真的有健康的胖子嗎？

眾所周知，肥胖會導致糖尿病、高血壓、心血管疾病和其他各種疾病的險。但是似乎肥胖人群中有15-20%的人並沒有這種風險，也就是說這些人屬於「健康的胖子」。這是為什麼呢？且聽科學家們的研究解釋。

近日發表在PLOS ONE期刊上的這項研究（論文信息見文末），解釋了一些胖子仍然保持健康的原因。

該研究確定了三種基因，它們似乎會影響脂肪是被區分開並且存儲在身體外圍還是會分解進入循環系統。如果血液中脂肪水平過高便會增加II型糖尿病的風險，還會導致心臟和肝臟的脂肪堆積。

哥本哈根大學的遺傳學教授 Haja Kadarmideen 說：「能夠存儲大量脂肪的人可能會肥胖，但不見得不健康。」

過去的研究發現，雖然超重或者肥胖會導致糖尿病、肝臟疾病和心臟病風險增加，但是肥胖人士中的15-20%似乎並沒有此類健康問題。

另一項有10萬丹麥人參與的研究發現，身體質量指數（BMI）處於「超重」水平的人相比於BMI為「健康」, 「體重不足」和「肥胖」的人壽命更長，這顯得體重、健康和壽命之間的關係並不是那麼的直截了當。

Kadarmideen說：「我們想要弄清楚一些肥胖的人仍然保持健康的原因。」

這項研究發現，個體的基因組成會影響健康問題突破了所謂的體重上限，而對於某些人來說，這個數值可能並不在「健康」BMI範圍內。相反的，一些BMI在健康範圍內的人（外表看起來很瘦卻內臟肥厚）卻會產生代謝問題，而這些代謝問題通常與肥胖相關。

這項研究有60名男性和女性參與，他們的肥胖呈現病態（BMI超過45），其中一半的人有代謝疾病，另一半的人則身體健康。參與者都接受了減肥手術。

科學家們掃描了數以萬計的基因以發現兩組之間的差異。最終，他們發現在這兩組中有3種基因，其活性和潛在基因序列相關的特性非常不同。當科學家們進一步測試脂肪和組織樣本中的基因活性時，他們得出結論，這些基因在調節脂肪在細胞中的運輸和存儲方面起到重要作用。該團隊希望能夠在更大規模的研究中復現這個發現。

在未來，可能能夠基於個體的基因特性和其他生物標記制定個性化的健康BMI值。Kadarmideen 說：「這項發現為診斷、藥物開發和以特定基因為靶向的治療鋪平了道路。」

他補充說，這項發現不應該被作為拋棄均衡飲食和鍛煉的借口，因為這些生活方式對健康有著重要影響，而這與體重無關。

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相關論文信息

題目Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals

作者Lisette J. A. Kogelman, Jingyuan Fu, Lude Franke, Jan Willem Greve, Marten Hofker, Sander S. Rensen, Haja N. Kadarmideen

發表日期December 1, 2016

DOI:http://dx.doi.org/10.1371/journal.pone.0167519

摘要

Background

Obesity is associated with severe co-morbidities such as type 2 diabetes and nonalcoholic steatohepatitis. However, studies have shown that 10–25 percent of the severely obese individuals are metabolically healthy. To date, the identification of genetic factors underlying the metabolically healthy obese (MHO) state is limited. Systems genetics approaches have led to the identification of genes and pathways in complex diseases. Here, we have used such approaches across tissues to detect genes and pathways involved in obesity-induced disease development.

Methods

Expression data of 60 severely obese individuals was accessible, of which 28 individuals were MHO and 32 were metabolically unhealthy obese (MUO). A whole genome expression profile of four tissues was available: liver, muscle, subcutaneous adipose tissue and visceral adipose tissue. Using insulin-related genes, we used the weighted gene co-expression network analysis (WGCNA) method to build within- and inter-tissue gene networks. We identified genes that were differentially connected between MHO and MUO individuals, which were further investigated by homing in on the modules they were active in. To identify potentially causal genes, we integrated genomic and transcriptomic data using an eQTL mapping approach.

Results

Both IL-6 and IL1B were identified as highly differentially co-expressed genes across tissues between MHO and MUO individuals, showing their potential role in obesity-induced disease development. WGCNA showed that those genes were clustering together within tissues, and further analysis showed different co-expression patterns between MHO and MUO subnetworks. A potential causal role for metabolic differences under similar obesity state was detected for PTPRE, IL-6R and SLC6A5.

Conclusions

We used a novel integrative approach by integration of co-expression networks across tissues to elucidate genetic factors related to obesity-induced metabolic disease development. The identified genes and their interactions give more insight into the genetic architecture of obesity and the association with co-morbidities.

論文鏈接http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0167519