Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity.
Applying data envelopment analysis to preventive medicine: a novel method for constructing a personalized risk model of obesity.
复制标题
DOI：
10.1371/journal.pone.0126443
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Kayama T
中科院分区：
文献类型：
--
作者：
Narimatsu H;Nakata Y;Nakamura S;Sato H;Sho R;Otani K;Kawasaki R;Kubota I;Ueno Y;Kato T;Yamashita H;Fukao A;Kayama T
数据包络分析(DEA)是一种尚未应用于肥胖研究领域的运筹学方法。然而，DEA可能被用来评估个人对肥胖的易感性，这可能有助于建立有效的肥胖发病风险模型。因此，我们进行了这项研究，以评估应用DEA预测肥胖的可行性，通过计算有效性分数和评估风险模型的有效性。在这项研究中，我们评估了高形研究的数据，这是一项基于人群的队列研究(带有跟踪研究)，对象是40岁的日本人。在我们的分析中，我们使用了DEA的面向投入的Charnes-Cooper-Rhodes模型，并将决策单元定义为单独的主体。投入被定义为(1)锻炼(以消耗的卡路里衡量)和(2)食物摄入量的反比(以摄取的卡路里衡量)。输出被定义为身体质量指数(BMI)的倒数。使用受试者单核苷酸多态的β系数，我们计算了他们的遗传易感性得分。基线调查的1,620名参与者和后续调查的708名参与者都获得了效率得分和GPS。为了比较这些关联的强弱，我们使用了多元线性回归模型。采用多元线性回归分析方法，以体重指数(BMI)为因变量，GPS和效率评分为解释变量，年龄、性别为人口学对照，评价遗传因素和效率评分对体重指数(BMI)的影响。我们的结果表明，所有因素都有统计学意义(p<；0.05)，调整后的R2值为0.66。因此，有可能使用数据包络分析来预测环境因素导致的肥胖，从而建立一个适合肥胖风险的模型。
Data envelopment analysis (DEA) is a method of operations research that has not yet been applied in the field of obesity research. However, DEA might be used to evaluate individuals’ susceptibility to obesity, which could help establish effective risk models for the onset of obesity. Therefore, we conducted this study to evaluate the feasibility of applying DEA to predict obesity, by calculating efficiency scores and evaluating the usefulness of risk models. In this study, we evaluated data from the Takahata study, which was a population-based cohort study (with a follow-up study) of Japanese people who are >40 years old. For our analysis, we used the input-oriented Charnes-Cooper-Rhodes model of DEA, and defined the decision-making units (DMUs) as individual subjects. The inputs were defined as (1) exercise (measured as calories expended) and (2) the inverse of food intake (measured as calories ingested). The output was defined as the inverse of body mass index (BMI). Using the β coefficients for the participants’ single nucleotide polymorphisms, we then calculated their genetic predisposition score (GPS). Both efficiency scores and GPS were available for 1,620 participants from the baseline survey, and for 708 participants from the follow-up survey. To compare the strengths of the associations, we used models of multiple linear regressions. To evaluate the effects of genetic factors and efficiency score on body mass index (BMI), we used multiple linear regression analysis, with BMI as the dependent variable, GPS and efficiency scores as the explanatory variables, and several demographic controls, including age and sex. Our results indicated that all factors were statistically significant (p < 0.05), with an adjusted R2 value of 0.66. Therefore, it is possible to use DEA to predict environmentally driven obesity, and thus to establish a well-fitted model for risk of obesity.
登录
查看更多内容
影响因子：
3.4
作者：
Alburquerque-Sendín, Francisco;Barberio-Mariano, Enzo;Rebelatto, José R.
通讯作者：
Rebelatto, José R.
影响因子：
3.1
作者：
Kohno, Kei;Narimatsu, Hiroto;Kato, Takeo
通讯作者：
Kato, Takeo
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
7.7
作者：
Funatogawa, Ikuko;Funatogawa, Takashi;Nakao, Mutsuhiro;Karita, Kanae;Yano, Eiji
通讯作者：
Yano, Eiji
影响因子：
5.4
作者：
BANKER, RD;CONRAD, RF;STRAUSS, RP
通讯作者：
STRAUSS, RP