Multivariate associations between NT-proBNP and cognitive test scores.

<p>The β scores are regression coefficients.</p>a<p>adjusted for age and sex.</p>b<p>adjusted for +MHVS.</p>c<p>adjusted for + smoking status, blood pressure, BMI, HbA1c, cholesterol, mode of treatment.</p>d<p>adjusted for + MI, angina, stroke, ABI.</p>e<p>adjusted for + ln(HADS depression).</p>*<p>significant at 0.05 level.</p>**<p>significant at 0.01 level.</p

Multivariate associations between NT-proBNP and depression scores.

<p>The β scores are regression coefficients.</p>a<p>adjusted for age and sex.</p>b<p>adjusted for +MHVS.</p>c<p>adjusted for + HbA1c, mode of treatment.</p>d<p>adjusted for + MI, angina, stroke, ABI.</p>*<p>significant at 0.05 level.</p>**<p>significant at 0.01 level.</p

The associations of sugar-sweetened, artificially sweetened and naturally sweet juices with all-cause mortality in 198,285 UK Biobank participants: a prospective cohort study

Background: Recent efforts to address the obesity epidemic have focused on sugar consumption, especially sugar-sweetened beverages. However, sugar takes many forms, is only one contributor to overall energy consumption and is correlated with other health-related lifestyle factors. The objective was to investigate the associations with allcause mortality of sugar- and artificially sweetened beverages and naturally sweet juices. Methods: Setting: UK Biobank, UK. Participants joined the UK Biobank study from 2006 to 2010 and were followed up until 2016; 198,285 men and women aged 40–69 years were eligible for this study (40% of the UK Biobank), of whom 3166 (1.6%) died over a mean of 7 years follow-up. Design: prospective population-based cohort study. Exposure variables: dietary consumption of sugar-sweetened beverages, artificially sweetened beverages, naturally sweet juices (100% fruit/vegetable juices) and total sugar intake, self-reported via 24-h dietary assessment tool completed between 2009 and 2012. Main outcome: all-cause mortality. Cox regression analyses were used to study the association between the daily intake of the above beverages and all-cause mortality. Models were adjusted for socio-demographic, economic, lifestyle and dietary confounders. Results: Total energy intake, total sugar intake and percentage of energy derived from sugar were comparable among participants who consumed > 2/day sugar-sweetened beverages and > 2/day fruit/vegetable juices (10,221 kJ/day versus 10,381 kJ/day; 183 g versus 190 g; 30.6% versus 31.0%). All-cause mortality was associated with total sugar intake (highest quintile adj. HR 1.28, 95% CI 1.06–1.55) and intake of sugar-sweetened beverages (> 2/day adj. HR 1.84, 95% CI 1.42–2.37) and remained so in sensitivity analyses. An association between artificially sweetened beverage intake and mortality did not persist after excluding deaths in the first 2 years of follow-up (landmark analysis) nor after excluding participants with recent weight loss. Furthermore, the inverse association between fruit/vegetable juice intake and mortality did not persist after additional adjustment for a diet quality score. Conclusions: Higher mortality is associated with sugar-sweetened beverages specifically. The lack of an adverse association with fruit/vegetable juices suggests that source of sugar may be important and the association with artificially sweetened beverage may reflect reverse causation. Conclusions: Higher mortality is associated with sugar-sweetened beverages specifically. The lack of an adverse association with fruit/vegetable juices suggests that source of sugar may be important and the association with artificially sweetened beverage may reflect reverse causation

Association of thyroid status with cognitive performance during follow-up.

<p>Abbreviations: Est, estimates; se, standard error; MMSE, Mini-Mental State Examination; LDCT, Letter-Digit Coding Test; PLTi, Picture-Word Learning Test immediate; PLTd, Picture-Word Learning Test delayed. Estimates represent the additional change in various cognitive performance tests per year in different subclinical thyroid status. Adjusted for sex, age, education, country, treatment, apo E genotype and test version where appropriate.</p

Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission

Objective: To determine to what extent changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials.Research Design and Methods: SomaSignal® predictive tests (each derived from ~5000 plasma proteins measurements using aptamer-based proteomics assay) were assessed in baseline and 1-year samples in trials (DiRECT n=118, DIADEM-I n=66) and control (DiRECT n=144, DIADEM-I n=76) participants. Results: Mean weight losses in DiRECT (UK) and DIADEM-I (Qatar) were 10.2 (SD 7.4) kg and 12.1 (SD 9.5) kg, respectively, versus 1.0 (3.7) kg and 4.0 (SD 5.4) kg in control groups. Cardiometabolic SomaSignal tests improved significantly (Bonferroni-adjusted p10kg predicted significant reductions in CV risk of -19.1% (CI -33.4 to -4.91) in DiRECT and -33.4% (CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months. Conclusion: Intentional weight loss in recent onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with larger (>10kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.</p

Additional file 1: of Associations of discretionary screen time with mortality, cardiovascular disease and cancer are attenuated by strength, fitness and physical activity: findings from the UK Biobank study

Table S1. Number of participants with missing data for covariates. Table S2. Cut-off points for age- and sex-specific physical activity tertiles. Table S3. Cut-off points for age- and sex-specific grip strength tertiles. Table S4. Cut-off points for age- and sex-specific fitness tertiles. Table S5. Cohort characteristics by categories of TV viewing. Table S6. Cohort characteristics by categories of PC screen time. Table S7. Cohort characteristics by age- and sex-specific tertiles of total physical activity. Table S8. Cohort characteristics by age- and sex-specific tertiles of cardiorespiratory fitness. Table S9. Cohort characteristics by age- and sex-specific tertiles of handgrip strength. Table S10. Correlation between TV viewing, total physical activity and grip strength. Figure S1. Cox proportional hazard model of the association of 1-h increments in screen time, TV viewing and PC screen time with CVD and cancer mortality. Figure S2. Cox proportional hazard models of the association of overall discretionary screen time with CVD and cancer mortality by physical activity, fitness and handgrip strength strata. Figure S3. Cox proportional hazard models of the association of overall discretionary TV viewing with CVD and cancer mortality by physical activity, fitness and handgrip strength strata. Figure S4. Cox proportional hazard models of the association of overall discretionary PC screen time with CVD and cancer mortality by physical activity, fitness and handgrip strength strata. Table S11. Cox proportional hazard estimates of the association of overall discretionary screen time with all-cause mortality, CVD and cancer incidence and mortality by physical activity, fitness and handgrip strength strata. Table S12. Cox proportional hazard estimates of the association of discretionary TV viewing with all-cause mortality, CVD and cancer incidence and mortality by physical activity, fitness and handgrip strength strata. Table S13. Cox proportional hazard estimates of the association of discretionary PC screen time with all-cause mortality, CVD and cancer incidence and mortality by physical activity, fitness and handgrip strength strata. (DOCX 1552 kb

Baseline characteristics of study participants grouped by thyroid status.

<p>Abbreviations: n, number; se, standard error; IADL, Instrumental Activities of Daily Living; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TIA, transient ischemic attack; HDL, high density cholesterol lipoprotein; LDL, low density cholesterol lipoprotein.</p>*<p> =  adjusted for sex and age at baseline, assessed by linear regression.</p

Association of subclinical thyroid status and various cognitive performance tests at baseline.

<p>Abbreviations: MMSE, Mini-Mental State Examination; LDCT, Letter-Digit Coding Test; PLTi, Picture-Word Learning Test immediate; PLTd, Picture-Word Learning Test delayed. Cognitive tests were presented in mean (standard error). Associations were assessed by linear regression analyses, adjusted for sex, age, education, country, apo E genotype and test version where appropriate.</p

Flowchart of study participants.

<p>Abbreviations: TSH, Thyroid Stimulating Hormone; FT4, free thyroxine.</p

Derivation and validation of a 10-year risk score for symptomatic abdominal aortic aneurysm

BACKGROUND: Abdominal aortic aneurysm (AAA) can occur in patients who are ineligible for routine ultrasound screening. A simple AAA risk score was derived and compared with current guidelines used for ultrasound screening of AAA. METHODS: United Kingdom Biobank participants without previous AAA were split into a derivation cohort (n=401820, 54.6% women, mean age 56.4 years, 95.5% White race) and validation cohort (n=83816). Incident AAA was defined as first hospital inpatient diagnosis of AAA, death from AAA, or an AAA-related surgical procedure. A multivariable Cox model was developed in the derivation cohort into an AAA risk score that did not require blood biomarkers. To illustrate the sensitivity and specificity of the risk score for AAA, a theoretical threshold to refer patients for ultrasound at 0.25% 10-year risk was modeled. Discrimination of the risk score was compared with a model of US Preventive Services Task Force (USPSTF) AAA screening guidelines. RESULTS: In the derivation cohort, there were 1570 (0.40%) cases of AAA over a median 11.3 years of follow-up. Components of the AAA risk score were age (stratified by smoking status), weight (stratified by smoking status), antihypertensive and cholesterol-lowering medication use, height, diastolic blood pressure, baseline cardiovascular disease, and diabetes. In the validation cohort, over 10 years of follow-up, the C-index for the model of the USPSTF guidelines was 0.705 (95% CI, 0.678–0.733). The C-index of the risk score as a continuous variable was 0.856 (95% CI, 0.837–0.878). In the validation cohort, the USPSTF model yielded sensitivity 63.9% and specificity 71.3%. At the 0.25% 10-year risk threshold, the risk score yielded sensitivity 82.1% and specificity 70.7% while also improving the net reclassification index compared with the USPSTF model +0.176 (95% CI, 0.120–0.232). A combined model, whereby risk scoring was combined with the USPSTF model, also improved prediction compared with USPSTF alone (net reclassification index +0.101 [95% CI, 0.055–0.147]). CONCLUSIONS: In an asymptomatic general population, a risk score based on patient age, height, weight, and medical history may improve identification of asymptomatic patients at risk for clinical events from AAA. Further development and validation of risk scores to detect asymptomatic AAA are needed