Ignite Creation Date:
2025-12-26 @ 10:46 PM
Ignite Modification Date:
2025-12-26 @ 10:46 PM
Study NCT ID:
NCT05488912
Status:
COMPLETED
Last Update Posted:
2025-03-28
First Post:
2022-07-26
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Fiber-rich Foods, Weight Status, and the Gut Microbiota in NH Hispanic Adults at Risk for Food Insecurity
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D050177', 'term': 'Overweight'}, {'id': 'D009765', 'term': 'Obesity'}], 'ancestors': [{'id': 'D044343', 'term': 'Overnutrition'}, {'id': 'D009748', 'term': 'Nutrition Disorders'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D001835', 'term': 'Body Weight'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITHOUT_DNA', 'description': 'Both fasting blood samples and blood samples collected during a mixed-meal tolerance test will be collected and used for glucose and hormone assessment.\n\nPre-collected stool samples will be obtained from participants for SCFA assessment.'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 61}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-03-28', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2024-10-10', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-03-27', 'studyFirstSubmitDate': '2022-07-26', 'studyFirstSubmitQcDate': '2022-08-02', 'lastUpdatePostDateStruct': {'date': '2025-03-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-08-05', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-10-10', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Food Insecurity and Access', 'timeFrame': 'August 2022 to August 2023', 'description': "Food insecurity scores will be generated at the household and individual level. Objective measures of food environment and access will be calculated using a modified version of the Retail Food environment Index (RFEI) that focuses on government assistance food sources within a radius around individuals' residential address (e.g., pantries, stores selling fresh food, SNAP retailers). Perceived food environment and access scores will be obtained from the NEMS-P. This questionnaire includes questions about food purchasing habits, priorities for food purchasing choices, characteristics of the local food environment including food availability at retailers and vendors, and food availability at home."}, {'measure': 'Fiber Intake', 'timeFrame': 'August 2022 to August 2023', 'description': 'Fiber intake will be calculated using validated analytical pipelines of the NHANES DSQ, developed at the National Cancer Institute. This methodology will be used to calculate predicted daily intakes of total fiber. The PI has previously used this methodology with Hispanic older adults. A comprehensive list of specific fiber-rich foods routinely consumed will be compiled, which will enable the identification of opportunities to increase local fiber-rich food production and consumption by highlighting foods commonly consumed by NH Hispanics (or gaps in current diets that could be fulfilled with local foods).'}, {'measure': 'Microbial Richness', 'timeFrame': 'August 2022 to August 2023', 'description': 'Microbial richness (number of different taxonomic groups per sample) will be established using Dirichlet multinomial mixtures. Relative abundance of bacterial groups (e.g., at the species, genus, phylum level) will also be calculated.'}, {'measure': 'Short-Chain Fatty Acids', 'timeFrame': 'August 2022 to August 2023', 'description': 'The SCFA assessment will focus on total SCFA, butyrate, acetate, and propionate. SCFA analysis will be measured using gas chromatography coupled with flame ionization detection.'}, {'measure': 'LPS', 'timeFrame': 'August 2022 to August 2023', 'description': 'The concentration of lipopolysaccharides (LPS) in the blood, a measure of gut permeability, will be measured. High circulating LPS, known as metabolic endotoxemia, is associated with insulin resistance, obesity, and other metabolic complications.'}, {'measure': 'Insulin', 'timeFrame': 'August 2022 to August 2023', 'description': 'Insulin levels will be measured using an enzyme-linked immunoassay (ELISA). The levels will be measured both from a fasting blood sample, and during MMTT. The fasting level will be used to for the calculation of the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR).'}, {'measure': 'Glucose', 'timeFrame': 'August 2022 to August 2023', 'description': 'Fasting glucose level will be used to for the calculation of the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR).'}, {'measure': 'GLP-1', 'timeFrame': 'August 2022 to August 2023', 'description': 'The levels of GLP-1 will be measured using an enzyme-linked immunoassay (ELISA). The levels of this hormone will be measured both from a fasting blood sample, and during MMTT.'}, {'measure': 'Ghrelin', 'timeFrame': 'August 2022 to August 2023', 'description': 'The levels of ghrelin will be measured using an enzyme-linked immunoassay (ELISA). The levels of this hormone will be measured both from a fasting blood sample, and during MMTT.'}], 'secondaryOutcomes': [{'measure': 'Fruit, Vegetable, and Whole Grain Intake', 'timeFrame': 'August 2022 to August 2023', 'description': 'Fruit, vegetable, and whole grain intake will be calculated using validated analytical pipelines of the NHANES DSQ, developed at the National Cancer Institute. These methodologies will be used to calculate predicted daily intakes of servings of fruits, vegetables, vegetables including legumes, and whole grains.'}, {'measure': 'Enterotype Classification of Gut Microbiota', 'timeFrame': 'August 2022 to August 2023', 'description': 'Individual enterotype classification of gut microbiota, a measure of the dominant taxonomic groups per individual, will be established using Dirichlet multinomial mixtures.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Overweight', 'Obesity', 'Food insecurity', 'Hispanic/Latino', 'Gut microbiome'], 'conditions': ['Food Insecurity', 'Overweight and Obesity']}, 'referencesModule': {'references': [{'type': 'BACKGROUND', 'citation': 'New Hampshire Department of Health and Human Services. Do I Qualify? NHEasy Gateway to Services https://nheasy.nh.gov/#/screening.'}, {'pmid': '22435709', 'type': 'BACKGROUND', 'citation': 'Besser RE, Jones AG, McDonald TJ, Shields BM, Knight BA, Hattersley AT. The impact of insulin administration during the mixed meal tolerance test. Diabet Med. 2012 Oct;29(10):1279-84. doi: 10.1111/j.1464-5491.2012.03649.x.'}, {'type': 'BACKGROUND', 'citation': 'NCCIH Clinical Research Toolbox. https://www.nccih.nih.gov/grants/toolbox (2020).'}, {'type': 'BACKGROUND', 'citation': '2020 Census Questionnaire. https://www.census.gov/programs-surveys/decennial-census/technical-documentation/questionnaires/2020.html (2020).'}, {'pmid': '25486370', 'type': 'BACKGROUND', 'citation': 'Jauregui-Lobera I, Garcia-Cruz P, Carbonero-Carreno R, Magallares A, Ruiz-Prieto I. Psychometric properties of Spanish version of the Three-Factor Eating Questionnaire-R18 (Tfeq-Sp) and its relationship with some eating- and body image-related variables. Nutrients. 2014 Dec 4;6(12):5619-35. doi: 10.3390/nu6125619.'}, {'pmid': '21443994', 'type': 'BACKGROUND', 'citation': 'Fernandez S, Olendzki B, Rosal MC. A dietary behaviors measure for use with low-income, Spanish-speaking Caribbean Latinos with type 2 diabetes: the Latino Dietary Behaviors Questionnaire. J Am Diet Assoc. 2011 Apr;111(4):589-99. doi: 10.1016/j.jada.2011.01.015.'}, {'pmid': '26597505', 'type': 'BACKGROUND', 'citation': 'Arredondo EM, Sotres-Alvarez D, Stoutenberg M, Davis SM, Crespo NC, Carnethon MR, Castaneda SF, Isasi CR, Espinoza RA, Daviglus ML, Perez LG, Evenson KR. Physical Activity Levels in U.S. Latino/Hispanic Adults: Results From the Hispanic Community Health Study/Study of Latinos. Am J Prev Med. 2016 Apr;50(4):500-508. doi: 10.1016/j.amepre.2015.08.029. Epub 2015 Nov 18.'}, {'type': 'BACKGROUND', 'citation': 'Marin, G., Sabogal, F., Marin, B. V., Otero-Sabogal, R. & Perez-Stable, E. J. Development of a Short Acculturation Scale for Hispanics. Hispanic Journal of Behavioral Sciences 9, 183-205 (1987).'}, {'type': 'BACKGROUND', 'citation': 'NHANES 2017-2018 Questionnaire Instruments. https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/questionnaires.aspx?BeginYear=2017.'}, {'pmid': '3123181', 'type': 'BACKGROUND', 'citation': 'Svedlund J, Sjodin I, Dotevall G. GSRS--a clinical rating scale for gastrointestinal symptoms in patients with irritable bowel syndrome and peptic ulcer disease. Dig Dis Sci. 1988 Feb;33(2):129-34. doi: 10.1007/BF01535722.'}, {'pmid': '26069274', 'type': 'BACKGROUND', 'citation': 'Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016 Jan;65(1):57-62. doi: 10.1136/gutjnl-2015-309618. Epub 2015 Jun 11.'}, {'pmid': '12672942', 'type': 'BACKGROUND', 'citation': 'Harrison GG, Stormer A, Herman DR, Winham DM. Development of a spanish-language version of the U.S. household food security survey module. J Nutr. 2003 Apr;133(4):1192-7. doi: 10.1093/jn/133.4.1192.'}, {'pmid': '26094227', 'type': 'BACKGROUND', 'citation': 'Green SH, Glanz K. Development of the Perceived Nutrition Environment Measures Survey. Am J Prev Med. 2015 Jul;49(1):50-61. doi: 10.1016/j.amepre.2015.02.004.'}, {'type': 'BACKGROUND', 'citation': 'New Hampshire Food Access Map. https://unhcoopext.maps.arcgis.com/apps/MapSeries/index.html?appid=5caa235e0e024beb8bebba50a0297d15&entry=2.'}, {'type': 'BACKGROUND', 'citation': 'Babey, S. H., Diamant, A., Hastert, T. A., Goldstein, H. & Al., E. Designed for Disease: The Link Between Local Food Environments and Obesity and Diabetes. (2008).'}, {'type': 'BACKGROUND', 'citation': 'Dietary Screener Questionnaire in the NHANES 2009-10: Background. https://epi.grants.cancer.gov/nhanes/dietscreen/.'}, {'pmid': '22402401', 'type': 'BACKGROUND', 'citation': 'Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012 Aug;6(8):1621-4. doi: 10.1038/ismej.2012.8. Epub 2012 Mar 8.'}, {'pmid': '31341288', 'type': 'BACKGROUND', 'citation': 'Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo-Rodriguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS 2nd, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vazquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019 Aug;37(8):852-857. doi: 10.1038/s41587-019-0209-9. No abstract available.'}, {'pmid': '27508062', 'type': 'BACKGROUND', 'citation': 'Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP. Bioconductor Workflow for Microbiome Data Analysis: from raw reads to community analyses. F1000Res. 2016 Jun 24;5:1492. doi: 10.12688/f1000research.8986.2. eCollection 2016.'}, {'pmid': '23444143', 'type': 'BACKGROUND', 'citation': 'Varemo L, Nielsen J, Nookaew I. Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res. 2013 Apr;41(8):4378-91. doi: 10.1093/nar/gkt111. Epub 2013 Feb 26.'}, {'type': 'BACKGROUND', 'citation': 'Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289-300 (1995).'}, {'pmid': '26100928', 'type': 'BACKGROUND', 'citation': 'Dao MC, Everard A, Aron-Wisnewsky J, Sokolovska N, Prifti E, Verger EO, Kayser BD, Levenez F, Chilloux J, Hoyles L; MICRO-Obes Consortium; Dumas ME, Rizkalla SW, Dore J, Cani PD, Clement K. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut. 2016 Mar;65(3):426-36. doi: 10.1136/gutjnl-2014-308778. Epub 2015 Jun 22.'}]}, 'descriptionModule': {'briefSummary': 'This study will include a group of 60 Hispanic adults living in New Hampshire with or without overweight/obesity. The study aims to assess food access and intake of fiber-rich foods, characterize fecal microbiota composition, and assess the relationship between the intake of fiber-rich foods and components of the gut microbiota-gut-brain axis. These aims will be accomplished through biospecimen collection including a pre-collected stool sample, a fasting blood sample, and a Mixed Meal Tolerance Test (MMTT). In addition, participants will answer questionnaires on dietary intake, food insecurity and access, physical activity, eating behavior, and sociodemographic characteristics.', 'detailedDescription': 'This study will include Hispanic adults living in New Hampshire with or without overweight/obesity. In a group of 60 participants, the study aims to assess food access and intake of fiber-rich foods, characterize fecal microbiota composition, and assess the relationship between the intake of fiber-rich foods and components of the gut microbiota-gut-brain axis.\n\nThe study involves biospecimen collection including a pre-collected stool sample, a fasting blood sample, and a Mixed Meal Tolerance Test (MMTT). In addition, participants will answer questionnaires on dietary intake, food insecurity and access, physical activity, eating behavior, and sociodemographic characteristics.\n\nPre-collected stool samples will be obtained from participants. Anthropometric measurements will be collected at the time of the study visit including height, weight, and waist and hip circumference. BMI will be calculated. An intra-venous catheter will be inserted by a healthcare professional to first collect a fasting blood sample, and will remain inserted for all following blood samples. Subjects will then undergo a Mixed Meal Tolerance Test (MMTT), a validated metabolic assessment in which the participant ingests a liquid mixed meal (e.g., Boost or Ensure), and blood samples are subsequently collected 15min, 30min, 60min and 120min after meal ingestion.\n\nIn the intervals between blood sample collections, subjects will complete questionnaires on dietary intake, food insecurity and access, physical activity, eating behavior, and sociodemographic characteristics. The following validated measures will be used to assess these aims:\n\n* USDA Household Food Sufficiency Questionnaire\n* Perceived Nutrition Environment Measurements Survey (NEMS-P)\n* Shortened version of the Three Factor Eating Questionnaire\n* Latino Dietary Behaviors Questionnaire (LDBQ)\n* Global Physical Activity Questionnaire\n* Short Acculturation Scale for Hispanics\n* NHANES Weight History Questionnaire\n* Medical History Questionnaire'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '55 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The study population will be composed of two groups of participants, one group which is composed of participants with a healthy BMI and the other which is composed of overweight/obese participants.\n\nAll participants will have self-identified as having an origin or cultural background in a Spanish-speaking country and be between the ages of 18 and 55. All participants will come from SNAP-eligible households. For this study, a SNAP-eligible household is one where the household income is 150% of the national poverty level.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Two groups of participants will be recruited:\n\n 1. Healthy BMI (20-25 kg/m2, n=30), and\n 2. Overweight/Obese BMI (\\>28 kg/m2, n=30)\n* Other inclusion criteria are as follows:\n\n * Adult men and women (18-55 years of age) residing in a SNAP-eligible households;\n * Self-identifying as Hispanic or Latino, and with origin or cultural background from a Spanish-speaking Latin American country;\n * Willingness and ability to provide a signed informed consent; and\n * Willingness to complete study visits and participate in all aspects of the study.\n\nExclusion Criteria:\n\n* Adults reporting any of the following conditions will be excluded from the study:\n\n * Diagnosed type 2 diabetes, chronic kidney or liver disease, cancer, chronic gastrointestinal conditions, cognitive impairment or incapacitating mental health problems, lack of mobility and physical independence, self-reported weight loss \\>5 kg within past 6 months, history of communicable or chronic diseases, medication use or surgery that would preclude safe and active study participation, bariatric surgery, antibiotic use within past 3 months, ongoing participation in other clinical trials, use of anti-obesity medications within the past year, inability to communicate in oral and written form in English and/or Spanish, and habitual consumption of more than two alcoholic drinks per day or of illegal drugs.'}, 'identificationModule': {'nctId': 'NCT05488912', 'acronym': 'FIRST', 'briefTitle': 'Fiber-rich Foods, Weight Status, and the Gut Microbiota in NH Hispanic Adults at Risk for Food Insecurity', 'organization': {'class': 'OTHER', 'fullName': 'University of New Hampshire'}, 'officialTitle': 'Fiber-rich Foods, Weight Status, and the Gut Microbiota: A Comparative Study in NH Hispanic Adults at Risk for Food Insecurity', 'orgStudyIdInfo': {'id': 'UNH-10-FY2021_49-01'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Healthy BMI (20-25 kg/m2, n=30)', 'description': 'A group of 30 Hispanic/Latino adults who are NH residents residing in SNAP-eligible households, and have a BMI between 20 and 25 kg/m2.'}, {'label': 'Overweight/Obese BMI (>28 kg/m2, n=30)', 'description': 'A group of 30 Hispanic/Latino adults who are NH residents residing in SNAP-eligible households, and have a BMI greater than or equal to 28 kg/m2.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '03824', 'city': 'Durham', 'state': 'New Hampshire', 'country': 'United States', 'facility': 'University of New Hampshire Health & Wellness', 'geoPoint': {'lat': 43.13397, 'lon': -70.92645}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO', 'description': 'IPD will not be shared with other researchers. Final data and results will be published and disseminated.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of New Hampshire', 'class': 'OTHER'}, 'collaborators': [{'name': 'New Hampshire Agricultural Experiment Station', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Assistant Professor of Human Nutrition', 'investigatorFullName': 'Maria Carlota Dao', 'investigatorAffiliation': 'University of New Hampshire'}}}}