Comparisons of the Relationships Between Multiple Lipid Indices and Diabetic Kidney Disease in Patients With Type 2 Diabetes: A Cross-Sectional Study

Association between Apolipoprotein B and diabetic nephropathy: insights from the National Health and Nutrition Examination Survey 2007-2016 and Mendelian randomization analysis

BACKGROUND

This study aimed to investigate the role of Apolipoprotein B (Apo B) in diabetic nephropathy (DN) from epidemiological and genetic perspectives.

METHODS

We employed weighted multivariable-adjusted logistic regression to assess the relationship between ApoB and DN risk, utilizing data from the National Health and Nutrition Examination Survey spanning 2007-2016. Then, we used restricted cubic splines (RCS) to flexibly model and visualize the relation of predicted ApoB levels with DN risk. Subsequently, a bidirectional two-sample Mendelian randomization study using genome-wide association study summary statistics was performed. The primary Inverse Variance Weighted method, along with supplementary MR approaches, was employed to verify the causal link between ApoB and DN. Sensitivity analyses were conducted to confirm the robustness of the results.

RESULTS

Our observational study enrolled 2242 participants with diabetes mellitus from NHANES. The multivariable logistic regression model indicated that elevated ApoB levels (>1.2 g/L), compared to low levels (<0.8 g/L), were significantly associated with DN risk (P < 0.05). The RCS model revealed a positive linear association with the risk of DN when ApoB levels exceeded 1.12 g/L (OR = 1.29, 95% CI: 1.07-1.57, P = 0.008). However, the MR IVW method did not reveal a direct causal effect of DN on ApoB (OR: 0.976; 95% CI: 0.950-1.004; P = 0.095), nor a direct causal effect of ApoB on DN (OR: 0.837; 95% CI: 0.950-1.078; P = 0.428).

CONCLUSION

The evidence from observational studies indicates a positive correlation between ApoB levels exceeding 1.12 g/L and the onset of DN. However, the causal effects of ApoB on DN and vice versa were not supported by the MR analysis.

Association of diabetic nephropathy with lipid metabolism: a Mendelian randomization study

OBJECTIVE

Patients with diabetic nephropathy (DN) often present with lipid profile abnormalities. While associations between these parameters and DN have been suggested, confounding factors obscure causal relationships. This study employed bidirectional Mendelian randomization (MR) to explore these links.

METHODS

Using genome-wide association study (GWAS) data, the primary analysis used the inverse-variance weighted (IVW) method, which was supported by MR-Egger regression and a weighted median estimator (WME). Sensitivity analyses, including heterogeneity, pleiotropy tests, leave-one-out, and reverse causality analyses, were conducted.

RESULTS

The IVW model revealed the following: (1) causal relationships between triglycerides (TG) (OR: 1.5807, 95% CI: 1.2578-1.9865, P = 0.0001), high-density lipoprotein cholesterol (HDL-C) (OR: 0.7342, 95% CI: 0.5729-0.9409, P = 0.0146), and apolipoprotein A1 (ApoA1) (OR: 0.6506, 95% CI: 0.5190-0.8156, P = 0.0002) and DN; (2) causal relationships between TG (OR: 1.0607, 95% CI: 1.0143-1.1093, P = 0.0098), HDL-C (OR: 0.9453, 95% CI: 0.9053-1.9871, P = 0.0109), and apolipoprotein B (ApoB) (OR: 1.0672, 95% CI: 0.0070-1.1310, P = 0.0280) and the urinary albumin-creatinine ratio (UACR); (3) no causal relationship between total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), ApoB and DN, or between TC, LDL-C, ApoA1 and UACR; (4) none of the results showed reverse causality.

CONCLUSION

TG is a risk factor for DN and UACR; HDL-C is protective for both; ApoA1 protects against DN; and ApoB is a risk factor for UACR. To further explore the underlying mechanisms between TG, HDL-C, ApoA1, ApoB, and their associations with DN and UACR, and to provide reference for the selection of lipid management and treatment strategies for clinical DN patients. This study demonstrated that causal relationships between TG, HDL-C, and ApoA1 with DN and between TG, HDL-C, and ApoB with the UACR.

The Non-High-Density Lipoprotein Cholesterol (Non-HDL-C) to HDL-C Ratio (NHHR) and Its Association with Chronic Kidney Disease in Chinese Adults with Type 2 Diabetes: A Preliminary Study

Objectives: The objective of this study was to examine the association between non-high-density lipoprotein cholesterol (non-HDL-C) to high-density lipoprotein cholesterol (HDL-C) ratio (NHHR) and chronic kidney disease (CKD) in Chinese adults with type 2 diabetes mellitus (T2DM). Methods: This study originated from a survey carried out in Zhejiang Province, located in eastern China, between March and November 2018. To explore the relationship between NHHR and CKD, a multivariable logistic regression model was employed. The dose-response relationship was assessed using restricted cubic spline (RCS) analysis, while generalized additive models (GAMs) were applied to examine the associations between NHHR and urinary albumin-to-creatinine ratio (UACR) as well as estimated glomerular filtration rate (eGFR). Subgroup analyses were performed across various demographic and clinical categories to assess the consistency of the NHHR-CKD association. The optimal NHHR cutoff for CKD diagnosis, its predictive accuracy, and its comparison with its components and HbA1c were determined through receiver operating characteristic (ROC) curve analysis. Results: The study enrolled 1756 participants, including 485 individuals with CKD and 1271 without CKD. Multivariable logistic regression revealed a significant positive association between NHHR and CKD, with each standard deviation (SD) increase in NHHR linked to a 23% higher odds of CKD (OR = 1.23, 95% CI: 1.09-1.37) after adjusting for potential confounders. When comparing quartiles, the fully adjusted ORs for Q2, Q3, and Q4 were 1.29 (0.92-1.79), 1.31 (0.94-1.83), and 1.87 (1.34-2.60), respectively, relative to Q1 (p for trend < 0.01). RCS analysis confirmed a linear dose-response relationship between NHHR and CKD in both sexes (p for nonlinearity > 0.05). GAMs indicated a significant positive correlation between NHHR and UACR (ρ = 0.109, p < 0.001) but no significant association with eGFR (ρ = -0.016, p = 0.502). Subgroup analyses demonstrated consistent associations across most subgroups, except for the 18-44 years age group, the well-controlled glycemic group, and the non-alcohol drinking group (p > 0.05). ROC curve analysis identified an optimal NHHR cutoff of 3.48 for CKD prediction, with an area under the curve (AUC) of 0.606 (95% CI: 0.577-0.635). Notably, NHHR outperformed its individual components and HbA1c in predictive performance. Conclusions: This study revealed a linear link between higher NHHR levels and increased CKD prevalence in Chinese T2DM patients. NHHR may also serve as a potential complementary biomarker for early CKD detection, though further prospective studies are needed to confirm its predictive value and clinical utility in high-risk T2DM populations.

Causal Association Between Cholesterol-Lowering Drugs and Diabetic Microvascular Complications: A Drug-Target Mendelian Randomization Study

Background: It remains unclear whether cholesterol-lowering therapy can reduce the incidence of microvascular complications in patients with diabetes. We aim to explore the potential causal relationship between three common types of cholesterol-lowering drugs and diabetic microvascular complications through drug-target Mendelian randomization (MR) study, laying the groundwork for the development of new medications. Methods: In this study, we collected single nucleotide polymorphisms (SNPs) associated with HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) inhibitors, PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitors, and NPC1L1 (Niemann-Pick C1-Like 1) inhibitors from published genome-wide association study statistics. Subsequently, drug-target MR analyses were performed to investigate the effects of these inhibitors on low-density lipoprotein cholesterol (LDL-C) level-mediated microvascular complications in diabetes mellitus. Coronary atherosclerosis as a positive control. Primary outcomes included diabetic nephropathy, diabetic retinopathy, and diabetic neuropathy from the FinnGen Consortium. Results: The MR analysis revealed significant associations between HMGCR inhibition and increased risks of diabetic nephropathy (OR [95%confidence interval (CI)] = 1.88 [1.50, 2.36], p = 5.55 × 10-8), retinopathy (OR [95%CI] = 1.86 [1.54, 2.24], p = 6.28 × 10-11), and neuropathy (OR [95%CI] = 2.63 [1.84, 3.75], p = 1.14 × 10-7) using the inverse variance weighted method. PCSK9 inhibitors have been associated with an increased risk of diabetic nephropathy (OR [95%CI] = 1.30 [1.07, 1.58], p = 0.009) and diabetic neuropathy (OR [95%CI] = 1.40 [1.15, 1.72], p = 0.001); NPC1L1 inhibitors significantly reduce the incidence of diabetic retinopathy (OR [95%CI] = 0.48 [0.28, 0.85], p = 0.01). The coronary heart disease as positive control. Conclusions: The findings show that HMGCR inhibitors and PCSK9 inhibitors may significantly increase the risk of diabetic microvascular complications. However, NPC1L1 inhibitors may provide protection against diabetic retinopathy.

An Investigation of the Correlation Between Retinal Nerve Fiber Layer Thickness with Blood Biochemical Indices and Cognitive Dysfunction in Patients with Type 2 Diabetes Mellitus

Objective

The study aimed to explore the correlation between retinal nerve fiber layer thickness (RNFLT) with blood biochemical indicators and cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) and the possible mechanism, thereby providing more theoretical basis for the occurrence and prevention of diabetes related complications.

Methods

Eighty T2DM patients treated in our hospital from March 2022 to September 2022 were selected as the study subjects, and the clinical data of the patients were retrospectively analyzed. All patients underwent fundus fluorescein angiography (FFA) to analyze the changes in retinal blood vessels. Patients who met the inclusion criteria were divided as the diabetic retinopathy (DR) group (n=46) and simple diabetes group (n=34). The RNFLT, blood biochemical indexes and changes in cognitive functions of the patients were detected. The correlation between RNFLT with blood biochemical indexes and cognitive dysfunction was analyzed.

Results

Compared with the simple diabetes group, patients in the DR group had much lower mean, nasal, inferior and superior thicknesses (P<0.01). There existed no significant difference in blood pressure, body mass index (BMI), blood lipids (triglycerides, cholesterol, low-density lipoprotein, high-density lipoprotein) between the two groups (P>0.05). Compared with the simple diabetes group, patients in the DR group had much higher fasting blood glucose (FBG), hemoglobin A1c (HbA1c), fasting insulin (FINS), insulin resistance (HOMA-IR) index, apolipoprotein B (ApoB)/apolipoprotein A1 (ApoA1) (P<0.001). Besides, the DR group had sharply lower scores on the Mini-Mental State Examination (MMSE) scale and higher levels of the Trail Making Test-A (TMT-A) and TMT-B (P<0.001). Spearman correlation analysis confirmed that the mean RNFLT was negatively correlated with the levels of FBG, HbA1c, HOMA-IR index, TMT-A and TMT-B (P<0.05), positively correlated with the score of mini-mental state examination (MMSE) (P<0.05), and was no significant correlation with FINS and ApoB/ApoA1 (P>0.05).

Conclusion

DR patients had significantly reduced RNFLT, elevated levels of blood glucose related indicators, and cognitive dysfunction. There existed a correlation between RNFLT and FBG, HbA1c, HOMA-IR index, TMT-A, TMT-B and MMSE.

A non-linear association between low-density lipoprotein cholesterol and the risk of diabetic kidney disease in patients with type 2 diabetes in China

Objective

To explore the intrinsic relationship between low-density lipoprotein cholesterol (LDL-C) and diabetic kidney disease (DKD) in patients with type 2 diabetes (T2D) in China.

Methods

This cross-sectional study included 1,313 patients with type 2 diabetes treated at the Affiliated Jinhua Hospital of Zhejiang University School of Medicine, located in Jinhua, China. The data were combined from two periods, 2017 and 2020-2021. Participants were categorized into groups with and without DKD. The relationship between LDL-C levels and DKD was evaluated employing logistics regression analysis and restricted cubic spline (RCS) curves.

Results

Generally, there was no statistical difference in LDL-C levels between DKD and non-DKD groups, however, a significantly non-linear relationship (Pnon-linear = 0.011) was observed between LDL-C levels and DKD prevalence after adjusting for confounding factors according to the RCS analysis. Two optimal cut-points of 2.97 and 3.61 mmol/L were selected out using random forest algorithm. With the middle LDL-C concentration (2.97-3.61 mmol/L) as the reference, the odds ratios for low (<2.97 mmol/L) and high (>3.61 mmol/L) concentrations were 1.45 (1.08-1.96) and 1.47 (1.01-2.15) respectively, after adjusting for confounding factors in the multivariate analyses. Notably, this association was more pronounced among female participants in the subgroup analyses.

Conclusion

A non-linear association was observed between LDL-C levels and the risk of DKD in patients with T2D in China. LDL-C levels below 2.97 mmol/L may elevate the risk of DKD, particularly in female patients with T2D.

Associations between cardiometabolic indices and the risk of diabetic kidney disease in patients with type 2 diabetes

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.

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

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.