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A quantitative analysis of the relationship between habitual energy expenditure, fitness and the metabolic cardiovascular syndrome

Published online by Cambridge University Press:  09 March 2007

Nicholas J. Wareham*
Affiliation:
Department of Community Medicine, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK
Susie J. Hennings
Affiliation:
Department of Community Medicine, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK
Christopher D. Byrne
Affiliation:
Department of Clinical Biochemistry, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK
C. Nicholas Hales
Affiliation:
Department of Clinical Biochemistry, University of Cambridge, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK
Andrew M. Prentice
Affiliation:
MRC Dunn Clinical Nutrition Centre, Hills Road, Cambridge CB2 2DH, UK
Nicholas E. Day
Affiliation:
Department of Community Medicine, Institute of Public Health, University of Cambridge, Cambridge CB2 2SR, UK
*
*Corresponding author: Dr N. J. Wareham, fax +44 (0) 1223 330330, email njw1004@medschl.cam.ac.uk
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Abstract

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Previous epidemiological studies have suggested an association between low levels of physical activity, fitness and the metabolic cardiovascular syndrome. However, many studies have used subjective non-quantitative questionnaire-based methods for assessing physical activity which do not distinguish between the different dimensions of this complex exposure, and in which measurement error in the exposure has not been estimated. These deficiencies in the measurement of this exposure complicate the interpretation of the results of epidemiological studies, and consequently make it difficult to design appropriate interventions and to estimate the expected benefit which would result from intervention. In particular, it is unclear whether public health advice should be to increase total energy expenditure, or to attempt to raise fitness by recommending periods of vigorous activity. To separate the effects of fitness and total energy expenditure in the aetiology of the metabolic cardiovascular syndrome, we measured the physical activity level (PAL), defined as total energy expenditure: BMR, and fitness (maximum O2 consumption (Vo2max per kg), measured in a sub-maximal test) in a cross-sectional population-based study of 162 adults aged 30–40 years. Heart-rate monitoring with individual calibration was used to measure total energy expenditure using the HRFlex method (Ceesay et al. 1989) which has been validated previously against doubly-labelled water and whole-body calorimetry. The relationship between a single measure of PAL, Vo2max per kg and the usual or habitual level for each exposure was measured in a sub-study of twenty-two subjects who undertook four repeated measures over the course of 1 year. This study design allows the reliability coefficient to be computed, which is used to adjust the observed associations for measurement error in the exposure. Twelve men (16.4%) and sixteen women (18.0%) were defined as having one or more features of the metabolic cardiovascular syndrome. The univariate odds ratio for each increasing quartile for PAL was 0.64 (95 % CI 0.43–0.94) and was 0.49 (95 % CI 0.32–0.74) for Vo2max per kg, suggesting that the association with the metabolic cardiovascular syndrome was stronger for fitness than for PAL. However, after adjustment for obesity and sex, and correction for exposure measurement error, the odds ratio per quartile for PAL was 0.32 (95 % CI 0.13–0.83) and 0.44 (95 % CI 0.24–0.78) for Vo2max per kg. Thus, although univariate analysis would suggest that fitness has a stronger association with the metabolic cardiovascular syndrome than PAL, this conclusion is reversed once confounding and the differences in measurement error are considered. We conclude from the present study that the metabolic cardiovascular syndrome is strongly associated with reduced habitual energy expenditure. The method employed to assess the exposure in the present study demonstrates the utility of assessing a known dimension of physical activity using a physiologically-based and objective measure with repeated estimation to adjust for measurement error. Such quantitative epidemiological data provide the basis for planning and evaluating the expected benefit of population-level interventions.

Type
Research Article
Copyright
Copyright © The Nutrition Society 1998

References

Armstrong, BK, White, E & Saracci, R (1994) Principles of Exposure Measurement in Epidemiology. Oxford: Oxford University Press.Google Scholar
Burchfiel, CM, Sharp, DS, Curb, JD, Rodriguez, BL, Hwang, L-J, Marcus, EB & Yano, K (1995) Physical activity and incidence of diabetes: The Honolulu Heart Program. American Journal of Epidemiology 141, 360368.CrossRefGoogle ScholarPubMed
Ceesay, SM, Prentice, AM, Day, KC, Murgatroyd, PR, Goldberg, GR & Scott, W (1989) The use of heart rate monitoring in the estimation of energy expenditure: a validation study using indirect whole-body calorimetry. British Journal of Nutrition 61, 175186.CrossRefGoogle ScholarPubMed
Consolazio, CF, Johnson, RE & Pecora, LJ (1963) Physiological Measurements of Metabolic Functions in Man. New York: McGraw Hill.Google Scholar
Eriksson, J, Taimela, S & Koivisto, VA (1997) Exercise and the metabolic syndrome. Diabetologia 40, 125135.Google ScholarPubMed
Friedewald, WT, Levy, RI & Fredrickson, DS (1972) Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clinical Chemistry 18, 499502.CrossRefGoogle ScholarPubMed
Goldberg, GR, Prentice, AM, Davies, HL & Murgatroyd, PR (1988) Overnight and basal metabolic rates in men and women. European Journal of Clinical Nutrition 42, 137144.Google ScholarPubMed
Haapanen, N, Milunpalo, S, Vuori, I, Oja, P & Pasanen, M (1997) Association of leisure time physical activity with the risk of coronary heart disease hypertension and diabetes in middle-aged men and women. International Journal of Epidemiology 26, 739747.CrossRefGoogle ScholarPubMed
Haffner, SM, Ferrannini, E, Hazuda, HP & Stern, MP (1992 a) Clustering of cardiovascular risk factors in confirmed prehypertensive individuals: Does the clock for coronary heart disease start ticking before the onset of clinical diabetes? Hypertension 20, 3845.CrossRefGoogle Scholar
Haffner, SM, Valdez, RA, Hazuda, HP, Mitchell, BD, Morales, PA & Stern, MP (1992 b) Prospective analysis of the insulin-resistance syndrome (syndrome X). Diabetes 41, 715722.Google Scholar
Hales, CN, Byrne, CD, Petry, CJ & Wareham, NJ (1996) Measurement of insulin and proinsulin. Diabetes Reviews 4, 320335.Google Scholar
Helmrich, SP, Ragland, DR, Leung, RW & Paffenbarger, RS (1991) Physical activity and reduced occurrence of non-insulin-dependent diabetes mellitus. New England Journal of Medicine 325, 147152.Google Scholar
James, WPT & Schofield, EC (1990) Human Energy Requirements. Oxford: Oxford Medical Publications.Google Scholar
Kunst, A, Draeger, B & Ziegenhorn, J (1983) UV-methods with hexokinase and glucose-6-phosphate dehydrogenase. In Methods of Enzymatic Analysis, Vol. 6, pp. 163172 [Bergmeyer, HU editor]. Deerfield, IL: Weinheim Verlag Chemie.Google Scholar
Livingstone, MBE, Coward, WA, Prentice, AM, Davies, PSW, Strain, JJ, McKenna, PG, Mahoney, CA, White, JA, Stewart, CM & Kerr, M-JJ (1992) Daily energy expenditure in free-living children: comparison of heart-rate monitoring with the doubly labeled water method. American Journal of Clinical Nutrition 56, 343352.Google Scholar
Livingstone, MBE, Prentice, AM, Coward, WA, Ceesay, SM, Strain, JJ, McKenna, PG, Nevin, GB, Barker, ME & Hickey, RJ (1990) Simultaneous measurement of free-living energy expenditure by the doubly labeled water method and heart-rate monitoring. American Journal of Clinical Nutrition 52, 5965.Google Scholar
Lynch, J, Helmrich, S, Lakka, T, Kaplan, GA, Cohen, RD, Salonen, R & Salonen, JT (1996) Moderately intense physical activities and high levels of cardiorespiratory fitness reduce the risk of non-insulin-dependent diabetes mellitus in middle-aged men. Archives of Internal Medicine 156, 13071314.Google Scholar
MacMahon, S, Peto, R, Cutler, J, Collins, R, Sorlie, P, Neaton, J, Abbott, R, Godwin, J, Dyer, A & Stamler, J (1990) Blood pressure, stroke, and coronary heart disease. Lancet 335, 765774.CrossRefGoogle ScholarPubMed
Manson, JE, Nathan, DM, Krolewski, AS, Stampfer, MJ, Willett, WC & Hennekens, CH (1992) A prospective study of exercise and incidence of diabetes among US male physicians. Journal of the American Medical Association 268, 6367.Google Scholar
Manson, JE, Rimm, EB, Stampfer, MJ, Colditz, GA, Willett, WC, Krolewski, AS, Rosner, B, Hennekens, CH & Speizer, FE (1991) Physical activity and incidence of non-insulin-dependent diabetes mellitus in women. Lancet 338, 774778.CrossRefGoogle ScholarPubMed
Paffenbarger, RS, Blair, SN, Lee, I-M & Hyde, RT (1993) Measurement of physical activity to assess health effects in free-living populations. Medicine and Science in Sports and Exercise 25, 6070.CrossRefGoogle ScholarPubMed
Perry, IJ, Wannamethee, SG, Walker, MK, Thomson, AG & Whincup, PH (1995) Prospective study of risk factors for development of non-insulin dependent diabetes in middle aged British men. British Medical Journal 310, 560564.Google Scholar
Reaven, GM (1988) Role of insulin resistance in human disease. Diabetes 37, 15951607.Google Scholar
Siconolfi, SF, Lasater, TM, Snow, RCK & Carleton, RA (1985) Self-reported physical activity compared with maximal oxygen uptake. American Journal of Epidemiology 122, 101105.CrossRefGoogle ScholarPubMed
Sobey, WJ, Beer, SF, Carrington, CA, Clark, PM, Frank, BH, Grey, IP, Luzio, SD, Owens, DR, Schneider, AE, Siddle, K, Temple, RC & Hales, CN (1989) Sensitive and specific two site immunoradio-metric assays for human insulin, proinsulin, 65–66 split and 32–33 split proinsulins. Biochemical Journal 260, 535541.Google Scholar
Spurr, GB, Prentice, AM, Murgatroyd, PR, Goldberg, GR, Reina, JC & Christman, NT (1988) Energy expenditure from minute-by-minute heart-rate recording: comparison with indirect calorimetry. American Journal of Clinical Nutrition 48, 552559.Google Scholar
Wareham, NJ, Hennings, SHJ, Prentice, AM & Day, NE (1997) Feasibility of heart-rate monitoring to estimate total level and pattern of energy expenditure in a population-based epidemio-logical study: the Ely young cohort feasibility study 1994–5. British Journal of Nutrition 78, 889900.CrossRefGoogle Scholar
World Health Organization (1985) Diabetes Mellitus.Geneva:WHO.Google Scholar