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Primary Care Corner with Geoffrey Modest MD: Microbiome 2

24 Jan, 17 | by EBM

By Dr. Geoffrey Modest

This is the second of two blogs on the microbiome, inspired by a recent review that highlighted several other health-related data besides the non-caloric artificial sweeteners (see Lynch SV. N Engl J Med 2016;375:2369).


  • ​The microbiome is huge, with 9.9 million microbial genes represented, as found from studying 1200 people in the US, China, and Europe. And it has >1000 species of microbes
  • Although the microbiome was previously felt to develop after birth, bacteria are found in the placentas of healthy mothers, in the amniotic fluid of preterm infants, and in meconium. And, the mode of infant delivery does influence postnatal microbial exposure: intravaginal delivery does seem to confer an infant microbiome taxonomically similar to the maternal gut and vaginally microbiota. Also the infant microbiome does become more similar to the adult one with the cessation of breast-feeding, and over the years bacterial diversity and functional capacity expand. The microbiome becomes less diverse in elderly, which could reflect coexisting conditions and age-related declines in immunocompetence.
  • Things that affect the microbiome include sex, age, diet, exposure to antimicrobial agents, changes in stool consistency, PPIs and other meds, travel, malnutrition, exercise (the effect of exercise on the microbiome is pretty clear in mice, not so clear in humans, since it is hard to sort out the effect of exercise vs different diets in those who exercise more). Also, host genetic features, host immune response, xenobiotics (including antibiotics), other drugs, infections, diurnal rhythms (see below), and environmental microbial exposures.
  • Clostridium difficile infections
    • This is probably the most advanced and practicable microbiome application. See for many studies and analyses. However about 90% of patients affected with severe, recurrent antibiotic-resistant C. difficile infections respond to fecal microbial transplants
  • Effects on immunity:
    • There are data that the infant microbiota at one month of age is significantly related to allergy in two-year-old children and to asthma in four-year-old children. Several of the products of the higher risk microbiota are associated with subclinical inflammation, which precedes childhood disease. Also other studies have found that children born by cesarean section, who do have differences in their microbiota, are more likely to develop type I diabetes, celiac disease, asthma, hospitalizations for gastroenteritis, and allergic rhinitis.
  • Obesity/metabolic syndrome/insulin resistance/diabetes
    • There are several studies finding that there are significant differences in the microbiome between obese and lean human subjects, with a decrease in Bacteroidetes and an increase in Firmicutes species in obese individuals. Studies have shown that taking microbiome samples from pairs of identical human twins, one lean and one obese, and placing them into genetically identical baby mice, have found that the mice with the microbiota from the obese twin develops more weight gain and more body fat, along with a less diverse microbiome, than those from the lean twin. Also, interestingly, women in their third trimester of pregnancy have an altered microbiome, which, when transplanted into mice, leads to more obesity, and that pro-obesity microbiome is more efficient in extracting energy from food [one common clinical issue with overweight/obese patients is that they may often eat much less than others but still do not lose weight, which has been shown in several studies, and attributed to their being more efficient in metabolizing foods. But perhaps this is mediated through the microbiome???]
    • Some proteins elaborated by E. coli stimulate glucagon-like peptide-1 (GLP-1) secretion, which could augment glycemic control in diabetics, where this hormone is less active than in nondiabetics. In addition, E. coli can elaborate peptide YY (produced in the ileum in response to feeding), which can activate anoxeretic pathways in the brain, mediating satiety.
  • Atherosclerosis/cerebral artery occlusion
    • There are pretty convincing studies that eating red meat leads to changes in the gut microbiota, which leads to increase production of trimethylamine-N-oxide (TMAO), which is a very strong risk factor for human atherosclerotic disease. And feeding meat to vegetarians does not increase TMAO until there are these microbiota changes from recurrent red meat diets. See blogs listed below for more details. Also, experimental data on mice show that cerebral arterial occlusion leads to 60% less damage in those with microbiota which are sensitive to antibiotics; mice given probiotics have less impairment after spinal cord injury.
  • Cancer
    • In mice, specific gut bacteria (most clearly shown for Bifidobacterium) enhance the efficacy of cancer immunotherapy, delaying melanoma growth. Human data has shown that certain microbiota species (B. Thetaiotaomicron or B. fragilis) can improve the effects of anti-tumor therapy targeting cytotoxic T-lymphocytes-associated antigen 4.
  • Autism
    • There are even some suggestive data that the microbiome may play a role in autism spectrum disorders. MIA mice, a maternal immune activation mouse model, exhibits autistic-like behavior, gut microbiome dysbiosis, increased gut mucosal permeability, and an increase in 4-ethylphenylsulfate (4EPS, a metabolite of gut bacteria). Injection of 4EPS into healthy, normal mice results in anxiety. And, feeding the MIA strain of mice a strain of Bacteroides fragilis normalized these adverse gut changes and decreased behavioral abnormalities, associated with decreasing circulating 4EPS levels. There are other neuropsych issues potentially related to the microbiome: gut bacteria can produce several neurotransmitters (eg norepinephrine, serotonin, dopamine, GABA, acetylcholine), and can change emotional behavior of mice (which seems to be related to central GABA receptor expression).
  • Other diseases with suggestive data of a linkage to microbiome dysbiosis include inflammatory bowel disease, kwashiorkor, juvenile rheumatoid arthritis, and multiple sclerosis. Also, in mice, stress leads to altered microbiota (less Bacteroides and more Clostridia), and in humans undergoing bariatric surgery, there are huge differences in the microbiome by either the Roux-en-Y gastric bypass or vertical banded gastroplasty, and this microbiome transplanted into germ-free mice leads to reduced fat deposition, suggesting that these microbiome changes themselves might play a direct role in decreasing adiposity (see Tremaroli V. Cell Metabolism2015; 22:228)​. And perhaps the changes in the microbiome, through the gut-brain relationship is part of the reason for the documented improvement in memory noted after bariatric surgery.
  • Diurnal rhythms (see Thaiss CA. Cell. 2014; 159: 514): the gut microbiota has diurnal variations that reflect feeding rhythms; humans with jet lag have dysbiosis; this jet lag leads to microbiome changes promoting glucose intolerance and obesity and are transferable to germ-free mice.


  • We should approach these studies on the microbiome with caution: some of the most impressive studies were done in animals in highly controlled conditions, and predictions in humans based on the studies is always fraught. For example, in general the use of probiotics in human adults has not shown as dramatic a response as found in rodents. (Although an interesting study of human neonatal probiotic supplementation in the first month of life was associated with a 60% reduction in the risk of pancreatic islet cell autoimmunity, a precursor to type 1 diabetes, before school-age). In addition, a stool sample may not be an adequate proxy for the microbial content of the entire GI tract. And, most of these studies have focused primarily on bacterial species in the microbiota, not taking into account the many other types of microorganisms found or their complex interactions.
  • One concern I have in general is our tendency towards reductionism. The microbiome appears to be a quite complex organ, composed of many different varieties of organisms which undoubtedly interact with each other in complex ways, and which are influenced by many known and undoubtedly unknown external cues (diet, antibiotic use, etc., etc.). So, for example, simply attempting to manipulate that microbiome through the introduction of one species or another of probiotics (i.e., our usual medical fix) may not deal with the complexity of this situation.
  • There have been a slew of other blogs on the microbiome over the years. See . One particularly interesting finding in one of the blogs was that one of metformin’s major action might be in its effects on the microbiome (see, which also reviews some of the TMAO data.
  • So, although I am pretty convinced of the importance of a healthy microbiome, it does seem to me that the major initiative should be around lifestyle changes overall: a healthy diet (and specifically one which is predominantly vegetarian), adequate exercise, perhaps adequate sleep (would be great to have more data on the effect of sleep patterns overall on the microbiome and if changing those patterns changes the microbiome), and minimizing exposure to unnecessary antibiotics (both in humans and in animals that make it into our food chain).

Primary Care Corner with Geoffrey Modest MD: Sugar Industry’s Role in Creating Policy

7 Oct, 16 | by EBM

By Dr. Geoffrey Modest

The NY Times ran a recent story on the historic role of the sugar industry, in close relationship with academia, in promoting the role of fats/undercutting the role of sugar in the development of coronary artery disease (CAD)

The Details:

  • In 1954, the Sugar Research Foundation (SRF) president stated that there was a chemical connection between fats and atherosclerosis, and that the carbohydrate industries could dramatically increase their market share by getting people to eat less fat. The industry spent $5.3 million (in 2016 dollars) to promote this, along with “that sugar is what keeps every human being alive and with energy to face our daily problems”
  • Since 1957, the British physiologist John Yudkin reported several studies finding that sugar was at least as important as fats in promoting CAD
  • The SRF in 1964 decided to pursue CAD research to see “the weak points there are in the experimentation [of the anti-sugar reports], and replicate the studies with appropriate corrections. Then we can publish the data and refute our detractors”
  • The SRF recruited Frederick Stare, chair of Harvard School of Public Health (HSPH), to join the SRF Advisory Board as an ad-hoc member.
  • There were a few articles in the Annals of Internal Medicine by D Mark Hegsted from HSPH linking sucrose to CAD, corroborating Yudkin, finding that “sucrose must be atherogenic”
  • In 1965, the SRF paid that same Hegsted and another person $48,900 (in 2016 dollars) to write a review article on sugar’s role, under the supervision of Stare, with a goal of undercutting the increasing scientific and public concerns about the role of sugar (the New York Herald Tribune had just run a full-page article about the Annals’ articles by Hegsted and others, highlighting the adverse role of sugar). This review article was delayed by ongoing evidence in the medical literature of sugar’s negative role in development CAD, but ultimately led to a 2-part NEJM review article, including both Hegsted and Stare as authors, which concluded that there was “no doubt” but that the only intervention to decrease CAD was to substitute polyunsaturated fat for saturated fat. The review article did comment on the sugar link but then discounted its role in CAD, through very inconsistent arguments. Although this review did disclose industry funding, it did not mention the SRF funding (it was not till 1984 that NEJM required authors to report financial disclosures/conflicts-of-interest).
  • One point that the review article highlighted and promoted was that it was okay to look just at serum cholesterol levels as a surrogate marker for CAD (at that time, we could only measure the total cholesterol levels), and ignore the often documented role of sugars in raising triglyceride levels.
  • The Sugar Association (the current name for SRF) then went on to undercut the role of sugar in dental caries, ultimately involved in shifting the 1971 emphasis of the National Institute of Dental Research’s National Caries Program to interventions other than restricting sucrose consumption.


  • As we know, as a result of reducing fat consumption as the target of the American Heart Association, various Nutrition societies etc; sugar/carbohydrate consumption has increased dramatically over the past many decades (e.g., witness the proliferation of low-fat foods, which basically substitute carbs for fats), as has the prevalence of obesity and its many unfortunate sequelae (diabetes, etc.). And there are pretty clear connections between sugar intake and CAD, as above, which date back to the 1950s. I did take a course on nutrition and health in the 1970s at the Harvard School of Public Health, when I read a book written by a decorated British military physician using cross-cultural epidemiologic studies to demonstrate a strong association between refined sugars and most chronic diseases at that time, though i cannot remember the author’s name. At that time Frederick Stare was still the head of HSPF, but retired in 1976 at least in part because of his role in the sugar industry: the nutrition dept at that time was openly up-in-arms against him). Hegsted later became the head of nutrition at the US Dept of Agriculture and played a key role in writing the government’s nutrition guidelines. It is pretty striking how the sugar industry was able to take the author of several anti-sugar articles in the Annals and (?? through $$, ??other political influences) was able to get him to write the 2-part NEJM review undercutting the role of sugar in CAD. And I do remember those reviews as being very well-read and oft-cited at the time.
  • The current article also brings up the concerns about using surrogate markers for clinical disease (as many of you know, I have very real concerns about using A1C levels instead of actual clinical outcomes as the measure of the benefit of new diabetes drugs: witness rosiglitazone, and the increasing reports of problems with most of the new meds. see many blogs in In the CAD case above, we did not measure LDL or HDL levels at the time, and we now know that increasing triglyceride levels physiologically track with decreasing HDL levels, and conversely, a low glycemic index diet is pretty consistently associated with increasing HDL and decreasing triglyceride levels. Another faulty surrogate marker…
  • And this article does reinforce the disproportionate role of the most prestigious journals (e.g. NEJM) in informing general clinical practice (and, i wouldn’t be surprised if these types of articles also influence future research done as well)
  • Of course, the above article is limited by what was available in the archives they searched, so the above findings should be interpreted not as “rock solid”, but pretty suggestive
  • But it really does reinforce the concern about the increasing role of industry in designing and performing clinical trials — there has been a dramatic shift from the 1970s, effected by President Reagan, where funding for research began to shift from the public to the private sectors, researchers shifting their affiliations from the largelyscientifically-independent medical schools to private companies (with the often attendant shift in their incomes), to the point that most clinical studies now reported in the literature are industry-funded. Although the disclosure requirements are much better defined, if company-sponsored studies are the only source of data available to clinicians, it is hard to ignore them (and this is reinforced by drug company detailing and direct-to-consumer advertising, etc.). See as well as for several blogs on drug company “shenanigans”, including covering up adverse information about the drugs they are making, the role of direct-to-consumer advertising, drug company unseemly profits, ghost-written scientific articles by drug companies, and drug companies/medical device makers marked inconsistency in reporting negative studies as required by law.


As an addendum, Karen Henley sent me an email about STAT, and its article on conflicts-of-interest:
My comments:

STAT has some great, daily, free emails dealing with Zika (a daily update on the medical, political and funding issues) as well as highlights of pharmaceutical shenanigans (of which there have been way too many over the past few years, most recently with Mylan and the epi-pens).

The STAT article commented on an article in the Indian Journal of Medical Ethics, which is actually an old journal which has been getting more attention over the past few years. They highlighted the rather profound conflicts-of-interest in the elite medical journals, highlighting the twists and turns of New England Journal of Medicine and their publishing an industry-sponsored article on Vioxx (rofecoxib), then refusing to publish the warning about the increasing numbers of patients (tens of thousands) who died from the drug (see the link in the STAT article). This was also exposed by Marcia Angell and others a few years ago in her very pointed concerns about the direction of NEJM, but I think the Indian journal also added a broad view of the issue of conflicts-of-interest –> which I think are a huge problem, pretty omnipresent, and make me very wary about being an early adopter of new medications.

Also, see prior blog , which highlights the huge conflicts of interest with the leaders of academic medical centers, including some near and dear highly-acclaimed academic medical centers (i.e., the Brigham), and with comments by Marcia Angell

Primary Care Corner with Geoffrey Modest MD: Normal BMI/Exercise Lower Cancer Risk

23 Sep, 16 | by EBM

By Dr. Geoffrey Modest

The International Agency for Research on Cancer (IARC) working group just assessed the relationship between overweight/obesity and cancers, finding 8 more cancers associated with obesity (see Lauby-Secretan B. N Engl J Med 201; 375: 794). They relied on over 1000 epidemiological/observational studies to assess this association, since there really are no large randomized clinical intervention trials with long-term follow-up assessing the effects of weight-loss vs maintaining weight to see if there is a difference in cancer incidence.

  • Background, worldwide estimates:
    • In 2014: 640 million adults in 2014 (an increase by a factor of 6 since 1975) were obese
    • In 2013: 110 million children and adolescents (an increase by a factor of 2 since 1980) were obese
    • In 2014: prevalence of obesity was 10.8% among men, 14.9% among women, and 5.0% among children; and globally more people are overweight or obese than are underweight.
    • In 2013: 4.5 million deaths worldwide were caused by overweight and obesity; the obesity-related cancer burden represents up to 9% of the cancer burden among women in North America, Europe, and the Middle East.
    • In 2012: 1 million new cancer cases and 8.2 million cancer-related deaths
  • The 8 new cancer associations:
    • Colon or rectum, RR = 1.3, with positive dose response relationships (e., the more overweight, the higher the risk)
    • Gastric cardia, RR = 1.8, with positive dose response relationships
    • Liver, RR = 1.8, with positive dose response relationships
    • Gallbladder, RR = 1.3, with positive dose response relationships (though in their analysis, comparing the top vs bottom decile of activity, this achieved a P=0.06 only)
    • Pancreas, RR = 1.5, with positive dose response relationships
    • Kidney, RR = 1.8, with positive dose response relationships
    • Esophageal adenocarcinoma, RR=8, with positive dose response relationships
  • In general the relative risks increased from 1.2 to 1.5 for overweight and from 1.5 to 1.8 for obesity for cancers of the colon, gastric cardia, liver, gallbladder, pancreas and kidney
  • These results were consistent in different geographic regions, and were similar for men and women
  • The previously known cancers with associations:
    • Breast cancer in postmenopausal women, RR of 1.1 per 5 BMI units, esp in estrogen-receptor positive tumors
    • Endometrial cancer: RR=1.5 for overweight,5 for BMI 30-35, 4.5 for BMI 35-40, and 7.1 for BMI>40
    • Ovarian cancer (epithelial): RR=1.1
    • Multiple myeloma, RR=1.2 for overweight, 1.2 for BMI 30-35, 1.5 for BMI 35-40, and 1.5 for BMI>40
    • Meningioma, RR = 1.5
    • Thyroid, RR=1.1
  • And there is some limited evidence of an obesity association with male breast cancer, fatal prostate cancer, and diffuse large B-cell lymphoma
  • For breast cancer, there was an association between increased BMI at the time of diagnosis and reduced survival
  • In terms of weight loss: the quality of the data are not great, but there are some suggestions that weight loss (including by bariatric surgery) may reduce the breast and endometrial cancer risks.
  • As supporting evidence:
    • Animal data (different animals) confirm an association between obesity and cancer at many different sites
    • Animal data also supports the effect of limiting weight gain vs food ad libitum for some cancers (mammary gland, colon, liver, pancreas, skin, pituitary) but inverse relationship with others (prostate, lymphoma, leukemia)


  • As with all of these observational studies, association does not imply causality. For example, is it the obesity itself which is associated with cancer? Or, are there specific things that obese people do differently than normal weight ones (e.g., eating certain oncogenic foods? not exercising enough? living in more toxic environments?)
  • The above results were similar for BMI and waist circumference when that data was available (waist circumference has a higher correlation with visceral obesity, which is the metabolically more active obesity associated with metabolic syndrome, increased inflammatory markers, )
  • In many of the above associations, the associations persisted in studies using mendelian randomization (see , which describes mendelian randomization and some of its limitations, but overall it is a process that assesses known genetic markers for a disease to help assess causality (to differentiate in this case whether the causality is if those genetically predisposed to obesity are more likely to get the cancer, not vice-versa or as independent phenomena)
  • Possible mechanisms: increased body fat is associated with multiple metabolic and endocrine changes (sex hormones, insulin and insulin-like growth factor, inflammation), which could promote tumor initiation and/or growth
  • It is important to keep in mind the strength of the associations above. Typically, in observational studies, a relative risk of under 1.5-2 often does not pan out as being really significant, despite the fact that it can be really significant in randomized controlled trials. So, a bit of a caution in over interpreting the above results for many of the cancers. The dose-response relationship does add some support the associations, however.


Another recent article came out on the relationship between physical activity and cancer (see doi:10.1001/jamainternmed.2016), finding that leisure-time physical activity was associated with lower risk of many cancers. Details:

  • 12 prospective US and European cohorts with self-reported physical activity from 1987-2004, including 1.44 million participants, looking at 26 different cancers
  • Mean age 59 (19-98), 57% female, mean follow-up 11 years (7-21), mean BMI 26, 54% ever-smokers
  • 186,932 cancers diagnosed
  • Leisure-time activity, defined as high if 6 or more METs. Median activity was 8 MET-h/week (equivalent to 150 minutes of moderate-intensity exercise, e.g. walking)
  • Results:
    • High vs low leisure-time activity was associated with lower risk of:
      • Esophageal adenocarcinoma (HR 0.58, i.e., 42% decreased risk)
      • Liver cancer (HR 0.73)
      • Lung cancer (HR 0.74)
      • Gastric cardia (HR 0.78)
      • Endometrial (HR 0.79)
      • Myeloid leukemia (HR 0.80)
      • Myeloma (HR 0.83)
      • Colon (HR 0.84)
      • Head and neck (HR 0.85)
      • Rectal (HR 0.87)
      • Bladder (HR 0.87)
      • Breast (HR 0.90)
    • In aggregate, there was a 7% lower risk of total cancer in those performing higher levels of physical activity [HR 0.93 (0.90-0.95)]
    • Adjusting for BMI (nullied the relationship above for liver, gastric cardia and endometrium) but otherwise only a small attenuation of the risk, on the order of 5-11% of the HR’s. Smoking status affected lung cancer but not the others
    • Some cancers were associated with more activity
      • Melanoma (HR 1.27)
      • Prostate cancer (HR 1.05)


  • One striking finding is the overlap of cancers which seem to be affected by both BMI and exercise, reinforcing that these lifestyle/environmental issues seem to be particularly important.
  • But, one needs to be particularly careful in meta-analyses in general and huge ones in particular: it is very hard to get granular data over time (what is “ever-smokers”? a few cigarettes at the beginning of the study? stopping smoking 2 packs/day near the end of the study?); how often did they track information, such as changes in BMI or physical activity over time? Was it just a one-shot assessment at the beginning of the study? And how did they then quantitate these typically changing variables over such a long follow-up?  This data acquisition is done differently in different studies, so how is this all put together mathematically? It is pretty striking the range of ages (19-98) and years of follow-up (7-21) in the individual studies, suggesting they were pretty heterogeneous. And, in general, the people in this large meta-analysis were reasonably lean (BMI=26), so it may be difficult to really control for BMI in their data (they divided the patients into BMI <25 vs >25, but did not have the BMI spread of the IARC study). This limits the interpretation of their finding in this exercise study that 3 of the highest risk cancers in the AIRC study for BMI had no relationship to exercise when controlling for BMI.
  • They only looked at leisure-time physical activity. It seems pretty intuitive that people with very physical jobs do have more exercise at work than those with office jobs (i.e., many of my patients are on their feet all day, walking around cleaning office buildings, etc. And it seems they should get some “exercise” credit for that.) There are not great studies which have looked at occupationally-related exercise, probably because it is hard to measure on an individual basis: even those with the same job category may have very different amounts of exercise if they clean a small office vs a large automated office building)
  • One concern is that the burden of obesity and lack of exercise is increasing, especially with migration to larger cities and with increasing Westernization around the world
  • But one potentially positive finding is that exercise is associated with lower cancer risk independent of BMI for many cancers (with above caveat): it is much easier to help people do exercise than to achieve sustained weight loss (see ). And there are reasonable postulated mechanisms by which exercise could decrease cancer: hormonal changes (sex steroids, insulin and insulin-like growth factos, adipokines; similar to the BMI mechanisms postulated above) as well as nonhormonal (decrease inflammation, improve immune function/surveillance, decrease oxidative stress, and increase GI transit time, the latter of which could decrease colon cancer incidence)
  • There are still many questions, even if one accepts the conclusions of these studies
    • Does instituting a more aggressive exercise program lead to decreased cancer (i.e., an intervention study would provide stronger conclusions than an observational study)
    • And how much exercise works? Is there a threshold? Is it different for different cancers? (this might be important in different parts of the world where different cancers predominate)
  • But, the real bottom line is that there have been many studies over the years showing that lifestyle/environment are associated with pretty much all of the chronic diseases in the world. The above studies simply reinforce the association with cancer. And it offers us as clinicians yet another way to talk with patients about the importance of a healthy lifestyle. The association with cancer may be a particularly useful tool in motivating patients to avoid progressing to a less healthy lifestyle over time or instituting changes to improve their lifestyle (for better or worse, patients given equal mortality scenarios from cancer or heart disease, for example, are more afraid of the cancer one…it just sounds scarier)

Primary Care Corner with Geoffrey Modest MD: Non-alcoholic Fatty Liver Disease 1

7 Sep, 16 | by EBM

By Dr. Geoffrey Modest

There have been several articles recently on non-alcoholic fatty liver disease (NAFLD) in a recent special issue of the journal Digestive Diseases and Sciences, as well as a recent release of NAFLD clinical management guidelines by the European Assn for the study of NAFLD. Since NAFLD is so common throughout the world, since it is amenable to lifestyle interventions, and since there was so much interesting info on NAFLD but so many unresolved questions, I will devote 3 blogs to this:

  1. Natural history of NAFLD
  2. Review of therapies, with more detail on a couple of topics (e.g. the role of the microbiome and of specific dietary components, esp. fructose)
  3. A review of the EASL guidelines for NAFLD


NAFLD Natural history (see Goh G. Dig Dis Sci 2016; 61: 1226)

  • NAFLD (nonalcoholic fatty liver disease) was first defined only in 1980, which is rather surprising given that it is: incredibly common (in the US its prevalence has increased from 5.5 to 11% between 1988 and 2008), the most common liver disease in the world with estimated prevalence of 20-30%, the most rapidly rising indication for liver transplantation, and likely to be the number one indication for liver transplantation by 2020. The fact that the prevalence is increasing, however, is not so surprising, since NAFLD is so closely related to insulin resistance, obesity and metabolic syndrome. It is important to keep in mind that with all of these statistics, there are real issues of differing definitions and ascertainment bias overall. For example, in one study the prevalence of ultrasound-diagnosed hepatic steatosis with normal liver enzymes was 16.4%, but the prevalence of hepatic steatosis with abnormal LFTs was 3.1% (i.e., determining NAFLD by ultrasound vs abnormal LFTs as the NAFLD yields very different prevalences).
  • NAFLD is formally defined as the accumulation of >5% fat in the liver, not attributable to alcohol, drugs or other secondary causes, and represents the spectrum from NAFL (non-alcoholic fatty liver, or steatosis) to necroinflammatory changes of NASH (non-alcoholic steatohepatitis), advanced fibrosis, cirrhosis and HCC (hepatocellular carcinoma). The biopsy may be indistinguishable from alcoholic steatohepatitis.
  • Long-term prognosis: the most common causes of death are cardiovascular (the number one cause: see Mantovani A. Dig Dis Sci 2016; 61: 1246), malignancy and liver disease. And it seems likely that a large % of those diagnosed with “cryptogenic cirrhosis” actually have NAFLD. The extent of these outcomes varies in different studies, from not much of an increase to being equivalent to hepatitis C. However, it seems that most studies find the mortality increases significantly as one goes from NAFL (steatosis only) to NASH, and seems overall to be much worse with more severe stages of NASH (e.g. one study with 18.5 years of follow-up found that liver-related mortality increased from 3% in non-NASH to 18% in those with NASH). A meta-analysis found that mortality was not much higher in those with simple steatosis vs the general population, but in those with NASH there was still an 81% increase in overall mortality  and 471% increase in liver-related mortality.
  • Progression of NAFLD: in a meta-analysis of 133 patients with simple steatosis, 39% developed progressive fibrosis, 53% remained stable and 8% improved. This translates to an average annual progression rate of 1 fibrosis stage over 14 years. Also, NASH progresses: a study of 221 patients found that 37% had progressive fibrosis on repeat biopsy over 5 years. Overall, it seems that in patients with NASH and no fibrosis, there is a 1 stage progression of fibrosis over 7 years. BUT there are a small group who have much more rapid progression.
  • 10-25% of patients with NASH progress to advanced fibrosis/cirrhosis. In a small Australian study comparing patients with NASH cirrhosis to those with hepatitis C, about 40% of each group developed liver-related complications over 7 years, though other studies have found lower mortality rates than hep C (but with more cardiovascular mortality). There are some data suggesting that higher serum ferritin levels (> 1.5x upper limit of normal) is associated with a higher likelihood of NASH and more advanced fibrosis.
  • BUT, one counterintuitive point that makes it difficult to rely simply on noninvasive testing: there is no relationship with the height of serum transaminases and the degree of hepatic inflammation or fibrosis. And, there can be significant hepatic inflammation without increased transaminases.


  • These data make it very difficult to figure out what is best to do with patients who have increased ALT levels (really common). In general, we screen for other causes of increased LFTs, especially for viral hepatitides (esp hep B and C, but I also check to make sure either immune to hep A naturally or by immunization, and immunize against hep B if nonimmune), autoimmune hepatitis (e.g. ANA, anti-smooth muscle, anti-liver-kidney microsomal antibody-1), iron overload (iron, TIBC, ferritin). And get an ultrasound.
  • One concern with NAFLD as an entity is that it really is a diagnosis of exclusion. That makes it more likely that NAFLD is not a single condition: there could well be unknown causes of fatty liver that are lumped together in “NAFLD”; there seem to be a variety of predisposing conditions (though insulin resistance is the most common, there are many people with NAFLD without that); and there are such variable prognoses (some never progress, some regress, some advance, and some advance very quickly).
  • Should we be screening for NAFLD (not currently recommended)? From the NHANES data, its incidence has increased from 5.5% around 1990 to 11% in 2008, and the % of cases of chronic liver disease attributable to NAFLD has increased from 47% to 75% during this time. (i.e., much more common than for other causes, which we do screen for). And there are interventions that help (see later blog). I personally do screen with LFTs in obese kids and all adults, and (not surprisingly, given the frequency of NAFLD, have found many cases, much more than hepatitis C). And I have had some success in convincing patients to lose weight and do more exercise based on these results. But should we be doing more inclusive screening with an ultrasound, to pick up the many cases where the LFTs are normal (and we know that LFT changes can be transient, and may never be found, despite the possibility of significant hepatic inflammation)????? I am not doing that, but it certainly seems reasonable….
  • What is the best way to follow those with steatosis on ultrasound or raised ALT levels suggestive of NAFLD? Should we be following ultrasounds routinely to look for progression? And if we do serial ultrasounds, how often? Biopsy is currently considered the only method to really see if there is active inflammation or fibrosis. Hopefully in the not-so-distant-future we will have reliable non-invasive tests: e.g. transient elastography which might helpdifferentiate NAFL from NASH and also track its progression (some small studies found it was able to differentiate degrees of steatosis, and another finding a stepwise increase in liver stiffness that correlated with the degree of biopsy-proven hepatic fibrosis). And there are potential serum markers: e.g. FIB-4 (a calculation involving age, AST, ALT and platelet count), which correlates well with the degree of hepatic fibrosis; and other markers look promising (e.g., cytokeratin-18, which reflects hepatocyte apoptosis). More studies are needed on these, but there are glimmers of hope that we can avoid biopsies.
  • And if we decide to do a liver biopsy, how often should they be done, in light of the pretty high progression rate to NASH and fibrosis? What about trying to pick up those who are rapid progressors?
  • One other consideration: since NAFLD is so common and has an attendant increased risk of cardiovascular disease, I have a low threshold to prescribe a statin, especially in middle-aged and older patients. See blog which reviews several of the studies, in patients with NAFLD, hepatitis B and C, finding clinical benefit. In NAFLD, there are some data showing that statins both reduce cardiovascular mortality, but also have some benefit in improving NAFLD histology or its future complications (advanced fibrosis, etc.), as well (also see Mantovani A. Dig Dis Sci 2016; 61: 1246 for more info).

Primary Care Corner with Geoffrey Modest MD: Weight Loss and Resting Metabolic Rate

17 Aug, 16 | by EBM

By Dr. Geoffrey Modest

One of the hardest tasks for us and our patients is maintaining weight loss in those who are overweight and obese. A recent NIH study looked at this issue, finding that people who had lost a lot of weight had long-term “metabolic adaptation” leading to a significant lowering of resting metabolic rate (RMR) and much less overall energy expenditure (see doi:10.1002/oby.21538 ). This study looked at 14 of the 16 “Biggest Loser” competitors from this televised weight-loss competition.


  • Baseline: median age 35, 6 men/8 women, weight 149 kg, BMI 49.5
  • At the end of the competition (30 weeks), through an aggressive program of diet and exercise, the mean weight loss was 58.3 kg, BMI deceased to 30, and the RMR decreased 610 kcal/day below baseline (this decrease in RMR was expected, as per a multitude of prior studies).
  • The following hormone levels improved dramatically after weight loss (at 30 weeks): insulin, C-peptide, triglycerides, HDL, adiponectin, T3, leptin, and the calculated HOMA-IR (which correlates with insulin resistance)
  • This weight loss was primarily from fat mass but with relative preservation of fat-free mass [likely from the intensive exercise training]
  • After 6 years:
    • Participants regained a mean of 41.0 kg (though wide variation: 1 person did not regain weight, though 5 were within 1% of their baseline weight or above), 80% of the weight gain was from fat However, 6 years later the RMR remained 704 kcal/d below baseline (actually non-significantly worse than the RMR after the remarkable initial weight loss), and metabolic adaptation was down 499 kcal/d [I believe this is basically RMR corrected for fat-free mass, but this was never clearly stated in the study]
  • The metabolic adaptation at the end of the competition (30 weeks) correlated with the amount of weight loss, did not correlate with the ultimate weight regained, but it did improve some in those who regained the most weight (and metabolic adaptationdid not improve at all in those who maintained the weight loss, with a dose-response curve)


  • Background: from the initial studies, the physiologic phenomenon of metabolic adaptation (also called “adaptive thermogenesis”, or AT) reflects the evolutionary imperative that the body readjusts to maximize efficiency in times of starvation by lowering energy expenditure. AT has been found to be independent of changes in fat-free mass and takes weeks to develop; in earlier studies it seemed to be independent of the magnitude of weight loss after reaching the peak of a 10% weight loss threshold (see Muller MJ. Obesity 2013; 21: 218). Adaptive thermogenesis is associated with a variety of changes related to decreases in resting and total energy expenditure, including decreased sympathetic nervous system activity, T3, and leptin. There are some early suggestions from animal studies that giving exogenous leptin restores at least some of the decreased RMR
  • Many studies have shown that in the setting of starvation, the body in fact lowers its metabolism to conserve energy and weight. One perhaps interesting issue is the role of genetics (I have seen nothing to answer these questions in searching around on this). For example, is there a difference in the metabolic adaptation/changes in RMR in those who are overweight but coming from families with lots of obesity vs those where there is not an apparent genetic burden for obesity? Overall, obese individuals are more likely to have lower RMR from several studies, but are those who are lean at baseline but have a lower RMR more likely to develop obesity than those with a higher RMR? Not so clear. At least some studies suggest that eating leads to thermogenesis (i.e., it might be that even in those with low metabolic rates, eating increases their metabolic rates enough that they do not become obese; and, therefore, perhaps there is no causal effect of low metabolic rate and eventual obesity). In fact some small studies noting lower RMR in obese women found that the RMR was actually higher in obese women if one corrected for fat-free body mass (see Hoffmans M. Int J Obesity 1979;3(2):111). A bit of a bag of worms….
  • Interestingly, bariatric surgery does not create the same issue of metabolic adaptation as does starvation: with surgery there seems to be an effective reset of the body’s weight set-point within a year of bariatric surgery, for unknown reasons (see Hao Z. Obesity (Silver Spring) 2016; 24: 654)
  • There were a couple of interesting studies (both from the same group) suggesting that weight loss by a low glycemic diet causes less decrease in RMR:
    • One found thatresting energy expenditure in overweight/obese young adults decreased much less with a low glycemic index diet (96 kcal/d, or 5.9%) vs a low fat diet (176 kcal/d, or 10.6%) [Those on low GI diet also had less hunger, improved insulin resistance, triglycerides, CRP and blood pressure]. See Pereira MA. JAMA 2004; 292: 2482
    • This was confirmed in another study (see EbbelingJAMA 2012; 307: 2627), finding that isocaloric feeding led to decreases in resting energy expenditure of 205 kcal/d in a low fat diet, 166 kcal/d in a low-glycemic index diet and 138 kcal/d in a very low-carbohydrate diet. Total energy expenditure decreased 423, 297 and 97 kcal/d respectively.
  • But, the bottom line from this study: at least in the case of those with severe morbid obesity (median BMI of 50), losing weight had the anticipated decrease in energy expenditure, but even 6 years later, this lowering of RMR through metabolic adaptation did not revert to their baseline. What does that mean? For one thing, it reinforces what many patients and clinicians know: losing weight is really hard to do, and if weight is lost, it is really really hard to keep it off in the long-term. Which doesn’t mean that we all should give up. Just that this understanding is really important, and we all (including patients) should really try to avoid the blame-game (some variant of “if you really want to lose weight, you can” morphing to “if you don’t lose weight, it reflects your lack of will-power, tenacity, ability to get things accomplished….”). and, my suggestion is that, given the remarkable difficulty in losing weight, those so motivated need lots of hand-holding: seeing them frequently to discuss how they are doing and collectively deciding on adjustments, encouraging lots of exercise to help maintain the weight loss, and overall collectively setting often small goals and slowly ramping them up as the patient is capable of doing (understanding that there will be bumps along the way). My guess is that this approach works much better than: “great, you understand what you need to do to lose weight, come back in 3-6 months”.

Primary Care Corner with Geoffrey Modest MD: BMI, Height and Socioeconomic Status

28 Apr, 16 | by EBM

By Dr. Geoffrey Modest

One clear limitation of observational epidemiologic studies is that they are only capable of showing an association, not causality. One way to assess possible causality is through a technique called “mendelian randomization”.  There was a recent study looking at the association between BMI or height and socioeconomic status (see BMJ 2016;352:i582​). In this study, one might suspect that the well-known association between BMI and socioeconomic status (SES) could be bidirectional (high BMI might predispose a person, through conscious or unconscious bias, to be less likely to be hired or make much money, hence the lower SES; and, low SES might predispose people to a higher BMI since they are less likely to be able to afford or even have access to more expensive vegetables and fruit, have less ability to exercise because of unsafe neighborhoods, etc.). Mendelian randomization is a process where one looks at genetic differences (which are randomly allocated at conception) as an unconfounded proxy for the risk factor (in this case, BMI and height). So, since the outcome of SES cannot influence the underlying genetic variation which is set at birth, by looking at the genetic code itself instead of the complexities of BMI or height (which may reflect the genes but also be confounded by the social environment), one might see that, for example, the genes associated with increased BMI or height really are the potential drivers of low SES. The ability to do this genetic testing is now greatly enhanced because of the widespread availability of cheap genome-wide studies, currently identifying 10’s of genetic variants associated with BMI and 100’s associated with height.

Details of this study:

  • 119,669 British men and women, aged 37 to 73 in a UK Biobank that had recruited 500,000 people from across the UK from 2006-2010, and included demographics, health status, lifestyle, blood pressure and stored samples of urine, sputum and blood. This analysis was limited to white people with “British ancestry” because those from other ethnic groups did not have enough representation to be adequately powered for a statistical association.
  • Main outcome: the relationship between BMI or height and the following 5 markers of SES: age completed full-time education, level of education, job class, annual household income, and Townsend deprivation index (a composite of unemployment, non-car ownership, non-home ownership, and household overcrowding).


  • Overall analyses:
    • Pretty much all of the five SES markers were very highly correlated with coronary artery disease, hypertension, type 2 diabetes and “long illness”
    • Tall stature was related inversely to all of the above SES indicators individually, equivalently for men and women; by genetic analysis, these associations were only significant for men
    • Higher BMI was related directly to all of the above SES indicators individually, equivalently for men and women​; by genetic analysis, was only significant for women, and only for the annual household income and the Townsend deprivation index
  • Genetic analysis found that these relationships were partly causal:
    • A 1-SD (6.3cm) taller stature “caused” :
      • 06 year older age of completing full-time education (0.02-0.09, p=0.01)
      • 12 times higher odds of working in a skilled profession (1.07-1.18, p<0.001)
      • $1615 higher annual income ($970-2260, p<0.001), with stronger association in men
    • A 1-SD higher BMI (4.6 kg/m2) “caused”:
      • $4200 lower annual income ($2400-6000, p<0.001) in women
      • 10 higher level of deprivation (0.04-0.16, p=0.001), but only in women

So, what are the implications of this study:

  • We know from many studies that lower SES is association with poorer health and shorter life expectancy
  • And, we know that adult height and BMI are associated with SES, with many studies in richer countries finding that taller people and those with lower BMI have higher SES and better health
  • I cited a previous mendelian randomization trial for alcohol and cardiovascular events, assessing the often-found association between drinking more and less CAD. Mendelian randomization found the opposite: those drinkers with a genetic variant for alcohol dehydrogenase had lower alcohol consumption and lower levels of CAD risk factors (blood pressure, inflammatory markers, etc.) compared to those without this allele, and that the lower risk of CAD events was present in those with this allele who actually were lower alcohol consumers (see BMJ 2014; 349:g4164 4 doi: 10.1136/bmj.g4164,as well as the editorial in Addiction: doi:10.1111/add.12828​). So this suggests that alcohol consumption by itself is not cardioprotective, but there was likely some confounding or bias inherent in the observational studies. And this bias was the cause of the prior held association of increased alcohol consumption and cardioprotection.
  • The current study did find that the observational associations between BMI and height with SES were in fact significantly stronger than the genetic analyses, suggesting that genetics played some role, but actually only a small part in the association (i.e., a significant part of the association between BMI/height and SES may well be social and possibly bidirectional)
  • The authors do acknowledge several limitations: even if there is a genetic predisposition to a higher BMI, do people with higher BMI move to areas where they are more comfortable? (Areas with others having higher BMI, and perhaps these are areas of lower SES, leading to their children being born in areas of lower SES, getting less good education, etc. And the higher BMI itself does have pretty clear adverse health effects, leading to more illness and less ability to be employed and decreasing SES. So, the genetic association may well be dwarfed by the social issues. A little harder to make equivalent arguments about height.
  • Although this is a huge dataset, it seems to me that there are a couple of limitations to the interpretation (I should add that these are my assessments based on what I glean of the mandelian randomization methodology, and would defer to anyone more statistically or biologically sophisticated than I — would welcome feedback and would like to send around contrary interpretations). One is: do we really know all of the important genetic “determinants”, and which really are the important ones? And is there any significant interaction between the many identified genetic markers (i.e., does it make sense to look at individual genetic markers singly as in this study or in combination?).  Second: while it is undoubtedly true that social circumstances cannot change one’s genes fundamentally, it is also true that the function of genes can change at pretty much anytime through epigenetics by DNA methylation and histone modification, leading to changes in the expression of these genes. For example, rats exposed to marijuana have changes in DNA methylation, which can affect function (see ). This blog goes through some of the basic principles of epigenetics, raising the concern that this post-conception phenomenon may have very profound clinical effects. In the case of the rats, where DNA methylation was actually passed to a subsequent generation, those infant mice were more prone to opioid addiction. And there are a variety of epigenetic changes associated with the environment, lifestyle, and chronic diseases themselves. For example, there are animal studies showing that there is a pretty strong association between nutrition and DNA methylation (e.g. J Nutr Biochem 2012; 23: 853); in fact there is a new journal “Environmental Epigenetics” with articles on such things as epigenetics as a mediator between air pollution and preterm birth, chemical exposures and autism, etc.
  • So, bottom line: there may well be genetic variants leading to obesity and short stature, and these may predict a small part in their determining decreased SES. But overall, I think this study reinforces that the relationship between BMI or height with SES is predominantly a social one: there are social biases, conscious or not, which directly affect the overall ability of overweight women and short men to achieve their full human potential, and this is reflected in their lower SES. And that lower SES may well contribute to higher BMI. I bring up this article because there are more and more studies using mendelian randomization, including a recent one on HDL-cholesterol, and that it seemed reasonable to think about the potential value and shortcomings of this technique.

Primary Care Corner with Geoffrey Modest MD: New Diabetes Cases Decreasing

14 Dec, 15 | by EBM

By Dr. Geoffrey Modest 

The CDC just released a somewhat encouraging report showing that newly diagnosed cases of diabetes in the US has started to decline (see overall graph below, and the various articles/subgroup analyses at ). A few observations:

  1. There seems to be an overall consistent trend to fewer cases since 2009, though the number of new cases is way above 1980 (the age-adjusted incidence in 1980 was about 3.5/1000 and in 2014 was 6.6/1000) and is basically the same as in 2004-5. Of note, the criteria for diagnosis of diabetes did change in 2010 to include the A1C>=6.5. No doubt this increased the number of diagnoses of diabetes, so the subsequent falloff may even be more significant.
  2. These data includes only those with diagnosed diabetes, and from current epidemiologic studies, it seems that about 25% of diabetics are currently unaware of their diagnosis
  3. The age-adjusted incidence of diagnosed diabetes has trended down for whites, blacks and hispanics, but was only significant for whites. Also, the overall incidence has consistently been much lower in whites (in 2014, was 6.4/1000, in 2009 was 8.0/1000) than blacks (was 8.4/1000 in 2014 and 11.5/1000 in 2009) and hispanics (was 8.5/1000 in 2014 and 11.9/1000 in 2009)
  4. The age-adjusted incidence of diagnosed diabetes has trended down for those with less than high-school education, those with high-school education and those with greater than high-school education, but was only significant for those with greater than high-school education. Also the overall incidence has consistently been much lower in those with greater than high-school education (in 2014, was 5.3/1000, in 2009 was 6.7/1000) than those with high-school education (was 7.8/1000 in 2014 and 9.0/1000 in 2009) and those with less than high-school education​ (was 11.1/1000 in 2014 and 15.4/1000 in 2009)​

So, what does this all mean and how do we interpret it?

  • Part of the issue may be that diabetes has a strong genetic component and some of the leveling off of new cases may be that the steep rise prior to 2008 reflected obesity/lifestyle issues in conjunction with genes, and we have perhaps reached the saturation point for the genetic component (i.e., those predisposed genetically to diabetes have largely already become diabetic)
  • Part of the issue may be changes in obesity. Hard to compare CDC data over the past 20 years, since there was a change in CDC methodology in 2011, but it appears at least that obesity has plateaued and downtrended a bit in adolescents.
  • Some really positive changes have been the decrease in soda consumption: over the past 20 years, there has been a >25% decrease in sales of full-calorie soda, with a “serious and sustained decline”. From 2004-12, children consumed 79% fewer sugar-sweetened beverage calories a day (4% cut in overall calories) — see
  • These changes seem to reflect public health initiatives to decrease soda consumption (since the changes are not related to increased taxes or other financial incentives)
  • McDonalds, for the first time, is closing more stores than they are opening…
  • More people are doing daily exercise than before
  • Unfortunately, the CDC data really shows that the significant changes in new diabetes incidence pertains mostly to white and more-educated people. That being noted, I should add that my experience in a poor minority community is that there really have been pretty consistent improvements overall in lifestyle. I have many more patients who eat better (much less soda/more water for drinks, decreases in junk food) and much more consistently do exercise (mostly walking outside when the weather is nice, or climbing up and down stairs for 10-15 minutes when not. And some who ride bikes or have some home exercise machines, or go to gyms). This has been a pretty striking change over the past 10 years or so. I suspect part of the issue is that I have spent a long time discussing lifestyle changes with my patients over many years, but also (and perhaps more important) is that there has been more general awareness of the importance of eating well and exercising which i am supporting and reinforcing.
  • Though, an important cautionary note. One concern I have raised in many past blogs is that we (scientists and physicians alike) often develop our models of disease based on what seem to be reasonable physiologic data, then generalize it and formalize it as recommendations. We always do this, and there really is no way around it. But we are often wrong. In the 1970s, it seemed reasonable to note that dietary fats are related to atherosclerotic disease (which was a particularly big killer then), and that some fats were worse than others (saturated fats seemed to be the worst then, though there were early data that trans fats were actually the worst by far and still took another 4 decades to be reduced/eliminated, polyusaturated were better but lowered HDL as well as LDL, then the best were monousaturates which raised HDL while lowering LDL). So, we endorsed a low-fat diet, which translated to a high-carb diet (e.g., low fat ice cream, etc., had fewer fats and more carbs). Many of us realized subsequently (though a lot of the data was available many years ago), that eggs really were not so bad in terms of clinical outcomes, and that the high glycemic/high carb diets may well have been the major factor propelling the obesity epidemic and diabetes. So, I think the take-home message here is that we will always be constucting biological/medical models (whether they be about dietary fat, homocysteine, postmenopausal estrogens, etc. etc.); that these models are natural for us to do and really important in determining policy (though best after the appropriate studies with important clinical outcomes are performed, but these often take many years to do, if done at all); but that we always need to be really vigilant in continually questioning the basis of these models through introspection and further studies, and not allowing a model such as the low-fat diet above to last for so long (I believe the goal is something like: do no harm….)


Primary Care Corner with Geoffrey Modest MD: Fructose Restriction and Cardiometabolic and Weight Improvement

11 Dec, 15 | by EBM

By Dr. Geoffrey Modest

There are several epidemiologic studies suggesting that fructose plays a role in the development of metabolic syndrome, as well as non-alcoholic fatty liver disease, type 2 diabetes, and cardiovascular disease. A small study just came out assessing 43 children with obesity and metabolic syndrome who were put on a 9 day fructose-restricted diet and then evaluated several metabolic parameters (see  doi:10.1002/oby.21371). This diet attempted to match each participant’s prior macronutrient intake profile


  • 27 Latino and 16 African-American children with obesity and metabolic syndrome (mean age 13.3, weight 93 kg, BMI 35.6)
  • A child-friendly diet: various no- or lower-sugar added processed foods including turkey hot dogs, pizza, bean burritos, baked potato chips, and popcorn


  • Mean caloric intake was 29 kcal/kg: 51% carbs, 16% protein, 33% fat (16% saturated, 9% polyunsat, 13% monounsat), which represents a change of: carbs decreased 4%, protein increased 2% and no change in fat consumption. Within the carbs ingested: dietary sugar decreased from 27.7 to 10.2% and fructose from 11.7 to 3.8%, with increase in dietary fiber
  • ​Despite efforts to maintain body weight, there was a 0.9 kg loss over the 10 days (33 reported they were unable to consume all the food provided…), with predominant weight loss in first 4 days (?water weight?)
  • Systolic BP did not change, but diastolic decreased 4.9 mmHg
  • ​Uric acid increased by 17.8 mmol/L, or 0.3 mg/dl (??, see below)
  • Fasting glucose decreased by 0.3 mmol/L (5 mg/dL), glucose area-under-the-curve decreased by 7.3%, fasting insulin decreased by 53%, and HOMA-IR, a measure of insulin resistance, decreased by 58%
  • Fasting triglycerides decreased by 46%, LDL by 12.5% and HDL by 20%
  • ALT declined nonsignificantly from 28.9 to 26.7 U/L, though AST significantly decreased from 27.4 to 23.8 U/L
  • Analysis restricted to those who did not lose weight did not materially change the above.

Fructose is a sugar with pretty different metabolism and effects: it is metabolized almost exclusively by the liver; it is a substrate for de novo lipogenesis and increased triglycerides; it leads to nonenzymatic fructation and reactive oxygen species causing cellular dysfunction; it does not suppress the hunger hormone ghrelin leading to excessive consumption, does not stimulate insulin release, unlike other sugars, thereby leading to insufficient plasma leptin levels and also to less satiety,and it stimulates the nucleus accumbens leading to increased reward and continued eating (so, there are several ways that consuming fructose does not decrease hunger, and perhaps this explains why so many of these kids were unable to eat all of their prepared food when on the fructose-restricted diet!!). There are also changes in the gut microbiome from fructose, perhaps related to the fact that it is one of the most poorly absorbed short-chain carbohydrates (for a more detailed assessment of the physiologic effects of fructose, see  Obesity Reviews 2012; 13: 799)

One unusual finding was that the changes from the fructose-restricted diet seemed to increase serum uric acid levels. This is a bit contrary to the typical physiologic finding of increased uric acid levels through up-regulated hepatic signal transduction pathways (see JAMA. 2013; 310(1):33), and to the finding of increased gout in the observational Nurses’ Health Study associated with higher fructose ingestion (see JAMA 2010; 304(20): 2270)

So, pretty impressive results in a short-term study, just by changing carbohydrate composition and without significant weight loss (the weight loss that occurred was likely water weight, given the rapidity of weight loss; and, both mathematical modeling controlling for weight and by looking exclusively at those who did not lose weight did not show significant directional differences in the outcomes measured). In particular, even short-term fructose restriction was associated with very impressive changes in markers of insulin resistance (fasting insulin levels and HOMA-IR). This study therefore adds support to strongly encouraging decreasing fructose consumption, the vast majority of which in Western diets is from high-fructose corn syrup and not free fructose from natural foods.

Primary Care Corner with Geoffrey Modest MD: Central Obesity

2 Dec, 15 | by EBM

By Dr. Geoffrey Modest

There are a plethora of older studies showing that central obesity has a much stronger association with cardiovascular and mortality outcomes than BMI. The role of BMI as a predictor of events is largely explained by the concomitant risk factors of hypertension, hyperlipidemia, glucose intolerance/diabetes. Central obesity, on the other hand, is associated with the more metabolically active visceral fat. Not surprisingly, several studies have shown that there is a strong correlation between an increased BMI and central obesity for most individuals. However, little attention is paid to people with normal BMI but central obesity, and the 2013 Am Heart Assn Obesity Society guidelines on obesity management only recommends checking waist circumference in those with high BMIs. In this context, there was a study looking at the clinical consequences of central obesity in individuals with normal weight (see  doi:10.7326/M14-2525​), using the NHANES III database (Third National Health and Nutrition Examination Survey).


  • 15184 adults (mean of 40 yo, 52.3% women, mean BMI 27, mean waist circumference 94 cm men/87 cm women, mean waist-to-hip ratio 0.94 men/0.85 women, 85% white, 11% African-American, mean BP 120/75, hypertension in 29%, diabetes 7%, history of  MI 3%, A1C 5.2%, LDL 123, 50% physically active) had general obesity assessed by BMI and central obesity by waist-to-hip ratio (WHR)
  • Of those with normal BMI (<25), 322 men (11.0%) and 105 women (3.3%) had increased WHR (>1.0); of those who were overweight by BMI (25-30), 1064 men (37.0%) and 289 women (12.0%) had increased waist-to-hip ratio (WHR). Of those who were obese (BMI>30), 928 men (63.0%) and 336 women (14.0%) had increased WHR. Overall analysis showed that both waist circumference and WHR were strongly correlated with BMI, though a little less so with WHR than waist circumference
  • Primary outcome: total and cardiovascular mortality, after adjustment for confounding factors (age, sex, education level, smoking history)

Results, using a BMI of 22 to represent people with normal BMI:

  • Over a mean follow-up of 14.3 years, there were 322 deaths (1413 women), 1404 due to cardiovascular disease
  • For a man with normal BMI and central obesity vs similar BMI and no central obesity: mortality risk HR 1.87 (1.53-2.29) [ie, 87% increase]
  • For a man with normal BMI and central obesity vs overweight man by BMI: mortality risk HR 2.24 (1.52-3.32), or vs obese man HR 2.42 (1.30-4.53)
  • For a woman with normal BMI and central obesity vs similar BMI and no central obesity: mortality risk HR 1.48 (1.35-1.62)
  • For a woman with normal BMI and central obesity vs overweight woman by BMI: mortality risk HR 1.40, or vs obese woman by BMI HR 1.32 (1.15-1.51)
  • Overall, “WHR, but not BMI was associated with high mortality risk”

A few points:

  • There have been a slew of metabolic differences found between visceral and peripheral fat, perhaps related to the fact that visceral fat products enter the portal circulation, are accumulated in the liver, and affect hepatic processes. For example, central obesity is associated with systemic inflammation (perhaps the reason there are greater associations with MI, as well as with diabetes, Alzheimers, etc). Also with more small, dense and more atherogenic LDL particles, hypertriglyceridemia, thrombotic risk factors, insulin resistance.
  • The INTERHEART study of cardiac risk factors in 52 countries found that the population-attributable risk to MI was 24.5% for central obesity but only 7.7% for BMI (see Lancet 2005; 366: 1640)
  • Central obesity (as opposed to BMI) is part of the definition of Metabolic Syndrome, and in the US/Western Europe is defined as abdominal circumference >40 inches in men (102 cm) or >35 inches in women (88 cm), though there are international definitions which vary by ethnicity, per the International Diabetes Federation recommendations (e.g., central obesity in South Asians is 90 cm for men and 80 cm for women).
  • The association between central obesity and cardiovascular risk could not be explained by the standard cardiac risk factors

So, what is the utility of waist circumference or other measures of central obesity vs BMI? It is clear from almost all of the studies that central obesity is more important as a risk factor than BMI. And, I think we should be using some measure of central obesity in our assessment of patients. However, BMI is still useful in that it provides a very easy measurement for providers and patients to track: it is probably more helpful to look at weight and BMI changes when patients are trying to lose weight, since we can appreciate very small changes and use that for motivational interviewing/reinforcing diet and exercise. Waist circumference or WHR will change more slowly and might therefore prove to be discouraging to providers and patients alike, and is also probably harder to perform with consistent accuracy. So, I think we should measure both, be especially aggressive in management of other cardiac risk factors in those with central obesity, and use changes in weight/BMI (including in those with normal BMI but central obesity) to reinforce the important lifestyle changes.

Primary Care Corner with Geoffrey Modest MD: Obesity in Kids and Cardiometabolic Risk

19 Oct, 15 | by EBM

By Dr. Geoffrey Modest

So, no great surprise, but it seems that cardiometabolic risk factors track the degree of obesity in children and young adults (see N Engl J Med 2015;373:1307-17​). A cross-sectional analysis of overweight and obese kids/young adults (age 3-19) in the National Health and Nutrition Examination Survey from 1999-2012 looked at measured height and weight, along with an array of cardiometabolic risk factors (lipids, A1c, etc.), assessing the relationship over different obesity levels.

Background (from the CDC):

  • Childhood obesity (defined as BMI >95th %ile) has more than doubled in children and quadrupled in adolescents in past 30 years: % of children aged 6-11 in the US who were obese increased from 7% in 1980 to 18% in 2012; for adolescents 12-19yo, it increased from 5% to 21%.
  • In 2012 >1/3 of children and adolescents were overweight or obese
  • Despite recent declines in obesity prevalence in preschool-aged kids, obesity is still way too prevalent: overall for those 2-19 yo, the prevalence “has remained fairly stable at about 17% and affects about 12.7 million children and adolescents for the past decade”, though the prevalence in those 2-5 yo has decreased significantly from 13.9% in 2003-4 to 8.4% in 2011-2, at which time the prevalence was 17.7% in 6-11 yo and 20.5% in 12-19 yo; the prevalence was highest in Hispanics (22.4%) and non-Hispanic blacks (20.2%) vs non-Hispanic whites (14.1%)

Details of study:

  • 8579 individuals (53.7% white, 16.5% black, 24.0% Hispanic; 52% male) with BMI>85th %ile, of whom 46.9% were overweight (BMI 85-95th%), 36.4% had class I obesity (95-120% of the 95th %ile), 11.9% had class II obesity (120-140% of the 95th %ile or BMI≥35), and 4.8% had class III obesity (≥140% of the 95th %ile, or BMI≥40).

Results, as progress from overweight to class I to class II to class III obesity:

  • LDL: 94.6, 98.4, 98.2, 96.5 (p=0.131, non-significant)
  • HDL: 49.4, 46.7, 43.5, 41.3 (p<0.001)
  • Systolic BP: 108.5, 111.0, 112.6,116.2 (p<0.001)
  • Diastolic BP 57.0, 58.8, 58.7, 64.5 (p<0.001)
  • Fasting triglycerides: 91.0, 113.2, 113.3, 143.2 (p<0.001)
  • Glycohemoglobin 5.15, 5.20, 5.30, 5.37 (p<0.001)
  • Fasting glucose: 93.2, 95.1, 96.7, 96.5 (p=0.001)
  • And, overall, these risk factors did have a sex difference: males did worse. In fact, the only significant ones for females were: HDL, systolic/diastolic BP, glycohemoglobin and glucose; and for each of these, the prevalence in males was much higher.

So, although this study tracked only the surrogate markers of cardiometabolic parameters (not so likely to have cardiac clinical events at this age…..), this study is important because:

  • Obesity in kids tracks with obesity in adults
  • This study, vs older ones, looks at levels of obesity and differences in risk factors, showing a graded response overall: the worse the obesity, the worse the risk factors. And, this study, I think, justifies subdividing obesity in kids into different levels (since there are differences in attributable cardiometabolic risks), as is done with adults.
  • And, though clinical events are the gold standard, autopsy studies have shown that there are fatty streaks in pretty much everyone aged 15-34; and there are advanced atherosclerotic lesions in 2% of men/0% of women​ aged 15-19 and 20% of men/8% of women aged 30-34. So, actual disease does begin early and, per usual, is seems better to deal with risk factors early on, before clinical disease manifests itself….

Also, for prior blog on the relationship between pediatric obesity and increased left ventricular mass from the Bogalusa Heart Study, see


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