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Zhao et al.3 To unravel the web of interconnected pathways in cell death and inflammation is a heroic task that keeps both cell-death and kidney research breathing—don’t take our breath away!
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DISCLOSURE
All the authors declared no competing interests.
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Linkermann A, Green DR. Necroptosis. N Engl J Med 2014; 370: 455–465. Vanden Berghe T, Linkermann A, JouanLanhouet S et al. Regulated necrosis: the expanding network of non-apoptotic cell death pathways. Nat Rev Mol Cell Biol 2014; 15: 135–147. Zhao H, Ning J, Lemaire A et al. Necroptosis and parthanatos are involved in remote lung injury after receiving ischemic renal allografts in rats. Kidney Int 2015; 87: 738–748.
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Linkermann A, Stockwell BR, Krautwald S et al. Regulated cell death and inflammation: an auto-amplification loop causes organ failure. Nat Rev Immunol 2014; 14: 759–767. Devalaraja-Narashimha K, Diener AM, Padanilam BJ. Cyclophilin D gene ablation protects mice from ischemic renal injury. Am J Physiol Renal Physiol 2009; 297: F749–F759. Linkermann A, Brasen JH, Darding M et al. Two independent pathways of regulated necrosis mediate ischemia-reperfusion injury. Proc Natl Acad Sci USA 2013; 110: 12024–12029. Park JS, Pasupulati R, Feldkamp T et al. Cyclophilin D and the mitochondrial permeability transition in kidney proximal tubules after hypoxic and ischemic injury. Am J Physiol Renal Physiol 2011; 301: F134–F150. Figueiredo N, Chora A, Raquel H et al. Anthracyclines induce DNA damage responsemediated protection against severe sepsis. Immunity 2013; 39: 874–884. Sosna J, Voigt S, Mathieu S et al. TNF-induced necroptosis and PARP-1-mediated necrosis represent distinct routes to programmed necrotic cell death. Cell Mol Life Sci 2014; 71: 331–348.
see clinical investigation on page 784
Friends, social networks, and progressive chronic kidney disease William M. McClellan1,2 and John J. Doran2 A report by Dunkler et al. reminds us that social factors are relevant for today’s clinical scientist and practitioner. They report that an increasing number of friends reduces the incidence and progression of chronic kidney disease in type 2 diabetes. The observation that ‘friends don’t let friends’ develop kidney disease suggests that social factors, as well as biomarkers, may be relevant in developing ‘personalized renal medicine’ and may identify areas for future nephrology research and education. Kidney International (2015) 87, 682–684. doi:10.1038/ki.2015.23
The emergence of scientific medicine during the late eighteenth and nineteenth centuries retained an earlier awareness of the interplay between physical causes, the 1 Department of Epidemiology, Emory University, Atlanta, Georgia, USA and 2Department of Medicine, Emory University, Atlanta, Georgia, USA Correspondence: William M. McClellan, Department of Epidemiology, Rollins School of Public Health, Emory University School of Medicine, 1518 Clifton Road, Atlanta, Georgia 30322, USA. E-mail:
[email protected]
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object of the new science, and social factors, which conveyed disease susceptibility through poorly understood mechanisms.1 For example, Virchow, commenting on an epidemic of typhus, noted, ‘Living conditions y are either natural or artificial depending on the spatial and temporal situation of the individual. y Artificial epidemics y are attributes of society, products of a false culture or of a culture that is not available to all classes. These are indicators of defects produced
by political and social organization, and therefore affect predominately those classes that do not participate in the advantages of the culture.’1 The scientific study of social factors associated with disease risk was, to some extent, sidelined in the ensuing twentieth century by scientific advances in understanding disease etiology, hygiene, and therapeutics, which revolutionized medicine. Flexner’s 1910 report illustrates the new emphasis, stressing the importance of basic medical sciences and bedside clinical instruction for reforming American medical education. The success of the subsequent scientific medical enterprise tended to relegate the study of the non-biological, social determinants of health to nursing, allied social sciences, and public health. For example, today, when we speak of ‘personalized medicine’ we are envisioning a future in which therapeutic emphasis during all stages of disease, including prevention, diagnosis, treatment, and follow-up, is directed by genomic, metabolomic, proteomic, and microbiomic biomarkers.2 Although this goal is estimable, one wonders where Virchow’s ‘artificial’ determinants of health and disease are to be found in this future model for medicine. The report by Dunkler et al.3 (this issue) reminds us that the social context of disease causation may be as relevant to today’s clinical scientist and practitioner as biomarker-directed risk stratification and therapy. The authors report a novel association between an individual’s number of social contacts and the incidence and progression of chronic kidney disease (CKD) in type 2 diabetes. Their observation reminds one of the popular meme that ‘friends don’t let friends,’ in this case, progress as rapidly to incident kidney disease. Further, it engenders the question of whether an understanding of mechanisms associated with friendship density and function may be relevant to personalized medicine as we currently define it. Dunkler et al.3 studied nearly 7000 participants in the Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET). Dunkler et al. measured progressive Kidney International (2015) 87
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Combined outcome 1.6 Relative odds of combined event
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Figure 1 | Adjusted models for renal end points. Association of the social network score and relative odds (solid line) with 95% confidence interval (dashed lines) with (a) incidence or progression of CKD, or death (combined outcome), and (b) incident albuminuria. The median of the first tertile is used as reference. Histograms show the distribution of the social network score in the respective outcome state. The model is adjusted for baseline age; duration of diabetes; glomerular filtration rate; status of albuminuria; sex; ONTARGET randomization arms; D-urinay albumin to creatinine ratio (UACR) to progression, which was defined as the difference between the participant-specific cut point of developing a new micro- or macroalbuminuria and UACR at baseline on the log-scale; body mass index; mean arterial blood pressure; fasting plasma glucose; and previous use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers.
CKD as a composite end point of incident end-stage renal disease, a decline in estimated glomerular filtration rate of more than 5% per year, progression of albuminuria of at least 30% over baseline, or incident microalbuminuria (albumin–creatinine ratio between 3.4 and 33.9 mg/mmol). Network size was measured using self-generated names of friends, a standard means of defining social network membership. Four questions were used to measure the network size: (1) ‘About how many people do you know personally and interact with who share your interests?’ (2) ‘About how many friends or family members do you have Kidney International (2015) 87
with whom you can speak frankly?’ (3) ‘About how many friends do you have who would drop by your home unexpectedly, and you wouldn’t be embarrassed if your home was untidy?’ (4) ‘Not including those who live with you, about how many people do you visit or visit you in an ordinary week?’ Individual event rates were substantial, with end-stage renal disease occurring in 4% of subjects, decline in estimated glomerular filtration rate in 21%, and progressive albuminuria in 14%, and a composite end point was based on a simple sum of these events. A larger number of close friends and
acquaintances was associated with a reduced risk of both the composite end point and its individual components. This protective benefit persisted after accounting for the competing risk of death and controlling for other potential confounders including reported financial worries, level of education, and lifestyle factors such as smoking. Supplementary results show a monotonic decline in risk of progression of CKD across the range of social network scores in both univariate and adjusted analyses (Figure 1). Similar results are reported for the entire ONTARGET population. Finally, the individual components of the social network score were weakly associated with decline in estimated glomerular filtration rate and strongly associated with progression of albuminuria. An important consideration in interpreting these results is that CKD is largely an asymptomatic state and thus the mechanisms linking friendship to progression of impaired kidney function are unlikely to be mediated by processes associated with disease awareness. Finally, as the authors note, these results replicate previously reported associations between the size of an individual’s social network and risk of death and associations between CKD progression and other lifestyle risk factors including socioeconomic status and physical exercise. The observation that one’s social network is associated with increased risk of progressive kidney disease is not entirely unexpected,4 as similar findings have repeatedly been reported for cardiovascular disease.5 Further, work by Christakis and his colleagues has shown that the characteristics of social network membership in the Framingham Heart Study (FHS), rather than its size, are associated with acquisition of kidney disease risk factors, including obesity and smoking.6 Notably, the spread over time and space of obesity in FHS network members extended beyond immediate friends to include social ties defined by ‘friends of friends’ (second-degree) and third-degree relationships. A major limitation of many of these studies, including the one by Dunkler 683
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et al.,3 is that the nature of the friendship links associated with the outcome is unspecified and may reflect affiliation between individuals sharing similar attributes, rather than influence directed from one individual to another. For example, one might speculate that social ties within families may be more (or less) influential than those with non-related friends. Work by Christakis’s group suggests that, with respect to the spread of obesity in social networks, the association between non-related individuals is as strong as, or stronger than, that observed among siblings and spouses.6 These associations are likely to be mutable and thus should be examined on a case-to-case basis. It would be wrong to suggest that the social and behavioral antecedents of disease have been ignored by contemporary kidney disease research. For example, among many others, Kimmel and his associates have conducted extensive studies of the social context of end-stage renal disease.7,8 Rather, the report by Dunkler et al.3 is an oppor-
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tunity to suggest that an important challenge facing the evolution of personal medicine in renal medicine is to continue to elucidate the biological and behavioral mechanisms that link ethnicity, income, and the characteristics of social networks to risk of kidney disease; and then to use that information to prepare physicians to deliver nephrology care in the 21st century.9 Is it possible that we might enhance the attraction of nephrology to trainees by broadening our definition of ‘personalized’ to include scientifically grounded understanding of the social attributes of health and disease? Perhaps in the future we will gain, and be able to incorporate into our therapeutic planning, an understanding of how one’s number of friends might influence our delivery of ‘the right drug at the right dose at the right time’ to individuals at risk of kidney disease and related outcomes.2
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DISCLOSURE
The authors declared no competing interests.
Rosen G. From Medical Police to Social Medicine. Science History Publications: New York, 1974. Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med 2010; 363: 301–304. Daniela D, Kohl M, Heinze G et al. Modifiable lifestyle and social factors affect chronic kidney disease in high-risk individuals with type 2 diabetes mellitus. Kidney Int 2015; 87: 784–791. Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. Am J Epidemiol 1979; 109: 186–204. Lett HS, Blumenthal JA, Babyak MA et al. Social support and coronary heart disease: epidemiologic evidence and implications for treatment. Psychosom Med 2005; 67: 869–878. Christakis NA, Fowler JH. Social contagion theory: examining dynamic social networks and human behavior. Stat Med 2013; 32: 556–577. Cukor D, Kimmel PL. Education and end of life in chronic kidney disease: disparities in black and white. Clin J Am Soc Nephrol 2010; 5: 163–166. Kimmel PL, Fwu CW, Eggers PW. Segregation, income disparities, and survival in hemodialysis patients. J Am Soc Nephrol 2013; 24: 293–301. Watt RG, Heilmann A, Sabbah W et al. Social relationships and health related behaviors among older US adults. BMC Public Health [online] 2014; 14: 533.
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