There are over 1,000,000 publications on diet and health and over 480,000 references on inflammation in the National Library of Medicine database. In addition, there have now been over 30,000 peer-reviewed articles published on the relationship between diet, inflammation, and health outcomes. Based on this voluminous literature, it is now recognized that low-grade, chronic systemic inflammation is associated with most non-communicable diseases (NCDs), including diabetes, obesity, cardiovascular disease, cancers, respiratory and musculoskeletal disorders, as well as impaired neurodevelopment and adverse mental health outcomes. Dietary components modulate inflammatory status. In recent years, the Dietary Inflammatory Index (DII^®), a literature-derived dietary index, was developed to characterize the inflammatory potential of habitual diet. Subsequently, a large and rapidly growing body of research investigating associations between dietary inflammatory potential, determined by the DII, and risk of a wide range of NCDs has emerged. In this narrative review, we examine the current state of the science regarding relationships between the DII and cancer, cardiometabolic, respiratory and musculoskeletal diseases, neurodevelopment, and adverse mental health outcomes. We synthesize the findings from recent studies, discuss potential underlying mechanisms, and look to the future regarding novel applications of the adult and children’s DII (C-DII) scores and new avenues of investigation in this field of nutritional research.

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

Inflammation is a potential mechanism linking obesity and cardiometabolic risk. Limited data on inflammatory markers in metabolically healthy obese and nonobese individuals exist. The aim of the study was to investigate the extent to which differences between metabolically healthy and unhealthy obese and nonobese adults, defined using a range of metabolic health definitions, are correlated with a range of inflammatory markers. A cross-sectional sample of 2047 men and women aged 45-74 years participated in the study. Participants were classified as obese (body mass index ≥ 30 kg/m(2)) and nonobese (body mass index < 30 kg/m(2)). Metabolic health status was defined using 5 existing metabolic health definitions based on a range of cardiometabolic abnormalities. Serum acute-phase reactants, adipocytokines, proinflammatory cytokines, and white blood cell counts were determined. According to most definitions, metabolically healthy obese and nonobese individuals presented with lower concentrations of complement component 3, C-reactive protein, TNF-α, IL-6, and plasminogen activator inhibitor-1; higher adiponectin levels; and reduced white blood cell count compared to their metabolically unhealthy counterparts. Logistic regression analysis identified greater likelihood of metabolically healthy obesity among individuals with lower levels of complement component 3 (odds ratios [ORs], 2-3.5), IL-6 (ORs, 1.7-2.9), plasminogen activator inhibitor-1 (ORs, 1.7-2.9), and white blood cells (ORs, 2.1-2.5) and higher adiponectin concentrations (ORs, 2.6-4.0). Favorable inflammatory status is positively associated with metabolic health in obese and nonobese individuals. These findings are of public health and clinical significance in terms of screening and stratification based on metabolic health phenotype to identify those at greatest cardiometabolic risk for whom appropriate therapeutic or intervention strategies should be developed.

There is a current lack of consensus on defining metabolically healthy obesity (MHO). Limited data on dietary and lifestyle factors and MHO exist. The aim of this study is to compare the prevalence, dietary factors and lifestyle behaviours of metabolically healthy and unhealthy obese and non-obese subjects according to different metabolic health criteria. Cross-sectional sample of 1,008 men and 1,039 women aged 45-74 years participated in the study. Participants were classified as obese (BMI ≥30kg/m²) and non-obese (BMI <30kg/m²). Metabolic health status was defined using five existing MH definitions based on a range of cardiometabolic abnormalities. Dietary composition and quality, food pyramid servings, physical activity, alcohol and smoking behaviours were examined. The prevalence of MHO varied considerably between definitions (2.2% to 11.9%), was higher among females and generally increased with age. Agreement between MHO classifications was poor. Among the obese, prevalence of MH was 6.8% to 36.6%. Among the non-obese, prevalence of metabolically unhealthy subjects was 21.8% to 87%. Calorie intake, dietary macronutrient composition, physical activity, alcohol and smoking behaviours were similar between the metabolically healthy and unhealthy regardless of BMI. Greater compliance with food pyramid recommendations and higher dietary quality were positively associated with metabolic health in obese (OR 1.45-1.53 unadjusted model) and non-obese subjects (OR 1.37-1.39 unadjusted model), respectively. Physical activity was associated with MHO defined by insulin resistance (OR 1.87, 95% CI 1.19-2.92, p = 0.006). A standard MHO definition is required. Moderate and high levels of physical activity and compliance with food pyramid recommendations increase the likelihood of MHO. Stratification of obese individuals based on their metabolic health phenotype may be important in ascertaining the appropriate therapeutic or intervention strategy.

Accumulating evidence identifies diet and inflammation as potential mechanisms contributing to cardiometabolic risk. However, inconsistent reports regarding dietary inflammatory potential, biomarkers of cardiometabolic health and metabolic syndrome (MetS) risk exist. Our objective was to examine the relationships between a food frequency questionnaire (FFQ)-derived dietary inflammatory index (DII^®), biomarkers of lipoprotein metabolism, inflammation and glucose homeostasis and MetS risk in a cross-sectional sample of 1992 adults. Energy-adjusted DII (E-DII) scores derived from an FFQ were calculated. Lipoprotein particle size and subclass concentrations were measured using nuclear magnetic resonance (NMR) spectroscopy. Serum acute-phase reactants, adipocytokines, pro-inflammatory cytokines and white blood cell (WBC) counts were determined. Insulin resistance was calculated by homeostasis model assessment (HOMA-IR). Our data indicate that a more pro-inflammatory diet, reflected by higher E-DII scores, was associated with potentially pro-atherogenic lipoprotein profiles characterised by increased numbers of large very low density lipoprotein (VLDL), small dense low density lipoprotein (LDL) and high density lipoprotein (HDL) particles and less large LDL and HDL particles (all p < 0.001). Inflammatory profiling identified a range of adverse phenotypes among those with higher E-DII scores, including higher complement component C3 (C3), C-reactive protein (CRP), (both p < 0.05), interleukin 6 (IL-6) and tumour necrosis factor (TNF)-α concentrations, higher WBC counts and neutrophil to lymphocyte ratio (NLR) and lower adiponectin levels (all p < 0.001). MetS risk was increased among those with higher E-DII scores (OR 1.37, 95% CI (1.01, 1.88), p < 0.05), after adjusting for potential confounders. In conclusion, habitual intake of a more pro-inflammatory diet is associated with unfavourable lipoprotein and inflammatory profiles and increased MetS risk.