted for all covariates (Model 3), AIP showed a significant positive correlation with SII [β (95% CI) 32.497 (5.425, 59.569), P = 0.021]. The smooth curve fitting results of AIP and SII are an “inverted U-shape” non-linear relationship, and the inflection point is at AIP = 0.82. This positive association between AIP and SII was found only in females and participants under 50. Specifically, for females, the positive correlation between AIP and SII was linear [β (95% CI) 80.791 (44.625, 116.958); P < 0.001]. In participants under 50, the positive correlation between AIP and SII was [β (95% CI) 34.198 (3.087, 65.310); P = 0.034], and there was also an “inverted U-shape” non-linear relationship with an inflection point of AIP = 0.549. For participants aged 20–50 years and males, the smooth curve showed a “down-flat-down” non-linear relationship. There is a significant positive correlation between AIP and SII. A positive association between AIP and SII was observed exclusively in females and among participants under 50. Furthermore, AIP and SII demonstrated a nonlinear relationship that resembles an “inverted U-shape”. These findings offer new insights into the prevention, treatment, and management of cardiovascular disease. However, further comprehensive cohort studies are necessary to validate the relationship between AIP and SII.

BackgroundThe evidence regarding the association between the systemic immune inflammatory index (SII) and mortality among individuals with diabetes is limited. This study aims to evaluate the associations between SII and all-cause and cause-specific mortality among individuals with diabetes.MethodsThe study included 8,668 participants with diabetes from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 with follow-up until 31 December 2019. The calculation of SII in this study was performed using the following formula: the neutrophil-to-lymphocyte ratio multiplied by the platelet count (10^9 cells/µL).ResultsThe study documented 2,463 deaths over 68,542 person-years, including 853 deaths from CVD and 424 from cancer. An increase in SII was significantly associated with higher all-cause and CVD mortality risk after multivariate adjustment. For each standard deviation increment in natural log transformed SII (lnSII), all-cause mortality increased by 17%, and CVD mortality increased by 34% (both P < 0.001). Additionally, the association between SII and all-cause mortality was U-shaped, with the inflection point at 6.02. The association between SII and CVD mortality was non-linear and J-shaped, where the risk increased significantly when lnSII exceeded 6.22. Furthermore, the association between SII and CVD mortality was attenuated in female and hyperlipidemia patients.ConclusionIn this study, we observed a significant positive association between the SII and both all-cause and CVD mortality in patients with diabetes. Additionally, it was discovered that this association exhibited a non-linear pattern. These findings suggest that maintaining SII within an optimal range may play a critical role in mitigating the risk of mortality.

BackgroundThe relationship between the systemic immune inflammatory index (SII) and the prognosis of hypertensive patients is unclear. This study aims to explore the association of SII with all-cause and cause-specific mortality in patients with hypertension.MethodsThis study included 8524 adults with hypertension from the National Health and Nutritional Examination Surveys (NHANES) 2011–2018, and followed for survival through December 31, 2019. Cox proportional hazards models were used to investigate the associations between SII and mortality from all causes, cardiovascular disease (CVD), and cancer. Restricted cubic spline, piecewise linear regression, subgroup and sensitivity analyses were also used.ResultsDuring a median follow-up of 4.58 years, 872 all-cause deaths occurred. After adjusting for covariates, higher SII was significantly associated with an elevated risk of CVD mortality. There was a 102% increased risk of CVD mortality per one-unit increment in natural log-transformed SII (lnSII) (P < 0.001). Consistent results were also observed when SII was examined as categorical variable (quartiles). The associations of SII with all-cause and cancer mortality were detected as U-shaped with threshold values of 5.97 and 6.18 for lnSII respectively. Below thresholds, higher SII was significantly associated with lower all-cause mortality (HR=0.79, 95%CI=0.64-0.97) and cancer mortality (HR=0.73, 95%CI=0.53-1.00). Above thresholds, SII was significantly positive associated with all-cause mortality (HR=1.93, 95%CI=1.55-2.40) and cancer mortality (HR=1.93, 95%CI=1.22-3.05). The results were robust in subgroup and sensitivity analyses.ConclusionHigher SII (either as a continuous or categorical variable) were significantly associated with a higher risk of CVD mortality. The U-shaped associations were observed between SII and all-cause and cancer mortality.

The relationship between body mass index (BMI) and the risk of asthma in the pediatric population is not fully understood. This study aimed to investigate the association between BMI and asthma in a large nationally representative sample. The study included 35,603 pediatric participants from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020. The association between BMI and asthma was examined using various statistical models, including logistic regression, piece-wise linear regression, and subgroup analyses, adjusting for potential confounding factors. When analyzing BMI as a continuous variable, a one-unit increase in BMI was associated with a 4% higher odds of asthma. A clear dose-response relationship was observed, where individuals in the higher BMI quartiles had progressively higher odds of asthma compared to those in the lowest quartile. Smooth curve fitting revealed a not entirely linear relationship, with a steeper increase in asthma risk at lower BMIs (below an inflection point of 21 kg/m²) compared to higher BMIs. Subgroup analyses consistently showed a positive association between BMI and asthma across different age, gender, race, socioeconomic, and smoking-related factors. Sensitivity analyses, including multiple imputation for missing data and alternative BMI metrics, confirmed the stability of the results. This study provides robust evidence for a positive and not entirely linear association between BMI and the risk of asthma in the pediatric population. These findings enhance the existing literature and underscore the necessity of considering BMI in both asthma research and clinical practice.

BackgroundMetabolic syndrome (MetS), a worldwide public health problem, affects human health and quality of life in a dramatic manner. A growing evidence base suggests that MetS is strongly associated with levels of systemic immune inflammation. The present study aimed to investigate the possible relationship between the systemic immune-inflammation index (SII), a novel inflammatory marker, and MetS to provide data support for effective MetS prevention by reducing the systemic inflammatory response.MethodsWe included adult participants with complete SII and MetS information from the 2011–2016 National Health and Nutrition Examination Survey (NHANES). MetS was defined as using the criteria developed by the Adult Treatment Program III of the National Cholesterol Education Program. The formula for SII was as follows: SII = platelet counts × neutrophil counts/ lymphocyte counts. Weighted linear regression was used to assess differences in variables across SII quartile groups after the SII score was divided into 4 quartiles. The independent interaction between SII and MetS was investigated using weighted multivariate logistic regression analysis and subgroup analysis, and the relationship between SII levels and 5 particular MetS items was further explored in depth.ResultsA total of 12,402 participants, 3,489 of whom were diagnosed with MetS, were included in this study. After correcting for covariates, the results of a logistic regression of multistage weighted complex sampling data revealed that participants with higher SII scores had a higher chance of developing MetS (odds ratio (OR) = 1.33, 95% confidence interval (CI): 1.14–1.55) and that SII levels could be used as an independent risk factor to predict that likelihood of MetS onset. In the Q1–Q4 SII quartile group, the risk of developing MetS was 1.33 times higher in the Q4 group, which had the highest level of systemic immune inflammation than in the Q1 group. After adjusting for all confounding factors, SII scores were found to have a negative correlation with high-density lipoprotein cholesterol (OR = 1.29; 95% CI, 0.99–1.67, P = 0.056) and a significant positive correlation with waist circumference (OR = 2.17; 95% CI, 1.65–2.87, P < 0.001) and blood pressure (BP) (OR = 1.65; 95% CI, 1.20–2.27, P = 0.003). Gender, age, and smoking status were shown to alter the positive association between SII and MetS in subgroup analyses and interaction tests (p for interaction < 0.05). Additionally, we demonstrated a nonlinear correlation between SII and MetS. The findings of the restricted cubic spline indicated that there was an inverted U-shaped association between SII and MetS.ConclusionsOur findings imply that increased SII levels are related to MetS, and SII may be a simple and cost-effective method to identify individuals with MetS. Therefore, protective measures such as early investigation and anti-inflammatory interventions are necessary to reduce the overall incidence of MetS.

Background The relationship between body mass index (BMI) changes over a long lifecycle and the risk of all-cause mortality among patients with cardiovascular disease (CVD) remains understudied. This study aims to investigate the association between BMI changes (from age 25 to after 50) and the risk of all-cause mortality in CVD patients. Methods This study leveraged data from the National Health and Nutrition Examination Survey (NHANES) 2001–2018 and the National Death Index (NDI) to construct a longitudinal cohort. Employing weighted multivariable Cox regression and Restricted Cubic Spline (RCS) analyses, we evaluated both the linear and nonlinear associations between BMI (at age 25 and after 50), its changes, and the risk of all-cause mortality among patients with CVD. Furthermore, we stratified the participants based on their BMI categories at these two time points to determine the relationship between different BMI trajectory patterns and all-cause mortality risk. Results A total of 2304 CVD patients were included in this study. During a median follow-up of 68 months, 774 participants died. The lowest risk of mortality was observed when BMI was 19.61 at age 25 and 26.55 after the age of 50. The impact of BMI change between these two time points on all-cause mortality risk exhibited a segmented effect with a threshold of 8.27. Specifically, when the change in BMI exceeded 8.27, it was positively associated with all-cause mortality risk [HR = 1.16, 95%CI=(1.00, 1.33)]. This relationship was most pronounced among CVD patients who were overweight at both ages 25 and after 50. Conclusion Among CVD patients, a U-shaped relationship is observed between BMI changes over a long lifecycle and the risk of all-cause mortality, where both excessive increases and decreases in BMI contribute to an elevated risk. BMI management strategies should be tailored to individual BMI trajectories, rather than solely focusing on weight loss.

The Systemic Immune-Inflammatory Index (SII) and Systemic Inflammatory Response Index (SIRI) are novel composite inflammatory markers. Previous studies suggest that obesity in individuals correlates with persistently low levels of chronic inflammation. This study aims to explore the association between SII and SIRI and Body Mass Index (BMI) among children and adolescents. A cross-sectional survey was conducted using the National Health and Nutrition Examination Survey (NHANES) dataset from 2 consecutive cycles from 2017-2020. Multivariate linear regression models were employed to examine the linear relationships between BMI and SII and SIRI. Non-linear associations were explored using smooth curve fitting and threshold effect analysis. A total of 2980 children and adolescents aged 6-19 years were included in this population-based study. In the population description of body mass index categories, we found progressively higher levels of SII and SIRI, notably peaking among obese children (SII mean ± SD: 528.83 ± 285.46; SIRI mean ± SD: 1.12 ± 0.79). Weighted multivariate linear regression confirmed a significant positive association between BMI and both inflammatory indices (P < 0.0001). Subgroup analyses revealed consistent correlations across gender divisions and highlighted a non-linear relationship between BMI and SII. SII and SIRI are positively associated with BMI in children and adolescents, indicating their potential as markers for assessing systemic inflammation in pediatric obesity. Further large-scale prospective studies are required to substantiate these findings.

To explore the association between body mass index (BMI) and total lumbar bone mineral density (BMD) in adults. This study included 9927 participants from 2011 to 2020 National Health and Nutrition Examination Survey (NHANES). The date on BMI, total lumbar BMD and other covariates were collected. Multivariate linear regression analyses were performed to evaluate the association between BMI and total lumbar BMD. Smoothing curve fitting and saturation effects analysis models were used to analyze the nonlinear relationships and saturation values. Multivariate linear regression analyses revealed that BMI was positively linked to total lumbar BMD in non-adjusted models (β = 0.003, 95% CI: 0.003-0.003, P < .00001). After adjusting for gender and race (β = 0.003, 95% CI: 0.003-0.004, P < .00001) and all covariates (β = 0.004, 95% CI: 0.003-0.004, P < .00001), the association still existed. Smoothing curve fitting showed that there was nonlinear correlation between BMI and total lumbar BMD with saturation effect. The BMI saturation value was 21.2 kg/m2 in the total lumbar BMD based on saturation effects analysis models. There was nonlinear positive correlation between BMI and total lumbar BMD with saturation effect. For adults, keeping the BMI at a reasonable value (21.2 kg/m2) would obtain an optimal balance between BMI and total lumbar BMD.

The incidence of stroke has increased globally, resulting in medical expenditures and social burdens over the past few decades. We aimed to explore the relationship between systemic immune inflammatory index (SII) and stroke using the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Based on NHANES data, 902 stroke patients and 27,364 non-stroke patients were included in this study. SII was the independent variable and stroke was the dependent variable. Univariate and multivariate logistic regression analyses were used to explore the association between SII and stroke. Restricted cubic spline (RCS) method was used to test the nonlinear association between SII and stroke. Weighted logistic regression analysis showed a significant association between SII and stroke (OR: 1.985, 95% CI: 1.245-3.166, p = 0.004). The interaction test showed that the association between SII and stroke was not significant between strata (p > 0.05). A significant positive association between SII and stroke risk (OR >1, p < 0.05) was observed in the crude model, model I and model II. RCS analysis showed no nonlinear positive association between SII and stroke risk after adjusting for all confounders. Our study determined that SII is associated with stroke risk. Given the inherent limitations of cross-sectional studies, further research is necessary to validate the causality of this association and to demystify the underlying mechanisms between inflammation and stroke.