1 University of Minnesota, School of Public Health, Division of Epidemiology, Minneapolis, MN, USA
2 Unilever Health Institute, Unilever Research & Development, Vlaardingen, The Netherlands
3 Institute for Nutrition Research, University of Oslo, Oslo, Norway
4 Wageningen University, Division of Human Nutrition and Epidemiology, Wageningen, The Netherlands, and Wageningen Centre for Food Sciences, Wageningen, The Netherlands
5 USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA
Correspondence: Mark A Pereira, Division of Epidemiology, 1300 South Second Street, Ste. 300, Minneapolis, MN 55454, USA. E-mail: pereira{at}epi.umn.edu
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Abstract |
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Materials and Methods The authors studied within-person variation in serum total and high density lipoprotein (HDL) cholesterol in 458 participants of 27 dietary intervention studies in Wageningen, The Netherlands, from 1976 to 1995.
Results For a median of 4 days between blood draws, the geometric mean of the within-person standard deviation was 0.13 mmol/l (5 mg/dl, coefficient of variation = 3.0%) for total cholesterol and 0.04 mmol/l (
1.5 mg/dl, coefficient of variation = 3.0%) for HDL cholesterol. In mixed-model linear regressions using within-person variance as the dependent variable and including lipid concentration and covariates listed below, within-person variance of both total cholesterol and HDL cholesterol was higher for greater number of days between blood draws and for self-selected diet rather than investigator-controlled diet. Within-person variance of total cholesterol only was higher for non-standardized versus standardized phlebotomy protocol and for female sex. The authors found evidence that the APOA4 347 (12/22 genotype) and MTP 493 (11 genotype) polymorphisms may increase the within-person variation in total cholesterol.
Conclusion Under certain study design (self-selected diet, use of non-standardized phlebotomy protocol) or participant characteristics (female, certain polymorphisms) within-person lipid variance is increased and required sample size will be greater. These findings may have important implications for the time and cost of such interventions.
Accepted 22 October 2003
Serum lipid levels vary considerably within individuals over short periods of time due to intrinsic factors, such as hormonal variation1 and illness,2 extrinsic factors such as diet,35 and analytical and quality control factors.68 However, little is known about the relative degree to which behavioural, biological, and genetic traits independently contribute to the within-person variation in serum cholesterol. Understanding the subject and study design characteristics that influence within-person variation in blood lipids has implications for the design of clinical trials examining, for example, dietary and drug interventions. Controlling the degree to which cholesterol fluctuations within individuals could increase the precision of its measurement and decrease the sample size needed to detect a particular effect size.
To address these issues the authors took advantage of a unique pooled data set from 27 dietary intervention studies of men and women from 1976 to 1995 at The Agricultural University of Wageningen, The Netherlands. The purpose was to determine which characteristics of the subjects and study design would predict the within-person variance (including biological, technological, and random sources) in serum total cholesterol and high density lipoprotein (HDL) cholesterol. A secondary purpose was to describe associations between 11 genetic polymorphisms involved in lipid metabolism919 and within-person variation in total cholesterol and HDL cholesterol. Finally, the authors sought to evaluate the potential importance of these predictors by quantifying the effect of within-person serum cholesterol variation on the sample size needed to detect a given intervention effect.
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Methods |
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Three studies, conducted in 1985 and 1986, were observed to have unusually high within-person variance. The within-person standard deviations of total cholesterol for these studies were 0.27, 0.29, and 0.39 mmol/l, two to three times larger than that of the other studies. The authors examined the mean lipid concentrations in these three studies and found an unusually large decrease between the first and the second measurements on the same treatment, compared with almost no difference in mean concentrations between the first and second measurements of the first treatment for all other studies pooled. The extreme difference in these three trials appeared to be due to unusually long periods between the two blood draws, when in fact much of the change in concentration was likely due to the dietary treatment rather than to biological variation. None of these three studies had multiple samples taken under the same conditions in subjects stabilized on their diets. The authors therefore excluded these three studies from analysis. Whereas the original pooled sample size comprised 585 individuals, after exclusions the present analyses included 458 individuals. Some 249 subjects participated in one trial, 132 in two trials, 63 in three trials, and 14 in four or more trials.
Specimen collection and analysis
For all studies, blood was collected after an overnight fast, serum was stored at 80°C, and total cholesterol and HDL cholesterol were determined with strict laboratory standardization as previously described.2224 Technical coefficients of variation for these assays were within the necessary requirements of the Centers for Disease Control and Prevention standardization programme. In some, but not all studies, phlebotomy technique was standardized by controlling posture of the subject before (standing) and during (either sitting or lying) the blood draw and having the same technicians draw blood from the same anatomical location at the same time of the same days of the week. Genetic polymorphisms were determined in DNA isolated from blood or mouth swabs using polymerase chain reaction (PCR) and restriction enzymes.919 Triglycerides or low density lipoprotein (LDL) cholesterol were not included in these analyses because triglycerides were only available on a subset of individuals (n = 342) and LDL lipoprotein cholesterol was calculated25 in these same subjects rather than directly measured.
Statistical methods
For each person, two or more serum lipid values were measured within each of two or more treatment arms within one or more studies. Analyses were in two steps: estimation of within-person variance and estimation of correlates of within-person variance. Within-person variance was estimated by half the squared difference between each follow-up measure and the first measure of an experimental arm. The interval (in days) was noted as a covariate, as we expected the variance to increase with increasing interval. Each of these estimates (correlated within arm) was theoretically distributed approximately as a 2 based on 1 d.f. A person contributed multiple estimates of variability from each study (in most cases one from each of two diet arms) in which he or she participated.
Correlates of within-person lipid variance were studied in a subsequent set of repeated measures regressions. In this case, the dependent variable was the natural logarithm of the within-person lipid variance of the given person, repeated over arms and studies. The independent variables included sex, age, 11 genetic polymorphisms, smoking (which rarely changed between studies), BMI, free-living total cholesterol, free-living HDL cholesterol, dietary control, phlebotomy standardization, and blood collection interval. For the genetic analyses there were a few instances of very small sample sizes resulting in uninterpretable results. Therefore, the less-frequently occurring homozygous and heterozygous individuals were grouped. This grouping did not appear to affect the results.
Exponentiation of the category-specific predicted natural logarithm of the within-person lipid variance yields an estimate of the geometric mean of the within-person variance itself. Like any log-normal variable, the geometric mean of the within-person lipid variance is about two-thirds of the mean of possible estimates of the within-person lipid variance; the estimated within-person standard deviation is about 82% of its mean.
Because many of the within-person variances are estimated from studies with only two diet treatment arms, each with two replications of serum lipid measurements, within-person variances per study are generally imprecisely measured, having two estimates based on 1 d.f. This investigation has little power to address whether within-person variance changes across studies. Although imprecision in the dependent variable does not bias regression coefficients, it does reduce precision of estimation, leading to higher P-values. Two approaches to address this limitation were used. First, the magnitude of the regression estimate was given more importance than the respective P-values (P-values were used descriptively). Second, analyses were repeated in the subgroup of participants who had more precisely defined estimates of within-person variance, because they participated in more than one study, or in studies in which more replicate serum lipid measurements were made. Findings were similar to those presented below (data not shown).
Another methodological point arises because the within-person variance in cholesterol increases with the passage of time from the date of the initial reference measurement.26 Intervals between measures in an arm ranged from 1 through 11 days. Preliminary regressions showed the log-variance tending to stabilize for intervals between 5 and 11 days (perhaps in part because there were few measurements at 5, 10, or 11 days), so we recoded all such intervals as 7 days. All regressions included the interval in days as a covariate.
We considered an alternative strategy for estimating variance components in a single step, namely, from mixed models with change in lipid concentration from the first measure in a given study arm as the dependent variable. We did not pursue this strategy as a solution to identifying within-person variance attributable to a particular predictor, because it is not possible in this method to assign a specific amount of within-person variance to a given stratum (e.g. male gender).
The primary analyses focused on within-person variance, because variances are additive across independent variables. However, it is more easily interpretable to express findings about within-person variation on the scale of measurement (i.e. standard deviation). Therefore, within-person standard deviation was computed as the square root of the geometric mean of the variance estimate and 95% CI per level of independent variables. For continuous variables tertiles were used to compute regression-adjusted least squares means.
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Results |
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Discussion |
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The biological mechanism behind within-person variation in cholesterol levels lies in intrinsic factors related to liver synthesis and tissue utilization, as regulated by genetic factors and their interactions with extrinsic factors. With regard to intrinsic factors, the hypothesis, based on previous findings,1 that women would have higher levels of within-person variation for total cholesterol was supported. This higher within-person variation in women likely reflects menstrual cycling. However, this hypothesis was not supported for HDL cholesterol. This higher level of variation, along with the smaller effects of dietary interventions on lipid levels in women in comparison to men, has been recently described from these same studies20 and may necessitate larger sample sizes for study of cholesterol interventions in women. Any effect of BMI and age on cholesterol variation may have been accounted for by adjustment for the free-living total cholesterol concentration. It is also quite possible that age and BMI are more important predictors of within-person variation in cholesterol in older and fatter populations.
Identification of genes that affect within-person variation in cholesterol independent of concentration may be important for improving the accuracy of screening, especially if these genes may also increase the risk for dyslipidaemia and coronary disease. In individuals with such mutations, effects of interventions and prediction of CHD risk based on one or two lipid measurements may be obscured by within-person variation. Although analyses of genetic polymorphisms were exploratory, two polymorphisms appeared to be associated with within-person variation in total (apolipoprotein A4 347 and microsomal triglyceride transfer protein 493) cholesterol. In one other study of men with peripheral arterial disease, those heterogeneous for the apolipoprotein B EcoRI polymorphism had higher within-individual variation of total serum cholesterol concentration.28 In contrast, in the present study a non-significant trend towards higher within-person variance for those who were homozygous for apolipoprotein B EcoRI was observed. However, these findings are tenuous due to small effect sizes, multiple comparisons, and the small sample sizes for some of the genotype subgroups.
One limitation of the present study is that there are many other extrinsic factors that are known to affect lipid concentration and that may therefore affect within-person lipid variation. These factors include physical activity level (through effects on synthesis and utilization and/or body composition), alcohol consumption, psychological stress, and acute illness.27 It is important to note that these factors did not appear to vary considerably in the trials included in the present analyses. Another limitation is the relative homogeneity of the population in terms of race, age, and BMI. Within-person variation is likely to vary within and among populations due to varying genetic and environmental factors. The conclusions drawn herein may therefore not necessarily apply to the planning of cholesterol interventions in other settings. Also, the individuals enrolled in these trials were free of chronic disease and did not have elevated serum lipid levels. Quantification of within-person variation in lipids, and the predictors of such in populations with elevated lipid concentrations or advanced atherosclerosis is desirable and may be particularly important for accurately assessing secondary prevention efforts.
These findings may have particular implications for the design of intervention studies when the outcome is serum cholesterol concentration. Given the estimation of within-person variability and the factors that predict this variability, the necessary sample size to detect important effects on serum cholesterol appears to be dependent on certain characteristics of the study design and the subjects who are recruited. One important issue concerns decreasing the days between replicate blood draws, which would decrease the within-person variation. However, as illustrated in Figure 1, this strategy cannot be recommended when designing studies as it is likely to result in a biased estimate of the true serum cholesterol concentration, and therefore a poor estimate of the change in serum cholesterol concentration from one treatment to another. As shown in Figure 1, the two measurements that are one day apart during each diet would demonstrate excellent precision but would underestimate true biological variability over time for this hypothetical individual (i.e. poor accuracy). The average cholesterol concentration from the two measurements is a poor estimate of the individuals' true mean during each dietary period. The response to the low saturated fat intervention will vary widely depending on whether the two consecutive measurements are taken during a peak or a trough in the periodicity of serum cholesterol. In this case, the true intervention effect for the individual would be 0.45 mmol/l (95% CI: 5.10, 4.65 mmol/l), but the observed effect would be 0.85 mmol/l (95% CI: 5.25, 4.40 mmol/l), an 89% overestimation. When each treatment lasts several weeks or more (as is usually the case), changes in cholesterol from one treatment to another are estimated more precisely and the chance of obtaining a significant intervention effect is optimized by taking multiple measurements on each treatment several days apart under controlled laboratory and experimental conditions. The within-person variance of total cholesterol, and therefore the sample size needed to detect a given effect size in trials with cross-over designs, may be considerably affected by the sex of the subjects and whether diet is controlled and phlebotomy is standardized. These findings may have important implications for the time and cost of such interventions.
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References |
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