Tupaia belangeri (Wagner, 1841), a Northern Treeshrew is an Animal Model of Metabolic Healthy Obesity
Tupaia belangeri (Wagner, 1841), a Northern Treeshrew is an Animal Model of Metabolic Healthy Obesity
Yanfei Cai1, Jiahong Feng1 and Wanlong Zhu1,2,3*
1Key Laboratory of Ecological Adaptive Evolution and Conservation on Animals-Plants in Southwest Mountain Ecosystem of Yunnan Province Higher Institutes College, School of Life Sciences, Yunnan Normal University, Kunming, Yunnan, China.
2Engineering Research Center of Sustainable Development and Utilization of Biomass Energy Ministry of Education, Yunnan Normal University, Kunming, Yunnan, China.
3Key Laboratory of Yunnan Province for Biomass Energy and Environment Biotechnology, 1st Yuhua District, Chenggong County, Kunming City, Yunnan Province, People’s Republic of China, 650500
ABSTRACT
Obesity has become a harmful factor for life expectancy through a series of diseases, but research on obesity in the past few decades has obtained only limited results. The discovery of the metabolic healthy obesity phenomenon brings a new direction for research of obesity problem, but the lack of an animal of metabolic healthy obesity limits its study. Tupaia belangeri is a new type of experimental animal emerging in recent years and is extremely widely used in various disease models because of its evolutionary status and high affinity with primates. Here, in order to judge whether this new experimental animal can serve as special materials in obesity research, we constructed an obesity model in T. belangeri by using high-fat food, then studied animal insulin sensitivity, blood pressure, blood biochemistry, routine blood, inflammatory response, and liver fat accumulation, etc. We found that T. belangeri had no metabolic disorders such as insulin resistance, hypertension, or hyperlipidemia after obesity, and there were also no typical obesity complications such as diabetes, inflammation, or fatty liver. To sum up, we suggest that T. belangeri can be used as an animal model of metabolic healthy obesity, and the special model organism of metabolic healthy obesity will provide us with new opportunities to study obesity problems.
Article Information
Received 05 May 2023
Revised 03 August 2023
Accepted 18 August 2023
Available online 17 November 2023
(early access)
Published 26 April 2024
Authors’ Contribution
YFC conceived and designed the study. JHF and YFC performed the experiments and acquired the data. WLZ analyzed the data and constructed the figures. WLZ wrote the manuscript. All authors have read and approved the final manuscript.
Key words
Tupaia belangeri, Metabolic healthy obesity, Animal model, Weight loss, Energy metabolism
DOI: https://dx.doi.org/10.17582/journal.pjz/20230505090553
* Corresponding author: [email protected]
0030-9923/2024/0003-1359 $ 9.00/0
Copyright 2024 by the authors. Licensee Zoological Society of Pakistan.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Introduction
Obesity is associated with chronic diseases such as insulin resistance (Kahn et al., 2006), hypertension (Gruber et al., 2021), hyperlipidemia, and diabete (Pillon et al., 2021). Obesity has also been linked to fatty liver (Xie et al., 2017), heart disease, and cancer (Kivimäki et al., 2017). With the increasing global prevalence of obesity, it has become an important factor endangering human health and reducing life expectancy, and its harm is expected to deepen in the future (Afshin et al., 2017; Kelly et al., 2008). Humans have made various attempts to solve the obesity problem (Cefalu et al., 2015), such as fasting (Ulgherait et al., 2021), exercise, and the administration of anti-feeding factor leptin (Duquenne et al., 2021; Montague et al., 1997). However, in the end, it has been found that the majority of dieters regained weight over the long-term, obesity rebounded very quickly after exercise stopped (Van Baak and Mariman, 2019), and exogenous leptin administration rarely worked in vivo (Blüher and Mantzoros, 2009; Myers et al., 2010). Although we have been working on this for decades, this problem has not been effectively solved (Roberto et al., 2015).
The discovery of the metabolic healthy obesity phenomenon brings new direction for research of obesity problem. The concept of healthy obesity was first conceived in the 1980s, and the first case of metabolic healthy obesity was found in the same year (Andres, 1980). As the phenomenon of metabolic healthy obesity has been widely reported, the discussion about healthy obesity has become popular. In recent researches, healthy obesity is that an individual still remains in a good health state despite being obese, which have no insulin resistance, hypertension, diabetes, dyslipidemia, fatty liver, etc. (Wildman, 2009; Bluher, 2012; Stefan et al., 2013). In addition, researches also showed that the healthy obesity phenomenon is fairly prevalent in the obese population (Lavie et al., 2018; Lin et al., 2017), so further research on healthy obesity also has broad application prospects. In short, these data all indicate that metabolic healthy obesity has great potential in the diagnosis, treatment, and research of obesity (Phillips, 2016; Stefan et al., 2018), but the lack of an animal model of metabolic healthy obesity limits its study.
Tupaia belangeri is a small mammal, originating from Borneo, which is widely distributed in the Oriental realm (Roberts et al., 2011; Sargis et al., 2014). T. belangeri belongs to the genus Tupaia of the family Tupaiidae of the order Scandentia in China. It has many characteristics, such as small body size, high brain-to-body mass ratio, and low feeding cost (Zhang et al., 2020). Compared with rodents, T. belangeri has a higher affinity with humans (Janečka Jan et al., 2007; Roberts et al., 2009). So, under the general trend of increasingly limited research use of primates, it shows great development and application prospects (Shi et al., 2020). Accordingly, it has been rapidly developed into a new type of experimental animal and used as a model organism to study basic biology and various medical disease models (Ruan et al., 2016; Che et al., 2021; Zhang et al., 2019), all of which suggest that T. belangeri has great potential for human disease research. T. belangeri might be potential as an animal model of healthy obese. In the present study, we induced the obesity model of T. belangeri using high-fat food. Then, we measured animal glucose tolerance, insulin tolerance, blood biochemical indexes, inflammatory markers, and liver fat accumulation, etc. We predicted that animals did not show metabolic disorders and typical obesity complications. Moreover, the masses of heart, liver, kidney, and other organs of the obese animals still remained normal.
Materials and Methods
Samples
Twenty-eight male T. belangeri (2 years old) animals were purchased from the Institute of Medical Biology, Chinese Academy of Medical Sciences (Kunming, China). Animals were housed at 25±1°C, 12L:12D (lights on at 7:00 am), 50-60% humidity, and 250 lx illumination, and were housed in single cages, food and water were provided ad libitum. All animals were adapted for 2 weeks before the start of the experiment, and then randomly divided into control (n=14) and experimental (n=14) groups. The control group was fed standard chow (fat 24%, protein 32%, carbohydrate 44%), and the experimental group was fed a high-fat diet (fat 57%, protein 21%, carbohydrate 22%).
Glucose tolerance test and insulin tolerance test
For the glucose tolerance test (GTT), animals were intraperitoneally injected with glucose at a dose of 2g/kg after 8 h of fasting. After injection, venous blood was taken at 0, 15, 30, 60, 90, and 120 min, and the blood glucose concentration was measured using a blood glucose meter (SinocareGA-3; Sinocare, Changsha, China). For insulin tolerance testing (ITT), animals were injected with insulin (Novolin R; Novo Nordisk, Bagsvaerd, Denmark) at a dose of 0.75 U/kg after 8 h of fasting, and the blood glucose concentration was measured at 0, 15, 30, 60, 90, and 120 min.
Blood pressure, heart rate, and temperature measurement
We used the tail-cuff method (Erken et al., 2013) to measure blood pressure and heart rate (BP-2000 Blood Pressure Analysis System; Visitech Systems, Apex, North Carolina). Animals were trained to adapt to the procedure several times before the measurement. During formal measurement, animals were allowed to adapt to the test room for 30 min, then the sensor was sleeved onto the tail of the animals. The blood flow signal was monitored by inflating and deflating the tail artery to measure the blood pressure and heart rate, and the average value was taken for three consecutive measurements. In addition, temperature is also the core index reflecting the physiological state. To determine whether the surface temperature distribution and nuclear temperature change after obesity, we imaged animals with an infrared imager (WIC640-SUW; Workswell, Roznov, Czech) to obtain the body surface temperature map (shooting distance: 1m) (Tattersall and Cadena, 2010). We measured the core temperature with a digital thermometer (inserted the probe into the anus approximately 2 cm and remained for 1 min to read).
Blood biochemistry and routine blood analysis
Blood was collected from the heart after fasting for 12h. 1/3 of the blood was anticoagulated with EDTA-K2, while 2/3 was placed at room temperature for 30 min, then centrifuged at 4000g, 4℃ for 10 min to obtain serum. An automatic biochemical analyzer (Cobas 8000 C702; Roche, Barthel, Switzerland) measured serum total cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, glucose, fructose amine, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, cholinesterase, glutaminase aminotransferase, total protein, albumin, globulin, total bilirubin, total bile acid, creatine kinase, lactate dehydrogenase, uric acid, urea nitrogen, and creatinine. Glycosylated hemoglobin level were measured using an automatic glycosylated hemoglobin analyzer (HLC-723GX; TOSOH, Yamaguchi, Japan) (Lahousen et al., 2002), and insulin and fasting C-peptide were detected using an automatic electrochemiluminescence analyzer (Cobas E601; Roche, Barthel, Switzerland). Routine blood tests were performed using an automatic blood cell counter (MEK-5105; NIHON KOHDEN, Tokyo, Japan), and the detection indexes included red blood cell count, white blood cell count, hemoglobin, and platelet count.
Enzyme-linked immunosorbent assay (ELISA)
Serum was obtained as described above, then the concentration level of CRP in the serum was measured using an ELISA kit (RayBiotech, Peachtree Corners, Georgia, USA), and α1-antitrypsin concentration level was detected with α1-antitrypsin kit (RayBiotech, Peachtree Corners, Georgia, USA).
Measurement of anatomical organs
To evaluate whether obesity affects the internal organs of T. belangeri, we used the cervical dislocation method and dissected the animals immediately. Then, the weights of the heart, kidney, large intestine, liver, lung, small intestine, spleen, stomach, testis, thymus, adrenal gland, pancreas, thyroid, and prostate were determined with an analytical balance.
Liver fat analysis
The liver was obtained after dissection. Liver fat accumulation was preliminarily assessed by weighing the liver and observing the adhesion of adipose tissue on its surface. Then, the triglyceride content in the liver was accurately determined using a triglyceride kit (Triglyceride Reagent T2449; Sigma-Aldrich, St. Louis, Missouri, USA). Specifically, 100 mg of tissue was homogenized in 500μl PBS, mixed with chloroform-methanol (2:1 (v/v)), transferred to the organic phase, air-dried overnight, resuspended in 1% Triton X-100 in absolute ethanol, and accurately quantified using a triglyceride assay kit.
Statistical analysis
All data were statistically analyzed using R language (version 4.1.1), and all data were tested for normality and variance homogeneity before analysis. Specifically, the Shapiro-Wilks test was used to evaluate the normality of data, and the F test was used to evaluate the homogeneity of the square difference of data. Repeated measurement variance analysis was used for the GTT and ITT test data, and the double-tailed t-test was used for other data. Statistical significance was set at P < 0.05.
Results
T. belangeri did not show impaired glucose tolerance or insulin resistance after obesity
At the end of the experiment, the body weight and adipose tissue weight between the control group and experimental obesity group reached extremely significant level (Fig. 1A, B), which showed that the construction of obesity model is successful. Unexpectedly, T. belangeri did not suffer from impaired glucose tolerance, and also maintained good insulin sensitivity (Fig. 1C, D). Moreover, the blood glucose of the two groups reached its peak 30 min after glucose injection, and the blood glucose value basically returned to the normal level after 120 minutes (Fig. 1C). Thirty minutes after insulin injection, the blood glucose levels of the two groups reached their lowest levels and returned to normal values 90 min later (Fig. 1D). In addition, through repeated measurement analysis of variance, a significant difference was observed within the intra-group over time (P < 0.05), but there was no significant difference between the groups (P > 0.05), indicating that both the control and obese groups responded to GTT and ITT and showed the same trend of change. In conclusion, through GTT and ITT, we confirmed that there was no impaired glucose tolerance or insulin resistance in T. belangeri after obesity.
Animal’s blood pressure, heart rate, and temperature were not abnormal
The results showed that there was no increase in blood pressure in the obese group (Fig. 1E), and no statistical difference was found in the heart rate analysis between the two groups (Fig. 1F). Therefore, obesity does not lead to hypertension or abnormal heart rate. In addition, body temperature analysis showed that not only did the core temperature not change (Fig. 1G), but the size and distribution gradient of body surface temperature did not change significantly (Supplementary Fig. S1). In general, by analyzing the representative physiological indicators, we found that animal can still maintain their normal state.
Blood biochemistry and routine blood indicators were normal
The blood lipid and blood glucose state were not significantly different (Fig. 2A, C). Testing and statistical analysis showed that aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase, cholinesterase, glutamine aminotransferase, total protein, albumin, globulin, total bilirubin and total bile acids, which reflect liver health, did not differ statistically between groups (Fig. 2D, H). There were also no significant differences in creatine kinase and lactate dehydrogenase, indicators of heart health (Fig. 2I). In addition, there were no significant differences in uric acid, urea nitrogen and creatinine. Nitrogen, or creatinine, an important indicator of renal function, was also not significantly different (Fig. 2J, L). Similarly, there were no significant differences in red blood cell count, white blood cell count, haemoglobin or platelet count in the obese group compared to the control group (Fig. 3A, D).
Obesity animal does not show inflammation
The results showed that the CRP level did not increase in the obese group compared to the control group (Fig. 3E). Notably, consistent with the above results, there was no statistical difference in the levels of α1 - antitrypsin between the two groups (Fig. 3F).
Despite obesity, there was no impact on internal organs weight
In order to determine whether obesity has an impact on the internal organs of animals, we dissected the animals immediately after execution and accurately weighed the animal organs with an analytical balance. The analysis revealed that not only the heart, kidney, large intestine, liver, lung, small intestine, spleen, and stomach, but also the testis, an organ reflecting reproductive function, showed no statistical difference between the two groups (Fig. 4A). Interestingly, we also found that the weight of the thymus, an immune organ, did not difference (Fig. 4B), which is consistent with the above conclusion that obesity does not cause inflammation. In addition, we confirmed that the weights of the adrenal gland, pancreas, and thyroid were not altered in obese animals (Fig. 4D, E, H). Finally, we also confirmed that the prostate, an organ associated with urination and reproduction, was unchanged (Fig. 4F). In conclusion, the above results demonstrate that, although the animals become obese, this does not affect the internal organs weight.
An obesity animal does not show liver fat accumulation
The results showed that the liver weight of obese animals did not increase (Fig. 4C), there was no fat attachment on the surface of the liver after obesity (Supplementary Fig. S2), and there was also no significant difference in triglyceride content in the liver between the two groups (Supplementary Fig. S2).
Discussion
Because obesity is usually accompanied by impaired glucose tolerance and insulin resistance (Kornfeld et al., 2013), we performed GTT and ITT to determine whether these typical symptoms also occurred in obese T. belangeri. In the present study, T. belangeri did not suffer from impaired glucose tolerance, and also maintained good insulin sensitivity. Blood pressure, heart rate, and body temperature are important indicators to reflect physiological state of the body (Ayres, 2020). In our results, it showed that there was no increase in blood pressure in the obese group, heart rate analysis and body temperature. Therefore, obesity does not lead to hypertension or abnormal heart rate. Previous studies have shown that hyperlipidemia and hyperglycemia are typical complications of obesity (Pillon et al., 2021), and that these chronic metabolic diseases seriously affect health and reduce life expectancy (Lamharzi et al., 2004). We found that the blood lipid and blood glucose state were not significantly different, which indicated that animals did not cause hyperlipidemia or hyperglycemia after obesity. Moreover, other blood biochemical and routine blood indicators showed no significant differences in red blood cell count, white blood cell count, haemoglobin or platelet count in the obese group compared to the control group, indicating that the animals remained healthy after obesity from the aspect of routine blood work.
In addition to insulin resistance, hypertension, hyperlipidemia, and hyperglycemia (Kahn et al., 2006; Gruber et al., 2021; Pillon et al., 2021), obesity is often associated with inflammatory reactions (Bapat et al., 2022), and CRP is a classic marker of vascular inflammation (Koenig et al., 1999). In addition, α1-antitrypsin is also an important inflammatory marker (Teckman et al., 1996). It showed that the CRP level did not increase in the obese group compared to the control group, which was consistent with the levels of α1-antitrypsin. Considering the typicality of hepatic fat accumulation in obesity (Xie et al., 2017), we analyzed fat accumulation in the liver. It showed that the liver weight of obese animals did not increase, there was no fat attachment on the surface and triglyceride content of the liver after obesity. These results indicate that animal does not accumulate fat in the liver after obesity. In conclusion, through the study of liver weight, fatty tissue adhesion on the liver surface, and triglyceride content in the liver, we confirmed that the animals did not show liver fat accumulation after obesity.
Based on the above studies, we found that T. belangeri can maintain glucose tolerance, blood pressure, blood biochemical, inflammatory markers, organs weight, and liver fat content normal status after obesity. This shows that this animal can maintain metabolic and physiological health after obesity, so it has a great potential in the study of metabolic health obesity as an animal model. Besides, we found that humans (Lavie et al., 2018; Lin et al., 2017) and Lasiopodomys brandtii (Liu et al., 2016), a rodent, also have a phenomenon of metabolic healthy obesity, and many other species that have not yet been studied may also maintain a healthy state after obesity. Although the phenomenon of metabolic health obesity exists in many species, whether the internal mechanism leading to this phenomenon is consistent across different species, whether it is all by the same key molecules and the same key pathways, is also a key question to be answered. All in all, healthy obesity may be an opportunity for understanding obesity problems, and it is worthy of further and extensive exploration in this field. In previous obesity research, we obtained relatively limited results when we focused on addressing obesity-related metabolic disorders and various disease consequences (Kivimäki et al., 2017). Therefore, this special animal model provides us with a new direction to study obesity, and also provides us with a new idea to research the current severe obesity problems. With the worsening global prevalence of obesity, it is of practical significance to explore new direction, and this new experimental animal may play an important role in further research.
Although our study revealed those findings, it has some limitations. First, health has many aspects (López-Otín and Kroemer, 2021), and we have mainly measured the metabolic aspect of animals. Whether other aspects, such as tissue, cellular and molecular levels, can also remain healthy require further research. Therefore, metabolic health cannot be equated with complete and overall health. The overall health of the animals needs more comprehensive evaluation. Besides, our experiments are observational and descriptive, and we have not further researched the internal molecular mechanism of the metabolic healthy obesity phenomenon. However, the research on the molecular mechanism of metabolic healthy obesity is the next most important work. Finally, the animals used in this experiment were males, but whether there was consistency in females requires further investigation. In summary, this study demonstrates that T. belangeri has a great potential in the study of metabolic health obesity as an animal model. It provides new opportunities and ideas for re-understanding obesity problems.
Acknowledgments
We thank Yazuo Yang, Yuting Liu, Yuqiu Liao, Bowen Yan, and Huibao Chen for their help with animal procedures. Thank are also done to the anonymous reviewers and the editor of the journal for their valuable comments.
Funding
This work was supported by the National Natural Scientific Foundation of China (No. 32160254; 31760118), Yunnan Ten Thousand Talents Plan Young and Elite Talents Project (YNWR-QNRC-2019-047), and Yunnan Provincial Middle-Young Academic and Technical Leader Candidate (2019HB013).
IRB approval
All animal procedures were compliance with the Animal Care and Use Committee of School of Life Science, Yunnan Normal University.
Ethical statement
This study was approved by the Ethics Committee (No.13-0901-011).
There is supplementary material associated with this article. Access the material online at: https://dx.doi.org/10.17582/journal.pjz/20230505090553
Statement of conflict of interest
The authors have declared no conflict of interest.
References
Afshin, A., Sur, P., Estep, K., Lee, A., Marczak, L., Moradi-Lakeh, M., Naghavi, M. and Vos, T., 2017. Health effects of overweight and obesity in 195 countries over 25 years. N. Eng. J. Med., 377: 13-27. https://doi.org/10.1056/NEJMoa1614362
Andres, R., 1980. Effect of obesity on total mortality. Int. J. Obes., 4: 381-386.
Ayres, J.S., 2020. The biology of physiological health. Cell, 181: 250-269. https://doi.org/10.1016/j.cell.2020.03.036
Bapat, S.P., Whitty, C., Mowery, C.T., Liang, Y., Yoo, A., Jiang, Z., Peters, M.C., Zhang, L.J., Vogel, I., Zhou, C., Nguyen, V.Q., Li, Z., Chang, C., Zhu, W.S., Hastie, A.T., He, H., Ren, X. and Qiu, W., 2022. Obesity alters pathology and treatment response in inflammatory disease. Nature, 604: 337-342. https://doi.org/10.1038/s41586-022-04536-0
Bluher, M., 2012. Are there still healthy obese patients? Curr. Opin. Endocrinol. Diabetes Obes., 19: 341-346. https://doi.org/10.1097/MED.0b013e328357f0a3
Blüher, S. and Mantzoros, C.S., 2009. Leptin in humans: Lessons from translational research. Am. J. clin. Nutr., 89: 991S-997S. https://doi.org/10.3945/ajcn.2008.26788E
Cefalu, W.T., Bray, G.A., Home, P.D., Garvey, W.T., Klein, S., Pi-Sunyer, F.X., Hu, F.B., Raz, I., Van, Gaal. L., Wolfe, B.M. and Ryan, D.H., 2015. Advances in the science, treatment, and prevention of the disease of obesity: Reflections from a diabetes care editors’ expert forum. Diabetes Care, 38: 1567-1582. https://doi.org/10.2337/dc15-1081
Che, P., Wang, M., Larson-Casey, J.L., Hu, R.H., Cheng, Y.E., Hamdaoui, M., Zhao, X.K., Grytz, R., Brent, Carter. A. and Ding, Q., 2021. A novel tree shrew model of pulmonary fibrosis. Lab. Investig., 101: 116-124. https://doi.org/10.1038/s41374-020-00476-3
Duquenne, M., Folgueira, C., Bourouh, C., Millet, M., Silva, A., Clasadonte, J., Imbernon, M., Fernandois, D., Martinez-Corral, I., Kusumakshi, S., Caron, E., Rasika, S. and Deliglia, E., 2021. Leptin brain entry via a tanycytic LepR–EGFR shuttle controls lipid metabolism and pancreas function. Nat. Metab., 3: 1071-1090. https://doi.org/10.1038/s42255-021-00432-5
Erken, H.A., Erken, G. and Genç, O., 2013. Blood pressure measurement in freely moving rats by the tail cuff method. Clin. exp. Hyperten., 35: 11-15. https://doi.org/10.3109/10641963.2012.685534
Ezzati, M., Bentham, J. and Bilano, V., 2017. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet, 390: 2627-2642.
Gruber, T., Pan, C., Wiedemann, T., Lefort, S., Le, Thuc. O., Legutko, B., García-Villalón, A.L., Ussar, S., Pfluger, P., Woods, S.C., Ertürk, A. and García-Cáceres, C., 2021. Obesity-associated hyperleptinemia alters the gliovascular interface of the hypothalamus to promote hypertension. Cell Metab., 33: 1155-1170.e1110. https://doi.org/10.1016/j.cmet.2021.04.007
Janecka, J.E., Miller, W., Pringle, T.H., Wiens, F., Zitzmann, A., Helgen, K.M., Springer, M.S. and Murphy, W.J., 2007. Molecular and genomic data identify the closest living relative of primates. Science, 318: 792-794. https://doi.org/10.1126/science.1147555
Kahn, S.E., Hull, R.L. and Utzschneider, K.M., 2006. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature, 444: 840-846. https://doi.org/10.1038/nature05482
Kelly, T., Yang, W. and Chen, C.S., 2008. Reynolds, K. and He, J. Global burden of obesity in 2005 and projections to 2030. Int. J. Obes., 32: 1431-1437. https://doi.org/10.1038/ijo.2008.102
Kivimäki, M., Kuosma, E., Luukkonen, R., Alfredsson, L., Fransson, E., Goldberg, M., Knutsson, A., Koskenvuo, M., Nordin, M., Oksanen, T., Pentti, J., Rugulies, R., Singh-Manoux, A., Steptoe, A., Theorell, T., Vahtera, J., Virtanen, M., Westerholm, P., Westerlund, H., Zins, M., Hamer, M. and Jokela, M., 2017. Overweight, obesity, and risk of cardiometabolic multimorbidity: Pooled analysis of individual-level data for 120 813 adults from 16 cohort studies from the USA and Europe. Lancet Publ. Hlth., 2: e277-e285. https://doi.org/10.1016/S2468-2667(17)30074-9
Koenig, W., Sund, M., Fröhlich, M., Fischer, H.G., Löwel, H., Döring, A., Hutchinson, W.L. and Pepys, M.B., 1999. C-Reactive protein, a sensitive marker of inflammation, predicts future risk of coronary heart disease in initially healthy middle-aged men: Results from the MONICA (monitoring trends and determinants in cardiovascular disease) Augsburg cohort study, 1984 to 1992. Circulation, 99: 237-242. https://doi.org/10.1161/01.CIR.99.2.237
Kornfeld, J.W., Baitzel, C., Könner, A.C., Nicholls, H.T., Vogt, M.C., Herrmanns, K., Scheja, L., Haumaitre, C., Wolf, A.M., Knippschild, U., Seibler, J., Cereghini, S., Heeren, J., Stoffel, M. and Brüning, J.C., 2013. Obesity-induced overexpression of miR-802 impairs glucose metabolism through silencing of Hnf1b. Nature, 494: 111-115. https://doi.org/10.1038/nature11793
Lahousen, T., Roller, R.E., Lipp, R.W. and Schnedl, W.J., 2002. Silent haemoglobin variants and determination of HbA(1c) with the HPLC Bio-Rad Variant II. J. Clin. Pathol., 55: 699. https://doi.org/10.1136/jcp.55.9.699
Lamharzi, N., Renard, C.B., Kramer, F., Pennathur, S., Heinecke, J.W., Chait, A. and Bornfeldt, K.E., 2004. Hyperlipidemia in concert with hyperglycemia stimulates the proliferation of macrophages in Atherosclerotic lesions: potential role of glucose-oxidized LDL. Diabetes, 53: 3217-3225. https://doi.org/10.2337/diabetes.53.12.3217
Lavie, C.J., Laddu, D. and Arena, R., 2018. Healthy weight and obesity prevention: JACC health promotion series. J. Am. College Cardiol., 72: 1506-1531. https://doi.org/10.1016/j.jacc.2018.08.1037
Lin, H., Zhang, L., Zheng, R. and Zheng, Y., 2017. The prevalence, metabolic risk and effects of lifestyle intervention for metabolically healthy obesity: A systematic review and meta-analysis: A PRISMA-compliant article. Medicine, 96: e8838. https://doi.org/10.1097/MD.0000000000008838
Liu, X.Y., Yang, D.B., Xu, Y.C., Gronning, M.O., Zhang, F., Wang, D.H. and Speakman, J.R., 2016. Photoperiod induced obesity in the Brandt’s vole (Lasiopodomys brandtii): A model of healthy obesity? Dis. Models Mech., 9: 1357-1366. https://doi.org/10.1242/dmm.026070
López-Otín, C. and Kroemer, G., 2021. Hallmarks of health. Cell, 184: 33-63. https://doi.org/10.1016/j.cell.2020.11.034
Montague, C.T., Farooqi, I.S., Whitehead, J.P., Soos, M.A., Rau, H., Wareham, N.J., Sewter, C.P., Digby, J.E., Mohammed, S.N., Hurst, J.A., Cheetham, C.H., Earley, A.R., Barnett, A.H., Prins, J.B. and O’Rahilly, S., 1997. Congenital leptin deficiency is associated with severe early-onset obesity in humans. Nature, 387: 903-908. https://doi.org/10.1038/43185
Myers, M.G., Leibel, R.L., Seeley, R.J. and Schwartz, M.W., 2010. Obesity and leptin resistance: Distinguishing cause from effect. Trends Endocrinol. Metab., 21: 643-651. https://doi.org/10.1016/j.tem.2010.08.002
Phillips, C.M., 2016. Metabolically healthy obesity: Personalised and public health implications. Trends Endocrinol. Metab., 27: 189-191. https://doi.org/10.1016/j.tem.2016.02.001
Pillon, N.J., Loos, R.J.F., Marshall, S.M. and Zierath, J.R., 2021. Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care. Cell, 184: 1530-1544. https://doi.org/10.1016/j.cell.2021.02.012
Roberto, C.A., Swinburn, B., Hawkes, C., Huang, T.T., Costa, S.A., Ashe, M., Zwicker, L., Cawley, J.H. and Brownell, K.D., 2015. Patchy progress on obesity prevention: Emerging examples, entrenched barriers, and new thinking. Lancet, 385: 2400-2409. https://doi.org/10.1016/S0140-6736(14)61744-X
Roberts, T.E., Lanier, H.C., Sargis, E.J. and Olson, L.E., 2011. Molecular phylogeny of treeshrews (Mammalia: Scandentia) and the timescale of diversification in Southeast Asia. Mol. Phylogenet. Evol., 60: 358-372. https://doi.org/10.1016/j.ympev.2011.04.021
Roberts, T.E., Sargis, E.J. and Olson, L.E., 2009. Networks, trees, and treeshrews: Assessing support and identifying conflict with multiple loci and a problematic root. Syst. Biol., 58: 257-270. https://doi.org/10.1093/sysbio/syp025
Ruan, G.P., Yao, X., Liu, J.F., He, J., Li, Z.A., Yang, J.Y., Pang, R.Q. and Pan, X.H., 2016. Establishing a tree shrew model of systemic lupus erythematosus and cell transplantation treatment. Stem Cell Res. Ther., 7: 121. https://doi.org/10.1186/s13287-016-0385-1
Sargis, E.J., Woodman, N., Morningstar, N.C., Reese, A.T. amd Olson, L.E., 2014. Island history affects faunal composition: The treeshrews (Mammalia: Scandentia: Tupaiidae) from the Mentawai and Batu Islands, Indonesia. Biol. J. Linn. Soc., 111: 290-304. https://doi.org/10.1111/bij.12195
Shi, Z., Xing, H., Qi, C., Fang, M. and Fu, J., 2020. Zhang X. Chinese tree shrews as a primate experimental animal eligible for the study of alcoholic liver disease: Characterization and confirmation by MRI. Exp. Anim., 69: 110-118. https://doi.org/10.1538/expanim.19-0073
Stefan, N., Häring, H.U. and Schulze, M.B., 2018. Metabolically healthy obesity: The low-hanging fruit in obesity treatment? The lancet. Diabetes Endocrinol., 6: 249-258. https://doi.org/10.1016/S2213-8587(17)30292-9
Stefan, N., Häring, H.U., Hu, F.B. and Schulze, M.B., 2013. Metabolically healthy obesity: Epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol., 1: 152-162. https://doi.org/10.1016/S2213-8587(13)70062-7
Sumithran, P. and Proietto, J., 2013. The defence of body weight: A physiological basis for weight regain after weight loss. Clin. Sci., 124: 231-241. https://doi.org/10.1042/CS20120223
Tattersall, G.J. and Cadena, V., 2010. Insights into animal temperature adaptations revealed through thermal imaging. Imag. Sci. J., 58: 261-268. https://doi.org/10.1179/136821910X12695060594165
Teckman, J.H., Qu, D. and Perlmutter, D.H., 1996. Molecular pathogenesis of liver disease in alpha1-antitrypsin deficiency. Hepatology (Baltimore, Med.), 24: 1504-1516. https://doi.org/10.1053/jhep.1996.v24.ajhep0241504
Ulgherait, M., Midoun, A.M., Park, S.J., Gatto, J.A., Tener, S.J., Siewert, J., Klickstein, N., Canman, J.C., Ja, W.W. and Shirasu-Hiza, M., 2021. Circadian autophagy drives iTRF-mediated longevity. Nature, 598: 353-358. https://doi.org/10.1038/s41586-021-03934-0
van Baak, M.A. and Mariman, E.C.M., 2019. Mechanisms of weight regain after weight loss-the role of adipose tissue. Nat. Rev. Endocrinol., 15: 274-287. https://doi.org/10.1038/s41574-018-0148-4
Wildman, R.P., 2009. Healthy obesity. Curr. Opin. Clin. Nutr. Metab. Care, 12: 438-443. https://doi.org/10.1097/MCO.0b013e32832c6db7
Xie, C., Yagai, T., Luo, Y., Liang, X., Chen, T., Wang, Q., Sun, D., Zhao, J., Sun, L., Jiang, C., Xue, X., Tian, Y., Wu, Y. and Jiang, C., 2017. Activation of intestinal hypoxia-inducible factor 2α during obesity contributes to hepatic steatosis. Nat. Med., 23: 1298-1308. https://doi.org/10.1038/nm.4412
Zhang, J., Luo, R.C., Man, X.Y., Lv, L.B., Yao, Y.G. and Zheng, M., 2020. The anatomy of the skin of the Chinese tree shrew is very similar to that of human skin. Zool. Res., 41: 208-212. https://doi.org/10.24272/j.issn.2095-8137.2020.028
Zhang, L., Shen, Z.L., Feng, Y., Li, D.Q., Zhang, N.N., Deng, Y.Q., Qi, X.P., Sun, X.M., Dai, J.J., Yang, C.G., Yang, Z.F., Qin, C.F. and Xia, X.S., 2019. Infectivity of Zika virus on primary cells support tree shrew as animal model. Emerg. Microbes Infect., 8: 232-241. https://doi.org/10.1080/22221751.2018.1559707
To share on other social networks, click on any share button. What are these?