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Factors Affecting Energy Metabolism: Genetics, Sex, and Aging

Continuing with citations from my article in Obesity Reviews on an aeteological framework for assessing obesity, we now turn to the some of the factors that can affect metabolic rate:

Genetics
Because heritable factors appear to be responsible for 45–75% of the inter‐individual variation in body mass index (BMI), the potential impact of genetic determinants of metabolic rate upon the predisposition to obesity must be considered. While numerous somatic and mitochondrial genes with potential effects on metabolic rate have been identified, their contribution to human obesity has yet to be defined Likewise, although there is preliminary evidence for intrauterine and perinatal programming of genes involved in energy metabolism, their role in human obesity remain unclear. What is apparent is that the genetic predisposition to obesity (including both energy intake and metabolism) is not explainable on the basis of a small number of common mutations exerting substantial effects on the individual tendency to weight gain. Thus, a great deal of work is still required before investigation into the multitude of genetic determinants of body weight can potentially impact clinical management. Currently, a careful clinical assessment of family history of obesity and related risk factors remains the best measure of genetic risk for obesity.

Sex
There is a clear effect of gender [sic] on metabolic requirements, whereby, for the same BMI, women consistently display lower metabolic rates (approximately 20% less) than men, largely accounted for by differences in fat‐free mass (FFM).

Aging
Aging is an important determinant of a decline in metabolic rate, with an estimated reduction of around 150 kcal per decade of adult life. Factors that result in the age‐related decline in energy requirements include changes in neuroendocrine factors (e.g. sympathetic activity, thyroid function, etc.) as well as a reduction in skeletal muscle quantity and quality (resulting from reduced physical activity, reduced protein intake and other less‐well‐understood factors).

Additional factors that can affect metabolic rate will be discussed in subsequent posts.

@DrSharma
Edmonton, AB

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The Heterogeneity of Obesity

In the same manner in that there is not one predisposing factor for the development of obesity, the phenotypic clinical presentation of obesity is likewise extraordinarily heterogenous. (This has some authors speaking of “obesities” rather than “obesity”).

While it is now well established that BMI is a measure of size rather than health, it is perhaps less well recognised how the different types of body fat and their storage in various fat depots and organs can contribute to cardiometabolic disease (location, location, location!).

Now, a comprehensive review by Ian Neeland from the University of Texas Southwestern Medical Center, Dallas, together with my colleagues Paul Poirier and JP Despres from Laval University in Quebec, published in Circulation discusses the cardiovascular and metabolic heterogeneity of obesity.

As the authors point out,

“Although the BMI has been a convenient and simple index to monitor the growth in obesity prevalence at the population level, many metabolic and clinical studies have revealed that obesity, when defined on the basis of the BMI alone, is a remarkably heterogeneous condition. For instance, patients with similar body weight or BMI values have been shown to display markedly different comorbidities and levels of health risk.”

Not only has BMI never emerged as a significant component in risk engines such as the Framingham risk score, there are many individuals with obesity who never develop metabolic complications or heart disease during the course of their life.

The paper offers a good review of what the author describe as adipose dysfunction or “adiposopathy” = “sick fat”. Thus, in some individuals, there is an accumulation of “unhealthy” fat (particularly visceral and ectopic fat), whereas in others, excess fat predominantly consists of “healthy” fat (predominantly in subcutaneous depots such as the hips and thighs).

The authors thus emphasise the importance of measuring fat location with methods ranging from simple anthropometric measures (e.g. waist circumference) to comprehensive imaging techniques (e.g. MRI).

The authors also provide a succinct overview of exactly how this “sick fat” contributes to cardiometabolic risk and briefly touches on the behavioural, medical, and surgical management of patients with obesity and elevated cardiometabolic risk.

I, for one, was also happy to see the inclusion of the Edmonton Obesity Staging System in their reflections on this complex issue.

This paper is certainly suggested reading for anyone interested in the link between obesity and cardiovascular disease.

@DrSharma
Edmonton, AB

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Can Liraglutide Help Grow New Fat Cells?

The human GLP-1 analogue liraglutide is now approved for the long-term medical treatment of obesity in an ever-increasing number of countries. Its safety and clinical effectiveness is now well established and there is no doubt that this is an important addition to the rather limited number of treatment options available to people living with obesity.

Interestingly, however, liraglutide has also been shown to promote the differentiation of pre-adipocytes or, in other words, promote the formation of new fat cells.

While this may seen worrying or even counter-intuitive, we much remember that having more (smaller) rather than fewer (bigger) fat cells actually has substantial metabolic advantage s- there is indeed ample data showing that large adipocyte cell size and limited capacity to grow fat cells (the extreme case of which is seen in people with lipodystrophy) is actually a key risk factor for metabolic problems including insulin resistance, possible by promoting the accumulation of ectopic fat (e.g. in liver and skeletal muscle).

Now, a paper by Yongmei Liand colleagues, published in Molecular Medicine Reports provides additional insight into the cellular pathways involved in liraglutide’s adipogenic effects.

Using a series of in vitro experiments, the researchers show that liraglutide does indeed promote the adipogenic differentiation of 3T3-L1 cells (a widely used murine preadipocyte cell line)  through a process that upregulates the expression of C/EBPα and PPARγ at the early phase of adipogenic differentiation, promots the expression of lipogenesis associated genes including aP2, and enhances the accumulated of lipids.

At the same time, liraglutide appears to suppress cell proliferation via the Hippo‑yes‑associated protein (YAP) signaling pathway, thereby allowing these cells to transform into mature adipocytes sooner.

How relevant these observations are for humans remains to be seen, but certainly the promotion of adipogenic differentiation may hold the potential for improving insulin sensitivity and reducing the metabolic risks associated with excess weight gain.

@DrSharma
Edmonton, AB

Disclaimer: I have received speaking and consulting honoraria from Novo Nordisk, the maker of liraglutide.

 

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Molecular Changes During Weight Gain – Everyone Is Different

As one may well imagine, changes in body weight (up or down) can profoundly affect a vast number of hormonal and metabolic pathways.

Now, a team of researchers led by Brian Piening and colleagues, in a paper published in Cell Systems used a broad “omics” based approach to study what happens when people lose ore gain weight.

Specifically, the goal of this study was to:

(1) assemble a comprehensive map of the molecular changes in humans (in circulating blood as well as the microbiome) that occur over the course of a carefully controlled weight gain and their reversibility with weight loss; and

(2) determine whether inulin sensitive (IS) and insulin resistant (IR) individuals who are matched for degree of obesity demonstrate unique biomolecular signatures and/or pathway activation during similar weight gain.

The study included 23 carefully selected healthy participants with BMI 25–35 kg/m2, were studied. Samples were collected at baseline. They then underwent a 30-day weight gain period (average 2.8 kg), followed by an eucaloric diet for 7 days, at which point a second fasted sample of blood and stool was collected. Each participant then underwent a caloric-restricted diet under nutritionist supervision for a subsequent 60-day period designed to return each participant back to his/her initial baseline weight, at which point a third set of fasted samples of blood and stool were collected. A subset of participants returned for a follow-up sampling approximately 3 months after the end of the perturbation.Insulin resistance was assessed at baseline using a modified insulin suppression test.

The large-scale multi-omics assays performed at all time points on each participant included genomics, proteomics, metabolomics and microbiomics.

Despite some differences between the IS and IR group (particularly in differential regulation of inflammatory/immune response pathways), overall, molecular changes were dominated by inter-personal variation (i.e. changes within the same individual), which accounted for more than 90% of the observed variance in some cases (e.g., cytokines). The most striking changes with weight gain were in inflammation response pathways (despite the rather modest weight gain) and were (fortunately) reversed by weight loss.

As the authors note,

“Comparing the variation in cytokine levels between multiple baselines in a single individual versus across individuals, we observed a striking difference: for almost all cytokines, the within-individual coefficient of variation was under 20%, whereas the variation across individuals was 40%–60%. This shows that our baseline cytokine profiles are unique to the individual, a point that has significant implications for one-size-fits-all clinical cytokine assays for the detection and/or monitoring of disease.”

On the opposite side of the spectrum, proteomics and metabolomics measurements had a substantial unexplained component (30% and 35%, respectively), highlighting the presence of unaccounted factors (e.g., food, exercise, and other changing environmental factors) or a subject-specific reaction to the perturbation.

Notably, not all of the responses we observed were consistent across IR and IS participants.

“In particular, for the microbiome, we observed that the microbe A. muciniphila was weight gain responsive only in insulin-sensitive participants. The abundance of this particular microbe in IR individuals did not change across perturbations and was barely or not detectable in most IR individuals.”

Clearly, these findings highlight the fact that each individual is biochemically unique, which the authors note, makes a strong case for personalized analysis in medicine.

Perhaps more importantly for researchers, nearly all of the data are publicly available, enabling exploration of inter-omic relationships and alterations across a longitudinal perturbation, thus providing a valuable resource for the development and validation of bioinformatic tools and pipelines integrating disparate data types.

@DrSharms
Edmonton, AB

 

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How The Body Weighs Itself – Evidence For A Bone “Gravitostat”

In my talks, I have often joked about how to best keep weight off – just carry around a backpack that contains the lost pounds to fool the body into thinking the weight is still there.

It turns out that what was intended as a joke, may in fact not be all too far from how the body actually regulates body weight.

As readers of these posts are well aware, body weight is tightly controlled by a complex neuroendocrine feedback system that effectively defends the body against weight loss (and somewhat, albeit less efficiently, protects against excessive weight gain).

Countless animal experiments (and human observations) show that following weight loss, more often than not, body weight is regained, generally precisely to the level of initial weight.

With the discovery of leptin in the early 90s, an important afferent part of this feedback system became clear. Loss of fat mass leads to a substantial decrease in leptin levels, which in turn results in increased appetite and decreased metabolic rate, both favouring weight regain and thus, restoration of body weight to initial levels.

Now, an international team of researchers led by John-Olov Jansson from the University of Gothenburg, Sweden, in a paper published in the Proceeding of the National Academy of Science (PNAS), provides compelling evidence for the existence of another afferent signal involved in body weight regulation – one derived from weight-bearing bones.

Prompted by observations that prolonged sedentariness can promote weight gain, independent of physical activity, the researchers hypothesised that,

“…there is a homeostat in the lower extremities regulating body weight with an impact on fat mass. Such a homeostat would (together with leptin) ensure sufficient whole body energy depots but still protect land-living animals from becoming too heavy. A prerequisite for such homeostatic regulation of body weight is that the integration center, which may be in the brain, receives afferent information from a body weight sensor. Thereafter, the integration center may adjust the body weight by acting on an effector.”

In a first series of experiments, the researchers observed that implanting a weight corresponding to about 15% of body weight into rodents (rats and mice), resulted in a rapid “spontaneous” adjustment in body weight so that the combined weight of the animal plus the weight implant corresponded more-or-less to that of control animals.

Within two weeks of implanting the weights, ∼80% of the increased loading was counteracted by reduced biological weight, largely due to reduced white adipose tissue (WAT), accompanied by a corresponding decrease in serum leptin levels. (Interestingly, this weight loss was also accompanied by a substantial improvement in insulin resistance and glucose homeostasis).

The decrease in “biological” body weight was mainly attributable to a reduction in caloric intake with no changes in fat oxidation, energy expenditure or physical activity.

Removal of the implanted weights resulted in rapid weight regain to initial levels, showing that the “weight sensor” was active in both directions.

Experiments showed that this “weight sensing” mechanism was largely independent of the leptin pathway and did not appear to involve grehlin, GLP-1, a-MSH, estrogen receptor-a, or the sympathetic nervous system.

Now for the interesting part: the observed effect of weight loading was not seen in mice depleted specifically of DMP1 osteocytes, demonstrating that the suppression of body weight by loading is dependent on osteocytes.

As the authors note, these findings are consistent with a growing body of data indicating that the skeleton is an endocrine organ that regulates energy and glucose metabolism. Indeed, it is well known that osteocytes can sense dynamic short term high-impact bone loading for local bone adaptation – now it appears, that osteocytes may also play a vital role in sensing overall body weight and signalling this to the brain centres that regulate energy balance and body weight.

Thus, in summary, not only have the authors provided compelling evidence for a “weight-sensing” role for bone osteocytes (presumably through their presence in the long weight-bearing bones of our lower extremities) but also provide a plausible biological explanation for the weight gain and change in fat mass seen with prolonged sedentariness (which literally takes the weight off the bone).

These findings may also finally explain why rats held at increased gravity for extended periods of time (simulated G2) become lean even when their energy intake matches their expenditure.

Perhaps, carrying around a heavy backpack may indeed help with long-term weight loss maintenance after all – who knew?

@DrSharma
Berlin, D

Hat tip to Jean-Philippe Chaput for alerting me to this article

 

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