Carbohydrate-to-fat ratio is positively associated with comprehensive frailty independent of protein intake in older adults

Carbohydrate-to-fat ratio is positively associated with comprehensive frailty independent of protein intake in older adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbohydrate-to-fat ratio is positively associated with comprehensive frailty independent of protein intake in older adults Hinako Nanri, Tsukasa Yoshida, Eiichi Yoshimura, Jun Kunisawa, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8653318/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Diet influences frailty, and protein intake has been a prime focus of prevention. However, frailty remains prevalent, even among older adults with relatively high protein intake, suggesting the influence of remaining energy distribution between carbohydrates and fat. We investigated the association of the carbohydrate-to-fat energy ratio (CFr) with comprehensive frailty in older Japanese adults and the influence of protein intake. Methods Cross-sectional baseline data from 5,679 community-dwelling adults aged ≥ 65 years in the Kyoto–Kameoka Study were analyzed. Diet was assessed using a validated 46-item food-frequency questionnaire. Percent energy from carbohydrates, fat, and protein was calculated. CFr was defined as the ratio of percent energy from carbohydrates to that from fat. Frailty was defined as a Kihon Checklist score ≥ 7. Multivariate logistic regression estimated odds ratios (ORs) and 95% confidence intervals for frailty across CFr quartiles, adjusting for sociodemographic, lifestyle, and health-related factors. Restricted cubic splines assessed overall dose–response and within sex-specific tertiles of protein intake. Results Frailty prevalence was 36%. Compared with the lowest CFr quartile, adjusted ORs for frailty in the second, third, and highest quartiles were 1.14, 1.45, and 2.02, respectively. Spline models showed little association at lower CFr values, with risk increasing at higher ratios. Higher CFr was consistently associated with greater frailty prevalence within low-, middle-, and high-protein tertiles, and formal tests showed no effect modification by protein intake. Conclusion A higher CFr is associated with a higher prevalence of comprehensive frailty in older Japanese adults, with broadly similar associations across protein-intake levels. comprehensive frailty carbohydrate-to-fat ratio protein intake older adults Kihon checklist Figures Figure 1 Figure 2 Figure 3 Introduction Amid global population aging, frailty is a multidimensional geriatric syndrome characterized by diminished physiological reserves and impaired recovery homeostasis after stress [ 1 ]. Frailty encompasses physical, cognitive, and social domains that interact to create complex health vulnerabilities. Frailty is associated with greater needs for long-term care, increased risk of hospital admission, and higher mortality [ 2 ]. As frailty can be slowed or prevented when identified early, clarifying modifiable determinants is a public-health priority; among these, diet is a key, tractable risk factor [ 3 ]. Comprehensive frailty, which captures not only physical vulnerability but also cognitive and social deficits, may better reflect the real-world risk of functional decline and loss of independence than physical frailty alone. Although protein intake has been a prime focus of frailty prevention, a “protein-focused” strategy alone may be insufficient to meaningfully lower the risk of frailty. Adequate nutritional intake, particularly sufficient protein, helps preserve muscle mass and strength and is consistently associated with a lower prevalence of physical and comprehensive frailty [ 4 – 8 ]. However, a cross-sectional study of 5,679 Japanese adults aged ≥ 65 years indicated that the prevalence of comprehensive frailty defined by the Kihon Checklist remained approximately 25–30%, even in the highest quartile of protein intake [ 7 , 8 ], indicating that the substantial frailty burden persists despite relatively high protein intake. This residual burden highlights the limitations of a protein-only strategy. Additionally, the feasible contribution of protein to total metabolizable energy is relatively narrow, typically approximately 10–20% of energy in human diets and well below a putative upper tolerance of approximately 35% [ 9 , 10 ]. In contrast, total digestible carbohydrate and total fat together account for approximately 80–85% of energy intake and span a much wider range (roughly 10–75% of energy) [ 11 – 14 ]. These features suggest that, once adequate protein is secured, how the remaining energy is partitioned between carbohydrates and fat may represent a key and potentially modifiable dimension of frailty prevention. Findings regarding the association between total carbohydrate or fat intake and frailty have been inconsistent [ 15 , 16 ]. In contrast, cross-sectional analyses using dietary pattern scores have reported that higher low-carbohydrate-diet scores, that is, a lower carbohydrate share, are associated with lower odds of frailty [ 17 , 18 ]. However, as macronutrient shares are compositional data constrained to sum up to 100%, changes in one macronutrient may imply compensatory changes in others. Consequently, ratio metrics such as the carbohydrate-to-fat energy ratio (CFr) can succinctly capture relative shifts under this constraint, mitigate collinearity, and improve interpretability. Consistent with this view, higher CFr and carbohydrate intake have been increasingly associated with adverse metabolic and cognitive profiles [ 18 , 19 ] and may contribute to the risk of frailty. These considerations motivated us to focus on CFr as a summary indicator of carbohydrate–fat balance in this study. Given that protein requirements and body composition differ by sex in older adults, the association between carbohydrate–fat balance and frailty may also plausibly vary by sex and by the adequacy of protein intake. However, it remains unclear whether the balance between carbohydrate and fat intake, independent of protein intake, is associated with comprehensive frailty in older adults. Moreover, no previous study has systematically examined whether any such association is consistent across different levels of protein intake, despite protein being a key determinant of frailty risk. The primary objective of this study was to quantify the association between the CFr and the prevalence of comprehensive frailty in community-dwelling older adults. The secondary objective was to examine whether this association differs across protein intake levels by evaluating dose–response relationships between CFr and frailty within sex-specific tertiles of protein intake. We hypothesized that a higher CFr would be associated with greater frailty prevalence and that this pattern would be broadly consistent across protein-intake tertiles. Methods Study participants The Kyoto–Kameoka study was a prospective cohort study that focused on incidents of falls and accidents and the reduction of long-term care needs among individuals aged ≥ 65 years in Kameoka City, Kyoto Prefecture, Japan [ 20 ]. In July 2011, a comprehensive survey, the Needs in the Sphere of Daily Life Survey (baseline survey), which included the 25-item Kihon Cecklis (KCL), was conducted. Valid responses were received from 13,294 participants (response rate, 72.9%). Six months later, in February 2012, an additional Health and Nutritional Survey that included a dietary survey was conducted and yielded valid responses from 8,319 participants (valid response rate, 69.4%). Both surveys were administered via postal questionnaires, and informed consent was obtained from those who returned the completed forms. The study was approved by the Ethics Committees of Kyoto University of Advanced Science (No. 24M20) and the National Institute of Biomedical Innovation, Health and Nutrition (NIBN-76-2). Of the 8,319 participants, 2,640 were excluded owing to missing dietary data, implausible energy intake ( 4,000 kcal/day for men; 3,500 kcal/day for women), missing Kihon Checklist scores, or missing covariates. The final analytic sample comprised 5,679 participants (2,744 men and 2,935 women). The same analytical sample was used for all primary analyses, including models fitted in the overall population and models stratified by sex-specific tertiles of protein intake. Dietary assessment Dietary intake was evaluated using a validated self-administered food frequency questionnaire [ 21 – 25 ]. Participants were asked to report the frequency of their consumption of 46 food and beverage items (including green tea and coffee) over the previous year to estimate their usual intake levels. Total energy and nutrient intakes were calculated using a program developed by the Department of Public Health, Nagoya City University School of Medicine [ 21 – 24 ], based on the Standard Tables of Food Composition in Japan (fifth revised edition) [ 26 ]. The percentages of total energy from protein and fat were calculated as the energy provided by each macronutrient divided by total energy intake. The percentage of total energy from carbohydrates was then calculated by difference as 100 minus the sum of the percentages from protein and fat. The CFr was defined as the ratio of percent energy from carbohydrates to percent energy from fat (CFr = eCarb/eFat), where eCarb and eFat denote the percentage of total energy intake from carbohydrate and fat, respectively. Definition of comprehensive frailty The KCL is a validated tool widely used to screen frailty in community-dwelling older adults [ 27 , 28 ]. It captures various frailty aspects, encompassing physical, social, and psychological domains through 25 items grouped into seven subdomains: i) questions 1–5 assess “instrumental activities of daily living,” ii) questions 6–10 evaluate “physical function and strength,” iii) questions 11–12 focus on “malnutrition,” iv) questions 13–15 address “oral function and eating,” v) questions 16–17 explore “socialization and housebound status,” vi) questions 18–20 measure “cognitive function and memory,” and vii) questions 21–25 examine “depression and mood.” Each item is scored as “+1” for a response indicating frailty risk (yes). A higher total score reflects poorer functioning and greater frailty, with comprehensive frailty defined as a score of ≥ 7 out of 25 [ 8 , 29 , 30 ] Covariates Additional data were collected via a self-administered questionnaire, which included questions on height, weight, family structure, education, economic difficulty, history of diseases (e.g., cardiovascular disease and stroke), alcohol consumption, smoking status, and self-reported health. Economic difficulty was assessed using a self-reported question on perceived financial status and dichotomized as difficult (very difficult or somewhat difficult) or not difficult (somewhat comfortable or comfortable). Alcohol consumption was categorized as daily, sometimes, seldom, and never. Smoking status was classified as daily, sometimes, former, and never. Self-reported health was rated as very good, good, fair, or poor, then grouped into very good/good or fair/poor. Participants’ ages were calculated based on the date of birth recorded in the city office’s resident register. Body mass index (BMI) was calculated by dividing self-reported weight (kg) by the square of self-reported height (m). The population density was calculated as the number of residents per unit area (km²). Statistical analysis The association between CFr and comprehensive frailty was evaluated in the overall analytical sample. Participants were then stratified by sex-specific tertiles of protein intake, and the analyses within each tertile were repeated to assess whether the association between CFr and frailty was consistent across different levels of protein intake. Details of each analytical model are described below. The baseline characteristics were summarized overall and across tertiles of protein intake as counts and percentages for categorical variables and means with standard deviations for continuous variables. Differences between groups were examined using Pearson chi-squared test for categorical variables and the Kruskal–Wallis rank-sum test for continuous variables. Multivariate logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for comprehensive frailty (Kihon Checklist) across quartiles of CFr, using the lowest quartile (Q1) as the reference. Covariates were sex (male/female), age (years, continuous), BMI (kg/m², continuous), total energy intake (kcal/day, continuous), alcohol status (everyday/sometimes/seldom/never), smoking status (everyday/sometimes/former/never), history of disease (hypertension, diabetes, dyslipidemia, cancer, cardiovascular disease, stroke, liver disease, chronic renal failure; yes/no), family structure (living alone/living with someone/others), educational attainment (≤ 9, 10–12, ≥ 13 years), economic difficulty (yes or no), population density (≥ 1,000 vs. < 1,000 people per km²), and self-rated health (good vs. poor). In the prespecified stratified analyses, protein intake (% energy) was categorized into tertiles. The CFr–frailty association within each protein tertile was re-estimated using the same covariate set as in the main model. The linear trend across CFr quartiles was assessed in the overall sample and within each protein tertile by entering an ordinal variable (Q1–Q4 coded 0–3) as a continuous term in the model ( p for trend). Restricted cubic spline logistic regression models were fitted with CFr as the exposure to examine potential nonlinearity. Three knots were placed a priori at the 10th, 50th, and 90th percentiles of the exposure distribution. The reference point was set to the median of Q1, and predictions were centered at this value (OR = 1 at the reference). ORs with 95% CIs are presented, and the curves are displayed on a logarithmic y-axis. Overall and nonlinear associations were evaluated using Wald χ² tests from the model analysis of variance (testing, respectively, all spline terms jointly and the nonlinear components with the linear term constrained), with a two-sided α = 0.05. Statistical analyses were performed using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria). Results Table 1 summarizes the participant characteristics. In the overall sample (n = 5,679), 48% were men and 52% were women; the mean age was 73 years (SD, 6 years), and the mean BMI was 22.7 kg/m² (SD, 3.3 kg/m²). Thirty-six percent met the criteria for comprehensive frailty. The mean total energy intake was 1,757 kcal/day (SD, 434 kcal/day); energy-density intakes averaged 31.7 g protein (SD, 6.2 g), 30 g fat (SD, 12 g), and 139 g carbohydrate (SD, 20 g) per 1,000 kcal. Total energy intake differed significantly across tertiles of protein intake (T1/T2/T3: n = 1,894/1,893/1,892). Significant differences were also observed for age; the proportions of participants living alone, with ≥ 13 years of education, living in areas with population density > 1,000 persons/km², and current drinkers (all p ≤ 0.001 except living alone [p = 0.029] and current drinking [p = 0.004]). Figure 1 shows violin plots of energy density for protein, fat, and carbohydrates alongside the CFr, stratified by tertiles of protein intake. As protein intake increased from T1 to T3, the median carbohydrate share decreased and the median fat share increased, with substantial but overlapping distributions across tertiles. Consequently, CFr declined stepwise from the low- to high-protein groups. Table 1 Baseline characteristics overall and sex-specific tertiles of protein (% energy) Baseline characteristics Overall Protein intake % energy Tertile 1 (Low) N = 1,894 1 Tertile 2 (Middle) N = 1,893 1 Tertile 3 (High) N = 1,892 1 Women, n 2,957 (52%) a 986 (52%) 986 (52%) 985 (52%) Age, years 73 (6) 73 (6) 72 (6) 73 (6) Height, cm 158 (9) 158 (9) 158 (9) 158 (9) Weight, kg 57 (11) 56 (10) 57 (10) 57 (11) Body mass index, kg/m 2 22.69 (3.33) 22.60 (3.14) 22.68 (3.17) 22.79 (3.66) Living alone, n 616 (11%) 227 (13%) 178 (9.9%) 211 (12%) Education attainment ≥ 13 years, n (%)s, n 1,325 (25%) 405 (23%) 429 (24%) 491 (28%) Economic difficulty, n 3,576 (65%) 1,230 (66%) 1,196 (65%) 1,150 (63%) Population density ≥ 1,000 people/km2, n 2,642 (47%) 812 (43%) 919 (49%) 911 (48%) History of disease, n 3,526 (62%) 1,172 (62%) 1,180 (62%) 1,174 (62%) Current drinker, n 2,442 (43%) 872 (46%) 799 (42%) 771 (41%) Current smoker, n 606 (11%) 218 (12%) 195 (10%) 193 (10%) Poor self-reported health, n 919 (16%) 297 (16%) 308 (16%) 314 (17%) Total energy intake, kcal/day 1,757 (434) 1,831 (467) 1,759 (375) 1,681 (442) a Values are n (%) for categorical variables and mean (SD) for continuous variables. Table 2 presents multivariable odds ratios (ORs) and 95% confidence intervals (CIs) for comprehensive frailty across quartiles of CFr (Q1 reference). Higher CFr was associated with progressively higher odds: Q2 OR, 1.14 (95% CI 0.93–1.38); Q3 OR, 1.45 (1.19–1.77); and Q4 OR, 2.02 (1.63–2.49); p for trend < 0.001. Prespecified stratified analyses showed broadly consistent dose–response patterns within protein-intake tertiles: in T1 (low), ORs were 1.56 (1.11–2.18), 1.80 (1.26–2.56), and 2.64 (1.78–3.92) for Q2–Q4, respectively ( p for trend < 0.001). In T2 (middle), ORs were 1.13 (0.80–1.59), 1.73 (1.20–2.49), and 1.91 (1.27–2.88) for Q2–Q4, respectively ( p for trend = 0.006). In T3 (high), ORs were 1.22 (0.85–1.74), 1.61 (1.13–2.31), and 1.74 (1.19–2.56) for Q2–Q4, respectively ( p for trend = 0.002). Table 2 Multivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for comprehensive frailty assessed using the Kihon Checklist according to quartiles (Q) of carbohydrate-fat ratio, overall and stratified by sex-specific tertiles (T) of protein intake. Q1 Q2 Q3 Q4 p a Reference OR (95% CI) OR (95% CI) OR (95% CI) All, n 1420 1420 1420 1420 Comprehensive frailty, n (%) 375 (26.4) 53 (37.3) 390 (27.5) 436 (30.7) 504 (35.5) Model 1.00 b 1.14 (0.93, 1.38) 1.45 (1.19, 1.77) 2.02 (1.63, 2.49) < 0.001 Protein intake category T1 (Low), n 474 473 473 474 Comprehensive frailty, n (%) 122 (25.7) 53 (37.3) 156 (33.0) 167 (35.3) 180 (38.0) Model 1.00 c 1.56 (1.11, 2.18) 1.80 (1.26, 2.56) 2.64 (1.78, 3.92) < 0.001 T2 (Middle), n 474 473 473 473 Comprehensive frailty, n (%) 119 (25.1) 53 (37.3) 119 (25.2) 136 (28.8) 150 (31.7) Model 1.00 c 1.13 (0.80, 1.59) 1.73 (1.20, 2.49) 1.91 (1.27, 2.88) 0.006 T3 (High), n 471 470 470 471 Comprehensive frailty, n (%) 115 (24.4) 53 (37.3) 129 (27.4) 151 (32.1) 159 (33.8) Model 1.00 c 1.22 (0.85, 1.74) 1.61 (1.13, 2.31) 1.74 (1.19, 2.56) 0.002 a p for trend across quartiles of carbohydrate-fat ratio was calculated by assigning the median value of the carbohydrate-fat ratio to each quartile and modeling this variable as a continuous term in the multivariate logistic regression model. b Odds ratios were estimated using multivariate logistic regression models adjusted for sex (male or female), age (years, continuous), total energy intake (kcal/day, continuous), alcohol status (everyday, sometimes, seldom, or never), smoking status (everyday, sometimes, former, or never), history of disease (hypertension, diabetes, dyslipidemia, cancer, cardiovascular disease, stroke, liver disease, and chronic renal failure; yes or no), family structure (living alone, living with someone, or others), educational attainment (≤ 9, 10–12, or ≥ 13 years), economic difficulty (yest or no), population density (≥ 1,000 or < 1,000 people per km²), and self-rated health (good or poor). Restricted cubic spline models in the overall sample showed a significant positive association between the CFr and the prevalence of comprehensive frailty ( p overall < 0.001) (Fig. 2 ). Using the median CFr value in the lowest quartile as the reference (OR = 1.0), the odds for frailty increased almost linearly with higher CFr values. Similar monotonic positive associations between CFr and comprehensive frailty were observed in each stratum when analyses were stratified by sex-specific tertiles of protein intake (Fig. 3 ). The overall Wald tests for CFr indicated significant results in all tertiles (T1 [low], p < 0.001; T2 [middle], p = 0.004; T3 [high], p = 0.004), whereas tests for nonlinearity were not (T1, p = 0.75; T2, p = 0.91; T3, p = 0.27), indicating approximately linear risk increases across the observed CFr range. The slope was broadly comparable across tertiles, and formal interaction testing did not support material effect modification by protein intake ( p for interaction = 0.82). Similar dose–response patterns were observed on stratifying according to a 1.0 g/kg/day cut-off of bodyweight-normalized protein intake (Supplementary Fig. 1). Discussion In this cross-sectional study of Japanese community-dwelling older adults, the CFr was positively associated with the prevalence of comprehensive frailty. Across CFr quartiles, multivariate-adjusted ORs of frailty increased monotonically; the highest quartile had approximately 2.0-fold higher odds than the lowest. Consistently, restricted cubic spline models demonstrated a near-linear positive association across the observed CFr range. This pattern was also evident within each tertile of protein intake, indicating that carbohydrate–fat allocation may be relevant even among individuals with high protein intake. Together, these results suggest that the balance between carbohydrate and fat may contribute to frailty risk beyond absolute macronutrient intakes. In supplementary domain-specific analyses using the KCL, higher CFr was also associated with higher odds of impairment across several functional domains, including instrumental activities of daily living, physical function/strength, oral function/eating, cognitive/memory function, and depressive mood (Supplementary Fig. 2). These associations were directionally consistent with the findings for comprehensive frailty, whereas no clear association was observed for malnutrition. Collectively, these domain-specific findings suggest that a higher CFr may relate to frailty through multiple functional pathways rather than a single domain. These findings align with a growing body of evidence suggesting that carbohydrate-dominant macronutrient patterns may be unfavorable for frailty. The Korean Frailty and Aging Cohort Study reported that consuming more than 65% of total energy from carbohydrates was associated with a higher prevalence of frailty [ 18 ], and higher carbohydrate intake was associated with a worse frailty index in the Baltimore Longitudinal Study of Aging [ 17 ]. Additionally, a higher CFr has been linked to adverse metabolic and cognitive profiles in older adults [ 19 ]. In parallel, evidence that adequate fat intake supports the maintenance of skeletal muscle mass and function [ 31 ] suggests that, even among high-protein consumers, optimizing carbohydrate–fat balance (i.e., avoiding an excessively high CFr), rather than focusing solely on absolute macronutrient amounts, may be nutritionally relevant for frailty prevention. However, prospective and interventional studies that jointly consider protein, fat, and carbohydrate are warranted to clarify the optimal macronutrient balance for frailty prevention in older adults. Across protein-intake tertiles, a higher CFr was associated with higher ORs for frailty in both quartile-based logistic and restricted cubic-spline models, with an approximately linear increase and no evidence of interaction by protein level. This pattern suggests that CFr contributes to frailty risk independently of protein intake. Additionally, mean CFr was lower in the highest protein tertile. Given that previous studies have emphasized the importance of protein in preventing frailty [ 8 , 32 ], these findings imply that, beyond securing adequate protein, avoiding a high CFr (i.e., preventing carbohydrate dominance of non-protein energy) may also be relevant for frailty prevention. However, shifts in macronutrient shares often co-occur with broader differences in diet quality and food sources. For instance, a lower CFr could reflect healthier substitutions (e.g., replacing refined carbohydrates with unsaturated fats while maintaining fiber) [ 33 – 35 ] but could also reflect higher intakes of saturated fat or ultra-processed foods; thus, “lower CFr” should not be interpreted as universally beneficial. Further studies are required to investigate whether targeting the CFr across different protein-intake levels yields measurable reductions in frailty risk. This study has several limitations. First, its cross-sectional design precludes causal inference and raises the possibility of reverse causation, such as prodromal frailty altering appetite or food choice, thereby shifting macronutrient balance. Second, dietary exposures were self-reported and derived from a single time point; thus, measurement error and day-to-day variability may have attenuated or distorted associations. Third, although a comprehensive set of covariates was adjusted for, residual and unmeasured confounding cannot be excluded. Fourth, the main exposure (CFr) is an informative allocation summary; however, as a ratio, it cannot be used to determine whether the observed associations are driven predominantly by increases in carbohydrates, decreases in fat, or both, nor does it specify the isocaloric substitution partner(s). Formal compositional data analyses (Aitchison geometry) or isocaloric substitution models could refine interpretation in future studies. Finally, the participants were Japanese community-dwelling older adults, and the findings may not generalize to populations with different dietary patterns, food environments, or ethnicities. Conclusion In this cross-sectional sample of 5,679 older adults, a higher CFr was associated with a greater prevalence of comprehensive frailty, increasing monotonically across quartiles and remaining consistent within protein-intake tertiles. These findings suggest that macronutrient allocation, specifically the carbohydrate–fat balance, matters beyond protein quantity alone. However, prospective and interventional studies that manipulate carbohydrate–fat allocation under adequate protein intake are warranted to determine whether lowering the CFr reduces frailty risk and to refine practical targets for macronutrient balance in frailty prevention among older adults. Statements and Declarations Acknowledgements This study was conducted with JSPS KAKENHI and supported by research grants provided to Hinako Nanri (24K02873), Yosuke Yamada (15H05363), and Misaka Kimura (24240091). We would like to thank all members of the Kyoto-Kameoka Study Group for their valuable contributions. We thank the administrative staff of Kameoka City and Kyoto Prefecture. We thank Shinkan Tokudome, former director of the National Institute of Nutrition and Health. Competing interests The authors declare that they have no conflict of interest. Funding This study was conducted with JSPS KAKENHI and was supported by a research grant provided to Misaka Kimura (24240091), Yosuke Yamada (15H05363), and Hinako Nanri (24K02873). We thank Shinkan Tokudome, who was a former director of the National Institute of Nutrition and Health. Author contributions Data acquisition: Hinako Nanri, Tsukasa Yoshida, Eiichi Yoshida, Hiroyuki Fujita, Misaka Kimura, and Yosuke Yamada; Conceptualization and hypothesis development: Jun Kunisawa; Analysis and interpretation of data: Hinako Nanri; Drafting of the manuscript: Hinako Nanri; Critical revision of the manuscript for important intellectual content: Hinako Nanri. All authors read and approved the final manuscript. Ethics approval The Needs in the Sphere of Daily Life Survey and Health and Nutritional Survey were administered via postal questionnaires, and informed consent was obtained from those who returned the completed forms. The study was approved by the Ethics Committees of Kyoto University of Advanced Science (No. 24M20) and the National Institute of Biomedical Innovation, Health and Nutrition (NIBN-76-2). Consent to participate Not applicable. Consent to publish Not applicable. Data availability statement Researchers can apply to the Kyoto-Kameoka Study Group for permission to use this data through the YY ( [email protected] ) on reasonable request. References Rockwood K (2005) Frailty and its definition: A worthy challenge. J Am Geriatr Soc 53:1069–1070. https://doi.org/10.1111/j.1532-5415.2005.53312.x Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K (2013) Frailty in elderly people. Lancet 381:752–762. https://doi.org/10.1016/S0140-6736(12)62167-9 Lorenzo-López L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodríguez-Villamil JL, Millán-Calenti JC (2017) Nutritional determinants of frailty in older adults: A systematic review. 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Lancet 390:2050–2062. https://doi.org/10.1016/S0140-6736(17)32252-3 US Department of Agriculture, Agricultural Research Service (2020) Energy intakes: Percentages of energy from protein, carbohydrate, fat, and alcohol, by gender and age, in the United States, 2017–2018 (What we eat in America, NHANES 2017–2018, Table 5. https://www.ars.usda.gov/ARSUserFiles/80400530/pdf/1718/Table_5_EIN_GEN_17.pdf. Accessed 16 Dec 2025 Ministry of Health (2023) L a W. Report of the national health and nutrition survey. Japan, p 2025 Sandoval-Insausti H, Pérez-Tasigchana RF, López-García E, García-Esquinas E, Rodríguez-Artalejo F, Guallar-Castillón P (2016) Macronutrients intake and incident frailty in older adults: A prospective cohort study. J Gerontol A Biol Sci Med Sci 71:1329–1334. https://doi.org/10.1093/gerona/glw033 Verspoor E, Voortman T, van Rooij FJA, Rivadeneira F, Franco OH, Kiefte-de Jong JC, Schoufour JD (2020) Macronutrient intake and frailty: The Rotterdam Study. Eur J Nutr 59:2919–2928. https://doi.org/10.1007/s00394-019-02131-0 Tanaka T, Kafyra M, Jin Y, Chia CW, Dedoussis GV, Talegawkar SA, Ferrucci L (2022) Quality specific associations of carbohydrate consumption and frailty index. Nutrients 14:5072. https://doi.org/10.3390/nu14235072 Yang N, Lee Y, Kim MK, Kim K (2023) Macronutrients intake and physical frailty in Korean older adults: A cohort-based cross-sectional study. Geriatr Gerontol Int 23:478–485. https://doi.org/10.1111/ggi.14597 Rogers PJ, Vural Y, Berridge-Burley N, Butcher C, Cawley E, Gao Z, Sutcliffe A, Tinker L, Zeng X, Flynn AN, Brunstrom JM, Brand-Miller JC (2024) Evidence that carbohydrate-to-fat ratio and taste, but not energy density or NOVA level of processing, are determinants of food liking and food reward. Appetite 193:107124. https://doi.org/10.1016/j.appet.2023.107124 Yamada Y, Nanri H, Watanabe Y, Yoshida T, Yokoyama K, Itoi A, Date H, Yamaguchi M, Miyake M, Yamagata E, Tamiya H, Nishimura M, Fujibayashi M, Ebine N, Yoshida M, Kikutani T, Yoshimura E, Ishikawa-Takata K, Yamada M, Nakaya T, Yoshinaka Y, Fujiwara Y, Arai H, Kimura M (2017) Prevalence of frailty assessed by Fried and Kihon checklist indexes in a prospective cohort study: Design and demographics of the Kyoto-Kameoka longitudinal study. J Am Med Dir Assoc 18:733.e7–733.e15. https://doi.org/10.1016/j.jamda.2017.02.022 Goto CTY, Imaeda N, Takekuma K, Kuriki K, Igarashi F, Ikeda M, Tokudome S (2006) Validation study of fatty acid consumption assessed with a short food frequency questionnaire against plasma concentration in middle-aged Japanese people. Scand J Nutr 50:77–82 Imaeda N, Goto C, Tokudome Y, Hirose K, Tajima K, Tokudome S (2007) Reproducibility of a short food frequency questionnaire for Japanese general population. J Epidemiol 17:100–107. https://doi.org/10.2188/jea.17.100 Tokudome S, Goto C, Imaeda N, Tokudome Y, Ikeda M, Maki S (2004) Development of a data-based short food frequency questionnaire for assessing nutrient intake by middle-aged Japanese. Asian Pac J Cancer Prev 5:40–43 Tokudome Y, Goto C, Imaeda N, Hasegawa T, Kato R, Hirose K, Tajima K, Tokudome S (2005) Relative validity of a short food frequency questionnaire for assessing nutrient intake versus three-day weighed diet records in middle-aged Japanese. J Epidemiol 15:135–145. https://doi.org/10.2188/jea.15.135 Watanabe D, Nanri H, Yoshida T, Yamaguchi M, Sugita M, Nozawa Y, Okabe Y, Itoi A, Goto C, Yamada Y, Ishikawa-Takata K, Kobayashi H, Kimura M, Kyoto-Kameoka Study Group KS (2019) Validation of energy and nutrition intake in Japanese elderly individuals estimated based on a short food frequency questionnaire compared against a 7-day dietary record: The Kyoto-Kameeoka study. Nutrients 11:688. https://doi.org/10.3390/nu11030688 Tokyo JPB, Ministry of Finance (2001) (Japanese) Science and Technology Agency of Japan: Standard Tables of Food Composition in Japan., 5th rev. edn. Arai H, Satake S (2015) English translation of the Kihon Checklist. Geriatr Gerontol Int 15:518–519. https://doi.org/10.1111/ggi.12397 Maseda A, Lorenzo-López L, López-López R, Arai H, Millán-Calenti JC (2017) Spanish translation of the Kihon Checklist (frailty index). Geriatr Gerontol Int 17:515–517. https://doi.org/10.1111/ggi.12892 Satake S, Senda K, Hong YJ, Miura H, Endo H, Sakurai T, Kondo I, Toba K (2016) Validity of the Kihon Checklist for assessing frailty status. Geriatr Gerontol Int 16:709–715. https://doi.org/10.1111/ggi.12543 Watanabe D, Yoshida T, Watanabe Y, Yamada Y, Miyachi M, Kimura M (2022) Validation of the Kihon Checklist and the frailty screening index for frailty defined by the phenotype model in older Japanese adults. BMC Geriatr 22:478. https://doi.org/10.1186/s12877-022-03177-2 Wang S, Zhang Y, Zhang D, Wang F, Wei W, Wang Q, Bao Y, Yu K (2023) Association of dietary fat intake with skeletal muscle mass and muscle strength in adults aged 20–59: NHANES 2011–2014. Front Nutr 10:1325821. https://doi.org/10.3389/fnut.2023.1325821 Coelho-Júnior HJ, Rodrigues B, Uchida M, Marzetti E (2018) Low protein intake is associated with frailty in older adults: A systematic review and meta-analysis of observational studies. Nutrients 10:1334. https://doi.org/10.3390/nu10091334 Sacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, Miller M, Rimm EB, Rudel LL, Robinson JG, Stone NJ, Van Horn LV; American Heart Association (2017) Dietary fats and cardiovascular disease: A presidential advisory from the American Heart Association. Circulation 136:e1–e23. https://doi.org/10.1161/CIR.0000000000000510 Te Morenga L, Mallard S, Mann J (2012) Dietary sugars and body weight: Systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346:e7492. https://doi.org/10.1136/bmj.e7492 Reynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L (2019) Carbohydrate quality and human health: A series of systematic reviews and meta-analyses. Lancet 393:434–445. https://doi.org/10.1016/S0140-6736(18)31809-9 Additional Declarations No competing interests reported. Supplementary Files Supplefigure.pptx Supplementary Figure 1 Restricted cubic spline curves showing the association between the CFr and the prevalence of comprehensive frailty according to bodyweight-normalized protein intake (< 1.0 vs. ≥ 1.0 g/kg/day). Panels (a) and (b) represent low (< 1.0 g/kg/day) and high (≥ 1.0 g/kg/day) protein intake, respectively. ORs and 95% CIs were estimated using multivariate logistic regression models with restricted cubic splines for CFr (three knots at the 10th, 50th, and 90th percentiles within each protein-intake category). Comprehensive frailty was defined using the Kihon Checklist. Models were adjusted for sex, age, BMI, total energy intake, alcohol status, smoking status, history of disease, family structure, educational attainment, economic difficulty, population density, and self-rated health. The reference value (OR = 1.0) was set at the median CFr of the lowest quartile within each protein-intake category; shaded areas indicate 95% CIs. p overall and p nonlinear are Wald χ² p- values for the overall and nonlinear spline terms of CFr, respectively. The multiplicative interaction between continuous CFr and bodyweight-normalized protein intake (< 1.0 vs. ≥ 1.0 g/kg/day) was tested by including a cross-product term in the model ( p for interaction = 0.94) Supplementary Figure 2 Shown are multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for impairment in each Kihon Checklist (KCL) domain per 1-unit increase in the carbohydrate-to-fat ratio (CFr). Models were adjusted for sex, age, BMI, total energy intake, alcohol status, smoking status, history of disease, family structure, educational attainment, economic difficulty, population density, and self-rated health. The vertical dashed line indicates an OR of 1.0, and point estimates with 95% CIs are shown numerically on the right. Results for malnutrition were not statistically significant, whereas higher CFr was associated with higher odds of impairment in most other functional domains. Domain-specific impairments were defined using established Kihon Checklist cut-offs: instrumental activities of daily living (IADL; Q1–5 ≥3), physical function/mobility (Q6–10 ≥3), malnutrition (Q11–12 =2), oral function/eating (Q13–15 ≥2), housebound/socialization (Q16: “No”), cognitive/memory function (Q18–20 ≥1), and depressive mood (Q21–25 ≥2). Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8653318","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":641556026,"identity":"da8f6477-c16d-4367-ae6d-546001a6f8fe","order_by":0,"name":"Hinako Nanri","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBACAwhpw8AmARFgJlZLGslaGA4zMEgQ6zBz6eZnjysKzufxSfceYPxSwcBuTkiL5Zxj5oZnDG4Xs8mcS2CWOcPAbNlAyGE3EswkGwxuJ7ZJ5BgwS7YxMBscIKgl/RtQyzmStOSAbDkA1sL4kUgtZUAtyYltMmcMDjOckSDGL+nbJBv+2CXOn91j+PBHhU0ywRBDAYd5GCSSDUjSwviDgcGONC2jYBSMglEwEgAAxMk6f+yDMF4AAAAASUVORK5CYII=","orcid":"","institution":"National Institutes of Biomedical Innovation, Health and Nutrition","correspondingAuthor":true,"prefix":"","firstName":"Hinako","middleName":"","lastName":"Nanri","suffix":""},{"id":641556029,"identity":"85b74f4f-79bd-4087-ab00-fc6135621686","order_by":1,"name":"Tsukasa Yoshida","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Tsukasa","middleName":"","lastName":"Yoshida","suffix":""},{"id":641556031,"identity":"24150f46-21b5-48ae-89b7-85757f58e3e7","order_by":2,"name":"Eiichi Yoshimura","email":"","orcid":"","institution":"National Institutes of Biomedical Innovation, Health and Nutrition","correspondingAuthor":false,"prefix":"","firstName":"Eiichi","middleName":"","lastName":"Yoshimura","suffix":""},{"id":641556033,"identity":"c897577d-f447-465d-85a0-fd145bbf3f98","order_by":3,"name":"Jun Kunisawa","email":"","orcid":"","institution":"National Institutes of Biomedical Innovation, Health and Nutrition","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Kunisawa","suffix":""},{"id":641556035,"identity":"5cfde3d1-648f-419d-ac01-8d2238de7db0","order_by":4,"name":"Hiroyuki Fujita","email":"","orcid":"","institution":"Kyoto University of Advanced Science","correspondingAuthor":false,"prefix":"","firstName":"Hiroyuki","middleName":"","lastName":"Fujita","suffix":""},{"id":641556036,"identity":"9ab86f5d-baa5-49c2-ac3c-0ecc2b846d71","order_by":5,"name":"Misaka Kimura","email":"","orcid":"","institution":"Kyoto University of Advanced Science","correspondingAuthor":false,"prefix":"","firstName":"Misaka","middleName":"","lastName":"Kimura","suffix":""},{"id":641556037,"identity":"2ba78a13-f3ea-4988-a89b-ac7d33e4b6bd","order_by":6,"name":"Yosuke Yamada","email":"","orcid":"","institution":"Tohoku University","correspondingAuthor":false,"prefix":"","firstName":"Yosuke","middleName":"","lastName":"Yamada","suffix":""}],"badges":[],"createdAt":"2026-01-20 22:15:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8653318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8653318/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109458209,"identity":"710a2997-bed2-4213-b902-6e36a4e73b23","added_by":"auto","created_at":"2026-05-18 10:26:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":226431,"visible":true,"origin":"","legend":"\u003cp\u003eProtein, fat, carbohydrate (% Energy), and carbohydrate-to fat energy ratio (CFr) across sex-specific tertiles of protein intake. Violin plots of protein, fat, and carbohydrate (percent of total energy, % Energy) and the CFr, stratified by tertiles of protein intake. Distributions of protein, fat, and carbohydrate (% Energy) are shown on the left axis, and CFr is scaled to the right axis. Points and vertical bars indicate medians and interquartile ranges within each protein-intake tertile\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8653318/v1/04dcc725139ec5192b5aba0c.png"},{"id":109458212,"identity":"96a41a0d-f495-46c8-a2f7-a5ec5ca98e1b","added_by":"auto","created_at":"2026-05-18 10:26:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":61868,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curves showing the association between the CFr and the prevalence of comprehensive frailty. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariate logistic regression with restricted cubic splines (three knots at the 10th, 50th, and 90th percentiles of CFr). Comprehensive frailty was defined using the Kihon Checklist. Models were adjusted for sex, age, body mass index (BMI), total energy intake, alcohol status, smoking status, history of disease, family structure, educational attainment, economic difficulty, population density, and self-rated health. The reference value (OR = 1.0) was set at the median CFr of the lowest quartile; shaded areas indicate 95% CIs. \u003cem\u003ep\u003c/em\u003e\u003csub\u003eoverall\u003c/sub\u003e and \u003cem\u003ep\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e were derived from Wald χ² tests for the overall and nonlinear spline terms, respectively\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8653318/v1/4e17529e2d28d266be171af0.png"},{"id":109458211,"identity":"d9724c59-fff0-443a-be17-eca7f8ab2b34","added_by":"auto","created_at":"2026-05-18 10:26:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":224718,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline curves showing the association between the CFr and the prevalence of comprehensive frailty, stratified by tertiles of protein intake. Panels (a), (b), and (c) represent the lowest (T1, Low), middle (T2, Middle), and highest (T3, High) tertiles of protein intake, respectively. ORs and 95% CIs were estimated using multivariate logistic regression models with restricted cubic splines for CFr (three knots at the 10th, 50th, and 90th percentiles within each protein-intake tertile). Comprehensive frailty was defined using the Kihon Checklist. Models were adjusted for sex, age, BMI, total energy intake, alcohol status, smoking status, history of disease, family structure, educational attainment, economic difficulty, population density, and self-rated health. The reference value (OR = 1.0) was set at the median CFr of the lowest quartile in each tertile; shaded areas indicate 95% CI. \u003cem\u003ep\u003c/em\u003e\u003csub\u003eoverall\u003c/sub\u003e and \u003cem\u003ep\u003c/em\u003enonlinear were derived from Wald χ² tests for the overall and nonlinear spline terms, respectively. The multiplicative interaction between continuous CFr and tertiles of protein intake was determined by including a cross-product term in the model (\u003cem\u003ep\u003c/em\u003e for interaction = 0.82)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8653318/v1/44d83cac0d93b09498dd9a30.png"},{"id":109800228,"identity":"3558123a-eb77-442e-98c1-9cb84e76a31b","added_by":"auto","created_at":"2026-05-22 15:36:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":705714,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8653318/v1/e8a32c5a-5c7d-4b99-ab87-a7dbfb3e8a3d.pdf"},{"id":109458210,"identity":"578f273b-3ac3-4aee-953d-c40edb52bd9b","added_by":"auto","created_at":"2026-05-18 10:26:43","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":86898,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline curves showing the association between the CFr and the prevalence of comprehensive frailty according to bodyweight-normalized protein intake (\u0026lt; 1.0 vs. ≥ 1.0 g/kg/day). Panels (a) and (b) represent low (\u0026lt; 1.0 g/kg/day) and high (≥ 1.0 g/kg/day) protein intake, respectively. ORs and 95% CIs were estimated using multivariate logistic regression models with restricted cubic splines for CFr (three knots at the 10th, 50th, and 90th percentiles within each protein-intake category). Comprehensive frailty was defined using the Kihon Checklist. Models were adjusted for sex, age, BMI, total energy intake, alcohol status, smoking status, history of disease, family structure, educational attainment, economic difficulty, population density, and self-rated health. The reference value (OR = 1.0) was set at the median CFr of the lowest quartile within each protein-intake category; shaded areas indicate 95% CIs. \u003cem\u003ep\u003c/em\u003e\u003csub\u003eoverall\u003c/sub\u003e and \u003cem\u003ep\u003c/em\u003e\u003csub\u003enonlinear\u003c/sub\u003e are Wald χ² \u003cem\u003ep-\u003c/em\u003evalues for the overall and nonlinear spline terms of CFr, respectively. The multiplicative interaction between continuous CFr and bodyweight-normalized protein intake (\u0026lt; 1.0 vs. ≥ 1.0 g/kg/day) was tested by including a cross-product term in the model (\u003cem\u003ep\u003c/em\u003e for interaction = 0.94)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShown are multivariable-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for impairment in each Kihon Checklist (KCL) domain per 1-unit increase in the carbohydrate-to-fat ratio (CFr). Models were adjusted for sex, age, BMI, total energy intake, alcohol status, smoking status, history of disease, family structure, educational attainment, economic difficulty, population density, and self-rated health. The vertical dashed line indicates an OR of 1.0, and point estimates with 95% CIs are shown numerically on the right. Results for malnutrition were not statistically significant, whereas higher CFr was associated with higher odds of impairment in most other functional domains. Domain-specific impairments were defined using established Kihon Checklist cut-offs: instrumental activities of daily living (IADL; Q1–5 ≥3), physical function/mobility (Q6–10 ≥3), malnutrition (Q11–12 =2), oral function/eating (Q13–15 ≥2), housebound/socialization (Q16: “No”), cognitive/memory function (Q18–20 ≥1), and depressive mood (Q21–25 ≥2).\u003c/p\u003e","description":"","filename":"Supplefigure.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8653318/v1/75f282a6a8c7139d1d020012.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carbohydrate-to-fat ratio is positively associated with comprehensive frailty independent of protein intake in older adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAmid global population aging, frailty is a multidimensional geriatric syndrome characterized by diminished physiological reserves and impaired recovery homeostasis after stress [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Frailty encompasses physical, cognitive, and social domains that interact to create complex health vulnerabilities. Frailty is associated with greater needs for long-term care, increased risk of hospital admission, and higher mortality [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As frailty can be slowed or prevented when identified early, clarifying modifiable determinants is a public-health priority; among these, diet is a key, tractable risk factor [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Comprehensive frailty, which captures not only physical vulnerability but also cognitive and social deficits, may better reflect the real-world risk of functional decline and loss of independence than physical frailty alone.\u003c/p\u003e \u003cp\u003eAlthough protein intake has been a prime focus of frailty prevention, a \u0026ldquo;protein-focused\u0026rdquo; strategy alone may be insufficient to meaningfully lower the risk of frailty. Adequate nutritional intake, particularly sufficient protein, helps preserve muscle mass and strength and is consistently associated with a lower prevalence of physical and comprehensive frailty [\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR39\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, a cross-sectional study of 5,679 Japanese adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years indicated that the prevalence of comprehensive frailty defined by the Kihon Checklist remained approximately 25\u0026ndash;30%, even in the highest quartile of protein intake [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], indicating that the substantial frailty burden persists despite relatively high protein intake. This residual burden highlights the limitations of a protein-only strategy. Additionally, the feasible contribution of protein to total metabolizable energy is relatively narrow, typically approximately 10\u0026ndash;20% of energy in human diets and well below a putative upper tolerance of approximately 35% [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In contrast, total digestible carbohydrate and total fat together account for approximately 80\u0026ndash;85% of energy intake and span a much wider range (roughly 10\u0026ndash;75% of energy) [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These features suggest that, once adequate protein is secured, how the remaining energy is partitioned between carbohydrates and fat may represent a key and potentially modifiable dimension of frailty prevention.\u003c/p\u003e \u003cp\u003eFindings regarding the association between total carbohydrate or fat intake and frailty have been inconsistent [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In contrast, cross-sectional analyses using dietary pattern scores have reported that higher low-carbohydrate-diet scores, that is, a lower carbohydrate share, are associated with lower odds of frailty [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, as macronutrient shares are compositional data constrained to sum up to 100%, changes in one macronutrient may imply compensatory changes in others. Consequently, ratio metrics such as the carbohydrate-to-fat energy ratio (CFr) can succinctly capture relative shifts under this constraint, mitigate collinearity, and improve interpretability. Consistent with this view, higher CFr and carbohydrate intake have been increasingly associated with adverse metabolic and cognitive profiles [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and may contribute to the risk of frailty. These considerations motivated us to focus on CFr as a summary indicator of carbohydrate\u0026ndash;fat balance in this study. Given that protein requirements and body composition differ by sex in older adults, the association between carbohydrate\u0026ndash;fat balance and frailty may also plausibly vary by sex and by the adequacy of protein intake. However, it remains unclear whether the balance between carbohydrate and fat intake, independent of protein intake, is associated with \u003cem\u003ecomprehensive\u003c/em\u003e frailty in older adults. Moreover, no previous study has systematically examined whether any such association is consistent across different levels of protein intake, despite protein being a key determinant of frailty risk.\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to quantify the association between the CFr and the prevalence of comprehensive frailty in community-dwelling older adults. The secondary objective was to examine whether this association differs across protein intake levels by evaluating dose\u0026ndash;response relationships between CFr and frailty within sex-specific tertiles of protein intake. We hypothesized that a higher CFr would be associated with greater frailty prevalence and that this pattern would be broadly consistent across protein-intake tertiles.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy participants\u003c/h2\u003e \u003cp\u003eThe Kyoto\u0026ndash;Kameoka study was a prospective cohort study that focused on incidents of falls and accidents and the reduction of long-term care needs among individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years in Kameoka City, Kyoto Prefecture, Japan [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In July 2011, a comprehensive survey, the Needs in the Sphere of Daily Life Survey (baseline survey), which included the 25-item Kihon Cecklis (KCL), was conducted. Valid responses were received from 13,294 participants (response rate, 72.9%). Six months later, in February 2012, an additional Health and Nutritional Survey that included a dietary survey was conducted and yielded valid responses from 8,319 participants (valid response rate, 69.4%). Both surveys were administered via postal questionnaires, and informed consent was obtained from those who returned the completed forms. The study was approved by the Ethics Committees of Kyoto University of Advanced Science (No. 24M20) and the National Institute of Biomedical Innovation, Health and Nutrition (NIBN-76-2).\u003c/p\u003e \u003cp\u003eOf the 8,319 participants, 2,640 were excluded owing to missing dietary data, implausible energy intake (\u0026lt;\u0026thinsp;500 or \u0026gt;\u0026thinsp;4,000 kcal/day for men; \u0026lt; 500 or \u0026gt;\u0026thinsp;3,500 kcal/day for women), missing Kihon Checklist scores, or missing covariates. The final analytic sample comprised 5,679 participants (2,744 men and 2,935 women). The same analytical sample was used for all primary analyses, including models fitted in the overall population and models stratified by sex-specific tertiles of protein intake.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDietary assessment\u003c/h3\u003e\n\u003cp\u003eDietary intake was evaluated using a validated self-administered food frequency questionnaire [\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Participants were asked to report the frequency of their consumption of 46 food and beverage items (including green tea and coffee) over the previous year to estimate their usual intake levels. Total energy and nutrient intakes were calculated using a program developed by the Department of Public Health, Nagoya City University School of Medicine [\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e24\u003c/span\u003e], based on the Standard Tables of Food Composition in Japan (fifth revised edition) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The percentages of total energy from protein and fat were calculated as the energy provided by each macronutrient divided by total energy intake. The percentage of total energy from carbohydrates was then calculated by difference as 100 minus the sum of the percentages from protein and fat. The CFr was defined as the ratio of percent energy from carbohydrates to percent energy from fat (CFr\u0026thinsp;=\u0026thinsp;eCarb/eFat), where eCarb and eFat denote the percentage of total energy intake from carbohydrate and fat, respectively.\u003c/p\u003e\n\u003ch3\u003eDefinition of comprehensive frailty\u003c/h3\u003e\n\u003cp\u003eThe KCL is a validated tool widely used to screen frailty in community-dwelling older adults [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It captures various frailty aspects, encompassing physical, social, and psychological domains through 25 items grouped into seven subdomains: i) questions 1\u0026ndash;5 assess \u0026ldquo;instrumental activities of daily living,\u0026rdquo; ii) questions 6\u0026ndash;10 evaluate \u0026ldquo;physical function and strength,\u0026rdquo; iii) questions 11\u0026ndash;12 focus on \u0026ldquo;malnutrition,\u0026rdquo; iv) questions 13\u0026ndash;15 address \u0026ldquo;oral function and eating,\u0026rdquo; v) questions 16\u0026ndash;17 explore \u0026ldquo;socialization and housebound status,\u0026rdquo; vi) questions 18\u0026ndash;20 measure \u0026ldquo;cognitive function and memory,\u0026rdquo; and vii) questions 21\u0026ndash;25 examine \u0026ldquo;depression and mood.\u0026rdquo; Each item is scored as \u0026ldquo;+1\u0026rdquo; for a response indicating frailty risk (yes). A higher total score reflects poorer functioning and greater frailty, with comprehensive frailty defined as a score of \u0026ge;\u0026thinsp;7 out of 25 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eAdditional data were collected via a self-administered questionnaire, which included questions on height, weight, family structure, education, economic difficulty, history of diseases (e.g., cardiovascular disease and stroke), alcohol consumption, smoking status, and self-reported health. Economic difficulty was assessed using a self-reported question on perceived financial status and dichotomized as difficult (very difficult or somewhat difficult) or not difficult (somewhat comfortable or comfortable). Alcohol consumption was categorized as daily, sometimes, seldom, and never. Smoking status was classified as daily, sometimes, former, and never. Self-reported health was rated as very good, good, fair, or poor, then grouped into very good/good or fair/poor. Participants\u0026rsquo; ages were calculated based on the date of birth recorded in the city office\u0026rsquo;s resident register. Body mass index (BMI) was calculated by dividing self-reported weight (kg) by the square of self-reported height (m). The population density was calculated as the number of residents per unit area (km\u0026sup2;).\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe association between CFr and comprehensive frailty was evaluated in the overall analytical sample. Participants were then stratified by sex-specific tertiles of protein intake, and the analyses within each tertile were repeated to assess whether the association between CFr and frailty was consistent across different levels of protein intake. Details of each analytical model are described below. The baseline characteristics were summarized overall and across tertiles of protein intake as counts and percentages for categorical variables and means with standard deviations for continuous variables. Differences between groups were examined using Pearson chi-squared test for categorical variables and the Kruskal\u0026ndash;Wallis rank-sum test for continuous variables. Multivariate logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for comprehensive frailty (Kihon Checklist) across quartiles of CFr, using the lowest quartile (Q1) as the reference. Covariates were sex (male/female), age (years, continuous), BMI (kg/m\u0026sup2;, continuous), total energy intake (kcal/day, continuous), alcohol status (everyday/sometimes/seldom/never), smoking status (everyday/sometimes/former/never), history of disease (hypertension, diabetes, dyslipidemia, cancer, cardiovascular disease, stroke, liver disease, chronic renal failure; yes/no), family structure (living alone/living with someone/others), educational attainment (\u0026le;\u0026thinsp;9, 10\u0026ndash;12, \u0026ge; 13 years), economic difficulty (yes or no), population density (\u0026ge;\u0026thinsp;1,000 vs. \u0026lt; 1,000 people per km\u0026sup2;), and self-rated health (good vs. poor). In the prespecified stratified analyses, protein intake (% energy) was categorized into tertiles. The CFr\u0026ndash;frailty association within each protein tertile was re-estimated using the same covariate set as in the main model. The linear trend across CFr quartiles was assessed in the overall sample and within each protein tertile by entering an ordinal variable (Q1\u0026ndash;Q4 coded 0\u0026ndash;3) as a continuous term in the model (\u003cem\u003ep\u003c/em\u003e for trend). Restricted cubic spline logistic regression models were fitted with CFr as the exposure to examine potential nonlinearity. Three knots were placed a priori at the 10th, 50th, and 90th percentiles of the exposure distribution. The reference point was set to the median of Q1, and predictions were centered at this value (OR\u0026thinsp;=\u0026thinsp;1 at the reference). ORs with 95% CIs are presented, and the curves are displayed on a logarithmic y-axis. Overall and nonlinear associations were evaluated using Wald χ\u0026sup2; tests from the model analysis of variance (testing, respectively, all spline terms jointly and the nonlinear components with the linear term constrained), with a two-sided α\u0026thinsp;=\u0026thinsp;0.05. Statistical analyses were performed using R (version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the participant characteristics. In the overall sample (n\u0026thinsp;=\u0026thinsp;5,679), 48% were men and 52% were women; the mean age was 73 years (SD, 6 years), and the mean BMI was 22.7 kg/m\u0026sup2; (SD, 3.3 kg/m\u0026sup2;). Thirty-six percent met the criteria for comprehensive frailty. The mean total energy intake was 1,757 kcal/day (SD, 434 kcal/day); energy-density intakes averaged 31.7 g protein (SD, 6.2 g), 30 g fat (SD, 12 g), and 139 g carbohydrate (SD, 20 g) per 1,000 kcal. Total energy intake differed significantly across tertiles of protein intake (T1/T2/T3: n\u0026thinsp;=\u0026thinsp;1,894/1,893/1,892). Significant differences were also observed for age; the proportions of participants living alone, with \u0026ge;\u0026thinsp;13 years of education, living in areas with population density\u0026thinsp;\u0026gt;\u0026thinsp;1,000 persons/km\u0026sup2;, and current drinkers (all p\u0026thinsp;\u0026le;\u0026thinsp;0.001 except living alone [p\u0026thinsp;=\u0026thinsp;0.029] and current drinking [p\u0026thinsp;=\u0026thinsp;0.004]). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows violin plots of energy density for protein, fat, and carbohydrates alongside the CFr, stratified by tertiles of protein intake. As protein intake increased from T1 to T3, the median carbohydrate share decreased and the median fat share increased, with substantial but overlapping distributions across tertiles. Consequently, CFr declined stepwise from the low- to high-protein groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics overall and sex-specific tertiles of protein (% energy)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBaseline characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eProtein intake % energy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTertile 1 (Low) \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,894\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTertile 2 (Middle) \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,893\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTertile 3 (High) \u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,892\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,957 (52%) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e986 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e986 (52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e985 (52%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73 (6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158 (9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.69 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.60 (3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.68 (3.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.79 (3.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving alone, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e616 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e178 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e211 (12%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment\u0026thinsp;\u0026ge;\u0026thinsp;13 years, n (%)s, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,325 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e405 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e429 (24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e491 (28%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic difficulty, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,576 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,230 (66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,196 (65%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,150 (63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation density\u0026thinsp;\u0026ge;\u0026thinsp;1,000 people/km2, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,642 (47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e812 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e919 (49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e911 (48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of disease, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,526 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,172 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,180 (62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,174 (62%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinker, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,442 (43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e872 (46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e799 (42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e771 (41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoker, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e606 (11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e218 (12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e195 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193 (10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor self-reported health, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e919 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e297 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e308 (16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e314 (17%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal energy intake, kcal/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,757 (434)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,831 (467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,759 (375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,681 (442)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003ea\u003c/sup\u003e Values are n (%) for categorical variables and mean (SD) for continuous variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents multivariable odds ratios (ORs) and 95% confidence intervals (CIs) for comprehensive frailty across quartiles of CFr (Q1 reference). Higher CFr was associated with progressively higher odds: Q2 OR, 1.14 (95% CI 0.93\u0026ndash;1.38); Q3 OR, 1.45 (1.19\u0026ndash;1.77); and Q4 OR, 2.02 (1.63\u0026ndash;2.49); \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Prespecified stratified analyses showed broadly consistent dose\u0026ndash;response patterns within protein-intake tertiles: in T1 (low), ORs were 1.56 (1.11\u0026ndash;2.18), 1.80 (1.26\u0026ndash;2.56), and 2.64 (1.78\u0026ndash;3.92) for Q2\u0026ndash;Q4, respectively (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In T2 (middle), ORs were 1.13 (0.80\u0026ndash;1.59), 1.73 (1.20\u0026ndash;2.49), and 1.91 (1.27\u0026ndash;2.88) for Q2\u0026ndash;Q4, respectively (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.006). In T3 (high), ORs were 1.22 (0.85\u0026ndash;1.74), 1.61 (1.13\u0026ndash;2.31), and 1.74 (1.19\u0026ndash;2.56) for Q2\u0026ndash;Q4, respectively (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate-adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for comprehensive frailty assessed using the Kihon Checklist according to quartiles (Q) of carbohydrate-fat ratio, overall and stratified by sex-specific tertiles (T) of protein intake.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive frailty, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e375 (26.4)\u003c/p\u003e \u003cp\u003e53 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e390 (27.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e436 (30.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e504 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 \u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 (0.93, 1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.19, 1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.02 (1.63, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein intake category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1 (Low), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive frailty, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (25.7)\u003c/p\u003e \u003cp\u003e53 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e180 (38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (1.11, 2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80 (1.26, 2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.64 (1.78, 3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 (Middle), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive frailty, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119 (25.1)\u003c/p\u003e \u003cp\u003e53 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119 (25.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e150 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.80, 1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.73 (1.20, 2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.91 (1.27, 2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (High), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComprehensive frailty, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115 (24.4)\u003c/p\u003e \u003cp\u003e53 (37.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (0.85, 1.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.13, 2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.74 (1.19, 2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e for trend across quartiles of carbohydrate-fat ratio was calculated by assigning the median value of the carbohydrate-fat ratio to each quartile and modeling this variable as a continuous term in the multivariate logistic regression model.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e Odds ratios were estimated using multivariate logistic regression models adjusted for sex (male or female), age (years, continuous), total energy intake (kcal/day, continuous), alcohol status (everyday, sometimes, seldom, or never), smoking status (everyday, sometimes, former, or never), history of disease (hypertension, diabetes, dyslipidemia, cancer, cardiovascular disease, stroke, liver disease, and chronic renal failure; yes or no), family structure (living alone, living with someone, or others), educational attainment (\u0026le;\u0026thinsp;9, 10\u0026ndash;12, or \u0026ge;\u0026thinsp;13 years), economic difficulty (yest or no), population density (\u0026ge;\u0026thinsp;1,000 or \u0026lt;\u0026thinsp;1,000 people per km\u0026sup2;), and self-rated health (good or poor).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eRestricted cubic spline models in the overall sample showed a significant positive association between the CFr and the prevalence of comprehensive frailty (\u003cem\u003ep\u003c/em\u003e\u003csub\u003eoverall\u003c/sub\u003e \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Using the median CFr value in the lowest quartile as the reference (OR\u0026thinsp;=\u0026thinsp;1.0), the odds for frailty increased almost linearly with higher CFr values. Similar monotonic positive associations between CFr and comprehensive frailty were observed in each stratum when analyses were stratified by sex-specific tertiles of protein intake (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The overall Wald tests for CFr indicated significant results in all tertiles (T1 [low], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; T2 [middle], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; T3 [high], \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), whereas tests for nonlinearity were not (T1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.75; T2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91; T3, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.27), indicating approximately linear risk increases across the observed CFr range. The slope was broadly comparable across tertiles, and formal interaction testing did not support material effect modification by protein intake (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.82). Similar dose\u0026ndash;response patterns were observed on stratifying according to a 1.0 g/kg/day cut-off of bodyweight-normalized protein intake (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional study of Japanese community-dwelling older adults, the CFr was positively associated with the prevalence of comprehensive frailty. Across CFr quartiles, multivariate-adjusted ORs of frailty increased monotonically; the highest quartile had approximately 2.0-fold higher odds than the lowest. Consistently, restricted cubic spline models demonstrated a near-linear positive association across the observed CFr range. This pattern was also evident within each tertile of protein intake, indicating that carbohydrate\u0026ndash;fat allocation may be relevant even among individuals with high protein intake. Together, these results suggest that the balance between carbohydrate and fat may contribute to frailty risk beyond absolute macronutrient intakes.\u003c/p\u003e \u003cp\u003eIn supplementary domain-specific analyses using the KCL, higher CFr was also associated with higher odds of impairment across several functional domains, including instrumental activities of daily living, physical function/strength, oral function/eating, cognitive/memory function, and depressive mood (Supplementary Fig.\u0026nbsp;2). These associations were directionally consistent with the findings for comprehensive frailty, whereas no clear association was observed for malnutrition. Collectively, these domain-specific findings suggest that a higher CFr may relate to frailty through multiple functional pathways rather than a single domain.\u003c/p\u003e \u003cp\u003eThese findings align with a growing body of evidence suggesting that carbohydrate-dominant macronutrient patterns may be unfavorable for frailty. The Korean Frailty and Aging Cohort Study reported that consuming more than 65% of total energy from carbohydrates was associated with a higher prevalence of frailty [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and higher carbohydrate intake was associated with a worse frailty index in the Baltimore Longitudinal Study of Aging [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Additionally, a higher CFr has been linked to adverse metabolic and cognitive profiles in older adults [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In parallel, evidence that adequate fat intake supports the maintenance of skeletal muscle mass and function [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] suggests that, even among high-protein consumers, optimizing carbohydrate\u0026ndash;fat balance (i.e., avoiding an excessively high CFr), rather than focusing solely on absolute macronutrient amounts, may be nutritionally relevant for frailty prevention. However, prospective and interventional studies that jointly consider protein, fat, and carbohydrate are warranted to clarify the optimal macronutrient balance for frailty prevention in older adults.\u003c/p\u003e \u003cp\u003eAcross protein-intake tertiles, a higher CFr was associated with higher ORs for frailty in both quartile-based logistic and restricted cubic-spline models, with an approximately linear increase and no evidence of interaction by protein level. This pattern suggests that CFr contributes to frailty risk independently of protein intake. Additionally, mean CFr was lower in the highest protein tertile. Given that previous studies have emphasized the importance of protein in preventing frailty [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e32\u003c/span\u003e], these findings imply that, beyond securing adequate protein, avoiding a high CFr (i.e., preventing carbohydrate dominance of non-protein energy) may also be relevant for frailty prevention. However, shifts in macronutrient shares often co-occur with broader differences in diet quality and food sources. For instance, a lower CFr could reflect healthier substitutions (e.g., replacing refined carbohydrates with unsaturated fats while maintaining fiber) [\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] but could also reflect higher intakes of saturated fat or ultra-processed foods; thus, \u0026ldquo;lower CFr\u0026rdquo; should not be interpreted as universally beneficial. Further studies are required to investigate whether targeting the CFr across different protein-intake levels yields measurable reductions in frailty risk.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, its cross-sectional design precludes causal inference and raises the possibility of reverse causation, such as prodromal frailty altering appetite or food choice, thereby shifting macronutrient balance. Second, dietary exposures were self-reported and derived from a single time point; thus, measurement error and day-to-day variability may have attenuated or distorted associations. Third, although a comprehensive set of covariates was adjusted for, residual and unmeasured confounding cannot be excluded. Fourth, the main exposure (CFr) is an informative allocation summary; however, as a ratio, it cannot be used to determine whether the observed associations are driven predominantly by increases in carbohydrates, decreases in fat, or both, nor does it specify the isocaloric substitution partner(s). Formal compositional data analyses (Aitchison geometry) or isocaloric substitution models could refine interpretation in future studies. Finally, the participants were Japanese community-dwelling older adults, and the findings may not generalize to populations with different dietary patterns, food environments, or ethnicities.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this cross-sectional sample of 5,679 older adults, a higher CFr was associated with a greater prevalence of comprehensive frailty, increasing monotonically across quartiles and remaining consistent within protein-intake tertiles. These findings suggest that macronutrient allocation, specifically the carbohydrate\u0026ndash;fat balance, matters beyond protein quantity alone. However, prospective and interventional studies that manipulate carbohydrate\u0026ndash;fat allocation under adequate protein intake are warranted to determine whether lowering the CFr reduces frailty risk and to refine practical targets for macronutrient balance in frailty prevention among older adults.\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with JSPS KAKENHI and supported by research grants provided to Hinako Nanri (24K02873), Yosuke Yamada (15H05363), and Misaka Kimura (24240091). We would like to thank all members of the Kyoto-Kameoka Study Group for their valuable contributions. We thank the administrative staff of Kameoka City and Kyoto Prefecture. We thank Shinkan Tokudome, former director of the National Institute of Nutrition and Health.\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted with JSPS KAKENHI and was supported by a research grant provided to Misaka Kimura (24240091), Yosuke Yamada (15H05363), and Hinako Nanri (24K02873). We thank Shinkan Tokudome, who was a former director of the National Institute of Nutrition and Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData acquisition: Hinako Nanri, Tsukasa Yoshida, Eiichi Yoshida, Hiroyuki Fujita,\u0026nbsp;Misaka Kimura, and Yosuke Yamada; Conceptualization and hypothesis development: Jun Kunisawa; Analysis and interpretation of data: Hinako Nanri; Drafting of the manuscript: Hinako Nanri; Critical revision of the manuscript for important intellectual content: Hinako Nanri. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Needs in the Sphere of Daily Life Survey and Health and Nutritional Survey were administered via postal questionnaires, and informed consent was obtained from those who returned the completed forms. The study was approved by the Ethics Committees of Kyoto University of Advanced Science (No. 24M20) and the National Institute of Biomedical Innovation, Health and Nutrition (NIBN-76-2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers can apply to the Kyoto-Kameoka Study Group for permission to use this data through the YY ([email protected]) on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRockwood K (2005) Frailty and its definition: A worthy challenge. 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Nutr Rev 79:66\u0026ndash;75. https://doi.org/10.1093/nutrit/nuaa104\u003c/li\u003e\n\u003cli\u003eCoelho-Junior HJ, Marzetti E, Picca A, Cesari M, Uchida MC, Calvani R (2020) Protein intake and frailty: A matter of quantity, quality, and timing. Nutrients 12:2915. https://doi.org/10.3390/nu12102915\u003c/li\u003e\n\u003cli\u003eKobayashi S, Suga H, Sasaki S, Three-generation Study of Women on Diets and Health Study Group (2017) Diet with a combination of high protein and high total antioxidant capacity is strongly associated with low prevalence of frailty among old Japanese women: A multicenter cross-sectional study. Nutr J 16:29. https://doi.org/10.1186/s12937-017-0250-9\u003c/li\u003e\n\u003cli\u003eNanri H, Yamada Y, Yoshida T, Okabe Y, Nozawa Y, Itoi A Yoshimura E, Watanabe Y, Yamaguchi M, Yokoyama K, Ishikawa-Takata K, Kobayashi H, Kimura M; Kyoto-Kameoka Study Group (2018) Sex difference in the association between protein intake and frailty: Assessed using the Kihon checklist indexes among older adults. J Am Med Dir Assoc 19:801\u0026ndash;805. https://doi.org/10.1016/j.jamda.2018.04.005\u003c/li\u003e\n\u003cli\u003eNanri H, Watanabe D, Yoshida T, Yoshimura E, Okabe Y, Ono M, Koizumi T, Kobayashi H, Fujita H, Kimura M, Yamada Y (2022) Adequate protein intake on comprehensive frailty in older adults: Kyoto-Kameoka study. 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J Gerontol A Biol Sci Med Sci 71:1329\u0026ndash;1334. https://doi.org/10.1093/gerona/glw033\u003c/li\u003e\n\u003cli\u003eVerspoor E, Voortman T, van Rooij FJA, Rivadeneira F, Franco OH, Kiefte-de Jong JC, Schoufour JD (2020) Macronutrient intake and frailty: The Rotterdam Study. Eur J Nutr 59:2919\u0026ndash;2928. https://doi.org/10.1007/s00394-019-02131-0\u003c/li\u003e\n\u003cli\u003eTanaka T, Kafyra M, Jin Y, Chia CW, Dedoussis GV, Talegawkar SA, Ferrucci L (2022) Quality specific associations of carbohydrate consumption and frailty index. Nutrients 14:5072. https://doi.org/10.3390/nu14235072\u003c/li\u003e\n\u003cli\u003eYang N, Lee Y, Kim MK, Kim K (2023) Macronutrients intake and physical frailty in Korean older adults: A cohort-based cross-sectional study. 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Nutrients 11:688. https://doi.org/10.3390/nu11030688\u003c/li\u003e\n\u003cli\u003eTokyo JPB, Ministry of Finance (2001) (Japanese) Science and Technology Agency of Japan: Standard Tables of Food Composition in Japan., 5th rev. edn.\u003c/li\u003e\n\u003cli\u003eArai H, Satake S (2015) English translation of the Kihon Checklist. Geriatr Gerontol Int 15:518\u0026ndash;519. https://doi.org/10.1111/ggi.12397\u003c/li\u003e\n\u003cli\u003eMaseda A, Lorenzo-L\u0026oacute;pez L, L\u0026oacute;pez-L\u0026oacute;pez R, Arai H, Mill\u0026aacute;n-Calenti JC (2017) Spanish translation of the Kihon Checklist (frailty index). Geriatr Gerontol Int 17:515\u0026ndash;517. https://doi.org/10.1111/ggi.12892\u003c/li\u003e\n\u003cli\u003eSatake S, Senda K, Hong YJ, Miura H, Endo H, Sakurai T, Kondo I, Toba K (2016) Validity of the Kihon Checklist for assessing frailty status. Geriatr Gerontol Int 16:709\u0026ndash;715. https://doi.org/10.1111/ggi.12543\u003c/li\u003e\n\u003cli\u003eWatanabe D, Yoshida T, Watanabe Y, Yamada Y, Miyachi M, Kimura M (2022) Validation of the Kihon Checklist and the frailty screening index for frailty defined by the phenotype model in older Japanese adults. BMC Geriatr 22:478. https://doi.org/10.1186/s12877-022-03177-2\u003c/li\u003e\n\u003cli\u003eWang S, Zhang Y, Zhang D, Wang F, Wei W, Wang Q, Bao Y, Yu K (2023) Association of dietary fat intake with skeletal muscle mass and muscle strength in adults aged 20\u0026ndash;59: NHANES 2011\u0026ndash;2014. Front Nutr 10:1325821. https://doi.org/10.3389/fnut.2023.1325821\u003c/li\u003e\n\u003cli\u003eCoelho-J\u0026uacute;nior HJ, Rodrigues B, Uchida M, Marzetti E (2018) Low protein intake is associated with frailty in older adults: A systematic review and meta-analysis of observational studies. Nutrients 10:1334. https://doi.org/10.3390/nu10091334\u003c/li\u003e\n\u003cli\u003eSacks FM, Lichtenstein AH, Wu JHY, Appel LJ, Creager MA, Kris-Etherton PM, Miller M, Rimm EB, Rudel LL, Robinson JG, Stone NJ, Van Horn LV; American Heart Association (2017) Dietary fats and cardiovascular disease: A presidential advisory from the American Heart Association. Circulation 136:e1\u0026ndash;e23. https://doi.org/10.1161/CIR.0000000000000510\u003c/li\u003e\n\u003cli\u003eTe Morenga L, Mallard S, Mann J (2012) Dietary sugars and body weight: Systematic review and meta-analyses of randomised controlled trials and cohort studies. BMJ 346:e7492. https://doi.org/10.1136/bmj.e7492\u003c/li\u003e\n\u003cli\u003eReynolds A, Mann J, Cummings J, Winter N, Mete E, Te Morenga L (2019) Carbohydrate quality and human health: A series of systematic reviews and meta-analyses. Lancet 393:434\u0026ndash;445. https://doi.org/10.1016/S0140-6736(18)31809-9\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"comprehensive frailty, carbohydrate-to-fat ratio, protein intake, older adults, Kihon checklist","lastPublishedDoi":"10.21203/rs.3.rs-8653318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8653318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDiet influences frailty, and protein intake has been a prime focus of prevention. However, frailty remains prevalent, even among older adults with relatively high protein intake, suggesting the influence of remaining energy distribution between carbohydrates and fat. We investigated the association of the carbohydrate-to-fat energy ratio (CFr) with comprehensive frailty in older Japanese adults and the influence of protein intake.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eCross-sectional baseline data from 5,679 community-dwelling adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years in the Kyoto\u0026ndash;Kameoka Study were analyzed. Diet was assessed using a validated 46-item food-frequency questionnaire. Percent energy from carbohydrates, fat, and protein was calculated. CFr was defined as the ratio of percent energy from carbohydrates to that from fat. Frailty was defined as a Kihon Checklist score\u0026thinsp;\u0026ge;\u0026thinsp;7. Multivariate logistic regression estimated odds ratios (ORs) and 95% confidence intervals for frailty across CFr quartiles, adjusting for sociodemographic, lifestyle, and health-related factors. Restricted cubic splines assessed overall dose\u0026ndash;response and within sex-specific tertiles of protein intake.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFrailty prevalence was 36%. Compared with the lowest CFr quartile, adjusted ORs for frailty in the second, third, and highest quartiles were 1.14, 1.45, and 2.02, respectively. Spline models showed little association at lower CFr values, with risk increasing at higher ratios. Higher CFr was consistently associated with greater frailty prevalence within low-, middle-, and high-protein tertiles, and formal tests showed no effect modification by protein intake.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eA higher CFr is associated with a higher prevalence of comprehensive frailty in older Japanese adults, with broadly similar associations across protein-intake levels.\u003c/p\u003e","manuscriptTitle":"Carbohydrate-to-fat ratio is positively associated with comprehensive frailty independent of protein intake in older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-18 10:26:39","doi":"10.21203/rs.3.rs-8653318/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d158a001-2783-4cd2-9c3f-ef0bba8938d8","owner":[],"postedDate":"May 18th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"80947814338958752593968211472402110744","date":"2026-05-11T10:29:31+00:00","index":30,"fulltext":""},{"type":"reviewerAgreed","content":"64483582842537952262968507354528652085","date":"2026-05-10T02:28:10+00:00","index":28,"fulltext":""},{"type":"reviewersInvited","content":"20","date":"2026-05-07T14:49:30+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T10:26:39+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-18 10:26:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8653318","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8653318","identity":"rs-8653318","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}