Background: We have shown that intrauterine growth restriction (IUGR) leads to increased preference for palatable foods at different ages in both humans and rodents. In IUGR rodents, altered striatal dopamine signaling associates with a preference for palatable foods.
Objectives: Our aim was to investigate if a multilocus genetic score reflecting dopamine-signaling capacity is differently associated with spontaneous palatable food intake in children according to the fetal growth status.
Methods: 192 four-year old children from a community sample from Montreal and Hamilton, Canada, were classified according to birth weight and administered a snack test meal containing regular as well as palatable foods. Intrauterine growth restriction was based on the birth weight ratio below 0.85; children were genotyped for polymorphisms associated with dopamine (DA) signaling, with the hypofunctional variants (TaqIA-A1 allele, DRD2-141C Ins/Ins, DRD4 7-repeat, DAT1-10-repeat, Met/Met-COMT) receiving the lowest scores, and a composite score was calculated reflecting the total number of the five genotypes. Macronutrient intake during the Snack Test was the outcome.
Results: Adjusting for z-score BMI at 48 months and sex, there was a significant interaction of the genetic profile and fetal growth on sugar intake [βˆ = -4.56, p = 0.04], showing a positive association between the genetic score and sugar intake in IUGR children, and no association in non-IUGR children. No significant interactions were seen in other macronutrients.
Conclusions: Variations in a genetic score reflecting DA signaling are associated with differences in sugar intake only in IUGR children, suggesting that DA function is involved in this behavioral feature in these children. This may have important implications for obesity prevention in this population.
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http://dx.doi.org/10.1016/j.appet.2017.10.021 | DOI Listing |
Hepatol Int
January 2025
Division of Gastroenterology and Hepatology, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan.
Background/purpose: Although metabolic dysfunction-associated steatotic liver disease (MASLD) has been proposed to replace the diagnosis of non-alcoholic fatty liver disease (NAFLD) with new diagnostic criteria since 2023, the genetic predisposition of MASLD remains to be explored.
Methods: Participants with data of genome-wide association studies (GWAS) in the Taiwan Biobank database were collected. Patients with missing data, positive for HBsAg, anti-HCV, and alcohol drinking history were excluded.
Discov Oncol
January 2025
Department of Clinical Laboratory, Laboratory Medicine Center, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China.
Gastric cancer (GC), one of the most common and heterogeneous malignancies, is the second leading cause of cancer death worldwide and is closely related to dietary habits. Fatty acid is one of the main nutrients of human beings, which is closely related to diabetes, hypertension and other diseases. However, the correlation between fatty acid metabolism and the development and progression of GC remains largely unknown.
View Article and Find Full Text PDFAnn Surg Oncol
January 2025
Department of Surgery, Duke University Medical Center, Durham, NC, USA.
Background: Bilateral risk-reducing mastectomies (RRMs) have been proven to decrease the risk of breast cancer in patients at high risk owing to family history or having pathogenic genetic mutations. However, few resources with consolidated data have detailed the patient experience following surgery. This systematic review features patient-reported outcomes for patients with no breast cancer history in the year after their bilateral RRM.
View Article and Find Full Text PDFNat Commun
January 2025
Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada.
Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors.
View Article and Find Full Text PDFBMJ Open Diabetes Res Care
January 2025
Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
Introduction: Altered serum levels of growth hormones, adipokines, and exocrine pancreas enzymes have been individually linked with type 1 diabetes (T1D). We collectively evaluated seven such biomarkers, combined with islet autoantibodies (AAb) and genetic risk score (GRS2), for their utility in predicting AAb/T1D status.
Research Design And Methods: Cross-sectional serum samples (n=154 T1D, n=56 1AAb+, n=77 ≥2AAb+, n=256 AAb-) were assessed for IGF1, IGF2, adiponectin, leptin, amylase, lipase, and trypsinogen (n=543, age range 2.
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