Alcohol consumption and body composition in a population-based sample of elderly Australian men

Background: Alcohol is calorie dense, and impacts&nbsp;activity, appetite and lipid processing. The aim of this&nbsp;study was to therefore investigate the association between&nbsp;alcohol consumption and components of body composition&nbsp;including bone, fat and lean tissue.Methods: Participants were recruited from a randomly&nbsp;selected, population-based sample of 534 men aged&nbsp;65 years and older enrolled in the Geelong Osteoporosis&nbsp;Study. Alcohol intake was ascertained using a food&nbsp;frequency questionnaire and the sample categorised as nondrinkers or alcohol users who consumed B2, 3&ndash;4 or C5&nbsp;standard drinks on a usual drinking day. Bone mineral&nbsp;density (BMD), lean body mass and body fat mass were&nbsp;measured using dual energy X-ray absorptiometry; overall&nbsp;adiposity (%body fat), central adiposity (%truncal fat) and&nbsp;body mass index (BMI) were calculated. Bone quality was&nbsp;determined by quantitative heel ultrasound (QUS).Results: There were 90 current non-drinkers (16.9 %),&nbsp;266 (49.8 %) consumed 1&ndash;2 drinks/day, 104 (19.5 %) 3&ndash;4&nbsp;drinks/day and 74 (13.8 %) C5 drinks/day. Those consuming C5 drinks/day had greater BMI (?4.8 %), fat mass&nbsp;index (?20.1 %), waist circumference (?5.0 %), %body&nbsp;fat (?15.2 %) and proportion of trunk fat (?5.3 %) and&nbsp;lower lean mass (-5.0 %) than non-drinkers after adjustment for demographic and lifestyle factors. Furthermore,&nbsp;they were more likely to be obese than non-drinkers&nbsp;according to criteria based on BMI (OR = 2.83, 95 %CI&nbsp;1.10&ndash;7.29) or waist circumference (OR = 3.36, 95 %CI&nbsp;1.32&ndash;8.54). There was an inverse relationship between&nbsp;alcohol consumption and QUS parameters and BMD at the&nbsp;mid forearm site; no differences were detected for BMD at&nbsp;other skeletal sites.Conclusion:&nbsp;Higher alcohol intake was associated with&nbsp;greater total and central adiposity and reduced bone&nbsp;quality.<br /

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Proximal correlates of metabolic phenotypes during ‘at-risk' and ‘case' stages of the metabolic disease continuum

Extent: 11p.OBJECTIVE: To examine the social and behavioural correlates of metabolic phenotypes during ‘at-risk’ and ‘case’ stages of the metabolic disease continuum. DESIGN: Cross-sectional study of a random population sample. PARTICIPANTS: A total of 718 community-dwelling adults (57% female), aged 18--92 years from a regional South Australian city. MEASUREMENTS: Total body fat and lean mass and abdominal fat mass were assessed by dual energy x-ray absorptiometry. Fasting venous blood was collected in the morning for assessment of glycated haemoglobin, plasma glucose, serum triglycerides, cholesterol lipoproteins and insulin. Seated blood pressure (BP) was measured. Physical activity and smoking, alcohol and diet (96-item food frequency), sleep duration and frequency of sleep disordered breathing (SDB) symptoms, and family history of cardiometabolic disease, education, lifetime occupation and household income were assessed by questionnaire. Current medications were determined by clinical inventory. RESULTS: 36.5% were pharmacologically managed for a metabolic risk factor or had known diabetes (‘cases’), otherwise were classified as the ‘at-risk’ population. In both ‘at-risk’ and ‘cases’, four major metabolic phenotypes were identified using principal components analysis that explained over 77% of the metabolic variance between people: fat mass/insulinemia (FMI); BP; lipidaemia/lean mass (LLM) and glycaemia (GLY). The BP phenotype was uncorrelated with other phenotypes in ‘cases’, whereas all phenotypes were inter-correlated in the ‘at-risk’. Over and above other socioeconomic and behavioural factors, medications were the dominant correlates of all phenotypes in ‘cases’ and SDB symptom frequency was most strongly associated with FMI, LLM and GLY phenotypes in the ‘at-risk’. CONCLUSION: Previous research has shown FMI, LLM and GLY phenotypes to be most strongly predictive of diabetes development. Reducing SDB symptom frequency and optimising the duration of sleep may be important concomitant interventions to standard diabetes risk reduction interventions. Prospective studies are required to examine this hypothesis.MT Haren, G Misan, JF Grant, JD Buckley, PRC Howe, AW Taylor, J Newbury and RA McDermot

Autosomal recessive Wolfram syndrome associated with an 8.5-kb mtDNA single deletion

Wolfram syndrome (MIM 222300) is characterized by optic atrophy, diabetes mellitus, diabetes insipidus, neurosensory hearing loss, urinary tract abnormalities, and neurological dysfunction. The association of clinical manifestations in tissues and organs unrelated functionally or embryologically suggested the possibility of a mitochondrial implication in the disease, which has been demonstrated in two sporadic cases. Nonetheless, familial studies suggested an autosomal recessive mode of transmission, and recent data demonstrated linkage with markers on the short arm of human chromosome 4. The patient reported here, as well as her parents and unaffected sister, carried a heteroplasmic 8.5-kb deletion in mtDNA. The deletion accounted for 23% of mitochondrial genomes in lymphocytes from the patient and approximately 5% in the tissues studied from members of her family. The presence of the deletion in the patient in a proportion higher than in her unaffected parents suggests a putative defect in a nuclear gene that acts at the mitochondrial level