Food-intake patterns assessed by using front-of-pack labeling program criteria associated with better diet quality and lower cardiometabolic risk.

BACKGROUND: Front-of-pack labeling systems may provide additional guidance to that already available to facilitate the identification of foods that improve diet quality. OBJECTIVE: We examined the association between choosing foods that meet criteria of an established front-of-pack labeling system with food-group and nutrient intakes and cardiometabolic risk factors. DESIGN: The association between the consumption of foods that met 2014 American Heart Association (AHA) Heart-Check Food Certification Program criteria and 2005 Healthy Eating Index (HEI-2005) scores, food-group intake, energy intake, nutrient intake, and cardiometabolic risk factors was analyzed in 11,296 men and women ≥ 19 y old by using 1-d dietary recall data from the NHANES 2007-2010. Individuals were categorized into consumers and nonconsumers of AHA Heart-Check Food Certification Program-certifiable foods and quartiles of intakes on the basis of the percentage of calories. RESULTS: The consumption of AHA Heart-Check Food Certification Program-certifiable foods was positively associated with HEI-2005 scores and fruit, vegetable, whole-grain, total sugar, fiber, potassium, calcium, and vitamin D intakes and inversely associated with the percentage of energy from saturated fat, monounsaturated fat, added sugars, alcohol, and intakes of cholesterol and sodium. The highest quartile of daily energy intake from AHA Heart-Check Food Certification Program-certifiable foods was associated with lower risk of obesity (26%), lower risk of elevated waist circumference (29%), and lower risk of metabolic syndrome (24%) than with lowest intakes (all P < 0.05). CONCLUSION: The choice of foods meeting one front-of-pack labeling system positively influences food-group and nutrient intakes and is associated with a higher diet quality and lower risk of cardiometabolic syndrome.

The GoodNEWS (Genes, Nutrition, Exercise, Wellness, and Spiritual Growth) Trial: a community-based participatory research (CBPR) trial with African-American church congregations for reducing cardiovascular disease risk factors--recruitment, measurement, and randomization.

INTRODUCTION: Although cardiovascular diseases (CVD) are the leading cause of death among Americans, significant disparities persist in CVD prevalence, morbidity, and mortality based on race and ethnicity. However, few studies have examined risk factor reduction among the poor and ethnic minorities. METHODS: Community-based participatory research (CBPR) study using a cluster randomized design--African-American church congregations are the units of randomization and individuals within the congregations are the units of analysis. Outcome variables include dietary change (Diet History Questionnaire), level of physical activity (7-Day Physical Activity Recall), lipoprotein levels, blood pressure, fasting glucose, and hemoglobin A1c. RESULTS: Eighteen (18) church congregations were randomized to either a health maintenance intervention or a control condition. Complete data were obtained on 392 African-American individuals, 18 to 70 years of age, predominantly employed women with more than a high school diploma. Treatment and intervention groups were similar at baseline on saturated fat intake, metabolic equivalent of tasks (METS) per day, and other risk factors for CVD. CONCLUSIONS: The GoodNEWS trial successfully recruited and evaluated CVD-related risk among African-American participants using a CBPR approach. Several logistical challenges resulted in extending the recruitment, preliminary training, and measurement periods. The challenges were overcome with the assistance of a local community consultant and a professional event planner. Our experience supports the need for incorporating non-traditional community-based staff into the design and operational plan of CBPR trials.

Searching for alternatives to full kinetic analysis in 18F-FDG PET: an extension of the simplified kinetic analysis method.

UNLABELLED: The most accurate way to estimate the glucose metabolic rate (or its influx constant) from (18)F-FDG PET is to perform a full kinetic analysis (or its simplified Patlak version), requiring dynamic imaging and the knowledge of arterial activity as a function of time. To avoid invasive arterial blood sampling, a simplified kinetic analysis (SKA) has been proposed, based on blood curves measured from a control group. Here, we extend the SKA by allowing for a greater variety of arterial input function (A(t)) curves among patients than in the original SKA and by accounting for unmetabolized (18)F-FDG in the tumor. METHODS: Ten A(t)s measured in patients were analyzed using a principal-component analysis to derive 2 principal components describing most of the variability of the A(t). The mean distribution volume of (18)F-FDG in tumors for these patients was used to estimate the corresponding quantity in other patients. In subsequent patient studies, the A(t) was described as a linear combination of the 2 principal components, for which the 2 scaling factors were obtained from an early and a late venous sample drawn for the patient. The original and extended SKA (ESKA) were assessed using fifty-seven (18)F-FDG PET scans with various tumor types and locations and using different injection and acquisition protocols, with the K(i) derived from Patlak analysis as a reference. RESULTS: ESKA improved the accuracy or precision of the input function (area under the blood curve) for all protocols examined. The mean errors (±SD) in K(i) estimates were -12% ± 33% for SKA and -7% ± 22% for ESKA for a 20-s injection protocol with a 55-min postinjection PET scan, 20% ± 42% for SKA and 1% ± 29% for ESKA (P < 0.05) for a 120-s injection protocol with a 55-min postinjection PET scan, and -37% ± 19% for SKA and -4% ± 6% for ESKA (P < 0.05) for a 20-s injection protocol with a 120-min postinjection PET scan. Changes in K(i) between the 2 PET scans in the same patients also tended to be estimated more accurately and more precisely with ESKA than with SKA. CONCLUSION: ESKA, compared with SKA, significantly improved the accuracy and precision of K(i) estimates in (18)F-FDG PET. ESKA is more robust than SKA with respect to various injection and acquisition protocols.

The influence of tumor oxygenation on hypoxia imaging in murine squamous cell carcinoma using [64Cu]Cu-ATSM or [18F]Fluoromisonidazole positron emission tomography.

[64Cu]Cu(II)-ATSM (64Cu-ATSM) and [18F]-Fluoromisonidazole (18F-FMiso) tumor binding as assessed by positron emisson topography (PET) was used to determine the responsiveness of each probe to modulation in tumor oxygenation levels in the SCCVII tumor model. Animals bearing the SCCVII tumor were injected with 64Cu-ATSM or 18F-FMiso followed by dynamic small animal PET imaging. Animals were imaged with both agents using different inspired oxygen mixtures (air, 10% oxygen, carbogen) which modulated tumor hypoxia as independently assessed by the hypoxia marker pimonidazole. The extent of hypoxia in the SCCVII tumor as monitored by the pimonidazole hypoxia marker was found to be in the following order: 10% oxygen>air>carbogen. Tumor uptake of 64Cu-ATSM could not be changed if the tumor was oxygenated using carbogen inhalation 90 min post-injection suggesting irreversible cellular uptake of the 64Cu-ATSM complex. A small but significant paradoxical increase in 64Cu-ATSM tumor uptake was observed for animals breathing air or carbogen compared to 10% oxygen. There was a positive trend toward 18F-FMiso tumor uptake as a function of changing hypoxia levels in agreement with the pimonidazole data. 64Cu-ATSM tumor uptake was unable to predictably detect changes in varying amounts of hypoxia when oxygenation levels in SCCVII tumors were modulated. 18F-FMiso tumor uptake was more responsive to changing levels of hypoxia. While the mechanism of nitroimidazole binding to hypoxic cells has been extensively studied, the avid binding of Cu-ATSM to tumors may involve other mechanisms independent of hypoxia that warrant further study.

The influence of tumor oxygenation on (18)F-FDG (fluorine-18 deoxyglucose) uptake: a mouse study using positron emission tomography (PET).

BACKGROUND: This study investigated whether changing a tumor's oxygenation would alter tumor metabolism, and thus uptake of (18)F-FDG (fluorine-18 deoxyglucose), a marker for glucose metabolism using positron emission tomography (PET). RESULTS: Tumor-bearing mice (squamous cell carcinoma) maintained at 37 degrees C were studied while breathing either normal air or carbogen (95% O(2), 5% CO2), known to significantly oxygenate tumors. Tumor activity was measured within an automatically determined volume of interest (VOI). Activity was corrected for the arterial input function as estimated from image and blood-derived data. Tumor FDG uptake was initially evaluated for tumor-bearing animals breathing only air (2 animals) or only carbogen (2 animals). Subsequently, 5 animals were studied using two sequential (18)F-FDG injections administered to the same tumor-bearing mouse, 60 min apart; the first injection on one gas (air or carbogen) and the second on the other gas. When examining the entire tumor VOI, there was no significant difference of (18)F-FDG uptake between mice breathing either air or carbogen (i.e. air/carbogen ratio near unity). However, when only the highest (18)F-FDG uptake regions of the tumor were considered (small VOIs), there was a modest (21%), but significant increase in the air/carbogen ratio suggesting that in these potentially most hypoxic regions of the tumor, (18)F-FDG uptake and hence glucose metabolism, may be reduced by increasing tumor oxygenation. CONCLUSION: Tumor (18)F-FDG uptake may be reduced by increases in tumor oxygenation and thus may provide a means to further enhance (18)F-FDG functional imaging.

Simplified kinetic analysis of tumor 18F-FDG uptake: a dynamic approach.

UNLABELLED: Standardized uptake value (SUV) is often used to quantify (18)F-FDG tumor use. Although useful, SUV suffers from known quantitative inaccuracies. Simplified kinetic analysis (SKA) methods have been proposed to overcome the shortcomings of SUV. Most SKA methods rely on a single time point (SKA-S), not on tumor uptake rate. We describe a hybrid between Patlak analysis and existing SKA-S methods, using multiple time points (SKA-M) but reduced imaging time and without measurement of an input function. We compared SKA-M with a published SKA-S method and with Patlak analysis. METHODS: Twenty-seven dynamic (18)F-FDG scans were performed on 11 cancer patients. A population-based (18)F-FDG input function was generated from an independent patient population. SKA-M was calculated using this population input function and either a short, late, dynamic acquisition over the tumor (starting 25-35 min after injection and ending approximately 55 min after injection) or dynamic imaging 10 or 25 min to approximately 55 min after injection but using only every second or third time point, to permit a 2- or 3-field-of-view study. SKA-S was also calculated. Both SKA-M and SKA-S were compared with the gold standard, Patlak analysis. RESULTS: Both SKA-M (1 field of view) and SKA-S correlated well with Patlak slope (r > 0.99, P < 0.001, and r = 0.96, P < 0.001, respectively), as did multilevel SKA-M (r > 0.99 and P < 0.001 for both). Mean values of SKA-M (25-min start time) and SKA-S were statistically different from Patlak analysis (P < 0.001 and P < 0.04, respectively). One-level SKA-M differed from the Patlak influx constant by only -1.0% +/- 1.4%, whereas SKA-S differed by 15.1% +/- 3.9%. With 1-level SKA-M, only 2 of 27 studies differed from K(i) by more than 20%, whereas with SKA-S, 10 of 27 studies differed by more than 20% from K(i). CONCLUSION: Both SKA-M and SKA-S compared well with Patlak analysis. SKA-M (1 or multiple levels) had lower variability and bias than did SKA-S, compared with Patlak analysis. SKA-M may be preferred over SUV or SKA-S when a large unmetabolized (18)F-FDG fraction is expected and 1-3 fields of view are sufficient.

Comparison of SUV and Patlak slope for monitoring of cancer therapy using serial PET scans.

The standardized uptake value (SUV) and the slope of the Patlak plot ( K) have both been proposed as indices to monitor the progress of disease during cancer therapy. Although a good correlation has been reported between SUV and K, they are not equivalent, and may not be equally affected by metabolic changes occurring during disease progression or therapy. We wished to compare changes in tumor SUV with changes in K during serial positron emission tomography (PET) scans for monitoring therapy. Thirteen patients enrolled in a protocol to treat renal cell carcinoma metastases were studied. Serial dynamic fluorodeoxyglucose (FDG) PET scans and computed tomography (CT) and magnetic resonance (MR) scans were performed once prior to treatment, once at 36+/-2 days after the start of treatment, and (in 7/13 subjects, 16/27 lesions) a third time at 92+/-9 days after the start of treatment. This resulted in a total of 33 scans, and 70 tumor Patlak and SUV values (one value for each lesion at each time point). SUV and K were measured over one to four predefined tumors/patient at each time point. The input function was obtained from regions of interest over the heart, combined, if necessary, with late blood samples. Over all tumors and scans, SUV and K correlated well ( r=0.97, P<0.0001). However, change in SUV with treatment over all tumor scan pairs was much less well correlated with the corresponding change in K ( r=0.73, P<0.0001). The absolute difference in % change was outside the 95% confidence limits expected from previous variability studies in 6 of 43 pairs of tumor scans, and greater than 50% in 2 of 43 tumor scan pairs. In four of the six cases, the two indices predicted opposing therapeutic outcomes. Similar results were obtained for SUV normalized by body weight or body surface area and for SUVs using mean or maximum count. Changes in CT and MR tumor cross-product dimensions correlated poorly with each other ( r=0.47, P=NS), and so could not be used to determine the "correct" PET index. Absolute values of SUV and K correlated well over the patient population. However, when monitoring individual patient therapy serially, large differences in the % changes in the two indices were occasionally found, sometimes sufficient to produce opposing conclusions regarding the progression of disease.