Background: Sarcopenia is defined as the loss of muscle mass and strength. Despite the seriousness of this disease, a single diagnostic criterion has not yet been established. Few studies have reported the prevalence of sarcopenia globally, and there is a high level of heterogeneity between studies, stemmed from the diagnostic criteria of sarcopenia and the target population. The aims of this systematic review and meta‐analysis were (i) to identify and summarize the diagnostic criteria used to define sarcopenia and severe sarcopenia and (ii) to estimate the global and region‐specific prevalence of sarcopenia and severe sarcopenia by sociodemographic factors.Methods: Embase, MEDLINE, and Web of Science Core Collections were searched using relevant MeSH terms. The inclusion criteria were cross‐sectional or cohort studies in individuals aged ≥18 years, published in English, and with muscle mass measured using dual‐energy x‐ray absorptiometry, bioelectrical impedance, or computed tomography (CT) scan. For the meta‐analysis, studies were stratified by diagnostic criteria (classifications), cut‐off points, and instruments to assess muscle mass. If at least three studies reported the same classification, cut‐off points, and instrument to measure muscle mass, they were considered suitable for meta‐analysis. Following this approach, 6 classifications and 23 subgroups were created. Overall pooled estimates with inverse‐variance weights obtained from a random‐effects model were estimated using the metaprop command in Stata.Results: Out of 19 320 studies, 263 were eligible for the narrative synthesis and 151 for meta‐analysis (total n = 692 056, mean age: 68.5 years). Using different classifications and cut‐off points, the prevalence of sarcopenia varied between 10% and 27% in the studies included for meta‐analysis. The highest and lowest prevalence were observed in Oceania and Europe using the European Working Group on Sarcopenia in Older People (EWGSOP) and EWGSOP2, respectively. The prevalence ranged from 8% to 36% in individuals <60 years and from 10% to 27% in ≥60 years. Men had a higher prevalence of sarcopenia using the EWGSOP2 (11% vs. 2%) while it was higher in women using the International Working Group on Sarcopenia (17% vs. 12%). Finally, the prevalence of severe sarcopenia ranged from 2% to 9%.Conclusions: The prevalence of sarcopenia and severe sarcopenia varied considerably according to the classification and cut‐off point used. Considering the lack of a single diagnostic for sarcopenia, future studies should adhere to current guidelines, which would facilitate the comparison of results between studies and populations across the globe.

Objective To investigate the association of grip strength with disease specific incidence and mortality and whether grip strength enhances the prediction ability of an established office based risk score. Design Prospective population based study. Setting UK Biobank. Participants 502 293 participants (54% women) aged 40-69 years. Main outcome measures All cause mortality as well as incidence of and mortality from cardiovascular disease, respiratory disease, chronic obstructive pulmonary disease, and cancer (all cancer, colorectal, lung, breast, and prostate). Results Of the participants included in analyses, 13 322 (2.7%) died over a mean of 7.1 (range 5.3-9.9) years’ follow-up. In women and men, respectively, hazard ratios per 5 kg lower grip strength were higher (all at P<0.05) for all cause mortality (1.20, 95% confidence interval 1.17 to 1.23, and 1.16, 1.15 to 1.17) and cause specific mortality from cardiovascular disease (1.19, 1.13 to 1.25, and 1.22, 1.18 to 1.26), all respiratory disease (1.31, 1.22 to 1.40, and 1.24, 1.20 to 1.28), chronic obstructive pulmonary disease (1.24, 1.05 to 1.47, and 1.19, 1.09 to 1.30), all cancer (1.17, 1.13 to 1.21, 1.10, 1.07 to 1.13), colorectal cancer (1.17, 1.04 to 1.32, and 1.18, 1.09 to 1.27), lung cancer (1.17, 1.07 to 1.27, and 1.08, 1.03 to 1.13), and breast cancer (1.24, 1.10 to 1.39) but not prostate cancer (1.05, 0.96 to 1.15). Several of these relations had higher hazard ratios in the younger age group. Muscle weakness (defined as grip strength <26 kg for men and <16 kg for women) was associated with a higher hazard for all health outcomes, except colon cancer in women and prostate cancer and lung cancer in both men and women. The addition of handgrip strength improved the prediction ability, based on C index change, of an office based risk score (age, sex, diabetes diagnosed, body mass index, systolic blood pressure, and smoking) for all cause (0.013) and cardiovascular mortality (0.012) and incidence of cardiovascular disease (0.009). Conclusion Higher grip strength was associated with a range of health outcomes and improved prediction of an office based risk score. Further work on the use of grip strength in risk scores or risk screening is needed to establish its potential clinical utility.

Combinations of lifestyle factors interact to increase mortality. Combinations of traditional factors such as smoking and alcohol are well described, but the additional effects of emerging factors such as television viewing time are not. The effect of socioeconomic deprivation on these extended lifestyle risks also remains unclear. We aimed to examine whether deprivation modifies the association between an extended score of lifestyle-related risk factors and health outcomes. Data for this prospective analysis were sourced from the UK Biobank, a prospective population-based cohort study. We assigned all participants an extended lifestyle score, with 1 point for each unhealthy lifestyle factor (incorporating sleep duration and high television viewing time, in addition to smoking, excessive alcohol, poor diet [low intake of oily fish or fruits and vegetables, and high intake of red meat or processed meats], and low physical activity), categorised as most healthy (score 0-2), moderately healthy (score 3-5), or least healthy (score 6-9). Cox proportional hazards models were used to examine the association between lifestyle score and health outcomes (all-cause mortality and cardiovascular disease mortality and incidence), and whether this association was modified by deprivation. All analyses were landmark analyses, in which participants were excluded if they had an event (death or cardiovascular disease event) within 2 years of recruitment. Participants with non-communicable diseases (except hypertension) and missing covariate data were excluded from analyses. Participants were also excluded if they reported implausible values for physical activity, sleep duration, and total screen time. All analyses were adjusted for age, sex, ethnicity, month of assessment, history of hypertension, systolic blood pressure, medication for hypercholesterolaemia or hypertension, and body-mass index categories. 328 594 participants aged 40-69 years were included in the study, with a mean follow-up period of 4·9 years (SD 0·83) after the landmark period for all-cause and cardiovascular disease mortality, and 4·1 years (0·81) for cardiovascular disease incidence. In the least deprived quintile, the adjusted hazard ratio (HR) in the least healthy lifestyle category, compared with the most healthy category, was 1·65 (95% CI 1·25-2·19) for all-cause mortality, 1·93 (1·16-3·20) for cardiovascular disease mortality, and 1·29 (1·10-1·52) for cardiovascular disease incidence. Equivalent HRs in the most deprived quintile were 2·47 (95% CI 2·04-3·00), 3·36 (2·36-4·76), and 1·41 (1·25-1·60), respectively. The HR for trend for one increment change towards least healthy in the least deprived quintile compared with that in the most deprived quintile was 1·25 (95% CI 1·12-1·39) versus 1·55 (1·40-1·70) for all-cause mortality, 1·30 (1·05-1·61) versus 1·83 (1·54-2·18) for cardiovascular disease mortality, and 1·10 (1·04-1·17) versus 1·16 (1·09-1·23) for cardiovascular disease incidence. A significant interaction was found between lifestyle and deprivation for all-cause and cardiovascular disease mortality (both pinteraction<0·0001), but not for cardiovascular disease incidence (pinteraction=0·11). Wide combinations of lifestyle factors are associated with disproportionate harm in deprived populations. Social and fiscal policies that reduce poverty are needed alongside public health and individual-level interventions that address a wider range of lifestyle factors in areas of deprivation. None.

Objective: To investigate the association of macronutrient intake with all cause mortality and cardiovascular disease (CVD), and the implications for dietary advice. Design: Prospective population based study. Setting: UK Biobank. Participants: 195 658 of the 502 536 participants in UK Biobank completed at least one dietary questionnaire and were included in the analyses. Diet was assessed using Oxford WebQ, a web based 24 hour recall questionnaire, and nutrient intakes were estimated using standard methodology. Cox proportional models with penalised cubic splines were used to study non-linear associations. Main outcome measures: All cause mortality and incidence of CVD. Results: 4780 (2.4%) participants died over a mean 10.6 (range 9.4-13.9) years of follow-up, and 948 (0.5%) and 9776 (5.0%) experienced fatal and non-fatal CVD events, respectively, over a mean 9.7 (range 8.5-13.0) years of follow-up. Non-linear associations were found for many macronutrients. Carbohydrate intake showed a non-linear association with mortality; no association at 20-50% of total energy intake but a positive association at 50-70% of energy intake (3.14 v 2.75 per 1000 person years, average hazard ratio 1.14, 95% confidence interval 1.03 to 1.28 (60-70% v 50% of energy)). A similar pattern was observed for sugar but not for starch or fibre. A higher intake of monounsaturated fat (2.94 v 3.50 per 1000 person years, average hazard ratio 0.58, 0.51 to 0.66 (20-25% v 5% of energy)) and lower intake of polyunsaturated fat (2.66 v 3.04 per 1000 person years, 0.78, 0.75 to 0.81 (5-7% v 12% of energy)) and saturated fat (2.66 v 3.59 per 1000 person years, 0.67, 0.62 to 0.73 (5-10% v 20% of energy)) were associated with a lower risk of mortality. A dietary risk matrix was developed to illustrate how dietary advice can be given based on current intake. Conclusion: Many associations between macronutrient intake and health outcomes are non-linear. Thus dietary advice could be tailored to current intake. Dietary guidelines on macronutrients (eg, carbohydrate) should also take account of differential associations of its components (eg, sugar and starch).