The association between blood glucose levels and lipids or lipid ratios in type 2 diabetes patients: A cross-sectional study

Abstract

Background:

Lipids and lipid ratios are associated with complications of diabetes mellitus type 2 (T2DM), such as cardiovascular disease, but the relationship between blood glucose levels and lipid or lipid ratios is not fully understood in T2DM patients. This study assesses the association between blood glucose levels and lipid or lipid ratios in a cohort of T2DM patients.

Methods:

A total of 1,747 Chinese T2DM patients from the Ningxia province of China were included in this cross-sectional study. Lipid parameters, including triglycerides (TG), total cholesterol (TC), high-density lipoprotein (HDL-C), low-density lipoprotein (LDL-C), and fasting blood glucose levels were measured quantitatively using standard methods. Fasting blood glucose was divided into three groups. A multiple mixed-effect linear regression model was conducted to identify a potential association between blood glucose and lipid parameters.

Results:

There was a positive association between blood glucose and TG levels (β=0.34, 95% CI: (0.20, 0.48), p<0.01); every 1 mmol/L increase in blood glucose levels resulted in a 0.34 mmol/L increase in TG. Blood glucose levels were also associated with high LDL (β=0.08, 95% CI: (0.02, 0.14), p<0.01), TG/HDL-C (β=0.31, 95% CI: (0.13, 0.49), p<0.01), and LDL-C/HDL-C (β=0.13, 95% CI: (0.06, 0.20), p<0.01) levels. After controlling for demographic variables, health-related behaviors, and physical health variables, a positive association between blood glucose levels and TG (β=0.31, 95% CI: (0.17, 0.45), p<0.01) and LDL-C (β=0.08, 95% CI: (0.02, 0.13), p<0.01) levels and an in increase in TG/HDL-C (β=0.28, 95% CI: (0.09, 0.46), p<0.01) and LDL-C/HDL-C (β=0.11, 95% CI: (0.04, 0.18), p<0.01) ratios was found.

Conclusion:

A correlation between blood glucose levels and serum lipids or lipid ratios has been established in this study. Blood glucose levels were positively associated with TG and LDL-C levels and elevated TG/HDL-C and LDL-C/HDL-C ratios.

Introduction

Diabetes mellitus type 2 is a multisystem, chronic, noncommunicable disease that has reached epidemic proportions (1) and developed into a significant public health concern worldwide (2, 3). Both the case number and the prevalence of diabetes have risen over the past few decades, and it is predicted that the number of T2DM patients will increase to 700 million by 2045 (4). T2DM is a metabolic disease caused by the relative or absolute deficiency of insulin and is associated with elevated blood glucose levels (5). Elevated blood glucose is also linked with several other diseases, such as dyslipidemia (6). Dyslipidemia and diabetes are closely associated, hyperglycemia not only causes apoptosis of β-cells in the islets of Langerhans (glucotoxicity) but also determines the degree of accumulation of oxidized LDLs (7), and blood lipids can successfully treat the adverse outcomes of this disease (8). More than 75% of T2DM patients have mixed dyslipidemia that is characterized by low levels of high‐density lipoprotein cholesterol (HDL‐C) and high levels of triglyceride (TG) (9).

Increased serum concentrations of total cholesterol (TC), total triglycerides (TG), and low-density lipoprotein cholesterol (LDL-c), as well as low HDL-C, are considered lipid parameters that could predict the risk of coronary heart disease (10). There are several lipid ratio parameters that have been defined by prior studies, such as TC/HDL-C, TG/HDL-C, and LDL-C/HDL-C (11–13). T2DM is widespread worldwide and is a severe risk factor for cardiovascular disease (14). T2DM patients often present with characteristic plasma lipid and lipoprotein abnormalities, including low HDL-C, high LDL-C (15), and elevated TC levels (15). It is critical for clinicians to effectively manage dyslipidemia to reduce the incidence of cardiovascular disease in this patient population.

According to a previous study, high blood glucose levels were positively associated with having high TG and high TC (16). Blood lipid levels in patients with T2DM are affected by the degree of glycemic control (17). Wang et al. reported that fasting plasma glucose levels were significantly associated with HDL and TC but not with LDL and TG among T2DM patients in Qingdao, China (18). The glycemic control of patients with T2DM is also significantly associated with the visceral adiposity index; a high index indicates poor glycemic control (19). Controlling lipid profiles in patients with T2DM is essential to reducing mortality and complications (20). However, the association between blood glucose levels and lipids or lipids ratio parameters closely linked to obesity in Ningxia province, remains unknown. The current study explores the association between blood glucose levels and lipid or lipid ratio parameters in a cohort of T2DM patients from this geographic region. We hypothesized that blood glucose levels were associated with higher TG, TC, and LDL levels, higher TG/HDL‐C and LDL‐C/HDL‐C ratios, and lower HDL-C levels in T2DM patients.

Methods

Study design and patients

1,747 T2DM patients from 10 hospitals in Ningxia province, China, were enrolled in this population-based cross-sectional study from August 2019 to November 2020. Participants were recruited using PPS sampling. A related sampling procedure has been described formerly (21). The total sample size was evaluated by the formula: n = (1 - P)/ϵ2 P. According to a previous study, the prevalence of dyslipidemia in Chinese diabetes patients was 54.3% (22). We set P = as 0.543 in this study. If α was 0.05, Z_α/2 was 1.96, and ϵ = 0.05, the calculated sample size was 1293. To prevent an invalid survey sample, we increased the sample by 20%, which made the minimum sample size in the survey 1551. The study was conducted during hospitalization. All participants over 18 years of age and had lived at their current address for a minimum of six months consented to participate in the study and agreed to complete a questionnaire and laboratory testing. Patients with severe mental disorders, a severe illness or language barrier that made communication impossible, pregnant or lactating women, diabetic ketoacidosis within the past month, malignant tumors, big surgery operation, or refusal to sign informed consent were excluded from the study. The Institutional Review Board of the Yinchuan Hospital of Traditional Chinese Medicine approved the study. Research procedures involving human participants conformed with the 1964 Helsinki declaration and its later amendments.

Dependent variables

All participants underwent a thorough physical examination and provided blood samples for lipid testing, including fasting serum TC, TG, HDL‐C, and LDL‐C. Blood samples were taken from the T2DM patients, and the serum was separated. Then, serum samples were tested within two hours. FPG was measured using the hexokinase method. The enzymatic method was applied to test TG and TC. HDL-C and LDL-C were measured using the direct quantitation (peroxidase scavenging) method. Automatic biochemical detectors (SIEMENSADVIAXPT, Germany) were used to test those biochemistry variables. All laboratory tests were conducted in the hospital laboratory using standard procedures.

Independent variables

The patients’ fasting blood glucose (FBG) levels were measured in the hospital laboratory. Blood glucose levels were defined as follows (1): slightly increased: FBG ≤11.1 mmol/L (2); moderately increased: FBG 11.2–16.6 mmol/L and (3) severely increased: FBG >16.6 mmol/L.

Health-related behaviors

Five self-reported health behaviors, current smoking status, alcohol use, physical activity, sleep duration, and sleep quality, were used as outcomes and operationally defined. Smoking was defined as smoking at least one cigarette per day for ≥6 months (23) and differentiated into current smoking versus non-smoking. Alcohol use was defined as consuming at least one glass of alcohol in the past month (24). Physical activity was determined by yes/no responses to the question, “do you engage in physical activity at least 30 minutes at work and/or during leisure time more than four days a week” (25). Sleep duration was identified by two questions, “how early do you usually go to sleep at night and how early do you usually wake up” (26). Sleep quality was determined by poor/moderate/good responses to the question “how do you rate your sleep quality” (21).

Physical health

Physical health characteristics included body mass index (BMI), according to the formula: BMI = weight (kg)/height (m²). New cases, mean no history of diabetes and newly diagnosed cases of type 2 diabetes (yes vs. no), other chronic diseases (yes vs. no), T2DM complications (yes vs. no), take hypoglycemic drugs and disease duration (continuous data).

Demographic variables

Age, marital status (married or single), residence place (rural or urban), occupation, and family income (average monthly family income self-reported in the local currency (RMB)) were considered demographic information.

Statistical analyses

We expressed continuous and categorical variables as mean (SD) and percentages (%). Comparisons were made between frequencies and means using t-tests and Chi-square tests. Multiple mixed-effect linear regression was used to examine the association between blood glucose levels and lipids and lipid ratio parameters. In the multiple mixed-effect linear regression model, the independents and dependent variables have a linear relationship, and the dependent variable must be continuous and at least interval-scale. Besides, the residuals follow the normal probability distribution and are independent. In this study, three separate models were used to control for each covariate: 1) adjusted for the demographic variables, 2) adjusted for demographics and health-related behaviors, and 3) adjusted for demographics, health-related behaviors, and physical health. Gender was fitted as a random intercept model. The regression models were shown with beta coefficients and 95% confidence intervals. All the analyses were performed using STATA software 14.0.

Results

Demographic characteristics of the participants

The patients had an average age of 58.6 (SD=12.1) years. More than half (54.8%) were male, 40.1% were farmers, and nearly one-fifth were illiterate (Table 1). A majority of the participants (63.8%) had a low blood glucose level, and 25.4% and 10.8% had moderate and high blood glucose levels, respectively. The female patients had less education, lower current smoking and alcohol use, and better sleep conditions than the males (P<0.05). Female patients also had higher HDL-C and LDL-C, and TG/HDL-C ratios than the male patients.

Bivariate regression model

Age, education attainment, current smoking, alcohol use, physical activity, BMI, new cases, other chronic diseases, and disease duration were associated with TG levels; current smoking, physical exercise, BMI, disease duration, T2DM complications, and taking medicine were associated with HDL levels; family income, physical exercise, new cases, and disease duration were associated with LDL levels; and age, education attainment, current smoking, alcohol use, physical exercise, BMI, new cases, disease duration, and T2DM complications were associated with TG/HDL-C (Table 2). Blood glucose levels were associated with high TG levels (β=0.34, 95% CI: (0.20, 0.48), p<0.01); 1 mmol/L level increase in blood glucose correlated with a 0.34 mmol/L increase in TG. Blood glucose levels were also associated with high levels of LDL (β=0.08, 95% CI: (0.02, 0.14), p<0.01) and elevated TG/HDL-C (β=0.31, 95% CI: (0.13, 0.49), p<0.01) and LDL-C/HDL-C (β=0.13, 95% CI: (0.06, 0.20), p<0.01) ratios. As blood glucose levels increased, there was a significant increase in TG and LDL-C levels and TG/HDL-C and LDL-C/HDL-C ratios (Figures 1, 4).

Figure 1

Figure 2

Figure 3

Figure 4

Multiple mixed-effect linear regression models measuring the association between blood glucose levels and lipids and lipid ratios

Model 1 found a positive association between blood glucose levels and TG (β=0.31, 95% CI: (0.17, 0.45), p<0.01) and LDL-C (β=0.08, 95% CI: (0.02, 0.13), p<0.01) levels and TG/HDL-C (β=0.28, 95% CI: (0.09, 0.46), p<0.01) and LDL-C/HDL-C (β=0.11, 95% CI: (0.04, 0.18), p<0.01) ratios after controlling for the demographic variables (Table 3). The association persevered when controlling for health-related behaviors (Model 2) and physical health variables (Model 3). All three models established no significant association between blood glucose levels and TC or HDL-C.

Discussion

The findings from this study add to the growing body of evidence supporting the association between blood glucose levels and serum lipid levels, and lipid ratios. Furthermore, to our knowledge, this is one of the first studies that examined the relationships between blood glucose levels and lipid ratios in a random cohort of Chinese T2DM patients. Blood glucose levels were positively associated with TG and LDL-C levels and TG/HDL-C and LDL-C/HDL-C ratios after controlling for possible confounders with a mixed-effect linear regression model. This suggests that those with higher blood glucose levels have a higher level of harmful lipids (TG, LDL, TG/HDL-C, LDL-C/HDL-C) that could increase their risk for T2DM complications.

The results of this study were supported by a previous study showing that blood glucose levels correlated positively with TG and LDL-C, which are linked to a higher risk of obesity and cardiovascular disease (27). In the context of T2DM, reasonable glycemic control could help improve lipid profiles. A previous study reported that short-term intensive glycemic control could significantly reduce T2DM-related triglyceride levels (28). According to another study, controlling blood lipid levels is one of the most significant factors influencing blood glucose control in patients with T2DM (29). In addition, blood glucose is associated with an increased risk of obesity (a determinant of lipid or lipid ratio parameters) (30).

It has been found that blood glucose levels are positively correlated with TG and LDL-C levels, while others have found no association (31, 32). Recently, a study reported that fasting plasma glucose is related to HDL and TC but not to LDL or TG (18). Meanwhile, a survey conducted in Eastern Sudan found that poor glycemic control was not related to TG but was related to elevated TC (32). Substantial heterogeneity in the study population, design, and variable definition, or an inadequate adjustment for medical confounding may explain the discrepancy. In addition, several factors affect the blood glucose levels and lipid profiles of people with T2DM. For example, diet is a significant factor affecting both (33), and particular eating patterns may contribute to different relationships between blood glucose levels and lipid profiles (34, 35).

Our findings suggest that blood glucose levels are positively associated with TG/HDL-C and LDL-C/HDL-C ratios. A ratio of lipid profile is more valuable than participant lipid values because it more accurately represents the complex interactions that affect the metabolism of lipoproteins (36). Researchers have indicated that the lipid ratio represents the ratio of atherogenic to antiatherogenic lipoproteins (37). It is assumed that individuals with high blood glucose levels have a high TC/HDL‐C ratio and are more likely to suffer stroke or atherosclerosis.

Strengths and limitations

As a strength of this study, it uses a patient-based and multi-stage random sampling design, in addition to a large sample size with lipid and lipid ratio measurements. However, there are several limitations. First, causal relationships between blood glucose and lipids cannot be determined because of its cross-sectional design. Second, despite controlling for numerous covariates, residual confounders could not be completely eliminated due to unmeasured factors. Especially, here lack of the information about the dosage of hypoglycemic drugs, so it may affect our results. Third, the study included patients from one province of China, so caution should be used when applying these findings to other regions of China or foreign countries.

Conclusion

A correlation between blood glucose levels and serum lipids or lipid ratios has been established in this study. Blood glucose levels were positively associated with TG and LDL-C levels and elevated TG/HDL-C and LDL-C/HDL-C ratios.

Funding

This work was supported by the Ningxia Natural Science Foundation (2022AAC05058).

Acknowledgments

We would like to express our sincere gratitude to the participants for their effort in this research. We also thank the investigators for their hard-working interviewing and data checking.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

• 1

  FaselisCKatsimardouAImprialosKDeligkarisPKallistratosMDimitriadisK. Microvascular complications of type 2 diabetes mellitus. Curr Vasc Pharmacol (2020) 18:117–24. doi: 10.2174/1570161117666190502103733

  • CrossRef
  • Google Scholar

• 2

  MaffiPSecchiA. The burden of diabetes: Emerging data. Dev Ophthalmol (2017) 60:1–5. doi: 10.1159/000459641

  • CrossRef
  • Google Scholar

• 3

  WangLLiXWangZBancksMPCarnethonMRGreenlandPet al. Trends in prevalence of diabetes and control of risk factors in diabetes among US adults, 1999-2018. JAMA (2021) 326:1–13. doi: 10.1001/jama.2021.9883

  • CrossRef
  • Google Scholar

• 4

  SaeediPPetersohnISalpeaPMalandaBKarurangaSUnwinNet al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes atlas, 9th edition. Diabetes Res Clin Pract (2019) 157:107843. doi: 10.1016/j.diabres.2019.107843

  • CrossRef
  • Google Scholar

• 5

  ZimmetPZMaglianoDJHermanWHShawJE. Diabetes: A 21st century challenge. Lancet Diabetes Endocrinol (2014) 2:56–64. doi: 10.1016/S2213-8587(13)70112-8

  • CrossRef
  • Google Scholar

• 6

  ZhangQHZhaoGYangNLZhangLJ. Fasting blood glucose levels in patients with different types of diseases. Prog Mol Biol Trans Sci (2019) 162:277–92. doi: 10.1016/bs.pmbts.2019.01.004

  • CrossRef
  • Google Scholar

• 7

  Muačević-KatanecDReinerZ. Diabetic dyslipidemia or ‘diabetes lipidus’? Expert Rev Cardiovasc Ther (2011) 9:341–8. doi: 10.1586/erc.11.17

  • CrossRef
  • Google Scholar

• 8

  KraussRM. Lipids and lipoproteins in patients with type 2 diabetes. Diabetes Care (2004) 27:1496–504. doi: 10.2337/diacare.27.6.1496

  • CrossRef
  • Google Scholar

• 9

  AthyrosVGDoumasMImprialosKPStavropoulosKGeorgianouEKatsimardouAet al. Diabetes and lipid metabolism. Hormones (2018) 17:61–7. doi: 10.1007/s42000-018-0014-8

  • CrossRef
  • Google Scholar

• 10

  GuXYYangXLLiYCaoJLiJXLiuXQet al. Usefulness of low-density lipoprotein cholesterol and non-High-Density lipoprotein cholesterol as predictors of cardiovascular disease in Chinese. Am J Cardiol (2015) 116:1063–70. doi: 10.1016/j.amjcard.2015.06.040

  • CrossRef
  • Google Scholar

• 11

  FujiharaKSuzukiHSatoAKodamaSHeianzaYSaitoKet al. Carotid artery plaque and LDL-to-HDL cholesterol ratio predict atherosclerotic status in coronary arteries in asymptomatic patients with type 2 diabetes mellitus. J Atheroscl Thromb (2013) 20:452–64. doi: 10.5551/jat.14977

  • CrossRef
  • Google Scholar

• 12

  TurakOAfarBOzcanFÖksüzFAli MendiMYaylaÇet al. The role of plasma Triglyceride/High-density lipoprotein cholesterol ratio to predict new cardiovascular events in essential hypertensive patients. J Clin Hypertension (2016) 18(8):772–7. doi: 10.1111/jch.12758

  • CrossRef
  • Google Scholar

• 13

  BittnerVJohnsonBDZinehIRogersWJVidoDMarroquinOCet al. The triglyceride/high-density lipoprotein cholesterol ratio predicts all-cause mortality in women with suspected myocardial ischemia: A report from the women’s ischemia syndrome evaluation (WISE). Am Heart J (2008) 157:548–55. doi: 10.1016/j.ahj.2008.11.014

  • CrossRef
  • Google Scholar

• 14

  GybergVDe BacquerDDe BackerGJenningsCKotsevaKMellbinLet al. Patients with coronary artery disease and diabetes need improved management: A report from the EUROASPIRE IV survey: A registry from the EuroObservational research programme of the European society of cardiology. Cardiovasc Diabetol (2015) 14:133. doi: 10.1186/s12933-015-0296-y

  • CrossRef
  • Google Scholar

• 15

  BahiruEHsiaoRPhillipsonDWatsonKE. Mechanisms and treatment of dyslipidemia in diabetes. Curr Cardiol Rep (2021) 23:26. doi: 10.1007/s11886-021-01455-w

  • CrossRef
  • Google Scholar

• 16

  GaoY. Effects of blood glucose control level on blood lipids and lipoproteins in patients with type 2 diabetes mellitus. Diabetes New World (2015) 35:3.

  • Google Scholar

• 17

  ZhaoYP. Effects of ACE gene and blood sugar control and their interaction on blood lipid levels in patients with type 2 diabetes mellitus. Hebei Med Univ (2006).

  • Google Scholar

• 18

  WangSJiXZhangZXueFZ. Relationship between lipid profiles and glycemic control among patients with type 2 diabetes in qingdao, China. Int J Environ Res Public Health (2020) 17:5317. doi: 10.3390/ijerph17155317

  • CrossRef
  • Google Scholar

• 19

  CaoYXunTSunKLiuZKXiangXJuanJet al. Relationship between glycemic control and visceral adiposity index among the patients with type 2 diabetes mellitus. Beijing da xue xue bao Yi xue ban (2017) 49:446.

  • Google Scholar

• 20

  BasuA. Role of berry bioactive compounds on lipids and lipoproteins in diabetes and metabolic syndrome. Nutrients (2019) 11:1983. doi: 10.3390/nu11091983

  • CrossRef
  • Google Scholar

• 21

  WangLLiJDangYMaHYNiuY. Relationship between social capital and depressive symptoms among type 2 diabetes mellitus patients in Northwest China: A mediating role of sleep quality. Front Psychiatry (2021) 12:725197. doi: 10.3389/fpsyt.2021.725197

  • CrossRef
  • Google Scholar

• 22

  ShahwanMJJairounAAFarajallahAShanabliS. Prevalence of dyslipidemia and factors affecting lipid profile in patients with type 2 diabetes. Diabetes Metab Syndrome (2019) 13:2387–92. doi: 10.1016/j.dsx.2019.06.009

  • CrossRef
  • Google Scholar

• 23

  WangXNWangLCaoRHYangXXiaoWKZhangYet al. Correlation between small and dense low-density lipoprotein cholesterol and cardiovascular events in Beijing community population. J Clin Hypertension (2021) 23:345–51. doi: 10.1111/jch.14150

  • CrossRef
  • Google Scholar

• 24

  ZhangYFengYChenSLiangSWangSXuKet al. Relationship between the duration of smoking and blood pressure in han and ethnic minority populations: A cross-sectional study in China. BMC Public Health (2021) 14;21(1):135. doi: 10.1186/s12889-020-09975-w

  • CrossRef
  • Google Scholar

• 25

  WangZHeizhatiMWangLLiMYangZLinMet al. Poor sleep quality is negatively associated with low cognitive performance in general population independent of self-reported sleep disordered breathing. BMC Public Health (2022) 3;22(1):3. doi: 10.1186/s12889-021-12417-w

  • CrossRef
  • Google Scholar

• 26

  UeshimaKFujiwaraTTakaoSSuzukiEIwaseTDoiHet al. Does social capital promote physical activity? A population-based study in Japan. PLoS One (2010) 5:e12135. doi: 10.1371/journal.pone.0012135

  • CrossRef
  • Google Scholar

• 27

  ZhangHZhaoXLiYMaoZHuoWJiangJet al. Night sleep duration and sleep initiation time with hypertension in Chinese rural population: the henan rural cohort. Eur J Pub Health (2020) 30:164–70. doi: 10.1093/eurpub/ckz142

  • CrossRef
  • Google Scholar

• 28

  ArthaIBhargahADharmawanNKPandeUWTriyanaKAMahariskiPAet al. High level of individual lipid profile and lipid ratio as a predictive marker of poor glycemic control in type-2 diabetes mellitus. Vasc Health Risk Manage (2019) 15:149–57. doi: 10.2147/VHRM.S209830

  • CrossRef
  • Google Scholar

• 29

  FujitaYFukushimaMSuzukiHTaniguchiANakaiYKuroeAet al. Short-term intensive glycemic control improves vibratory sensation in type 2 diabetes. Diabetes Res Clin Pract (2008) 80:e16–9. doi: 10.1016/j.diabres.2007.12.011

  • CrossRef
  • Google Scholar

• 30

  KadotaAHozawaAOkamuraTKadowakTNakmauraKMurakamiYet al. Relationship between metabolic risk factor clustering and cardiovascular mortality stratified by high blood glucose and obesity: NIPPON DATA90, 1990-2000. Diabetes Care (2007) 30:1533–8. doi: 10.2337/dc06-2074

  • CrossRef
  • Google Scholar

• 31

  LaverdyOGHuebWASprandelMCOKalil-FilhoRMaranhãoRC. Effects of glycemic control upon serum lipids and lipid transfers to HDL in patients with type 2 diabetes mellitus: Novel findings in unesterified cholesterol status. Exp Clin Endocrinol Diabetes (2015) 123:232–9. doi: 10.1055/s-0034-1396863

  • CrossRef
  • Google Scholar

• 32

  OmarSMMusaIROsmanOEAdamI. Assessment of glycemic control in type 2 diabetes in the Eastern Sudan. BMC Res Notes (2018) 11:373. doi: 10.1186/s13104-018-3480-9

  • CrossRef
  • Google Scholar

• 33

  Hernandez-rodasMCValenzuelaRVidelaLA. Relevant aspects of nutritional and dietary interventions in non-alcoholic fatty liver disease. Int J Mol Sci (2015) 16:25168–98. doi: 10.3390/ijms161025168

  • CrossRef
  • Google Scholar

• 34

  UlvenSMLederLElindEOttestadIChristensenJTelle-HansenVHet al. Exchanging a few commercial, regularly consumed food items with improved fat quality reduces total cholesterol and LDL-cholesterol: A double-blind, randomized controlled trial. Br J Nutr (2016) 116:1383–93. doi: 10.1017/S0007114516003445

  • CrossRef
  • Google Scholar

• 35

  AbdelhamidASBrownTJBrainardJSBiswasPThorpeGCMooreHJet al. Omega-3 fatty acids for the primary and secondary prevention of cardiovascular disease. Cochrane Database systematic Rev (2020) 11(11):CD003177. doi: 10.1002/14651858.CD003177.pub3

  • CrossRef
  • Google Scholar

• 36

  LiuXYanLXueF. The associations of lipids and lipid ratios with stroke: A prospective cohort study. J Clin Hypertension (2019) 21:127–35. doi: 10.1111/jch.13441

  • CrossRef
  • Google Scholar

• 37

  MillánJPintóXMuozAZúñigaMRubiés-PratJPallardoLFet al. Lipoprotein ratios: Physiological significance and clinical usefulness in cardiovascular prevention. Vasc Health Risk Manage (2009) 5:757–65.

  • Google Scholar