Details

Autor(en) / Beteiligte

• Espeland, Mark A., PhD
• Bray, George A., MD
• Neiberg, Rebecca, MS
• Rejeski, W. Jack, PhD
• Knowler, William C., MD, DrPH
• Lang, Wei, PhD
• Cheskin, Lawrence J., MD
• Williamson, Don, PhD
• Lewis, C. Beth, MD
• Wing, Rena, PhD

Titel

Describing Patterns of Weight Changes Using Principal Components Analysis: Results from the Action for Health in Diabetes (Look AHEAD) Research Group

Ist Teil von

• Annals of epidemiology, 2009-10, Vol.19 (10), p.701-710

Ort / Verlag

United States: Elsevier Inc

Erscheinungsjahr

2009

Link zum Volltext

Quelle

MEDLINE

Beschreibungen/Notizen

• Purpose To demonstrate how principal components analysis can be used to describe patterns of weight changes in response to an intensive lifestyle intervention. Methods Principal components analysis was applied to monthly percent weight changes measured on 2,485 individuals enrolled in the lifestyle arm of the Action for Health in Diabetes (Look AHEAD) clinical trial. These individuals were 45 to 75 years of age, with type 2 diabetes and body mass indices greater than 25 kg/m2 . Associations between baseline characteristics and weight loss patterns were described using analyses of variance. Results Three components collectively accounted for 97.0% of total intrasubject variance: a gradually decelerating weight loss (88.8%), early versus late weight loss (6.6%), and a mid-year trough (1.6%). In agreement with previous reports, each of the baseline characteristics we examined had statistically significant relationships with weight loss patterns. As examples, males tended to have a steeper trajectory of percent weight loss and to lose weight more quickly than women. Individuals with higher hemoglobin A1c (glycosylated hemoglobin; HbA1c ) tended to have a flatter trajectory of percent weight loss and to have mid-year troughs in weight loss compared to those with lower HbA1c. Conclusions Principal components analysis provided a coherent description of characteristic patterns of weight changes and is a useful vehicle for identifying their correlates and potentially for predicting weight control outcomes.