A High-Protein/Low-Fat Diet May Interact with Vitamin D-Binding Protein Gene Variants to Moderate the Risk of Depression in Apparently Healthy Adults

Pooyan, Sara; Rahimi, Mohammad; Mollahosseini, Mehdi; Khorraminezhad, Leila; Nasir, Yasaman; Maghbooli, Zhila; Mirzaei, Khadijeh

doi:10.1159/000492497

Cited by 18 publications

(10 citation statements)

References 37 publications

(51 reference statements)

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“…With regard to depression, in a large epidemiologic study, elevated dietary intake of protein at baseline was associated with more severe depressive symptoms at 10-year follow-up in a dose-dependent manner, in women only (87). Others have reported that interactions between dietary protein levels and genetic polymorphisms can moderate the risk of depression (88).…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of Gut Microbiota and Major Depression

et al. 2019

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Background: Recently discovered relationships between the gastrointestinal microbiome and the brain have implications for psychiatric disorders, including major depressive disorder (MDD). Bacterial transplantation from MDD patients to rodents produces depression-like behaviors. In humans, case-control studies have examined the gut microbiome in healthy and affected individuals. We systematically reviewed existing studies comparing gut microbial composition in MDD and healthy volunteers.Methods: A PubMed literature search combined the terms “depression,” “depressive disorder,” “stool,” “fecal,” “gut,” and “microbiome” to identify human case-control studies that investigated relationships between MDD and microbiota quantified from stool. We evaluated the resulting studies, focusing on bacterial taxa that were different between MDD and healthy controls.Results: Six eligible studies were found in which 50 taxa exhibited differences (p < 0.05) between patients with MDD and controls. Patient characteristics and methodologies varied widely between studies. Five phyla—Bacteroidetes, Firmicutes, Actinobacteria, Fusobacteria, and Protobacteria—were represented; however, divergent results occurred across studies for all phyla. The largest number of differentiating taxa were within phylum Firmicutes, in which nine families and 12 genera differentiated the diagnostic groups. The majority of these families and genera were found to be statistically different between the two groups in two identified studies. Family Lachnospiraceae differentiated the diagnostic groups in four studies (with an even split in directionality). Across all five phyla, nine genera were higher in MDD (Anaerostipes, Blautia, Clostridium, Klebsiella, Lachnospiraceae incertae sedis, Parabacteroides, Parasutterella, Phascolarctobacterium, and Streptococcus), six were lower (Bifidobacterium, Dialister, Escherichia/Shigella, Faecalibacterium, and Ruminococcus), and six were divergent (Alistipes, Bacteroides, Megamonas, Oscillibacter, Prevotella, and Roseburia). We highlight mechanisms and products of bacterial metabolism as they may relate to the etiology of depression.Conclusions: No consensus has emerged from existing human studies of depression and gut microbiome concerning which bacterial taxa are most relevant to depression. This may in part be due to differences in study design. Given that bacterial functions are conserved across taxonomic groups, we propose that studying microbial functioning may be more productive than a purely taxonomic approach to understanding the gut microbiome in depression.

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“…The sequencing process was performed using the ABI PRISM 3730 automated sequencer (Applied Biosystems, Foster City, CA, USA). 29 The fragments containing 3 genotypes were distinguished: CC, CT, and TT.…”

Section: Methodsmentioning

confidence: 99%

The Interaction Between Dietary Total Antioxidant Capacity and MC4R Gene and HOMA-IR in Metabolically Healthy and Unhealthy Overweight and Obese Women

Elhamkia

Setayesh

Yarizadeh

et al. 2022

Nutr Metab Insights

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Introduction: The current prevalence of the metabolically healthy obesity is about 3%. Genetic and nutrition are influencers of such phenotypes. The main goal of this study was to assess the interaction between Dietary Total Antioxidant Capacity (DTAC) and the genotypes of MC4R and Insulin resistance in metabolically healthy/unhealthy overweight and obese women in Iran. Material And Methods: This cross-sectional study was conducted on 237 overweight-obese women with a mean age of 36. The value of Dietary total antioxidant capacity (DTAC) was calculated using the following indices: Total reactive antioxidant potential (TRAP), Trolox equivalent antioxidant capacity (TEAC), and ferric reducing ability of plasma (FRAP). The Metabolic health status was evaluated using the Karelis criteria. Melanocortin 4 receptor single nucleotide polymorphisms were determined by the restriction fragment length polymorphism (PCR-RFLP) method. Also, insulin resistance was evaluated through homeostasis model assessment (HOMA). Result: Our data noted that 72.96% of participants presented Unhealthy Metabolically and 26.94% Healthy Metabolically including 33.5% of the total had T/T genotype, 23.8% had the C/T genotype, and 42.5% had the C/C genotype ( P = .05). A linear regression model test showed that the probability of metabolically healthy obesity was significantly higher in patients with the T/C genotype. The test value was statistically significant (95% CI: 0.000-0.001; P = .056, β = 0). No statistically significant relation was observed between study parameters and DTAC values. HOMA-Index was higher in all unhealthy subjects significantly. Conclusions: The findings indicated that there are significant associations between genotypes of rs1333048 SNP and DTAC. The C/C genotype subjects with higher DTAC had a better lipid profile and were metabolically healthier.

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“…10% of the samples were directly sequenced for con rmation the PCR-RFLP results. The sequencing process performed using the ABI PRISM 3730 automated sequencer (Applied Biosystems, Foster City, CA, USA) [29]. Ampli ed DNA (7 μl) was digested with 0.5 μl of BCII restriction enzyme (Fermentase, Germany) at 56 °C overnight.…”

Section: The Genotype Determinationmentioning

confidence: 99%

Association of Dietary Total Antioxidant Capacity and MC4R Polymorphisms in Metabolically Healthy and Unhealthy Overweight/Obese Women

Elhamkia

Setayesh

Yarizadeh

et al. 2021

Preprint

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Background: 3-15% of people with obesity display a metabolically healthy phenotype. An inflammatory diet impairs metabolic signaling pathways and eliciting metabolic syndrome. The main purpose of this study was to investigate how the Dietary Total Antioxidant Capacity (DTAC) and the MC4R variants affect the metabolic health status in overweight and obese Iranian women.Methods and results: This cross-sectional study was conducted on 237 overweight-obese Tehrani women with mean age of 36 years.The DTAC was calculated using the following indices: total reactive antioxidant potential (TRAP), Trolox equivalent antioxidant capacity (TEAC), and the ferric-reducing ability of plasma (FRAP). The Metabolic health was evaluated using the Karelis criteria. The MC4R genotypes were determined by the restriction fragment length polymorphism (PCR-RFLP) method. Approximately 43% of participants were identified as Metabolically Unhealthy Obesity (MUO), 6% Metabolically Healthy Obesity (MHO) including 42.5% of the total had T/T genotype ,23.8% had the C/T genotype, and 33.5% had the C/C genotype (P=0.05). In the C/C genotype people 75% were unhealthy whereas it was lower in T/T and T/C genotype people. Further, The C/C genotype with higher DTAC quartile had higher Karlis score than other DTAC quartile. A liner model test supported that the probability of MHO was significantly higher in subjects with the T/C genotype (β=-0.001, 95% CI= -0.001-4.796; P ≤ 0.1). Conclusions: Our results indicated that the C/C genotype with higher DTAC had favorable lipid profile and were metabolically healthier. It is plausible that dietary modification targeting antioxidant intake may attenuate metabolic impairments associated with genetic predisposition.

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“…As for proteins, fermentation and bacterial putrefaction increase their metabolism, resulting in the production of both essential amino acids and toxic products (i.e., putrescine, phenol and ammonia), associated to an increased risk of depression [ 96 ].…”

Section: Behavioural Correlates Of Gut Dysbiosismentioning

confidence: 99%

Cognitive-Behavioural Correlates of Dysbiosis: A Review

Luca

Chattipakorn

Sriwichaiin

et al. 2020

IJMS

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Evidence suggests an association between an altered gut microbiota (dysbiosis), cognitive performance and behaviour. This paper provides an overview of the current literature regarding the cognitive-behavioural correlates of dysbiosis, with special attention on the clinical and biochemical mechanisms underlying the association between dysbiosis, cognition (mild cognitive impairment and dementia) and behaviour (depression, schizophrenia, addiction). After providing an overview of the evidence, the review discusses the molecular aspects that could account for the cognitive-behavioural correlates of dysbiosis. Shedding light on this topic could provide insights regarding the pathogenesis of these burdening neuropsychiatric disorders and even suggest future therapeutic strategies.

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“…For instance, differences in bone density between three visfatin genotypes (GG, GT, TT) were assessed by ANOVA test in a nutrigenomics study on obese and overweight healthy adults [ 37 ]. However, a t -test was used to examine the differences in lipid profile, inflammatory parameters between two vitamin D-binding protein (polymorphism, rs4588) genotypes (CC, AC + AA) [ 38 ]. Furthermore, s two-way ANOVA is used to measure the effect of two different categorical variables on one continuous variable.…”

Section: Traditional Statistical Analysis In Nutrition Studiesmentioning

confidence: 99%

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

et al. 2020

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Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.

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A High-Protein/Low-Fat Diet May Interact with Vitamin D-Binding Protein Gene Variants to Moderate the Risk of Depression in Apparently Healthy Adults

Cited by 18 publications

References 37 publications

Systematic Review of Gut Microbiota and Major Depression

The Interaction Between Dietary Total Antioxidant Capacity and MC4R Gene and HOMA-IR in Metabolically Healthy and Unhealthy Overweight and Obese Women

Association of Dietary Total Antioxidant Capacity and MC4R Polymorphisms in Metabolically Healthy and Unhealthy Overweight/Obese Women

Cognitive-Behavioural Correlates of Dysbiosis: A Review

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

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