Endocrinol Metab.  2020 Jun;35(2):217-226. 10.3803/EnM.2020.35.2.217.

Effects of Cardiovascular Risk Factor Variability on Health Outcomes

• Lee SH
• Kim MK
• Rhee EJ

• KMID: 2503252
• DOI: http://doi.org/10.3803/EnM.2020.35.2.217

Abstract

Innumerable studies have suggested “the lower, the better” for cardiovascular risk factors, such as body weight, lipid profile, blood pressure, and blood glucose, in terms of health outcomes. However, excessively low levels of these parameters cause health problems, as seen in cachexia, hypoglycemia, and hypotension. Body weight fluctuation is related to mortality, diabetes, obesity, cardiovascular disease, and cancer, although contradictory findings have been reported. High lipid variability is associated with increased mortality and elevated risks of cardiovascular disease, diabetes, end-stage renal disease, and dementia. High blood pressure variability is associated with increased mortality, myocardial infarction, hospitalization, and dementia, which may be caused by hypotension. Furthermore, high glucose variability, which can be measured by continuous glucose monitoring systems or self-monitoring of blood glucose levels, is associated with increased mortality, microvascular and macrovascular complications of diabetes, and hypoglycemic events, leading to hospitalization. Variability in metabolic parameters could be affected by medications, such as statins, antihypertensives, and hypoglycemic agents, and changes in lifestyle patterns. However, other mechanisms modify the relationships between biological variability and various health outcomes. In this study, we review recent evidence regarding the role of variability in metabolic parameters and discuss the clinical implications of these findings.

Keyword

Variability; Glucose; Blood pressure; Body weight; Lipids; Mortality; Cardiovascular diseases

Figure

• 

  Fig. 1 Mechanisms of weight cycling effects on cardiometabolic health outcomes. Modified from Rhee [15]. GFR, glomerular filtration rate.

• 

  Fig. 2 High variability in body weight, lipid profile, blood pressure, and blood glucose is associated with increased risks of all-cause mortality, cardiovascular diseases, and other health outcomes.

Reference

1. Grundy SM, Stone NJ, Bailey AL, Beam C, Birtcher KK, Blumenthal RS, et al. 2018 AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of blood cholesterol: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. Circulation. 2019; 139:e1082–143.

2. Mach F, Baigent C, Catapano AL, Koskinas KC, Casula M, Badimon L, et al. 2019 ESC/EAS guidelines for the management of dyslipidaemias: lipid modification to reduce cardiovascular risk. Eur Heart J. 2020; 41:111–88.

3. Sabatine MS, Giugliano RP, Keech AC, Honarpour N, Wiviott SD, Murphy SA, et al. Evolocumab and clinical outcomes in patients with cardiovascular disease. N Engl J Med. 2017; 376:1713–22.

4. SPRINT Research Group. Wright JT Jr, Williamson JD, Whelton PK, Snyder JK, Sink KM, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015; 373:2103–16.

5. Park S. Ideal target blood pressure in hypertension. Korean Circ J. 2019; 49:1002–9.

6. American Diabetes Association. 6. Glycemic targets: standards of medical care in diabetes-2020. Diabetes Care. 2020; 43(Suppl 1):S66–76.

7. Kim MK, Ko SH, Kim BY, Kang ES, Noh J, Kim SK, et al. 2019 Clinical practice guidelines for type 2 diabetes mellitus in Korea. Diabetes Metab J. 2019; 43:398–406.

8. Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlof B, et al. Prognostic significance of visit-to-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 2010; 375:895–905.

9. Keys A, Brozek J, Henschel A, Mickelsen O, Taylor HL. The biology of human starvation. Minneapolis: University of Minnesota Press;1950.

10. Chehregosha H, Khamseh ME, Malek M, Hosseinpanah F, Ismail-Beigi F. A view beyond HbA1c: role of continuous glucose monitoring. Diabetes Ther. 2019; 10:853–63.

11. Zenari L, Marangoni A. What are the preferred strategies for control of glycaemic variability in patients with type 2 diabetes mellitus? Diabetes Obes Metab. 2013; 15(Suppl 2):17–25.

12. 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.

13. Banach M, Stulc T, Dent R, Toth PP. Statin non-adherence and residual cardiovascular risk: there is need for substantial improvement. Int J Cardiol. 2016; 225:184–96.

14. 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.

15. Rhee EJ. Weight cycling and its cardiometabolic impact. J Obes Metab Syndr. 2017; 26:237–42.

16. 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.

17. Cereda E, Malavazos AE, Caccialanza R, Rondanelli M, Fatati G, Barichella M. Weight cycling is associated with body weight excess and abdominal fat accumulation: a cross-sectional study. Clin Nutr. 2011; 30:718–23.

18. 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.

19. Arnold AM, Newman AB, Cushman M, Ding J, Kritchevsky S. Body weight dynamics and their association with physical function and mortality in older adults: the cardiovascular health study. J Gerontol A Biol Sci Med Sci. 2010; 65:63–70.

20. Cologne J, Takahashi I, French B, Nanri A, Misumi M, Sadakane A, et al. Association of weight fluctuation with mortality in Japanese adults. JAMA Netw Open. 2019; 2:e190731.

21. 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.

22. 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.

23. Field AE, Byers T, Hunter DJ, Laird NM, Manson JE, Williamson DF, et al. Weight cycling, weight gain, and risk of hypertension in women. Am J Epidemiol. 1999; 150:573–9.

24. Yokomichi H, Ohde S, Takahashi O, Mochizuki M, Takahashi A, Yoda Y, et al. Weight cycling and the subsequent onset of type 2 diabetes mellitus: 10-year cohort studies in urban and rural Japan. BMJ Open. 2017; 7:e014684.

25. Neamat-Allah J, Barrdahl M, Husing A, Katzke VA, Bachlechner U, Steffen A, et al. Weight cycling and the risk of type 2 diabetes in the EPIC-Germany cohort. Diabetologia. 2015; 58:2718–25.

26. Rhee EJ, Cho JH, Kwon H, Park SE, Park CY, Oh KW, et al. Increased risk of diabetes development in individuals with weight cycling over 4 years: the Kangbuk Samsung Health study. Diabetes Res Clin Pract. 2018; 139:230–8.

27. 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.

28. 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.

29. 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.

30. Welti LM, Beavers DP, Caan BJ, Sangi-Haghpeykar H, Vitolins MZ, Beavers KM. Weight fluctuation and cancer risk in postmenopausal women: the women’s health initiative. Cancer Epidemiol Biomarkers Prev. 2017; 26:779–86.

31. Stevens VL, Jacobs EJ, Sun J, McCullough ML, Patel AV, Gaudet MM, et al. Weight cycling and risk of endometrial cancer. Cancer Epidemiol Biomarkers Prev. 2012; 21:747–52.

32. LaRosa JC, Grundy SM, Waters DD, Shear C, Barter P, Fruchart JC, et al. Intensive lipid lowering with atorvastatin in patients with stable coronary disease. N Engl J Med. 2005; 352:1425–35.

33. Heeschen C, Hamm CW, Laufs U, Snapinn S, Bohm M, White HD, et al. Withdrawal of statins increases event rates in patients with acute coronary syndromes. Circulation. 2002; 105:1446–52.

34. Daskalopoulou SS, Delaney JA, Filion KB, Brophy JM, Mayo NE, Suissa S. Discontinuation of statin therapy following an acute myocardial infarction: a population-based study. Eur Heart J. 2008; 29:2083–91.

35. Colivicchi F, Bassi A, Santini M, Caltagirone C. Discontinuation of statin therapy and clinical outcome after ischemic stroke. Stroke. 2007; 38:2652–7.

36. Pineda A, Cubeddu LX. Statin rebound or withdrawal syndrome: does it exist? Curr Atheroscler Rep. 2011; 13:23–30.

37. Sposito AC, Carvalho LS, Cintra RM, Araujo AL, Ono AH, Andrade JM, et al. Rebound inflammatory response during the acute phase of myocardial infarction after simvastatin withdrawal. Atherosclerosis. 2009; 207:191–4.

38. Bookstein L, Gidding SS, Donovan M, Smith FA. Day-to-day variability of serum cholesterol, triglyceride, and high-density lipoprotein cholesterol levels: impact on the assessment of risk according to the National Cholesterol Education Program guidelines. Arch Intern Med. 1990; 150:1653–7.

39. Robinson D, Bevan EA, Hinohara S, Takahashi T. Seasonal variation in serum cholesterol levels: evidence from the UK and Japan. Atherosclerosis. 1992; 95:15–24.

40. 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.

41. 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.

42. Lee EY, Yang Y, Kim HS, Cho JH, Yoon KH, Chung WS, et al. Effect of visit-to-visit LDL-, HDL-, and non-HDL-cholesterol variability on mortality and cardiovascular outcomes after percutaneous coronary intervention. Atherosclerosis. 2018; 279:1–9.

43. 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.

44. Han BH, Han K, Yoon KH, Kim MK, Lee SH. Impact of mean and variability of high-density lipoprotein-cholesterol on the risk of myocardial infarction, stroke, and mortality in the general population. J Am Heart Assoc. 2020; 9:e015493.

45. Rhee EJ, Han K, Ko SH, Ko KS, Lee WY. Increased risk for diabetes development in subjects with large variation in total cholesterol levels in 2,827,950 Koreans: a nationwide population-based study. PLoS One. 2017; 12:e0176615.

46. Lee SH, Kim HS, Park YM, Kwon HS, Yoon KH, Han K, et al. HDL-cholesterol, its variability, and the risk of diabetes: a nationwide population-based study. J Clin Endocrinol Metab. 2019; 104:5633–41.

47. Kim MK, Han K, Koh ES, Kim HS, Kwon HS, Park YM, et al. Variability in total cholesterol is associated with the risk of end-stage renal disease: a nationwide population-based study. Arterioscler Thromb Vasc Biol. 2017; 37:1963–70.

48. Koh ES, Kim M, Kim MK, Han K, Shin SJ, Kwon HS, et al. Intra-individual variability in high density lipoprotein cholesterol and risk of end-stage renal disease: a nationwide population-based study. Atherosclerosis. 2019; 286:135–41.

49. Lin YH, Huang JC, Wu PY, Chen SC, Chiu YW, Chang JM, et al. Greater low-density lipoprotein cholesterol variability is associated with increased progression to dialysis in patients with chronic kidney disease stage 3. Oncotarget. 2017; 9:3242–53.

50. Bardini G, Innocenti M, Rotella CM, Giannini S, Mannucci E. Variability of triglyceride levels and incidence of microalbuminuria in type 2 diabetes. J Clin Lipidol. 2016; 10:109–15.

51. Lee HJ, Lee SR, Choi EK, Han KD, Oh S. Low lipid levels and high variability are associated with the risk of new-onset atrial fibrillation. J Am Heart Assoc. 2019; 8:e012771.

52. Lee SR, Choi EK, Han KD, Lee SH, Oh S. Effect of the variability of blood pressure, glucose level, total cholesterol level, and body mass index on the risk of atrial fibrillation in a healthy population. Heart Rhythm. 2020; 17:12–9.

53. Smit RA, Trompet S, Sabayan B, le Cessie S, van der Grond J, van Buchem MA, et al. Higher visit-to-visit low-density lipoprotein cholesterol variability is associated with lower cognitive performance, lower cerebral blood flow, and greater white matter hyperintensity load in older subjects. Circulation. 2016; 134:212–21.

54. Lee SH, Han K, Cho H, Park YM, Kwon HS, Kang G, et al. Variability in metabolic parameters and risk of dementia: a nationwide population-based study. Alzheimers Res Ther. 2018; 10:110.

55. 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.

56. Porkka KV, Taimela S, Kontula K, Lehtimaki T, Aalto-Setala K, Akerblom HK, et al. Variability gene effects of DNA polymorphisms at the apo B, apo A I/C III and apo E loci on serum lipids: the Cardiovascular Risk in Young Finns Study. Clin Genet. 1994; 45:113–21.

57. Pereira MA, Weggemans RM, Jacobs DR Jr, Hannan PJ, Zock PL, Ordovas JM, et al. Within-person variation in serum lipids: implications for clinical trials. Int J Epidemiol. 2004; 33:534–41.

58. Smit RAJ, Jukema JW, Postmus I, Ford I, Slagboom PE, Heijmans BT, et al. Visit-to-visit lipid variability: clinical significance, effects of lipid-lowering treatment, and (pharmaco) genetics. J Clin Lipidol. 2018; 12:266–76.

59. Ballantyne CM, Neutel J, Cropp A, Duggan W, Wang EQ, Plowchalk D, et al. Results of bococizumab, a monoclonal antibody against proprotein convertase subtilisin/kexin type 9, from a randomized, placebo-controlled, dose-ranging study in statin-treated subjects with hypercholesterolemia. Am J Cardiol. 2015; 115:1212–21.

60. Kastelein JJ, Nissen SE, Rader DJ, Hovingh GK, Wang MD, Shen T, et al. Safety and efficacy of LY3015014, a monoclonal antibody to proprotein convertase subtilisin/kexin type 9 (PCSK9): a randomized, placebo-controlled phase 2 study. Eur Heart J. 2016; 37:1360–9.

61. Parati G, Ochoa JE, Lombardi C, Bilo G. Assessment and management of blood-pressure variability. Nat Rev Cardiol. 2013; 10:143–55.

62. Gosmanova EO, Mikkelsen MK, Molnar MZ, Lu JL, Yessayan LT, Kalantar-Zadeh K, et al. Association of systolic blood pressure variability with mortality, coronary heart disease, stroke, and renal disease. J Am Coll Cardiol. 2016; 68:1375–86.

63. Muntner P, Whittle J, Lynch AI, Colantonio LD, Simpson LM, Einhorn PT, et al. Visit-to-visit variability of blood pressure and coronary heart disease, stroke, heart failure, and mortality: a cohort study. Ann Intern Med. 2015; 163:329–38.

64. Choi S, Shin J, Choi SY, Sung KC, Ihm SH, Kim KI, et al. Impact of visit-to-visit variability in systolic blood pressure on cardiovascular outcomes in Korean National Health Insurance Service-National Sample Cohort. Am J Hypertens. 2017; 30:577–86.

65. Nagai M, Hoshide S, Ishikawa J, Shimada K, Kario K. Visit-to-visit blood pressure variations: new independent determinants for carotid artery measures in the elderly at high risk of cardiovascular disease. J Am Soc Hypertens. 2011; 5:184–92.

66. 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.

67. 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.

68. 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.

69. 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.

70. Basson MD, Klug MG, Hostetter JE, Wynne J. Visit-to-visit variability of blood pressure is associated with hospitalization and mortality in an unselected adult population. Am J Hypertens. 2018; 31:1113–9.

71. 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.

72. Seo SM, Chung WB, Choi IJ, Koh YS, Ihm SH, Kim PJ, et al. Visit-to-visit variability of systolic blood pressure predicts all-cause mortality in patients received percutaneous coronary intervention with drug-eluting stents. Heart Vessels. 2018; 33:489–97.

73. 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.

74. Suh S, Kim JH. Glycemic variability: how do we measure it and why is it important? Diabetes Metab J. 2015; 39:273–82.

75. Monnier L, Wojtusciszyn A, Molinari N, Colette C, Renard E, Owens D. Respective contributions of glycemic variability and mean daily glucose as predictors of hypoglycemia in type 1 diabetes: are they equivalent? Diabetes Care. 2020; 43:821–7.

76. Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care. 2019; 42:1593–603.

77. Peled S, Pollack R, Elishoov O, Haze A, Cahn A. Association of inpatient glucose measurements with amputations in patients hospitalized with acute diabetic foot. J Clin Endocrinol Metab. 2019; 104:5445–52.

78. 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 all-cause mortality in type 2 diabetes: the ADVANCE trial. Diabetes Care. 2014; 37:2359–65.

79. Lin CC, Yang CP, Li CI, Liu CS, Chen CC, Lin WY, et al. Visit-to-visit variability of fasting plasma glucose as predictor of ischemic stroke: competing risk analysis in a national cohort of Taiwan Diabetes Study. BMC Med. 2014; 12:165.

80. 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.

81. Akirov A, Diker-Cohen T, Masri-Iraqi H, Shimon I. High glucose variability increases mortality risk in hospitalized patients. J Clin Endocrinol Metab. 2017; 102:2230–41.

82. Gupta R, Wood DA. Primary prevention of ischaemic heart disease: populations, individuals, and health professionals. Lancet. 2019; 394:685–96.