Associations of Body Mass Index, Visceral Fat Area, Waist Circumference, and Waist-to-Hip Ratio with Cognitive Function in Western China: Results from WCHAT Study

Impact of lighting environment on human performance and prediction modeling of personal visual comfort in enclosed cabins

Enclosed cabins are of great significance in various fields, including national defense, scientific research, and industrial applications. It is important to clarify the impact of the lighting environment in these cabins on the people operating within them. This study investigated the effects of the lighting environment in enclosed cabins on the physiological, operational, and comfort performance of operators through simulated experiments. In Addition, using the Random Forest Algorithm and ExpandNet technique, we developed a prediction model to evaluate the comfort level of the lighting environment for personnel in enclosed cabins. The results indicated that pupil diameter exhibited the highest sensitivity to ambient light. The appropriate luminance combination of the screen and the ambient scene have a positive effect on human performance. In particular, it was observed that the average cognitive performance and comfort of participants tended to be relatively high in the luminance combinations 13, 14, and 15 at CCT 5500 K. The screen luminance of these combinations are all 284.75 cd/m2. Although no statistically significant relationship was found between the cognitive performance of the participants and their comfort, the comfort of the participants tended to decrease after the cognitive operations was completed. According to the proposed personal comfort prediction model, the visual comfort of different people varies even under the same lighting conditions. This study provides a solid theoretical basis for improving the design of lighting environments in enclosed spaces and contributes to developing a pleasant and productive working environment within limited cabins.

Methylome-wide studies of six metabolic traits

Exploring the molecular correlates of metabolic health measures may identify the shared and unique biological processes and pathways that they track. Here, we performed epigenome-wide association studies (EWASs) of six metabolic traits: body mass index (BMI), body fat percentage, waist-hip ratio (WHR), and blood-based measures of glucose, high-density lipoprotein (HDL) cholesterol, and total cholesterol. We considered blood-based DNA methylation (DNAm) from >750,000 CpG sites in over 17,000 volunteers from the Generation Scotland (GS) cohort. Linear regression analyses identified between 304 and 11,815 significant CpGs per trait at P<3.6×10-8, with 37 significant CpG sites across all six traits. Further, we performed a Bayesian EWAS that jointly models all CpGs simultaneously and conditionally on each other, as opposed to the marginal linear regression analyses. This identified between 3 and 27 CpGs with a posterior inclusion probability ≥ 0.95 across the six traits. Next, we used elastic net penalised regression to train epigenetic scores (EpiScores) of each trait in GS, which were then tested in the Lothian Birth Cohort 1936 (LBC1936; European ancestry) and Health for Life in Singapore (HELIOS; Indian-, Malay- and Chinese-ancestries). A maximum of 27.1% of the variance in BMI was explained by the BMI EpiScore in the subset of Malay-ancestry Singaporeans. Four metabolic EpiScores were associated with general cognitive function in LBC1936 in models adjusted for vascular risk factors (Standardised βrange: 0.08 - 0.12, PFDR < 0.05). EpiScores of metabolic health are applicable across ancestries and can reflect differences in brain health.

Which factors affect phenoconversion in isolated rapid eye movement sleep behavior disorder?

AIM

Isolated REM sleep behavior disorder (IRBD) is characterized by loss of the normal atonia of REM sleep. Patients with IRBD are at substantial risk of developing the synuclein-related neurodegenerative diseases (NDD). Few predictors of phenoconversion (from IRBD to NDD) have been identified such as age >65 years, hyposmia, constipation, elevated Epworth sleepiness scale (ESS). We aimed to detect rate and risk factors of phenoconversion.

METHOD

The study designed as retrospectively. NDD was developed in 18 (27.27%) patients while NDD wasn't developed in 48 (72.73%) patients after ten years. The data of the first visit (age, gender, hyposmia, constipation, ESS, comorbidities, physical/neurological examinations, laboratory, and polysomnography) were compared between NDD (n:18) and IRBD (46) groups. The statistical program IBM SPSS Statistics Version 20.0 was used for all analyzes. The threshold for statistical significance for each test was set at 0.05.

RESULTS

Although, most first-visit data (age, gender, hyposmia, constipation, ESS, laboratory, polysomnography) were not different between NDD (n:18) and IRBD (n:48) groups, diabetes mellitus (DM) frequency (p:0.021), mean duration of DM (0.027), chest circumference (p:0.017), and hip circumference (p:0.045) were found higher in NDD than IRBD. If the risk of phenoconversion calculated by logistic regression analysis was different only in terms of DM frequency (p:0.030) [odds ratio: 4.909 (1.17-20.19)].

CONCLUSION

The present study showed that the phenoconversion rate for ten years is 27.27%, and IRBD patients with diabetes mellitus increase the phenoconversion risk nearly five times.

Socio-Demographic, Lifestyle, and Clinical Characteristics of Early and Later Weight Status in Older Adults: Secondary Analysis of the ASPREE Trial and ALSOP Sub-Study

OBJECTIVE

To identify the socio-demographic, lifestyle, and clinical characteristics associated with self-reported weight status in early (age 18 years) and late (age ≥ 70 years) adulthood.

METHODS

The number of participants was 11,288, who were relatively healthy community-dwelling Australian adults aged ≥70 years (mean age 75.1 ± 4.2 years) in the Aspirin in Reducing Events in the Elderly (ASPREE) Longitudinal Study of Older Persons (ALSOP) sub-study. Self-reported weight at the study baseline (age ≥ 70 years) and recalled weight at age 18 years were collected. Height measured at baseline was used to calculate the BMI at both time points. Individuals were categorised into one of five 'lifetime' weight status groups: healthy weight (at both age 18 year and ≥70 years), overweight (at either or both times), non-obese (age 18 year) to obesity (age ≥70 years), obesity (age 18 years) to non-obese (age ≥ 70 years), and early and later life obesity (at age 18 years and ≥70 years).

RESULTS

Participants who experienced obesity in early and/or late adulthood were at a higher risk of adverse clinical characteristics. Obesity in late adulthood (regardless of early adulthood weight status) was associated with high proportions of hypertension, diabetes, and dyslipidaemia, whereas obesity in early adulthood (regardless of late adulthood weight status) was associated with lower cognitive scores (on all four measures).

DISCUSSION/CONCLUSION

Healthy or overweight weight status in early and later adulthood was associated with more favourable socioeconomic, lifestyle, and clinical measures. Obesity in early adulthood was associated with lower cognitive function in later adulthood, whereas obesity in later adulthood was associated with hypertension, diabetes, and dyslipidaemia.

The factors associated with cognitive function among community-dwelling older adults in Taiwan

BACKGROUND

This research aimed to investigate the associations of anthropometric measurements, physiological parameters, chronic disease comorbidities, and social and lifestyle factors with cognitive function amongst community-dwelling older adults in Taiwan.

METHODS

This was an observational, cross-sectional study involving 4,578 participants at least 65 years old, recruited between January 2008 and December 2018 from the Annual Geriatric Health Examinations Program. Cognitive function was assessed using the short portable mental state questionnaire (SPMSQ). Multivariable logistic regression was done to analyze the factors associated with cognitive impairment.

RESULTS

Among the 4,578 participants, 103 people (2.3%) with cognitive impairment were identified. Associated factors were age (odds ratio (OR) = 1.16, 95% confidence interval (CI) = 1.13,1.20), male gender (OR = 0.39, 95% CI = 0.21,0.72), diabetes mellitus (DM) (OR = 1.70, 95% CI = 1.03, 2.82), hyperlipidemia (OR = 0.47, 95% CI = 0.25, 0.89), exercise (OR = 0.44, 95% CI = 0.34, 0.56), albumin (OR = 0.37, 95% CI = 0.15, 0.88), and high-density lipoprotein (HDL) (OR = 0.98, 95% CI = 0.97, 1.00). Whereas waistline, alcohol intake in recent six months, and hemoglobin was not significantly associated with cognitive impairment (all p > 0.05).

CONCLUSIONS

Our findings suggested that people with older age and a history of DM had a higher risk of cognitive impairment. Male gender, a history of hyperlipidemia, exercise, a high albumin level, and a high HDL level seemed to be associated with a lower risk of cognitive impairment amongst older adults.

Obesity marker trajectories and cognitive impairment in older adults: a 10-year follow-up in Taichung community health study for elders

BACKGROUND

Obesity and cognitive impairment prevalence increases as age increases. Recent growing evidence finds links between obesity and cognitive impairment in older adults. However, the association between the two is controversial. This study aims to identify obesity marker trajectory patterns, and to assess whether these patterns are associated with cognitive impairment and cognitive decline during a 10-year follow-up period among community-dwelling older adults.

METHODS

A total of 626 older adults aged 65 and older were involved in the study, with at least two repeated measurements at baseline, one-year or 10-year follow-up. Cognitive function was measured through the Mini Mental State Examination. Obesity markers included body mass index, waist circumference, waist-to-hip (WHR), fat mass (FM), and abdominal fat (AF) measured by dual-energy X-ray absorptiometry. Multivariate logistic regression analyses were performed to estimate the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of cognitive impairment and cognitive decline for obesity marker trajectory patterns.

RESULTS

After a 10-year follow-up, 168 older adults with incident cognitive impairment and 156 with rapid cognitive decline were defined as the top 25th percentile of cognitive decline. Four distinct trajectory groups of obesity markers were identified. In multivariate logistic regression analyses, a low likelihood of cognitive impairment was observed in the consistently high-level group from FM trajectory (ORs = 0.41, 95% CI = 0.20-0.85); the high-level U-shaped group from WHR trajectory (0.43, 0.22-0.84); and the median-level flat inverse U-shaped, consistently high-level, and low-level flat U-shaped groups from AF trajectory (0.44, 0.26-0.77; 0.33, 0.18-0.61; 0.39, 0.18-0.82). In addition, a low likelihood of rapid decline was found in the low-level, slightly increasing trend group from WHR trajectory (0.43, 0.22-0.85).

CONCLUSION

FM and AF trajectories with consistent high levels and WHR trajectory with high level with U-shaped group are associated with low risks of incident cognitive impairment in older adults. Similarly, WHR trajectory with a low but slowly increasing trend is associated with a decreased risk of cognitive decline.

The Role of Dietary Patterns and Dietary Quality on Body Composition of Adolescents in Chinese College

There is limited evidence regarding the effects of dietary pattern and dietary quality on the risk of unhealthy weight status and related body composition in Chinese adolescence. In particular, studies using bioelectrical impedance analyzer (BIA) in these subjects are rare. The aim of this study was to evaluate the role of diet in body composition, to find a healthy dietary pattern for Chinese youth, and to promote the application of BIA among this population. A total of 498 participants aged from 18 to 22 years old were included. Dietary patterns were identified by principal components analysis. Energy-adjusted dietary inflammatory index (DII) and diet balance index (DBI) were calculated based on semi-quantitative food frequency questionnaire. Multivariate linear regression and logistic regression analysis were used to examine the relationship of dietary patterns, dietary quality with body mass index (BMI), fat mass index (FMI), fat-free mass index (FFMI), and the effect of dietary factors on BMI levels. The majority of participants with overweight and obesity had abdominal obesity, and there was 3.7% abdominal obesity in normal BMI individuals. Four dietary patterns were detected in the subjects. The pattern with the higher energy intake, which was close to the Western diet, was positively correlated with BMI (β = 0.326, p = 0.018) and FMI (β = 0.201, p = 0.043), while being negatively correlated with FFMI (β = −0.183, p = 0.021). Individuals who followed the pattern similar to the Mediterranean diet had a higher basal metabolic rate (BMR), and the highest fat free mass, soft lean mass, and skeletal muscle mass (p < 0.05) but the lowest FMI, visceral fat area (VFA), waist−hip ratio, and FMI/FFMI ratio (p < 0.05). Higher energy-adjusted DII was associated with high BMI. Higher bound score (HBS) (β = −0.018, p = 0.010) and diet quality distance (DQD) (β = −0.012, p = 0.015) were both negatively correlated with FFMI. In conclusion, fat or muscle indexes, such as BMR, FMI, and FFMI, had an important role in predicting overweight and obesity, which suggested the importance of applying BIA among Chinese college students. Students who followed healthful dietary patterns or the high-quality diet that is similar to the Mediterranean diet but not close to the Western diet were more likely to have a healthy BMI and normal body composition.