Predicted Pleistocene-Holocene range shifts of the tiger (Panthera tigris)

Aim In this article, we modelled the potential range shifts of tiger (Panthera tigris) populations over the Late Pleistocene and Holocene, to provide new insights into the evolutionary history and interconnectivity between populations of this endangered species. Location Asia. Methods We used an ecological niche approach and applied a maximum entropy (Maxent) framework to model potential distributions of tigers. Bioclimatic conditions for the present day and mid-Holocene, and for the Last Glacial Maximum (LGM), were used to represent interglacial and glacial conditions of the Late Pleistocene, respectively. Results Our results show that the maximum potential tiger range during modern climates (without human impacts) would be continuous from the Indian subcontinent to north-east Siberia. During the LGM, distributions are predicted to have contracted to southern China, India and Southeast Asia and remained largely contiguous. A potential distribution gap between Peninsular Malaya and Sumatra could have effectively separated tigers on the Sunda Islands from those in continental Asia during interglacials. Main conclusions The continuous modelled distribution of tigers in mainland Asia supports the idea of mainly unimpeded gene flow between all populations throughout the Late Pleistocene and Holocene. Thus, our data support a pragmatic approach to tiger conservation management, especially of mainland populations, as it is likely that only recent anthropogenic changes caused separation of these populations. In contrast, Sunda tigers are likely to have separated and differentiated following the Last Glacial Maximum and thus warrant separate management

Association of cardiovascular biomarkers with incident heart failure with preserved and reduced ejection fraction

Importance:  Nearly half of all patients with heart failure have preserved ejection fraction (HFpEF) as opposed to reduced ejection fraction (HFrEF), yet associations of biomarkers with future heart failure subtype are incompletely understood. Objective:  To evaluate the associations of 12 cardiovascular biomarkers with incident HFpEF vs HFrEF among adults from the general population. Design, Setting, and Participants:  This study included 4 longitudinal community-based cohorts: the Cardiovascular Health Study (1989-1990; 1992-1993 for supplemental African-American cohort), the Framingham Heart Study (1995-1998), the Multi-Ethnic Study of Atherosclerosis (2000-2002), and the Prevention of Renal and Vascular End-stage Disease study (1997-1998). Each cohort had prospective ascertainment of incident HFpEF and HFrEF. Data analysis was performed from June 25, 2015, to November 9, 2017. Exposures:  The following biomarkers were examined: N-terminal pro B-type natriuretic peptide or brain natriuretic peptide, high-sensitivity troponin T or I, C-reactive protein (CRP), urinary albumin to creatinine ratio (UACR), renin to aldosterone ratio, D-dimer, fibrinogen, soluble suppressor of tumorigenicity, galectin-3, cystatin C, plasminogen activator inhibitor 1, and interleukin 6.Main Outcomes and Measures: development of incident HFpEF and incident HFrEF. Results:  Among the 22 756 participants in these 4 cohorts (12 087 women and 10 669 men; mean [SD] age, 60 [13] years) in the study, during a median follow-up of 12 years, 633 participants developed incident HFpEF, and 841 developed HFrEF. In models adjusted for clinical risk factors of heart failure, 2 biomarkers were significantly associated with incident HFpEF: UACR (hazard ratio [HR], 1.33; 95% CI, 1.20-1.48; P < .001) and natriuretic peptides (HR, 1.27; 95% CI, 1.16-1.40; P < .001), with suggestive associations for high-sensitivity troponin (HR, 1.11; 95% CI, 1.03-1.19; P = .008), plasminogen activator inhibitor 1 (HR, 1.22; 95% CI, 1.03-1.45; P = .02), and fibrinogen (HR, 1.12; 95% CI, 1.03-1.22; P = .01). By contrast, 6 biomarkers were associated with incident HFrEF: natriuretic peptides (HR, 1.54; 95% CI, 1.41-1.68; P < .001), UACR (HR, 1.21; 95% CI, 1.11-1.32; P < .001), high-sensitivity troponin (HR, 1.37; 95% CI, 1.29-1.46; P < .001), cystatin C (HR, 1.19; 95% CI, 1.11-1.27; P < .001), D-dimer (HR, 1.22; 95% CI, 1.11-1.35; P < .001), and CRP (HR, 1.19; 95% CI, 1.11-1.28; P < .001). When directly compared, natriuretic peptides, high-sensitivity troponin, and CRP were more strongly associated with HFrEF compared with HFpEF. Conclusions and Relevance:  Biomarkers of renal dysfunction, endothelial dysfunction, and inflammation were associated with incident HFrEF. By contrast, only natriuretic peptides and UACR were associated with HFpEF. These findings highlight the need for future studies focused on identifying novel biomarkers of the risk of HFpE