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Wednesday, June 13, 2012

Longitudinal associations between sleep duration and subsequent weight gain: A systematic review

a Doctoral Program in Population Health and Clinical Outcomes Research, Department of Preventive Medicine, HSC Level 3, Stony Brook University, Stony Brook, NY 11794-8338, USAb Department of Preventive Medicine, Graduate Program in Public Health, HSC Level 3, room 071, Stony Brook University, Stony Brook, NY 11794-8338, USAReceived 31 December 2010. Revised 19 May 2011. Accepted 23 May 2011. Available online 23 July 2011.View full text To systematically examine the relationship between sleep duration and subsequent weight gain in observational longitudinal human studies.

Systematic review of twenty longitudinal studies published from 2004–October 31, 2010.

While adult studies (n = 13) reported inconsistent results on the relationship between sleep duration and subsequent weight gain, studies with children (n = 7) more consistently reported a positive relationship between short sleep duration and weight gain.

While shorter sleep duration consistently predicts subsequent weight gain in children, the relationship is not clear in adults. We discuss possible limitations of the current studies: 1) the diminishing association between short sleep duration on weight gain over time after transition to short sleep, 2) lack of inclusion of appropriate confounding, mediating, and moderating variables (i.e., sleep complaints and sedentary behavior), and 3) measurement issues.

prs.rt("abs_end");Sleep; Obesity; Weight gain; Longitudinal studiesBMI, Body mass index; CDC, Centers for Disease Control and Prevention

Figures and tables from this article:

Fig. 1. Illustration of literature search.

View Within ArticleFig. 2. Patel & Hu Model2 with media use added.

View Within ArticleTable 1. Adult studies.

View table in articleView Within ArticleTable 2. Adult Study Independent Variables.

View table in articleView Within ArticleTable 3. Children Studies.

View table in articleView Within ArticleTable 4. Children Study Independent Variables.

View table in articleView Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

prs.rt('data_end');

View the original article here

Longitudinal associations between sleep duration and subsequent weight gain: A systematic review

a Doctoral Program in Population Health and Clinical Outcomes Research, Department of Preventive Medicine, HSC Level 3, Stony Brook University, Stony Brook, NY 11794-8338, USAb Department of Preventive Medicine, Graduate Program in Public Health, HSC Level 3, room 071, Stony Brook University, Stony Brook, NY 11794-8338, USAReceived 31 December 2010. Revised 19 May 2011. Accepted 23 May 2011. Available online 23 July 2011.View full text To systematically examine the relationship between sleep duration and subsequent weight gain in observational longitudinal human studies.

Systematic review of twenty longitudinal studies published from 2004–October 31, 2010.

While adult studies (n = 13) reported inconsistent results on the relationship between sleep duration and subsequent weight gain, studies with children (n = 7) more consistently reported a positive relationship between short sleep duration and weight gain.

While shorter sleep duration consistently predicts subsequent weight gain in children, the relationship is not clear in adults. We discuss possible limitations of the current studies: 1) the diminishing association between short sleep duration on weight gain over time after transition to short sleep, 2) lack of inclusion of appropriate confounding, mediating, and moderating variables (i.e., sleep complaints and sedentary behavior), and 3) measurement issues.

prs.rt("abs_end");Sleep; Obesity; Weight gain; Longitudinal studiesBMI, Body mass index; CDC, Centers for Disease Control and Prevention

Figures and tables from this article:

Fig. 1. Illustration of literature search.

View Within ArticleFig. 2. Patel & Hu Model2 with media use added.

View Within ArticleTable 1. Adult studies.

View table in articleView Within ArticleTable 2. Adult Study Independent Variables.

View table in articleView Within ArticleTable 3. Children Studies.

View table in articleView Within ArticleTable 4. Children Study Independent Variables.

View table in articleView Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

prs.rt('data_end');

View the original article here

Efficient Disease Risk Prediction a Long Way Off, Experts Say

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AppId is over the quota

THURSDAY, May 24 (HealthDay News) -- Detailed information about a person's genetic makeup and their environmental risk factors does not significantly change their disease risk prediction, according to the results of a new simulation study.

The researchers, from the Harvard School of Public Health, said that much more research is needed before information on patients' genetic variants could actually help doctors prevent or treat certain conditions.

"Overall, our findings suggest that the potential complexity of genetic and environmental factors related to disease will have to be understood on a much larger scale than initially expected to be useful for risk prediction," study author Hugues Aschard, a research fellow in the epidemiology department, said in a Harvard news release. "The road to efficient genetic risk prediction, if it exists, is likely to be long," he added.

In conducting the study, the investigators examined whether or not disease risk prediction for breast cancer, type 2 diabetes and rheumatoid arthritis would improve if environmental risk factors were considered along with genetic risk. The study authors called this interplay of genetic and environmental factors a "synergistic effect."

The researchers simulated a wide range of possible interactions between environmental risk factors and common genetic risk markers related to the three diseases to determine if this simulation model would improve risk prediction.

For breast cancer, 15 common genetic variations associated with the disease plus certain environmental factors -- such as age at first menstrual period and first birth, and number of close relatives who had breast cancer -- were considered. In examining type 2 diabetes, the researchers looked at 31 genetic variations along with risk factors such as family history, obesity, smoking and physical activity. For rheumatoid arthritis, they considered 31 genetic variations, as well as smoking and breast-feeding.

These disease models, however, showed no significant improvement in risk prediction, and the researchers concluded that with this method, risk prediction sensitivity would improve by no more than 1 percent to 3 percent.

"Statistical models of synergy among genetic markers are not 'game changers' in terms of risk prediction in the general population," said Aschard.

Study senior author Peter Kraft, an associate professor of epidemiology at the Harvard School of Public Health, added: "For most people, your doctor's advice before seeing your genetic test for a particular disease will be exactly the same as after seeing your tests."

The study authors noted that additional research on genetic and environmental interactions can provide important clues about the cause of disease, which may lead to improved prevention and treatment.

The study was published online May 24 and will appear in the June 8 print issue of the American Journal of Human Genetics.

-- Mary Elizabeth Dallas MedicalNewsCopyright © 2012 HealthDay. All rights reserved. SOURCE: Harvard School of Public Health, news release, May 24, 2012



View the original article here

Efficient Disease Risk Prediction a Long Way Off, Experts Say

AppId is over the quota
AppId is over the quota

THURSDAY, May 24 (HealthDay News) -- Detailed information about a person's genetic makeup and their environmental risk factors does not significantly change their disease risk prediction, according to the results of a new simulation study.

The researchers, from the Harvard School of Public Health, said that much more research is needed before information on patients' genetic variants could actually help doctors prevent or treat certain conditions.

"Overall, our findings suggest that the potential complexity of genetic and environmental factors related to disease will have to be understood on a much larger scale than initially expected to be useful for risk prediction," study author Hugues Aschard, a research fellow in the epidemiology department, said in a Harvard news release. "The road to efficient genetic risk prediction, if it exists, is likely to be long," he added.

In conducting the study, the investigators examined whether or not disease risk prediction for breast cancer, type 2 diabetes and rheumatoid arthritis would improve if environmental risk factors were considered along with genetic risk. The study authors called this interplay of genetic and environmental factors a "synergistic effect."

The researchers simulated a wide range of possible interactions between environmental risk factors and common genetic risk markers related to the three diseases to determine if this simulation model would improve risk prediction.

For breast cancer, 15 common genetic variations associated with the disease plus certain environmental factors -- such as age at first menstrual period and first birth, and number of close relatives who had breast cancer -- were considered. In examining type 2 diabetes, the researchers looked at 31 genetic variations along with risk factors such as family history, obesity, smoking and physical activity. For rheumatoid arthritis, they considered 31 genetic variations, as well as smoking and breast-feeding.

These disease models, however, showed no significant improvement in risk prediction, and the researchers concluded that with this method, risk prediction sensitivity would improve by no more than 1 percent to 3 percent.

"Statistical models of synergy among genetic markers are not 'game changers' in terms of risk prediction in the general population," said Aschard.

Study senior author Peter Kraft, an associate professor of epidemiology at the Harvard School of Public Health, added: "For most people, your doctor's advice before seeing your genetic test for a particular disease will be exactly the same as after seeing your tests."

The study authors noted that additional research on genetic and environmental interactions can provide important clues about the cause of disease, which may lead to improved prevention and treatment.

The study was published online May 24 and will appear in the June 8 print issue of the American Journal of Human Genetics.

-- Mary Elizabeth Dallas MedicalNewsCopyright © 2012 HealthDay. All rights reserved. SOURCE: Harvard School of Public Health, news release, May 24, 2012



View the original article here

Prenatal Factors influence Kwashiorkor: Evidence for the Predictive Adaptation Model

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Severe acute malnutrition in childhood manifests as oedematous (kwashiorkor, marasmic kwashiorkor) and non-oedematous (marasmus) syndromes with very different prognoses. Kwashiorkor differs from marasmus in the patterns of protein, amino acid and lipid metabolism when patients are acutely ill as well as after rehabilitation to ideal weight for height. Metabolic patterns among marasmic patients define them as metabolically thrifty, while kwashiorkor patients function as metabolically profligate. Such differences might underlie syndromic presentation and prognosis. However, no fundamental explanation exists for these differences in metabolism, nor clinical pictures, given similar exposures to undernutrition. We hypothesized that different developmental trajectories underlie these clinical-metabolic phenotypes: if so this would be strong evidence in support of predictive adaptation model of developmental plasticity.


We reviewed the records of all children admitted with severe acute malnutrition to the Tropical Metabolism Research Unit Ward of the University Hospital of the West Indies, Kingston, Jamaica during 1962–1992. We used Wellcome criteria to establish the diagnoses of kwashiorkor (n = 391), marasmus (n = 383), and marasmic-kwashiorkor (n = 375). We recorded participants’ birth weights, as determined from maternal recall at the time of admission. Those who developed kwashiorkor had 333 g (95% confidence interval 217 to 449, p<0.001) higher mean birthweight than those who developed marasmus.


These data are consistent with a model suggesting that plastic mechanisms operative in utero induce potential marasmics to develop with a metabolic physiology more able to adapt to postnatal undernutrition than those of higher birthweight. Given the different mortality risks of these different syndromes, this observation is supportive of the predictive adaptive response hypothesis and is the first empirical demonstration of the advantageous effects of such a response in humans. The study has implications for understanding pathways to obesity and its cardio-metabolic co-morbidities in poor countries and for famine intervention programs.

Posted in evolutionary medicine


 

Prenatal Factors influence Kwashiorkor: Evidence for the Predictive Adaptation Model

AppId is over the quota AppId is over the quota

Severe acute malnutrition in childhood manifests as oedematous (kwashiorkor, marasmic kwashiorkor) and non-oedematous (marasmus) syndromes with very different prognoses. Kwashiorkor differs from marasmus in the patterns of protein, amino acid and lipid metabolism when patients are acutely ill as well as after rehabilitation to ideal weight for height. Metabolic patterns among marasmic patients define them as metabolically thrifty, while kwashiorkor patients function as metabolically profligate. Such differences might underlie syndromic presentation and prognosis. However, no fundamental explanation exists for these differences in metabolism, nor clinical pictures, given similar exposures to undernutrition. We hypothesized that different developmental trajectories underlie these clinical-metabolic phenotypes: if so this would be strong evidence in support of predictive adaptation model of developmental plasticity.


We reviewed the records of all children admitted with severe acute malnutrition to the Tropical Metabolism Research Unit Ward of the University Hospital of the West Indies, Kingston, Jamaica during 1962–1992. We used Wellcome criteria to establish the diagnoses of kwashiorkor (n = 391), marasmus (n = 383), and marasmic-kwashiorkor (n = 375). We recorded participants’ birth weights, as determined from maternal recall at the time of admission. Those who developed kwashiorkor had 333 g (95% confidence interval 217 to 449, p<0.001) higher mean birthweight than those who developed marasmus.


These data are consistent with a model suggesting that plastic mechanisms operative in utero induce potential marasmics to develop with a metabolic physiology more able to adapt to postnatal undernutrition than those of higher birthweight. Given the different mortality risks of these different syndromes, this observation is supportive of the predictive adaptive response hypothesis and is the first empirical demonstration of the advantageous effects of such a response in humans. The study has implications for understanding pathways to obesity and its cardio-metabolic co-morbidities in poor countries and for famine intervention programs.

Posted in evolutionary medicine


 

Turning Up The Heat: Immune Brinksmanship In The Acute-phase Response

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Abstract
The acute-phase response (APR) is a systemic response to severe trauma, infection, and cancer, although many of the numerous cytokine-mediated components of the APR are incompletely understood. Some of these components, such as fever, reduced availability of iron and zinc, and nutritional restriction due to anorexia, appear to be stressors capable of causing harm to both the pathogen and the host. We review how the host benefits from differences in susceptibility to stress between pathogens and the host. Pathogens, infected host cells, and neoplastic cells are generally more stressed or vulnerable to additional stress than the host because: a) targeted local inflammation works in synergy with APR stressors; b) proliferation/growth increases vulnerability to stress; c) altered pathogen physiology results in pathogen stress or vulnerability; and d) protective heat shock responses are partially abrogated in pathogens since their responses are utilized by the host to enhance immune responses. Therefore, the host utilizes a coordinated system of endogenous stressors to provide additional levels of defense against pathogens. This model of immune brinksmanship can explain the evolutionary basis for the mutually stressful components of the APR.

Posted in evolutionary medicine


 

Turning Up The Heat: Immune Brinksmanship In The Acute-phase Response

AppId is over the quota AppId is over the quota

Abstract
The acute-phase response (APR) is a systemic response to severe trauma, infection, and cancer, although many of the numerous cytokine-mediated components of the APR are incompletely understood. Some of these components, such as fever, reduced availability of iron and zinc, and nutritional restriction due to anorexia, appear to be stressors capable of causing harm to both the pathogen and the host. We review how the host benefits from differences in susceptibility to stress between pathogens and the host. Pathogens, infected host cells, and neoplastic cells are generally more stressed or vulnerable to additional stress than the host because: a) targeted local inflammation works in synergy with APR stressors; b) proliferation/growth increases vulnerability to stress; c) altered pathogen physiology results in pathogen stress or vulnerability; and d) protective heat shock responses are partially abrogated in pathogens since their responses are utilized by the host to enhance immune responses. Therefore, the host utilizes a coordinated system of endogenous stressors to provide additional levels of defense against pathogens. This model of immune brinksmanship can explain the evolutionary basis for the mutually stressful components of the APR.

Posted in evolutionary medicine


 

The evolution of evolutionary molecular medicine

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This article introduces a special issue of the Journal of Molecular Medicine on Evolutionary Molecular Medicine. The first paragraphs are below.


New technologies have always been the driving forces for major developments in science. Medicine is no exception. New sequencing technologies have enabled us to begin understanding the genomic and molecular origins of life and the reasons for disease; they are also transforming evolutionary biology into a new, precise, molecular science that has enormous promise for advancing medicine and public health [1]. This issue of the Journal of Molecular Medicine has invited papers to discuss this exciting development.


Evolution comes to medicine, genomics comes to evolution Medical doctors are trained to taking a detailed history from their patients, their personal history, a family history (and tree if indicated), and the time course symptoms and laboratory tests. Now we look back into the history of mankind and to the origins of life 3.5 billion years ago to understand why we get sick. The history-taking process has thus been extended from the individual to his phylogenetic ancestors. The transformation of medicine by genomics will eventually be recognized among the most significant in a long history of innovations. The beginnings of modern medicine were made…(see article for more)

Posted in evolutionary medicine


 

The evolution of evolutionary molecular medicine

AppId is over the quota AppId is over the quota

This article introduces a special issue of the Journal of Molecular Medicine on Evolutionary Molecular Medicine. The first paragraphs are below.


New technologies have always been the driving forces for major developments in science. Medicine is no exception. New sequencing technologies have enabled us to begin understanding the genomic and molecular origins of life and the reasons for disease; they are also transforming evolutionary biology into a new, precise, molecular science that has enormous promise for advancing medicine and public health [1]. This issue of the Journal of Molecular Medicine has invited papers to discuss this exciting development.


Evolution comes to medicine, genomics comes to evolution Medical doctors are trained to taking a detailed history from their patients, their personal history, a family history (and tree if indicated), and the time course symptoms and laboratory tests. Now we look back into the history of mankind and to the origins of life 3.5 billion years ago to understand why we get sick. The history-taking process has thus been extended from the individual to his phylogenetic ancestors. The transformation of medicine by genomics will eventually be recognized among the most significant in a long history of innovations. The beginnings of modern medicine were made…(see article for more)

Posted in evolutionary medicine


 

MEDLINE citation counts by year of publication [updated]

MEDLINE consists of filled citations indexed mesh ® (medical subject headings ®).

Years of publication with # Citations # Citations published in U.S.% of the citations published in the US * search are performed in PubMed ® on May 14, 2012.

** From December 2006 citations subset OLDMEDLINE, which have all their original terms of mapped to current mesh found in MEDLINE. Has more citations in PubMed with the earlier date of publication; some OLDMEDLINE status, but some are PubMed status or "as provided by the publisher" status.

*** PubMed covers both dates of print and electronic publications in search of the date of publication; in this connection, the citations may be counted as more than one year. Totals do not include these duplicates.

Comments:
-Any search repeated on later can bring different results (usually the higher numbers, as the NLM may have been processed more filled with citations for many reasons, for example, the delay in receiving or new journal for indexing by going back to volume 1, or data from back issues, such as those that are complex in PubMed, or from other sources).
-Counters are limited to a subset of MEDLINE [sb] PubMed and contain no outside citations. To search PubMed MEDLINE excluding the OLDMEDLINE subset of citations, add not jsubsetom to search.
-The country of publication means the country where the published journal. These data are available for only a subset of the OLDMEDLINE from 1964-65.


View the original article here


This post was made using the Auto Blogging Software from WebMagnates.org This line will not appear when posts are made after activating the software to full version.

MEDLINE citation counts by year of publication [updated]

MEDLINE consists of filled citations indexed mesh ® (medical subject headings ®).

Years of publication with # Citations # Citations published in U.S.% of the citations published in the US * search are performed in PubMed ® on May 14, 2012.

** From December 2006 citations subset OLDMEDLINE, which have all their original terms of mapped to current mesh found in MEDLINE. Has more citations in PubMed with the earlier date of publication; some OLDMEDLINE status, but some are PubMed status or "as provided by the publisher" status.

*** PubMed covers both dates of print and electronic publications in search of the date of publication; in this connection, the citations may be counted as more than one year. Totals do not include these duplicates.

Comments:
-Any search repeated on later can bring different results (usually the higher numbers, as the NLM may have been processed more filled with citations for many reasons, for example, the delay in receiving or new journal for indexing by going back to volume 1, or data from back issues, such as those that are complex in PubMed, or from other sources).
-Counters are limited to a subset of MEDLINE [sb] PubMed and contain no outside citations. To search PubMed MEDLINE excluding the OLDMEDLINE subset of citations, add not jsubsetom to search.
-The country of publication means the country where the published journal. These data are available for only a subset of the OLDMEDLINE from 1964-65.


View the original article here


This post was made using the Auto Blogging Software from WebMagnates.org This line will not appear when posts are made after activating the software to full version.

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