Abstract:BackgroundDyslipidemia is a well-recognized risk factor for diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D). Growing evidences have shown that compared with the traditional lipid parameters, some lipid ratios may provide additional information of lipid metabolism. Thus, the present study aimed to investigate which lipid index was most related to DKD.MethodsThis study was a cross-sectional study that enrolled patients with T2D from January 2021 to October 2021. Each participant was screened… Show more
“…Some studies have shown that AIP could represent the quantity of sdLDL particles (subfraction of LDL-C) [ 34 ]. sdLDL, characterized by difficulty in being cleared from the circulation, susceptibility to oxidation, and ease of uptake by macrophages to form foam cells, contributes to an elevated risk of microvascular complications [ 35 ]. Nonetheless, the clinical applicability of sdLDL is restricted because of its intricate and expensive measurement process [ 13 ].…”
Background
Some studies have assessed the predictive role of the atherogenic index of plasma (AIP) for macrovascular diseases. This prospective investigation aimed to elucidate whether AIP is associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR) incidence.
Methods
The data were extracted from 4831 participants, of whom 2943 and 3360 participants with type 2 diabetes (T2D) were included in the DKD and DR follow-up analyses, respectively. Cox regression models were performed to test the relationships of AIP value at baseline with the risk of incident DKD and DR. Group-based trajectory modelling was utilized to discern AIP trajectories during the follow-up period. Subsequently, logistic regressions were applied to ascertain the influence of AIP trajectories on the incidence of DKD and DR.
Results
During the follow-up period, 709 (24.1%) and 193 (5.7%) participants developed DKD and DR, respectively. The median (interquartile range) follow-up time was 24.2 (26.3) months for DKD and 25.7 (27.0) months for DR. According to the multivariate Cox regression models, baseline AIP was positively and linearly related to the occurrence of DKD, with a hazard ratio of 1.75 (95% confidence interval [CI] 1.36–2.26). Three distinct trajectories of AIP were identified throughout the follow-up time: Low (31.4%), Median (50.2%), and High (18.3%). Compared to participants with the Low AIP trajectory, those with High and Median AIP trajectories presented 117% (95% CI: 1.62–2.91) and 84% (95% CI 1.46–2.32) greater odds of developing DKD, respectively. However, neither baseline levels nor trajectories of AIP were shown to be related to DR after adjusting for confounding factors.
Conclusions
Baseline levels and trajectories of AIP were independently related to elevated DKD risk, indicating that AIP could be used as a predictor for identifying T2D participants at higher risk of DKD. No association between AIP and DR was detected.
“…Some studies have shown that AIP could represent the quantity of sdLDL particles (subfraction of LDL-C) [ 34 ]. sdLDL, characterized by difficulty in being cleared from the circulation, susceptibility to oxidation, and ease of uptake by macrophages to form foam cells, contributes to an elevated risk of microvascular complications [ 35 ]. Nonetheless, the clinical applicability of sdLDL is restricted because of its intricate and expensive measurement process [ 13 ].…”
Background
Some studies have assessed the predictive role of the atherogenic index of plasma (AIP) for macrovascular diseases. This prospective investigation aimed to elucidate whether AIP is associated with diabetic kidney disease (DKD) and diabetic retinopathy (DR) incidence.
Methods
The data were extracted from 4831 participants, of whom 2943 and 3360 participants with type 2 diabetes (T2D) were included in the DKD and DR follow-up analyses, respectively. Cox regression models were performed to test the relationships of AIP value at baseline with the risk of incident DKD and DR. Group-based trajectory modelling was utilized to discern AIP trajectories during the follow-up period. Subsequently, logistic regressions were applied to ascertain the influence of AIP trajectories on the incidence of DKD and DR.
Results
During the follow-up period, 709 (24.1%) and 193 (5.7%) participants developed DKD and DR, respectively. The median (interquartile range) follow-up time was 24.2 (26.3) months for DKD and 25.7 (27.0) months for DR. According to the multivariate Cox regression models, baseline AIP was positively and linearly related to the occurrence of DKD, with a hazard ratio of 1.75 (95% confidence interval [CI] 1.36–2.26). Three distinct trajectories of AIP were identified throughout the follow-up time: Low (31.4%), Median (50.2%), and High (18.3%). Compared to participants with the Low AIP trajectory, those with High and Median AIP trajectories presented 117% (95% CI: 1.62–2.91) and 84% (95% CI 1.46–2.32) greater odds of developing DKD, respectively. However, neither baseline levels nor trajectories of AIP were shown to be related to DR after adjusting for confounding factors.
Conclusions
Baseline levels and trajectories of AIP were independently related to elevated DKD risk, indicating that AIP could be used as a predictor for identifying T2D participants at higher risk of DKD. No association between AIP and DR was detected.
Background
This study was designed to assess the associations between emerging cardiometabolic indices—the atherogenic index of plasma (AIP), the stress hyperglycemia ratio (SHR), the triglyceride-glucose (TyG) index, and the homeostasis model assessment of insulin resistance (HOMA-IR)—and the incidence of diabetic kidney disease (DKD) in type 2 diabetes (T2D) patients.
Methods
We consecutively enrolled 4351 T2D patients. The AIP, SHR, TyG index, and HOMA-IR were calculated from baseline parameters. DKD was defined as a urine albumin/creatinine ratio > 30 mg/g or an eGFR < 60 mL/min per 1.73 m. All participants were categorized into tertiles based on the cardiometabolic indices. Multivariate logistic regression models, restricted cubic splines, and receiver operating characteristic (ROC) curves were used for analysis.
Results
A total of 1371 (31.5%) patients were diagnosed with DKD. A restricted cubic spline showed a J-shaped association of the AIP and TyG index with DKD, a log-shaped association between HOMA-IR and DKD, and a U-shaped association between the SHR and DKD incidence. Multivariate logistic regression revealed that individuals in the highest tertile of the four cardiometabolic indices had a significantly greater risk of DKD than did those in the lowest tertile (AIP: OR = 1.08, 95% CI = 1.02–1.14, P = 0.005; SHR: OR = 1.42, 95% CI = 1.12–1.81, P = 0.004; TyG index: OR = 1.86, 95% CI = 1.42–2.45, P < 0.001; HOMA-IR: OR = 2.24, 95% CI = 1.52–3.30, P < 0.001). The receiver operating characteristic curves showed that the HOMA-IR score was better than other indices at predicting the risk of DKD, with an optimal cutoff of 3.532.
Conclusions
Elevated AIP, SHR, TyG index and HOMA-IR are associated with a greater risk of DKD in patients with T2D. Among these indices, the HOMA-IR score demonstrated the strongest association with and predictive value for DKD incidence.
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