scholarly journals Genetically Determined Obesity and Adipose Distribution Impact Human Blood Trait Variation across Cell Lineages

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1876-1876
Author(s):  
Christopher Thom ◽  
Madison Wilken ◽  
Stella T. Chou ◽  
Benjamin F Voight

Abstract Introduction Genome wide association studies (GWAS) have catalogued thousands of loci that influence blood traits, but genetic mechanisms and impacted cell types are often unknown. While some regulatory factors are blood cell-autonomous, other extrinsic regulatory factors respond to systemic physiology. Epidemiologic studies have correlated generalized obesity (increased body mass index, BMI) and high cholesterol with anemia, but cell types and mechanisms that mediate these effects are incompletely understood, and these studies do not provide evidence for causality. Thus, we wanted to use genetic effects of BMI and cholesterol on hemoglobin level (HGB) and other blood traits to infer causal relationships, identify relevant cell types, and propose genetic mechanisms. Methods Mendelian randomization (MR), akin to a "genetic randomized controlled trial", uses variants linked to an exposure trait to estimate causal effects of that exposure on a given outcome. 1 Random variant allele allocation at meiosis enables this approach to address confounding and reverse causality that can otherwise preclude causal inference from epidemiologic and cohort studies. Further, effects of multiple factors can be parsed using multivariable MR and causal mediation analyses to help explain genetic associations. 2 Our study used gender- and age-adjusted GWAS summary statistics from European individuals. Results We hypothesized that increased genetically determined BMI would decrease HGB. Using a MR framework, we found that a 5 kg/m 2 (1 standard deviation unit) increase in BMI decreased hemoglobin by 0.06±0.01 g/dL (p=1x10 -5, Fig). Increased BMI also decreased erythrocyte count, and unexpectedly also decreased platelet and white blood cell counts (all with p<5x10 -3). These data showed that obesity-related mechanisms extended beyond the erythroid lineage, perhaps impacting multipotent hematopoietic progenitor cells (HPCs). Similar to BMI, a 1 SD unit increase in total cholesterol decreased HGB (0.10±0.03 g/dL, p=2x10 -3, Fig). However, genetic effects of cholesterol were restricted to erythroid traits (HGB and hematocrit). Multivariable and mediation analyses confirmed that effects of BMI and cholesterol functioned through distinct genetic mechanisms. We speculated that multilineage effects from BMI could reflect a genetic predisposition to accumulate bone marrow adipose tissue, which can impact HPCs and hematopoiesis. 3 A tendency for 'central' adiposity increases one's waist-hip ratio (WHR), and increases cardiovascular disease risk concordant with BMI. Unexpectedly, we found that increased WHR, in contrast to BMI, increased HGB (0.08±0.02 g/dL, p=9x10 -6) as well as erythrocyte, platelet, and white blood cell counts (all with p<4x10 -3, Fig). In multivariable experiments, the effects of WHR were exacerbated after accounting for BMI at the individual or population level. Thus, obesity impacts blood traits through genetically determined adipose distribution. Conclusions Our results confirm that BMI and cholesterol negatively impact HGB at a genetic level, consistent with clinical observations. The unexpected multilineage effects of genetically determined BMI most likely reflects a tendency to accumulate bone marrow adipose tissue, which in turn impacts HPCs and downstream blood cell production. Our findings suggest that adjustment for BMI and adiposity traits may be considered in blood trait GWAS analyses and illuminate opportunities to functionally dissect related genes and molecular pathways. References 1. Hemani, G. et al. Elife 7, (2018). PMID: 29846171. 2. Sanderson, E. et al. Int. J. Epidemiol. 48, 713-727 (2019). PMID: 30535378. 3. Wang, H. et al. Front. Endocrinol.. 9, 694 (2018). PMID: 30546345. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.

1996 ◽  
Vol 76 (02) ◽  
pp. 184-186 ◽  
Author(s):  
Kenji lijima ◽  
Fumiyo Murakami ◽  
Yasushi Horie ◽  
Katsumi Nakamura ◽  
Shiro Ikawa ◽  
...  

SummaryA 74-year-old female developed pneumonia following herpes simplex encephalitis. Her white blood cell counts reached 28,400/μl, about 90% of which consisted of granulocytes. The polymorphonuclear (PMN) elastase/α1-arantitrypsin complex levels increased and reached the maximum of 5,019 ng/ml, indicating the release of a large amount of elastase derived from the granulocytes. The mechanism of PMN elastase release was most likely to be granulocyte destruction associated with phagocytosis. The cleavage of fibrinogen and fibrin by PMN elastase, independent of plasmin, was indicated by the presence of the fragments in immunoprecipitated plasma from the patient corresponding to elastase-induced FDP D and DD fragments and the absence of fragments corresponding to plasmin-induced FDP D and DD fragments on SDS-PAGE. These findings suggested that the large amount of PMN elastase released from the excessive numbers of granulocytes in this patient with herpes simplex encephalitis and pneumonia, induced the cleavage of fibrinogen and fibrin without the participation of plasmin.


2021 ◽  
pp. 096228022110259
Author(s):  
Shintaro Yamamuro ◽  
Tomohiro Shinozaki ◽  
Satoshi Iimuro ◽  
Yutaka Matsuyama

Modern causal mediation theory has formalized several types of indirect and direct effects of treatment on outcomes regarding specific mediator variables. We reviewed and unified distinct approaches to estimate the “interventional” direct and indirect effects for multiple mediators and time-varying variables. This study was motivated by a clinical trial of elderly type-2 diabetic patients in which atorvastatin was widely prescribed to control patients’ cholesterol levels to reduce diabetic complications, including cardiovascular disease. Among atorvastatin’s preventive side-effects (pleiotropic effects), we focus on its anti-inflammatory action as measured by white blood cell counts. Hence, we estimate atorvastatin’s interventional indirect effects through cholesterol lowering and through anti-inflammatory action, and interventional direct effect bypassing these two actions. In our analysis, total effect (six-year cardiovascular disease risk difference) estimated by standard plug-in g-formula of −3.65% (95% confidence interval: −10.29%, 4.38%) is decomposed into indirect effect via low-density lipoprotein cholesterol (−0.90% [−1.91%, −0.07%]), via white blood cell counts (−0.03% [−0.22%, 0.11%]), and direct effect (−2.84% [−9.71%, 5.41%]) by the proposed parametric mediational g-formula. The SAS program and its evaluation via simulated datasets are provided in the Supplemental materials.


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