The significant increase in worldwide morbidity and mortality from non-communicable diseases (NCDs) indicates that the efficacy of existing strategies addressing this crisis may need improvement. Early identification of the metabolic irregularities associated with the disease process may be a key to developing early intervention strategies. Unhealthy lifestyle behaviours are well established drivers of the development of several NCDs, but the impact of such behaviours on health can vary considerably between individuals. How can it be determined if an individual’s unique set of lifestyle behaviours is producing disease? Accumulating evidence suggests that lifestyle-associated activation of oxidative and inflammatory processes is primary driver of the cell and tissue damage which underpins the development of NCDs. However, the benefit of monitoring subclinical inflammation and oxidative activity has not yet been established. After reviewing relevant studies in this context, we suggest that quantification of oxidative stress and inflammatory biomarkers during the disease-free prodromal stage of NCD development may have clinical relevance as a timely indicator of the presence of subclinical metabolic changes, in the individual, portending the development of disease. Monitoring markers of oxidative and inflammatory activity may therefore enable earlier and more efficient strategies to both prevent NCD development and/or monitor the effectiveness of treatment.

BackgroundUric acid (UA) has been suggested as a novel risk factor for diabetes. However, its definite role in this prevalent disease is still the subject of much discussion because it is always accompanied with other major risk factors such as obesity and high visceral adiposity. In order to clarify the role of UA in diabetes, this study aimed to investigate the associations between plasma UA and fasting plasma glucose, HbA1c, lipid profile and inflammatory markers after accounting for the contribution of other diabetes risk factors such as BMI and VAT fat mass.MethodsIn the present cross-sectional study, 100 non-diabetic middle-aged males (n = 48) and females (n = 52) were recruited. Central fat distribution measures including android to gynoid fat ratio, VAT and subcutaneous adipose tissue (SAT) fat mass were determined using dual-energy X-ray absorptiometry (DXA). Biochemical analysis was done using methods well established for clinical and research laboratories. Multiple linear regression analysis was performed to analyse the association between plasma UA and the biochemical and central fat distribution measures.ResultsUA was positivly associated with body mass index (BMI) (r (98) = 0.42, P ≤ 0.001), android to gynoid fat ratio (r (98) = 0.62, P ≤ 0.001) and VAT fat mass (r (96) = 0.55, P ≤ 0.001). UA was also positively associated with plasma glucose (r (98) = 0.33, P ≤ 0.001), hemoglobin A1c (r (93) = 0.25, P = 0.014), plasma triglyceride (r s (95) = 0.40, P ≤ 0.001), HDL cholesterol (r (98) = − 0.61, P ≤ 0.001) and CRP (r s (98) = 0.23, P = 0.026). However, these associations were no longer significant after accounting for BMI or/and VAT fat mass. No significant association was observed between UA and SAT fat mass (r (97) = 0.02, P ≥ 0.05), Total cholesterol (r (98) = 0.03, P ≥ 0.05), LDL cholesterol (r (98) = 0.13, P ≥ 0.05), TNF-α (r (97) = 0.12, P ≥ 0.05) and IL-6 (r (96) = −0.02, P ≥ 0.05).ConclusionResults from this study suggest, for the first time, that the association between plasma UA and glucose in a non-diabetic population is not direct but rather dependent on VAT fat mass.

High protein intake and reduced levels of the essential pyridine nucleotide nicotinamide adenine dinucleotide (NAD+) have both been independently associated with promotion of the ageing phenotype. However, it has not yet been shown whether these two independent observations are biochemically linked. To investigate this possibility, we used a cross-sectional study design of 100 apparently healthy middle-aged males (n = 48) and females, in which we assessed average dietary intakes of multiple components using a validated questionnaire. We also analysed fasting blood levels of urea, NAD+ and its metabolites, and inflammation-linked biomarkers, including interleukin-6 (IL-6), Kynurenine (Kyn), and Tryptophan (Trp). One-way ANOVA and ANCOVA were then performed for statistical analysis. Our results have shown for the first time that plasma levels of NAD+ and Total NAD(H) were lower with increasing protein intake (F (2, 92) = 4.61, P = 0.012; F (2, 92) = 4.55, P = 0.013, respectively). The associated decrease in NAD+ and NAD(H) levels was even stronger with increasing plasma levels of the protein breakdown product urea (F (2, 93) = 25.11, P≤0.001; F (2, 93) = 21.10, P≤0.001). These associations were all independent of age, gender and energy intake. However, no significant association was observed between protein intake or plasma urea, and plasma levels of NAD+ metabolites. We also observed that plasma levels of the inflammatory cytokine IL-6, and both Kyn, and Trp, but not the Kyn/Trp ratio were higher with increasing plasma urea levels (F (2, 94) = 3.30, P = 0.041; F (2, 95) = 7.41, P≤0.001; F (2, 96) = 4.23, P = 0.017, respectively). These associations were dependent on eGFR and energy intake, except for the urea and Trp association that was independent of all. In conclusion, we report for the first time, a novel association between protein intake, its metabolism, and plasma NAD+ levels with a possible link to inflammation. These findings provide further insight into how protein restriction may contribute to the anti-ageing process observed in several studies.