目的探究中国成年人身体质量指数(body mass index,BMI)的长期变化轨迹及影响因素,为制定精准体重管理策略提供依据。方法利用中国家庭追踪调查2010—2022年共7期数据,以基线年龄≥18岁的6134名成年人为研究对象。采用组基轨迹模型(grou...目的探究中国成年人身体质量指数(body mass index,BMI)的长期变化轨迹及影响因素,为制定精准体重管理策略提供依据。方法利用中国家庭追踪调查2010—2022年共7期数据,以基线年龄≥18岁的6134名成年人为研究对象。采用组基轨迹模型(group-based trajectory modeling,GBTM)拟合BMI变化轨迹,采用多分类logistic回归分析各轨迹组的影响因素。结果GBTM模型识别出稳定正常组(25.29%)、渐进超重组(39.73%)、持续超重组(27.47%)、波动肥胖组(7.52%)4种BMI轨迹;多分类logistic回归分析显示,年龄为30~44岁(RRR=1.476,95%CI:1.205~1.807)和45~59岁(RRR=1.478,95%CI:1.199~1.822)、居住在城镇(RRR=1.252,95%CI:1.086~1.443)、锻炼(RRR=1.292,95%CI:1.101~1.517))是渐进超重组的危险因素,自评健康为不健康(RRR=0.593,95%CI:0.480~0.733)为渐进超重组的保护因素。男性(RRR=1.202,95%CI:1.032~1.401)、年龄为30~44岁(RRR=1.649,95%CI:1.315~2.067)和45~59岁(RRR=1.527,95%CI:1.209~1.929)、居住在城镇(RRR=1.361,95%CI:1.167~1.587)、在婚(RRR=1.634,95%CI:1.273~2.098)、家庭人均纯收入高(RRR=1.346,95%CI:1.113~1.628)、锻炼(RRR=1.359,95%CI:1.144~1.614)、患慢性病(RRR=1.317,95%CI:1.059~1.638)是持续超重组的危险因素;自评健康为不健康(RRR=0.670,95%CI:0.532~0.843)为持续超重组的保护因素。居住在城镇(RRR=1.861,95%CI:1.481~2.339)、在婚(RRR=1.671,95%CI:1.136~2.458)、饮酒(RRR=1.467,95%CI:1.095~1.966)、锻炼(RRR=1.316,95%CI:1.024~1.691)、患慢性病(RRR=1.837,95%CI:1.359~2.483)是波动肥胖组的危险因素。结论中国成年人BMI变化存在显著的群体异质性,需重点关注渐进超重与持续超重两类主要群体,并对居住在城镇、已婚、锻炼人群及中年、慢性病患者等高风险群体实施精准干预。展开更多
The concept of mass elevation effect (massenerhebungseffect, MEE) was introduced by A. de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and sn...The concept of mass elevation effect (massenerhebungseffect, MEE) was introduced by A. de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and snowline to occur at higher elevations in the central Alps than on their outer margins. It also has been widely observed in other areas of the world, but there have not been significant, let alone quantitative, researches on this phenomenon. Especially, it has been usually completely neglected in developing fitting mod- els of timberline elevation, with only longitude or latitude considered as impacting factors. This paper tries to quantify the contribution of MEE to timberline elevation. Considering that the more extensive the land mass and especially the higher the mountain base in the interior of land mass, the greater the mass elevation effect, this paper takes mountain base elevation (MBE) as the magnitude of MEE. We collect 157 data points of timberline elevation, and use their latitude, longitude and MBE as independent variables to build a multiple linear regression equation for timberline elevation in the southeastern Eurasian continent. The results turn out that the contribution of latitude, longitude and MBE to timberline altitude reach 25.11%, 29.43%, and 45.46%, respectively. North of northern latitude 32°, the three factors' contribution amount to 48.50%, 24.04%, and 27.46%, respectively; to the south, their contribution is 13.01%, 48.33%, and 38.66%, respectively. This means that MBE, serving as a proxy indi- cator of MEE, is a significant factor determining the elevation of alpine timberline. Compared with other factors, it is more stable and independent in affecting timberline elevation. Of course, the magnitude of the actual MEE is certainly determined by other factors, including mountain area and height, the distance to the edge of a land mass, the structures of the mountains nearby. These factors need to be inctuded in the study of MEE quantification in the future. This paper could help build up a high-accuracy and multi-scale elevation model for alpine timberline and even other altitudinal belts.展开更多
摘要目的探究中国成年人身体质量指数(body mass index,BMI)的长期变化轨迹及影响因素,为制定精准体重管理策略提供依据。方法利用中国家庭追踪调查2010—2022年共7期数据,以基线年龄≥18岁的6134名成年人为研究对象。采用组基轨迹模型(group-based trajectory modeling,GBTM)拟合BMI变化轨迹,采用多分类logistic回归分析各轨迹组的影响因素。结果GBTM模型识别出稳定正常组(25.29%)、渐进超重组(39.73%)、持续超重组(27.47%)、波动肥胖组(7.52%)4种BMI轨迹;多分类logistic回归分析显示,年龄为30~44岁(RRR=1.476,95%CI:1.205~1.807)和45~59岁(RRR=1.478,95%CI:1.199~1.822)、居住在城镇(RRR=1.252,95%CI:1.086~1.443)、锻炼(RRR=1.292,95%CI:1.101~1.517))是渐进超重组的危险因素,自评健康为不健康(RRR=0.593,95%CI:0.480~0.733)为渐进超重组的保护因素。男性(RRR=1.202,95%CI:1.032~1.401)、年龄为30~44岁(RRR=1.649,95%CI:1.315~2.067)和45~59岁(RRR=1.527,95%CI:1.209~1.929)、居住在城镇(RRR=1.361,95%CI:1.167~1.587)、在婚(RRR=1.634,95%CI:1.273~2.098)、家庭人均纯收入高(RRR=1.346,95%CI:1.113~1.628)、锻炼(RRR=1.359,95%CI:1.144~1.614)、患慢性病(RRR=1.317,95%CI:1.059~1.638)是持续超重组的危险因素;自评健康为不健康(RRR=0.670,95%CI:0.532~0.843)为持续超重组的保护因素。居住在城镇(RRR=1.861,95%CI:1.481~2.339)、在婚(RRR=1.671,95%CI:1.136~2.458)、饮酒(RRR=1.467,95%CI:1.095~1.966)、锻炼(RRR=1.316,95%CI:1.024~1.691)、患慢性病(RRR=1.837,95%CI:1.359~2.483)是波动肥胖组的危险因素。结论中国成年人BMI变化存在显著的群体异质性,需重点关注渐进超重与持续超重两类主要群体,并对居住在城镇、已婚、锻炼人群及中年、慢性病患者等高风险群体实施精准干预。
基金Foundation: National Natural Science Foundation of China, No.41030528 No.40971064 Innovation Project of State Key Laboratory of Resources and Environmental Information System (LREIS)
摘要The concept of mass elevation effect (massenerhebungseffect, MEE) was introduced by A. de Quervain about 100 years ago to account for the observed tendency for temperature-related parameters such as tree line and snowline to occur at higher elevations in the central Alps than on their outer margins. It also has been widely observed in other areas of the world, but there have not been significant, let alone quantitative, researches on this phenomenon. Especially, it has been usually completely neglected in developing fitting mod- els of timberline elevation, with only longitude or latitude considered as impacting factors. This paper tries to quantify the contribution of MEE to timberline elevation. Considering that the more extensive the land mass and especially the higher the mountain base in the interior of land mass, the greater the mass elevation effect, this paper takes mountain base elevation (MBE) as the magnitude of MEE. We collect 157 data points of timberline elevation, and use their latitude, longitude and MBE as independent variables to build a multiple linear regression equation for timberline elevation in the southeastern Eurasian continent. The results turn out that the contribution of latitude, longitude and MBE to timberline altitude reach 25.11%, 29.43%, and 45.46%, respectively. North of northern latitude 32°, the three factors' contribution amount to 48.50%, 24.04%, and 27.46%, respectively; to the south, their contribution is 13.01%, 48.33%, and 38.66%, respectively. This means that MBE, serving as a proxy indi- cator of MEE, is a significant factor determining the elevation of alpine timberline. Compared with other factors, it is more stable and independent in affecting timberline elevation. Of course, the magnitude of the actual MEE is certainly determined by other factors, including mountain area and height, the distance to the edge of a land mass, the structures of the mountains nearby. These factors need to be inctuded in the study of MEE quantification in the future. This paper could help build up a high-accuracy and multi-scale elevation model for alpine timberline and even other altitudinal belts.