Diabetes Metab J.  2022 Jan;46(1):49-62. 10.4093/dmj.2021.0316.

Association between Variability of Metabolic Risk Factors and Cardiometabolic Outcomes

Affiliations
  • 1Division of Endocrinology & Metabolism, Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea

Abstract

Despite strenuous efforts to reduce cardiovascular disease (CVD) risk by improving cardiometabolic risk factors, such as glucose and cholesterol levels, and blood pressure, there is still residual risk even in patients reaching treatment targets. Recently, researchers have begun to focus on the variability of metabolic variables to remove residual risks. Several clinical trials and cohort studies have reported a relationship between the variability of metabolic parameters and CVDs. Herein, we review the literature regarding the effect of metabolic factor variability and CVD risk, and describe possible mechanisms and potential treatment perspectives for reducing cardiometabolic risk factor variability.

Keyword

Blood pressure; Body weight; Cholesterol; Gamma-glutamyltransferase; Glucose; Heart rate

Figure

  • 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.


Cited by  1 articles

Variability of Metabolic Risk Factors: Causative Factor or Epiphenomenon?
Hye Jin Yoo
Diabetes Metab J. 2022;46(2):257-259.    doi: 10.4093/dmj.2022.0060.


Reference

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.
Article
2. Zanchetti A. Bottom blood pressure or bottom cardiovascular risk?: how far can cardiovascular risk be reduced? J Hypertens. 2009; 27:1509–20.
Article
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.
Article
4. Barnett MP, Bangalore S. Cardiovascular risk factors: it’s time to focus on variability! J Lipid Atheroscler. 2020; 9:255–67.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
32. Suh S, Kim JH. Glycemic variability: how do we measure it and why is it important? Diabetes Metab J. 2015; 39:273–82.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
55. Simpson WG. Biomarker variability and cardiovascular disease residual risk. Curr Opin Cardiol. 2019; 34:413–7.
Article
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.
Article
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.
Article
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.
Article
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.
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.
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.
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
71. Kishi T. Baroreflex failure and beat-to-beat blood pressure variation. Hypertens Res. 2018; 41:547–52.
Article
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.
Article
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.
Article
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.
Article
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.
Article
76. Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood-pressure variability. Nat Rev Cardiol. 2013; 10:143–55.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
116. Bilchick KC, Berger RD. Heart rate variability. J Cardiovasc Electrophysiol. 2006; 17:691–4.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
Article
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.
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.
Article
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.
Article
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.
Article
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.
Article
127. Mason JE, Starke RD, Van Kirk JE. Gamma-glutamyl transferase: a novel cardiovascular risk biomarker. Prev Cardiol. 2010; 13:36–41.
Article
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.
Article
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