Methods: This retrospective cohort study was conducted between 24th February 2020 and 20th April 2020. All consecutive patients in the entire State of Kuwait diagnosed with COVID-19 according to WHO guidelines and admitted to Jaber Al-Ahmad Al-Sabah Hospital were included. Patients received standardized investigations and treatments. Multivariable analysis was used to determine the associations between risk factors and outcomes (admission to intensive care and/or mortality).
Findings: Of 1096 patients, the median age was 41 years and 81% of patients were male. Most patients were asymptomatic on admission (46.3%), of whom 35 later developed symptoms, and 59.7% had no signs of infection. Only 3.6% of patients required an ICU admission and 1.7% were dead at the study's cutoff date. On multivariable analysis, the risk factors found to be significantly associated with admission to intensive care were age above 50 years old, a qSOFA score above 0, smoking, elevated CRP and elevated procalcitonin levels. Asthma, smoking and elevated procalcitonin levels correlated significantly with mortality in our cohort.
SETTING: Five medical and cardiology wards of a tertiary care center in Malaysia.
SUBJECTS: Five hundred cardiac inpatients, who received ACEIs concomitantly with other interacting drugs.
METHOD: This was a prospective cohort study of 500 patients with cardiovascular diseases admitted to Penang Hospital between January to August 2006, who received ACEIs concomitantly with other interacting drugs. ACEI-drug interactions of clinical significance were identified using available drug information resources. Drug Interaction Probability Scale (DIPS) was used to assess the causality of association between ACEI-drug interactions and the adverse outcome (hyperkalemia).
MAIN OUTCOME MEASURE: Hyperkalemia as an adverse clinical outcome of the interaction was identified from laboratory investigations.
RESULTS: Of the 489 patients included in the analysis, 48 (9.8%) had hyperkalemia thought to be associated with ACEI-drug interactions. Univariate analysis using binary logistic regression revealed that advanced age (60 years or more), and taking more than 15 medications were independent risk factors significantly associated with hyperkalemia. However, current and previous smoking history appeared to be a protective factor. Risk factors identified as predictors of hyperkalemia secondary to ACEI-drug interactions by multi-logistic regression were: advanced age (adjusted OR 2.3, CI 1.07-5.01); renal disease (adjusted OR 4.7, CI 2.37-9.39); hepatic disease (adjusted OR 5.2, CI 1.08-25.03); taking 15-20 medications (adjusted OR 4.4, CI 2.08-9.19); and taking 21-26 medications (adjusted OR 9.0, CI 1.64-49.74).
CONCLUSION: Cardiac patients receiving ACEIs concomitantly with potentially interacting drugs are at high risk of experiencing hyperkalemia. Old age, renal disease, hepatic disease, and receiving large number of medications are factors that may significantly increase their vulnerability towards this adverse outcome; thus, frequent monitoring is advocated.
Methods: In 12 cohorts from 6 European countries, individual estimates of annual mean air pollution levels at the baseline residence were estimated by standardized land-use regression models developed within the ESCAPE and TRANSPHORM projects: particulate matter (PM) ≤2.5, ≤10, and 2.5-10 μm in diameter (PM2.5, PM10, and PMcoarse), PM2.5 absorbance, nitrogen oxides (NO2 and NOx) and elemental composition of PM. We estimated cohort-specific associations of air pollutant concentrations and traffic intensity with total, malignant, and nonmalignant brain tumor, in separate Cox regression models, adjusting for risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.
Results: Of 282194 subjects from 12 cohorts, 466 developed malignant brain tumors during 12 years of follow-up. Six of the cohorts also had data on nonmalignant brain tumor, where among 106786 subjects, 366 developed brain tumor: 176 nonmalignant and 190 malignant. We found a positive, statistically nonsignificant association between malignant brain tumor and PM2.5 absorbance (hazard ratio and 95% CI: 1.67; 0.89-3.14 per 10-5/m3), and weak positive or null associations with the other pollutants. Hazard ratio for PM2.5 absorbance (1.01; 0.38-2.71 per 10-5/m3) and all other pollutants were lower for nonmalignant than for malignant brain tumors.
Conclusion: We found suggestive evidence of an association between long-term exposure to PM2.5 absorbance indicating traffic-related air pollution and malignant brain tumors, and no association with overall or nonmalignant brain tumors.
OBJECTIVE: We examined the association between long-term exposure to ambient air pollution and incidence of postmenopausal breast cancer in European women.
METHODS: In 15 cohorts from nine European countries, individual estimates of air pollution levels at the residence were estimated by standardized land-use regression models developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) and Transport related Air Pollution and Health impacts – Integrated Methodologies for Assessing Particulate Matter (TRANSPHORM) projects: particulate matter (PM) ≤2.5μm, ≤10μm, and 2.5–10μm in diameter (PM2.5, PM10, and PMcoarse, respectively); PM2.5 absorbance; nitrogen oxides (NO2 and NOx); traffic intensity; and elemental composition of PM. We estimated cohort-specific associations between breast cancer and air pollutants using Cox regression models, adjusting for major lifestyle risk factors, and pooled cohort-specific estimates using random-effects meta-analyses.
RESULTS: Of 74,750 postmenopausal women included in the study, 3,612 developed breast cancer during 991,353 person-years of follow-up. We found positive and statistically insignificant associations between breast cancer and PM2.5 {hazard ratio (HR)=1.08 [95% confidence interval (CI): 0.77, 1.51] per 5 μg/m3}, PM10 [1.07 (95% CI: 0.89, 1.30) per 10 μg/m3], PMcoarse[1.20 (95% CI: 0.96, 1.49 per 5 μg/m3], and NO2 [1.02 (95% CI: 0.98, 1.07 per 10 μg/m3], and a statistically significant association with NOx [1.04 (95% CI: 1.00, 1.08) per 20 μg/m3, p=0.04].
CONCLUSIONS: We found suggestive evidence of an association between ambient air pollution and incidence of postmenopausal breast cancer in European women. https://doi.org/10.1289/EHP1742.
RESEARCH DESIGN AND METHODS: The Prospective Urban Rural Epidemiology (PURE) study enrolled 143,567 adults aged 35-70 years from 4 high-income countries (HIC), 12 middle-income countries (MIC), and 5 low-income countries (LIC). The mean follow-up was 9.0 ± 3.0 years.
RESULTS: Among those with diabetes, CVD rates (LIC 10.3, MIC 9.2, HIC 8.3 per 1,000 person-years, P < 0.001), all-cause mortality (LIC 13.8, MIC 7.2, HIC 4.2 per 1,000 person-years, P < 0.001), and CV mortality (LIC 5.7, MIC 2.2, HIC 1.0 per 1,000 person-years, P < 0.001) were considerably higher in LIC compared with MIC and HIC. Within LIC, mortality was higher in those in the lowest tertile of wealth index (low 14.7%, middle 10.8%, and high 6.5%). In contrast to HIC and MIC, the increased CV mortality in those with diabetes in LIC remained unchanged even after adjustment for behavioral risk factors and treatments (hazard ratio [95% CI] 1.89 [1.58-2.27] to 1.78 [1.36-2.34]).
CONCLUSIONS: CVD rates, all-cause mortality, and CV mortality were markedly higher among those with diabetes in LIC compared with MIC and HIC with mortality risk remaining unchanged even after adjustment for risk factors and treatments. There is an urgent need to improve access to care to those with diabetes in LIC to reduce the excess mortality rates, particularly among those in the poorer strata of society.
METHODS AND RESULTS: Data was sourced from participants in the Western Australian Pregnancy (Raine) Cohort Study. At 14 and 17 y, dietary intake, anthropometric and biochemical data were measured and z-scores for an 'energy dense, high fat and low fibre' DP were estimated using reduced rank regression (RRR). Associations between DP z-scores and cardiometabolic risk factors were examined using regression models. Tracking of DP z-scores was assessed using Pearson's correlation coefficient. A 1 SD unit increase in DP z-score between 14 and 17 y was associated with a 20% greater odds of high metabolic risk (95% CI: 1.01, 1.41) and a 0.04 mmol/L higher fasting glucose in boys (95% CI: 0.01, 0.08); a 28% greater odds of a high-waist circumference (95% CI: 1.00, 1.63) in girls. An increase of 3% and 4% was observed for insulin and HOMA (95% CI: 1%, 7%), respectively, in boys and girls, for every 1 SD increase in DP z-score and independently of BMI. The DP showed moderate tracking between 14 and 17 y of age (r = 0.51 for boys, r = 0.45 for girls).
CONCLUSION: An 'energy dense, high fat, low fibre' DP is positively associated with cardiometabolic risk factors and tends to persist throughout adolescence.
METHODS: We examined determinants and tracking of a dietary pattern (DP) associated with metabolic risk and its key food groups among 860 adolescents in the Western Australian Pregnancy (Raine) Cohort study. Food intake was reported using a food frequency questionnaire (FFQ) at 14 and 17 years. Z-scores for an 'energy-dense, high-fat, low-fibre' DP were estimated by applying reduced rank regression at both ages. Tracking was based on the predictive value (PV) of remaining in the DPZ-score or food intake quartile at 14 and 17 years. Early-life exposures included: maternal age; maternal pre-pregnancy body mass index; parent smoking status during pregnancy; and parent socio-economic position (SEP) at 14 and 17 years. Associations between the DPZ-scores, early-life factors and SEP were analysed using regression analysis.
RESULTS: Dietary tracking was strongest among boys with high DPZ-scores, high intakes of processed meat, low-fibre bread, crisps and savoury snacks (PV > 1) and the lowest intakes of vegetables, fruit and legumes. Lower maternal education (β = 0.09, P = 0.002 at 14 years; β = 0.14, P
OBJECTIVES: We examined trajectories across adolescence and early adulthood for 2 major dietary patterns and their associations with childhood and parental factors.
METHODS: Using data from the Western Australian Pregnancy Cohort (Raine Study), intakes of 38 food groups were estimated at ages 14, 17, 20 and 22 y in 1414 participants using evaluated FFQs. Using factor analysis, 2 major dietary patterns (healthy and Western) were consistently identified across follow-ups. Sex-specific group-based modeling assessed the variation in individual dietary pattern z scores to identify group trajectories for each pattern between ages 14 and 22 y and to assess their associations with childhood and parental factors.
RESULTS: Two major trajectory groups were identified for each pattern. Between ages 14 and 22 y, a majority of the cohort (70% males, 73% females) formed a trajectory group with consistently low z scores for the healthy dietary pattern. The remainder had trajectories showing either declining (27% females) or reasonably consistent healthy dietary pattern z scores (30% males). For the Western dietary pattern, the majority formed trajectories with reasonably consistent average scores (79% males, 81% females) or low scores that declined over time. However, 21% of males had a trajectory of steady, marked increases in Western dietary pattern scores over time. A lower maternal education and higher BMI (in kg/m2) were positively associated with consistently lower scores of the healthy dietary pattern. Lower family income, family functioning score, maternal age, and being in a single-parent family were positively related to higher scores of the Western dietary pattern.
CONCLUSIONS: Poor dietary patterns established in adolescence are likely to track into early adulthood, particularly in males. This study highlights the transition between adolescence and early adulthood as a critical period and the populations that could benefit from dietary interventions.
Methods: : We utilized data among 1020 infants from a mother-offspring cohort, who were Singapore citizens or permanent residents of Chinese, Malay or Indian ethnicity with homogeneous parental ethnic backgrounds, and did not receive chemotherapy, psychotropic drugs or have diabetes mellitus. Ethnicity was self-reported at recruitment and later confirmed using genotype analysis. Subject-specific BMI curves were fitted to infant BMI data using natural cubic splines with random coefficients to account for repeated measures in each child. We estimated characteristics of the child's BMI peak [age and magnitude at peak, average pre-peak velocity (aPPV)]. Systolic (SBP) and diastolic blood pressure (DBP), BMI, sum of skinfolds (SSF) and fat-mass index (FMI) were measured during a follow-up visit at age 48 months. Weighted multivariable linear regression was used to assess the predictors (maternal BMI, gestational weight gain, ethnicity, infant sex, gestational age, birthweight-for-gestational age and breastfeeding duration) of infant BMI peak and its associations with outcomes at 48 months. Comparisons between ethnicities were tested using Bonferroni post-hoc correction.
Results: : Of 1020 infants, 80.5% were followed up at the 48-month visit. Mean (SD) BMI, SSF and FMI at 48 months were 15.6 (1.8) kg/m 2 , 16.5 (5.3) mm and 3.8 (1.3) kg/m 2 , respectively. Mean (SD) age at peak BMI was 6.0 (1.6) months, with a magnitude of 17.2 (1.4) kg/m 2 and pre-peak velocity of 0.7 (0.3) kg/m 2 /month. Compared with Chinese infants, the peak occurred later in Malay {B [95% confidence interval (CI): 0.64 mo (0.36, 0.92)]} and Indian infants [1.11 mo (0.76, 1.46)] and was lower in magnitude in Indian infants [-0.45 kg/m 2 (-0.69, -0.20)]. Adjusting for maternal education, BMI, gestational weight gain, ethnicity, infant sex, gestational age, birthweight-for-gestational-age and breastfeeding duration, higher peak and aPPV were associated with greater BMI, SSF and FMI at 48 months. Age at peak was positively associated with BMI at 48 months [0.15 units (0.09, 0.22)], whereas peak magnitude was associated with SBP [0.17 units (0.05, 0.30)] and DBP at 48 months [0.10 units (0.01, 0.22)]. Older age and higher magnitude at peak were associated with increased risk of overweight at 48 months [Relative Risk (95% CI): 1.35 (1.12-1.62) for age; 1.89 (1.60-2.24) for magnitude]. The associations of BMI peak with BMI and SSF at 48 months were stronger in Malay and Indian children than in Chinese children.
Conclusions: : Ethnic-specific differences in BMI peak characteristics, and associations of BMI peak with early childhood cardio-metabolic markers, suggest an important impact of early BMI development on later metabolic outcomes in Asian populations.