Age-standardised rates of cardiovascular disease (CVD) are substantially higher in men than women. This explains why CVD has traditionally been seen as a “man’s problem”. However, CVD is the leading cause of death in women, worldwide, and is one of the most common causes of disability-adjusted life-years lost. In general, this is under-recognised and, in several ways, women are disadvantaged in terms of CVD. Both in primary and secondary prevention, there is evidence that women are undertreated, compared to men. Women often experience heart disease in a different way compared to men, and lack of recognition of this has been shown to have adverse consequences. Female patients of male cardiac physicians have been found to have worse outcomes than their male counterparts, with no such gender differential for female cardiologists. Clinical trials in CVD primarily recruit male patients, yet, it is well recognised that some drugs act differently in women and men. Diabetes and smoking, and perhaps other risk factors, confer a greater proportional excess cardiovascular risk to women than to men, whilst adverse pregnancies and factors concerned with the female reproductive cycle give women added vulnerability to CVD. However, women’s health research is skewed towards mother and child health, an area where, arguably, the greatest public health gains have already been made, and breast cancer. Hence there is a need to redefine what is meant by “women’s health” to encompass the whole lifecycle, with a stronger emphasis on CVD and other non-communicable diseases. Sex-specific analyses of research data should be the norm, whenever feasible.

Objective To improve classification of movement behaviours in free-living accelerometer data using machine-learning methods, and to investigate the association between machine-learned movement behaviours and risk of incident cardiovascular disease (CVD) in adults. Methods Using free-living data from 152 participants, we developed a machine-learning model to classify movement behaviours (moderate-to-vigorous physical activity behaviours (MVPA), light physical activity behaviours, sedentary behaviour, sleep) in wrist-worn accelerometer data. Participants in UK Biobank, a prospective cohort, were asked to wear an accelerometer for 7 days, and we applied our machine-learning model to classify their movement behaviours. Using compositional data analysis Cox regression, we investigated how reallocating time between movement behaviours was associated with CVD incidence. Results In leave-one-participant-out analysis, our machine-learning method classified free-living movement behaviours with mean accuracy 88% (95% CI 87% to 89%) and Cohen’s kappa 0.80 (95% CI 0.79 to 0.82). Among 87 498 UK Biobank participants, there were 4105 incident CVD events. Reallocating time from any behaviour to MVPA, or reallocating time from sedentary behaviour to any behaviour, was associated with lower CVD risk. For an average individual, reallocating 20 min/day to MVPA from all other behaviours proportionally was associated with 9% (95% CI 7% to 10%) lower risk, while reallocating 1 hour/day to sedentary behaviour from all other behaviours proportionally was associated with 5% (95% CI 3% to 7%) higher risk. Conclusion Machine-learning methods classified movement behaviours accurately in free-living accelerometer data. Reallocating time from other behaviours to MVPA, and from sedentary behaviour to other behaviours, was associated with lower risk of incident CVD, and should be promoted by interventions and guidelines.

Significance Statement: Randomized clinical trials of treatments to slow CKD progression often require large sample sizes and long follow-up to understand their effects on clinical events. This is especially true in patients with earlier stages of kidney disease who are unlikely to experience ESKD for many years. Surrogate study end points that occur earlier during disease progression could help. To evaluate whether eGFR decline over time may be a good surrogate end point, the authors did a meta-analysis of 14 cohorts. They found that slower eGFR decline was significantly associated with lower risk of ESKD in all populations, including those with better kidney function. The results suggest that change in the slope of eGFR decline may be a good surrogate end point for ESKD in clinical trials, particularly in longer trials with patients with rapidly progressive disease. Background: Decline in eGFR is a biologically plausible surrogate end point for the progression of CKD in clinical trials. However, it must first be tested to ensure strong associations with clinical outcomes in diverse populations, including patients with higher eGFR. Methods: To investigate the association between 1-, 2-, and 3-year changes in eGFR (slope) with clinical outcomes over the long term, we conducted a random effects meta-analysis of 3,758,551 participants with baseline eGFR≥60 ml/min per 1.73 m² and 122,664 participants with eGFR<60 ml/min per 1.73 m² from 14 cohorts followed for an average of 4.2 years. Results: Slower eGFR decline by 0.75 ml/min per 1.73 m² per year over 2 years was associated with lower risk of ESKD in participants with baseline eGFR≥60 ml/min per 1.73 m² (adjusted hazard ratio, 0.70; 95% CI, 0.68 to 0.72) and eGFR<60 ml/min per 1.73 m² (0.71; 95% CI, 0.68 to 0.74). The relationship was stronger with 3-year slope. For a rapidly progressing population with predicted 5-year risk of ESKD of 8.3%, an intervention that reduced eGFR decline by 0.75 ml/min per 1.73 m² per year over 2 years would reduce the ESKD risk by 1.6%. For a hypothetical low-risk population with a predicted 5-year ESKD risk of 0.58%, the same intervention would reduce the risk by only 0.13%. Conclusions: Slower decline in eGFR was associated with lower risk of subsequent ESKD, even in participants with eGFR≥60 ml/min per 1.73 m², but those with the highest risk would be expected to benefit the most.

Background: Higher levels of physical activity (PA) are associated with a lower risk of cardiovascular disease (CVD). However, uncertainty exists on whether the inverse relationship between PA and incidence of CVD is greater at the highest levels of PA. Past studies have mostly relied on self-reported evidence from questionnaire-based PA, which is crude and cannot capture all PA undertaken. We investigated the association between accelerometer-measured moderate, vigorous, and total PA and incident CVD. Methods and findings: We obtained accelerometer-measured moderate-intensity and vigorous-intensity physical activities and total volume of PA, over a 7-day period in 2013–2015, for 90,211 participants without prior or concurrent CVD in the UK Biobank cohort. Participants in the lowest category of total PA smoked more, had higher body mass index and C-reactive protein, and were diagnosed with hypertension. PA was associated with 3,617 incident CVD cases during 440,004 person-years of follow-up (median (interquartile range [IQR]): 5.2 (1.2) years) using Cox regression models. We found a linear dose–response relationship for PA, whether measured as moderate-intensity, vigorous-intensity, or as total volume, with risk of incident of CVD. Hazard ratios (HRs) and 95% confidence intervals for increasing quarters of the PA distribution relative to the lowest fourth were for moderate-intensity PA: 0.71 (0.65, 0.77), 0.59 (0.54, 0.65), and 0.46 (0.41, 0.51); for vigorous-intensity PA: 0.70 (0.64, 0.77), 0.54 (0.49,0.59), and 0.41 (0.37,0.46); and for total volume of PA: 0.73 (0.67, 0.79), 0.63 (0.57, 0.69), and 0.47 (0.43, 0.52). We took account of potential confounders but unmeasured confounding remains a possibility, and while removal of early deaths did not affect the estimated HRs, we cannot completely dismiss the likelihood that reverse causality has contributed to the findings. Another possible limitation of this work is the quantification of PA intensity-levels based on methods validated in relatively small studies. Conclusions: In this study, we found no evidence of a threshold for the inverse association between objectively measured moderate, vigorous, and total PA with CVD. Our findings suggest that PA is not only associated with lower risk for of CVD, but the greatest benefit is seen for those who are active at the highest level.

Aims/hypothesis Diabetes has been shown to be a risk factor for some cancers. Whether diabetes confers the same excess risk of cancer, overall and by site, in women and men is unknown. Methods A systematic search was performed in PubMed for cohort studies published up to December 2016. Selected studies reported sex-specific relative risk (RR) estimates for the association between diabetes and cancer adjusted at least for age in both sexes. Random-effects meta-analyses with inverse-variance weighting were used to obtain pooled sex-specific RRs and women-to-men ratios of RRs (RRRs) for all-site and site-specific cancers. Results Data on all-site cancer events (incident or fatal only) were available from 121 cohorts (19,239,302 individuals; 1,082,592 events). The pooled adjusted RR for all-site cancer associated with diabetes was 1.27 (95% CI 1.21, 1.32) in women and 1.19 (1.13, 1.25) in men. Women with diabetes had ~6% greater risk compared with men with diabetes (the pooled RRR was 1.06, 95% CI 1.03, 1.09). Corresponding pooled RRRs were 1.10 (1.07, 1.13) for all-site cancer incidence and 1.03 (0.99, 1.06) for all-site cancer mortality. Diabetes also conferred a significantly greater RR in women than men for oral, stomach and kidney cancer, and for leukaemia, but a lower RR for liver cancer. Conclusions/interpretation Diabetes is a risk factor for all-site cancer for both women and men, but the excess risk of cancer associated with diabetes is slightly greater for women than men. The direction and magnitude of sex differences varies by location of the cancer.

Cardiovascular disease (CVD) is the leading cause of death in women and men. Yet biological and social factors differ between the sexes, while the importance of CVD in women may be underestimated due to the higher age-specific rates in men and the historical bias towards the male model of CVD. Consequently, sex differences in risk factor associations with CVD occur, but these are not always recognised. This article argues that sex disaggregation should be the norm in CVD research, for both humanitarian and clinical reasons. A tutorial on how to design and analyse sex comparisons is provided, including ways of reducing bias and increasing efficiency. This is presented both in the context of analysing individual participant data from a single study and a meta-analysis of sex-specific summary data. Worked examples are provided for both types of research. Fifteen key recommendations are included, which should be considered when undertaking sex comparisons of CVD associations. Paramount among these is the need to estimate sex differences, as ratios of relative risks or differences in risk differences, rather than merely test them for statistical significance. Conversely, when there is no evidence of statistical or clinical significance of a sex difference, the conclusions from the research should not be sex-specific.