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Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and diabetic kidney disease in patients with diabetes in the United States: a cross-sectional study

Lipids in Health and Disease volume 23, Article number: 317 (2024) Cite this article

Abstract

Background

This paper investigated the link between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and diabetic kidney disease (DKD) in adult diabetic patients and identified the optimal NHHR value for impacting DKD.

Methods

This cross-sectional research made use of records from the National Health and Nutrition Examination Survey (NHANES) executed between 2005 and 2016. The link of NHHR to DKD risk was analyzed by logistic regression and restricted cubic spline (RCS) models. The stability and reliability of the results were assessed by subgroup analysis and sensitivity analysis.

Results

In total, 4,177 participants were involved. As a continuous variable, NHHR was markedly connected to an increased risk of DKD (OR 1.07, 95% CI 1.02, 1.12, P < 0.01). When NHHR was grouped in quartiles, relative to the reference set, the highest NHHR group was also linked to a heightened risk of DKD (OR 1.23, 95% CI 1.01, 1.50, P < 0.05). The outcome of RCS show a “J” shaped correlation between NHHR and DKD risk (P for nonlinear = 0.0136). The risk of developing DKD was the lowest when NHHR equals 2.66. Subgroup analysis revealed that the link of NHHR to DKD persisted in participants aged below 40, females, non-smokers, and those without hyperuricemia. Sensitivity analysis demonstrated a certain robustness in this association.

Conclusion

A meaningful link is present between NHHR and DKD. An NHHR value of around 2.66 could represent the ideal cutoff for assessing DKD risk.

Introduction

The occurrence of diabetes, a global epidemic, has been on a constant rise in America [1]. Diabetic kidney disease (DKD) is a frequently observed diabetes-related complication, impacting nearly 40% of diabetic patients. One of the key elements to chronic kidney disease worldwide is DKD. A reduction in glomerular function and persistent proteinuria are the hallmarks of DKD [2, 3]. DKD could eventually progress to kidney failure, necessitating hemodialysis or renal transplant, which places significant strains on public health infrastructure and patient support systems [4]. The complex pathogenesis and progression of DKD entail a combination of metabolic, hemodynamic, and inflammatory factors. Hypertension and hyperglycemia emerge as striking risk factors for DKD [2]. Dyslipidemia is more frequently observed in individuals suffering from type 2 diabetes [5] and is tightly linked to the threat of atherosclerotic vascular disease [6]. Therefore, it is vital to identify potential lipid markers impacting the onset or advancement of DKD for the prevention or delay of DKD progression to advanced kidney disease.

The main manifestations of dyslipidemia in diabetic individuals are hypertriglyceridemia and decreased high-density lipoprotein cholesterol (HDL-C) [6], which increase the risk of renal damage and cardiovascular mortality [7]. An early study confirmed that the plasma lipoprotein profile of patients with DKD is more prone to contribute to atherosclerosis than that of unaffected patients [8]. The correlation between common lipoproteins and DKD had been discussed [7, 9,10,11], with findings suggesting that lipid abnormalities play a part in either improving or worsening kidney function in diabetic individuals. Additionally, research indicates that Apolipoprotein (apo) C-I may affect HDL elevation by participating in the metabolism of other proteins [12], potentially further impacting DKD. Furthermore, cholesteryl ester transfer protein inhibitors are believed to reduce the likelihood of diabetes and chronic kidney disease [13]. The ratio of non-high-density lipoprotein cholesterol (non-HDL-C) to HDL-C, known as the NHHR, is a novel combined lipid marker under a recent study [14], confirming its superiority as a biomarker for atherosclerosis evaluation [15, 16]. NHHR has exhibited high predictive capability in evaluating the likelihood of diabetes onset compared to conventional lipid parameters [14].

While numerous findings have shown the link of lipoproteins to DKD, the link between NHHR and DKD has yet to be established. Here, a cross-sectional study on the basis of National Health and Nutrition Examination Survey (NHANES) was performed to explore the latent link of NHHR to DKD in American adult diabetic population.

Materials and methods

Study population

NHANES is an underway national examination, with a view to analyzing the health and nutrition profile of the American population (https://www.cdc.gov/nchs/nhanes/index.htm). This database comprises a wealth of clinical, laboratory, and nutritional data from participants of various ages, races, and geographical backgrounds. The survey employs an elaborate, multi-stage probability sampling method to enhance the reliability and accuracy of the sample. Since the data was anonymized to protect privacy, no additional ethical approval was required.

Researchers retrieved data from the NHANES spanning from 2005 to 2016, involving 60,936 survey participants. A total of 4,177 members were enrolled in the research after excluding 26,756 respondents under the age of 18, 3,809 individuals lacking relevant data on NHHR, estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR), 591 pregnant participants, 24,710 non-diabetic participants, and 893 participants with missing weighted data (Fig. 1).

Fig. 1
figure 1

Flowchart for selecting participants for inclusion in the research: Flowchart for selecting participants from the 2005–2016 NHANES studies

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NHHR assessment

In the research, NHHR was considered as the dependent variable, and based on former research [17,18,19], the calculation method for NHHR is as follows:

$$\begin{array}{l}NHHR = Non{\rm{ - }}HDL{\rm{ - }}C/HDL{\rm{ - }}C\\Non{\rm{ - }}HDL{\rm{ - }}C = Total\,Cholesterol\ - HDL{\rm{ - }}C\end{array}$$
(1)

Definition of DKD

The diagnostic criteria for diabetes are as follows: Self-reported diagnosis, application of insulin or oral diabetes medications, fasting blood glucose ≥ 126 mg/dL, or hemoglobin A1c concentration ≥ 6.5% [20]. Among participants diagnosed with diabetes according to the above criteria, those with a UACR ≥ 30 mg/g or an eGFR < 60 ml/min/1.73 m2 are considered to have DKD [21]; otherwise, they are deemed not to have DKD.

Covariates assessment

The variables selected for this study include age, gender, race, education level, marital status, poverty income ratio (PIR), and body mass index (BMI). Additionally, serum uric acid concentrations from laboratory tests exceeding 420 mmol/L in men and 360 mmol/L in women are indicative of hyperuricemia [22]. Dietary cholesterol intake (in milligrams) was derived from two 24-hour dietary history inquiries. The initial dietary history inquiry was performed in person, followed by a secondary inquiry conducted over the phone in a few days. The final dietary cholesterol intake (in milligrams) was determined by averaging the cholesterol amounts reported in the two interviews [19]. In the surveys, participants were considered “physically active” if they engaged in intense workouts for a minimum of 10 consecutive minutes outside of occupational or commuting activities; all other cases were labeled “inactive”. A history of hypertension was derived from self-reported assessment or blood pressure exceeding 140/90 mmHg [17]. This study also included alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels [23]. Participants who had consumed at least 12 different types of alcoholic beverages in any given year were considered drinkers. The smoking status of participants was established based on whether they had smoked over 100 cigarettes over the course of their life [17].

Statistical analysis

On account of the complex sampling design of NHANES, researchers utilized weights obtained from the NHANES official website to correct for sampling bias, ensuring the representativeness and reliability of the results. The detailed computation technique for sampling weights was presented as: Dietary two-day sample weight (WTDR2D) / 6. The mean (standard error) was applied to express continuous variables following a gaussian distribution, while the median (first quartile, third quartile) was used for those with non-normal distribution. Percentages (%) were utilized to express categorical variables. Weighted student’s t-test, Mann-Whitney U test, and χ² test were employed to assess intergroup differences.

In this study, researchers used a linear regression model to analyze the relationship between NHHR and eGFR and natural logarithm of UACR [ln(UACR)], with β values and 95% confidence intervals (CI) recorded. Logistic regression models were utilized to explore the link of NHHR to DKD. Additionally, to explore the straight or curved relationship of the regression equation, restricted cubic spline (RCS) with 4 knots was employed, using the median as the reference value.

The robustness of the link among different subgroups was investigated using stratified analysis. Specific subgroup factors can be found in the covariates above.

For the results stability, researchers conducted two sensitivity analyses. Firstly, participants with a BMI ≥ 30 kg/m2 were excluded to examine the influence of obesity on lipid levels and diabetes [24]. Additionally, the link of dietary cholesterol intake to the progression of type 2 diabetes has been confirmed [25]. To minimize the impact of diet on lipid levels, individuals with cholesterol intake surpassing 300 mg were removed.

Data evaluation was made with R (version 4.1.3) and EmpowerStats (version 4.2), where P < 0.05 was considered significant.

Results

Baseline characteristics

In accordance with this eligibility criteria, a total of 4,177 members were contained in the research(Fig. 1), with 40.15% having DKD. The baseline features of the involved members were shown in Table 1. Weighted outputs indicated a median age of 60 years, with 51.34% being male. The median NHHR for the non-DKD and DKD groups were 3.0 and 2.9, respectively (P > 0.05). Baseline characteristics exhibiting significant differences between the two groups included age, race, education level, marital status, smoking status, family PIR, alcohol consumption, history of hypertension, physical activity, dietary cholesterol, eGFR, and ln(UACR) (P values all < 0.05).

Table 1 Baseline characteristics of participants stratified according to DKD
Full size table

Association between NHHR and DKD

Table 2 displays the link of NHHR to DKD in diabetic patients, the relationship between NHHR and ln(UACR), and the link between NHHR and eGFR. By treating NHHR as a continuous variable, a notable correlation was observed with an elevated risk of DKD (OR 1.07, 95%CI 1.02, 1.12). The connection between NHHR and ln(UACR) (β 0.07, 95%CI 0.04, 0.10) is remarkable, but not significant with lower eGFR (β -0.03, 95%CI -0.40, 0.35). When NHHR was considered as a categorical variable, the DKD risk for participants in the 2nd, 3rd, and 4th quartiles were 0.84, 1.05, and 1.23 separately, with their corresponding 95% CIs of (0.69, 1.01), (0.87, 1.27), and (1.01, 1.50) exhibiting a trend that was not statistically significant (P > 0.05). The average changes in ln(UACR) for participants in the 2nd, 3rd, and 4th quartiles relative to the reference quartile were respectively − 0.06, 0.08, and 0.22, presenting a significant trend (P < 0.01). As for eGFR, the average changes were − 0.15 (95% CI -1.81, 1.51), -0.40 (95% CI -2.09, 1.29), and − 0.71 (95% CI -2.46, 1.04) respectively, showing no significant trend (P > 0.05). A notable nonlinear link of NHHR to DKD was proved by RCS model (P for nonlinear < 0.05) (Fig. 2). It can also be observed that when NHHR approaches 2.66, the log odds of DKD occurrence and non-occurrence are approximately equal to 0, indicating an equal risk at this point. As NHHR deviates from 2.66, the risk of developing DKD gradually increases.

Table 2 Correlation of NHHR levels with DKD, ln(UACR) and eGFR in patients with diabetes mellitus
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Fig. 2
figure 2

RCS plots to assess the level of NHHR in diabetic people in relation to the log odds of DKD. The covariates that were adjusted to the model were the same as described above

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Subgroup analysis and sensitivity analysis

The link of NHHR to DKD shown in the subgroup analysis (Fig. 3) remained significant in participants aged below 40, females, non-smokers, and those without a history of hyperuricemia. However, subgroup analysis based on alcohol consumption, race, marital status, education level, BMI, family PIR, physical activity, and history of hypertension did not show this correlation. Furthermore, there was a notable relationship between smoking and NHHR (P for interaction < 0.05).

Fig. 3
figure 3

Stratified analysis of NHHR and occurrence of DKD in diabetic patients. The NHHR was analyzed using continuous variables, and interaction tests were also conducted to derive the interaction P-value. The red line segments represent the 95% CI for each group, and the ends of the lines represent the upper and lower 95% CI limits. The blue diamond reflects the midpoint of the line segment and illustrates the effect value

Full size image

Tables 3 and 4 show the outcomes of sensitivity analyses. By excluding individuals with BMI ≥ 30 kg/m2, the link between continuous NHHR and DKD, along with ln(UACR), stayed noteworthy in the fully adjusted model. Also, by excluding individuals with dietary cholesterol intake over 300 mg, the association between continuous NHHR and DKD, as well as ln(UACR), stayed meaningful within the fully adjusted model.

Table 3 Sensitivity analysis
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Table 4 Sensitivity analysis
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Discussion

In the research, the authors selected data from NHANES 2005–2016 to discover the underlying connection of NHHR to DKD. After thorough adjustment for covariates in the continuous model, a positive link of NHHR to DKD was found. In the categorical model, a positive association with the risk of DKD was displayed by the maximum group. This positive correlation persisted in further sensitivity analyses. Additionally, the relationship between NHHR and DKD continued to be significant among participants under the age of 40, females, non-smokers, and those without a history of hyperuricemia. Furthermore, NHHR had a non-linear connection with DKD risk, with NHHR = 2.66 identified as the optimal threshold based on calculations.

NHHR is a novel lipid index for assessing atherosclerosis, as it encompasses comprehensive information on both harmful and protective lipid particles in relation to atherosclerosis, potentially reflecting the balance between these two classes of lipoproteins [14]. While the specific relationship of NHHR and DKD has not been confirmed in previous studies, some researchers have used triglycerides (TG)/HDL-C to analyze the relationship between chronic kidney disease and urinary albumin excretion, with the results indicating an elevated ratio related to a higher likelihood of chronic kidney disease and increased urinary protein excretion [26]. The association between lipid parameters and DKD has been explored, with a prospective research [27] demonstrating that higher concentrations of lipoprotein(a) in serum are linked to a higher occurrence of DKD. A meta-analysis [28] has also confirmed this conclusion. Russo, Giuseppina T, et al. [29] suggested that elevated TG and reduced HDL-C concentration are independent hazard factors for developing DKD. In a cross-sectional study [9], DKD was significantly connected to serum LDL-C, TG, total cholesterol, and very low-density lipoprotein cholesterol (VLDL-C) (all OR > 1, all P < 0.05). The “U-shaped” correlation involving HDL-C and cardiovascular diseases in diabetic patients was shown by a retrospective cohort study [30], implying that elevated HDL-C concentration could also be related to unfavorable clinical results. Other studies validated similar findings, revealing that raised HDL-C concentration was correlated with a larger likelihood of dying from cardiovascular disease [31, 32]. Several previous analyses have consistently revealed a meaningful link of disruptions in plasma lipid profiles to the onset and advancement of DKD, potentially lending support to this research findings indirectly. In the study, the relationship between NHHR and DKD in females remains consistent with the findings of some previous researchers [33,34,35]. This may be attributed to greater changes in central obesity and insulin resistance in women, which lead to endothelial dysfunction and systemic inflammation [36]. Piani, Federica et al. [37] analyzed the impact of gender differences on DKD from a renal hemodynamic perspective, noting that females with diabetes exhibit higher glomerular hydrostatic pressure and lower renal blood flow in comparison to males, which further supports the conclusions. However, some studies [38,39,40] have reached conclusions that are contrary to this research, which may be attributed to the safeguarding benefits of estrogen on the kidneys through the regulation of the extracellular matrix, alleviation of renal fibrosis, and modulation of the manifestation of transforming growth factor-β [41,42,43]. Besides, further research has indicated that estrogen can counteract oxidative stress, thereby alleviating the state of renal ischemia and hypoxia [44]. The effects of estrogen on the female kidney are also influenced by age and hyperglycemia [37]. Therefore, the gender differences in diabetic kidney disease require further investigation for confirmation. To sum up, for patients with diabetes, mixed dyslipidemia is characterized not only by elevated TG, reduced HDL-C, and raised small dense low-density lipoprotein-cholesterol levels [45], but also by elevated plasma levels of arginine vasopressin (AVP), especially for males [46]. Consequently, the authors have attempted to explore innovative lipid parameters to advance the comprehension of lipoproteins in DKD risk.

In this study, the results indicate that within the completely adjusted RCS model, the association between NHHR and DKD exhibits a “J-shaped” non-linear association. When NHHR is 2.66, there is a 50% probability of developing DKD. However, when NHHR deviates from 2.66, the risk of developing DKD increases accordingly. The positive correlation between NHHR and DKD persists in women. Lipid abnormalities in diabetes include not only quantitative abnormalities in lipoproteins but also qualitative and kinetic abnormalities [47]. Normal HDL-C protects cardiovascular health by mediating cholesterol reverse transport and exerting anti-inflammatory, antioxidant, and anti-thrombotic effects [48]. However, HDL-C, as a complex lipoprotein particle, may have different roles in cholesterol reverse transport depending on its subtypes. For example, research [49] has shown that HDL-C containing both apoA-I and apoA-II exhibits more pronounced pro-atherogenic characteristics compared to HDL-C containing only apoA-I. Furthermore, this protective impact of HDL-C seems to be compromised by diabetes, as research has found that in females with type 2 diabetes, not only is HDL-C concentration reduced, but the distribution of HDL subtypes also shifts towards less anti-atherogenic characteristics [50]. Therefore, low NHHR within a specific range does not provide absolute protection against the risk of developing DKD. Non-HDL-C refers to all cholesterol-carrying proteins other than HDL-C [51]. Under conditions of hyperglycemia-induced oxidative stress, the concentration of oxidized low-density lipoprotein (ox-LDL) in non-HDL gradually increases [52], leading to the deposition of ox-LDL in glomeruli and inducing podocyte damage [53]. Studies have found that in experimental hyperglycemia, excessive ox-LDL accumulation within podocytes and mesangial cells correlates with lipid peroxidation in the kidney and loss of renin, impacting glomerular lesions [54]. Acting through the oxidized low-density lipoprotein receptor 1, ox-LDL activates the AMPK, PI3K, and NF-κB signaling pathways, promoting the upregulation of pro-inflammatory cytokines, facilitating foam cell formation, driving atherosclerosis progression [55, 56], and potentially resulting in atherosclerotic renal damage. Furthermore, experimental studies [57] have observed renal tissue damage in mice with a high-cholesterol consumption, showing reduced interstitial spaces between glomeruli and the encompassing Bowman’s capsule. A cohort study [58] involving adolescents with type 1 diabetes revealed that VLDL-C concentrations were associated with markers of glomerular hemodynamic function, indicating the contribution of lipid abnormalities in DKD progression. In conclusion, elevated non-HDL-C levels may increase the chance of developing DKD. Besides, regarding the disturbance of plasma lipid profile, it is important to consider not only lipoprotein cholesterol itself, but also to the changes in plasma arginine vasopressin. Copeptin is a peptide that originates from the same precursor as AVP but is more stable. Both copeptin and AVP have been demonstrated to be linked to DKD and cardiovascular incidents in diabetic people [59,60,61]. Copeptin mediates renal injury by activating the renin-angiotensin-aldosterone system (RAAS) in diabetic patients [62]. Furthermore, AVP can activate antagonism of vasopressin V2 receptor (V2R) widely distributed in renal collecting ducts and endothelial cells, promoting proteinuria and glomerular hyperfiltration [63], which ultimately leads to renal dysfunction.

In this study, it is discovered that NHHR may serve as a potential novel biomarker to assist clinicians in assessing DKD, providing a possible avenue for the future research and development of new therapies. Furthermore, the emergence of new lipid metrics contributes to strengthening health education for diabetic patients and developing more personalized treatment plans. While postmenopausal women with diabetes are often the focus of concern, it is crucial to recognize that younger women, particularly those under 40, also require attention due to their lipid profiles. The NHHR indicator can act as a useful resource for assessing the hazard of DKD in this population, providing an early warning signal for timely intervention, and it may also provide a new direction for public health management targeting this group.

Strengths and limitations

The study possesses several advantages. To begin with, the authors harnessed the data from NHANES, which is known for its use of representative stratified sampling and meticulous weight adjustments. Additionally, this research included a substantial sample size. Finally, covariates were rigorously adjusted for based on relevant prior studies. However, the study is not devoid of limitations. Firstly, while researchers identified a potential link between non-HDL-C levels and DKD, the cross-sectional approach prevents the definitive formation of a causal connection. Next, despite accounting for some confounding factors in the stratified analysis, not all possible confounders were taken into consideration. Thirdly, the inability to discern the diabetes type among participants may hinder result interpretation. Lastly, the study relied on the United States-based data, prompting the need for further investigation to ascertain the applicability of the findings to other regions.

Conclusions

For American adults with diabetes, there exists a non-linear relationship between NHHR and the risk of DKD. The positive link of NHHR to DKD remains significant in individuals aged below 40, females, non-smokers, and those without a history of hyperuricemia. The study provides valuable evidence for assessing the likelihood of kidney disease in diabetic patients. Monitoring the NHHR in clinical environments can facilitate the detection of high-risk patients for DKD. It is recommended that diabetic patients with abnormal lipid levels undergo lipid-lowering therapy to mitigate the risk of developing DKD.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

NHHR:

Non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio

DKD:

Diabetic kidney disease

NHANES:

National Health and Nutrition Examination Survey

RCS:

Restricted cubic spline

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low-density lipoprotein cholesterol

VLDL-C:

Very low-density lipoprotein cholesterol

Apo:

Apolipoprotein

Non-HDL-C:

Non-high-density lipoprotein cholesterol

UACR:

Urine albumin-to-creatinine ratio

eGFR:

Estimated glomerular filtration rate

PIR:

Poverty income ratio

BMI:

Body mass index

ALT:

Alanine aminotransferase

AST:

Aspartate aminotransferase

WTDR2D:

Dietary two-day sample weight

Ln(UACR):

Natural logarithm of UACR

CI:

Confidence intervals

OR:

Odds ratios

TG:

Triglycerides

ox-LDL:

Oxidized low-density lipoprotein

AVP:

Arginine vasopressin

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Acknowledgements

The authors would like to thank the staff and participants associated with the NHANES study for their significant contributions.

Funding

This research was funded by the Natural Science Foundation of Shandong Province (ZR2020MH361, ZR2023MH300), China Postdoctoral Science Foundation (2021M692750), Special funding for Mount Tai Scholar Project (tsqn202211356, tsqn202312377) and Shandong Medical & Health Science and Technology Development Foundation (202303051688, 202304071664) .

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  1. Wenbo Wang and Yujie Li contributed equally to this work.

Authors and Affiliations

  1. Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People’s Republic of China

    Jingjing Pan, Changnian Li, Jiayi Zhang & Zhishen Ruan

  2. Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong Province, People’s Republic of China

    Zhenhua Sun, Xiaoying Yu, Qianhui Wan, Wenbo Wang & Yujie Li

  3. Department of Geriatrics, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, NO.1 Jingba Road, Jinan, 250000, Shandong Province, People’s Republic of China

    Yujie Li

  4. Department of Orthopaedics, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, NO.1 Jingba Road, Jinan, 250000, Shandong Province, People’s Republic of China

    Wenbo Wang

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Contributions

YL and WW proposed the strategy, JP conducted the data extraction and organization as well as the final draft, CL provided statistical methodological guidance, JZ, ZS, and XY analyzed and validated the data, and QW and ZR produced the charts. All authors conducted the review and approved the final manuscript.YL and WW are the guarantors of this work and are responsible for the completeness and accuracy of the work.

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Correspondence to Wenbo Wang or Yujie Li.

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The study was conducted ethically in accordance with the Declaration of Helsinki (revised 2013). The National Center for Health Statistics (NCHS) Research Ethics Review Board approved the NHANES protocols.

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The authors declare no competing interests.

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Pan, J., Li, C., Zhang, J. et al. Association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and diabetic kidney disease in patients with diabetes in the United States: a cross-sectional study. Lipids Health Dis 23, 317 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02308-5

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X