The Gut Microbiome among Postmenopausal Latvian Women in Relation to Dietary Habits.

In recent years, many studies have been initiated to characterise the human gut microbiome in relation to different factors like age, lifestyle, and dietary habits. This study aimed to evaluate the impact of yoghurt intake on the gut microbiome among postmenopausal women and how overall dietary habits modulate the gut microbiome. In total, 52 participants were included in the study and two groups-a control (n = 26) and experimental group (n = 26)-were established. The study was eight weeks long. Both study groups were allowed to consume a self-selected diet, but the experimental group had to additionally consume 175 g of plain organic milk yoghurt on a daily basis for eight weeks. In addition, a series of questionnaires were completed, including a questionnaire on the subject's sociodemographic background, health status, and lifestyle factors, as well as a food frequency questionnaire. Stool samples were collected for the analysis of the gut microbiome (both prior to and after the eight weeks of the study). Sequencing of V4-V5 regions of the 16S rRNA gene was used to determine the bacterial composition of stool samples. The dominant phylum from the gut microbiome was Firmicutes (~70% to 73%), followed by Bacteroidota (~20% to 23%). Although no significant changes in the gut microbiome were related to daily consumption of yoghurt, we report that consumption of food products like grains, grain-based products, milk and milk products, and beverages (tea, coffee) is associated with differences in the composition of the gut microbiome. Establishing nutritional strategies to shape the gut microbiome could contribute to improved health status in postmenopausal women, but further research is needed.

Could Artificial Intelligence/Machine Learning and Inclusion of Diet-Gut Microbiome Interactions Improve Disease Risk Prediction? Case Study: Coronary Artery Disease.

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and the main leading cause of morbidity and mortality worldwide, posing a huge socio-economic burden to the society and health systems. Therefore, timely and precise identification of people at high risk of CAD is urgently required. Most current CAD risk prediction approaches are based on a small number of traditional risk factors (age, sex, diabetes, LDL and HDL cholesterol, smoking, systolic blood pressure) and are incompletely predictive across all patient groups, as CAD is a multi-factorial disease with complex etiology, considered to be driven by both genetic, as well as numerous environmental/lifestyle factors. Diet is one of the modifiable factors for improving lifestyle and disease prevention. However, the current rise in obesity, type 2 diabetes (T2D) and CVD/CAD indicates that the "one-size-fits-all" approach may not be efficient, due to significant variation in inter-individual responses. Recently, the gut microbiome has emerged as a potential and previously under-explored contributor to these variations. Hence, efficient integration of dietary and gut microbiome information alongside with genetic variations and clinical data holds a great promise to improve CAD risk prediction. Nevertheless, the highly complex nature of meals combined with the huge inter-individual variability of the gut microbiome poses several Big Data analytics challenges in modeling diet-gut microbiota interactions and integrating these within CAD risk prediction approaches for the development of personalized decision support systems (DSS). In this regard, the recent re-emergence of Artificial Intelligence (AI) / Machine Learning (ML) is opening intriguing perspectives, as these approaches are able to capture large and complex matrices of data, incorporating their interactions and identifying both linear and non-linear relationships. In this Mini-Review, we consider (1) the most used AI/ML approaches and their different use cases for CAD risk prediction (2) modeling of the content, choice and impact of dietary factors on CAD risk; (3) classification of individuals by their gut microbiome composition into CAD cases vs. controls and (4) modeling of the diet-gut microbiome interactions and their impact on CAD risk. Finally, we provide an outlook for putting it all together for improved CAD risk predictions.

New bioelectrical impedance analysis equations for children and adolescents based on the deuterium dilution technique.

BACKGROUND AND AIMS: Body composition in childhood is not only a marker of the prevalence of obesity, but it can also be used to assess associated metabolic complications. Bioelectrical impedance analysis (BIA) shows promise as an easy to use, rapid, and non-invasive tool to evaluate body composition. The objectives of this study were to: (a) develop BIA prediction equations to estimate total body water (TBW) and fat-free mass (FFM) in European children and early adolescents and to validate the analysis with the deuterium dilution as the reference technique and (b) compare our results with previously published paediatric BIA equations. METHODS: The cohort included 266 healthy children and adolescents between 7 and 14 years of age, 46% girls, in five European countries: Bosnia and Herzegovina, Latvia, Montenegro, North Macedonia, and Portugal. TBW and FFM were the target variables in the developed regression models. For model development, the dataset was randomly split into training and test sets, in 70:30 ratio, respectively. Model tuning was performed with 10-fold cross-validation that confirmed the unbiased estimate of its performance. The final regression models were retrained on the whole dataset. RESULTS: Cross-validated regression models were developed using resistance index, weight, and sex as the optimal predictors. The new prediction equations explained 87% variability in both TBW and FFM. Limits of agreement between BIA and reference values, were within ±17% of the mean, (-3.4, 3.7) and (-4.5, 4.8) kg for TBW and FFM, respectively. BIA FFM and TBW estimates were within one standard deviation for approximately 83% of the children. BIA prediction equations underestimated TBW and FFM by 0.2 kg and 0.1 kg respectively with no proportional bias and comparable accuracy among different BMI-for-age subgroups. Comparison with predictive equations from published studies revealed varying discrepancy rates with the deuterium dilution measurements, with only two being equivalent to the equations developed in this study. CONCLUSIONS: The small difference between deuterium dilution and BIA measurements validated by Bland-Altman analysis, supports the application of BIA for epidemiological studies in European children using the developed equations.

Eating Patterns and Food Choices of Latvian Infants during Their First Year of Life.

Introduction: Pregnancy, infancy, and early childhood are periods of rapid growth and development. The role of nutrition is very important during these critical growth and development periods. The aim of the study was to investigate infant feeding practices through the first year of life in Latvia, and to compare feeding practices with nutritional guidelines in Latvia and other European countries. Methods: We analysed cross-sectional study data from food frequency questionnaires with additional questions on breastfeeding and complementary feeding introduction. A total of 266 infants from all Latvian regions from birth to 1-year-old were included in the study. Breastfeeding rates were assessed by month of age. Complementary feeding was assessed using three age groups (0⁻3.9 months, 4⁻6.9 months, and 7⁻12.9 months), whereas two groups were used to assess food frequency and portion sizes (0⁻5.9 months and 6⁻12.9 months). Results: The breastfeeding rate during the first month of life was 89%. At 6 months, 68% of infants received breast milk, but by 12 months, only 45% still received breast milk. Complementary foods were introduced at a mean age of 5 months (standard deviation = 1). Before 4 months of age, 9% of infants were receiving complementary food, the majority (85%) between 4 and 6 months of age. There were 6% of infants who were introduced to complementary foods after 7 months of age. First complementary food choices were mainly porridge (64%), vegetables (21%), and fruits (10%). After 6 months of age, foods from almost all food groups were present in each infant diet at least once per day, such as vegetables (85%), potatoes (85%), fruits (81%), dairy (78%), and meat (73%), less than once per day-grains (88%), fats (73%), cow's milk (66%), eggs (45%), fish (36%), and legumes (28%). Conclusion: Breastfeeding rate during first months of life is high in Latvia. Breastfeeding was sustained at the age of 6 months, in the highest rate among Baltic countries. However, only 45% continued breastfeeding at the age of 12 months, in accordance with WHO recommendations. A guideline on complementary feeding is followed by the majority of parents. There are deviations from guidelines in inclusion of some foods in the diet and frequency of consumption.

Salt Consumption in Latvian Population: A Pilot Study.

Background and objective: High dietary sodium intake is associated with multiple health risks, and the average sodium intake in Latvia is higher than the World Health Organization has recommended. In Latvia, no study so far has combined self-reported dietary data on sodium and potassium intake with objective measurements in 24-h urine samples. This pilot study aimed to cross-validate both methods and to assess any possible factors interfering with the collection of samples and data in large, population-based future studies of sodium and potassium intake in Latvian adults. Materials and methods: A stratified random sample of healthy Latvian adults aged 19⁻64 (n = 30) was drawn. Dietary data of sodium and potassium was collected using one 24-h dietary recall and a two-day food diary. Sodium and potassium excretion was measured by one 24-h urinary collection. Results: Median intake of sodium and potassium based on dietary data was 2276.4 mg/day (interquartile range (IQR), 1683.3⁻3979.4) and 2172.0 mg/day (IQR, 1740.6⁻3506.5), respectively. Median intake of sodium and potassium based on urinary data was 3500.3 mg/day (IQR, 2191.0⁻5535.0) and 2965.4 mg/day (IQR, 2530.2⁻3749.9), respectively. Urinary data showed significantly higher results than dietary records (Wilcoxon signed rank test, p = 0.023). Only 13% of the subjects did not exceed the WHO-recommended limit of 2000 mg of sodium per day, and only 33% consumed at least the recommended allowance of 3510 mg of potassium per day. Median intake of salt was 8.8 g/day (IQR, 5.5⁻13.8) (according to urinary data). Conclusions: The findings from the present study showed considerable underestimation of dietary sodium and potassium intake based on self-reported dietary data. Urinary data revealed more accurate results, and showed that Latvian adults exceed the amount of salt recommended and consume less potassium than recommended. The pilot study also showed that the chosen methods are adequate for implementation in large, population-based studies to evaluate dietary intake of salt, sodium, and potassium in populations of Latvian adults.

Dietary intake and food sources of choline in European populations.

Choline is an important nutrient for humans. Choline intake of the European population was assessed considering the European Food Safety Authority European Comprehensive Food Consumption Database and the United States Department of Agriculture Nutrient Database. Average choline intake ranges were 151-210 mg/d among toddlers (1 to ≤3 years old), 177-304 mg/d among other children (3 to ≤10 years old), 244-373 mg/d among adolescents (10 to ≤18 years old), 291-468 mg/d among adults (18 to ≤65 years old), 284-450 mg/d among elderly people (65 to ≤75 years old) and 269-444 mg/d among very elderly people (≥75 years old). The intakes were higher among males compared with females, mainly due to larger quantities of food consumed per day. In most of the population groups considered, the average choline intake was below the adequate intake (AI) set by the Institute of Medicine in the USA. The main food groups contributing to choline intake were meat, milk, grain, egg and their derived products, composite dishes and fish. The main limitations of this study are related to the absence of choline composition data of foods consumed by the European population and the subsequent assumption made to assess their intake levels. Given the definition of AI, no conclusion on the adequacy of choline intake can be drawn for most European population groups. Such results improve the knowledge on choline intake in Europe that could be further refined by the collection of choline composition data for foods as consumed in Europe.

Dietary intake and major sources of plant lignans in Latvian men and women.

Higher intake of lignans, diphenolic plant compounds, may reduce the risk of certain types of cancer and cardiovascular diseases. We assessed the dietary intake of four lignans: matairesinol, secoisolariciresinol, lariciresinol and pinoresinol. Furthermore, for the breads we supplemented the data with two more lignans: syringaresinol and medioresinol. Study subjects were 172 men and 97 women aged 40-75 years, residing in Riga, the capital of Latvia, all living at home, eating habitual food. Median total lignan intake was 2259 (range 1169-5759) µg/day. Secoisolariciresinol contributed 58% and syringaresinol 22% of lignan intake. Bread was the major food source of lignans in men (86%), whereas in women it was bread (57%) and flaxseed (35%).