OBJECTIVE

We aim to investigate the impact of ideal cardiovascular health metrics (ICVHMs) on the association between famine exposure and adulthood diabetes risk.

RESEARCH DESIGN AND METHODS

This study included 77,925 participants from the China Cardiometabolic Disease and Cancer Cohort (4C) Study who were born around the time of the Chinese Great Famine and free of diabetes at baseline. They were divided into three famine exposure groups according to the birth year, including nonexposed (1963–1974), fetal exposed (1959–1962), and childhood exposed (1949–1958). Relative risk regression was used to examine the associations between famine exposure and ICVHMs on diabetes.

RESULTS

During a mean follow-up of 3.6 years, the cumulative incidence of diabetes was 4.2%, 6.0%, and 7.5% in nonexposed, fetal-exposed, and childhood-exposed participants, respectively. Compared with nonexposed participants, fetal-exposed but not childhood-exposed participants had increased risks of diabetes, with multivariable-adjusted risk ratios (RRs) (95% CIs) of 1.17 (1.05–1.31) and 1.12 (0.96–1.30), respectively. Increased diabetes risks were observed in fetal-exposed individuals with nonideal dietary habits, nonideal physical activity, BMI ≥24.0 kg/m2, or blood pressure ≥120/80 mmHg, whereas significant interaction was detected only in BMI strata (P for interaction = 0.0018). Significant interactions have been detected between number of ICVHMs and famine exposure on the risk of diabetes (P for interaction = 0.0005). The increased risk was observed in fetal-exposed participants with one or fewer ICVHMs (RR 1.59 [95% CI 1.24–2.04]), but not in those with two or more ICVHMs.

CONCLUSIONS

The increased risk of diabetes associated with famine exposure appears to be modified by the presence of ICVHMs.

Emerging evidence indicated that early life development was associated with the risk of type 2 diabetes mellitus (T2DM) in adulthood (1,2). Low birth weight was associated with a higher risk of diabetes in later life (35). In addition, studies of the Ukraine famine and the Dutch “Hunger Winter” famine suggested that exposure to starvation in utero was associated with an elevated risk of T2DM in later life (6,7). As one of the largest catastrophes in human history, the Chinese Great Famine has aroused much attention from scholars (8). The Chinese Great Famine had overwhelmingly cardiometabolic consequences, including increasing the risk of obesity (9,10) and metabolic syndrome (11). Several previous epidemiological studies have shown an association between Chinese famine exposure and the risk of T2DM (1214). However, whether and what factors in later life might modify this association have not been extensively investigated.

Rapid economic development and associated dramatic lifestyle changes have led to a substantial increase in the prevalence of T2DM in China (1517). The association between early life development and risk of T2DM may be modified by lifestyle in adulthood (5,14,18,19). In 2010, the American Heart Association proposed seven components critical to ideal cardiovascular health (CVH), including four ideal health behaviors (nonsmoking within the last year, ideal BMI, physical activity at goal levels, and a dietary pattern recommended) and three biological factors (ideal total cholesterol [TC], blood pressure [BP], and fasting plasma glucose) (20). There has been accumulating evidence suggesting that ideal CVH might be a marker of insulin sensitivity and related to lower risk of T2DM (2123). However, previous studies only examined the effect of diet and adult obesity on the association between famine exposure and the risk of T2DM (14,19). To the best of our knowledge, there have been no studies to explore the impact of these ideal CVH metrics (ICVHMs) on the association between famine exposure and adulthood diabetes risk. Therefore, we conducted this prospective study in a nationwide large cohort of the China Cardiometabolic Disease and Cancer Cohort (4C) Study, with two aims: 1) to examine the association between early life famine exposure and risk of T2DM later in life, and 2) to explore whether the ICVHMs might modify the association between famine exposure and risk of diabetes.

Study Population

The China Cardiometabolic Disease and Cancer Cohort (4C) Study was a multicenter, prospective, population-based cohort study investigating the associations of glucose homeostasis with clinical outcomes, including diabetes, cardiovascular disease, cancer, and all-cause mortality. A total of 20 communities from various geographic regions in China were selected to represent the general population in China. Eligible men and women aged ≥40 years were identified from local resident registration systems. Trained community health workers visited eligible individuals’ homes and invited them to participate in the study. A total of 193,846 individuals were recruited for the study at baseline from 2011 to 2012 (2426). During 2014–2016, all participants were invited to participate in an in-person follow-up visit. Lifestyle risk factors and medical history were queried by trained staff using the same standard questionnaire as at baseline. Anthropometric and BP measurements, oral glucose tolerance tests, and blood samples were obtained using the same protocol that was used in the baseline examination.

The study was approved by the Medical Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University (Shanghai, China). Written informed consent was obtained from all participants.

Data Collection

All questionnaire data collection and anthropometric measurements were performed by trained staff according to a standard protocol at local health stations or community clinics at each study center. Using a detailed questionnaire, we collected information on sociodemographic characteristics, lifestyle factors, as well as medical history through personal interviews. Education levels were divided into high school education or above versus less than high school. The type and frequency of alcohol consumptions and smoking habits were recorded. Participants were classified as never, former, or current drinkers according to alcohol drinking habits. The information on intensity, duration, and frequency of physical activity was gathered using the short form of the International Physical Activity Questionnaire, and the metabolic equivalent minutes per week were used to estimate physical activities (one metabolic equivalent represents the energy expenditure for an individual at rest) (27). A previously evaluated and validated dietary questionnaire (25) was used to collect information on dietary intake over the past 12 months. The questionnaire was designed to capture information on frequency and quantity of major food items such as red meat, fruits and vegetables, dairy, and Chinese traditional food like pickles and salty vegetables.

Height and weight were measured to the nearest 0.1 kg and 0.1 cm separately with participants wearing lightweight clothes and no shoes. BP was tested two times using an automated electronic device (Model HEM-752 with Fuzzy logic; Omron) in a seated position after at least a 5-min quiet rest, and the means of two measurements were used in the final data analysis.

Biochemical Evaluation

A blood sample was collected in the morning after an overnight fast (at least 10 h). Sera were aliquoted into 0.5-mL Eppendorf tubes within 2 h after blood collection and shipped in dry ice at −80°C to the central laboratory located at Shanghai Institute of Endocrine and Metabolic Diseases, which is certified by the College of American Pathologists. All participants underwent an oral glucose tolerance test, and plasma glucose was obtained at 0 and 2 h during the test. Plasma glucose concentrations were analyzed locally using a glucose oxidase or hexokinase method within 2 h after blood sample collection under a stringent quality-control program. All regional laboratories passed a national standardization program and study-specific quality assurance program. Serum TC was measured at the central laboratory at Ruijin Hospital using an autoanalyzer (ARCHITECT ci16200 analyzer; Abbott Laboratories, Abbott Park, IL).

Definitions

Famine Exposure

According to the birth time during and around the Chinese Great Famine, we defined famine exposure subgroups as nonexposed (born between 1 January 1963 and 31 December 1974), fetal exposed (born between 1 January 1959 and 31 December 1962), and childhood exposed (1 January 1949 and 31 December 1958), as in previous studies (12,28). Famine severity was determined according to the excess death rate for each province (12,29), which was calculated as the mortality rate change from the average level in 1956–1958 to the highest rate during 1959–1962 (29). Based on the information, an excess mortality rate of 100% was used as a threshold value to define severely and less severely affected areas.

ICVHMs

ICVHMs were adapted from the recommendations of the Goals and Metrics Committee of the Strategic Planning Task Force of the American Heart Association (20): never smoked or quit smoking >12 months prior, BMI <24.0 kg/m2, physical activity at goal (at least 150 min/week of moderate-intensity physical activity, 75 min/week of vigorous-intensity aerobic physical activity, or an equivalent combination of moderate- and vigorous-intensity aerobic activities), dietary score ≥3 (including four components: fruits and vegetables ≥4.5 cups/day; fish ≥198 g/week; sweets/sugar-sweetened beverages ≤450 kcal/week; and soy protein ≥25 g/day), TC <200 mg/dL (untreated; to convert to millimoles per liter, multiply by 0.0259), and BP <120/80 mmHg (untreated) (Supplementary Table 1). Fasting plasma glucose was not included as a CVH metric in the main analysis because plasma glucose is used to define T2DM (23). The number of ICVHMs was categorized as ≤1, 2, 3, 4, and ≥5 CVH metrics at the ideal level.

Diabetes

Incident diabetes was defined as fasting plasma glucose ≥7.0 mmol/L, and/or 2-h postload plasma glucose ≥11.1 mmol/L, and/or a self-reported previous diagnosis by health care professionals during follow-up among participants without diabetes at baseline.

Statistical Analyses

The baseline characteristics of the study population by famine exposure groups were compared using the Pearson χ2 test for categorical variables and Student t test or Mann-Whitney U test for continuous variables. Cumulative incidence of diabetes was calculated during the follow-up. The association of incident diabetes with famine exposure was examined using relative risk regression models (24,30). Model 1 was unadjusted. Model 2 was adjusted for sex and age (continuous variable). Model 3 included variables in model 2 plus family history of diabetes (yes or no), drinking status (current drinker or not), and education status (less than high school or high school or above). Individual CVH metrics were further adjusted in model 4.

The modifying effect of ICVHMs on the association of famine exposure and diabetes was evaluated in stratified analyses by strata of the six individual components of ICVHMs and the number of ICVHMs. In the subgroup analyses, individual CVH metrics were mutually adjusted, and the P value was corrected for multiple testing via false discovery rate using the Benjamini-Hochberg method. We further compared the risk estimates in the strata of sex and famine exposure severity. To demonstrate possible interactions of famine exposure and ICVHMs in the development of diabetes, we generated interaction terms using the cross products of famine exposure with each component of the ICVHMs. The interaction was tested using the likelihood ratio test by comparing the full model including the interaction term with the reduced model excluding the interaction term. The df for the P for interaction was calculated based on the number of exposure groups (nonexposed, fetal exposed, and childhood exposed) and the number of subgroups for each effect modifier in the subgroup analysis. To reduce the bias related to age differences between famine and postfamine births, an “age-balanced” method was used, in which both postfamine and prefamine births were combined as unexposed control subjects (13,31).

All analyses were conducted using SAS 9.2 (SAS Institute, Cary, NC), and a two-tailed P < 0.05 was considered as statistically significant.

Among 193,846 participants examined at baseline, 170,240 (87.8%) were followed up in 2014–2016. Of them, participants with missing baseline information on plasma glucose measurement (n = 6,074), diagnosed or screen-detected diabetes at baseline (n = 34,497), missing data on BMI (n = 2,497), smoking status (n = 4,193), diet habits (n = 4,429), and physical activity (n = 2,583) were excluded. Additionally, 26,166 participants born before 31 December 1948 and 11,876 without glucose measurement at follow-up visit were also excluded, leaving 77,925 for the current analysis (Supplementary Fig. 1). Among them, 23,926 (30.70%) were men, and the mean age of the study participants was 54.5 ± 7.6 years. Baseline characteristics of participants according to categories of famine exposure are presented in Table 1. In addition to the obvious age difference between groups, the fetal-exposed group has a greater proportion of individuals with higher education than the other two groups. There is an increasing trend of TC and BP level and a decreasing trend of four or five or more ICVHMs from nonexposed to fetal-exposed to childhood-exposed groups.

Table 1

Baseline characteristics of 77,925 participants according to famine exposure in early life

NonexposedFamine exposure
FetalChildhood
Number of participants (%) 23,582 (30.3) 13,195 (16.9) 41,148 (52.8) 
Age at baseline, years 44.8 ± 2.7 50.6 ± 1.3 57.3 ± 2.8 
Male sex 7,185 (30.5) 3,559 (27.0) 13,182 (32.0) 
BMI, kg/m2 24.2 ± 3.6 24.4 ± 3.4 24.4 ± 3.5 
High school education or above 10,518 (44.6) 7,547 (57.2) 13,906 (33.8) 
Current cigarette smoking 4,976 (21.1) 2,536 (19.2) 8,324 (20.2) 
Current alcohol drinking 2,448 (10.4) 1,290 (9.8) 4,347 (10.6) 
Moderate and vigorous physical activity 2,881 (12.2) 1,788 (13.6) 6,344 (15.4) 
Family history of diabetes 3,403 (14.4) 2,104 (16.0) 5,009 (12.2) 
Healthy diet 13,867 (58.8) 7,662 (58.1) 22,745 (55.3) 
TC, mmol/L 4.6 ± 1.1 5.0 ± 1.1 5.1 ± 1.1 
Systolic BP, mmHg 123.0 ± 17.1 127.0 ± 18.0 131.9 ± 19.6 
Diastolic BP, mmHg 76.9 ± 11.1 78.1 ± 11.1 78.6 ± 10.8 
Increased TC 6,550 (27.8) 5,393 (40.9) 18,856 (45.8) 
Increased BP 13,538 (57.4) 8,684 (65.8) 30,824 (74.9) 
Fasting plasma glucose, mmol/L 5.3 ± 0.5 5.4 ± 0.6 5.5 ± 0.6 
2-h plasma glucose, mmol/L 6.5 ± 1.6 6.7 ± 1.6 6.9 ± 1.7 
ICVHMs    
 ≤1 2,255 (9.6) 1,588 (12.0) 5,860 (14.2) 
 2 4,833 (20.5) 3,291 (24.9) 11,854 (28.8) 
 3 6,909 (29.3) 4,044 (30.7) 12,645 (30.7) 
 4 6,091 (25.8) 2,918 (22.1) 7,570 (18.4) 
 ≥5 3,494 (14.8) 1,354 (10.3) 3,219 (7.8) 
NonexposedFamine exposure
FetalChildhood
Number of participants (%) 23,582 (30.3) 13,195 (16.9) 41,148 (52.8) 
Age at baseline, years 44.8 ± 2.7 50.6 ± 1.3 57.3 ± 2.8 
Male sex 7,185 (30.5) 3,559 (27.0) 13,182 (32.0) 
BMI, kg/m2 24.2 ± 3.6 24.4 ± 3.4 24.4 ± 3.5 
High school education or above 10,518 (44.6) 7,547 (57.2) 13,906 (33.8) 
Current cigarette smoking 4,976 (21.1) 2,536 (19.2) 8,324 (20.2) 
Current alcohol drinking 2,448 (10.4) 1,290 (9.8) 4,347 (10.6) 
Moderate and vigorous physical activity 2,881 (12.2) 1,788 (13.6) 6,344 (15.4) 
Family history of diabetes 3,403 (14.4) 2,104 (16.0) 5,009 (12.2) 
Healthy diet 13,867 (58.8) 7,662 (58.1) 22,745 (55.3) 
TC, mmol/L 4.6 ± 1.1 5.0 ± 1.1 5.1 ± 1.1 
Systolic BP, mmHg 123.0 ± 17.1 127.0 ± 18.0 131.9 ± 19.6 
Diastolic BP, mmHg 76.9 ± 11.1 78.1 ± 11.1 78.6 ± 10.8 
Increased TC 6,550 (27.8) 5,393 (40.9) 18,856 (45.8) 
Increased BP 13,538 (57.4) 8,684 (65.8) 30,824 (74.9) 
Fasting plasma glucose, mmol/L 5.3 ± 0.5 5.4 ± 0.6 5.5 ± 0.6 
2-h plasma glucose, mmol/L 6.5 ± 1.6 6.7 ± 1.6 6.9 ± 1.7 
ICVHMs    
 ≤1 2,255 (9.6) 1,588 (12.0) 5,860 (14.2) 
 2 4,833 (20.5) 3,291 (24.9) 11,854 (28.8) 
 3 6,909 (29.3) 4,044 (30.7) 12,645 (30.7) 
 4 6,091 (25.8) 2,918 (22.1) 7,570 (18.4) 
 ≥5 3,494 (14.8) 1,354 (10.3) 3,219 (7.8) 

Data are n (%) or mean ± SD.

During up to 5 years of follow-up (mean 3.6 years), we identified a total of 4,842 (6.21%) individuals with incident diabetes. The mean (SD) follow-up time was 3.67 (0.97), 3.63 (0.91), and 3.59 (0.90) and the cumulative incidence of diabetes was 4.15%, 6.0%, and 7.47% for the nonexposed, fetal-exposed, and childhood-exposed group to famine, respectively. Compared with nonexposed participants, both fetal-exposed (age- and sex-adjusted risk ratio [RR] 1.21 [95% CI 1.09–1.35]) and childhood-exposed (age- and sex-adjusted RR 1.20 [95% CI 1.04–1.38]) participants had increased risks of diabetes in adulthood. The risk of incident diabetes remained significantly increased after adjusting for all other covariates, including individual CVH metrics in fetal-exposed participants (RR 1.17 [95% CI, 1.05–1.31]), but not in childhood-exposed participants (RR 1.12 [95% CI 0.96–1.30]). Moreover, the increased risk of diabetes associated with fetal famine exposure was observed in severely affected areas, but not in less severely affected areas (P for interaction <0.001) (Table 2). Furthermore, in the age-balanced analysis, compared with the individuals combined of prefamine and postfamine births as the reference group, the increased risk of diabetes in the fetal-exposed individuals remained statistically significant (RR 1.11 [95% CI 1.03–1.19]) (Supplementary Table 2).

Table 2

RRs (95% CIs) for incident T2DM according to famine exposure in early life among 77,925 participants

NonexposedFamine exposure
FetalChildhood
Case subjects/total number 978/23,582 792/13,195 3,072/41,148 
Cumulative incidence, % 4.15 6.00 7.47 
Model 1 1.00 (reference) 1.45 (1.32–1.59) 1.80 (1.68–1.93) 
Model 2 1.00 (reference) 1.21 (1.09–1.35) 1.20 (1.04–1.38) 
Model 3 1.00 (reference) 1.19 (1.07–1.33) 1.11 (0.95–1.29) 
Model 4 1.00 (reference) 1.17 (1.05–1.31) 1.12 (0.96–1.30) 
Severely exposed areas 1.00 (reference) 1.20 (1.05–1.37) 1.14 (0.95–1.37) 
Less severely exposed areas 1.00 (reference) 1.07 (0.87–1.30) 1.05 (0.80–1.37) 
NonexposedFamine exposure
FetalChildhood
Case subjects/total number 978/23,582 792/13,195 3,072/41,148 
Cumulative incidence, % 4.15 6.00 7.47 
Model 1 1.00 (reference) 1.45 (1.32–1.59) 1.80 (1.68–1.93) 
Model 2 1.00 (reference) 1.21 (1.09–1.35) 1.20 (1.04–1.38) 
Model 3 1.00 (reference) 1.19 (1.07–1.33) 1.11 (0.95–1.29) 
Model 4 1.00 (reference) 1.17 (1.05–1.31) 1.12 (0.96–1.30) 
Severely exposed areas 1.00 (reference) 1.20 (1.05–1.37) 1.14 (0.95–1.37) 
Less severely exposed areas 1.00 (reference) 1.07 (0.87–1.30) 1.05 (0.80–1.37) 

Model 1 was unadjusted; model 2 was adjusted for age and sex; and model 3 included model 2 plus education attainment (less than high school or high school or above), drinking status (current drinker or not), and family history of diabetes (yes or no). Model 4 included model 3 plus individual CVH metrics.

Subgroup analysis stratified by the six ICVHMs was further carried out, and individual CVH metrics were mutually adjusted in the regression model (Table 3). Compared with nonexposed individuals, significantly increased diabetes risk was observed among fetal famine-exposed individuals with nonideal dietary habits, nonideal physical activity, BMI ≥24 kg/m2, or BP ≥120/80 mmHg, with RRs (95% CIs) of 1.29 (1.10–1.52), 1.16 (1.03–1.30), 1.20 (1.05–1.36), and 1.22 (1.08–1.38), respectively. Significant interaction was detected only in BMI strata and famine exposure on risk of diabetes (P for interaction = 0.0018), but not across dietary habits, physical activity, and BP (all P for interaction ≥0.05). The increased diabetes risk associated with fetal famine exposure is significant in both ideal (RR 1.14 [95% CI 1.01–1.30]) and nonideal smoking status groups (RR 1.28 [95% CI 1.02–1.59]) (P for interaction = 0.0621). The RR (95% CI) of diabetes risk associated with fetal exposure to famine among individuals with ideal and nonideal TC levels was 1.17 (1.01–1.35) and 1.15 (0.97–1.37), respectively. No significant interactions have been detected across TC strata (P for interaction = 0.4103). When individual CVH metrics were not mutually adjusted, we observed significant differences in smoking status (P for interaction = 0.0013) and BMI (P for interaction = 0.0003) across strata (Supplementary Table 3). Multiple testing via false discovery rate analyses and sensitivity analyses with further adjustment of area (rural/urban), marriage status, occupation, and economic status showed similar results (Supplementary Tables 4 and 5). In age-balanced analysis, the risk estimates do not change significantly (Supplementary Table 6).

Table 3

Multivariable-adjusted RRs (95% CIs) for incident T2DM according to famine exposure and combined ICVHMs

Case subjects/nCumulative incidence, %NonexposedFamine exposureP for interaction
FetalChildhood
Diet pattern      0.5392 
 Nonideal 2,219/33,651 6.59 1.00 (reference) 1.29 (1.10–1.52) 1.25 (1.00–1.56)  
 Ideal 2,623/44,274 5.92 1.00 (reference) 1.07 (0.93–1.25) 1.01 (0.82–1.25)  
Physical activity      0.5766 
 Nonideal 4,181/66,912 6.25 1.00 (reference) 1.16 (1.03–1.30) 1.06 (0.90–1.25)  
 Ideal 661/11,013 6.00 1.00 (reference) 1.25 (0.91–1.72) 1.52 (0.996–2.31)  
Smoking status      0.0621 
 Nonideal 1,110/15,836 7.01 1.00 (reference) 1.28 (1.02–1.59) 1.03 (0.75–1.42)  
 Ideal 3,732/62,089 6.01 1.00 (reference) 1.14 (1.01–1.30) 1.14 (0.96–1.36)  
BMI      0.0018 
 Nonideal 3,307/40,399 8.19 1.00 (reference) 1.20 (1.05–1.36) 1.09 (0.91–1.31)  
 Ideal 1,535/37,526 4.09 1.00 (reference) 1.10 (0.90–1.35) 1.17 (0.88–1.54)  
TC      0.4103 
 Nonideal 2,222/30,799 7.21 1.00 (reference) 1.15 (0.97–1.37) 1.17 (0.93–1.46)  
 Ideal 2,620/47,126 5.56 1.00 (reference) 1.17 (1.01–1.35) 1.06 (0.86–1.30)  
BP      0.9710 
 Nonideal 3,920/53,046 7.39 1.00 (reference) 1.22 (1.08–1.38) 1.18 (1.00–1.40)  
 Ideal 922/24,879 3.71 1.00 (reference) 0.97 (0.76–1.23) 0.87 (0.61–1.23)  
Number of ICVHMs      0.0005 
 ≤1 947/9,703 9.76 1.00 (reference) 1.59 (1.24–2.04) 1.28 (0.90–1.80)  
 2 1,553/19,978 7.77 1.00 (reference) 1.03 (0.85–1.26) 1.02 (0.78–1.33)  
 3 1,452/23,598 6.15 1.00 (reference) 1.00 (0.83–1.22) 1.01 (0.77–1.32)  
 4 658/16,579 3.97 1.00 (reference) 1.14 (0.84–1.55) 1.26 (0.82–1.93)  
 ≥5 232/8,067 2.88 1.00 (reference) 1.09 (0.67–1.78) 0.79 (0.39–1.60)  
Case subjects/nCumulative incidence, %NonexposedFamine exposureP for interaction
FetalChildhood
Diet pattern      0.5392 
 Nonideal 2,219/33,651 6.59 1.00 (reference) 1.29 (1.10–1.52) 1.25 (1.00–1.56)  
 Ideal 2,623/44,274 5.92 1.00 (reference) 1.07 (0.93–1.25) 1.01 (0.82–1.25)  
Physical activity      0.5766 
 Nonideal 4,181/66,912 6.25 1.00 (reference) 1.16 (1.03–1.30) 1.06 (0.90–1.25)  
 Ideal 661/11,013 6.00 1.00 (reference) 1.25 (0.91–1.72) 1.52 (0.996–2.31)  
Smoking status      0.0621 
 Nonideal 1,110/15,836 7.01 1.00 (reference) 1.28 (1.02–1.59) 1.03 (0.75–1.42)  
 Ideal 3,732/62,089 6.01 1.00 (reference) 1.14 (1.01–1.30) 1.14 (0.96–1.36)  
BMI      0.0018 
 Nonideal 3,307/40,399 8.19 1.00 (reference) 1.20 (1.05–1.36) 1.09 (0.91–1.31)  
 Ideal 1,535/37,526 4.09 1.00 (reference) 1.10 (0.90–1.35) 1.17 (0.88–1.54)  
TC      0.4103 
 Nonideal 2,222/30,799 7.21 1.00 (reference) 1.15 (0.97–1.37) 1.17 (0.93–1.46)  
 Ideal 2,620/47,126 5.56 1.00 (reference) 1.17 (1.01–1.35) 1.06 (0.86–1.30)  
BP      0.9710 
 Nonideal 3,920/53,046 7.39 1.00 (reference) 1.22 (1.08–1.38) 1.18 (1.00–1.40)  
 Ideal 922/24,879 3.71 1.00 (reference) 0.97 (0.76–1.23) 0.87 (0.61–1.23)  
Number of ICVHMs      0.0005 
 ≤1 947/9,703 9.76 1.00 (reference) 1.59 (1.24–2.04) 1.28 (0.90–1.80)  
 2 1,553/19,978 7.77 1.00 (reference) 1.03 (0.85–1.26) 1.02 (0.78–1.33)  
 3 1,452/23,598 6.15 1.00 (reference) 1.00 (0.83–1.22) 1.01 (0.77–1.32)  
 4 658/16,579 3.97 1.00 (reference) 1.14 (0.84–1.55) 1.26 (0.82–1.93)  
 ≥5 232/8,067 2.88 1.00 (reference) 1.09 (0.67–1.78) 0.79 (0.39–1.60)  

Adjusted for age, sex, education attainment (less than high school or high school or above), drinking status (current drinker or not), and family history of diabetes (yes or no). Individual CVH metrics were mutually adjusted.

The incidence of diabetes according to famine exposure and the number of ICVHMs are displayed in Fig. 1. There was an inverse relationship between the number of ICVHMs and the incidence of diabetes. The highest incidence was found in those with fetal famine exposure and with no ICVHMs in adulthood.

Figure 1

Cumulative incidence of diabetes according to famine exposure and the number of ICVHMs.

Figure 1

Cumulative incidence of diabetes according to famine exposure and the number of ICVHMs.

When analyzing the effect of the number of ICVHMs on the association between exposure to famine in early life and the risk of incident diabetes, significant interactions have been detected (P for interaction = 0.0005). We found the risk was significantly increased in participants with one or less ICVHMs (RR 1.59 [95% CI 1.24–2.04] for fetal-exposed), but not in those with two or more ICVHMs (Table 3). In sensitivity analyses and age-balanced analyses, the risk estimates do not change significantly (P for interaction = 0.0003 and 0.0041, respectively) (Supplementary Tables 5 and 6).

Sex difference and the severity of famine exposure were further explored in stratified analysis (Fig. 2). We found that the association between famine exposure and number of ICVHMs on the risk of diabetes was present in both men and women. The incremental risk of diabetes associated with fetal famine exposure was greatest among women with one or less ICVHMs (RR 1.66 [95% CI 1.11–2.49]) and was lowest among men with five or more ICVHMs (RR 0.64 [95% CI 0.18–2.22]) (Fig. 2A). Furthermore, the increased risk was significant in those with fetal exposure in severely affected areas and with one or less ICVHM (RR 1.52 [95% CI 1.14–2.02]), while in their counterparts, a marginal increased risk was observed (RR 1.61 [95% CI 0.96–2.70]) (Fig. 2C). No significantly increased risk of T2DM was observed for participants with childhood famine exposure in both sexes (Fig. 2B) and both famine-severity areas (Fig. 2D).

Figure 2

Multivariable-adjusted RRs (95% CIs) of incident diabetes for participants with famine exposure in relation to number of ICVHMs according to sex and area categories. A total of 77,925 participants (23,582 nonexposed, 13,195 fetal exposed, and 41,148 childhood exposed) were included in the analysis. The reference group is nonexposed individuals, with the same number of ICVHMs as the famine-exposed groups. RRs (95% CIs) were adjusted for age, sex, education attainment (less than high school or high school or greater), drinking status (current drinker or not), and family history of diabetes (yes or no). Interaction between the combination of famine exposure status with number of ICVHMs and sex on diabetes: P for interaction = 0.863 (A) and P for interaction = 0.484 (B). Interaction between the combination of famine exposure status with number of ICVHMs and level of affected area on diabetes: P for interaction = 0.485 (C) and P for interaction = 0.598 (D).

Figure 2

Multivariable-adjusted RRs (95% CIs) of incident diabetes for participants with famine exposure in relation to number of ICVHMs according to sex and area categories. A total of 77,925 participants (23,582 nonexposed, 13,195 fetal exposed, and 41,148 childhood exposed) were included in the analysis. The reference group is nonexposed individuals, with the same number of ICVHMs as the famine-exposed groups. RRs (95% CIs) were adjusted for age, sex, education attainment (less than high school or high school or greater), drinking status (current drinker or not), and family history of diabetes (yes or no). Interaction between the combination of famine exposure status with number of ICVHMs and sex on diabetes: P for interaction = 0.863 (A) and P for interaction = 0.484 (B). Interaction between the combination of famine exposure status with number of ICVHMs and level of affected area on diabetes: P for interaction = 0.485 (C) and P for interaction = 0.598 (D).

In this large population-based study, we found that prenatal exposure to famine is associated with an increased risk of T2DM in adulthood. More importantly, an interaction/effect modification between famine and number of ICVHMs was observed, and this increased risk was diminished in individuals with two or more ICVHMs. To our knowledge, this is the first and largest epidemiological study investigating the modifying effects of ICVHMs on the association between famine exposure and the risks of T2DM in adulthood.

Famine exposure in early life was shown to be related to the risk of T2DM in epidemiological studies previously (6,7,12). Findings from the Ukraine and Dutch famines provide strong support for an association between famine exposure in early life and T2DM (6,7). In China, the first evidence on the association between the famine exposure and T2DM came from the 2002 China National Nutrition and Health Survey, which demonstrated that fetal famine exposure in severely affected areas was associated with an increased risk of hyperglycemia in adulthood (odds ratio 3.92 [95% CI 1.64–9.39]) (19). The effect of prenatal famine exposure was confirmed by other studies subsequently (12,14,28,32,33). Results from the Dongfeng-Tongji cohort (33) suggested that participants who were exposed to severe famine in childhood had a 38% higher T2DM risk than those exposed to less severe famine (odds ratio 1.38 [95% CI 1.05–1.81]). The China Kadoorie Biobank study (14) also confirmed that famine exposure in early life increased the risk of T2DM in adulthood (hazard ratio 1.25 [95% CI 1.07–1.45]). However, most of these studies were limited, with a relatively small population (28,32,33) or limited areas (32). Our current study provided further supporting evidence that exposure to famine in early life influences the risk of T2DM development later in life in a large prospective nationwide cohort. Partly consistent with previous findings (12,19), we found that individuals with fetal famine exposure appeared to have significantly increased risk of T2DM compared with nonexposed participants. Importantly, only participants in severely affected areas with fetal famine exposure have a higher risk of diabetes.

Previous epidemiologic studies on the modifying factors and potential mechanisms of famine exposure and diabetes risk in later life are limited. Stratified analysis from the 2002 China National Nutrition and Health Survey has examined dietary factors, economic status, and BMI in 7,874 rural adults born between 1954 and 1964 and concluded that the increased risk of hyperglycemia in adulthood related to fetal exposure to the severe Chinese famine appears to be exacerbated by a nutritionally rich environment in later life (19). The China Kadoorie Biobank study (14) reported that coexistence of prenatal experience of undernutrition and abdominal obesity in adulthood was associated with a higher risk of T2DM. In the current study, for the first time, we examined the effect of ICVHMs and the number of these metrics on the association between famine exposure in early life and the risk of T2DM in a large nationwide Chinese cohort. Interestingly, we found that the increased risk of diabetes due to famine exposure might be modified by a healthy lifestyle or metabolic metrics (such as ideal BMI) in adulthood. These findings imply the importance of a healthy lifestyle in the prevention of T2DM among individuals who experienced the fetal undernutrition (5). In addition, unlike the heterogeneity of men and women reported previously (28,34), our study found that fetal famine exposure was associated with increased risk of diabetes in both male and female. Furthermore, fetal famine exposure significantly increased the risk of diabetes only in severely affected areas but not less severely affected areas, which reinforces our conclusions on famine exposure and diabetes risk.

There are several potential mechanisms underlying the association of famine exposure in early life and diabetes in adulthood. One is the developmental origin hypothesis (i.e., early nutrition status influences the epigenetic changes) (35,36). Epigenetic dysregulation was reported in diabetic islets in a study of comprehensive DNA methylation in diabetic and nondiabetic pancreatic islets (37). Furthermore, differential DNA methylations were reported in those exposed to Dutch famine, which suggested that prenatal starvation might promote an adverse metabolic phenotype in later life by epigenetic modulation (38). The “fetal programming hypothesis” suggested that the thrifty phenotype would reduce β-cell function and is more prone to develop T2DM under conditions of a sudden move to overnutrition. Our observation supports an effect of a transform lifestyle from starvation to overnutrition. Additionally, exposure to famine in early life increases the susceptibility to chronic diseases in adulthood potentially through a memory of the effects of early nutritional environments, which was also called “metabolic imprinting” (39).

It is worth mentioning that aging effect is an unavoidable issue for the analysis between famine exposure and health outcomes. The age gap may account for difference in the physical activity, employment, residence (urban/rural), and economic status. As the incidence of diabetes is highly correlated with aging, this age difference between individuals born during the famine and postfamine control individuals can introduce substantial bias in analysis. To overcome this issue, we applied age-balanced analysis and stratified analysis with the severity level of famine exposure, as proposed by the previous work (7) and recent re-examination of available studies (13,31). We demonstrated that fetal famine exposure was associated with increased diabetes risk using age-balanced analysis. This association was seen in severely affected areas.

The strengths of this study include a nationwide prospective cohort design, the large sample size, and the detailed information about lifestyle factors. The diagnosis of diabetes was not self-reported, but based on oral glucose tolerance test at both baseline and follow-up visit. Our study does have a number of important limitations. First, the Chinese famine did not have a definite beginning or ending time, making it difficult to precisely define the famine exposure. Misclassification of famine exposure was inevitable. However, using birth date to define famine exposure was the most common method in studies on the Chinese famine (11,19). Second, the study participants were only followed for a mean of 3.6 years. This relatively short follow-up duration reduced the number of incident diabetes and the study’s statistical power. Third, the study participants only had one follow-up visit, and glycemic measures were obtained at only two time points (the baseline and follow-up visits). This could limit the accuracy of the timing of diagnoses for diabetes. Fourth, we evaluated a diet score mainly based on the information of fruit, vegetable, soy protein, and level of caloric intake, not including the sodium intake (40), which may underestimate the actual effect of a healthy diet with T2DM. Fifth, the possibility of residual confounding due to unmeasured or poorly measured confounders such as maternal health and maternal child-feeding behaviors could not be ruled out. Finally, although age-balanced analysis has been recommended by previous studies, there might exist limitations of combining the age group. Famine exposure has been shown to be associated with cardiometabolic risk and adult mortality; thus, survival bias might be possible.

In conclusion, we found that famine exposure in early life significantly increased risk of T2DM in later life. This association could be attenuated by two or more ICVHMs in adulthood, and an interaction/effect modification between famine and number of ICVHMs was observed. Our findings emphasize the importance of a healthy lifestyle in adulthood in prevention of T2DM even in presence of the adverse prenatal or early life factors.

This article contains supplementary material online at https://doi.org/10.2337/figshare.12185469.

J.L., M.L., Y.X., Y.B., Y.Q., Q.L., and T.W. contributed equally to this work.

Acknowledgments. The authors thank all of the study participants.

Funding. The research reported in this publication was supported by the National Basic Research Program of China (973 Program) (award 2015CB553601), the Ministry of Science and Technology of the People’s Republic of China (awards 2016YFC1305600, 2016YFC1305202, 2016YFC1304904, and 2017YFC1310700), the National Natural Science Foundation of China (awards 81700764, 81670795, 81621061, and 81561128019), National Major Scientific and Technological Special Project for “Significant New Drugs Development” (award 2017ZX09304007), and the Innovative Research Team of High-Level Local Universities in Shanghai.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. J.L., Y.B., G.N., and W.W. conceived and designed the study. J.L., M.L., and R.D. analyzed data. Y.Q., Q.L., R.H., L.S., Q.S., Z.Z., X.Y., L.Y., G.Q., Q.W., G.C., Z.G., G.W., F.S., Z.L., Li C., Y.H., Z.Y., X.T., Y.Z., C.L., Y.W., S.W., T.Y., H.D., Lu.C., J.Z., and Y.M. collected data. All authors were involved in writing and revising the paper and had final approval of the submitted and published versions. Y.B., G.N., and W.W. are the guarantors of this work and, as such, had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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