Prevalence of protein intake below recommended in community‐dwelling older adults: a meta‐analysis across cohorts from the PROMISS consortium

European Horizon 2020 PROMISS Project ‘PRevention Of Malnutrition In Senior Subjects in the EU’, (grant agreement no. 678732). The content only reflects the author’s view and the commission is not responsible for any use that may be made of the information it contains.Background: Lower protein intake in older adults is associated with loss of muscle mass and strength. The present study aimed to provide a pooled estimate of the overall prevalence of protein intake below recommended (according to different cut-off values) among community-dwelling older adults, both within the general older population and within specific subgroups. Methods: As part of the PRevention Of Malnutrition In Senior Subjects in the EU (PROMISS) project, a meta-analysis was performed using data from four cohorts (from the Netherlands, UK, Canada, and USA) and four national surveys [from the Netherlands, Finland (two), and Italy]. Within those studies, data on protein and energy intake of community-dwelling men and women aged ≥55 years were obtained by either a food frequency questionnaire, 24 h recalls administered on 2 or 3 days, or food diaries administered on 3 days. Protein intake below recommended was based on the recommended dietary allowance of 0.8 g/kg body weight (BW)/d, by using adjusted BW (aBW) instead of actual BW. Cut-off values of 1.0 and 1.2 were applied in additional analyses. Prevalences were also examined for subgroups according to sex, age, body mass index (BMI), education level, appetite, living status, and recent weight loss. Results: The study sample comprised 8107 older persons. Mean ± standard deviation protein intake ranged from 64.3 ± 22.3 (UK) to 80.6 ± 23.7 g/d [the Netherlands (cohort)] or from 0.94 ± 0.38 (USA) to 1.17z ± 0.30 g/kg aBW/d (Italy) when related to BW. The overall pooled prevalence of protein intake below recommended was 21.5% (95% confidence interval: 14.0–30.1), 46.7% (38.3–55.3), and 70.8% (65.1–76.3) using the 0.8, 1.0, and 1.2 cut-off value, respectively. A higher prevalence was observed among women, individuals with higher BMI, and individuals with poor appetite. The prevalence differed only marginally by age, education level, living status, and recent weight loss. Conclusions: In community-dwelling older adults, the prevalence of protein intake below the current recommendation of 0.8 g/kg aBW/d is substantial (14–30%) and increases to 65–76% according to a cut-off value of 1.2 g/kg aBW/d. To what extent the protein intakes are below the requirements of these older people warrants further investigation.publishersversionpublishe

Methodology and implementation of the WHO European Childhood Obesity Surveillance Initiative (COSI)

Establishment of the WHO European Childhood Obesity Surveillance Initiative (COSI)has resulted in a surveillance system which provides regular, reliable, timely, andaccurate data on children's weight status—through standardized measurement ofbodyweight and height—in the WHO European Region. Additional data on dietaryintake, physical activity, sedentary behavior, family background, and schoolenvironments are collected in several countries. In total, 45 countries in the EuropeanRegion have participated in COSI. The first five data collection rounds, between 2007and 2021, yielded measured anthropometric data on over 1.3 million children. In COSI,data are collected according to a common protocol, using standardized instrumentsand procedures. The systematic collection and analysis of these data enables inter-country comparisons and reveals differences in the prevalence of childhood thinness,overweight, normal weight, and obesity between and within populations. Furthermore,it facilitates investigation of the relationship between overweight, obesity, and poten-tial risk or protective factors and improves the understanding of the development ofoverweight and obesity in European primary-school children in order to supportappropriate and effective policy responses.The authors gratefully acknowledge support through a grant from the Russian Government in the context of the WHO European Office for the Prevention and Control of NCDs. The ministries of health of Austria, Croatia, Greece, Italy, Malta, Norway, and the Russian Federation provided financial support for the meetings at which the protocol, data collection procedures, and analyses were discussed. Data collection in countries was made possible through funding from the following: Albania: WHO through the Joint Programme on Children, Food Security and Nutrition “Reducing Malnutrition in Children,” funded by the Millennium Development Goals Achievement Fund, and the Institute of Public Health. Austria: Federal Ministry of Labor, Social Affairs, Health and Consumer Protection of Austria. Bulgaria: Ministry of Health, National Center of Public Health and Analyses, and WHO Regional Office for Europe. Bosnia and Herzegovina: WHO country office support for training and data management. Croatia: Ministry of Health, Croatian Institute of Public Health, and WHO Regional Office for Europe. Czechia: Ministry of Health of the Czech Republic, grant number 17-31670A and MZCR—RVO EU 00023761. Denmark: Danish Ministry of Health. Estonia: Ministry of Social Affairs, Ministry of Education and Research (IUT 42-2), WHO Country Office, and National Institute for Health Development. Finland: Finnish Institute for Health and Welfare. France: Santé publique France (the French Agency for Public Health). Georgia: WHO. Greece: International Hellenic University and Hellenic Medical Association for Obesity. Hungary: WHO Country Office for Hungary. Ireland: Health Service Executive. Italy: Ministry of Health. Kazakhstan: Ministry of Health of the Republic of Kazakhstan, WHO, and UNICEF. Kyrgyzstan: World Health Organization. Latvia: Ministry of Health and Centre for Disease Prevention and Control. Lithuania: Science Foundation of Lithuanian University of Health Sciences and Lithuanian Science Council and WHO. Malta: Ministry of Health. Montenegro: WHO and Institute of Public Health of Montenegro. North Macedonia: Government of North Macedonia through National Annual Program of Public Health and implemented by the Institute of Public Health and Centers of Public Health; WHO country office provides support for training and data management. Norway: the Norwegian Ministry of Health and Care Services, the Norwegian Directorate of Health, and the Norwegian Institute of Public Health. Poland: National Health Programme, Ministry of Health. Portugal: Ministry of Health Institutions, the National Institute of Health, Directorate General of Health, Regional Health Directorates, and the kind technical support from the Center for Studies and Research on Social Dynamics and Health (CEIDSS). Romania: Ministry of Health. Russian Federation: WHO. San Marino: Health Ministry, Educational Ministry, and Social Security Institute and Health Authority. Serbia: WHO and the WHO Country Office (2015-540940 and 2018/873491-0). Slovakia: Biennial Collaborative Agreement between WHO Regional Office for Europe and Ministry of Health SR. Slovenia: Ministry of Education, Science and Sport of the Republic of Slovenia within the SLOfit surveillance system. Spain: Spanish Agency for Food Safety and Nutrition. Sweden: Public Health Agency of Sweden. Tajikistan: WHO Country Office in Tajikistan and Ministry of Health and Social Protection. Turkmenistan: WHO Country Office in Turkmenistan and Ministry of Health. Turkey: Turkish Ministry of Health and World Bank.info:eu-repo/semantics/publishedVersio

Associations between dietary inflammatory scores and biomarkers of inflammation in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort

Background: Since the first version of the dietary inflammatory index (DII & REG;) developed in the past decade, several other versions have been developed. However, to date no study has attempted to compare these versions with respect to their associations with biomarkers of inflammation. Objective: We aimed to investigate the relationship between four dietary inflammatory scores [DII, two energy-adjusted derivatives (E-DII and E-DIIr), and the Inflammatory Score of the Diet (ISD)], and circulating levels of several inflammatory markers and adipokines. Methods: This study included 17 637 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with at least one marker of inflammation measured in blood. Associations between the four scores and C-reactive protein (CRP), interleukin (IL)6, IL10, IL1RA, tumor necrosis factor-a (TNFa), soluble tumor necrosis factor receptor-1 (sTNFR1), sTNFR2, leptin, soluble leptin receptor (sLeptin R), adiponectin, and High Molecular Weight (HMW) adiponectin were evaluated using multivariable linear regressions adjusted for potential confounders. Results: Positive associations were observed between the four dietary inflammatory scores and levels of CRP, IL6, sTNFR1, sTNFR2 and leptin. However, only the DII and the ISD were positively associated with IL1RA levels and only the DII and the E-DIIr were positively associated with TNFa levels. The proportion of variance of each biomarker explained by the scores was lower than 2%, which was equivalent to the Conclusions: Our results suggest that the four dietary inflammatory scores were associated with some biomarkers of inflammation and could be used to assess the inflammatory potential of diet in European adults but are not sufficient to capture the inflammatory status of an individual. These findings can help to better understand the inflammatory potential of diet, but they need to be replicated in studies with repeated dietary measurements. Crown Copyright & COPY; 2023 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-N

FTO genetic variants, dietary intake and body mass index: insights from 177 330 individuals

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177 330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m2, P = 1.9 × 10−105), and all participants (0.30 [0.30, 0.35] kg/m2, P = 3.6 × 10−107). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10−16), and relative weak associations with lower total energy intake (−6.4 [−10.1, −2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (−0.07 [−0.11, −0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10−9) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposity

Gene-centric meta-analyses for central adiposity traits in up to 57 412 individuals of European descent confirm known loci and reveal several novel associations

Waist circumference (WC) and waist-to-hip ratio (WHR) are surrogate measures of central adiposity that are associated with adverse cardiovascular events, type 2 diabetes and cancer independent of body mass index (BMI). WC and WHR are highly heritable with multiple susceptibility loci identified to date. We assessed the association between SNPs and BMI-adjusted WC and WHR and unadjusted WC in up to 57 412 individuals of European descent from 22 cohorts collaborating with the NHLBI's Candidate Gene Association Resource (CARe) project. The study population consisted of women and men aged 20–80 years. Study participants were genotyped using the ITMAT/Broad/CARE array, which includes ∼50 000 cosmopolitan tagged SNPs across ∼2100 cardiovascular-related genes. Each trait was modeled as a function of age, study site and principal components to control for population stratification, and we conducted a fixed-effects meta-analysis. No new loci for WC were observed. For WHR analyses, three novel loci were significantly associated (P < 2.4 × 10−6). Previously unreported rs2811337-G near TMCC1 was associated with increased WHR (β ± SE, 0.048 ± 0.008, P = 7.7 × 10−9) as was rs7302703-G in HOXC10 (β = 0.044 ± 0.008, P = 2.9 × 10−7) and rs936108-C in PEMT (β = 0.035 ± 0.007, P = 1.9 × 10−6). Sex-stratified analyses revealed two additional novel signals among females only, rs12076073-A in SHC1 (β = 0.10 ± 0.02, P = 1.9 × 10−6) and rs1037575-A in ATBDB4 (β = 0.046 ± 0.01, P = 2.2 × 10−6), supporting an already established sexual dimorphism of central adiposity-related genetic variants. Functional analysis using ENCODE and eQTL databases revealed that several of these loci are in regulatory regions or regions with differential expression in adipose tissue

FTO genetic variants, dietary intake and body mass index: insights from 177 330 individuals

FTO is the strongest known genetic susceptibility locus for obesity. Experimental studies in animals suggest the potential roles of FTO in regulating food intake. The interactive relation among FTO variants, dietary intake and body mass index (BMI) is complex and results from previous often small-scale studies in humans are highly inconsistent. We performed large-scale analyses based on data from 177 330 adults (154 439 Whites, 5776 African Americans and 17 115 Asians) from 40 studies to examine: (i) the association between the FTO-rs9939609 variant (or a proxy single-nucleotide polymorphism) and total energy and macronutrient intake and (ii) the interaction between the FTO variant and dietary intake on BMI. The minor allele (A-allele) of the FTO-rs9939609 variant was associated with higher BMI in Whites (effect per allele = 0.34 [0.31, 0.37] kg/m2, P = 1.9 × 10−105), and all participants (0.30 [0.30, 0.35] kg/m2, P = 3.6 × 10−107). The BMI-increasing allele of the FTO variant showed a significant association with higher dietary protein intake (effect per allele = 0.08 [0.06, 0.10] %, P = 2.4 × 10−16), and relative weak associations with lower total energy intake (−6.4 [−10.1, −2.6] kcal/day, P = 0.001) and lower dietary carbohydrate intake (−0.07 [−0.11, −0.02] %, P = 0.004). The associations with protein (P = 7.5 × 10−9) and total energy (P = 0.002) were attenuated but remained significant after adjustment for BMI. We did not find significant interactions between the FTO variant and dietary intake of total energy, protein, carbohydrate or fat on BMI. Our findings suggest a positive association between the BMI-increasing allele of FTO variant and higher dietary protein intake and offer insight into potential link between FTO, dietary protein intake and adiposit

Risk thresholds for alcohol consumption : combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies

Background Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. Methods We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12.5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5.6 years [5th-95th percentile 1.04-13.5]) from 71 011 participants from 37 studies. Findings In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5.4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around or below 100 g per week. Alcohol consumption was roughly linearly associated with a higher risk of stroke (HR per 100 g per week higher consumption 1.14, 95% CI, 1.10-1.17), coronary disease excluding myocardial infarction (1.06, 1.00-1.11), heart failure (1.09, 1.03-1.15), fatal hypertensive disease (1.24, 1.15-1.33); and fatal aortic aneurysm (1.15, 1.03-1.28). By contrast, increased alcohol consumption was loglinearly associated with a lower risk of myocardial infarction (HR 0.94, 0.91-0.97). In comparison to those who reported drinking >0-100-200-350 g per week had lower life expectancy at age 40 years of approximately 6 months, 1-2 years, or 4-5 years, respectively. Interpretation In current drinkers of alcohol in high-income countries, the threshold for lowest risk of all-cause mortality was about 100 g/week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data support limits for alcohol consumption that are lower than those recommended in most current guidelines. Copyright (C) The Author(s). Published by Elsevier Ltd.Peer reviewe

Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses

BACKGROUND: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. METHODS: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million personyears of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. RESULTS: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eG FR values 105 mL.min(-1).1.73 m(-2), compared with those with eG FR between 60 and 105 mL.min(-1).1.73 m(-2). Mendelian randomization analyses for CHD showed an association among participants with eGFR 105 mL.min(-1).1.73 m(-2). Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin Alc, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. CONCLUSIONS: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function

Alcohol consumption and risk of cardiovascular disease among hypertensive women

This study investigated the relation between alcohol consumption and the risk of cardiovascular disease (CVD) among 10 530-hypertensive women from the EPIC-NL cohort. Alcohol consumption was assessed using a validated food-frequency questionnaire and participants were followed for occurrence of CVD. During 9.4 years follow-up, we documented 580 coronary heart disease (CHD) events and 254 strokes, 165 of which were ischemic. An inverse association (P trend = 0.009) between alcohol consumption and risk of CHD was observed with a multivariate-adjusted hazard ratio of 0.72 (95% confidence interval: 0.52-1.01) for those consuming 70-139.9 g alcohol/week compared to lifetime abstainers. Of different beverages, only red wine consumption was associated with a reduced risk of CHD. A U-shaped relation (P = 0.08) was observed for total stroke with a hazard ratio of 0.65 (0.44-0.95) for consuming 5-69.9 g alcohol/week compared with lifetime abstainers. Similar results were observed for ischemic stroke with a hazard ratio of 0.56 (0.35-0.89) for consuming of 5-69.9 g alcohol/week. We conclude that moderate alcohol consumption is associated with a reduced risk of CHD among hypertensive women. Light alcohol consumption tended to be related to a lower risk of stroke. Current guidelines for alcohol consumption in the general population also apply to hypertensive women

Comparison of protein intake per eating occasion, food sources of protein and general characteristics between community-dwelling older adults with a low and high protein intake

Background & aims: Adequate protein intake is required to maintain muscle health in old age, but a low protein intake is very common in older adults. There is little insight in the general and dietary profile of older adults with a low protein intake. Therefore, this study aimed to compare community-dwelling older adults with a low and a high protein intake with regard to protein intake per eating occasion, food sources of protein and general participant characteristics. Methods: Data were used from 727 Dutch community-dwelling older adults aged ≥70 years. Protein intake at meal and snack moments was measured with two non-consecutive dietary record assisted 24-h recalls. Low protein intake was defined as below the Recommended Dietary Allowance of 0.8 g protein per kg adjusted body weight per day (g/kg aBW/d). Differences in protein and food intakes between those with a low and a high protein intake were assessed with the Mann–Whitney U test and Chi-square test. Eating occasions were compared with regard to differences between the low and high protein intake group by using MANOVA. Characteristics of older adults with low protein intake were selected by using a multiple logistic backward elimination procedure. Results: Low protein intake was present in 15% of the participants. At all eating occasions, median protein intake was lower in the low compared to the high protein intake group (breakfast, 7.8 vs. 10.8 g; lunch, 12.6 vs. 24.3 g; dinner, 21.8 vs. 31.1 g; snack moments, 6.7 vs. 9.7 g; P < 0.001), and was also consistently lower relative to energy intake. The contribution of animal protein to total protein intake was lower among the low protein intake group. Both groups obtained most protein from dairy, meat and cereals, but meat contributed less (21.5 vs. 28.2%) and cereals more (21.9 vs. 19.6%) among the low than the high protein intake group (all P < 0.01). Differences in protein intake, percentage of energy from protein and contribution of animal to total protein intake between the groups were largest at lunch compared to the other eating occasions. Out of a long list of variables, low protein intake was only associated with following a diet, being obese vs. normal-weight and drinking alcohol on none vs. some but <5 days/week (P < 0.05). Conclusions: At all eating occasions, Dutch community-dwelling older adults with a protein intake <0.8 g/kg aBW/d ate less protein (also relative to their energy intake) and a lower proportion of animal protein compared to those with a high protein intake. These differences were largest at lunch. Major food sources of protein – in both groups – were dairy, meat and cereals. We could only identify following a diet, being obese and not drinking alcohol as general characteristics of older adults with a low protein intake.</p