Ellie Paige

Body-mass index and all-cause mortality: Individual-participant-data meta-analysis of 239 prospective studies in four continents.

Summary Background Overweight and obesity are increasing worldwide. To help assess their relevance to mortality in different populations we conducted individual-participant data meta-analyses of prospective studies of body-mass index (BMI), limiting confounding and reverse causality by restricting analyses to never-smokers and excluding pre-existing disease and the first 5 years of follow-up. Methods Of 10 625 411 participants in Asia, Australia and New Zealand, Europe, and North America from 239 prospective studies (median follow-up 13·7 years, IQR 11·4–14·7), 3 951 455 people in 189 studies were never-smokers without chronic diseases at recruitment who survived 5 years, of whom 385 879 died. The primary analyses are of these deaths, and study, age, and sex adjusted hazard ratios (HRs), relative to BMI 22·5–<25·0 kg/m2. Findings All-cause mortality was minimal at 20·0–25·0 kg/m2 (HR 1·00, 95% CI 0·98–1·02 for BMI 20·0–<22·5 kg/m2; 1·00, 0·99–1·01 for BMI 22·5–<25·0 kg/m2), and increased significantly both just below this range (1·13, 1·09–1·17 for BMI 18·5–<20·0 kg/m2; 1·51, 1·43–1·59 for BMI 15·0–<18·5) and throughout the overweight range (1·07, 1·07–1·08 for BMI 25·0–<27·5 kg/m2; 1·20, 1·18–1·22 for BMI 27·5–<30·0 kg/m2). The HR for obesity grade 1 (BMI 30·0–<35·0 kg/m2) was 1·45, 95% CI 1·41–1·48; the HR for obesity grade 2 (35·0–<40·0 kg/m2) was 1·94, 1·87–2·01; and the HR for obesity grade 3 (40·0–<60·0 kg/m2) was 2·76, 2·60–2·92. For BMI over 25·0 kg/m2, mortality increased approximately log-linearly with BMI; the HR per 5 kg/m2 units higher BMI was 1·39 (1·34–1·43) in Europe, 1·29 (1·26–1·32) in North America, 1·39 (1·34–1·44) in east Asia, and 1·31 (1·27–1·35) in Australia and New Zealand. This HR per 5 kg/m2 units higher BMI (for BMI over 25 kg/m2) was greater in younger than older people (1·52, 95% CI 1·47–1·56, for BMI measured at 35–49 years vs 1·21, 1·17–1·25, for BMI measured at 70–89 years; pheterogeneity<0·0001), greater in men than women (1·51, 1·46–1·56, vs 1·30, 1·26–1·33; pheterogeneity<0·0001), but similar in studies with self-reported and measured BMI. Interpretation The associations of both overweight and obesity with higher all-cause mortality were broadly consistent in four continents. This finding supports strategies to combat the entire spectrum of excess adiposity in many populations. Funding UK Medical Research Council, British Heart Foundation, National Institute for Health Research, US National Institutes of Health.

Income-related inequalities in chronic conditions, physical functioning and psychological distress among older people in Australia: cross-sectional findings from the 45 and up study

BackgroundThe burden of chronic disease continues to rise as populations age. There is relatively little published on the socioeconomic distribution of this burden in older people. This study quantifies absolute and relative income-related inequalities in prevalence of chronic diseases, severe physical functioning limitation and high psychological distress in mid-age and older people in Australia.MethodsCross-sectional study of 208,450 participants in the 45 and Up Study, a population-based cohort of men and women aged 45–106 years from New South Wales, Australia. Chronic conditions included self-reported heart disease, diabetes, Parkinson’s disease, cancer and osteoarthritis; physical functioning limitation (severe/not) was measured using Medical Outcomes Study measures and psychological distress (high/not) using the Kessler Psychological Distress Scale. For each outcome, prevalence was estimated in relation to annual household income (6 categories). Prevalence differences (PDs) and ratios (PRs) were generated, comparing the lowest income category (<

The relationship between body mass index and hospitalisation rates, days in hospital and costs : findings from a large prospective linked data study

20,000) to the highest (≥

Consequences Of Natural Perturbations In The Human Plasma Proteome

70,000), using Poisson regression with robust standard errors, weighted for age, sex and region of residence. Analyses were stratified by age group (45–64, 65–79 and ≥80 years) and sex and adjusted for age and country of birth.ResultsWith few exceptions, there were income gradients in the prevalence of chronic conditions among all age-sex groups, with prevalence decreasing with increasing income. Of the chronic diseases, PDs were highest for diabetes (ranging between 5.69% and 10.36% across age-sex groups) and in women, also for osteoarthritis (5.72% to 8.14%); PRs were highest for osteoarthritis in men aged 45–64 years (4.01), otherwise they were highest for diabetes (1.78 to 3.43). Inequalities were very high for both physical functioning limitation and psychological distress, particularly among those aged 45–64 (PDs between 18.67% and 29.23% and PRs between 4.63 and 16.51). Absolute and relative inequalities tended to decrease with age, but remained relatively high for diabetes and physical functioning in the elderly (≥80 years).ConclusionsSignificant inequalities in the prevalence of chronic conditions, physical functioning and psychological distress persist into old age. The additional health burden placed on those who are already disadvantaged is likely to become an increasingly important issue in an ageing population.

Using Australian Pharmaceutical Benefits Scheme data for pharmacoepidemiological research: challenges and approaches

Background Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation—admissions, days and costs—associated with above-normal BMI. Methods Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated. Results There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%). Conclusions The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would result in long-term savings to the health care system is not known from this study.

How weight change is modelled in population studies can affect research findings: empirical results from a large-scale cohort study

Proteins are the primary functional units of biology and the direct targets of most drugs, yet there is limited knowledge of the genetic factors determining inter-individual variation in protein levels. Here we reveal the genetic architecture of the human plasma proteome, testing 10.6 million DNA variants against levels of 2,994 proteins in 3,301 individuals. We identify 1,927 genetic associations with 1,478 proteins, a 4-fold increase on existing knowledge, including trans associations for 1,104 proteins. To understand consequences of perturbations in plasma protein levels, we introduce an approach that links naturally occurring genetic variation with biological, disease, and drug databases. We provide insights into pathogenesis by uncovering the molecular effects of disease-associated variants. We identify causal roles for protein biomarkers in disease through Mendelian randomization analysis. Our results reveal new drug targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Mid- and Long-Term Health Risks in Living Kidney Donors: A Systematic Review and Meta-analysis.

The Pharmaceutical Benefits Scheme (PBS) dataset provides detailed information about subsidised medicines dispensed in Australia and is increasingly used for pharmacoepidemiological research. Use of the PBS dataset provides unique opportunities for such research, but comes with its own set of challenges that must be considered and addressed. This paper outlines some issues that commonly arise when using PBS data - relating to accurate identification of medicine dispensings and how to define medicine exposure - and suggests some possible approaches for dealing with them. The paper is intended as an introductory resource for researchers.

Characteristics of antidepressant medication users in a cohort of mid-age and older Australians

Objectives To investigate how results of the association between education and weight change vary when weight change is defined and modelled in different ways. Design Longitudinal cohort study. Participants 60 404 men and women participating in the Social, Environmental and Economic Factors (SEEF) subcomponent of the 45 and Up Study—a population-based cohort study of people aged 45 years or older, residing in New South Wales, Australia. Outcome measures The main exposure was self-reported education, categorised into four groups. The outcome was annual weight change, based on change in self-reported weight between the 45 and Up Study baseline questionnaire and SEEF questionnaire (completed an average of 3.3 years later). Weight change was modelled in four different ways: absolute change (kg) modelled as (1) a continuous variable and (2) a categorical variable (loss, maintenance and gain), and relative (%) change modelled as (3) a continuous variable and (4) a categorical variable. Different cut-points for defining weight-change categories were also tested. Results When weight change was measured categorically, people with higher levels of education (compared with no school certificate) were less likely to lose or to gain weight. When weight change was measured as the average of a continuous measure, a null relationship between education and annual weight change was observed. No material differences in the education and weight-change relationship were found when comparing weight change defined as an absolute (kg) versus a relative (%) measure. Results of the logistic regression were sensitive to different cut-points for defining weight-change categories. Conclusions Using average weight change can obscure important directional relationship information and, where possible, categorical outcome measurements should be included in analyses.

Neutrophil-mediated IL-6 receptor trans-signaling and the risk of chronic obstructive pulmonary disease and asthma

Living kidney donation is the gold standard treatment of end-stage renal disease (ESRD); more than 8000 living-donor kidney transplantations were done in 2013 in the United States, Brazil, and Japan alone (1). Although living donation is highly beneficial to recipients, it remains a complex ethical, moral, and medical issue. It is practiced with the expectation that risk for minimal short- and long-term harm to the donor is outweighed by the psychological benefits of altruism and improved recipient health (2). A short-term reduction in glomerular filtration rate after nephrectomy is a known consequence of kidney donation (3). However, the mid- and long-term health risks remain uncertain, despite their critical role in informing clinical guidelines for follow-up and supporting the process of informed consent (4, 5). Although narrative reviews (1, 6, 7) and individual studies have reported on the longer-term health risks of living kidney donation, most have not been systematically assessed and quantified. For example, the 3 previously published meta-analyses (each involving up to 6 studies comparing donors vs. a nondonor control group) focused only on a limited number of outcomes (such as hypertension and renal function) and involved only about half of the currently available data (3, 8, 9). Interpretation of the evidence has also been complicated by diverse selection criteria for nondonor control groups (for example, general population vs. based on donation criteria), follow-up durations, and analytic approaches (for example, different matching criteria or adjustment for potential confounders) (4, 10). To help quantify the mid- and long-term risks of living kidney donation, we conducted a systematic review and meta-analysis of observational studies comparing living kidney donors with control participants (nondonors) for a broad range of health outcomes. Methods Data Sources and Searches This review was done using a predefined protocol published in PROSPERO (CRD42017072284) and in accordance with MOOSE (Meta-analysis of Observational Studies in Epidemiology) guidelines. Studies published from April 1964 to 20 July 2017 were identified, without language restriction, through electronic searches using PubMed, Embase, Scopus, and PsycINFO. We supplemented this search by scanning reference lists of relevant articles (including studies as well as reviews and meta-analyses) and by backward and forward citation searching of all included studies. The computer-based searches combined terms related to living organ donation, health related quality of life, and epidemiological studies without health outcomes restriction (Supplement). Supplement. Supplementary Online Content Study Selection Studies were eligible for inclusion if they reported associations between living kidney donation and any health outcomes, disease traits, or health-related quality of life (HRQoL) using a validated instrument; had a mean follow-up after donation of at least 1 year; and provided a comparison group of control participants who had not donated a kidney. Outcomes evaluated in only 1 study (gout and kidney stones) were not included. Data Extraction and Quality Assessment Two investigators independently extracted data on the following characteristics using standardized protocols: sample size; study design; sampling population; location; year of publication; years of baseline survey (year of kidney donation); follow-up duration; participant sex, age range, and ethnicity; number of donors and control participants; selection criteria for control participants; outcomes recorded; outcome definitions and methods of ascertainment (Supplement Table 1); reported risk estimates; and degree of statistical adjustment used or, where relevant, mean level of disease traits and HRQoL assessment scores in donors and control participants. Discrepancies were resolved by discussion and adjudication by a third reviewer. We used the most up-to-date or comprehensive information when more than 1 article reported on the same study. Study quality was evaluated with the Newcastle-Ottawa Scale (NOS) (11), which uses a star system (maximum of 9 stars) to assess quality in the following 3 domains: selection of participants, comparability of study groups, and ascertainment of outcomes of interest. Studies with 4 stars or more were rated as medium or higher quality. We classified selection of control participants as +++ if they were eligible for nephrectomy based on medical status and assessment of renal function; ++ if they were eligible for nephrectomy based on medical status but without assessment of renal function, or if renal function was assessed but limited information on medical history was available; and + if limited screening or information on controls selection was available. Furthermore, we classified matching of donors with control participants as +++ if it was done according to age, sex, sociodemographic factors, or other factors potentially influencing the outcome of interest (for example, body mass index, blood pressure, medical history, and smoking history); ++ if it was based on age, sex, and sociodemographic factors; + if it was based only on age or sex; and null if no matching was done. Data Synthesis and Analysis Primary analyses involved only within-study comparisons to limit potential biases. We restricted primary analyses to studies with an NOS score of at least 4 and baseline recruitment period ending in or after 2000 to provide more reliable and contemporary summary estimates. Supplementary analyses involved all available studies. We used reported relative risks (RRs) or unadjusted RRs or odds ratios calculated from study-specific data to quantify the association between living kidney donation and each of the binary outcomes of interest. Hazard ratios and odds ratios were assumed to approximate the same measure of RR. Incidence rates per 1000 person-years in donors were extracted from studies or calculated as the ratio of events in donors and control participants over the number of person-years at risk. This number in each group was extracted from each study or estimated by multiplying the mean (or median) years of follow-up by the number of donors and control participants. For pregnancy outcomes, incidence of adverse outcomes per 100 pregnancies was calculated by dividing the number of adverse events by the number of pregnancies. We assessed continuous outcomes (disease traits and HRQoL outcomes) by comparing mean differences in living kidney donors with those in control participants for each study. Standardized mean difference (SMD) was calculated within each study as the mean difference between cases and control participants divided by the pooled SD (12). This estimate allows comparison among disparate outcome measures reported with different units. Summary RRs, incidence rates, and SMDs comparing donors with control participants were calculated for each outcome by pooling the study-specific estimates using the random-effects profile likelihood meta-analysis method (13). Consistency of findings across individual studies for outcomes reported in 3 or more studies was assessed by the I 2 statistic. Evidence of publication bias across studies was assessed for outcomes with more than 10 studies using funnel plots and the Egger test. All analyses were 2-sided, used a significance level of P< 0.05, and were done with Stata, version 14.2 (StataCorp). Role of the Funding Source None of the funding organizations were involved in the design or conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Results Overall, 52 unique studies from 17 countries involving 118426 donors and 117656 control participants met the inclusion criteria (Figure 1 and Appendix Table). Twenty-three studies were based in North America, 10 in Europe, 2 in Australia, 5 in South America, and 12 in other or several regions. Donors were primarily recruited from hospital registries: 39 studies recruited donors from hospitals and 11 from national or regional donor registries (2 studies did not report this information). The average follow-up ranged from 1 to 24 years, and 14 studies reported a mean or median follow-up of 10 years or more. Selection of the control population differed between studies, with 8 studies selecting control participants from population-based studies, 11 from the general population, 14 from siblings and other volunteers, and 19 from other sources. Twenty-seven studies used several characteristics to match the control and donor populations, and 17 excluded control participants with 2 or more contraindications for nephrectomy. Of these, only 5 selected the control population on the basis of completion of living donor screening or eligibility for nephrectomy based on medical status and renal function tests (Supplement Table 2). Twenty-eight studies were judged to have an NOS score of at least 4 (Supplement Table 3) and had a baseline recruitment period ending in or after 2000. Figure 1. Evidence search and selection. Appendix Table. Summary of 52 Included Unique Prospective Studies on Outcomes of Living Kidney Donation* Association With Disease Traits Twenty-six studies reported associations with disease traits, of which 17 reported information on blood pressure, 6 on metabolic markers, and 17 on markers of renal function (Appendix Table). In the primary analysis (up to 8 studies, 1040 donors, and 1032 control participants), living kidney donors had higher mean diastolic blood pressure (SMD, 0.17 [95% CI, 0.03 to 0.34]) and lower levels of high-density lipoprotein cholesterol than control participants (SMD, 0.29 [CI, 0.52 to 0.11]). Donors also had poorer renal function than control participants, with a lower mean estimated glomerular filtration rate (SMD, 1.59 [CI, 1.86 to 0.33]) and higher mean level of serum creatinine (SMD, 1.0

A record linkage study of antidepressant medication use and weight change in Australian adults.

Objectives: We aimed to investigate antidepressant use, including the class of antidepressant, in mid-age and older Australians according to sociodemographic, lifestyle and physical and mental health-related factors. Methods: Baseline questionnaire data on 111,705 concession card holders aged ⩾45 years from the 45 and Up Study—a population-based cohort study from New South Wales, Australia—were linked to administrative pharmaceutical data. Current- and any-antidepressant users were those dispensed medications with Anatomical Therapeutic Chemical classification codes beginning N06A, within ⩽6 months and ⩽19 months before baseline, respectively; non-users had no antidepressants dispensed ⩽19 months before baseline. Multinomial logistic regression was used to calculate adjusted relative risk ratios (aRRRs) for predominantly self-reported factors in relation to antidepressant use. Results: Some 19% of the study population (15% of males and 23% of females) were dispensed at least one antidepressant during the study period; 40% of participants used selective serotonin reuptake inhibitors (SSRIs) only and 32% used tricyclic antidepressants (TCAs) only. Current antidepressant use was markedly higher in those reporting: severe versus no physical impairment (aRRR 3.86(95%CI 3.67–4.06)); fair/poor versus excellent/very good self-rated health (4.04(3.83–4.25)); high/very high versus low psychological distress (7.22(6.81–7.66)); ever- versus never-diagnosis of depression by a doctor (18.85(17.95–19.79)); low-dose antipsychotic use versus no antipsychotic use (12.26(9.85–15.27)); and dispensing of ⩾10 versus <5 other medications (5.97(5.62–6.34)). Sociodemographic and lifestyle factors were also associated with use, although to a lesser extent. Females, older people, those with lower education and those with poorer health were more likely to be current antidepressant users than non-users and were also more likely to use TCAs-only versus SSRIs-only. Conclusions: Use of antidepressants is substantially higher in those with physical ill-health and in those reporting a range of adverse mental health measures. In addition, sociodemographic factors, including sex, age and education were also associated with antidepressant use and the class of antidepressant used.