■先前的研究已经建立了白蛋白尿和各种炎症反应之间的联系,强调C-反应蛋白增加1mg/L,白蛋白尿的可能性增加2%。最近的研究表明,全身免疫炎症指数(SII)与尿蛋白排泄增加之间呈正相关。此外,全身炎症反应指数(SIRI)水平升高也与蛋白尿患病率升高相关.全身炎症综合指数(AISI)提供了更全面的炎症指标,与SII和SIRI相比,提供对全身炎症状态的广泛评估。然而,AISI与蛋白尿之间的具体关系尚不清楚.这项研究旨在探索美国成年人的这种关联。
■我们分析了2007-2018年国家健康与营养检查调查(NHANES)的数据,不包括孕妇和18岁以下的个人。AISI数据缺失的案件,尿白蛋白浓度,和其他协变量也被排除。使用以下公式计算AISI: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。白蛋白尿定义为尿白蛋白与肌酐之比超过30mg/g。连续变量以平均值±标准误差的形式表示,和分类变量的百分比。我们使用加权t检验和卡方检验进行基线比较。我们应用加权多变量逻辑回归和广义加性模型(GAM)来探索AISI和蛋白尿之间的关联,并评估潜在的非线性关系。
■该研究包括32273名参与者,平均年龄(46.75±0.24)岁。该队列包括48.73%的男性和51.27%的女性。蛋白尿的患病率为9.64%。log2AISI的平均对数值为7.95±0.01,分为三位数:四分位数1(Q1)(4.94至7.49),第二季度(7.49至8.29),和第三季度(8.29至10.85)。随着log2AISI的增加,高血压的患病率也是如此,糖尿病,充血性心力衰竭,和蛋白尿,均显示有统计学意义的增加(P<0.001)。同样,使用抗高血压药,降脂,降糖药物也更为普遍(P<0.001)。在三组年龄方面观察到统计学上的显着差异,种族和民族,正规教育,酒精消费,吸烟状况,收缩压和舒张压,身体质量指数,估计肾小球滤过率,HbA1c,丙氨酸氨基转移酶,天冬氨酸转氨酶,白蛋白,肌酐,尿酸,高密度脂蛋白胆固醇(P<0.05)。然而,各组间总胆固醇或性别比例无显著差异.log2AISI和蛋白尿之间的关联使用加权多变量逻辑回归评估,并且详细的结果呈现在表2中。在模型1中,不调整协变量,log2AISI每增加一个单位与蛋白尿风险增加32%相关(比值比[OR]=1.32,95%置信区间[CI]:1.27~1.38,P<0.001).模型2根据年龄进行了调整,性别,种族,和教育水平,并表现出类似的趋势,log2AISI每增加一个单位与31%的风险增加相关(OR=1.31,95%CI:1.26-1.37,P<0.001)。模型3,对所有协变量进行了进一步调整,显示log2AISI每增加一个单位与蛋白尿风险增加20%相关(OR=1.20,95%CI:1.15-1.26,P<0.001)。该研究还将log2AISI从连续变量转换为分类变量进行分析。与Q1相比,在调整所有协变量后,Q3的白蛋白尿风险,显著升高(OR=1.37,95%CI:1.22~1.55,P<0.001)。与Q1相比,Q2也显示出更高的风险(OR=1.13,95%CI:1.06-1.36,P=0.004)。趋势测试表明log2AISI增加与蛋白尿风险增加之间存在剂量效应关系。GAM揭示了log2AISI和蛋白尿之间的非线性关系,男女之间有明显的趋势。基于转折点的分段回归显示出女性的显着影响,尽管节段之间的斜率差异不显著。在男人中,观察到显著的阈值效应;低于7.25的log2AISI,log2AISI的增加并没有增加蛋白尿的风险,但是在这个门槛之上,风险显著增加。作为敏感性分析的一部分,通过将结局变量更改为大量白蛋白尿并校正所有协变量进行加权多变量逻辑回归.分析表明,log2AISI每增加一个单位,发生大量白蛋白尿的风险增加了31%(OR=1.31,95%CI:1.15-1.49,P<0.001)。与Q1相比,Q3的蛋白尿风险增加了69%(OR=1.69,95%CI:1.27-2.25,P<0.001),在第二季度,它增加了40%(OR=1.40,95%CI:1.03-1.92,P=0.030)。亚组分析和交互作用结果显示,AISI与蛋白尿风险之间的正相关性在男性中比在女性中强。同样,与血压正常的人相比,高血压患者的关联性更强,与正常体重的人相比,超重的人更高。此外,吸烟者和饮酒者比不吸烟者和不饮酒者显示AISI与蛋白尿风险之间有更强的正相关。这些结果表明性,血压,身体质量指数,吸烟,饮酒与AISI相互作用,影响蛋白尿的风险。
■在美国成年人中,AISI与蛋白尿风险增加之间存在强烈的正相关。随着log2AISI的增加,白蛋白尿的风险也是如此。然而,需要通过大规模前瞻性研究进一步验证这一结论.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.