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| Name | Class |
|---|---|
| National Dairy Council | OTHER |
| University of Minnesota (UM) Advanced Research and Diagnostic Laboratory (ARDL) | UNKNOWN |
| Azenta Life Sciences | UNKNOWN |
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Individuals with lactase non-persistence (LNP; determined by a functional variant in the LCT gene [rs4988235, GG genotype]) are susceptible to lactose intolerance in adulthood due to deficiency of lactase, the enzyme which digests milk lactose sugars. However, many LNP individuals still drink ≥1 cup of milk daily. Recent analysis in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) found that consumption of 1 serving (cup) of milk/day was associated with ~30% lower risk of type 2 diabetes among LNP individuals, but not among individuals with lactase persistence (LP). This beneficial effect might be partially explained by favorable alterations in gut microbiota and related metabolites associated with higher milk consumption among LNP individuals. Based on these observational study findings, the investigator team proposes to conduct a randomized, controlled trial of lactose-containing vs. lactose-free milk in LNP individuals with pre-diabetes, to comprehensively investigate the effects of milk intake on the gut microbiome and glycemic outcomes.
The trial will feature a 2-week milk washout period, followed by 1:1 randomization to lactose-containing (1% or 2%) or lactose-free (1% or 2%) milk for 12 weeks (4 weeks each of ½ cup, 1 cup, and 2 cups milk). Before and after the 12 weeks, visits will entail lactose challenge hydrogen breath tests (HBT; i.e., lactose tolerance tests) and blood tests for fasting glucose, hemoglobin A1c, and metabolomics; while stool samples and continuous glucose monitoring (CGM) data will be collected at home using provided kits/devices.
Specific aims of the study are to: (1) establish feasibility and tolerability of a randomized trial of lactose-containing vs. lactose-free milk; (2) to examine the effect of lactose-containing milk on gut microbiome species, functions, and metabolites in LNP individuals with pre-diabetes; and (3) to examine the effect of lactose-containing milk on glycemic outcomes in LNP individuals with pre-diabetes.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Lactose-Containing Milk | Active Comparator | Participants will be randomized to lactose-containing milk in strata of age (<60, ≥60) and sex (female, male). Within each age and sex stratum, 10 participants will be randomized into two intervention groups in a 1:1 ratio |
|
| Lactose-Free Milk | Active Comparator | Participants will be randomized to lactose-free milk in strata of age (<60, ≥60) and sex (female, male). Within each age and sex stratum, 10 participants will be randomized into two intervention groups in a 1:1 ratio |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Lactose-Containing Milk | Dietary Supplement | Participants will be asked to drink regular milk (1% or 2%) for 12 weeks as follows:
Participants will continue drinking 2 cups milk/day for 2 weeks after the 12-week follow-up visit. |
| Measure | Description | Time Frame |
|---|---|---|
| Gastrointestinal symptoms | Gastrointestinal symptoms, specifically abdominal pain, bloating, flatulence, and diarrhea, will be recorded daily from screening visit through 12 weeks of milk intervention. The occurrence and severity of these four adverse events will be summarized and reported by study arm. Average frequencies of none-mild vs. moderate-severe symptoms will be compared between treatment groups by week of study, as well as for specific time intervals corresponding to milk doses (weeks 1-4, 5-8, 9-12). | Daily From Screening visit to Week 12 |
| Change in Expired Breath Hydrogen | Expired breath hydrogen after lactose challenge will be measured during the baseline visit and after 12 weeks of milk intervention at the time of the follow-up visit using Hydrogen Breath Test (HBT) kits. Breath tubes will be mailed to an external laboratory where stable isotope analysis for expired breath hydrogen will be conducted. Expired breath hydrogen will be expressed as incremental Area Under the Curve (iAUC). Change in iAUC from baseline to week 12 will be summarized using basic descriptive statistics (group means and standard deviations), and change in iAUC will be compared between treatment groups. | From Baseline to Week 12 |
| Change in gut microbiome features - Relative Abundance of Species | Stool samples will be collected using home stool microbiome kits at baseline, 4-, 8-, and 12-week timepoints. Shotgun sequencing will be conducted. Change in relative abundance of species (with >1% mean relative abundance) from baseline will summarized, using basic descriptive statistics (group means and standard deviations). Change in relative abundance of species from baseline will be compared between the treatment groups. | From Baseline to Week 12 |
| Change in gut microbiome features - Functional Pathway Relative Abundance | Stool samples will be collected using home stool microbiome kits at baseline, 4-, 8-, and 12-week timepoints. Shotgun sequencing will be conducted. Change in relative abundance of functional pathways (with >1% mean relative abundance) from baseline will summarized, using basic descriptive statistics (group means and standard deviations). Change in relative abundance of functional pathways from baseline will be compared between the treatment groups. |
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Inclusion Criteria:
Exclusion Criteria:
Stratified in a 1:1 ratio
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Brandilyn Peters-Samuelson, PhD | Contact | 718-430-3281 | brandilyn.peterssamuelson@einsteinmed.edu | |
| Qibin Qi, PhD | Contact | 718-430-4203 | qibin.qi@einsteinmed.edu |
| Name | Affiliation | Role |
|---|---|---|
| Brandilyn Peters-Samuelson, PhD | Albert Einstein College of Medicine | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| HCHS/SOL Bronx Field Center | Recruiting | The Bronx | New York | 10458 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31518657 | Background | Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R; IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract. 2019 Nov;157:107843. doi: 10.1016/j.diabres.2019.107843. Epub 2019 Sep 10. | |
| 33069326 |
| Label | URL |
|---|---|
| R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2021. | View source |
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| ID | Term |
|---|---|
| D007787 | Lactose Intolerance |
| D018149 | Glucose Intolerance |
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D008286 | Malabsorption Syndromes |
| D007410 | Intestinal Diseases |
| D005767 | Gastrointestinal Diseases |
| D004066 | Digestive System Diseases |
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| Metabolic Solutions Inc. |
| INDUSTRY |
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|
| Lactose-Free Milk | Dietary Supplement | Participants will be asked to drink 1% or 2% lactose-free milk for 12 weeks as follows:
Participants will continue drinking 2 cups milk/day for 2 weeks after the 12-week follow-up visit. |
|
| From Baseline to Week 12 |
| Change in gut microbiome features - Metabolomics | Targeted metabolic profiling will be performed on serum and stool samples (baseline and week 12) using LC-MS/MS methods for absolute quantitation of 70 metabolites associated with gut bacterial metabolism. Change in stool and serum metabolites from baseline will be summarized using basic descriptive statistics (group means and standard deviations). Change in stool and serum metabolites from baseline will be compared between the treatment groups. | From Baseline to Week 12 |
| Change in glycemic outcomes - Fasting glucose | Blood sera samples for fasting glucose will be collected at baseline and Week 12. Fasting glucose, i.e., blood sugar levels following an 8-hour fast, will be analyzed via standard analytical chemistry approaches and reported in mg/dL or mmol/L units. Ranges vary but a fasting glucose level <99 mg/dL is considered 'normal', between 100-125 mg/dL is within the 'pre-diabetic' range, >126 mg/dL is within the 'diabetic' range. Change in fasting glucose from baseline will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Baseline to Week 12 |
| Change in glycemic outcomes - Hemoglobin A1c (HbA1c) | Whole blood samples for HbA1c will be collected at baseline and Week 12. HbA1c, used to measure the amount of hemoglobin with attached glucose and reflects average blood glucose levels over the past several months, will be analyzed via standard analytical chemistry approaches. Ranges vary, however, a 'normal' HbA1c is generally <5.7%, 5.7-6.4% is in the 'pre-diabetic' range and a value of 6.5% or greater is in the 'diabetic' range. Change in HbA1c from baseline will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Baseline to Week 12 |
| Change in glycemic outcomes - Continuous Glucose Monitoring (CGM) mean glucose | During screening visit participants will have a 2-week continuous glucose monitor (CGM) applied to the skin on the upper arm in advance of the 2-week milk washout period. The CGM will be returned during the baseline visit 2 weeks later. After the 12 week visit, another 2-week CGM will be applied during which time participants will continue drinking milk concurrent with the 2-week CGM (i.e., until 14 weeks). Change in mean glucose (mg/dL) from screening to week 14 will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Screening visit to Week 14 visit |
| Change in glycemic outcomes - Continuous Glucose Monitoring (CGM) glycemic variability | During screening visit participants will have a 2-week continuous glucose monitor (CGM) applied to the skin on the upper arm in advance of the 2-week milk washout period. The CGM will be returned during the baseline visit 2 weeks later. After the 12 week visit, another 2-week CGM will be applied during which time participants will continue drinking milk concurrent with the 2-week CGM (i.e., until 14 weeks). Change in glycemic variability (%CV) from screening to week 14 will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Screening visit to Week 14 visit |
| Change in glycemic outcomes - Continuous Glucose Monitoring (CGM) time above range | During screening visit participants will have a 2-week continuous glucose monitor (CGM) applied to the skin on the upper arm in advance of the 2-week milk washout period. The CGM will be returned during the baseline visit 2 weeks later. After the 12 week visit, another 2-week CGM will be applied during which time participants will continue drinking milk concurrent with the 2-week CGM (i.e., until 14 weeks). Change in time above range (%) from screening to week 14 will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Screening visit to Week 14 visit |
| Change in glycemic outcomes - Continuous Glucose Monitoring (CGM) time in range | During screening visit participants will have a 2-week continuous glucose monitor (CGM) applied to the skin on the upper arm in advance of the 2-week milk washout period. The CGM will be returned during the baseline visit 2 weeks later. After the 12 week visit, another 2-week CGM will be applied during which time participants will continue drinking milk concurrent with the 2-week CGM (i.e., until 14 weeks). Change in time in range (%) from screening to week 14 will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Screening visit to Week 14 visit |
| Change in glycemic outcomes - Continuous Glucose Monitoring (CGM) time below range | During screening visit participants will have a 2-week continuous glucose monitor (CGM) applied to the skin on the upper arm in advance of the 2-week milk washout period. The CGM will be returned during the baseline visit 2 weeks later. After the 12 week visit, another 2-week CGM will be applied during which time participants will continue drinking milk concurrent with the 2-week CGM (i.e., until 14 weeks). Change in time below range (%) from screening to week 14 will be summarized using descriptive statistics (means and standard deviations) and compared between the treatment groups. | From Screening visit to Week 14 visit |
| Change in Flatulence | The Smart Underwear device will be worn externally on regular underwear near the rectum/perineum during specified daytime wear periods. The device continuously detects hydrogen in expelled flatus and records supporting temperature and movement data. These data will be used to derive the frequency of flatus events per wear period, which reflects intestinal gas production and gut microbial activity. De-identified data will be transferred after each wear period through the Human Flatus Atlas mobile app and uploaded to servers. Change in frequency of flatus events per wear period will be summarized using basic descriptive statistics (group means and standard deviations). | From Screening to Week 1, from Week 1 to Week 10, and from Week 1 to Week 14 |
| Background |
| GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020 Oct 17;396(10258):1204-1222. doi: 10.1016/S0140-6736(20)30925-9. |
| 37909353 | Background | Parker ED, Lin J, Mahoney T, Ume N, Yang G, Gabbay RA, ElSayed NA, Bannuru RR. Economic Costs of Diabetes in the U.S. in 2022. Diabetes Care. 2024 Jan 1;47(1):26-43. doi: 10.2337/dci23-0085. |
| 31683759 | Background | Uusitupa M, Khan TA, Viguiliouk E, Kahleova H, Rivellese AA, Hermansen K, Pfeiffer A, Thanopoulou A, Salas-Salvado J, Schwab U, Sievenpiper JL. Prevention of Type 2 Diabetes by Lifestyle Changes: A Systematic Review and Meta-Analysis. Nutrients. 2019 Nov 1;11(11):2611. doi: 10.3390/nu11112611. |
| 35916901 | Background | Wareham NJ. Personalised prevention of type 2 diabetes. Diabetologia. 2022 Nov;65(11):1796-1803. doi: 10.1007/s00125-022-05774-7. Epub 2022 Aug 2. |
| 30318098 | Background | Liu B, Sun Y, Bao W. Creating and supporting a healthy food environment for type 2 diabetes prevention. Lancet Planet Health. 2018 Oct;2(10):e423-e424. doi: 10.1016/S2542-5196(18)30211-0. No abstract available. |
| 23945722 | Background | Aune D, Norat T, Romundstad P, Vatten LJ. Dairy products and the risk of type 2 diabetes: a systematic review and dose-response meta-analysis of cohort studies. Am J Clin Nutr. 2013 Oct;98(4):1066-83. doi: 10.3945/ajcn.113.059030. Epub 2013 Aug 14. |
| 38253929 | Background | Luo K, Chen GC, Zhang Y, Moon JY, Xing J, Peters BA, Usyk M, Wang Z, Hu G, Li J, Selvin E, Rebholz CM, Wang T, Isasi CR, Yu B, Knight R, Boerwinkle E, Burk RD, Kaplan RC, Qi Q. Variant of the lactase LCT gene explains association between milk intake and incident type 2 diabetes. Nat Metab. 2024 Jan;6(1):169-186. doi: 10.1038/s42255-023-00961-1. Epub 2024 Jan 22. |
| 28426286 | Background | Segurel L, Bon C. On the Evolution of Lactase Persistence in Humans. Annu Rev Genomics Hum Genet. 2017 Aug 31;18:297-319. doi: 10.1146/annurev-genom-091416-035340. Epub 2017 Apr 19. |
| 28690131 | Background | Storhaug CL, Fosse SK, Fadnes LT. Country, regional, and global estimates for lactose malabsorption in adults: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2017 Oct;2(10):738-746. doi: 10.1016/S2468-1253(17)30154-1. Epub 2017 Jul 7. |
| 32899182 | Background | Anguita-Ruiz A, Aguilera CM, Gil A. Genetics of Lactose Intolerance: An Updated Review and Online Interactive World Maps of Phenotype and Genotype Frequencies. Nutrients. 2020 Sep 3;12(9):2689. doi: 10.3390/nu12092689. |
| 32560312 | Background | Robles L, Priefer R. Lactose Intolerance: What Your Breath Can Tell You. Diagnostics (Basel). 2020 Jun 17;10(6):412. doi: 10.3390/diagnostics10060412. |
| 20629478 | Background | Wilt TJ, Shaukat A, Shamliyan T, Taylor BC, MacDonald R, Tacklind J, Rutks I, Schwarzenberg SJ, Kane RL, Levitt M. Lactose intolerance and health. Evid Rep Technol Assess (Full Rep). 2010 Feb;(192):1-410. |
| 38159728 | Background | JanssenDuijghuijsen L, Looijesteijn E, van den Belt M, Gerhard B, Ziegler M, Ariens R, Tjoelker R, Geurts J. Changes in gut microbiota and lactose intolerance symptoms before and after daily lactose supplementation in individuals with the lactase nonpersistent genotype. Am J Clin Nutr. 2024 Mar;119(3):702-710. doi: 10.1016/j.ajcnut.2023.12.016. Epub 2023 Dec 28. |
| 9853544 | Background | Carroccio A, Montalto G, Cavera G, Notarbatolo A. Lactose intolerance and self-reported milk intolerance: relationship with lactose maldigestion and nutrient intake. Lactase Deficiency Study Group. J Am Coll Nutr. 1998 Dec;17(6):631-6. doi: 10.1080/07315724.1998.10718813. |
| 26095206 | Background | Zheng X, Chu H, Cong Y, Deng Y, Long Y, Zhu Y, Pohl D, Fried M, Dai N, Fox M. Self-reported lactose intolerance in clinic patients with functional gastrointestinal symptoms: prevalence, risk factors, and impact on food choices. Neurogastroenterol Motil. 2015 Aug;27(8):1138-46. doi: 10.1111/nmo.12602. Epub 2015 Jun 19. |
| 29902437 | Background | Agus A, Planchais J, Sokol H. Gut Microbiota Regulation of Tryptophan Metabolism in Health and Disease. Cell Host Microbe. 2018 Jun 13;23(6):716-724. doi: 10.1016/j.chom.2018.05.003. |
| 34127525 | Background | Qi Q, Li J, Yu B, Moon JY, Chai JC, Merino J, Hu J, Ruiz-Canela M, Rebholz C, Wang Z, Usyk M, Chen GC, Porneala BC, Wang W, Nguyen NQ, Feofanova EV, Grove ML, Wang TJ, Gerszten RE, Dupuis J, Salas-Salvado J, Bao W, Perkins DL, Daviglus ML, Thyagarajan B, Cai J, Wang T, Manson JE, Martinez-Gonzalez MA, Selvin E, Rexrode KM, Clish CB, Hu FB, Meigs JB, Knight R, Burk RD, Boerwinkle E, Kaplan RC. Host and gut microbial tryptophan metabolism and type 2 diabetes: an integrative analysis of host genetics, diet, gut microbiome and circulating metabolites in cohort studies. Gut. 2022 Jun;71(6):1095-1105. doi: 10.1136/gutjnl-2021-324053. Epub 2021 Jun 14. |
| 34680047 | Background | Gojda J, Cahova M. Gut Microbiota as the Link between Elevated BCAA Serum Levels and Insulin Resistance. Biomolecules. 2021 Sep 28;11(10):1414. doi: 10.3390/biom11101414. |
| 35727887 | Background | Guha S, Majumder K. Comprehensive Review of gamma-Glutamyl Peptides (gamma-GPs) and Their Effect on Inflammation Concerning Cardiovascular Health. J Agric Food Chem. 2022 Jul 6;70(26):7851-7870. doi: 10.1021/acs.jafc.2c01712. Epub 2022 Jun 21. |
| 30279333 | Background | Li X, Yin J, Zhu Y, Wang X, Hu X, Bao W, Huang Y, Chen L, Chen S, Yang W, Shan Z, Liu L. Effects of Whole Milk Supplementation on Gut Microbiota and Cardiometabolic Biomarkers in Subjects with and without Lactose Malabsorption. Nutrients. 2018 Oct 2;10(10):1403. doi: 10.3390/nu10101403. |
| 30445663 | Background | Aguayo-Mazzucato C, Diaque P, Hernandez S, Rosas S, Kostic A, Caballero AE. Understanding the growing epidemic of type 2 diabetes in the Hispanic population living in the United States. Diabetes Metab Res Rev. 2019 Feb;35(2):e3097. doi: 10.1002/dmrr.3097. Epub 2018 Dec 4. |
| 29999504 | Background | Ugidos-Rodriguez S , Matallana-Gonzalez MC , Sanchez-Mata MC . Lactose malabsorption and intolerance: a review. Food Funct. 2018 Aug 15;9(8):4056-4068. doi: 10.1039/c8fo00555a. |
| 31672155 | Background | Kaplan RC, Wang Z, Usyk M, Sotres-Alvarez D, Daviglus ML, Schneiderman N, Talavera GA, Gellman MD, Thyagarajan B, Moon JY, Vazquez-Baeza Y, McDonald D, Williams-Nguyen JS, Wu MC, North KE, Shaffer J, Sollecito CC, Qi Q, Isasi CR, Wang T, Knight R, Burk RD. Gut microbiome composition in the Hispanic Community Health Study/Study of Latinos is shaped by geographic relocation, environmental factors, and obesity. Genome Biol. 2019 Nov 1;20(1):219. doi: 10.1186/s13059-019-1831-z. |
| 37237391 | Background | Wang Z, Peters BA, Bryant M, Hanna DB, Schwartz T, Wang T, Sollecito CC, Usyk M, Grassi E, Wiek F, Peter LS, Post WS, Landay AL, Hodis HN, Weber KM, French A, Golub ET, Lazar J, Gustafson D, Sharma A, Anastos K, Clish CB, Burk RD, Kaplan RC, Knight R, Qi Q. Gut microbiota, circulating inflammatory markers and metabolites, and carotid artery atherosclerosis in HIV infection. Microbiome. 2023 May 27;11(1):119. doi: 10.1186/s40168-023-01566-2. |
| 31672156 | Background | Sanders JG, Nurk S, Salido RA, Minich J, Xu ZZ, Zhu Q, Martino C, Fedarko M, Arthur TD, Chen F, Boland BS, Humphrey GC, Brennan C, Sanders K, Gaffney J, Jepsen K, Khosroheidari M, Green C, Liyanage M, Dang JW, Phelan VV, Quinn RA, Bankevich A, Chang JT, Rana TM, Conrad DJ, Sandborn WJ, Smarr L, Dorrestein PC, Pevzner PA, Knight R. Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads. Genome Biol. 2019 Oct 31;20(1):226. doi: 10.1186/s13059-019-1834-9. |
| 22388286 | Background | Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012 Mar 4;9(4):357-9. doi: 10.1038/nmeth.1923. |
| Background | Zhu Q, Huang S, Gonzalez A, et al. OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures. 2021:2021.04.04.438427. doi:10.1101/2021.04.04.438427 %J bioRxiv |
| 26476454 | Background | Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016 Jan 4;44(D1):D457-62. doi: 10.1093/nar/gkv1070. Epub 2015 Oct 17. |
| 31586394 | Background | Caspi R, Billington R, Keseler IM, Kothari A, Krummenacker M, Midford PE, Ong WK, Paley S, Subhraveti P, Karp PD. The MetaCyc database of metabolic pathways and enzymes - a 2019 update. Nucleic Acids Res. 2020 Jan 8;48(D1):D445-D453. doi: 10.1093/nar/gkz862. |
| 23630581 | Background | McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013 Apr 22;8(4):e61217. doi: 10.1371/journal.pone.0061217. Print 2013. |
| Background | Oksanen J, Blanchet FG, Kindt R, et al. Multivariate analysis of ecological communities in R: vegan tutorial. R package version 1.7. 01/01 2013; |
| 22711789 | Background | Chen J, Bittinger K, Charlson ES, Hoffmann C, Lewis J, Wu GD, Collman RG, Bushman FD, Li H. Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics. 2012 Aug 15;28(16):2106-13. doi: 10.1093/bioinformatics/bts342. Epub 2012 Jun 17. |
| 25532675 | Background | Turner-McGrievy GM, Wirth MD, Shivappa N, Wingard EE, Fayad R, Wilcox S, Frongillo EA, Hebert JR. Randomization to plant-based dietary approaches leads to larger short-term improvements in Dietary Inflammatory Index scores and macronutrient intake compared with diets that contain meat. Nutr Res. 2015 Feb;35(2):97-106. doi: 10.1016/j.nutres.2014.11.007. Epub 2014 Dec 3. |
| 25964261 | Background | Thompson FE, Dixit-Joshi S, Potischman N, Dodd KW, Kirkpatrick SI, Kushi LH, Alexander GL, Coleman LA, Zimmerman TP, Sundaram ME, Clancy HA, Groesbeck M, Douglass D, George SM, Schap TE, Subar AF. Comparison of Interviewer-Administered and Automated Self-Administered 24-Hour Dietary Recalls in 3 Diverse Integrated Health Systems. Am J Epidemiol. 2015 Jun 15;181(12):970-8. doi: 10.1093/aje/kwu467. Epub 2015 May 10. |
| 20609344 | Background | Lavange LM, Kalsbeek WD, Sorlie PD, Aviles-Santa LM, Kaplan RC, Barnhart J, Liu K, Giachello A, Lee DJ, Ryan J, Criqui MH, Elder JP. Sample design and cohort selection in the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol. 2010 Aug;20(8):642-9. doi: 10.1016/j.annepidem.2010.05.006. |
| 20609343 | Background | Sorlie PD, Aviles-Santa LM, Wassertheil-Smoller S, Kaplan RC, Daviglus ML, Giachello AL, Schneiderman N, Raij L, Talavera G, Allison M, Lavange L, Chambless LE, Heiss G. Design and implementation of the Hispanic Community Health Study/Study of Latinos. Ann Epidemiol. 2010 Aug;20(8):629-41. doi: 10.1016/j.annepidem.2010.03.015. |
| 29187837 | Background | Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome Datasets Are Compositional: And This Is Not Optional. Front Microbiol. 2017 Nov 15;8:2224. doi: 10.3389/fmicb.2017.02224. eCollection 2017. |
| D002239 | Carbohydrate Metabolism, Inborn Errors |
| D008661 | Metabolism, Inborn Errors |
| D030342 | Genetic Diseases, Inborn |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D006943 | Hyperglycemia |
| D044882 | Glucose Metabolism Disorders |
| D003920 | Diabetes Mellitus |
| D004700 | Endocrine System Diseases |