Oral health problems such as tooth loss, periodontal disease and dry mouth are very common conditions in older people (≥65 years) and have significant impacts on health(Reference Ramsay, Whincup and Watt1). Poor oral health in older age has been associated with the development of chronic conditions, such as disability, CVD and increased risk of mortality(Reference Kotronia, Wannamethee and Papacosta2–Reference Joshy, Arora and Korda4). Moreover, older people are at risk of following a poor diet, particularly avoidance of specific foods or frequent consumption of unhealthy food, which can adversely influence their nutritional intake, quality of life and general well-being(Reference Moynihan5). Poor oral health can play an important part in changes in dietary habits and variety of food intake, but at the same time an unhealthy diet may also affect oral health(Reference Moynihan5), suggesting a bidirectional relationship between poor oral health and diet in older people.
Tooth loss, in particular, has been found to be associated with low intakes of fruits and vegetables, vitamin C, vitamin E, as well as higher consumption of processed foods and fatty acids in adults (> 18 years)(Reference Hung, Colditz and Joshipura6–Reference Watson, McGowan and McCrum10). It is possible that the association between oral health and fat intake may be due to higher consumption of processed meat which is a source of saturated fat which is easy to chew and requires little preparation(Reference Savoca, Arcury and Leng11–Reference Rohrmann and Linseisen13). Tooth loss was also associated with poor diet quality and low protein intake in older people(Reference Ritchie, Joshipura and Hung7,Reference Savoca, Arcury and Leng12,Reference Zhu and Hollis14,Reference Bomfim, de Souza and Corrente15) . However, these findings were not consistent in all studies(Reference Gaewkhiew, Sabbah and Bernabé16,Reference Ervin and Dye17) . Furthermore, having a good oral function (optimal number of teeth and ability to chew) was found to be associated with higher consumption of protein and vegetables and decreased consumption of energy content and fats in some studies(Reference Zhu and Hollis14,Reference Iwasaki, Yoshihara and Ogawa18,Reference Sheiham, Steele and Marcenes19) , but similar to tooth loss, results were not consistently reported by other studies(Reference Ervin and Dye17,Reference Iwasaki, Kimura and Yoshihara20) . Moreover, periodontal disease was associated with low intake of percentage of energy content from fat in adults(Reference Hamasaki, Kitamura and Kawashita21), whereas dry mouth, particularly hyposalivation, was associated with lower consumption of fruits and vegetables in older people(Reference Iwasaki, Yoshihara and Ito22). Additionally, studies have found that poor diet quality and increased intake of saturated fat were associated with the progression of periodontal disease (chronic gum disease) and tooth loss in older adults(Reference Iwasaki, Borgnakke and Ogawa23,Reference Iwasaki, Manz and Moynihan24) . Furthermore, diets rich in antioxidants and low in saturated fats led to an improvement in the severity of periodontal disease in adults(Reference Woelber, Bremer and Vach25).
These studies support the presence of a bidirectional association of poor oral health with diet quality and intake. However, there is no consensus on the specific nutrients associated with poor oral health in older people. Furthermore, the majority of previous studies have focused on tooth loss and periodontal disease. There is lack of evidence on the associations of other markers of oral health such as dry mouth and self-rated oral health with dietary intake in older people. Hence, we aimed to examine the associations of objectively and subjectively assessed oral health markers with diet quality and dietary intake, as well as changes in diet quality over 10 years, and their associations with poor oral health in samples of community-dwelling older people in the UK and USA.
Subjects and methods
We used data from the British Regional Heart Study (BRHS) in the UK and the Health, Aging and Body Composition (HABC) Study in the USA. The study is reported according to the STROBE guidelines.
The British Regional Heart Study
This is an ongoing cohort study which at baseline comprised 7735 males aged 40–59 years. Individuals were recruited from twenty-four towns across the UK in 1978–80 and have been followed up since(Reference Lennon, Ramsay and Papacosta26). For the present study, data from the 20-year and 30-year follow-up were used. In 1998–2000, 4252 males (77 % response rate) aged 60–79 years participated in the 20-year follow-up physical examination and completed postal questionnaires (see Fig. 1). A 30-year follow-up of the cohort was undertaken in 2010–2012 and was attended by 2147 surviving participants aged 71–92 years. Participants completed a postal questionnaire (68 % response rate), participated in the physical and oral health examination (n 1722) (55 % response rate) and had blood samples taken(Reference Lennon, Ramsay and Papacosta26). Examinations were conducted by a trained research nurse and took place in each of the twenty-four towns of the study in community-based venues. Information on missing data and attrition is presented in online Supplementary Fig. S1. Ethical approval was provided by the National Research Ethics Service Committee, London. Written informed consent was obtained from individuals for their participation in the study, according to the Declaration of Helsinki(27).
The Health, Aging and Body Composition Study
The HABC Study is a prospective population-based study investigating deterioration in physical function of older individuals and how changes in body composition influence health in older age. Baseline data collection took place in 1997–1998, where 3075 White and African American males and females aged 70–79 years were recruited (see Fig. 2). Random selection of White participants was performed through Medicare, whereas African American were selected through neighbourhoods with a ZIP code around Memphis and Pittsburgh(Reference Stewart, Weyant and Garcia28). At baseline, individuals who were not able to walk 0·25 miles or climb ten steps were excluded from the study. In year 2 (1998–1999), data from males and females aged 71–80 years (n 3075) were used. Measurements included an oral health (n 1975) and physical assessment, collection of blood samples and completion of questionnaires. Oral examination was conducted at the research centre by a dental hygienist or a periodontist. Details on missing data and attrition are presented in online Supplementary Fig. S2. All participants provided written informed consent. Ethical approval was provided by University of Pittsburgh, University of Tennessee – Memphis, UCSF and NIH(Reference Stewart, Weyant and Garcia28).
Flow chart of British Regional Heart Study participants followed up from 1998–2000 until 2010–2012.
Flow chart of the Health, Aging and Body Composition Study participants aged 71–80 years at year 2. * Eligible for a periodontal disease assessment.
Oral health markers
In both studies, oral health was assessed through an oral examination and completion of questionnaires and included objectively and subjectively assessed oral health markers. Objective markers (oral examination) included count of natural teeth and periodontal disease measures (loss of attachment and pocket depth)(Reference Ramsay, Whincup and Watt1,Reference Weyant, Newman and Kritchevsky29) . Details of the examination of periodontal disease can be found elsewhere(Reference Ramsay, Whincup and Watt1,Reference Weyant, Newman and Kritchevsky29) . Subjective (self-rated) oral health markers (questionnaires) comprised self-rated oral health, dry mouth, difficulty eating due to mouth, teeth or dentures problems, sensitivity to hot/cold/sweets and limitation of food due to gum problems. In the HABC Study, one question was used to assess dry mouth (dry mouth when eating). In the BRHS, the Xerostomia Inventory Scale, which consists of eleven questions, was used to measure dry mouth and groups (number of symptoms) were created according to the responses provided(Reference Thomson, Chalmers and Spencer30).
Dietary intake and diet quality
In the BRHS, dietary intake was assessed at two time points at the 20-year (age 60–79 years) and 30-year (aged 71–92 years) follow-up by a postal FFQ which was developed for use in the WHO’s Monitoring Trends and Determinants in Cardiovascular Disease Survey, and has been validated in the British population(Reference Yarnell, Fehily and Milbank31). For the measurement of diet quality, the Elderly Dietary Index (EDI) was utilised, which is based on the US Modified MyPyramid for Older Adults and other recommendations for older people(Reference Parsons, Papachristou and Atkins32). The EDI score comprises nine food components (meat, fish and seafood, legumes, fruit, vegetables, cereals, bread, olive oil and dairy)(Reference Parsons, Papachristou and Atkins32,Reference Atkins, Whincup and Morris33) . Consumption of each food group was used to create the EDI score to indicate diet quality. Further details on the FFQ and EDI score can be found elsewhere(Reference Atkins, Whincup and Morris33). Dietary intake included protein, carbohydrates, energy content from total and saturated fat, cholesterol, fruits and vegetables, sugar and a range of micronutrients (vitamins C and E, Fe, vitamin A, β-carotene, vitamin K1). The total macronutrient and micronutrient intakes were calculated by a validated computer program which is based on UK food composition tables(Reference Atkins, Whincup and Morris33,Reference Holland, Welch and Unwin34) . The frequency of foods consumed was multiplied by the standard portion sizes for each food and by the nutrient composition of the foods obtained from the UK food composition tables(Reference Parsons, Papachristou and Atkins32,Reference Holland, Welch and Unwin34) . Then, according to these intakes, total energy intake was also calculated. Additionally, in order to examine whether potential associations between oral health and fat intake are a result of high consumption of processed meat, we created a variable about the intake of processed meat. In the BRHS, consumption of bacon, salami, tinned meat, corned beef, pork and beef sausages, meat pie and pasties was used to create this variable. In the HABC Study, processed meat consisted of ham, bacon, sausage, bologna and fried chicken. This variable was based on the number and frequency of processed meat consumed.
In the HABC Study, a 108-item, interviewer-administered modified version of the Block FFQ (Block Dietary Data Systems) was used for the assessment of dietary intake in year 2 (1998–1999)(Reference Block, Hartman and Dresser35,Reference Hengeveld, Wijnhoven and Olthof36) . More details on the FFQ can be found elsewhere(Reference Hengeveld, Wijnhoven and Olthof36). The Healthy Eating Index (HEI) was used to assess diet quality and shows whether an individual adheres to the Dietary Guidelines for Americans of 1995 and the Food Pyramid of 1992(Reference Hengeveld, Wijnhoven and Olthof36). HEI score is a validated tool for the assessment of diet quality for the US population and is defined according to different sex and age groups(Reference Hengeveld, Wijnhoven and Olthof36,Reference Bowman, Lino and Gerrior37) . Therefore, it can be used to assess dietary intake of different age groups, including older people. HEI consists of ten components: nine food components (grains, vegetables, fruit, milk and meat, intakes of percentages of energy content from total and saturated fat, total cholesterol and total Na) and one component which assesses diet variety. In the HABC Study, dietary intake also included trans-fat intake, percentage of energy from sweets and soda, and a number of micronutrients (vitamins B1, B2, B5, B6, C, D, E, A and B12, Cu, Mg, Mn, Fe, Ca, P, K, Zn, folic acid). Similar to the BRHS, we created a variable about the consumption of processed meat. In both studies, protein intake was measured as g/kg body weight per d.
Covariates
In both studies, measures of socio-economic position, smoking, alcohol and history of doctor-diagnosed CVD and diabetes were self-reported through questionnaires(Reference Lennon, Ramsay and Papacosta26,Reference Weyant, Newman and Kritchevsky29) . In the BRHS, occupational social class was used to assess socio-economic position on the basis of longest-held occupation at baseline(Reference Ramsay, Whincup and Watt38). In the HABC Study, the highest level of education accomplished was the indicator of socio-economic position(Reference Weyant, Newman and Kritchevsky29). To create BMI, body weight and height assessed at physical examinations were utilised(Reference Ruhl, Harris and Ding39,Reference Parsons, Sartini and Welsh40) . For both studies, total energy intake was derived from FFQ questionnaires, and regular use of prescribed medications causing dry mouth (xerostomia) was identified(41).
Statistical analysis
Logistic and regression analyses were performed for the BRHS and HABC Study separately to assess the associations of oral health markers with diet quality and majority of nutrients. Ordinal regression was performed for the associations between (a) poor oral health and three categories of processed meat, and (b) change in EDI score over time and oral health problems (four categories) later in life. We observed no violations of the assumptions for logistic and ordinal regression analyses. Two variables for number of natural teeth were created: (a) 0, 1–7, 8–14, 15–20 and ≥ 21 teeth; and (b) < 21 teeth and ≥ 21 teeth(Reference Hobdell, Petersen and Clarkson42). Loss of attachment and pocket depth were markers of periodontal disease and the following categories were generated: in the BRHS – pocket depth > 20 % sites affected > 3·5 mm and loss of attachment > 20 % sites affected > 5·5 mm; in the HABC Study – both markers > 20 % sites affected ≥ 3 mm(Reference Ramsay, Whincup and Watt38,Reference Bretz, Weyant and Corby43) . Self-rated oral health was a binary variable grouped as excellent/good and fair/poor in both studies. In the BRHS, dry mouth was divided into 0, 1–2 or ≥ 3 dry mouth symptoms. In the HABC Study, dry mouth was categorised as either having dry mouth when eating or not. Moreover, to examine the burden of poor oral health, we created a variable indicating an accumulation of oral problems – in the BRHS, four oral health problems were used: < 21 natural teeth, ≥ 3 dry mouth symptoms, any difficulty eating and sensitivity to hot/cold/sweets(Reference Ramsay, Papachristou and Watt44); in the HABC Study, the measure included: < 21 natural teeth, dry mouth when eating, any difficulty eating and limitation of food due to gum problems. The variable was then categorised as 0, 1, 2 and ≥ 3 problems. In the BRHS, EDI scores were divided into quartiles, with the bottom quartile (worst diet quality) as the outcome variable in logistic regression models. In the HABC Study, HEI scores were categorised as good (> 80), needs improvement (51–80) and low (< 51) diet quality(Reference Bowman, Lino and Gerrior37); ‘low HEI score’ (poor diet quality) was the outcome whereas needs improvement and good HEI scores were grouped together (reference group) in logistic regression models. In both studies, macro- and micronutrients were divided into quartiles and either the bottom or top quartile were outcomes in logistic regression models in order to indicate worse dietary intake(Reference Thompson, Subar, Coulston and Boushey45). Consumption of processed meat was categorised as: 1 = rarely/monthly (little intake), 2 = 1 type of processed meat ≥ 2 times per week (moderate intake), 3 = ≥ 2 types of processed meat ≥ 2 times per week; the third category (≥ 2 types of processed meat ≥ 2 times per week – indicating high intake) was the outcome in ordinal regression. Protein intake was a binary variable; in the BRHS low protein consumption was categorised as < 0·75 g/kg per d and high as ≥ 0·75 g/kg per d(46), and in the HABC Study low as < 0·8 g/kg per d and high as ≥ 0·80 g/kg per d(47). Recommended values of fruits and vegetables intake were used as cut-off values in logistic regression. In the BRHS, individuals with intake smaller than five portions/d and, in the HABC Study, participants with intake smaller than six portions/d were classified as having low intake of fruits and vegetables(48,49) . In the BRHS, regression models were adjusted for age, social class, smoking, alcohol, history of CVD and diabetes, BMI and energy intake. In the HABC Study, age, sex, race, education, smoking, alcohol, history of CVD and diabetes, BMI and energy intake were included as potential confounders in the models.
Additionally, in the BRHS, change in the EDI scores was examined over 10 years between age 60–79 years and 71–92 years. Change in EDI scores was calculated as a 4-level variable (1 = persistent poor EDI score, 2 = decrease in score, 3 = improvement, 4 = persistent good score). Ordinal and logistic regression analyses were performed to test the association of change in EDI score with oral health markers at age 71–92 years. All analyses were performed using SAS, version 9.4 software (SAS Institute, Inc.).
In both studies, we compared the distribution of characteristics between those with and without missing data to examine potential differences between the two groups.
Results
Baseline characteristics and prevalence of oral health markers can be found in Table 1. The mean age of BRHS participants (n 2147) was 78·8 years, 52 % were in non-manual social class, 20 % had no remaining natural teeth and 35 % reported fair/poor self-rated oral health. Moreover, 62 % had at least one dry mouth symptom, whereas 36 % reported at least two oral health problems. In the HABC Study (n 2998), mean age of study subjects was 74·7 years, 52 % were female, 58 % White and 42 % reported post-secondary education. Also, 11 % of the HABC Study sample had no remaining natural teeth, 31 % fair/poor self-rated oral health, 4 % had dry mouth when eating and 22 % at least two oral health problems.
Population characteristics and prevalence of oral health problems in the British Regional Heart Study (BRHS) and the Health, Aging and Body Composition (HABC) Study
(Mean values and standard deviations; numbers and percentages)
* Baseline data (year 1).
Oral health and diet quality
OR and 95 % CI for the associations between oral health markers and diet quality in the BRHS and HABC Study are presented in Table 2.
Objective oral health markers
In the BRHS, having 8–14 teeth was associated with the bottom quartile (poor diet quality) of EDI score (OR 1·57, 95 % CI 1·05, 2·34), after adjusting for confounders. In the HABC Study, complete and partial tooth loss, and periodontal disease (loss of attachment) were associated with poor diet quality in the age-adjusted models (0 v. ≥ 21 teeth, OR 1·78, 95 % CI 1·03, 3·08; periodontal disease, OR 2·21, 95 % CI 1·33, 3·70). The associations for tooth loss were attenuated and did not remain after adjustment. For periodontal disease, the association was also attenuated after adjusting for confounders (OR 1·69, 95 % CI 0·98, 2·94).
Association of oral health markers with Elderly Dietary Index (EDI) and Healthy Eating Index (HEI) scores in older British men aged 71–92 years in the British Regional Heart Study (BRHS) and older American men and women aged 71–80 years in the Health, Aging and Body Composition (HABC) Study
(Odds ratios and 95 % confidence intervals)
* BRHS: age, social class, smoking, alcohol, history of CVD and diabetes, BMI, energy intake; HABC Study: age, sex, race, education, smoking, alcohol, history of CVD and diabetes, BMI.
† BRHS: Includes < 21 teeth, difficulty eating, symptoms of dry mouth and sensitivity to hot, cold or sweet; HABC Study: dry mouth when eating, < 21 remaining teeth, any difficulty eating or chewing, limit of food due to gum problems.
Subjective oral health markers
In the BRHS, fair/poor self-rated oral health and at least three oral health problems were associated with the bottom quartile (poor diet quality) of EDI score (OR 1·51, 95 % CI 1·14, 1·99; OR 1·83, 95 % CI 1·11, 3·01, respectively), after full adjustment.
In the HABC Study, fair/poor self-rated oral health, dry mouth and accumulation of oral health problems were associated with low HEI score (poor diet quality) in age-adjusted models (OR 1·68, 95 % CI 1·26, 2·24 for self-rated oral health, OR 1·84, 95 % CI 1·03, 3·29 for dry mouth), but these associations did not remain after adjusting for confounders.
Oral health and dietary intake
Table 3 presents the associations of poor oral health with energy intake, percentage of energy from saturated fat, and fruits and vegetables intakes in the BRHS. Associations between oral health problems and protein, vitamin C and α-tocopherol intakes are presented in online Supplementary Appendix A, Fig. A1. Associations between oral health and processed meat are presented in online Supplementary Appendix B, Fig. B1.
Association of oral health markers with bottom quartile of energy intake, top quartile of percentage of energy from saturated fat and low intake of fruits and vegetables in older British men aged 71–92 years in the British Regional Heart Study
(Odds ratios and 95 % confidence intervals)
* Age, social class, smoking, alcohol, history of CVD and diabetes, and BMI; for fruits and vegetables, energy intake was added in the model.
† < 21 remaining teeth, ≥ 3 dry mouth symptoms, difficulty eating, sensitivity to hot/cold/sweets.
Objective oral health markers
In the fully adjusted model, partial tooth loss (15–20 teeth v. ≥ 21) was associated with the top quartile of energy intake (OR 1·54, 95 % CI 1·11, 2·16). We did not observe any associations between markers of poor oral health and low protein intake. Complete and partial tooth loss were associated with increased consumption of processed meat and low fruits and vegetables intake (processed meat, OR 2·03, 95 % CI 1·40, 2·95; OR 1·64, 95 % CI 1·12, 2·38, respectively) after adjustment for confounders. The associations between oral health and processed meat did not remain after further adjustment for energy intake. A borderline association was observed for periodontal disease and low fruits and vegetables intake in the age-adjusted model (OR 1·52, 95 % CI 1·00, 2·30). This association did not change significantly after full adjustment (OR 1·52, 95 % CI 0·96, 2·40). Complete and partial tooth loss, and loss of attachment were associated with the bottom quartile of α-tocopherol in the fully adjusted models (see online Supplementary Appendix A, Fig. A1). We did not observe any other associations between oral health markers and vitamins and minerals.
Subjective oral health problems
In the BRHS, ≥ 3 dry mouth symptoms and ≥ 3 oral health problems were associated with the top quartile of energy intake (OR 1·58, 95 % CI 1·17, 2·13; OR 2·32, 95 % CI 1·46, 3·67, respectively) in the fully adjusted models. Moreover, fair/poor self-rated oral health and having at least three oral health problems were associated with top quartile of percentage of energy content from saturated fat, and low intake of fruits and vegetables. Having ≥ 3 oral health problems was associated with increased consumption of processed meat (OR 2·16, 95 % CI 1·22, 3·18) after adjustment for confounders. This association did not remain after adjusting for energy intake. Furthermore, fair/poor self-rated oral health was associated with the bottom quartile of vitamin C in the fully adjusted model (OR 1·38, 95 % CI 1·04, 1·84) (see online Supplementary Appendix A, Fig. A1). Finally, having ≥ 3 oral health problems was associated with the bottom quartile of α-tocopherol in the fully adjusted models (see online Supplementary Appendix A, Fig. A1). We did not observe other associations between oral health markers and vitamins and minerals.
Associations of markers of poor oral health with dietary intake in the HABC Study can be found in Table 4 and online Supplementary Appendix C, Fig. C1. Associations for oral health markers with processed meat are presented in online Supplementary Appendix B, Fig. B1.
Association of oral health markers with bottom quartile of energy intake, top quartile of percentage of energy content from saturated fat and low intake of fruits and vegetables in older American men and women aged 71–80 years in the Health, Aging and Body Composition Study
(Odds ratios and 95 % confidence intervals)
* Adjusted for age, sex, race, education, smoking, alcohol, history of CVD and diabetes, BMI; for fruits and vegetables, energy intake was added to the model.
† < 21 remaining teeth dry mouth when eating, any difficulty eating or chewing, limit of food due to gum problems.
Objective oral health markers
Periodontal disease (pocket depth) was associated with the top quartile of energy intake (OR 1·75, 95 % CI 1·29, 2·37) in the fully adjusted models (Table 4). Periodontal disease (loss of attachment) was the only oral health marker associated with the top quartile of percentage of energy content from saturated fat after adjustment (Table 4), whereas complete and partial tooth loss were associated with the top quartile of energy content from trans-fat (see online Supplementary Appendix C, Fig. C1). Moreover, complete tooth loss (0 v. ≥ 21 teeth) was associated with low intake of fruits and vegetables in the fully adjusted model (OR 1·55, 95 % CI 1·03, 2·34) (Table 4). No significant associations were reported between poor oral health and low protein intake (see online Supplementary Appendix C, Fig. C1). Additionally, periodontal disease (pocket depth) was associated with increased intake of processed meat after adjustment for age, sex, race, education, smoking, alcohol, history of CVD and diabetes, and BMI. After adjusting further for energy intake, this association did not remain significant (see online Supplementary Appendix B, Fig. B1). We did not observe other associations between oral health markers and vitamins and minerals.
Subjective oral health markers
Having ≥ 3 oral health problems was associated with the top quartile of energy intake (OR 1·49, 95 % CI 1·04, 2·15) in the fully adjusted model. For micronutrients, only having ≥ 3 oral health problems was associated with the bottom quartile of vitamin D intake (OR 1·77, 95 % CI 1·21, 2·58, fully adjusted) (see online Supplementary Appendix A, Fig. A1). We did not observe other associations between oral health markers and vitamins and minerals.
Change in Elderly Dietary Index score and prevalence of oral health problems (10-year follow-up)
The associations for change in EDI score over a 10-year follow-up (age 60–79 to 71–92 years), with poor oral health at 71–92 years, are presented in Table 5. In the age-adjusted model, persistent low EDI score (low score at both time points) was associated with partial tooth loss (< 21 teeth v. ≥ 21), periodontal disease, fair/poor self-rated oral health and accumulation of oral health problems. After adjusting for confounders, associations for persistent low EDI score with tooth loss and having two oral health problems remained significant (OR 1·66, 95 % CI 1·09, 2·52; OR 1·96, 95 % CI 1·07, 3·56, respectively).
Change in dietary quality (Elderly Dietary Index (EDI) scores) over 10 years (age 60–79 to 71–92 years) and the association with having oral health problems at 71–92 years in the British Regional Heart Study
(Odds ratios and 95 % confidence intervals)
* Age, social class, smoking, alcohol, history of CVD and diabetes, BMI, energy intake.
† < 21 remaining teeth, ≥ 3 dry mouth symptoms, difficulty eating, sensitivity to hot/cold/sweets.
Missing data
In the BRHS, those with missing data were slightly older (1 year), were more likely to be in manual social class, consumed less alcohol, were less physically active and were more likely to report fair/poor general health. In the HABC Study, participants with missing data were more likely to be African American, current and former smokers, spent fewer years in school, consumed less alcohol and were more likely to report poor general health.
Discussion
In the present study, oral health problems in older age were associated with poor diet quality, higher intake of total energy, percentage of energy content from saturated and trans-fat, low intake of fruits and vegetables, and high intake of processed meat. Additionally, in the BRHS, persistent low diet quality in older ages over a 10-year follow-up was associated with having oral health problems later in life.
In the BRHS, tooth loss, fair/poor self-rated oral health and ≥ 3 oral health problems were associated with poor diet quality (lowest EDI scores). Similar associations for tooth loss have been supported by previous studies(Reference Ritchie, Joshipura and Hung7,Reference Zhu and Hollis14) . Individuals experiencing tooth loss were more likely to reduce consumption of specific vegetables (such as apples, carrots) as well as foods high in fibre, which influence diet quality(Reference Watson, McGowan and McCrum10,Reference Sheiham and Steele50,Reference Moynihan, Snow and Jepson51) . To our knowledge, this is one of the first studies to report an association between fair/poor self-rated oral health and diet quality. This highlights the importance of the individual’s perception of the oral health status and that it is possible that older people with fair/poor self-rated oral health have unhealthier diets. Our results also indicate that a greater burden of oral health problems (an accumulation of oral health problems) is associated with poorer diet quality. No independent significant associations for oral health markers with diet quality were reported in the HABC Study. However, the small number of individuals in the low HEI score group could have influenced the significance of the results observed.
In the BRHS, dry mouth was associated with increased energy intake. Additionally, fair/poor self-rated oral health was associated with high intake of percentage of energy content from saturated fat and low intake of fruits and vegetables. These findings again indicate that self-reported measures of oral health could be indicators of a compromised diet. In the HABC Study, periodontal disease was associated with top quartiles of both total energy intake and percentage of energy content from saturated fat, in accordance with some previous studies(Reference Najeeb, Zafar and Khurshid52,Reference Jauhiainen, Suominen and Kanerva53) . One study reported that increased intake of saturated fat was associated with progression of periodontal disease in older people(Reference Iwasaki, Manz and Moynihan24). Furthermore, tooth loss was associated with low intake of fruits and vegetables and top quartiles of percentage of energy content from trans-fat and energy content from sweets and soda. Additionally, in both studies, poor oral health was associated with high intake of processed meat. The associations for low intake of fruits and vegetables are in accordance with previous studies(Reference Ritchie, Joshipura and Hung7,Reference Tsakos, Herrick and Sheiham8) . In both our study populations, overall, we observed an increased consumption of meat and fish, cakes/cookies/desserts as well as high intake of liquid and solid fats. Additionally, mean intakes of percentage of energy from saturated and trans-fat were above the current recommendations(47). However, associations for poor oral health with processed meat did not remain significant after adjusting for energy intake, suggesting that older people with oral health problems may follow diets high in energy content and therefore are more likely to consume more processed meat too. We observed no associations between oral health markers and protein intake, which may be the result of overall levels of protein intake in both populations being within the recommended values(47).
Persistent poor diet quality over 10 years of follow-up in the BRHS was associated with partial tooth loss and accumulation of oral health problems, an association that was independent of confounders. These findings point to a potential bidirectional relationship between poor oral health and diet in older age. A previous study reported that following a diet rich in antioxidants and vitamins and low in saturated and trans-fatty acids is linked to fewer teeth being lost(Reference Kotsakis, Chrepa and Shivappa54). The presence of oral health problems may lead to a compromised diet and poor diet quality due to food avoidance and change of dietary habits, whereas an unhealthy diet may also contribute to the deterioration of oral health in older people(Reference Moynihan55).
A strength of our study is that we examined a wide range of macro- and micronutrients in two comparable studies of community-dwelling older people in two Western populations and were able to investigate bidirectional associations between poor oral health and diet quality in the BRHS. Moreover, this is one of the few studies examining the role of subjective oral health measures, such as dry mouth and self-rated oral health.
Our study has also limitations. Measurement of some oral health measures (i.e. periodontal disease, dry mouth) was different in the two studies, and some dietary intake variables (i.e. energy content from trans-fat, micronutrients) were not available in both studies. Furthermore, different measures assessed diet quality in the two studies (EDI in BRHS; HEI in HABC Study), and therefore this may account for differences in the diet quality results between the two studies. It is also possible that our findings may not be representative of the general populations of the UK and USA because the BRHS consisted of White males, whereas the HABC Study comprised a subpopulation of older people in the USA (White and African American males and females from Pittsburgh and Memphis). The majority of our findings were cross-sectional, and therefore cannot establish causal relationships. When dietary intake is assessed by self-reported measures, including the FFQ method, it can lead to misreporting of intakes and this can introduce bias in the form of measurement error(Reference Poslusna, Ruprich and de Vries56,Reference Livingstone and Black57) . This could influence the estimation of energy and micronutrient intakes(Reference Poslusna, Ruprich and de Vries56,Reference Livingstone and Black57) . Nevertheless, the FFQ used in the present study have been validated in the British and US populations(Reference Yarnell, Fehily and Milbank31,Reference Hengeveld, Wijnhoven and Olthof36,Reference Ahluwalia, Dwyer and Terry58) . We did not exclude participants with unintentional weight loss or extreme energy intake. However, in both studies, very small number of participants reported unintentional weight loss due to cancer or heart failure (BRHS n 59, 2·7 %; HABC Study n 13, 0·4 %, for cancer) or excessive energy intake (BRHS n 2, 0·1 %; HABC Study n 77, 2·6 %). Therefore, the results are unlikely to be biased due to unintentional weight loss or extreme energy intake. Furthermore, the presence of survivor bias is possible, since healthier participants are more likely to have attended the examinations. We also adjusted for a range of confounders, but residual confounding may be present, resulting in overestimation of the observed associations. In both studies, multiple comparisons were performed, therefore increasing the risk of false-positive results. As the number of hypothesis testing increases, so does the risk of observing false-positive results(Reference Lindquist and Mejia59).
In conclusion, we observed that oral health problems were associated with poor diet quality and intake of micro- and macronutrients in two studies of community-dwelling older people in the UK and USA. Also, persistent poor diet quality was found to be associated with poor oral health later in life in the BRHS. These findings support a bidirectional association between poor oral health and diet. Future studies should further investigate the association between changes in diet and changes in oral health in older people. Additionally, future research should also focus on older people living in care homes to provide more evidence on how oral health influences diet and food choices. Further evidence can help design effective dietary programmes tailored to the oral health of older people, which could reduce the burden of poor oral health and its consequences in older people.
Acknowledgements
This research was supported by the British Heart Foundation Programme grant (RG/08/013/25942), The Dunhill Medical Trust (grant no. R396/1114 and R592/0717) and the National Institute on Aging (contracts N01-AG-6–2101; N01-AG-6–2103; N01-AG-6–2106; NIA grant R01-AG028050; NINR grant R01-NR012459; NIH/NIDCR grant R03 DE028505–02). The British Heart Foundation, Dunhill Medical Trust and National Institute on Aging had no role in the design, analysis or writing of this article.
Study concept and design: E. K., S. E. R., S. G. W., A. O. P. and P. H. W. Acquisition of data: S. E. R., S. G. W., A. O. P., P. H. W., L. T. L. and R. J. W. Analysis and interpretation of data: all authors. Drafting of the manuscript: all authors. Critical revision of the manuscript for important intellectual content: all authors.
The authors declare that there are no conflicts of interest.
Supplementary material
For supplementary materials referred to in this article, please visit https://doi.org/10.1017/S0007114521000180
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