CD36 polymorphism, sugary drinks, and sedentarism are associated with hypertriglyceridemic waist phenotype

Background: The hypertriglyceridemic waist (HTGW) phenotype is characterized by concomitant increases in waist circumference (WC) and blood triglyceride levels (TG), which have been identified as a predictor of metabolic disorders. This study aimed to analyze associations between food consumption, exercise, and the CD36 gene rs1761667 G>A polymorphism with the HTGW phenotype in adult Mexicans. Methods: This cross-sectional study included a total of 255 participants (both genders, between 18-64 years of age). The HTGW phenotype was defined as WC >88 cm in women, WC >102 cm in men, and TG >150 mg/dL. Body composition was analyzed by electrical bioimpedance. Dietary intakes (macro and micronutrients) were evaluated through a validated 64-item food frequency questionnaire and a 24-h recall. Physical exercise was subjectively recorded asking the participants if they regularly performed some systematic exercise or sport of moderate intensity at least 150-300 minutes a week. Biochemical tests were determined by an automated system. A Taqman real-time assay was used to detect the rs1761667 (G>A) polymorphism of the CD36 gene. A multivariate logistic regression model was performed to analyze the variables potentially associated with the HTGW phenotype (adjusted for age, energy intake, and total fat mass). Results: Overall, 21.6% of the population presented the HTGW phenotype; compared to the HTGW-, also, they were older, had more body fat, higher glucose, cholesterol and insulin levels, and high blood pressure. Female sex (OR=2.92, 95% CI: 1.12-7.60, p=0.028), body mass index (OR=1.19, 95% CI: 1.07-1.32, p=0.001), total cholesterol (OR=1.01, 95% CI:1.00-1.02, p=0.039), daily consumption of sugary drinks (OR=6.94, 95% CI: 1.80-26.8, p=0.005), and the CD36 AG genotype (OR=3.81, 95% CI: 1.08-13.4, p=0.037) were positively associated with the HTGW phenotype, while performing exercise played a protective role (OR=0.23, 95% CI: 0.08-0.62, p=0.004). Overall, the model predicted the HTGW phenotype in 47% (R2=0.47, p≤0.001). Conclusion: The CD36 AG genotype, daily consumption of sugary drinks and sedentarism are risk factors for the HTGW phenotype in Mexicans.

A Nutrigenetic Strategy for Reducing Blood Lipids and Low-Grade Inflammation in Adults with Obesity and Overweight

The pathogenesis of obesity and dyslipidemia involves genetic factors, such as polymorphisms related to lipid metabolism alterations predisposing their development. This study aimed to evaluate the effect of a nutrigenetic intervention on the blood lipid levels, body composition, and inflammation markers of adults with obesity and overweight. Eleven genetic variants associated with dyslipidemias in Mexicans were selected, and specific nutrigenetic recommendations for these polymorphisms were found. One hundred and one adults were recruited and assigned to follow either a standard or nutrigenetic diet for eight weeks. Anthropometric, biochemical, body composition, and inflammation markers were evaluated through standardized methods. Weighted genetic risk scores (wGRSs) were computed using the study polymorphisms. After intervention, both diets significantly decreased the anthropometric parameters and body composition (p < 0.05). Only the nutrigenetic diet group showed significant reductions in VLDL-c (p = 0.001), triglycerides (p = 0.002), TG:HDL (p = 0.002), IL-6 (p = 0.002), and TNF-α (p = 0.04). wGRSs had a high impact on the ΔTGs and ΔVLDL-c of both groups (standard diet: ΔTGs: Adj R2 = 0.69, p = 0.03; ΔVLDL-c: Adj R2 = 0.71, p = 0.02; nutrigenetic diet: ΔTGs: Adj R2 = 0.49, p = 0.03 and ΔVLDL-c: R2 = 0.29, p = 0.04). This nutrigenetic intervention improved lipid abnormalities in patients with excessive body weight. Hence, nutrigenetic strategies could be coadjuvant tools and enhance the standard dietary treatment for cardiometabolic diseases.

Epigenetic Biomarkers of Metabolic Responses to Lifestyle Interventions

Studies have examined the possible utility of epigenetic phenomena (DNA methylation changes, covalent histone modifications, and miRNA expression patterns) in predicting individual responses to different lifestyle programs. Nonetheless, most available evidence is focused on identifying epigenetic marks eventually associated with body composition and adiposity outcomes, whereas their roles in metabolic endings remain less explored. This document comprehensively reviewed the evidence regarding the use of epigenetic signatures as putative biomarkers of metabolic outcomes (glycemic, lipid, blood pressure, and inflammatory/oxidative stress features) in response to different lifestyle interventions in humans. Although more investigation is still necessary in order to translate this knowledge in clinical practice, these scientific insights are contributing to the design of advanced strategies for the precise management of cardiometabolic risk, gaining understanding on metabolic heterogeneity, allowing for the prediction of metabolic outcomes, and facilitating the design of epigenome-based nutritional strategies for a more customized approach for metabolic alterations treatment under the scope of precision nutrition.

Prevalence of food addiction and its association with lifestyle factors in undergraduate students from Northwest Mexico

Background: The aim of this study was to determine the prevalence of food addiction (FA) in undergraduate students from Northwest Mexico and to examine its association with lifestyle factors, eating behaviors and food consumption.Methods: This cross-sectional study included a total of 326 undergraduate students, both sexes, between 18 and 25 years of age, who were enrolled in a bachelor's degree program at a public or private university in the city of Tijuana, Baja California, Mexico. FA was assessed using the modified Yale Food Addiction Scale Version 2.0 (mYFAS 2.0). Lifestyle (sleep patterns, physical exercise, alcohol intake, and smoking) and nutritional information (eating behaviors and food frequency consumption) was obtained through a clinical history. A multivariate logistic regression model was fitted to assess the factors associated with FA.Results: The whole prevalence of FA was 12.9%. In general, mild FA was the most frequent (5.2%), followed by severe (4.3%) and moderate (3.4%) categories. In the multivariate model, insomnia conferred a higher risk for FA (OR = 2.08, 95% CI, 1.04-4.17, p = 0.040), while the habitual consumption of fruits showed a protective effect (OR = 0.50, 95% CI, 0.25-0.98, p = 0.046). Overall, the model predicted FA in 12% (R2=0.12, p = 0.011).Conclusion: The prevalence of FA is 12.9% among undergraduate students from Northwest Mexico. Although caution should be exercised, insomnia seems to increase the risk of FA, while the habitual consumption of fruits appears to have a protective role. Additional studies are needed to validate these results.

Lipid-Related Genetic Variants for Personalized Dietary Interventions: A Systematic Review

Dyslipidemias are known risk factors for chronic diseases. Precision nutrition interventions are designed according to characteristics, such as diet, phenotype, and genotype. This systematic review aims to define a panel of genetic variants associated with lipid abnormalities that could be later used in nutrigenetic intervention studies. A systematic review is conducted following the PRISMA-P. Studies published from January 2010 to December 2020 in English language and humans are included from PubMed and ScienceDirect databases. Articles that demonstrate a strong association between polymorphisms (single nucleotide variation) of genes involved in lipid metabolism and increased risk for dyslipidemia are included. A total of 3031 articles are screened, but only 51 articles fulfill the inclusion criteria. The genes included are FABP2, MTTP related to CM synthesis and secretion; LPL, LIPC involved in triglyceride hydrolysis; CETP, APOA1, LCAT, ABCA1, and APOA5 related to lipoprotein metabolism, and APOE, LDLR, SCARB1, APOC3 involved in lipid clearance. In this systematic review, genetic variants related to chylomicron synthesis, triglyceride hydrolysis, lipoprotein metabolism, and lipid clearance demonstrate a strong association with lipid abnormalities, which can be used to design precision nutrition interventions that may help to prevent and treat dyslipidemia effectively.

Crosstalk between Gut Microbiota and Epigenetic Markers in Obesity Development: Relationship between Ruminococcus, BMI, and MACROD2/SEL1L2 Methylation

Changes in gut microbiota composition and in epigenetic mechanisms have been proposed to play important roles in energy homeostasis, and the onset and development of obesity. However, the crosstalk between epigenetic markers and the gut microbiome in obesity remains unclear. The main objective of this study was to establish a link between the gut microbiota and DNA methylation patterns in subjects with obesity by identifying differentially methylated DNA regions (DMRs) that could be potentially regulated by the gut microbiota. DNA methylation and bacterial DNA sequencing analysis were performed on 342 subjects with a BMI between 18 and 40 kg/m². DNA methylation analyses identified a total of 2648 DMRs associated with BMI, while ten bacterial genera were associated with BMI. Interestingly, only the abundance of Ruminococcus was associated with one BMI-related DMR, which is located between the MACROD2/SEL1L2 genes. The Ruminococcus abundance negatively correlated with BMI, while the hypermethylated DMR was associated with reduced MACROD2 protein levels in serum. Additionally, the mediation test showed that 19% of the effect of Ruminococcus abundance on BMI is mediated by the methylation of the MACROD2/SEL1L2 DMR. These findings support the hypothesis that a crosstalk between gut microbiota and epigenetic markers may be contributing to obesity development.

An Interplay between Oxidative Stress (Lactate Dehydrogenase) and Inflammation (Anisocytosis) Mediates COVID-19 Severity Defined by Routine Clinical Markers

Viral infections activate the innate immune response and the secretion of inflammatory cytokines. They also alter oxidative stress markers, which potentially can have an involvement in the pathogenesis of the disease. The aim of this research was to study the role of the oxidative stress process assessed through lactate dehydrogenase (LDH) on the severity of COVID-19 measured by oxygen saturation (SaO₂) and the putative interaction with inflammation. The investigation enrolled 1808 patients (mean age of 68 and 60% male) with COVID-19 from the HM Hospitals database. To explore interactions, a regression model and mediation analyses were performed. The patients with lower SaO₂ presented lymphopenia and higher values of neutrophils-to-lymphocytes ratio and on the anisocytosis coefficient. The regression model showed an interaction between LDH and anisocytosis, suggesting that high levels of LDH (>544 U/L) and an anisocytosis coefficient higher than 10% can impact SaO₂ in COVID-19 patients. Moreover, analysis revealed that LDH mediated 41% (p value = 0.001) of the effect of anisocytosis on SaO₂ in this cohort. This investigation revealed that the oxidative stress marker LDH and the interaction with anisocytosis have an important role in the severity of COVID-19 infection and should be considered for the management and treatment of the oxidative phenomena concerning this within a precision medicine strategy.

The Role of Nutrition on Meta-inflammation: Insights and Potential Targets in Communicable and Chronic Disease Management

PURPOSE OF REVIEW

Chronic low-grade inflammation may contribute to the onset and progression of communicable and chronic diseases. This review examined the effects and eventual mediation roles of different nutritional factors on inflammation.

RECENT FINDINGS

Potential nutritional compounds influencing inflammation processes include macro and micronutrients, bioactive molecules (polyphenols), specific food components, and culinary ingredients as well as standardized dietary patterns, eating habits, and chrononutrition features. Therefore, research in this field is still required, taking into account critical aspects of heterogeneity including type of population, minimum and maximum intakes and adverse effects, cooking methods, physiopathological status, and times of intervention. Moreover, the integrative analysis of traditional variables (age, sex, metabolic profile, clinical history, body phenotype, habitual dietary intake, physical activity levels, and lifestyle) together with individualized issues (genetic background, epigenetic signatures, microbiota composition, gene expression profiles, and metabolomic fingerprints) may contribute to the knowledge and prescription of more personalized treatments aimed to improving the precision medical management of inflammation as well as the design of anti-inflammatory diets in chronic and communicable diseases.

Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Association of the Gut Microbiota with the Host's Health through an Analysis of Biochemical Markers, Dietary Estimation, and Microbial Composition

This study aims to analyze the relationship between gut microbiota composition and health parameters through specific biochemical markers and food consumption patterns in the Spanish population. This research includes 60 Spanish adults aged 47.3 ± 11.2 years old. Biochemical and anthropometric measurements, and a self-referred dietary survey (food frequency questionnaire), were analyzed and compared with the participant´s gut microbiota composition analyzed by 16s rDNA sequencing. Several bacterial strains differed significantly with the biochemical markers analyzed, suggesting an involvement in the participant´s metabolic health. Lower levels of Lactobacillaceae and Oscillospiraceae and an increase in Pasteurellaceae, Phascolarctobacterium, and Haemophilus were observed in individuals with higher AST levels. Higher levels of the Christensenellaceae and a decrease in Peptococcaceae were associated with higher levels of HDL-c. High levels of Phascolarctobacterium and Peptococcus and low levels of Butyricicoccus were found in individuals with higher insulin levels. This study also identified associations between bacteria and specific food groups, such as an increase in lactic acid bacteria with the consumption of fermented dairy products or an increase in Verrucomicrobiaceae with the consumption of olive oil. In conclusion, this study reinforces the idea that specific food groups can favorably modulate gut microbiota composition and have an impact on host´s health.

Multi-Omics Nutritional Approaches Targeting Metabolic-Associated Fatty Liver Disease

Currently, metabolic-associated fatty liver disease (MAFLD) is a leading global cause of chronic liver disease, and is expected to become one of the most common indications of liver transplantation. MAFLD is associated with obesity, involving multiple mechanisms such as alterations in lipid metabolism, insulin resistance, hyperinflammation, mitochondrial dysfunction, cell apoptosis, oxidative stress, and extracellular matrix formation. However, the onset and progression of MAFLD is variable among individuals, being influenced by intrinsic (personal) and external environmental factors. In this context, sequence structural variants across the human genome, epigenetic phenomena (i.e., DNA methylation, histone modifications, and long non-coding RNAs) affecting gene expression, gut microbiota dysbiosis, and metabolomics/lipidomic fingerprints may account for differences in MAFLD outcomes through interactions with nutritional features. This knowledge may contribute to gaining a deeper understanding of the molecular and physiological processes underlying MAFLD pathogenesis and phenotype heterogeneity, as well as facilitating the identification of biomarkers of disease progression and therapeutic targets for the implementation of tailored nutritional strategies. This comprehensive literature review highlights the potential of nutrigenetic, nutriepigenetic, nutrimetagenomic, nutritranscriptomics, and nutrimetabolomic approaches for the prevention and management of MAFLD in humans through the lens of precision nutrition.

Dietary protein and the glycemic index handle insulin resistance within a nutritional program for avoiding weight regain after energy-restricted induced weight loss

Background and aim

The role of dietary protein and glycemic index on insulin resistance (based on TyG index) within a nutritional program for weight loss and weight maintenance was examined.

Methods

This study analyzed 744 adults with overweight/obesity within the DIOGenes project. Patients who lost at least 8% of their initial weight (0–8 weeks) after a low-calorie diet (LCD) were randomly assigned to one of five ad libitum diets designed for weight maintenance (8–34 weeks): high/low protein (HP/LP) and high/low glycemic index (HGI/LGI), plus a control. The complete nutritional program (0–34 weeks) included both LCD plus the randomized diets intervention. The TyG index was tested as marker of body mass composition and insulin resistance.

Results

In comparison with the LP/HGI diet, the HP/LGI diet induced a greater BMI loss (p < 0.05). ∆TyG was positively associated with resistance to BMI loss (β = 0.343, p = 0.042) during the weight maintenance stage. In patients who followed the HP/LGI diet, TyG (after LCD) correlated with greater BMI loss in the 8–34 weeks period (r = −0.256; p < 0.05) and during the 0–34 weeks intervention (r = −0.222, p < 0.05) periods. ΔTyG₁ value was associated with ΔBMI₂ (β = 0.932; p = 0.045) concerning the HP/LGI diet.

Conclusions

A HP/LGI diet is beneficial not only for weight maintenance after a LCD, but is also related to IR amelioration as assessed by TyG index changes. Registration Clinical Trials NCT00390637.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-022-00707-y.

Holistic Integration of Omics Tools for Precision Nutrition in Health and Disease

The combination of multiple omics approaches has emerged as an innovative holistic scope to provide a more comprehensive view of the molecular and physiological events underlying human diseases (including obesity, dyslipidemias, fatty liver, insulin resistance, and inflammation), as well as for elucidating unique and specific metabolic phenotypes. These omics technologies include genomics (polymorphisms and other structural genetic variants), epigenomics (DNA methylation, histone modifications, long non-coding RNA, telomere length), metagenomics (gut microbiota composition, enterotypes), transcriptomics (RNA expression patterns), proteomics (protein quantities), and metabolomics (metabolite profiles), as well as interactions with dietary/nutritional factors. Although more evidence is still necessary, it is expected that the incorporation of integrative omics could be useful not only for risk prediction and early diagnosis but also for guiding tailored dietary treatments and prognosis schemes. Some challenges include ethical and regulatory issues, the lack of robust and reproducible results due to methodological aspects, the high cost of omics methodologies, and high-dimensional data analyses and interpretation. In this review, we provide examples of system biology studies using multi-omics methodologies to unravel novel insights into the mechanisms and pathways connecting the genotype to clinically relevant traits and therapy outcomes for precision nutrition applications in health and disease.

Genetic and epigenetic nutritional interactions influencing obesity risk and adiposity outcomes

PURPOSE OF REVIEW

This article aims to critically overview the current interplay of genetic/epigenetic factors and several nutritional aspects influencing obesity susceptibility and adiposity outcomes for obesity management and weight status monitoring.

RECENT FINDINGS

Single nucleotide polymorphisms located in or near genes participating in energy homeostasis, fatty acid metabolism, appetite control, brain regulation, and thermogenesis have been associated with body composition measures (body weight, body mass index, waist circumference, body fat percentage, and visceral adipose tissue) depending on nutrient intakes, dietary patterns, and eating behaviors. Moreover, studies analyzing interactions between the epigenome and dietary intakes in relation to adiposity outcomes are reported. The main epigenetic mechanisms include methylation levels of promoter sequences, telomere length, and micro-ribonucleic acid expression profiles, whereas covalent histone modifications remain less studied.

SUMMARY

Exploring potential interactions between the genetic/epigenetic background and nutritional features is improving the current understanding of the obesity physiopathogenesis and the usefulness of translating this precision information in the clinical setting for weight gain prediction, the design of personalized nutrition therapies as well as individual responsiveness estimation to dietary advice. The analysis of further relationships between the genotype, the epigenotype and other precision markers including the gut microbiota and the metabolome is warranted.

Hematological- and Immunological-Related Biomarkers to Characterize Patients with COVID-19 from Other Viral Respiratory Diseases

COVID-19 has overloaded health system worldwide; thus, it demanded a triage method for an efficient and early discrimination of patients with COVID-19. The objective of this research was to perform a model based on commonly requested hematological variables for an early featuring of patients with COVID-19 form other viral pneumonia. This investigation enrolled 951 patients (mean of age 68 and 56% of male) who underwent a PCR test for respiratory viruses between January 2019 and January 2020, and those who underwent a PCR test for detection of SARS-CoV-2 between February 2020 and October 2020. A comparative analysis of the population according to PCR tests and logistic regression model was performed. A total of 10 variables were found for the characterization of COVID-19: age, sex, anemia, immunosuppression, C-reactive protein, chronic obstructive pulmonary disease, cardiorespiratory disease, metastasis, leukocytes and monocytes. The ROC curve revealed a sensitivity and specificity of 75%. A deep analysis showed low levels of leukocytes in COVID-19-positive patients, which could be used as a primary outcome of COVID-19 detection. In conclusion, this investigation found that commonly requested laboratory variables are able to help physicians to distinguish COVID-19 and perform a quick stratification of patients into different prognostic categories.

Longwise Cluster Analysis for the Prediction of COVID-19 Severity within 72 h of Admission: COVID-DATA-SAVE-LIFES Cohort

The use of routine laboratory biomarkers plays a key role in decision making in the clinical practice of COVID-19, allowing the development of clinical screening tools for personalized treatments. This study performed a short-term longitudinal cluster from patients with COVID-19 based on biochemical measurements for the first 72 h after hospitalization. Clinical and biochemical variables from 1039 confirmed COVID-19 patients framed on the “COVID Data Save Lives” were grouped in 24-h blocks to perform a longitudinal k-means clustering algorithm to the trajectories. The final solution of the three clusters showed a strong association with different clinical severity outcomes (OR for death: Cluster A reference, Cluster B 12.83 CI: 6.11−30.54, and Cluster C 14.29 CI: 6.66−34.43; OR for ventilation: Cluster-B 2.22 CI: 1.64−3.01, and Cluster-C 1.71 CI: 1.08−2.76), improving the AUC of the models in terms of age, sex, oxygen concentration, and the Charlson Comorbidities Index (0.810 vs. 0.871 with p < 0.001 and 0.749 vs. 0.807 with p < 0.001, respectively). Patient diagnoses and prognoses remarkably diverged between the three clusters obtained, evidencing that data-driven technologies devised for the screening, analysis, prediction, and tracking of patients play a key role in the application of individualized management of the COVID-19 pandemics.

Interaction of ACEI antihypertensive agent's administration with the inflammatory status at admission concerning COVID-19 clinical stay outcomes

Interactions between anti-hypertensive agents (ACEI), comorbidities, inflammation, and stress status may impact hospital stay duration in COVID-19 patients. This retrospective study analyzed epidemiological data, comorbidities, metabolic/inflammatory markers, and clinical information from 165 SARS-CoV-2 positive patients. In a multiple linear regression model, an IL-6 higher than 100 mg/L, glucose at admission (baseline levels at the hospital entry), and the interaction between ACEI administration and LDH predicted the days of hospital admission (P < 0.001). In conclusion, hypertensive patients suffering more severe inflammatory condition assessed by LDH levels clinically benefited more and reduced the hospital stay when prescribed ACEI agents than those with lower systemic baseline inflammation at admission.

The triglyceride-glucose index as an adiposity marker and a predictor of fat loss induced by a low-calorie diet

BACKGROUND

This study aimed to investigate the putative role of the triglyceride-glucose index (TyG index) computed as ln[TG (mg/dl) × glucose (mg/dl)/2] and derived proxies as predictors of adiposity and weight loss changes after a low-calorie diet (LCD) intervention.

METHODS

A total of 744 adult participants from the multicentre DIOGenes intervention study were prescribed a LCD (800 kcal/day) during 8 weeks. Body composition and fat content at baseline and after 8 weeks were estimated by DEXA/BIA. A multivariate analysis approach was used to estimate the difference in ΔWeight_1-2 (kg), ΔBMI_1-2 (kg/m² ) or ΔFat_1-2 (%) between the basal value (point 1) and after 8 weeks following a LCD (point 2), respectively. The TyG index at baseline (TyG₁ ), after following the LCD for 8 weeks (TyG₂ ) or the TyG index differences between both time points (ΔTyG_1-2 ) were analysed as predictors of weight and fat changes.

RESULTS

TyG₁ was associated with ΔWeight_1-2 (kg) and ΔBMI_1-2 (kg/m² ), with β = 0.812 (p = .017) and β = 0.265 (p = .018), respectively. Also, TyG₂  values were inversely related to ΔFat_1-2 (%), β = -1.473 (p = .015). Moreover, ΔTyG_1-2 was associated with ΔWeight_1-2 (kg) and ΔFat_1-2 (%), β = 0.689 (p = .045) and β = 1.764 (p = .002), respectively. Furthermore, an association between TyG₂ and resistance to fat loss was found (p = .015).

CONCLUSION

TyG₁ index is a good predictor of weight loss induced by LCD. Moreover, TyG₂ was closely related to resistance to fat loss, while ΔTyG_1-2  values were positively associated with body fat changes. Therefore, TyG index and derived estimations could be used as markers of individualized responses to energy restriction and a surrogate of body composition outcomes in clinical/epidemiological settings in obesity conditions.

Effect of Metformin on Glycemic Control Regarding Carriers of the SLC22A1/OCT1 (rs628031) Polymorphism and Its Interactions with Dietary Micronutrients in Type 2 Diabetes

PURPOSE

Differences in metformin effect on glycemic control in type 2 Diabetes (T2D) have been associated with diet, obesity, years since T2D diagnosis and genetic factors, such as the Met408Val (rs628031) SLC22A1/OCT1 gene polymorphism. This study aimed to analyze the effect of metformin and diet on glycemic control and its association with the Met408Val polymorphism in patients with T2D from western Mexico.

PATIENTS AND METHODS

A total of 240 T2D adult patients were enrolled in this cross-sectional study. Anti-hyperglycemic therapy, dietary intake, body composition and glycemic profile were recorded and the determination of genotypes of SLC22A1/OCT1 gene (rs628031) was performed using an allelic discrimination assay.

RESULTS

The type of metformin therapy was 47% monotherapy, 45% dual therapy (metformin+glibenclamide or metformin+insulin) and 8% triple therapy (metformin+glibenclamide+insulin). Individuals with metformin monotherapy had a higher glycemic control frequency (%HbA1c <7.0) compared with the dual and triple treatment schemes (77% vs 35% and 15%, respectively; p<0.001). Interestingly, a high potassium intake was documented in the three anti-hyperglycemic therapies and a lower intake of micronutrients, including calcium, magnesium, and zinc. An interaction was found between calcium intake and carriers of the risk allele A (408Val) with %HbA1c (P interaction=0.028), and potassium intake with the TyG index (P interaction=0.027). In addition, there was a positive correlation between calcium intake and %HbA1c (r=0.682; p=0.010), and potassium intake vs TyG index (r=0.593; p=0.033) in risk allele A (408Val) carriers with metformin monotherapy. Genotype frequencies were GG homozygotes (76.6%), GA heterozygotes (21.5%) and AA homozygotes (1.9%). The allele frequency was 87.4% for the ancestral allele G and 12.6% for the risk allele A.

CONCLUSION

These findings suggest a differing effect of metformin on glycemic control regarding calcium and potassium intake and the Met408Val SLC22A1/OCT1 gene polymorphism in T2D patients.

A predictive regression model of the obesity-related inflammatory status based on gut microbiota composition

BACKGROUND AND AIM

Fecal microbiome disturbances are linked to different human diseases. In the case of obesity, gut microbiota seems to play a role in the development of low-grade inflammation. The purpose of the present study was to identify specific bacterial families and genera associated with an increased obesity-related inflammatory status, which would allow to build a regression model for the prediction of the inflammatory status of obese and overweight subjects based on fecal microorganisms.

METHODS

A total of 361 volunteers from the Obekit trial (65 normal-weight, 110 overweight, and 186 obese) were classified according to four variables: waist/hip ratio (≥0.86 for women and ≥1.00 for men), leptin/adiponectin ratio (LAR, ≥3.0 for women and ≥1.4 for men), and plasma C-reactive protein (≥2 mg/L) and TNF levels (≥0.85 pg/mL). An inflammation score was designed to classify individuals in low (those subjects who did exceed the threshold value in 0 or 1 variable) or high inflammatory index (those subjects who did exceed the threshold value in 2 or more variables). Fecal 16 S rRNA sequencing was performed for all participants, and differential abundance analyses for family and genera were performed using the MicrobiomeAnalyst web-based platform.

RESULTS

Methanobacteriaceae, Christensenellaceae, Coriobacteriaceae, Bifidobacteriaceae, Catabacteriaceae, and Dehalobacteriaceae families, and Methanobrevibacter, Eggerthella, Gemmiger, Anaerostipes, and Collinsella genera were significantly overrepresented in subjects with low inflammatory index. Conversely, Carnobacteriaceae, Veillonellaceae, Pasteurellaceae, Prevotellaceae and Enterobacteriaceae families, and Granulicatella, Veillonella, Haemophilus, Dialister Parabacteroides, Prevotella, Shigella, and Allisonella genera were more abundant in subjects with a high inflammatory index. A regression model adjusted by BMI, sex, and age and including the families Coriobacteriaceae and Prevotellaceae and the genus Veillonella was developed.

CONCLUSION

A microbiota-based regression model was able to predict the obesity-related inflammatory status (area under the ROC curve = 0.8570 ± 0.0092 Harrell's optimism-correction) and could be useful in the precision management of inflammobesity.

Interactions of Comorbidity and Five Simple Environmental Unhealthy Habits Concerning Physical and Mental Quality of Life in the Clinical Setting

The objective of this study was to examine the interactions between comorbidity and five lifestyle single habits concerning different subscales of quality of life (QoL). For the study, 302 patients were consecutively recruited at the internal medicine department of a tertiary teaching hospital. Lifestyle habits, comorbidities and QoL were recorded according to validated questionnaires. Five single unhealthy habits, such as tobacco consumption, dietary intake of ultra-processed pastries, raw nuts or carbonated drinks, sleep time and physical activity patterns were selected according to previously published data. The main outcomes of the study were the scores of the eight subscales of the SF-36 QoL survey. The aggregate of unhealthy habits showed statistically significant association to every category in the SF-36 questionnaire, both in the univariate and the multivariate analysis when adjusting by age, sex and comorbidity. An interaction was found between comorbidity and unhealthy habits in both physical and mental summaries of SF-36. In conclusion, the lifestyle assessment according to five unhealthy habits is associated with a worse QoL. The interaction between comorbidity and unhealthy habits is especially clear in diseased patients due to the interplay between illness and lifestyle in the prediction of QoL.

Proinflammatory and Hepatic Features Related to Morbidity and Fatal Outcomes in COVID-19 Patients

OBJECTIVE

to screen putative associations between liver markers and proinflammatory-related features concerning infectious morbidity and fatal outcomes in COVID-19 patients.

METHODS

a total of 2094 COVID-19 positive patients from the COVID-DATA-SAFE-LIFES cohort (HM hospitals consortium) were classified according to median values of hepatic, inflammatory, and clinical indicators. Logistic regression models were fitted and ROC cures were generated to explain disease severity and mortality.

RESULTS

intensive care unit (ICU) assistance plus death outcomes were associated with liver dysfunction, hyperinflammation, respiratory insufficiency, and higher associated comorbidities. Four models including age, sex, neutrophils, D-dimer, oxygen saturation lower than 92%, C-reactive protein (CRP), Charlson Comorbidity Index (CCI), FIB-4 and interactions with CRP, neutrophils, and CCI explained ICU plus death variance in more than 28%. The predictive values of ROC curves were: FIB-4 (0.7339), AST/ALT ratio (0.7107), CRP (0.7003), CCI index (0.6778), neutrophils (0.6772), and platelets (0.5618) concerning ICU plus death outcomes.

CONCLUSIONS

the results of this research revealed that liver and proinflammatory features are important determinants of COVID-19 morbidity and fatal outcomes, which could improve the current understanding of the COVID-19 physiopathology as well as to facilitate the clinical management and therapy decision-making of this disease under a personalized medicine scope.