Cigarette smoking, e-cigarette use, and sociodemographic correlates of mental health and tobacco-related disease risk in the All of Us research program.

SIGNIFICANCE: Research on the conditions under which electronic cigarette (EC) use produces a net reduction in the population harm attributable to combusted cigarette (CC) use requires the triangulation of information from cohort(s) of smokers, non-smokers, EC users, and dual-users of all varieties. MATERIALS AND METHODS: This project utilizes data from the All of Us Research Program to contrast a panel of wellness and disease-risk indicators across a range of self-reported tobacco-use profiles, including smokers, current, and former EC users. This article focuses on the tobacco use history and current tobacco use status among All of Us participants enrolled between May 2017 and February 2023 (Registered Controlled Tier Curated Data Repository [CDR] v7). RESULTS: The present analytic sample included an unweighted total of N = 412 211 individuals with information on ever-use of both CC and EC. Among them, 155 901 individuals have a history of CC use, with 65 206 identified as current smokers. EC usage is reported by 64 002 individuals, with 16 619 being current users. Model predicted analyses identified distinct patterns in CC and EC usage across demographic and socioeconomic variables, with younger ages favoring ECs. DISCUSSION: Age was observed to significantly affect EC usage, and gender differences reveal that males were significantly more likely to use CC and/or EC than females or African Americans of any gender. Higher educational achievement and income were associated with lower use of both CC and EC, while lower levels of mental health were observed to increase the likelihood of using CC and EC products. CONCLUSION: Findings suggest the potential for the All of Us Research Program for investigation of causal factors driving both behavioral use transitions and cessation outcomes.

Motile Living Biobots Self-Construct from Adult Human Somatic Progenitor Seed Cells.

Fundamental knowledge gaps exist about the plasticity of cells from adult soma and the potential diversity of body shape and behavior in living constructs derived from genetically wild-type cells. Here anthrobots are introduced, a spheroid-shaped multicellular biological robot (biobot) platform with diameters ranging from 30 to 500 microns and cilia-powered locomotive abilities. Each Anthrobot begins as a single cell, derived from the adult human lung, and self-constructs into a multicellular motile biobot after being cultured in extra cellular matrix for 2 weeks and transferred into a minimally viscous habitat. Anthrobots exhibit diverse behaviors with motility patterns ranging from tight loops to straight lines and speeds ranging from 5-50 microns s-1 . The anatomical investigations reveal that this behavioral diversity is significantly correlated with their morphological diversity. Anthrobots can assume morphologies with fully polarized or wholly ciliated bodies and spherical or ellipsoidal shapes, each related to a distinct movement type. Anthrobots are found to be capable of traversing, and inducing rapid repair of scratches in, cultured human neural cell sheets in vitro. By controlling microenvironmental cues in bulk, novel structures, with new and unexpected behavior and biomedically-relevant capabilities, can be discovered in morphogenetic processes without direct genetic editing or manual sculpting.

A Meta-Analysis of Bioelectric Data in Cancer, Embryogenesis, and Regeneration.

Developmental bioelectricity is the study of the endogenous role of bioelectrical signaling in all cell types. Resting potentials and other aspects of ionic cell physiology are known to be important regulatory parameters in embryogenesis, regeneration, and cancer. However, relevant quantitative measurement and genetic phenotyping data are distributed throughout wide-ranging literature, hampering experimental design and hypothesis generation. Here, we analyze published studies on bioelectrics and transcriptomic and genomic/phenotypic databases to provide a novel synthesis of what is known in three important aspects of bioelectrics research. First, we provide a comprehensive list of channelopathies-ion channel and pump gene mutations-in a range of important model systems with developmental patterning phenotypes, illustrating the breadth of channel types, tissues, and phyla (including man) in which bioelectric signaling is a critical endogenous aspect of embryogenesis. Second, we perform a novel bioinformatic analysis of transcriptomic data during regeneration in diverse taxa that reveals an electrogenic protein to be the one common factor specifically expressed in regeneration blastemas across Kingdoms. Finally, we analyze data on distinct Vmem signatures in normal and cancer cells, revealing a specific bioelectrical signature corresponding to some types of malignancies. These analyses shed light on fundamental questions in developmental bioelectricity and suggest new avenues for research in this exciting field.

A multianalytical approach to evaluate the association of 55 SNPs in 28 genes with obesity risk in North Indian adults.

OBJECTIVES: The aim of the study was to investigate the association of 55 SNPs in 28 genes with obesity risk in a North Indian population using a multianalytical approach. METHODS: Overall, 480 subjects from the North Indian population were studied using strict inclusion/exclusion criteria. SNP Genotyping was carried out by Sequenom Mass ARRAY platform (Sequenom, San Diego, CA) and validated Taqman® allelic discrimination (Applied Biosystems® ). Statistical analyses were performed using SPSS software version 19.0, SNPStats, GMDR software (version 6) and GENEMANIA. RESULTS: Logistic regression analysis of 55 SNPs revealed significant associations (P < .05) of 49 SNPs with BMI linked obesity risk whereas the remaining 6 SNPs revealed no association (P > .05). The pathway-wise G-score revealed the significant role (P = .0001) of food intake-energy expenditure pathway genes. In CART analysis, the combined genotypes of FTO rs9939609 and TCF7L2 rs7903146 revealed the highest risk for BMI linked obesity. The analysis of the FTO-IRX3 locus revealed high LD and high order gene-gene interactions for BMI linked obesity. The interaction network of all of the associated genes in the present study generated by GENEMANIA revealed direct and indirect connections. In addition, the analysis with centralized obesity revealed that none of the SNPs except for FTO rs17818902 were significantly associated (P < .05). CONCLUSIONS: In this multi-analytical approach, FTO rs9939609 and IRX3 rs3751723, along with TCF7L2 rs7903146 and TMEM18 rs6548238, emerged as the major SNPs contributing to BMI linked obesity risk in the North Indian population.

Association of FTO and IRX3 genetic variants to obesity risk in north India.

BACKGROUND: Obesity is an increasingly important health problem worldwide as well as in developing countries like India. Recent genetic studies suggest that obesity associated FTO and IRX3 are functionally linked and many effects due to genetic variants in FTO gene act through IRX3. AIM: To evaluate the association of FTO and IRX3 genetic variants towards obesity risk. SUBJECTS AND METHODS: North Indian individuals categorised as non-obese (BMI < 30 kg/m(2)) and obese (BMI ≥ 30 kg/m(2)) were selected. FTO rs8050136, rs1421085, rs9939609, rs17817449 and IRX3 rs3751723 were genotyped by means of validated Taqman® allelic discrimination to evaluate their association with obesity by means of single locus logistic regression by SPSS ver. 19 and multi-locus linkage and haplotype analysis by SNPStats and gene-gene interaction with Generalised Multifactor Dimensionality Reduction (GMDR) ver.6. RESULTS: In single locus analysis, FTO rs8050136 CA (p = 0.0001; OR (95% CI) = 2.4 (1.7-3.4) and AA (p = 0.0001; OR (95% CI) = 3.1 (1.9-5.2); FTO rs1421085 TA (p = 0.0001; OR (95% CI) = 2.1 (1.4-3.0) and AA (p = 0.0001; OR (95% CI) = 3.0 (1.8-5.0); FTO rs9939609 TC (p = 0.0001; OR (95% CI) = 2.1 (1.5-3.1) and CC (p = 0.0001; OR (95% CI) = 4.2 (2.5-7.3) along with TG (p = 0.001; OR (95% CI) = 2.1 (1.3-3.2) and GG (p = 0.021; OR (95% CI) = 3.8 (1.2-11.8) genotypes of FTO rs17817449 with GT (p = 0.0001; OR (95% CI) = 2.1 (1.5-3.1) and TT (p = 0.012; OR (95% CI) = 3.3 (1.8-3.6) genotypes of IRX3 rs3751723 were significantly associated with obesity. In multi-locus analysis, SNPs of FTO and IRX3 were in strong linkage disequilibrium and in haplotype and GMDR analysis the SNPs were significantly associated with obesity risk (p < 0.05). CONCLUSION: This is the first study to reveal that genetic variants of both FTO and IRX3 genes are in high linkage disequilibrium (LD) and are associated with obesity risk in North Indians.

Analysis of MC4R rs17782313, POMC rs1042571, APOE-Hha1 and AGRP rs3412352 genetic variants with susceptibility to obesity risk in North Indians.

BACKGROUND: Obesity is a multi-factorial disorder influenced by genetic and environmental factors. The physiological pathways associated with obesity are complex and involve several genes. AIM: The aim of this survey is to evaluate the association of genetic variants of melanocortin-4-receptor (MC4R), pro-opiomelanocortin (POMC), apolipoprotein E (APOE) and agouti-related protein (AGRP) with obesity in the North Indian population. METHODS: MC4R rs17782313, POMC rs1042571, APOE-Hha1 and AGRP rs3412352 polymorphisms were investigated for their association in 396 obese individuals with BMI ≥ 30 kg/m(2) and 300 healthy non-obese individuals with BMI < 30 kg/m(2). Genotyping was performed using Taqman probes and PCR-RFLP methods. Single locus logistic regression analysis was conducted using (SPSS), ver.19 and PLINK software Version 1.01 and high order genetic interactions associated with obesity risk were analysed using MDR software (version 2.3.0.2). RESULTS: The genotypes of MC4R rs17782313, POMC rs1042571 and APOE-Hha1 were significantly associated with obese individuals (BMI ≥ 30 kg/m(2)) when compared with non-obese individuals (BMI < 30 kg/m(2)). No association of AGRP rs34123523 was seen with obesity. CONCLUSIONS: The best interaction model for predicting obesity risk by MDR analysis was the three factor model including POMC (C > T), MC4R (T > C) and APOE (Hha1) polymorphisms. Genetic variants in MC4R, POMC and APOE genes might play significant roles in predisposing obesity (BMI ≥ 30 kg/m(2)) in the North Indian population.