Graphical abstract

INTRODUCTION

Despite efforts to lower metabolic risk factors for atherosclerotic cardiovascular disease (ASCVD) defined by the Framingham Heart Study, such as dyslipidemia, hypertension (HTN), and diabetes mellitus (DM) [1], residual cardiovascular risk has been reported even in those achieving target goals of risk factors [2-4]. Suspicion regarding the adverse effects of these metabolic risk variabilities has been brought up several decades ago [5-7], but researchers have given attention to it recently, and accumulating evidence has been reported to support it. Not only lowering the mean levels of cardiometabolic risk variables but also preventing fluctuation have become new target treatment goals, and the effect of lowering variability as a treatment option has been actively investigated. In this paper, we review the literature on the effects of metabolic factor variability on cardiovascular disease (CVD) and mortality.

GLUCOSE VARIABILITY

Mechanism of adverse effect of glucose variability

Several in vivo and in vitro studies have demonstrated the effect of glucose variability (GV) on adverse cardiovascular events (Fig. 1). The primary mechanism is suggested to be the activation of oxidative stress and production of inflammatory cytokines that induce endothelial damage and dysfunction. Research on human coronary artery endothelial cells under fluctuating glucose levels has shown an increase in tumor necrosis factor-α, intercellular adhesion molecule-1, and interleukin-6 [8,9]. Oxidative stress produced by GV leads to apoptosis through the nuclear factor-erythroid 2-related factor 2 (Nrf2)/heme oxygenase-1 (HO-1) pathway [10]. Overproduction of reactive oxygen species (ROS) in mitochondria by GV induces vascular endothelial cell growth factor (VEGF) production and triggers undesirable angiogenesis [11]. GV also induces dysfunction of endothelial nitric oxide (NO) synthase, leading to vasoconstriction from decreased NO levels [12].

Costantino et al. [12] reported that epigenetic modification of p66Shc, the adaptor protein, causes inadequate angiogenesis and vascular damage in type 2 diabetes mellitus (T2DM). Some studies suggested microRNA (miRNA) produced in glucose fluctuating condition influence endothelial dysfunction. Guo et al. [13] indicated miRNA-1273-3p is related to GV-induced autophagy, attenuation of cell proliferation, endothelial dysfunction. La Sala et al. [14] revealed upregulated miRNA-185 in high glucose fluctuation inhibit elevation of glutathione peroxidases-1 which normally suppress ROS damage. Furthermore, glucose fluctuation contributes to activated monocyte and macrophage recruitment, resulting in vascular smooth muscle cell proliferation and migration [15]. In addition, high GV may cause platelet activation, aggregation, and autonomic dysfunction in endothelial cells [16].

Glucose variability and cardiovascular disease risk

Since the time Monnier et al. [7] demonstrated that not only sustained hyperglycemia but also glucose swing cause oxidative stress and accompanying microvascular and macrovascular complications, numerous studies have published reinforcing evidence (Table 1) [3,17-31] and attempted to find an appropriate index reflecting inter- and intra-day GV. In addition to the traditionally used standard deviation (SD) and coefficient of variation (CV) showing dispersion of data, various metrics have been reported and applied [32]. The mean of daily difference calculated using the glucose value of 2 consecutive days simultaneously reflects between-day glycemic variation, and Continuous Overlapping Net Glycemic Action (CONGA), measured by the SD of glucose value for the defined period of time, reveals within-day glycemic fluctuation. In addition, indices indicating risk and patient prognosis (lability index) and assessing long-term diabetic complications (time in range) have been used in several studies [16,33].

Early studies were conducted on patients who underwent percutaneous coronary intervention (PCI) or coronary artery bypass graft and measured the periprocedural glucose level of participants [23-25,34-36]. Fluctuating glucose levels are associated with major adverse cardiovascular events (MACEs), atrial fibrillation, and post-procedural complications. However, most of the studies were conducted in an in-hospital setting and used short-term glycemic variability. Subsequent studies performed secondary analysis of randomized clinical trials including T2DM patients, and reported an association between long-term GV and CVD risk [3,26-31]. Hirakawa et al. [3] reported that increased glycosylated hemoglobin (HbA1c) variability was associated with vascular events (hazard ratio, 1.64; 95% confidence interval [CI], 1.05 to 2.55); Zhou et al. [18] reported that high fasting blood glucose (FBG) variability increased CVD risk; and Wan et al. [37] demonstrated this relationship is applied to all age groups regardless of DM duration. The EMpagliflozin and daPAgliflozin in patients hospiTalized for acute decompensated Heart failure (EMPATHY) trial recently conducted in Japanese T2DM patients identified that adverse effects of intermittent hyperglycemia were enhanced in patients with HbA1c levels below 7% [19]. These findings indicate the harmfulness of glycemic variation not only in metabolically high-risk patients but also in relatively well-controlled T2DM patients or people whose metabolic risk is low.

Accumulating evidence suggests that glucose fluctuation increases CVD risk in T2DM or patients who undergo coronary artery procedures; however, the possibility of a reverse causality effect must be addressed. Two studies [20,38] demonstrated the relationship between intermittent hyperglycemia and high CVD using data from people without DM and previous CVD from the Korean National Health Insurance. Analysis of data from a Denmark private clinic cohort with non-diabetics also showed that HbA1c variability is a predictor of MACE [21]. In addition, Kim et al. [39] illustrated that increased FBG variability is predictive of gestational DM.

Glucose variability depending on medication

In selecting anti-diabetic agents, their effects on GV as well as improving hyperglycemia should be considered. Dipeptidyl peptidase-4 (DPP4) enzyme inhibitors, glucagon-like peptide 1 (GLP1) receptor agonists, and sodium-glucose co-transporter 2 (SGLT2) inhibitors might be desirable oral hypoglycemic agents (OHAs) that reduce GV. Kim et al. [40] reported that sitagliptin reduced GV compared to glimepiride in T2DM patients who used metformin. Bae et al. [41] demonstrated a significant reduction in MACE and SD of glucose in patients treated with teneligliptin compared with placebo. Although several clinical trials have also reported that DPP4 inhibitors blunted glucose fluctuation [42-44], other studies reported that DPP4 inhibitors were unsuccessful in reducing ASCVD risk [45,46]. The Variability of Glucose in Patients with Type 2 Diabetes Treated with Four Different Insulin Combination Regimens (VARIATION) study illustrated that GLP1 receptor agonists and basal insulin decreased GV and hypoglycemic events, compared to basal and bolus insulin or basal insulin plus OHAs [47]. The FLuctuATion reduction with inSUlin and GLP-1 Added together (FLAT SUGAR) trial also demonstrated that adding GLP1 receptor agonist to basal insulin and metformin is better than metformin plus basal/bolus regimen for GV improvement [48]. The superiority of DPP4 inhibitor and GLP1 receptor agonist on GV might be attributable to their intrinsic mechanism of action. While GV aggravation is induced by decline of endogenous insulin and insufficient glucagon suppression, DPP4 inhibitor and GLP1 receptor agonist, which keep incretin level, inhibit postprandial glucagon secretion as well as improve pancreatic-βcell function [49-51]. As for SGLT2 inhibitors, despite limited evidence of the superiority of GV to other OHA, Nomoto et al. [52] showed similar GV between dapagliflozin plus glargine and DPP4 inhibitor plus glargine. In addition, adding dapagliflozin to long-acting insulin and other OHA ameliorated GV in a study including 36 subjects from a Chinese hospital [53]. However, a recently published placebo-controlled, randomized study with 84 Korean patients reported that adding dapagliflozin to basal insulin plus OHA did not show superiority to placebo in terms of GV [54]. Future large-scale studies are needed to demonstrate the effects of SGLT2 inhibitors on GV.

CHOLESTEROL VARIABILITY

Mechanism of adverse effects of cholesterol variability

Several theories explain the detrimental effects of lipid variability on CV risk. One hypothesis is that cholesterol variability causes transient hypercholesterolemia and fluctuation of cholesterol plaque composite and structure, inducing endothelial dysfunction and cholesterol plaque instability [55]. Clark et al. [56] demonstrated that high variability of low-density lipoprotein (LDL), non-high-density lipoprotein (HDL) cholesterol, and total cholesterol/HDL (measured by SD) was correlated with percent atheroma volume progression by analyzing nine clinical studies that used intravascular ultrasonography to assess coronary atheroma burden. Second, the existence of cholesterol variability implies general frailty and epiphenomenon of systemic conditions [57]. Another theory is that the adverse effects of lipid variability are linked to statin non-adherence [58,59]. Mann et al. [60] reported that visit-to-visit lipid variability was associated with statin non-adherence after adjusting for mean LDL, age, and sex using pharmacy data. Statin discontinuation induces a rebound phenomenon, and the favorable effect of statin is acutely lost when it is discontinued [58]. Patients who discontinued statin therapy after acute coronary syndrome showed higher mortality than those who did not.

Studies on cholesterol variability and cardiovascular risk

Since the importance of lipid variability in coronary disease was reported in an observational study on air force officers [5], it took >30 years to shed light on lipid variability. The Framingham study showed that high LDL variability in a population without cardiovascular risk was associated with future CVD risk [61]. In addition, the Treating New Target (TNT) study reported that LDL variability in people with known CVD risk increases coronary and cardiovascular events by 16% and 11%, respectively, regardless of absolute LDL levels [62].

Factors that affect lipid variability have been investigated. The TNT study illustrated that 80 mg of atorvastatin lowered LDL variability than 10 mg of atorvastatin. In contrast, doses and types of statins showed no effect on lipid variability in several other studies [63,64]. Boey et al. [63] reported no significant differences in LDL variability among patients treated with simvastatin (10, 20, and 40 mg). The VOYAGER (an indiVidual patient data meta-analysis Of statin therapY in At risk Groups: Effects of Rosuvastatin, atorvastatin and simvastatin) meta-analysis [64] consists of 37 clinical studies using atorvastatin, simvastatin, and rosuvastatin and reported low relevance between lipid variability and types of statins. Discordant results were shown regarding the influence of previous statin use on the harmful effects of lipid variability. Most studies focused on patients with coronary artery disease (CAD) or underwent PCI, and Kim et al. [22] and Zhu et al. [65] excluded people with previous CV events to leave out possible reverse causality. The outcomes were in line with those of previous studies showing the association between lipid variability and CV risk. Furthermore, Kim et al. [58] illustrated that the adverse effect of cholesterol variability was magnified in patients without conventional risk factors and in patients who did not take lipid-lowering medication. However, a Korean nationwide population-based cohort study conducted with statin-naïve healthy young people [66] revealed no significant association between lipid variability and CV risk. Moreover, total cholesterol variability augments the risk of atrial fibrillation [67] and dementia [68] which increases morbidity and mortality in the elderly population.

Limitations and expected direction of future studies

Future well-designed studies addressing improvements on several points are required (Fig. 2). The intervals and numbers of blood lipid measurements must be standardized. Smith et al. [69] reported that lipid variability tends to increase with longer daily, weekly, and monthly measurement interval. The appropriate interval and frequency of cholesterol measurement for optimal variability estimation that reflects CVD risk should be investigated [55]. Controversies on proper statistical methods and variability indices must also be addressed. A large number of previous studies compared the highest quartile (Q4) to the lowest quartile (Q1) or high quartile (Q4) to low quartiles (Q1–3); such approaches might exaggerate the association [70]. In addition, Cox regression is adequate to compare between-group variability but not within-individual variability or correlation across repeated measurements. Multilevel or mixed linear regression models and joint modelling are suggested as proper tools for this type of analysis [70]. Blood lipid level variability was calculated using the following formulas: CV, average successive variability [ASV=∑(Xi–Xi+1)/n], SD, or variation independent of mean [VIM=100%×(SD/meanβ), where β is the regression coefficient]. Although VIM is less relevant to the mean than SD and CV, controversy over association with mean value and adequacy for variability estimation still exists [70]. The frequency of deviation outside the designated range or the greatest absolute change is suggested as an alternative index [55]. Future studies are also required to define the normal range of cholesterol variability so that clinicians can pursue an optimal approach to reduce lipid variability. Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor which has a long half-life, is expected to maintain a stable cholesterol level. Lipid-lowering agents and treatment strategies that lower not only mean cholesterol levels but also variability are required [37].

BLOOD PRESSURE VARIABILITY

Mechanism of adverse effects of blood pressure variability

Blood pressure (BP) fluctuation is necessary to maintain homeostasis and to provide adequate organ perfusion, but consistent blood pressure variability (BPV) reflects dysfunction of regulatory function, leading to pathological problems. Baroreflex dysfunction [71], prolonged sympathetic activation, and increased arterial stiffness cause short-term BP variations [72,73]. Seasonal changes and behavioral factors, such as sleep disturbance or work-related stress, can cause long-term BPV [74,75]. Periodic high BP induced by these factors induces shear stress on the vessel wall and cardiovascular system, which consequently increases intima-media thickness, vascular dysfunction, and atherosclerosis progression [76]. Furthermore, patients’ poor adherence to drugs and inappropriate titration of BP medication lead to visit-to-visit BPV [75]. In addition, Kim et al. [22] suggested that general frailty caused by systemic conditions might play a role in intermittent high BP.

Studies on blood pressure variability and CVD risk

Parati et al. [75] suggested the classification of BPV according to time frame as very-short-term (beat-to-beat), short-term (day-to-night, hour-to-hour), mid-term (over various days), and long-term (over months, over seasons, over clinic visits). Short-term BP fluctuations from ambulatory BP monitoring is considered to cause target organ damage, particularly left ventricular hypertrophy [77], and predict cardiovascular events and death [78]. Studies on home-monitored BP fluctuation (mid-term) were few, but they proved that BPV was associated with cardiovascular events and mortality [79-82]. Research over 19 years discovered the predictive value of home BPV on cardiovascular events over clinic-based BPV [83]. Several clinical trials and cohort studies identified that visit-to-visit BP fluctuation was predictive of CADs, myocardial infarction (MI), and related mortality, and this association was confirmed in several meta-analyses [22,75,84-87]. Parati et al. [75] indicated that long-term BPV is more strongly associated with CVD than short-term BPV, whereas Meng et al. [84] illustrated that within-visit systolic BPV was inappropriate for CVD prediction. Kim et al. [22] and de Havenon et al. [85] reported that adverse effects of BPV on cardiovascular risk and all-cause mortality were maintained in a general healthy population. However, the predictive value of BPV on CV events was not significant in patients with end-stage renal disease [86].

Blood pressure variability depending on medications

Several clinical trials have revealed that calcium-channel blockers (CCBs) and non-loop diuretics cause lower BPV than beta-blockers (BBs), angiotensin receptor blockers (ARBs), and angiotensin-converting-enzyme inhibitors (ACEi) [88-90]. A meta-analysis including 398 trials [91] reported that the ratio of variance (VR) increased in the order of CCB, thiazide, ACEi, ARB, and BB (VR=0.81, 0.87, 1.08, 1.16, and 1.17, respectively). The combination of antihypertensive medications has been researched in subsequent studies. Sato et al. [92] reported that adding amlodipine was better than adding thiazide in patients with inadequate BP control with ARB monotherapy in terms of low BP fluctuation. Rakugi et al. [93] demonstrated that the combination of ARB and CCB decreased BPV compared to a combination of ARB and diuretics. Nagai et al. [94] suggested that CCB plus diuretics are more favorable than ARB plus CCB, which is superior to ARB and diuretics in terms of lowering BPV. Although the mechanism of different effect on BPV among medications is uncertain, decrease of arterial stiffness via vasodilation by CCB and non-loop diuretics is suggested as a possible explanation [88,91,95].

WEIGHT VARIABILITY

A large proportion of patients succeed in weight reduction but end up regaining lost weight, a phenomenon known as weight cycling. However, Hamm et al. [6] reported that individuals who gain and lose weight have a higher risk of coronary disease mortality compared to weight gain only or no weight change groups. The Framingham cohort study also verified that body weight variability (BWV) increases all-cause mortality and coronary heart disease using biannual body weight data for a mean of 32 years [96]. The association between BWV and all-cause mortality was shown in subsequent cohort studies and meta-analyses [97,98], although few studies reported contradictory results [99]. The adverse effects of weight cycling was independent of traditional CVD risk factors [100] and appeared to be enhanced in the obese population [100,101]. Kim et al. [102] demonstrated that not only BWV, but also waist circumference variability increased all-cause mortality and stroke risk based on a nationwide cohort study in Korea. Two main mechanisms are suggested to explain the harmful effects of weight variability: the repeated overshoot theory and increased visceral energy partitioning theory. The repeated overshoot theory indicates that weight cycling causes fluctuations in renal and cardiovascular risk factors, such as glomerular pressure, glucose, lipids, heart rate, and BP, which place extra burden on the kidneys and heart and lead to vascular injury [103]. Because the fluctuations are not always symmetrical; that is, the period of weight gain overrides that of weight loss, the net effect on the individual is unfavorable. Weight cycling also causes weight loss related to body adaptation in resting energy expenditure, thus reducing basal metabolism even after weight regain [104]. Byrne et al. [105] discovered that the recovery rate of skeletal muscle mass was faster in extremities than in the body trunk, which gives rise to central fat deposition when individuals regain weight. In addition, the relationship between fasting hyperinsulinemia and weight cycling in Japanese subjects might explain this phenomenon [106].

HEART RATE VARIABILITY

In contrast to other cardiometabolic risk factors, low heart rate variability (HRV) has been proven by Kleiger et al. [107] to increase mortality. They examined patients who survived acute myocardial infarction (AMI) and measured HRV, which showed that the relative risk of mortality was 5.3 in those with HRV <50 ms compared to those with HRV >100 ms. This association is supported by other cohort studies involving patients with recent AMI [108,109] with the predictive value of low HRV being preserved regardless of left ventricular ejection fraction or ventricular arrhythmia [109]. The adverse effect of low HRV is attributable to the reduced ability to antagonize sympathetic activation through the vagal mechanism in patients with AMI [109]. T2DM patients without previous CAD who have low heart rate fluctuation also show increased mortality and sudden cardiac death [110,111]. Cardiac autonomic neuropathy, which is important for adaptation to a given environmental change, might explain these results [110]. Subsequent studies consisting of the general population with or without previous cardiovascular heart disease also illustrated that low heart rate fluctuation increases all-cause mortality [112,113] and CV events [114]. Based on carotid intima-media thickness data, Pereira et al. [115] demonstrated that low HRV is related to subclinical atherosclerosis, and Tsuji et al. [114] suggested that low HRV implies enhanced subclinical cardiac disease risk in the general population. BBs, ACEi, and statins have been shown to increase HRV [116] and the additional use of ivabradine on BB increases HRV in patients with systolic heart failure or stable CAD [117,118].

GAMMA-GLUTAMYL TRANSFERASE VARIABILITY

The association between high gamma-glutamyl transferase (GGT) levels and cardiovascular events and mortality has been proven in a British prospective cohort study including 7,613 patients [119]. Several cohort studies in different populations and meta-analyses have shown supporting evidence [120-122]. Recently, Chung et al. [123] demonstrated that not only mean GGT levels but also GGT variability predicted MI, stroke, and cardiovascular mortality in the general population from a Korean cohort. Lee et al. [124] illustrated the predictive value of GGT fluctuation on all-cause mortality in T2DM patients without previous CVD or liver disease, and this effect is magnified in a young, male population consuming high alcohol and cigarettes. Subsequent studies showed that variability in GGT increased the hazard ratio of hospitalization for heart failure [125], risk of dementia, and development of end-stage renal disease [126] in the Korean population. The possible mechanism is that GGT has catabolic activity on the antioxidant glutathione, causing a proportional increase in GGT levels with oxidative stress and inflammation [127]. GGT fluctuations reflect the oscillation of oxidative stress and possibly alters the stability of atherosclerotic plaques, manifesting as LDL variability [124,128].

CONCLUSIONS

In this review, we examined the current literature on the variability of metabolic risk factors for cardiovascular events and mortality. Except for a few discrepancies over subgroups, the variability of glucose, BP, cholesterol, and GGT increased cardiovascular morbidity and mortality, while reduced HRV yielded unfavorable outcomes. The mechanisms underlying the adverse effects of variability are being investigated, but inflammation and oxidative stress caused by metabolic factor variation cause organ damage. Based on the current findings, clinicians should recognize the variability of patients’ metabolic factors and prescribe appropriate drugs to minimize this effect. The trend of values should be evaluated at proper intervals and using suitable methods, as well as measuring a single value at the patient’s visit. To offer ideal treatment to patients, selecting a treatment regimen that could reduce both the absolute values and variability of metabolic risk factors is important. Therefore, further research should be performed to find better medication regimens that decrease glucose, BP, and cholesterol variability and to find a desirable lifestyle modification method to address fluctuations in weight. In addition, checking whether patient variability arises from inappropriate regimens or inadequate compliance is important. Patients should be educated on the hazardous effects of variability on health outcomes to encourage drug adherence.

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

This work was supported by the Korea University Guro Hospital (Korea Research-Driven Hospital) and a grant funded by Korea University Medicine (K2115701).

Acknowledgements

None

Fig. 1.

Potential pathogenesis of the adverse effects of glucose variability on cardiovascular risk. miRNA, microRNA; DM, diabetes mellitus; TNF-α, tumor necrosis factor-α; ICAM-1, intercellular adhesion molecule-1; ROS, reactive oxygen species; VEGF, vascular endothelial cell growth factor.

Fig. 2.

Evidence that previous studies have demonstrated on lipid variability-mediated cardiovascular complications, limitations of previous studies, and suggested directions for future studies. CVD, cardiovascular disease; VIM, variation independent of mean; SD, standard deviation; CV, coefficient of variation; ARV, average real variability.

REFERENCES

• 1. Sytkowski PA, Kannel WB, D’Agostino RB. Changes in risk factors and the decline in mortality from cardiovascular disease. The Framingham Heart Study. N Engl J Med 1990;322:1635-41.ArticlePubMed
• 2. Zanchetti A. Bottom blood pressure or bottom cardiovascular risk?: how far can cardiovascular risk be reduced? J Hypertens 2009;27:1509-20.ArticlePubMed
• 3. Hirakawa Y, Arima H, Zoungas S, Ninomiya T, Cooper M, Hamet P, et al. Impact of visit-to-visit glycemic variability on the risks of macrovascular and microvascular events and allcause mortality in type 2 diabetes: the ADVANCE trial. Diabetes Care 2014;37:2359-65.ArticlePubMedPDF
• 4. Barnett MP, Bangalore S. Cardiovascular risk factors: it’s time to focus on variability! J Lipid Atheroscler 2020;9:255-67.ArticlePubMedPMCPDF
• 5. Groover ME Jr, Jernigan JA, Martin CD. Variations in serum lipid concentration and clinical coronary disease. Am J Med Sci 1960;239:133-9.ArticlePubMed
• 6. Hamm P, Shekelle RB, Stamler J. Large fluctuations in body weight during young adulthood and twenty-five-year risk of coronary death in men. Am J Epidemiol 1989;129:312-8.PubMed
• 7. Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, et al. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA 2006;295:1681-7.ArticlePubMed
• 8. Quagliaro L, Piconi L, Assaloni R, Da Ros R, Maier A, Zuodar G, et al. Intermittent high glucose enhances ICAM-1, VCAM-1 and E-selectin expression in human umbilical vein endothelial cells in culture: the distinct role of protein kinase C and mitochondrial superoxide production. Atherosclerosis 2005;183:259-67.ArticlePubMed
• 9. Wang JS, Yin HJ, Guo CY, Huang Y, Xia CD, Liu Q. Influence of high blood glucose fluctuation on endothelial function of type 2 diabetes mellitus rats and effects of Panax Quinquefolius Saponin of stem and leaf. Chin J Integr Med 2013;19:217-22.ArticlePubMedPDF
• 10. Liu TS, Pei YH, Peng YP, Chen J, Jiang SS, Gong JB. Oscillating high glucose enhances oxidative stress and apoptosis in human coronary artery endothelial cells. J Endocrinol Invest 2014;37:645-51.ArticlePubMedPDF
• 11. Sun J, Xu Y, Sun S, Sun Y, Wang X. Intermittent high glucose enhances cell proliferation and VEGF expression in retinal endothelial cells: the role of mitochondrial reactive oxygen species. Mol Cell Biochem 2010;343:27-35.ArticlePubMedPDF
• 12. Costantino S, Paneni F, Battista R, Castello L, Capretti G, Chiandotto S, et al. Impact of glycemic variability on chromatin remodeling, oxidative stress, and endothelial dysfunction in patients with type 2 diabetes and with target HbA1c levels. Diabetes 2017;66:2472-82.ArticlePubMedPDF
• 13. Guo J, Sang Y, Yin T, Wang B, Yang W, Li X, et al. miR-1273g3p participates in acute glucose fluctuation-induced autophagy, dysfunction, and proliferation attenuation in human umbilical vein endothelial cells. Am J Physiol Endocrinol Metab 2016;310:E734-43.ArticlePubMed
• 14. La Sala L, Cattaneo M, De Nigris V, Pujadas G, Testa R, Bonfigli AR, et al. Oscillating glucose induces microRNA-185 and impairs an efficient antioxidant response in human endothelial cells. Cardiovasc Diabetol 2016;15:71.ArticlePubMedPMC
• 15. Zhou JJ, Nuyujukian DS, Reaven PD. New insights into the role of visit-to-visit glycemic variability and blood pressure variability in cardiovascular disease risk. Curr Cardiol Rep 2021;23:25.ArticlePubMedPMCPDF
• 16. Nusca A, Tuccinardi D, Albano M, Cavallaro C, Ricottini E, Manfrini S, et al. Glycemic variability in the development of cardiovascular complications in diabetes. Diabetes Metab Res Rev 2018;34:e3047.ArticlePubMedPDF
• 17. Subramaniam B, Lerner A, Novack V, Khabbaz K, Paryente-Wiesmann M, Hess P, et al. Increased glycemic variability in patients with elevated preoperative HbA1C predicts adverse outcomes following coronary artery bypass grafting surgery. Anesth Analg 2014;118:277-87.ArticlePubMed
• 18. Zhou JJ, Schwenke DC, Bahn G, Reaven P; VADT Investigators. Glycemic variation and cardiovascular risk in the veterans affairs diabetes trial. Diabetes Care 2018;41:2187-94.ArticlePubMedPMCPDF
• 19. Sato M, Inaishi J, Saisho Y, Sato Y, Komuro I, Itoh H. Association of visit-to-visit glycemic variability with risk of cardiovascular diseases in high-risk Japanese patients with type 2 diabetes: a subanalysis of the EMPATHY trial. J Diabetes Investig 2021;12:2190-6.ArticlePubMedPMCPDF
• 20. Yu JH, Han K, Park S, Lee DY, Nam GE, Seo JA, et al. Effects of long-term glycemic variability on incident cardiovascular disease and mortality in subjects without diabetes: a nationwide population-based study. Medicine (Baltimore) 2019;98:e16317.PubMedPMC
• 21. Ghouse J, Skov MW, Kanters JK, Lind B, Isaksen JL, Blanche P, et al. Visit-to-visit variability of hemoglobin A1c in people without diabetes and risk of major adverse cardiovascular events and all-cause mortality. Diabetes Care 2019;42:134-41.ArticlePubMedPDF
• 22. Kim MK, Han K, Park YM, Kwon HS, Kang G, Yoon KH, et al. Associations of variability in blood pressure, glucose and cholesterol concentrations, and body mass index with mortality and cardiovascular outcomes in the general population. Circulation 2018;138:2627-37.ArticlePubMed
• 23. Xia J, Xu J, Li B, Liu Z, Hao H, Yin C, et al. Association between glycemic variability and major adverse cardiovascular and cerebrovascular events (MACCE) in patients with acute coronary syndrome during 30-day follow-up. Clin Chim Acta 2017;466:162-6.ArticlePubMed
• 24. Zhang JW, He LJ, Cao SJ, Yang Q, Yang SW, Zhou YJ. Effect of glycemic variability on short term prognosis in acute myocardial infarction subjects undergoing primary percutaneous coronary interventions. Diabetol Metab Syndr 2014;6:76.ArticlePubMedPMCPDF
• 25. Gerbaud E, Darier R, Montaudon M, Beauvieux MC, CoffinBoutreux C, Coste P, et al. Glycemic variability is a powerful independent predictive factor of midterm major adverse cardiac events in patients with diabetes with acute coronary syndrome. Diabetes Care 2019;42:674-81.ArticlePubMedPDF
• 26. Zinman B, Marso SP, Poulter NR, Emerson SS, Pieber TR, Pratley RE, et al. Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2). Diabetologia 2018;61:48-57.ArticlePubMedPDF
• 27. Segar MW, Patel KV, Vaduganathan M, Caughey MC, Butler J, Fonarow GC, et al. Association of long-term change and variability in glycemia with risk of incident heart failure among patients with type 2 diabetes: a secondary analysis of the ACCORD trial. Diabetes Care 2020;43:1920-8.ArticlePubMedPMCPDF
• 28. Wan EY, Yu EY, Chin WY, Ng FT, Chia SM, Wong IC, et al. Age-specific associations of glycated haemoglobin variability with cardiovascular disease and mortality in patients with type 2 diabetes mellitus: a 10-year cohort study. Diabetes Obes Metab 2020;22:1316-27.PubMed
• 29. Critchley JA, Carey IM, Harris T, DeWilde S, Cook DG. Variability in glycated hemoglobin and risk of poor outcomes among people with type 2 diabetes in a large primary care cohort study. Diabetes Care 2019;42:2237-46.ArticlePubMedPDF
• 30. Echouffo-Tcheugui JB, Zhao S, Brock G, Matsouaka RA, Kline D, Joseph JJ. Visit-to-visit glycemic variability and risks of cardiovascular events and all-cause mortality: the ALLHAT study. Diabetes Care 2019;42:486-93.ArticlePubMedPMCPDF
• 31. Wang A, Liu X, Xu J, Han X, Su Z, Chen S, et al. Visit-to-visit variability of fasting plasma glucose and the risk of cardiovascular disease and all-cause mortality in the general population. J Am Heart Assoc 2017;6:e006757.ArticlePubMedPMC
• 32. Suh S, Kim JH. Glycemic variability: how do we measure it and why is it important? Diabetes Metab J 2015;39:273-82.ArticlePubMedPMC
• 33. Alfieri V, Myasoedova VA, Vinci MC, Rondinelli M, Songia P, Massaiu I, et al. The role of glycemic variability in cardiovascular disorders. Int J Mol Sci 2021;22:8393.ArticlePubMedPMC
• 34. Nusca A, Lauria Pantano A, Melfi R, Proscia C, Maddaloni E, Contuzzi R, et al. Glycemic variability assessed by continuous glucose monitoring and short-term outcome in diabetic patients undergoing percutaneous coronary intervention: an observational pilot study. J Diabetes Res 2015;2015:250201.ArticlePubMedPMCPDF
• 35. Kuroda M, Shinke T, Otake H, Sugiyama D, Takaya T, Takahashi H, et al. Effects of daily glucose fluctuations on the healing response to everolimus-eluting stent implantation as assessed using continuous glucose monitoring and optical coherence tomography. Cardiovasc Diabetol 2016;15:79.ArticlePubMedPMC
• 36. Clement KC, Alejo D, DiNatale J, Whitman GJ, Matthew TL, Clement SC, et al. Increased glucose variability is associated with atrial fibrillation after coronary artery bypass. J Card Surg 2019;34:549-54.ArticlePubMedPDF
• 37. Wan EY, Yu EY, Chin WY, Barrett JK, Mok AH, Lau CS, et al. Greater variability in lipid measurements associated with cardiovascular disease and mortality: a 10-year diabetes cohort study. Diabetes Obes Metab 2020;22:1777-88.ArticlePubMedPMCPDF
• 38. Kim JA, Lee JS, Chung HS, Roh E, Lee YB, Hong SH, et al. Impact of visit-to-visit fasting plasma glucose variability on the development of type 2 diabetes: a nationwide population-based cohort study. Diabetes Care 2018;41:2610-6.ArticlePubMedPDF
• 39. Kim JA, Kim J, Roh E, Hong SH, Lee YB, Baik SH, et al. Association of fasting plasma glucose variability with gestational diabetes mellitus: a nationwide population-based cohort study. BMJ Open Diabetes Res Care 2020;8:e001084.ArticlePubMedPMC
• 40. Kim HS, Shin JA, Lee SH, Kim ES, Cho JH, Son HY, et al. A comparative study of the effects of a dipeptidyl peptidase-IV inhibitor and sulfonylurea on glucose variability in patients with type 2 diabetes with inadequate glycemic control on metformin. Diabetes Technol Ther 2013;15:810-6.ArticlePubMed
• 41. Bae JC, Kwak SH, Kim HJ, Kim SY, Hwang YC, Suh S, et al. Effects of teneligliptin on HbA1c levels, continuous glucose monitoring-derived time in range and glycemic variability in elderly patients with T2DM (TEDDY study). Diabetes Metab J 2022;46:81-92.ArticlePubMedPDF
• 42. Rizzo MR, Barbieri M, Marfella R, Paolisso G. Reduction of oxidative stress and inflammation by blunting daily acute glucose fluctuations in patients with type 2 diabetes: role of dipeptidyl peptidase-IV inhibition. Diabetes Care 2012;35:2076-82.PubMedPMC
• 43. Guerci B, Monnier L, Serusclat P, Petit C, Valensi P, Huet D, et al. Continuous glucose profiles with vildagliptin versus sitagliptin in add-on to metformin: results from the randomized Optima study. Diabetes Metab 2012;38:359-66.ArticlePubMed
• 44. Marfella R, Barbieri M, Grella R, Rizzo MR, Nicoletti GF, Paolisso G. Effects of vildagliptin twice daily vs. sitagliptin once daily on 24-hour acute glucose fluctuations. J Diabetes Complications 2010;24:79-83.ArticlePubMed
• 45. White WB, Cannon CP, Heller SR, Nissen SE, Bergenstal RM, Bakris GL, et al. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N Engl J Med 2013;369:1327-35.ArticlePubMed
• 46. Scirica BM, Bhatt DL, Braunwald E, Steg PG, Davidson J, Hirshberg B, et al. Saxagliptin and cardiovascular outcomes in patients with type 2 diabetes mellitus. N Engl J Med 2013;369:1317-26.ArticlePubMed
• 47. Bajaj HS, Venn K, Ye C, Patrick A, Kalra S, Khandwala H, et al. Lowest glucose variability and hypoglycemia are observed with the combination of a GLP-1 receptor agonist and basal insulin (VARIATION Study). Diabetes Care 2017;40:194-200.ArticlePubMedPDF
• 48. FLAT-SUGAR Trial Investigators. Glucose variability in a 26-week randomized comparison of mealtime treatment with rapid-acting insulin versus GLP-1 agonist in participants with type 2 diabetes at high cardiovascular risk. Diabetes Care 2016;39:973-81.ArticlePubMedPDF
• 49. Ahren B, Foley JE, Ferrannini E, Matthews DR, Zinman B, Dejager S, et al. Changes in prandial glucagon levels after a 2-year treatment with vildagliptin or glimepiride in patients with type 2 diabetes inadequately controlled with metformin monotherapy. Diabetes Care 2010;33:730-2.ArticlePubMedPMCPDF
• 50. Seino Y, Kuwata H, Yabe D. Incretin-based drugs for type 2 diabetes: focus on East Asian perspectives. J Diabetes Investig 2016;7 Suppl 1(Suppl 1):102-9.ArticlePubMedPDF
• 51. Drucker DJ, Nauck MA. The incretin system: glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes. Lancet 2006;368:1696-705.ArticlePubMed
• 52. Nomoto H, Miyoshi H, Sugawara H, Ono K, Yanagiya S, Oita M, et al. A randomized controlled trial comparing the effects of dapagliflozin and DPP-4 inhibitors on glucose variability and metabolic parameters in patients with type 2 diabetes mellitus on insulin. Diabetol Metab Syndr 2017;9:54.ArticlePubMedPMCPDF
• 53. Luo M, Kong X, Wang H, Zhai X, Cai T, Ding B, et al. Effect of dapagliflozin on glycemic variability in patients with type 2 diabetes under insulin glargine combined with other oral hypoglycemic drugs. J Diabetes Res 2020;2020:6666403.ArticlePubMedPMCPDF
• 54. Lee SH, Min KW, Lee BW, Jeong IK, Yoo SJ, Kwon HS, et al. Effect of dapagliflozin as an add-on therapy to insulin on the glycemic variability in subjects with type 2 diabetes mellitus (DIVE): a multicenter, placebo-controlled, double-blind, randomized study. Diabetes Metab J 2021;45:339-48.ArticlePubMedPDF
• 55. Simpson WG. Biomarker variability and cardiovascular disease residual risk. Curr Opin Cardiol 2019;34:413-7.ArticlePubMed
• 56. Clark D 3rd, Nicholls SJ, St John J, Elshazly MB, Kapadia SR, Tuzcu EM, et al. Visit-to-visit cholesterol variability correlates with coronary atheroma progression and clinical outcomes. Eur Heart J 2018;39:2551-8.ArticlePubMed
• 57. Waters DD, Bangalore S, Fayyad R, DeMicco DA, Laskey R, Melamed S, et al. Visit-to-visit variability of lipid measurements as predictors of cardiovascular events. J Clin Lipidol 2018;12:356-66.ArticlePubMed
• 58. Kim MK, Han K, Kim HS, Park YM, Kwon HS, Yoon KH, et al. Cholesterol variability and the risk of mortality, myocardial infarction, and stroke: a nationwide population-based study. Eur Heart J 2017;38:3560-6.ArticlePubMedPMC
• 59. Takenouchi A, Tsuboi A, Kitaoka K, Minato S, Kurata M, Fukuo K, et al. Visit-to-visit low-density lipoprotein cholesterol variability is an independent determinant of carotid intimamedia thickness in patients with type 2 diabetes. J Clin Med Res 2017;9:310-6.ArticlePubMedPMC
• 60. Mann DM, Glazer NL, Winter M, Paasche-Orlow MK, Muntner P, Shimbo D, et al. A pilot study identifying statin nonadherence with visit-to-visit variability of low-density lipoprotein cholesterol. Am J Cardiol 2013;111:1437-42.ArticlePubMedPMC
• 61. Kreger BE, Odell PM, D’Agostino RB, Wilson PF. Long-term intraindividual cholesterol variability: natural course and adverse impact on morbidity and mortality: the Framingham Study. Am Heart J 1994;127:1607-14.ArticlePubMed
• 62. Bangalore S, Breazna A, DeMicco DA, Wun CC, Messerli FH; TNT Steering Committee and Investigators. Visit-to-visit low-density lipoprotein cholesterol variability and risk of cardiovascular outcomes: insights from the TNT trial. J Am Coll Cardiol 2015;65:1539-48.PubMed
• 63. Boey E, Gay GM, Poh KK, Yeo TC, Tan HC, Lee CH. Visit-to-visit variability in LDL- and HDL-cholesterol is associated with adverse events after ST-segment elevation myocardial infarction: a 5-year follow-up study. Atherosclerosis 2016;244:86-92.ArticlePubMed
• 64. Karlson BW, Wiklund O, Palmer MK, Nicholls SJ, Lundman P, Barter PJ. Variability of low-density lipoprotein cholesterol response with different doses of atorvastatin, rosuvastatin, and simvastatin: results from VOYAGER. Eur Heart J Cardiovasc Pharmacother 2016;2:212-7.ArticlePubMed
• 65. Zhu Y, Lu JM, Yu ZB, Li D, Wu MY, Shen P, et al. Intra-individual variability of total cholesterol is associated with cardiovascular disease mortality: a cohort study. Nutr Metab Cardiovasc Dis 2019;29:1205-13.ArticlePubMed
• 66. Park JB, Kim DH, Lee H, Hwang IC, Yoon YE, Park HE, et al. Mildly abnormal lipid levels, but not high lipid variability, are associated with increased risk of myocardial infarction and stroke in “statin-naive” young population a nationwide cohort study. Circ Res 2020;126:824-35.ArticlePubMed
• 67. Roh E, Chung HS, Lee JS, Kim JA, Lee YB, Hong SH, et al. Total cholesterol variability and risk of atrial fibrillation: a nationwide population-based cohort study. PLoS One 2019;14:e0215687.ArticlePubMedPMC
• 68. Chung HS, Lee JS, Kim JA, Roh E, Lee YB, Hong SH, et al. Variability in total cholesterol concentration is associated with the risk of dementia: a nationwide population-based cohort study. Front Neurol 2019;10:441.ArticlePubMedPMC
• 69. Smith SJ, Cooper GR, Myers GL, Sampson EJ. Biological variability in concentrations of serum lipids: sources of variation among results from published studies and composite predicted values. Clin Chem 1993;39:1012-22.ArticlePubMedPDF
• 70. Bautista LE, Rueda-Ochoa OL. Methodological challenges in studies of the role of blood lipids variability in the incidence of cardiovascular disease. Lipids Health Dis 2021;20:51.ArticlePubMedPMCPDF
• 71. Kishi T. Baroreflex failure and beat-to-beat blood pressure variation. Hypertens Res 2018;41:547-52.ArticlePubMedPDF
• 72. Bae EH, Lim SY, Han KD, Oh TR, Choi HS, Kim CS, et al. Association between systolic and diastolic blood pressure variability and the risk of end-stage renal disease. Hypertension 2019;74:880-7.ArticlePubMed
• 73. Tatasciore A, Renda G, Zimarino M, Soccio M, Bilo G, Parati G, et al. Awake systolic blood pressure variability correlates with target-organ damage in hypertensive subjects. Hypertension 2007;50:325-32.ArticlePubMed
• 74. Myers MG, Godwin M, Dawes M, Kiss A, Tobe SW, Grant FC, et al. Conventional versus automated measurement of blood pressure in primary care patients with systolic hypertension: randomised parallel design controlled trial. BMJ 2011;342:d286.ArticlePubMedPMC
• 75. Parati G, Torlasco C, Pengo M, Bilo G, Ochoa JE. Blood pressure variability: its relevance for cardiovascular homeostasis and cardiovascular diseases. Hypertens Res 2020;43:609-20.ArticlePubMedPDF
• 76. Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood-pressure variability. Nat Rev Cardiol 2013;10:143-55.ArticlePubMedPDF
• 77. Frattola A, Parati G, Cuspidi C, Albini F, Mancia G. Prognostic value of 24-hour blood pressure variability. J Hypertens 1993;11:1133-7.ArticlePubMed
• 78. Palatini P, Reboldi G, Beilin LJ, Casiglia E, Eguchi K, Imai Y, et al. Added predictive value of night-time blood pressure variability for cardiovascular events and mortality: the Ambulatory Blood Pressure-International Study. Hypertension 2014;64:487-93.ArticlePubMed
• 79. Kikuya M, Ohkubo T, Metoki H, Asayama K, Hara A, Obara T, et al. Day-by-day variability of blood pressure and heart rate at home as a novel predictor of prognosis: the Ohasama study. Hypertension 2008;52:1045-50.ArticlePubMed
• 80. Johansson JK, Niiranen TJ, Puukka PJ, Jula AM. Prognostic value of the variability in home-measured blood pressure and heart rate: the Finn-Home Study. Hypertension 2012;59:212-8.ArticlePubMed
• 81. Asayama K, Kikuya M, Schutte R, Thijs L, Hosaka M, Satoh M, et al. Home blood pressure variability as cardiovascular risk factor in the population of Ohasama. Hypertension 2013;61:61-9.ArticlePubMed
• 82. Juhanoja EP, Niiranen TJ, Johansson JK, Puukka PJ, Thijs L, Asayama K, et al. Outcome-driven thresholds for increased home blood pressure variability. Hypertension 2017;69:599-607.ArticlePubMed
• 83. Ntineri A, Kalogeropoulos PG, Kyriakoulis KG, Aissopou EK, Thomopoulou G, Kollias A, et al. Prognostic value of average home blood pressure and variability: 19-year follow-up of the Didima study. J Hypertens 2018;36:69-76.PubMed
• 84. Meng Y, Magnussen CG, Wu F, Buscot MJ, Juonala M, Pahkala K, et al. Within-visit SBP variability from childhood to adulthood and markers of cardiovascular end-organ damage in mid-life. J Hypertens 2021;39:1865-75.ArticlePubMed
• 85. de Havenon A, Delic A, Yaghi S, Wong KH, Majersik JJ, Stulberg E, et al. Midlife blood pressure variability and risk of allcause mortality and cardiovascular events during extended follow-up. Am J Hypertens 2021;34:1269-75.ArticlePubMedPMCPDF
• 86. Gregg LP, Hedayati SS, Yang H, Van Buren PN, Banerjee S, Navaneethan SD, et al. Association of blood pressure variability and diuretics with cardiovascular events in patients with chronic kidney disease stages 1-5. Hypertension 2021;77:948-59.ArticlePubMed
• 87. Yu ZB, Li D, Chen XY, Zheng PW, Lin HB, Tang ML, et al. Association of visit-to-visit variability of blood pressure with cardiovascular disease among type 2 diabetes mellitus patients: a cohort study. Diabetes Metab J 2019;43:350-67.ArticlePubMedPMCPDF
• 88. Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlof B, et al. Effects of beta blockers and calcium-channel blockers on within-individual variability in blood pressure and risk of stroke. Lancet Neurol 2010;9:469-80.PubMed
• 89. Muntner P, Levitan EB, Lynch AI, Simpson LM, Whittle J, Davis BR, et al. Effect of chlorthalidone, amlodipine, and lisinopril on visit-to-visit variability of blood pressure: results from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial. J Clin Hypertens (Greenwich) 2014;16:323-30.ArticlePubMedPMCPDF
• 90. Smith TR, Drozda JP Jr, Vanslette JA, Hoeffken AS, Nicholson RA. Medication class effects on visit-to-visit variability of blood pressure measurements: analysis of electronic health record data in the “real world”. J Clin Hypertens (Greenwich) 2013;15:655-62.ArticlePubMedPMCPDF
• 91. Webb AJ, Fischer U, Mehta Z, Rothwell PM. Effects of antihypertensive-drug class on interindividual variation in blood pressure and risk of stroke: a systematic review and meta-analysis. Lancet 2010;375:906-15.ArticlePubMed
• 92. Sato N, Saijo Y, Sasagawa Y, Morimoto H, Takeuchi T, Sano H, et al. Visit-to-visit variability and seasonal variation in blood pressure: Combination of Antihypertensive Therapy in the Elderly, Multicenter Investigation (CAMUI) Trial subanalysis. Clin Exp Hypertens 2015;37:411-9.ArticlePubMed
• 93. Rakugi H, Ogihara T, Saruta T, Kawai T, Saito I, Teramukai S, et al. Preferable effects of olmesartan/calcium channel blocker to olmesartan/diuretic on blood pressure variability in very elderly hypertension: COLM study subanalysis. J Hypertens 2015;33:2165-72.PubMedPMC
• 94. Nagai M, Dote K, Kato M, Sasaki S, Oda N, Kagawa E, et al. Visit-to-visit blood pressure variability and classes of antihypertensive agents; associations with artery remodeling and the risk of stroke. Curr Pharm Des 2016;22:383-9.ArticlePubMed
• 95. Parati G, Castiglioni P, Omboni S, Faini A. Effects on 24-hour blood pressure variability of ace-inhibition and calcium channel blockade as monotherapy or in combination. Sci Rep 2018;8:13779.ArticlePubMedPMCPDF
• 96. Lissner L, Odell PM, D’Agostino RB, Stokes J 3rd, Kreger BE, Belanger AJ, et al. Variability of body weight and health outcomes in the Framingham population. N Engl J Med 1991;324:1839-44.ArticlePubMed
• 97. Zhang Y, Hou F, Li J, Yu H, Li L, Hu S, et al. The association between weight fluctuation and all-cause mortality: a systematic review and meta-analysis. Medicine (Baltimore) 2019;98:e17513.PubMedPMC
• 98. Zou H, Yin P, Liu L, Liu W, Zhang Z, Yang Y, et al. Bodyweight fluctuation was associated with increased risk for cardiovascular disease, all-cause and cardiovascular mortality: a systematic review and meta-analysis. Front Endocrinol (Lausanne) 2019;10:728.ArticlePubMedPMC
• 99. Stevens VL, Jacobs EJ, Sun J, Patel AV, McCullough ML, Teras LR, et al. Weight cycling and mortality in a large prospective US study. Am J Epidemiol 2012;175:785-92.ArticlePubMed
• 100. Bangalore S, Fayyad R, Laskey R, DeMicco DA, Messerli FH, Waters DD. Body-weight fluctuations and outcomes in coronary disease. N Engl J Med 2017;376:1332-40.ArticlePubMed
• 101. Oh TJ, Moon JH, Choi SH, Lim S, Park KS, Cho NH, et al. Body-weight fluctuation and incident diabetes mellitus, cardiovascular disease, and mortality: a 16-year prospective cohort study. J Clin Endocrinol Metab 2019;104:639-46.ArticlePubMedPDF
• 102. Kim DH, Nam GE, Han K, Kim YH, Park KY, Hwang HS, et al. Variabilities in weight and waist circumference and risk of myocardial infarction, stroke, and mortality: a nationwide cohort study. Endocrinol Metab (Seoul) 2020;35:933-42.ArticlePubMedPMCPDF
• 103. Montani JP, Schutz Y, Dulloo AG. Dieting and weight cycling as risk factors for cardiometabolic diseases: who is really at risk? Obes Rev 2015;16 Suppl 1:7-18.ArticlePubMed
• 104. Dulloo AG, Jacquet J. The control of partitioning between protein and fat during human starvation: its internal determinants and biological significance. Br J Nutr 1999;82:339-56.ArticlePubMed
• 105. Byrne NM, Weinsier RL, Hunter GR, Desmond R, Patterson MA, Darnell BE, et al. Influence of distribution of lean body mass on resting metabolic rate after weight loss and weight regain: comparison of responses in white and black women. Am J Clin Nutr 2003;77:1368-73.ArticlePubMed
• 106. Yatsuya H, Tamakoshi K, Yoshida T, Hori Y, Zhang H, Ishikawa M, et al. Association between weight fluctuation and fasting insulin concentration in Japanese men. Int J Obes Relat Metab Disord 2003;27:478-83.ArticlePubMedPDF
• 107. Kleiger RE, Miller JP, Bigger JT Jr, Moss AJ. Decreased heart rate variability and its association with increased mortality after acute myocardial infarction. Am J Cardiol 1987;59:256-62.ArticlePubMed
• 108. Wichterle D, Simek J, La Rovere MT, Schwartz PJ, Camm AJ, Malik M. Prevalent low-frequency oscillation of heart rate: novel predictor of mortality after myocardial infarction. Circulation 2004;110:1183-90.ArticlePubMed
• 109. La Rovere MT, Bigger JT Jr, Marcus FI, Mortara A, Schwartz PJ. Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. ATRAMI (Autonomic Tone and Reflexes After Myocardial Infarction) Investigators. Lancet 1998;351:478-84.PubMed
• 110. Benichou T, Pereira B, Mermillod M, Tauveron I, Pfabigan D, Maqdasy S, et al. Heart rate variability in type 2 diabetes mellitus: a systematic review and meta-analysis. PLoS One 2018;13:e0195166.ArticlePubMedPMC
• 111. Kataoka M, Ito C, Sasaki H, Yamane K, Kohno N. Low heart rate variability is a risk factor for sudden cardiac death in type 2 diabetes. Diabetes Res Clin Pract 2004;64:51-8.ArticlePubMed
• 112. Tsuji H, Venditti FJ Jr, Manders ES, Evans JC, Larson MG, Feldman CL, et al. Reduced heart rate variability and mortality risk in an elderly cohort. The Framingham Heart Study. Circulation 1994;90:878-83.ArticlePubMed
• 113. Dekker JM, Crow RS, Folsom AR, Hannan PJ, Liao D, Swenne CA, et al. Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: the ARIC Study. Atherosclerosis Risk In Communities. Circulation 2000;102:1239-44.ArticlePubMed
• 114. Tsuji H, Larson MG, Venditti FJ Jr, Manders ES, Evans JC, Feldman CL, et al. Impact of reduced heart rate variability on risk for cardiac events. The Framingham Heart Study. Circulation 1996;94:2850-5.ArticlePubMed
• 115. Pereira VL Jr, Dobre M, Dos Santos SG, Fuzatti JS, Oliveira CR, Campos LA, et al. Association between carotid intima media thickness and heart rate variability in adults at increased cardiovascular risk. Front Physiol 2017;8:248.ArticlePubMedPMC
• 116. Bilchick KC, Berger RD. Heart rate variability. J Cardiovasc Electrophysiol 2006;17:691-4.ArticlePubMed
• 117. Bohm M, Borer JS, Camm J, Ford I, Lloyd SM, Komajda M, et al. Twenty-four-hour heart rate lowering with ivabradine in chronic heart failure: insights from the SHIFT Holter substudy. Eur J Heart Fail 2015;17:518-26.ArticlePubMed
• 118. Tendera M, Talajic M, Robertson M, Tardif JC, Ferrari R, Ford I, et al. Safety of ivabradine in patients with coronary artery disease and left ventricular systolic dysfunction (from the BEAUTIFUL Holter Substudy). Am J Cardiol 2011;107:805-11.ArticlePubMed
• 119. Wannamethee G, Ebrahim S, Shaper AG. Gamma-glutamyltransferase: determinants and association with mortality from ischemic heart disease and all causes. Am J Epidemiol 1995;142:699-708.ArticlePubMed
• 120. Choi KM, Han K, Park S, Chung HS, Kim NH, Yoo HJ, et al. Implication of liver enzymes on incident cardiovascular diseases and mortality: a nationwide population-based cohort study. Sci Rep 2018;8:3764.ArticlePubMedPMCPDF
• 121. Kunutsor SK, Abbasi A, Adler AI. Gamma-glutamyl transferase and risk of type II diabetes: an updated systematic review and dose-response meta-analysis. Ann Epidemiol 2014;24:809-16.ArticlePubMed
• 122. Targher G. Elevated serum gamma-glutamyltransferase activity is associated with increased risk of mortality, incident type 2 diabetes, cardiovascular events, chronic kidney disease and cancer: a narrative review. Clin Chem Lab Med 2010;48:147-57.PubMed
• 123. Chung HS, Lee JS, Kim JA, Roh E, Lee YB, Hong SH, et al. γ-Glutamyltransferase variability and the risk of mortality, myocardial infarction, and stroke: a nationwide population-based cohort study. J Clin Med 2019;8:832.ArticlePubMedPMC
• 124. Lee DY, Han K, Yu JH, Park S, Seo JA, Kim NH, et al. Prognostic value of long-term gamma-glutamyl transferase variability in individuals with diabetes: a nationwide population-based study. Sci Rep 2020;10:15375.ArticlePubMedPMCPDF
• 125. Hong SH, Lee JS, Kim JA, Lee YB, Roh E, Yu JH, et al. Gamma-glutamyl transferase variability and the risk of hospitalisation for heart failure. Heart 2020;106:1080-6.ArticlePubMed
• 126. Lee DY, Han K, Yu JH, Park S, Heo JI, Seo JA, et al. Gammaglutamyl transferase variability can predict the development of end-stage of renal disease: a nationwide population-based study. Sci Rep 2020;10:11668.ArticlePubMedPMCPDF
• 127. Mason JE, Starke RD, Van Kirk JE. Gamma-glutamyl transferase: a novel cardiovascular risk biomarker. Prev Cardiol 2010;13:36-41.ArticlePubMed
• 128. Lee YB, Han K, Park S, Kim SM, Kim NH, Choi KM, et al. Gamma-glutamyl transferase variability and risk of dementia: a nationwide study. Int J Geriatr Psychiatry 2020;35:1105-14.ArticlePubMedPDF

Figure & Data

References

Citations

Citations to this article as recorded by  

• 4-week results of “Linni Slim” synbiotic in patients with metabolic syndrome
  A. S. Rudoy, N. N. Silivinchik
  Experimental and Clinical Gastroenterology.2024; (3): 87.     CrossRef
• Association between weight loss and cardiovascular outcomes and mortality in Korea: A nationwide cohort study
  So Yoon Kwon, Gyuri Kim, Seohyun Kim, Jae Hyeon Kim
  Diabetes Research and Clinical Practice.2024; 214: 111767.     CrossRef
• Identifying Personal and Lifestyle Determinants Associated With Glycemic Variability Among Healthy Non-Diabetes Adults
  SuJin Song
  CardioMetabolic Syndrome Journal.2024; 4(2): 93.     CrossRef
• Long-term variability in physiological measures in relation to mortality and epigenetic aging: prospective studies in the USA and China
  Hui Chen, Tianjing Zhou, Shaowei Wu, Yaying Cao, Geng Zong, Changzheng Yuan
  BMC Medicine.2023;[Epub]     CrossRef
• Dose–response relationship between physical activity and cardiometabolic risk in obese children and adolescents: A pre-post quasi-experimental study
  Zekai Chen, Lin Zhu
  Frontiers in Physiology.2023;[Epub]     CrossRef
• Association of body weight change with all-cause and cause-specific mortality: A nationwide population-based study
  So Yoon Kwon, Gyuri Kim, Jungkuk Lee, Jiyun Park, You-Bin Lee, Sang-Man Jin, Kyu Yeon Hur, Jae Hyeon Kim
  Diabetes Research and Clinical Practice.2023; 199: 110666.     CrossRef
• Association between lipid variability and the risk of mortality in cancer patients not receiving lipid-lowering agents
  Seohyun Kim, Gyuri Kim, So Hyun Cho, Rosa Oh, Ji Yoon Kim, You-Bin Lee, Sang-Man Jin, Kyu Yeon Hur, Jae Hyeon Kim
  Frontiers in Oncology.2023;[Epub]     CrossRef
• Association between visit-to-visit lipid variability and risk of ischemic heart disease: a cohort study in China
  Yonghao Wu, Peng Shen, Lisha Xu, Zongming Yang, Yexiang Sun, Luhua Yu, Zhanghang Zhu, Tiezheng Li, Dan Luo, Hongbo Lin, Liming Shui, Mengling Tang, Mingjuan Jin, Kun Chen, Jianbing Wang
  Endocrine.2023; 84(3): 914.     CrossRef
• Variability of Metabolic Risk Factors: Causative Factor or Epiphenomenon?
  Hye Jin Yoo
  Diabetes & Metabolism Journal.2022; 46(2): 257.     CrossRef
• Long-Term Variability in Physiological Measures in Relation to Mortality and Epigenetic Aging: Prospective Studies in the US and China
  Hui Chen, Tianjing Zhou, Shaowei Wu, Yaying Cao, Geng Zong, Changzheng Yuan
  SSRN Electronic Journal .2022;[Epub]     CrossRef

About this article

Park MJ, Choi KM. Association between Variability of Metabolic Risk Factors and Cardiometabolic Outcomes. Diabetes Metab J. 2022;46(1):49-62.

Received: Nov 10, 2021; Accepted: Dec 07, 2021

DOI: https://doi.org/10.4093/dmj.2021.0316.

Download citation ↓