Individual aspects of social capital have been shown to have significant associations with health outcomes. However, research has seldom tested different elements of social capital simultaneously, ...whilst also adjusting for other well-known health determinants over time. This longitudinal individual-level study investigates how temporal changes in social capital, together with changes in material conditions and other health determinants affect associations with self-rated health over a six year period. We use data from the British Household Panel Survey, a randomly selected cohort which is considered representative of the United Kingdom's population, with the same individuals (N=9303) providing responses to identical questions in 1999 and 2005. Four measures of social capital were used: interpersonal trust, social participation, civic participation and informal social networks. Material conditions were measured by total income (both individual and weighted household income), net of taxation. Other health determinants included age, gender, smoking, marital status and social class. After the baseline sample was stratified by health status, associations were examined between changes in health status and changes in all other considered variables. Simultaneous adjustment revealed that inability to trust demonstrated a significant association with deteriorating self-rated health, whereas increased levels of social participation were significantly associated with improved health status over time. Low levels of household and individual income also demonstrated significant associations with deteriorating self-rated health. In conclusion, it seems that interpersonal trust and social participation, considered valid indicators of social capital, appear to be independent predictors of self-rated health, even after adjusting for other well-known health determinants. Understandably, how trust and social participation influence health outcomes may help resolve the debate surrounding the role of social capital within the field of public health.
There has been an abundance of research discussing the health implications of generalised trust and happiness over the past two decades. Both attitudes have been touted as independent predictors of ...morbidity and mortality, with strikingly similar trajectories and biological pathways being hypothesised. To date, however, neither trust nor happiness have been considered simultaneously as predictors of mortality. This study, therefore, aims to investigate the effects of generalised trust and happiness on all-cause and cause-specific mortality. The distinction between different causes of death (i.e. cardiovascular vs. cancer-related mortality) allowed us to assess if psychosocial mechanisms could account for associations between generalised trust, happiness and mortality. The study sample was derived from US General Social Survey data from 1978 to 2010 (response rates ranged from 70 to 82 per cent), and combined with death records from the National Death Index. The analytical sample comprised 23,933 individuals with 5382 validated deaths from all-cause mortality by 2014. Analyses were performed with Cox regression models and competing-risk models. In final models, generalised trust, but not happiness, showed robust and independent associations with all-cause mortality. Regarding cause-specific mortality, trust only showed a significant relationship with cardiovascular mortality. The distinct patterns of association between generalised trust and all-cause/cause-specific mortality suggest that their relationship could be being driven by cardiovascular mortality. In turn, this supports the feasibility of psychosocial pathways as possible biological mechanisms from distrust to mortality.
•Happiness and generalised trust both touted as independent predictors of mortality.•Trust but not happiness predicts all-cause mortality.•Trust predicts mortality caused by CVD but not by neoplasia.•Psychosocial mechanisms might drive the association between trust and health.
The global financial crisis of 2008 was described by the IMF as the worst recession since the Great Depression. This historic event provided the backdrop to this United Kingdom (UK) longitudinal ...study of changes in associations between social capital and psychological wellbeing. Past longitudinal studies have reported that the presence of social capital may buffer against adverse mental health outcomes. This study adds to existing literature by employing data from the British Household Panel Survey and tracking the same individuals (N = 11,743) pre- and immediately post-crisis (years 2007–09). With longitudinal, multilevel logistic regression modelling, we aimed to compare the buffering effects of individual-level social capital (generalised trust and social participation) against worse psychological wellbeing (GHQ-12) during and immediately after the 2008 financial crisis. After comparing the same individuals over time, results showed that stocks of social capital (generalised trust) were significantly depleted across the UK during the crisis, from 40% trusting others in 2007 to 32% in 2008. Despite this drop, the buffering effect of trust against worse psychological wellbeing was pronounced in 2008; those not trusting had an increased risk of worse psychological wellbeing in 2008 compared with the previous year in fully adjusted models (OR = 1.49, 95% CI (1.34–1.65). Levels of active participation increased across the timeframe of this study but were not associated with psychological health. From our empirical evidence, decision makers should be made aware of how events such as the crisis (and the measures taken to counter its effects) could negatively impact on a Nation's trust levels. Furthermore, past research implies that the positive effects of trust on psychological wellbeing evident in this study may only be short-term; therefore, decision makers should also prioritise policies that restore trust levels to improve the psychological wellbeing of the population.
•The 2008 financial crisis coincided with a depletion of generalised trust.•Despite this, trust remained an important buffer against poor psychological wellbeing.•Decision makers should prioritise initiatives to increase trust in times of crisis.
Prediabetes is a state of glycaemic dysregulation below the diagnostic threshold of type 2 diabetes (T2D). Globally, ~352 million people have prediabetes, of which 35-50% develop full-blown diabetes ...within five years. T2D and its complications are costly to treat, causing considerable morbidity and early mortality. Whether prediabetes is causally related to diabetes complications is unclear. Here we report a causal inference analysis investigating the effects of prediabetes in coronary artery disease, stroke and chronic kidney disease, complemented by a systematic review of relevant observational studies. Although the observational studies suggest that prediabetes is broadly associated with diabetes complications, the causal inference analysis revealed that prediabetes is only causally related with coronary artery disease, with no evidence of causal effects on other diabetes complications. In conclusion, prediabetes likely causes coronary artery disease and its prevention is likely to be most effective if initiated prior to the onset of diabetes.
The driving force behind the current global type 2 diabetes epidemic is insulin resistance in overweight and obese individuals. Dietary factors, physical inactivity, and sedentary behaviors are the ...major modifiable risk factors for obesity. Nevertheless, many overweight/obese people do not develop diabetes and lifestyle interventions focused on weight loss and diabetes prevention are often ineffective. Traditionally, chronically elevated blood glucose concentrations have been the hallmark of diabetes; however, many individuals will either remain 'prediabetic' or regress to normoglycemia. Thus, there is a growing need for innovative strategies to tackle diabetes at scale. The emergence of biomarker technologies has allowed more targeted therapeutic strategies for diabetes prevention (precision medicine), though largely confined to pharmacotherapy. Unlike most drugs, lifestyle interventions often have systemic health-enhancing effects. Thus, the pursuance of lifestyle precision medicine in diabetes seems rational. Herein, we review the literature on lifestyle interventions and diabetes prevention, describing the biological systems that can be characterized at scale in human populations, linking them to lifestyle in diabetes, and consider some of the challenges impeding the clinical translation of lifestyle precision medicine.
Aims/hypothesis
Excess adiposity is differentially associated with increased risk of cardiometabolic disease in men and women, according to observational studies. Causal inference studies largely ...assume a linear relationship between BMI and cardiometabolic outcomes, which may not be the case. In this study, we investigated the shapes of the causal relationships between BMI and cardiometabolic diseases and risk factors. We further investigated sex differences within the causal framework.
Methods
To assess causal relationships between BMI and the outcomes, we used two-stage least-squares Mendelian randomisation (MR), with a polygenic risk score for BMI as the instrumental variable. To elucidate the shapes of the causal relationships, we used a non-linear MR fractional polynomial method, and used piecewise MR to investigate threshold relationships and confirm the shapes.
Results
BMI was associated with type 2 diabetes (OR 3.10; 95% CI 2.73, 3.53), hypertension (OR 1.53; 95% CI 1.44, 1.62) and coronary artery disease (OR 1.20; 95% CI 1.08, 1.33), but not chronic kidney disease (OR 1.08; 95% CI 0.67, 1.72) or stroke (OR 1.08; 95% CI 0.92, 1.28). For cardiometabolic risk factors, BMI was positively associated with glucose, HbA
1c
, triacylglycerol levels and both systolic and diastolic BP. BMI had an inverse causal relationship with total cholesterol, LDL-cholesterol and HDL-cholesterol. The data suggest a non-linear causal relationship between BMI and blood glucose levels, HbA
1c
and lipid fractions (
p
<0.001), more strongly in men than women. The piecewise MR results were consistent with the fractional polynomial results. The causal effect of BMI on coronary artery disease, total cholesterol and LDL-cholesterol was different in men and women, but this sex difference was only significant for LDL-cholesterol after controlling for multiple testing (
p
<0.001). Further, the causal effect of BMI on coronary artery disease varied by menopause status in women.
Conclusions/interpretation
We describe the shapes of causal effects of BMI on cardiometabolic diseases and risk factors, and report sex differences in the causal effects of BMI on LDL-cholesterol. We found evidence of non-linearity in the causal effect of BMI on diseases and risk factor biomarkers. Reducing excess adiposity is highly beneficial for health, but there is greater need to consider biological sex in the management of adiposity.
Graphical abstract
Novel biomarkers are key to addressing the ongoing pandemic of type 2 diabetes mellitus. While new technologies have improved the potential of identifying such biomarkers, at the same time there is ...an increasing need for informed prioritization to ensure efficient downstream verification. We have built BALDR, an automated pipeline for biomarker comparison and prioritization in the context of diabetes. BALDR includes protein, gene, and disease data from major public repositories, text-mining data, and human and mouse experimental data from the IMI2 RHAPSODY consortium. These data are provided as easy-to-read figures and tables enabling direct comparison of up to 20 biomarker candidates for diabetes through the public website
https://baldr.cpr.ku.dk
.
Identifying factors that influence individuals' smoking behavior remains a huge public health concern. This study aimed to investigate changes in individuals' cigarette smoking while considering ...well-known smoking determinants, including social capital, its presence being postulated to reduce smoking.
From British Household Panel Survey data, two baseline smoking cohorts were created ("smoking" and "not smoking"). The same individuals from this nationally representative sample (N
= 8114, aged 16-91 years) were interviewed on four occasions between years 2000 and 2007 to investigate changes in cigarette smoking behavior. Logistic regression models with random effects compensated for within-individual behavior over time. Temporal pathways were investigated by lagging independent variables (t - 1) in relation to our cigarette-use outcome at time (t).
Active social participation at (t - 1) was positively associated with smoking cessation at (t) (odds ratio OR = 1.39; 95% confidence interval CI 1.07-1.82). Separating from one's spouse at (t - 1) increased risk for smoking relapse/initiation at (t) (OR = 6.63; 95% CI 1.70-28.89). Conversely, being married protected against smoking cigarettes (OR = 1.87; 95% CI 1.15-3.04). These associations held in our robustness checks.
Initial marital breakdown predicted a high risk of smoking relapse/initiation. The timing of this life event provides a critical window where adverse smoking behavior might occur. Conversely, the positive effects of active social participation on cigarette cessation remained consistent, its absence further predicting smoking relapse/initiation. Robustness of results confirms the important role that active participation has on cigarette smoking behavior. Group smoking cessation interventions could harness participatory elements to better achieve their goals.
By investigating temporal relationships between well-known smoking determinants and cigarette smoking outcomes, we identified that being "separated" (not "divorced") at time (t) predicted a higher risk of smoking relapse/initiation at (t). Tailored health messages could be employed to highlight the increased risk of cigarette smoking relapse/initiation during this stressful life event. Conversely, active social participation (a common social capital proxy) consistently predicted smoking cessation over time. Future group smoking cessation interventions could be designed explicitly to harness participatory elements to better achieve their goals.
Objective
The first‐line approach for childhood obesity is lifestyle intervention (LI); however, success varies. This study aimed first to identify distinct subgroups of response in children living ...with overweight and obesity and second to elucidate predictors for subclusters.
Methods
Based on the obesity patient follow‐up registry the APV (Adipositas‐Patienten‐Verlaufsdokumentation) initiative, a total of 12,453 children and adolescents (median age: 11.5 IQR: 9.7–13.2 years; BMI z score BMIz: 2.06 IQR: 1.79–2.34; 52.6% girls) living with overweight/obesity and participating in outpatient LI were studied. Longitudinal k‐means clustering was used to identify individual BMIz response curve for up to 2 years after treatment initiation. Multinomial logistic regression was used to elucidate predictors for cluster membership.
Results
A total of 36.3% of children and adolescents experienced “no BMIz loss.” The largest subcluster (44.8%) achieved “moderate BMIz loss,” with an average delta‐BMIz of −0.23 (IQR: −0.33 to −0.14) at study end. A total of 18.9% had a “pronounced BMIz loss” up to −0.61 (IQR: −0.76 to −0.49). Younger age and lower BMIz at LI initiation, larger initial BMIz loss, and less social deprivation were linked with higher likelihood for moderate or pronounced BMIz loss compared with the no BMIz loss cluster (all p < 0.05).
Conclusions
These results support the importance of patient‐tailored intervention and earlier treatment escalation in high‐risk individuals who have little chance of success.
PDF
