Редактирование: APOC3

Apolipoprotein C-III precursor (Apo-CIII) (ApoC-III) (Apolipoprotein C3)

==Publications==

{{medline-entry
|title=Positional Obstructive Sleep Apnea Syndrome in Elderly Patients.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/32050596
|abstract=[i]Background[/i] The purpose of this study was to evaluate the prevalence of position-dependent obstructive sleep apnea (POSA) in elderly patients (≥65 years old). Adult (range 19-65 years old) and elderly patients were also compared in order to show differences in the incidence of POSA between these two groups of patients. [i]Methods[/i] A prospective bi-center study was performed between January 2018 and May 2019. A total of 434 participants underwent polysomnography (PSG) study at home (Embletta MPR). Body position during the PSG recordings was determined. Patients were subdivided in two groups: those aged between 19 and 65 years old (adult patients) and ≥65 years old (elderly patients). POSA patients were defined using Cartwright's system, Bignold classification, and the new Amsterdam Positional OSA Classification (APOC). [i]Results[/i] The prevalence of POSA in elderly patients differed according to the classification system used: 49.3% using Cartwright's classification system, 20.5% with the Bignold classification, and 22.6%, 38.9%, and 5.4% of APOC 1, APOC 2, and [[APOC3]] sub-classes were respectively identified for the APOC classification system. No difference between adult and elderly patients regarding the prevalence of POSA was observed. No statistical differences emerged between the two groups of patients in terms of supine ([i]p[/i] = 0.9) and non-supine AHI ([i]p[/i] = 0.4). [i]Conclusions[/i] A significant number of elderly patients could be considered treatable with positional therapy according to the APOC classification. However, the efficacy and applicability of positional therapy in elderly patients must be confirmed by further research.
|mesh-terms=* Adult
* Aged
* Humans
* Middle Aged
* Polysomnography
* Posture
* Prospective Studies
* Sleep Apnea, Obstructive
* Supine Position
* Young Adult
|keywords=* aging effects
* obstructive sleep apnea
* polysomnography
* positional sleep apnea
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7042812
}}
{{medline-entry
|title=No Association between Variation in Longevity Candidate Genes and Aging-related Phenotypes in Oldest-old Danes.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/26946122
|abstract=In this study we explored the association between aging-related phenotypes previously reported to predict survival in old age and variation in 77 genes from the DNA repair pathway, 32 genes from the growth hormone 1/ insulin-like growth factor 1/insulin (GH/IGF-1/INS) signalling pathway and 16 additional genes repeatedly considered as candidates for human longevity: [[APOE]], [[APOA4]], [[APOC3]], [[ACE]], [[CETP]], [[HFE]], [[IL6]], [[[[IL6]]R]], [[MTHFR]], [[TGFB1]], SIRTs 1, 3, 6; and HSPAs 1A, 1L, 14. Altogether, 1,049 single nucleotide polymorphisms (SNPs) were genotyped in 1,088 oldest-old (age 92-93 years) Danes and analysed with phenotype data on physical functioning (hand grip strength), cognitive functioning (mini mental state examination and a cognitive composite score), activity of daily living and self-rated health. Five SNPs showed association to one of the phenotypes; however, none of these SNPs were associated with a change in the relevant phenotype over time (7 years of follow-up) and none of the SNPs could be confirmed in a replication sample of 1,281 oldest-old Danes (age 94-100). Hence, our study does not support association between common variation in the investigated longevity candidate genes and aging-related phenotypes consistently shown to predict survival. It is possible that larger sample sizes are needed to robustly reveal associations with small effect sizes. 
|mesh-terms=* Activities of Daily Living
* Aged, 80 and over
* Aging
* Cognition
* Denmark
* Female
* Genotype
* Hand Strength
* Humans
* Linear Models
* Longevity
* Male
* Phenotype
* Polymorphism, Single Nucleotide
* Signal Transduction
* Surveys and Questionnaires
|keywords=* Association study
* Human aging
* Oldest-old
* Single nucleotide polymorphisms
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4841709
}}
{{medline-entry
|title=Nutritional habits, lifestyle, and genetic predisposition in cardiovascular and metabolic traits in Turkish population.
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/26856649
|abstract=Cardiovascular and metabolic traits (CMT) are influenced by complex interactive processes including diet, lifestyle, and genetic predisposition. The present study investigated the interactions of these risk factors in relation to CMTs in the Turkish population. We applied bootstrap agglomerative hierarchical clustering and Bayesian network learning algorithms to identify the causative relationships among genes involved in different biological mechanisms (i.e., lipid metabolism, hormone metabolism, cellular detoxification, aging, and energy metabolism), lifestyle (i.e., physical activity, smoking behavior, and metropolitan residency), anthropometric traits (i.e., body mass index, body fat ratio, and waist-to-hip ratio), and dietary habits (i.e., daily intakes of macro- and micronutrients) in relation to CMTs (i.e., health conditions and blood parameters). We identified significant correlations between dietary habits (soybean and vitamin B12 intakes) and different cardiometabolic diseases that were confirmed by the Bayesian network-learning algorithm. Genetic factors contributed to these disease risks also through the pleiotropy of some genetic variants (i.e., [[F5]] rs6025 and [[MTR]] rs180508). However, we also observed that certain genetic associations are indirect since they are due to the causative relationships among the CMTs (e.g., [[APOC3]] rs5128 is associated with low-density lipoproteins cholesterol and, by extension, total cholesterol). Our study applied a novel approach to integrate various sources of information and dissect the complex interactive processes related to CMTs. Our data indicated that complex causative networks are present: causative relationships exist among CMTs and are affected by genetic factors (with pleiotropic and non-pleiotropic effects) and dietary habits.
|mesh-terms=* Aging
* Anthropometry
* Bayes Theorem
* Cardiovascular Diseases
* Diet
* Energy Metabolism
* Feeding Behavior
* Female
* Genetic Predisposition to Disease
* Humans
* Life Style
* Lipid Metabolism
* Lipoproteins, LDL
* Male
* Middle Aged
* Risk Factors
* Turkey
* Waist-Hip Ratio
|keywords=* Cardiometabolic traits
* Diet
* Genetic predisposition
* Interactive mechanisms
* Turkey
|full-text-url=https://sci-hub.do/10.1016/j.nut.2015.12.027
}}