In this study, we found 985 genes that change expression in the cortex and the medulla of the kidney with age. Some of the genes whose transcripts increase in abundance with age are known to be specifically expressed in immune cells, suggesting that immune surveillance or inflammation increases with age. The age-regulated genes show a similar aging profile in the cortex and the medulla, suggesting a common underlying mechanism for aging. Expression profiles of these age-regulated genes mark not only age, but also the relative health and physiology of the kidney in older individuals. Finally, the set of aging-regulated kidney genes suggests specific mechanisms and pathways that may play a role in kidney degeneration with age. A study of human aging in the kidney reveals similar changes in the transcriptional profile in cortex and medulla, suggesting that a common underlying aging process is taking place This indicates that aging is a complex process driven by diverse molecular pathways and biochemical events. As such, a powerful approach to study aging is to use systems biology, which allows a multitude of factors affecting aging to be analyzed in parallel. For example, DNA microarrays and gene expression chips have been used to perform a genome-wide analysis of changes in gene expression in old age. Extensive studies in ed genes . Severaled genes , 2000 aned genes , 2004. Tsir2, and oxidative damage have each been shown to affect lifespan in diverse model organisms  in the renal cortex. The primary function of the tubules within the medulla is to concentrate or dilute the urine so as to maintain fluid balance. The major age-related change in kidney function is a 25% decline in the glomerular filtration rate starting at age 40 . The abiIn this study, we present a molecular portrait of the aging process in the human kidney by analyzing gene expression as a function of age on a genome-wide scale. We show that age regulation is similar in the cortex and the medulla, and that age-regulated genes in the kidney are broadly expressed. We show that the expression profiles of age-regulated genes correlate well with the morphological and physiological state of the kidney in old age. Finally, we analyze the set of age-regulated genes to identify specific metabolic processes and cellular functions that change as a function of age, and discuss their possible roles in specifying the functional lifespan of the kidney.http://genome-www5.stanford.edu/) . Using our dataset, the expression level for every gene as a function of age could be plotted. For example, the expression of CDO1 (which encodes a cysteine dioxygenase type 1 protein) tended to increase with age. There was also variation between subjects and between the cortex and the medulla . We saw large differences in expression between tissue types and between the sexes. These differences were of similar magnitude for both young and old subjects, so that aging in one tissue (or sex) typically ran parallel to aging in the other , Tissuei is one if sample i was a medulla sample (and zero for cortex), and ijɛ is a random error term. The coefficients kjβ for k = 0, 1, 2, and 3 are values to be estimated from data. Our primary interest is in βj1, which describes how quickly the expression of gene j changes with age, withβj1 = 0 for genes with no linear age relationship.In kjcan differ from zero, even when the true coefficient is zero. We judged statistical significance through p-values, where a value of pji near zero corresponds to a large absolute value |β^kj|unlikely to have arisen by chance. Such p-values do not distinguish genes that increase with age from those that decrease with age. We also use one-tailed p-values, written p˜kj, taking values near zero to be significantly decreasing trends and those near one to be significantly increasing trends  gave no reason to suspect that a curved relationship was needed. Similarly, a piecewise linear age relationship (with bends at ages 50 and 70) was not significantly better than a linear one. Large and statistically significant differences in expression were found for the two tissue types, and so the tissue type was included in p < 0.001 for both sex and age.To make p < 0.001), which is considerably greater than would be expected by chance . Of these, 742 genes increase expression with age and 243 decrease expression with age  were localized to an isolated region of the kidney. Our normal samples were obtained from the region of the kidney furthest from the carcinoma, were not directly contaminated with cancer cells, and appeared normal histologically see . This prp-value for whether renal cell carcinoma affected each of the 44,928 genes present on the Affymetrix DNA chip. The smallest p-value was 0.00013. We would expect to see almost six such p-values by chance alone. This result indicates that the presence of renal cell carcinoma does not significantly affect the expression of any gene in the normal tissue from the same kidney, compared to normal tissues taken from kidneys without renal cell carcinoma.Third, we used regression models to directly test whether our aging studies were affected by seven medical factors: renal cell carcinoma, transitional cell carcinoma, size of tumor, hypertension, systolic blood pressure, diastolic blood pressure, and diabetes mellitus. For renal cell carcinoma, we used a regression model predicting expression from age, sex, tissue type, and a zero/one variable indicating whether the sample came from a patient with renal cell carcinoma or not. The result gave a p < 0.001) age regulation using either of these models. Renal cell carcinoma does not significantly affect the age slopes for these genes . The regression slopes changed very little with and without these factors, indicating that these factors do not strongly affect our analysis of age regulation .Fourth, five of the samples were from kidneys that did not have tumors, and two of these were from donor kidneys used for transplantation that had no associated pathology at all. The expression profile from these five patients was similar to the profile from other samples used in our study. In summary, it is unlikely that these disease and medical factors have confounded our analysis of age-regulated changes in gene expression.Changes in the expression for some of the 985 age-regulated genes may directly reflect the aging process in the kidney; these genes would serve both as aging markers and provide clues about molecular mechanisms for aging in the kidney. Other changes may result from an age-related change in the relative proportion of cell types within the kidney, such as would result from increased infiltration of immune cells with age. Finally, the expression changes may reflect the downstream response of the kidney to an age-related process elsewhere, such as would result from age-related changes in blood pressure or vascular supply.Since the cortex and medulla contain different cell types and have distinct functions, it was of interest to test whether they age similarly. It is possible that the pattern of degeneration in a particular cell type reflects those metabolic pathways that are used most heavily by that cell. For example, there could be deterioration in cell adhesion in glomerular epithelial cells that form part of the filtration barrier in the cortex, while there could be an age-related decline in ion traffic or water flow across the apical or basolateral membranes of tubular epithelial cells in the medulla. Alternatively, distinct cell types could show a common pattern of age-related decline involving pathways common to all cells, such as protein synthesis and mitochondrial function. This degeneration of core cellular processes would affect every cell function, including filtration by glomerular epithelial cells and water and solute reabsorption by tubular epithelial cells.βj4 × Tissue × Age was added to the model in βj4. j4-values. Genes showing tissue-specific slopes would appear in peaks on the left and right. The figure shows neither of these peaks, indicating there is no statistically significant difference in aging between the two tissue types.To test whether age-related gene expression changes are different in cortex and medulla, we considered a model in which a term of the form To further investigate coordinate aging in the cortex and medulla, we searched for age-regulated genes in each of these tissues independently, and then tested whether age-regulated genes in one were also age-regulated in the other. Specifically, to find age-regulated genes in the cortex, we fit the modelusing the cortex samples only. To find age-regulated genes in the medulla, we fit the modelp < 0.001).using only the medulla samples. We found 634 genes in the cortex samples and 72 genes in the medulla samples that showed significant changes in expression with age (p < 0.001) in the cortex samples. If aging in the medulla were unrelated to aging in the cortex, we would expect to see a flat histogram. The actual histogram has a strong peak of genes on the right, indicating that significantly age-regulated genes in the cortex tend to also be significantly age-regulated in the medulla. Of the 634 genes that increased expression with age in the cortex, 22 also increased expression with age in the medulla, compared with the 0.6 genes expected at p = 0.001. We obtained similar results when we took the converse approach, first selecting the 72 age-regulated genes in the medulla, and then testing whether they were also age-regulated in the cortex (data not shown).Having identified age-regulated genes in the cortex, we next examined whether they were also age-regulated in the medulla. r = 0.487. Models 2 and 3 allow us to investigate whether the cortex and medulla age at the same rate as specified in model 1. For the 22 genes that are significantly age-related in both tissues, the age coefficients have a high correlation (r = 0.96), and the slopes themselves are numerically close (2 expression per year), corresponding to only a 6% divergence in expression over 50 y. Given the strong similarities in the aging profiles of these two tissue types, we are able to increase the statistical power of our analysis by pooling the cortex and medulla datasets (resulting in model 1).Next, we compared the slope of expression with respect to age in the cortex to that in the medulla C. The rely close D. We fouWe examined the list of 985 age-regulated genes, and immediately found evidence for increased expression of genes from immunocytes. Many of the 985 age-regulated genes are expressed specifically in B cells (e.g., immunoglobulin mu, kappa, and lambda), T cells (e.g., T cell receptor beta), or neutrophils (e.g., neutrophil cytosolic factors 1 and 4) see . Nearly Immunohistochemical experiments using antibodies directed against markers specific for B cells, T cells, or neutrophils showed that the kidney samples contained a small proportion of immune cells (less than 1%) in sporadic clusters scattered throughout each section (data not shown). The number of immune cells varied greatly from section to section, and thus it was not possible to use immunohistochemistry to confirm a quantitative increase in the numbers of immune cells in the kidney with age.2 of the expression level for each gene, and then calculated an average expression level for the blood (five samples) and the kidney (134 samples). Of the 985 genes that change expression with age, 538 are expressed at higher levels in blood cells than in the kidney samples. Age-related changes in the RNA abundance of these genes may reflect either changes in the fraction of immune cells in the kidney or age-related changes in expression in kidney cells. The remaining 447 are expressed at higher levels in the kidney than in whole blood, and age regulation of these genes is likely to reflect expression changes in kidney cells themselves , indicating that regulation of expression due to differentiation (between the cortex and medulla) is much greater than that related to aging. This result is consistent with the idea that aging results from a slow degeneration of a core transcriptome in the cortex and the medulla of the kidney.Aging is thought to be caused by slow degeneration of the transcriptome (the entire set of genes expressed in a tissue), rather than a qualitative change in expression, as occurs during tissue specification. As such, changes in gene expression associated with aging should be less than expression differences between different types of tissues. To confirm this idea, we compared the magnitude of gene expression differences due to differentiation (cortex versus medulla) to those due to aging. We used the same approach as before to evaluate differences in expression in cortex versus medulla on a genome-wide scale. For every gene, we calculated the istogram . Genes cTo address whether different organs have distinct or common aging profiles, we analyzed whether the 447 age-regulated genes in the kidney were expressed specifically in the kidney or broadly in many tissues. If the kidney has its own specific pattern of aging, one might expect that the set of 447 aging-regulated genes would be enriched for those expressed specifically in the kidney, such as genes that have direct roles in forming the filtration barrier or in regulating ion or water reabsorption. If there is a common profile for aging shared among tissues, one might expect that most of the list of 447 aging-regulated genes would be expressed in many tissues.We determined the level of expression of the age-regulated genes in different tissues using data from a previous study reporting a genome-wide profile of gene expression in 26 different human tissues with Affymetrix gene arrays . Of the The expression levels of these 447 age-regulated genes constitute a molecular profile of aging, and we can examine the expression profile of individual patients to observe how they compare to the average for their age group. Older individuals tended to express age-induced genes at higher levels and age-repressed genes at lower levels than younger individuals. However, certain individuals had unusual expression profiles, in which genes were expressed at levels more typical of a different age group. For example, patient 81 was 78 y old but had an expression profile as though she were older see . Her kidDo the molecular gene expression profiles correlate with the physiological ages of the kidney samples? That is, does patient 81 have a kidney showing excessive age-related damage and does patient 95 have a kidney with unusually good health? To answer these questions, we determined the morphological and physiological states of the kidneys from each of the patients by examining histological stains. As people grow older, there is a general decline in the morphological appearance of the kidney: (1) the glomeruli lose their structure and their capillaries are replaced with fibrous tissue (glomerular sclerosis), (2) the tubules collapse and atrophy, and the interstitial space between them widens and scars , and (3) there is a thickening of the innermost layer of the arteriole wall due to the accumulation of hyaline material . We gave three scores to each kidney section corresponding to the appearance of the glomeruli, the tubules, and the arterioles. Scores ranged from zero for normal appearance for youthful patients to four for an advanced state of glomerular sclerosis, tubular atrophy/interstitial fibrosis, or arterial intimal hyalinosis see . We thenWe then compared the chronicity index to the gene expression profiles of the 447 age-regulated genes as a function of age see . In genemortalin-2 (which encodes Heat Shock Protein 70), which decreases expression in the kidney in old age. Heat shock proteins act as protein chaperones, and likely function to counteract cell senescence by alleviating the accumulation of damaged proteins in old cells. In human fibroblasts, overexpression of mortalin-2 extends lifespan in vitro  extends longevity, and several genes encoding heat shock proteins decrease expression in old age , all but one of which increase expression in old age see . Forty-nAnother functional group is a set of 11 genes encoding ribosomal proteins, all of which increase expression with age. Protein synthesis rates are known to decline as animals grow older, and increased expression of these ribosomal protein genes may serve to offset this.Changes in the expression of regulatory genes with age may have particularly strong effects on kidney metabolism and function, since these changes are likely to initiate cascades of changes in downstream genes. We examined our list of 447 age-regulated genes for those that are likely to function as regulatory genes. Of the 447 age-regulated genes, 15 encode transcription factors and 51 encode proteins that are part of signaling pathways.As filtration of the blood takes place in the glomerulus, age-regulated genes that are enriched in the glomerulus may be especially important for understanding how kidney function declines with age. We identified genes enriched in the glomerulus using data from a previous study, in which cDNA microarrays were used to compare expression levels in the glomeruli relative to the rest of the kidney . Of the Old age is associated with a functional decline in a myriad of molecular and cellular processes. To gain a global perspective of the diverse pathways that change with age, we performed a whole-genome analysis of gene expression as a function of age for kidney samples from 74 patients ranging in age from 27 to 92 y. Many factors affect gene expression in addition to age, including variability between individuals, between different tissues within the kidney, and between sexes. The large number of samples in our dataset provided good power for identifying age-regulated genes in noisy data despite small changes in expression, and allowed us to use a statistical linear regression model to identify 985 genes that change expression with age.The results from this work show that transcriptional differences between young and old individuals involve an accumulation of small changes in expression from many genes, rather than resulting from large expression changes in a small number of genes. This observation suggests that functional decline in old age is not the result of the complete failure of a small number of cellular processes. Rather, it is the slight weakening of many pathways that cumulatively causes a significant decrease in cell function. Studying aging by analyzing one pathway at a time is difficult, because any single pathway might show only a small change with respect to age and might contribute only a small amount to the overall functional decline in old age. By contrast, functional genomics is a powerful approach to study aging, because many genes can be simultaneously scanned in parallel for small changes in expression.(r) of the regression slopes for these 985 genes was only 0.085 between the kidney and muscle aging experiments and hence accounts for only 0.0072 of the variance between these two tissues  in which the pathology was localized and did not involve the part of the kidney sampled. Key factors from the medical record for each patient used in this study are listed in Frozen tissues were placed in cryomolds, embedded in Cryo Tissue Tek O.C.T. Compound  and cut into 4-μm sections (Leica Microsystems, Wetzlar, Germany). Sections were stained with hematoxylin and eosin, and then histologically evaluated to exclude samples showing abnormal histology. Histology slides were also marked into two main functional sections, the cortex and medulla, to help aid in accurate dissection of these two areas. We reviewed radiological findings for all tumors and histology for all slides. We excluded any cases in which radiological imaging, gross examination at the time of resection, or histological review of the removed tissue indicated that there might be tumor involvement of the normal areas. Cases with incomplete or unclear medical records were excluded from this study.Frozen kidney tissue samples were dissected into cortex and medulla sections. Portions were weighed (0.05–0.75 g), cut into small pieces on dry ice, and then placed in 1 ml of TRIzol Reagent  per 50–100 mg of tissue. The tissue was homogenized using a PowerGen700 homogenizer (Fisher Scientific, Pittsburgh, Pennsylvania, United States), and the total RNA was isolated according to the TRIzol Reagent protocol.A standard protocol designed by Affymetrix  for their HG-U133A and HG-U133B high-density oligonucleotide arrays was slightly modified by the Stanford Genome Technology Center , and all samples were processed in their facility see . Eight mhttp://genome-www5.stanford.edu/) and at the Web site http://cmgm.stanford.edu/approximatelykimlab/aging_kidney/. The Affymetrix probe IDs and the locus link IDs for the genes discussed in the paper are in Using the dChip program , microarp-values we use are based on t-tests from standard linear regression theory. Under the hypothesis H0 that kjβ = 0, the estimated coefficient β^kjis a random variable. The least squares value is a particular number, β^LSkj. The p-value measures the extent to which the least squares value is surprisingly large assuming H0 holds. Specifically, the two-tailed p-value isThe p-value we use isand the one-tailed kjis employed to test H0 against an alternative hypothesis of kjβ < 0. We use it because it distinguishes between significant increasing and significant decreasing coefficients. Under H0, the distribution of p is U(0,1), and so is that of p˜. Numerically, the equationSometimes p˜holds.t-test is derived under an assumption of normally distributed errors. The data showed estimated errors with heavier than normal tails. The t-test is known to be robust against heavy-tailed errors.The A linear regression is more appropriate for these data than is an analysis of variance (ANOVA) on age groups, because the latter is aimed at piecewise constant expression patterns, and it is not plausible that expression should change sharply at a given age. A genome-wide ANOVA (data not shown) did, however, find a similar group of age-related genes. Unlike ANOVA, regression summarizes the age effect in one coefficient. This is advantageous for interpretation and for statistical power when there is little nonlinearity.p-values for all the genes. If the histogram of p˜values differed sharply from uniform, and if the smallest p-values were small compared to 1/44,928, then the coefficient was included.The decision of whether to include a variable in model 1 was based on the collection of p-value of 0.001. Such a gene list can be expected to have about 44 genes in it by chance, even if all of the coefficients are really zero. Thus, of the 985 age-related genes, it is plausible that about 44 of them are false positives. We have chosen to work with a fixed significance level, instead of attempting to fix the false discovery rate, because our test statistics are strongly correlated.Gene lists were made using a threshold 2 expression in the cortex from that in the medulla, and fit a regression of the difference versus age and sex. Such an analysis removes intra-subject correlations. There was again no evidence of genes aging differently in the two tissue types (data not shown).We were concerned that intra-subject correlations might have affected our results. For each of 59 subjects with both cortex and medulla samples, we subtracted logFigure S1Each row shows the presence of a medical or related factor. Age of patients is shown on the y-axis. Only transitional cell carcinoma showed a strong age bias. We have identified over 20 different factors that might potentially confound our study on aging, such as race, blood pressure, diabetes, and type and size of tumor adjacent to the normal section see .(221 KB PDF).Click here for additional data file.Figure S2We used regression models to directly test whether our aging studies were affected by seven medical factors: renal cell carcinoma, transitional cell carcinoma, size of tumor, hypertension, systolic blood pressure, diastolic blood pressure, or diabetes mellitus. Scatterplots show age-related slopes using a regression model that includes a term for the medical factor compared to slopes from a regression model that does not include that medical factor.p < 0.001) age regulation using either a model with a renal cell carcinoma term or without a renal cell carcinoma term. The vertical and horizontal axes show the slope from a model with and without the renal cell carcinoma term, respectively. The slopes change very little with and without the renal cell carcinoma term. As one might expect, many of the genes that are significant at the 0.001 level are just barely so. There were 866 genes significant in both models, 119 significant only when renal cell carcinoma was not in the model, and 86 significant only when renal cell carcinoma was in the model. The overall picture of age relationship changes very little whether a term for renal cell carcinoma is included in the model or not.(A) Effect of renal cell carcinoma (RCC) on age-related expression. We selected genes that showed statistically significant . We have not used false discovery rate techniques for this problem, because the age coefficients for different genes are far from independent. The scatterplot shows that transitional cell carcinoma does not affect age-related slopes very much.(B) Effect of transitional cell carcinoma (TCC) on age-related expression. Scatterplot showing age-related slopes with and without a term for transitional cell carcinoma. Transitional cell carcinoma was present in 13 patients, all of whom were old. Thus if transitional cell carcinoma affected gene expression in adjacent normal tissue, then it might bias our results on aging. (B) shows data for presence or absence of transitional cell carcinoma in the model. The gene with the smallest (C) Tumor size does not affect age regulation.(D) Hypertension (HTN) does not affect age regulation.(E) Systolic blood pressure (SBP) does not affect age regulation.(F) Diastolic blood pressure (DPB) does not affect age regulation.(G) Diabetes mellitus (DM) does not affect age regulation.(307 KB JPG).Click here for additional data file.Figure S3p < 0.004). A Pearson correlation value of 0.085 implies that 0.72% of the variance in the muscle regression coefficients is due to variance in the associated kidney regression coefficients. We note that the muscle dataset had a small sample size (n = 16), which may not be large enough to sufficiently detect similarity in age regulation with the kidney.We obtained the muscle dataset from the GEO database . To comp(59 KB XLS).Click here for additional data file.Figure S4C. elegans (D. melanogaster (We compared patterns of gene expression in the aging time course data from  elegans  and D. mnogaster  to thoseD. melanogaster. Open triangles denote age-regulated genes in humans and their orthologs in flies. Open circles denote age-regulated genes in flies and their orthologs in humans. The scatterplot shows the regression slopes from the human kidney and the fly aging datasets (r = −0.05 (p = 0.27) for human and fly, while the age-regulated fly genes paired with human orthologs show a Pearson correlation r = −0.05 (p = 0.12).(A) Regression slopes of age-regulated genes from human kidney and datasets . SpecifiC. elegans. Open circles denote age-regulated genes in humans and their orthologs in worms. Open triangles denote age-regulated genes in worms and their orthologs in humans. The scatterplot shows the regression slopes from the human kidney and C. elegans aging datasets (r = 0.05 (p = 0.54). The age-regulated worm genes paired with human orthologs show a Pearson correlation r = −0.01 (p = 0.08).(B) Regression slopes of age-regulated genes from human kidney and datasets . The ageThese results show no evidence for overlap in the aging process between different species.(509 KB PDF).Click here for additional data file.Protocol S1(40 KB DOC).Click here for additional data file.Table S1(33 KB XLS).Click here for additional data file.Table S2(13 KB XLS).Click here for additional data file.Table S3(135 KB XLS).Click here for additional data file.Table S4(75 KB XLS).Click here for additional data file.Table S5(53 KB XLS).Click here for additional data file.


Translational research requires a team approach to scientific inquiry and product development. Translational research teams consist of basic and clinical scientists who can be members of both academic and industrial communities. The conception, pre-clinical testing, and clinical evaluation of a diagnostic or therapeutic approach demands an intense interaction between investigators with diverse backgrounds. As the barriers between industry and academia are removed, issues of potential conflict of interest become more complex. Translational researchers must become aware of the situations which constitute conflict of interest and understand how such conflicts can impact their research programs. Finally, the translational research community must participate in the dialogue ongoing in the public and private sectors and help shape the rules that will govern conflicts that arise during the evolution of their research programs. By its nature, translational research crosses boundaries between basic science and clinical application. It places researchers in new contexts and ushers in a range of new contacts and relationships. Crossing these boundaries contributes directly to the creativity and social impact of translational medicine. But crossing these boundaries also gives rise to new and often conflicting obligations between researchers, their employers, and their industry sponsors. The public is rightfully concerned that the financial interests of researchers, their institutions, and their corporate sponsors may bias research. Yet history also teaches us that industry collaboration is often essential in realizing the promise of translational research. Industry collaboration has figured prominently in many translational research successes including recombinant growth hormone, angioplasty, stenting for coronary artery disease, and many new medications and diagnostic devices .Translational researchers must, therefore, understand what financial conflicts of interest are and how they are managed. Their industry partners must understand the constraints placed on researchers by federal and university policies as well as state laws. Relevant policies in the United States include the regulations issued by the Public Health Service and published as part of the Code of Federal Regulations (42 CFR 50.601–50.607) and in the National Science Foundation Grant Policy Guide (Section 510) . Laws goConflicts may arise whenever researchers' outside, personal financial interests have the potential to compromise an investigator's professional judgment and independence in the design, conduct, or publication of research. The most commonly regulated financial interests include consulting fees or compensation for personal services, equity or other ownership interests, royalties, and intellectual property rights. A researcher may, for example, receive consulting income or equity in exchange for service on a scientific advisory board of a company that then sponsors clinical research in her lab. Another researcher may be paid for talks to physician groups about an approved medication while simultaneously conducting research on potential off-label uses of the drug. The investigator or an immediate family member may hold stock in the research sponsor. These examples are all common cases and most institutions have relatively standardized ways of managing such common conflicts.Greater challenges are created when the financial relationships between commercial interests and investigators are either ambiguous or complex. Ambiguity can result in a number of ways, but one of the most frequent occurs when investigators approach consulting as an extension of discussions among academic colleagues. Thinking that talking to a corporate representative is the same as talking to an academic colleague, for example, may lead the investigator to make inappropriate disclosures that compromise intellectual property rights or contractual obligations. Often it is not any one financial interest, but rather the combination of multiple financial interests that makes a situation unmanageable. It is extraordinarily difficult, for example, to manage situations in which an investigator is the founder of a startup company, an inventor on a patent licensed to the company, a consultant to the company, and the recipient of other government and industry grants for closely related research.Spurred by a combination of bad experiences and new regulations, most research institutions now have policies in place for the management of some aspects of personal financial interests in research. One strategy is simply to prohibit personal financial interests in research. The Association of American Universities, for example, advocates outright prohibition in cases involving research on human subjects unless there are "compelling circumstances" that justify an exception . ProhibiAlthough effective, prohibitions are blunt tools and in our opinion should be used only as a last resort. We say this not only because prohibiting financial interests may leave any number of other equally biasing interests in place , but alsFortunately, there are usually less draconian alternatives to outright prohibition. These strategies seek to ensure the integrity of the research, guarantee public scrutiny and access, and, of course, to protect human participants . One of Research integrity is further protected by a vigilant stance regarding publication restrictions. Industry partners have a legitimate interest in protecting proprietary information, but this can usually be honored by providing a short period for review prior to submitting a manuscript for publication. No contract or agreement, however, should give the sponsor the right to control publication. Work that requires such control is more appropriately done in industry rather than in academic laboratories. The close attention to publication restrictions is particularly important when a researcher may have a student working on an industry sponsored project. Junior and student scientists working in a research program, who may not have any relationship with a company, must be able to have freedom in pursuing aspects of projects outside the bounds of the research agreement and publishing data in a timely fashion.Translational research that results in the development of a new company presents particular opportunities and challenges. Because of the importance of small companies to economic growth, public research universities often view the number of university-related startups as an index of their contribution to the state economy. More specific institutional interests are created when universities take equity in startups through licensing agreements. Unlike more established firms, start-up companies are often highly dependent on obtaining favorable research outcomes from a particular project. In many cases, prohibition may be the only way to manage the tangle of institutional and individual interests than can result in this situation. Universities may create "firewalls" between the management of equity and researchers . They maRecently, the American Association of Medical Colleges published suggested guidelines for the management of conflict of interest based on input from a number of medical schools . Such doUnrecognized and unmanaged conflicts of financial interest represent major risk factors for programs of translational research. Even when properly disclosed and managed, however, outside financial interests may limit your research activities in a variety of ways, ranging from mild to severe. If nothing else, time and staff resources must be allocated to institutional and extra-institutional review processes. Because approval is required before funding is released or human subjects are enrolled, the pace of research is slowed by outside financial interests.Scientists with personal financial interests in the research will usually find themselves barred from participating in sensitive research activities, especially those involving direct contact with human subjects. This increases the costs of research as additional staff or consultants are hired to do the work that the financially interested party would have otherwise performed. But even this strategy may prove difficult if key staff or alternative expert consultants also have financial interests. There is a tipping point beyond which so many potential team members are financially involved that the interests simply can not be managed and outright prohibition becomes the only option.In the most extreme circumstances it is possible for researchers to research themselves out of a job. Their personal financial interests in a research sponsor may be so great or so complex that their employers are unable to accept further funding from that sponsor. A line of translational research may simply end for a researcher when he or she is barred from carrying the work into the clinical arena as a result of individual or institutional financial interests. This can occur even when the researcher is not actively seeking financial gain. Early basic science work, for example, may lead to an invention that the university decides to patent and license. In most universities the researcher is entitled to a share of whatever revenue is produced. The researcher now has a personal financial interest that requires management and may disqualify her or him from participation in later clinical work designed to translate the basic science into practice. This is an extreme case, of course, but it does happen and it illustrates the often unseen and unintended implications of how financial interests are usually managed.Public concern about personal and institutional interests in research is not going to go away. Nor is the need for industry collaboration in translational research. Indeed the need for collaborations between industry and academia is only going to grow as we move beyond a sequential "bench to bedside" model to acknowledge the benefits of combining clinical and basic biological studies . If finaIndividual investigators should recognize that disclosure of personal financial interests, while perhaps uncomfortable, is vital to continued public confidence in science. They must also balance their wish for personal gain against the additional oversight and management of their activities that will inevitably result. They should recognize that some financial interests are more easily managed than others. Consulting income paid as cash, for example, is much more easily managed than consulting income paid with equity in the company. The latter creates a long-term financial interest and may imply management influence. The understandable desire to "share the wealth" with team members by assisting them in obtaining outside financial interests of their own backfires when it then becomes necessary to remove them from the tasks that they were hired to do in the first place.The decision to create a company in order to further a translational research agenda is appealing, often appropriate, but always more complex than researchers typically appreciate. It is a risky conceit to believe that one's success in obtaining research grants and managing a research team is adequate preparation to launch a business. This is borne out by the fact that technologies licensed to companies founded by faculty inventors are generally less successful than those licensed to companies with non-academic founders . Even whIn spite of a growing entrepreneurial spirit within academia, the culture gap between industry and academia remains large. Industries seeking to partner with academic researchers must understand that universities are not simply laboratories for hire. They should not assume that they will own or control publication of the research they support. Universities are not set up to guarantee the same level of security and secrecy than can be obtained in industry laboratories. The open character of universities is in fact an asset- it is the foundation on which the creative engine rests. Finally it is useful to appreciate that "indirect costs" are real costs for universities. Indeed, even at the full rate, universities subsidize research contracts [13]. Efforts to avoid indirect costs, like efforts to negotiate contracts that contain publication restrictions and overreaching intellectual property clauses, ultimately have the effect of disrupting working relationships and slowing the pace of research. By the same token, industry collaborators should recognize that efforts to "build relationships" with academic researchers and their staff by providing extra financial incentives and benefits are often counterproductive, not only because they fail to buy loyalty, but also because they create unacceptable financial conflicts of interest for academic personnel.Universities, too, have much to learn about managing financial interests and collaborating creatively with their industry partners. Although a consensus regarding institutional conflicts of interest is emerging, universities still have much to do in terms of implementation, particularly with regard to credible external review of clinical research opportunities in which the institution holds a financial interest. Managing or avoiding institutional conflicts of interest will also require a more realistic view of technology transfer opportunities. Although "big hits" do occur, they occur only rarely and revenue from technology transfer operations is typically only a tiny fraction of revenue from sponsored research. Universities run the risk of disrupting their larger research mission by overly aggressive efforts to capture and commercialize intellectual property. Open publication and teaching should always be the most significant "knowledge transfer" functions of a research university.The increasing complexity of translational research also challenges universities to devise new kinds of collaboration with industry. Mankoff and her colleagues, for example, recently proposed the creation of centers that obtain revenue from existing therapies, while simultaneously providing material for biological studies and supporting experimental therapies . Before Individual researchers, industries, and universities have much to learn about the management of financial interests. Given the potential for financial interests to disrupt or even end programs of translational research, we believe that all parties would benefit from greater attention and greater creativity in the management of conflicts of interest. A dialogue must be established between the individual investigator, industry, academic institutions, and the public to define the issues and develop rational solutions. Such a dialogue could be initiated by the development of a focus on such topics at national meetings, by individual organizations whose membership is involved in translational research developing interdisciplinary panels to discuss the issues and attempt to develop guidelines, and by an integration of conflict of interest topics in the training of junior scientists. Solutions should enhance, not impede, translational research.


The compound was also reported to function as a potential anti-cancer drug by rescuing the DNA-binding activity and, consequently, the transcription activation function of mutant p53 protein in mammalian tissue culture cells and in mice.CP-31398 is a small molecule that has been reported to stabilize the DNA-binding core domain of the human tumor suppressor protein p53 We performed a series of gene expression experiments to test the activity of CP-31398 in yeast and in human cell cultures. With these cell-based assays, we were unable to detect any specific stimulation of mutant p53 activity by this compound. Concentrations of CP-31398 that were reported to be active in the published work were highly toxic to the human H1299 lung carcinoma and Saos-2 cell lines in our experiments.in vivo experiments are in agreement with the recently published biochemical analysis of CP-31398 showing that this molecule does not bind p53 as previously claimed, but intercalates into DNA.In our experiments, the small molecule CP-31398 was unable to reactivate mutant p53 protein. The results of our  CIP, a cyclin dependent kinase inhibitor that is a potent inhibitor of the cell cycle. Another class of p53 target genes, of which bax is the most known representative, mediates p53-induced apoptosis. Other p53 target genes prevent the process of angiogenesis [The tumor suppressor protein p53 protects organisms from malignancy by either inducing programmed cell death or by arresting the cell cycle in response to cellular stress. The intracellular concentration of p53 is tightly regulated at the posttranslational level and the protein is very unstable under physiological conditions. Upon stress, p53 is stabilized and can act as a potent transcription factor that activates a plethora of downstream target genes ,2. The pNot surprisingly, p53 is inactivated in a wide variety of human cancers ,3. Most in vitro assay, activity of these compounds was subsequently confirmed in cell culture experiments and in a xenograft tumor mouse model [A report by Foster et al.  generatese model . One of We tested CP-31398 in a yeast cell-based assay and in human tissue culture cells. We could not detect any reactivation of mutant p53 in these cellular assays. Our results are in agreement with, and provide support to the results obtained by Rippin et al. , which iSaccharomyces cerevisiae does not contain p53 homologous proteins. However, it has been demonstrated that p53 expressed in yeast can function as a potent transcriptional activator of artificial genes bearing its specific recognition sequence [CIP1 promoter [HIS3 and lacZ genes , which is a competitive inhibitor of the HIS3 gene product. The p53-dependent activation of this reporter gene is convenient for library screening, while expression of the bacterial lacZ gene allows verification and quantitation of the transcriptional activity of the various p53 forms and putative modulators.The yeast sequence . To testpromoter  was inses figure . The p53lacZ and HIS3 reporter genes, which resulted in increased β-galactosidase activity  reduced reporter gene activity, both in the case of wild type p53 expression and in the case of p5R173A expression. Results of growth assays on selective plates to indirectly measure HIS3 expression paralleled our data from the lacZ experiments (data not shown).We tested the effect of CP-31398 on human p53 activity in our p53-responsive yeast strain. Yeast cells expressing either wild type p53 or the mutant p53R173A were grown in media containing increasing concentrations of CP-31398. Activation of transcription of the p53-dependent reporter gene was assessed by measuring β-galactosidase activity in extracts from these cells figure . No signSince these negative results regarding the lack of expected effects of CP-31398 on p53 could be due to our assay system in yeast, we tested CP-31398 in experiments with human tissue culture cells. We transfected the human p53-null H1299 lung carcinoma cell line that was also used for some of the experiments described by Foster et al.  with plaCell death and decreased reporter gene activity was not dependent on the expression of p53 since treatment with CP-31398 of the same cell lines expressing the unrelated activator GAL4-VP16 co-transfected with the respective reporter construct caused similar toxicity and lower reporter gene activity figure .We next tested whether CP-31398 might have an effect on endogenous wild type p53 in the human cell line HeLa. These cells express wild type p53 protein, but p53 levels are low because of the presence of the viral HPV E6 protein, which targets p53 for degradation . We tranWe subjected extracts from HeLa cells treated with CP-31398 to western blot analysis. The p53 signals correlated with increasing CP-31398 concentrations, whereas the actin control signals did not figure . These rWe also measured changes in p53 levels in HeLa cells after treatment with increasing concentration of daunorubicin, a known anticancer agent that is highly cytotoxic by a number of proposed mechanisms – intercalation into DNA among them . We founin vitro screen, was reported to stabilize the p53 DNA-binding core domain and to reactivate mutant p53 in vivo [We assessed the proposed p53 stabilizing action of CP-31398 in yeast cells and in human cells. CP-31398, a compound isolated in an antibody-based  in vivo . We were in vivo  led to ein vivo and in vitro studies question whether DNA binding itself is regulated at all and suggest that induction of p53 activity primarily occurs at the level of increasing protein concentration within the nucleus [The p53 protein expressed within yeast cells functions as a potent transcriptional activator. Reconstitution of transcriptional activation by p53 in a heterologous, yet cellular system such as a yeast cell should be suitable to assess DNA-binding and transcriptional activation activity regardless of posttranslational modifications and other influences that are inevitable when p53 is studied in the context of its regulatory network in mammalian cells. It has been proposed that such posttranslational modifications like acetylation and phosphorylation activate the latent DNA binding activity of p53 by allosteric mechanisms . However nucleus ,19,20. TIn contrast to the results obtained with the exogenous expression of wild type p53 in yeast cells or with the H1299 and Saos-2 human cells, we observed a strong increase in wild type p53-dependent reporter gene activation in HeLa cells. These cells showed no apparent cell death after treatment with CP-31398. Wang et al.  reportedOur results strongly suggest a classical p53 stabilization through reduced degradation due to genotoxic effects caused by CP-31398. In fact, wild type p53 levels changed quite dramatically in HeLa cells, which are resitant to the apoptotic effects of p53, whereas the other human cell lines did not survive the treatment, probably because they underwent apoptosis in response to CP-31398 . In suppin vivo by CP-31398, a potential anti-cancer compound. Concentrations of CP-31398 that were reported to be active in the published work were highly toxic to human cells in our experiments. The results of our in vivo experiments are in agreement with the recently published biochemical analysis of CP-31398, which shows that this molecule does not bind p53 as previously claimed, but rather intercalates into DNA.In contrast to the results reported by Foster et al. , we did S. cerevisiae strain JPY5 [MATura3-52 his3Δ200 leu2Δ1 trp1Δ63 lys2Δ385). The p53 responsive yeast strain was constructed by integration of the reporter construct described in the result section and in figure HIS3 locus by homologous recombination. The integrating p21 reporter plasmid was linearized with AflII that cuts in the 3' untranslated region (3'UTR) of the S. cerevisiae HIS3 gene.The yeast strain used in our experiments is a derivative of the ain JPY5  (MATura3Yeast genetic techniques and media were as described in . For selHinDIII restriction sites by polymerase chain reaction (PCR). The PCR product was introduced into the HinDIII sites of pGAD424, removing the GAL4AD ORF from pGAD424. All the point mutant p53 variants were generated by assembled PCR with mismatched primer pairs and subsequent cloning into pGAD424 analogous to wild type p53. The yeast reporter plasmid was derived from pDE96  [CIP1 promoter  into the XbaI and SalI sites of the vector backbone. The plasmid WWP-luc is described in [All p53 forms tested in yeast were expressed from the vector pGAD424 (Clontech, Inc). Wild type p53 was subcloned from a mammalian expression vector with primers containing 3, lacZ)  by introribed in .HinDIII p53 fragments from the yeast expression vectors into the GAL4 expression plasmid pSCETV-GAL4(1-93)RV, this resulted in p53 expression under the control of the CMV promoter. The mammalian GAL4 dependent reporter Gal5-luc contains five GAL4 responsive binding sites in front of the luciferase cassette [The mammalian p53 expression plasmids were constructed by subcloning the cassette . Gal4-VPcassette .Yeast β-galactosidase assays in solution using permeabilized cells were performed as described in . ActivitCells were obtained from ATCC (American Type Culture Collection, Manassas, Virginia, USA) and cultured according to the recommendations of ATCC.® transfection reagent (Qiagen, Inc) according to manufacturers recommendations for transfection of all cell lines. Cells for luciferase assays and western blotting were harvested by scraping 48 hours after transfection and subjected to three freeze thaw cycles in 100 mM potassium phosphate pH 7.8 1 mM dithiothreitol buffer. Supernatants were clarified by centrifugation (5 min, 13000 rpm) and resuspended in 100 μl extraction buffer. 10 μl of extract was mixed with 100 μl luciferase assay solution (Promega) and analyzed in a luminometer (EG&G Berthold Lumat LB 9507). β-galactosidase assays were performed according to standard methods using 50 μl of the extract and luciferase units were normalized according to β-galactosidase values. All measurements were performed from at least two independent transfections experiments.We used PolyfectProtein extracts were prepared as described above. Proteins were separated by SDS-PAGE, electrophoretically transferred to nitrocellulose membranes, and western blotting was performed according to standard procedures. Anti-p53 antibody DO-1 (Santa Cruz Biotechnology, Inc) reacts with an amino terminal epitope mapping between amino acid residues 11–25 of wild type and mutant p53. Anti-actin antibody  is an affinity purified goat polyclonal antibody raised against a peptide mapping to the carboxy terminus of human actin.All experimental work was carried out by ST. AB conceived of the study and participated in its design and coordination. Both authors read and approved the final manuscript.


To compare the effects of isocaloric, energy-restricted very low-carbohydrate ketogenic (VLCK) and low-fat (LF) diets on weight loss, body composition, trunk fat mass, and resting energy expenditure (REE) in overweight/obese men and women.Randomized, balanced, two diet period clinical intervention study. Subjects were prescribed two energy-restricted  diets: a VLCK diet with a goal to decrease carbohydrate levels below 10% of energy and induce ketosis and a LF diet with a goal similar to national recommendations (%carbohydrate:fat:protein = ~60:25:15%).2) and 13 premenopausal women (age 34.0 ± 2.4 y, body mass 76.3 ± 3.6 kg, body mass index 29.6 ± 1.1 kg/m2).15 healthy, overweight/obese men , and resting energy expenditure (REE) were determined at baseline and after each diet intervention. Data were analyzed for between group differences considering the first diet phase only and within group differences considering the response to both diets within each person.Actual nutrient intakes from food records during the VLCK (%carbohydrate:fat:protein = ~9:63:28%) and the LF (~58:22:20%) were significantly different. Dietary energy was restricted, but was slightly higher during the VLCK  compared to the LF  diet for men. Both between and within group comparisons revealed a distinct advantage of a VLCK over a LF diet for weight loss, total fat loss, and trunk fat loss for men (despite significantly greater energy intake). The majority of women also responded more favorably to the VLCK diet, especially in terms of trunk fat loss. The greater reduction in trunk fat was not merely due to the greater total fat loss, because the ratio of trunk fat/total fat was also significantly reduced during the VLCK diet in men and women. Absolute REE  was decreased with both diets as expected, but REE expressed relative to body mass , was better maintained on the VLCK diet for men only. Individual responses clearly show the majority of men and women experience greater weight and fat loss on a VLCK than a LF diet.This study shows a clear benefit of a VLCK over LF diet for short-term body weight and fat loss, especially in men. A preferential loss of fat in the trunk region with a VLCK diet is novel and potentially clinically significant but requires further validation. These data provide additional support for the concept of metabolic advantage with diets representing extremes in macronutrient distribution. Recent reports showing a greater weight loss with a free-living very low-carbohydrate ketogenic (VLCK) than a low-fat diet after 3 and 6 months -5 has geAlthough several studies have shown that VLCK diets result in greater reductions in body mass, it remains unclear how these diets affect the composition of weight loss and the distribution of fat loss. Some early reports show that VLCK diets result in preferential loss of fat and preservation of lean body mass -12, suggVolek and Westman  have revA total of twenty-eight healthy volunteers (15 men and 13 women) were recruited by flyers and word-of-mouth. Subjects were between 20 and 55 y, nonsmokers, and greater than 25 percent body fat determined via dual-energy X-ray absorptiometry (DEXA). Subjects went through a thorough screening procedure to ensure they would be committed to completing the study. Exclusion criteria included a body mass >145 kg , post-menopausal women, overt diabetes, cardiovascular, respiratory, gastrointestinal, thyroid or any other metabolic disease, weight change ± 2 kg over the last month, adherence to special diets, use of nutritional supplements , and use of medications to control blood lipids or glucose. The majority of subjects were sedentary and were instructed not to start an exercise program during the study. Those who were active were instructed to maintain the same level of physical activity throughout the study. Baseline characteristics of men and women stratified by diet order are shown in Table 1  of each diet. Men consumed each diet for 50 days whereas women consumed the diets for approximately 30 days in order to control for possible effects of menstrual phase on some of the dependent variables measured in this study [Our primary research question was to compare VLCK and LF diets on weight loss, fat loss, and trunk fat loss. We addressed this in several ways. First, subjects were initially randomly assigned to either a LF or VLCK weight loss diet. Weight loss, body composition (fat mass and lean body mass), trunk fat, and resting energy expenditure (REE) were assessed before and after each diet (Phase I). Because there is often a great deal of variation in response to diet, we decided that a direct comparison of responses to a VLCK and LF diet should be made in the same person. To achieve this aim, we asked subjects to switch to the opposite diet after completion of the first diet period (Phase II), after which the same measurements were assessed (i.e., each subject consumed a VLCK and LF diet). This experimental approach allowed us to compare these two diets in two ways: a is study ,24. All Both experimental diets were designed to be hypoenergetic . Energy levels were assigned to the nearest 200 kcal increment based on REE obtained using indirect calorimetry at the start of the study and appropriate activity factors. Standard diabetic exchange lists were used to ensure a constant energy and macronutrient balance of protein (~20% energy), fat (~25% energy), and carbohydrate (~55% of energy) during the LF diet. The LF diet was also designed to contain <10% saturated fat and <300 mg cholesterol (i.e., a Step-I diet). Foods encouraged during the LF diet included whole grains , fruit/fruit juices, vegetables, vegetable oils, and low-fat dairy and meat products. We developed customized diabetic exchange lists for the VLCK diet period in order to ensure a constant energy and balance of protein (~30% energy), fat (~60% energy), and carbohydrate (~10% of energy) throughout the day. There were no restrictions on the type of fat from saturated and unsaturated sources or cholesterol levels. Foods commonly consumed on the VLCK diet were beef (e.g., hamburger, steak), poultry (e.g., chicken, turkey), fish, oils, various nuts/seeds and peanut butter, moderate amounts of vegetables, salads with low-carbohydrate dressing, moderate amounts of cheese, eggs, protein powder, and water or low-carbohydrate diet drinks. Low-carbohydrate bars and shakes  were provided to subjects during the VLC diet. A daily multi-vitamin/mineral complex that provided micronutrients at levels ≤ 100% of the RDA was given to subjects during both experimental diets.All subjects received extensive initial instruction and follow-up by registered dietitians on how to translate foods/meals into diabetic exchanges. Subjects were also provided with a packet outlining specific lists of appropriate foods, recipes, and sample meal plans that were compatible with their individual preferences for both experimental diets. Subjects received thorough instructions for completing detailed weighed food records during three 7-day periods  for each diet. Food measuring utensils and scales were provided to subjects to ensure accurate reporting of food/beverage amounts consumed. Food diaries were analyzed for energy and macro/micronutrient content (NUTRITIONIST PRO™, Version 1.3, First Databank Inc, The Hearst Corporation, San Bruno, CA). The program had no missing values for the nutrients reported. The database was extensively modified by our group to include new foods and recipes.To ensure that carbohydrates were restricted throughout the VLCK diet, subjects tested their urine daily using reagent strips (Bayer Corporation, Elkhart, IN) at the same time of day and recorded the result on log sheets. The test is specific for acetoacetic acid, which produces a relative color change when it reacts with nitroprusside. We have found this to be a very sensitive indicator of carbohydrate restriction and compliance to a VLCK diet in our prior studies ,21,22,25Body mass and body composition were measured in the morning after a 12 h overnight fast. Body mass was recorded to the nearest 100 g on a digital scale (OHAUS Corp., Florham Park, NJ) with subjects either nude or wearing only underwear. Whole body and regional body composition were assessed using a fan-beam DEXA (Prodigy™, Lunar Corporation, Madison, WI). Regional analysis of the trunk was assessed according to anatomical landmarks by the same technician using computer algorithms (enCORE version 6.00.270). Coefficients of variation for lean body mass, fat mass, and bone mineral content on repeat scans with repositioning on a group of men and women in our laboratory were 0.4, 1.4, and 0.6%, respectively.2) and VCO2 were averaged during the last 20 min for determination of REE [Resting energy expenditure measurements were made by indirect calorimetry  after an overnight fast (>12 h) with subjects resting supine in comfortable thermoneutral conditions. The metabolic cart was calibrated with a standard gas mixture each morning. Subjects were instructed to relax quietly in a dimly lit room without sleeping for 30 min and oxygen consumption (VOn of REE . We asseP ≤ 0.05.Changes in body weight, body composition, and REE between diets were assessed using independent t-tests for between group comparisons (i.e., Phase I responses) and dependent t-tests were used to assess within group comparisons. All statistical analyses were performed with Statistica 5.5 for windows (StatSoft Inc, Tulsa, OK). Significance was set at There were no differences in dietary nutrient intakes between groups at baseline. Subjects complied very well with the given instructions for both diet interventions according to analysis of diets records. During the diet interventions, all dietary nutrients were significantly different between the VLCK and LF diets with the exception of total dietary energy (women only) and alcohol  has been shown to directly inhibit appetite [One argument is that the greater weight loss on y intake , and thiy intake . A reducappetite . Also, tappetite . In thisMetabolic advantage may occur on a VLCK diet due to the demand on protein turnover for gluconeogenesis , greaterAlthough the issue of whether VLCK diets result in greater weight loss compared to LF diets has obvious significance, a primary purpose of this study and an equally important question relates to the composition of weight loss. In a meta-analysis, Garrow and Summerbell  predict A novel and potentially clinically significant finding was a preferential loss of fat in the trunk region with a VLCK diet, which was approximately three-fold greater during the VLCK than the LF diet. Upper body fat carries a greater health risk than fat stored in other regions of the body and thus an effective weight loss approach should consider the regional distribution of fat loss. Proportionally, trunk fat mass comprised less of the total fat mass after the VLCK but not the LF diet. The mechanisms regulating composition of weight loss and distribution of fat loss during VLCK diets remain unclear, but could be mediated in part by changes in hormones such as insulin, leptin, or cortisol that could differentially impact nutrient partitioning.In summary, this study showed greater weight loss and fat loss preferentially from the trunk region in subjects on a closely monitored free-living VLCK diet compared to a LF diet. These diets were prescribed to be energy restricted and isocaloric. The superiority of the VLCK diet over the LF diet was most dramatic for men, but when individual responses were examined, a group of women clearly showed metabolic advantage as well. Indeed, 12/13 women experienced greater fat loss in the trunk region during the VLCK diet compared to the low-fat diet. Such a response is consistent with a metabolic advantage of VLCK diets. The ultimate proof for such a theory will depend on the findings from carefully controlled feeding and metabolic studies that encompass physiological measurements to isolate plausible mechanisms.Table 1. Baseline characteristics of men and women based on their starting diet. Table 2. Daily intakes of dietary energy and nutrients at baseline and during both diets.Click here for file


Warfarin therapy is effective for the prevention of stroke in patients with atrial fibrillation. However, warfarin therapy is underutilized even among ideal anticoagulation candidates. The purpose of this study was to examine the use of warfarin in both inpatients and outpatients with atrial fibrillation within a Veterans Affairs (VA) hospital system.This retrospective medical record review included outpatients and inpatients with atrial fibrillation. The outpatient cohort included all patients seen in the outpatient clinics of the VA Connecticut Healthcare System during June 2000 with a diagnosis of atrial fibrillation. The inpatient cohort included all patients discharged from the VA Connecticut Healthcare System West Haven Medical Center with a diagnosis of atrial fibrillation during October 1999 – March 2000. The outcome measure was the rate of warfarin prescription in patients with atrial fibrillation.A total of 538 outpatients had a diagnosis of atrial fibrillation and 73 of these had a documented contraindication to anticoagulation. Among the 465 eligible outpatients, 455 (98%) were prescribed warfarin. For the inpatients, a total of 212 individual patients were discharged with a diagnosis of atrial fibrillation and 97 were not eligible for warfarin therapy. Among the 115 eligible inpatients, 106 (92%) were discharged on warfarin.Ideal anticoagulation candidates with atrial fibrillation are being prescribed warfarin at very high rates within one VA system, in both the inpatient and outpatient settings; we found warfarin use within our VA was much higher than that observed for Medicare beneficiaries in our state. Warfarin therapy is highly effective for the prevention of ischaemic stroke in atrial fibrillation . DespiteMany of the previous studies about the use of warfarin in atrial fibrillation have focused on the prescription of warfarin on discharge from an acute hospitalization. Since some patients may be discharged from the hospital with a plan to begin warfarin therapy as an outpatient, these prior studies may have underestimated the use of warfarin for patients with atrial fibrillation. The Veterans Affairs (VA) Healthcare System is a useful setting for studying the use of warfarin therapy in both the inpatient and outpatient arenas because the electronic medical record contains prescription medication data as well as the inpatient and outpatient medical records (including progress notes, laboratory data, radiology reports, and other consult reports).The objective of this study was to examine the use of warfarin in both inpatients and outpatients with atrial fibrillation within a VA setting. Specifically, we used the same methodology as the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation , so thatWe assembled two retrospective cohorts of patients to evaluate both inpatients and outpatients with atrial fibrillation. The medical records of both the inpatients and the outpatients were reviewed to confirm the diagnosis of atrial fibrillation, to identify any exclusion criteria, and to determine if patients were being prescribed warfarin.Using the criteria developed by the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation , a physiMedical record abstraction was conducted by two of the authors (KR, SK) using standard definitions. All of the exclusions were reviewed by three of the authors (KR, SK, DMB), a sample of the charts of patients with an exclusion criteria was re-abstracted (DMB), and any disagreements were resolved by consensus.The outpatient cohort included all of the patients seen in the outpatient clinics of the VA Connecticut Health Care System during the month of June 2000 with a diagnosis of atrial fibrillation. Outpatient clinics include both primary care and subspecialty clinics. Some of these clinics have a particular interest in the care of patients with atrial fibrillation such as cardiology and anti-coagulation clinics, however, most of the clinics do not have such a special interest .The inpatient cohort included all patients discharged from the VA Connecticut Health Care System West Haven Campus with any discharge diagnosis of atrial fibrillation (primary or secondary diagnosis) during the period of October 1, 1999 through March 31, 2000. Some of the patients in the inpatient cohort were readmitted during our study period; this report includes data from individual patients for their first hospital stay.The Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation project developed a set of exclusion criteria to identify ideal candidates for warfarin therapy; we used this exclusion criteria for the current study. Patients were excluded if they met one or more of the following: current sinus rhythm; bleeding disorder; endocarditis or pericarditis (within 6 months); seizures; intracranial hemorrhage; intracranial surgery or biopsy; lone atrial fibrillation; dual chamber pacemaker; alcohol or drug abuse; allergy to warfarin; hepatic failure; schizophrenia or active psychotic disorder; comfort care or terminal illness with life expectancy less than 6 months; un-repaired intracranial aneurysm; extensive metastatic cancer; brain cancer; malignant hypertension; peptic ulcer disease; hemorrhage; documentation that the patient refused warfarin therapy; prior complication or allergy related to past use of warfarin; or physician documentation of a rationale for not prescribing warfarin, including risk for bleeding, risk for falls, mental status impairment, liver disease, arthritis requiring non-steroidal anti-inflammatory medications or aspirin, pending surgery or other invasive procedure, terminal illness, patient's inability to obtain necessary blood work, or history of patient's non-adherence to warfarin . PatientFor all of the patients, warfarin prescription was determined from the medical record. Patients receiving warfarin from the VA pharmacy were readily identified from the VA pharmacy component of the medical record. Patients receiving warfarin privately were identified from the progress notes. For inpatients, warfarin prescription was evaluated at the time of discharge from the hospital. For outpatients, warfarin prescription was evaluated at the time of examination of the medical record. Each outpatient medical record was examined in detail to determine the presence or absence of warfarin prescription. For those outpatients patients in whom this determination was difficult or if the data collector had a question about the patient, then the medical record was examined again by three of the authors to determine the presence or absence of warfarin prescription. This study received Institutional Review Board approval.Student's t-tests were used to compare dimensional variables, and Fisher Exact and chi-square tests were used to assess binary variables. Two-sided p-values <0.05 were considered to be statistically significant. Exact binomial 95% confidence interval were calculated for the proportions of patients using warfarin in the inpatient and outpatient cohorts. The SAS System software release 6.12 (Cary, N.C.) was used for data analysis.A total of 538 patients from the VA Connecticut outpatient clinics were identified as having a diagnosis of atrial fibrillation . Of these, 73 patients had one or more contraindication to anticoagulation Table . Of the The ten patients who were not prescribed warfarin did not differ from those who received warfarin with respect to age . Among the ten patients who were not prescribed warfarin: one was a dialysis patient who received his medical care primarily from private physicians outside of the VA, he was eventually placed on warfarin; the medical record of one 90-years-old patient, who also received the majority of his health care from private physicians, indicated that his private physician had elected not to prescribe anticoagulation "because of age"; one patient had a history of alcohol use; and in the remaining 7 patients there was no documentation of a reason for why the warfarin had not been prescribed .A total of 212 individual patients were discharged with a diagnosis of atrial fibrillation . During the admission these 212 patients, 17 died during their hospitalization, 3 were transferred to a facility outside of the VA Connecticut Healthcare System, and 77 had one or more contraindication to anticoagulation. Of the 115 remaining eligible patients, 106  were discharged on warfarin.The nine patients who were not prescribed warfarin did not differ from those who were prescribed warfarin with respect to age . To determine if some of the eligible inpatients who were not discharged on warfarin later received warfarin in the outpatient setting, we examined the outpatient records of the nine inpatients who were not discharged on warfarin: 5 died; 3 no longer receive care at our medical center (and no medication data were available); and for 1 patient, the medical record stated that he was offered warfarin therapy but that he refused to accept it. Similarly, we evaluated a sample of 50 eligible patients who had been discharged on warfarin therapy and examined their warfarin use post-discharge: 7 no longer receive care at our medical center (and no medication data are available); 4 were taken off of warfarin (3 because they were cardioverted as outpatients, and for 1 patient the warfarin was discontinued after an episode of bright red blood per rectum); and 2 patients have died.Among the eligible inpatients 12/115 (10%) had history of prior stroke or transient ischemic attacks; 3/12 (25%) were not prescribed warfarin on discharge. No reasons were documented for why these patients were not given warfarin.There was overlap between the inpatient and outpatient cohorts such that a total of 722 unique patients were identified among the 212 inpatients and the 538 outpatients.We found high rates of warfarin prescription in ideal anticoagulation candidates with atrial fibrillation treated within this VA system. A total of 561 of 580 ideal anticoagulation candidates 97%) were prescribed warfarin: 98% of ideal outpatient anticoagulation candidates and 92% of ideal inpatient anticoagulation candidates. These rates of warfarin use for atrial fibrillation are substantially higher than those reported previously from private sector academic and community hospitals. For example, as part of the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation, medical records were reviewed from a random sample of Medicare beneficiaries, hospitalized during the period 1998–1999, with any discharge diagnosis of atrial fibrillation from each state 7% were p. The excWe report the anticoagulation rates from one VA healthcare system, and our findings may not be generalizable to other VA healthcare systems or to non-VA hospitals. Specifically, our results may not be generalizable to women with atrial fibrillation. Furthermore, given that the out-patient cohort for this study was obtained from a one-month sample, we may have selected patients who are more likely to be seen at an out-patient clinic, and therefore, our findings may also have limited generalizability to atrial fibrillation patients who do not require or who do not have access to regular out-patient clinical care. Although we have found higher rates of warfarin use than most of the previous studies in this area -9, our fSeveral possible factors might account for the high use of warfarin in VA hospitals. First, the actual rates of warfarin prescription may be higher in VA facilities where anticoagulation clinics are well established, the electronic medical record ensures that all services (primary care and consult services) have access to a patient's medical record, the staff has academic affiliations, and the VA culture embraces quality improvement and medical error reduction initiatives. Within the VA Connecticut Health Care System, pharmacist-directed anticoagulation clinics are available at two sites, in West Haven and Newington, Connecticut. Veterans who choose to obtain warfarin from the VA pharmacy are usually followed at one of these two anti-coagulation clinics. Veterans can elect to purchase warfarin from private pharmacies and have their anticoagulation intensity monitored privately . Second, the higher rate may result from data collection differences between studies. Specifically, given the comprehensive VA electronic medical record, VA-based researchers may be able to identify more contraindications for anticoagulation. No quality improvement projects to increase the use of warfarin for patients with atrial fibrillation in the VA Connecticut Healthcare System were initiated during or immediately prior to the study period. A limitation of the current study is that we were unable to determine the specific reasons for why such a high rate of warfarin use was observed.The retrospective nature of this study permitted us to evaluate clinical practices without altering physicians' behavior. However, this retrospective chart review may have limitations. First, we may not have identified those patients who are prescribed warfarin by private practitioners and obtain their warfarin from non-VA pharmacies. This would result in even higher rates of warfarin prescription than we have reported.Second, we assembled our cohort using diagnosis codes for atrial fibrillation and did not use electrocardiographic data. Those patients who had atrial fibrillation by electrocardiogram, but who were not identified as having atrial fibrillation by their clinicians, would not have been included in this study. Because such patients are unlikely to receive warfarin our estimates of warfarin use are higher than would have been observed if we had used electrocardiography to identify atrial fibrillation patients. While many studies of the use of warfarin for atrial fibrillation have also assembled cohorts using diagnosis codes and not electrocardiographic data, the VA-based study by Bradley et al. used electrocardiographic criteria and their findings are similar to ours .Third, some may argue that we excluded patients who would benefit from anticoagulation. For example, patients with atrial fibrillation and numerous other risk factors for stroke might benefit from anticoagulation despite the presence of a contraindication to anticoagulation such as a risk for falls. We chose to use the exclusion criteria developed for the Medicare Health Care Quality Improvement Program's National Stroke Project – Atrial Fibrillation  so that Fourth, this study includes a total of 722 unique patients. Although some studies of warfarin use in patients with atrial fibrillation have included similar numbers of patients  , many stWe conclude that high rates of adherence to treatment guidelines regarding the use of anticoagulation in patients with atrial fibrillation can be achieved. Our experience, and that of Bradley et al., indicates that high rates of warfarin use can be achieved across at least two VA settings .The authors declare that they have no competing interests.All of the authors are employed by the VA Connecticut Healthcare system. Dr. Bravata is supported by an Advanced Research Career Development Award from the Department of Veteran Affairs Health Services Research & Development Service.All of the authors contributed to this manuscript, participated in the research design and manuscript preparation. Two of the authors (SK, KR) conduct the data collection. Three of the authors (DMB, SK, KR) reviewed the data and conducted the analyses.The pre-publication history for this paper can be accessed here: