In our previous study, there were similarities in several cardiometabolic risk factors among spouse pairs . Similarly, many previous studies have shown a high degree of statistically significant similarities or concordances among spouse pairs for cardiometabolic risk factors (anthropometric traits, lifestyle habits, and diseases) [8–19]. This may be owing to environmental factors playing a greater role in spousal similarities than genetic factors. Here, we hypothesized that, when using random male-female pairs rather than spouse pairs, the similarities in cardiometabolic risk factors will be reduced. Using random male-female pairs, we found few significant similarities in cardiometabolic risk factors, including continuous risk factors (anthropometric traits, blood indicators, blood pressure, HbA1c level, and lipid traits), lifestyle habits (smoking, drinking, and physical activity) and diseases (hypertension, T2DM and metabolic syndrome). These findings support our hypothesis that, when using random male-female rather than spouse pairs, the similarities in cardiometabolic risk factors are reduced.
To our knowledge, this is the first study to explore and compare spouse pairs and random male-female pairs. Furthermore, we used a large sample size of over 5,000 pairs to compare and determine concordance for various circulatory and metabolic indicators (blood indicators, lifestyle-related factors, and the prevalence of diseases). Regarding the anthropometric traits, blood indicators, blood pressure, HbA1c level, and lipid traits (continuous variables, Table 2), the age-adjusted correlation coefficients among random male-female pairs were extremely low (-0.007 − 0.071); however, there was a statistically significant association for TC (correlation coefficient = 0.071). However, this finding should be interpreted with caution because it has low clinical significance owing to the large number of participants.
After quantifying spousal concordance for cardiometabolic risk factors, it was suggested that prevention interventions targeting spouse pairs rather than individuals may be more effective . For example, in a randomized controlled trial focusing on the weight loss effect of exercise training, both overweight spouses achieved significant weight loss . Therefore, focusing on corrective intervention for lifestyle-related factors, which are correctable factors, may improve test values and even prevent diseases. Couples with unfavourable lifestyles may be able to correct their lifestyles and prevent illness by competing with and encouraging each other. Since most couples of a similar age have similar health statuses, it may be possible to prevent cardiometabolic-related diseases by actively encouraging one another to attend health checks (primary prevention) and disease screenings (secondary prevention) [29–31].
This study had some limitations. First, owing to the use of a cross-sectional study design, the timing of new onset hypertension, diabetes, and hyperlipidaemia could not be determined. We only determined the prevalence of cardiometabolic diseases. Thus, future studies should include non-symptomatic participants at baseline and investigate the degree of concordance in new onset cardiometabolic diseases among random male-female pairs during follow-up. Second, the male-female pairs in this study were selected from spouse pairs. An unmarried status has been associated with an increased frequency of unhealthy behaviour (especially in relation to smoking) and psychological issues (especially depression) [32, 33]. Participants in this study who were married likely had higher physical and psychological health levels compared with unmarried individuals. Regardless, in this study, the random male-female pairs were selected from a healthy population and had few significant similarities in cardiometabolic risk factors. We hypothesized that, if unmarried individuals were included, even fewer associations would exist. Third, participants who undergo health check-ups may have a higher-level health consciousness than those who do not , which could have caused a volunteer bias in our study. Fourth, for this study, we only targeted the general population in Japan. In our previous study, we performed an analysis using large-scale biobank data from two facilities, one in Japan and the other in the Netherlands. Spouse pairs showed similarities in several cardiometabolic risk factors at both the facilities. As this was a single-centre study, the generalizability of the study findings is limited.