- Email: [email protected]
Influence of sedentary behavior, physical activity, and cardiorespiratory fitness on the atherogenic index of plasma
Journal of Clinical Lipidology (2016) -, -–-
Original Contribution
Influence of sedentary behavior, physical activity, and cardiorespiratory fitness on the atherogenic index of plasma Meghan K. Edwards, MS, Michael J. Blaha, MD, MPH, Paul D. Loprinzi, PhD* Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, Oxford, MS, USA (Ms Edwards); Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins Hospital, Baltimore, MD, USA (Dr Blaha); and Jackson Heart Study Vanguard Center of Oxford, Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, Oxford, MS, USA (Dr Loprinzi) KEYWORDS: Cardiovascular disease; Epidemiology; High-density lipoprotein; Lipid profile; Triglycerides
BACKGROUND: Atherogenic index of plasma (AIP), calculated as LOG10 (triglycerides/high-density lipoprotein-cholesterol), may have greater utility over other metrics in predicting risk for cardiovascular disease (CVD). Previous work demonstrates the associations of physical activity (PA), sedentary behavior, and cardiorespiratory fitness (CRF) with triglycerides high-density lipoprotein (HDL-C) and CVD. OBJECTIVE: Limited research has examined these parameters and their potential additive associations with AIP, which was the purpose of this study. METHODS: Data from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) were used (N 5 307 adults 20–49 years). Sedentary behavior and moderate-to-vigorous physical activity (MVPA) were assessed via accelerometry. CRF was assessed via submaximal treadmill testing. Using median values, a PACS (Physical Activity Cardiorespiratory Sedentary) score (ranging from 0–3) was created, indicating the number of these positive characteristics (eg, above median CRF) each participant possessed. RESULTS: Above median MVPA was associated with significantly lower AIP values (b 5 20.09; 95% CI, 20.17 to 20.01; P 5 .03), whereas above-median CRF (b 5 20.0009; 95% CI, 20.09 to 0.08; P 5 .98) and below-median sedentary behavior (b 5 20.02; 95% CI, 20.13 to 0.08; P 5 .60) were not. Compared to those with a PACS score of 0, those with a score of 1 or 2 did not have significantly reduced AIP values (b 5 0.02; 95% CI, 20.06 to 0.10; P 5 .59, and b 5 0.007; 95% CI, 20.12 to 0.13; P 5 .90, respectively); however, those with a score of 3 did (b 5 20.14; 95% CI, 20.28 to 20.001; P 5 .04). CONCLUSION: Interventions targeting improvements in lipid profile (AIP) may wish to promote adequate MVPA over CRF or decreased sedentary behavior. Ó 2016 National Lipid Association. All rights reserved.
No funding was used to prepare this manuscript. * Corresponding author. The University of Mississippi, Director of Research Engagement—Jackson Heart Study Vanguard Center of Oxford, Director, Physical Activity Epidemiology Laboratory, Center for Health Behavior Research, School of Applied Sciences, Department of Health,
1933-2874/Ó 2016 National Lipid Association. All rights reserved. http://dx.doi.org/10.1016/j.jacl.2016.10.014
Exercise Science, and Recreation Management, 229 Turner Center, University, Oxford, MS 38677, USA. E-mail address: [email protected] Submitted July 28, 2016. Accepted for publication October 27, 2016.
2
Introduction Cardiovascular disease (ie, disorders of the heart and/or blood vessels) is the leading cause of mortality in the United States, accounting for nearly one third of all deaths.1 According to a report from the National Center for Health Statistics, life expectancy could increase by nearly 7 years if all forms of early onset of major cardiovascular disease (CVD) were prevented,2 along with improvements in nonCVD morbidity (eg, type II diabetes, depression, and certain types of cancer), as well as quality of life.3 In addition to its considerable cost of human lives (800.9 thousand deaths in 2013 in the US.),1 CVD presents an enormous financial burden. It has been estimated that coronary heart disease (CHD), which accounts for 50% of CVD mortality,4 costs the United States $108.9 billion annually in health care services, medications, and loss of work productivity.5 Fortunately, there are ways to combat and prevent the deleterious consequences of CVD. It is well established that lifestyle changes can reduce the risk for CVD morbidity and mortality.6 Lifestyle behaviors known to associate with CVD include, for example, poor dietary habits and physical inactivity. There is substantial evidence to support the effectiveness of physical activity and dietary changes on the outcome of CVD, including several statements from the American Heart Association7 on exercise,8 physical activity interventions,9 and diet/lifestyle recommendations.10 Notably, these behaviors have been shown to associate with a number of risk factors that may contribute to CVD, such as elevated total cholesterol, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), blood pressure, and glucose, as well as decreased high-density lipoprotein cholesterol (HDLC).11–14 Identifying predictive CVD risk factors is inarguably a critical step in the prevention of CVD and premature CVD-related mortality, as it assists in formulating the goals/objectives of intervention strategies (eg, decrease bodyweight, improve physical activity levels) aimed at risk reduction. An equally important step is to develop and implement the most effective screening strategies, designed to identify (as early as possible) individuals who are at the greatest risk for developing CVD. As an example of recent efforts to improve CVD screening strategies, the American College of Cardiology/ American Heart Association (ACC/AHA) task force recently developed the pooled cohort equations,15 which were specifically designed to predict 10-year risk of developing a first atherosclerotic cardiovascular disease–related event. Notably, these equations have recently been shown to have predictive validity with respect to CVD-specific,16 cancer-specific,17 and all-cause mortality.16 In addition to these new predictive equations, various indices have been developed to assess individual CVD risk factors. For example, several measures of lipid profile have been explored for their associations with health-related outcomes (eg, CVD). Atherogenic index of plasma (AIP) is calculated by logarithmically transforming the ratio of TG/HDL-C18
Journal of Clinical Lipidology, Vol -, No -, - 2016 and has been shown to have promising utility as a predictor of CVD, to a greater extent than other measures of lipid profile (TG/HDL-C ratio,19 Castelli Risk Index 1 [total cholesterol/HDL-C], Castelli risk index II [LDL-C/HDLC], and Atherogenic Coefficient [non-HDL-C/HDL-C]).20 This greater predictor of CVD from the AIP may possibly be due to a stronger correlation of AIP with lipoprotein particle size (specifically, the ratio of the smallest LDL particles to the largest LDL particles, which better correlates with total concentration of atherogenic lipoprotein particles).21 The potential utility of AIP as a predictor for CVD served as motivation for the present investigation of modifiable factors that may be associated with AIP. Specifically, we were interested in exploring the potential individual and additive associations of physical activity (specifically MVPA), sedentary behavior, and cardiorespiratory fitness (CRF) with AIP. This is a worthwhile investigation for various reasons, which are detailed in the subsequent two paragraphs. The health benefits of exercise are known include positive effects on lipid profile, mainly decreased TG and increased HDL-C levels11 (both of which are included within the AIP calculation).21 Notably, several studies have previously demonstrated an inverse association between aerobic exercise and AIP.22–24 We felt it important to also include CRF within the current model, given previous indications that CRF may be more closely associated with certain health outcomes (eg, CVD-related events and CVD-specific mortality) than physical activity levels.25 This may be due to the fact that age, gender, genotype, smoking status, weight status, and presence of medical conditions (eg, hypertension) are all considered, along with physical activity levels, to be determinants of CRF. Although physical activity is considered a principal determinant of CRF, with previous evidence to suggest a (positive) dose-response relationship between the two,26,27 CRF may be influenced by a number of additional modifiable and nonmodifiable determinants,25 thus making it important to examine its unique association with AIP in addition to any potential combined (with physical activity and/or sedentary behavior) associations on AIP. Although limited in investigation, a significant association between CRF and AIP is plausible, given significant (favorable) associations of CRF with CVD risk factors, including TG and HDL-C levels.28 Notably, sedentary behavior was included within our evaluated model given the associations of physical inactivity with CVD-related outcomes (eg, a dose-response relationship between sitting time and CVD-specific mortality, as well as CVD-related events),29 and recent suggestions that sedentary behavior may negatively influence health outcomes, independent of MVPA.30 The notion that sedentary behavior may independently associate with health outcomes has inspired examinations of the joint effects of sedentary behavior and MVPA on mortality31–33 and health-related quality of life34 for instance. To our knowledge, no previous study has explored the potential
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independent association of sedentary behavior on AIP. Additionally, to our knowledge, no previous study has evaluated both the independent and combined associations of MVPA, sedentary behavior, and CRF with AIP, which was the purpose of this study.
Methods Design and participants Data were extracted from the 2003–2004 NHANES. Analyses are based on data from 307 adults (20–49 years) who provided complete data for the study variables during a dietary-fasted morning session; notably, only adults 20– 49 years were eligible for the NHANES treadmill-based cardiorespiratory fitness assessment. The NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention that uses a representative sample of noninstitutionalized United States civilians selected by a complex, multistage, stratified, clustered probability design. The multistage design consists of 4 stages, including the identification of counties, segments (city blocks), random selection of households within the segments, and random selection of individuals within the households. Procedures were approved by the National Center for Health Statistics review board. Consent was obtained from all participants before data collection. Further information on NHANES methodology and data collection is available on the NHANES website (http:// www.cdc.gov/nchs/nhanes.htm).
Atherogenic index of plasma AIP was assessed from a blood sample and calculated as: LOG10 (triglycerdies/HDL-C), with triglycerides and HDL-C expressed in mmol/L. AIP was expressed as a continuous variable and also categorized as nonelevated or elevated AIP (.0.24).21
Measurement of MVPA and sedentary behavior Moderate-to-vigorous physical activity (MVPA) and sedentary behavior were assessed using the ActiGraph 7164 accelerometer. SAS (version 9.2) was used to reduce accelerometry data to those with $4 days of $10 h/d of monitored data and integrate it into 1-minute time intervals. Nonwear time was identified as $60 consecutive minutes of zero activity counts, with allowance for 1–2 minutes of activity counts between 0 and 100. The Troiano cut-point (2020 counts/min) was used to determine time spent in MVPA.35 Sedentary behavior was defined as counts/min #99.36 Using these cut points, total daily estimates of sedentary behavior and MVPA were calculated. Notably, a number of comparison studies have evaluated multiple accelerometer monitors and have demonstrated evidence of both adequate levels of relative validity (eg, when
3 compared to oxygen uptake) inter-instrument reliability).37–39
and
reliability
(eg,
Measurement of cardiorespiratory fitness (ie, maximum oxygen consumption [VO2max]) At the mobile examination survey, participants aged 12– 49 years old (only those 20–49 evaluated herein) were eligible for the treadmill-based cardiorespiratory fitness component.40 Trained health technicians performed the treadmillbased cardiorespiratory fitness exam. The protocol used was a submaximal treadmill test. Participants were assigned to one of eight treadmill test protocols. Determining which treadmill test protocol to use was based on the participant’s predicted VO2max using the Jackson41 prediction equation. The objective of each protocol was to elicit a heart rate that was approximately 75% of the participant’s agepredicted maximum heart rate (ie, 220-age) by the conclusion of the test. Each treadmill protocol included a 2-minute warm-up period, two 3-minute exercise stages, and a 2-minute cool-down period. During the treadmill test, heart rate was monitored continuously using an automated monitor with four electrodes connected to the thorax and abdomen of the participant and was recorded at the end of the warm-up period, each exercise stage, and each minute of recovery. Because the relationship between heart rate and oxygen consumption is assumed to be linear during exercise,42 VO2max was estimated by measuring the heart rate response to known levels of submaximal work. Notably, this NHANES VO2max assessment has demonstrated evidence of convergent validity by associating with healthrelated outcomes43 as well as concurrent validity by correlating with indirect calorimetry (r 5 0.79).44
Calculation of physical activity, cardiorespiratory, and sedentary behavior index score A PACS (Physical Activity Cardiorespiratory Sedentary) score45,46 was created that ranged from 0–3, indicating the number of positive characteristics. For each variable, a score of 0/1 was created using the median split method. Thus, those above the sample MVPA median of 32.2 min/d were given a score of ‘‘1’’; those below the sample sedentary median of 437.5 min/d were given a score of ‘‘1’’; and those above the sample VO2max median of 38.2 mL/kg/min were given a score of ‘‘1.’’ As an example, those who were above the median MVPA and VO2max and below the median sedentary behavior level were given a PACS index score of 3.
Statistical analysis All analyses accounted for the complex survey design used in NHANES. Multivariable linear and logistic regression models were used; AIP served as the outcome variable
Journal of Clinical Lipidology, Vol -, No -, - 2016
4 with the PACS index score serving as an independent variable (PACS score of 0 served as referent). Covariates included age (years; continuous), gender, race-ethnicity (Mexican American, non-Hispanic white, non-Hispanic black, other), serum cotinine (ng/mL; continuous), obesity (yes/no based on $ 30 kg/m2; measured height and weight), health-related quality of life,34 and healthy eating index.14 We also considered other covariates (eg, medication use), but their inclusion did not alter our observations. Statistical significance was set at P , .05.
Results Characteristics of the study variables are displayed in Table 1. Participants, on average, were 34 years, with gender equally distributed across the sample (54% men). The zero-order correlation between sedentary behavior with MVPA and CRF, respectively, were, r 5 20.23 (P , .001) and r 5 20.10 (P 5 .06). Further, the correlation coefficient for the association between MVPA and CRF was, r 5 0.33 (P , .05). In alignment with these correlation coefficients, there was no evidence of multicollinearity in the model as the highest individual variance inflation factor in the model was 1.6. With regard to the independent models, above-median MVPA (badjusted 5 20.09; 95% CI, 20.17 to 20.01; P 5 .03) but not above-median fitness (badjusted 5 20.0009; 95% CI, 20.09 to 20.08; P 5 .98) or below-median sedentary behavior (badjusted 5 20.02; 95% CI, 20.13 to 0.08; P 5 .60) were associated with AIP when expressed as a continuous variable. Results were similar when AIP was expressed as a binary (elevated/not elevated) variable: MVPA (ORadjusted 5 0.52; 95% CI, 0.28–0.95; P 5 .03); fitness (ORadjusted 5 0.84; 95% CI, 0.38–1.86; P 5 .65); and sedentary behavior (ORadjusted 5 0.93; 95% CI, 0.37– 2.35; P 5 .88). To illustrate, those above the median MVPA level (32 min/d a day; similar to government
Table 1
MVPA guidelines) had a 48% reduced odds of having an elevated AIP (ORadjusted 5 0.52; 95% CI, 0.28–0.95; P 5 .03). Notably, results were unchanged when expressing MVPA, fitness, and sedentary behavior as continuous variables instead of binary variables (data not shown). With regard to the combined model, and when compared to those with a PACS of 0, results were as follow: PACS score of 1 (badjusted 5 0.02; 95% CI, 20.06 to 0.10; P 5 .59), 2 (badjusted 5 0.007; 95% CI, 20.12 to 0.13; P 5 .90), and 3 (badjusted 5 20.14; 95% CI, 20.28 to 20.001; P 5 .04). Results were similar when AIP was expressed as a binary (elevated/not elevated) variable: PACS score of 1 (ORadjusted 5 0.67; 95% CI, 0.26–1.71; P 5 .38); PACS score of 2 (ORadjusted 5 0.77; 95% CI, 0.23–2.55; P 5 .65); and PACS score of 3 (ORadjusted 5 0.32; 95% CI, 0.10–1.00; P 5 .05). These findings of a PACS score of 3 (vs 0) being statistically significant align with the results of a three-way multiplicative interaction analysis (binteraction 5 20.16; 95% CI, 20.30, 20.03; P 5 .02). Finally, additional analyses evaluated the association of the sedentary behavior, cardiorespiratory fitness and MVPA on the triglyceride/HDLC ratio (ie, not the logarithm of this ratio). For this relationship, neither sedentary behavior (b 5 20.22; 95% CI, 20.89 to 0.43; P 5 .47), cardiorespiratory fitness (b 5 0.09; 95% CI, 20.27 to 0.47; P 5 .58) or MVPA (b 5 20.37; 95% CI, 20.85 to 0.20; P 5 .19) were associated with the triglyceride/HDLC ratio.
Discussion The present study used a multivariable model including MVPA, CRF, and sedentary behavior, in efforts to examine any independent or combined associations of these risk factors and AIP, which has been shown to associate with CVD.19,20 The main findings of this investigation are twofold: First, MVPA, but not sedentary behavior or
Weighted characteristics of the analyzed sample, 2003–2004 NHANES (N 5 307)
Characteristics
Mean/Proportion
SE
95% CI
Age (y) Male (%) Non-Hispanic white (%) Obese (%; $30 kg/m2) Elevated (.0.24) AIP AIP (mean) HRQOL (range 5 0–4) Cotinine (mean ng/mL) HEI (mean) Cardiorespiratory fitness (mean mL/kg/min) MVPA (mean min/d) Sedentary behavior (mean min/d)
34.2 54.1 72.1 25.5 17.8 20.05 0.16 61.0 51.0 39.3 35.4 442.9
0.48 2.5 3.5 2.6 2.5 0.02 0.02 9.1 0.76 0.65 1.28 5.73
33.1 to 35.2
20.09 to 20.007 0.12 to 0.21 41.6 to 80.4 49.4 to 52.6 37.9 to 40.6 32.7 to 38.2 430.7 to 455.1
AIP, atherogenic index of plasma; HEI, healthy eating index; HRQOL, health-related quality of life; MVPA, moderate-to-vigorous physical activity; SE, standard error.
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CRF, was significantly, independently associated with lower AIP values. Notably, this association was present when AIP was expressed as either a continuous or a binary variable. When expressed as a binary variable, MVPA was found to be significantly associated with a 48% reduced odds of having an elevated AIP (.0.24 mmol/L).21 Second, having favorable levels (where ‘‘favorable was considered as below median levels of sedentary behavior [442.9 min/ day] and above median levels of MVPA [35.4 min/day] and CRF [39.3 mL/kg/min]) of all three risk factors was significantly associated with a 68% reduced odds of having an elevated AIP, whereas having favorable levels of two factors or only one factor were not. Of note, none of these three parameters were statistically significantly associated with the nonlogarithmically transformed triglyceride/ HDLC ratio. Collectively, these findings suggest that having favorable levels of all three factors (ie, ,7.4 h/d of sedentary behavior, .35.4 min/d of MVPA, and a CRF value above 39.3) is associated with the lowest AIP values, but if one factor had to be selected to promote initially, our findings suggest that MVPA may be the sensible choice. Given evidence of a direct association between MVPA and CRF,26,27 it conceivable that increases in MVPA may also elicit improvements in CRF. Additionally, if a person is acquiring more MVPA throughout the day, they likely will displace some amount sedentary behavior time,47 thus increasing the odds that they may achieve favorable levels of all three factors. Notable strengths of the present study include the employed national sample, objective measurements of MVPA and sedentary behavior, and the evaluation of both independent and combined associations of three distinct variables with an under investigated marker of lipid profile that has been shown to associate with CVD, AIP.21 This study is limited by its relatively small sample size for lipid-related epidemiological studies. Another limitation is the cross-sectional design, which precludes the ability to ascertain causality. However, the implied conclusion that increases in MVPA are inversely associated with AIP is in alignment with previous intervention studies evaluating the effects of aerobic exercise on AIP.22–24 The biologic plausibility of this observation lies with previous work exploring the associations between exercise and lipid profile, including research highlighted within a recent review on this topic by Mann et al.6 For example, exercise is thought to lead to weight loss, improve insulin sensitivity, and leads to decreased free fatty acid flux to the liver with associated decreased production of VLDL. Mechanistically, exercise has also been shown to increase levels of lecithincholesterol acyltransferase (LCAT),48 a key enzyme for esterification of free cholesterol and transfer to maturing HDL capable of conducting reverse cholesterol transport49 and may also result in increases in lipoprotein lipase (LPL),50 the primary enzyme responsible for removing lipoprotein-associated triglycerides from the circulation.51 Furthermore, oxidative metabolism of fatty acids by muscle fibers is improved by exercise.52 In the absence of physical
5 activity and exercise, accumulations of visceral fat lead to increased production of large VLDL, with subsequent transfer of triglycerides to HDL and LDL via cholesterol ester transfer protein (CETP) and breakdown to small dense lipoprotein particles, such as the more atherogenic smalldense LDL particle, via lipases. When contemplating the rationale for the observed insignificant, independent associations between both CRF and sedentary behavior with AIP, it is important to consider the possible attenuation of the negative effects of sedentary behavior by MVPA,31,53 as well as the limitations of utilizing a submaximal treadmill test to calculate cardiorespiratory fitness (submaximal testing CRF values, of course, do not correlate perfectly with directly measured maximal oxygen uptake).44 In conclusion, the present study investigated the independent and combined associations of MVPA, sedentary behavior, and CRF with AIP. We recommend health care professionals to advocate for favorable levels of all three included risk factors (high CRF and MVPA levels and low sedentary behavior levels), which we found to be associated with the lowest AIP. If selecting a single risk factor to target for improvements, our results indicate that MVPA may be the most advantageous option to achieve favorable levels in AIP. Future prospective work on this topic is needed to potentially corroborate the present findings and would facilitate a deeper understanding of the best practices to promote a healthy lipid profile and reduced risk for CVD.
Acknowledgment Author contributions: All authors have approved the final version submitted for publication. Author Ms. Edwards drafted the first draft of the manuscript, critically revised the manuscript and interpreted the statistical results. Dr Blaha critically revised the manuscript and interpreted the statistical results. Dr Loprinzi computed the analyses, critically revised the manuscript and interpreted the statistical results.
Financial disclosure The authors report no conflicts of interest.
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41. Jackson AS, Blair SN, Mahar MT, Wier LT, Ross RM, Stuteville JE. Prediction of functional aerobic capacity without exercise testing. Med Sci Sports Exerc. 1990;22(6):863–870. 42. American College of Sports Medicine. ACSM’s Guidelines for Exercise Testing and Prescription. 6th edition Philadelphia, PA: Lippincott Williams and Wilkins Company; 1995. 43. Loprinzi PD, Zebardast N, Ramulu PY. Cardiorespiratory fitness and vision loss among young and middle-age U.S. adults. Am J Health Promot. 2015;29(4):226–229. 44. Wang CY, Haskell WL, Farrell SW, et al. Cardiorespiratory fitness levels among US adults 20-49 years of age: findings from the 19992004 National Health and Nutrition Examination Survey. Am J Epidemiol. 2010;171(4):426–435. 45. Loprinzi PD. Less Sitting, More Physical Activity, and Higher Cardiorespiratory Fitness: Associations With Weight Status. Mayo Clin Proc. 2016;91(4):538–539. 46. Edwards MK, Loprinzi PD. Associations between accelerometerassessed sedentary behavior, physical activity and objectivelymeasured cardiorespiratory fitness with red blood cell distribution width. Int J Cardiol. 2016;221:755–758. 47. Prince SA, Saunders TJ, Gresty K, Reid RD. A comparison of the effectiveness of physical activity and sedentary behaviour
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interventions in reducing sedentary time in adults: a systematic review and meta-analysis of controlled trials. Obes Rev. 2014;15(11): 905–919. Riedl I, Yoshioka M, Nishida Y, et al. Regulation of skeletal muscle transcriptome in elderly men after 6 weeks of endurance training at lactate threshold intensity. Exp Gerontol. 2010;45(11):896–903. Calabresi L, Franceschini G. Lecithin:cholesterol acyltransferase, high-density lipoproteins, and atheroprotection in humans. Trends Cardiovasc Med. 2010;20(2):50–53. Harrison M, Moyna NM, Zderic TW, et al. Lipoprotein particle distribution and skeletal muscle lipoprotein lipase activity after acute exercise. Lipids Health Dis. 2012;11:64. Eckel RH. Lipoprotein lipase. A multifunctional enzyme relevant to common metabolic diseases. N Engl J Med. 1989;320(16):1060–1068. Shaw I, Shaw BS, Krasilshchikov O. Comparison of aerobic and combined aerobic and resistance training on low-density lipoprotein cholesterol concentrations in men. Cardiovasc J Afr. 2009;20(5): 290–295. Petersen CB, Bauman A, Tolstrup JS. Total sitting time and the risk of incident diabetes in Danish adults (the DANHES cohort) over 5 years: a prospective study. Br J Sports Med. 2016;50: 1382–1387.
Original Contribution
Influence of sedentary behavior, physical activity, and cardiorespiratory fitness on the atherogenic index of plasma Meghan K. Edwards, MS, Michael J. Blaha, MD, MPH, Paul D. Loprinzi, PhD* Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, Oxford, MS, USA (Ms Edwards); Ciccarone Center for the Prevention of Heart Disease, Johns Hopkins Hospital, Baltimore, MD, USA (Dr Blaha); and Jackson Heart Study Vanguard Center of Oxford, Physical Activity Epidemiology Laboratory, Department of Health, Exercise Science and Recreation Management, The University of Mississippi, Oxford, MS, USA (Dr Loprinzi) KEYWORDS: Cardiovascular disease; Epidemiology; High-density lipoprotein; Lipid profile; Triglycerides
BACKGROUND: Atherogenic index of plasma (AIP), calculated as LOG10 (triglycerides/high-density lipoprotein-cholesterol), may have greater utility over other metrics in predicting risk for cardiovascular disease (CVD). Previous work demonstrates the associations of physical activity (PA), sedentary behavior, and cardiorespiratory fitness (CRF) with triglycerides high-density lipoprotein (HDL-C) and CVD. OBJECTIVE: Limited research has examined these parameters and their potential additive associations with AIP, which was the purpose of this study. METHODS: Data from the 2003–2004 National Health and Nutrition Examination Survey (NHANES) were used (N 5 307 adults 20–49 years). Sedentary behavior and moderate-to-vigorous physical activity (MVPA) were assessed via accelerometry. CRF was assessed via submaximal treadmill testing. Using median values, a PACS (Physical Activity Cardiorespiratory Sedentary) score (ranging from 0–3) was created, indicating the number of these positive characteristics (eg, above median CRF) each participant possessed. RESULTS: Above median MVPA was associated with significantly lower AIP values (b 5 20.09; 95% CI, 20.17 to 20.01; P 5 .03), whereas above-median CRF (b 5 20.0009; 95% CI, 20.09 to 0.08; P 5 .98) and below-median sedentary behavior (b 5 20.02; 95% CI, 20.13 to 0.08; P 5 .60) were not. Compared to those with a PACS score of 0, those with a score of 1 or 2 did not have significantly reduced AIP values (b 5 0.02; 95% CI, 20.06 to 0.10; P 5 .59, and b 5 0.007; 95% CI, 20.12 to 0.13; P 5 .90, respectively); however, those with a score of 3 did (b 5 20.14; 95% CI, 20.28 to 20.001; P 5 .04). CONCLUSION: Interventions targeting improvements in lipid profile (AIP) may wish to promote adequate MVPA over CRF or decreased sedentary behavior. Ó 2016 National Lipid Association. All rights reserved.
No funding was used to prepare this manuscript. * Corresponding author. The University of Mississippi, Director of Research Engagement—Jackson Heart Study Vanguard Center of Oxford, Director, Physical Activity Epidemiology Laboratory, Center for Health Behavior Research, School of Applied Sciences, Department of Health,
1933-2874/Ó 2016 National Lipid Association. All rights reserved. http://dx.doi.org/10.1016/j.jacl.2016.10.014
Exercise Science, and Recreation Management, 229 Turner Center, University, Oxford, MS 38677, USA. E-mail address: [email protected] Submitted July 28, 2016. Accepted for publication October 27, 2016.
2
Introduction Cardiovascular disease (ie, disorders of the heart and/or blood vessels) is the leading cause of mortality in the United States, accounting for nearly one third of all deaths.1 According to a report from the National Center for Health Statistics, life expectancy could increase by nearly 7 years if all forms of early onset of major cardiovascular disease (CVD) were prevented,2 along with improvements in nonCVD morbidity (eg, type II diabetes, depression, and certain types of cancer), as well as quality of life.3 In addition to its considerable cost of human lives (800.9 thousand deaths in 2013 in the US.),1 CVD presents an enormous financial burden. It has been estimated that coronary heart disease (CHD), which accounts for 50% of CVD mortality,4 costs the United States $108.9 billion annually in health care services, medications, and loss of work productivity.5 Fortunately, there are ways to combat and prevent the deleterious consequences of CVD. It is well established that lifestyle changes can reduce the risk for CVD morbidity and mortality.6 Lifestyle behaviors known to associate with CVD include, for example, poor dietary habits and physical inactivity. There is substantial evidence to support the effectiveness of physical activity and dietary changes on the outcome of CVD, including several statements from the American Heart Association7 on exercise,8 physical activity interventions,9 and diet/lifestyle recommendations.10 Notably, these behaviors have been shown to associate with a number of risk factors that may contribute to CVD, such as elevated total cholesterol, triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), blood pressure, and glucose, as well as decreased high-density lipoprotein cholesterol (HDLC).11–14 Identifying predictive CVD risk factors is inarguably a critical step in the prevention of CVD and premature CVD-related mortality, as it assists in formulating the goals/objectives of intervention strategies (eg, decrease bodyweight, improve physical activity levels) aimed at risk reduction. An equally important step is to develop and implement the most effective screening strategies, designed to identify (as early as possible) individuals who are at the greatest risk for developing CVD. As an example of recent efforts to improve CVD screening strategies, the American College of Cardiology/ American Heart Association (ACC/AHA) task force recently developed the pooled cohort equations,15 which were specifically designed to predict 10-year risk of developing a first atherosclerotic cardiovascular disease–related event. Notably, these equations have recently been shown to have predictive validity with respect to CVD-specific,16 cancer-specific,17 and all-cause mortality.16 In addition to these new predictive equations, various indices have been developed to assess individual CVD risk factors. For example, several measures of lipid profile have been explored for their associations with health-related outcomes (eg, CVD). Atherogenic index of plasma (AIP) is calculated by logarithmically transforming the ratio of TG/HDL-C18
Journal of Clinical Lipidology, Vol -, No -, - 2016 and has been shown to have promising utility as a predictor of CVD, to a greater extent than other measures of lipid profile (TG/HDL-C ratio,19 Castelli Risk Index 1 [total cholesterol/HDL-C], Castelli risk index II [LDL-C/HDLC], and Atherogenic Coefficient [non-HDL-C/HDL-C]).20 This greater predictor of CVD from the AIP may possibly be due to a stronger correlation of AIP with lipoprotein particle size (specifically, the ratio of the smallest LDL particles to the largest LDL particles, which better correlates with total concentration of atherogenic lipoprotein particles).21 The potential utility of AIP as a predictor for CVD served as motivation for the present investigation of modifiable factors that may be associated with AIP. Specifically, we were interested in exploring the potential individual and additive associations of physical activity (specifically MVPA), sedentary behavior, and cardiorespiratory fitness (CRF) with AIP. This is a worthwhile investigation for various reasons, which are detailed in the subsequent two paragraphs. The health benefits of exercise are known include positive effects on lipid profile, mainly decreased TG and increased HDL-C levels11 (both of which are included within the AIP calculation).21 Notably, several studies have previously demonstrated an inverse association between aerobic exercise and AIP.22–24 We felt it important to also include CRF within the current model, given previous indications that CRF may be more closely associated with certain health outcomes (eg, CVD-related events and CVD-specific mortality) than physical activity levels.25 This may be due to the fact that age, gender, genotype, smoking status, weight status, and presence of medical conditions (eg, hypertension) are all considered, along with physical activity levels, to be determinants of CRF. Although physical activity is considered a principal determinant of CRF, with previous evidence to suggest a (positive) dose-response relationship between the two,26,27 CRF may be influenced by a number of additional modifiable and nonmodifiable determinants,25 thus making it important to examine its unique association with AIP in addition to any potential combined (with physical activity and/or sedentary behavior) associations on AIP. Although limited in investigation, a significant association between CRF and AIP is plausible, given significant (favorable) associations of CRF with CVD risk factors, including TG and HDL-C levels.28 Notably, sedentary behavior was included within our evaluated model given the associations of physical inactivity with CVD-related outcomes (eg, a dose-response relationship between sitting time and CVD-specific mortality, as well as CVD-related events),29 and recent suggestions that sedentary behavior may negatively influence health outcomes, independent of MVPA.30 The notion that sedentary behavior may independently associate with health outcomes has inspired examinations of the joint effects of sedentary behavior and MVPA on mortality31–33 and health-related quality of life34 for instance. To our knowledge, no previous study has explored the potential
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independent association of sedentary behavior on AIP. Additionally, to our knowledge, no previous study has evaluated both the independent and combined associations of MVPA, sedentary behavior, and CRF with AIP, which was the purpose of this study.
Methods Design and participants Data were extracted from the 2003–2004 NHANES. Analyses are based on data from 307 adults (20–49 years) who provided complete data for the study variables during a dietary-fasted morning session; notably, only adults 20– 49 years were eligible for the NHANES treadmill-based cardiorespiratory fitness assessment. The NHANES is an ongoing survey conducted by the Centers for Disease Control and Prevention that uses a representative sample of noninstitutionalized United States civilians selected by a complex, multistage, stratified, clustered probability design. The multistage design consists of 4 stages, including the identification of counties, segments (city blocks), random selection of households within the segments, and random selection of individuals within the households. Procedures were approved by the National Center for Health Statistics review board. Consent was obtained from all participants before data collection. Further information on NHANES methodology and data collection is available on the NHANES website (http:// www.cdc.gov/nchs/nhanes.htm).
Atherogenic index of plasma AIP was assessed from a blood sample and calculated as: LOG10 (triglycerdies/HDL-C), with triglycerides and HDL-C expressed in mmol/L. AIP was expressed as a continuous variable and also categorized as nonelevated or elevated AIP (.0.24).21
Measurement of MVPA and sedentary behavior Moderate-to-vigorous physical activity (MVPA) and sedentary behavior were assessed using the ActiGraph 7164 accelerometer. SAS (version 9.2) was used to reduce accelerometry data to those with $4 days of $10 h/d of monitored data and integrate it into 1-minute time intervals. Nonwear time was identified as $60 consecutive minutes of zero activity counts, with allowance for 1–2 minutes of activity counts between 0 and 100. The Troiano cut-point (2020 counts/min) was used to determine time spent in MVPA.35 Sedentary behavior was defined as counts/min #99.36 Using these cut points, total daily estimates of sedentary behavior and MVPA were calculated. Notably, a number of comparison studies have evaluated multiple accelerometer monitors and have demonstrated evidence of both adequate levels of relative validity (eg, when
3 compared to oxygen uptake) inter-instrument reliability).37–39
and
reliability
(eg,
Measurement of cardiorespiratory fitness (ie, maximum oxygen consumption [VO2max]) At the mobile examination survey, participants aged 12– 49 years old (only those 20–49 evaluated herein) were eligible for the treadmill-based cardiorespiratory fitness component.40 Trained health technicians performed the treadmillbased cardiorespiratory fitness exam. The protocol used was a submaximal treadmill test. Participants were assigned to one of eight treadmill test protocols. Determining which treadmill test protocol to use was based on the participant’s predicted VO2max using the Jackson41 prediction equation. The objective of each protocol was to elicit a heart rate that was approximately 75% of the participant’s agepredicted maximum heart rate (ie, 220-age) by the conclusion of the test. Each treadmill protocol included a 2-minute warm-up period, two 3-minute exercise stages, and a 2-minute cool-down period. During the treadmill test, heart rate was monitored continuously using an automated monitor with four electrodes connected to the thorax and abdomen of the participant and was recorded at the end of the warm-up period, each exercise stage, and each minute of recovery. Because the relationship between heart rate and oxygen consumption is assumed to be linear during exercise,42 VO2max was estimated by measuring the heart rate response to known levels of submaximal work. Notably, this NHANES VO2max assessment has demonstrated evidence of convergent validity by associating with healthrelated outcomes43 as well as concurrent validity by correlating with indirect calorimetry (r 5 0.79).44
Calculation of physical activity, cardiorespiratory, and sedentary behavior index score A PACS (Physical Activity Cardiorespiratory Sedentary) score45,46 was created that ranged from 0–3, indicating the number of positive characteristics. For each variable, a score of 0/1 was created using the median split method. Thus, those above the sample MVPA median of 32.2 min/d were given a score of ‘‘1’’; those below the sample sedentary median of 437.5 min/d were given a score of ‘‘1’’; and those above the sample VO2max median of 38.2 mL/kg/min were given a score of ‘‘1.’’ As an example, those who were above the median MVPA and VO2max and below the median sedentary behavior level were given a PACS index score of 3.
Statistical analysis All analyses accounted for the complex survey design used in NHANES. Multivariable linear and logistic regression models were used; AIP served as the outcome variable
Journal of Clinical Lipidology, Vol -, No -, - 2016
4 with the PACS index score serving as an independent variable (PACS score of 0 served as referent). Covariates included age (years; continuous), gender, race-ethnicity (Mexican American, non-Hispanic white, non-Hispanic black, other), serum cotinine (ng/mL; continuous), obesity (yes/no based on $ 30 kg/m2; measured height and weight), health-related quality of life,34 and healthy eating index.14 We also considered other covariates (eg, medication use), but their inclusion did not alter our observations. Statistical significance was set at P , .05.
Results Characteristics of the study variables are displayed in Table 1. Participants, on average, were 34 years, with gender equally distributed across the sample (54% men). The zero-order correlation between sedentary behavior with MVPA and CRF, respectively, were, r 5 20.23 (P , .001) and r 5 20.10 (P 5 .06). Further, the correlation coefficient for the association between MVPA and CRF was, r 5 0.33 (P , .05). In alignment with these correlation coefficients, there was no evidence of multicollinearity in the model as the highest individual variance inflation factor in the model was 1.6. With regard to the independent models, above-median MVPA (badjusted 5 20.09; 95% CI, 20.17 to 20.01; P 5 .03) but not above-median fitness (badjusted 5 20.0009; 95% CI, 20.09 to 20.08; P 5 .98) or below-median sedentary behavior (badjusted 5 20.02; 95% CI, 20.13 to 0.08; P 5 .60) were associated with AIP when expressed as a continuous variable. Results were similar when AIP was expressed as a binary (elevated/not elevated) variable: MVPA (ORadjusted 5 0.52; 95% CI, 0.28–0.95; P 5 .03); fitness (ORadjusted 5 0.84; 95% CI, 0.38–1.86; P 5 .65); and sedentary behavior (ORadjusted 5 0.93; 95% CI, 0.37– 2.35; P 5 .88). To illustrate, those above the median MVPA level (32 min/d a day; similar to government
Table 1
MVPA guidelines) had a 48% reduced odds of having an elevated AIP (ORadjusted 5 0.52; 95% CI, 0.28–0.95; P 5 .03). Notably, results were unchanged when expressing MVPA, fitness, and sedentary behavior as continuous variables instead of binary variables (data not shown). With regard to the combined model, and when compared to those with a PACS of 0, results were as follow: PACS score of 1 (badjusted 5 0.02; 95% CI, 20.06 to 0.10; P 5 .59), 2 (badjusted 5 0.007; 95% CI, 20.12 to 0.13; P 5 .90), and 3 (badjusted 5 20.14; 95% CI, 20.28 to 20.001; P 5 .04). Results were similar when AIP was expressed as a binary (elevated/not elevated) variable: PACS score of 1 (ORadjusted 5 0.67; 95% CI, 0.26–1.71; P 5 .38); PACS score of 2 (ORadjusted 5 0.77; 95% CI, 0.23–2.55; P 5 .65); and PACS score of 3 (ORadjusted 5 0.32; 95% CI, 0.10–1.00; P 5 .05). These findings of a PACS score of 3 (vs 0) being statistically significant align with the results of a three-way multiplicative interaction analysis (binteraction 5 20.16; 95% CI, 20.30, 20.03; P 5 .02). Finally, additional analyses evaluated the association of the sedentary behavior, cardiorespiratory fitness and MVPA on the triglyceride/HDLC ratio (ie, not the logarithm of this ratio). For this relationship, neither sedentary behavior (b 5 20.22; 95% CI, 20.89 to 0.43; P 5 .47), cardiorespiratory fitness (b 5 0.09; 95% CI, 20.27 to 0.47; P 5 .58) or MVPA (b 5 20.37; 95% CI, 20.85 to 0.20; P 5 .19) were associated with the triglyceride/HDLC ratio.
Discussion The present study used a multivariable model including MVPA, CRF, and sedentary behavior, in efforts to examine any independent or combined associations of these risk factors and AIP, which has been shown to associate with CVD.19,20 The main findings of this investigation are twofold: First, MVPA, but not sedentary behavior or
Weighted characteristics of the analyzed sample, 2003–2004 NHANES (N 5 307)
Characteristics
Mean/Proportion
SE
95% CI
Age (y) Male (%) Non-Hispanic white (%) Obese (%; $30 kg/m2) Elevated (.0.24) AIP AIP (mean) HRQOL (range 5 0–4) Cotinine (mean ng/mL) HEI (mean) Cardiorespiratory fitness (mean mL/kg/min) MVPA (mean min/d) Sedentary behavior (mean min/d)
34.2 54.1 72.1 25.5 17.8 20.05 0.16 61.0 51.0 39.3 35.4 442.9
0.48 2.5 3.5 2.6 2.5 0.02 0.02 9.1 0.76 0.65 1.28 5.73
33.1 to 35.2
20.09 to 20.007 0.12 to 0.21 41.6 to 80.4 49.4 to 52.6 37.9 to 40.6 32.7 to 38.2 430.7 to 455.1
AIP, atherogenic index of plasma; HEI, healthy eating index; HRQOL, health-related quality of life; MVPA, moderate-to-vigorous physical activity; SE, standard error.
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CRF, was significantly, independently associated with lower AIP values. Notably, this association was present when AIP was expressed as either a continuous or a binary variable. When expressed as a binary variable, MVPA was found to be significantly associated with a 48% reduced odds of having an elevated AIP (.0.24 mmol/L).21 Second, having favorable levels (where ‘‘favorable was considered as below median levels of sedentary behavior [442.9 min/ day] and above median levels of MVPA [35.4 min/day] and CRF [39.3 mL/kg/min]) of all three risk factors was significantly associated with a 68% reduced odds of having an elevated AIP, whereas having favorable levels of two factors or only one factor were not. Of note, none of these three parameters were statistically significantly associated with the nonlogarithmically transformed triglyceride/ HDLC ratio. Collectively, these findings suggest that having favorable levels of all three factors (ie, ,7.4 h/d of sedentary behavior, .35.4 min/d of MVPA, and a CRF value above 39.3) is associated with the lowest AIP values, but if one factor had to be selected to promote initially, our findings suggest that MVPA may be the sensible choice. Given evidence of a direct association between MVPA and CRF,26,27 it conceivable that increases in MVPA may also elicit improvements in CRF. Additionally, if a person is acquiring more MVPA throughout the day, they likely will displace some amount sedentary behavior time,47 thus increasing the odds that they may achieve favorable levels of all three factors. Notable strengths of the present study include the employed national sample, objective measurements of MVPA and sedentary behavior, and the evaluation of both independent and combined associations of three distinct variables with an under investigated marker of lipid profile that has been shown to associate with CVD, AIP.21 This study is limited by its relatively small sample size for lipid-related epidemiological studies. Another limitation is the cross-sectional design, which precludes the ability to ascertain causality. However, the implied conclusion that increases in MVPA are inversely associated with AIP is in alignment with previous intervention studies evaluating the effects of aerobic exercise on AIP.22–24 The biologic plausibility of this observation lies with previous work exploring the associations between exercise and lipid profile, including research highlighted within a recent review on this topic by Mann et al.6 For example, exercise is thought to lead to weight loss, improve insulin sensitivity, and leads to decreased free fatty acid flux to the liver with associated decreased production of VLDL. Mechanistically, exercise has also been shown to increase levels of lecithincholesterol acyltransferase (LCAT),48 a key enzyme for esterification of free cholesterol and transfer to maturing HDL capable of conducting reverse cholesterol transport49 and may also result in increases in lipoprotein lipase (LPL),50 the primary enzyme responsible for removing lipoprotein-associated triglycerides from the circulation.51 Furthermore, oxidative metabolism of fatty acids by muscle fibers is improved by exercise.52 In the absence of physical
5 activity and exercise, accumulations of visceral fat lead to increased production of large VLDL, with subsequent transfer of triglycerides to HDL and LDL via cholesterol ester transfer protein (CETP) and breakdown to small dense lipoprotein particles, such as the more atherogenic smalldense LDL particle, via lipases. When contemplating the rationale for the observed insignificant, independent associations between both CRF and sedentary behavior with AIP, it is important to consider the possible attenuation of the negative effects of sedentary behavior by MVPA,31,53 as well as the limitations of utilizing a submaximal treadmill test to calculate cardiorespiratory fitness (submaximal testing CRF values, of course, do not correlate perfectly with directly measured maximal oxygen uptake).44 In conclusion, the present study investigated the independent and combined associations of MVPA, sedentary behavior, and CRF with AIP. We recommend health care professionals to advocate for favorable levels of all three included risk factors (high CRF and MVPA levels and low sedentary behavior levels), which we found to be associated with the lowest AIP. If selecting a single risk factor to target for improvements, our results indicate that MVPA may be the most advantageous option to achieve favorable levels in AIP. Future prospective work on this topic is needed to potentially corroborate the present findings and would facilitate a deeper understanding of the best practices to promote a healthy lipid profile and reduced risk for CVD.
Acknowledgment Author contributions: All authors have approved the final version submitted for publication. Author Ms. Edwards drafted the first draft of the manuscript, critically revised the manuscript and interpreted the statistical results. Dr Blaha critically revised the manuscript and interpreted the statistical results. Dr Loprinzi computed the analyses, critically revised the manuscript and interpreted the statistical results.
Financial disclosure The authors report no conflicts of interest.
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