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孙丽荣, 霍醒伟, 贾珊珊, 等. 全身炎症综合指数与白蛋白尿的关联:基于2007–2018年国家健康与营养调查的横断面研究[J]. koko体育app 学报(医学版), 2024, 55(3): 671-679. DOI:
引用本文: 孙丽荣, 霍醒伟, 贾珊珊, 等. 全身炎症综合指数与白蛋白尿的关联:基于2007–2018年国家健康与营养调查的横断面研究[J]. koko体育app 学报(医学版), 2024, 55(3): 671-679. DOI:
SUN Lirong, HUO Xingwei, JIA Shanshan, et al. Association Between the Aggregate Index of Systemic Inflammation and Albuminuria: A Cross-Sectional Study of National Health and Nutrition Examination Survey 2007-2018[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(3): 671-679. DOI:
Citation: SUN Lirong, HUO Xingwei, JIA Shanshan, et ✱al. Association Between the Aggregate 🥂Index of Systemic Inflammation and Albuminuria: A Cross-Sectional Study of National Health and Nutrition Examination Survey 2007-2018[J]. Journal of Sichuan University (Medical Sciences), 2024, 55(3): 671-679. DOI:

全身炎症综合指数与白蛋白尿的关联:基于2007–2018年国家健康与营养调查的横断面研究

Association Between the Aggregate Index of Systemic Inflammation and Albuminuria: A Cross-Sectional Study of National Health and Nutrition Examination Survey 2007-2018

  • 摘要:
    目的  探讨美国成人全身炎症综合指数(aggregate index of systemic inflammation, AISI)与白蛋白尿之间的关联。
    方法  研究使用了2007–2018年美国国家健康与营养调查(National Health and Nutrition Examination Survey, NHANES)的数据,排除了怀孕妇女和18岁以下的个体,缺少AISI、尿白蛋白浓度及其他共变量数据的病例也被排除。AISI的计算公式为:AISI=(血小板计数×中性粒细胞计数×单核细胞计数)/淋巴细胞计数,并根据log2AISI三分位数将研究变量分为3组,即Q1(4.94~7.49),Q2(7.49~8.29),Q3(8.29~10.85)。白蛋白尿是指尿白蛋白与肌酐比值超过30 mg/g。通过加权多变量逻辑回归和亚组分析研究AISI与白蛋白尿之间的独立关系。广义相加模型用于检验非线性关联。
    结果  本研究共分析了32273名参与者。白蛋白尿的患病率为9.64%。按AISI水平分类的基线特征显示随着AISI水平的增加,白蛋白尿的患病率相应增加。在完全调整模型中,加权多变量逻辑回归揭示了与最低AISI水平相比,更高AISI水平的个体患白蛋白尿风险更高〔比值比(odds ratio, OR)=1.37,95%置信区间(confidence interval, CI):1.21~1.55,P<0.001〕;AISI与大量白蛋白尿风险间的关系强调了这一正向关系的稳健性。广义相加模型结果表明AISI与白蛋白尿之间存在非线性关系。阈值效应分析提示当log2AISI小于7.25时,log2AISI的增加并不增加白蛋白尿的风险,但当log2AISI超过7.25时,log2AISI的增加显著增加白蛋白尿的风险。亚组分析和交互作用提示性别、血压、体质量指数、吸烟和饮酒与 AISI 存在交互作用,影响蛋白尿的风险。
    结论 美国成人AISI与白蛋白尿风险之间存在稳健的正向关联。然而,有必要通过大规模前瞻性研究进一步验证该结论。
     
    Abstract:
    Objective  Prior studies have established a connection between albuminuria and various inflammatory reactions, highlighting that an increase in C-reactive protein by 1 mg/L increases the likelihood of albuminuria by 2%. Recent investigations indicate a positive correlation between the systemic immune-inflammation index (SII) and increased urinary protein excretion. In addition, elevated levels of the systemic inflammatory response index (SIRI) also correlate with a higher prevalence of albuminuria. The aggregate index of systemic inflammation (AISI) offers a more comprehensive indicator of inflammation, providing an extensive assessment of systemic inflammatory status compared to SII and SIRI. Yet, the specific relationship between AISI and albuminuria remains unclear. This study aims to explore this association in U.S. adults.
    Methods  We analyzed data from the National Health and Nutrition Examination Survey (NHANES) for 2007-2018, excluding pregnant women and individuals under 18. Cases with missing data on AISI, urinary albumin concentration, and other covariates were also excluded. AISI was computed using the formula: AISI=(platelet count×neutrophil count×monocyte count)/lymphocyte count. Albuminuria was defined as the urinary albumin-to-creatinine ratio exceeding 30 mg/g. Continuous variables were presented in the form of the mean±standard error, and categorical variables in percentages. We utilized weighted t-tests and chi-square tests for baseline comparisons. We applied weighted multivariable logistic regression and generalized additive models (GAM) to explore the association between AISI and albuminuria and to assess potential nonlinear relationships.
    Results  The study included 32273 participants, with an average age of (46.75±0.24) years old. The cohort comprised 48.73% males and 51.27% females. The prevalence of albuminuria was 9.64%. The average logarithmic value of log2AISI was 7.95±0.01, and were categorized into tertiles as follows: Quartile 1 (Q1) (4.94 to 7.49), Q2 (7.49 to 8.29), and Q3 (8.29 to 10.85). As log2AISI increased, so did the prevalence of hypertension, diabetes, congestive heart failure, and albuminuria, all showing statistically significant increases (P<0.001). Similarly, the use of antihypertensive, lipid-lowering, and hypoglycemic drugs was also more prevalent (P<0.001). Statistically significant differences were observed across the three groups concerning age, race and ethnicity, formal education, alcohol consumption, smoking status, systolic and diastolic blood pressures, body mass index, estimated glomerular filtration rate, HbA1c, alanine aminotransferase, aspartate aminotransferase, albumin, creatinine, uric acid, and high-density lipoprotein cholesterol (P<0.05). However, no significant differences were noted in the total cholesterol or the sex ratios among the groups. The association between log2AISI and albuminuria was assessed using weighted multivariable logistic regression, and the detailed results are presented in Table 2. In model 1, without adjusting for covariates, each unit increase in log2AISI was associated with a 32% increase in the risk of albuminuria (odds ratio OR=1.32, 95% confidence interval CI: 1.27-1.38, P<0.001). Model 2 was adjusted for age, gender, race, and education level, and showed a similar trend, with each unit increase in log2AISI associated with a 31% increased risk (OR=1.31, 95% CI: 1.26-1.37, P<0.001). Model 3, which was further adjusted for all covariates, revealed that each unit increase in log2AISI was associated with a 20% increase in the risk of albuminuria (OR=1.20, 95% CI: 1.15-1.26, P<0.001). The study also transformed log2AISI from a continuous to a categorical variable for analysis. Compared with Q1, the risk of albuminuria in Q3, after adjusting for all covariates, significantly increased (OR=1.37, 95% CI: 1.22-1.55, P<0.001). Q2 also demonstrated a higher risk compared with Q1 (OR=1.13, 95% CI: 1.06-1.36, P=0.004). The trend test indicated a dose-effect relationship between increasing log2AISI and the rising risk of albuminuria. GAM revealed a nonlinear relationship between log2AISI and albuminuria, with distinct trends noted between sexes. Segmented regression based on turning points showed significant effects among women, although the slope difference between the segments was not significant. In men, a significant threshold effect was observed; below the log2AISI of 7.25, increases in log2AISI did not enhance the risk of albuminuria, but above this threshold, the risk significantly increased. As part of a sensitivity analysis, weighted multivariable logistic regression was performed by changing the outcome variable to macroalbuminuria and adjusting for all covariates. The analysis showed that for every unit increase in log2AISI, the risk of developing macroalbuminuria increased by 31% (OR=1.31, 95% CI: 1.15-1.49, P<0.001). Compared with Q1, the risk of albuminuria in Q3 increased by 69% (OR=1.69, 95% CI: 1.27-2.25, P<0.001), and in Q2, it increased by 40% (OR=1.40, 95% CI: 1.03-1.92, P=0.030). Subgroup analysis and interaction results showed that the positive association between AISI and proteinuria risk was stronger in men than in women. Similarly, the association was stronger in people with hypertension compared with those with normal blood pressure, and higher in overweight people compared with those of normal weight. Furthermore, smokers and drinkers showed a stronger positive association between AISI and the risk of proteinuria than non-smokers and non-drinkers do. These results suggest that sex, blood pressure, body mass index, smoking, and alcohol consumption interact with AISI to influence the risk of proteinuria.
    Conclusion  There is a robust positive association between AISI and increased risks of albuminuria in US adults. As log2AISI increases, so does the risk of albuminuria. However, further validation of this conclusion through large-scale prospective studies is warranted.
     

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