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Predictive value of small dense low-density lipoprotein cholesterol for cardiovascular events in Chinese elder diabetes mellitus patients



As a subcomponent of low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C) has been suggested to be a better predictor of cardiovascular diseases (CVD). The aim of this research was to evaluate the predictive value of the sdLDL-C in cardiovascular events (CVs) in Chinese elderly patients with type 2 diabetes mellitus (DM).


A total of 386 consecutive type 2 DM patients were included into this study during December 2014 to December 2016. The serum sdLDL-C level of each subject was measured by homogeneous method. During a period of 48-month’s follow-up, the occurrence of CVs and associated clinical information were recorded. Receiver operating characteristic (ROC) curves were used to assess the predictive value of serum sdLDL-C to occurrence of major CVs.


A total of 92 CVs occurred during the study period. The ROC curve analysis manifested that sdLDL-C in the study population had a matchable discriminatory power (AUC for sdLDL-C was 0.7366, P = 0.003). In addition, Kaplan-Meier event-free survival curves displayed an obvious increase of CVs risk for sdLDL‐C 26 mg/dL (log-rank = 9.10, P = 0.003). This phenomenon had analogous results in patients who received statins at baseline (log rank = 7.336, P = 0.007). Cox regression analysis revealed that the increase in HbA1c, glucose, LDL-C, sdLDL-C, non-high-density lipoprotein cholesterol (non-HDL-C) and apolipoprotein B (ApoB) and the decrease in apolipoprotein AI (ApoAI) were obviously interrelated with heightened CVs risk. Multiple Cox regression demonstrated that the increase of sdLDL-C and hemoglobin A1c (HbA1c) was significantly correlated with CVs. The results of the study indicated that high sdLDL-C level (> 10 mg/dL) was a risk factor for CVs in the multivariate model (HR 1.281, 95% CI 1.225–16.032; P < 0.01).


sdLDL-C level could be an effective predictor in predicting the future CVs for Chinese elderly patients with type 2 DM and dyslipidemia.


Type 2 diabetes mellitus (DM) patients are more likely to have cardiovascular disease (CVD) partially due to dyslipidemia characterized by elevated low-density lipoprotein cholesterol (LDL-C), small dense LDL-C(sdLDL-C), remnant lipoprotein cholesterol (RLP-C) and decreased HDL-C in serum [1, 2]. Moreover, previous researches had discovered that higher sdLDL-C and RLP-C levels were associated with elevated risk of CVD events [3, 4]. Moreover, serum levels of lipoprotein(a) (Lp(a)) can be risk factors for adverse events [5].

A cross-sectional study has shown that sdLDL-C concentration was closely related to the severity of cardiovascular disease and was independent of classic coronary risk factors [6]. Moreover, another study has revealed that a higher RLP-C concentration was a risk factor for cardiovascular events (CVs) independent of other risk factors in diabetic patients [7]. However, few studies in the past have assessed the relationship between sdLDL-C and RLP-C and CVs in Chinese elderly patients with type 2 diabetes mellitus (DM).

The serum sdLDL-C level could be easily detected through automated analysis. However, these methods have not yet been impressed on large number of type 2 DM patients, especially among the Chinese elderly population. The study attempted to investigate whether sdLDL-C can predict CVs or sdLDL-C as a complement to the routine lipid profile should be considered in clinical practice in order to improve the management of CVD and risks factors for their progression in Chinese elderly patients with type 2 DM [8].


Subjects and study design

This research included 418 consecutive patients aged ≥ 65 years with type 2 DM at Suzhou Ninth People′s Hospital, Suzhou, China, between December 2014 and December 2016, and all of the subjects had no history of CVD. DM was identified according to 2009 American Diabetes Association Criteria for diabetes diagnosis [9]. The body mass index (BMI), estimate of glomerular filtration rate (eGFR) and smoking status (current smokers and at least one cigarette per day) were recorded. The diagnosis of hypertension was based on blood pressure measurement (systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg) and/or use of antihypertensive medications within 2 weeks of enrollment [10]. Dyslipidemia was defined as the fasting serum LDL-C ≥ 140 (mg/dL), high-density lipoprotein cholesterol (HDL-C) < 40 (mg/dL) or triglyceride (TG) ≥ 150 (mg/dL) and/or the current use of lipid-lowering medication [11]. Exclusion criteria were: age ≥ 90 years (n = 2), presence of malignancy (n = 3), known thyroid disorders (n = 3), lost of blood examination data (n = 6), infectious disease (n = 7), lost during follow-up (n = 7) and severe hepatic and nephrotic diseases (n = 4).

Finally, a total of 386 patients (mean age of 72.7 ± 5.4 year, range from 65 to 86 years) were included into the study (269 males and 117 females). They received follow-ups ranging from 20 to 48 months, with an average of 28 months. Blood biochemical examinations were performed for subjects yearly during the follow-up period. The associated clinical and laboratory data was collected between September and November 2019. The endpoints were: (1) CVD death, (2) the date of the first occurrence of CVs, and (3) the date of the patient′s last visit to Suzhou Ninth People′s Hospital. CVs were defined as (1) death caused by cardiovascular disease, (2) onset of acute coronary syndrome, (3) congestive heart failure, (4) need for coronary or other arterial revascularization, (5) stroke and registered in Table 1.

Table 1 Cardiovascular events were defined and registered during the follow-up

Laboratory measurements

The blood samples were collected after 12 h of fasting in the morning. After collection, the samples were centrifuged immediately and stored at – 80 ℃ until assay within the same day. High-sensitivity C-reactive protein (hsCRP), HDL-C, Hemoglobin A1c (HbA1c), LDL-C, TG, fasting blood glucose, apolipoprotein A-I(ApoA-I), apolipoprotein B(ApoB) and lipoprotein (a) were detected through standard biochemical tests as reported previously [12]. The sdLDL-C and RLP-C were detected by a detergent-based fully-automatic homogeneous method (Denka Seiken kit, Tokyo, Japan) [13].

Statistical analyses

All statistical analyses were performed using the SAS 9.1 software package. The Chi-square test was used to analyze categorical variables. Wilcoxon test and independent student t-test was used to compare the means between the CVs group and non-CVs group. Correlation coefficients between sdLDL-C and other parameters were determined by Spearman's rank analysis. Kaplan-Meier method was used to compare the occurrence of CVs between the lower sdLDL-C and higher sdLDL-C group, and the differences were assessed with log-rank test. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used for assess the ability of sdLDL-C, HbA1c, and RLP-C to predict CVs. Cox regression and multivariate Cox regression analysis were employed to determine these independent predictors. All statistical analyses were double-tailed, and statistical significance was considered at the level of P < 0.05.


The occurrences of CVs were summarized in Table 1. The patients with CVs had obviously higher BMI and were more likely to be under calcium channel blockers and insulin therapy compared with non-CVs patients (Table 2). A comparison of laboratory findings manifested the levels of glucose, HbA1c, LDL-C, non-HDL-C, sdLDL-C, RLP-C, ApoB and ApoA-1 in the CVs group were significantly different from those of the non-CVs group (Table 3). Whereas there was no statistically significant difference in TG, hsCRP, HDL-C, eGFR, sdLDL-C/LDL-C ratioand lipoprotein (a) between the two groups.

Table 2 The clinical characteristics of the enrolled patients at baseline
Table 3 The laboratory characteristics of the enrolled patients at baseline

In this study, first-time CVs were observed in 92 patients. Kaplan-Meier method showed an obvious increase of CVs risk for the median levels of sdLDL‐C (Fig. 1a). Similar trend was found in patients who received statins (Fig. 1b). Cox regression analysis showed that increase in sdLDL-C and HbA1c was associated with a higher risk for CVs (Table 4). To determine whether sdLDL-C was an independent risk factor, we performed Cox multivariate regression analysis. The models were built after adjustment age, gender and CVs risk factors. Model 1 (including Glucose, HbA1c, LDL-C, Non-HDL-C, sdLDL-C, ApoA-I and ApoB) and Model 2 (only including sdLDL-C and HbA1c) showed that just sdLDL-C and HbA1c remained significantly associated with the risk of CVs. These results suggest that elevated Glucose and dyslipidemia might contribute to CVs.

Fig. 1

The Kaplan–Meier event free survival of patients stratified by the median small dense low-density lipoprotein cholesterol (sdLDL-C) concentration (26 mg/dL). a All patients, b patients treated with statins

Table 4 Predictors for cardiovascular events according to Cox,s proportional hazard analysis

Spearman's correlation analysis suggested that compared with LDL-C, the serum level of sdLDL-C exhibited more significant correlations with various parameters, suggesting that sdLDL-C might the major factor among LDL-C contribute to CVs.

We performed the ROC analysis in order to test the discriminatory power of sdLDL-C for the CVs (Fig. 2). The result indicate that the AUC of the sdLDL-C has a strong discriminating power against CVs, and its optimal cut-off value is 36.2mg/dL (AUC = 0.736, P = 0.003) than HbA1C and RLP-C.

Fig. 2

Receiver operating characteristic curves showed discriminatory power of sdLDL-C, HbA1C and RLP-C on CVs


Diabetes mellitus patients are often accompanied by dyslipidemia, which is the major controllable risk factor associated with cardiovascular disease (CVD) events [14]. Dyslipidemia has been confirmed as one of the principal processes underlying CVD, while sdLDL-C is considered as an emerging risk factor for CVD. Indeed, about 70% of elderly patients with type 2 DM die from CVD [15, 16].

SdLDL particles were defined as LDL with an average diameter of < 25.5 nm and had some features related to atherogenic potential: (1) increased penetration into the arterial wall by the small sizes, (2) a longer circulation time with low affinity for the LDL receptors, (3) high susceptibility to oxidation by containing little antioxidative vitamins [17]. Previous studies concerning the elevated levels of sdLDL-C has been associated with CVs [18, 19]. However, to the best of our knowledge, few studies have investigated on the influence of sdLDL-C levels on the onset of CVs in Chinese elderly patients with type2 DM.

In the present study, Spearman's rank correlation analysis suggest that sdLDL-C might the major factor among LDL-C contribute to CVs. Furthermore, ROC curve assay indicated that the AUC of the sdLDL-C has a strong discriminating power against CVs, and its optimal cut-off value is 36.2mg/dL (AUC=0.736, P = 0.003) than HbA1C and RLP-C (Fig. 2). Kaplan-Meier event-free survival curve displayed a obvious increase of CVs risk for the median levels of sdLDL‐C (Fig.1a). This phenomenon had analogous results in patients who received statins at baseline (Fig. 1b). Cox regression analysis showed that increase in sdLDL-C and HbA1c revealed a higher risk for CVs. From Table 5, we do see the correlation between sdLDL-C and RLP-C, but in the multivariable model, the RLP-C variable has been excluded from the regression automatically because less correlation with the outcome compare to sdLDL-C (Table 4).

Table 5 Spearman’s correlation of LDL-C and sdLDL-C to inflammatory biomarkers and age

To determine whether sdLDL-C was an independent risk factor, we use the step-wise COX regression, even if the correlated variables are included, some of them will be excluded from the model automatically if they are not relevant compared to the investigational factor. The models were built after adjustment age, gender and CVs risk factors. Model 1 including Glucose, HbA1c, LDL-C, Non-HDL-C, sdLDL-C, ApoA-I and ApoB and Model 2 only including sdLDL-C and HbA1c showed that just sdLDL-C and HbA1c remained significantly associated with the risk of CVs These results suggest that elevated Glucose and dyslipidemia might contribute to CVs.

Indeed, sdLDL-C levels has the ability to predict CVD better than total LDL-C [18]. In addition, the Québec Cardiovascular Study has shown that sdLDL-C is interrelated with an raised risk of CAD in men [6, 7]. On the other hand, remnant lipoproteins are rich in TG and the main components include VLDL in the fasting state [20]. Obviously, the current study not only confirmed the sdLDL-C concentrations was an independent risk predictor for CVs, but also provided novel information concerning the role of RLP‐C in predicting CVs in diabetic patients [21,22,23,24].

In this study, both sdLDL-C and HbA1c are independent predictors for future cardiovascular events, and Table 5 shows that sdLDL-C and HbA1c are not significantly correlated. These inconsistent results may be partly due to HbA1c levels are stable over a period of time, while sdLDL-C is dynamically changing. Then, studies on the relationship between HbA1c and sdLDL-C at different time points over a period of time may present different results. Of course, sdLDL-C levels of type 2 DM patients are affected by many lipid-lowering agents used [25]. In order to manage type 2 DM patients better, it is necessary to analyze sdLDL-C in time to guide the health management of type 2 DM patients. This finding suggested that sdLDL-C should be focused on in the process of type 2 DM patients health management.

There are some limitations in this cohort study. First of all, the sample size is small and all the patients are Chinese, which may lead to a bias to fully observe the results and/or severity of CAD. Besides, of all the lipid biomarkers, only sdLDL-C was found independently associated with CVs among elderly patients with diabetes mellitus; whether it is because of the effect of statin therapy needs further investigation. Finally, the comparison of the predictive ability of sdLDL-C for CVs to the patient subgroup needs further research. Another limitation of the present study is that during the follow-up period the examinations of many patients were not collected and there were too many missing data; as a result of this, available data during the follow-up were not fully credible, which could easily lead to bias. Further limitation of the present study is that many patients had multiple antidiabetic drug combinations, and it was difficult to compare the detailed antidiabetic agents used.


The current study demonstrated that sdLDL-C was an effective predictor in predicting the future CVs of elderly patients with type 2 DM and dyslipidemia.

Availability of data and materials

Unfortunately, the initial data cannot be shared as it contains confidential information.



Diabetes mellitus


Cardiovascular events


Cardiovascular diseases


Small dense low-density lipoprotein cholesterol


Remnant lipoprotein cholesterol


Low-density lipoprotein cholesterol


High-density lipoprotein cholesterol


Hemoglobin A1c



Lp (a):

Lipoprotein (a)

ROC curves:

Receiver operating characteristic curves


  1. 1.

    Hirano T. Pathophysiology of diabetic dyslipidemia. J AtherosclerThromb. 2018;25:771–82.

    CAS  Google Scholar 

  2. 2.

    Santos HO, Lavie CJ. Weight loss and its influence on high-density lipoprotein cholesterol (HDL-C) concentrations: a noble clinical hesitation[J]. ClinNutr ESPEN. 2021;42:90–2.

    Article  Google Scholar 

  3. 3.

    Ai M, Otokozawa S, Asztalos BF, Ito Y, Nakajima K, White CC, Cupples LA, Wilson PW, Schaefer EJ. Small dense LDL cholesterol and coronary heart disease: results from the Framingham Offspring Study. Clin Chem. 2010;56:967–76.

    CAS  Article  Google Scholar 

  4. 4.

    Saeed A, Feofanova EV, Yu B, Sun W, Virani SS, Nambi V, Coresh J, Guild CS, Boerwinkle E, Ballantyne CM, Hoogeveen RC. Remnant-like particle cholesterol, low-density lipoprotein triglycerides, and incident cardiovascular disease. J Am CollCardiol. 2018;72:156–69.

    CAS  Article  Google Scholar 

  5. 5.

    Santos HO, Kones R, Rumana U, et al. Lipoprotein(a): current evidence for a physiologic role and the effects of nutraceutical strategies[J]. ClinTher. 2019;41(9):1780–97.

    CAS  Google Scholar 

  6. 6.

    Hoogeveen RC, Gaubatz JW, Sun W, Dodge RC, Crosby JR, Jiang J, Couper D, Virani SS, Kathiresan S, Boerwinkle E, Ballantyne CM. Small dense low-density lipoprotein-cholesterol concentrations predict risk for coronary heart disease: the Atherosclerosis Risk In Communities (ARIC) study. ArteriosclerThrombVasc Biol. 2014;34:1069–77.

    CAS  Article  Google Scholar 

  7. 7.

    St-Pierre AC, Cantin B, Dagenais GR, Mauriège P, Bernard P-M, Després J-P, Lamarche B. Low-density lipoprotein subfractions and the long-term risk of ischemic heart disease in men. ArteriosclerThrombVascBiol. 2005;25:553–9.

    CAS  Google Scholar 

  8. 8.

    Santos HO, Earnest CP, Tinsley GM, et al. Small dense low-density lipoprotein-cholesterol (sdLDL-C): analysis, effects on cardiovascular endpoints and dietary strategies[J]. ProgCardiovasc Dis. 2020;63(4):503–9.

    Article  Google Scholar 

  9. 9.

    American Diabetes Association. Executive summary: standards of medical care in diabetes—2011. Diabetes Care. 2011;34(Suppl 1):S4-10.

    Google Scholar 

  10. 10.

    Chobanian AV, Bakris GL, Black HR, Cushman WC, Green LA, Izzo JL Jr, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the jnc 7report. JAMA. 2003;289:2560–72.

    CAS  Article  Google Scholar 

  11. 11.

    Expert Panel on Detection Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA. 2001;285:2486–97.

    Article  Google Scholar 

  12. 12.

    Shen H, Zhou J, Shen G, Yang H, Lu Z, Wang H. Correlation between serum levels of small, dense low-density lipoprotein cholesterol and carotid stenosis in cerebral infarction patients >65 years of age. Ann Vasc Surg. 2014;28:375–80.

    Article  Google Scholar 

  13. 13

    Ito Y, Hirao Y. Development of a novel homogeneous assay for remnant like particle cholesterol (abstract 484). Atheroscler Suppl. 2011;12:103.

    Article  Google Scholar 

  14. 14.

    Khosravi A, Tabib AA, Golshadi I, et al. The relationship between weight and CVD risk factors in a sample population from Central Iran (Based on IHHP)[J]. Arya Atheroscler. 2012;8(2):82–9.

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Kannel WB, McGee DL. Diabetes and cardiovascular disease. JAMA. 1979;241:2035–8.

    CAS  Article  Google Scholar 

  16. 16.

    Grundy SM, Benjamin IJ, Burke GL, Chait A, Eckel RH, Howard BV, et al. Diabetes and cardiovascular disease: a statement for health professionals from the American Heart Association. Circulation. 1999;100:1134–46.

    CAS  Article  Google Scholar 

  17. 17.

    Rizzo M, Berneis K. Should we measure routinely the LDL peak particle size ? Int J Cardiol. 2006;107:166–70.

    Article  Google Scholar 

  18. 18.

    Arai H, Kokubo Y, Watanabe M, Sawamura T, Ito Y, Minagawa A, Okamura T, Miyamato Y. Small dense low-density lipoproteins cholesterol can predict incident cardiovascular disease in an urban Japanese cohort: the Suita study. J AtherosclerThromb. 2013;20:195–203.

    CAS  Google Scholar 

  19. 19.

    Ito Y, Ohta M, Ikezaki H, Hirao Y, Machida A, Schaefer EJ, Furusyo N. Development and population results of a fully automated homogeneous assay for LDL triglyceride. JALM. 2017;2:746–56.

    Google Scholar 

  20. 20.

    Chapman MJ, Ginsberg HN, Amarenco P, Andreotti F, Boren J, Catapano AL, et al. Triglyceride-rich lipoproteins and high-density lipoprotein cholesterol in patients at high risk of cardiovascular disease: evidence and guidance for management. Eur Heart J. 2011;32:1345–61.

    CAS  Article  Google Scholar 

  21. 21.

    Schwartz GG, Abt M, Bao W, et al. Fasting triglycerides predict recurrent ischemic events in patients with acute coronary syndrome treated with statins. J Am CollCardiol. 2015;65:2267–75.

    CAS  Article  Google Scholar 

  22. 22.

    Masuda D, Yamashita S. Postprandial hyperlipidemia and remnant lipoproteins. J AtherosclerThromb. 2017;24:95–109.

    CAS  Google Scholar 

  23. 23.

    Kugiyama K, Doi H, Takazoe K, et al. Remnant lipoprotein levels in fasting serum predict coronary events in patients with coronary artery disease. Circulation. 1999;99:2858–60.

    CAS  Article  Google Scholar 

  24. 24.

    Nguyen SV, Nakamura T, Kugiyama K. High remnant lipoprotein predicts recurrent cardiovascular events on statin treatment after acute coronary syndrome. Circ J. 2014;78:2492–500.

    CAS  Article  Google Scholar 

  25. 25.

    Rizzo M, Nikolic D, Banach M, Patti AM, Montalto G, Rizvi AA. Incretin-based therapies, glucometabolic health and endovascular inflammation. Curr Pharm Des. 2014;20:4953–60.

    CAS  Article  Google Scholar 

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This study was supported by Department of Clinical Laboratory Medicine and Xu Yuan Neurological and Cardiovascular Disease Research Team, Suzhou Ninth People′s Hospital, Suzhou, China.


Fifth Batch of Gusu Health Personnel Project (GSWS2019087).

Author information




XL and CX planned the study and wrote the manuscript. LJF, XY and YHL contributed to data collection and enrolled patients. ZXW performed the flow mediated dilatation test. ZJ, XJH and SH supervised the study and reviewed/edited the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Jun Zhou, Jianhong Xu or Hao Shen.

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This study was approved by the Committee on Human Research of Suzhou Ninth People′s Hospital. All patients signed a dedicate informed consent.

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Not applicable.

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The authors declare no conflicts of interest.

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Xu, L., Chen, X., Lu, J. et al. Predictive value of small dense low-density lipoprotein cholesterol for cardiovascular events in Chinese elder diabetes mellitus patients. Diabetol Metab Syndr 13, 49 (2021).

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  • Cardiovascular event
  • Small dense low-density lipoprotein cholesterol
  • Receiver operating characteristic curve
  • Diabetes mellitus