Dietary patterns of rural older adults are associated with weight and nutritional status.
OBJECTIVE
To characterize dietary patterns of rural older adults and relate patterns to weight and nutritional status.


METHODS
Cross-sectional.


METHODS
Rural Pennsylvania.


METHODS
One hundred seventy-nine community-dwelling adults aged 66 to 87 years.


METHODS
A home visit was conducted to collect demographic, health behavior, and anthropometric data and a blood sample. Five 24-hour dietary recall were administered. Cluster analysis classified participants into dietary patterns using food subgroup servings. Chi-square, analysis of covariance, and logistic regression were used to assess differences across clusters.


RESULTS
A low-nutrient-dense cluster (n=107), with higher intake of breads, sweet breads/desserts, dairy desserts, processed meats, eggs, and fats/oils, and a high-nutrient-dense cluster (n=72) with higher intake of cereals, dark green/yellow vegetables, other vegetables, citrus/melons/berries, fruit juices, other fruits, milks, poultry, fish, and beans, were identified. Those in the high-nutrient-dense cluster had lower energy intake; higher energy-adjusted intake of fiber, iron, zinc, folate, and vitamins B(6), B(12), and D; higher Healthy Eating Index scores; higher plasma vitamin B(12) levels; and a lower waist circumference. Those with a low-nutrient-dense dietary pattern were twice as likely to be obese, twice as likely to have low plasma vitamin B(12) levels, and three to 17 times more likely to have low nutrient intake.


CONCLUSIONS
This study provides support for recommending a high-nutrient-dense dietary pattern for older adults. Behavioral interventions encouraging diets characterized by high-nutrient-dense foods may improve weight and nutritional status of older adults.
Ledikwe
Jenny H
JH
Department of Nutritional Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, USA.
Smiciklas-Wright
Helen
H
Mitchell
Diane C
DC
Miller
Carla K
CK
Jensen
Gordon L
GL
D000368:Aged; D000704:Analysis of Variance; D001824:Body Constitution; D001835:Body Weight; D016009:Chi-Square Distribution; D016000:Cluster Analysis; D003677:Deficiency Diseases / Q000097:blood / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D004034:Diet Surveys; D005247:Feeding Behavior; D005260:Female; D015577:Geriatric Assessment; D007722:Health Knowledge, Attitudes, Practice; D006801:Humans; D016015:Logistic Models; D008297:Male; D020380:Needs Assessment; D015596:Nutrition Assessment; D009752:Nutritional Status; D009765:Obesity / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D010414:Pennsylvania / Q000453:epidemiology; D011502:Protein-Energy Malnutrition / Q000097:blood / Q000453:epidemiology / Q000209:etiology / Q000517:prevention & control; D012423:Rural Health / Q000706:statistics & numerical data*