Borderline-High Mean Corpuscular Volume Levels Are Associated with Arterial Stiffness among the Apparently Healthy Korean Individuals

Gene-Environment Interactions and Gene-Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants

PhenoAge and BioAge are two commonly used biological age (BA) measures. The author here searched for gene-environment interactions (GxE) and gene-gene interactions (GxG) on PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 111,996 Taiwan Biobank (TWB) participants, including a discovery set of 86,536 TWB2 individuals and a replication set of 25,460 TWB1 individuals. Searching for variance quantitative trait loci (vQTLs) provides a convenient way to evaluate GxE and GxG. A total of 4 nearly independent (linkage disequilibrium measure r2 < 0.01) PhenoAgeAccel-vQTLs are identified from 5,303,039 autosomal TWB2 SNPs (p < 5E-8), whereas no vQTLs are found from BioAgeAccel. These 4 PhenoAgeAccel-vQTLs (rs35276921, rs141927875, rs10903013, and rs76038336) are further replicated by TWB1 (p < 5E-8). They are located in the OR51B5, FAM234A, and AXIN1 genes. All 4 PhenoAgeAccel-vQTLs are significantly associated with PhenoAgeAccel (p < 5E-8). A phylogenetic heat map of the GxE analyses showed that smoking exacerbated the PhenoAgeAccel-vQTLs' aging effects, while higher educational attainment attenuated the PhenoAgeAccel-vQTLs' aging effects. Body mass index, chronological age, alcohol consumption, and sex do not prominently modulate PhenoAgeAccel-vQTLs' aging effects. Based on these vQTL results, rs141927875-rs35276921 interaction (p = 4.7E-61) and rs76038336-rs10903013 interaction (p = 3.3E-116) on PhenoAgeAccel are detected.

Association between biomarkers of inflammation and dyslipidemia in drug resistant tuberculosis in Uganda

BACKGROUND

Active tuberculosis (TB) significantly increases the risk of cardiovascular disease, but the underlying mechanisms remain unclear. This study aimed to investigate the association between inflammation biomarkers and dyslipidemia in patients with drug-resistant TB (DR-TB).

METHODS

This was a secondary analysis of data from a cross-sectional multi-center study in Uganda conducted 2021. Participants underwent anthropometric measurements and laboratory tests included a lipid profile, full haemogram and serology for HIV infection. Dyslipidemia was defined as total cholesterol > 5.0 mmol/l and/or low-density lipoprotein cholesterol > 4.14 mmol/l, and/or triglycerides (TG) ≥ 1.7 mmol/l, and/or high density lipoprotein cholesterol (HDL-c) < 1.03 mmol/l for men and < 1.29 mmol/l for women. Biomarkers of inflammation were leukocyte, neutrophil, lymphocyte, monocyte, and platelet counts, as well as neutrophil/lymphocyte (NLR), platelet/lymphocyte, and lymphocyte/monocyte (LMR) ratios, mean corpuscular volume (MCV), and the systemic immune inflammation index (SII) (neutrophil × platelet/lymphocyte). Modified Poisson Regression analysis was used for determining the association of the biomarkers and dyslipidemia.

RESULTS

Of 171 participants, 118 (69.0%) were co-infected with HIV. The prevalence of dyslipidemia was 70.2% (120/171) with low HDL-c (40.4%, 69/171) and hypertriglyceridemia (22.5%, 38/169) being the most common components. Patients with dyslipidemia had significantly higher lymphocyte (P = 0.008), monocyte (P < 0.001), and platelet counts (P = 0.014) in addition to a lower MCV (P < 0.001) than those without dyslipidemia. Further, patients with dyslipidemia had lower leucocyte (P < 0.001) and neutrophil (P = 0.001) counts, NLR (P = 0.008), LMR (P = 0.006), and SII (P = 0.049). The MCV was inversely associated with low HDL-C (adjusted prevalence ratio (aPR) = 0.97, 95% CI 0.94-0.99, P = 0.023) but was positively associated with hypertriglyceridemia (aPR = 1.04, 95% CI 1.00-1.08, P = 0.052).

CONCLUSIONS

Individuals with dyslipidemia exhibited elevated lymphocyte, monocyte, and platelet counts compared to those without. However, only MCV demonstrated an independent association with specific components of dyslipidemia. There is need for further scientific inquiry into the potential impact of dyslipidemia on red cell morphology and a pro-thrombotic state among patients with TB.

Development of multiple intracranial aneurysms: beyond the common risk factors

OBJECTIVE

The prevalence of multiple intracranial aneurysms (MIAs) has increased over the last decades. Because MIAs have been identified as an independent risk factor for formation, growth, and rupture of intracranial aneurysms (IAs), a more profound understanding of the underlying pathophysiology of MIAs is needed. Therefore, the authors' extensive institutional aneurysm database was analyzed to elucidate differences between patients with a single IA (SIA) and those with MIAs.

METHODS

A total of 2446 patients seen with or for IAs at the University Hospital of Essen, Essen, Germany, from January 2003 to June 2016 were included in this retrospective cohort study and were separated into MIA and SIA subgroups. Patient data were screened for sociodemographic and radiographic parameters, preexisting medical conditions, and results of blood examinations. These parameters were analyzed for their correlations with MIAs and absolute number of IAs.

RESULTS

MIAs were identified in 853 (34.9%) patients. In multivariable analysis, MIAs were independently associated with female sex (p = 0.001), arterial hypertension (p = 0.023), tobacco abuse (p = 0.009), AB blood group (p = 0.010), and increased admission values for C-reactive protein (p = 0.006), mean corpuscular volume (p = 0.009), and total serum protein (p = 0.034), but not with diagnostic modality (3D vs 2D digital subtraction angiography, p = 0.912). Absolute number of IAs was independently associated with female sex (p < 0.001), arterial hypertension (p = 0.014), familial predisposition to IA (p = 0.015), tobacco consumption (p = 0.025), increased mean corpuscular volume (p = 0.002), and high platelet count (p = 0.007).

CONCLUSIONS

In this sizable consecutive series of patients with IAs, the authors confirmed the impact of common IA risk factors on the genesis of MIAs. In addition, specific hemorheological and hemocytological features may also contribute to the development of MIAs.

Age‐related changes in mean corpuscular volumes in patients without anaemia: An analysis of large‐volume data from a single institute

Although the mean corpuscular volume (MCV) has been associated with various diseases, these associations in relation to the age‐related trends in MCV remain unclear. Therefore, we used a dataset with over one million values to identify the relationship between ageing and MCV changes. All laboratory data obtained between November 1998 and November 2019 at Chungbuk National University Hospital were retrospectively collected. After excluding cases with missing values for individual complete blood count parameters, outlier MCV values, and ages less than 1 year and more than 88 years, 977,335 MCV values were obtained from 309,393 patients. Principal component analysis of blood components with ages and analysis of the median value changes for each blood component across decade‐wise age groups were conducted to identify relationships between ageing and changes in blood components. The median values of MCV showed gradual increments with age. The linear relationship for patients aged 1–25 years had a larger slope than that for patients aged 26–88 years. For MCV, the equation for patients aged 1–25 years was 0.40*(age) + 81.24 in females and 0.45*(age) + 79.58 in males. The equation for patients aged 26–90 years was 0.04*(age) + 88.97 in females and 0.06*age + 88.30 in males. Among patients aged >40 years, the MCV value was higher in men than in women. Analysis of a large dataset showed that the MCV gradually increased with age and the linear relationship differed between patients aged 1–25 and 26–88 years.