Dietary Macronutrient Composition in Relation to Circulating HDL and Non-HDL Cholesterol: A Federated Individual-Level Analysis of Cross-Sectional Data from Adolescents and Adults in 8 European Studies

Abstract

Background Associations between increased dietary fat and decreased carbohydrate intake with circulating HDL and non-HDL cholesterol have not been conclusively determined. Objective We assessed these relations in 8 European observational human studies participating in the European Nutritional Phenotype Assessment and Data Sharing Initiative (ENPADASI) using harmonized data. Methods Dietary macronutrient intake was recorded using study-specific dietary assessment tools. Main outcome measures were lipoprotein cholesterol concentrations: HDL cholesterol (mg/dL) and non-HDL cholesterol (mg/dL). A cross-sectional analysis on 5919 participants (54% female) aged 13–80 y was undertaken using the statistical platform DataSHIELD that allows remote/federated nondisclosive analysis of individual-level data. Generalized linear models (GLM) were fitted to assess associations between replacing 5% of energy from carbohydrates with equivalent energy from total fats, SFAs, MUFAs, or PUFAs with circulating HDL cholesterol and non-HDL cholesterol. GLM were adjusted for study source, age, sex, smoking status, alcohol intake and BMI. Results The replacement of 5% of energy from carbohydrates with total fats or MUFAs was statistically significantly associated with 0.67 mg/dL (95% CI: 0.40, 0.94) or 0.99 mg/dL (95% CI: 0.37, 1.60) higher HDL cholesterol, respectively, but not with non-HDL cholesterol concentrations. The replacement of 5% of energy from carbohydrates with SFAs or PUFAs was not associated with HDL cholesterol, but SFAs were statistically significantly associated with 1.94 mg/dL (95% CI: 0.08, 3.79) higher non-HDL cholesterol, and PUFAs with –3.91 mg/dL (95% CI: –6.98, –0.84) lower non-HDL cholesterol concentrations. A statistically significant interaction by sex for the association of replacing carbohydrates with MUFAs and non-HDL cholesterol was observed, showing a statistically significant inverse association in males and no statistically significant association in females. We observed no statistically significant interaction by age. Conclusions The replacement of dietary carbohydrates with fats had favorable effects on lipoprotein cholesterol concentrations in European adolescents and adults when fats were consumed as MUFAs or PUFAs but not as SFAs.

Short-Term Intake of Yellowstripe Scad versus Salmon Did Not Induce Similar Effects on Lipid Profile and Inflammatory Markers among Healthy Overweight Adults despite Their Comparable EPA+DHA Content

“…Although the observed improvement in the salmon group may not be clinically meaningful to the study population, it may at least be useful as primary prevention. Moreover, it is possible that other dietary nutrients and physical activity levels could have influenced the results, although both diet groups did not show significant differences in terms of diet quality [ 57 , 58 ]. The increasing trend of carbohydrate intake and reducing trend of physical activity level observed in the YSS group after 8 weeks were much greater than the salmon group, though not statistically significant.…”

Identification and Characterization of Human Observational Studies in Nutritional Epidemiology on Gut Microbiomics for Joint Data Analysis

“…In addition, 17 studies (74%) were of longitudinal design, which may have allowed the researchers to examine the temporal relationships of the gut microbiota, as well as the dynamic nature of those factors that may modulate the gut microbiota composition, such as dietary intake [22]. Though the identified studies in the KP-INTIMIC used various types of dietary assessment methodologies, successful federated joint meta-analysis can still be conducted, as shown previously [24]. Another advantage of conducting joint data analysis using data from the identified studies is related to the increase in the statistical power [13].…”

“…Here, we present a description of the design, characteristics, methods, and available data and metadata of epidemiological observational studies on food, diet, and gut microbiomics identified within the context of KP-INTIMIC. As in a previous JPI Knowledge Hub, the European Nutritional Phenotype Assessment and Data Sharing Initiative (EN-PADASI) [23,24], the identified studies may be used in future federated individual-level joint data analyses examining the human gut microbiome and the role of diet in health and disease in the areas of early infancy, the development of sub-chronic and chronic diseases during lifespan, and aging. "Federated" refers to the joint analyses of individual-level data using the statistical platform DataSHIELD, a flexible, modular, free, open-source solution that allows the conduct of analysis without the need of individual studies to physically transfer their data into a single central database, as a way to circumvent privacy, ethical, and legal constrains of observational studies that often preclude the physical sharing of data [25].…”

Metaproteomics Approach and Pathway Modulation in Obesity and Diabetes: A Narrative Review

“…Additionally, regarding host proteins, T1D patients exhibited an increased expression of mucin-2 and a reduction in elastase 3A expression with respect to healthy individuals, suggesting both an increased mucin synthesis in charge of gastrointestinal epithelium protection and a reduction in exocrine pancreas functionality (Table 2). [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and observed a significant depletion of the genus Enterococcus in T1D subjects with high levels of HbA1c compared with healthy individuals. In this study, the metaproteomics was used to investigate the urinary proteome alterations in T1D subjects compared to their healthy siblings.…”

“…Recently, the Joint Programming Initiative Knowledge Platform-INtesTInalMICrobiomics (JPI KP-INTIMIC) has collected meta-data from human observational studies on gut microbiomics [ 20 ]. The human gut microbial consortium can be thus jointly analyzed in federated individual-level meta-analyses using DataSHIELD [ 21 , 22 ], possibly also with federated standardization of omics data [ 23 ].…”