Background:Total daily energy expenditure (TDEE) is vital for energy balance and cardiometabolic health, yet its trajectory across the lifespan, particularly in females, remains poorly understood. Objectives:We sought to examine the effects of aging and sex on body composition and TDEE. Methods:In a cross-sectional analysis of data from research centers across 9 European Countries and the United States from the International Atomic Energy Agency database, TDEE and body composition measures of 2326 participants (1560W/766M; 50.7 ± 12 .6 y) were stratified across age groups: young (30-39 y; YOUNG), middle-aged (40-54 y; MID), and old (55-70 y; OLD). Doubly labeled water was used to estimate TDEE and fat-free mass (FFM). Fat mass (FM) was calculated as the difference between body mass and FFM, and %fat was ratio between FM and body mass as a percentage. Linear models were used for analysis. Results:Females demonstrated greater FM and lower FFM with each age group, compared with males (P < 0.001). In females, OLD had lower absolute TDEE than YOUNG (-217 kcal/d, P < 0.001) and MID (-208 kcal/d, P < 0.001). Male absolute TDEE was lowered across all age groups (OLD compared with YOUNG: -334 kcal/d; OLD compared with MID: -210 kcal/d; MID compared with YOUNG: -124 kcal/d; P < 0.001). Adjusted TDEE was similar within age groups between females and males. Conclusions:These results suggest that age influences changes in body composition and energy expenditure similarly between males and females. The most significant change in TDEE occurs as individuals transition from middle age to older adulthood. Females generally have a higher percentage of %fat and FM, along with lower FFM, compared with males across all age groups. These findings are important for understanding how aging affects metabolism and body composition, which could inform sex-specific health strategies and interventions.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96years. The resultant regression equation predicts expected TEE from easily acquired variables, such as body weight, age and sex, with 95% predictive limits that can be used to screen for misreporting by participants in dietary studies. We applied the equation to two large datasets (National Diet and Nutrition Survey and National Health and Nutrition Examination Survey) and found that the level of misreporting was >50%. The macronutrient composition from dietary reports in these studies was systematically biased as the level of misreporting increased, leading to potentially spurious associations between diet components and body mass index.

BACKGROUND:Apolipoprotein L1 gene (APOL1) variants are risk factors for chronic kidney disease (CKD) among Black Americans. Data are sparse on the genetic epidemiology of CKD and the clinical association of APOL1 variants with CKD in West Africans, a major group in the Black population. METHODS:We conducted a case-control study involving participants from Ghana and Nigeria who had CKD stages 2 through 5, biopsy-proven glomerular disease, or no kidney disease. We analyzed the association of CKD with APOL1 variants among participants with high-risk genotypes (two APOL1 risk alleles) and those with low-risk genotypes (fewer than two APOL1 risk alleles) by fitting logistic-regression models that controlled for covariates, including clinical site, age, and sex. RESULTS:Among 8355 participants (4712 with CKD stages 2 through 5, 866 with glomerular diseases, and 2777 with no kidney disease), the prevalence of monoallelic APOL1 variants was 43.0% and that of biallelic APOL1 variants was 29.7%. Participants with two APOL1 risk alleles had higher odds of having CKD than those with one risk allele or no risk alleles (adjusted odds ratio, 1.25; 95% confidence interval [CI], 1.11 to 1.40), as well as higher odds of focal segmental glomerulosclerosis (adjusted odds ratio, 1.84; 95% CI, 1.30 to 2.61). Participants with one APOL1 risk allele had higher odds of having CKD than those with no risk alleles (adjusted odds ratio, 1.18; 95% CI, 1.04 to 1.33), as well as higher odds of focal segmental glomerulosclerosis (adjusted odds ratio, 1.61; 95% CI, 1.04 to 2.48). The inclusion of covariates did not modify the association of monoallelic and biallelic APOL1 variants with CKD or focal segmental glomerulosclerosis. CONCLUSIONS:In this study, monoallelic APOL1 variants were associated with 18% higher odds of CKD and 61% higher odds of focal segmental glomerulosclerosis; biallelic APOL1 variants were associated with 25% higher odds of CKD and 84% higher odds of focal segmental glomerulosclerosis. (Funded by the National Human Genome Research Institute and others.).

Increasing awareness of HIV preexposure prophylaxis (HIVPrEP) is an important HIV prevention strategy. In Brighton, HIVPrEP is provided by a single provider from two sites: the sexual health clinic and a community setting. As part of our yearly evaluation project, we explored HIVPrEP awareness among patients accessing our service. We sent an SMS link to an anonymous online survey to patients after their sexual health appointment in July/ August 2022 including asking if respondents know about HIVPrEP. Overall, 289/1927 (15%) responded including 157 (54%) cismen, 116 (40%) ciswomen and 15 (5%) trans/ nonbinary identifying people. One hundred and seventynine (62%, 95% CI 56.1 to 67.6) respondents stated they were aware of HIVPrEP. There were differences in awareness by gender, age, sexuality and ethnicity (table 1). It is concerning that HIVPrEP awareness was relatively poor among groups other than MSM. The delivery of HIV interventions including HIV testing, and HIVPrEP needs to become more innovative to reach people less informed.

The common bed bug, Cimex lectularius L., is a difficult urban pest to control. A simulated field study was conducted to compare the efficacy of steam application and an insecticide mixture spray (0.05% acetamiprid and 0.06% bifenthrin mixture) against C. lectularius. Three types of furniture (desk chair, upholstered armchair, and wooden table) were treated in the laboratory. The efficacy of the treatments was evaluated by visual inspection and placement of interceptor traps under the legs of the furniture. One hundred mixed stages of an insecticide-resistant population of C. lectularius were released onto each furniture item. After a 10-day acclimation period, each furniture item received steam treatment, insecticide spray, or no treatment. The second application of treatment was conducted 14 d later. Bed bug counts from interceptors and visual inspections were recorded at 13 d and 28 d after the initial treatment. At 28 d, the mean (± SE) live bed bug count in the steam, spray, and control group was 1 ± 0, 2 ± 1, and 83 ± 10, respectively. Both treatment methods were highly effective in controlling bed bugs on furniture. The mean bed bug count from interceptors in the steam, spray, and control groups were 0.3 ± 0.2, 11 ± 7, and 47 ± 9, respectively. There was no significant difference in the efficacy between steam and spray treatments based on either visual inspection or bed bug counts from interceptors. However, based on interceptor counts, the steam treatment caused faster bed bug population reduction than insecticide sprays.

Obesity is caused by a prolonged positive energy balance 1 , 2 . Whether reduced energy expenditure stemming from reduced activity levels contributes is debated 3 , 4 . Here we show that in both sexes, total energy expenditure (TEE) adjusted for body composition and age declined since the late 1980s, while adjusted activity energy expenditure increased over time. We use the International Atomic Energy Agency Doubly Labelled Water database on energy expenditure of adults in the United States and Europe ( n = 4,799) to explore patterns in total (TEE: n = 4,799), basal (BEE: n = 1,432) and physical activity energy expenditure ( n = 1,432) over time. In males, adjusted BEE decreased significantly, but in females this did not reach significance. A larger dataset of basal metabolic rate (equivalent to BEE) measurements of 9,912 adults across 163 studies spanning 100 years replicates the decline in BEE in both sexes. We conclude that increasing obesity in the United States/Europe has probably not been fuelled by reduced physical activity leading to lowered TEE. We identify here a decline in adjusted BEE as a previously unrecognized factor.

Vy, Ha My T.; Coca, Steven G.; Sawant, Ashwin; Sakhuja, Ankit; Gutierrez, Orlando M.; Cooper, Richard; Loos, Ruth J.F.; Horowitz, Carol R.; Do, Ron; Nadkarni, Girish N. Author Information

Objectives This is a protocol for a Cochrane Review (intervention). The objectives are as follows: Primary objective: to determine the effect of structured physical activity and exercise therapy compared to placebo or normal physical activity on HRQOL of people with IBD (UC, CD, IBDU). Secondary objective: to evaluate the impact of these physical activity interventions on maintenance of remission and fatigue.

There is considerably greater variation in metabolic rates between men than between women, in terms of basal, activity and total (daily) energy expenditure (EE). One possible explanation is that EE is associated with male sexual characteristics (which are known to vary more than other traits) such as musculature and athletic capacity. Such traits might be predicted to be most prominent during periods of adolescence and young adulthood, when sexual behaviour develops and peaks. We tested this hypothesis on a large dataset by comparing the amount of male variation and female variation in total EE, activity EE and basal EE, at different life stages, along with several morphological traits: height, fat free mass and fat mass. Total EE, and to some degree also activity EE, exhibit considerable greater male variation (GMV) in young adults, and then a decrease in the degree of GMV in progressively older individuals. Arguably, basal EE, and also morphometrics, do not exhibit this pattern. These findings suggest that single male sexual characteristics may not exhibit peak GMV in young adulthood, however total and perhaps also activity EE, associated with many morphological and physiological traits combined, do exhibit GMV most prominently during the reproductive life stages.

LivestockVol. 28, No. 4 EditorialAllowing cows a choiceRichard CooperRichard CooperSearch for more papers by this authorRichard CooperPublished Online:11 Jul 2023https://doi.org/10.12968/live.2023.28.4.149AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Chamberlain AT, Powell CD, Arcier E, Aldenhoven N. The relationship between on-farm environmental conditions inside and outside cow sheds during the summer in England: can Temperature Humidity Index be predicted from outside conditions? Animal - Open Space. 2022; 1:1. https://doi.org/10.1016/j.anopes.2022.100019 Crossref, Google ScholarDawkins MS. The Science of Animal Welfare, Understanding What Animals Want. Oxford: Oxford University Press; 2021 Crossref, Google Scholar FiguresReferencesRelatedDetails 2 July 2023Volume 28Issue 4ISSN (print): 2053-0862ISSN (online): 2053-0870 Metrics History Published online 11 July 2023 Published in print 2 July 2023 Information© MA Healthcare LimitedPDF download

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Richard Cooper; Black:White Health Inequalities, Genes Versus Environment—A New Chapter?. Ethnicity and Disease 1 January 2023; 33 (1): 51–54. doi: https://doi.org/10.18865/1731 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes.Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: “Some College” (yes/no, for any education beyond high school) and “Graduated College” (yes/no, for completing a 4-year college degree). Genome-wide significant (p < 5 × 10−8) and suggestive (p < 1 × 10−6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals).Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue.Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.

Empirical science in the eighteenth and nineteenth centuries transformed public health. Improvement in nutrition and living conditions were the driving forces, linked to basic sanitation. The principles of public health also proved highly effective in prevention of chronic disease, such as cardiovascular disease and cancer. However, the dominant force in biomedicine has become genomics and “precision medicine,” both of which ignore the role of environmental exposures, and focus on individual, not collective risk. Genetic determinism and technological solutions have narrowed the scope of research aimed at improving population health, and reduced the benefits that biomedical science and public health could provide. The COVID-19 pandemic is the same story in bold print.

Veterinary RecordVolume 191, Issue 12 p. 506-506 Bovine Health Risks of overfeeding copper to cattle Richard Cooper, Richard Cooper Specialist in cattle health and production Map of Ag, Cumbria House, Gilwilly Road, Penrith, Cumbria, CA11 9FFSearch for more papers by this authorJames Husband, James Husband Specialist in bovine health management Map of Ag, Cumbria House, Gilwilly Road, Penrith, Cumbria, CA11 9FFSearch for more papers by this authorJulia Moorhouse, Corresponding Author Julia Moorhouse head of veterinary services julia.moorhouse@mapof.ag Map of Ag, Cumbria House, Gilwilly Road, Penrith, Cumbria, CA11 9FFSearch for more papers by this author Richard Cooper, Richard Cooper Specialist in cattle health and production Map of Ag, Cumbria House, Gilwilly Road, Penrith, Cumbria, CA11 9FFSearch for more papers by this authorJames Husband, James Husband Specialist in bovine health management Map of Ag, Cumbria House, Gilwilly Road, Penrith, Cumbria, CA11 9FFSearch for more papers by this authorJulia Moorhouse, Corresponding Author Julia Moorhouse head of veterinary services julia.moorhouse@mapof.ag Map of Ag, Cumbria House, Gilwilly Road, Penrith, Cumbria, CA11 9FFSearch for more papers by this author First published: 16 December 2022 https://doi.org/10.1002/vetr.2568Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume191, Issue1217–31 December 2022Pages 506-506 RelatedInformation

ABSTRACTCommon SNPs are predicted to collectively explain 40-50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes. Here we show, using GWAS data from 5.4 million individuals of diverse ancestries, that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a median size of ~90 kb, covering ~21% of the genome. The density of independent associations varies across the genome and the regions of elevated density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs account for 40% of phenotypic variance in European ancestry populations but only ~10%-20% in other ancestries. Effect sizes, associated regions, and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely explained by linkage disequilibrium and allele frequency differences within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than needed to implicate causal genes and variants. Overall, this study, the largest GWAS to date, provides an unprecedented saturated map of specific genomic regions containing the vast majority of common height-associated variants.

In mammals, trait variation is often reported to be greater among males than females. However, to date, mainly only morphological traits have been studied. Energy expenditure represents the metabolic costs of multiple physical, physiological, and behavioral traits. Energy expenditure could exhibit particularly high greater male variation through a cumulative effect if those traits mostly exhibit greater male variation, or a lack of greater male variation if many of them do not. Sex differences in energy expenditure variation have been little explored. We analyzed a large database on energy expenditure in adult humans (1494 males and 3108 females) to investigate whether humans have evolved sex differences in the degree of interindividual variation in energy expenditure. We found that, even when statistically comparing males and females of the same age, height, and body composition, there is much more variation in total, activity, and basal energy expenditure among males. However, with aging, variation in total energy expenditure decreases, and because this happens more rapidly in males, the magnitude of greater male variation, though still large, is attenuated in older age groups. Considerably greater male variation in both total and activity energy expenditure could be explained by greater male variation in levels of daily activity. The considerably greater male variation in basal energy expenditure is remarkable and may be explained, at least in part, by greater male variation in the size of energy-demanding organs. If energy expenditure is a trait that is of indirect interest to females when choosing a sexual partner, this would suggest that energy expenditure is under sexual selection. However, we present a novel energetics model demonstrating that it is also possible that females have been under stabilizing selection pressure for an intermediate basal energy expenditure to maximize energy available for reproduction.

Common single-nucleotide polymorphisms (SNPs) are predicted to collectively explain 40–50% of phenotypic variation in human height, but identifying the specific variants and associated regions requires huge sample sizes 1 . Here, using data from a genome-wide association study of 5.4 million individuals of diverse ancestries, we show that 12,111 independent SNPs that are significantly associated with height account for nearly all of the common SNP-based heritability. These SNPs are clustered within 7,209 non-overlapping genomic segments with a mean size of around 90 kb, covering about 21% of the genome. The density of independent associations varies across the genome and the regions of increased density are enriched for biologically relevant genes. In out-of-sample estimation and prediction, the 12,111 SNPs (or all SNPs in the HapMap 3 panel 2 ) account for 40% (45%) of phenotypic variance in populations of European ancestry but only around 10–20% (14–24%) in populations of other ancestries. Effect sizes, associated regions and gene prioritization are similar across ancestries, indicating that reduced prediction accuracy is likely to be explained by linkage disequilibrium and differences in allele frequency within associated regions. Finally, we show that the relevant biological pathways are detectable with smaller sample sizes than are needed to implicate causal genes and variants. Overall, this study provides a comprehensive map of specific genomic regions that contain the vast majority of common height-associated variants. Although this map is saturated for populations of European ancestry, further research is needed to achieve equivalent saturation in other ancestries.

BACKGROUND:Automated and data-driven methods for screening using natural language processing (NLP) and machine learning may replace resource-intensive manual approaches in the usual care of patients hospitalized with conditions related to unhealthy substance use. The rigorous evaluation of tools that use artificial intelligence (AI) is necessary to demonstrate effectiveness before system-wide implementation. An NLP tool to use routinely collected data in the electronic health record was previously validated for diagnostic accuracy in a retrospective study for screening unhealthy substance use. Our next step is a noninferiority design incorporated into a research protocol for clinical implementation with prospective evaluation of clinical effectiveness in a large health system.OBJECTIVE:This study aims to provide a study protocol to evaluate health outcomes and the costs and benefits of an AI-driven automated screener compared to manual human screening for unhealthy substance use.METHODS:A pre-post design is proposed to evaluate 12 months of manual screening followed by 12 months of automated screening across surgical and medical wards at a single medical center. The preintervention period consists of usual care with manual screening by nurses and social workers and referrals to a multidisciplinary Substance Use Intervention Team (SUIT). Facilitated by a NLP pipeline in the postintervention period, clinical notes from the first 24 hours of hospitalization will be processed and scored by a machine learning model, and the SUIT will be similarly alerted to patients who flagged positive for substance misuse. Flowsheets within the electronic health record have been updated to capture rates of interventions for the primary outcome (brief intervention/motivational interviewing, medication-assisted treatment, naloxone dispensing, and referral to outpatient care). Effectiveness in terms of patient outcomes will be determined by noninferior rates of interventions (primary outcome), as well as rates of readmission within 6 months, average time to consult, and discharge rates against medical advice (secondary outcomes) in the postintervention period by a SUIT compared to the preintervention period. A separate analysis will be performed to assess the costs and benefits to the health system by using automated screening. Changes from the pre- to postintervention period will be assessed in covariate-adjusted generalized linear mixed-effects models.RESULTS:The study will begin in September 2022. Monthly data monitoring and Data Safety Monitoring Board reporting are scheduled every 6 months throughout the study period. We anticipate reporting final results by June 2025.CONCLUSIONS:The use of augmented intelligence for clinical decision support is growing with an increasing number of AI tools. We provide a research protocol for prospective evaluation of an automated NLP system for screening unhealthy substance use using a noninferiority design to demonstrate comprehensive screening that may be as effective as manual screening but less costly via automated solutions.TRIAL REGISTRATION:ClinicalTrials.gov NCT03833804; https://clinicaltrials.gov/ct2/show/NCT03833804.INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/42971.