Association of Lactase Persistence Genotypes (rs4988235) and Ethnicity with Dairy Intake in a Healthy U.S. Population

Chin, Elizabeth; Huang, Liping; Bouzid, Yasmine; Kirschke, Catherine P.; Durbin‐Johnson, Blythe; Baldiviez, Lacey M.; Bonnel, Ellen L.; Keim, Nancy L.; Korf, Ian F; Stephensen, Charles B.; Lemay, Danielle G.

doi:10.3390/nu11081860

Cited by 27 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lactose was estimated by manually looking up ASA24-reported foods into Nutrition Data System for Research (NDSR). The servings of total dairy are from the standard ASA24 output, with servings of soymilk subtracted [10,11]. The blue dashed line indicates the linear line of best fit.…”

Section: Figurementioning

confidence: 99%

“…We use lactose as a case study and estimate values from foods recorded from ASA24 recalls by a California cohort of healthy adults [25]. To address the first scenario, we trained and tested nine machine learning models on the ability to predict the amount of lactose in a given ASA24 food, Nutrients 2019, 11,3045 4 of 27 comparing the predicted lactose values to the lactose obtained by manually looking up ASA24 foods into NDSR. For the second scenario, we developed a database matching algorithm that used only the 62 nutrients shared between the ASA24 output and the NCC database to return the best NCC food match for a given ASA24 food, as well as another algorithm that used both the nutrients and text descriptions of the foods.…”

Section: Figurementioning

confidence: 99%

See 1 more Smart Citation

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

et al. 2019

Self Cite

View full text Add to dashboard Cite

The Automated Self-Administered 24-Hour Dietary Assessment Tool (ASA24) is a free dietary recall system that outputs fewer nutrients than the Nutrition Data System for Research (NDSR). NDSR uses the Nutrition Coordinating Center (NCC) Food and Nutrient Database, both of which require a license. Manual lookup of ASA24 foods into NDSR is time-consuming but currently the only way to acquire NCC-exclusive nutrients. Using lactose as an example, we evaluated machine learning and database matching methods to estimate this NCC-exclusive nutrient from ASA24 reports. ASA24-reported foods were manually looked up into NDSR to obtain lactose estimates and split into training (n = 378) and test (n = 189) datasets. Nine machine learning models were developed to predict lactose from the nutrients common between ASA24 and the NCC database. Database matching algorithms were developed to match NCC foods to an ASA24 food using only nutrients ("Nutrient-Only") or the nutrient and food descriptions ("Nutrient + Text"). For both methods, the lactose values were compared to the manual curation. Among machine learning models, the XGB-Regressor model performed best on held-out test data (R 2 = 0.33). For the database matching method, Nutrient + Text matching yielded the best lactose estimates (R 2 = 0.76), a vast improvement over the status quo of no estimate. These results suggest that computational methods can successfully estimate an NCC-exclusive nutrient for foods reported in ASA24. . Both databases have comparable nutrient completeness, with FNDDS at 100% and NCC at 92-100% completeness, but the databases differ in the number of nutrients reported. The ASA24 output includes 65 nutrients [5] and licensed 2018 NCC Database files include 166 nutrients and food components. Sixty-two nutrients are shared between the ASA24 output and NCC database. The NCC database also outputs nutrients and food components such as lactose, soluble and insoluble fiber, sugar alcohols, and individual amino acids while ASA24 does not [6]. While both ASA24 and NDSR/NCC database have widespread use and contain thousands of foods, there is no unique identifier to match each food on a one-to-one basis. Only a small number of foods in ASA24 have an exact known counterpart in NCC. Manual lookup of foods reported in ASA24 into NDSR based on text descriptions and nutrient profiles is time-consuming but is currently the only method to obtain values of NDSR-exclusive nutrients. This presents a major hurdle for investigating nutrient intake when using ASA24 if the research question requires assessment of a nutrient absent in the underlying database.Our research group is investigating a series of questions that require assessment of a nutrient that is not reported in the ASA24 output: lactose. Most adults worldwide are unable to digest lactose, the primary carbohydrate in milk. Some populations, however, are able to digest lactose into adulthood in a heritable trait known as lactase persistence (LP) [7]. LP genotypes may influence dairy and more specifically, lactose, ...

show abstract

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some studies have found that milk or total dairy consumption is associated with the LP genotype [43][44][45][46][47]. Other studies have shown that LP genetics has no influence over whether a participant is a cow milk consumer [48][49][50]. Most of those studies were conducted in populations with high frequencies of the LP phenotype and high amounts of milk consumption.…”

Section: Discussionmentioning

confidence: 99%

Association between Dairy Intake and Linear Growth in Chinese Pre-School Children

et al. 2020

View full text Add to dashboard Cite

Stunting remains a major public health issue for pre-school children globally. Dairy product consumption is suboptimal in China. The aim of this study was to investigate the relationship between dairy intake and linear growth in Chinese pre-school children. A national representative survey (Chinese Nutrition and Health Surveillance) of children aged under 6 years was done in 2013. Stratified multistage cluster sampling was used to select study participants. A food frequency questionnaire was used to collect dietary information. We calculated height-for-age Z-scores (HAZs) and estimated stunting using the 2006 WHO growth standard. In total, 12,153 children aged two to four years old (24 to <60 months) were studied from 55 counties in 30 provinces in China. Approximately 39.2% (4759/12,153) of those children consumed dairy at least once per day, 11.9% (1450/12,153) consumed dairy at least once in the last week, and nearly half (48.9%, 5944/12,153) did not have any dairy in the last week. The HAZ was −0.15 ± 1.22 and the prevalence of stunting was 6.5% (785/12,153). The HAZ for children who consumed dairy at least once per day or per week was 0.11 points or 0.13 points higher than the children without dairy intake. The risk of stunting for children who consumed dairy at least once per day was 28% lower than the children without dairy intake in the last week, and the risk was similar between weekly dairy consumption and no dairy consumption (AOR: 1.03, 95% CI: 0.74–1.42) after adjusting for potential confounders, including socioeconomic characteristics, lifestyle, health status, and the intake frequency of other foods. Dairy intake was significantly associated with a higher HAZ and a lower risk of stunting for children aged 2–4 years old in China. The proportion of dairy intake was still low in Chinese pre-school children. The promotion of dairy consumption might be an effective and feasible measurement for improving linear growth in Chinese pre-school children.

show abstract

“…Asians are known to have higher prevalence of intolerance than other ethnicities. It has been reported that in all ethnic populations and especially among Caucasians, LP individuals tend to consume more cheese and recent total dairy intake (35). In LP, lactose is broken down by the enzyme lactase in the small intestine, resulting in the formation of D-galactose, which has been extensively shown to induce neurodegeneration through oxidative stress in animal models.…”

Section: Discussionmentioning

confidence: 99%

Dairy and fish consumption and the risk of mild cognitive impairment and dementia

Chua¹,

Gwee²,

Nyunt³

et al. 2020

Preprint

View full text Add to dashboard Cite

Background There are inconsistent evidence for the association between fish consumption and dementia risk, and emerging evidence suggesting that dairy consumption is associated with lower dementia risk, based on a limited number of prospective cohort studies. Methods We investigated the association of dairy and fish consumption with the risk of developing mild cognitive impairment (MCI) and dementia. High intake of daily consumption of milk and dairy products and consumption of fish at least three servings per week were determined at baseline for 1,164 community-living and cognitively normal Chinese individuals aged 55 + in the Singapore Longitudinal Ageing Study (SLAS-1), who were followed-up (average 3.5 years) for incident cases of MCI and dementia determined by established criteria. Results The participants’ mean (SD) age was 65.1 (6.9) years, 66.8% were female. Controlling for sex, age, low or no education, APOE-e4 status, physical, social and productive activity, depression (GDS ≥ 5), central obesity, prediabetes/diabetes, hypertension, abnormal lipids, cardiac diseases, and daily fruit/vegetable intake in multivariable analysis, daily intake of milk and dairy products was significantly associated with lower risk of incident MCI (OR = 0.61; 95% CI = 0.39–0.96) or MCI-dementia (OR = 0.62; 95% CI = 0.40–0.95). Fish intake was not significantly associated with lowered risk of incident MCI and MCI-dementia, (OR = 0.74, 95%CI = 0.49–1.13 for MCI; OR = 0.76, 95% CI = 0.51–1.14 for MCI-dementia). Conclusions High dairy intake was associated with approximately 40% reduced risk of MCI or dementia. There is weak support in this study of an association between fish intake and MCI-dementia risk. Trial Registration: ClinicalTrials.gov NCT03405675. Registered 23 January 2018 (retrospectively registered).

show abstract

Association of Lactase Persistence Genotypes (rs4988235) and Ethnicity with Dairy Intake in a Healthy U.S. Population

Cited by 27 publications

References 45 publications

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

Nutrient Estimation from 24-Hour Food Recalls Using Machine Learning and Database Mapping: A Case Study with Lactose

Association between Dairy Intake and Linear Growth in Chinese Pre-School Children

Dairy and fish consumption and the risk of mild cognitive impairment and dementia

Contact Info

Product

Resources

About