Optimizing Exposome-wide Assessments in Cardiometabolic Risk

This thesis is focused on cardiovascular disease (CVD) and type 2 diabetes mellitus (T2D), two concomitant conditions that appear with growing concern. In our work, we aim to improve the identification of individuals at-risk of cardiometabolic disease through the characterization of complex environmental exposures (i.e. diet, physical activity), that temporally vary, and the health effects on cardiometabolic traits and disease. Our projects were based upon the Västerbotten Health Survey (VHU) and the Malmö Diet and Cancer (MDCS) studies, which included extensive data on lifestyle, biological intermediates, and clinical outcomes. In Paper I, we utilized the so-called environmental-wide association approach (EWAS), using longitudinal data from > 31,000 adults in VHU study. Under generalized linear models, from ~ 300 candidate exposures, 11 modifiable variables were associated with most of the cardiometabolic traits; the prioritised variables belonged to smoking, coffee intake, physical activity, alcohol intake, and context-specific lifestyle domains. In Paper II, we implemented a machine learning-based model to identify individuals with variable susceptibility to lifestyle risk factors for T2D and CVD. Individuals with sensitivity to blood lipids, and blood pressure associated predictors were at higher risk to develop cardiometabolic disease. Furthermore, when pooling across sensitive groups from the two cohorts, the findings suggest a particular vulnerable subpopulation with different risk profile. In Paper III, a series of causal-inference experiments from VHU and publicly available genome-wide association study (GWAS) summary statistics were used to triangulate evidence of the direct and mediated effects by adiposity and physical activity, of macronutrient intake (fat, carbohydrates, protein and sugar) and cardiometabolic disease. Using structural equation modelling, the mediation analyses enhanced with Mendelian randomization analysis, showed a likely causal putative association between carbohydrate intake and T2D. In addition, the integrative genomic analyses suggested a candidate causal variant localized to the established T2D gene TCF7L2. In Paper IV, we conducted a systematic review and metanalysis of observational studies, complemented by Mendelian randomization analysis using GWAS summary statistics, investigating causal associations of individuals with high, yet normal, glycaemia associated with cardiovascular complications. Prediabetes was likely causally associated with coronary heart disease; suggesting higher, but not diabetic levels of blood glucose confer a risk, thus, effective preventive strategies may prove successful in prediabetes

A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.</p

Genotype-stratified treatment for monogenic insulin resistance: a systematic review

This is the final version. Available from Nature Research via the DOI in this record. Data availability: All data used in this review is available from publicly available and herein referenced sources. A list of included studies is provided in Supplementary Data 1. All data generated or analyzed during this study are included in this published article and its supplementary information files. Source data for the figures are available as Supplementary Data 2.BACKGROUND: Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology. METHODS: Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy. RESULTS: 10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions. CONCLUSIONS: The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.Wellcome TrustWellcome Trus

Next-generation epidemiology : the role of high-resolution molecular phenotyping in diabetes research

Epidemiologists have for many decades reported on the patterns and distributions of diabetes within and between populations and have helped to elucidate the aetiology of the disease. This has helped raise awareness of the tremendous burden the disease places on individuals and societies; it has also identified key risk factors that have become the focus of diabetes prevention trials and helped shape public health recommendations. Recent developments in affordable high-throughput genetic and molecular phenotyping technologies have driven the emergence of a new type of epidemiology with a more mechanistic focus than ever before. Studies employing these technologies have identified gene variants or causal loci, and linked these to other omics data that help define the molecular processes mediating the effects of genetic variation in the expression of clinical phenotypes. The scale of these epidemiological studies is rapidly growing; a trend that is set to continue as the public and private sectors invest heavily in omics data generation. Many are banking on this massive volume of diverse molecular data for breakthroughs in drug discovery and predicting sensitivity to risk factors, response to therapies and susceptibility to diabetes complications, as well as the development of disease-monitoring tools and surrogate outcomes. To realise these possibilities, it is essential that omics technologies are applied to well-designed epidemiological studies and that the emerging data are carefully analysed and interpreted. One might view this as next-generation epidemiology, where complex high-dimensionality data analysis approaches will need to be blended with many of the core principles of epidemiological research. In this article, we review the literature on omics in diabetes epidemiology and discuss how this field is evolving. [Figure not available: see fulltext.

Predicting sensitivity and resilience to modifiable risk factors for cardiometabolic morbidity and mortality

Background Lifestyle exposures play a major role in the development of disease, yet people vary in their susceptibility. A critical step towards precision medicine is identifying individuals who are resilient or sensitive to the environment, and, assess whether the allocation to these predicted groups are more or less likely to develop cardiometabolic disease.Methods We have used repeated data from the VHU study (n=35440) to identify sensitive and resilient individuals using prediction intervals at the 5th and 95th quantile. Three exposure susceptibility groups were derived per cardiometabolic score using quantile regression forests in the training dataset; next, in the validation dataset, we assessed the different risks of the groups using Cox proportional hazard models for CVD and diabetes.Results The results of our study suggest that, after ∼10 y of follow-up, individuals with sensitivity to the environmental exposures associated with systolic and diastolic blood pressure, blood lipids, and glucose were at higher risk of developing cardiometabolic disease. Moreover, when hazards were pooled with the replication cohort, for those individuals sensitive to the exposures associated with blood pressure traits, the hazards remained significant.Conclusions Identifying individuals who are predicted to be sensitive are at higher risk of developing disease, this population may be a clinical target for prevention or early intervention and public health strategies

Estimating the Direct Effect between Dietary Macronutrients and Cardiometabolic Disease, Accounting for Mediation by Adiposity and Physical Activity

Assessing the causal effects of individual dietary macronutrients and cardiometabolic disease is challenging owing to the complexity to distinguish direct effects from those mediated or confounded by other factors. To estimate these effects, intake of protein, carbohydrate, sugar, fat, and its subtypes were obtained using food frequency data derived from a Swedish population-based cohort (n~60,000). Data on clinical outcomes (i.e., type 2 diabetes (T2D) and cardiovascular disease (CVD) incidence) were obtained by linking health registry data. We assessed the magnitude of direct and mediated effects of diet, adiposity and physical activity on T2D and CVD using structural equation modelling (SEM). To strengthen causal inference, we used Mendelian randomization (MR) to model macronutrient intake exposures against clinical outcomes. We identified likely causal effects of genetically predicted carbohydrate intake (including sugar intake) and T2D, independent of adiposity and physical activity. Pairwise, serial-and parallel-mediational configurations yielded similar results. In the integrative genomic analyses, the candidate causal variant localized to the established type 2 diabetes gene TCF7L2. These findings may be informative when considering which dietary modifications included in nutritional guidelines are most likely to elicit health-promoting effects

Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality

People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person's degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual's cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as 'sensitive' to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup

Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality

People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person’s degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual’s cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as ‘sensitive’ to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup

Exposome-wide ranking of modifiable risk factors for cardiometabolic disease traits

The present study assessed the temporal associations of ~ 300 lifestyle exposures with nine cardiometabolic traits to identify exposures/exposure groups that might inform lifestyle interventions for the reduction of cardiometabolic disease risk. The analyses were undertaken in a longitudinal sample comprising &gt; 31,000 adults living in northern Sweden. Linear mixed models were used to assess the average associations of lifestyle exposures and linear regression models were used to test associations with 10-year change in the cardiometabolic traits. ‘Physical activity’ and ‘General Health’ were the exposure categories containing the highest number of ‘tentative signals’ in analyses assessing the average association of lifestyle variables, while ‘Tobacco use’ was the top category for the 10-year change association analyses. Eleven modifiable variables showed a consistent average association among the majority of cardiometabolic traits. These variables belonged to the domains: (i) Smoking, (ii) Beverage (filtered coffee), (iii) physical activity, (iv) alcohol intake, and (v) specific variables related to Nordic lifestyle (hunting/fishing during leisure time and boiled coffee consumption). We used an agnostic, data-driven approach to assess a wide range of established and novel risk factors for cardiometabolic disease. Our findings highlight key variables, along with their respective effect estimates, that might be prioritised for subsequent prediction models and lifestyle interventions