Association between full service and fast food restaurant density, dietary intake and overweight/obesity among adults in Delhi, India

Abstract Background The food environment has been implicated as an underlying contributor to the global obesity epidemic. However, few studies have evaluated the relationship between the food environment, dietary intake, and overweight/obesity in low- and middle-income countries (LMICs). The aim of this study was to assess the association of full service and fast food restaurant density with dietary intake and overweight/obesity in Delhi, India. Methods Data are from a cross-sectional, population-based study conducted in Delhi. Using multilevel cluster random sampling, 5364 participants were selected from 134 census enumeration blocks (CEBs). Geographic information system data were available for 131 CEBs (n = 5264) from a field survey conducted using hand-held global positioning system devices. The number of full service and fast food restaurants within a 1-km buffer of CEBs was recorded by trained staff using ArcGIS software, and participants were assigned to tertiles of full service and fast food restaurant density based on their resident CEB. Height and weight were measured using standardized procedures and overweight/obesity was defined as a BMI ≥25 kg/m2. Results The most common full service and fast food restaurants were Indian savory restaurants (57.2%) and Indian sweet shops (25.8%). Only 14.1% of full service and fast food restaurants were Western style. After adjustment for age, household income, education, and tobacco and alcohol use, participants in the highest tertile of full service and fast food restaurant density were less likely to consume fruit and more likely to consume refined grains compared to participants in the lowest tertile (both p < 0.05). In unadjusted logistic regression models, participants in the highest versus lowest tertile of full service and fast food restaurant density were significantly more likely to be overweight/obese: odds ratio (95% confidence interval), 1.44 (1.24, 1.67). After adjustment for age, household income, and education, the effect was attenuated: 1.08 (0.92, 1.26). Results were consistent with further adjustment for tobacco and alcohol use, moderate physical activity, and owning a bicycle or motorized vehicle. Conclusions Most full service and fast food restaurants were Indian, suggesting that the nutrition transition in this megacity may be better characterized by the large number of unhealthy Indian food outlets rather than the Western food outlets. Full service and fast food restaurant density in the residence area of adults in Delhi, India, was associated with poor dietary intake. It was also positively associated with overweight/obesity, but this was largely explained by socioeconomic status. Further research is needed exploring these associations prospectively and in other LMICs

At What Stage of Neural Processing Does Cocaine Act to Boost Pursuit of Rewards?

Dopamine-containing neurons have been implicated in reward and decision making. One element of the supporting evidence is that cocaine, like other drugs that increase dopaminergic neurotransmission, powerfully potentiates reward seeking. We analyze this phenomenon from a novel perspective, introducing a new conceptual framework and new methodology for determining the stage(s) of neural processing at which drugs, lesions and physiological manipulations act to influence reward-seeking behavior. Cocaine strongly boosts the proclivity of rats to work for rewarding electrical brain stimulation. We show that the conventional conceptual framework and methods do not distinguish between three conflicting accounts of how the drug produces this effect: increased sensitivity of brain reward circuitry, increased gain, or decreased subjective reward costs. Sensitivity determines the stimulation strength required to produce a reward of a given intensity (a measure analogous to the KM of an enzyme) whereas gain determines the maximum intensity attainable (a measure analogous to the vmax of an enzyme-catalyzed reaction). To distinguish sensitivity changes from the other determinants, we measured and modeled reward seeking as a function of both stimulation strength and opportunity cost. The principal effect of cocaine was a two-fourfold increase in willingness to pay for the electrical reward, an effect consistent with increased gain or decreased subjective cost. This finding challenges the long-standing view that cocaine increases the sensitivity of brain reward circuitry. We discuss the implications of the results and the analytic approach for theories of how dopaminergic neurons and other diffuse modulatory brain systems contribute to reward pursuit, and we explore the implications of the conceptual framework for the study of natural rewards, drug reward, and mood

Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes.

Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57-1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628-0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs

Stem Cells in Bone and Articular Cartilage Tissue Regeneration

Articular cartilage is a thin layer of hyaline cartilage that covers the surface of the bones of diarthrodial joints. It is an avascular, alymphatic and aneural tissue, with a smooth opalescent appearance. Cartilage is a highly organised and specialised tissue allowing free articulation, painless movement and transmission of force through the skeleton. Compared to other tissues, articular cartilage has a low rate of metabolic activity (Pearle et al. 2005 ). The tissue is maintained by a single specialised cell, the chondrocyte (Buckwalter et al. 1999 ), and is comprised of a highly organised matrix with a large extracellular matrix (ECM) to cell volume ratio. The basic structure is composed of a 3D collagen scaffold and aggregating proteoglycans (Jeffery et al. 1991 ). The arrangement, direction and location of these collagen fibrils vary, along with the cell density, matrix composition and overall thickness throughout the tissue, providing different mechanical properties across the joint. The composition of the ECM reflects its mechanical properties such as tensile strength (mainly collagens type II, IX, and XI) and compressive stiffness (such as proteoglycans and aggrecan). Small proteoglycans, including decorin, biglycan and fibromodulin, bind to other matrix macromolecules and thereby help to stabilise the matrix (Buckwalter and Mankin 1998a ). Additionally, collagen type VI and non- collagenous proteins, such as anchorin CII, tenascin and fi bronectin, are important mediators of cell–matrix interactions (Poole et al. 2001 ). The ECM acts as a signal transducer for the chondrocytes, creating mechanical, electrical and physicochemical signals that help to direct the synthetic and degradative activity of chondrocytes (Buckwalter and Mankin 1998a ). Articular cartilage is divided into four zones: the superficial (tangential), transitional, radial and calcifi ed (Eyre 2002 ). The physical and biochemical differences between the zones are important to allow the cartilage to resist both extrinsic and intrinsic forces due to mechanical stress and swelling in the proteoglycan- rich areas (Knudson and Knudson 2001 ). Cartilage tissue contains a large proportion of water (65–80 % by wet weight). Chondrocytes comprise approximately 5–10 % of the tissue total volume and collagens form 10–30 %, whilst proteoglycans and other molecules consist of 5–10 % of the tissue wet weight (Eyre 2002 ; Archer et al. 2003a ; Bhosale and Richardson 2008 ; Hunziker et al. 2007 )

Stem Cells in Bone and Articular Cartilage Tissue Regeneration

Articular cartilage is a thin layer of hyaline cartilage that covers the surface of the bones of diarthrodial joints. It is an avascular, alymphatic and aneural tissue, with a smooth opalescent appearance. Cartilage is a highly organised and specialised tissue allowing free articulation, painless movement and transmission of force through the skeleton. Compared to other tissues, articular cartilage has a low rate of metabolic activity (Pearle et al. 2005 ). The tissue is maintained by a single specialised cell, the chondrocyte (Buckwalter et al. 1999 ), and is comprised of a highly organised matrix with a large extracellular matrix (ECM) to cell volume ratio. The basic structure is composed of a 3D collagen scaffold and aggregating proteoglycans (Jeffery et al. 1991 ). The arrangement, direction and location of these collagen fibrils vary, along with the cell density, matrix composition and overall thickness throughout the tissue, providing different mechanical properties across the joint. The composition of the ECM reflects its mechanical properties such as tensile strength (mainly collagens type II, IX, and XI) and compressive stiffness (such as proteoglycans and aggrecan). Small proteoglycans, including decorin, biglycan and fibromodulin, bind to other matrix macromolecules and thereby help to stabilise the matrix (Buckwalter and Mankin 1998a ). Additionally, collagen type VI and non- collagenous proteins, such as anchorin CII, tenascin and fi bronectin, are important mediators of cell–matrix interactions (Poole et al. 2001 ). The ECM acts as a signal transducer for the chondrocytes, creating mechanical, electrical and physicochemical signals that help to direct the synthetic and degradative activity of chondrocytes (Buckwalter and Mankin 1998a ). Articular cartilage is divided into four zones: the superficial (tangential), transitional, radial and calcifi ed (Eyre 2002 ). The physical and biochemical differences between the zones are important to allow the cartilage to resist both extrinsic and intrinsic forces due to mechanical stress and swelling in the proteoglycan- rich areas (Knudson and Knudson 2001 ). Cartilage tissue contains a large proportion of water (65–80 % by wet weight). Chondrocytes comprise approximately 5–10 % of the tissue total volume and collagens form 10–30 %, whilst proteoglycans and other molecules consist of 5–10 % of the tissue wet weight (Eyre 2002 ; Archer et al. 2003a ; Bhosale and Richardson 2008 ; Hunziker et al. 2007 )

Alterations in the chondrocyte surfaceome in response to pro-inflammatory cytokines

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