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Impact of baseline and trajectory of the atherogenic index of plasma on incident diabetic kidney disease and retinopathy in participants with type 2 diabetes: a longitudinal cohort study

Lipids in Health and Disease volume 23, Article number: 11 (2024) Cite this article

Abstract

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

Type 2 diabetes (T2D) has become a serious worldwide health challenge with a growing prevalence [1]. The primary cause of mortality in diabetic patients is chronic vascular complications [2], which can affect life expectancy and impose financial burdens [3]. These vascular complications are influenced by metabolic factors, of which dyslipidaemia is vital [4]. Notably, patients with diabetes exhibit a typical dyslipidaemia pattern characterized by elevated triglycerides (TG), reduced high-density lipoprotein cholesterol (HDL-C), and increased small dense low-density lipoprotein (sdLDL), which differs from what has been observed in the nondiabetic population [5, 6]. Furthermore, despite attaining the target for low-density lipoprotein cholesterol (LDL-C), patients with diabetes still experience vascular complications [7], suggesting that the traditional use of a single lipid indicator (LDL-C) is no longer sufficient for elucidating vascular damage in individuals with diabetes. Hence, the exploration of a comprehensive blood lipid profile for improved prediction and assessment of diabetic vascular complications warrants attention.

The atherogenic index of plasma (AIP) was introduced to assess the atherosclerosis burden [8, 9]. Numerous studies have reported that AIP serves as a novel predictive indicator for cardiovascular diseases, regardless of conventional factors [10,11,12,13]. Moreover, AIP could predict insulin resistance [14], prediabetes [15], and diabetes onset [16,17,18]. In a second analysis conducted by Fu et al., it was revealed that AIP could function as a predictor of cardiovascular events in individuals with T2D [19]. Considering the coexistence of micro- and macrovascular complications in diabetes, questions have arisen as to whether AIP contributes to the incidence of microvascular diseases, including diabetic kidney disease (DKD) and diabetic retinopathy (DR). Until now, several cross-sectional studies have investigated the correlations of AIP with microvascular complications in T2D [20,21,22,23]; however, those studies yielded inconsistent results, which prompted us to investigate these relationships in a large prospective cohort.

Furthermore, there has been growing interest in investigating the correlation between individuals’ longitudinal trajectories of lipid indices and outcomes. Yan et al. reported that higher trajectories of lipid accumulation product (LAP) had a strong impact on the incidence of T2D beyond baseline LAP measurements [24]. Another study revealed significant correlations between HDL-C and TG trajectories and the onset of T2D [25]. Nevertheless, long-term AIP trajectories, which could indicate the prolonged influence of lipid overaccumulation, have not been previously published in diabetic complications; in particular, the associations of these trajectories with DKD and DR are unknown.

As few large longitudinal cohort studies have examined the relationship of AIP levels with microvascular complications in participants with T2D, the current study was to explore whether the AIP could be a predictive biomarker of DKD and DR and to assess the impact of the long-term trajectories of AIP across multiple follow-up visits on adverse outcomes.

Methods

Participants

T2D participants were recruited from the National Metabolic Management Center (MMC) at Ruijin Hospital, Shanghai Jiao Tong University School of Medicine [26,27,28]. A total of 4831 T2D participants with either DKD or DR follow-up data were included between June 2017 and June 2023. Generally, participants were recommended to undergo DKD testing every six months and DR examinations every year at the Ruijin center. Individuals without DKD follow-up data (n = 255), diagnosed with DKD at baseline (n = 1445), without baseline AIP data (n = 13), or with less than six months of follow-up (n = 175) were excluded from the survival analysis dataset for DKD (n = 2943). Furthermore, after excluding those with fewer than three AIP measurements, 2652 participants were included in the construction of the long-term AIP trajectory for DKD analysis. Similarly, for DR analysis, after excluding those without DR follow-up data (n = 1213), those diagnosed with referable DR at baseline (n = 220), those lacking baseline AIP data (n = 34), those with less than six months of follow-up (n = 4), and those with less than three AIP measurements(n = 190), this study enrolled 3360 T2D participants for DR survival analysis and 3170 for trajectory construction. A flowchart is displayed in Supplemental Fig. 1. The study protocol received approval from the Ruijin Hospital Ethics Committee. All participants signed informed consent before enrolment.

Clinical data collection

At the MMC, participants generally underwent clinical examinations and completed standardized questionnaires at each visit. The recorded information was stored in an electronic medical system exclusively designed for MMC. Baseline covariates including sex, age, ideal smoking, diabetes duration, drinking status, body mass index (BMI), glycated haemoglobin (HbA1c), systolic blood pressure (SBP), and LDL-C were retrieved from the MMC. In this project, people who never smoked or successfully stopped smoking for over one year were classified as ‘ideal smoking’ status. More elaborate definitions and collection methods for covariates were provided in previous publications [26, 29].

AIP at baseline and follow-up visits

The AIP was calculated as the logarithm (base 10) of the ratio of TG (mmol/L) to HDL-C (mmol/L). According to the standard operating procedures of MMC, participants were advised to visit the Ruijin center two to four times a year [30]. TG and HDL-C measurements were routinely taken at each visit, allowing us to obtain AIP values at baseline and follow-up visits. Participants who had at least three AIP measurements during the follow-up were selected for the construction of the AIP trajectory, and the number of AIP measurements for each participant varied between 3 and 21, averaging 7 measurements.

Definition of DKD and DR

DKD was defined as an estimated glomerular filtration rate < 60 mL/min/1.73m2, a urinary albumin/creatinine ratio ≥ 3.39 mg/mmol, or both. The incidence of DR during follow-up in this study was defined as the onset of referable DR (moderate nonproliferative DR or worse) by fundus photography [31].

Statistical analysis

Characteristics at baseline are presented as the mean ± standard deviation (SD) or median (interquartile range [IQR]) for continuous variables and as the number (proportion) for categorical variables. Comparisons between the participants who developed outcomes and those who did not were conducted using the Student’s t-test, the Wilcoxon rank-sum test, or the Chi-square test. Missing baseline covariates were imputed through multiple imputation.

Kaplan-Meier curves were plotted according to baseline AIP tertiles, followed by comparison using the log-rank test. Cox regression models were employed to assess the associations between baseline AIP and the risk of DKD and DR. Three models were stepwise adjusted for covariates: Model 1 was an unadjusted model; Model 2 was adjusted for age, sex, HbA1c, diabetes duration, BMI, SBP, ideal smoking, and drinking status; and Model 3 was additionally adjusted for LDL-C. To examine the potential nonlinear relationship between outcome incidence and baseline AIP levels, the current study plotted restricted cubic splines with three knots, adjusting for all confounders.

Group-based trajectory modelling was conducted to discern distinct longitudinal AIP value patterns during the follow-up visits, testing models with groups spanning from 2 to 5 [32]. The number of groups and the optimal trajectory shape (linear, quadratic, or cubic) were determined by the Bayesian information criterion and model interpretability. Each group comprised over 2% of the total population, and the average posterior probability of membership exceeded 70%. In the end, the best-fitting model consisted of three distinct trajectories. Subsequently, logistic regressions were applied to ascertain the influence of AIP trajectory groups on the onset of outcomes. The data were analysed with R software (version 4.1.2) and Stata 17.0. P < 0.05 was considered significant.

Results

Characteristics of study participants

During a median (IQR) follow-up time of 24.2 (26.3) months for DKD and 25.7 (27.0) months for DR, 709 of 2943 participants experienced new-onset DKD, and 193 of 3360 participants developed referable DR. The baseline clinical characteristics of the participants are presented based on outcome status (Table 1). Notably, the baseline AIP was higher in participants who developed DKD than in those who did not (P = 0.001). Compared with participants without incident DKD during follow-up, those who developed DKD appeared to be more women, older, with a longer diabetes duration, and higher BMI, SBP, and TG levels (all P < 0.05). For the analysis of DR, participants with DR development tended to be older, have higher HbA1c and SBP levels, have a lower BMI, and have a longer diabetes duration than those without DR (all P < 0.05).

Table 1 Baseline characteristics of the study participants according to outcome status
Full size table

Baseline AIP was linked to the incidence of DKD rather than DR

Kaplan-Meier analyses revealed that individuals in tertile 3 of AIP had the highest risk of DKD incidence (log-rank P < 0.001), as presented in Supplemental Fig. 2. Table 2 presents the hazard ratio (HR) and 95% confidence interval (CI) obtained from Cox regression analyses, elucidating the relationships of baseline AIP with the risk of DKD and DR. Multivariate-adjusted models indicated that each one-unit rise in baseline AIP was correlated with a 75% elevated probability (HR 1.75; 95% CI 1.36–2.26) of DKD. When baseline AIP were stratified into tertiles and examined as a categorical variable, similar results were detected. Specifically, tertile 3 (HR 1.47; 95% CI 1.21–1.79) and tertile 2 (HR 1.27; 95% CI 1.05–1.54) of AIP presented a greater risk of incident DKD when using tertile 1 as the reference. Restricted cubic spline regression revealed a linear and positive relationship between baseline AIP and the incidence of DKD (P nonlinear = 0.187, P overall < 0.001) (Fig. 1). Moreover, no correlation was observed between DR and baseline AIP, which was considered either a continuous or categorical variable, in the three models.

Table 2 Association of AIP level at baseline with the incidence of DKD and DR
Full size table
Fig. 1
figure 1

Restricted cubic spline regression for baseline AIP levels and DKD. Adjusted for age, sex, HbA1c, duration of diabetes, BMI, SBP, ideal smoking, drinking, and LDL-C. The solid line indicates the smoothed fitted relationship and the red shaded area represents 95% CI. Abbreviations: AIP atherogenic index of plasma, DKD diabetic kidney disease, CI confidence interval

Full size image

AIP trajectories were related to incident DKD but not DR

After excluding those with fewer than three recorded AIP visits, a cohort of 2652 participants with T2D was included in the group-based trajectory modelling in the DKD longitudinal dataset (Supplemental Fig. 1). This model delineated three distinct trajectory classes: Low (834 participants, 31.4%), Median (1332 participants, 50.2%), and High (486 participants, 18.3%), each exhibiting stable AIP levels throughout the follow-up period (Fig. 2A). Table 3 displays the odds ratio (OR) and 95% CI of AIP trajectories for the occurrence of DKD, as determined through logistic regression models. With respect to the Low trajectory, the Median and High AIP trajectories presented 69% (95% CI: 1.36–2.10) and 74% (95% CI 1.33–2.28) higher odds of developing DKD, respectively. After controlling for baseline covariates in Model 3, the Median and High trajectories exhibited odds of DKD incidence that were 1.84-fold (95% CI 1.46–2.32) and 2.17-fold (95% CI 1.62–2.91), respectively. Regarding trajectories of AIP levels over the follow-up visits and the results of logistic regression analyses for DR (Fig. 2B; Table 3), various AIP trajectories exhibited no significant differences in the occurrence of DR (all P > 0.05).

Fig. 2
figure 2

Trajectories of AIP during follow-up visits in the DKD (A) and DR (B) longitudinal analysis datasets. DR refers to referable DR. Abbreviations: AIP atherogenic index of plasma, DKD diabetic kidney disease, DR diabetic retinopathy

Full size image
Table 3 OR and 95% CI of AIP Trajectories on DKD and DR in the T2D participants
Full size table

Discussion

This investigation revealed that elevated baseline AIP was linked to an increased risk of DKD but not DR. The correlation between baseline AIP and DKD risk followed a positive and linear dose-response association. Moreover, the present study identified three distinct trajectories of AIP with repeated measurements of this novel index across the follow-up visits, which showed that the High and Median AIP trajectories carried higher odds of incident DKD. These findings indicated that high levels of AIP could predict the occurrence of DKD.

The AIP, as a simple and easily accessible indicator, integrates HDL-C and TG concentrations, offering a more comprehensive representation of the pathogenesis and specific nature of dyslipidaemia compared to elevated TG or reduced HDL-C levels alone [33]. 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]. Thus, the novel lipid indicator AIP might function as a more effective marker for evaluating vascular risk.

Unlike the findings of prior research revealing that AIP was associated with macrovascular events [19], the present investigation focused primarily on examining the relationships between AIP and microvascular diseases in the T2D population. Previous studies investigating the associations between AIP and kidney damage in individuals with T2D were cross-sectional, revealing inconsistent results, and longitudinal cohort studies were scarce. For instance, several retrospective studies reported that an increased AIP was linked to the development of microalbuminuria in 4358 and 335 Chinese T2D patients, respectively [20, 22]. However, in a study involving 2523 patients with T2D, no significant difference in the diabetic nephropathy prevalence, determined by urinary microalbumin, was observed among AIP tertiles [21]. Hence, the current study utilized a large longitudinal cohort to further provide evidence that baseline AIP was not only correlated with the incidence of DKD but also exhibited a positive and linear relationship. The present results underscored the predictive value of AIP for DKD, independent of LDL-C.

Indeed, both DR and DKD are diabetic microvascular complications, but the current study found no association between AIP, either at baseline or in the trajectories, and the occurrence of DR. This phenomenon might be explained by the different influences of circulating lipids on the initiation of kidney and eye damage in individuals with diabetes [36]. Epidemiological studies have indicated that the links between lipid profiles and retinopathy are not as strong as those observed for kidney disease [36,37,38]. In contrast to the kidney, the blood-retinal barrier’s integrity shields the retina from possible harm caused by plasma lipoproteins [39], and the regulation or dysregulation of intraretinal lipid transport may be more crucial in the development of DR than plasma lipid levels, which possibly explains the absence of a correlation between AIP and DR. Further multicenter clinical investigations are warranted to confirm the correlation between AIP and the onset of DR.

Considering the dynamic changes in the AIP over time, prior research relying on a single AIP measurement at baseline could not accurately capture long-term exposure. Based on the MMC medical system, researchers collected multiple AIP measurements at different visits, thereby enabling the identification of long-term AIP trajectories in a real-world study. Importantly, assessing the effect of AIP trajectories on the occurrence of DKD and DR provided more reliable and robust results than analysing only the impact of baseline AIP levels. This investigation was the first to elucidate the influence of long-term trends in the AIP on the incidence of DKD and DR in participants with T2D, and these results provided insights into the cumulative load of AIP for DKD incidence. In the clinical setting, graphical shapes of AIP values during follow-up visits could be plotted to discern T2D participants with similarly higher AIP trajectories. It is likely that the corresponding group could benefit from more frequent screening and more intensive management of DKD.

The progression of DKD is intricate and involves complex interactions among factors such as oxidative stress, endothelial dysfunction, and abnormal cytokine and growth factor production [40]. However, the mechanism of the relationship between AIP and incident DKD is unclear. One possible explanation was that AIP was calculated from TG and HDL, and DKD development was correlated with reduced HDL-C and elevated TG [41]. HDL particles might offer protection in the process, including the transport of cholesterol in the reverse direction, along with exerting antioxidant and anti-inflammatory effects [42]. HDL-C also has the potential to improve glycaemic regulation by influencing glucose absorption in skeletal muscle [43] and safeguarding islet β-cell function [44]. These defensive functions of HDL particles appear to be impaired in the context of diabetes. On the other hand, people who did not have T2D but had high TG levels seldom developed kidney disease, illustrating that dyslipidaemia was not enough to cause kidney damage. Indeed, the metabolic dysfunction characteristics of diabetes (hyperglycaemia, insulin resistance) potentially play a crucial role in facilitating the lipotoxic effects on the microvascular network [41]. The kidney is particularly susceptible to lipotoxic damage, which is mediated by factors such as malfunctioning adipose tissue, insulin resistance, elevated plasma nonesterified fatty acids, and obesity [45].

Study strengths and limitations

First, this study was the first longitudinal design to examine the value of AIP as a predictor of microvascular diseases in T2D. Second, this cohort consisted of a relatively large sample size and included multiple follow-up visits. These enabled us to effectively monitor AIP trajectory patterns, ultimately bolstering the robustness and reliability of the findings.

On the other hand, several limitations should be considered. First, the current research was single-center, and additional validation in multiple centers is necessary. Second, as this study predominantly consisted of Chinese individuals with T2D, the generalizability of the findings to other races was limited. Although these limitations impact the clinical use of AIP as a predictor of DKD, this study underscores the significance of closely monitoring AIP in participants with T2D over the follow-up period.

Conclusions

The current investigation indicates a linear correlation between elevated baseline AIP and a greater risk of developing DKD but not DR. The High and Median AIP trajectories, denoting prolonged exposure to elevated AIP levels, carry the greater odds of incident DKD. These results suggest that AIP may be a valuable predictor for evaluating the risk of developing DKD, rather than DR, in participants with T2D, which implies that regular screening and active management of AIP in diabetic patients may offer potential benefits in preventing the incidence of DKD.

Data availability

Due to the privacy concerns of the participants, the datasets of the current study are not publicly accessible, but are available from the corresponding author on reasonable request.

Abbreviations

AIP:

Atherogenic index of plasma

T2D:

Type 2 diabetes

DKD:

Diabetic kidney disease

DR:

Diabetic retinopathy

TG:

Triglycerides

HDL-C:

High-density lipoprotein cholesterol

sdLDL:

Small dense low-density lipoprotein

LDL-C:

Low-density lipoprotein cholesterol

MMC:

Metabolic Management Center

BMI:

Body mass index

SBP:

Systolic blood pressure

HbA1c:

Glycated haemoglobin

SD:

Standard deviation

IQR:

Interquartile range

OR:

Odds ratio

HR:

Hazard ratio

CI:

Confidence interval

References

  1. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, Stein C, Basit A, Chan JCN, Mbanya JC, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119.

    Article  PubMed  Google Scholar 

  2. Morrish NJ, Wang SL, Stevens LK, Fuller JH, Keen H. Mortality and causes of death in the WHO multinational study of Vascular Disease in Diabetes. Diabetologia. 2001;44(Suppl 2):14–21.

    Article  Google Scholar 

  3. Ward A, Alvarez P, Vo L, Martin S. Direct medical costs of complications of diabetes in the United States: estimates for event-year and annual state costs (USD 2012). J Med Econ. 2014;17:176–83.

    Article  PubMed  Google Scholar 

  4. Horton WB, Barrett EJ. Microvascular Dysfunction in Diabetes Mellitus and Cardiometabolic Disease. Endocr Rev. 2021;42:29–55.

    Article  PubMed  Google Scholar 

  5. Chehade JM, Gladysz M, Mooradian AD. Dyslipidemia in type 2 diabetes: prevalence, pathophysiology, and management. Drugs. 2013;73:327–39.

    Article  CAS  PubMed  Google Scholar 

  6. Goldberg RB. Dyslipidemia in diabetes: when and how to treat? Endocrinol Metab Clin North Am. 2022;51:603–24.

    Article  PubMed  Google Scholar 

  7. Judge EP, Phelan D, O’Shea D. Beyond statin therapy: a review of the management of residual risk in diabetes mellitus. J R Soc Med. 2010;103:357–62.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Dobiásová M. [AIP–atherogenic index of plasma as a significant predictor of cardiovascular risk: from research to practice]. Vnitr Lek. 2006;52:64–71.

    PubMed  Google Scholar 

  9. Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apob-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34:583–8.

    Article  PubMed  Google Scholar 

  10. Chen M, Fang CY, Guo JC, Pang LM, Zhou Y, Hong Y, Yang LF, Zhang J, Zhang T, Zhou BF, Hu GQ. Predictive value of atherogenic index of plasma and atherogenic index of plasma combined with low-density lipoprotein cholesterol for the risk of acute myocardial infarction. Front Cardiovasc Med. 2023;10:1117362.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Hamzeh B, Pasdar Y, Mirzaei N, Faramani RS, Najafi F, Shakiba E, Darbandi M. Visceral adiposity index and atherogenic index of plasma as useful predictors of risk of cardiovascular diseases: evidence from a cohort study in Iran. Lipids Health Dis. 2021;20:82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ma X, Sun Y, Cheng Y, Shen H, Gao F, Qi J, Yang L, Wang Z, Shi D, Liu Y, et al. Prognostic impact of the atherogenic index of plasma in type 2 diabetes mellitus patients with acute coronary syndrome undergoing percutaneous coronary intervention. Lipids Health Dis. 2020;19:240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Wu TT, Gao Y, Zheng YY, Ma YT, Xie X. Atherogenic index of plasma (AIP): a novel predictive indicator for the coronary artery disease in postmenopausal women. Lipids Health Dis. 2018;17:197.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yin B, Wu Z, Xia Y, Xiao S, Chen L, Li Y. Non-linear association of atherogenic index of plasma with insulin resistance and type 2 diabetes: a cross-sectional study. Cardiovasc Diabetol. 2023;22:157.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Zheng X, Zhang X, Han Y, Hu H, Cao C. Nonlinear relationship between atherogenic index of plasma and the risk of prediabetes: a retrospective study based on Chinese adults. Cardiovasc Diabetol. 2023;22:205.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Shi Y, Wen M. Sex-specific differences in the effect of the atherogenic index of plasma on prediabetes and diabetes in the NHANES 2011–2018 population. Cardiovasc Diabetol. 2023;22:19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Yi Q, Ren Z, Bai G, Zhu S, Li S, Li C, Wu H, Zhu Y, Song P. The longitudinal effect of the atherogenic index of plasma on type 2 diabetes in middle-aged and older Chinese. Acta Diabetol. 2022;59:269–79.

    Article  CAS  PubMed  Google Scholar 

  18. Lan Y, Chen G, Wu D, Ding X, Huang Z, Wang X, Balmer L, Li X, Song M, Wang W, et al. Temporal relationship between atherogenic dyslipidemia and inflammation and their joint cumulative effect on type 2 diabetes onset: a longitudinal cohort study. BMC Med. 2023;21:31.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Fu L, Zhou Y, Sun J, Zhu Z, Xing Z, Zhou S, Wang Y, Tai S. Atherogenic index of plasma is associated with major adverse cardiovascular events in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2021;20:201.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Xu J, Zhou H, Xiang G. Association of Atherogenic Index of plasma with retinopathy and nephropathy in adult patients with type 2 diabetes Mellitus aged > 18 years. Can J Diabetes 2022.

  21. Li Z, Huang Q, Sun L, Bao T, Dai Z. Atherogenic index in type 2 diabetes and its relationship with chronic microvascular complications. Int J Endocrinol. 2018;2018:1765835.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Qi L, Kang N, Chen X, Li Z, Deng C, Chen S. Predictive value of plasma atherogenic index for Microalbuminuria in newly diagnosed patients with type 2 diabetes Mellitus. Diabetes Metab Syndr Obes. 2022;15:1245–52.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Akdoğan M, Ustundag-Budak Y, Huysal K. The association of hematologic inflammatory markers with atherogenic index in type 2 diabetic retinopathy patients. Clin Ophthalmol. 2016;10:1797–801.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yan G, Li F, Elia C, Zhao Y, Wang J, Chen Z, Yuan H, Lu Y. Association of lipid accumulation product trajectories with 5-year incidence of type 2 diabetes in Chinese adults: a cohort study. Nutr Metab (Lond). 2019;16:72.

    Article  PubMed  Google Scholar 

  25. Sun Q, Liu J, Wu L, Sun Y, Jin J, Wang S, Wu J, Jing Y, Zhou H, Dong C. Associations of visit-to-visit variabilities and trajectories of serum lipids with the future probability of type 2 diabetes mellitus. Lipids Health Dis. 2021;20:168.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fang Q, Shi J, Zhang J, Peng Y, Liu C, Wei X, Hu Z, Sun L, Hong J, Gu W, et al. Visit-to-visit HbA1c variability is associated with aortic stiffness progression in participants with type 2 diabetes. Cardiovasc Diabetol. 2023;22:167.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Zhang Y, Wang W, Ning G. Metabolic Management Center: an innovation project for the management of metabolic diseases and complications in China. J Diabetes. 2019;11:11–3.

    Article  CAS  PubMed  Google Scholar 

  28. Zhang Y, Wang Y, Ning G, He P, Wang W. Protecting older people: a high priority during the COVID-19 pandemic. Lancet. 2022;400:729–30.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Wang S, Shi J, Peng Y, Fang Q, Mu Q, Gu W, Hong J, Zhang Y, Wang W. Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: a real-world single-centre study. Cardiovasc Diabetol. 2021;20:82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhao Q, Li H, Ni Q, Dai Y, Zheng Q, Wang Y, Ke T, Li L, Zhao D, Dong Q, et al. Follow-up frequency and clinical outcomes in patients with type 2 diabetes: a prospective analysis based on multicenter real-world data. J Diabetes. 2022;14:306–14.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Fang Q, Xiang M, Shi J, Zhou Y, Peng Y, Wang S, Liu C, Hong J, Gu W, Wang W, Zhang Y. Subclinical atherosclerosis associates with diabetic retinopathy incidence: a prospective study. Acta Diabetol. 2022;59:1041–52.

    Article  PubMed  Google Scholar 

  32. Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38.

    Article  PubMed  Google Scholar 

  33. Fernández-Macías JC, Ochoa-Martínez AC, Varela-Silva JA, Pérez-Maldonado IN. Atherogenic index of plasma: Novel Predictive Biomarker for Cardiovascular illnesses. Arch Med Res. 2019;50:285–94.

    Article  PubMed  Google Scholar 

  34. Qin Z, Zhou K, Li Y, Cheng W, Wang Z, Wang J, Gao F, Yang L, Xu Y, Wu Y, et al. The atherogenic index of plasma plays an important role in predicting the prognosis of type 2 diabetic subjects undergoing percutaneous coronary intervention: results from an observational cohort study in China. Cardiovasc Diabetol. 2020;19:23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Lu CF, Liu WS, Chen ZH, Hua LY, Wang XQ, Huang HY. Comparisons of the relationships between multiple lipid indices and Diabetic kidney disease in patients with type 2 diabetes: a cross-sectional study. Front Endocrinol (Lausanne). 2022;13:888599.

    Article  PubMed  Google Scholar 

  36. Eid S, Sas KM, Abcouwer SF, Feldman EL, Gardner TW, Pennathur S, Fort PE. New insights into the mechanisms of diabetic complications: role of lipids and lipid metabolism. Diabetologia. 2019;62:1539–49.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Sacks FM, Hermans MP, Fioretto P, Valensi P, Davis T, Horton E, Wanner C, Al-Rubeaan K, Aronson R, Barzon I, et al. Association between plasma triglycerides and high-density lipoprotein cholesterol and microvascular kidney disease and retinopathy in type 2 diabetes mellitus: a global case-control study in 13 countries. Circulation. 2014;129:999–1008.

    Article  CAS  PubMed  Google Scholar 

  38. Morton J, Zoungas S, Li Q, Patel AA, Chalmers J, Woodward M, Celermajer DS, Beulens JW, Stolk RP, Glasziou P, Ng MK. Low HDL cholesterol and the risk of diabetic nephropathy and retinopathy: results of the ADVANCE study. Diabetes Care. 2012;35:2201–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Modjtahedi BS, Bose N, Papakostas TD, Morse L, Vavvas DG, Kishan AU. Lipids and Diabetic Retinopathy. Semin Ophthalmol. 2016;31:10–8.

    Article  PubMed  Google Scholar 

  40. van Dijk C, Berl T. Pathogenesis of diabetic nephropathy. Rev Endocr Metab Disord. 2004;5:237–48.

    Article  PubMed  Google Scholar 

  41. Russo GT, De Cosmo S, Viazzi F, Pacilli A, Ceriello A, Genovese S, Guida P, Giorda C, Cucinotta D, Pontremoli R, Fioretto P. Plasma triglycerides and HDL-C levels predict the Development of Diabetic kidney disease in subjects with type 2 diabetes: the AMD annals Initiative. Diabetes Care. 2016;39:2278–87.

    Article  CAS  PubMed  Google Scholar 

  42. Tabet F, Rye KA. High-density lipoproteins, inflammation and oxidative stress. Clin Sci (Lond). 2009;116:87–98.

    Article  CAS  PubMed  Google Scholar 

  43. Drew BG, Duffy SJ, Formosa MF, Natoli AK, Henstridge DC, Penfold SA, Thomas WG, Mukhamedova N, de Courten B, Forbes JM, et al. High-density lipoprotein modulates glucose metabolism in patients with type 2 diabetes mellitus. Circulation. 2009;119:2103–11.

    Article  CAS  PubMed  Google Scholar 

  44. Brunham LR, Kruit JK, Hayden MR, Verchere CB. Cholesterol in beta-cell dysfunction: the emerging connection between HDL cholesterol and type 2 diabetes. Curr Diab Rep. 2010;10:55–60.

    Article  CAS  PubMed  Google Scholar 

  45. Opazo-Ríos L, Mas S, Marín-Royo G, Mezzano S, Gómez-Guerrero C, Moreno JA, Egido J. Lipotoxicity and Diabetic Nephropathy: Novel mechanistic insights and Therapeutic opportunities. Int J Mol Sci 2020, 21.

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Acknowledgements

This research sincerely appreciates the support of all MMC participants.

Funding

This work was supported by grants from the National Natural Science Foundation of China (grant no. 82270896 to YZ), “Management strategy of the tertiary prevention and treatment of diabetes based on DIP system” supported by China Health Promotion Foundation (to WW), the Capacity building for multidisciplinary cooperation in diagnosis and treatment of major metabolic diseases (grant no. Z155080000004 to WW), the Shanghai Medical and Health Development Foundation (grant no. DMRFP_II_01 to YZ, grant no. DMRFP_II_05 to CZ), Innovative research team of high-level local universities in Shanghai, and Chinese Academy of Engineering (No.22-HN-ZD-01).

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Author notes

  1. Jia Zhang, Cong Liu, and Ying Peng contributed equally to this work.

Authors and Affiliations

  1. Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 RuiJin Er Road, Shanghai, China

    Jia Zhang, Cong Liu, Ying Peng, Qianhua Fang, Xing Wei, Cui Zhang, Lin Sun, Zhuomeng Hu, Jie Hong, Weiqiong Gu, Weiqing Wang, Juan Shi & Yifei Zhang

  2. Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Jia Zhang, Cong Liu, Ying Peng, Qianhua Fang, Xing Wei, Cui Zhang, Lin Sun, Zhuomeng Hu, Jie Hong, Weiqiong Gu, Weiqing Wang, Juan Shi & Yifei Zhang

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  1. Jia Zhang
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  2. Cong Liu
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  13. Yifei Zhang
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Contributions

YZ and JS contributed to the study’s design and manuscript revisions and served as the corresponding author. JZ, CL, and YP conducted data analysis, drafted the main manuscript, and served as co-first authors. WW conceived and supervised the study. QF, XW, CZ, LS, and ZH contributed to data collection. JH and WG participated in study’s design. All authors approved the final manuscript.

Corresponding authors

Correspondence to Juan Shi or Yifei Zhang.

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Ethics approval and consent to participate

The study protocol was approved by the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine. This study was carried out in strict compliance with the principles of the Declaration of Helsinki. All participants signed the written informed consent.

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Not applicable.

Competing interests

The authors declare no competing interests.

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Zhang, J., Liu, C., Peng, Y. et al. Impact of baseline and trajectory of the atherogenic index of plasma on incident diabetic kidney disease and retinopathy in participants with type 2 diabetes: a longitudinal cohort study. Lipids Health Dis 23, 11 (2024). https://doi.org/10.1186/s12944-024-02003-5

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  • DOI: https://doi.org/10.1186/s12944-024-02003-5

Keywords

Lipids in Health and Disease

ISSN: 1476-511X