Bharathi Ankalmadagu Venkatasubbareddy, Kurpad Anura Vishwanath, Thomas Tinku, Yusuf Salim, Saraswathi Govindachar, Vaz Mario
St. John's Research Institute, St. John's National Academy of Health Sciences, Opp BDA Complex, Koramangala, Bangalore-560034, Karnataka, India.
Asia Pac J Clin Nutr. 2008;17(1):178-85.
To develop Food Frequency Questionnaires (FFQs) and nutrient databases for urban and rural Indian populations with diverse dietary habits for the PURE (Prospective Urban and Rural Epidemiological) pilot study.
24 hour dietary recalls were obtained from 84 rural and 60 urban subjects. From a comprehensive food list, separate FFQs were developed for the two groups. Nutrient analysis of the FFQ required the selection of foods, development of recipes and application of these to cooked foods to develop a nutrient database. The FFQs were piloted in 80 urban and 77 rural subjects. Separately for each group, a stepwise regression method was used to identify foods contributing to a cumulative 90% of variance to total energy intake. Nutrient and food group intakes were compared using an independent t-test.
The urban and the rural FFQs contained 129 and 102 foods respectively, of which 82 foods were common to both. Fourteen urban foods and eight rural foods explained a cumulative 90% of variance for total energy intake. Daily intakes for most nutrients and food groups were two to three fold higher in the urban than in the rural group.
In Indian populations with diverse dietary habits, using standard methods to develop separate FFQs can capture dietary intakes adequately. To develop nutrient databases, substitution of local food composition tables with data from other sources using standard methods to match foods can be adopted.
为开展PURE(城乡前瞻性流行病学)试点研究,针对饮食习惯各异的印度城乡人群开发食物频率问卷(FFQ)和营养数据库。
收集了84名农村和60名城市受试者的24小时饮食回忆。从一份综合食物清单中,为两组分别编制了单独的FFQ。FFQ的营养分析需要选择食物、制定食谱并将其应用于熟食以建立营养数据库。在80名城市和77名农村受试者中对FFQ进行了预试验。对每组分别采用逐步回归方法来确定对总能量摄入方差累积贡献率达90%的食物。使用独立t检验比较营养和食物组摄入量。
城市和农村的FFQ分别包含129种和102种食物,其中82种食物两组共有。14种城市食物和8种农村食物解释了总能量摄入方差的累积贡献率达90%。大多数营养素和食物组的每日摄入量城市组比农村组高两到三倍。
在饮食习惯各异的印度人群中,采用标准方法编制单独的FFQ能够充分获取饮食摄入量。为建立营养数据库,可采用标准方法用其他来源的数据替代当地食物成分表以匹配食物。