Search

aimed to investigate the association between age at first childbirth and lung function in Chinese women and further test whether this association is mediated by body mass index (BMI). Methods: This cross-sectional study is a partial survey of the China Kadoorie Biobank (CKB) which was conducted in Xinxiang City, Henan Province between 2004 and 2008. A total of 16,584 postmenopausal women aged 30– 79 years were enrolled. Multiple linear and logistic regression were used to investigate the association between age at first childbirth and lung function and overweight/obesity. The mediation analysis was performed using the PROCESS procedure for SPSS. Results: The mean (SD) age at first childbirth was 23.1 (2.7) years. Women with first childbirth aged ≤ 19 years and 20– 22 years had lower lung function than women who gave first childbirth aged 23– 25 years. Per 1-year increase in the age at first childbirth was associated with a 3.31 mL increase in FEV1 (95% CI = 1.27– 5.35), 3.91 mL increase in FVC (95% CI = 1.63– 6.18), 0.15% increase in FEV1, % predicted (95% CI = 0.05– 0.24) and 0.14% increase in FVC, % predicted (95% CI = 0.05– 0.22). There was no clear association between age at first childbirth and FEV1/FVC ratio. BMI played a contribution to the association between age at first childbirth and FEV1 and the proportion was 16.4% (indirect effect: β = 0.65, 95% CI = 0.46– 0.89; total effect: β = 3.96, 95% CI = 1.92– 5.99). Similarly, the proportion to FVC, FEV1, % predicted, and FVC, % predicted was 25.0%, 16.6%, and 25.0%, respectively. Conclusion: Early age at first childbirth was associated with lower lung function and BMI mediated the association. It is important to test lung function and popularize the knowledge of weight control in women who gave first childbirth at an early age. [ABSTRACT FROM AUTHOR]

Purpose: To understand the relationship between body mass index (BMI), age, prostate volume (PV), prostate-specific antigen (PSA), International Prostate Symptom Score (IPSS) and quality of life (QoL) in Zhengzhou.Materials and Methods: In the cross-sectional study, men living in Zhengzhou were invited to participate in this study. Men who were 40 years or older were subjected to the IPSS and related examination. A total of 1360 participants were included. Body mass index < 18.5 kg/m2 was determined as underweight, 18.5-24.99 kg/m2 normal, 25-29.99 kg/m2 overweight, and ≥30 kg/m2 obese.Results: The mean BMI was 24.92 ± 3.37 kg/m2. The mean PSA was 1.06 ± 0.85 ng/mL. The mean PV was 20.10 ± 9.96 mL. The mean age was 62.72 ± 11.03 years. The mean IPSS was 5.87 ± 3.48 scores. The mean QoL was 2.33 ± 1.28 scores. PSA showed a significant tendency to decrease with increasing BMI (r = -0.061, p = 0.018, ptrend = 0.037). The same with age (r = -0.109, p < .001; ptrend = .045). But the result suggested that both IPSS and QoL were positively correlated with BMI (r = 0.120, p < .001, ptrend < .001; r = 0.083, p = .001, ptrend = .021, respectively). PV increased with increasing BMI (r = 0.110, p < .001, ptrend = 0.045 ).Conclusions: Age, PSA decreased with increasing BMI. But larger PV, IPSS, and QoL were associated with higher BMI. [ABSTRACT FROM AUTHOR]

Objectives: The objective of this study was the investigation of age- and sex-associations in a set of blood plasma metabolites in healthy male and female subjects. Methods: A comparison study design with male and female subjects of various ages was used. Metabolic profiling was performed using electrospray ionization tandem mass spectrometry that yielded 186 metabolite concentrations for each study participant. The key age-related metabolites were identified using an integrative analysis of absolute concentrations, metabolite ratios and the differential correlation of pairwise metabolite concentrations. All of the age-associated metabolites were adjusted prior to the analysis to account for differences in Body Mass Index (BMI). Results: A total of 236 plasma samples from 140 female and 96 male subjects aged 20 to 82 years-old were collected and analyzed in the study. 13 and 14 age-associated metabolites (|r| > 0.33 and p < 6.6×10−5), 438 and 337 age-associated metabolite ratios (|r| > 0.37 and p < 3.5×10−6), and 5 and 10 core metabolites were discovered in the female and male groups, respectively. 80% of the metabolites displaying associations with age belonged to sphingolipids and phosphatidylcholines, and the two sexes shared less than 50% of the age-associated metabolites. Conclusion: The study found that changes in metabolite concentrations, metabolite ratios and differential correlations were age and sex-specific. [ABSTRACT FROM AUTHOR]

No risk assessment tools for the efficacy of folic acid treatment for hyperhomocysteinaemia (HHcy) have been developed. We aimed to use two common genetic risk score (GRS) methods to construct prediction models for the efficacy of folic acid therapy on HHcy, and the best gene–environment prediction model was screened out. A prospective cohort study enrolling 638 HHcy patients was performed. We used a logistic regression model to estimate the associations of two GRS methods with the efficacy. Performances were compared using area under the receiver operating characteristic curve (AUC). The simple count genetic risk score (SC-GRS) and weighted genetic risk score (wGRS) were found to be independently associated with the efficacy of folic acid treatment for HHcy. Using the SC-GRS, per risk allele increased with a 1·46-fold increased failure risk (P < 0·001) after adjustment for traditional risk factors, including age, sex, BMI, smoking, alcohol consumption, history of diabetes, history of hypertension, history of hyperlipidaemia, history of stroke and history of CHD. When used the wGRS, the association was strengthened (OR = 2·08, P < 0·001). Addition of the SC-GRS and wGRS to the traditional risk model significantly improved the predictive ability by AUC (0·859). A precise gene–environment predictive model with good performance was developed for predicting the treatment failure rate of folic acid therapy for HHcy. [ABSTRACT FROM AUTHOR]

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts. [ABSTRACT FROM AUTHOR]