Dataset Information

Association of systemic immunity-inflammation index with metabolic syndrome in U.S. adult: a cross-sectional study.

ABSTRACT:

Background

Metabolic syndrome (MetS) is a pathological condition characterized by the abnormal clustering of several metabolic components and has become a major public health concern. We aim to investigate the potential link of Systemic immunity-inflammation index (SII) on MetS and its components.

Methods and result

Weighted multivariable logistic regression was conducted to assess the relationship between SII and MetS and its components. Restricted cubic spline (RCS) model and threshold effect analysis were also performed. A total of 6,999 U.S. adults were enrolled. Multivariate model found that SII were positively associated with MetS (OR = 1.18;95CI%:1.07-1.30) and hypertension (OR = 1.22; 95CI%:1.12-1.34) in a dose-dependent manner. When SII was converted into a categorical variable, the risk of MetS increased by 36% and the risk of hypertension increased by 53% in the highest quantile of SIIs. The RCS model confirmed linear associations between SII and MetS, as well as a non-linear association between SII and certain components of MetS, including hypertension, hyperglycemia, low HDL, and hyperlipidemia. Meanwhile, the relationship between SII and hypertension presents a J-shaped curve with a threshold of 8.27, above which the risk of hypertension increases. Furthermore, in MetS and hypertension, age, sex, body mass index (BMI), and race were not significantly associated with this positive association based on subgroup analyses and interaction tests(p for interaction > 0.05).

Conclusions

The present study indicated that there was a higher SII association with an increased risk of MetS and hypertension in adults. However, further prospective cohort studies are required to establish a causal relationship between SII and MetS, as well as its components.

SUBMITTER: Zeng P

PROVIDER: S-EPMC10788994 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Publications

Association of systemic immunity-inflammation index with metabolic syndrome in U.S. adult: a cross-sectional study.

Zeng Peng P Jiang Cheng C Liu Anbang A Yang Xinyuan X Lin Feng F Cheng Lingli L

BMC geriatrics 20240115 1

<h4>Background</h4>Metabolic syndrome (MetS) is a pathological condition characterized by the abnormal clustering of several metabolic components and has become a major public health concern. We aim to investigate the potential link of Systemic immunity-inflammation index (SII) on MetS and its components.<h4>Methods and result</h4>Weighted multivariable logistic regression was conducted to assess the relationship between SII and MetS and its components. Restricted cubic spline (RCS) model and th ...[more]

PMID: 38225566

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Project description:BackgroundSignificant evidence suggests that asthma might originate from low-grade systemic inflammation. Previous studies have established a positive association between the systemic immune-inflammation index (SII) and the systemic inflammation response index (SIRI) levels and the risk of stroke. However, it remains unclear whether SII, SIRI and the prevalence of stroke are related in individuals with asthma.MethodsThe present cross-sectional study used data from the National Health and Nutrition Examination Survey (NHANES) conducted between 1999 and 2018. SII was calculated using the following formula: (platelet count × neutrophil count)/lymphocyte count. SIRI was calculated using the following formula: (neutrophil count × monocyte count)/lymphocyte count. The Spearman rank correlation coefficient was used to determine any correlation between SII, SIRI, and the baseline characteristics. Survey-weighted logistic regression was employed to calculate odds ratios (ORs) and 95% confidence intervals (CIs) to determine the association between SII, SIRI, and stroke prevalence. The predictive value of SII and SIRI for stroke prevalence was assessed through receiver operating characteristic (ROC) curve analysis, with the area under the ROC curve (AUC) being indicative of its predictive value. Additionally, clinical models including SIRI, coronary heart disease, hypertension, age, and poverty income ratio were constructed to evaluate their clinical applicability.ResultsBetween 1999 and 2018, 5,907 NHANES participants with asthma were identified, of which 199 participants experienced a stroke, while the remaining 5,708 participants had not. Spearman rank correlation analysis indicated that neither SII nor SIRI levels exhibited any significant correlation with the baseline characteristics of the participants (r<0.1). ROC curves were used to determine the optimal cut-off values for SII and SIRI levels to classify participants into low- and high-level groups. Higher SII and SIRI levels were associated with a higher prevalence of stroke, with ORs of 1.80 (95% CI, 1.18-2.76) and 2.23 (95% CI, 1.39-3.57), respectively. The predictive value of SIRI (AUC=0.618) for stroke prevalence was superior to that of SII (AUC=0.552). Furthermore, the clinical model demonstrated good predictive value (AUC=0.825), with a sensitivity of 67.1% and specificity of 87.7%.ConclusionIn asthmatics, higher levels of SII and SIRI significantly increased the prevalence of stroke, with its association being more pronounced in individuals with coexisting obesity and hyperlipidaemia. SII and SIRI are relatively stable novel inflammatory markers in the asthmatic population, with SIRI having a better predictive value for stroke prevalence than SII.

Project description:ObjectiveThe systemic immunity-inflammation index (SII) is a newly developed biomarker that provides an integrated measure of inflammation in the body. We aim to evaluate the relationship between SII and body fat distribution.MethodsAdults from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 were included. The SII was computed using lymphocyte (LC), neutrophil (NC), and platelet (PC) counts as its components. Body fat distribution was assessed by (total, android, gynoid) percentage fat, total abdominal fat area, subcutaneous adipose tissue area, visceral adipose tissue area, and the ratio of visceral to subcutaneous adipose tissue area (V/S ratio). Multivariable weighted linear regression and subgroup analysis were use to examine the relationships between fat distribution and SII. Restricted cubic splines (RCS) and threshold effect analysis were used to examine analyze nonlinear associations.ResultsAfter exclusions, a total of 11,192 adults with a weighted mean age of 38.46 ± 0.26 years were studied. In multivariable weighted linear regression, each level increase in log2SII was associated with increased of 0.23 SDs total percentage fat (95% CI = 0.03, 0.43) and 0.26 SDs android percentage fat (95% CI = 0.06, 0.47). Besides, the subgroup analysis showed that the positive association between SII and android percentage fat was mainly among obese individuals (BMI > 30 kg/m2) and non-obese individuals without DM or hypertension. Meanwhile, the relationship between SII and the V/S ratio was found to be significant in the female subgroup, the obese subgroup, individuals with non-alcoholic fatty liver disease (NAFLD), and those without diabetes mellitus. Finally, SII exhibited an inverted U-shaped relationship with total percentage fat, android percent fat and total abdominal fat. Accordingly, threshold effect analysis indicated a positive association between lower SII levels and total percentage fat, android percentage fat and total abdominal fat area.ConclusionsIn the nationwide study, it was observed that the SII exhibited a significant correlation with higher levels of body fat, specifically android fat. This association was particularly noticeable within specific subgroups of the population.

Project description:BackgroundSystemic immune-inflammation index (SII) is associated with increased risk in a wide range of illnesses. However, few studies have explored the associations between SII and the risk of malnutrition. Therefore, this study aimed to investigate the association between SII and malnutrition in a nationwide, multicenter, cross-sectional study involving Chinese hospitalized patients.DesignFrom August 2020 to August 2021, a total of 40,379 hospitalized patients met the inclusion and exclusion criteria. Detailed demographic data, diagnoses, as well as physical and laboratory examination results were recorded. The diagnosis of malnutrition was used with two distinct methods: the Malnutrition Screening Tool 2002 (NRS 2002) + Global Leaders Initiative on Malnutrition (GLIM) criteria and the controlling nutritional status (CONUT) score. The risk factors for malnutrition were analyzed using binary logistic regression and multiple logistic regression to obtain odds ratios (OR) and 95% confidence intervals (CI). Restricted cubic spline (RCS), linear spline, and receiver operating characteristic (ROC) analysis were also used.ResultsThe prevalence of malnutrition diagnosed by the two methods was 13.4% and 14.9%, respectively. In the NRS 2002 + GLIM diagnostic model, lnSII showed statistical significance between the malnutrition and non-malnutrition group (6.28 ± 0.78 vs. 6.63 ± 0.97, p < 0.001). A positive association was observed between higher SII and the risk of malnutrition in both before and after adjustment models compared to the first quartile (Q3 vs. Q1, OR = 1.27, 95%CI: 1.15-1.40; Q4 vs. Q1, OR = 1.83, 95%CI: 1.67-2.00). However, a significant reduction in prevalence was observed when SII was in the second quartile (Q2 vs. Q1, OR < 1), as indicated by a restricted cubic spline with a U trend (p for nonlinear <0.001). According to the CONUT score, the prevalence of individuals with normal nutritional status decreased with increasing SII, while the occurrence of three different degrees of malnutrition generally increased. The Kappa value between the two diagnostic methods was 0.23, and the merged data observed an area under the ROC curve of 0.73 (95%CI: 0.714-0.742).ConclusionThe U-shaped association between SII and the prevalence of malnutrition was observed. Both lower and higher SII levels (either continuous or categorical variable) were significantly associated with an increased risk of malnutrition.

Project description:BackgroundAsthma is associated with persistent airway inflammation, and numerous studies have investigated inflammatory markers causing asthma. However, the systemic immune-inflammation index (SII) is a novel inflammatory marker, with scarce research reporting on the correlation between SII and asthma and asthma-related events.ObjectiveThe purpose of this study was to assess the relationship between SII and asthma and asthma-related events (including whether asthma is still present, asthma flare-ups in the past year, and asthma duration) using data from the National Health and Nutrition Examination Survey (NHANES).MethodsThe study utilized data from NHANES 2009-2018 with asthma and asthma-related events as dependent variables and SII as an independent variable. Multifactor logistic regression was employed to assess the correlation between the independent and dependent variables. Smoothed curve-fitting and threshold effect analyses were also carried out to determine the presence of non-linear relationships. Subgroup analyses were then performed to identify sensitive populations.ResultsIn this study, we analyzed data from 40,664 participants to elucidate the association between SII and asthma and its related events. The study findings indicated a positive correlation between SII and asthma, with a relative risk increase of 0.03% for asthma incidence per one percentage point increase in SII (OR = 1.0003, 95% CI: 1.0002, 1.0004). For individuals still suffering from asthma, higher SII also indicated a positive correlation with ongoing asthma (OR = 1.0004, 95% CI: 1.0001, 1.0006). However, no statistically significant association was observed between SII and asthma exacerbations within the following year (OR = 1.0001, p > 0.05). When considering the duration of asthma, we observed a slight positive correlation with SII (β = 0.0017, 95% CI: 0.0005, 0.0029). Additionally, a significant non-linear relationship between SII and asthma duration emerged at the threshold of 504.3 (β = 0.0031, 95% CI: 0.0014-0.0048, p = 0.0003). Subgroup analysis revealed a stronger correlation between SII and asthma in male patients (OR = 1.0004, 95% CI: 1.0002-1.0006) and individuals aged 60 and above (OR = 1.0005, 95% CI: 1.0003-1.0007). No gender differences were observed for individuals still suffering from asthma. However, the positive correlation between SII and asthma was more pronounced in participants under 20 years old (OR = 1.0004 in Model 3, 95% CI: 1.0002-1.0006). Specific sensitive subgroups for asthma exacerbation recurrence within the past year were not identified. When considering asthma duration, we observed this association to be significant in male individuals (β = 0.0031 in Model 3, 95% CI: 0.0014-0.0049) as well as individuals aged 20 to 39 (β = 0.0023 in Model 3, 95% CI: 0.0005-0.0040).ConclusionOur study concludes that SII is positively correlated with the persistence of asthma yet has limited predictive power for asthma recurrence. This highlights SII's potential as a tool for assessing asthma risk and formulating targeted management strategies.

Dataset Information

Association of systemic immunity-inflammation index with metabolic syndrome in U.S. adult: a cross-sectional study.

Background

Methods and result

Conclusions

Publications

Association of systemic immunity-inflammation index with metabolic syndrome in U.S. adult: a cross-sectional study.

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