Prostaglandin profiling reveals a role for haematopoietic prostaglandin D synthase in adipose tissue macrophage polarisation in mice and humans.

BACKGROUND/OBJECTIVES: Obesity has been associated with both changes in adipose tissue lipid metabolism and inflammation. A key class of lipid-derived signalling molecules involved in inflammation are the prostaglandins. In this study, we aimed to determine how obesity affects the levels of prostaglandins within white adipose tissue (WAT) and determine which cells within adipose tissue produce them. To avoid the effects of cellular stress on prostaglandin levels, we developed a multivariate statistical approach in which metabolite concentrations and transcriptomic data were integrated, allowing the assignment of metabolites to cell types. SUBJECTS/METHODS: Eicosanoids were measured by liquid chromatography-tandem mass spectrometry and mRNA levels using real-time PCR. Eicosanoid levels and transcriptomic data were combined using principal component analysis and hierarchical clustering in order to associate metabolites with cell types. Samples were obtained from C57Bl/6 mice aged 16 weeks. We studied the ob/ob genetically obese mouse model and diet-induced obesity model. We extended our results in mice to a cohort of morbidly obese humans undergoing bariatric surgery. RESULTS: Using our modelling approach, we determined that prostglandin D₂ (PGD₂) in adipose tissue was predominantly produced in macrophages by the haematopoietic isoform of prostaglandin D synthase (H-Pgds). Analysis of sub-fractionated WAT confirmed that H-Pgds was expressed in adipose tissue macrophages (ATMs). Furthermore, H-Pgds expression in ATMs isolated from lean and obese mice was consistent with it affecting macrophage polarisation. Functionally, we demonstrated that H-PGDS-produced PGD₂ polarised macrophages toward an M2, anti-inflammatory state. In line with a potential anti-inflammatory role, we found that H-PGDS expression in ATMs was positively correlated with both peripheral insulin and adipose tissue insulin sensitivity in humans. CONCLUSIONS: In this study, we have developed a method to determine the cellular source of metabolites within an organ and used it to identify a new role for PGD₂ in the control of ATM polarisation.HQL-79 was a kind gift of Professor Yoshihiro Urade. Professor Vidal-Puig was funded by the BHF, MRC and BBSRC. Dr Virtue was funded by the BBSRC and the BHF. Dr Eiden, Dr Masoodi and Dr Griffin were funded by the MRC. Dr Mok was funded by the Wellcome Trust.This is the final published version. It first appeared at http://www.nature.com/ijo/journal/vaop/ncurrent/full/ijo201534a.htm

A meta-analysis of the relationship between brain dopamine receptors and obesity: a matter of changes in behavior rather than food addiction?

Addiction to a wide range of substances of abuse has been suggested to reflect a ‘Reward Deficiency Syndrome'. That is, drugs are said to stimulate the reward mechanisms so intensely that, to compensate, the population of dopamine D(2) receptors (DD2R) declines. The result is that an increased intake is necessary to experience the same degree of reward. Without an additional intake, cravings and withdrawal symptoms result. A suggestion is that food addiction, in a similar manner to drugs of abuse, decrease DD2R. The role of DD2R in obesity was therefore examined by examining the association between body mass index (BMI) and the Taq1A polymorphism, as the A1 allele is associated with a 30–40% lower number of DD2R, and is a risk factor for drug addiction. If a lower density of DD2R is indicative of physical addiction, it was argued that if food addiction occurs, those with the A1 allele should have a higher BMI. A systematic review found 33 studies that compared the BMI of those who did and did not have the A1 allele. A meta-analysis of the studies compared those with (A1/A1 and A1/A2) or without (A2/A2) the A1 allele; no difference in BMI was found (standardized mean difference 0.004 (s.e. 0.021), variance 0.000, Z=0.196, P<0.845). It was concluded that there was no support for a reward deficiency theory of food addiction. In contrast, there are several reports that those with the A1 allele are less able to benefit from an intervention that aimed to reduce weight, possibly a reflection of increased impulsivity

Validation of a Novel Multivariate Method of Defining HIV-Associated Cognitive Impairment

Background. The optimum method of defining cognitive impairment in virally suppressed people living with HIV is unknown. We evaluated the relationships between cognitive impairment, including using a novel multivariate method (NMM), patientreported outcome measures (PROMs), and neuroimaging markers of brain structure across 3 cohorts.Methods. Differences in the prevalence of cognitive impairment, PROMs, and neuroimaging data from the COBRA, CHARTER, and POPPY cohorts (total n = 908) were determined between HIV-positive participants with and without cognitive impairment defined using the HIV-associated neurocognitive disorders (HAND), global deficit score (GDS), and NMM criteria.Results. The prevalence of cognitive impairment varied by up to 27% between methods used to define impairment (eg, 48% for HAND vs 21% for NMM in the CHARTER study). Associations between objective cognitive impairment and subjective cognitive complaints generally were weak. Physical and mental health summary scores (SF-36) were lowest for NMM-defined impairment (P&lt;.05). There were no differences in brain volumes or cortical thickness between participants with and without cognitive impairment defined using the HAND and GDS measures. In contrast, those identified with cognitive impairment by the NMM had reduced mean cortical thickness in both hemispheres (P&lt;.05), as well as smaller brain volumes (P&lt;.01). The associations with measures of white matter microstructure and brain-predicted age generally were weaker.Conclusion. Different methods of defining cognitive impairment identify different people with varying symptomatology and measures of brain injury. Overall, NMM-defined impairment was associated with most neuroimaging abnormalities and poorer selfreported health status. This may be due to the statistical advantage of using a multivariate approach

Cognitive function and drivers of cognitive impairment in a European and a Korean cohort of people living with HIV

Although cognitive impairments are still prevalent in the current antiretroviral therapy era, limited investigations have compared the prevalence of cognitive disorder in people living with HIV (PLWH) and its determinants in different regions and ethnicities. We compared cognitive performance across six domains using comparable batteries in 134 PLWH aged ≥45 years from the COBRA study (Netherlands, UK), and 194 PLWH aged ≥18 years from the NeuroAIDS Project (South Korea). Cognitive scores were standardized and averaged to obtain domain and global T-scores. Associations with global T-scores were evaluated using multivariable regression and the ability of individual tests to detect cognitive impairment (global T-score ≤45) was assessed using the area-under-the-receiver-operating-characteristic curve (AUROC). The median (interquartile range) age of participants was 56 (51, 62) years in COBRA (88% white ethnicity, 93% male) and 45 (37, 52) years in NeuroAIDS (100% Korean ethnicity, 94% male). The rate of cognitive impairment was 18.8% and 18.0%, respectively (p = 0.86). In COBRA, Black-African ethnicity was the factor most strongly associated with cognitive function (11.1 [7.7, 14.5] lower scores vs. white ethnicity, p < 0.01), whereas in NeuroAIDS, age (0.6 [0.1, 1.3] per 10-year, p<0.01) and education (0.7 [0.5, 0.9] per year, p<0.01) were significantly associated with cognitive function with anemia showing only a weak association (−1.2 [−2.6, 0.3], p=0.12). Cognitive domains most associated with cognitive impairment were attention (AUROC = 0.86) and executive function (AUROC = 0.87) in COBRA and processing speed (AUROC = 0.80), motor function (AUROC = 0.78) and language (AUROC = 0.78) in NeuroAIDS. Two cohorts of PLWH from different geographical regions report similar rates of cognitive impairment but different risk factors and cognitive profiles of impairment