Allostatic load as a predictor of all-cause and cause-specific mortality in the general population: Evidence from the Scottish Health Survey

Allostatic load is a multiple biomarker measure of physiological ‘wear and tear’ that has shown some promise as marker of overall physiological health, but its power as a risk predictor for mortality and morbidity is less well known. This study has used data from the 2003 Scottish Health Survey (SHeS) (nationally representative sample of Scottish population) linked to mortality records to assess how well allostatic load predicts all-cause and cause-specific mortality. From the sample, data from 4,488 men and women were available with mortality status at 5 and 9.5 (rounded to 10) years after sampling in 2003. Cox proportional hazard models estimated the risk of death (all-cause and the five major causes of death in the population) according to allostatic load score. Multiple imputation was used to address missing values in the dataset. Analyses were also adjusted for potential confounders (sex, age and deprivation). There were 258 and 618 deaths over the 5-year and 10-year follow-up period, respectively. In the fully-adjusted model, higher allostatic load (poorer physiological ‘health’) was not associated with an increased risk of all-cause mortality after 5 years (HR = 1.07, 95% CI 0.94 to 1.22; p = 0.269), but it was after 10 years (HR = 1.08, 95% CI 1.01 to 1.16; p = 0.026). Allostatic load was not associated with specific causes of death over the same follow-up period. In conclusions, greater physiological wear and tear across multiple physiological systems, as measured by allostatic load, is associated with an increased risk of death, but may not be as useful as a predictor for specific causes of death.REF Compliant by Deposit in Stirling's Repositor

Allostatic load as a predictor of all-cause and cause-specific mortality in the general population: Evidence from the Scottish Health Survey

Allostatic load is a multiple biomarker measure of physiological ‘wear and tear’ that has shown some promise as marker of overall physiological health, but its power as a risk predictor for mortality and morbidity is less well known. This study has used data from the 2003 Scottish Health Survey (SHeS) (nationally representative sample of Scottish population) linked to mortality records to assess how well allostatic load predicts all-cause and cause-specific mortality. From the sample, data from 4,488 men and women were available with mortality status at 5 and 9.5 (rounded to 10) years after sampling in 2003. Cox proportional hazard models estimated the risk of death (all-cause and the five major causes of death in the population) according to allostatic load score. Multiple imputation was used to address missing values in the dataset. Analyses were also adjusted for potential confounders (sex, age and deprivation). There were 258 and 618 deaths over the 5-year and 10-year follow-up period, respectively. In the fully-adjusted model, higher allostatic load (poorer physiological ‘health’) was not associated with an increased risk of all-cause mortality after 5 years (HR = 1.07, 95% CI 0.94 to 1.22; p = 0.269), but it was after 10 years (HR = 1.08, 95% CI 1.01 to 1.16; p = 0.026). Allostatic load was not associated with specific causes of death over the same follow-up period. In conclusions, greater physiological wear and tear across multiple physiological systems, as measured by allostatic load, is associated with an increased risk of death, but may not be as useful as a predictor for specific causes of death

Atmospheric Methane : Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations

Atmospheric methane's rapid growth from late 2006 is unprecedented in the observational record. Assessment of atmospheric methane data attributes a large fraction of this atmospheric growth to increased natural emissions over the tropics, which appear to be responding to changes in anthropogenic climate forcing. Isotopically lighter measurements of (Figure presented.) are consistent with the recent atmospheric methane growth being mainly driven by an increase in emissions from microbial sources, particularly wetlands. The global methane budget is currently in disequilibrium and new inputs are as yet poorly quantified. Although microbial emissions from agriculture and waste sources have increased between 2006 and 2022 by perhaps 35 Tg/yr, with wide uncertainty, approximately another 35–45 Tg/yr of the recent net growth in methane emissions may have been driven by natural biogenic processes, especially wetland feedbacks to climate change. A model comparison shows that recent changes may be comparable or greater in scale and speed than methane's growth and isotopic shift during past glacial/interglacial termination events. It remains possible that methane's current growth is within the range of Holocene variability, but it is also possible that methane's recent growth and isotopic shift may indicate a large-scale reorganization of the natural climate and biosphere is under way

Atmospheric Methane: Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations

Atmospheric methane's rapid growth from late 2006 is unprecedented in the observational record. Assessment of atmospheric methane data attributes a large fraction of this atmospheric growth to increased natural emissions over the tropics, which appear to be responding to changes in anthropogenic climate forcing. Isotopically lighter measurements of d13C-CH4 are consistent with the recent atmospheric methane growth being mainly driven by an increase in emissions from microbial sources, particularly wetlands. The global methane budget is currently in disequilibrium and new inputs are as yet poorly quantified. Although microbial emissions from agriculture and waste sources have increased between 2006 and 2022 by perhaps 35 Tg/yr, with wide uncertainty, approximately another 35–45 Tg/yr of the recent net growth in methane emissions may have been driven by natural biogenic processes, especially wetland feedbacks to climate change. A model comparison shows that recent changes may be comparable or greater in scale and speed than methane's growth and isotopic shift during past glacial/interglacial termination events. It remains possible that methane's current growth is within the range of Holocene variability, but it is also possible that methane's recent growth and isotopic shift may indicate a large-scale reorganization of the natural climate and biosphere is under way

A New Method for Analyzing ¹⁴C of Methane in Ancient Air Extracted from Glacial Ice

We present a new method developed for measuring radiocarbon of methane (¹⁴CH₄) in ancient air samples extracted from glacial ice and dating 11,000–15,000 calendar years before present. The small size (~20 μg CH₄ carbon), low CH₄ concentrations ([CH₄], 400–800 parts per billion [ppb]), high carbon monoxide concentrations ([CO]), and low ¹⁴C activity of the samples created unusually high risks of contamination by extraneous carbon. Up to 2500 ppb CO in the air samples was quantitatively removed using the Sofnocat reagent. ¹⁴C procedural blanks were greatly reduced through the construction of a new CH₄ conversion line utilizing platinized quartz wool for CH₄ combustion and the use of an ultra-high-purity iron catalyst for graphitization. The amount and ¹⁴C activity of extraneous carbon added in the new CH₄ conversion line were determined to be 0.23 ± 0.16 μg and 23.57 ± 16.22 pMC, respectively. The amount of modern (100 pMC) carbon added during the graphitization step has been reduced to 0.03 μg. The overall procedural blank for all stages of sample handling was 0.75 ± 0.38 pMC for ~20-μg, ¹⁴C-free air samples with [CH₄] of 500 ppb. Duration of the graphitization reactions for small (<25 μg C) samples was greatly reduced and reaction yields improved through more efficient water vapor trapping and the use of a new iron catalyst with higher surface area. ¹⁴C corrections for each step of sample handling have been determined. The resulting overall ¹⁴CH₄ uncertainties for the ancient air samples are ~1.0 pMC.This is the publisher’s final pdf. The published article is copyrighted by the University of Arizona and can be found at: https://journals.uair.arizona.edu/index.php/radiocarbon/index

Genetic mechanisms of critical illness in COVID-19.

Host-mediated lung inflammation is present1, and drives mortality2, in the critical illness caused by coronavirus disease 2019 (COVID-19). Host genetic variants associated with critical illness may identify mechanistic targets for therapeutic development3. Here we report the results of the GenOMICC (Genetics Of Mortality In Critical Care) genome-wide association study in 2,244 critically ill patients with COVID-19 from 208 UK intensive care units. We have identified and replicated the following new genome-wide significant associations: on chromosome 12q24.13 (rs10735079, P = 1.65 × 10-8) in a gene cluster that encodes antiviral restriction enzyme activators (OAS1, OAS2 and OAS3); on chromosome 19p13.2 (rs74956615, P = 2.3 × 10-8) near the gene that encodes tyrosine kinase 2 (TYK2); on chromosome 19p13.3 (rs2109069, P = 3.98 ×  10-12) within the gene that encodes dipeptidyl peptidase 9 (DPP9); and on chromosome 21q22.1 (rs2236757, P = 4.99 × 10-8) in the interferon receptor gene IFNAR2. We identified potential targets for repurposing of licensed medications: using Mendelian randomization, we found evidence that low expression of IFNAR2, or high expression of TYK2, are associated with life-threatening disease; and transcriptome-wide association in lung tissue revealed that high expression of the monocyte-macrophage chemotactic receptor CCR2 is associated with severe COVID-19. Our results identify robust genetic signals relating to key host antiviral defence mechanisms and mediators of inflammatory organ damage in COVID-19. Both mechanisms may be amenable to targeted treatment with existing drugs. However, large-scale randomized clinical trials will be essential before any change to clinical practice

Seven Key Investments for Health Equity across the Lifecourse: Scotland versus the rest of the UK

AbstractWhile widespread lip service is given in the UK to the social determinants of health (SDoH), there are few published comparisons of how the UK's devolved jurisdictions ‘stack up’, in terms of implementing SDoH-based policies and programmes, to improve health equity over the life-course. Based on recent SDoH publications, seven key societal-level investments are suggested, across the life-course, for increasing health equity by socioeconomic position (SEP). We present hard-to-find comparable analyses of routinely collected data to gauge the relative extent to which these investments have been pursued and achieved expected goals in Scotland, as compared with England and Wales, in recent decades. Despite Scotland's longstanding explicit goal of reducing health inequalities, it has recently been doing slightly better than England and Wales on only one broad indicator of health-equity-related investments: childhood poverty. However, on the following indicators of other ‘best investments for health equity’, Scotland has not achieved demonstrably more equitable outcomes by SEP than the rest of the UK: infant mortality and teenage pregnancy rates; early childhood education implementation; standardised educational attainment after primary/secondary school; health care system access and performance; protection of the population from potentially hazardous patterns of food, drink and gambling use; unemployment. Although Scotland did not choose independence on September 18th, 2014, it could still (under the planned increased devolution of powers from Westminster) choose to increase investments in the underperforming categories of interventions for health equity listed above. However, such discussion is largely absent from the current post-referendum debate. Without further significant investments in such policies and programmes, Scotland is unlikely to achieve the ‘healthier, fairer society’ referred to in the current Scottish Government's official aspirations for the nation

Flowchart of survey response and data linkage.

<p>Flowchart of survey response and data linkage.</p