METHODS: Two hundred subjects (104 patients, 96 controls) underwent extensive clinical phenotyping. Stool samples were analyzed using 16S rRNA gene sequencing. Fecal metabolomics were performed using two platforms, nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry.
RESULTS: Fecal microbiome and metabolome composition in PD was significantly different from controls, with the largest effect size seen in NMR-based metabolome. Microbiome and NMR-based metabolome compositional differences remained significant after comprehensive confounder analyses. Differentially abundant fecal metabolite features and predicted functional changes in PD versus controls included bioactive molecules with putative neuroprotective effects (eg, short chain fatty acids [SCFAs], ubiquinones, and salicylate) and other compounds increasingly implicated in neurodegeneration (eg, ceramides, sphingosine, and trimethylamine N-oxide). In the PD group, cognitive impairment, low body mass index (BMI), frailty, constipation, and low physical activity were associated with fecal metabolome compositional differences. Notably, low SCFAs in PD were significantly associated with poorer cognition and low BMI. Lower butyrate levels correlated with worse postural instability-gait disorder scores.
INTERPRETATION: Gut microbial function is altered in PD, characterized by differentially abundant metabolic features that provide important biological insights into gut-brain pathophysiology. Their clinical relevance further supports a role for microbial metabolites as potential targets for the development of new biomarkers and therapies in PD. ANN NEUROL 2021;89:546-559.
METHODS: For this systematic review and meta-analysis, we searched PubMed, Embase, Scopus, and the Cochrane Library from inception to May 1, 2019, for relevant original research articles without any language restrictions. The literature search and data extraction were done independently by two investigators. Primary outcomes were the prevalence of non-obese or lean people within the NAFLD group and the prevalence of non-obese or lean NAFLD in the general, non-obese, and lean populations; the incidence of NAFLD among non-obese and lean populations; and long-term outcomes of non-obese people with NAFLD. We also aimed to characterise the demographic, clinical, and histological characteristics of individuals with non-obese NAFLD.
FINDINGS: We identified 93 studies (n=10 576 383) from 24 countries or areas: 84 studies (n=10 530 308) were used for the prevalence analysis, five (n=9121) were used for the incidence analysis, and eight (n=36 954) were used for the outcomes analysis. Within the NAFLD population, 19·2% (95% CI 15·9-23·0) of people were lean and 40·8% (36·6-45·1) were non-obese. The prevalence of non-obese NAFLD in the general population varied from 25% or lower in some countries (eg, Malaysia and Pakistan) to higher than 50% in others (eg, Austria, Mexico, and Sweden). In the general population (comprising individuals with and without NAFLD), 12·1% (95% CI 9·3-15·6) of people had non-obese NAFLD and 5·1% (3·7-7·0) had lean NAFLD. The incidence of NAFLD in the non-obese population (without NAFLD at baseline) was 24·6 (95% CI 13·4-39·2) per 1000 person-years. Among people with non-obese or lean NALFD, 39·0% (95% CI 24·1-56·3) had non-alcoholic steatohepatitis, 29·2% (21·9-37·9) had significant fibrosis (stage ≥2), and 3·2% (1·5-5·7) had cirrhosis. Among the non-obese or lean NAFLD population, the incidence of all-cause mortality was 12·1 (95% CI 0·5-38·8) per 1000 person-years, that for liver-related mortality was 4·1 (1·9-7·1) per 1000 person-years, cardiovascular-related mortality was 4·0 (0·1-14·9) per 1000 person-years, new-onset diabetes was 12·6 (8·0-18·3) per 1000 person-years, new-onset cardiovascular disease was 18·7 (9·2-31·2) per 1000 person-years, and new-onset hypertension was 56·1 (38·5-77·0) per 1000 person-years. Most analyses were characterised by high heterogeneity.
INTERPRETATION: Overall, around 40% of the global NAFLD population was classified as non-obese and almost a fifth was lean. Both non-obese and lean groups had substantial long-term liver and non-liver comorbidities. These findings suggest that obesity should not be the sole criterion for NAFLD screening. Moreover, clinical trials of treatments for NAFLD should include participants across all body-mass index ranges.
FUNDING: None.
DESIGN: Population-based, retrospective cohort study. Participants were followed up for 5 years from 2006 to 2010. Mortality data were obtained via record linkages with the Malaysian National Registration Department. Multiple Cox regression was applied to compare risk of CVD and all-cause mortality between BMI categories adjusting for age, gender and ethnicity. Models were generated for all participants, all participants the first 2 years of follow-up, healthy participants, healthy never smokers, never smokers, current smokers and former smokers.
SETTING: All fourteen states in Malaysia.
SUBJECTS: Malaysian adults (n 32 839) aged 18 years or above from the third National Health and Morbidity Survey.
RESULTS: Total follow-up time was 153 814 person-years with 1035 deaths from all causes and 225 deaths from CVD. Underweight (BMI<18·5 kg/m2) was associated with a significantly increased risk of all-cause mortality, while obesity (BMI ≥30·0 kg/m2) was associated with a heightened risk of CVD mortality. Overweight (BMI=25·0-29·9 kg/m2) was inversely associated with risk of all-cause mortality. Underweight was significantly associated with all-cause mortality in all models except for current smokers. Overweight was inversely associated with all-cause mortality in all participants. Although a positive trend was observed between BMI and CVD mortality in all participants, a significant association was observed only for severe obesity (BMI≥35·0 kg/m2).
CONCLUSIONS: Underweight was associated with increased risk of all-cause mortality and obesity with increased risk of CVD mortality. Therefore, maintaining a normal BMI through leading an active lifestyle and healthy dietary habits should continue to be promoted.
METHODS: The cross-sectional study was conducted from September 2017 to June 2018 in the paediatrics wards of a tertiary referral paediatric government hospital, a tertiary teaching hospital and a government district hospital in Malaysia. The sample comprised paediatric patients aged 2-12 years within 24-72 hours of hospital admission. Data was collected using the 3-Minute Nutrition Screening-Paediatrics tool. Data was analysed using SPSS 20.
RESULTS: Of the 341 patients screened, 284(83.3%) were included; 170(59.9%) boys and 114(40.1%) girls. The overall median age was 4.85 years (interquartile range: 4.33 years). The median length of hospital stay was 3 days (interquartile range: 3 days). There were 72(25.4%) participants at high under-nutrition risk, with the highest proportion being at the district government hospital 31(33%). Among those with high risk, 5.4% subjects had severe acute malnutrition, 9.7% had severe chronic malnutrition, and 11.1% had severe thinness.
CONCLUSION: The 3-Minute Nutrition Screening-Paediatrics scale was found to be effective as a nutrition screening tool for hospitalised children in Malaysia.
Methods: Data from the National Health and Morbidity Survey (NHMS) 2018 was analysed. This survey applied a multistage stratified cluster sampling design to ensure national representativeness. Malnutrition was identified using a validated Mini Nutrition Assessment-Short Form (MNA-SF). Variables on sociodemographic, health status, and dietary practices were also obtained. The complex sampling analysis was used to determine the prevalence and associated factors of at-risk or malnutrition among the elderly.
Result: A total of 3,977 elderly completed the MNA-SF. The prevalence of malnutrition and at-risk of malnutrition was 7.3% and 23.5%, respectively. Complex sample multiple logistic regression found that the elderly who lived in a rural area, with no formal or primary level of education, had depression, Instrumental Activity of Daily Living (IADL) dependency, and low quality of life (QoL), were underweight, and had food insecurity and inadequate plain water intake were at a significant risk of malnutrition (malnutrition and at-risk), while Chinese, Bumiputra Sarawak, and BMI more than 25 kgm-2 were found to be protective.
Conclusions: Currently, three out of ten elderly in Malaysia were at-risk or malnutrition. The elderly in a rural area, low education level, depression, IADL dependency, low QoL, underweight, food insecurity, and inadequate plain water intake were at risk of malnutrition in Malaysia. The multiagency approach is needed to tackle the issue of malnutrition among the elderly by considering all predictors identified from this study.
METHODS: Data were derived from the Global School-Based Student Health Survey (GSHS). Data from 71176 adolescents aged 12-15 years residing in 23 countries were analyzed. The Centers for Disease Control and Prevention (CDC) 2000 growth charts were used to identify underweight, normal weight, and overweight/ obesity. Weighted age- and gender-adjusted prevalence of weight categories and tobacco use was calculated. Multivariate logistic regression analysis was performed to estimate the association between weight categories and tobacco use for each country, controlling for covariates. Pooled odds ratios and confidence intervals were computed using random- or fixed-effects meta-analyses.
RESULTS: A significant association between weight categories and tobacco use was evident in only a few countries. Adolescents reporting tobacco use in French Polynesia, Suriname, and Indonesia, had 72% (95% CI: 0.15-0.56), 55% (95% CI: 0.24-0.84), and 24% (95% CI: 0.61-0.94) lower odds of being underweight, respectively. Adolescents reporting tobacco use in Uganda, Algeria, and Namibia, had 2.30 (95% CI: 1.04-5.09), 1.71 (95% CI: 1.25-2.34), and 1.45 (95% CI: 1.00-2.12) times greater odds of being overweight/obese, but those in Indonesia and Malaysia had 33% (95% CI: 0.50-0.91) and 16% (95% CI: 0.73-0.98) lower odds of being overweight/obese.
CONCLUSIONS: The association between tobacco use and BMI categories is likely to be different among adolescents versus adults. Associating tobacco use with being thin may be more myth than fact and should be emphasized in tobacco prevention programs targeting adolescents.