PubMed Health⌕ Search

PubMed · 16151460

Greater than predicted decrease in resting energy expenditure with age: cross-sectional and longitudinal evidence.

Abstract

OBJECTIVE: To determine whether the age-related decrease in resting energy expenditure (REE) is explained by variations in body composition. DESIGN: In Study 1, adult subjects (20-70 years) from the Quebec Family Study were classified into five different age groups. Body composition was measured by hydrodensitometry to determine fat mass and fat-free mass as predictors of REE. In the youngest group of individuals these predictors were used to plot a reference regression that was then used to predict REE in the other age groups. In Study 2, this issue was investigated in a longitudinal design (6-year follow-up). Subjects were subdivided into three groups and a reference regression was plotted at the beginning of the follow-up and was then used to predict REE 6 years later in the three age groups. SUBJECTS: In Study 1, 627 adults (288 men and 339 women), aged between 20 and 70 years. In Study 2, 191 adults (93 men and 98 women). RESULTS: In Study 1, measured REE was 329, 302, 528 and 636 kJ/day (P < 0.0001) below predicted REE at 34, 44, 54 and 64 years, respectively. In Study 2 the most marked deviation from predicted REE in response to the 6-year follow-up in men was observed in young adults (-548 kJ/day, P < 0.001) while in women, the largest deviation occurred later in life (-720 kj/day, P < 0.001). CONCLUSION: Aging is accompanied by a decrease in REE that is significantly greater than what is predicted by variations in body composition. This decrease may reach a mean level of about 500-800 kj/day.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

G Alfonzo-González, E Doucet, C Bouchard, A Tremblay. 2006. Greater than predicted decrease in resting energy expenditure with age: cross-sectional and longitudinal evidence.. https://doi.org/10.1038/sj.ejcn.1602262

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Quantifying the treatment effect explained by markers in the presence of measurement error.

Surrogate markers or intermediate markers are important in identifying subjects with high risk of a serious disease or for monitoring disease progression of a subject on treatment. Quantifying the proportion of treatment effect (PTE) explained by markers has been studied extensively. Due to reasons such as biological variation, limited machine precision, etc. markers are generally measured with error. The estimated PTE ignoring the measurement error could be biased, which may lead to incorrect conclusions. In this article, we adjust for the measurement error using regression calibration to construct a less biased estimator of excess relative odds, a quantity to measure the treatment effect explained by markers. The method is applied to data from a clinical study in osteoporosis.

Absorptiometry, Photon↗

Less lipoatrophy and better lipid profile with abacavir as compared to stavudine: 96-week results of a randomized study.

OBJECTIVE: To assess lipoatrophy, other toxicities, and efficacy associated with abacavir as compared with stavudine in HIV-infected antiretroviral-naive patients. METHODS: This was a prospective, randomized, open trial, stratified by viral load and CD4 cell count, conducted January 2001 to July 2004. Two hundred thirty-seven adult patients with HIV infection initiating antiretroviral therapy were assigned to receive abacavir (n = 115) or stavudine (n = 122), both combined with lamivudine and efavirenz. The primary endpoint was the proportion of patients with lipoatrophy as assessed by physician and patient observation at 96 weeks. RESULTS: A lower proportion of patients assigned to abacavir developed clinical signs of lipoatrophy (4.8% vs. 38.3%; P < 0.001). These observations were confirmed by anthropometric data. Dual energy x-ray absorptiometry (DEXA) scans performed in 57 patients showed significantly greater total limb fat loss in the stavudine arm (-1579 vs. 913 g; P < 0.001). The lipid profile in abacavir patients presented more favorable changes in the levels of triglycerides (P = 0.03), high-density lipoprotein cholesterol (HDLc; P < 0.001), and apolipoprotein A1 (P < 0.001) as well as in the ratio between total cholesterol and HDLc (P = 0.005). Throughout the study, a higher proportion of patients in the stavudine group received lipid-lowering agents as compared to the abacavir group (17% vs. 4%; P = 0.002). Similar virologic and immunologic responses were observed. CONCLUSIONS: Assuming the limitations inherent to clinical assessment, this study shows a notably weaker association of abacavir with lipoatrophy than stavudine. DEXA scans and anthropometric measurements supported the clinical findings. In addition, the lipid changes that occurred were more favorable in patients receiving abacavir.

Absorptiometry, Photon↗

Estimating the response rate in the presence of measurement error.

In clinical research, it is often of interest to estimate the response rate (i.e. the proportion of subjects who achieve a clinically meaningful threshold) for a particular variable. The standard estimator of the response rate is generally biased in the presence of measurement error. The estimation accounting for the measurement error utilizing fully nonparametric (NP) methods is complicated and may not be efficient. Therefore, we propose a model-based approach assuming a parametric model for the true value and only the first few moments for the measurement error. The estimator for the true response rate and the variance for the estimator are derived. An innovative method using bootstrap simulation is proposed to check the model assumption. Simulations show that the proposed estimator outperforms a fully NP estimator if the model assumption for X holds. This method is applied to address a commonly occurring question in osteoporosis regarding response to treatment in terms of longitudinal changes in bone mineral density (BMD). Bootstrap simulations showed that the model utilized is appropriate. The proposed method can also be applied in other fields of clinical research.

Absorptiometry, Photon↗