Because this post is without data, it is certain to be disappointing to many. Nonetheless, investigation begins somewhere - a thought, an idea, a simple observation - and then may blossom into a complete project. Speaking of which, it would be a rather involved project to collect all published data and all of our unpublished observations regarding the genetics of O - overweight vs. obesity.
People who know our work, know that we have examined a lot of genotype-phenotype associations. While overweight/obesity is not our forte in the manner that blood lipids are, suffice to say that we have spent a fair amount of effort looking at genetic factors contributing to BMI. See, for example, this paper on APOA2 and this on PER2, relating chronobiology to obesity. Others have shown that variants in genes FTO, MC4R, LAMA2, SOX6, NEGR1, NPC1, TMEM18 and many others contribute to the risk of obesity.
Our basic observation is - we rarely see a genetic variant that associates with both overweight and obesity. Here, overweight in most populations is defined as a body mass index, or BMI, between 25 and 30 kg/m2 (where the kg reflects body weight in kilograms and m2 is the square of body height in meters). Obese is a BMI above 30. For some populations, these numbers are different due to different basic characteristics of body size.
So, what could this observation imply? My personal opinion is one centered on differences in the metabolic and biochemical (inflammation, adipocytokine) profiles of adipose tissue in the overweight vs. the obese individual. Thus, there may be an entirely different set of genetic variants, in combination with lifestyle choices of diet, exercise, lark-vs-owl chronotype and such, that contribute to the overweight situation compared to those that magnify the condition to one of obesity.
To me, this is an interesting topic and I would greatly appreciate your thoughts and comments.
Tuesday, July 6, 2010
Friday, June 18, 2010
Maps: McDonalds and obesity
A recently published map entitled "The Contiguous United States Visualized by distance to the nearest McDonald's" correlates somewhat but overall not so well with the 2008 map published by the CDC of obesity trends across the United States.
For example, the New England states show close proximity to the restaurants but some of the lowest percentages of obese individuals among the population. In South Carolina, over 30% of the population is considered obese (BMI > 30 kg/m^2) and greater than its neighboring states, but the proximity to McDonald's seems no different than in Georgia, North Carolina and other states in the region. Alabama and Mississippi have some of the highest obesity trends in the USA, but lower prximity to McDonald's. Of note, there is a high concentration of McDonald's franchises in the Chicagoland area because this is the location of the company's headquarters and test kitchen. Lastly, there are high concentrations of the restaurants in the main population centers of Utah (Salt Lake City) and Colorado (Denver), but both these states show rather low population trends of obesity.
Where there is one, you will find many more. In many instances the presence of one type of establishment acts as a seed for others. Thus, the above in essence uses the location of and priximity to McDonald's as a proxy for other fast food restaurants. The National Health and Nutrition Examination Survey (NHANES), with data from 2007 - 2008, can give some information on meals eaten in the home, meals eaten with the family and money spent on meals outside the home. Those data can be found here. I have not analyzed these data nor have seen a published analysis. I'll have to take a look.
For example, the New England states show close proximity to the restaurants but some of the lowest percentages of obese individuals among the population. In South Carolina, over 30% of the population is considered obese (BMI > 30 kg/m^2) and greater than its neighboring states, but the proximity to McDonald's seems no different than in Georgia, North Carolina and other states in the region. Alabama and Mississippi have some of the highest obesity trends in the USA, but lower prximity to McDonald's. Of note, there is a high concentration of McDonald's franchises in the Chicagoland area because this is the location of the company's headquarters and test kitchen. Lastly, there are high concentrations of the restaurants in the main population centers of Utah (Salt Lake City) and Colorado (Denver), but both these states show rather low population trends of obesity.
Where there is one, you will find many more. In many instances the presence of one type of establishment acts as a seed for others. Thus, the above in essence uses the location of and priximity to McDonald's as a proxy for other fast food restaurants. The National Health and Nutrition Examination Survey (NHANES), with data from 2007 - 2008, can give some information on meals eaten in the home, meals eaten with the family and money spent on meals outside the home. Those data can be found here. I have not analyzed these data nor have seen a published analysis. I'll have to take a look.
Wednesday, June 16, 2010
Genome compexity and the number of genes
This month marks the 10th anniversary of (one of) the announcement(s) of the completion of the human genome. Several have taken this occasion to comment on the successes of genome-based biomedical research, or lack thereof.
At "The Loom," Carl Zimmer has a neat graphic depicting estimates of the number of genes present in the human genome. This number has, more or less, steadily fallen as progress in sequencing and then in filling in remaining gaps in the reference genome sequence has moved forward. My comments to that blog entry are:
The reduction in the estimates of the number of protein-coding genes in the human genome parallels our increased understanding of the complexities involved in regulation of gene activity. For example, many types of non-coding RNAs have been described as well as their roles in modulating the information flow from DNA to protein.
At the same time, I believe that the human genome’s reduced “tool kit” (in terms of number of protein-coding genes) shows a certain level of our genome’s sophistication. Think of the many different ways one can use a screwdriver – say to open a can of paint, or its handle as a hammer. In other words, different proteins can join to different networks in a tissue- or developmental-specific manner. In conjunction with this are the alternatively spliced mRNAs, which often lead to different protein isoforms (proteins that are mostly the same, but with perhaps one different functional subdomain). Think of a Phillips vs. regular screwdriver.
Thus, fewer genes has not meant there are fewer protein isoforms nor less complex protein-protein or protein-small molecule interaction networks. To the contrary, there is an increased complexity and that is one reason it has been difficult to define all the players in a particular human affliction such as type 2 diabetes or cancer.
Here, I would like to add a couple of other points:
1. Genetic variation, whether common, moderately rare or even unique to an individual or family, no doubt has a role in adding to the complexity of interactions among the (relatively) small number of genes and small molecules. For example, our research is considering transcription factor binding sites and seed sites for mRNA-mRNA interactions that are created by minor alleles of SNPs.
2. Where much of the above leads is toward differential pathway dynamics. Because the number of protein-coding genes is low while the human organism, its response to a number of different situations (consider how long tobacco smoking or a poor diet must be endured, on average, before life-threatening phenotypes emerge), and its great capacity to develop, survive and even thrive with numerous genetic aberrations are all complex, many of the answers we research seek simply remain to be discovered. We just do not know all the players - proteins, RNAs, genome state (e.g., methylation) and small molecules - and so cannot fully describe a type 2 diabetes pathway in a series of affected tissues, or a given cancer for that matter. Progress is being made - sequencing of the genomes of a tumor and healthy tissue from the same individual have uncovered common pathways and perhaps drug targets. There, however, remains much more to describe before the full potential of human genomics research will be realized.
At "The Loom," Carl Zimmer has a neat graphic depicting estimates of the number of genes present in the human genome. This number has, more or less, steadily fallen as progress in sequencing and then in filling in remaining gaps in the reference genome sequence has moved forward. My comments to that blog entry are:
The reduction in the estimates of the number of protein-coding genes in the human genome parallels our increased understanding of the complexities involved in regulation of gene activity. For example, many types of non-coding RNAs have been described as well as their roles in modulating the information flow from DNA to protein.
At the same time, I believe that the human genome’s reduced “tool kit” (in terms of number of protein-coding genes) shows a certain level of our genome’s sophistication. Think of the many different ways one can use a screwdriver – say to open a can of paint, or its handle as a hammer. In other words, different proteins can join to different networks in a tissue- or developmental-specific manner. In conjunction with this are the alternatively spliced mRNAs, which often lead to different protein isoforms (proteins that are mostly the same, but with perhaps one different functional subdomain). Think of a Phillips vs. regular screwdriver.
Thus, fewer genes has not meant there are fewer protein isoforms nor less complex protein-protein or protein-small molecule interaction networks. To the contrary, there is an increased complexity and that is one reason it has been difficult to define all the players in a particular human affliction such as type 2 diabetes or cancer.
Here, I would like to add a couple of other points:
1. Genetic variation, whether common, moderately rare or even unique to an individual or family, no doubt has a role in adding to the complexity of interactions among the (relatively) small number of genes and small molecules. For example, our research is considering transcription factor binding sites and seed sites for mRNA-mRNA interactions that are created by minor alleles of SNPs.
2. Where much of the above leads is toward differential pathway dynamics. Because the number of protein-coding genes is low while the human organism, its response to a number of different situations (consider how long tobacco smoking or a poor diet must be endured, on average, before life-threatening phenotypes emerge), and its great capacity to develop, survive and even thrive with numerous genetic aberrations are all complex, many of the answers we research seek simply remain to be discovered. We just do not know all the players - proteins, RNAs, genome state (e.g., methylation) and small molecules - and so cannot fully describe a type 2 diabetes pathway in a series of affected tissues, or a given cancer for that matter. Progress is being made - sequencing of the genomes of a tumor and healthy tissue from the same individual have uncovered common pathways and perhaps drug targets. There, however, remains much more to describe before the full potential of human genomics research will be realized.
Thursday, May 27, 2010
Clusterin variants extend links between Alzheimer disease and blood lipids
A recent report in the American Journal of Clinical Nutrition shows that genetic variation in the gene encoding clusterin (CLU or APOJ) showed strong and significant association with plasma fatty acids in an Alaskan Eskimo population. This is relevant to liver function, heart disease and Alzheimer disease (AD). In fact, other characteristics of CLU serve to strengthen the links between AD and blood lipids in a manner similar to APOE (apolipoprotein E).
There are in excess of 100 scientific publications describing different aspects of CLU gene and protein function. Those are not easily summarized here but can be accessed here. Briefly, the protein has no known function and seems to be involved in several basic biological events such as cell death, tumor progression, and neurodegenerative disorders (provided by RefSeq).
I have collected some interesting data on CLU:
Two separate GWAS have shown an association with Alzheimer disease. These are papers by Harold, et al. (2009) and Lambert, et al. (2009) in populations in Europe or of European ancestry. Otowa, et al (2009) showed also by GWAS an association to panic disorder in a Japanese population.
According to SymAtlas, this gene is very highly expressed in human liver.
Our preliminary analysis indicates that CLU is under positive selection in the human lineage (based on amino acid substitution rates). This could indicate responsiveness to some character of the environment. Diet perhaps?
Proteomic analysis of aortas of Apoe -/- mice showed a large increase in Clu protein expression (Wu, Tan, et al. (2007) J. Proteome Res. 6:4728).
Lastly and perhaps most interestingly, CLU is differentially expressed in human individuals with low vs high response to caloric restriction in terms of weight loss (Bouchard Vohl 2010 Am J Clin Nutr 91:309).
There are in excess of 100 scientific publications describing different aspects of CLU gene and protein function. Those are not easily summarized here but can be accessed here. Briefly, the protein has no known function and seems to be involved in several basic biological events such as cell death, tumor progression, and neurodegenerative disorders (provided by RefSeq).
I have collected some interesting data on CLU:
Two separate GWAS have shown an association with Alzheimer disease. These are papers by Harold, et al. (2009) and Lambert, et al. (2009) in populations in Europe or of European ancestry. Otowa, et al (2009) showed also by GWAS an association to panic disorder in a Japanese population.
According to SymAtlas, this gene is very highly expressed in human liver.
Our preliminary analysis indicates that CLU is under positive selection in the human lineage (based on amino acid substitution rates). This could indicate responsiveness to some character of the environment. Diet perhaps?
Proteomic analysis of aortas of Apoe -/- mice showed a large increase in Clu protein expression (Wu, Tan, et al. (2007) J. Proteome Res. 6:4728).
Lastly and perhaps most interestingly, CLU is differentially expressed in human individuals with low vs high response to caloric restriction in terms of weight loss (Bouchard Vohl 2010 Am J Clin Nutr 91:309).
Friday, May 21, 2010
Lipoprotein-associated phospholipase A2 and heart disease-risk
Researchers at UC Davis have discovered that a substance found in blood, which is linked with inflammation, serves as a predictor of coronary artery disease in African-Americans. These results have been published recently in J. Clinical Endocrinology and Metabolism.
The compound in question is lipoprotein-associated phospholipase A2 (Lp-PLA2). This is also known as PLA2G7. While this blood factor is also associated with risk of heart disease in Whites, that association is not always accurate.
A colleague of mine offers that this result is interesting. Publication in JCEM rather than a cardiology journal may be related to the relatively small samples - "336 Caucasians and 224 African-Americans who were about to undergo diagnostic coronary arteriography."
With respect to the differences, obesity prevalence is 51% greater in African Americans than Whites, which could be relevant to inflammation. Alternatively, coronary disease in African Americans may be more advanced than in Whites at the point at which arteriography is performed.
I agree - especially in terms of disparities in health care among groups of ethnic minority in the USA.
----------
Reference
Enkhmaa B, Anuurad E, Zhang W, Pearson TA, Berglund L. (2010) Association of Lp-PLA(2) activity with allele-specific Lp(a) levels in a bi-ethnic population. Atherosclerosis. in press.
The compound in question is lipoprotein-associated phospholipase A2 (Lp-PLA2). This is also known as PLA2G7. While this blood factor is also associated with risk of heart disease in Whites, that association is not always accurate.
A colleague of mine offers that this result is interesting. Publication in JCEM rather than a cardiology journal may be related to the relatively small samples - "336 Caucasians and 224 African-Americans who were about to undergo diagnostic coronary arteriography."
With respect to the differences, obesity prevalence is 51% greater in African Americans than Whites, which could be relevant to inflammation. Alternatively, coronary disease in African Americans may be more advanced than in Whites at the point at which arteriography is performed.
I agree - especially in terms of disparities in health care among groups of ethnic minority in the USA.
----------
Reference
Enkhmaa B, Anuurad E, Zhang W, Pearson TA, Berglund L. (2010) Association of Lp-PLA(2) activity with allele-specific Lp(a) levels in a bi-ethnic population. Atherosclerosis. in press.
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