Abstract
Background & Aims
Age-related loss of skeletal muscle mass and strength begins at 40 years of age, and limited evidence suggests that niacin supplementation increases levels of nicotinamide adenine dinucleotide in mouse muscle tissue. In addition, skeletal muscle has a key role in the body's processing of glucose. Therefore, this study aimed to investigate the relationship between dietary niacin and skeletal muscle mass, strength, and glucose homeostasis in people aged 40 years and older.
Methods
This study was an American population-based cross-sectional analysis using data from the National Health and Nutrition Examination Survey (NHANES). Considering that some outcomes are only measured in specific survey cycles and subsamples, we established three data sets: a grip strength dataset (2011–2014, n=3772), a body mass components dataset (2011–2018, n=3279), and a glucose homeostasis dataset (1999–2018, n=9189). Dietary niacin and covariates were measured in all survey cycles. Linear regression or logistic regression models that adjusted for several main covariates, such as physical activity and diet, was used to evaluate the relationship between dietary niacin and grip strength, total lean mass, appendicular lean mass, total fat, trunk fat, total bone mineral content, homeostasis model assessment of insulin resistance (HOMA-IR), fasting blood glycose, fasting insulin and sarcopenia risk. Subgroup analyses, a trend test, an interaction test, and a restricted cubic spline were used for further exploration.
Results
Higher dietary niacin intake was significantly correlated with higher grip strength (β 0.275, 95% confidence intervals [CI] 0.192–0.357), higher total lean mass (β 0.060, 95% CI 0.045–0.074), higher appendicular lean mass (β 0.025, 95% CI 0.018–0.033), and higher total bone mineral content (β 0.005, 95% CI 0.004–0.007). By contrast, higher dietary niacin intake was significantly associated with lower total fat (β −0.061, 95% CI −0.076 to −0.046), lower trunk fat (β −0.041, 95% CI −0.050 to −0.032) and lower sarcopenia risk (OR 0.460, 95% CI 0.233 to 0.907). In addition, dietary niacin significantly reduced HOMA-IR, fasting blood glucose (in participants without diabetes), and fasting insulin (p <0.05).
Conclusion
Niacin is associated with improved body composition (characterized by increased muscle mass and decreased fat content) and improved glucose homeostasis in dietary doses. Dietary niacin supplementation is a feasible way to alleviate age-related muscular loss.
Key words: Dietary niacin, muscle, glucose homeostasis, NHANES
Introduction
Sarcopenia is an age-related, progressive, systemic skeletal muscle disorder, usually characterized by the loss of muscle mass and strength (1). The most effective method to assess muscle mass is dual-energy X-ray absorptiometry, which can be used to estimate lean body mass, fat distribution, and bone mineral density. Grip strength is an effective method to evaluate muscle strength. Studies have shown that muscle strength drops significantly after the age of 40 years. Compared with people younger than 40 years, the muscle strength of people older than 40 years decreases by 16.6%–40.9% (2). Low muscle mass and strength are associated with a range of adverse outcomes, including fractures, weakness, and mortality (3, 4, 5).
Skeletal muscle is not only important for maintaining physical activity but is also a key site for glucose uptake and storage, which has an important impact on insulin sensitivity (6). With increasing age, the decrease of skeletal muscle mass and strength can aggravate the risk of insulin resistance through many mechanisms, including mitochondrial dysfunction, intramuscular lipid accumulation, and increased oxidative stress (6). This changes the metabolic homeostasis of the whole body and can lead to obesity and diabetes (7). Therefore, it is necessary to take measures to improve muscle atrophy in middle age to prevent functional degradation and metabolic disorders in older individuals.
Nutrition and exercise are the key to skeletal muscle growth and maintenance. Niacin, also known as vitamin B3, is a water-soluble B vitamin found in many foods and supplements (8). A recent clinical trial found that supplementation of nicotinamide adenine dinucleotide (NAD+) booster niacin increased blood NAD+ eightfold in adult patients with mitochondrial disease, and muscle NAD+ reached the level of the control group (9). An animal experiment found that short-term supplementation of nicotinoside increased NAD+ in muscle tissue of mice, and enhanced the treadmill endurance and grip strength of the mice (10). A study by Beltrà and coworkers found that niacin supplementation effectively corrected tissue NAD+ level and improved mitochondrial metabolism and cachexia in mice with cachexia (11). A growing body of evidence shows that niacin can improve skeletal muscle mass and strength. However, no study has explored the relationship between dietary niacin and muscle mass, strength, and insulin resistance in middle-aged and older adults in a representative large population sample, and it is unclear which variables will affect the relationship between niacin and the outcomes of interest. Therefore, the purpose of this study was to use the National Health and Nutrition Examination Survey (NHANES) database to fill the above knowledge gaps.
Methods
This cross-sectional study was prepared in accordance with the requirements of the STROBE statement (12).
Study design
We used publicly available data from the NHANES database. NHANES is a research program aimed at evaluating the health and nutritional status of adults and children in the United States. In NHANES, a stratified, multi-stage, and probability-cluster design is used to obtain a nationally representative sample of non-institutionalized civilians in the United States. Demographic, socioeconomic, diet, health, and other data are collected through questionnaires, and physical and laboratory examinations are performed via mobile examination units (13). The National Center for Health Statistics Research Ethics Review Board approved the NHANES research program and obtained the written informed consent of all participants.
Study population
Participants over the age of 40 years were selected for this study (2). Some outcomes were not available in every survey cycle; therefore, to maximize the use of data, three datasets were included: the grip strength dataset, the body mass components dataset, and the glucose homeostasis dataset. Dietary niacin and covariates were derived from the same survey cycles as the corresponding outcomes in three different datasets.
Grip strength dataset
Muscle strength (often measured by grip strength) is central to diagnosing sarcopenia because it is easy to measure and has a strong capability of predicting outcomes (14). In NHANES, grip strength was investigated over two survey cycles: 2011–2012 and 2013–2014. Participants were asked to squeeze a dynamometer as hard as possible with one hand and to exhale while squeezing to avoid the build-up of pressure in the chest. Participants then repeated the test with the other hand. Each hand was tested three times, alternating hands between trials and resting for 60 sec between measurements of the same hand. The combined grip strength (kg) was calculated as the sum of the maximum readings of the left and right hands. It is expressed relative to body weight (kg/kg weight).
Body mass components dataset
Participants aged 40–60 years underwent dual-energy X-ray absorptiometry during the 4-year survey cycles 2011–2018. Participants were excluded if they were pregnant, had a self-reported history of imaging contrast (barium) use in the past 7 days, weighted more than 450 pounds, or were taller than 6 feet 5 inches. For the eligible participants, measurements were taken of the total lean mass (excluding bone mineral content), appendix lean mass (sum of lean body mass in the limbs, excluding bone mineral content), total fat mass, trunk fat mass, and bone mineral content. The above measurements were expressed relative to body weight (kg/kg weight). Based on the criteria of The Foundation for the National Institutes of Health (FNIH), we defined sarcopenia as appendicular lean mass/BMI < 0.789 kg for males and appendicular lean mass/BMI < 0.512 kg for females(15).
Glucose homeostasis dataset
The NHANES database includes measurements of fasting blood glucose and fasting insulin over 10 survey cycles from 1999 to 2018. In addition, homeostasis model assessment of insulin resistance (HOMA-IR) was calculated, which is an indicator of the level of insulin resistance in individuals. The HOMA-IR formula is fasting blood glucose (mmol/L) × fasting insulin (µU/ml)/22.5.
Dietary intake
In NHANES, the U.S. Department of Agriculture (USDA) dietary data collection instrument (involving the automated multiple pass method) is used to collect two 24-h dietary recalls from the participants. The first is conducted face-to-face at a mobile examination center and the second is conducted by telephone interview after an interval of 3–10 days. The USDA Food and Nutrient Database for Dietary Studies, 2.0 (FNDDS 2.0) is then used to calculate various nutrient intakes (16). To ensure high response rates and accuracy, the first 24-hour dietary recall data was used in this study as dietary intake. Dietary niacin, energy intake, alcohol and macronutrient intake per day were determined using this method in all survey cycles.
Outcomes
Outcomes of interest included grip strength, total lean body mass (excluding bone mineral content), appendix lean body mass (excluding bone mineral content), total fat mass, trunk fat mass, bone mineral content, HOMA-IR, fasting blood glucose, and fasting insulin. In addition, we also analyzed the correlation between dietary niacin intake and sarcopenia risk.
Other covariates
The covariates in this study included age, ethnicity, sex, PIR (family income-to-poverty ratio), educational attainment, smoking history, daily alcohol intake, diabetes history, hypertension history, physical activity, healthy eating index-2015 (HEI-2015), body mass index (BMI), total daily energy intake, and the percentage of energy contributed by fat, carbohydrate and protein to total daily energy intake.
Ethnicities were divided into Non-Hispanic Black, Non-Hispanic White, Mexican American, and other/multi-racial. PIR was divided into low income (PIR <1.30), middle income (PIR 1.30–3.49), and high income (PIR ≥3.50). Education was divided into some college or above, high school graduate or general educational development, and less than high school graduate. Smoking history was divided into never (smoking less than 100 cigarettes ever), former (smoking more than 100 cigarettes ever but not smoking at all now), and current (smoking more than 100 cigarettes ever and smoking some days or every day). Participants were diagnosed with diabetes if they met one of the following criteria: 1) a physician diagnosis of diabetes; 2) a glycohemoglobin level >6.5%; 3) fasting glucose ≥7.0 mmol/l; 4) random blood glucose ≥11.1 mmol/l; 5) two-hour blood glucose during oral glucose tolerance testing ≥11.1 mmol/l; and 6) use of antidiabetic medication or insulin. Hypertension was defined as hypertension previously diagnosed by a physician, the use of antihypertensive drugs, systolic blood pressure ≥140 mmHg, and/or diastolic blood pressure ≥90 mmHg (17). Physical activity was self-reported by the participants using the Global Physical Activity Questionnaire. As previously described, we divided physical activity into <600, 600–7,999, and ≥8,000 metabolic equivalent of task (min/week) (18). HEI-2015 was obtained using an official calculation method and was used to assess the quality of participants' diet (19). Higher HEI-2015 scores represent a higher quality of diet. BMI is the square of weight (kg) divided by height (m). From the first 24-hour dietary recall, each participant's daily intakes of carbohydrates, fats, and proteins were obtained and then multiplied by the corresponding energy of each substance (4 kcal/g for carbohydrates and protein and 9 kcal/g for fat), and then divided by the total daily energy intake to obtain the respective percentages.
Statistical analysis
All analyses were performed in SAS (version 9.4) and R (version 4.2.2). Taking into account the complex sampling design used in the NHANES database, each dataset was weighted when it was analyzed to produce nationally representative estimates. Continuous data were expressed as weighted mean ± standard error and categorical variables were expressed as weighted percentages. The relationship between niacin intake and outcomes was analyzed by weighted linear regression models or weighted logistic regression models. Three models were built: model 1 without any adjustments for covariates; model 2 was adjusted for age, sex, ethnicity, PIR, education, smoking history, and alcohol intake; and model 3 that was based on model 2 but further adjusted for total energy intake, physical activity, HEI-2015, BMI, fat energy percentage, carbohydrate energy percentage, protein energy percentage, diabetes, and hypertension. Regression results were expressed as regression coefficients (β) or odd ratio (OR) and 95% confidence intervals (CI). The restriction cubic spline function was used to describe the nonlinear relationship between niacin intake and the results. In addition, subgroup analyses were performed based on age, sex, ethnicity, PIR, educational attainment, smoking history, physical activity, BMI, hypertension, and diabetes, and interactions were calculated to explore changes in niacin intake on outcomes in different subgroups. Bilateral p<0.05 was considered statistically significant.
Results
Baseline characteristics of the participants
For the grip strength dataset, a total of 3,772 participants with complete data were included in the study after excluding missing data on combined grip strength, first 24-hour dietary recall, and other covariates. Similarly, for the body mass components dataset and the glucose homeostasis dataset, 3,279 and 9,189 participants, respectively, were included in the study after excluding missing data (Fig. S1). The baseline characteristics of each dataset participant is shown in Table 1. In the body mass components dataset, 310 people were diagnosed as sarcopenia (9.45%). The daily intake of niacin was about 0.32 mg/kg weight.
Table 1.
Population-weighted baseline characteristics of participants over 40 years old in grip strength dataset, body mass components dataset and glucose homeostasis dataset
| Characteristics | Grip strength dataseta(n=3772) | Body mass components datasetb(n=3279) | Glucose homeostasis datasetc(n=9189) |
|---|---|---|---|
| Age, mean (SE), y | 56.55 (0.24) | 49.41 (0.17) | 56.08 (0.19) |
| Sex, n (%) | |||
| Female | 1813 (48.06) | 1561 (47.61) | 4310 (46.90) |
| Male | 1959 (51.94) | 1718 (52.39) | 4879 (53.10) |
| Ethnicity, n (%) | |||
| Non-Hispanic Black | 897 (23.78) | 717 (21.87) | 1665 (18.12) |
| Non-Hispanic White | 1694 (44.91) | 1218 (37.15) | 4752 (51.71) |
| Mexican American | 373 (9.89) | 439 (13.39) | 1357 (14.77) |
| Other/Multi-Racial | 808 (21.42) | 905 (27.6) | 1415 (15.40) |
| Educational attainment, n (%) | |||
| Some college or above | 2231 (59.15) | 1998 (60.93) | 4964 (54.02) |
| High school graduate or GED | 820 (21.74) | 708 (21.59) | 2110 (22.96) |
| Less than high school graduate | 721 (19.11) | 573 (17.47) | 2115 (23.02) |
| PIR, n (%) | |||
| <1.3 | 1026 (27.20) | 862 (26.29) | 2256 (24.55) |
| 1.3–3.49 | 1320 (34.99) | 1097 (33.46) | 3398 (36.98) |
| ≥3.50 | 1426 (37.80) | 1320 (40.26) | 3535 (38.47) |
| BMI, kg/m2, n (%) | |||
| <25 kg/m2 | 1051 (27.86) | 873 (26.62) | 2486 (27.05) |
| ≥25 kg/m2 | 2721 (72.14) | 2406 (73.38) | 6703 (72.95) |
| Smoking status, n (%) | |||
| Former | 1094 (29.00) | 711 (21.68) | 2964 (32.26) |
| Never | 1989 (52.73) | 1856 (56.6) | 4498 (48.95) |
| Current | 689 (18.27) | 712 (21.71) | 1727 (18.79) |
| Diabetes, n (%) | |||
| Yes | 836 (22.16) | 576 (17.57) | 2118 (23.05) |
| No | 2936 (77.84) | 2703 (82.43) | 7071 (76.95) |
| Hypertension, n (%) | |||
| Yes | 1955 (51.83) | 1313 (40.04) | 4799 (52.23) |
| No | 1817 (48.17) | 1966 (59.96) | 4390 (47.77) |
| Physical activity, MET min/week, n (%) | |||
| <600 | 832 (22.06) | 603 (18.39) | 3326 (36.20) |
| 600–7999 | 2432 (64.48) | 2029 (61.88) | 4935 (53.71) |
| ≥8000 | 508 (13.47) | 647 (19.73) | 928 (10.10) |
| Alcohol use, mean (SE), g | 13.27 (0.92) | 14.77 (1.01) | 11.95 (0.51) |
| HEI-2015 scores, mean (SE) | 53.86 (0.53) | 52.15 (0.47) | 52.14 (0.27) |
| Weight, mean (SE), kg | 82.65 (0.42) | 83.02 (0.46) | 83.05 (0.29) |
| Total energy, mean (SE), kcal/day | 2149.87 (19.00) | 2269.03 (21.86) | 2168.34 (12.94) |
| Energy contribution from carbohydrate, mean (SE), % | 0.48 (0.00) | 0.47 (0.00) | 0.48 (0.00) |
| Energy contribution from fat, mean (SE), % | 0.34 (0.00) | 0.35 (0.00) | 0.35 (0.00) |
| Energy contribution from protein, mean (SE), % | 0.16 (0.00) | 0.16 (0.00) | 0.16 (0.00) |
| Niacin, mean (SE), mg/kg weight | 0.32 (0.00) | 0.34 (0.00) | 0.31 (0.00) |
a. Grip strength dataset (2011–2014); b. Body mass components datase (2011–2018); c. glucose homeostasis dataset (1999–2018); PIR, family income-to-poverty ratio; BMI, body mass index; MET, metabolic equivalent of task; HEI-2015, healthy eating index-2015
Niacin intake and body mass components
In all models, niacin intake was significantly positively correlated with appendicular lean mass, total lean mass, and total bone mineral content, and negatively correlated with total fat and trunk fat (Table 2). Assuming linearity, for every 1 mg/ kg weight increase in the daily niacin amount, appendicular lean mass increased by 0.025 kg/kg weight (95% CI 0.018–0.033 kg/kg weight), total lean mass increased by 0.060 kg/kg weight (95% CI 0.045–0.074 kg/kg weight), and total bone mineral content increased by 0.005 kg/kg weight (95% CI 0.004–0.007 kg/kg weight). For every 1 mg/kg weight increase in daily niacin, total fat decreased by 0.061 kg/kg weight (95% CI −0.076 to −0.046 kg/kg weight) and trunk fat reduced by 0.041 kg/kg weight (95% CI −0.050 to −0.032 kg/kg weight). Subgroup analyses showed that age, sex, smoking status, BMI and hypertension were significant factors affecting niacin intake and body mass components. In subgroups of people aged 50–60 years, niacin intake had a stronger association with body mass components. Niacin intake had a stronger positive association with total lean mass and total bone mineral content and a stronger negative association with total fat in the female subgroup. In addition, increases in niacin intake in participants with hypertension resulted in enhanced increases in total lean mass and total bone mineral content and enhanced decreases in total fat and trunk fat. In addition, subgroups with a BMI >25 kg/m2 were more likely to experience an enhanced increase in total lean mass and appendicular lean mass and an enhanced decrease in total fat with dietary niacin supplementation (Fig. Figure 1, Figure 1, Pinteraction<0.05). The remaining subgroup analyses of niacin intake and body mass components is shown in Fig. S2 A–E. The nonlinear relationship between niacin intake and body mass components is shown in Fig. Figure 2, Figure 2.
Table 2.
Univariate and multivariate linear regression analysis of daily niacin intake (1mg/kg weight) and muscle mass and strength
| Model 1a |
Model 2b |
Model 3c |
||||
|---|---|---|---|---|---|---|
| β (95% CI) | Pvalue | β (95% CI) | Pvalue | β (95% CI) | Pvalue | |
| Grip strength, kg/kg weight | 0.461 (0.420, 0.503) | <0.0001 | 0.314 (0.270, 0.358) | <0.0001 | 0.275 (0.192, 0.357) | <0.0001 |
| Appendicular lean mass, kg/kg weight | 0.071 (0.061, 0.081) | <0.0001 | 0.039 (0.032, 0.046) | <0.0001 | 0.025 (0.018, 0.033) | <0.0001 |
| Total lean mass, kg/kg weight | 0.142 (0.124, 0.161) | <0.0001 | 0.086 (0.072, 0.100) | <0.0001 | 0.060 (0.045, 0.074) | <0.0001 |
| Total fat, kg/kg weight | −0.147 (−0.166, −0.128) | <0.0001 | −0.090 (−0.105, −0.075) | <0.0001 | −0.061 (−0.076, −0.046) | <0.0001 |
| Trunk fat, kg/kg weight | −0.080 (−0.089, −0.070) | <0.0001 | −0.063 (−0.073, −0.053) | <0.0001 | −0.041 (−0.050, −0.032) | <0.0001 |
| Total bone mineral content, kg/kg weight | 0.008 (0.007, 0.009) | <0.0001 | 0.007 (0.006, 0.009) | <0.0001 | 0.005 (0.004, 0.007) | <0.0001 |
a. Unadjusted model; b. Adjusted for age, sex, ethnicity, PIR, educational attainment, smoking status and alcohol use; c. Adjusted for age, sex, ethnicity, PIR, educational attainment, smoking status, alcohol use, DM, hypertension, total energy, physical activity, HEI-2015 scores, BMI, energy contribution from carbohydrate, energy contribution from fat and energy contribution from protein.
Figure 1.
An analysis of dietary niacin intake and (A) total lean mass, (B) appendicular lean mass, (C) total fat, (D) trunk fat, (E) total bone mineral content, and (F) grip strength stratified by baseline characteristics
Adjusted for age, sex, ethnicity, PIR (family income-to-poverty ratio), educational attainment, smoking status, alcohol use, diabetes, hypertension, total energy, physical activity, HEI-2015 (healthy eating index-2015) scores, body mass index, energy contribution from carbohydrate, energy contribution from fat, and energy contribution from protein.
Figure 2.
Restriction cubic spline plot between dietary niacin intake and (A) total lean mass, (B) appendicular lean mass, (C) total fat, (D) trunk fat, (E) total bone mineral content, and (F) grip strength
Adjusted for age, sex, ethnicity, PIR (family income-to-poverty ratio), educational attainment, smoking status, alcohol use, diabetes, hypertension, total energy, physical activity, HEI-2015 (healthy eating index-2015) scores, body mass index, energy contribution from carbohydrate, energy contribution from fat, and energy contribution from protein.
Besides, niacin intake was negatively correlated with sarcopenia risk (Table 3). In model 3, which adjusted for all covariates, the highest level of niacin intake (Q4) remained negatively correlated with sarcopenia (OR = 0.460, 95% CI, 0.233-0.907, Ptrend = 0.020). The nonlinear relationship between niacin intake and sarcopenia risk is shown in Fig. 3.
Table 3.
Univariate and multivariate logistic regression analysis of daily niacin intake (1mg/kg weight) and the risk of sarcopenia
| Model 1a |
Model 2b |
Model 3c |
||||
|---|---|---|---|---|---|---|
| OR (95% CI) | Pvalue | OR (95% CI) | Pvalue | OR (95% CI) | Pvalue | |
| Daily niacin intake (mg/kg weight, quartile) | ||||||
| Q1 | Ref | Ref | Ref | |||
| Q2 | 0.759 (0.479, 1.204) | 0.237 | 0.728 (0.434, 1.220) | 0.222 | 0.778 (0.475, 1.274) | 0.310 |
| Q3 | 0.628 (0.395, 0.998) | 0.048 | 0.584 (0.356, 0.960) | 0.035 | 0.635 (0.383, 1.051) | 0.076 |
| Q4 | 0.457 (0.280, 0.744) | 0.002 | 0.402 (0.239, 0.676) | <0.001 | 0.460 (0.233, 0.907) | 0.026 |
| Ptrend | <0.001 | <0.001 | 0.020 | |||
a. Unadjusted model; b. Adjusted for age, sex, ethnicity, PIR, educational attainment, smoking status and alcohol use; c. Adjusted for age, sex, ethnicity, PIR, educational attainment, smoking status, alcohol use, DM, hypertension, total energy, physical activity, HEI-2015 scores, energy contribution from carbohydrate, energy contribution from fat and energy contribution from protein.
Figure 3.
Restriction cubic spline plot between dietary niacin intake and sarcopenia risk
Adjusted for age, sex, ethnicity, PIR (family income-to-poverty ratio), educational attainment, smoking status, alcohol use, diabetes, hypertension, total energy, physical activity, HEI-2015 (healthy eating index-2015) scores, energy contribution from carbohydrate, energy contribution from fat, and energy contribution from protein.
Niacin intake and grip strength
Niacin intake was significantly positively correlated with grip strength, and this correlation remained significant in model 3, which adjusted for many covariates (Table 2). Assuming linearity, for every 1 mg/kg weight increase in daily niacin intake, grip strength increased by 0.275 kg/ kg weight (95% CI 0.192–0.357 kg/kg weight). Subgroup analysis showed a stronger positive correlation between niacin intake and grip strength in subgroup populations who never smoking, had a BMI ≥25 kg/m2, and had hypertension (Fig. 1F, Pinteraction<0.05). The remaining subgroup analysis of niacin intake and grip strength is shown in Fig. S2 F. The nonlinear relationship between niacin intake and grip strength is shown in Fig. 2F.
Niacin intake and glucose homeostasis
When analyzing the association of niacin intake with HOMA-IR, fasting blood glucose, and fasting insulin, the population was divided into participants with and without diabetes. Niacin intake was significantly negatively correlated with HOMA-IR and fasting insulin, regardless of diabetes, implying that dietary niacin improved insulin resistance and lowered fasting insulin levels (Table 4). Assuming linearity, for HOMA-IR, for every 1 mg/kg weight increase in dietary niacin, the HOMA-IR of participants with and without diabetes decreased by 6.557 (95% CI −11.058 to −2.066) and 1.790 (95% CI −2.746 to −0.835), respectively. For fasting insulin, for every 1 mg/kg weight increase in dietary niacin, the fasting insulin of participants with and without diabetes decreased by 16.363 µU/ml (95% CI −26.739 to −5.988 µU/ml) and 6.942 (95% CI −10.215 to −3.669 µU/ml), respectively.
Table 4.
Univariate and multivariate linear regression analysis of daily niacin intake (1mg/kg weight) and glucose homeostasis
| Model 1a |
Model 2b |
Model 3c |
|||||
|---|---|---|---|---|---|---|---|
| β (95% CI) | Pvalue | β (95% CI) | Pvalue | β (95% CI) | Pvalue | ||
| HOMA-IR | DM | −4.974 (−8.176, −1.771) | 0.003 | −5.169 (−8.625, −1.713) | 0.004 | −6.557 (−11.058, −2.066) | 0.005 |
| n-DM | −1.472 (−2.250, −0.694) | <0.001 | −1.654 (−2.461, −0.846) | <0.0001 | −1.790 (−2.746, −0.835) | <0.001 | |
| Fasting blood glucose, mmol/L | DM | 0.880 (−0.381, 2.141) | 0.170 | 0.599 (−0.669, 1.868) | 0.350 | 0.030 (−1.660, 1.721) | 0.970 |
| n-DM | −0.179 (−0.313, −0.045) | 0.010 | −0.268 (−0.403, −0.133) | <0.001 | −0.185 (−0.352, −0.019) | 0.030 | |
| Fasting insulin, µU/ml | DM | −14.132 (−21.337, −6.928) | <0.001 | −13.707 (−21.763, −5.651) | <0.001 | −16.363 (−26.739, −5.988) | 0.002 |
| n-DM | −5.759 (−8.449, −3.068) | <0.0001 | −6.350 (−9.139, −3.560) | <0.0001 | −6.942 (−10.215, −3.669) | <0.0001 | |
a. Unadjusted model; b. Adjusted for age, sex, ethnicity, PIR, educational attainment, smoking status and alcohol use; c. Adjusted for age, sex, ethnicity, PIR, educational attainment, smoking status, alcohol use, hypertension, total energy, physical activity, HEI-2015 scores, BMI, energy contribution from carbohydrate, energy contribution from fat and energy contribution from protein.
In addition, dietary niacin significantly reduced fasting blood glucose levels only in participants without diabetes. Assuming linearity, fasting blood glucose in participants without diabetes was reduced by 0.185 mmol/L (95% CI −0.352 to −0.019 mmol/L) for every 1 mg/kg weight increase in dietary niacin. Further subgroup analyses showed that age, smoking status, BMI, and diabetes were important factors affecting the association of niacin with HOMA-IR and fasting insulin. In subgroups who had smoked before, never smoked, had a BMI of ≥25 kg/m2, or had diabetes, the negative correlation between niacin and HOMA-IR and fasting insulin was more significant. Moreover, dietary niacin intake was more strongly inversely associated with HOMA-IR in the 40–60 years age group (Fig. 4, Pinteraction <0.05). Fig. S3 shows the other subgroup analyses. The nonlinear relationship between niacin intake and glucose homeostasis is shown in Fig. 5.
Figure 4.
An analysis of dietary niacin intake and (A) HOMA-IR and (B) fasting insulin stratified by baseline characteristics
Adjusted for age, sex, ethnicity, PIR (family income-to-poverty ratio), educational attainment, smoking status, alcohol use, diabetes, hypertension, total energy, physical activity, HEI-2015 (healthy eating index-2015) scores, body mass index, energy contribution from carbohydrate, energy contribution from fat, and energy contribution from protein.
Figure 5.
Restriction cubic spline plot between dietary niacin intake and (A) HOMA-IR, (B) fasting blood glucose, and (C) fasting insulin stratified by diabetes
Adjusted for age, sex, ethnicity, PIR (family income-to-poverty ratio), educational attainment, smoking status, alcohol use, hypertension, total energy, physical activity, HEI-2015 (healthy eating index-2015) scores, body mass index, energy contribution from carbohydrate, energy contribution from fat, and energy contribution from protein.
Discussion
Reduction in skeletal muscle mass and strength is an important part of diagnosing sarcopenia, which is associated with the occurrence of multiple adverse outcomes and causes a heavy social burden (20). Previous studies have focused on people aged ≥65 years, but studies have shown that skeletal muscle mass and strength gradually decrease from the age of 40 years and beyond (2, 21). Therefore, early intervention is needed to prevent or mitigate age-related sarcopenia. The objective of this study was to explore the relationship between daily dietary niacin intake and grip strength, muscle mass, and glucose homeostasis in the United States population aged 40 years and older.
Our study found that daily dietary niacin intake was positively correlated with total lean mass, appendicular lean mass, total bone mineral content, and grip strength, and negatively correlated with sarcopenia risk, total fat and trunk fat. In addition, dietary niacin was negatively correlated with HOMA-IR and fasting insulin in participants with and without diabetes, and fasting blood glucose in participants without diabetes. To our knowledge, this is the first study to explore the relationship between dietary niacin and these outcomes of interest in a representative large cohort, while adjusting for a number of important covariates. Our results suggest that dietary niacin supplementation is an effective strategy for preventing and improving age-related muscle mass and strength loss.
Higher dietary niacin intake improved muscle and fat distribution throughout the body, consistent with the findings of a previous randomized controlled trial. In this study involving overweight African-American women, niacin-bound chromium caused a significant loss of fat and sparing of muscle (22). In our study, subgroup analyses also found that dietary niacin was more significantly associated with changes in muscle and fat in participants aged 50–60years, with hypertension and a BMI ≥25 kg/m2. A large prospective cohort study by Han et al. showed that the skeletal muscle mass index measured by bioelectrical impedance was inversely associated with the risk of hypertension in men, which may be related to insulin resistance because of decreased muscle mass, enhanced inflammatory and oxidative pathways, and increased arterial stiffness (23, 24). Moreover, aging and obesity can lead to a decrease in skeletal muscle mass and performance (25, 26). Therefore, in people with increased skeletal muscle damage and fat accumulation, the effect of dietary niacin is more significant.
In our study, dietary niacin intake was also significantly positively correlated with grip strength. This finding may be explained by the significant enhancement of skeletal muscle mass that results from dietary niacin because there is a strong positive correlation between skeletal muscle mass and strength (27). In addition, a cross-sectional study by Kim et al. showed that dietary niacin reduced levels of pro-inflammatory cytokines, including interleukin-6 (IL-6) and tumor necrosis factor -α (TNF-α), in the elderly, thereby improving diet quality and reducing sarcopenia (28). Similar to the subgroup analysis of dietary niacin and skeletal muscle mass, dietary niacin had a stronger positive correlation with grip strength in subgroups with a BMI >25 kg/m2 or hypertension.
This study found that in the range of daily dietary niacin intake in the general United States population (in the glucose homeostasis dataset, the maximum dosage of daily dietary niacin was 238 mg and the average dosage was 25 mg), dietary niacin was negatively correlated with HOMA-IR and fasting insulin, and in participants without diabetes, it was negatively correlated with fasting blood glucose. This means that niacin improves insulin resistance and glucose homeostasis at dietary doses. However, some previous studies have suggested that niacin promotes insulin resistance, which is a significant side effect when niacin is used as a lipid-lowering drug (29, 30). The reason for this inconsistent result lies in the dosage of niacin. When niacin is used as a lipid-lowering drug, its daily dose is 2–6 g, which far exceeds the daily dietary dose (31).
In addition, dietary niacin-induced gains in muscle mass and decreases in adipose tissue throughout the body also helps to improve insulin resistance. Skeletal muscle accounts for 40–50% of lean body mass in adults and is responsible for most of the body's postprandial glucose processing. However, sarcopenia because of aging reduces the amount of muscle available for disposing of glucose (32). Excess adipose tissue in the body accumulates between muscle cells, around muscles and other organs, and secretes large amounts of pro-inflammatory cytokines such as TNF-α, IL-1, and IL-6. This exacerbates the inflammatory state of the body and leads to skeletal muscle atrophy by increasing apoptosis and upregulating the decay of the protein body of silk protein, further impairing the body's energy metabolism and glucose homeostasis (33, 34).
The restrictive cube spline further confirms our findings. Niacin intake was approximately linearly correlated with most outcomes across the dose range of this study, with no significant plateau phase. Analysis of this study showed that niacin was associated with improvements in body composition, grip strength, and glucose homeostasis in people over 40 years of age at dietary doses.
The main strength of this study was that the study data came from a large, nationally representative database. In addition, many potential confounding factors were corrected, making the results more reliable and accurate. However, there were some limitations in this study. First, as a cross-sectional study, we were only able to determine the association between dietary niacin and the outcome of interest, but not causation. This study only included the general population in the United States, so the relationship between dietary niacin and skeletal muscle mass, strength, and glucose homeostasis in other countries and ethnic groups needs further investigation. In addition, dietary niacin intake was derived from participants' 24-hour dietary recall, which may be biased and may not reflect the participants' typical dietary patterns.
Conclusion
Niacin intake at dietary doses in people aged 40 years and older is associated with increased skeletal muscle mass and strength and loss of body fat, and it may improve glucose homeostasis. Dietary niacin may help to slow the occurrence of age-related sarcopenia and reduce the incidence of adverse events in middle-aged and elderly people.
Acknowledgments
We thank Carol Wilson, PhD, from Liwen Bianji (Edanz) (www.liwenbianji.cn) for editing the English text of a draft of this manuscript.
Contributor Information
Yun Lu, Email: luyun@qdu.edu.cn.
Shang-Long Liu, Email: liushanglong@qdu.edu.cn.
Ethics declarations
The study protocols for NHANES approved by NCHS Research Ethics Review Board. All participants provided written informed consent before participating in the study.
Conflicts of Interest
The authors declare no conflict of interest.
Electronic Supplementary Material
Supplementary material is available in the online version of this article at https://doi.org/10.1007/s12603-023-1967-0.
Supplementary material, approximately 850 KB.
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