Dataset Information

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population.

ABSTRACT:

Objective

Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.

Methods

A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were divided into three groups based on estimated glomerular filtration rate (eGFR) and urinary albumin-to-creatinine ratio (ACR): non-DKD group, DKD stages 1-2 Alb group, and DKD-non-Alb+DKD stage 3 Alb group. The possible association of SII with DKD was investigated by correlation and multivariate logistic regression analysis, and receiver-operating characteristic (ROC) curves analysis.

Results

Moving from the non-DKD group to the DKD-non-Alb+DKD stage 3 Alb group, SII level was gradually increased (P for trend <0.01). Partial correlation analysis revealed that SII was positively associated with urinary ACR and prevalence of DKD, and negatively with eGFR (all P<0.01). Multivariate logistic regression analysis showed that SII remained independently significantly associated with the presence of DKD after adjustment for all confounding factors [(odds ratio (OR), 2.735; 95% confidence interval (CI), 1.840-4.063; P < 0.01)]. Moreover, compared with subjects in the lowest quartile of SII (Q1), the fully adjusted OR for presence of DKD was 1.060 (95% CI 0.773-1.455) in Q2, 1.167 (95% CI 0.995-1.368) in Q3, 1.266 (95% CI 1.129-1.420) in the highest quartile (Q4) (P for trend <0.01). Similar results were observed in presence of DKD stages 1-2 Alb or presence of DKD-non- Alb+DKD stage 3 Alb among SII quartiles. Last, the analysis of ROC curves revealed that the best cutoff values for SII to predict DKD, Alb DKD stages 1- 2, and DKD-non-Alb+ DKD stage 3 Alb were 609.85 (sensitivity: 48.3%; specificity: 72.8%), 601.71 (sensitivity: 43.9%; specificity: 72.3%), and 589.27 (sensitivity: 61.1%; specificity: 71.1%), respectively.

Conclusion

Higher SII is independently associated with an increased risk of the presence and severity of DKD, and SII might be a promising biomarker for DKD and its distinct phenotypes in Chinese population.

SUBMITTER: Yan P

PROVIDER: S-EPMC10795757 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population.

Yan Pijun P Yang Yuxia Y Zhang Xing X Zhang Yi Y Li Jia J Wu Zujiao Z Dan Xiaofang X Wu Xian X Chen Xiping X Li Shengxi S Xu Yong Y Wan Qin Q

Frontiers in endocrinology 20240104

<h4>Objective</h4>Systemic immune-inflammation index (SII), a novel inflammatory marker, has been reported to be associated with diabetic kidney disease (DKD) in the U.S., however, such a close relationship with DKD in other countries, including China, has not been never determined. We aimed to explore the association between SII and DKD in Chinese population.<h4>Methods</h4>A total of 1922 hospitalized patients with type 2 diabetes mellitus (T2DM) included in this cross-sectional study were div ...[more]

PMID: 38239983

Similar Datasets

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: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: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 immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population.

Objective

Methods

Results

Conclusion

Publications

Association of systemic immune-inflammation index with diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study in Chinese population.

Similar Datasets

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