Diet Quality Is an Indicator of Disease Risk Factors in Hispanic College Freshmen

Published:February 22, 2019DOI:



      No studies have assessed the relationship between diet quality, using the Healthy Eating Index (HEI), and adiposity, physical activity, and metabolic disease risk factors in a Hispanic college population.


      To assess associations between diet quality and adiposity, metabolic health, and physical activity levels in a Hispanic college freshman population.


      This was a cross-sectional study. Measurements were obtained during a 4-hour in-person visit and included demographic information via questionnaire, height, weight, waist circumference, body mass index, body fat via BodPod, hepatic fat, visceral adipose tissue (VAT) and subcutaneous adipose tissue via magnetic resonance imaging, glucose, insulin, homeostatic model assessment of insulin resistance (HOMA-IR), and lipids via blood draw from fasting subjects, physical activity (ie, step counts per day and time spent in different intensity levels) via 7-day accelerometry, and dietary intake via three to four 24-hour dietary recalls. Dietary quality was calculated using the HEI-2015.


      Hispanic college freshmen (n=92), 18 to 19 years, 49% male, who were enrolled at University of Texas at Austin from 2014 to 2015.

      Main outcome measures

      Main outcome measures were diet quality and adiposity, metabolic health, and physical activity levels.

      Statistical analyses performed

      Linear regressions determined if dietary quality is related to adiposity, metabolic, and physical activity outcomes. A priori covariates included sex, body fat, and body mass index percentile (for metabolic models), and moderate and vigorous physical activity (MVPA, for adiposity and metabolic models).


      The average HEI-2015 total score was 54.9±13.4. A 1-point increase in HEI score was associated with 1.5 mL lower VAT (P=0.013); 8 minutes per day higher light activity (P=0.008), and 107 more step counts per day (P=0.002); and 0.10 μg/mL lower insulin (P=0.046) and 0.5 U lower HOMA-IR (P<0.001).


      Results suggest that small improvements in diet quality may be positively associated with a reduction in metabolic disease risk, during a critical time period in a young person’s life.


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        • Racette S.B.
        • Deusinger S.S.
        • Strube M.J.
        • Highstein G.R.
        • Deusinger R.H.
        Weight changes, exercise, and dietary patterns during freshman and sophomore years of college.
        J Am Coll Health. 2005; 53: 245-251
        • Silliman K.
        • Rodas-Fortier K.
        • Neyman M.
        A survey of dietary and exercise habits and perceived barriers to following a healthy lifestyle in a college population.
        Cal J Health Promot. 2004; 18: 281
        • Yahia N.
        • Brown C.A.
        • Rapley M.
        • Chung M.
        Level of nutrition knowledge and its association with fat consumption among college students.
        BMC Public Health. 2016; 16: 1-10
        • Nelson M.C.
        • Story M.
        • Larson N.I.
        • Neumark-Sztainer D.
        • Lytle L.A.
        Emerging adulthood and college-aged youth: An overlooked age for weight-related behavior change.
        Obesity (Silver Spring). 2008; 16: 2205-2211
        • Lien N.
        • Lytle L.A.
        • Klepp K.-I.
        Stability in consumption of fruit, vegetables, and sugary foods in a cohort from age 14 to age 21.
        Prev Med. 2001; 33: 217-226
        • Demory-Luce D.
        • Morales M.
        • Nicklas T.
        • Baranowski T.
        • Zakeri I.
        • Berenson G.
        Changes in food group consumption patterns from childhood to young adulthood: The Bogalusa Heart Study.
        J Am Diet Assoc. 2004; 104: 1684-1691
        • Harmon B.E.
        • Forthofer M.
        • Bantum E.O.
        • Nigg C.R.
        Perceived influence and college students’ diet and physical activity behaviors: An examination of ego-centric social networks.
        BMC Public Health. 2016; 16: 1-10
        • Suminski R.R.
        • Petosa R.
        • Utter A.C.
        • Zhang J.J.
        Physical activity among ethnically diverse college students.
        J Am Coll Health. 2002; 51: 75-80
        • Vadeboncoeur C.
        • Townsend N.
        • Foster C.
        A meta-analysis of weight gain in first year university students: Is freshman 15 a myth?.
        BMC Obes. 2015; 2: 22
        • Driskell J.A.
        • Kim Y.-N.
        • Goebel K.J.
        Few differences found in the typical eating and physical activity habits of lower-level and upper-level university students.
        J Am Diet Assoc. 2005; 105: 798-801
      1. Colby SL, Ortman JM. Projections of the Size and Composition of the U.S. Population: 2014 to 2060. Current Population Reports, P25-1143. Washington, DC: US Census Bureau; 2014.

        • Krogstad J.M.
        Facts about Latinos and education.
        (Published July 28, 2016. Accessed May 5, 2018)
        • Pérez C.M.
        • Sánchez H.
        • Ortiz A.P.
        Prevalence of overweight and obesity and their cardiometabolic comorbidities in Hispanic adults living in Puerto Rico.
        J Community Health. 2013; 38: 1140-1146
        • Flores Y.N.
        Risk factors for chronic liver disease in Blacks, Mexican Americans, and Whites in the United States: Results from NHANES IV, 1999-2004.
        Am J Gastroenterol. 2008; 103: 2231-2238
        • Centers for Disease Control and Prevention (CDC)
        Health disparities experienced by Hispanics—United States.
        MMWR Morb Mortal Wkly Rep. 2004; : 935-937
        • Davis J.N.
        • Alexander K.E.
        • Ventura E.E.
        • et al.
        Associations of dietary sugar and glycemic index with adiposity and insulin dynamics in overweight Latino youth.
        Am J Clin Nutr. 2007; 86: 1331
        • Davis J.N.
        • Ventura E.E.
        • Weigensberg M.J.
        • et al.
        The relation of sugar intake to β cell function in overweight Latino children.
        Am J Clin Nutr. 2005; 82: 1004-1010
        • Spruijt-Metz D.
        • Belcher B.
        • Anderson D.
        • et al.
        A high sugar, low fiber meal leads to higher leptin and physical activity levels in overweight Latina females as opposed to a low sugar, high fiber meal.
        J Am Diet Assoc. 2009; 109: 1058
        • Alexander K.E.
        • Ventura E.E.
        • Spruijt-Metz D.
        • Weigensberg M.J.
        • Goran M.I.
        • Davis J.N.
        Association of breakfast skipping with visceral fat and insulin indices in overweight Latino youth.
        Obesity (Silver Spring). 2009; 17: 1528-1533
        • Freeland-Graves J.H.
        • Nitzke S.
        Position of the academy of nutrition and dietetics: Total diet approach to healthy eating.
        J Acad Nutr Diet. 2013; 113: 307-317
        • US Department of Health and Human Services, US Department of Agriculture
        Dietary Guidelines for Americans, 2010.
        7th ed. US Government Printing Office, Washington, DC2010
        • US Department of Health and Human Services, US Department of Agriculture
        Dietary Guidelines for Americans 2015-2020.
        8th ed. US Government Printing Office, Washington, DC2015
        • Guenther P.M.
        • Casavale K.O.
        • Reedy J.
        • et al.
        Update of the Healthy Eating Index: HEI-2010.
        J Acad Nutr Diet. 2013; 113: 569-580
        • Schap T.
        • Kuczynski K.
        • Hiza H.
        Healthy Eating Index—Beyond the score.
        J Acad Nutr Diet. 2017; 117: 519-521
        • Schwingshackl L.
        • Bogensberger B.
        • Hoffmann G.
        Diet quality as assessed by the Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension Score, and Health Outcomes: An updated systematic review and meta-analysis of cohort studies.
        J Acad Nutr Diet. 2018; 118 (e111): 74-100
        • Plotnikoff R.C.
        • Costigan S.A.
        • Williams R.L.
        • et al.
        Effectiveness of interventions targeting physical activity, nutrition and healthy weight for university and college students: A systematic review and meta-analysis.
        Int J Behav Nutr Phys Act. 2015; 12: 45
        • House B.T.
        • Shearrer G.E.
        • Boisseau J.B.
        • Bray M.S.
        • Davis J.N.
        Decreased eating frequency linked to increased visceral adipose tissue, body fat, and BMI in Hispanic college freshmen [published online March 6, 2018]..
        BMC Nutr. 2018; 4: 1-12
      2. EPI Info 2000 [computer program]. Version 2000. Centers for Disease Control and Prevention, Atlanta, GA2000
        • Kuczmarski R.J.
        • Ogden C.L.
        • Guo S.S.
        • et al.
        2000 CDC growth charts for the United States: Methods and development.
        Vital Health Stat 11. 2002; : 1-190
        • National Center for Health Statistics
        National Health and Nutrition Examination Survey (NHANES): Anthropometry Procedures Manual 2007.
        National Center for Health Statistics, Hyattsville, MD2007
        • Ginde S.R.
        • Geliebter A.
        • Rubiano F.
        • et al.
        Air displacement plethysmography: Validation in overweight and obese subjects.
        Obesity (Silver Spring). 2005; 13: 1232-1237
        • Hu H.H.
        • Kim H.W.
        • Nayak K.S.
        • Goran M.I.
        Comparison of fat–water MRI and single-voxel MRS in the assessment of hepatic and pancreatic fat fractions in humans.
        Obesity (Silver Spring). 2010; 18: 841-847
        • Hu H.H.
        • Nayak K.S.
        • Goran M.I.
        Assessment of abdominal adipose tissue and organ fat content by magnetic resonance imaging.
        Obes Rev. 2011; 12: e504-e515
        • Otsu N.
        A threshold selection method from gray-level histograms.
        IEEE Trans Syst Man Cybern B Cybern. 1979; 9: 62-66
      3. MATLAB [computer program]. Version R2013a. Math Works Inc, Natick, MA2013
      4. Nutrition Data Systems for Research [computer program]. Version 2014. Nutrition Coordinating Center, Minneapolis, MN2014
        • Feskanich D.
        • Sielaff B.H.
        • Chong K.
        • Buzzard I.M.
        Computerized collection and analysis of dietary intake information.
        Comput Methods Programs Biomed. 1989; 30: 47-57
        • Krebs-Smith S.M.
        • Pannucci T.E.
        • Subar A.F.
        • et al.
        Update of the healthy eating index: HEI-2015.
        J Acad Nutr Diet. 2018; 118: 1591-1602
        • Reedy J.
        • Lerman J.L.
        • Krebs-Smith S.M.
        • et al.
        Evaluation of the Healthy Eating Index-2015.
        J Acad Nutr Diet. 2018; 118: 1622-1633
        • National Cancer Institute, Division of Cancer Control and Population Sciences
        Comparing the HEI-2015, HEI-2010, and HEI-2005.
        (Published 2018. Accessed May 1, 2018)
        • National Cancer Institute
        Healthy Eating Index—Overview of the methods & calculations.
        (Published 2018. Accessed May 1, 2018)
        • Nutrition Coordinating Center
        Healthy Eating Index.
        (Published 2018. Accessed May 1, 2018)
        • Freedson P.S.
        • Melanson E.
        • Sirard J.
        Calibration of the Computer Science and Applications, Inc. accelerometer.
        Med Sci Sports Exerc. 1998; 30: 777-781
        • Friedewald W.T.
        • Levy R.I.
        • Fredrickson D.S.
        Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.
        Clin Chem. 1972; 18: 499-502
        • Matthews D.R.
        • Hosker J.P.
        • Rudenski A.S.
        • Naylor B.A.
        • Treacher D.F.
        • Turner R.C.
        Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.
        Diabetologia. 1985; 28: 412-419
        • Gayoso-Diz P.
        • Otero-González A.
        • Rodriguez-Alvarez M.X.
        • et al.
        Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: Effect of gender and age: EPIRCE cross-sectional study.
        BMC Endocr Disord. 2013; 13: 47
        • Friedemann C.
        • Heneghan C.
        • Mahtani K.
        • Thompson M.
        • Perera R.
        • Ward A.M.
        Cardiovascular disease risk in healthy children and its association with body mass index: Systematic review and meta-analysis [published online September 25, 2012]..
        BMJ. 2012; 345: 1-16
        • Harris P.A.
        • Taylor R.
        • Thielke R.
        • Payne J.
        • Gonzalez N.
        • Conde J.G.
        Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support.
        J Biomed Inform. 2009; 42: 377-381
      5. SAS Software [computer program]. Version 9. SAS Institute Inc, Cary, NC2004
      6. SPSS Statistics [computer program]. Version 22. IBM Corporation, Armonk, NY2013
        • Schwingshackl L.
        • Hoffmann G.
        Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: A systematic review and meta-analysis of cohort studies.
        J Acad Nutr Diet. 2015; 115 (e785): 780-800
        • Raynor H.A.
        • Champagne C.M.
        Position of the Academy of Nutrition and Dietetics: Interventions for the treatment of overweight and obesity in adults.
        J Acad Nutr Diet. 2016; 116: 129-147
        • Flegal K.M.
        • Kruszon-Moran D.
        • Carroll M.D.
        • Fryar C.D.
        • Ogden C.L.
        Trends in obesity among adults in the United States, 2005 to 2014.
        JAMA. 2016; 315: 2284-2291
        • Ogden C.L.
        • Carroll M.D.
        • Kit B.K.
        • Flegal K.M.
        Prevalence of childhood and adult obesity in the United States, 2011-2012.
        JAMA. 2014; 311: 806-814
        • Centers for Disease Control and Prevention
        Obesity and overweight.
        (Published 2017. Accessed May 1, 2018)
        • Danaei G.
        • Ding E.L.
        • Mozaffarian D.
        • et al.
        The preventable causes of death in the United States: Comparative risk assessment of dietary, lifestyle, and metabolic risk factors.
        PLoS Med. 2009; 6: e1000058
        • Fox C.S.
        • Massaro J.M.
        • Hoffmann U.
        • et al.
        Abdominal visceral and subcutaneous adipose tissue compartments. Association with Metabolic Risk Factors in the Framingham Heart Study.
        Circulation. 2007; 116: 39-48
        • Bjørndal B.
        • Burri L.
        • Staalesen V.
        • Skorve J.
        • Berge R.K.
        Different adipose depots: Their role in the development of metabolic syndrome and mitochondrial response to hypolipidemic agents [published online February 15, 2011]..
        J Obes. 2011; 2011: 1-15
        • Vega G.L.
        • Adams-Huet B.
        • Peshock R.
        • Willett D.
        • Shah B.
        • Grundy S.M.
        Influence of body fat content and distribution on variation in metabolic risk.
        J Clin Endocrinol Metab. 2006; 91: 4459-4466
        • Loprinzi P.D.
        • Smit E.
        • Mahoney S.
        Physical activity and dietary behavior in US adults and their combined influence on health.
        Mayo Clin Proc. 2014; 89: 190-198
        • Durstine J.L.
        • Gordon B.
        • Wang Z.
        • Luo X.
        Chronic disease and the link to physical activity.
        J Sport Health Sci. 2013; 2: 3-11
        • Huang T.T.-K.
        • Harris K.J.
        • Lee R.E.
        • Nazir N.
        • Born W.
        • Kaur H.
        Assessing overweight, obesity, diet, and physical activity in college students.
        J Am Coll Health. 2003; 52: 83-86
        • McCullough M.L.
        • Feskanich D.
        • Stampfer M.J.
        • et al.
        Diet quality and major chronic disease risk in men and women: Moving toward improved dietary guidance.
        Am J Clin Nutr. 2002; 76: 1261
        • Pereira M.A.
        • Kartashov A.I.
        • Ebbeling C.B.
        • et al.
        Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis.
        Lancet. 2005; 365: 36-42
        • Powell L.M.
        • Han E.
        • Chaloupka F.J.
        Economic contextual factors, food consumption, and obesity among U.S. adolescents.
        J Nutr. 2010; 140: 1175-1180
        • Malik V.S.
        • Willett W.C.
        • Hu F.B.
        Global obesity: Trends, risk factors and policy implications.
        Nat Rev Endocrinol. 2012; 9: 13
        • McKeown N.M.
        • Meigs J.B.
        • Liu S.
        • Saltzman E.
        • Wilson P.W.
        • Jacques P.F.
        Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort.
        Diabetes Care. 2004; 27: 538-546
      7. Salas-Salvadó J, Martinez-González MÁ, Bulló M, Ros E. The role of diet in the prevention of type 2 diabetes. Nutr Metab Cardiovasc Dis. 2011;21:B32-B48.

        • Ford E.S.
        • Mokdad A.H.
        Fruit and vegetable consumption and diabetes mellitus incidence among US adults.
        Prev Med. 2001; 32: 33-39
        • Goran M.I.
        • Bergman R.N.
        • Cruz M.L.
        • Watanabe R.
        Insulin resistance and associated compensatory responses in African-American and Hispanic children.
        Diabetes Care. 2002; 25: 2184-2190
        • Menke A.
        • Casagrande S.
        • Geiss L.
        • Cowie C.C.
        Prevalence of and trends in diabetes among adults in the United States, 1988-2012.
        JAMA. 2015; 314: 1021-1029
        • Ma Y.
        • Olendzki B.C.
        • Pagoto S.L.
        • et al.
        Number of 24-hour diet recalls needed to estimate energy intake.
        Ann Epidemiol. 2009; 19: 553-559
        • Johnson R.K.
        Dietary intake—How do we measure what people are really eating?.
        Obesity (Silver Spring). 2002; 10: S63-S68
        • Johnson R.K.
        • Soultanakis R.P.
        • Matthews D.E.
        Literacy and body fatness are associated with underreporting of energy intake in US low-income women using the multiple-pass 24-hour recall: A doubly labeled water study.
        J Am Diet Assoc. 1998; 98: 1136-1140
        • Subar A.F.
        • Freedman L.S.
        • Tooze J.A.
        • et al.
        Addressing current criticism regarding the value of self-report dietary data.
        J Nutr. 2015; 145: 2639-2645
        • Ford E.S.
        • Kohl III, H.W.
        • Mokdad A.H.
        • Ajani U.A.
        Sedentary behavior, physical activity, and the metabolic syndrome among US adults.
        Obes Res. 2005; 13: 608-614
        • Ritterbeck M.
        • Glass J.
        The 26 healthiest colleges in the U.S.
        (Accessed May 1, 2018)
        • Dehority S.
        • Guarneri B.
        • Millado N.
        • Olivero T.
        • Radding B.
        • Tuthill M.
        The 25 fittest colleges in America. Health 2017.
        (Published 2017. Accessed May 1, 2018)


      M. J. Landry is a PhD candidate, Department of Nutritional Sciences, The University of Texas at Austin.


      S. Vandyousefi is a PhD candidate, Department of Nutritional Sciences, The University of Texas at Austin.


      E. Khazaee is a PhD candidate, Department of Nutritional Sciences, The University of Texas at Austin.


      R. Ghaddar is a PhD candidate, Department of Nutritional Sciences, The University of Texas at Austin.


      F. M. Asigbee is a postdoctoral fellow, Department of Nutritional Sciences, The University of Texas at Austin.


      J. B. Boisseau is a graduate student, Department of Nutritional Sciences, The University of Texas at Austin.


      B. T. House is a functional medicine practitioner, Department of Nutritional Sciences, The University of Texas at Austin.


      J. N. Davis is an associate professor, Department of Nutritional Sciences, The University of Texas at Austin.