Alessa Hala B, Chomistek Andrea K, Hankinson Susan E, Barnett Junaidah B, Rood Jennifer, Matthews Charles E, Rimm Eric B, Willett Walter C, Hu Frank B, Tobias Deirdre K
1Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA; 2Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN; 3Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; 4Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA; 5Nutritional Immunology Laboratory, Human Nutrition Research Center on Aging and Friedman School of Nutrition Science & Policy, Tufts University, Boston, MA; 6Clinical Chemistry Laboratory and Stable Isotope Library, Pennington Biomedical Research Center, Baton Rouge, LA; 7Nutritional Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; 8Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, and 9Division of Preventive Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.
Med Sci Sports Exerc. 2017 Sep;49(9):1817-1825. doi: 10.1249/MSS.0000000000001287.
Although physical activity is an established risk factor for chronic disease prevention, the specific mechanisms underlying these relationships are poorly understood. We examined the associations between total activity counts and moderate-vigorous physical activity (MVPA) measured by accelerometer, and physical activity energy expenditure measured by doubly labeled water, with plasma levels of proinsulin, insulin, c-peptide, insulin growth factor binding protein-3, insulin growth factor-1, adiponectin, leptin, and leptin-sR.
We conducted a cross-sectional analysis of 526 healthy US women in the Women's Lifestyle Validation Study, 2010 to 2012. We performed multiple linear regression models adjusting for potential lifestyle and health-related confounders to assess the associations between physical activity, measured in quartiles (Q) and biomarkers.
Participants in Q4 versus Q1 of total activity counts had lower proinsulin (-20%), c-peptide (-7%), insulin (-31%), and leptin (-46%) levels, and higher adiponectin (55%), leptin-sR (25%), and insulin growth factor-1 (9.6%) levels (all P trend ≤ 0.05). Participants in Q4 versus Q1 of MVPA had lower proinsulin (-26%), c-peptide (-7%), insulin (-32%), and leptin (-40%) levels, and higher adiponectin (31%) and leptin-sR (22%) levels (all P trend ≤ 0.05). Further adjustment for body mass index (BMI) attenuated these associations, but the associations with adipokines remained significant. Those in Q4 versus Q1 of physical activity energy expenditure had lower leptin (-21%) and higher leptin-sR (10%) levels (all P trend ≤ 0.05), after additional adjustment for BMI. In the sensitivity analysis, the associations were similar but attenuated when physical activity was measured using the subjective physical activity questionnaire.
Our data suggest that greater physical activity is modestly associated with favorable levels of cardiometabolic and endocrine biomarkers, where the strongest associations were found with accelerometer-measured physical activity. These associations may be only partially mediated through BMI, further supporting the role of physical activity in the reduction of cardiometabolic and endocrine disease risk, independent of adiposity.
尽管身体活动是预防慢性病的既定风险因素,但这些关系背后的具体机制仍知之甚少。我们研究了通过加速度计测量的总活动计数和中等强度至剧烈身体活动(MVPA),以及通过双标水测量的身体活动能量消耗,与胰岛素原、胰岛素、C肽、胰岛素生长因子结合蛋白-3、胰岛素生长因子-1、脂联素、瘦素和瘦素可溶性受体(leptin-sR)的血浆水平之间的关联。
在2010年至2012年的女性生活方式验证研究中,我们对526名健康美国女性进行了横断面分析。我们进行了多元线性回归模型,对潜在的生活方式和健康相关混杂因素进行调整,以评估按四分位数(Q)测量的身体活动与生物标志物之间的关联。
总活动计数处于Q4组的参与者与Q1组相比,胰岛素原水平降低20%、C肽降低7%、胰岛素降低31%、瘦素降低46%,脂联素升高55%、瘦素可溶性受体升高25%、胰岛素生长因子-1升高9.6%(所有P趋势≤0.05)。MVPA处于Q4组的参与者与Q1组相比,胰岛素原水平降低26%、C肽降低7%、胰岛素降低32%、瘦素降低40%,脂联素升高31%、瘦素可溶性受体升高22%(所有P趋势≤0.05)。进一步对体重指数(BMI)进行调整后,这些关联减弱,但与脂肪因子的关联仍然显著。在对BMI进行额外调整后,身体活动能量消耗处于Q4组的参与者与Q1组相比,瘦素降低21%,瘦素可溶性受体升高10%(所有P趋势≤0.05)。在敏感性分析中,当使用主观身体活动问卷测量身体活动时,关联相似但减弱。
我们的数据表明,更多的身体活动与心血管代谢和内分泌生物标志物的良好水平适度相关,其中与加速度计测量的身体活动关联最强。这些关联可能仅部分通过BMI介导,进一步支持了身体活动在降低心血管代谢和内分泌疾病风险中独立于肥胖的作用。