Impact of Older Age Adiposity on Incident Diabetes: A Community-Based Cohort Study in China

Study population and data collection

Studied populations were derived from the Anhui cohort study in China. The methods of the baseline investigations [21] and the follow-up in the cohort study [22,23] have been fully described before. In brief, we randomly selected 1,810 people aged ≥65 years who had lived for at least 5 years in Yiming sub-district of Hefei city in 2001 and 1,709 aged ≥60 years from all 16 villages in Tangdian District of Yingshang County in 2003. A total of 3,336 individuals participated in this study (urban participants n=1,736), with an overall response rate of 94.8%. Informed consent was obtained from each participant. In the case of those who were unable to provide informed consent due to disability or limited level of education, their next of kin or care givers were invited to provide approval for participation. The participants were interviewed at home by a trained survey team from the Anhui Medical University.

The main materials used in the interviews were a general health and risk factors questionnaire, and a Geriatric Mental Status (GMS)—a comprehensive semi-structured mental state interview. In the general health and risk factors record, we collected data relating to sociodemographic information, lifestyles, social networks and support, psychosocial aspects, cardiovascular disease and other disease risk factors [22]. We documented chronic diseases in the general health and risk factors questionnaire. Diabetes was defined based on a recorded doctor-diagnosis in the questionnaire interview for those who answered “Yes” to the question, “Have you ever been told by a doctor that you have diabetes?” [23]. The GMS questionnaire data was read by the Automated Geriatric Examination for Computer Assisted Taxonomy (AGECAT) to diagnose depression and dementia cases for each participant [19]. According to standard procedures, we measured the participants’ blood pressure, body weight, height, and WC. The body weight of each participant was recorded to the nearest 0.1 kg by digital scales with light clothing on, whilst height was measured without shoes to the nearest 0.5 cm by a portable audiometer. The WC was measured to the nearest 0.1 centimetre by using a plastic tape placed at the mid-way between the lowest rib and the iliac crest. We calculated BMI for each participant by dividing measured body weight with the square of height (kg/m²).

Follow-up of cohort

One year after the baseline survey, we re-interviewed 2,608 participants (wave 2) using the same protocol as at baseline. From 2007 to 2009, we successfully re-examined 1,757 surviving cohort members (wave 3), and in 2010 to 2011, we carried out a wave 4 survey and re-interviewed 944 participants. At each wave interview of the follow-up, we documented diabetes based on doctor-diagnosis recorded in the questionnaire.

The vital status of the cohort members was monitored until December 2011. At each survey wave, we conducted home visits to obtain information about participants’ survival status through multiple sources including resident committees, family members, neighbours, and friends. In the urban cohort, we also reviewed electronic registration databases from the Centre for Disease Control and Police Registration in Hefei city to identify mortality and causes of deaths. In total we identified 671 deaths during the follow-up of the cohort. For those that were deceased, we used a standard verbal autopsy questionnaire to interview their next of kin responsible or reviewed their death certificate to determine the date and causes of death, including diabetes diagnosis [23].

Data analysis

Of the 3,336 participants, 191 had baseline diabetes and 336 were lost to follow-up. After excluding them, 2,809 participants remained for analysis. Over 10 years follow-up 178 participants were identified to have developed diabetes. We described baseline characteristics of participants using mean± standard deviation (SD) and percentage (%) and examined differences in their distributions between diabetic and non-diabetic participants in the follow-up using a one-way analysis of variance (ANOVA) for continuous variables and a chi-square test for categorical variables.

We computed person-years at risk to the end of follow up, date of DM diagnosis, date of death, or date of loss to follow-up. We used multivariate adjusted Cox regression models to examine baseline adiposity associated with incident diabetes, calculating hazard ratio (HR) and 95% confidence interval (CI). We examined the continuous BMI and WC, and then their quartiles (Q) associated with incident DM. Following these, we investigated the cut-off points of BMI and WC for overweight and obesity measurements associated with DM. We used three sets of BMI criteria for analysis to compare their predictive values to diabetes; (1) BMI-World Health Organization (WHO) (the criteria recommended for the world population by the WHO) [24], (2) BMI-China (recommended for the Chinese population [25]), and (3) BMI-Asian/Hong Kong (recommended for the Asia and Hong Kong Chinese populations [26]), and two sets of WC cut-off points for analysis; (1) WHO recommended its action level cut-off point [27], and (2) the Chinese Medical Association recommended criteria central obesity for Chinese adults [11,12]. These cut-off points are shown in Supplementary Table 1. We further included the combination of BMI cut-off points with WC central obesity criteria for analysis (the most predictive cut-off point criteria from both BMI and WC would be selected). Finally, we stratified the data by gender for analysis to test differences between men and women in the association of adiposity with the incidence of DM. We computed a ratio of two HRs and tested the differences in the HRs using the methods in our previous studies [22]. All analyses were performed using SPSS version 26.0 (IBM Co., Armonk, NY, USA).

Disclosure

Ethical approval for this cohort study was obtained from the Research Ethics Committee, Anhui Medical University, China (Ref. none, granted in 2001 and 2007) and the Research Ethics Committee, University of Wolverhampton (Ref. A1- Favourable, granted in 2010).

BMI and WC measured for adiposity associated with incidence of diabetes

Previous studies suggested similar predictive values of BMI and WC on the incidence of diabetes [9,14], but these would be subject to which cut-off points were used. A recent meta-analysis study [17] showed that compared to BMI ≥30 kg/m², WC ≥102 cm in men or 88 in women cm was a better predictor for the development of diabetes at ages over 60 years. Lee et al. [28] performed a meta-analysis, including 21 studies with 154,998 participants and 9,342 cases of incidence of diabetes to compare the ability of commonly used anthropometric measures associated with incident diabetes. They found that a one SD increment in WC was more strongly associated with the 5-year risk of incidence of DM than BMI in the general population, but there was no appreciable difference between these measures in the predictive accuracy for diabetes. Further analysis for the subgroups by age showed that the point estimates for the impacts of BMI and WC on incident DM seemed greater in populations aged <50 years than those aged ≥50, but the 95% CIs overlapped. In previous literature [17,28], most studies were conducted in the west and HICs and in young and middle-aged populations, and their findings may not be generalisable to people in China, particularly in the older Chinese population. Compared to white Caucasians, Chinese and Asians appear to have higher morbidity at lower cut-off points for BMI and WC [29]. We have analysed different cut-off points of BMI and found that the BMI-Asian/Hong Kong criteria was the best predictor of incident DM in older Chinese people.

Few studies measured both BMI and WC simultaneously to predict diabetes in older people. Recently, there have been several studies published to examine which of the two obesity measures is the better predictor of incident diabetes in the Chinese population [23,30-33]. Jia et al. [31] followed 48,015 men and 13,688 women adults aged 18 to 85 years old, which were derived from the health examinations of employees of the Kailuan Company in Tangshan city, China, for a median duration of two years and found that WC measurements could be a better predictor of DM than BMI. In a cohort study of 15,752 Chinese people aged ≥50 years with 4-year follow-up, Xu et al. [33] found that the predictive value of WC for incident diabetes risk (adjusted odds ratio, 1.93; 95% CI, 1.71 to 2.17) was higher than BMI (adjusted odds ratio, 1.76; 95% CI, 1.50 to 2.06). The authors suggested that measuring abdominal obesity was a better predictor of diabetes risk in participants than measuring general obesity. However, these findings were mainly in middle-aged populations, and only a few studies included and focused on the older population. Our study of Chinese aged ≥60 years with 10-year follow-up, demonstrated adiposity significantly increased the risk of DM, with a stronger predictive value of BMI than WC in terms of their continuous variable and quartile variable data analysis.

Gender differences in the association of adiposity with incident diabetes

Gender differences in the impact of older age adiposity on diabetes risk is not well investigated [17]. Most studies on gender differences have focused on middle-aged populations [34,35]. In Scotland, Logue et al. [34] examined 51,920 men and 43,137 women aged ≥20 years, and found that men were diagnosed with T2DM at a lower BMI than women across age groups. A meta-analysis by Seo et al. [17] found that central obesity may be a more serious risk factor for diabetes in people aged ≥60 years and in women than their counterparts. Lee et al. [28] reported a stronger impact of WC in women than men as well, but the impact of BMI on DM was stronger in men than women. In a cohort study of 990 men and 1,033 women aged 70 years and over in China who were examined at baseline and after 36 months, Woo et al. [30] found that both BMI and WC were positively associated with diabetes, in men but not women. In the United States, Biggs et al. [13] examined 4,193 men and women aged ≥65 years with a median follow-up of 12.4 years, and found that compared to women, men had a higher risk of T2DM at a lower BMI and WC. Our study showed no significant gender differences in the impact of adiposity measured by BMI and WC on the risk of diabetes, suggesting that the contributions of general and central obesity on incident DM were equally important for men and women.

Strengths and limitations of the study

To the best of our knowledge, this study is the first to examine the predictive values of adiposity measured by different cut-off points of BMI and WC in older age for incidence of DM in China. It included 2,809 older people living in the rural and urban communities with a long-term follow-up, and adjusted for many important confounders. The predictive values for incidence of DM using different cut-off points of BMI and WC and the combination of BMI and WC were explored. This has allowed for more predictive values of adiposity measurement on diabetes risk to be investigated rather than the traditional snapshot of obesity measurements such as BMI, or WC alone. The study has limitations. First, the information about diabetes was largely obtained from a self-reported doctor diagnosis at the interview, rather than actual glycaemia, which may miss those who did not have detected or diagnosed DM, leading to an underestimation of the association of adiposity with incident DM in this study. However, previous studies have shown that self-reported diabetes in older people are acceptably reliable [36], and self-reported doctor diagnosis of diabetes has been validated in the older Chinese population [37]. Thus, the underestimated effect should be minimised. Nevertheless, a large-scale cohort study based on measurement of actual glycaemia is required to examine the impact of older age obesity on incidence of diabetes. Second, there were 336 participants who were lost to follow-up in the original cohort; we do not know whether they had a higher or lower level of risk in developing diabetes than the 2,809 cohort members. This may have led to either an over- or under-estimation of the predictive values. However, the rate of loss to follow-up in our study is similar to those in some studies undertaken in Western countries [38], and due to its small proportion of the loss to follow-up (10.7%) the effect would be minimised. Third, our study examined the associations of BMI and WC at baseline with followed-up incidence of diabetes, but did not consider any body weight changes including weight loss, weight gain, and stable weight during the follow-up period for analysis. Thus, our findings of the associations of adiposity measured in older age with incidence of DM may be more conservative. More research is needed to examine the adiposity changes in the follow-up of the cohort associated with incidence of DM in older people.

Implications

Our study has shown that increased BMI and WC in older age were significantly associated with a greater risk of developing diabetes in both men and women. This highlights the importance of management of overall and central obesity in older people. Our study does not support the paradox of ‘beneficial impacts of overweight and obesity in older age’ due to reduced risk of dementia and all-cause mortality [39]. Together with our findings of this and other studies of overweight and obesity in older age associated with morbidities and mortality (e.g., COVID-19) [40], we strongly recommend that older men and women in China should maintain an ideal bodyweight (using the BMI-Asian/Hong-Kong criteria for management) and girth growth (using WC-China criteria for management) to reduce the risk of DM and other co-morbidities.

Our findings demonstrate that the combined BMI and WC measurement for adiposity was a strong predictor of incident diabetes in older Chinese. This suggests that such a combination measure for adiposity in older people should be used for bodyweight management in older people, since it would require little extra cost or equipment and could increase clinicians’ ability to identify individuals at high risk for diabetes. To reduce the risk of diabetes in late life, older people should undergo lifestyle interventions such as increased physical activity, bodyweight management, a reduced fat, sugar, and calorie diet [41,42].

In conclusion, adiposity in older age significantly increased the risk of incident diabetes in both men and women. It would be considered more appropriate to use the Asian/Hong Kong criteria of BMI cut-off point and WC-China to analyse adiposity in older age instead of the other BMI and WC criterion mentioned. Our study suggested that the combined use of BMI and WC was a stronger predictor of diabetes risk in older people. There were no significant gender differences in the predictive values of BMI and WC measurements on diabetes risk, and thus it would be of equal importance for controlling adiposity in older men and women to reduce the risk of DM.