Testosterone Linked to Weight Loss in Obese Men

May 9, 2012 -- Testosterone replacement may promote weight loss in obese older men who have low levels of the male sex hormone, a new study shows.

But before men try to lose weight by bumping up their testosterone, experts agree that more studies are needed to show that the treatment is both safe and effective.

Researchers followed a group of mostly older, overweight men receiving injections of the hormone for up to five years to treat erectile dysfunction and other symptoms associated with low testosterone.

Their findings were presented at the 19th European Congress on Obesity in Lyon, France.

The men who were treated the longest lost more than 30 pounds on average over the course of the study, and also showed improvements in blood pressure, blood glucose, and LDL (bad) cholesterol.

Researcher Farid Saad, DVM, says the dramatic weight loss came as a surprise.

"This study was not performed for the purpose of promoting weight loss," he tells WebMD. "This was an incidental finding that was entirely unexpected."

Low Testosterone Common in Obese Men

The study included middle-aged and older overweight or obese men with low testosterone levels being treated with testosterone replacement at a single urology clinic.

A total of 214 men remained in the study for at least two years, and just over half of these men were followed for another three years or more.

All the study participants received a long-acting injected testosterone, with two injections given during the first six weeks of treatment followed by injections every three months as long as they remained in the study.

Men who were treated the longest lost the most weight and saw the biggest reductions in waist circumference and body mass index (BMI).

After five years of follow-up, the average weight loss was 35 pounds and the average waist circumference dropped from about 42 to 38 inches. Most men also saw improvements in triglyceride levels, blood pressure, blood sugar, and LDL cholesterol.

Saad is a researcher with Bayer Pharma AG, of Berlin, Germany. Bayer manufacturers the long-acting testosterone treatment the men received, which has not been approved for use in the U.S.

Saad says the men in the study may have lost weight on the testosterone therapy because they had more energy to exercise.

"This is a theory, and, certainly, more research is needed to confirm our findings," he tells WebMD.

Testosterone for Weight Loss: 'More Study Needed'

There are concerns that prolonged testosterone therapy could increase prostate cancer risk, but there was no evidence of this among the men in the study.

University of Buffalo endocrinologist Sandeep Dhindsa, MD, agrees that more study is needed to confirm the safety and usefulness of replacement therapy for weight loss in men with low testosterone.

Dhindsa's own research, published two years ago, showed that low testosterone is common in obese men, especially those with diabetes.

"The weight loss reported in this study was much greater than that reported in other studies," he tells WebMD. "Given this outcome and the safety questions about this treatment, it is important to replicate these findings."

These findings were presented at a medical conference. They should be considered preliminary as they have not yet undergone the "peer review" process, in which outside experts scrutinize the data prior to publication in a medical journal.

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What does it mean to say that something causes 16% of cancers?

A few days ago, news reports claimed that 16 per cent of cancers around the world were caused by infections. This isn’t an especially new or controversial statement, as there’s clear evidence that some viruses, bacteria and parasites can cause cancer (think HPV, which we now have a vaccine against). It’s not inaccurate either. The paper that triggered the reports did indeed conclude that “of the 12.7 million new cancer cases that occurred in 2008, the population attributable fraction (PAF) for infectious agents was 16·1%”.

But for me, the reports aggravated an old itch. I used to work at a cancer charity. We’d get frequent requests for such numbers (e.g. how many cancers are caused by tobacco?). However, whenever such reports actually came out, we got a lot confused questions and comments. The problem is that many (most?) people have no idea what it actually means to say that X% of cancers are caused by something, where those numbers come from, or how they should be used.

Formally, these numbers – the population attributable fractions (PAFs) – represent the proportion of cases of a disease that could be avoided if something linked to the disease (a risk factor) was avoided. So, in this case, we’re saying that if no one caught HPV or any other cancer-causing infection, then 16.1% of cancers would never happen. That’s around 2 million cases attributable to these causes.

From answering enquiries and talking to people, I reckon that your average reader believes that we get these numbers because keen scientists examined lots of medical records, and did actual tallies. We used to get questions like “How do you know they didn’t get cancer because of something else?” and “What, did they actually count the people who got cancer because of [insert risk factor here]?”

No, they didn’t. Those numbers are not counts.

Those 2 million cases don’t correspond to actual specific people. I can’t tell you their names.

Instead, PAFs are the results of statistical models that mash together a lot of data from previous studies, along with many assumptions.

At a basic level, the models need a handful of ingredients. You need to know how common the risk factor is – so, for example, what proportion of cancer patients carry the relevant infections? You need to know how big the effect is – if someone is infected, their risk of cancer goes up by how many times? If you have these two figures, you can calculate a PAF as a percentage. If you also know the incidence of a cancer in a certain population during a certain year, you can convert that percentage into a number of cases.

There’s always a certain degree of subjectivity. Consider the size of the effect – different studies will produce different estimates, and the value you choose to put into the model has a big influence on the numbers that come out. And people who do these analyses will typically draw their data from dozens if not hundreds of sources.

In the infection example, some sources are studies that compare cancer rates among people with or without the infections. Others measure proteins or antibodies in blood samples to see who is infected. Some are international registries of varying quality. The new infection paper alone combines data from over 50 papers and sources, and some of these are themselves analyses of many earlier papers. Bung these all into one statistical pot, simmer gently with assumptions and educated guesses, and voila – you have your numbers.

This is not to say that these methods aren’t sound (they are) or that these analyses aren’t valuable (they can tell public health workers about the scale of different challenges). But it’s important to understand what’s actually been done, because it shows us why PAFs can be so easily misconstrued.

The numbers aren’t about assigning blame.

For a start, PAFs don’t necessarily add up. Many causes of cancer interact with one another. For example, being very fat and being very inactive can both increase the risk of cancer, but they are obviously linked. You can’t calculate the PAFs for different causes of cancer, and bung them all into a nice pie chart, because the slices of the pie will overlap.

Cancers are also complex diseases. Individual tumours arise because of a number of different genetic mutations that build up over the years, potentially due to different causes. You can’t take a single patient and assign them to a “radiation” or “infection” or “smoking” bucket. Those 16.1% of cancers that are linked to infections may also have other “causes”. Cancer is more like poverty (caused by a number of events throughout one’s life, some inherited and some not) rather than malaria (caused by a very specific infection delivered via mosquito).

You can’t find trends by comparing PAFs across different studies.

The latest paper tells us that 16.1% of cancers are attributable to infections. In 2006, a similar analysis concluded that 17.8% of cancers are attributable to infections. And in 1997, yet another study put the figure at 15.6%. If you didn’t know how the numbers were derived, you might think: Aha! A trend! The number of infection-related cancers was on the rise but then it went down again.

That’s wrong. All these studies relied on slightly different methods and different sets of data. The fact that the numbers vary tells us nothing about whether the problem of infection-related cancers has got ‘better’ or ‘worse’. (In this case, the estimates are actually pretty close, which is reassuring. I have seen ones that vary more wildly. Try looking for the number of cancers caused by alcohol or poor diets, if you want some examples).

Unfortunately, we have this tricky habit of seeing narratives even when there aren’t any. Journalists do this all the time. A typical interview would go like this: “So, you’re saying infections cause 16.1% of cancers, but a few years ago, you said they cause 17.8% of cancers.” And then, the best-case scenario would be: “So, why did it go down?” And the worst-case one: “Scientists are always changing their minds. How can we trust you if you can’t get a simple thing like this right?”

The numbers are hard to compare, and obscure crucial information.

Executives and policy-makers love PAFs, and they especially love comparing them across different risk factors. They are nice, solid numbers that make for strong bullet points and eye-grabbing Powerpoint slides. They have a nasty habit of becoming influential well beyond their actual scientific value. I have seen them used as the arbitrators of decisions, lined up on a single graphic that supposedly illustrates the magnitude of different problems. But of course, they do no such thing.

For a start, the PAF model relies on a strong assumption of causality. You’re implying that the risk factor you’re studying clearly causes the disease in question. That’s warranted in some cases, including many of the infections discussed in the new paper. In others… well, not so much.

Here’s an example. I could do two sets of calculations using exactly the same methods and tell you how many cases of cancer were attributable to radon gas, or not eating enough fruit and vegetables. A casual passer-by might compare the two, look at which number was bigger, and draw conclusions about which risk factor was more important. But this would completely obscure the fact that there is very strong evidence that radon gas causes cancer, but only tenuous evidence that a lack of fruit and vegetables does. Comparing the two numbers makes absolutely no sense.

There are other subtle questions you might also need to ask if you were going to commit money to a campaign, or call for policy changes, or define your strategy. How easily could you actually alter exposure to a risk factor? Does the risk factor cause cancers that have no screening programmes, or that are particularly hard to treat? Is it becoming more of a problem? PAFs obscure all of these issues. That would be fine if they were used appropriately, with due caution and caveats. But from experience, they’re not.

What PAFs are good for

They’re basically a way of saying that a problem is this big (I hold my hands bout an inch apart), or that it’s this big (they’re a foot apart now) or THIS big (stretched out to the sides). They’re our best guess based on the best available data. In the case of infections, the message is that they cause more cancers than people might expect.

Used carefully, I have no real problem with PAFs, but I think that they’re blunt instruments, often wielded clumsily. We could do a much better job at communicating what they actually mean, and how they are derived. I’d be happier if we quoted ranges based on confidence intervals. I’d be even happier if we stopped presenting them to one decimal place – that imbues them with a rigour that I honestly don’t think they deserve. And if, whenever we talked about PAFs, we liberally used the suffix “-ish”? Well, I’d be this happy.

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Caffeine protects against brain degeneration in diabetes

Badly controlled diabetes is known to affect the brain, causing memory and learning problems and even increased incidence of dementia. How this occurs is not clear but a study in mice with type 2 diabetes has discovered how diabetes affects the hippocampus, causing memory loss, and also how caffeine can prevent this.

Curiously, the neurodegeneration that Rodrigo Cunha, from the Centre for Neuroscience and Cell Biology of the University of Coimbra in Portugal, sees as result of diabetes is the same that occurs at the first stages of several neurodegenerative diseases, including Alzheimer’s and Parkinson’s, suggesting that caffeine (or drugs with similar mechanisms) could help them too.

Type 2 diabetes, which accounts for about 90% of all diabetic cases, is a full-blown public health disaster – 285 million people affected worldwide - 6.4% of the world population - with numbers expected to almost double by 2030, without counting pre-diabetic individuals. The problem is that the disease is triggered by obesity, sedentary lifestyle and bad eating habits (although there is also a genetic predisposition), all of which are increasingly widespread.

In the new study, João Duarte, Rodrigo Cunha and colleagues take advantage of a new mouse model of diabetes type 2, which, like humans, develops the disease in adults as result of a high-fat diet, to look at one of the least understood complications of diabetes – the disease effect on the brain, more specifically, on memory. They also investigate a possible protective effect by caffeine as this psychostimulant has been suggested to prevent memory loss in a series of neurodegenerative diseases, maybe even in diabetes, although how this happens is not known. when we consider that coffee is the world leading beverage right after water, with about 500 billion cups consumed annually, this effect, if true, needs to be better understood.

With that aim the researchers compared 4 groups of mice - diabetic or normal animals without or with caffeine (equivalent to 8 cups of coffee a day) in their water – to find that long-term consumption of caffeine not only diminished the weight gain and the high levels of blood sugar typical of diabetes, but also prevented the mice's memory loss (diabetic animals had significantly poorer memory than normal ones). This confirmed that caffeine seems to, in fact, protect against diabetes as well as prevent memory impairment, probably by interfering with the neurodegeneration caused by toxic sugar levels.

And in fact, further investigation allowed Cunha and colleagues to find that the memory problems were caused by degeneration in the hippocampus – a brain region linked to memory and learning, which is often atrophied in diabetics. On the other hand a molecule called adenosine receptor A2AR seemed to be the key for caffeine’s memory rescue since its density – which is known to increase with noxious insults - was high in diabetic animals but normal in those treated with caffeine.

So does this mean that we should drink eight cups of coffee a day to prevent memory loss in old age or diabetes?

Not really as Rodrigo Cunha, the team leader explains: “Indeed, the dose of caffeine shown to be effective is just too excessive. All we can take from here is that a moderate consumption of caffeine should afford a moderate benefit, but still a benefit. Such experimental design is common in pre-clinical studies: in order to highlight a clear benefit, one dramatizes the tested doses. But it's an important first step. Our ultimate goal is the design of a drug more potent and selective (i.e. with less potential side effects) than caffeine itself; animal studies enable us to pinpoint the likely target of caffeine with protective benefits in type 2 diabetes. So now we will be testing chemical derivates of caffeine, which act as selective adenosine A2A receptor antagonists,to try to prevent diabetic encephalopathy. It might turn out to be a therapeutic breakthrough for this devastating disease”.

And, with a disease that is already affecting 6.4% of the population and growing, a breakthrough can never come too soon…

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The relationship between TV viewing and eating

Television viewing and unhealthy eating habits in US adolescents appear to be linked in a national survey of students in the fifth to tenth grades, according to a report published in the May issue of Archives of Pediatrics & Adolescent Medicine, a JAMA Network publication. The study is part of the Nutrition and the Health of Children and Adolescents theme issue.

Television viewing (TVV) by young people has been associated with unhealthy eating and food choices that may track into early adulthood. Young people in the US fall short of recommendations for whole fruit, whole grains, legumes and dark green or orange vegetables, while exceeding recommendations for fat, sodium and added sugar that can increase the risk of obesity and chronic disease throughout a lifetime, the authors write in their study background.

Leah M. Lipsky, Ph.D., M.H.S., and Ronald J. Iannotti, Ph.D., of the Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Md., examined the association of television viewing with eating behaviors in US adolescents. They used data from the 2009-2010 Health Behavior in School-Aged Children Study, a survey of adolescents conducted every four years in the US. The survey included a nationally representative group of 12,642 students in the fifth through 10th grades with a mean (average) age of 13.4 years.

"Television viewing time was associated with lower odds of consuming fruit or vegetables daily and higher odds of consuming candy and sugar-sweetened soda daily, skipping breakfast at least one day per week and eating at a fast food restaurant at least one day per week in models adjusted for computer use, physical activity, age, sex, race/ethnicity and family affluence," the authors comment. "The relationship of TVV with this unhealthy combination of eating behaviors may contribute to the documented relationship of TVV with cardiometabolic risk factors." According to the results, the odds of eating fruits and vegetables daily were higher for younger than older students, for girls compared with boys, and for white students and other groups compared with black and Hispanic youth. The odds of eating sweets daily were highest for older than younger youth, for girls compared to boys and for black youth compared with other racial/ethnic groups.

The results also indicate that the odds of drinking soda at least daily were highest for older versus younger youth, for boys versus girls and for black and Hispanic young people compared with other racial/ethnic groups. Skipping breakfast was more common for older than younger students, for girls compared with boys and for black, Hispanic and other youth compared with white students.

"Future research should elucidate the independent contributions of TVV, food advertising and TV snacking on dietary intake in this population," the authors conclude. "If these relationships are causal, efforts to reduce TVV or to modify the nutritional content of advertised foods may lead to substantial improvements in adolescents' dietary intake."

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Zambo declares dengue outbreak

ZAMBOANGA CITY -- The City Government, through the City Health Office, has declared a dengue outbreak in this southern port city.

The declaration was made as the number of dengue cases from January 1 up to the first week of May this year has increased by 413, or more than 50 percent compared to the number of cases recorded in the same period of last year.

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There were a total of 361 dengue cases recorded in the same period in 2011, but the cases reached 774 this year, said City Health Officer Dr. Rodelin Agbulos.

He said they have recorded 10 deaths out of the 774 cases.

Last year, he said, they only recorded four deaths.

Agbulos also said that five villages in the city have registered high number of dengue cases. These areas include Tumaga, with 63 cases; Tugbungan, 57; Guiwan, 51; San Roque, 47; and Canelar, 31.

The other hard-hit villages are Zambowood, Cabatangan, Sta. Maria, Putik, Mercedes, Sangali, Maasin, Divisoria, Pasobolong, Lumbangan, Rio Hondo and Tetuan.

Agbulos noted that the villages with high number of dengue cases usually have rivers and cemeteries that serve as breeding sites of dengue-carrying mosquitoes.

To combat the disease, Agbulos said the City Health Office has scheduled a citywide clean-up drive on Saturday as part of the City Government’s campaign against dengue.

In view of this, Mayor Celso Lobregat has authorized the release of over half a million pesos from the City Government’s emergency funds to purchase larvicides and other chemicals that will be used in the war against the mosquito-borne disease.

The City Health Office is set to carry out a comprehensive plan in cooperation with the villages, other health agencies and facilities, especially the community, to ensure an effective and efficient anti-dengue campaign.

Dengue fever is an infection caused by dengue virus, which is transmitted to humans through the bites of an infective female Aedes mosquito.

Aedes mosquitoes are “day biters” and biting activities peak at 6 a.m. to 8 a.m. and at 4 p.m. to 6 p.m.

The signs and symptoms of dengue fever are: on-and-off fever lasting for two to seven days; loss of appetite; nausea/vomiting; abdominal pain; body weakness; small reddish spots on chest area, arms and legs; bleeding signs (nose and gum bleeding, vomiting blood, bloody stools and abdominal pain); restlessness; weak, rapid pulse; cold, clammy skin; and difficulty in breathing. (Bong Garcia/Sunnex)

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Friend request: help pay my bill

ONCE Facebook was for social interaction, friendship and media-sharing; now it's morphing into a venue for serious pursuits such as paying bills.

This process took another step yesterday when Telstra released a Facebook application that lets customers top up pre-paid mobile accounts from their Facebook home page -- while they continue to interact with friends.

The app also makes use of the Facebook friends network -- some might say controversially -- by offering a button that lets a customer ask a friend to help pay their bill.

The plea for credit can appear on their wall. Telstra customers then use the Credit Me2U to transfer money from one pre-paid account to another.

Telstra Mobile executive director Warwick Bray said: "In just a few clicks, customers can use the app to check their balance information, recharge with a stored debit or credit card and view up to 180 days of usage and recharge history.

"And should a customer run out of credit, they can ask their mates for a top-up using the 'request credit from friends' feature, which provides the option to put a call out on their Facebook wall or via a direct message."

Users search for and download the application, install it, and verify their mobile phone account. The bill-paying facility then appears as a link on their Facebook homepage.

Telstra said the app pointed to "a future where all types of transactions are embedded in the world's most popular social network".

"This is part of Telstra's strategy to improve customer satisfaction by making it more convenient for people to manage their mobile services at a time and location that suites them," the telco said.

Its new app coincides with Facebook's revamping of its application network. In a developer's blog post yesterday, Facebook said it would open its own app centre, where users could download free and paid apps and run them from within their Facebook account. Apps could be accessed in a browser, or from apps on Apple/Android devices.

While apps have been part of Facebook for years, the difference is their organisation into a single online location and their categorisation into genres.