SCI 발표논문

SCI 발표논문

← SCI 발표논문 목록

Sci Rep. 2021;11(1):7007.

Correlation of dynamic membrane fluctuations in red blood cells with diabetes mellitus and cardiovascular risks.

, , , ,

제1저자 손민지 · 교신저자 임수

PMID 33772071 PMC7997877 DOI 10.1038/s41598-021-86528-0 IF 4.9 내분비대사내과
소속 (4)
  1. Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea.
  2. Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
  3. Tomocube Inc., Daejeon, 34051, Republic of Korea.
  4. Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea. limsoo@snu.ac.kr.

Abstract

The rheological and physiological properties of red blood cells (RBCs) are affected by many factors in the vascular environment. Among them, membrane fluctuations (MFs), particularly dynamic fluctuations in RBC cell membrane thickness (RBC-MFs), are likely to be altered by the level of glycation of haemoglobin in patients with diabetes mellitus (DM). We investigated the associations of RBC-MFs with physiological variables associated with DM and cardiovascular diseases (CVDs). Forty-one healthy control subjects and 59 patients with DM were enrolled. Five-microliter samples of blood were collected and diluted 400 times. To measure the RBC-MFs, holotomography was used, which non-invasively and precisely analyses the optical characteristics of RBCs. Associations between the RBC-MFs and biochemical parameters related to glucose homeostasis and lipid profiles were investigated. Independent associations of the RBC-MFs with the presence of CVDs were also analysed. RBC-MFs were lower in patients with DM than in healthy participants (61.64 ± 7.49 nm vs 70.65 ± 6.65 nm, P = 1.4 × 10-8). RBC-MFs correlated modestly with glycated haemoglobin level (ρ = - 0.47) and weakly with age (ρ = - 0.36), duration of diabetes (ρ = - 0.36), fasting plasma glucose level (ρ = - 0.37), and the 10-year Framingham risk score (ρ = - 0.38) (all P < 0.05). Low RBC-MFs were independently associated with the presence of CVDs after adjusting for CVD risk factors. The weak but significant associations of RBC-MFs with cardiometabolic risk factors and CVDs suggest that such deformity of circulating RBCs may be a useful marker of vascular complications of DM.

Figures

Figure 1
Figure 1 Correlations between the membrane fluctuations of red blood cells and clinical factors. (A) HbA1c (%), (B) FPG, fasting plasma glucose (mg/dL), (C) Groups divided by HbA1c levels as follows: < 6.5%, 6.5%–7.4%, 7.5%–8.9%, and ≥ 9%.
Figure 2
Figure 2 Correlations between cardiovascular risks and the membrane fluctuation of red blood cells. The upper part of the figure shows Spearman’s correlation coefficients (ρ values) and the lower side of the figure shows correlations by direction and thickness. Only significant correlations (P < 0.05) are retained in the picture. MF, membrane fluctuations; DM(Y), duration of diabetes mellitus (year); S(PPY), smoking duration (packs per year); D(Y) drinking duration (years); FRS, 10-year coronary heart disease risk by Framingham risk score; SBP, systolic blood pressure; DBP, diastolic blood pressure; wt, weight; WC, waist circumference; BMI, body mass index; HbA1c, glycated haemoglobin; FPG, fasting plasma glucose; Tchol, total cholesterol; TG, triglycerides; HDL, high-density lipoprotein cholesterol; LDL, low-density lipoprotein cholesterol; eGFR, estimated glomerular filtration ratio; PCR, urinary protein-to-creatinine ratio; ACR, urinary albumin-to-creatinine ratio.
Figure 3
Figure 3 Receiver-operating-characteristic (ROC) curves for CVD by multivariable logistic regression models with or without red blood cell membrane fluctuations (RBC-MFs). (A) Model 1 tested in all participants; (B) Model 2 tested in patients with diabetes mellitus (DM).
Figure 4
Figure 4 Correlation between red blood cell-membrane fluctuations (RBC-MFs) and 10-year cardiovascular disease (CVD) risks. (A) Ten-year CHD risk by FRS, (B) Ten-year ASCVD risks by ACC/AHA guidelines, (C) Ten-year CHD risk with risk criteria as follows: low (< 10%), intermediate (10–19%), and high (≥ 20%), (D) 10-year ASCVD risk with risk criteria as follows: low (< 5%), borderline (5–7.4%), intermediate (7.5–19.9%), and high (≥ 20%). CHD, coronary heart disease; FRS, Framingham risk score; ASCVD, atherosclerotic CVD; ACC, American College of Cardiology; AHA, American Heart Association.
Figure 5
Figure 5 Workflow of the HT system for analysing holotomographic results. (A) depicts the used HT system. (B–E) shows analysis procedure. (B) From the multiple 2D hologram patterns of a sample obtained with various illumination angles, both the phase and amplitude images were retrieved. (C) From these phase and amplitude images, a 3D RI tomogram is reconstructed. (D) Also, the time-lapse phase images of the RBC were recorded with normal illumination. (E) Then, from the measure phase images, the time-lapse cell height images were calculated by considering the mean RI values obtained in (C).

그림 출처: PubMed Central 공개 논문 (PMC7997877)

Comment · 정정

Conflict of interest statement

The authors declare no competing interests.

그 밖의 정보
출판 형태
Journal Article, Research Support, Non-U.S. Gov't
저널
Scientific reports · eISSN 2045-2322
전자 출판
2021-03-26
MeSH
Adult · Aged · Aged, 80 and over · Biomarkers / blood · Blood Glucose / metabolism · Cardiovascular Diseases / pathology · Cell Membrane / physiology · Cross-Sectional Studies · Diabetes Mellitus / pathology · Dyslipidemias / blood / pathology · Erythrocytes / physiology · Female · Glucose / metabolism · Glycated Hemoglobin / analysis · Heart Disease Risk Factors · Humans · Lipid Metabolism / physiology · Lipids / blood · Male · Microscopy, Atomic Force · Middle Aged · Optical Tweezers · Pilot Projects · Young Adult
Substances
Biomarkers · Blood Glucose · Glucose · Glycated Hemoglobin · Lipids
참고문헌
49편