ABSTRACT
Background
No evidence-based recommendations regarding optimal breakfast frequency and timing and type 2 diabetes mellitus (T2DM) exist for older adults because of limited studies.
Objectives
We sought to prospectively assess relations between breakfast frequency and timing and T2DM risk among older adults and determine whether these depended on sex or cardiometabolic risk factors.
Methods
Weekly breakfast frequency and usual daily breakfast time were assessed by questionnaire at baseline in 3747 older adults (aged ≥ 65 y) from the Cardiovascular Health Study (CHS) who were free of cancer and T2DM and followed for 17.6 y. Multivariable-adjusted hazard ratios (aHRs) with 95% CIs estimated from Cox proportional hazards models were used to quantify associations with T2DM.
Results
Most CHS participants (median age: 74 y; IQR: 71–78 y) consumed breakfast daily (85.5%), and 73% had their first daily eating occasion between 07:00 and 09:00, both of which were associated with higher socioeconomic status, factors that are indicative of a healthier lifestyle, and lower levels of cardiometabolic risk indicators at baseline. During follow-up, 547 T2DM cases were documented. No strong evidence was observed linking breakfast frequency and risk of T2DM. Compared with participants whose breakfast timing (first eating occasion of the day) was 07:00–09:00, those who broke fast after 09:00 had an aHR for T2DM of 0.71 (95% CI: 0.51, 0.99). This association was present in participants with impaired fasting glucose at baseline (aHR: 0.61; 95% CI: 0.39, 0.95) but not in those without (aHR: 0.83; 95% CI: 0.50, 1.38). No associations between eating frequency or timing and T2DM were observed within other prespecified subgroups.
Conclusions
Eating breakfast daily was not associated with either higher or lower risk of T2DM in this cohort of older adults, whereas a later (after 09:00) daily first eating occasion time was associated with lower T2DM risk in participants with impaired fasting glucose at baseline.
This trial was registered at clinicaltrials.gov as NCT00005133.
Keywords: type 2 diabetes mellitus, epidemiology, nutrition, prevention, breakfast frequency and timing, older adults
See corresponding editorial on page 293.
Introduction
Older adults (aged ≥65 y) have the highest prevalence of type 2 diabetes mellitus (T2DM) of any age group (1), and the number of cases among this age group is projected to increase by >4.5-fold from 2005 to 2050 (2). Older adults with T2DM are at substantial risk of developing acute and chronic complications such as geriatric syndromes, kidney disease, major lower-extremity amputation, blindness, stroke, and incident coronary artery disease (3–6), and are at higher risk of institutionalization (7) and premature death (1, 8) than older adults without T2DM. Given these long-term complications, the reduced quality of life, the associated high health care costs, and the rising incidence and prevalence of T2DM (2, 9), T2DM among older adults has become a major challenge in both the clinical and public health realms, making prevention a priority.
Diet is one of the most important modifiable risk factors associated with T2DM in North America (10–13). However, no evidence-based recommendations exist for older adults regarding the timing and/or frequency of meals, snacks, and caloric beverages for cardiometabolic protection because of limited studies on the topic. Breakfast eating timing and frequency have been linked with several cardiometabolic risk factors in the general population such as ghrelin secretion (14), weight gain (15–18), dyslipidemia (19, 20), blood pressure (21), and insulin resistance (19, 20); however, the long-term effect of breakfast eating timing and frequency patterns on new-onset T2DM risk remains unclear. Previous systematic reviews and meta-analyses of 10 observational studies reported that skipping breakfast was associated with significantly increased risk of T2DM in adults aged 14–99 y after comprehensive adjustment for demographic, diet, and lifestyle factors (22, 23), yet no studies assessed breakfast timing (the timing of the first eating occasion of the day when a person breaks fast). None of the included studies were conducted exclusively in older adults; therefore, it remains unknown whether these findings are generalizable to older adults. Further, the associations that have been observed to date are modest (22, 23) and require replication in additional cohorts representing socioeconomic diversity. We previously reported in younger adults that skipping meals to lose weight was associated with increased risk of T2DM in some subgroups characterized by sex and cardiometabolic risk factors (24), but it remains unknown whether any potential relations between the specific meal of breakfast's frequency/timing and the long-term risk of incident T2DM similarly depend on sex and common cardiometabolic risk indicators (BMI and impaired fasting glucose) in older adults.
The objectives of our present study were to 1) characterize the breakfast frequency and timing patterns of a cohort of older, community-dwelling American adults; 2) prospectively determine whether these breakfast frequency and timing patterns were associated with risk of incident T2DM; and 3) test whether this association depended on sex or common cardiometabolic risk factors at baseline [BMI (in kg/m2) ≥30 and impaired fasting glucose].
Methods
Data source and study population
The Cardiovascular Health Study (CHS; NCT00005133) is a population-based, observational, prospective cohort study initiated by the National Heart, Lung, and Blood Institute to identify risk factors related to the development and progression of coronary heart disease and stroke in US adults aged 65 y and older. The design, recruitment, and methods have been reported previously (25). Briefly, 5201 community-dwelling, ambulatory males and females aged 65 y and older from 4 US communities (Forsyth County, NC; Sacramento County, CA; Washington County, MD; and Pittsburgh, PA) were recruited between 1989 and 1990 from randomly generated Health Care Financing Administration Medicare eligibility lists. To increase representation of the black community, a supplemental cohort of 687 predominantly black participants was recruited and enrolled between 1992 and 1993 using similar methods from 3 of the original centers.
Baseline assessments consisted of home interviews and in-clinic evaluations, where standardized physical examinations, blood sampling, diagnostic testing, and questionnaires were conducted by trained personnel to determine the health status, medical history, medication use, and lifestyle habits of participants. Participants attended in-clinic evaluations annually and were contacted by telephone at 6-mo intervals between 1989 and 1999. Semiannual follow-up was continued by telephone after 1999 to ascertain health status and events information. To supplement routine follow-up on incident T2DM, data from the CHS were linked to Centers for Medicare and Medicaid Services (CMS) claims data from 1991 until 2010 using participants’ Social Security number, sex, and date of birth (26, 27). For the present study, we considered the 1992–1993 visit as the baseline assessment to allow for the inclusion of CMS claims data for identification of incident T2DM in the follow-up period. Each center's institutional review board reviewed and approved the study protocol, and all participants gave written informed consent. Institutional review board approval was obtained for the present analyses at the Nova Scotia Health Authority.
In this present analysis, participants were excluded (in this order) if they were deceased by the 1992–1993 visit (n = 354), had prevalent diabetes at baseline (n = 916), their baseline diabetes status was missing (n = 108), they were lost to follow-up in the CHS (n = 624), did not answer the eating timing and frequency questions (n = 21), had cancer (except nonmelanoma skin cancer) during the 5 y preceding baseline (n = 100), or if their follow-up data on incident diabetes were missing (n = 18). We excluded participants with a history of cancer because these participants may have changed their eating timing and frequency patterns owing to health concerns, treatment, treatment side effects, or symptoms. After exclusions, 3747 participants were available for the breakfast frequency analysis (Supplemental Figure 1). Of the 3747 participants included in the breakfast frequency analysis, 3 participants did not answer the question on breakfast timing and were excluded from this analysis. Therefore, 3744 participants were available for the breakfast timing analysis.
Eating timing and frequency and other dietary assessment
A nutrition history questionnaire was used to ascertain information on breakfast frequency, meal frequency, after-dinner snacking, fruit and vegetable intake, breakfast timing, and evening food and drink consumption timing at the baseline visit for both cohorts. Participants were asked to respond to the following questions: “How often do you eat breakfast? Every day, some days, rarely, weekends only, never,” “On Monday through Friday how many meals do you usually eat per day?,” “On Monday through Friday how many snacks do you eat after dinner?,” “About how many servings of vegetables do you eat per day or week, not counting salad or potatoes?,” “How many fruits do you usually eat per day or per week, not counting juices?,” “On Monday through Friday about what time do you usually first eat or drink something after waking up?,” and “On Monday through Friday about what time do you usually last eat or drink something before going to bed?” These last 2 questions were used to calculate the time that participants reported “breaking fast” after going to bed and waking up. According to the American Heart Association, a commonly used definition of breakfast in epidemiologic studies is the consumption of food or beverage (excluding water) between 05:00 and 09:00 (28). Because very few participants (1.6%) ate before 05:00, we used the 07:00 cutoff in categorizing the breakfast timing variable (before 07:00; 07:00–09:00; and after 09:00).
Measurement of covariables and cardiometabolic risk factors
Information on age, sex, race, marital status, household income, education, smoking status, alcohol intake, exercise intensity, depressive symptoms, BMI, hypertension, and hypercholesterolemia was collected at the baseline visit. Of note, only 2 options for sex were presented to participants at baseline: female or male. To ascertain information on race, participants were asked to respond to the following prompt: “Please look at this card and tell me which best describes your race: White, Black, American Indian/Alaskan native, Asian/Pacific Islander, Other.” We created a new dichotomous variable for race (largest racial group in the CHS: no, yes) because some categories of the original race variable contained too few participants for analysis. Repeated assessment of demographic characteristics, lifestyle habits, prescription medication use, health conditions, anthropometric measures, and laboratory values occurred during annual study visits. Hypertension was defined in the CHS as seated average systolic blood pressure ≥160 mm Hg or seated average diastolic blood pressure ≥95 mm Hg or use of antihypertensive medication with self-reported history of hypertension. Hypercholesterolemia was defined as total cholesterol ≥240 mg/dL as per the National Cholesterol Education Program Adult Treatment Panel III guidelines (29). Information on prescription medication use during the 2 preceding weeks was collected directly from the medication containers at annual clinic visits in the first 10 y and by semiannual telephone contact thereafter. Fasting glucose was measured at clinic visits in 1989–1990, 1992–1993, 1996–1997, 1998–1999, and 2005–2006; nonfasting glucose was measured in 1994–1995. We created a new dichotomous variable for fasting glucose (<100 mg/dL, ≥100 mg/dL) because a value of ≥100 mg/dL is used to diagnose impaired fasting glucose, which increases an individual's risk of progressing to T2DM (30).
Ascertainment of incident T2DM
Incident T2DM was defined as initiating use of an insulin or oral hypoglycemic medication, measured fasting (≥8 h) glucose ≥126 mg/dL, random (<8 h fasting) glucose ≥200 mg/dL, or the existence of ≥2 inpatient (i.e., hospital, nursing home, or home health services), ≥3 outpatient (outpatient or carrier health services), or ≥1 inpatient and ≥1 outpatient International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) Medicare claim codes for diabetes (prefix 250.xx) over a 2-y period (31). Time-to-incident T2DM was defined as the time in days from the 1992–1993 CHS clinic visit to the earliest of diabetes identified by the CHS data or by CMS records.
Statistical analysis
All statistical analyses were conducted using Stata statistical software version 14.2 (StataCorp LP, 2016), according to an analysis plan developed a priori. All analyses were conducted at a 2-tailed α level of 0.05. We grouped participants based on breakfast frequency pattern (nondaily breakfast consumer: participants who reported eating breakfast “never,” “some days,” “rarely,” or “weekends only”; daily breakfast consumer: “every day”) and breakfast timing pattern (before 07:00, 07:00–09:00, after 09:00) and summarized baseline characteristics, comparing the groups for each breakfast frequency and timing variable using t tests, 1-factor ANOVA, Wilcoxon's rank-sum tests, or Kruskal–Wallis tests for continuous variables (depending on the distribution) and chi-square tests for categorical variables.
To quantify the risk of incident T2DM associated with eating timing and frequency patterns, we used multivariable-adjusted Cox proportional hazards regression models to estimate multivariable-adjusted hazard ratios (aHRs) and 95% CIs. Follow-up time was calculated from the date of the 1992–1993 visit until the date of first diagnosis of T2DM, loss to follow-up, or death, whichever came first; all follow-up was administratively censored in 2010. The proportional hazards assumption was verified using Schoenfeld residuals. In the basic multivariable model 1, we adjusted for age (y) and sex (female, male). In multivariable model 2, we further adjusted for demographic factors: marital status (married, not married), largest racial group in the CHS (no, yes), education (≤high school graduation, >high school graduation), and annual household income (<$25,000, $25,000–$49,999, ≥$50,000). In multivariable model 3, we further adjusted for lifestyle and mental health factors: smoking status (never, former, current), alcohol (number of alcoholic beverages per week: 0, <7, 7–14, >14), exercise intensity (none/low, moderate/high), and depression score [Centre for Epidemiological Studies-Depression Scale (CES-D) score (32, 33); normal: <10; at risk of clinical depression: ≥10 (33)]. In multivariable model 4 (our main model reported for this study), we further adjusted for the dietary factors: fruit and vegetable intake (≥3 servings/d; yes, no), snacks after dinner (yes, no), and meal frequency (<3, ≥3 meals/d). In models in which breakfast timing was the main exposure, we also adjusted for daily breakfast consumption (yes, no). In models in which daily breakfast consumption was the main exposure, we also adjusted for breakfast timing (before 07:00, 07:00–09:00, after 09:00). We examined whether the relations between skipping breakfast and incident T2DM and breakfast timing and incident T2DM depended on sex, BMI, and impaired fasting glucose by conducting subgroup analyses, which were decided upon a priori. Interactions were tested by including interaction terms of the exposure (breakfast frequency or timing) with the cardiometabolic risk factor within the Cox models; however, our a priori objective and power calculations were for a stratified analysis and were potentially not powered for further tests of interaction. To mitigate potential survivor bias, we repeated all analyses excluding participants who were >70 y old at baseline.
BMI is a potential mediator and was continuously adjusted for in subsequent Cox regression models to test whether our results were materially altered by its inclusion, which is a method of examining potential mediation that was previously used (34–37). Assessment of most covariates was repeated over the follow-up period, so we were able to use time-varying covariates in our Cox proportional hazards regression models to assess long-term effects and minimize within-person measurement error. Time-varying covariables were updated at each available time point and the most recent nonmissing values were carried forward if values were missing at subsequent visits. Less than 2% of data were missing for any variable so we conducted complete case analysis.
Results
In this sample of 3747 older American adults without prevalent T2DM, 547 cases of incident T2DM were documented during a median follow-up of 10.7 y (maximum follow-up was 17.6 y). Most participants reported eating breakfast daily (85.5%), whereas few participants (2.8%) reported skipping breakfast daily. At baseline, people who did not consume breakfast daily were younger than those who did consume breakfast daily; were 65.0% females; were more likely to be not in the largest racial group in the CHS (nonwhite); were less educated; were more likely unmarried; had lower annual household income; were more likely to smoke; exercised less intensely; had a higher number of depressive symptoms; had greater fasting glucose, fasting insulin, BMI, and waist circumference; and more often consumed snacks after dinner (Table 1). Participants who did not consume breakfast daily also consumed fewer meals per day and fewer servings of fruits and vegetables per day than participants who did consume breakfast daily. Breakfast timing (breaking fast later than 09:00) was similarly associated with the many baseline variables that have been shown to either increase or decrease risk of T2DM (Table 1).
TABLE 1.
Characteristics of the CHS participants at baseline by breakfast frequency and timing1
| Breakfast frequency | |||||||
|---|---|---|---|---|---|---|---|
| Not-daily3 | Daily | Breakfast timing (first eating occasion of the day2) | |||||
| Characteristics | (0–6 times/wk) (n = 545) | (7 times/wk) (n = 3202) | P value | Before 07:00 (n = 609) | 07:00–09:00 (n = 2733) | After 09:00 (n = 402) | P value |
| Age, y | 73.1 ± 4.6 | 75.2 ± 5.3 | <0.001 | 74.6 ± 5.3 | 75.0 ± 5.2 | 74.5 ± 5.5 | 0.419 |
| Sex | <0.001 | ||||||
| Female | 354 (65.0) | 1899 (59.3) | 0.013 | 319 (52.4) | 1691 (61.9) | 242 (60.2) | |
| Male | 191 (35.1) | 1303 (40.7) | 290 (47.6) | 1042 (38.1) | 160 (39.8) | ||
| Largest racial group in the CHS4 | <0.001 | <0.001 | |||||
| No | 179 (32.8) | 416 (13.0) | 83 (13.6) | 364 (13.3) | 146 (36.3) | ||
| Yes | 366 (67.2) | 2786 (87.0) | 526 (86.4) | 2369 (86.7) | 256 (63.7) | ||
| Education | <0.001 | <0.001 | |||||
| ≤High school | 342 (63.0) | 1655 (51.8) | 351 (57.9) | 1399 (51.3) | 246 (61.4) | ||
| >High school | 201 (37.0) | 1542 (48.2) | 255 (42.1) | 1331 (48.8) | 155 (38.7) | ||
| Household income, US$/y | <0.001 | <0.001 | |||||
| <25,000 | 356 (69.0) | 1660 (55.6) | 348 (61.3) | 1402 (54.9) | 263 (70.1) | ||
| 25,000–49,999 | 100 (19.4) | 855 (28.6) | 142 (25.0) | 734 (28.7) | 79 (21.1) | ||
| ≥50,000 | 60 (11.6) | 471 (15.8) | 78 (13.7) | 420 (16.4) | 33 (8.8) | ||
| Married | 313 (57.4) | 2226 (69.5) | <0.001 | 390 (64.0) | 1906 (69.8) | 241 (60.0) | <0.001 |
| Smoking status | <0.001 | 0.008 | |||||
| Never | 200 (36.8) | 1527 (47.7) | 284 (46.6) | 1276 (46.7) | 167 (41.5) | ||
| Former | 242 (44.6) | 1396 (43.6) | 265 (43.5) | 1198 (43.9) | 174 (43.3) | ||
| Current | 101 (18.6) | 278 (8.7) | 60 (9.9) | 256 (9.4) | 61 (15.2) | ||
| Alcohol use, drinks/wk | 0.154 | 0.112 | |||||
| None | 265 (48.8) | 1700 (53.1) | 325 (53.4) | 1409 (51.6) | 229 (57.3) | ||
| <7 | 204 (37.6) | 1069 (33.4) | 189 (31.0) | 957 (35.0) | 127 (31.8) | ||
| 7–14 | 38 (7.0) | 251 (7.8) | 58 (9.5) | 204 (7.5) | 27 (6.8) | ||
| >14 | 36 (6.6) | 180 (5.6) | 37 (6.1) | 161 (5.9) | 17 (4.3) | ||
| Exercise intensity | 0.022 | 0.008 | |||||
| No exercise | 65 (11.9) | 304 (9.5) | 55 (9.0) | 259 (9.5) | 55 (13.7) | ||
| Low | 257 (47.2) | 1417 (44.3) | 289 (47.5) | 1191 (43.6) | 193 (48.0) | ||
| Moderate | 185 (33.9) | 1149 (35.9) | 212 (34.8) | 995 (36.4) | 125 (31.1) | ||
| High | 38 (7.0) | 332 (10.4) | 53 (8.7) | 288 (10.5) | 29 (7.2) | ||
| Hypertension | 215 (39.5) | 1238 (38.7) | 0.728 | 222 (36.5) | 1073 (39.3) | 157 (39.1) | 0.434 |
| Hypercholesterolemia | 109 (20.0) | 583 (18.2) | 0.309 | 108 (17.7) | 511 (18.7) | 73 (18.2) | 0.842 |
| Fasting glucose, mg/dL | 98.9 ± 10.4 | 97.6 ± 9.7 | 0.003 | 97.5 ± 9.5 | 97.7 ± 9.9 | 98.4 ± 10.4 | 0.101 |
| Fasting insulin, IU/mL | 13.4 [10.4–17.4] | 12.4 [10.4–16.4] | 0.002 | 12.4 [9.4–15.4] | 12.4 [10.4–16.4] | 13.4 [10.4–18.4] | <0.001 |
| BMI, kg/m2 | 27.6 ± 5.5 | 26.2 ± 4.3 | <0.001 | 26.3 ± 4.3 | 26.3 ± 4.5 | 27.1 ± 5.3 | <0.001 |
| Waist circumference, cm | 98.3 ± 14.5 | 95.7 ± 12.5 | <0.001 | 96.1 ± 13.0 | 96.0 ± 12.8 | 96.7 ± 13.4 | 0.378 |
| CES-D score | <0.001 | 0.019 | |||||
| Low (<10) | 427 (78.4) | 2712 (84.7) | 523 (85.9) | 2295 (84.0) | 319 (79.4) | ||
| High (≥10) | 118 (21.7) | 489 (15.3) | 86 (14.1) | 437 (16.0) | 83 (20.7) | ||
| Fruits and vegetables, servings/d | <0.001 | 0.001 | |||||
| <3 | 485 (89.8) | 2582 (81.1) | 482 (79.8) | 2227 (82.0) | 355 (88.5) | ||
| ≥3 | 55 (10.2) | 601 (18.9) | 122 (20.2) | 488 (18.0) | 46 (11.5) | ||
| Breakfast timing | <0.001 | — | |||||
| Before 07:00 | 77 (14.2) | 532 (16.6) | — | — | — | ||
| 07:00–09:00 | 308 (56.6) | 2425 (75.8) | — | — | — | ||
| After 09:00 | 159 (29.2) | 243 (7.6) | — | — | — | ||
| Meal frequency, n/d | 2.0 ± 0.6 | 2.8 ± 0.6 | <0.001 | 2.7 ± 0.6 | 2.7 ± 0.5 | 2.3 ± 0.6 | <0.001 |
| Snacks after dinner | 350 (64.9) | 1904 (59.9) | 0.026 | 310 (51.5) | 1666 (61.3) | 275 (69.4) | <0.001 |
Values are n (%), mean ± SD, or median [IQR]. Data were analyzed using t tests, 1-factor ANOVA, Wilcoxon's rank-sum tests, or Kruskal–Wallis tests when the dependent variable was continuous (depending on the distribution) and chi-square tests when the dependent variable was categorical. CES-D, Centre for Epidemiological Studies-Depression Scale; CHS, Cardiovascular Health Study.
Three participants did not report the usual time of their first eating occasion of the day. Therefore, 3744 participants were included in the breakfast timing analysis.
A total of 103 participants reported skipping breakfast daily. Therefore, this group was too small to analyze separately.
The largest racial group in our sample of CHS participants was white.
No association was observed between eating breakfast less than daily (never, rarely, weekends only, and some days) and risk of T2DM either before (aHR: 0.92; 95% CI: 0.70, 1.23) or after adjustment for BMI (aHR: 0.89; 95% CI: 0.67, 1.19) (Table 2). Likewise, no association was observed between eating breakfast irregularly (never, rarely, weekends only) and risk of T2DM either before (aHR: 0.74; 95% CI: 0.50, 1.10) or after adjustment for BMI (aHR: 0.73; 95% CI: 0.49, 1.08) as compared with eating breakfast regularly (some days, every day) (Table 3). Similarly, no associations between eating breakfast irregularly and risk of T2DM were observed in the subgroups of males, females, those with BMI <30, with BMI ≥30, with impaired fasting glucose, or without impaired fasting glucose (Table 4).
TABLE 2.
Skipping breakfast and multivariable-adjusted risk of T2DM1
| Not-daily breakfast consumer (0–6 times/wk) (n = 545) | Daily breakfast consumers (7 times/wk) (n = 3202) | P value | |
|---|---|---|---|
| T2DM cases, n | 82 | 465 | |
| Person-time, y (%) | 5762.867 (14.6) | 33,694.949 (85.4) | |
| Adjusted for age and sex | 1.02 (0.80, 1.30) | 1.00 (Referent) | 0.879 |
| + Demographic factors2 | 0.89 (0.69, 1.15) | 1.00 (Referent) | 0.390 |
| + Lifestyle and mental health factors3 | 0.89 (0.69, 1.15) | 1.00 (Referent) | 0.373 |
| + Dietary factors4 | 0.92 (0.70, 1.23) | 1.00 (Referent) | 0.587 |
| Additional adjustment for potential mediator | |||
| + BMI5 | 0.89 (0.67, 1.19) | 1.00 (Referent) | 0.444 |
Values are HRs (95% CIs) from a multivariable-adjusted analysis using Cox proportional hazards regression unless otherwise indicated. Time-varying covariables were updated at each available time point. T2DM, type 2 diabetes mellitus.
In addition to age (y) and sex (female, male), this model adjusted for demographic factors: marital status (married, not married), largest racial group in the Cardiovascular Health Study (no, yes), education (≤high school graduation, >high school graduation), and annual household income (<$25,000, $25,000–$49,999, ≥$50,000).
In addition to age, sex, and demographic factors, this model further adjusted for lifestyle and mental health factors: smoking status (never, former, current), alcohol (number of alcoholic beverages per week: 0, <7, 7–14, >14), exercise intensity (none/low, moderate/high), and Centre for Epidemiological Studies-Depression Scale score (high: ≥10; low: <10).
In addition to age, sex, demographic, lifestyle, and mental health factors, this model further adjusted for dietary factors: fruit and vegetable intake (3 servings/d; yes, no), breakfast timing (before 07:00, 07:00–09:00, after 09:00), meal frequency (<3, ≥3 meals/d), and snacks after dinner (yes, no). This model is the main model reported for this analysis.
In addition to age, sex, demographic, lifestyle, mental health, and dietary factors, this model further adjusted for BMI (kg/m2).
TABLE 3.
Irregular breakfast consumption and multivariable-adjusted risk of T2DM1
| Irregular breakfast consumer: rarely, weekends only, never (n = 272) | Regular breakfast consumer: every day, some days (n = 3475) | P value | |
|---|---|---|---|
| T2DM cases, n | 36 | 511 | |
| Person-time, y (%) | 2908.038 (7.4) | 36,549.777 (92.6) | |
| Adjusted for age and sex | 0.88 (0.63, 1.23) | 1.00 (Referent) | 0.452 |
| + Demographic factors2 | 0.78 (0.55, 1.11) | 1.00 (Referent) | 0.170 |
| + Lifestyle and mental health factors3 | 0.76 (0.53, 1.08) | 1.00 (Referent) | 0.124 |
| + Dietary factors4 | 0.74 (0.50, 1.10) | 1.00 (Referent) | 0.135 |
| Additional adjustment for potential mediator | |||
| + BMI5 | 0.73 (0.49, 1.08) | 1.00 (Referent) | 0.119 |
Values are HRs (95% CIs) from a multivariable-adjusted analysis using Cox proportional hazards regression unless otherwise indicated. Time-varying covariables were updated at each available time point. T2DM, type 2 diabetes mellitus.
In addition to age (y) and sex (female, male), this model adjusted for demographic factors: marital status (married, not married), largest racial group in the Cardiovascular Health Study (no, yes), education (≤high school graduation, >high school graduation), and annual household income (<$25,000, $25,000–$49,999, ≥$50,000).
In addition to age, sex, and demographic factors, this model further adjusted for lifestyle and mental health factors: smoking status (never, former, current), alcohol (number of alcoholic beverages per week: 0, <7, 7–14, >14), exercise intensity (none/low, moderate/high), and Centre for Epidemiological Studies-Depression Scale score (high: ≥10; low: <10).
In addition to age, sex, demographic, lifestyle, and mental health factors, this model further adjusted for dietary factors: fruit and vegetable intake (3 servings/d; yes, no), breakfast timing (before 07:00, 07:00–09:00, after 09:00), meal frequency (<3, ≥3 meals/d), and snacks after dinner (yes, no). This model is the main model reported for this analysis.
In addition to age, sex, demographic, lifestyle, mental health, and dietary factors, this model further adjusted for BMI (kg/m2).
TABLE 4.
Breakfast frequency and multivariable-adjusted risk of type 2 diabetes mellitus stratified by risk factors1
| Cases, n | Person time, y (%) | Not-daily breakfast consumer (0–6 times/wk) | P value | Daily breakfast consumer (7 times/wk) | Interaction P value | |
|---|---|---|---|---|---|---|
| Sex | 0.431 | |||||
| Female | 340 | 25,096.986 (63.6) | 0.83 (0.58, 1.19) | 0.312 | 1.00 (Referent) | |
| Male | 207 | 14,360.83 (36.4) | 1.11 (0.70, 1.76) | 0.661 | 1.00 (Referent) | |
| Baseline BMI, kg/m2 | 0.459 | |||||
| <30 | 386 | 32,680.172 (82.9) | 0.78 (0.54, 1.12) | 0.181 | 1.00 (Referent) | |
| ≥30 | 161 | 6758.79 (17.1) | 1.16 (0.72, 1.86) | 0.539 | 1.00 (Referent) | |
| Baseline impaired fasting glucose | 0.995 | |||||
| No | 206 | 26,695.86 (67.7) | 0.74 (0.47, 1.17) | 0.198 | 1.00 (Referent) | |
| Yes | 341 | 12,761.955 (32.3) | 0.98 (0.68, 1.42) | 0.933 | 1.00 (Referent) |
Values are HRs (95% CIs) from a multivariable-adjusted analysis using Cox proportional hazards regression unless otherwise indicated. Time-varying covariables were updated at each available time point. P values for interaction terms between breakfast frequency and each risk factor were not significant (all interaction P values > 0.43). Models adjusted for age (y), sex (female, male; except when stratified by sex), marital status (married, not married), largest racial group in the Cardiovascular Health Study (no, yes), education (≤high school graduation, >high school graduation), annual household income (<$25,000, $25,000–$49,999, ≥$50,000), smoking status (never, former, current), alcohol (number of alcoholic beverages per week: 0, <7, 7–14, >14), exercise intensity (none/low, moderate/high), Centre for Epidemiological Studies-Depression Scale score (high: ≥10; low: <10), fruit and vegetable intake (3 servings/d; yes, no), breakfast timing (before 07:00, 07:00–09:00, after 09:00), snacks after dinner (yes, no), and meal frequency (<3, ≥3 meals/d).
Approximately 73% of participants broke fast between 07:00 and 09:00. Participants who broke fast after 09:00 had 29% lower risk of T2DM than participants who broke fast between 07:00 and 09:00 after adjustment for demographic, lifestyle, mental health, and dietary factors (aHR: 0.71; 95% CI: 0.51, 0.99) (Table 5). The strength of the evidence was decreased after adjustment for the potential mediator of BMI (aHR: 0.72; 95% CI: 0.51, 1.00). In stratified analyses, among participants with impaired fasting glucose at baseline, those who reported breaking fast after 09:00 had a 39% lower risk of T2DM than those who reported breaking fast between 07:00 and 09:00 (aHR: 0.61; 95% CI: 0.39, 0.95), whereas this association was not significant among participants who did not have impaired fasting glucose at baseline (aHR: 0.83; 95% CI: 0.50, 1.38) (Table 6). No association was observed between breakfast timing and risk of T2DM within the subgroups of sex and baseline BMI.
TABLE 5.
Breakfast timing and multivariable-adjusted risk of T2DM1
| Breakfast timing (first eating occasion of the day) | |||||
|---|---|---|---|---|---|
| Before 07:00 (n = 609) | P value | 07:00–09:00 (n = 2733) | After 09:00 (n = 402) | P value | |
| T2DM cases, n | 91 | 405 | 51 | ||
| Person-time, y (%) | 6288.493 (16.0) | 29,006.667 (73.6) | 4126.157 (10.5) | ||
| Adjusted for age and sex | 1.03 (0.82, 1.30) | 0.789 | 1.00 (Referent) | 0.89 (0.66, 1.19) | 0.421 |
| + Demographic factors2 | 0.99 (0.78, 1.25) | 0.939 | 1.00 (Referent) | 0.74 (0.54, 1.01) | 0.061 |
| + Lifestyle and mental health factors3 | 0.99 (0.79, 1.26) | 0.959 | 1.00 (Referent) | 0.72 (0.53, 0.99) | 0.042 |
| + Dietary factors4 | 1.00 (0.79, 1.27) | 0.989 | 1.00 (Referent) | 0.71 (0.51, 0.99) | 0.041 |
| Additional adjustment for potential mediator | |||||
| + BMI5 | 1.01 (0.80, 1.28) | 0.950 | 1.00 (Referent) | 0.72 (0.51, 1.00) | 0.047 |
Values are HRs (95% CIs) from a multivariable-adjusted analysis using Cox proportional hazards regression unless otherwise indicated. Time-varying covariables were updated at each available time point. T2DM, type 2 diabetes mellitus.
In addition to age (y) and sex (female, male), this model adjusted for demographic factors: marital status (married, not married), largest racial group in the Cardiovascular Health Study (no, yes), education (≤high school graduation, >high school graduation), and annual household income (<$25,000, $25,000–$49,999, ≥$50,000).
In addition to age, sex, and demographic factors, this model further adjusted for lifestyle and mental health factors: smoking status (never, former, current), alcohol (number of alcoholic beverages per week: 0, <7, 7–14, >14), exercise intensity (none/low, moderate/high), and Centre for Epidemiological Studies-Depression Scale score (high: ≥10; low: <10).
In addition to age, sex, demographic, lifestyle, and mental health factors, this model further adjusted for dietary factors: fruit and vegetable intake (3 servings/d; yes, no), daily breakfast consumption (yes, no), meal frequency (<3, ≥3 meals/d), and snacks after dinner (yes, no). This model is the main model reported for this analysis.
In addition to age, sex, demographic, lifestyle, mental health, and dietary factors, this model further adjusted for BMI (kg/m2).
TABLE 6.
Breakfast timing and multivariable-adjusted risk of type 2 diabetes mellitus stratified by risk factors1
| Cases, n | Person time, y (%) | Before 07:00 (n = 609) | P value | 07:00–09:00 (n = 2733) | After 09:00 (n = 402) | P value | Interaction P value | |
|---|---|---|---|---|---|---|---|---|
| Sex | 0.744 | |||||||
| Female | 340 | 25,096.986 (63.6) | 0.96 (0.69, 1.32) | 0.787 | 1.00 (Referent) | 0.74 (0.49, 1.11) | 0.144 | |
| Male | 207 | 14,360.83 (36.4) | 1.03 (0.73, 1.47) | 0.851 | 1.00 (Referent) | 0.71 (0.41, 1.23) | 0.217 | |
| Baseline BMI, kg/m2 | 0.776 | |||||||
| <30 | 386 | 32,680.172 (82.9) | 1.04 (0.79, 1.37) | 0.776 | 1.00 (Referent) | 0.70 (0.46, 1.05) | 0.086 | |
| ≥30 | 161 | 6758.79 (17.1) | 0.91 (0.57, 1.47) | 0.706 | 1.00 (Referent) | 0.70 (0.41, 1.19) | 0.191 | |
| Baseline impaired fasting glucose | 0.476 | |||||||
| No | 206 | 26,695.86 (67.7) | 1.06 (0.72, 1.55) | 0.780 | 1.00 (Referent) | 0.83 (0.50, 1.38) | 0.475 | |
| Yes | 341 | 12,761.955 (32.3) | 0.99 (0.73, 1.34) | 0.923 | 1.00 (Referent) | 0.61 (0.39, 0.95) | 0.027 |
Values are HRs (95% CIs) from a multivariable-adjusted analysis using Cox proportional hazards regression unless otherwise indicated. Time-varying covariables were updated at each available time point. P values for interaction terms between breakfast timing and each risk factor were not significant (all interaction P values > 0.48). Models adjusted for age (y), sex (female, male; except when stratified by sex), marital status (married, not married), largest racial group in the Cardiovascular Health Study (no, yes), education (≤high school graduation, >high school graduation), annual household income (<$25,000, $25,000–$49,999, ≥$50,000), smoking status (never, former, current), alcohol (number of alcoholic beverages per week: 0, <7, 7–14, >14), exercise intensity (none/low, moderate/high), Centre for Epidemiological Studies-Depression Scale score (high: ≥10; low: <10), fruit and vegetable intake (3 servings/d; yes, no), daily breakfast consumption (yes, no), snacks after dinner (yes, no), and meal frequency (<3, ≥3 meals/d).
Discussion
To the best of our knowledge, the present study is the first to assess whether breakfast frequency and timing patterns are related to an increased risk of T2DM in a large cohort of older males and females (≥65 y of age). The majority of CHS participants consumed breakfast daily and broke fast between 07:00 and 09:00. This pattern was associated with higher socioeconomic status, a healthy lifestyle, and lower levels of cardiometabolic risk indicators. We did not detect an association between eating breakfast daily (breakfast frequency) and risk of T2DM; however, we observed a reduced risk of T2DM among participants who broke fast after 09:00 (breakfast timing), especially among those with baseline impaired fasting glucose.
Our finding that skipping breakfast is cross-sectionally associated with fasting glucose, fasting insulin, BMI, and waist circumference is in alignment with some previous studies that found associations between breakfast frequency and indicators of metabolic and hormonal perturbation (19, 38–40). Although not conducted exclusively in older adults, 1 previous prospective study found that skipping meals to lose weight was associated with several cardiometabolic risk factors at baseline, which is concordant with the present study (24). Our study is also in agreement with previous studies that demonstrated breakfast eaters tend to have a healthier lifestyle and be of higher socioeconomic status (34–37, 41). Of note, the addition of demographic, lifestyle, and dietary factors to the Cox regression models that included age and sex resulted in a change in direction of estimates in addition to a >10% change in estimate size, indicating that the relations between these factors are multidirectional, impactful, and complex.
In a recent meta-analysis of studies on breakfast skipping and T2DM, ever skipping breakfast was associated with an increased risk of T2DM when compared with never skipping breakfast (25). However, unlike the present study, participants in the 6 studies included in the meta-analysis were, on average, middle-aged, and none of the included studies were conducted exclusively in older adults. The first prospective study to examine breakfast consumption and risk of T2DM among females, which was included in the meta-analysis, conducted a sensitivity analysis and found the direct association between irregular breakfast consumption and T2DM risk was observed only among younger females (<65 y of age) and not among older females (≥65 y of age) (Nurses’ Health Study) (36), which is consistent with our findings.
Compared with younger individuals, older adults have reduced individual organ and tissue mass and tissue-specific organ metabolic rate, which contribute to a reduction in resting metabolic rate and, in turn, changes in body composition (42). Therefore, older adults may have different metabolic and nutritional needs, and may differ in rates of glucose metabolism compared with younger individuals, which may partly explain why our results differed from those of studies in younger adults. In addition, CHS participants were older than the traditional age of retirement in the United States at baseline. Reduced work-related stress and improved circadian rhythms in retirement may have had a protective effect against the development of T2DM that outweighed the effect of skipping breakfast (43, 44). In addition, survivor bias may have offset any adverse cardiometabolic effects conferred by eating timing and frequency patterns. However, our sensitivity analysis excluding the oldest participants did not generate materially altered results (data not shown).
To the best of our knowledge, the present study was the first to assess whether breakfast timing was associated with the risk of developing T2DM in any age group. Several possible explanations exist for why breaking fast after 09:00 was associated with a lower risk of T2DM in the present study, including in the subgroup of impaired fasting glucose at baseline. Firstly, the Dawn and Somogyi phenomena may have been present among participants, especially those at cardiometabolic risk (45, 46). Compared with eating later, eating earlier in the morning could have resulted in daily elevated postprandial blood glucose, further disrupting metabolic homeostasis and increasing the risk of T2DM. Secondly, people with metabolic risk factors such as older age, high BMI, and hyperglycemia may have been more likely to adopt practices usually recommended by a dietitian, such as eating breakfast and eating regularly, which could have generated a reverse causation–type effect such as indication bias. Thirdly, confounding by demographic and lifestyle factors that were unmeasured in the CHS, such as sleep duration, may have been present.
Strengths and limitations
The present study included a large sample of older adults and females [who were historically underrepresented in epidemiologic and clinical research (47, 48)], a long duration of follow-up, prospective design, exclusion of prevalent T2DM, a wide range of socioeconomic and health statuses, and comprehensive repeated assessment of various demographic, lifestyle, dietary, and health-related factors, which we were able to control for in our Cox regression models. Including time-dependent factors in our analysis allowed us to minimize misclassification bias and to adjust for time-dependent confounding. We were also able to supplement the follow-up of incident T2DM with CMS claims data to ensure capture was complete. However, the present study had some limitations that should be considered. Breakfast frequency and timing were assessed at 1 point in time and may have changed within the 18-y follow-up period, so we were not able to include time-varying exposures (breakfast frequency and timing) in our analysis, which would have been our ideal method (49–51). As a result, nondifferential misclassification error may have been present, which would have attenuated true associations. Breakfast frequency and timing patterns were self-reported, and interpretation may have differed between participants. To our knowledge, a standard method for assessing meal timing and frequency by questionnaire has not been validated to date. In addition, we did not have details on the composition, quality, or variety of breakfast foods consumed. A limited number of CHS participants reported “never” eating breakfast; thus, we cannot exclude a possible increased risk of T2DM with never eating breakfast in older adults. We also cannot rule out possible residual confounding by unknown or unmeasured factors (e.g., circadian rhythms, light/dark exposure, sleep pattern, food insecurity, and stress). Glucose measures were not available every year, and thus some cases of T2DM may have been missed. Additional prospective longitudinal cohort and clinically based observational studies that enroll participants of diverse ages, genders, races, and ethnic groups in different countries are needed to address the limitations and confirm the findings of the present study.
Conclusions
Our study has provided novel evidence that a later (after 09:00) daily first eating occasion time may be associated with a reduction in long-term risk of incident T2DM among older adults with impaired fasting glucose. However, because the number of cases in the present study was modest and we did not observe a significant interaction P value with impaired fasting glucose, further studies are required to provide clinical evidence and confirmation of the biological mechanism at play. If replicated in other studies in older adults, the findings from our study could be used as evidence in the future when dietary guideline panels develop recommendations for breakfast frequency and timing for older adults.
Supplementary Material
ACKNOWLEDGEMENTS
We sincerely thank the CHS for providing access to the data.
The authors’ responsibilities were as follows—LEC: conceived the study idea and design, procured the data, had full access to all the data presented, and takes responsibility for the integrity of the data and accuracy of the data analysis; ASC: performed the statistical analyses and drafted the manuscript with LEC and RAM; and all authors: contributed to additional drafts of the manuscript and read and approved the final manuscript. The authors report no conflicts of interest.
Notes
Supported by National Heart, Lung, and Blood Institute (NHLBI) contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, and 75N92021D00006 and grants U01HL080295 and U01HL130114, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by National Institute on Aging grants R01AG023629 and K24AG065525. A full list of principal Cardiovascular Health Study investigators and institutions can be found at https://CHS-NHLBI.org. LEC was supported by a Dalhousie University Internal Medicine Research Foundation Junior Faculty award. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The funding sources were not involved in data collection, data analysis, or manuscript drafting.
Supplemental Figure 1 is available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/ajcn/.
Abbreviations used: aHR, multivariable-adjusted hazard ratio; CHS, Cardiovascular Health Study; CMS, Centers for Medicare and Medicaid Services; T2DM, type 2 diabetes mellitus.
Contributor Information
Allie S Carew, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada; Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Rania A Mekary, School of Pharmacy, Massachusetts College of Pharmacy and Health Sciences University, Boston, MA, USA.
Susan Kirkland, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada; Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Olga Theou, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada; School of Physiotherapy, Dalhousie University, Halifax, Nova Scotia, Canada.
Ferhan Siddiqi, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada.
Robin Urquhart, QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada; Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Michelle George, Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Chris Blanchard, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada.
Mary L Biggs, Department of Biostatistics, University of Washington, Seattle, WA, USA.
Luc Djoussé, Division on Aging, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA.
Kenneth J Mukamal, Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Brookline, MA, USA.
Leah E Cahill, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada; QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada; Department of Community Health and Epidemiology, Dalhousie University, Halifax, Nova Scotia, Canada.
Data Availability
Data described in the article, code book, and analytic code will not be made available because the data are not publicly available from the CHS.
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This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data described in the article, code book, and analytic code will not be made available because the data are not publicly available from the CHS.