Associations between diabetes and both cardiovascular disease and all-cause mortality are modified by grip strength: evidence from UK Biobank, a prospective population-based cohort study

OBJECTIVE Grip strength and diabetes are predictors of mortality and cardiovascular disease (CVD), but whether these risk factors interact to predispose to adverse health outcomes is unknown. This study determined the interactions between diabetes and grip strength and their association with health outcomes. RESEARCH DESIGN AND METHODS We undertook a prospective, general population cohort study by using UK Biobank. Cox proportional hazards models were used to explore the associations between both grip strength and diabetes and the outcomes of all-cause mortality and CVD incidence/mortality as well as to test for interactions between diabetes and grip strength. RESULTS 347,130 UK Biobank participants with full data available (mean age 55.9 years, BMI 27.2 kg/m2, 54.2% women) were included in the analysis, of which 13,373 (4.0%) had diabetes. Over a median follow-up of 4.9 years (range 3.3–7.8 years), 6,209 died (594 as a result of CVD), and 4,301 developed CVD. Participants with diabetes were at higher risk of all-cause and CVD mortality and CVD incidence. Significant interactions (P < 0.05) existed whereby the risk of CVD mortality was higher in participants with diabetes with low (hazard ratio [HR] 4.05 [95% CI 2.72, 5.80]) versus high (HR 1.46 [0.87, 2.46]) grip strength. Similar results were observed for all-cause mortality and CVD incidence. CONCLUSIONS Risk of adverse health outcomes among people with diabetes is lower in those with high grip strength. Low grip strength may be useful to identify a higher-risk subgroup of patients with diabetes. Intervention studies are required to determine whether resistance exercise can reduce risk

Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants

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

Child maltreatment and cardiovascular disease: quantifying mediation pathways using UK Biobank

Background: Child maltreatment is associated with cardiovascular disease (CVD), but mediation pathways have not been fully elucidated. The aim of the current study was to determine and quantify the underlying pathways linking child maltreatment and CVD. Methods: We conducted a retrospective cohort study using the UK Biobank. The number and types of child maltreatment, including abuse and neglect, were recalled by the participants. Lifestyle, biological, physical, and mental health factors measured at baseline were explored as potential mediators. Incident CVD was ascertained through record linkage after baseline measurement. Age, sex, ethnicity, area-based deprivation, and education level were adjusted for as confounders. Cox proportional hazard models were conducted to test for associations between child maltreatment and incident CVD. Results: A total of 152,040 participants who completed the child maltreatment assessment were included in the analyses, and one third reported at least one type of child maltreatment. There was a dose-response relationship between the number of maltreatment types and incident CVD. On average, each additional type of child maltreatment was associated with an 11% (95% CI 8–14%, P < 0.0001) increased risk of CVD. The majority (56.2%) of the association was mediated through depressive symptoms, followed by smoking (14.7%), high-density lipoprotein cholesterol (8.7%), and sleep duration (2.4%). Conclusion: Child maltreatment is associated with incident CVD through a combination of mental health, lifestyle, and biological pathways. Therefore, in addition to interventions to reduce the occurrence of child maltreatment, attention should be targeted at promoting healthy lifestyles and preventing, identifying, and treating depression among children and adults who have previously been maltreated

New versus old guidelines for sarcopenia classification: What is the impact on prevalence and health outcomes?

Introduction: recently, the European Working Group on Sarcopenia in Older People (EWGSOP) established a new operational definition and cut-off points for sarcopenia. The aim of this study was, therefore, to compare the prevalence of sarcopenia and its associations with different health outcomes using the old (EWGSOP1) and new (EWGSOP2) definitions of sarcopenia in the UK Biobank cohort. Methods: sarcopenia was defined as low grip strength plus low muscle mass. Using both EWGSOP cut-off points, we created specific sarcopenia variables. Prevalence of sarcopenia derived using both EWGSOP definitions was calculated and compared as well as prospective health outcomes including all-cause mortality as well as incidence and mortality from cardiovascular disease (CVD), respiratory disease and chronic obstructive pulmonary disease (COPD). Results: the prevalence of sarcopenia based on the EWGSOP1 and EWGSOP2 classifications were 8.14 and 0.36%, respectively. Sarcopenia defined by EWGSOP1 was associated with a higher risk of respiratory disease and COPD as well as mortality from all-cause, CVD and respiratory diseases. However, only respiratory incidence remained associated with sarcopenia when EWGSOP2 was used (HR: 1.32 [95% CI: 1.05–1.66]). Moreover, although individuals classified as sarcopenic using both classifications had the highest risk of all-cause mortality and respiratory disease, those with sarcopenia based on EWGSOP1 only experienced a more extensive range of poorer health outcomes. Conclusion: in comparison with EWGSOP1, the new classification (EWGSOP2) produced a lower estimate of sarcopenia prevalence and fewer associations with adverse health outcomes. Although these associations were higher, many become non-significant

Factors associated with sarcopenia: a cross-sectional analysis using UK Biobank

Introduction: The critical sociodemographic, lifestyle and diseases factors influencing sarcopenia, defined by the current European Working Group on Sarcopenia 2 (EWGSOP2) classification and cut-off points, have not yet been fully elucidated. This study aimed, therefore, to determine sociodemographic, anthropometric, lifestyle and health-related factors associated with sarcopenia using the new EWGSOP2 definition. Study design: 396,283 participants (52.8% women, age 38-73 years) were included in this cross-sectional study. The potential factors associated with sarcopenia were allocated to four categories: sociodemographic (sex, age, education, income and professional qualification), anthropometric (nutritional status, abdominal obesity, body fat and birth weight), lifestyle (physical activity, smoking, sleeping and sitting time, TV viewing, alcohol, and dietary intakes) and health status (self-reported prevalent diseases). P-values were corrected for multiple testing using the Bonferroni method. Results: Age, women, lower education, higher deprivation, underweight, lower birth weight, and chronic diseases such as rheumatoid arthritis, chronic bronchitis and osteoporosis were associated with a higher likelihood of sarcopenia. Conversely, overweight, obesity, as well as a self-reported higher intake of energy, protein, vitamins (B12 and B9) and minerals (potassium, calcium and magnesium) were associated with lower odds of sarcopenia. Conclusion: Women, people aged over 65 years, underweight people and those with rheumatoid arthritis were most likely to have sarcopenia. Considering the increase in the ageing population, sarcopenia is likely to become more prevalent. Identifying factors associated with sarcopenia could inform future strategies for early identification of individuals at high risk of sarcopenia and therefore the implementation of preventive strategies against the disease

Sarcopenic obesity and its association with respiratory disease incidence and mortality

Background: Sarcopenic obesity is defined as a combination of sarcopenia and obesity. Previous studies have shown a positive association between sarcopenia and respiratory disease, while other studies have identified that obese individuals have a lower risk for respiratory diseases. This study aimed to investigate the association of obesity, sarcopenia and sarcopenic obesity with respiratory disease incidence and mortality. Methods: Data from 170,083 participants from the prospective UK Biobank study were included. Sarcopenic obesity was defined as the combination of sarcopenia with one of the following obesity criteria: BMI ≥30 kg/m2, waist circumference (WC) ≥ 88 cm in women or ≥ 102 cm in men, or the two highest quintiles of body fat. Respiratory disease incidence and mortality were the outcomes. Results: The mean follow-up period was 7.0 years. 5,459 (3.2%) participants developed respiratory diseases and 780 (0.5%) died from respiratory diseases. Compared to individuals without obesity or sarcopenia, those who were obese (Hazard Ratio (HR): 1.13 [95 CI: 1.03; 1.23]), sarcopenic (HR: 1.23 [95% CI: 1.10; 1.36]) or sarcopenic obese (based on BMI) (HR: 1.51 [95% CI: 1.30; 1.77]), had a higher risk of respiratory disease incidence. However, the risk of respiratory disease mortality was higher in sarcopenic individuals and lower in obese individuals. No associations were identified between sarcopenic obesity and respiratory mortality (HR: 1.12 [95% CI: 0.76; 1.63]). Similar patterns were found when obesity was defined using WC or body fat. Conclusion: – Obesity, sarcopenia and sarcopenic obesity were associated with a higher risk of respiratory disease incidence. However, while obesity was associated with lower, and sarcopenia with higher respiratory mortality risk, no associations between sarcopenic obesity and respiratory mortality were identified

Associations of fat and carbohydrate intake with cardiovascular disease and mortality: prospective cohort study of UK Biobank participants

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)

Global prevalence of sarcopenia and severe sarcopenia: a systematic review and meta‐analysis

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

The combination of physical activity and sedentary behaviors modifies the genetic predisposition to obesity

Objective: This study aimed to investigate whether the association between a validated genetic profile risk score for BMI (GPRS‐BMI) (based on 93 single‐nucleotide polymorphisms) and phenotypic obesity (BMI) was modified by the combined categories of physical activity (PA) and sedentary behaviors in a large population‐based study. Methods: This study included cross‐sectional baseline data from 338,216 white European adult men and women aged 37 to 73 years. Interaction effects of GPRS‐BMI with the combined categories of PA and sedentary behaviors on BMI were investigated. Results: There was a significant interaction between GPRS‐BMI and the combined categories of objectively measured PA and total sedentary behavior (P[interaction]  =  3.5 × 10−6); among physically inactive and highly sedentary individuals, BMI was higher by 0.60 kg/m2 per 1‐SD increase in GPRS‐obesity (P  =  8.9 × 10−50), whereas the relevant BMI difference was 38% lower among physically active individuals and those with low sedentary time (β: 0.37 kg/m2; P  =  2.3 × 10−51). A similar pattern was observed for the combined categories of objective PA and TV viewing (inactive/high TV viewing β: 0.60 vs. active/low TV viewing β: 0.40 kg/m2; P[interaction]  =  2.9 × 10−6). Conclusions: This study provides evidence that combined categories of PA and sedentary behaviors modify the extent to which genetic predisposition to obesity results in higher BMI

Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study

Objective: To investigate whether the association between a genetic profile risk score for obesity (GPRS-obesity) (based on 93 SNPs) and body mass index (BMI) was modified by physical activity (PA), cardiorespiratory fitness, commuting mode, walking pace and sedentary behaviours. Methods: For the analyses we used cross-sectional baseline data from 310,652 participants in the UK Biobank study. We investigated interaction effects of GPRS-obesity with objectively measured and self-reported PA, cardiorespiratory fitness, commuting mode, walking pace, TV viewing, playing computer games, PC-screen time and total sedentary behaviour on BMI. Body mass index (BMI) was the main outcome measure. Results: GPRS-obesity was associated with BMI (β:0.54 kg.m−2 per standard deviation (SD) increase in GPRS, [95% CI: 0.53; 0.56]; P = 2.1 × 10−241). There was a significant interaction between GPRS-obesity and objectively measured PA (P[interaction] = 3.3 × 10−11): among inactive individuals, BMI was higher by 0.58 kg.m−2 per SD increase in GPRS-obesity (p = 1.3 × 10−70) whereas among active individuals the relevant BMI difference was less (β:0.33 kg.m−2, p = 6.4 × 10−41). We observed similar patterns for fitness (Unfit β:0.72 versus Fit β:0.36 kg.m−2, P[interaction] = 1.4 × 10−11), walking pace (Slow β:0.91 versus Brisk β:0.38 kg.m−2, P[interaction] = 8.1 × 10−27), discretionary sedentary behaviour (High β:0.64 versus Low β:0.48 kg.m−2, P[interaction] = 9.1 × 10−12), TV viewing (High β:0.62 versus Low β:0.47 kg.m−2, P[interaction] = 1.7 × 10−11), PC-screen time (High β:0.82 versus Low β:0.54 kg.m−2, P[interaction] = 0.0004) and playing computer games (Often β:0.69 versus Low β:0.52 kg.m−2, P[interaction] = 8.9 × 10−10). No significant interactions were found for commuting mode (car, public transport, active commuters). Conclusions: Physical activity, sedentary behaviours and fitness modify the extent to which a set of the most important known adiposity variants affect BMI. This suggests that the adiposity benefits of high PA and low sedentary behaviour may be particularly important in individuals with high genetic risk for obesity