U-shaped association between low-density lipid cholesterol and diabetes mellitus in patients with hypertension

Liu, Lin; Geng, Suxia; Huang, Jiayi; Yu, Yu-Ling; Chen, Chaolei; Huang, Yu-Qing; Feng, Yingqing

doi:10.1186/s12944-019-1105-5

Cited by 11 publications

(16 citation statements)

References 27 publications

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“…Subsequent cohort studies provided further evidence for the J-shaped association between mean HbA1c and all-cause mortality [ 33 – 35 ]. Furthermore, recent studies have found that similar to HbA1c, a U-shaped relationship is demonstrated between the lipid indices and adverse outcomes [ 36 – 38 ]. These findings explain the “reverse epidemiology” observed in both the present study and existing studies, where risk factors for the outcome lower the event risk instead, such as the lowering of intracranial hemorrhage and AF risk under raised mean LDL-C in this cohort [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Lee¹,

Zhou

Wong

et al. 2021

BMC Endocr Disord

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Introduction Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and variability of HbA1c and lipids for adverse outcomes. Methods This retrospective cohort study consists of type 1 and type 2 diabetic patients who were prescribed insulin at outpatient clinics of Hong Kong public hospitals, from 1st January to 31st December 2009. Standard deviation (SD) and coefficient of variation were used to measure the variability of HbA1c, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and triglyceride. The primary outcome is all-cause mortality. Secondary outcomes were diabetes-related complications. Result The study consists of 25,186 patients (mean age = 63.0, interquartile range [IQR] of age = 15.1 years, male = 50%). HbA1c and lipid value and variability were significant predictors of all-cause mortality. Higher HbA1c and lipid variability measures were associated with increased risks of neurological, ophthalmological and renal complications, as well as incident dementia, osteoporosis, peripheral vascular disease, ischemic heart disease, atrial fibrillation and heart failure (p < 0.05). Significant association was found between hypoglycemic frequency (p < 0.0001), HbA1c (p < 0.0001) and lipid variability against baseline neutrophil-lymphocyte ratio (NLR). Conclusion Raised variability in HbA1c and lipid parameters are associated with an elevated risk in both diabetic complications and all-cause mortality. The association between hypoglycemic frequency, baseline NLR, and both HbA1c and lipid variability implicate a role for inflammation in mediating adverse outcomes in diabetes, but this should be explored further in future studies.

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Section: Discussionmentioning

confidence: 99%

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Lee¹,

Zhou

Wong

et al. 2021

BMC Endocr Disord

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“…In prediabetic patients, dyslipidemia is usually manifested as elevated levels of LDL-C, TC, TG, and reduced HDL-C levels [ 22 , 24 ]; among them, abnormally elevated LDL-C and decreased HDL-C levels will damage the function of pancreatic islet β cells to further aggravate IR, which will form a vicious cycle, and accelerate the progression from prediabetes to type 2 DM [ 25 ]. A series of clinical studies that have been completed have also confirmed that both LDL-C and HDL-C were important lipid parameters associated with the risk of glycemic metabolism [ 26 – 28 ]. In a recent study, Pan et al using a two-sample Mendelian randomization analysis demonstrated that elevated LDL-C levels can significantly increase the risk of developing type 2 DM [ 27 ]; while in the study of the Chinese hypertensive population by Liu et al, it was found a U-shaped correlation between the level of LDL-C and the incidence of DM [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

“…A series of clinical studies that have been completed have also confirmed that both LDL-C and HDL-C were important lipid parameters associated with the risk of glycemic metabolism [ 26 – 28 ]. In a recent study, Pan et al using a two-sample Mendelian randomization analysis demonstrated that elevated LDL-C levels can significantly increase the risk of developing type 2 DM [ 27 ]; while in the study of the Chinese hypertensive population by Liu et al, it was found a U-shaped correlation between the level of LDL-C and the incidence of DM [ 26 ]. In contrast to LDL-C, HDL-C was inversely associated with the risk of glycemic metabolism.…”

Section: Discussionmentioning

confidence: 99%

Association of LDL:HDL ratio with prediabetes risk: a longitudinal observational study based on Chinese adults

et al. 2022

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Background Low-density lipoprotein:high-density lipoprotein cholesterol ratio (LDL:HDL ratio) has a good performance in identifying diabetes mellitus (DM) and insulin resistance. However, it is not yet clear whether the LDL:HDL ratio is associated with a high-risk state of prediabetes. Methods This cohort study retrospectively analyzed the data of 100,309 Chinese adults with normoglycemia at baseline. The outcome event of interest was new-onset prediabetes. Using multivariate Cox regression and smoothing splines to assess the association of LDL:HDL ratio with prediabetes. Results During an average observation period of 37.4 months, 12,352 (12.31%) subjects were newly diagnosed with prediabetes. After adequate adjustment for important risk factors, the LDL:HDL ratio was positively correlated with the prediabetes risk, and the sensitivity analysis further suggested the robustness of the results. Additionally, in stratified analysis, we discovered significant interactions between LDL:HDL ratio and family history of DM, sex, body mass index and age (all P-interaction < 0.05); among them, the LDL:HDL ratio-related prediabetes risk decreased with the growth of body mass index and age, and increased significantly in women and people with a family history of DM. Conclusions The increased LDL:HDL ratio in the Chinese population indicates an increased risk of developing prediabetes, especially in women, those with a family history of DM, younger adults, and non-obese individuals.

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“…The inverse relationship between HDL-C level and cardiovascular adverse outcomes is well established, 36 and reinforced by recent findings of the inversed association between high lipoprotein function and atherosclerotic burden. 37 Recent studies exploring the relationship between cholesterol indices and cardiovascular events demonstrate that the J-shaped relationship is mainly present in LDL-C. 20,38 The U-shaped relationship between HDL-C and allcause mortality reported may be attributed to other causes of death, such as infection and external causes, and confounded by alcoholism which raises HDL. [39][40][41] These findings suggest that the J-shaped relationship between total cholesterol and SCD may be driven by LDL-C, given the observed linear association between HDL-C and AMI.…”

Section: Discussionmentioning

confidence: 99%

Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death

Lee¹,

Zhou

Guo

et al. 2021

Endocrino Diabet & Metabol

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Introduction:The present study evaluated the application of incorporating non-linear J/U-shaped relationships between mean HbA1c and cholesterol levels into risk scores for predicting acute myocardial infarction (AMI) and non-AMI-related sudden cardiac death (SCD) respectively, amongst patients with type 2 diabetes mellitus.Methods: This was a territory-wide cohort study of patients with type 2 diabetes mellitus above the age 40 and free from prior AMI and SCD, with or without prescriptions of anti-diabetic agents between January 1st, 2009 to December 31st, 2009 at government-funded hospitals and clinics in Hong Kong. Patients recruited were followed up until 31 December 2019 or their date of death. Risk scores were developed for predicting incident AMI and non-AMI-related SCD. The performance of conditional inference survival forest (CISF) model compared to that of random survival forests (RSF) model and multivariate Cox model. Results:This study included 261 308 patients (age = 66.0 ± 11.8 years old, male = 47.6%, follow-up duration = 3552 ± 1201 days, diabetes duration = 4.77 ± 2.29 years). Mean HbA1c and low high-density lipoprotein-cholesterol (HDL-C) were significant predictors of AMI on multivariate Cox regression. Mean HbA1c was linearly associated with AMI, whilst HDL-C was inversely associated with AMI. Mean HbA1c and total cholesterol were significant multivariate predictors with a J-shaped relationship with non-AMI-related SCD. The AMI and SCD risk scores had an area under the curve (AUC) of 0.666 (95% confidence interval (CI) = [0.662, 0.669]) and 0.677 (95% CI = [0.673, 0.682]), respectively. CISF significantly improves prediction performance of both outcomes compared to RSF and multivariate Cox models. Conclusion:A holistic combination of demographic, clinical and laboratory indices can be used for the risk stratification of patients with type 2 diabetes mellitus for AMI and SCD.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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U-shaped association between low-density lipid cholesterol and diabetes mellitus in patients with hypertension

Cited by 11 publications

References 27 publications

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning

Association of LDL:HDL ratio with prediabetes risk: a longitudinal observational study based on Chinese adults

Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death

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